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Income Inequality in Natural Resource-Rich Countries Empirical Evidence from Chile
Javier Beltraacuten
MSc (Economics)
Submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
School of Economics and Finance
QUT Business School
Queensland University of Technology
2020
i
Keywords
Count data models
Data Envelopment Analysis
Dutch disease
Economic diversity
Incivilities
Income inequality
Local government efficiency
Natural resource dependence
Panel data
Paradox of plenty
Racial diversity
Resource curse hypothesis
Social cohesion
Spatial analysis
ii
Abstract
Persistently high indicators of relative economic disadvantage such as measures of income
inequality can give rise to a feeling of discontent in the population which in turn can trigger
costly social conflicts For instance inequality has been suggested as one of the main causes of
social outburst considering recent events in many countries around the world This has generated
in extant literature an increasing number of criticisms of current political and socio-economic
models This research considers the Chilean economy which is recognised as an example of the
success of standard economic thinking however it is also well-known for its persistently high
levels of inequality an adverse indicator of economic performance This thesis contributes with
three essays to the understanding of the sources and potential consequences of income inequality
in Chile The data consider a panel of 324 Chilean counties and their corresponding municipalities
for the 2006ndash2017 period
The first essay investigates the association between income inequality and the endowment
of natural resources The Gini coefficient of each county is used as a measure of income inequality
The influence of natural resources on income inequality is captured by using the proportion of
employment in the primary sector as a proxy for the degree of dependence on natural resources in
each county Previous literature has identified a significant spatial dimension of income inequality
in Chile but this spatial dimension has been largely neglected in the domain of policy design and
implementation Thus the analysis in this essay applies spatial regression models for cross-
sectional and panel data while controlling for other socioeconomic and demographic
characteristics The main finding is that contrary to what theory predicts our measure of natural
resource dependence in terms of employment shows a robust and significant negative association
with income inequality The main implication of this empirical result is that a transformation
process towards activities less dependent on natural resources reinforces rather than reduces the
persistence of income inequality at least through the channel of employment Hence this
transformation process imposes additional challenges to central and local governments in their
goal of reducing income inequality Empirical analysis also shows a significant degree of positive
spatial autocorrelation of income inequality This means that counties with similar levels of income
iii
inequality tend to cluster in space The regression analysis confirms the importance of capturing
geographical heterogeneity in the explanation of income inequality however gives less support
to a process of spatial dependence like a spillover effect of income inequality among
neighbouring counties
Among the potential consequences of income inequality the literature highlights its
possible impacts on the efficiency in the provision of public services by local authorities however
empirical evidence is very little For this reason the second essay analyses the technical efficiency
of municipal local governments in Chile and examine if income inequality has significant impacts
on the variations in the efficiency levels across municipalities An input-oriented Data
Envelopment Analysis is used to measure municipal efficiency Results reveal that the municipal
production technology is characterized by variable returns to scale but scale inefficiencies only
explain a small proportion of total inefficiency This justify a need for analysing the influence of
variables which are beyond the control of local authorities in explaining differences in municipal
efficiency The main hypothesis tested was whether income inequality has a negative influence on
municipal efficiency whilst a measure of natural resource dependence at the county level was used
as an instrument to control for the effects of possible endogeneity issues Results showed that
changes in income inequality could be associated with changes in the municipal efficiency level
in the same magnitude but in the opposite direction This confirms that local authorities in counties
characterized by high levels of income inequality face greater challenges to achieve more efficient
performance This result suggests that policies aimed at reducing income inequality can also
increase the efficiency of local governments Our results also reveal that policies such as
amalgamation de-amalgamation or cooperation among municipalities should be designed
specifically for each region rather than as a standard national strategy
Finally the third essay analyses how social cohesion is associated with the levels of
economic and racial diversity Social cohesion is proxied using the reported number of antisocial
behaviours catalogued as incivilities Incivilities are those antisocial behaviours which violate
social norms but are not usually considered as criminal Research has documented the implications
of incivilities on human stress health public behaviour and increasing feelings of insecurity and
fear among the population Few studies have explicitly considered incivilities as a dependent
variable to identify their determinants or use them to analyse the weakening of social cohesion and
iv
the growing feeling of social unrest in contemporary societies Economic diversity is proxied using
the Gini coefficient in each county and racial diversity through the number of new visas granted
as proportion of the county population Our findings show that incivilities are strongly associated
with racial diversity and to a lesser extent with economic diversity The rate of incivilities also
shows a negative association with the level of income and a positive relationship with poverty and
unemployment rates These results give empirical support to the idea that both relative and
absolute indicators of economic deprivation play an important role in understanding the growing
problem of incivilities in highly unequal economies like Chile Results also show that the rate of
incivilities is negatively related to the degree of financial autonomy of municipalities These
findings represent promising areas for central and local governments in the implementation of
policies aimed at increasing social cohesion through the reduction of incivilities and other types of
antisocial behaviours
v
Table of Contents
Keywords i
Abstract ii
Table of Contents v
List of Figures viii
List of Tables ix
List of Abbreviations x
Statement of Original Authorship xi
Acknowledgements xii
Chapter 1 Introduction 13
Income inequality and dependence on natural resources 14
Local government efficiency and income inequality 16
Social cohesion and economic diversity 19
Contributions 21
Thesis outline 23
Chapter 2 Natural Resources Curse or Blessing Evidence on Income Inequality at the County Level in Chile 24
21 Introduction 24
22 Inequality and Natural Resources 28 221 Theoretical Framework 28
Cross-country literature 29 Single country evidence 32
222 The relevance of the spatial approach 33
23 Research problem and hypotheses 35
24 Data and Methods 36 241 Operationalization of key variables 36 242 Control variables 40 243 Methods 41 244 Spatial Model Specification 43
25 Results 44 251 Exploratory Spatial Data Analysis (ESDA) 44 252 Cross-sectional analysis 45 253 Panel Data analysis 48
26 Discussion and conclusions 51
Chapter 3 The Impact of Income Inequality on the Efficiency of Municipalities in Chile 55
vi
31 Introduction 55
32 Related Literature 61 321 Measuring efficiency of local governments 61 322 Explaining differences in LGE 63 323 The trade-off between efficiency and equity 64
33 Methodology 66 331 Chilean Municipalities and period of analysis 66 332 Measuring municipal efficiency 68 333 Inputs and outputs used in DEA 70 334 Regression model 71 335 The instrument 75
34 Results and discussion 77 341 DEA results 77
Returns to scale 78 Efficiency measure 80
342 Regression results 82 Exploratory spatial analysis 82 Cross-sectional analysis 83 Panel data analysis 84
35 Conclusions 88
Chapter 4 Social Cohesion Incivilities and Diversity Evidence at the municipal level in Chile 91
41 Introduction 91
42 Related Literature 95 421 The Community Heterogeneity Thesis 95 422 The literature on incivilities 97 423 The ldquoIncivilities Thesisrdquo 99
4 3 Methodology 100 431 Period of analysis and data sample 100 432 Operationalisation of the response variable and exploratory analysis 101 433 Measures of community heterogeneity and control variables 105 434 Methods 108 435 Hypotheses 111
44 Results and Discussion 112
4 5 Conclusions 118
Chapter 5 Conclusions 120
Bibliography 126
Appendices 139
Appendix A Summary statistics income inequality 139
Appendix B Summary statistics for NRD measures by region 140
Appendix C Regional administrative division and defined zones 141
Appendix D Summary statistics numeric controls and correlation matrix 142
vii
Appendix E Static spatial panel models 143
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis 145
Appendix G Linear panel data models 146
Appendix H Spatial panel models (Generalized Moments (GM) estimation) 147
Appendix I Inputs and outputs used in DEA analysis 148
Appendix J Technical and scale efficiency 149
Appendix K Correlation matrix 150
Appendix L Returns to scale by year and zone 151
Appendix M Returns to scale by year (maps) 152
Appendix N Efficiency status by year (maps) 153
Appendix O Spatial distribution efficiency scores by year (maps) 154
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis 155
Appendix Q OLS regressions for cross-sectional and panel data 157
Appendix R Quantile maps incivilities rate by group (average total period) 159
Appendix S Correlation matrix numeric covariates 160
Appendix T Negative Binomial regressions 161
Appendix U Coefficients economic and racial diversity by geographical zone 162
viii
List of Figures
Figure 21 Average share in GDP of economic activities (2006ndash17) 37
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17) 38
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17) 39
Figure 23 Moran scatter plots for variables gini and pss_casen 45
Figure 31 Geographical distribution of Chilean regions and macrozones 74
Figure 32 Evolution of efficiency scores and the proportion of firms by sector 77
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE) 78
Figure 34 Returns to scale by zone 79
Figure 35 Evolution mean efficiency scores (VRS) by zone 81
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017 102
Figure 42 Evolution total number of incivilities by category 104
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017) 104
Figure 44 Annual average number of incivilities per county 109
Figure C1 Geographical distribution of Chilean regions and 3 zones 141
Figure D1 Correlation matrix numeric explanatory variables 142
Figure F1 Moran scatter plot OLS residuals 145
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018) 148
Figure K1 Correlation matrix contextual factors 150
Figure M1 Spatial distribution of returns to scale by county per year 152
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year 153
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year 154
Figure P1 Moran scatter plot efficiency scores and OLS residuals 155
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17) 159
Figure S1 Correlation matrix numeric covariates 160
ix
List of Tables
Table 21 Cross-sectional Model Comparison (six-year average data) 47
Table 22 ML Spatial SAR Models 50
Table 23 ML Spatial SEM Models 50
Table 24 ML Spatial SARAR Models 51
Table 31 Descriptive statistics Inputs and Output variables used in DEA analysis 71
Table 32 Summary Statistics Numeric Contextual Factors 74
Table 33 Summary efficiency scores (VRS) by zone and region 80
Table 34 Cross-sectional (censored) regressions 84
Table 35 Panel data regressions 87
Table 41 Summary statistics total count of incivilities and by category (full sample and period) 103
Table 42 Summary statistics numeric explanatory variables 108
Table 43 Poisson regressions 113
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region 115
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group 116
Table A1 Summary statistics Gini coefficients by year and zone 139
Table B1 Summary statistics NRD measures by region 140
Table D1 Summary Statistics Numeric Explanatory Variables 142
Table F1 Analysis OLS residuals Anselin Method 145
Table G1 Panel regressions (non-spatial) 146
Table H1 GM Spatial Models 147
Table L1 Returns to scale (percentage of municipalities) 151
Table P1 Analysis OLS residuals Anselin Method 155
Table P2 OLS and spatial regression models for the six-year averaged data 156
Table Q1 OLS cross-sectional regression per year 157
Table Q2 OLS panel regressions Pooled random effects and instrumental variable 158
Table T1 Negative Binomial regressions 161
Table U1 Coefficients economic and racial diversity in pooled Poisson models by geographic zone 162
x
List of Abbreviations
Constant returns to scale CRS
Data envelopment analysis DEA
Decreasing returns to scale DRS
Efficiency scores ES
Exploratory spatial data analysis ESDA
Generalized methods of moments GMM
Gross Domestic Product GDP
Increasing returns to scale IRS
Local government efficiency LGE
Maximum likelihood ML
Municipal common fund MCF
Natural resource dependence NRD
Natural resource endowment NRE
Ordinary Least Squares OLS
Organization for Economic Cooperation and Development OECD
Own permanent revenues OPR
Resource curse hypothesis RCH
Spatial autoregressive model SAR
Spatial error model SEM
Variable returns to scale VRS
xi
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet requirements
for an award at this or any other higher education institution To the best of my knowledge and
belief the thesis contains no material previously published or written by another person except
where due reference is made
Signature QUT Verified Signature
Date _________04092020_________
xii
Acknowledgements
First I would like to thank my wife Lilian who joined me in this challenge and patiently
supported me all these years I would also like to thank our family who always supported us from
Chile I especially thank my sister Silvia who took care of our house and dog
I am also grateful to my supervisory team Dr Radhika Lahiri and Dr Vincent Hoang who
supported and guided me in the process of making this thesis a reality
I also thank the Deans of the Faculty of Economics and Business at my beloved University
of Talca Dr Arcadio Cerda and Dr Rodrigo Herrera who trusted me and supported me in this
process In the same way I would like to thank all the support of the director of the Commercial
Engineering career Mr Milton Inostroza
Finally I would like to thank the government of Chile for the financial support that made
my stay and studies possible here at the Queensland University of Technology
13
Chapter 1 Introduction
Efficiency and equity issues are often considered together in the evaluation of economic
performance While higher efficiency usually measured by growth rates of income per capita
correlates with improvements in measures of well-being the link between inequality and well-
being is less clear This is reflected not only in the type and amount of research related to efficiency
and equity but also in the role that both play in the design of the economic policy For instance
several market-oriented countries have focused primarily on economic growth trusting in a trickle-
down process where financial benefits given to the wealthy are expected to ultimately benefit the
poor However despite the growing interest in the issue of inequality there is a considerable lack
of studies about its consequences
Although some level of inequality is inevitable or even necessary for economic activity this
study is motivated by the argument that relatively high levels of inequality can be associated with
many problems such as persistent unemployment increasing fiscal expenses indebtedness and
political instability (Berg amp Ostry 2011) Inequality can also have other severe social
consequences including increased crime rates teenage pregnancy obesity and fewer
opportunities for low-income households to invest in health and education (Atkinson 2015) In
addition when the role of money and concentration of economic power undermine political
outcomes inequality of opportunities hampers social and economic mobility trust and social
cohesion In summary inequality can increase the fragility of the economic and social situation in
a country reducing economic growth and making it less inclusive and sustainable
14
A country well-known for its market-oriented economy and high level of dependence on
natural resources is Chile Chilean success in terms of economic growth contrasts with its inability
to reduce the persistently high levels of social and economic inequality particularly in the last
three decades Using data for the 2006-2017 period and considering 324 out of 345 Chilean
counties this thesis presents three essays with empirical evidence aiming to explain the
phenomenon of persistent income inequality and some of its potential consequences The first
essay aims to analyse how the evolution and variability of income inequality throughout the
country are associated with the degree of natural resource dependence The second essay studies
the relevance of income inequality in explaining cross-county differences in the performance of
local governments (municipalities) Finally the third essay explores the link between social
cohesion and community heterogeneity highlighting the importance of economic and racial
diversity
Income inequality and dependence on natural resources
The first essay explores how cross-county differences in income inequality are associated
with differences in the degree of dependence on natural resources We use the Gini coefficient in
each county as our dependent variable and the proportion of employment in the primary sector as
our measure of natural resource dependence The main hypothesis is that income inequality should
be positively related to the degree of natural resource dependence To test our hypothesis we use
a spatial econometric approach This approach is motivated by the study of Paredes Iturra and
Lufin (2016) who explore the geographic heterogeneity of income inequality in Chile finding
evidence of a significant spatial dimension
15
The theoretical and empirical literature has mostly proposed a positive link between
inequality and natural resources Although most of the evidence corresponds to cross-country
comparisons there is also increasing body of research at the local level A rationale underpinning
the positive link suggested in the literature is that in natural resource-rich countries ownership is
concentrated in small groups and extraction activities require low-skilled workers (Gylfason amp
Zoega 2003 Leamer Maul Rodriguez amp Schott 1999) Another market-based argument often
labelled as the ldquoDutch Diseaserdquo proposes that natural resource windfalls could be associated with
a crowding-out effect on the manufacturing sector (Corden amp Neary 1982 Easterly 2007) This
process encourages rent-seeking behaviours discourages investment in physical and human
capital and delays the process of technology adoption and industrialization (Auty 2001 Bulte
Damania amp Deacon 2005 Gylfason amp Zoega 2003) The result could be a lower economic
growth which is the central idea under the ldquoResource Curse Hypothesisrdquo (Auty 1993 Sachs amp
Warner 2001)
An ldquoinstitutionalrdquo argument for the positive association between inequality and the
endowment of natural resources is based on the so-called ldquoParadox of Plentyrdquo (Borge Parmer amp
Torvik 2015 Dauvin amp Guerreiro 2017) The idea is that both national and local authorities have
less incentive to operate efficiently when they experience windfalls in their revenues for
instance from natural resources This could end with corrupted authorities exerting patronage
clientelism and designing public policies to favour specific groups of the population (Uslaner amp
Brown 2005) Evidence also suggests that the final effect of natural resource booms on income
inequality will depend on authoritiesrsquo capacity to manage these additional resources the extent of
commuting and migration among regions and the potential increase in the demand for non-tradable
16
goods which are intensive in unskilled workers (Aroca amp Atienza 2011 Cust amp Poelhekke 2015
Fleming amp Measham 2015b Howie amp Atakhanova 2014 Michaels 2011)
Contrary to most theoretical and empirical evidence we find that income inequality shows
a robust and significant negative association with our proxy for natural resource dependence This
result suggests that the process of transformation to an economy less dependent on natural
resources could have exacerbated rather than alleviated the persistence of income inequality The
decrease in the participation of the primary sector in employment in favour mainly of the tertiary
sector highlights the importance of the latter to explain the current high levels of inequality and its
future evolution Another important result is that spatial linear models show practically the same
results as traditional linear models This could be interpreted as the spatial dimension previously
found in income inequality is not the result of spatial dependence in the variable itself for instance
due to a process of spillover among counties Hence the usually found positive spatial
autocorrelation of income inequality (similar levels in neighbouring counties) could be explained
by spatial patterns in other variables or to the spatial heterogeneity that characterizes the Chilean
economy
Local government efficiency and income inequality
Essay 2 delves deep into the potential trade-off between efficiency and equity We measure
the efficiency of Chilean municipalities which correspond to the organizations in charge of
managing each county Municipal efficiency is understood as ldquotechnical efficiencyrdquo that is the
possibility that each municipality has reached the same level of outputs with less use of inputs
Then we analyse how income inequality controlling for other contextual factors such as
socioeconomic demographic geographical and political characteristics may help to explain
17
differences in municipal performance Our main hypothesis is that municipal efficiency is
inversely associated with income inequality Moreover we seek a causal interpretation of this
relationship
Municipal performance could be influenced by income inequality in direct and indirect ways
In a direct sense income inequality is used to capture the degree of heterogeneity and complexity
in the demand for public services that citizens exert over local authorities Hence higher levels of
income inequality should be associated with a more complex set of public services and therefore
with lower levels of municipal efficiency (Jottier Ashworth amp Heyndels 2012) Furthermore
when high levels of inequality exist the richest groups can exert a higher influence over local
authorities resulting in low quality and quantity of services for most of the population Among
indirect effects high and persistent inequality could be the source of corrupted institutions and
local authorities favouring themselves or specific groups This undermines citizensrsquo participation
in civic activities and their willingness to monitor municipal performance (Uslaner amp Brown
2005) Additionally the potential benefits of decentralization on the way local governments
deliver public services will be limited when the context is characterized by corrupted politicians
and a limited administrative and financial capacity (Scott 2009)
We measure municipal efficiency using an input-oriented Data Envelopment Analysis
(DEA) to obtain efficiency scores for our sample of 324 municipalities in each year from 2006 to
2017 Then we study the influence on municipal efficiency of income inequality and our set of
contextual factors using a panel of six years corresponding to those years for which household
income information is available 2006 2009 2011 2013 2015 and 2017 Our dependent variable
is the set of efficiency scores which are relative measures of efficiency They are relative to the
18
municipalities included in the sample and they do not imply that higher technical efficiency gains
cannot be achieved Thus we use both cross-sectional and panel censored regression models To
tackle endogeneity issues and suggest a causal interpretation we also propose using the proportion
of firms in the primary sector as an instrument for income inequality
We find an average efficiency score of 83 meaning that Chilean municipalities could
reduce the use of inputs by 17 without reducing their outputs We also measure municipal
efficiency under different assumptions related to returns to scale This allows us to disaggregate
technical efficiency to assess whether inefficiencies are due to management issues (pure technical
efficiency) or scale issues (scale efficiency) Although the results show that most municipalities
operate under increasing or decreasing returns to scale scale inefficiencies only explain a small
proportion of total municipal inefficiencies This highlights the need to look for contextual factors
outside the control of local authorities to explain differences in municipal performance
Geographical representations of our results in terms of returns to scale and efficiency scores
show some spatial clustering process among municipalities Spatial statistics tests confirm that
efficiency scores show a significant positive spatial autocorrelation This means that neighbouring
municipalities tend to show similar levels of efficiency This similar performance could be due to
a process of spatial dependence (eg efficiency spillovers among neighbouring municipalities) or
due to the existence of structural-geographical similarities (spatial heterogeneity) To assess the
spatial dimension in municipal efficiency abstracting from temporal fluctuations we use a cross-
section of data consisting of the six-year average for the variables in our panel After running a
regression of efficiency scores against the set of controls the analysis of OLS residuals shows that
the spatial autocorrelation is almost completely removed This means that the spatial pattern in
19
municipal efficiency can be explained (controlled) by other variables such as regional indicator
variables rather than efficiency itself Given this result we proceed to study the influence of
income inequality on municipal efficiency using traditional (non-spatial) regression analysis
In contrast to literature that emphasizes an equity-efficiency trade-off (Andersen amp Maibom
2020 Berg amp Ostry 2011 Browning amp Johnson 1984 Okun 2015) that is greater equality leads
to lower efficiency we find that municipal efficiency is inversely associated with income
inequality This implies that more equal counties are also those with higher municipal efficiency
Furthermore the coefficient of income inequality is close to one when we use the instrumental
variable approach This means that a reduction in income inequality ceteris paribus should be
associated with an increase in the same magnitude in municipal efficiency This result has strong
policy implications The non-existence of the trade-off suggests that there is more to be gained by
targeting policies towards the reduction of inequality than conventional theories suggest For
instance these policies may help increase the levels of efficiency and well-being at least at the
municipal level
Social cohesion and economic diversity
The third essay studies the relationship between the degree of social cohesion and diversity
in Chile Extant literature has argued that one of the main factors influencing social cohesion is
the degree of economic and ethnic-racial diversity within a society This diversity erodes social
cohesion reducing trust and corrupting institutions (Letki 2008 Rothstein amp Uslaner 2005
Tolsma Van der Meer amp Gesthuizen 2009 Uslaner 2011 2013)
To measure social cohesion scholars have traditionally used measures of social capital trust
or feelings of insecurity (Ariely 2014 Chan To amp Chan 2006 Letki 2008) We suggest the use
20
of the rate of incivilities per 1000 inhabitants as a proxy for social cohesion Incivilities correspond
to those antisocial behaviours (eg groups of rowdy teens and public drunkenness) or visible
neighbourhood conditions (eg graffiti and abandoned buildings) that tend not to be treated as
crime Using the rate of incivilities is arguably a more objective and reliable measure of social
cohesion particularly in countries where institutions of order and security are among the most
trusted An increase in the rate of incivilities rather than changes in crime rates should better
capture the worsening in social cohesion experienced in countries such as Chile where crime rates
are not growing but social conflicts are on the rise Thus the main hypothesis in this essay is that
the rate of incivilities (social cohesion) should be positively (negatively) associated with economic
and racial diversity
Using panel count data models we start analysing how differences in incivilities rates
between and within counties are associated with differences in indicators of relative and absolute
economic disadvantage We use the Gini coefficient of each county as our measure of economic
diversity Although we find a significant and positive association between the rate of incivilities
and the level of income inequality the magnitude of the link seems to be small Among absolute
indicators of economic disadvantage only the level of income shows a strong effect Next we
include our measure of racial diversity We use the number of new visas granted to foreigners as
a proportion of the county population Results show a significant and strong positive association
between the rate of incivilities and racial diversity
To check the robustness of our results we analyse the impact of our measures of economic
and racial diversity running our models separately for each Chilean region and clustering them
geographically We also split the total number of incivilities in four categories to see which type
21
of incivilities show the greatest association with our measures of diversity In general results
support the ldquocommunity heterogeneity hypothesisrdquo that higher community heterogeneity is
associated with higher rates of incivilities (Alesina amp La Ferrara 2002 Letki 2008 Tolsma et al
2009) However results do not support the ldquoincivilities thesisrdquo that the distribution of incivilities
tends to mirror the distribution of income inequality (Skogan 1999 Taylor 1999)
Three results stand out among the set of control variables First the level of education shows
and independent and significant negative association with the rate of incivilities This is in contrast
to previous studies where education acts mainly as a moderator of the effect of economic and racial
diversity on social cohesion (Tolsma et al 2009) The results also show that there is no significant
relationship between the rate of incivilities and the proportion of young population This is relevant
because policies aimed to reduce incivilities usually put the focus on specific groups such as young
people which are linked to physical and social incivilities when social control is weakened
Finally the degree of financial municipal autonomy also shows a significant negative association
with the rate of incivilities This result suggests that municipalities can contribute independently
or together with the central government to reduce incivilities and strengthen social cohesion
Contributions
The three essays in this thesis provide several important insights into the analysis of the
causes and consequences of income inequality particularly in the context of Chile ndash a typical
resource rich economy with persistently high levels of income inequality
Essay 1 advances the understanding of the relationship between income inequality and
natural resources in Chile extending the empirical analysis from the regional level to the county
level In addition the geographic heterogeneity of income inequality is explored with the inclusion
22
of alternative sources of spatial dependence as a potential dimension of the causal relationship
between income inequality and natural resources This essay demonstrates the relevance of natural
resources in explaining the persistence of income inequality even after controlling for other
socioeconomics and institutional factors Findings from this study have potential contribution not
only in the design of policies aimed to reduce income inequality but also in addressing the current
developmental bias between the metropolitan region and the rest of the country
Essay 2 is one of the first studies that undertake a longitudinal analysis of the effects of
income inequality on the efficiency of municipal governments in Chile To capture the role of the
municipal governments in the provision to local people of public services such as education and
health we specify several inputs and outputs in our efficiency model which is different from the
conventional specification in the existing literature For example the number of medical
consultations in public health facilities and the number of enrolled students in public schools are
used as outputs instead of general indicators such as county population Our empirical analysis
also utilises a larger sample of municipalities and covers a much longer period spanning from 2006
to 2017 This essay also investigates the contextual factors beyond the control of local authorities
that can explain variations in the efficiency of municipal governments across the country
Empirical findings from Essay 2 help us increase our understanding of the production
technology of municipalities the sources of inefficiencies and specifically the impact of income
inequality on the performance of local authorities The results deliver two main policy
implications First municipal inefficiencies in the provision of public goods and services differ
across Chilean municipalities In addition efficiency levels show some degree of spatial
autocorrelation This implies that policies such as amalgamation or cooperation among
23
municipalities could have effects beyond the municipalities involved which must be considered
Second the causal effect that income inequality has on municipal efficiency provides another
dimension into the design and implementation of development policies
Essay 3 explores for the first time the effects of economic and racial diversity on social
cohesion in Chile This essay considers incivilities as manifestation of social cohesion and
investigates as extant literature suggests whether indicators of relative economic disadvantage
such as income inequality are among the main factors driving social disorganization and social
unrest Empirical findings suggest that on the one hand economic heterogeneity captured by the
Gini coefficient has a disparate effect both in terms of magnitude and significance on the rate of
incivilities across the country On the other hand the impact of racial heterogeneity appears to be
stronger more significant and of a similar magnitude throughout the country Results also provide
new insights into the design of national policies addressing social disorders particularly those
policies focussed on specific groups of the population and the role of local authorities Overall the
findings provide an opportunity to advance the understanding of the process of weakening in the
social cohesion experienced in Chile and the conflicts that have risen from this process
Thesis outline
The remainder of the thesis is organized as follows Chapter 2 presents essay 1 examining
the association between income inequality and the degree of dependence on natural resources
Chapter 3 presents essay 2 which looks for a causal relationship between municipal efficiency and
income inequality Chapter 4 presents essay 3 analysing the relationship between social cohesion
and economic and racial diversity Finally Chapter 5 presents some concluding remarks
24
Chapter 2 Natural Resources Curse or Blessing Evidence on
Income Inequality at the County Level in Chile
21 Introduction
A phenomenon of increasing inequality of incomes and wealth in recent decades has been
documented by leading scholars and international organizations such as the International Monetary
Fund (Berg amp Ostry 2017 Ostry Berg amp Tsangarides 2014) and the Organization for Economic
Cooperation and Development (Cingano 2014) These efforts have placed the topic of inequality
at the top of the current economic debate recognizing inequality as a determinant not only of
economic growth but also of human development They also have highlighted the necessity for
more research on the drivers of inequality and mechanisms through which it manifests aiming to
design effective policies in reducing economic and social inequalities
Various factors have been analysed as the sources of high and increasing levels of inequality
Among the most significant factors are the levels of income at initial stages of economic
development (Kuznets 1955) Globalization (Milanovic 2016) skill-biased technological change
(Tinbergen 1975) investment in human capital (Murphy amp Topel 2016) institutions
redistributive policy and country-specific characteristics (Acemoglu 1995 2002 Acemoglu
Aghion amp Violante 2001 Acemoglu Johnson amp Robinson 2001) Our focus in this essay is on
the importance that the natural resource endowment (NRE) or lack thereof can play in the
determination of income disparities
25
This essay studies the patterns and evolution of income inequality in the context of a natural
resource-rich country Using the case of the Chilean economy we aim to understand and
disentangle how a phenomenon of high- and persistent-income inequality is related to the
endowment of natural resources that a country owns Chile is an interesting case to study because
despite showing a successful history of economic growth inequality among individuals and among
aggregated spatial units has shown a strong persistence (Paredes et al 2016) Furthermore Chile
has remained among the most unequal countries in the world1
Theory and empirical evidence do not establish a clear link between income inequality and
NRE In addition NRE has received considerably less attention (Auty 2001 ElGindi 2017) and
most of the evidence has been focused on cross-country comparisons For instance NRE can
influence inequality by determining its initial levels (Engerman amp Sokoloff 1994 1997
Engerman Sokoloff Urquiola amp Acemoglu 2002) shaping the evolution of institutions
(Acemoglu 2002) make the educational system less intellectually challenging and moulding the
structure of economic activity (Leamer et al 1999) So studying how cross-county differences in
NRE are associated with the distribution of income within a country has theoretical empirical and
policy implications
In this study we offer empirical evidence on the relationship between income inequality and
the endowment of natural resources using data at the county level in Chile for the period 2006-
2017 Income inequality is measured by the Gini coefficient The importance of NRE is proxied
using a measure of natural resource dependence (NRD) defined as the percentage of the total
1 A 2014 OECD report on income inequality (httpwwwoecdorgsocialincome-distribution-databasehtm) showed Chile as the country with the highest Gini coefficient of disposable income among OECD countries OECD also indicates Chile as the country with the widest gap between the richest 10 percent and the poorest 10 percent of countryrsquos population among its 34 members (OECD 2014)
26
employment in each county corresponding to the primary sector (agriculture forestry fishing and
mining)
The main hypothesis to be tested is whether income inequality is positively associated with
the degree of NRD The transmission mechanisms through which natural resources could influence
socioeconomic outcomes could be based on the market or institutions The market-based approach
argues that natural resource booms could be associated with an appreciation of the real exchange
rate and a crowding out effect over other more productive economic activities such as
manufacturing It could also delay the adoption of new technologies and reduce incentives to invest
in physical and human capital (Gylfason amp Zoega 2003) Based on the ldquoResource Curse
Hypothesisrdquo (RCH) natural resources could be a curse when the political and institutional
framework is weak and natural resources are concentrated in space such as oil and minerals
(Deacon 2011) 2 Among institutional channels a higher NRD or natural resource booms could
be associated with rent seeking misallocation of labour and entrepreneurial talent institutional
and political decline or even violent conflicts For instance the ldquoParadox of Plentyrdquo sustains that
windfalls of revenues as a consequence of resource booms could be related to a lack of incentives
to perform efficiently corruption patronage and local authorities favouring their voters or being
captured by the richest groups (Dauvin amp Guerreiro 2017) Hence a higher NRD or natural
resource booms could be the explanation not only for low levels of growth in regions more
dependent on natural resources but also it could be the root of income disparities
2 There is a wide strand of research on the Resource Curse Hypothesis however the evidence so far is not conclusive Evidence in favour of RCH has been mainly found in developing resource rich countries (Auty 1993 2001 Badeeb Lean amp Clark 2017 Blanco amp Grier 2012 Borge et al 2015 Brunnschweiler amp Bulte 2008 Sachs amp Warner 2001 Van der Ploeg 2011)
27
To test our hypothesis that is whether the levels of income inequality across counties are
positively associated with the degree of NRD we use a spatial econometric approach We use this
approach because attributes such as income inequality in one region may not be independent of
attributes in neighbouring regions (Armstrong amp Taylor 2000) This process of spatial dependence
invalidates the use of traditional (non-spatial) approaches
This study seeks to make two contributions to research First previous empirical evidence
shows a significant spatial dimension of income inequality in Chile (Paredes et al 2016)
However this dimension has been barely explored with most studies limiting the degree of
disaggregation to a regional scale (Aroca amp Bosch 2000) We use a spatial approach which makes
it possible to model and test the significance of the spatial dimension in the analysis of income
inequality and its relationship with other variables Second previous research for the Chilean
economy linking inequality with NRE has been mainly focused on explaining differences between
regions or the importance and effects of the mining-copper sector (Aroca amp Atienza 2011 Ebert
amp La Menza 2015 Lagos amp Blanco 2010 Rehner Baeza amp Barton 2014) We extend this
analysis using data for local economies Identifying and quantifying the impact of NRE on income
inequality at the county level is likely to be more informative for policies aiming to address the
current developmental bias between the metropolitan region and the rest of the country Moreover
the analysis of the role of natural resources in conjunction with other potential sources of inequality
may shed lights in understanding the persistence of the high levels of inequality observed in the
Chilean economy All in all this study could contribute to the design of policies that
simultaneously help reduce inequality increase efficiency and promote sustainable and inclusive
growth
28
Our main finding shows that after controlling for other potential sources of income
inequality such as educational level demographic characteristics and the level of public
government expenditure the degree of dependence on natural resources has a significant effect on
income inequality However contrary to our expectations the effect is negative This result
suggests that the natural or policy-driven process of transformation from primary and extractive
activities to manufacturing and service sectors imposes additional challenges to central and local
authorities aiming to reduce income inequality
In section 22 we review the literature on the relationship between income inequality and
natural resources In section 23 we establish our research problem and main hypothesis Section
24 describes our data and methods and section 25 the empirical results We finish with section
26 discussing our main results concluding and proposing avenues for future research
22 Inequality and Natural Resources
221 Theoretical Framework
Explanations for income inequality can be associated with individual institutional political
and contextual characteristics Individual characteristics include age gender and mainly the level
of education and skills of the population in the labour force For instance globalization and
technological change lead firms to increase the demand for skilled labour deepening income
inequality between skilled and unskilled workers (Atkinson 2015 Milanovic 2016 Tinbergen
1975) Among institutional characteristics labour unions collective bargaining and the minimum
wage have been suggested as explanations of income inequality (Acemoglu Aghion et al 2001
Atkinson 2015) Policy design associated with market regulation progressive taxation and
redistribution can also impact the levels and patterns of inequality
29
A key factor in understanding the levels and differences in income distribution within a
country may be its endowment of natural resources NRE shapes the structure of the economy
(Leamer et al 1999) it is associated with the creation of institutions that define the political
culture and it can also influence the performance of other sectors (Watkins 1963) In addition
NRE determines initial conditions market competition ownership over resources rent seeking
and the geographical concentration of the population and economic activity
Cross‐countryliterature
Bourguignon and Morrison (1990) introduce one of the earliest theoretical frameworks
describing the relationship between inequality and NRE They develop a small open economy
model where income distribution is a function of NRE ownership structure and trade protection
Giving cross-sectional evidence for a group of developing countries they conclude that the impact
of NRE particularly mineral resources and land depends on the number and size of the firms
whether they are public or private and the level of protection A higher concentration of production
in a few private firms a big share of production oriented to foreign instead of domestic markets
and protection increasing the relative price of scarce resources are some of the reasons explaining
why some countries are less egalitarian than others
NRE could also influence the evolution and levels of inequality by determining the initial
distribution of incomes This is known as the ldquoEngerman-Sokoloff Hypothesisrdquo (Engerman amp
Sokoloff 1997 Engerman et al 2002) In addition Leamer (1999) proposes that inequality and
development paths in each economy are a function of its economic structure which in turn depends
on ldquofundamentalsrdquo and ldquosymptomsrdquo On the one hand ldquofundamentalsrdquo refer to resource
endowment production structure closeness to markets and governments interventions On the
30
other hand ldquosymptomsrdquo are related to institutions employment structure and net export structure
Using this conceptual framework Leamer argues that natural Resource-Rich Countries (RRC) can
experience a higher level of inequality because can have a ldquodumbbell educational systemrdquo
ownership is concentrated in small groups and extraction activities require low-skilled workers
This implies fewer incentives to educate citizens until very late in the development process
resulting in human capital not prepared to take advantage of the process of technological progress
and delaying the emergence of more efficient and competitive sectors such as manufacturing and
services
Using 1980 and 1990 data for a group of countries classified according to land abundance
Leamer (1999) provides evidence showing that on the one hand land-scarce countries concentrate
their production and employment in sectors that promote equality such as capital-intensive
manufacturing chemical or machinery On the other hand countries abundant in natural resources
concentrate their production trade or employment in sectors that promote income inequality such
as the production of food beverages extraction activities or forestry
Gylfason and Zoega (2003) using a framework based on standard growth models also
proposed a positive relationship between NRE and inequality They assume that workers can work
in the primary sector or in the manufacturing (including services) sector In addition wage income
is equally distributed in the manufacturing sector but unequally in the primary sector (because of
initial distribution competition rent seeking etc) Therefore inequality will be greater when a
bigger proportion of labour is dedicated to extraction activities in the primary sector This
phenomenon is further amplified because of lower incentives to invest in physical and human
capital to adopt new technologies and to increase the share of the manufacturing sector
31
Diverse mechanisms explaining the link between NRE and inequality have been proposed
arguing that NRE determines simultaneously economic growth and inequality (Gylfason amp Zoega
2003) NRE could impact economic growth through the real exchange rate and the crowding-out
effect on manufacturing (ldquoDutch Diseaserdquo) reducing incentives to invest in physical and human
capital (Easterly 2007) and influencing the processes of technology adoption industrialization
and diversification of the economy in a manner that is less conducive to growth (Bulte et al 2005)
These potential explanations related to the called ldquoResource Curse Hypothesisrdquo do not have strong
empirical support (Auty 2001 Bulte et al 2005)
NRE may also influence economic growth through the quality of institutions (Acemoglu
1995 Acemoglu Aghion et al 2001 Acemoglu amp Robinson 2002 Engerman amp Sokoloff 1997
Engerman et al 2002) the concentration of ownership political power and rent-seeking NRE
acts by shaping institutional context and social infrastructure a phenomenon that is stronger when
resources are spatially concentrated such as minerals and plantations (Bulte et al 2005) NRE
could also have a significant effect on social cohesion and instability spreading its influence like
a disease (Brunori Ferreira amp Peragine 2013 Kanbur amp Venables 2005 Milanovic 2016
Ocampo 2004)
Considering a non-tradable sector intensive in unskilled workers Goderis and Malone
(2011) develop a model where the natural resources sector experiences an exogenous gift of
resource income They analyse the impact over income inequality of resource booms proxied by
changes in a commodity price index They conclude that inequality decreases in the short run but
increases after the initial reduction
32
Fum and Hodler (2010) show that natural resources increase inequality but this is
conditional on the level of ethnical polarization of society Carmignani (2013 2010) confirms this
positive relationship using different measures of dependence and abundance and goes further
arguing that inequality constitutes an indirect channel through which NRE affects human
development
Singlecountryevidence
Most of the studies about the relationship between inequality and NRE derive from cross-
country analyses Evidence for specific countries has been mainly based on case studies Howie
and Atakhanova (2014) based on the model of Goderis and Malone (2011) find for the case of
Kazakhstan that income and consumption inequality decreased significantly after booms in the oil-
and-gas sector because of resource booms increase the demand for non-tradable goods which are
intensive in unskilled workers The results depend on the level of rurality institutional quality
education levels and public spending on health and education Fleming and Measham (2015b
2015a) evaluate the impact of booms in the mining and oil sectors in Australia They find that a
boom in the mining sector increases income inequality due to commuting and migration among
regions This phenomenon can be exacerbated when the demanding access to natural resource
revenues is associated with the creation of more local administrative units (counties provinces and
even regions) but the government capacity is not simultaneously improved (Cust amp Poelhekke
2015 Michaels 2011) Furthermore the benefits that a region can receive in the form of fiscal
transfers can be more than compensated by the loses due to city-to-mine commuting such as the
case of mining regions in Chile (Aroca amp Atienza 2011)
33
Other studies at the local level have analysed the impact of the mining sector in Peru (Aragoacuten
amp Rud 2013 Loayza amp Rigolini 2016 Loayza Teran amp Rigolini 2013) Spain (Domenech
2008) and Canada (Papyrakis amp Raveh 2014) and the effects of oil windfalls in Brazil (Caselli amp
Michaels 2013)
In summary there is a wide range of potential mechanisms through which NRE could
influence income inequality Although most of them seem to suggest a positive relationship others
such as commuting and increased within-county demand for non-tradable goods and services
could lead to a negative association This highlights the need to know the sign of this association
in the Chilean economy where the trend shows a reduction in the degree of NRD After controlling
for other factors a positive link would support the argument that the reduction in the degree of
NRD has been relevant in the reduction experienced by income inequality in the same period
However a negative link would support the position that the reduction in NRD has contributed to
explain the persistence of income inequality and its slow reduction
222 The relevance of the spatial approach
Inequalities within countries are still the most important form of inequality from the political
point of view (Milanovic 2016) People from a geographic area within a country are influenced
and care most about their status relative to the people in other areas in the same country The
influence among regions involves multiple aspects (eg economic political and environmental)
These potential interactions have been traditionally ignored assuming independence among
observations related to different regions Moreover neglecting the process of spatial interaction in
key indicators of the economic and social performance of a country may mislead the design of the
public policy
34
The spatial dimension could play a significant role in understanding the distribution of
income within a country One strand of efforts aiming to capture the geographic heterogeneity of
inequality has been focussed on decomposing general indicators such as the Gini coefficient or the
Theil Index Evidence for different countries including the US (Doran amp Jordan 2016) China
(Akita 2003 Gustafsson amp Shi 2002 Ye Ma Ye Chen amp Xie 2017 Yue Zhang Ye Cheng
amp Leipnik 2014) Japan (Ohtake 2008) South Africa (Leibbrandt Finn amp Woolard 2012) and
Chile (Paredes et al 2016) shows that regional inequality is sensitive to the geographic scale of
analysis These studies also show a significant spatial component in the explanation of inequality
of income expenditure or gross domestic product for each country
Another strand explicitly uses exploratory spatial data analysis (ESDA) and spatial
econometrics ESDA has been used to provide new insights about the nature of regional disparities
of incomes and growth rates (Celebioglu amp Dallrsquoerba 2010 Yue et al 2014) Spatial econometric
models aim to assess and address the nature of the spatial effects These effects could be the result
of ldquospatial heterogeneityrdquo that is different relationships in distinct locations or ldquospatial
dependencerdquo which implies cross-sectional interactions (spillover effects) among units from
distinct but near locations
Spatial spillovers have been analysed to study both positive and negative spatial correlation
among less resource-abundant counties and resource-abundant counties On the one hand less
resource-abundant counties may experience positive spillovers because their industries supply
more goods and services to meet the increasing regional demand They can also be benefited from
positive agglomeration externalities and higher investment in private and public infrastructure
(Allcott amp Keniston 2014 Michaels 2011) On the other hand negative spillovers could be the
35
result of a high degree of interregional migration that limits the rise in wages and higher local
prices due to the increase in the share of the non-tradable sector In addition local governments
could have a limited capacity to translate the revenues from resource booms into effective public
policies promoting a sustained local development (Beine Coulombe amp Vermeulen 2015 Caselli
amp Michaels 2013 Papyrakis amp Raveh 2014)
23 Research problem and hypotheses
We can conclude from our overview of the literature that the theoretical and empirical
evidence about the link between inequality and natural resources is inconclusive This does not
make clear whether the process of reduction in the degree of dependence on natural resources
such as that experienced by the Chilean economy helps to explain the sustained but slow reduction
in income inequality or its high persistence
The research question guiding this study relates to how the natural resource endowment
determines the paths and structure of income inequality in natural resource-rich countries Using
the case of Chile the main hypotheses to be tested is whether a higher degree of dependence on
natural resources is associated with higher levels of income inequality To do that we use data at
the county level and we explicitly include the spatial dimension Our aim is to arrive at a more
comprehensive understanding of the drivers and transmission mechanisms explaining the
evolution and patterns shown by income inequality In addition we test whether the spatial
dimension plays a significant role in explaining differences in income distribution in Chile
36
24 Data and Methods
We use county-level data for the years 2006 2009 2011 2013 2015 and 2017 The reason
for not using contiguous years is that income data at the household level are only available every
two-three years from the Chilean National Socioeconomic Characterization Survey (CASEN in its
Spanish acronym)3 For the period 2006-2017 the Chilean administrative division considers 15
regions 54 provinces and 346 counties Data on income are available for 324 counties and six
years resulting in a panel with 1944 observations4
We start evaluating the spatial dimension in our data and analysing the link between
inequality and NRD using a cross-sectional setting To this end we use the ldquosix-year averagerdquo
(2006 2009 2011 2013 2015 2017) for our variables given the low time variability showed by
our measures of income inequality and NRD Results are then compared with those of a panel data
setting
241 Operationalization of key variables
The dependent variable in the present study income inequality at the county level is
measured calculating the Gini coefficient using three definitions of household income labour
autonomous and monetary income5 Labour income corresponds to the incomes obtained by all
members in the household excluding domestic service consisting of wages and salaries earnings
3 CASEN survey is conducted by the Chilean Ministry of Social Development covering topics such as education employment income and health CASEN is considered nationally representative and it is the main source for measures of inequality and poverty used for the design and evaluation of social policies in Chile 4 The six waves of CASEN for our study considered an average of 75599 households and 252081 individuals 5 The Gini coefficient is chosen because provides an overall estimate of income inequality It summarizes what proportion of the population gains what proportion of the total income The Gini coefficient can range between 0 (everyone in the population has the same income) and 1 (one person earns 100 per cent of the income in the community)
37
from independent work and self-provision of goods Autonomous income is the sum of labour
income and non-labour income (including capital income) consisting of rents interest and dividend
earnings pension healthcare benefits and other private transfers Finally monetary income is
defined as the sum of autonomous income and monetary subsidies which correspond to cash
transfers by the public sector through social programs Appendix A shows summary statistics for
the Gini coefficient of our three measures of income
The main independent variable in our study is the degree of dependence on natural resources
in each county To have an idea of the importance of each economic activity in the Chilean
economy particularly those activities related to natural resources Figure 21 shows their average
share in Chilean Gross Domestic Product (GDP) for the period 2006-17 We can observe that the
leading activities are those related to the primary sector especially mining and to the tertiary
sector where financial personal commerce restaurants and hotels services stand out The shares
of each economic activity in GDP vary significantly between Chilean regions and such
information is not available at the county level
Figure 21 Average share in GDP of economic activities (2006ndash17)
38
Leamer (1999) argues that when the main source of income is labour income (as indeed
happens for the Chilean case) using employment shares allows a better approach to measuring
dependence on natural resources Using employment data from CASEN survey we define our
measure of NRD as the employment in the primary sector (mining fishing forestry and
agriculture) as a percentage of the total employment in each county We name this variable
pss_casen where ldquopssrdquo stands for ldquoprimary sector sharerdquo We built other two proxies of NRD
using data from the ldquoServicio de Impuestos Internosrdquo (SII) which is the agency in charge of
collecting taxes in Chile The variable pss measures the percentage of employment in the primary
sector and the variable pss_firms measures the number of firms in the primary sector as a
percentage of the total number of firms in each county Appendix B shows summary statistics for
our three measures of NRD disaggregated by region
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17)
39
Figure 22 shows the evolution of our measure of inequality (using the Gini coefficient of
autonomous income) and our three potential proxies for NRD for the period 2006-2017 We
observe that both income inequality and the degree of NRD show a downward trend This seems
to support our hypothesis of a positive link between inequality and NRD however we need to
control of other sources of inequality before getting such a conclusion In what follows we use the
variable gini as our measure of income inequality capturing the Gini coefficient of autonomous
income Our measure of NRD is the variable pss_casen defined previously
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17)
Note Gini and NRD averages 2006-09-11-13-15-17 for 324 Chilean counties divided into five equal groups Source Own elaboration based on CASEN survey
40
Figure 23 shows quantile maps for income inequality (on the left) and NRD (on the right)
using the six-years average dataset6 On the one hand we observe that high levels of inequality
seem to be clustered in the Centre-South of the country where agriculture forestry and fishery are
the predominant economic activities Only isolated counties show high inequality in the Centre
(Metropolitan area where the countyrsquos capital is located) and North (Mining) areas On the other
hand our measure of NRD seems to show an opposite spatial pattern than income inequality with
high levels in the Centre and North of the country
242 Control variables
To control for county characteristics we use a set of socio-economic demographic and
institutional variables Economic factors are captured by the natural log of the mean autonomous
household income per capita (in thousands of Chilean pesos of 2017) lnincome the poverty rate
poverty the unemployment rate unemployment the percentage of the population living in rural
areas rural and the average years of education of the population over 15 years old education
Demographic factors include the proportion of the population in the labour force labour_force
and the natural log of population density (population divided by county area) lndensity
We also include the natural log of the total municipal public expenditure per capita
lnmuni_expenditure to control for municipal heterogeneity This heterogeneity is mainly related
to the capacity of municipalities to generate their own revenues In addition the richest
municipalities are in the Metropolitan region which concentrates economic power and around 40
6 After sorting a variable in ascending order quantile maps use the quantiles (quartiles quintiles deciles etc) as class breaks to divide the distribution of the variable where each class includes approximately the same number of observations (counties)
41
of the population This has basically implied a lag in the development of regions other than the
metropolitan region
The spatial distribution of our measures of income inequality and NRD displayed in Figure
23 seems to show different patterns in the North Centre and South of the country Appendix C
shows the administrative division of Chile in 15 regions and how we have grouped them in three
zones North Centre and South We consider as the ldquoCentrerdquo area that formed by the Metropolitan
region (XIII) and its two neighbouring regions V and VI Using the Centre area as our reference
we include in our analysis two dummy variables indicating whether a county is located in the North
area (regions XV I II III and IV) or South area (regions VII VIII IX XIV X XI and XII)
Appendix D shows summary statistics for the set of numeric control variables and the
correlation matrix between our measure of NRD pss_casen and the set of numeric controls
243 Methods
To assess and then consider the spatial nature of the data we need to define the set of relevant
neighbours for each country This is operationalized by building a matrix called ldquoWrdquo with a ldquo1rdquo
for neighbouring counties and a ldquo0rdquo for non-neighbouring counties We could build W using
contiguity-based (whether counties share a border or point) or geography-based (taking the
distances among the centroids of each county polygon) spatial weights Specifically we build a W
matrix considering the 5-nearest counties7 Two reasons explain the choice of k-nearest
neighbours First we cannot use a contiguity criterium because we do not have information about
all the counties and there are some geographically isolated counties Second given the significant
7 We assign a ldquo1rdquo to the five nearest counties to each county based on the distances among the polygon centroids Then W is ldquorow standardizedrdquo This facilitates the interpretation of the spatial lag of a variable as the ldquoweighted average valuerdquo of the same variable in neighbouring counties
42
differences in county areas (ldquobig countiesrdquo in northern and southern regions) using a distance-
band criterium with a not enough large distance band can lead to many ldquoislandsrdquo in extreme regions
and a multi-modal distribution for the number of neighbours
We start testing our inequality and NRD variables for spatial autocorrelation in order to
evaluate statistically the clustering patterns shown in Figure 23 Next we run an OLS regression
of inequality against NRD and our set of controls and we test the spatial autocorrelation of OLS
residuals If we cannot reject the null hypothesis of random spatial distribution we do not need
spatial models to analyse income inequality which would give contrasting evidence to previous
suggestions about the relevance of the spatial dimension of income inequality in Chile (Paredes
2013 Paredes et al 2016) If we find significant spatial autocorrelation in the OLS residuals this
justifies the use of spatial models and highlight the need to find the correct spatial structure8
If inequality in one county spillovers or influences inequality in neighbouring counties the
spatial lag of inequality should be included as an explanatory variable and we should use a spatial
autoregressive model (SAR) If some unobserved variable is the explanation for the clustering of
counties with similar inequality then this will be better captured including a spatial lag of the
errors and we should use a spatial error model (SEM) (Anselin 1988 Anselin amp Bera 1998)
Finally when our main explanatory variable or some of the controls show spatial autocorrelation
a spatial lag of the explanatory variable(s) should be included in our model
8 The existence of spatial autocorrelation violates the standard assumption of independence among observations needed for OLS regression This will result in OLS coefficients biased and inconsistent (Anselin 1988)
43
244 Spatial Model Specification
A model that includes the three forms of spatial dependence described above is called the
Cliff-Ord Model The model in its cross-sectional representation could be expressed as
119910 120582119882119910 119883120573 119882119883120574 119906 (21)
where
119906 120588119882119906 120576 (22)
119882 is our weight matrix that works as an NxN spatial lag operator9 Thus 119882119910 119882119883 and 119882119906
are the spatial lags for the dependent variable explanatory variables and the error term
respectively The parameter 120582 capture the spatial dependence in the dependent variable 120574 the
spatial dependence in the explanatory variables 120588 capture the spatial dependence in the error term
and 120598 is a vector of idiosyncratic errors For instance if ldquoyrdquo is income inequality and ldquoXrdquo a measure
of NRD the level of inequality in one county will be explained by the degree of NRD in the same
county 119883120573 the average degree of NRD in neighbouring counties 119882119883120574 the average level of
inequality in neighbouring counties 120582119882119910 and the average value of residuals in neighbouring
counties 12058811988211990610
From equations (21) and (22) the SAR and SEM models can be seen as special cases of
the Cliff-Ord representation after imposing restrictions over the spatial parameters 120582 120574 and 120588 For
the specification of the spatial panel models we follow the terminology by Croissant and Millo
9 The spatial lag is a weighted sum of the values observed at neighbouring locations 10 On the one hand the impact that income inequality in one county has over income inequality in neighbouring counties is called a ldquoglobal spilloverrdquo and it is associated with the feedback effect among neighbours (one county is its neighboursrsquo neighbourrdquo) on the other hand the influence that the degree of NRD in neighbouring counties has over inequality in one county is called a ldquolocal spilloverrdquo
44
(2018) Spatial panel models including the spatial lag of the dependent variable (SAR) the spatial
lag of the residuals (SEM) or both (SARAR) are described in Appendix E
25 Results
251 Exploratory Spatial Data Analysis (ESDA)
To analyse the significance of the spatial dimension in our data set we use the six-year
average of our variables Spatial autocorrelation is tested using the Moranrsquos I statistic11 Moranrsquos
I measures the correlation of one variable with itself in space12 Figure 24 shows the Moran scatter
plots where the standardized variable (Gini coefficient and NRD for each county) appears in the
horizontal axis against its spatial lag (average value in the 5-nearest neighbouring counties) The
Moranrsquos I (slope of the line in the Moran scatter plot) of income inequality shows a significant
positive spatial autocorrelation that is counties with high (low) inequality tend to be close to each
other
11 There are many statistics to formally test the significance of the spatial dimension in the distribution of our data The null hypothesis assumes spatial randomness which means that there is not spatial structure in the data so any spatial pattern is equally likely to occur and values in one location do not depend on values in other locations The alternative hypotheses can be the existence of positive or negative spatial autocorrelation Positive spatial autocorrelation means similar values in neighbouring locations (less variability than under spatial randomness) Negative spatial autocorrelation means dissimilar values in neighbouring locations (more variability than under spatial randomness) 12 Traditional measures of correlation such as the Pearsonrsquos coefficient measure the degree of linear correlation between two different variables Measures of spatial autocorrelation assess the correlation between the values of one variable in one location related to the values of the same variable in other neighbouring locations
45
Figure 23 Moran scatter plots for variables gini and pss_casen
Moranrsquos I is a measure of global spatial autocorrelation this means it is intended to capture
the clustering property of the entire data set To identify where are the significant hot-spots
(clusters of counties showing high income inequality) or cold-spots (clusters of counties showing
low income inequality) we need local indicators of spatial association (LISA) Using the local
Moranrsquos I (not reported) we find significant hot-spots in the South of the country (mainly
agricultural regions) and significant cold-spots in the Centre (Metropolitan area) of the country
The next step is to check whether the clustering pattern in inequality is the result of a process of
spatial dependence in the variable itself or it can be explained by other variables related to
inequality
252 Cross-sectional analysis
We start analysing differences in income inequality between counties using the six-year
average data and running an OLS regression for the model
119892119894119899119894 120573 120573 119901119904119904_119888119886119904119890119899 120573 119897119899119894119899119888119900119898119890 120573 119901119900119907119890119903119905119910 120573 119906119899119890119898119901119897119900119910119898119890119899119905 120573 119897119886119887119900119906119903_119891119900119903119888119890 120573 119890119889119906119888119886119905119894119900119899 120573 119897119899119889119890119899119904119894119905119910 120573 119903119906119903119886119897 120573 119897119899119898119906119899_119890119909119901119890119899119889119894119905119906119903119890 120573 119899119900119903119905ℎ 120573 119904119900119906119905ℎ
(23)
46
The Moran scatter plot and spatial statistical test of OLS residuals from equation (23) are
in Appendix F OLS residuals show a small but significant positive autocorrelation (Moranrsquos I =
0121) This means that income inequality continues showing a significant degree of spatial
autocorrelation after controlling for the set of covariates In addition robust Lagrange Multiplier
(LM) tests show that a spatial error model is preferred over a spatial lag model (Anselin Bera
Florax amp Yoon 1996)13 This means that income inequality tends to cluster in zones larger than a
county so the analysis should be performed on a larger scale such as provinces regions or macro
zones If the SAR model were preferred it would mean that income inequality in one county is
influenced by the level of income inequality in neighbouring counties To find the spatial structure
that best fits the clustering process of income inequality we run the full set of spatial model
specifications in a cross-sectional setting and results are shown in Table 21
Column 2 in Table 21 shows the results of our ldquoOLSrdquo model The ldquoSLXrdquo model includes
spatial dependence only through the explanatory variables The ldquoSARrdquo model includes the spatial
lag of the dependent variable as a regressor and the ldquoSEMrdquo model includes spatial dependence
through the error term The ldquoSARARrdquo model includes both the spatial lag of the response and the
errors and the ldquoSDMrdquo and ldquoSDEMrdquo are the extensions of the ldquoSARrdquo and ldquoSEMrdquo models
respectively including the spatial lag of the explanatory variables Finally a model including
spatial lags for the response errors and explanatory variables (the ldquoSARARXrdquo model) is shown in
the last column
13 Following the ldquoAnselin methodrdquo when both non robust LM tests are significant we should select between the robust LR tests From Table F1 in Appendix F only the robust LM test for the SEM model (RLMerr) appears as significant
47
Table 21
Cross-sectional Model Comparison (six-year average data)
48
Opposite to our hypothesis we observe a significant and negative coefficient for our measure
of NRD This means that counties more dependent on natural resources show lower levels of
inequality Education years population density and municipal expenditure per capita are also
negatively related to inequality On the other hand the level of income the poverty rate and the
proportion of the population living in rural areas show a positive relationship with income
inequality There is no significant influence of the unemployment rate and the proportion of the
population in the labour force In addition the SAR SEM and SARAR models show a
significantly higher average inequality in the South of the country related to the Centre area
The main finding from our cross-sectional analysis is that there is a significant and negative
relationship between inequality and NRD which is quite robust to the model specification
253 Panel Data analysis
Like the cross-sectional case we start estimating the panel without spatial effects Results
for the pooled fixed effects (FE) and random effects (RE) specifications of equation (3) are in
Appendix G Spatial models were estimated using Maximum Likelihood (ML) and Generalized
Moments (GM) and assuming that the spatial structure (W matrix) is the same for all years14
Tables 22 23 and 24 show results for the ML estimation of the SAR SEM and SARAR models
using the pooled FE and RE specifications Results for the GM estimation are in Appendix H
All our spatial models include time fixed effects In the case of the pooled and RE models they
additionally include indicator variables for those counties located in the North and South of the
country
14 In traditional (non-spatial) panels data are stacked as time series for each observational unit (county) In the case of spatial panels data are stacked as slice of cross-sections for each year in the panel
49
The main result is that the negative and significant effect of NRD on income inequality is
robust to most of the spatial panel specifications In addition the coefficient for the variable
pss_casen changes slightly among panel specifications (pooled FE and RE) but does not change
among spatial models (SAR SEM and SARAR)
Another important finding is related to the significance of the spatial dimension of income
inequality When spatial models cross-sectional or panel are compared to non-spatial models
there are no major differences in the magnitude of the coefficients or their significance This could
mean that the positive spatial autocorrelation shown by income inequality seems to be better
explained by a process of spatial heterogeneity rather than spatial dependence The practical
implication of this result is that including dummy variables for aggregated units (eg regions or
groups of regions) could be enough to control for the spatial dimension in the modelling and
analysis of income inequality
Among control variables years of education seems to be the main variable for the design of
long-term policies aimed at reducing inequality This result is in line with previous evidence for
cross-country studies (Leamer et al 1999) and specific countries (Howie amp Atakhanova 2014)
Municipal expenditure per capita also shows a significant and negative association with income
inequality in the pooled and RE spatial specifications This means that higher municipal
expenditure helps to reduce inequality between counties but its effect is more limited within
counties This result support the importance of local governments (Fleming amp Measham 2015a)
however the negative coefficient appears as evidence against the ldquoParadox of Plentyrdquo (Borge et
al 2015)
50
Table 22
ML Spatial SAR Models
Table 23
ML Spatial SEM Models
51
Table 24
ML Spatial SARAR Models
26 Discussion and conclusions
In this essay we delve deep into the sources of income inequality analysing its association
with the degree of dependence on natural resources using county-level data for the 2006ndash2017
period in Chile Given recent evidence for the Chilean economy suggesting a significant spatial
dimension we assess and incorporate explicitly the spatial structure of income inequality using
spatial methods We use cross-sectional and panel data to evaluate the significance of the spatial
dimension and we test whether NRD has a positive effect on income inequality
Contrary to what theory predicts NRD shows a significant and negative association with
income inequality This result is robust to the type of analysis (cross-sectional vs panel data) the
approach (spatial vs non-spatial) and the inclusion of different controls The negative and
significant coefficient implies that if the degree of NRD would not have experienced a 10 drop
during this period income inequality could have fallen in 2 additional points So the downward
trend in the participation of the primary sector in terms of employment in the Chilean economy
52
could be one of the main reasons explaining the high persistence in the levels of income inequality
This means that those areas that undergo a process of productive transformation mainly towards
the services sector would be facing greater problems to reduce inequality This process of
productive transformation natural or policy-driven highlights the importance of policies focused
on human capital and the role of local governments in reducing inequality
The main implication for policymakers is that a reduction in NRD does not help to reduce
inequality generating additional challenges for local and central governments in its attempt to
transform the structure of their economies to fewer dependent ones on natural resources The
finding of a significant spatial dimension suggests that defining macro zones capturing the spatial
heterogeneity in the data should be done before analysing the relationship among variables and the
design and evaluation of specific policies Particularly relevant in those areas experiencing a
reduction in NRD are migration commuting and the characteristics of the tertiary (services) sector
In addition our findings show that education and municipal expenditure could be effective policy
tools in the fight to reduce inequality in Chile
Although our results seem quite robust they do not allow us to make causal inferences about
the effect of NRD on income inequality However we could think of the following explanation to
explain the negative relationship found and the differences between geographical areas
Areas highly dependent on NR used to demand a high proportion of low-skill labour This
has change in sectors such as the mining sector in the northern area which has simultaneously
experienced an increase in activities related to the service sector such as retail restaurants
transport and housing However those services associated with more skilled labour such as the
finance sector remain concentrated in the capital region The reduction in the degree of NRD
(employment in extractive activities) implies lower labour force but more specialized with most
53
of the low-skilled labour transferred to a service sector characterized by low productivity and low
wages
Non-spatial models show that the North and South particularly the latter present
significantly higher levels of inequality This could be associated with the type of resources with
ldquopointrdquo resources such as minerals in the North and ldquodiffusedrdquo resources such as agriculture in the
South This translates into higher average incomes in the Centre and North areas and lower average
incomes in the South
The reduction in NRD implies not only a movement of the labour force from extractive
activities to manufacturing or services with the latter characterized by low productivity and low
salaries of the labour force We could also speculate that most of the high incomes move to the
central area where the economic power and ownership over firms and resources are concentrated
This would explain low inequality associated with higher average incomes in the central area and
high inequality associated with lower average incomes in the South A more in-depth analysis
capturing the mobility of wealth and labour force between counties or more aggregated areas is
needed to better understand the causal mechanism involved
Our findings open avenues for future research in different strands First studies on the causes
of income inequality should take the role of NRD into consideration which has been overlooked
so far Given that the spatial dimension of income inequality seems to be explained by a
phenomenon of spatial heterogeneity estimation strategies such as spatial regime models or
geographically weighted regression should be used (Chi amp Zhu 2019) Second the effect of NRD
on income inequality could manifest through different channels such as education fiscal transfers
and institutions We could extend our analysis to identify which of these competing channels is
the most relevant Transforming some continuous variables such as educational level to a
54
categorical variable or defining new indicator variables for instance whether a local government
shows or not an efficient performance we could classify counties in different groups and then
check whether there are differences or not in the relationship between income inequality and NRD
A third strand could be to disaggregate our measure of NRD for different industries This
would allow us to test differences among industries and to identify the sectors that promote greater
equality and which greater inequality Forth the analysis of the consequences of income inequality
on other economic and social phenomena such as efficiency economic growth and social cohesion
has a growing interest in researchers and policymakers Our findings suggest that to answer the
question of whether income inequality has a causal impact on other variables we could include a
measure of NRD as an instrument to address endogeneity issues For instance two interesting
topics for future research are the analysis of how differences in income inequality between counties
could help to explain differences in the level of efficiency of local governments and differences in
the degree of social cohesion (unrest) throughout the country Those are the issues to be addressed
in the next two essays
55
Chapter 3 The Impact of Income Inequality on the Efficiency of
Municipalities in Chile
31 Introduction
In Chile municipalities are the smallest administrative unit for which citizens choose their
local authorities playing an important role in the provision of public goods and services at the
local level Municipalities have a similar set of objectives but the level of financial resources
available to finance their activities is highly heterogeneous This could result in significant
differences in the levels of performance between municipalities Despite their importance there is
little empirical evidence about the efficiency of local governments in Chile This essay aims to
measure the technical efficiency of Chilean municipalities and to analyse how local characteristics
particularly those related to income distribution at the county level could help to explain
differences in municipal performance
Cross-country studies situate Chile as an efficient country in international comparisons about
efficiency of government spending (Herrera amp Pang 2005 Loacutepez amp Miller 2008) However
evidence for Chile at the local level is relatively sparse suggesting significant levels of
inefficiency For instance Pacheco Sanchez and Villena (2013) found a mean efficiency level of
around 70 using a sample of 309 municipalities for the period 2008-2010 This suggests that
municipalities could achieve the same level of output by reducing the usage of inputs by an average
of 30 Their study also showed that those municipalities more dependent on the central
56
government or those located in counties with lower income per capita are more efficient than their
counterparts
Most empirical research on Local Government Efficiency (LGE) has been conducted for
member countries of the Organization for Economic Cooperation and Development (OECD) of
which Chile has been a member since 2010 In the case of European countries such as Spain and
Italy which share similar characteristics such as the monetary union and levels of GDP per head
efficiency studies have been mainly motivated by budgetary constraints (Balaguer-Coll Brun-
Martos Maacuterquez-Ramos amp Prior 2019 lo Storto 2013) The Chilean context differs in three
main ways from its OECD counterparts First except for the Metropolitan Region that concentrates
most of the population Chilean regions are highly dependent on natural resources Second Chile
is also characterized by one of the highest levels of income inequality among OECD countries
which contrast with the situation of developed natural resource-rich countries such as Australia
and Norway Third although budget constraints are also a relevant issue Chilean municipalities
have experienced a sustained increase in the level of financial resources and expenditure
Another relevant distinction when we benchmark the performance of municipalities across
different countries is the type of public services they provide On the one hand in most of the
countries included Chile the main role of local governments is to provide ldquoservices to peoplerdquo
such as public education and public health On the other hand there are countries such as Australia
where local governments mainly provide ldquoservices to propertyrdquo including waste management
maintenance of local roads and the provision of community facilities such as libraries swimming
pools and parks (Dollery Wallis amp Akimov 2010 Drew Kortt amp Dollery 2015 McQuestin
Drew amp Dollery 2018)
57
Despite contextual differences Chilean municipalities seem not to perform differently from
municipalities in other developed and natural resource-rich countries where income inequality is
significantly less than in Chile (Narboacuten-Perpintildeaacute amp De Witte 2018a) This result highlights the
need to study the role of income inequality and the degree of dependence on natural resources over
LGE characteristics that have been largely overlooked in the literature
We measure and analyse differences in municipal performance using a two-stage approach
In the first stage we measure municipal efficiency using an input-oriented Data Envelopment
Analysis (DEA) to get a set of ldquoefficiency scoresrdquo In the second stage we regress efficiency scores
against our measure of income inequality controlling for a set of contextual factors describing the
economic socio-demographic and political context of each county
We use a sample of 324 municipalities for the period 2006-2017 During this period Chile
was divided into 346 counties belonging to 15 regions This period was characterized by important
external and internal shocks including the Global Financial Crisis (GFC) one of the biggest
earthquakes in Chilean history in 2010 and three municipal elections The availability of
information allows us to measure efficiency for the full period but the influence of contextual
factors is analysed using a panel of six years (2006 2009 2011 2013 2015 and 2017) for which
household income information is available
The main hypothesis tested in the second stage is whether higher levels of income inequality
are associated with lower levels of efficiency Previous evidence shows that when progress is not
evenly shared persistent within-country inequality reduces the effectiveness and efficiency of the
public sector (Ortega Sanjuaacuten amp Casquero 2017 Tandon 2005)
Income inequality has been used to control for a wide range of idiosyncratic factors
associated with historical institutional and cultural factors affecting efficiency (Greene 2016
58
Ortega et al 2017) For instance at the local level income inequality has been considered as an
indicator of economic heterogeneity in the population where higher inequality is associated with
a more heterogeneous set of conflicting demands for public services which adversely affect an
efficient provision (Ashworth Geys Heyndels amp Wille 2014 Geys amp Moesen 2009) Higher
levels of income inequality could also relate to economically privileged groups having a greater
capacity to influence the political system for their own benefit rather than that of the majority
When high inequality is persistent the feeling of frustration and disappointment in the population
could reduce not only trust and cooperation among individuals but also trust in institutions which
would negatively affect government efficiency (Boix amp Posner 1998 Coffeacute amp Geys 2005) For
instance national or local authorities could end exerting patronage and clientelism and showing
rising levels of corruption (Uslaner 2011 Uslaner amp Brown 2005)
One of the main gaps in extant literature is the need to conduct more analysis of LGE using
panel data taking into consideration endogeneity issues and controlling for unobserved
heterogeneity (Narboacuten-Perpintildeaacute amp De Witte 2018a) To address the above we set-up a panel with
time and county-specific effects and we propose the use of a measure of natural resource
dependence (NRD) as an instrument for income inequality Based on the ldquoParadox of Plentyrdquo
fiscal revenues from natural resources windfalls could be associated with an over expansion of the
public sector fostering rent-seeking and corruption and reducing local government efficiency
(Dauvin amp Guerreiro 2017 Manzano amp Rigobon 2001) In the Chilean case most of the revenues
generated by local governments included those from natural resources end up in a common fund
which benefits all municipalities The aim of this common fund is precisely to reduce inequalities
among municipalities so although we do not expect a direct impact of natural resources on LGE
we could expect an indirect effect through other indicators particularly income inequality
59
As far as we know this is the first study analysing the influence of income inequality as a
determinant of municipal efficiency in Chile Moreover this is the first study in the context of a
natural resource-rich country which specifically suggests a measure of natural resource
dependence as an instrument to correct for endogeneity bias We propose the use of the proportion
of firms in the primary sector as proxy for the degree of NRD in each county We argue that this
variable is a better proxy than using the proportion of employment in the manufacturing sector
which has been proposed in previous studies (Alesina amp La Ferrara 2002) During the period
analysed our proxy remained relatively stable and showed a significant relationship with income
inequality In addition it is less likely that it has directly affected municipal efficiency
This study adds to the literature in two other ways First the extant literature suggests that
efficiency measurement could be highly sensitive to the chosen technique as well as the selection
of inputs and outputs (Narboacuten-Perpintildeaacute amp De Witte 2018a) Inputs are usually proxied by a single
measure of total public expenditures and outputs by general proxies such as population andor the
number of businesses in each county We offer a novel approach for the selection of inputs and
outputs On the one hand we disaggregate government expenditures into four components
(operation personnel health and education) and we use the number of public schools and health
facilities in each county as a proxy for physical capital On the other hand we use four outputs
aiming to capture the wide variety of goods and services supplied by each municipality Through
this approach we aim to better describe the production function of each municipality capturing
not only the variety of inputs and outputs but also differences in size among municipalities
A third contribution relates to the measurement of LGE in the Chilean context We measure
technical and scale efficiency using a larger sample and a longer period This has empirical and
policy relevance On the one hand it helps us to select the correct DEA model and allows us to
60
determine the importance of scale inefficiencies as explanation for differences in municipal
performance On the other hand efficiency measures increase the information available for both
central and local governments to better understand the production technology that best describes
each municipality and to carry out policies to improve efficiency
We believe that our selection of inputs and outputs the use of a large dataset and the joint
analysis using cross-sectional and panel data provide a more accurate and robust analysis of
municipal efficiency Likewise knowing whether inequality has a significant influence on
municipal efficiency may provide useful insights and guidance for policymakers not only in Chile
but also for countries sharing similar characteristics
DEA results show an average level of technical efficiency (inefficiency) of around 83
(17) This means that municipalities could reduce on average a 17 the use of inputs without
reducing the outputs There are significant differences among geographic areas with the Centre
area (where the countyrsquos capital is located) displaying higher efficiency than the rest of the country
When municipal efficiency is measured under different assumptions about returns to scale results
reveal a production technology with variable returns to scales and around 75 of the
municipalities displaying scale inefficiencies However when technical efficiency is
disaggregated between pure technical efficiency and scale efficiency results show that scale
inefficiency explains a small proportion of the total municipal technical inefficiency This finding
justifies a deeper analysis of the reasons why municipalities could operate inefficiently and why
municipal performance could vary among municipalities
Efficiency scores also show a significant degree of positive spatial autocorrelation This
means that municipal efficiency shows a general clustering process with neighbouring
municipalities showing similar levels of efficiency A further analysis shows that most of the
61
spatial pattern in municipal efficiency is exogenous that is could be associated to other variables
Hence we conduct most of our regression analysis using traditional (non-spatial) methods and
leaving spatial regressions in the appendixes
Findings from cross-sectional and panel regressions support the hypothesis that municipal
performance is significantly and negatively associated with income inequality at the county level
The coefficient of income inequality is close to one which means that reductions in income
inequality ceteris paribus could be associated with increases in municipal efficiency in the same
proportion This result supports the strand of research arguing that there is not a trade-off at least
at the municipal level between equity and efficiency (Andersen amp Maibom 2020 Berg amp Ostry
2011 2017) The main policy implications are that authorities in more unequal counties would
face higher challenges to perform efficiently and policies pertaining to inequality and efficiency
should not be designed independently
The chapter is structured as follows Section 32 provides a brief literature review on related
local government efficiency Section 33 introduces the methodological background and empirical
models Section 34 presents the empirical results and discussions Section 35 concludes the
chapter
32 Related Literature
321 Measuring efficiency of local governments
Studies on measuring LGE can be grouped in those analysing the provision of single services
such as health or education and those assessing overall efficiency (lo Storto 2013) Once inputs
and outputs have been defined efficiency is measured using parametric andor non-parametric
techniques Among the former group Stochastic Frontier Analysis (SFA) seems to be preferred
62
by scholars aiming to measure efficiency and to analyse the link with environmental variables
using a single procedure (Pacheco et al 2013 Tsekeris Sotiris Tsekeris amp Papaioannou 2018)
On the non-parametric group Data Envelopment Analysis (DEA) is by far the most used technique
(Afonso amp Fernandes 2006 Balaguer-Coll amp Prior 2009 lo Storto 2013)
The selection of inputs and outputs depends not only on the aimed of the study (specific
sector vs whole measure of efficiency) but also on the role that municipalities play in different
countries There are countries such as Australia (Drew et al 2015) and Spain (Balaguer-Coll amp
Prior 2009) where local governments mainly supply services to ldquopropertyrdquo such as waste
management and road maintenance In these cases efficiency has been mainly measured using
total indicators of local government expenditure and outputs have been proxied using general
indicators such as population or number of business (Drew et al 2015) On the other hand in
countries such as Italy (lo Storto 2013) and Portugal (Afonso amp Fernandes 2008) in Europe or
Brazil (de Sousa Cribari-Neto amp Stosic 2005) and Chile (Pacheco et al 2013) in South America
municipalities mainly supply services to ldquopeoplerdquo Here in addition to financial expenditures or
revenues inputs have included the number of local government employees the number of schools
or the number of hospitals and health centres School-age population the number of students
enrolled in primary and secondary schools and the number of beds in hospitals have been
considered as outputs Based on the study of Narboacuten-Perpintildeaacute amp De Witte (2018a) a wider list of
inputs and outputs used in previous studies can be found in Appendix I
Studies from different countries show important differences in the average efficiency scores
both between and within countries These studies also differ in the samples methodologies and
variables included A summary showing the range and variability of the mean efficiency scores
founds in countries all around the world can be found in Narboacuten-Perpintildeaacute amp De Witte (2018a)
63
These authors also show that OECD natural resource-rich countries such as Australia Belgium
and Chile show similar results in terms of mean efficiency scores with LGE studies being less
frequent in Latin American countries
Measuring efficiency of local governments as decision-making units (DMU) presents many
challenges and difficulties (Borger Kerstens Moesen amp Vanneste 1994 Ravallion 2005)
Worthington and Dollery (2000) mention problems with the selection and measurement of inputs
the identification of different stakeholders the hidden characteristic of the ldquolocal government
technologyrdquo and the multidimensionality of the services provided by local governments All these
issues make difficult to identify and distinguish between outputs and outcomes with outputs
commonly proxied by general indicators such as county area or county population Because
efficiency measures are highly sensitive to the chosen technique and the selection of inputs and
outputs Narboacuten-Perpintildeaacute amp De Witte (2018a) suggest formulating different specifications and
using less general and unspecified indicators Moreover the complexity in defining outputs and
the use of general indicators make more likely that contextual factors affect municipal efficiency
322 Explaining differences in LGE
To explain differences in local government performance researchers have basically
distinguished between ldquodiscretionaryrdquo and ldquonon-discretionaryrdquo factors Discretionary factors refer
to the degree of discretion of local authorities in the selection and management of inputs and
outputs On the other hand scholars have investigated the influence on LGE of contextual factors
beyond authoritiesrsquo control These factors reflective at the environment where municipalities
operate include economic socio-demographic geographic financial political and institutional
characteristics (da Cruz amp Marques 2014 Narboacuten-Perpintildeaacute amp De Witte 2018b)
64
In general the evidence about the influence of contextual factors has delivered mixed and
country-specific results (Narboacuten-Perpintildeaacute amp De Witte 2018b) Sampaio de Sousa et al (2005)
using data for Brazilian municipalities finds that population density and urbanization rate have
strong positive effects on efficiency scores Benito et al (2010) show that lower levels of
efficiency of Spanish municipalities are associated with a greater economic level a less stable
population and a bigger size of the local government Afonso (2008) finds that per capita income
level and education are not significant factors influencing LGE of Portuguese municipalities He
also finds that municipalities in Northern areas show greater efficiency than their counterparts in
Southern areas More recently Tsekeris (2018) finds that spatial variations in efficiency in Greece
can be attributed to factors related to inter-regional market access specialization and sectoral
concentration resource-factor endowments and political factors among others Characteristics
describing each local government have also been used including municipal indebtedness (Benito
et al 2010) fiscal deficits (Sinha 2017) degree of fiscal autonomy (Boetti Piacenza amp Turati
2009) and individual characteristics of local authorities such as age gender and political ideology
Narboacuten‐Perpintildeaacute amp De Witte (2018b) conclude after analysing 63 articles that studies on the
influence of contextual factors have mostly used cross-sectional data with little attention to
endogeneity issues which makes any causal interpretation doubtful
323 The trade-off between efficiency and equity
The existence of a potential trade-off between efficiency and equity is in the core of
economic discussion (Andersen amp Maibom 2020 Berg amp Ostry 2011 Browning amp Johnson
1984 Okun 2015)15 The argument that economic growth (one of the most common efficiency
15 Redistributive policies distort incentives and lead to suboptimal outcomes and thus efficiency losses
65
measures) could be negatively affected in the search for greater equality has been translated not
only into economic policies that favour economic growth over those that reduce inequality but
also in the emphasis of scholarly research Thus theoretical and empirical research has been
mainly focussed on efficiency and policy implications of a great diversity of shocks and policies
leaving the analysis of inequality as one of measurement and mostly descriptive Additionally
empirical evidence of the trade-off is scant and inconclusive (Andersen amp Maibom 2020
Browning amp Johnson 1984)
Among economic contextual factors that could affect LGE income inequality has been
largely ignored An exception is a cross-country comparison conducted by Ortega (2017) who
analyses the role of inequality on government efficiency in developing countries He finds that
more unequal countries could have higher difficulties to achieve specific health outcomes Income
inequality has even been considered as part of the outputs to measure efficiency particularly for
the case of European and OECD countries (Afonso Schuknecht amp Tanzi 2010 Antonelli amp De
Bonis 2018)
At the local level income inequality has been mainly used as a proxy for the effect of income
heterogeneity Economic inequality could have a direct and an indirect effect on government
efficiency The direct effect poses that higher income inequality could reduce municipal efficiency
because it is associated with a more complex and competing set of public services demanded by
the population (Jottier et al 2012) The indirect effect puts the focus in the link between inequality
social capital and levels of corruption Economic diversity could reduce trust in people and
institutions when related to high and persistent levels of income inequality It could also affect the
willingness to participate in community and political groups the existence of a shared objective
by citizens and the perception of a prosperous future (Uslaner amp Brown 2005)
66
The evidence is ambiguous For instance Geys and Moesen (2009) find that income
inequality has little relation to efficiency of Flemish municipalities and Ashworth et al (2014)
find a negative relationship for the Norwegian case Findings also indicate that inequality is the
strongest determinant of trust and that trust has a greater effect on communal participation than on
political participation (Uslaner amp Brown 2005)
33 Methodology
We follow a two-stage approach widely used in this kind of analysis A DEA analysis is
conducted in the first stage to get efficiency scores for each municipality Then regression analysis
is conducted in the second stage aiming to identify contextual variables other than differences in
the management of inputs that can help to explain the heterogeneity in municipal performance
331 Chilean Municipalities and period of analysis
The territory of Chile is divided into regions and these into provinces which for purposes of
the local administration are divided into counties The local administration of each county resides
in a municipality which is administrated by a Mayor assisted by a Municipal Council16
Municipalities represent the decentralization of the central power in Chile They are autonomous
organizations with legal personality and own patrimony whose purpose is to satisfy the needs of
the local community and ensure their participation in the economic social and cultural progress of
the county Municipalities have a diversity of functions related to public health education and
social assistance among others
16 The Mayor and City Council are elected by suffrage for the citizens of the respective commune every 4 years
67
To achieve their goals two are the main sources of municipal incomes own permanent
revenues (OPR) and the Municipal Common Fund (MCF) OPR are incomes generated by the
county and they are an indicator of the self-financing capacity of each municipality OPR are not
subject to restrictions regarding their investment and they are mainly generated by territorial taxes
commercial patents and circulation permits17 The MCF is a fund that aims to redistribute
community income to ensure compliance with the purpose of the municipalities and their proper
functioning Sources to finance the MCF come from municipal revenues The distribution
mechanism of the fund is regulated by parameters such as whether municipalities generate OPR
per capita lower than the national average and the number of poor people in the commune in
relation to the number of poor people in the country
This study covers the period from 2006 to 2017 During this period Chile was divided into
15 regions 54 provinces and 346 counties18 Although the information on inputs and outputs is
available for the entire period information on contextual factors at the county level such as
household income is only available every two-three years In addition some counties are excluded
from household surveys due to their difficult access Hence we use a sample of 324 municipalities
to measure municipal efficiency for the whole period (3888 observations) However the analysis
of contextual factors is conducted for those years when household income information is available
2006 2009 2011 2013 2015 and 2017 (1944 observations)
17 The territorial tax is a tax on agricultural and non-agricultural real estate Specifically of this income only 40 is left to the municipality as its own financing while the remaining 60 is allocated to the MCF (in the case of the four largest communes- Santiago Providencia Las Condes and Vitacura-percentages are 35 and 65 respectively) Unlike the territorial tax commercial patents are regulated mainly by the municipality which chooses the rate to be charged subject to a range established by law and is responsible of their collection Finally regarding the circulation permits 375 is of municipal benefit while 645 is directed to the MCF 18 There are 346 counties managed by 345 municipalities where counties ldquoCabo de Hornosrdquo and ldquoAntaacuterticardquo are managed by the municipality of ldquoCabo de Hornosrdquo
68
332 Measuring municipal efficiency
Municipal efficiency is measured using Data Envelopment Analysis (Coelli Prasada Rao
OrsquoDonnell amp Battese 2005) This is a non-parametric approach that uses linear programming to
measure efficiency for a group of municipalities as decision making units (DMUrsquos) The main
advantage of using DEA and the reason why DEA is used for the case of Chilean municipalities
is its flexibility in handling multiple inputs and outputs without the need to specify a functional
form (Balaguer-Coll amp Prior 2009 Mikušovaacute 2015 Tigga amp Mishra 2015) Following Afonso
and Fernandes (2008) the relationship between inputs and outputs for each municipality could be
represented by the following equation
119884 119891 119883 119894 1 119899 (31)
In equation (31) 119884 is the set of outputs and 119883 the set of inputs for each of the n
municipalities Using linear programming the production frontier is constructed and a vector of
efficiency scores is obtained The frontier represents full technical efficiency mdash the point at which
the highest output occurs given specified inputs or the point at which the lowest amount of inputs
is used to produce a specified quantity of output Efficiency scores under DEA are relative
measures of efficiency They measure a municipalityrsquos efficiency against the other measured
municipalities in the sample and not a hypothetical lsquoperfect municipalityrsquo The further from the
frontier the less technically efficient a municipality is
We use an input-oriented approach because Chilean municipalities have a greater control
over the management of inputs relative to the outputs they have to manage Obtaining efficiency
scores requires an assumption about the returns to scale exhibited by each municipality When
DMUrsquos are homogeneous the CCR model (Charnes Cooper amp Rhodes 1978) which assumes
69
constant return-to-scale (CRS) is the appropriate specification The CCR model assumes full
proportionality between outputs and inputs and that DMUrsquos operate at their optimal When DMUrsquos
are highly heterogeneous as is the case with local governments in most countries it is not realistic
to assume complete proportionality between inputs and outputs nor that all DMUrsquos operate at their
optimum scale In this situation a variable returns-to-scale (VRS) or BCC model (Banker
Charnes amp Cooper 1984) is the preferred formulation
Assuming VRS imposes minimum restrictions on the efficient frontier and allows for
comparisons only among municipalities of similar scale (Coelli et al 2005 Wu Huang amp Pan
2014) This means that when we use the CCR model (assuming CRS) on a heterogeneous sample
of DMUrsquos the resulting measure of technical efficiency that we obtain is related not only to the
management of inputs but also to issues of scale19 To empirically check the validity of the VRS
assumption we measure technical efficiency under CRS VRS and non-increasing returns-to-scale
(NIRS) and we analyse the existence of scale inefficiencies This allows us to check the relevance
of scale effects as a potential explanation of differences in municipal efficiency Appendix J
shows the specification of the DEA model under VRS and how ldquototal technical efficiencyrdquo
(assuming CRS and therefore due to management and scale issues) could be disaggregated in ldquopure
technical efficiencyrdquo (under VRS and related only to management issues) and scale efficiency (due
to scale issues)
19 Assuming VRS can also lead to measurement problems such as overestimation of efficiency scores This is the case when an important proportion of DMUrsquos shows CRS so assuming VRS ignores the information about proportionality between inputs and outputs (Podinovski 2004)
70
333 Inputs and outputs used in DEA
Following the literature on local government expenditure efficiency (Afonso amp Fernandes
2008 de Sousa et al 2005 Dlouhyacute 2018 Tandon 2005 Tigga amp Mishra 2015) and trying to
reflect as well as possible the functioning of municipalities five inputs and four outputs were
selected Input and output data were obtained from the National System of Municipal Information
(SINIM in its Spanish acronym) and they are expressed in thousands of Chilean pesos of 201720
Inputs are Municipal Operational Expenditure X1 (including expenses on goods and
services social assistance investment and transfers to community organizations) Municipal
Personnel Expenditure X2 (including full time and part-time workers) Total Municipal
Expenditure in Education sector X3 Total Municipal Expenditure in Health sector X4 and the
Number of Municipal Buildings X5 (proxied by the number of public facilities in education and
health sectors)
Output variables were selected highlighting the relevance of education and health sectors
and trying to capture the wide range of local services provided by municipalities The variable
ldquoOwn Permanent Revenuesrdquo Y1 is used to capture the scale and diversity of municipal
activities21 The ldquoMonthly Average Enrolmentrdquo in municipal education establishments related to
the school-age population in each county Y2 is used as educational output As health output the
ldquoNumber of Medical Consultationsrdquo in public facilities Y3 is considered Finally the number of
community organizations Y4 is used as output reflecting the promotion of community
development by each municipality Table 31 shows the summary statistics of input and output
20 The data from SINIM database was mostly obtained using the sinimr package (Salas 2019) 21 According to SINIM database this variable aims to measure the management of the municipalityrsquos own resources with respect to the population of the commune
71
variables for the whole sample and period Inputs and outputs excepting the Monthly Average
Enrolment Y2 are measured in per capita terms using county population information from the
National Institute of Statistics (INE in its Spanish acronym)
Table 31
Descriptive statistics Inputs and Output variables used in DEA analysis
334 Regression model
Contextual factors could play an important role not only in explaining why some
municipalities operate inefficiently but also why municipal performance differs among them
These factors may affect municipal performance modifying incentives for local authorities to
operate efficiently and their capability to take advantage of economies of scale They also define
the conditions for cooperation or competition among municipalities and the citizensacute ability and
willingness to monitor local authorities (Afonso amp Fernandes 2008)
Information on income at the household level for each county was obtained from the
ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) CASEN is
conducted every two-three years being the reason why consecutive years are not considered in
72
our regression analysis The other contextual factors used as controls were obtained from different
sources including SINIM INE and the ldquoServicio de Impuestos Internosrdquo (SII)22
Our main hypothesis is whether higher levels of income inequality are associated with lower
levels of municipal efficiency To test our hypothesis the empirical model is defined as
120579 120573 119892119894119899119894 119885 120573 120575 120572 120598 (32)
Where 120579 is the vector of DEA efficiency scores 119892119894119899119894 is the Gini coefficient of each
county 120575 are year-specific effects 120572 are county-specific constants 120598 is a vector of error terms
and 119885 is a vector of controls Next we discuss the motivation for these controls
The level of purchasing power of countiesrsquo citizens is proxied by the variable log(income)
which is the natural log of the mean household income per capita in thousands of Chilean pesos of
2017 On the one hand poorer counties could display higher efficiency due to their necessity to
take care of their constraint resources (Pacheco et al 2013) On the other hand richer counties
could show higher efficiency because richer citizens exert higher monitoring over local authorities
and demand better quality public services in return for their tax payments (Afonso et al 2010)
The possibility for municipalities to take advantage of economies of scale and urbanization is
captured by three variables First the variable log(density) which correspond to the natural log of
population density Second the dummy variable reg_cap indicating whether a county is a regional
capital or not Third the variable agroland which correspond to the proportion of land for
agricultural use which is informed to the SII We expect a positive effect of log(density) but
negative for regcap and agroland
22 The SII is the institution in charge of collecting taxes in Chile
73
Socio-demographic characteristics are captured including a Dependence Index IDD IDD
corresponds to the number of people under 15 years or over 65 years per 100 people in the active
population (those people between 15 and 65 years old) A higher proportion of young and older
population could be associated with a higher demand for municipal services relating to education
and health making harder to offer public services efficiently The citizensrsquo capacity to monitor
local authorities is proxied including the variable education (average years of education for the
population older than 15 years) and the variable housing (proportion of households which are
owners of the property where they live in each county) In both cases we expect a positive
association with LGE
Among municipal characteristics the variable professional (percentage of municipal
personnel with a professional degree) is used to control for the quality of municipal services and
it is expected a positive impact The variable mcf (proportion of total municipal income coming
from the MCF) is included to capture the influence of financial dependence on the central
government A higher dependence from MCF could be associated with higher efficiency when it
is linked to more control from central government (Worthington amp Dollery 2000) However when
MCF discourages the generation of own resources and proper management of resources from the
fund a lower efficiency should be expected (Bravo 2014) In addition the dummy variable mayor
is included to capture differences among mayors supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo
political parties related to those ldquoINDEPENDENTrdquo mayors
Table 32 report summary statistics for the set of numeric contextual factors and Appendix
K the corresponding correlation matrix Despite the high correlation between income and
education variables we include both in the regression section as they capture different county
characteristics
74
Table 32
Summary Statistics Numeric Contextual Factors
Figure 31 Geographical distribution of Chilean regions and macrozones
Previous evidence on growth and convergence of Chilean regions have found that regions
tend to cluster spatially (Aroca amp Bosch 2000) Aiming to capture this regional clustering process
75
and considering the high concentration in the number of municipalities and population in the
central area we group municipalities in four ldquozonesrdquo We define as reference the ldquoCentre-Northrdquo
zone consisting of regions XIII (where the countryrsquos capital is located) and its two neighbouring
regions V and VI The ldquoNorthrdquo zone includes regions XV I II III and IV The ldquoCentre-Southrdquo
zone includes regions VII VIII and IX Finally the ldquoSouthrdquo zone embraces regions XIV X XI
and XII Figure 31 displays the regional administrative division and zones considered in this
essay
Efficiency scores (ES) are limited to have values between 0 and 1 However they are relative
measures (relative to the sample of municipalities) This implies that when a municipality is on the
frontier (ES = 1) it does not mean that potentially larger technical efficiency gains cannot be made
Hence equation 32 is estimated using OLS and censored regressions We start running cross-
sectional regressions for each of the six years Then we compare the results with those from panel
regressions Because fixed-effects panel Tobit models could be affected by the incidental
parameters problem (Henningsen 2010 2019) we use random-effects panel Tobit models
including indicator variables for years and zones Finally to deal with the potential endogeneity
problem we also use an instrumental variable approach The instrument is described next
335 The instrument
Government effectiveness and income distribution are both structural components of
economies (Ortega et al 2017 Ravallion 2005) In the search for a causal interpretation for the
influence of income inequality on municipal efficiency we need an instrument which must be
correlated with the variable to be instrumented (in our case income inequality) and uncorrelated
with the error term in the efficiency equation (32) Previous literature has used as instruments for
Gini the number of townships governments in a previous period the percentage of revenues from
76
intergovernmental transfers in a previous period and the current share of the labour force in the
manufacturing sector (Alesina amp La Ferrara 2002) Using the share of the labour force in a specific
sector is unlikely to reduce the problem of endogeneity particularly in countries where local
governments supply mostly ldquoservices to peoplerdquo and the main source of income inequality is
labour income
We propose as an instrument the proportion of firms in the primary sector (mining fishing
forestry and agriculture)
119901119904119904_119891119894119903119898119904Number of firms in the primary sector
Total number of firms (33)
On the one hand this instrument is likely to be correlated with local income inequality in
natural resource-rich countries23 On the other hand we contend that our instrument is less likely
to be correlated with the error term in the efficiency equation First the main services supplied by
Chilean municipalities are services to people (health and education) not to firms Second most of
the revenues collected by municipalities included those associated with natural resources end up
in the municipal common fund whose objective is precisely to reduce inequalities among
municipalities Third services to firms are expected to be more significant with the tertiary sector
We argue that our instrument captures natural and structural conditions which directly
influence income inequality but it does not directly affect LGE Figure 32 shows the evolution
of the annual average efficiency score and the proportion of firms in the primary secondary
(manufacturing) and tertiary (services) sectors We observe that sectors shares have remained
relatively stable with a slight reduction in the participation of the primary sector in favour of the
23 Results confirm a significant association between income inequality and the degree of dependence on natural resources at the county level
77
tertiary sector by the end of the period This is contrary to the evolution of municipal efficiency
which shows a cyclical behaviour as will be shown in the next section
Figure 32 Evolution of efficiency scores and the proportion of firms by sector
34 Results and discussion
341 DEA results
Figure 33 displays the evolution of our three measures of efficiency Overall technical
efficiency pure technical efficiency and scale efficiency are around 78 83 and 95
respectively with fluctuations over the years Therefore around three quarters of the overall
78
inefficiency is attributed to inefficiency in the management of inputs and around one quarter to
scale inefficiencies24
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE)
Returnstoscale
Figure 34 reports by zone and for the whole period the proportion of municipalities
showing CRS decreasing returns to scale (DRS) or increasing returns to scale (IRS) Most of the
municipalities operate under variable (increasing or decreasing) returns to scale which could be
explained by the high heterogeneity in size among municipalities A summary of RTS
disaggregated by year and zone is in Appendix L Policies oriented to increase efficiency usually
24 The average scale efficiency score of 95 means that municipalities could get an additional 5 of inputs saving if municipalities were able to change their size to achieve their optimal scale
79
consider amalgamation de-amalgamation or ways of cooperation among municipalities To have
a better idea about where and how feasible is the implementation of such policies Appendix M
shows maps with the administrative division of the country in its 345 municipalities and which
municipalities show CRS IRS or DRS in each of the six years of data
Figure 34 Returns to scale by zone
Based on results for the whole period (Figure 34) the North has the highest proportion of
municipalities exhibiting DRS This suggests the need to reduce the size of municipalities splitting
those ldquotoo bigrdquo creating new administrative areas or giving more autonomy to current
municipalities25 The opposite occurs in the Centre-North area where municipalities mostly
exhibit IRS This indicates the need to merge municipalities An alternative strategy to the
amalgamation process is increasing inter-municipal cooperation (Balaguer-Coll et al 2019)
25 This has been the policy followed in Chile Although two new regions were created (XV in the North and XIV in the South) the number of municipalities has not changed
80
which seems to be a more plausible option in Chile Finally evidence on scale efficiency for the
Centre-South and South areas is not clear in terms of the adequate strategy to improve efficiency
Efficiencymeasure
Although most municipalities show scale inefficiencies (Figure 34) only a small proportion
of total inefficiency is associated to scale issues (Figure 33) Together this results justify not only
the use of the BCC model (efficiency scores under VRS) to capture municipal heterogeneity but
also highlights the need to look for other factors outside the control of local authorities which
could be influencing municipal performance
Table 33
Summary efficiency scores (VRS) by zone and region
Table 33 summarizes ES under VRS using data for the six years of CASEN survey A mean
efficiency score of 83 is found for the full sample and period This means that on average
inefficient municipalities can reduce the use of inputs by 17 to get the same current output By
81
comparing average ES per zone it can be concluded that municipalities in the North Centre-North
Centre-South and South could achieve the same level of output with 17 12 18 and 23 fewer
resources respectively Results also show that one third of the municipalities present an efficiency
score equal to one
Figure 35 shows the evolution of the mean ES for the four zones over the 12 years period
A clear drop in the efficiency level is observed in 2009 (the financial crisis year) excepting for the
North area Likewise although mean efficiency returned to its pre-crisis levels in 2014-2015 a
new drop in efficiency was experienced in 2016 It also seems that the 2010 earthquake did not
generate a significant effect on municipal efficiency Figure 35 also shows that although levels
of efficiency seem to differ among zones they follow a similar trend through time with the only
exception of the North which corresponds to the mining area In addition efficiency seems to be
significantly higher in the Centre-North area This is explained by the high mean level of efficiency
in region XIII which includes the countryrsquos capital city
Figure 35 Evolution mean efficiency scores (VRS) by zone
82
To know which and where are the efficient municipalities and if they are surrounded by
municipalities with a similar level of efficiency Appendix N has maps showing the ldquoefficiency
statusrdquo of each municipality that is whether they are efficient (ES = 1) or inefficient (ES lt 1)
Additionally Appendix O shows ldquoequal intervalsrdquo maps describing the spatial distribution of ES
among municipalities for each of the six years26 Results show that efficient municipalities can be
found all through the country the ldquoefficiency statusrdquo could change from one year to another and
municipalities with similar level-status of efficiency tend to cluster in space
342 Regression results
Exploratoryspatialanalysis
DEA efficiency scores and their geographical representations seem to show that municipal
efficiency presents a spatial clustering pattern This means that municipal performance could be
influenced not only by contextual factors of the county where municipality belongs but also by the
level of efficiency of neighbouring municipalities and their characteristics To test the significance
of the spatial dimension in municipal efficiency we use a cross-section of data considering the six-
year average of efficiency scores the Gini coefficient and the set of controls
We use the Moranrsquos I indicator to test for spatial autocorrelation Moranrsquos I is a measure of
the correlation between the level of efficiency in one municipality and its ldquospatial lagrdquo that is the
average level of efficiency in neighbouring municipalities We define as the relevant neighbours
for each municipality the 5-nearest municipalities This is obtained using the distances among the
26 An equal intervals map uses the same principle as a histogram to organize the observations into categories that divide the range of the variable into equal interval bins For the equal interval classification the value range between the lower and upper bound in each bin is constant across bins but the number of observations in each bin is typically not equal
83
polygonsrsquo centroids (latitude and longitude) of each county Results confirm that municipal
efficiency show a significant level of positive spatial autocorrelation This means that
municipalities tend to have neighbouring municipalities with similar performance
The positive spatial autocorrelation shown by municipal efficiency could be due to the
performance in one municipality is influenced by the performance in neighbouring municipalities
(spatial dependence in the variable itself) or due to structural differences among regions-zones
(spatial heterogeneity) To check the source of the spatial autocorrelation we run an OLS
regression of ES against income inequality and controls and then we test OLS residuals for spatial
autocorrelation Moranrsquos I over OLS residuals although significant is barely higher than zero (see
Appendix P) This means that the spatial effect itself is not a serious issue and can be handle for
instance including zone indicators variables hence we proceed to analyse the influence of income
inequality on LGE using non-spatial regression27
Cross‐sectionalanalysis
We start reporting censored regressions for each year in our panel Efficiency scores have
been rescaled to numbers between 0 and 100 to facilitate the interpretation of the results All
regressions include dummy variables for three of the four zones in which we have grouped Chilean
regions Results are in Table 3428 Income inequality shows a negative sign in all years which is
consistent with our hypothesis that inequality is negatively related to municipal efficiency
However only in three of the six years the effect of income inequality appears as statistically
27 In the case of having incorporated the spatial dimension the two most commonly used forms are including in the model the spatial lag of the dependent variable (spatial autoregressive model SAR) or the spatial lag of the error (spatial error model SEM) Following the method suggested by Anselin et al (1996) Lagrange Multiplier (LM) tests on the OLS residuals showed that the SAR model is preferred over the SEM Moranrsquos I LM tests and spatial regressions can be found in Appendix P 28 Regression results using OLS are in Appendix Q
84
significant Only the income level displays a significant and positive influence on efficiency for
the whole period A higher population density also consistently favours municipal efficiency On
the other hand as we expected a higher IDD makes it more difficult to achieve an efficient
performance29 Consistent with results in Pacheco et al (Pacheco et al 2013) municipal
efficiency show a significant an positive association with the MCF only in the first half of our
period of analysis with the second half showing an insignificant relationship
Table 34
Cross-sectional (censored) regressions
Paneldataanalysis
Estimation results for the six-year panel are reported in Table 35 Columns (1) and (2) show
the results for the pooled and random effects censored models only controlling for zone and year
29 The set of cross-sectional regressions was also run for each zone independently Results confirm that the set of significant contextual factors differs not only among years but also among geographic areas This support previous evidence suggesting that cross-sectional analysis should be treated with caution (Narboacuten-Perpintildeaacute amp De Witte 2018b)
85
dummies Income inequality appears as non-significant Zone indicator variables confirm that
municipalities located in the Centre-South and South of the country display a lower average level
of efficiency compared to the Centre-North area Time dummies mostly show negative
coefficients This is a signal that shocks such as the GFC and the earthquake in 2010 may have
had a negative impact on efficiency but that impact was not permanent The results for the pooled
and RE models including the full set of controls are reported in columns (3) and (4) These results
show a significant negative influence of income inequality on LGE
When income inequality is instrumented by the variable pss_firms most of the coefficients
remain unchanged except for those associated with the income variables gini and log(income)
This result implies that our original model suffers for instance from the omitted variable bias
This means that LGE and income inequality are determined simultaneously by some variable not
included in our model Columns (5) and (6) show results using our instrument for income
inequality The ldquotrue coefficientrdquo of variable gini remains negative but the magnitude of the
relationship is higher The negative coefficient for gini implies on the one hand that municipalities
located in more unequal counties face more challenges to achieve an efficient management of
public resources On the other hand the coefficient in column (6) is close to one The interpretation
is that for each point of reduction in income inequality ceteris paribus LGE should increase in the
same proportion Next we discuss some of the results associated with the controls variables
Contrary to Pacheco et al (2013) income level has a positive coefficient meaning that richer
counties in terms of income per capita show higher efficiency This could be explained by higher
monitoring and increasing demands from citizen and taxpayers In addition it is expected a higher
efficiency in municipalities located in counties with a higher population density and those with a
lower proportion of land for agricultural use This result is mainly explained by municipalities
86
located in the Centre area The opposite happens with municipalities in the South implying that
they are too ldquosmallrdquo to take advantage of agglomeration economies There is also a limit for
agglomeration and scale economies which is shown by the negative coefficient of the variable
regcap although this coefficient loses its significance in the IV approaches30
Unexpectedly efficiency was found to be negatively associated with the variable education
This result is similar to the case of Czech municipalities (Šťastnaacute amp Gregor 2014) where
explanations include a weakened monitoring effect due to the fact that more educated citizens
present greater mobility and labour cost disadvantages for municipalities with better educated
labour force In Chile an additional explanation could be the relationship between education and
voter turnout Since 2012 voting is no longer mandatory in Chile This fact considerably reduced
voter turnout which in turn may have influenced the monitoring and control effect of more
educated voters For the case of variable IDD results show that local authorities in counties with
higher proportion of aging and young population (related to those in the active population) face a
greater challenge in their quest to offer public services efficiently
The influence of mcf is like that found by Pacheco et al (2013) with municipalities more
dependent on central transfers showing more efficiency31 Political influence captured by the
variable mayor did not show a significant effect This result is like other studies concluding that
the ideological position did not have a significant influence on efficiency (Benito et al 2010
Boetti et al 2009 Cordero Pedraja-Chaparro Pisaflores amp Polo 2017)
30 This negative coefficient suggests that the negative effects of agglomeration economies such as overcrowding pollution high cost of land and traffic congestion could predominate over the positive effects of scale economies in regional capitals 31 When the analysis is conducted for each zone independently MCF displays a negative influence in the North and Centre-North areas but a positive influence in the Centre-South and South areas These results confirm that on the one hand richer municipalities (those in the North and Centre-North) have less incentives for efficient management of their resources On the other hand results support the relevance of MCF helping poorer municipalities (those in the South) to reach their outcomes
87
Table 35
Panel data regressions
88
35 Conclusions
The trade-off between equity and efficiency is in the core of the economic discussion This
ldquoprinciplerdquo has been used by policymakers to prioritize the design of policies focused on economic
growth delaying those policies aimed at reducing economic inequalities This essay offers
empirical evidence of a negative relationship between inequality and efficiency that is a reduction
of income inequality could have positive effects on economic efficiency at least at the level of
local governments
We followed a traditional Two-Stage approach commonly used in the analysis of LGE We
compared cross-sectional and panel data results and we have added an instrumental variable
approach to give a causal interpretation to the link between efficiency and inequality We proposed
the use of a measure of natural resource dependence to instrumentalize the impact of income
inequality on LGE Given that our units of analysis are municipalities and not counties we argue
that our measure of NRD is correlated with income inequality and it does not have a direct
influence on LGE
We found that Chilean municipalities perform better than previous studies suggest
Municipal efficiency depicted significant levels of positive spatial autocorrelation and most of the
municipalities showed to be operating under increasing or decreasing returns to scale This would
imply that the policies generally used to improve efficiency such as amalgamation or cooperation
should be implemented observing the reality of each region and not as strategies at the national
level We also found that scale inefficiency explains a small proportion of the average total
inefficiency reason why the analysis of external factors that could affect the municipal efficiency
takes greater relevance
89
Income inequality plays an important part in explaining municipal efficiency In fact it was
found that reductions in income inequality could result in increases in municipal efficiency in a
similar proportion An unexpected finding was that the levels of education shows a negative
association with municipal performance This could be due to a low average level of education or
the existence of an omitted variable This variable could be the significant reduction in voting
turnout rates for local and national elections due to changes in the voting system during the period
of our analysis All in all our results may help to shed light on the potential consequences of
changes in contextual factors and the design of strategies aimed to increase municipal efficiency
in countries with similar characteristics to the Chilean economy For instance policies oriented to
take advantage of economies of scale can be formulated merging municipalities or establishing
networks in specific sectors such as education or health
Further work needs to be done both in measurement and in the explanation of differences in
municipal performance in Chile One area of future work will be to identify the factors that better
predict why municipalities operates under increasing decreasing or constant returns to scale
Multinomial logistic regression and the application of machine learning algorithms to SINIM data
sets appear as suitable methods for that purpose Intertemporal DEA (Drew amp Dollery 2015)
should be used to measure municipal efficiency capturing changes in total factor productivity In
addition municipalities operate under different levels of geographical authorities such as the
provincial mayor and the regional governor Hence it would be useful to know how each
municipality performs within each region-zone related to how performs to the whole country This
should be done conducting a metafrontier analysis (OrsquoDonnell Rao amp Battese 2008)
We have also identified through a cross sectional spatial exploratory analysis that on
average municipalities with similar levels of efficiency tend to cluster in space Regarding to
90
analyse the importance of contextual factors on municipal efficiency a deeper analysis should use
censored spatial models to check the significance of the spatial dimension in cross-sectional and
panel contexts Another interesting avenue for future research is associated with the negative
association found between LGE and education The significant reduction in votersacute turnout since
the law of automatic registration and voluntary voting in 2011 appears as a natural experiment to
analyse its effects on efficiency indicators such as municipal performance Incorporating variables
such as the voting turnout in each county or classifying municipalities based on individual
institutional political and economic characteristics could help to shed light on which of these
channels is the most relevant when analysing the impact of inequality on municipal efficiency
Finally we argued that an important part of the influence of income inequality over LGE
could be through its indirect effect on trust social capital and social cohesion The final essay will
delve deep in that relationship
91
Chapter 4 Social Cohesion Incivilities and Diversity
Evidence at the municipal level in Chile
41 Introduction
A deterioration in social cohesion could carry significant costs such as a reduction in
generalized trust between individuals and in institutions a society caught in a vicious circle of
inequality and citizens increasingly distanced from civic life (Uslaner 2011) A growing feeling
of frustration and discontentment can eventually translate into a social outbreak with uncertain
results This is precisely what have been happening in many countries around the world included
Chile
ldquoSocial cohesion is a state of affairs concerning both the vertical and the horizontal
interactions among members of society as characterized by a set of attitudes and norms that
includes trust a sense of belonging and the willingness to participate and help as well as their
behavioural manifestationsrdquo (Chan et al 2006) This definition highlights the multidimensionality
in the concept of social cohesion which has been measured using objective andor subjective
indicators of trust social norms solidarity willingness to participate in social and political groups
and feelings of belonging (Ariely 2014 Chan et al 2006) Multidimensionality also implies that
the impact of determinants of social cohesion such as economic and racial diversity could be
different for each of its various dimensions (Ariely 2014)
A common characteristic to all societies is that they are made up of different groups that
differ with respect to race ethnicity income religion language local identity etc The
92
Community Heterogeneity Thesis (Coffeacute amp Geys 2006) argues that individuals prefer to interact
with others that are like themselves Hence high levels of diversity particularly economic and
racial represent a complex scenario to maintain social cohesion One of the most common factors
adduced for social cohesion is income inequality with higher levels linked to lower levels of trust
(Ariely 2014 Rothstein amp Uslaner 2005)
Traditional measures of social cohesion may not be adequately capturing the deterioration
in social connections For instance measures of (lack of) trust include a strong subjective element
On the other hand proxies for social participation such as volunteering jobs or joining to social
organizations have not been supported by empirical evidence as a source of generalized social trust
(Rothstein amp Uslaner 2005) We proposed to use the rate of incivilities which we argue is a more
appropriate measure of the degree of worsening in the social context
Incivilities are those visible disorders in the public space that violate respectful social norms
and tend not to be treated as crimes by the criminal justice system There are two types of
incivilities social and physical Social incivilities include antisocial behaviours such as public
drinking noisy neighbours and fighting in public places Physical incivilities include among
others vandalism graffiti abandoned cars and garbage on the streets Because citizens and
political authorities cannot always distinguish between incivilities and crime they are usually
treated as an additional category of crime This implies that policies aimed to reduce incivilities
are generally based on punitive actions However theory and evidence on incivilities suggest that
factors explaining incivilities and crime could be different (Lewis 2017 Taylor 1999)
In Chile crime rates have shown a sustained downward trend after reaching its highest level
in 2011 On the other hand incivilities rate has shown a sustained upward trend which coincides
with the increasing victimization and feeling of insecurity in the population This has motivated
93
Chilean authorities based on the ldquoBroken Windows Theoryrdquo to propose new punitive actions (or
increase the severity of the current ones) to those who commit this type of antisocial behaviours
The ldquoBroken Windows Theoryrdquo states that higher rates of incivilities are a signal of social
disorganization which result in higher crime rates (J Q Wilson amp Kelling 1982) This is expected
to have consequences on familiesrsquo decisions such as moving away from public spaces or even
leaving their neighbourhoods
As far as we know there is no previous evidence about the potential causes of incivilities in
Chile Efforts to identify the factors explaining incivilities could help not only to reduce the risk
factors favouring violent and property crimes but also to guide interventions aimed to change
social behaviours and strengthen social cohesion in highly unequal societies Thus the main
contribution of the present study is to provide a deeper comprehension of the problem of incivilities
and how they can help to better understand the weakening of social cohesion that many
contemporary societies experience
We aim to offer the first evidence on the factors explaining the evolution and the differences
in incivilities rates in Chile We set up a panel for six years (2006 2009 2011 2013 2015 and
2017) and 324 counties (1944 observations) We start exploring the evolution and geographical
distribution of incivilities Then we investigate whether economic and racial diversity after
controlling for other socioeconomic demographic and municipal characteristics can be regarded
as key predictors of incivilities
We use the Gini coefficient to proxy economic heterogeneity and the number of new visas
granted to foreigners as proportion of the county population as proxy for racial diversity The main
hypothesis is whether economic and racial diversity have a positive association with the rate of
incivilities In addition to the arguments regarding the ldquoCommunity Heterogeneity Thesisrdquo Taylor
94
(1999) and Skogan (1986 1999 2015) argue that incivilities are caused by inequality and the lack
of informal mechanisms of social control Based on the ldquoIncivilities Thesisrdquo the patterns of
incivilities should mirror the patterns of inequality (Taylor 1999) Then higher inequality should
be associated with higher physical and social vulnerability of the population This reduces social
control and increases social disorganization which triggers antisocial or negligent behaviours
Our main result reveals a strong positive association between the rate of incivilities and the
number of new visas granted per year The relationship with income inequality although also
positive seems to be less significant These findings give strong support to the ldquoCommunity
Heterogeneity Thesisrdquo and to a lesser extent to the ldquoIncivilities Thesisrdquo When the analysis is
disaggregated geographically racial diversity shows a clear positive effect The impact of income
inequality seems to be conditional depending on the level of income showing no effect in poorer
regions Results also show that the impact of economic and racial diversity differs by type of
incivility For example income inequality shows a strong association with ldquoStreet Tradingrdquo while
racial diversity with ldquoAlcohol Consumptionrdquo Two are the main policy implications On the one
hand a national strategy to address the problems associated with foreign immigration could help
to reduce incivilities For instance a joint effort between national and local authorities to curb
immigration and its distribution throughout the country On the other hand our results show that
the relationship between incivilities and economic diversity differs depending on the region or
geographical area Hence the impact on social cohesion of policies aimed to tackle economic
inequalities should be analysed in each specific context
The rate of incivilities also shows a negative association with the level of municipal financial
autonomy This implies that municipalities can effectively carry out policies to reduce incivilities
beyond the efforts of the central government Another important finding is that our results do not
95
support the hypothesis that a higher proportion of the young population is associated with higher
rates of incivilities Hence policies aimed to reduce incivilities should be focused on the causes of
incivilities rather than the criminalization of behaviours or stigmatization of specific population
groups
The structure of the chapter is as follows Section 42 outlines the relevant literature on social
cohesion and incivilities Section 43 describes the data variables and methodology and
establishes the hypotheses of the study Section 44 contains the results and discussions Section
45 presents the main conclusions
42 Related Literature
421 The Community Heterogeneity Thesis
The idea under ldquoThe Community Heterogeneity Thesisrdquo is that if individuals prefer to
interact with others who are similar to themselves in terms of income race or ethnicity high levels
of income inequality and racial diversity facilitate a context for lower tolerance and antisocial
behaviours lowering the ldquostaterdquo of social cohesion (Alesina 2000 Coffeacute amp Geys 2006 Letki
2008) Alessina and Ferrara (2002) give support to this hypothesis arguing that individuals have a
natural aversion to heterogeneity However the most popular explanation is the principle of
homophily people prefer to interact with others who share the same ethnic heritage have the same
social status and hence share experiences and tastes (Letki 2008 McPherson Smith-Lovin amp
Cook 2001 Tolsma et al 2009) For instance Delhey and Newton (2005) find for a sample of
60 countries that income inequality and ethnicity are strongly and negatively correlated with trust
Tolsma et al (2009) using data for Dutch neighbourhoods and municipalities find that social
cohesion is negatively and consistently affected by economic deprivation but not by ethnic
96
heterogeneity These authors also conclude that the effect of neighbourhood and municipal
characteristics on social cohesion depends on residentsrsquo income and educational level
Rothstein and Uslaner (2005) give two theoretical reasons why economic and racial diversity
should be causally related to social trust a key element of social cohesion First optimism about
the future makes less sense when there is more economic inequality which generally translates into
inequality of opportunities especially in areas such as education and the labour market Second
the distribution of resources and opportunities plays a key role in establishing the belief that people
share a common destiny and have similar fundamental values In highly unequal societies people
are likely to stick with their own kind Perceptions of injustice will reinforce negative stereotypes
of other groups making social trust and accommodation more difficult
Uslaner (2002 2011) and Uslaner and Brown (2005) find that high levels of inequality are
the single major factor driving down trust in people who are different from yourself Evidence for
USA finds that inequality is the strongest determinant of generalized trust over time (Rothstein amp
Uslaner 2005) Reducing inequality and then increasing generalized trust should have positive
consequences at the individual and aggregates levels At the individual level it may lead to greater
tolerance and more acts of altruism for people of different backgrounds At the aggregate level it
may lead to greater economic growth more redistribution from the rich to the poor and less
corruption (Uslaner 2002 2013) Letki (2008) argues that when neighbourhood socio-economic
context (apart from just an individualrsquos socioeconomic status) is considered it turns out to be the
main factor triggering negative attitudes and lack of trust in out-group members
The increasing diversity caused by immigration can also reduce the conditions necessary for
social cohesion (Ariely 2014 Holtug amp Mason 2010) Christel Kesler and Irene Bloemraad
(2010) find for nineteen advanced democracies between 1981 and 2000 that increasing migration
97
generally decreases trust civic engagement and political participation The authors also find that
in more equal countries with clear policies in favour of cultural minorities the negative effects of
migration are mitigated or even reversed Letki (2008) states that deprivation and disorder tend to
be strongly correlated with racial diversity Because we propose the use of the number of disorders
or antisocial behaviours known as ldquoincivilitiesrdquo as our measure of social cohesion we describe the
literature on incivilities in the next section
422 The literature on incivilities
The study of incivilities has been a continuing concern mainly for developed countries since
the 1980s The focus has changed from individual and psychological explanations to ecological
(contextual) and social explanations (Taylor 1999) The individual approach basically considered
perceptions of incivilities as an explanatory variable of fear of crime The ecological explanation
argues that indicators of economic disadvantage (eg income levels income inequality
unemployment rate and poverty rate) are the keys to understand a process of social disorganization
and lack of informal control These economic factors lead to higher rates of inappropriate or
negligent behaviours and ultimately to higher crime rates (Blau amp Blau 1982 Messner Rosenfeld
amp Baumer 2004 Phan Orsquobrien Mendolia amp Paloyo 2017 Sampson 1986)
The negative impact of incivilities is not merely reflected in its association with crime rates
(Skogan 2015) Physical and social incivilities could worsen neighbourhoods by affecting quality
of life perception of the environment and public and private behaviours Previous research has
indicated that a higher level of incivilities is associated with health problems (Branas et al 2011
Cohen et al 2000 Hill amp Angel 2005 Ross 2011 Ross amp Mirowsky 2001) greater
victimization and fear of crime (Brunton-Smith Jackson amp Sutherland 2014 Mijanovich amp
Weitzman 2003) and multiple negative economic effects For instance incivilities could be
98
related to a reduction in commercial activity lower investment in real estate reduction in house
prices (Skogan 2015) and population instability (Hipp 2010)
To describe the state of the art in the study of incivilities and their consequences Skogan
(2015) used the concept of untidiness to characterize the research on incivilities The study of
incivilities has had multiple approaches (economic ecological and psychological) Incivilities
have also been measured using multiple sources of information (police reports surveys trained
observation) which result in different measures (perceptions vs count data) However the question
about what specific factors have the strongest effect on incivilities has been overlooked and
perceptions about incivilities have been used mainly as a predictor of crime fear of crime and
victimization
There are two types of incivilities social and physical Social incivilities are a matter of
behaviour including groups of rowdy teens public drunkenness people fighting and street hassles
Physical incivilities involve visual signs of negligence and decay such as abandoned buildings
broken streetlights trash-filled lots and graffiti (Skogan 1999 2015 Taylor 1999) Three reasons
justify the distinction between physical and social incivilities First like multiple dimensions of
social cohesion different structural and social conditions could be responsible for different types
and categories of incivilities Second punitive sanctions are expected to have a greater impact on
physical than on social incivilities since the latter are more related to behaviours rooted in citizensrsquo
culture Third physical incivilities should be more related to absolute measures of economic
disadvantage (eg poverty or unemployment rates) and social incivilities to relative indicators of
economic disadvantage (eg such as income inequality) This line of research is based on the
ldquoincivilities thesisrdquo which states that to understand the distribution of disorders it is necesary to
analyse the patterns of structured inequalities (Skogan 1986 Taylor 1999)
99
423 The ldquoIncivilities Thesisrdquo
Incivilities theories began with a focus on psychological dynamics (Garofalo 1978) moved
forward to an interest in social psychological processes (J Q Wilson amp Kelling 1982) and finally
evolved into a focus on community dynamics and outcomes (Skogan 1999) Individual and group
behaviours in tandem with ecological features have been proposed as the key factors explaining
incivilities and their posterior influence on social control quality of life and more serious crime
(J Q Wilson amp Kelling 1982)
In terms of ecological factors particularly those related to economic conditions Skogan
(1986) was the first linking the distribution of incivilities to the patterns of structural inequality If
incivilities mirror inequality structure this will have consequences in residentsrsquo health and safety
due to its levels of vulnerability In addition structured inequality associated with the proportion
of the manufacturing sector (eg when fabrics tend to move from cities to farther areas) will be
related to higher social disorganization and differences between urban and rural areas (W J
Wilson 1996) In addition a persistent feeling of relative deprivation (persistently high levels of
income inequality) could lead to fellow inhabitants of the community to commit antisocial
behaviours showing their frustration with the current economic model
The literature on incivilities posits that their causes are different from those of crime (Lewis
2017) Unlike crime analysis especially property crimes information on the location where the
incivility takes place is the same as the location where the perpetrator resides To achieve a
comprehensive understanding of the different types of incivilities it is crucial to consider
incivilities data covering an entire territory and not just specific areas (Hooghe Vanhoutte
Hardyns amp Bircan 2010) If we add to this the availability of panel data it could be possible not
100
only to identify the main determinants of incivilities but also the causal mechanism from income
inequality towards incivilities rate
In Chile citizen security crime and delinquency are among the most significant issues for
citizens based on opinion polls Existing research has found weak evidence of a significant
relationship between crime and indicators of socio-economic disadvantage such as income
inequality and unemployment rate with significant effects only on property crime (Beyer amp
Vergara 2006 Nuntildeez Rivera Villavicencio amp Molina 2003 Rivera Gutieacuterrez amp Nuacutentildeez 2009)
Crime deterrence variables such as the probability of being caught or the number of police
resources have also shown ambiguous results (Beyer amp Vergara 2006 Rivera et al 2009
Vergara 2012) Evidence at the county level shows that crime is higher in urban counties those
with a lower mean income per capita and counties located in the North of the country In addition
at least half of the crimes reported in one county are perpetrated by criminals from other counties
(Rivera et al 2009) No studies could be found about the determinants of incivilities
4 3 Methodology
431 Period of analysis and data sample
Chile is a relatively small country in Latin America with a population of 18346018
inhabitants in 2017 The country is divided into 345 municipalities with on average 53104
inhabitants (median value 18705) Municipalities are the organ of the State Administration
responsible to solve local needs Municipalities are not only the relevant political and
administrative local unit of analysis but also they represent the feeling of lsquocommunityrsquo among
the inhabitants of each municipality (Hooghe et al 2010) Our data includes many sources of
101
heterogeneity among municipalities such as indicators of economic deprivation population
density demographic characteristics and whether the county is a regional or provincial capital
We use a sample of 324 municipalities covering most of the Chilean territory for the period
2006ndash17 Data on incivilities is obtained from the ldquoCentre of Studies and Analysis of Crimerdquo
which is part of the ldquoSubsecretaria de Prevencion del Delitordquo (SPD in its Spanish acronym) of the
Chilean government32 Information on income inequality and control variables is obtained from
the ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) the
ldquoNational Institute of Statisticsrdquo (INE in its Spanish acronym) the ldquoNational Municipal
Information Systemrdquo (SINIM in its Spanish acronym) and the Immigration Department of the
Government of Chile Our panel only includes the years for which CASEN survey is available
2006 2009 2011 2013 2015 and 2017
432 Operationalisation of the response variable and exploratory analysis
Official Chilean records contain information for the total number of cases of incivilities per
year at the county level The number of cases is the sum of complains and detentions reported at
the police Our dependent variable 119894119899119888119894119907119894119897119894119905119894119890119904 correspond to the number of cases per year Due
to population differences comparisons between counties are made using the incivilities rate per
1000 population calculated as
119894119899119888119894119907_119903119886119905119890 lowast 1000 (41)
where 119894119899119888119894119907_119903119886119905119890 is the incivilities rate 119905 is the year 119894 the county and 119899 is the population of the
county per year
32 httpceadspdgovclestadisticas-delictuales
102
Figure 41 illustrates at the top the evolution of the total number (cases reported) of
incivilities and crimes at the country level for the period 2006-1733 At the bottom Figure 41
shows the evolution of the mean county rate per 1000 inhabitants We observe that both the number
of incivilities and the number of crimes has reached similar annual figures however average
county rates per 1000 population show different trends Crime rate displays a sustained fall after
reaching its pick in 2011 Incivilities average county rate which also reached a pick and posterior
drop in 2011 has recovered its upward trend since 2016 considerably exceeding the crime rate
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017
33 Crime refers to ldquocrimes of greater social connotationrdquo (DMCS in its Spanish acronym) which includes violent and property crimes
103
Chilean records classify incivilities in nine categories most of them associated with social
incivilities Summary statistics for the total and for each of the nine categories are presented in
Table 41 In addition Figure 42 shows the evolution of incivilities by category for the whole
period We see that the global trend in incivilities is mainly due to a substantial increase in ldquoStreet
Tradingrdquo and ldquoPublic Alcohol Consumptionrdquo A common element of Figures 41 and 42 is the
significant change in trend experienced by crimes and incivilities in 2011 That year the SPD
became dependent on the Ministry of Interior of the Chilean Government This event put the issue
of crime and delinquency within national priorities for the central government
Table 41
Summary statistics total count of incivilities and by category (full sample and period)
Unlike crime rates we do not expect significant cross-county spillover effects in incivilities
However the questions of where incivilities are concentrated and why they are there can be of
great interest (Skogan 2015) Figure 43 shows quantile maps for the rate of incivilities per 1000
inhabitants for the initial and final years in our panel
104
Figure 42 Evolution total number of incivilities by category
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017)
105
We observe that the range of values has increased significantly from 2006 to 2017 but the
spatial distribution remains almost unchanged On the one hand high incivilities rates in the North
could be associated with the mining activity On the other hand high rates in the Centre area
(where the countyrsquos capital is located) could be related to the higher population density and the
concentration of the economic activity34
To see how the different types of incivilities are distributed throughout the country we have
grouped those similar categories in four groups ldquoPublic Damagerdquo (ldquoPublic Disturbsrdquo ldquoPublic
Damagerdquo ldquoOtherrdquo) ldquoThreatsrdquo (ldquoPublic Fightrdquo ldquoThreatsrdquo and ldquoAnnoying Noisesrdquo) ldquoAlcohol
Consumptionrdquo (ldquoPublic Alcohol Consumptionrdquo and ldquoDrunkennessrdquo) and ldquoStreet Tradingrdquo This
distinction in groups could be relevant if we expect different patterns and different effects of
community heterogeneity on social cohesion among counties For instance we expect higher
levels of Public Damage in big urban cities Street trading is more likely in urban cities but also in
tourist areas The spatial distribution of these four groups for the six-year-average rate per 1000
inhabitants can be found in Appendix R
433 Measures of community heterogeneity and control variables
Social cohesion income inequality and racial diversity are all ldquocommunityrdquo (not individual)
characteristics Thus to understand their relationship we need aggregated data at least at the
county-municipal level With more disaggregated data like at the suburbs level the required
heterogeneity among groups of citizens is lost (Wilkinson 1999) Like Coffeacute and Geys (2006) we
use the Gini coefficient to capture economic heterogeneity However instead of a measured for
34 We also analysed the spatial distribution of crime rates In general areas with high levels of incivilities not necessarily are associated with high levels of crime This could imply that factors explaining incivilities and crime are different
106
the diversity of nationalities we use the proportion of foreign population to capture racial
heterogeneity Income data is obtained from the CASEN survey The Gini coefficient is calculated
for each county and included through the variable gini Racial heterogeneity is included through
the variable foreign which is the annual number of new VISAS granted to foreigners as a
proportion of the county population Chile has experienced a significant increase in immigration
since 2011 Immigration has been concentrated in the metropolitan region and mining regions in
the North of the country We expect a positive relationship between immigration and incivilities
although as with the relationship between immigration and crime the foundations for this
hypothesis are not strong (Hooghe et al 2010 Sampson 2008)
Economic development is another explanation for social cohesion frequently appealed to
explain trust with wealthier societies considered to exhibit higher levels of trust (Delhey amp
Newton 2005) In this study we include the natural log of the mean household income per capita
log(income) We also include the poverty rate poverty and the unemployment rate
unemployment Unlike the variable log(income) these variables are expected to be positively
associated with the number of incivilities When a relative indicator of economic heterogeneity
such as income inequality is included as determinant of social cohesion we should expect less
effect from absolute indicators of economic disadvantage such as poverty and unemployment rates
(Hooghe et al 2010 Tolsma et al 2009)
Among demographic variables the percentage of inhabitants between 10 and 24 years old is
included through the variable youth The variable women defined as the proportion of the female
population in each county is also included Variable youth is expected to have an ambiguous effect
Although young people have lower victimization and report rates they also represent the group
more likely to commit antisocial behaviours when a community has a low capacity of self-
107
regulation (eg when there is low parental supervision) The female population is associated with
a higher report of incivilities related to the male population
It is argued that crime and incivilities are essentially urban problems (Christiansen 1960
Wirth 1938) We include the variable log(density) defined as the log of population density (the
number of inhabitants divided by the area of each county in square kilometres) and a dummy
variable capital indicating whether a county is an administrative capital (provincial or regional)
Two additional variables are included to capture the level of informal social control exerted
by families living in each municipality First the variable education which is defined as the
average years of education of people over 15 years old Second the variable housing which capture
the proportion of families which are owners of their housing unit Although education and housing
are related to both the possibility of reporting and committing an incivility we expect a negative
association with the rate of incivilities
In Chile crime has been mainly a problem faced by the police and the Central Government
Administration To control for current law enforcement policies we include the variable
deterrence defined as the number of arrests as a proportion of the total number of incivilities cases
In addition municipalities can develop their own initiatives to deal with crime and incivilities
depending on their capacity to generate its own resources The level of financial autonomy from
central transfers is captured by the variable autonomy This variable is obtained from SINIM and
it is defined as the proportion of the budget revenue of each municipality that comes from its own
permanent sources of revenues A categorical variable mayor is also included This variable
indicates whether the municipality mayor is supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo political
parties (related to those ldquoINDEPENDENTrdquo mayors)
108
Table 42 presents descriptive statistics for our measures of income and racial heterogeneity
and the set of numeric control variables The Pearson correlation among these variables is shown
in Appendix S
Table 42
Summary statistics numeric explanatory variables
434 Methods
The annual count of incivilities as is characteristic for count data is highly concentrated in
a relatively small range of values In addition the distribution is right-skewed due to the presence
of important outliers (counties with a high number of incivilities) Figure 44 shows the
distribution of the six-year average number of incivilities for each of the 15 regions in Chile35 We
observe that regions differ in the number of counties in which they are divided In addition
counties within each region show important differences in the number of incivilities For instance
35 Regions are ordered from left to right and from top to bottom in the way they are geographically distributed from North to South So the northernmost region of the country is the ldquoXVrdquo and the southernmost region is the ldquoXIIrdquo The Metropolitan region (where the countryrsquos capital is located) is region ldquoXIIIrdquo in the centre of the country (see Appendix C)
109
excepting the Metropolitan region ldquoXIIIrdquo most counties in regions located in the centre of the
country (middle row in Figure 44) show a range of incivilities between 0 and 2000 The number
of incivilities is considerably lower in counties located in the northern (top row in Figure 44) and
southern (bottom row in Figure 44) regions of the country compared to regions in the centre of
the country It also seems clear from Figure 44 that the number of incivilities does not follow a
normal distribution
Figure 44 Annual average number of incivilities per county
The number of incivilities can be better described by a Poisson distribution In this case the
number of incivilities is ldquothe countrdquo and the number of incivilities per year is ldquothe rate per unit
timerdquo We are interested in modelling the average number of incivilities per year usually called 120582
as a function of a set of contextual factors to explain differences in incivilities between and within
110
counties The main characteristic of the Poisson distribution is that the mean is equal to the
variance This implies that as the mean rate for a Poisson variable increases the variance also
increases The main implication is we cannot use OLS to model 120582 as a function of the set of
contextual factors because the equal variance assumption in linear regression is violated
The rate of incivilities between counties is not directly comparable due to population
differences We expect counties with more people to have more reports of incivilities since there
are more people who could be affected To capture differences in population which is called the
exposure of our response variable 120582 it is necessary to include a term on the right side of our model
called an offset We will use the log of the county population in thousands as our offset36
Additionally similar to the case of crime data incivilities show a significant degree of
overdispersion (variance higher than the mean) suggesting that there is more variation in the
response than the Poisson model implies37 We also model and regress incivilities assuming a
Negative Binomial distribution to address overdispersion An advantage of this approach is that it
introduces a dispersion parameter in addition to 120582 which gives the model more flexibility38
Considering as the response variable the count of incivilities per year the model can be
expressed as follow
120582 119890119909119901 120573 120573 119892119894119899119894 120573 119891119900119903119890119894119892119899 119883120574 120572 120579 (42)
36 If we think of 120582 as the average number of incivilities per year then represents the number per 1000
inhabitants so that the yearly count is adjusted to be comparable across counties of different sizes Adjusting the yearly count by population is equivalent to adding 119897119900119892 1199011199001199011199061198971198861199051198941199001198991000 to the right-hand side of the regression equation 37 Without adjusting for overdispersion we use incorrect artificially small standard errors leading to artificially small p-values for model coefficients 38 The Negative Binomial model posits selecting a 120582 (average number of incivilities) for each county and then generating a count using a Poisson random variable with the selected 120582 With this approach the counts will be more dispersed than would be expected for observations based on a single Poisson variable with rate 120582
111
where 120582 is the rate of incivilities 119883 is our vector of controls 120572prime119904 are county-specific constants
and 120579prime119904 are time-specific constants Accounting for differences in county population we have
119890119909119901 120573 120573 119892119894119899119894 119883120574 120572 120579 (43)
where 119901119900119901119906119897119886119905119894119900119899 is the county population per year Hence the model to be estimated using
Maximum Likelihood Estimation (MLE) is
119897119900119892 120582 120573 120573 119892119894119899119894 119883120574 120572 120579 119897119900119892 (44)
Finally to account for different effects depending on the type of incivilities we also run
equation (44) for each of the four groups of incivilities defined in section (432)
435 Hypotheses
Based on the community heterogeneity hypothesis the relationship between social cohesion
and diversity should be stronger for lower levels of income and less educated groups of people
(Tolsma et al 2009) Hence contrary to evidence for developed and more equal countries we
expect a significant positive association for the Chilean case where more than 50 of the
population is economically vulnerable (OECD 2017)
The main hypotheses to be tested in this essay is whether the number of incivilities is
positively associated with the level of economic and racial heterogeneity at the county level We
start analysing this association for the full sample and period Next we analyse whether the
relationship between incivilities and our measures of diversity differs by geographic area (region
or zone) Finally we check whether the effect of economic and racial diversity is different
depending on the group of incivilities
112
44 Results and Discussion
Overall our results show that the rate of incivilities displays a stronger and more significant
relationship with racial diversity than with economic heterogeneity This association differs for
different geographic areas and for different types of incivilities Absolute economic indicators
except for income show a significant but small effect Increases in the average levels of income
or education and more financial autonomy for municipalities seem to be effective ways to reduce
the rate of incivilities
We estimate equation (44) assuming that the number of incivilities follows a Poisson
distribution Regional and temporal heterogeneity are captured through the inclusion of dummy
variables for five years (with 2006 as the reference year) and fourteen regional dummies (with
region XIII as the reference region) Results are reported in Table 4339 This table is structured in
two blocks of regressions pooled models in columns (1)-(4) and fixed-effects models in columns
(5)-(8)40 The first column in each block only includes economic indicators relative and absolute
trying to test which ones are more relevant and whether incivilities tend to mirror income
inequality (the ldquoincivilities thesisrdquo) The second column adds the variable foreign to account for
the effect of racial diversity (Letki 2008) The third column includes education to check whether
the association between economic and racial diversity with social cohesion changes (gets less
significant) when we control for educational level (Tolsma et al 2009) The final column in each
block corresponds to the full model specification which includes the rest of controls
39 Pooled estimations were obtained using the R command glm() and compared with results obtained from the command poisson in STATA 15 Fixed effects and random effects models were estimated using the STATA command xtpoisson Negative Binomial models were estimated using the command glmnb() in the MASS package for R and commands nbreg and xtnbreg in STATA 15 Results for Negative Binomial regressions are in Appendix T 40 We have omitted results with random effects due to the literature on panel count data models suggest that the most robust estimator is Poisson regression with Fixed Effects (Santos Silva amp Tenreyro 2010 2011)
113
Table 43
Poisson regressions
114
The positive and significant coefficient for the variable gini besides being small it becomes
insignificant in the fixed effects specification which includes the full set of controls This result
does not seem to be enough evidence to support our hypothesis that more unequal counties display
higher rates of incivilities On the other hand racial diversity through the variable foreign shows
a consistent positive association with the rate of incivilities41 Together coefficients for gini and
foreign seems to support the ldquocommunity heterogeneity thesisrdquo (Letki 2008) but not the
ldquoincivilities thesisrdquo (Skogan 1999) To check this finding we run the pooled full model
specification for each region and results are shown in Table 44 where regions have been ordered
from North to South The sign of the coefficient of the variable gini differs for different regions
Moreover the relationship is insignificant in some of the most unequal regions which are in the
South of the country (VIII IX and XIV) This result rejects the hypothesis that incivilities mirror
structural income inequalities For the variable foreign 12 out of 15 regions confirm the positive
association with the rate of incivilities42
We also run our pooled full model separately for each group of incivilities defined at the end
of section (432) Income inequality keeps its significant but small association with each group of
incivilities (see Table 45) Our measure of racial diversity shows a stronger association with
ldquoAlcohol Consumptionrdquo related to ldquoPublic Damagerdquo and ldquoThreatsrdquo The link with ldquoStreet Tradingrdquo
appears as non-significant These results support our general finding that on the one hand racial
heterogeneity exert a more significant influence on the rate of incivilities than economic
41 To get the effects of the variables gini and foreign over the rate of incivilities per 1000 inhabitants we need to exponentiate their coefficients Thus an increase in one unit in the variable gini increases the rate of incivilities per 1000 inhabitantsrdquo in 1005 times (e^0005) in the pooled full model Similarly an increase in one point in variable foreign is expected to increase the ldquorate of incivilities per 1000 inhabitantsrdquo in 10876 times (e^0084) in the full pooled model and 10471 (e^0046) times in the full Fixed Effects model 42 Given that depending on the geographic location regions share certain characteristics the model was also run clustering regions in 4 zones (see Figure 31 in chapter 3) Results by zone are in Appendix U
115
heterogeneity On the other hand results confirm our hypothesis that the effect of diversity is
different for different types of incivilities
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region
Back to our general results in Table 43 the significant and negative coefficient of the
income variable and to a lesser extent the significant and positive coefficients of poverty and
unemployment provide evidence that absolute rather than relative economic indicators may be
more important explanations of the rate of incivilities This is opposite to evidence for the analysis
116
of crime rates such as in Hoodge (2010) and support the idea that determinants of incivilities are
different from those of crime Our results are also opposite to those for Dutch municipalities where
economic indicators turned out to be more important than ethnic heterogeneity (Tolsma et al
2009) The coefficient for the variable log(income) could be interpreted as counties with an income
level under the average face higher problems of antisocial behaviours such as incivilities In
addition as the income level moves far away from its average low level the problem of incivilities
is less relevant43 In terms of policy implications only those policies that achieve a significant
increase in the average level of county income seem to be effective in reducing incivilities and
strengthening social cohesion
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group
43 We ran our model splitting the sample of counties in quintiles based on their income level The coefficient for the variable gini only showed a significant (and positive) coefficient for the second third and fourth quintile A non-significant coefficient for the first quintile could imply that other types of inequalities (eg health education) are more relevant in the poorest counties In the case of the fifth quintile (the richest group of counties) they have better facilities in terms of infrastructure police resources self-protection etc which could reduce the potential triggers of incivilities
117
The inclusion of the variable education significantly improved the goodness of fit of the
models and did not generate significant changes in the coefficients of our measures of economic
and racial diversity This result rejects the proposition that the relationship between social
cohesion and diversity becomes less strong when controlled by education (Tolsma et al 2009)
Additionally it highlights the topic of education as a relevant determinant of the rate of incivilities
and social cohesion
Among control variables there are also some important results Opposite to what we
expected the variable youth shows a negative or non-significant coefficient Although this result
could be due to the lack of ldquophysical incivilitiesrdquo in Chilean records it indicates that it is incorrect
to stereotype this group as the main responsible for high incivilities rates The significant and
negative coefficient of the variable autonomy in the fixed effects specification could also have
important policy implications It is a signal that local governments can play an important role in
reducing incivilities or complementing the efforts from the central government Another
interesting result is the significant coefficient of the variable housing The latter finding is
particularly important in the sense that a negative sign supports public policies oriented to increase
homeownership as effective ways to improve social cohesion However the small magnitude of
the coefficient that even showed the opposite sign in some model specifications could be
explained for the high level of segregation that these policies have generated in Chilean society
As mentioned in the Introduction and Literature Review so far only a few studies have
used measures of disorders or incivilities as dependent variable to explain changes in social
cohesion (Skogan 2015) In addition there is no evidence in Chile analysing the determinants of
incivilities separately from those of crimes The importance of our results on identifying the
importance of economic and racial diversity on social cohesion lies mainly in its generality An
118
important number of countries all around the world share a similar context characterized by high
levels of inequality and an explosive increase in immigration These countries are also
experiencing a worsening in social cohesion which increases the risk of a social outburst
4 5 Conclusions
The main goal of this essay was to determine whether differences in incivilities at the county
level mirror differences in income distribution and racial diversity Previous literature suggests a
positive and strong association between social cohesion and indicators of economic disadvantage
relative deprivation and racial diversity (Letki 2008 Tolsma et al 2009 Uslaner amp Brown 2005)
While not all our results were significant they showed helpful insights about how and where
economic and racial diversity are more likely to influence the rate of incivilities and social
cohesion
We used data for the period 2006ndash17 economic heterogeneity was measured through the
Gini coefficient at the county level and racial heterogeneity was proxied by the number of granted
visas to foreigners as proportion of county population We found strong evidence of a significant
and positive association between the rate of incivilities and racial diversity but not with income
inequality Contrary to previous evidence at the municipal level (Coffeacute amp Geys 2006 Tolsma et
al 2009) in general our results give support for the ldquohomophily principlerdquo and the ldquocommunity
heterogeneity hypothesisrdquo However results also showed that the effect of economic and racial
diversity varies throughout the Chilean regions and for the different types of incivilities
We also found that policies aimed at controlling the behaviour of young people did not have
strong empirical support In terms of the role that local governments may have in facing the
119
growing problem of incivilities we found evidence that efforts managed from the municipalities
can be an important complement to those from the central government
Future research should go further on the role of local authorities on incivilities and social
cohesion On the one hand municipalities could have a direct impact on social cohesion through
the implementation of programs complementary to those of central authorities oriented to reduce
incivilities and crime On the other hand social cohesion could be indirectly affected when local
authorities display an inefficient performance supplying public services to citizens or they are
recognized as corrupted institutions We suggest that policy makers from central government
should give local authorities a greater role in fighting antisocial behaviours and crime Evaluating
programs in specific municipalities could help to elucidate the causal effect of for instance higher
fiscal autonomy on the rate of incivilities
Another interesting area for future work will be to analyse how housing policies have
contributed to the phenomenon of segregation of Chilean society and in the process of weakening
social cohesion Finally our main result highlights the need of a deeper analysis of the impact that
foreign immigration is having in Chile For instance disaggregating information by country of
origin and the reasons why immigrants are arriving to the country or specific regions will surely
help to understand the impacts of immigration
120
Chapter 5 Conclusions
This thesis investigated in three essays the issue of income inequality in Chile using county-
level data for the period 2006-2017 The first essay supplied empirical evidence about the
importance of the degree of dependence on natural resources in terms of employment in explaining
cross-county differences in income inequality The second essay analysed the potential causal
effect that income inequality has on the level of technical efficiency of local governments
providing public goods and services Lastly the third essay studied the relationship between social
cohesion measured through the number of antisocial behaviours classified as ldquoincivilitiesrdquo and
the levels of income and racial heterogeneity
Findings from the first essay support the idea that the endowment of natural resources plays
a significant role in explaining income inequality in Chile However contrary to what most
theoretical and empirical evidence postulates our findings showed a robust negative association
between the two variables This means that the reduction experienced in Chile in the degree of
dependence on natural resources in terms of employment has contributed to the persistence of high
levels of income inequality The exploratory analysis indicated that income inequality shows a
general clustering process characterized by a significant and positive spatial autocorrelation
Regarding the previous evidence for Chile (Paredes et al 2016) the regression analysis confirmed
the relevance of the spatial dimension of income inequality through a process of spatial
heterogeneity giving less support to the existence of a process of spatial dependence (spillover
effect) in the variable itself
121
Essay 2 studied the potential trade-off between efficiency and equity analysing the influence
of income inequality on the efficiency of local governments at the municipal level To identify the
causal effect of income inequality on municipal efficiency we proposed the use of the proportion
of firms in the primary sector as an instrument for income inequality Findings confirmed our
hypothesis that efficiency is lower in more unequal counties This result suggests the non-existence
of the trade-off between equity and efficiency Hence policies intended to reduce inequality could
help to increase efficiency at least at the level of municipal local governments
The third essay analysed how social cohesion proxied by the rate of incivilities is associated
with the levels of economic diversity proxied by income inequality and the levels of racial
diversity proxied by the number of new visas grated as proportion of the county population
Findings gave strong support to the hypothesis that the rate of incivilities is positively related to
racial diversity and to a lesser extent to economic diversity In addition the rate of incivilities
appears negatively related to the degree of financial autonomy of municipalities This means that
local governments can effectively contribute to the reduction of incivilities which could help
reduce victimization and crime rates ultimately strengthening social cohesion
Taken together findings from essays 2 and 3 highlight the important role that income
inequality could play in other relevant economic and social dimensions These findings add to the
understanding of the potential consequences of income inequality particularly in natural resource
rich countries with persistently high levels of inequality
The present study has mainly investigated income inequality at the county level In addition
Chilean municipalities play an important role providing ldquoservices to peoplerdquo so our findings could
be applied in other highly unequal countries with a high degree of dependence on natural resources
and local governments with similar responsibilities For instance in Latin America apart from
122
Chile and Brazil there are no studies on the efficiency of local governments Other limitations are
associated with the availability of information For instance important indicators such as GDP per
capita are only available at the regional level and information of incomes is not available annually
In addition given the heterogeneity among municipalities some type of grouping of municipalities
should be performed before looking for causal relationships or conducting program evaluation
Despite these limitations we believe this study could be the basis for different strands of future
research on the topic of inequality local government efficiency and social cohesion
It was stated in chapter 2 based on the resource curse hypothesis literature there are two
elements that determine whether NR are a curse or blessing in terms of socioeconomic outcomes
First the curse is more likely in countries with weak political and governance institutions Second
different types of resources affect institutions differently with resources that are concentrated in
space so-called ldquopointrdquo resources tend to impair institutions while ldquodiffuserdquo resources do not
(Deacon 2011 Isham Woolcock Pritchett amp Busby 2005) Our results showed a negative
relationship between income inequality and our measure of natural resource dependence even after
controlling for zone fixed effects and for the level of government expenditure This result could
be interpreted as a signal that NR has a direct effect on income inequality in addition to the indirect
impact through market or institutional channels Using other potential institutional transmission
channels will shed light about the true effect that the endowment of natural resources has over
income inequality Variables that could capture these institutional channels include the level of
employment in the public sector measures of rule of law and corruption and changes in the
creation of new business in the secondary and tertiary sectors related to the primary sector
Based on results from chapter 3 most of the municipalities show scale inefficiencies One
immediate area for future work will involve using our set of contextual factors to predict the status
123
of municipalities in terms of scale inefficiencies Defining as dependent variable whether a
municipality shows constant decreasing or increasing returns to scale we could run a multinomial
logistic regression to predict municipal status For instance we would expect that a one-unit
increase in the Gini coefficient should increase the probability of scale inefficiencies (increasing
or decreasing returns to scale rather than constant returns to scale) Because the aim in this case
would be predicting a certain result in terms of returns to scale the next step should involve to
split the full sample in training and testing data sets and to use some resampling methods such as
bootstrapping This will allow us to evaluate the performance and accuracy of our model
predictions using different random samples of municipalities Results from Machine Learning
algorithms will help us to assess the generalizability of our results to other data sets
Future work should also benefit greatly by using data on different Latin American countries
to (1) compare the responsibilities of local governments (2) select a common set of inputs and
output to evaluate LGE (3) identify the relevance of scales inefficiencies in explaining differences
in performance and (4) analyse the influence of contextual characteristics over LGE Differences
in the main primary sector activity in each country such as oil in Brazil mining in Chile or Coffee
in Colombia could be responsible for differences in LGE among countries These differences could
be associated with sources of revenue management of expenditure and definitions of outputs or
contextual effects such as corrupted institutions or the delay in the development of other sectors
such as manufacturing or services
To delve deep on reasons explaining the social crisis experienced by Chilean society and
other countries one area of future work will be to analyse the relationship between diversity and
the origins of social revolutions Based on Tiruneh (2014) the three most important factors that
explain the onset of social revolutions are economic development regime type and state
124
ineffectiveness Interesting questions include whether the characteristics of Chilean context at the
end of 2019 are enough to trigger the transformation of the political and socioeconomic system
Social revolutions particularly violent revolutions are less likely in more democratic educated
and wealthy societies So it would be relevant to identify the factors explaining the violence that
has characterized the social crisis in Chile Finally the democratic regime has been maintained in
the last decades with changes between left and right governments This could imply that more
important than the regime has been the efficiency or ineffectiveness of the governments to satisfy
the needs of the population
Future work should also cover the disaggregation of information regarding foreign
population in terms of the reasons for new granted visas and the country of origin Official data
allows us to disaggregate whether the benefit is permanent (students and employees with contract)
or temporary Furthermore most of the new visas were traditionally granted to neighbouring
countries (Peru and Bolivia) a trend that has changed in the recent years to include countries such
as Colombia Venezuela and Haiti An analysis of how economic and social indicators have been
affected by changes in the composition of foreigners their reasons for immigrating to the country
and their geographical distribution have implications for economic policy at both the national and
local levels At the national level such analysis should be a key input when proposing changes to
the national immigration policy At the local level it could help define the role of municipalities
to assess the benefits and challenges of immigration These challenges are mainly related to the
provision of public goods and services such as health and education which in Chile are the
responsibility of the municipalities
The findings of this thesis suggest that policymakers should encourage policies that reduce
income inequality The key role that municipalities could play to strengthen social cohesion and
125
the increasingly important role that foreign population is acquiring in most modern societies are
also interesting avenues for future research However the picture is still incomplete and more
research is needed incorporating other dimensions of inequality This is essential if we want to
understand the reasons that could have triggered the social outbursts experienced by various
economies across the globe
126
Bibliography
Acemoglu D (1995) Reward structures and the allocation of talent European Economic Review 39(1) 17ndash33 httpsdoiorghttpsdoiorg1010160014-2921(94)00014-Q
Acemoglu D (2002) Technical Change Inequality and the Labor Market Journal of Economic Literature 40(1) 7ndash72 httpsdoiorg1012570022051026976
Acemoglu D Aghion P amp Violante G L (2001) Deunionization Technical Change and Inequality Carnegie-Rochester Conference Series on Public Policy 55(1) 229ndash264 httpsdoiorg101016S0167-2231(01)00058-6
Acemoglu D Johnson S amp Robinson J A (2001) The Colonial Origins of Comparative Development An Empirical Investigation The American Economic Review 91(5) 1369ndash1401 httpsdoiorg101257aer9151369
Acemoglu D amp Robinson J A (2002) The Political Economy of the Kuznets Curve Review of Development Economics 6(2) 183ndash203 httpsdoiorg1011111467-936100149
Afonso A amp Fernandes S (2006) Measuring local government spending efficiency Evidence for the Lisbon region Regional Studies 40(1) 39ndash53 httpsdoiorg10108000343400500449937
Afonso A amp Fernandes S (2008) Assessing and explaining the relative efficiency of local government The Journal of Socio-Economics 37(5) 1946ndash1979 httpsdoiorg101016jsocec200703007
Afonso A Schuknecht L amp Tanzi V (2010) Income distribution determinants and public spending efficiency Journal of Economic Inequality 8(3) 367ndash389 httpsdoiorg101007s10888-010-9138-z
Akita T (2003) Decomposing regional income inequality in China and Indonesia using two-stage nested Theil decomposition method The Annals of Regional Science 37(1) 55ndash77 httpsdoiorg101007s001680200107
Alesina A (2000) Participation in heterogeneous communities The Quarterly Journal of Economics 115(3) 847ndash904 httpsdoiorg101162003355300554935
Alesina A amp La Ferrara E (2002) Who trusts others Journal of Public Economics 85(2) 207ndash234 httpsdoiorg101016S0047-2727(01)00084-6
Allcott H amp Keniston D (2014) Dutch Disease or Agglomeration The Local Economic Effects of Natural Resource Booms in Modern America (N W P N 20508 Ed) NBER Working Paper No 20508 (Vol w20508) NBER Working Paper No 20508 NBER Working Paper No 20508 httpsdoiorgNBER Working Paper No 20508
Andersen T M amp Maibom J (2020) The big trade-off between efficiency and equitymdashis it there Oxford Economic Papers 72(2) 391ndash411
127
Anselin L (1988) Spatial econometrics methods and models (Vol 4) DordrechtBoston Kluwer Academic Publishers
Anselin L amp Bera A K (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics Statistics Textbooks and Monographs (Vol 155)
Anselin L Bera A K Florax R amp Yoon M J (1996) Simple diagnostic tests for spatial dependence Regional Science and Urban Economics 26(1) 77ndash104 httpsdoiorg1010160166-0462(95)02111-6
Antonelli M A amp De Bonis V (2018) The efficiency of social public expenditure in European countries a two-stage analysis Applied Economics 1ndash14 httpsdoiorg1010800003684620181489522
Aragoacuten F M amp Rud J P (2013) Natural Resources and Local Communities Evidence from a Peruvian Gold Mine American Economic Journal Economic Policy 5(2) 1ndash25 httpsdoiorg101257pol521
Ariely G (2014) Does Diversity Erode Social Cohesion Conceptual and Methodological Issues Political Studies 62(3) 573ndash595 httpsdoiorg1011111467-924812068
Armstrong H amp Taylor J (2000) Regional economics and policy (3rd ed) Oxford Blackwell
Aroca P amp Atienza M (2011) Economic implications of long distance commuting in the Chilean mining industry Resources Policy 36(3) 196ndash203 httpsdoiorg101016jresourpol201103004
Aroca P amp Bosch M (2000) Crecimiento convergencia y espacio en las regiones chilenas 1960 - 1998 Estudios de Economiacutea 27 199ndash224 Retrieved from httprepositoriouchileclbitstreamhandle2250127853Patricio_Arocapdfsequence=1
Ashworth J Geys B Heyndels B amp Wille F (2014) Competition in the political arena and local government performance Applied Economics 46(19) 2264ndash2276 httpsdoiorg101080000368462014899679
Atkinson A B (2015) Inequality What Can Be Done Harvard University Press
Auty R (1993) Sustaining development in mineral economies the resource curse thesis London [ua] Routledge
Auty R (2001) Resource abundance and economic development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Badeeb R A Lean H H amp Clark J (2017) The evolution of the natural resource curse thesis A critical literature survey Resources Policy 51 123ndash134 httpsdoiorg101016jresourpol201610015
Balaguer-Coll M T Brun-Martos M I Maacuterquez-Ramos L amp Prior D (2019) Local government efficiency determinants and spatial interdependence Applied Economics
128
51(14) 1478ndash1494 httpsdoiorg1010800003684620181527458
Balaguer-Coll M T amp Prior D (2009) Short- and long-term evaluation of efficiency and quality An application to Spanish municipalities Applied Economics 41(23) 2991ndash3002 httpsdoiorg10108000036840701351923
Banker R D Charnes A amp Cooper W W (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis Management Science 30(9) 1078ndash1092
Beine M Coulombe S amp Vermeulen W N (2015) Dutch Disease and the Mitigation Effect of Migration Evidence from Canadian Provinces The Economic Journal 125(589) 1574ndash1615 httpsdoiorg101111ecoj12171
Benito B Bastida F amp Garciacutea J A (2010) Explaining differences in efficiency an application to Spanish municipalities Applied Economics 42(4) 515ndash528 httpsdoiorg10108000036840701675560
Berg A amp Ostry J (2011) Equality and efficiency Finance amp Development 48(3) 12ndash15
Berg A amp Ostry J (2017) Inequality and Unsustainable Growth Two Sides of the Same Coin IMF ECONOMIC REVIEW 65(4) 792ndash815 httpsdoiorg101057s41308-017-0030-8
Beyer H amp Vergara R (2006) Delincuencia en Chile Determinantes y rol de las poliacuteticas puacuteblicas Instituto de Economiacutea UC
Blanco L amp Grier R (2012) Natural resource dependence and the accumulation of physical and human capital in Latin America Resources Policy 37(3) 281ndash295 httpsdoiorghttpdoiorg101016jresourpol201201005
Blau J R amp Blau P M (1982) The cost of inequality Metropolitan structure and violent crime American Sociological Review 114ndash129
Boetti L Piacenza M amp Turati G (2009) Fiscal decentralization and spending efficiency of local governments An Empirical Investigation on a Sample Of 4
Boix C amp Posner D (1998) Social capital Explaining its origins and effects on government performance British Journal Of Political Science 28 686ndash693
Borge L E Parmer P amp Torvik R (2015) Local natural resource curse JOURNAL OF PUBLIC ECONOMICS 131 101ndash114 httpsdoiorg101016jjpubeco201509002
Borger B Kerstens K Moesen W amp Vanneste J (1994) Explaining differences in productive efficiency An application to Belgian municipalities Public Choice 80(3) 339ndash358 httpsdoiorg101007BF01053225
Bourguignon F amp Morrisson C (1990) Income distribution development and foreign trade A cross-sectional analysislowast European Economic Review 34(6) 1113ndash1132 httpsdoiorghttpsdoiorg1010160014-2921(90)90071-6
129
Branas C C Cheney R A MacDonald J M Tam V W Jackson T D amp Ten Have T R (2011) A difference-in-differences analysis of health safety and greening vacant urban space American Journal of Epidemiology 174(11) 1296ndash1306
Bravo J (2014) Fondo Comuacuten Municipal y su desincentivo a la recaudacioacuten en Chile Temas de La Agenda Legislativa-Centro de Poliacuteticas Puacuteblicas UC 9(68)
Browning E K amp Johnson W R (1984) The Trade-Off between Equality and Efficiency Journal of Political Economy 92(2) 175ndash203 httpsdoiorg101086261219
Brunnschweiler C N amp Bulte E H (2008) The resource curse revisited and revised A tale of paradoxes and red herrings Journal of Environmental Economics and Management 55(3) 248ndash264 httpsdoiorghttpsdoiorg101016jjeem200708004
Brunori P Ferreira F H G amp Peragine V (2013) Inequality of opportunity income inequality and economic mobility Some international comparisons In Getting Development Right (pp 85ndash115) Springer
Brunton-Smith I Jackson J amp Sutherland A (2014) Bridging structure and perception On the neighbourhood ecology of beliefs and worries about violent crime British Journal of Criminology 54(4) 503ndash526
Bulte E H Damania R amp Deacon R T (2005) Resource intensity institutions and development World Development 33(7) 1029ndash1044 httpsdoiorg101016jworlddev200504004
Carmignani F (2013) Development outcomes resource abundance and the transmission through inequality Resource and Energy Economics 35(3) 412ndash428 httpsdoiorg101016jreseneeco201304007
Carmignani Fabrizio amp Avom D (2010) The social development effects of primary commodity export dependence Ecological Economics 70(2) 317ndash330 httpsdoiorg101016jecolecon201009003
Caselli F amp Michaels G (2013) Do Oil Windfalls Improve Living Standards Evidence from Brazil American Economic Journal Applied Economics 5(1) 208ndash238 httpsdoiorg101257app51208
Celebioglu F amp Dallrsquoerba S (2010) Spatial disparities across the regions of Turkey An exploratory spatial data analysis Annals of Regional Science 45(2) 379ndash400 httpsdoiorg101007s00168-009-0313-8
Chan J To H-P amp Chan E (2006) Reconsidering social cohesion Developing a definition and analytical framework for empirical research Social Indicators Research 75(2) 273ndash302
Charnes A Cooper W W amp Rhodes E (1978) Measuring the efficiency of decision making units European Journal of Operational Research 2(6) 429ndash444
130
Chi G amp Zhu J (2019) Spatial Regression Models for the Social Sciences SAGE Publications Retrieved from httpsbooksgooglecomaubooksid=zHGkvwEACAAJ
Christiansen K O (1960) Industrialization and urbanization in relation to crime and juvenile delinquency International Review of Criminal Policy 16 3ndash8
Cingano F (2014) Trends in income inequality and its impact on economic growth (Vol 163) Paris OECD Publishing httpsdoiorg1017875jxrjncwxv6j-en
Coelli T J Prasada Rao D S OrsquoDonnell C J amp Battese G E (2005) An introduction to efficiency and productivity analysis An Introduction to Efficiency and Productivity Analysis Springer Science amp Business Media httpsdoiorg101007b136381
Coffeacute H amp Geys B (2005) Institutional Performance and Social Capital An Application to the Local Government Level Journal of Urban Affairs 27(5) 485ndash501 httpsdoiorg101111j0735-2166200500249x
Coffeacute H amp Geys B (2006) Community Heterogeneity A Burden for the Creation of Social Capital Social Science Quarterly 87(5) 1053ndash1072 httpsdoiorg101111j1540-6237200600415x
Cohen D Spear S Scribner R Kissinger P Mason K amp Wildgen J (2000) ldquo Broken windowsrdquo and the risk of gonorrhea American Journal of Public Health 90(2) 230
Corden W M amp Neary J P (1982) Booming sector and de-industrialisation in a small open economy The Economic Journal 92(368) 825ndash848
Cordero J M Pedraja-Chaparro F Pisaflores E C amp Polo C (2017) Efficiency assessment of Portuguese municipalities using a conditional nonparametric approach Journal of Productivity Analysis 48(1) 1ndash24 httpsdoiorg101007s11123-017-0500-z
Croissant Y amp Millo G (2018) Panel Data Econometrics with R John Wiley amp Sons
Cust J amp Poelhekke S (2015) The Local Economic Impacts of Natural Resource Extraction 7 251ndash268 httpsdoiorg101146annurev-resource-100814-125106
da Cruz N F amp Marques R C (2014) Revisiting the determinants of local government performance Omega 44 91ndash103 httpsdoiorg101016JOMEGA201309002
Dauvin M amp Guerreiro D (2017) The Paradox of Plenty A Meta-Analysis World Development 94 httpsdoiorg101016jworlddev201701009
de Sousa M da C S Cribari-Neto F amp Stosic B D (2005) Explaining DEA technical efficiency scores in an outlier corrected environment the case of public services in Brazilian municipalities Brazilian Review of Econometrics 25(2) 287ndash313
Deacon R T (2011) The Political Economy of the Natural Resource Curse A Survey of Theory and Evidence Foundations and Trends in Microeconomics 111-208
Delhey J amp Newton K (2005) Predicting cross-national levels of social trust global pattern or
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Nordic exceptionalism European Sociological Review 21(4) 311ndash327
Dlouhyacute M (2018) Measuring Geographic Inequalities Dealing with Multiple Health Resources by Data Envelopment Analysis Frontiers in Public Health 6 53 httpsdoiorg103389fpubh201800053
Dollery B Wallis J amp Akimov A (2010) One Size Does Not Fit All The Special Case of Remote Small Local Councils in Outback Queensland Local Government Studies 36(1) 21ndash42 httpsdoiorg10108003003930903435716
Domenech J (2008) Mineral resource abundance and regional growth in Spain 1860ndash2000 Journal of International Development The Journal of the Development Studies Association 20(8) 1122ndash1135
Doran J amp Jordan D (2016) Decomposing US regional income inequality from 1969 to 2009 Applied Economics Letters 23(11) 781ndash784 httpsdoiorg1010801350485120151109030
Drew J amp Dollery B (2015) The State of Things The Dynamic Efficiency of Australian State and Territories Economic Papers A Journal of Applied Economics and Policy 34(3) 165ndash176
Drew J Kortt M amp Dollery B (2015) What Determines Efficiency in Local Government A DEA Analysis of NSW Local Government Economic Papers A Journal of Applied Economics and Policy 34(4) 243ndash256 httpsdoiorg1011111759-344112118
Easterly W (2007) Inequality does cause underdevelopment Insights from a new instrument Journal of Development Economics 84(2) 755ndash776 httpsdoiorghttpdxdoiorg101016jjdeveco200611002
Ebert L amp La Menza T (2015) Chile copper and resource revenue A holistic approach to assessing commodity dependence Resources Policy 43(Supplement C) 101ndash111 httpsdoiorghttpsdoiorg101016jresourpol201410007
ElGindi T (2017) Natural resource dependency neoliberal globalization and income inequality Are they related A longitudinal study of developing countries (1980ndash2010) Current Sociology 65(1) 21ndash53 httpsdoiorg1011770011392116632031
Engerman S L amp Sokoloff K L (1994) Factor Endowments Institutions and Differential Paths of Growth Among New World Economies National Bureau of Economic Research
Engerman S L amp Sokoloff K L (1997) Factor endowments institutions and differential paths of growth among new world economies How Latin America Fell Behind 260ndash304
Engerman S L Sokoloff K L Urquiola M amp Acemoglu D (2002) Factor Endowments Inequality and Paths of Development among New World Economies [with Comments] EconomampxedA 3(1) 41ndash109 Retrieved from httpwwwjstororgezp01libraryquteduaustable20065432
132
Faumlre R Grosskopf S amp Lovell C A K (1985) The measurement of efficiency of production (Vol 6) Springer Science amp Business Media
Fleming D A amp Measham T G (2015a) Income Inequality across Australian Regions during the Mining Boom 2001-11 Australian Geographer 46(2) 203ndash216 httpsdoiorg1010800004918220151020596
Fleming D A amp Measham T G (2015b) Local economic impacts of an unconventional energy boom the coal seam gas industry in Australia Australian Journal of Agricultural and Resource Economics 59(1) 78ndash94 httpsdoiorg1011111467-848912043
Fum R M amp Hodler R (2010) Natural resources and income inequality The role of ethnic divisions Economics Letters 107(3) 360ndash363 httpsdoiorg101016jeconlet201003008
Garofalo J (1978) The fear of crime Broadening our perspective
Geys B amp Moesen W (2009) Exploring Sources of Local Government Technical Inefficiency Evidence from Flemish Municipalities Public Finance and Management 9(1) 1ndash29
Goderis B amp Malone S W (2011) Natural Resource Booms and Inequality Theory and Evidence The Scandinavian Journal of Economics 113(2) 388ndash417 httpsdoiorg101111j1467-9442201101659x
Greene W H (2016) Productivity and Efficiency Analysis (W H Greene L Khalaf R Sickles M Veall amp M-C Voia Eds) (1st ed 20) Cham Springer International Publishing httpsdoiorg101007978-3-319-23228-7
Gustafsson B amp Shi L (2002) Income inequality within and across counties in rural China 1988 and 1995 Journal of Development Economics 69(1) 179ndash204 httpsdoiorg101016S0304-3878(02)00058-5
Gylfason T amp Zoega G (2003) Inequality and Economic Growth Do Natural Resources Matter In T T Eicher S (Ed) Inequality and Growth Theory and Policy Implications (pp 255ndash292) The MIT Press
Henningsen A (2010) Estimating Censored Regression Models in R using the censReg Package R Package Vignettes Collection 5(2) 12
Henningsen A (2019) censReg Censored Regression (Tobit) Models R package version 05-30 httpscranr-projectorgpackage=censReg Retrieved from httpscranr-projectorgpackage=censReg
Herrera S amp Pang G (2005) Efficiency of Public Spending in Developing Countriesthinsp An Efficiency Frontier Approach World Bank Washington DC
Hill T D amp Angel R J (2005) Neighborhood disorder psychological distress and heavy drinking Social Science amp Medicine 61(5) 965ndash975
133
httpsdoiorghttpsdoiorg101016jsocscimed200412027
Hipp J R (2010) RESIDENT PERCEPTIONS OF CRIME AND DISORDER HOW MUCH IS ldquoBIASrdquo AND HOW MUCH IS SOCIAL ENVIRONMENT DIFFERENCES Criminology 48(2) 475ndash508 httpsdoiorg101111j1745-9125201000193x
Holtug N amp Mason A (2010) Introduction Immigration diversity and social cohesion SAGE Publications Sage UK London England
Hooghe M Vanhoutte B Hardyns W amp Bircan T (2010) Unemployment Inequality Poverty and Crime Spatial Distribution Patterns of Criminal Acts in Belgium 2001ndash06 The British Journal of Criminology 51(1) 1ndash20 httpsdoiorg101093bjcazq067
Howie P amp Atakhanova Z (2014) Resource boom and inequality Kazakhstan as a case study Resources Policy 39(1) 71ndash79 httpsdoiorg101016jresourpol201311004
Isham J Woolcock M Pritchett L amp Busby G (2005) The Varieties of Resource Experience Natural Resource Export Structures and the Political Economy of Economic Growth The World Bank Economic Review 19(2) 141ndash174 httpsdoiorg101093wberlhi010
Jottier D Ashworth J amp Heyndels B (2012) Understanding Votersrsquo Preferences How the Electoratersquos Complexity Affects Prediction Accuracy and Wishful Thinking among Politicians with Respect to Election Outcomes Kyklos 65(3) 340ndash370 httpsdoiorg101111j1467-6435201200542x
Kanbur S M R amp Venables A (2005) Spatial inequality and development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Kesler C amp Bloemraad I (2010) Does immigration erode social capital The conditional effects of immigration-generated diversity on trust membership and participation across 19 countries 1981ndash2000 Canadian Journal of Political ScienceRevue Canadienne de Science Politique 43(2) 319ndash347
Kuznets S (1955) Economic Growth and Income Inequality The American Economic Review 45(1) 1ndash28 Retrieved from httpwwwjstororgstable1811581
Lagos G amp Blanco E (2010) Mining and development in the region of Antofagasta Resources Policy 35(4) 265ndash275 httpsdoiorghttpsdoiorg101016jresourpol201007006
Leamer E E Maul H Rodriguez S amp Schott P K (1999) Does natural resource abundance increase Latin American income inequality Journal of Development Economics 59(1) 3ndash42 httpsdoiorg101016s0304-3878(99)00004-8
Leibbrandt M Finn A amp Woolard I (2012) Describing and decomposing post-apartheid income inequality in South Africa Development Southern Africa 29(1) 19ndash34 httpsdoiorg1010800376835X2012645639
134
Letki N (2008) Does Diversity Erode Social Cohesion Social Capital and Race in British Neighbourhoods Political Studies 56(1) 99ndash126 httpsdoiorg101111j1467-9248200700692x
Lewis D A (2017) Fear of crime Incivility and the production of a social problem Routledge
lo Storto C (2013) Evaluating Technical Efficiency of Italian Major Municipalities A Data Envelopment Analysis model Procedia - Social and Behavioral Sciences 81 346ndash350 httpsdoiorg101016JSBSPRO201306440
Loayza N amp Rigolini J (2016) The Local Impact of Mining on Poverty and Inequality Evidence from the Commodity Boom in Peru World Development 84 219ndash234 httpsdoiorg101016jworlddev201603005
Loayza N Teran A M y amp Rigolini J (2013) Poverty Inequality and the Local Natural Resource Curse World Bank Policy Research Working Paper (6366) httpsdoiorg1015961813-9450-6366
Loacutepez R amp Miller S J (2008) Chile The Unbearable Burden of Inequality World Development 36(12) 2679ndash2695 httpsdoiorg101016jworlddev200801012
Manzano O amp Rigobon R (2001) Resource Curse or Debt Overhang National Bureau of Economic Research Working Paper Series No 8390 1 httpsdoiorghttpwwwnberorgpapersw9424bib
McPherson M Smith-Lovin L amp Cook J M (2001) Birds of a Feather Homophily in Social Networks Annual Review of Sociology 27(1) 415ndash444 httpsdoiorg101146annurevsoc271415
McQuestin D Drew J amp Dollery B (2018) Do Municipal Mergers Improve Technical Efficiency An Empirical Analysis of the 2008 Queensland Municipal Merger Program Australian Journal of Public Administration 77(3) 442ndash455 httpsdoiorg1011111467-850012286
Messner S F Rosenfeld R amp Baumer E P (2004) Dimensions of Social Capital and Rates of Criminal Homicide American Sociological Review 69(6) 882ndash903 httpsdoiorg101177000312240406900607
Michaels G (2011) THE LONG TERM CONSEQUENCES OF RESOURCE-BASED SPECIALISATION The Economic Journal 121(551) 31ndash57 httpsdoiorg101111j1468-0297201002402x
Mijanovich T amp Weitzman B C (2003) Which ldquobroken windowsrdquo matter School neighborhood and family characteristics associated with youthsrsquo feelings of unsafety Journal of Urban Health 80(3) 400ndash415
Mikušovaacute P (2015) An Application of DEA Methodology in Efficiency Measurement of the Czech Public Universities Procedia Economics and Finance 25 569ndash578 httpsdoiorg101016S2212-5671(15)00771-6
135
Milanovic B (2016) Global inequality Harvard University Press
Millo G amp Piras G (2012) splm Spatial panel data models in R Journal of Statistical Software 47(1) 1ndash38
Murphy K M amp Topel R H (2016) Human Capital Investment Inequality and Economic Growth JOURNAL OF LABOR ECONOMICS 34(2) S99ndashS127 httpsdoiorg101086683779
Narboacuten-Perpintildeaacute I amp De Witte K (2018a) Local governmentsrsquo efficiency a systematic literature reviewmdashpart I International Transactions in Operational Research 25(2) 431ndash468 httpsdoiorg101111itor12364
Narboacuten-Perpintildeaacute I amp De Witte K (2018b) Local governmentsrsquo efficiency a systematic literature reviewmdashpart II International Transactions in Operational Research 25(4) 1107ndash1136 httpsdoiorg101111itor12389
Nuntildeez J Rivera J Villavicencio X amp Molina O (2003) Determinantes socioeconoacutemicos y demograacuteficos del crimen en Chile Estudios de Economiacutea 30(1) 55ndash85
OrsquoDonnell C J Rao D S P amp Battese G E (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios Empirical Economics 34(2) 231ndash255 httpsdoiorg101007s00181-007-0119-4
Ocampo J A (2004) Latin Americarsquos Growth and Equity Frustrations During Structural Reforms The Journal of Economic Perspectives 18(2) 67ndash88 httpsdoiorg1012570895330041371349
OECD (2014) Focus on inequality and growth OECD
OECD (2017) Howrsquos Life 2017 Life Satisfaction Oecd httpsdoiorg101787how_life-2017-en
Ohtake F (2008) Inequality in Japan Asian Economic Policy Review 3(1) 87ndash109 httpsdoiorg101111j1748-3131200800093x
Okun A M (2015) Equality and efficiency the big tradeoff Washington DC Brookings Institution Press
Ortega B Sanjuaacuten J amp Casquero A (2017) Determinants of efficiency in reducing child mortality in developing countries The role of inequality and government effectiveness Health Care Management Science 20(4) 500ndash516 httpsdoiorg101007s10729-016-9367-1
Ostry J Berg A amp Tsangarides C (2014) Redistribution inequality and growth International Monetary Fund
Pacheco F Saacutenchez R amp Villena M (2013) Eficiencia de los Gobiernos Locales y sus Determinantes Un anaacutelisis de Fronteras Estocaacutesticas en Datos de Panel para
136
Municipalidades Chilenas Santiago de Chile Chile
Papyrakis E amp Raveh O (2014) An Empirical Analysis of a Regional Dutch Disease The Case of Canada Environmental and Resource Economics 58(2) 179ndash198 httpsdoiorg101007s10640-013-9698-z
Paredes D (2013) The Role of Human Capital Market Potential and Natural Amenities in Understanding Spatial Wage Disparities in Chile Spatial Economic Analysis 8(2) 154ndash175 httpsdoiorg101080174217722013774094
Paredes D Iturra V amp Lufin M (2016) A spatial decomposition of income inequality in Chile Regional Studies 50(5) 771ndash789
Phan P Van Orsquobrien M Mendolia S amp Paloyo A (2017) National pro-poor spending programmes and their effect on income inequality and poverty Evidence from Vietnam Applied Economics 49(55) 5579ndash5590 httpsdoiorg1010800003684620171313957
Podinovski V V (2004) Bridging the Gap between the Constant and Variable Returns-to-Scale Models Selective Proportionality in Data Envelopment Analysis The Journal of the Operational Research Society 55(3) 265ndash276 Retrieved from httpwwwjstororgstable4102006
Ravallion M (2005) On Measuring Aggregate Social Efficiency Economic Development and Cultural Change 53(2) 273ndash292 httpsdoiorg101086425380
Rehner J Baeza S A amp Barton J R (2014) Chilersquos resource-based export boom and its outcomes Regional specialization export stability and economic growth Geoforum 56(Supplement C) 35ndash45 httpsdoiorghttpsdoiorg101016jgeoforum201406007
Rivera J Gutieacuterrez M amp Nuacutentildeez J (2009) Caracterizacioacuten socioeconoacutemica y espacial de la criminalidad en Chile Revista CEPAL
Ross C E (2011) Collective threat trust and the sense of personal control Journal of Health and Social Behavior 52(3) 287ndash296
Ross C E amp Mirowsky J (2001) Neighborhood disadvantage disorder and health Journal of Health and Social Behavior 258ndash276
Rothstein B amp Uslaner E M (2005) All for all Equality corruption and social trust World Politics 58(1) 41ndash72
Sachs J D amp Warner A M (2001) The curse of natural resources European Economic Review 45(4ndash6) 827ndash838 httpsdoiorg101016S0014-2921(01)00125-8
Salas R (2019) sinimr Chilean Municipalities Information System Wrapper Retrieved from httpsgithubcomrobsalascosinimr
Sampson R J (1986) Crime in Cities The Effects of Formal and Informal Social Control Crime and Justice 8 271ndash311
137
Sampson R J (2008) Rethinking crime and immigration Contexts 7(1) 28ndash33
Santos Silva J M C amp Tenreyro S (2010) On the existence of the maximum likelihood estimates in Poisson regression Economics Letters 107(2) 310ndash312 httpsdoiorghttpsdoiorg101016jeconlet201002020
Santos Silva J M C amp Tenreyro S (2011) poisson Some convergence issues Stata Journal 11(2) 207ndash212 Retrieved from httpwwwstata-journalcomarticlehtmlarticle=st0225
Scott Z (2009) Decentralisation local development and social cohesion an analytical review GSDRC Research Paper 5
Sinha R P (2017) Fiscal Performance Benchmarking of Indian States-A Robust Frontier Approach The Central European Review of Economics and Management 1(4) 225ndash249
Skogan W (1986) Fear of crime and neighborhood change Crime and Justice 8 203ndash229
Skogan W (1999) Measuring what matters Crime disorder and fear In Measuring what matters Proceedings from the Policing Research Institute meetings (pp 37ndash53) National Institute of Justice Washington DC
Skogan W (2015) Disorder and Decline The State of Research Journal of Research in Crime and Delinquency 52(4) 464ndash485 httpsdoiorg1011770022427815577836
Šťastnaacute L amp Gregor M (2014) Public sector efficiency in transition and beyond evidence from Czech local governments Applied Economics 47(7) 1ndash20 httpsdoiorg101080000368462014978077
Tandon A (2005) Measuring Efficiency of Macro Systems An Application to Millennium Development Goal Attainment Asian Development Review 22(2) 108ndash125
Taylor R B (1999) The incivilities thesis Theory measurement and policy Measuring What Matters 65 88
Tigga N S amp Mishra U S (2015) On Measuring Technical Efficiency of the Health System in India An Application of Data Envelopment Analysis Journal of Health Management 17(3) 285ndash298 httpsdoiorg1011770972063415589229
Tinbergen J (1975) Demographic Development and the Exhaustion of Natural Resources Population and Development Review 1(1) 23ndash32 httpsdoiorg1023071972269
Tiruneh G (2014) Social Revolutions Their Causes Patterns and Phases SAGE Open 4(3) 2158244014548845 httpsdoiorg1011772158244014548845
Tolsma J Van der Meer T amp Gesthuizen M (2009) The impact of neighbourhood and municipality characteristics on social cohesion in the Netherlands Acta Politica 44(3) 286ndash313
Tsekeris Sotiris T Tsekeris T amp Papaioannou S (2018) Regional determinants of technical efficiency evidence from the Greek economy Regional Studies [London]thinsp Carfax
138
httpsdoiorg1010800034340420171390312
Uslaner E (2002) The moral foundations of trust Cambridge University Press
Uslaner E (2011) CORRUPTION AND INEQUALITY DICE Report 9(2) 20ndash24
Uslaner E (2013) Trust and corruption revisited how and why trust and corruption shape each other Quality amp Quantity 47(6) 3603ndash3608 httpsdoiorg101007s11135-012-9742-z
Uslaner E amp Brown M (2005) Inequality trust and civic engagement American Politics Research 33(6) 868ndash894
Van der Ploeg F (2011) Natural Resources Curse or Blessing Journal of Economic Literature 49(2) 366ndash420 httpsdoiorg101257jel492366
Vergara R (2012) Crime Prevention Programs Evidence From CHILE The Developing Economies 50(1) 1ndash24
Watkins M H (1963) A staple theory of economic growth Canadian Journal of Economics and Political ScienceRevue Canadienne de Economiques et Science Politique 29(2) 141ndash158
Wilkinson R G (1999) Income inequality social cohesion and health clarifying the theorymdasha reply to Muntaner and Lynch International Journal of Health Services 29(3) 525ndash543
Wilson J Q amp Kelling G L (1982) Broken windows Atlantic Monthly 249(3) 29ndash38
Wilson W J (1996) When Work Disappears Political Science Quarterly 111(4) 567ndash595 httpsdoiorg1023072152085
Wirth L (1938) Urbanism as a Way of Life American Journal of Sociology 44(1) 1ndash24
Worthington A amp Dollery B (2000) An empirical survey of frontier efficiency measurement techniques in local government Local Government Studies 26(2) 23ndash52 httpsdoiorg10108003003930008433988
Wu P-C Huang T-H amp Pan S-C (2014) Country Performance Evaluation The DEA Model Approach Social Indicators Research 118(2) 835ndash849 httpsdoiorg101007s11205-013-0443-3
Ye X Ma L Ye K Chen J amp Xie Q (2017) Analysis of Regional Inequality from Sectoral Structure Spatial Policy and Economic Development A Case Study of Chongqing China Sustainability 9(4) 633 Retrieved from httpwwwmdpicom2071-105094633
Yue W Z Zhang Y T Ye X Y Cheng Y Q amp Leipnik M R (2014) Dynamics of Multi-Scale Intra-Provincial Regional Inequality in Zhejiang China Sustainability 6(9) 5763ndash5784 httpsdoiorg103390su6095763
139
Appendices
Appendix A Summary statistics income inequality
Table A1
Summary statistics Gini coefficients by year and zone
140
Appendix B Summary statistics for NRD measures by region
Table B1
Summary statistics NRD measures by region
141
Appendix C Regional administrative division and defined zones
Figure C1 Geographical distribution of Chilean regions and 3 zones
142
Appendix D Summary statistics numeric controls and correlation matrix
Table D1
Summary Statistics Numeric Explanatory Variables
Figure D1 Correlation matrix numeric explanatory variables
143
Appendix E Static spatial panel models
Following Millo amp Piras (2012) a model including a spatial lag of the dependent variable and
spatial autoregressive disturbances but not spatial lags for the explanatory variable(s) is called
SARAR model A static spatial SARAR panel could be expressed as
119910 120582 119868 otimes119882 119910 119883120573 119906 (E1)
where y is an 119873 1 vector of observations on the dependent variable X is a 119873 119896 matrix of
observations on the non-stochastic exogenous regressors 119868 an identity matrix of dimension 119879 otimes
is the kronecker operator 119882 is the 119873 119873 spatial weights matrix of known constants whose
diagonal elements are set to zero and 120582 the corresponding spatial parameter44
The disturbance vector is the sum of two terms
119906 120580 otimes 119868 120583 120576 (E2)
where 120580 is a 119879 1 vector of ones 119868 an 119873 119873 identity matrix 120583 is a vector of time-invariant
individual specific effects (not spatially autocorrelated) and 120576 a vector of spatially autocorrelated
innovations that follow a spatial autoregressive process of the form
120576 120588 119868 otimes119882 120576 120584 (E3)
If we assume that spatial correlation applies to both the individual effects 120583 and the remainder
error components 120576 Kapoor et al (2007) propose that the disturbance term 119906 follows a first order
spatial autoregressive process of the form
119906 120588 119868 otimes119882 119906 120576 (E4)
44 Unlike ldquotraditional panelsrdquo which are organized as different time series for each cross-sectional unit ldquospatial panelsrdquo are organized as a series of cross-sections for each year
144
where 119882 is the spatial weights matrix and 119903ℎ119900 the corresponding spatial autoregressive
parameter To further allow for the innovations to be correlated over time the innovations vector
in Equation 7 follows an error component structure
120576 120580 otimes 119868 120583 120584 (E5)
where 120583 is the vector of cross-sectional specific effects 120584 a vector of innovations that vary
both over cross-sectional units and time periods 120580 is a vector of ones and 119868 an 119873 119873 identity
matrix45
Spatial panel models are usually estimated by Maximum Likelihood or GMM46 The SAR
SEM or SARAR models could be estimated with Random or Fixed effects For instance A fixed
effect spatial lag model can be written in stacked form as
119910 120582 119868 otimes119882 119910 120580 otimes 119868 120583 119883120573 120576 (E6)
where 120582 is the spatial autoregressive coefficient 119882 a non-stochastic spatial weights matrix
120580 a column vector of ones of dimension 119879 119868 an 119873 119873 identity matrix and 120576 sim 119873 0120590 On
the other hand a fixed effects spatial error model assuming the disturbance specification by
Kapoor et al (2007) can be written as
119910 120580119879 otimes 119868119873 120583 119883120573 119906119906 120588 119868119879 otimes119882119873 119906 120576
(E7)
where 120588 is the spatial autocorrelation coefficient and 120576 is a well-behaved error term
45 In the regression implementation the specification given by equations (22) and (3) is denoted by ldquobrdquo for ldquoBaltagirdquo On the other hand the specification given by equations (23) and (31) is denoted by ldquokkprdquo for ldquoKapoor Kelejian and Pruchardquo 46 We use the R package splm which allow both types of regression procedure
145
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis
Table F1
Analysis OLS residuals Anselin Method
Figure F1 Moran scatter plot OLS residuals
146
Appendix G Linear panel data models
Table G1
Panel regressions (non-spatial)
147
Appendix H Spatial panel models (Generalized Moments (GM) estimation)
Table H1
GM Spatial Models
148
Appendix I Inputs and outputs used in DEA analysis
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018)
149
Appendix J Technical and scale efficiency
Following lo Storto (2013) under an input-oriented specification assuming VRS with n
municipalities using k inputs to produce m outputs the DEA model for a given i-th municipality
is specified with the following mathematical programming problem
119898119894119899 120579119904119906119887119895119890119888119905 119905119900 119910 119884120582 0120579119909 119883120582 01198991prime120582 1 120582 0prime
Where 119910 is the column vector of the outputs and 119909 is the column vector of the inputs
Moreover we can define X as the (k times n) input matrix and Y as the (m times n) output matrix 120579 is a
scalar (that satisfies 120579 1) more specifically it is the efficiency score that measures technical
efficiency It measures the distance between a municipality and the efficiency frontier defined as
a linear combination of the best practice observations With 120579 1 the municipality is inside the
frontier (ie it is inefficient) while 120579 1 implies that the municipality is on the frontier (ie it is
efficient) The vector 120582 is an (n times 1) vector of constants that measures the weights used to compute
the location of an inefficient municipality if it were to become efficient
The total technical efficiency 119879119864 can be decomposed into pure technical efficiency
119879119864 and scale efficiency 119878119864 where 119878119864 119879119864 119879119864 (Coelli et al 2005) To find out
whether a municipality is scale efficient and qualify the type of returns of scale a DEA model
under non-increasing returns to scale 119879119864 is implemented where 119878119864 119879119864 119879119864 Hence
the following rule can be applied (Faumlre Grosskopf amp Lovell 1985)
bull If 119878119864 1 then a municipality is scale efficient both under CRS and VRS
bull If 119878119864 1 it operates under increasing returns to scale
bull If 119878119864 1 it operates under decreasing returns to scale
150
Appendix K Correlation matrix
Figure K1 Correlation matrix contextual factors
151
Appendix L Returns to scale by year and zone
Table L1
Returns to scale (percentage of municipalities)
152
Appendix M Returns to scale by year (maps)
Figure M1 Spatial distribution of returns to scale by county per year
153
Appendix N Efficiency status by year (maps)
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year
154
Appendix O Spatial distribution efficiency scores by year (maps)
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year
155
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis
Table P1
Analysis OLS residuals Anselin Method
Figure P1 Moran scatter plot efficiency scores and OLS residuals
156
Table P2
OLS and spatial regression models for the six-year averaged data
157
Appendix Q OLS regressions for cross-sectional and panel data
Table Q1
OLS cross-sectional regression per year
158
Table Q2
OLS panel regressions Pooled random effects and instrumental variable
159
Appendix R Quantile maps incivilities rate by group (average total period)
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17)
160
Appendix S Correlation matrix numeric covariates
Figure S1 Correlation matrix numeric covariates
161
Appendix T Negative Binomial regressions
Table T1
Negative Binomial regressions
162
Appendix U Coefficients economic and racial diversity by geographical zone
Table U1
Coefficients economic and racial diversity in pooled Poisson models by geographic zone
i
Keywords
Count data models
Data Envelopment Analysis
Dutch disease
Economic diversity
Incivilities
Income inequality
Local government efficiency
Natural resource dependence
Panel data
Paradox of plenty
Racial diversity
Resource curse hypothesis
Social cohesion
Spatial analysis
ii
Abstract
Persistently high indicators of relative economic disadvantage such as measures of income
inequality can give rise to a feeling of discontent in the population which in turn can trigger
costly social conflicts For instance inequality has been suggested as one of the main causes of
social outburst considering recent events in many countries around the world This has generated
in extant literature an increasing number of criticisms of current political and socio-economic
models This research considers the Chilean economy which is recognised as an example of the
success of standard economic thinking however it is also well-known for its persistently high
levels of inequality an adverse indicator of economic performance This thesis contributes with
three essays to the understanding of the sources and potential consequences of income inequality
in Chile The data consider a panel of 324 Chilean counties and their corresponding municipalities
for the 2006ndash2017 period
The first essay investigates the association between income inequality and the endowment
of natural resources The Gini coefficient of each county is used as a measure of income inequality
The influence of natural resources on income inequality is captured by using the proportion of
employment in the primary sector as a proxy for the degree of dependence on natural resources in
each county Previous literature has identified a significant spatial dimension of income inequality
in Chile but this spatial dimension has been largely neglected in the domain of policy design and
implementation Thus the analysis in this essay applies spatial regression models for cross-
sectional and panel data while controlling for other socioeconomic and demographic
characteristics The main finding is that contrary to what theory predicts our measure of natural
resource dependence in terms of employment shows a robust and significant negative association
with income inequality The main implication of this empirical result is that a transformation
process towards activities less dependent on natural resources reinforces rather than reduces the
persistence of income inequality at least through the channel of employment Hence this
transformation process imposes additional challenges to central and local governments in their
goal of reducing income inequality Empirical analysis also shows a significant degree of positive
spatial autocorrelation of income inequality This means that counties with similar levels of income
iii
inequality tend to cluster in space The regression analysis confirms the importance of capturing
geographical heterogeneity in the explanation of income inequality however gives less support
to a process of spatial dependence like a spillover effect of income inequality among
neighbouring counties
Among the potential consequences of income inequality the literature highlights its
possible impacts on the efficiency in the provision of public services by local authorities however
empirical evidence is very little For this reason the second essay analyses the technical efficiency
of municipal local governments in Chile and examine if income inequality has significant impacts
on the variations in the efficiency levels across municipalities An input-oriented Data
Envelopment Analysis is used to measure municipal efficiency Results reveal that the municipal
production technology is characterized by variable returns to scale but scale inefficiencies only
explain a small proportion of total inefficiency This justify a need for analysing the influence of
variables which are beyond the control of local authorities in explaining differences in municipal
efficiency The main hypothesis tested was whether income inequality has a negative influence on
municipal efficiency whilst a measure of natural resource dependence at the county level was used
as an instrument to control for the effects of possible endogeneity issues Results showed that
changes in income inequality could be associated with changes in the municipal efficiency level
in the same magnitude but in the opposite direction This confirms that local authorities in counties
characterized by high levels of income inequality face greater challenges to achieve more efficient
performance This result suggests that policies aimed at reducing income inequality can also
increase the efficiency of local governments Our results also reveal that policies such as
amalgamation de-amalgamation or cooperation among municipalities should be designed
specifically for each region rather than as a standard national strategy
Finally the third essay analyses how social cohesion is associated with the levels of
economic and racial diversity Social cohesion is proxied using the reported number of antisocial
behaviours catalogued as incivilities Incivilities are those antisocial behaviours which violate
social norms but are not usually considered as criminal Research has documented the implications
of incivilities on human stress health public behaviour and increasing feelings of insecurity and
fear among the population Few studies have explicitly considered incivilities as a dependent
variable to identify their determinants or use them to analyse the weakening of social cohesion and
iv
the growing feeling of social unrest in contemporary societies Economic diversity is proxied using
the Gini coefficient in each county and racial diversity through the number of new visas granted
as proportion of the county population Our findings show that incivilities are strongly associated
with racial diversity and to a lesser extent with economic diversity The rate of incivilities also
shows a negative association with the level of income and a positive relationship with poverty and
unemployment rates These results give empirical support to the idea that both relative and
absolute indicators of economic deprivation play an important role in understanding the growing
problem of incivilities in highly unequal economies like Chile Results also show that the rate of
incivilities is negatively related to the degree of financial autonomy of municipalities These
findings represent promising areas for central and local governments in the implementation of
policies aimed at increasing social cohesion through the reduction of incivilities and other types of
antisocial behaviours
v
Table of Contents
Keywords i
Abstract ii
Table of Contents v
List of Figures viii
List of Tables ix
List of Abbreviations x
Statement of Original Authorship xi
Acknowledgements xii
Chapter 1 Introduction 13
Income inequality and dependence on natural resources 14
Local government efficiency and income inequality 16
Social cohesion and economic diversity 19
Contributions 21
Thesis outline 23
Chapter 2 Natural Resources Curse or Blessing Evidence on Income Inequality at the County Level in Chile 24
21 Introduction 24
22 Inequality and Natural Resources 28 221 Theoretical Framework 28
Cross-country literature 29 Single country evidence 32
222 The relevance of the spatial approach 33
23 Research problem and hypotheses 35
24 Data and Methods 36 241 Operationalization of key variables 36 242 Control variables 40 243 Methods 41 244 Spatial Model Specification 43
25 Results 44 251 Exploratory Spatial Data Analysis (ESDA) 44 252 Cross-sectional analysis 45 253 Panel Data analysis 48
26 Discussion and conclusions 51
Chapter 3 The Impact of Income Inequality on the Efficiency of Municipalities in Chile 55
vi
31 Introduction 55
32 Related Literature 61 321 Measuring efficiency of local governments 61 322 Explaining differences in LGE 63 323 The trade-off between efficiency and equity 64
33 Methodology 66 331 Chilean Municipalities and period of analysis 66 332 Measuring municipal efficiency 68 333 Inputs and outputs used in DEA 70 334 Regression model 71 335 The instrument 75
34 Results and discussion 77 341 DEA results 77
Returns to scale 78 Efficiency measure 80
342 Regression results 82 Exploratory spatial analysis 82 Cross-sectional analysis 83 Panel data analysis 84
35 Conclusions 88
Chapter 4 Social Cohesion Incivilities and Diversity Evidence at the municipal level in Chile 91
41 Introduction 91
42 Related Literature 95 421 The Community Heterogeneity Thesis 95 422 The literature on incivilities 97 423 The ldquoIncivilities Thesisrdquo 99
4 3 Methodology 100 431 Period of analysis and data sample 100 432 Operationalisation of the response variable and exploratory analysis 101 433 Measures of community heterogeneity and control variables 105 434 Methods 108 435 Hypotheses 111
44 Results and Discussion 112
4 5 Conclusions 118
Chapter 5 Conclusions 120
Bibliography 126
Appendices 139
Appendix A Summary statistics income inequality 139
Appendix B Summary statistics for NRD measures by region 140
Appendix C Regional administrative division and defined zones 141
Appendix D Summary statistics numeric controls and correlation matrix 142
vii
Appendix E Static spatial panel models 143
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis 145
Appendix G Linear panel data models 146
Appendix H Spatial panel models (Generalized Moments (GM) estimation) 147
Appendix I Inputs and outputs used in DEA analysis 148
Appendix J Technical and scale efficiency 149
Appendix K Correlation matrix 150
Appendix L Returns to scale by year and zone 151
Appendix M Returns to scale by year (maps) 152
Appendix N Efficiency status by year (maps) 153
Appendix O Spatial distribution efficiency scores by year (maps) 154
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis 155
Appendix Q OLS regressions for cross-sectional and panel data 157
Appendix R Quantile maps incivilities rate by group (average total period) 159
Appendix S Correlation matrix numeric covariates 160
Appendix T Negative Binomial regressions 161
Appendix U Coefficients economic and racial diversity by geographical zone 162
viii
List of Figures
Figure 21 Average share in GDP of economic activities (2006ndash17) 37
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17) 38
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17) 39
Figure 23 Moran scatter plots for variables gini and pss_casen 45
Figure 31 Geographical distribution of Chilean regions and macrozones 74
Figure 32 Evolution of efficiency scores and the proportion of firms by sector 77
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE) 78
Figure 34 Returns to scale by zone 79
Figure 35 Evolution mean efficiency scores (VRS) by zone 81
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017 102
Figure 42 Evolution total number of incivilities by category 104
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017) 104
Figure 44 Annual average number of incivilities per county 109
Figure C1 Geographical distribution of Chilean regions and 3 zones 141
Figure D1 Correlation matrix numeric explanatory variables 142
Figure F1 Moran scatter plot OLS residuals 145
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018) 148
Figure K1 Correlation matrix contextual factors 150
Figure M1 Spatial distribution of returns to scale by county per year 152
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year 153
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year 154
Figure P1 Moran scatter plot efficiency scores and OLS residuals 155
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17) 159
Figure S1 Correlation matrix numeric covariates 160
ix
List of Tables
Table 21 Cross-sectional Model Comparison (six-year average data) 47
Table 22 ML Spatial SAR Models 50
Table 23 ML Spatial SEM Models 50
Table 24 ML Spatial SARAR Models 51
Table 31 Descriptive statistics Inputs and Output variables used in DEA analysis 71
Table 32 Summary Statistics Numeric Contextual Factors 74
Table 33 Summary efficiency scores (VRS) by zone and region 80
Table 34 Cross-sectional (censored) regressions 84
Table 35 Panel data regressions 87
Table 41 Summary statistics total count of incivilities and by category (full sample and period) 103
Table 42 Summary statistics numeric explanatory variables 108
Table 43 Poisson regressions 113
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region 115
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group 116
Table A1 Summary statistics Gini coefficients by year and zone 139
Table B1 Summary statistics NRD measures by region 140
Table D1 Summary Statistics Numeric Explanatory Variables 142
Table F1 Analysis OLS residuals Anselin Method 145
Table G1 Panel regressions (non-spatial) 146
Table H1 GM Spatial Models 147
Table L1 Returns to scale (percentage of municipalities) 151
Table P1 Analysis OLS residuals Anselin Method 155
Table P2 OLS and spatial regression models for the six-year averaged data 156
Table Q1 OLS cross-sectional regression per year 157
Table Q2 OLS panel regressions Pooled random effects and instrumental variable 158
Table T1 Negative Binomial regressions 161
Table U1 Coefficients economic and racial diversity in pooled Poisson models by geographic zone 162
x
List of Abbreviations
Constant returns to scale CRS
Data envelopment analysis DEA
Decreasing returns to scale DRS
Efficiency scores ES
Exploratory spatial data analysis ESDA
Generalized methods of moments GMM
Gross Domestic Product GDP
Increasing returns to scale IRS
Local government efficiency LGE
Maximum likelihood ML
Municipal common fund MCF
Natural resource dependence NRD
Natural resource endowment NRE
Ordinary Least Squares OLS
Organization for Economic Cooperation and Development OECD
Own permanent revenues OPR
Resource curse hypothesis RCH
Spatial autoregressive model SAR
Spatial error model SEM
Variable returns to scale VRS
xi
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet requirements
for an award at this or any other higher education institution To the best of my knowledge and
belief the thesis contains no material previously published or written by another person except
where due reference is made
Signature QUT Verified Signature
Date _________04092020_________
xii
Acknowledgements
First I would like to thank my wife Lilian who joined me in this challenge and patiently
supported me all these years I would also like to thank our family who always supported us from
Chile I especially thank my sister Silvia who took care of our house and dog
I am also grateful to my supervisory team Dr Radhika Lahiri and Dr Vincent Hoang who
supported and guided me in the process of making this thesis a reality
I also thank the Deans of the Faculty of Economics and Business at my beloved University
of Talca Dr Arcadio Cerda and Dr Rodrigo Herrera who trusted me and supported me in this
process In the same way I would like to thank all the support of the director of the Commercial
Engineering career Mr Milton Inostroza
Finally I would like to thank the government of Chile for the financial support that made
my stay and studies possible here at the Queensland University of Technology
13
Chapter 1 Introduction
Efficiency and equity issues are often considered together in the evaluation of economic
performance While higher efficiency usually measured by growth rates of income per capita
correlates with improvements in measures of well-being the link between inequality and well-
being is less clear This is reflected not only in the type and amount of research related to efficiency
and equity but also in the role that both play in the design of the economic policy For instance
several market-oriented countries have focused primarily on economic growth trusting in a trickle-
down process where financial benefits given to the wealthy are expected to ultimately benefit the
poor However despite the growing interest in the issue of inequality there is a considerable lack
of studies about its consequences
Although some level of inequality is inevitable or even necessary for economic activity this
study is motivated by the argument that relatively high levels of inequality can be associated with
many problems such as persistent unemployment increasing fiscal expenses indebtedness and
political instability (Berg amp Ostry 2011) Inequality can also have other severe social
consequences including increased crime rates teenage pregnancy obesity and fewer
opportunities for low-income households to invest in health and education (Atkinson 2015) In
addition when the role of money and concentration of economic power undermine political
outcomes inequality of opportunities hampers social and economic mobility trust and social
cohesion In summary inequality can increase the fragility of the economic and social situation in
a country reducing economic growth and making it less inclusive and sustainable
14
A country well-known for its market-oriented economy and high level of dependence on
natural resources is Chile Chilean success in terms of economic growth contrasts with its inability
to reduce the persistently high levels of social and economic inequality particularly in the last
three decades Using data for the 2006-2017 period and considering 324 out of 345 Chilean
counties this thesis presents three essays with empirical evidence aiming to explain the
phenomenon of persistent income inequality and some of its potential consequences The first
essay aims to analyse how the evolution and variability of income inequality throughout the
country are associated with the degree of natural resource dependence The second essay studies
the relevance of income inequality in explaining cross-county differences in the performance of
local governments (municipalities) Finally the third essay explores the link between social
cohesion and community heterogeneity highlighting the importance of economic and racial
diversity
Income inequality and dependence on natural resources
The first essay explores how cross-county differences in income inequality are associated
with differences in the degree of dependence on natural resources We use the Gini coefficient in
each county as our dependent variable and the proportion of employment in the primary sector as
our measure of natural resource dependence The main hypothesis is that income inequality should
be positively related to the degree of natural resource dependence To test our hypothesis we use
a spatial econometric approach This approach is motivated by the study of Paredes Iturra and
Lufin (2016) who explore the geographic heterogeneity of income inequality in Chile finding
evidence of a significant spatial dimension
15
The theoretical and empirical literature has mostly proposed a positive link between
inequality and natural resources Although most of the evidence corresponds to cross-country
comparisons there is also increasing body of research at the local level A rationale underpinning
the positive link suggested in the literature is that in natural resource-rich countries ownership is
concentrated in small groups and extraction activities require low-skilled workers (Gylfason amp
Zoega 2003 Leamer Maul Rodriguez amp Schott 1999) Another market-based argument often
labelled as the ldquoDutch Diseaserdquo proposes that natural resource windfalls could be associated with
a crowding-out effect on the manufacturing sector (Corden amp Neary 1982 Easterly 2007) This
process encourages rent-seeking behaviours discourages investment in physical and human
capital and delays the process of technology adoption and industrialization (Auty 2001 Bulte
Damania amp Deacon 2005 Gylfason amp Zoega 2003) The result could be a lower economic
growth which is the central idea under the ldquoResource Curse Hypothesisrdquo (Auty 1993 Sachs amp
Warner 2001)
An ldquoinstitutionalrdquo argument for the positive association between inequality and the
endowment of natural resources is based on the so-called ldquoParadox of Plentyrdquo (Borge Parmer amp
Torvik 2015 Dauvin amp Guerreiro 2017) The idea is that both national and local authorities have
less incentive to operate efficiently when they experience windfalls in their revenues for
instance from natural resources This could end with corrupted authorities exerting patronage
clientelism and designing public policies to favour specific groups of the population (Uslaner amp
Brown 2005) Evidence also suggests that the final effect of natural resource booms on income
inequality will depend on authoritiesrsquo capacity to manage these additional resources the extent of
commuting and migration among regions and the potential increase in the demand for non-tradable
16
goods which are intensive in unskilled workers (Aroca amp Atienza 2011 Cust amp Poelhekke 2015
Fleming amp Measham 2015b Howie amp Atakhanova 2014 Michaels 2011)
Contrary to most theoretical and empirical evidence we find that income inequality shows
a robust and significant negative association with our proxy for natural resource dependence This
result suggests that the process of transformation to an economy less dependent on natural
resources could have exacerbated rather than alleviated the persistence of income inequality The
decrease in the participation of the primary sector in employment in favour mainly of the tertiary
sector highlights the importance of the latter to explain the current high levels of inequality and its
future evolution Another important result is that spatial linear models show practically the same
results as traditional linear models This could be interpreted as the spatial dimension previously
found in income inequality is not the result of spatial dependence in the variable itself for instance
due to a process of spillover among counties Hence the usually found positive spatial
autocorrelation of income inequality (similar levels in neighbouring counties) could be explained
by spatial patterns in other variables or to the spatial heterogeneity that characterizes the Chilean
economy
Local government efficiency and income inequality
Essay 2 delves deep into the potential trade-off between efficiency and equity We measure
the efficiency of Chilean municipalities which correspond to the organizations in charge of
managing each county Municipal efficiency is understood as ldquotechnical efficiencyrdquo that is the
possibility that each municipality has reached the same level of outputs with less use of inputs
Then we analyse how income inequality controlling for other contextual factors such as
socioeconomic demographic geographical and political characteristics may help to explain
17
differences in municipal performance Our main hypothesis is that municipal efficiency is
inversely associated with income inequality Moreover we seek a causal interpretation of this
relationship
Municipal performance could be influenced by income inequality in direct and indirect ways
In a direct sense income inequality is used to capture the degree of heterogeneity and complexity
in the demand for public services that citizens exert over local authorities Hence higher levels of
income inequality should be associated with a more complex set of public services and therefore
with lower levels of municipal efficiency (Jottier Ashworth amp Heyndels 2012) Furthermore
when high levels of inequality exist the richest groups can exert a higher influence over local
authorities resulting in low quality and quantity of services for most of the population Among
indirect effects high and persistent inequality could be the source of corrupted institutions and
local authorities favouring themselves or specific groups This undermines citizensrsquo participation
in civic activities and their willingness to monitor municipal performance (Uslaner amp Brown
2005) Additionally the potential benefits of decentralization on the way local governments
deliver public services will be limited when the context is characterized by corrupted politicians
and a limited administrative and financial capacity (Scott 2009)
We measure municipal efficiency using an input-oriented Data Envelopment Analysis
(DEA) to obtain efficiency scores for our sample of 324 municipalities in each year from 2006 to
2017 Then we study the influence on municipal efficiency of income inequality and our set of
contextual factors using a panel of six years corresponding to those years for which household
income information is available 2006 2009 2011 2013 2015 and 2017 Our dependent variable
is the set of efficiency scores which are relative measures of efficiency They are relative to the
18
municipalities included in the sample and they do not imply that higher technical efficiency gains
cannot be achieved Thus we use both cross-sectional and panel censored regression models To
tackle endogeneity issues and suggest a causal interpretation we also propose using the proportion
of firms in the primary sector as an instrument for income inequality
We find an average efficiency score of 83 meaning that Chilean municipalities could
reduce the use of inputs by 17 without reducing their outputs We also measure municipal
efficiency under different assumptions related to returns to scale This allows us to disaggregate
technical efficiency to assess whether inefficiencies are due to management issues (pure technical
efficiency) or scale issues (scale efficiency) Although the results show that most municipalities
operate under increasing or decreasing returns to scale scale inefficiencies only explain a small
proportion of total municipal inefficiencies This highlights the need to look for contextual factors
outside the control of local authorities to explain differences in municipal performance
Geographical representations of our results in terms of returns to scale and efficiency scores
show some spatial clustering process among municipalities Spatial statistics tests confirm that
efficiency scores show a significant positive spatial autocorrelation This means that neighbouring
municipalities tend to show similar levels of efficiency This similar performance could be due to
a process of spatial dependence (eg efficiency spillovers among neighbouring municipalities) or
due to the existence of structural-geographical similarities (spatial heterogeneity) To assess the
spatial dimension in municipal efficiency abstracting from temporal fluctuations we use a cross-
section of data consisting of the six-year average for the variables in our panel After running a
regression of efficiency scores against the set of controls the analysis of OLS residuals shows that
the spatial autocorrelation is almost completely removed This means that the spatial pattern in
19
municipal efficiency can be explained (controlled) by other variables such as regional indicator
variables rather than efficiency itself Given this result we proceed to study the influence of
income inequality on municipal efficiency using traditional (non-spatial) regression analysis
In contrast to literature that emphasizes an equity-efficiency trade-off (Andersen amp Maibom
2020 Berg amp Ostry 2011 Browning amp Johnson 1984 Okun 2015) that is greater equality leads
to lower efficiency we find that municipal efficiency is inversely associated with income
inequality This implies that more equal counties are also those with higher municipal efficiency
Furthermore the coefficient of income inequality is close to one when we use the instrumental
variable approach This means that a reduction in income inequality ceteris paribus should be
associated with an increase in the same magnitude in municipal efficiency This result has strong
policy implications The non-existence of the trade-off suggests that there is more to be gained by
targeting policies towards the reduction of inequality than conventional theories suggest For
instance these policies may help increase the levels of efficiency and well-being at least at the
municipal level
Social cohesion and economic diversity
The third essay studies the relationship between the degree of social cohesion and diversity
in Chile Extant literature has argued that one of the main factors influencing social cohesion is
the degree of economic and ethnic-racial diversity within a society This diversity erodes social
cohesion reducing trust and corrupting institutions (Letki 2008 Rothstein amp Uslaner 2005
Tolsma Van der Meer amp Gesthuizen 2009 Uslaner 2011 2013)
To measure social cohesion scholars have traditionally used measures of social capital trust
or feelings of insecurity (Ariely 2014 Chan To amp Chan 2006 Letki 2008) We suggest the use
20
of the rate of incivilities per 1000 inhabitants as a proxy for social cohesion Incivilities correspond
to those antisocial behaviours (eg groups of rowdy teens and public drunkenness) or visible
neighbourhood conditions (eg graffiti and abandoned buildings) that tend not to be treated as
crime Using the rate of incivilities is arguably a more objective and reliable measure of social
cohesion particularly in countries where institutions of order and security are among the most
trusted An increase in the rate of incivilities rather than changes in crime rates should better
capture the worsening in social cohesion experienced in countries such as Chile where crime rates
are not growing but social conflicts are on the rise Thus the main hypothesis in this essay is that
the rate of incivilities (social cohesion) should be positively (negatively) associated with economic
and racial diversity
Using panel count data models we start analysing how differences in incivilities rates
between and within counties are associated with differences in indicators of relative and absolute
economic disadvantage We use the Gini coefficient of each county as our measure of economic
diversity Although we find a significant and positive association between the rate of incivilities
and the level of income inequality the magnitude of the link seems to be small Among absolute
indicators of economic disadvantage only the level of income shows a strong effect Next we
include our measure of racial diversity We use the number of new visas granted to foreigners as
a proportion of the county population Results show a significant and strong positive association
between the rate of incivilities and racial diversity
To check the robustness of our results we analyse the impact of our measures of economic
and racial diversity running our models separately for each Chilean region and clustering them
geographically We also split the total number of incivilities in four categories to see which type
21
of incivilities show the greatest association with our measures of diversity In general results
support the ldquocommunity heterogeneity hypothesisrdquo that higher community heterogeneity is
associated with higher rates of incivilities (Alesina amp La Ferrara 2002 Letki 2008 Tolsma et al
2009) However results do not support the ldquoincivilities thesisrdquo that the distribution of incivilities
tends to mirror the distribution of income inequality (Skogan 1999 Taylor 1999)
Three results stand out among the set of control variables First the level of education shows
and independent and significant negative association with the rate of incivilities This is in contrast
to previous studies where education acts mainly as a moderator of the effect of economic and racial
diversity on social cohesion (Tolsma et al 2009) The results also show that there is no significant
relationship between the rate of incivilities and the proportion of young population This is relevant
because policies aimed to reduce incivilities usually put the focus on specific groups such as young
people which are linked to physical and social incivilities when social control is weakened
Finally the degree of financial municipal autonomy also shows a significant negative association
with the rate of incivilities This result suggests that municipalities can contribute independently
or together with the central government to reduce incivilities and strengthen social cohesion
Contributions
The three essays in this thesis provide several important insights into the analysis of the
causes and consequences of income inequality particularly in the context of Chile ndash a typical
resource rich economy with persistently high levels of income inequality
Essay 1 advances the understanding of the relationship between income inequality and
natural resources in Chile extending the empirical analysis from the regional level to the county
level In addition the geographic heterogeneity of income inequality is explored with the inclusion
22
of alternative sources of spatial dependence as a potential dimension of the causal relationship
between income inequality and natural resources This essay demonstrates the relevance of natural
resources in explaining the persistence of income inequality even after controlling for other
socioeconomics and institutional factors Findings from this study have potential contribution not
only in the design of policies aimed to reduce income inequality but also in addressing the current
developmental bias between the metropolitan region and the rest of the country
Essay 2 is one of the first studies that undertake a longitudinal analysis of the effects of
income inequality on the efficiency of municipal governments in Chile To capture the role of the
municipal governments in the provision to local people of public services such as education and
health we specify several inputs and outputs in our efficiency model which is different from the
conventional specification in the existing literature For example the number of medical
consultations in public health facilities and the number of enrolled students in public schools are
used as outputs instead of general indicators such as county population Our empirical analysis
also utilises a larger sample of municipalities and covers a much longer period spanning from 2006
to 2017 This essay also investigates the contextual factors beyond the control of local authorities
that can explain variations in the efficiency of municipal governments across the country
Empirical findings from Essay 2 help us increase our understanding of the production
technology of municipalities the sources of inefficiencies and specifically the impact of income
inequality on the performance of local authorities The results deliver two main policy
implications First municipal inefficiencies in the provision of public goods and services differ
across Chilean municipalities In addition efficiency levels show some degree of spatial
autocorrelation This implies that policies such as amalgamation or cooperation among
23
municipalities could have effects beyond the municipalities involved which must be considered
Second the causal effect that income inequality has on municipal efficiency provides another
dimension into the design and implementation of development policies
Essay 3 explores for the first time the effects of economic and racial diversity on social
cohesion in Chile This essay considers incivilities as manifestation of social cohesion and
investigates as extant literature suggests whether indicators of relative economic disadvantage
such as income inequality are among the main factors driving social disorganization and social
unrest Empirical findings suggest that on the one hand economic heterogeneity captured by the
Gini coefficient has a disparate effect both in terms of magnitude and significance on the rate of
incivilities across the country On the other hand the impact of racial heterogeneity appears to be
stronger more significant and of a similar magnitude throughout the country Results also provide
new insights into the design of national policies addressing social disorders particularly those
policies focussed on specific groups of the population and the role of local authorities Overall the
findings provide an opportunity to advance the understanding of the process of weakening in the
social cohesion experienced in Chile and the conflicts that have risen from this process
Thesis outline
The remainder of the thesis is organized as follows Chapter 2 presents essay 1 examining
the association between income inequality and the degree of dependence on natural resources
Chapter 3 presents essay 2 which looks for a causal relationship between municipal efficiency and
income inequality Chapter 4 presents essay 3 analysing the relationship between social cohesion
and economic and racial diversity Finally Chapter 5 presents some concluding remarks
24
Chapter 2 Natural Resources Curse or Blessing Evidence on
Income Inequality at the County Level in Chile
21 Introduction
A phenomenon of increasing inequality of incomes and wealth in recent decades has been
documented by leading scholars and international organizations such as the International Monetary
Fund (Berg amp Ostry 2017 Ostry Berg amp Tsangarides 2014) and the Organization for Economic
Cooperation and Development (Cingano 2014) These efforts have placed the topic of inequality
at the top of the current economic debate recognizing inequality as a determinant not only of
economic growth but also of human development They also have highlighted the necessity for
more research on the drivers of inequality and mechanisms through which it manifests aiming to
design effective policies in reducing economic and social inequalities
Various factors have been analysed as the sources of high and increasing levels of inequality
Among the most significant factors are the levels of income at initial stages of economic
development (Kuznets 1955) Globalization (Milanovic 2016) skill-biased technological change
(Tinbergen 1975) investment in human capital (Murphy amp Topel 2016) institutions
redistributive policy and country-specific characteristics (Acemoglu 1995 2002 Acemoglu
Aghion amp Violante 2001 Acemoglu Johnson amp Robinson 2001) Our focus in this essay is on
the importance that the natural resource endowment (NRE) or lack thereof can play in the
determination of income disparities
25
This essay studies the patterns and evolution of income inequality in the context of a natural
resource-rich country Using the case of the Chilean economy we aim to understand and
disentangle how a phenomenon of high- and persistent-income inequality is related to the
endowment of natural resources that a country owns Chile is an interesting case to study because
despite showing a successful history of economic growth inequality among individuals and among
aggregated spatial units has shown a strong persistence (Paredes et al 2016) Furthermore Chile
has remained among the most unequal countries in the world1
Theory and empirical evidence do not establish a clear link between income inequality and
NRE In addition NRE has received considerably less attention (Auty 2001 ElGindi 2017) and
most of the evidence has been focused on cross-country comparisons For instance NRE can
influence inequality by determining its initial levels (Engerman amp Sokoloff 1994 1997
Engerman Sokoloff Urquiola amp Acemoglu 2002) shaping the evolution of institutions
(Acemoglu 2002) make the educational system less intellectually challenging and moulding the
structure of economic activity (Leamer et al 1999) So studying how cross-county differences in
NRE are associated with the distribution of income within a country has theoretical empirical and
policy implications
In this study we offer empirical evidence on the relationship between income inequality and
the endowment of natural resources using data at the county level in Chile for the period 2006-
2017 Income inequality is measured by the Gini coefficient The importance of NRE is proxied
using a measure of natural resource dependence (NRD) defined as the percentage of the total
1 A 2014 OECD report on income inequality (httpwwwoecdorgsocialincome-distribution-databasehtm) showed Chile as the country with the highest Gini coefficient of disposable income among OECD countries OECD also indicates Chile as the country with the widest gap between the richest 10 percent and the poorest 10 percent of countryrsquos population among its 34 members (OECD 2014)
26
employment in each county corresponding to the primary sector (agriculture forestry fishing and
mining)
The main hypothesis to be tested is whether income inequality is positively associated with
the degree of NRD The transmission mechanisms through which natural resources could influence
socioeconomic outcomes could be based on the market or institutions The market-based approach
argues that natural resource booms could be associated with an appreciation of the real exchange
rate and a crowding out effect over other more productive economic activities such as
manufacturing It could also delay the adoption of new technologies and reduce incentives to invest
in physical and human capital (Gylfason amp Zoega 2003) Based on the ldquoResource Curse
Hypothesisrdquo (RCH) natural resources could be a curse when the political and institutional
framework is weak and natural resources are concentrated in space such as oil and minerals
(Deacon 2011) 2 Among institutional channels a higher NRD or natural resource booms could
be associated with rent seeking misallocation of labour and entrepreneurial talent institutional
and political decline or even violent conflicts For instance the ldquoParadox of Plentyrdquo sustains that
windfalls of revenues as a consequence of resource booms could be related to a lack of incentives
to perform efficiently corruption patronage and local authorities favouring their voters or being
captured by the richest groups (Dauvin amp Guerreiro 2017) Hence a higher NRD or natural
resource booms could be the explanation not only for low levels of growth in regions more
dependent on natural resources but also it could be the root of income disparities
2 There is a wide strand of research on the Resource Curse Hypothesis however the evidence so far is not conclusive Evidence in favour of RCH has been mainly found in developing resource rich countries (Auty 1993 2001 Badeeb Lean amp Clark 2017 Blanco amp Grier 2012 Borge et al 2015 Brunnschweiler amp Bulte 2008 Sachs amp Warner 2001 Van der Ploeg 2011)
27
To test our hypothesis that is whether the levels of income inequality across counties are
positively associated with the degree of NRD we use a spatial econometric approach We use this
approach because attributes such as income inequality in one region may not be independent of
attributes in neighbouring regions (Armstrong amp Taylor 2000) This process of spatial dependence
invalidates the use of traditional (non-spatial) approaches
This study seeks to make two contributions to research First previous empirical evidence
shows a significant spatial dimension of income inequality in Chile (Paredes et al 2016)
However this dimension has been barely explored with most studies limiting the degree of
disaggregation to a regional scale (Aroca amp Bosch 2000) We use a spatial approach which makes
it possible to model and test the significance of the spatial dimension in the analysis of income
inequality and its relationship with other variables Second previous research for the Chilean
economy linking inequality with NRE has been mainly focused on explaining differences between
regions or the importance and effects of the mining-copper sector (Aroca amp Atienza 2011 Ebert
amp La Menza 2015 Lagos amp Blanco 2010 Rehner Baeza amp Barton 2014) We extend this
analysis using data for local economies Identifying and quantifying the impact of NRE on income
inequality at the county level is likely to be more informative for policies aiming to address the
current developmental bias between the metropolitan region and the rest of the country Moreover
the analysis of the role of natural resources in conjunction with other potential sources of inequality
may shed lights in understanding the persistence of the high levels of inequality observed in the
Chilean economy All in all this study could contribute to the design of policies that
simultaneously help reduce inequality increase efficiency and promote sustainable and inclusive
growth
28
Our main finding shows that after controlling for other potential sources of income
inequality such as educational level demographic characteristics and the level of public
government expenditure the degree of dependence on natural resources has a significant effect on
income inequality However contrary to our expectations the effect is negative This result
suggests that the natural or policy-driven process of transformation from primary and extractive
activities to manufacturing and service sectors imposes additional challenges to central and local
authorities aiming to reduce income inequality
In section 22 we review the literature on the relationship between income inequality and
natural resources In section 23 we establish our research problem and main hypothesis Section
24 describes our data and methods and section 25 the empirical results We finish with section
26 discussing our main results concluding and proposing avenues for future research
22 Inequality and Natural Resources
221 Theoretical Framework
Explanations for income inequality can be associated with individual institutional political
and contextual characteristics Individual characteristics include age gender and mainly the level
of education and skills of the population in the labour force For instance globalization and
technological change lead firms to increase the demand for skilled labour deepening income
inequality between skilled and unskilled workers (Atkinson 2015 Milanovic 2016 Tinbergen
1975) Among institutional characteristics labour unions collective bargaining and the minimum
wage have been suggested as explanations of income inequality (Acemoglu Aghion et al 2001
Atkinson 2015) Policy design associated with market regulation progressive taxation and
redistribution can also impact the levels and patterns of inequality
29
A key factor in understanding the levels and differences in income distribution within a
country may be its endowment of natural resources NRE shapes the structure of the economy
(Leamer et al 1999) it is associated with the creation of institutions that define the political
culture and it can also influence the performance of other sectors (Watkins 1963) In addition
NRE determines initial conditions market competition ownership over resources rent seeking
and the geographical concentration of the population and economic activity
Cross‐countryliterature
Bourguignon and Morrison (1990) introduce one of the earliest theoretical frameworks
describing the relationship between inequality and NRE They develop a small open economy
model where income distribution is a function of NRE ownership structure and trade protection
Giving cross-sectional evidence for a group of developing countries they conclude that the impact
of NRE particularly mineral resources and land depends on the number and size of the firms
whether they are public or private and the level of protection A higher concentration of production
in a few private firms a big share of production oriented to foreign instead of domestic markets
and protection increasing the relative price of scarce resources are some of the reasons explaining
why some countries are less egalitarian than others
NRE could also influence the evolution and levels of inequality by determining the initial
distribution of incomes This is known as the ldquoEngerman-Sokoloff Hypothesisrdquo (Engerman amp
Sokoloff 1997 Engerman et al 2002) In addition Leamer (1999) proposes that inequality and
development paths in each economy are a function of its economic structure which in turn depends
on ldquofundamentalsrdquo and ldquosymptomsrdquo On the one hand ldquofundamentalsrdquo refer to resource
endowment production structure closeness to markets and governments interventions On the
30
other hand ldquosymptomsrdquo are related to institutions employment structure and net export structure
Using this conceptual framework Leamer argues that natural Resource-Rich Countries (RRC) can
experience a higher level of inequality because can have a ldquodumbbell educational systemrdquo
ownership is concentrated in small groups and extraction activities require low-skilled workers
This implies fewer incentives to educate citizens until very late in the development process
resulting in human capital not prepared to take advantage of the process of technological progress
and delaying the emergence of more efficient and competitive sectors such as manufacturing and
services
Using 1980 and 1990 data for a group of countries classified according to land abundance
Leamer (1999) provides evidence showing that on the one hand land-scarce countries concentrate
their production and employment in sectors that promote equality such as capital-intensive
manufacturing chemical or machinery On the other hand countries abundant in natural resources
concentrate their production trade or employment in sectors that promote income inequality such
as the production of food beverages extraction activities or forestry
Gylfason and Zoega (2003) using a framework based on standard growth models also
proposed a positive relationship between NRE and inequality They assume that workers can work
in the primary sector or in the manufacturing (including services) sector In addition wage income
is equally distributed in the manufacturing sector but unequally in the primary sector (because of
initial distribution competition rent seeking etc) Therefore inequality will be greater when a
bigger proportion of labour is dedicated to extraction activities in the primary sector This
phenomenon is further amplified because of lower incentives to invest in physical and human
capital to adopt new technologies and to increase the share of the manufacturing sector
31
Diverse mechanisms explaining the link between NRE and inequality have been proposed
arguing that NRE determines simultaneously economic growth and inequality (Gylfason amp Zoega
2003) NRE could impact economic growth through the real exchange rate and the crowding-out
effect on manufacturing (ldquoDutch Diseaserdquo) reducing incentives to invest in physical and human
capital (Easterly 2007) and influencing the processes of technology adoption industrialization
and diversification of the economy in a manner that is less conducive to growth (Bulte et al 2005)
These potential explanations related to the called ldquoResource Curse Hypothesisrdquo do not have strong
empirical support (Auty 2001 Bulte et al 2005)
NRE may also influence economic growth through the quality of institutions (Acemoglu
1995 Acemoglu Aghion et al 2001 Acemoglu amp Robinson 2002 Engerman amp Sokoloff 1997
Engerman et al 2002) the concentration of ownership political power and rent-seeking NRE
acts by shaping institutional context and social infrastructure a phenomenon that is stronger when
resources are spatially concentrated such as minerals and plantations (Bulte et al 2005) NRE
could also have a significant effect on social cohesion and instability spreading its influence like
a disease (Brunori Ferreira amp Peragine 2013 Kanbur amp Venables 2005 Milanovic 2016
Ocampo 2004)
Considering a non-tradable sector intensive in unskilled workers Goderis and Malone
(2011) develop a model where the natural resources sector experiences an exogenous gift of
resource income They analyse the impact over income inequality of resource booms proxied by
changes in a commodity price index They conclude that inequality decreases in the short run but
increases after the initial reduction
32
Fum and Hodler (2010) show that natural resources increase inequality but this is
conditional on the level of ethnical polarization of society Carmignani (2013 2010) confirms this
positive relationship using different measures of dependence and abundance and goes further
arguing that inequality constitutes an indirect channel through which NRE affects human
development
Singlecountryevidence
Most of the studies about the relationship between inequality and NRE derive from cross-
country analyses Evidence for specific countries has been mainly based on case studies Howie
and Atakhanova (2014) based on the model of Goderis and Malone (2011) find for the case of
Kazakhstan that income and consumption inequality decreased significantly after booms in the oil-
and-gas sector because of resource booms increase the demand for non-tradable goods which are
intensive in unskilled workers The results depend on the level of rurality institutional quality
education levels and public spending on health and education Fleming and Measham (2015b
2015a) evaluate the impact of booms in the mining and oil sectors in Australia They find that a
boom in the mining sector increases income inequality due to commuting and migration among
regions This phenomenon can be exacerbated when the demanding access to natural resource
revenues is associated with the creation of more local administrative units (counties provinces and
even regions) but the government capacity is not simultaneously improved (Cust amp Poelhekke
2015 Michaels 2011) Furthermore the benefits that a region can receive in the form of fiscal
transfers can be more than compensated by the loses due to city-to-mine commuting such as the
case of mining regions in Chile (Aroca amp Atienza 2011)
33
Other studies at the local level have analysed the impact of the mining sector in Peru (Aragoacuten
amp Rud 2013 Loayza amp Rigolini 2016 Loayza Teran amp Rigolini 2013) Spain (Domenech
2008) and Canada (Papyrakis amp Raveh 2014) and the effects of oil windfalls in Brazil (Caselli amp
Michaels 2013)
In summary there is a wide range of potential mechanisms through which NRE could
influence income inequality Although most of them seem to suggest a positive relationship others
such as commuting and increased within-county demand for non-tradable goods and services
could lead to a negative association This highlights the need to know the sign of this association
in the Chilean economy where the trend shows a reduction in the degree of NRD After controlling
for other factors a positive link would support the argument that the reduction in the degree of
NRD has been relevant in the reduction experienced by income inequality in the same period
However a negative link would support the position that the reduction in NRD has contributed to
explain the persistence of income inequality and its slow reduction
222 The relevance of the spatial approach
Inequalities within countries are still the most important form of inequality from the political
point of view (Milanovic 2016) People from a geographic area within a country are influenced
and care most about their status relative to the people in other areas in the same country The
influence among regions involves multiple aspects (eg economic political and environmental)
These potential interactions have been traditionally ignored assuming independence among
observations related to different regions Moreover neglecting the process of spatial interaction in
key indicators of the economic and social performance of a country may mislead the design of the
public policy
34
The spatial dimension could play a significant role in understanding the distribution of
income within a country One strand of efforts aiming to capture the geographic heterogeneity of
inequality has been focussed on decomposing general indicators such as the Gini coefficient or the
Theil Index Evidence for different countries including the US (Doran amp Jordan 2016) China
(Akita 2003 Gustafsson amp Shi 2002 Ye Ma Ye Chen amp Xie 2017 Yue Zhang Ye Cheng
amp Leipnik 2014) Japan (Ohtake 2008) South Africa (Leibbrandt Finn amp Woolard 2012) and
Chile (Paredes et al 2016) shows that regional inequality is sensitive to the geographic scale of
analysis These studies also show a significant spatial component in the explanation of inequality
of income expenditure or gross domestic product for each country
Another strand explicitly uses exploratory spatial data analysis (ESDA) and spatial
econometrics ESDA has been used to provide new insights about the nature of regional disparities
of incomes and growth rates (Celebioglu amp Dallrsquoerba 2010 Yue et al 2014) Spatial econometric
models aim to assess and address the nature of the spatial effects These effects could be the result
of ldquospatial heterogeneityrdquo that is different relationships in distinct locations or ldquospatial
dependencerdquo which implies cross-sectional interactions (spillover effects) among units from
distinct but near locations
Spatial spillovers have been analysed to study both positive and negative spatial correlation
among less resource-abundant counties and resource-abundant counties On the one hand less
resource-abundant counties may experience positive spillovers because their industries supply
more goods and services to meet the increasing regional demand They can also be benefited from
positive agglomeration externalities and higher investment in private and public infrastructure
(Allcott amp Keniston 2014 Michaels 2011) On the other hand negative spillovers could be the
35
result of a high degree of interregional migration that limits the rise in wages and higher local
prices due to the increase in the share of the non-tradable sector In addition local governments
could have a limited capacity to translate the revenues from resource booms into effective public
policies promoting a sustained local development (Beine Coulombe amp Vermeulen 2015 Caselli
amp Michaels 2013 Papyrakis amp Raveh 2014)
23 Research problem and hypotheses
We can conclude from our overview of the literature that the theoretical and empirical
evidence about the link between inequality and natural resources is inconclusive This does not
make clear whether the process of reduction in the degree of dependence on natural resources
such as that experienced by the Chilean economy helps to explain the sustained but slow reduction
in income inequality or its high persistence
The research question guiding this study relates to how the natural resource endowment
determines the paths and structure of income inequality in natural resource-rich countries Using
the case of Chile the main hypotheses to be tested is whether a higher degree of dependence on
natural resources is associated with higher levels of income inequality To do that we use data at
the county level and we explicitly include the spatial dimension Our aim is to arrive at a more
comprehensive understanding of the drivers and transmission mechanisms explaining the
evolution and patterns shown by income inequality In addition we test whether the spatial
dimension plays a significant role in explaining differences in income distribution in Chile
36
24 Data and Methods
We use county-level data for the years 2006 2009 2011 2013 2015 and 2017 The reason
for not using contiguous years is that income data at the household level are only available every
two-three years from the Chilean National Socioeconomic Characterization Survey (CASEN in its
Spanish acronym)3 For the period 2006-2017 the Chilean administrative division considers 15
regions 54 provinces and 346 counties Data on income are available for 324 counties and six
years resulting in a panel with 1944 observations4
We start evaluating the spatial dimension in our data and analysing the link between
inequality and NRD using a cross-sectional setting To this end we use the ldquosix-year averagerdquo
(2006 2009 2011 2013 2015 2017) for our variables given the low time variability showed by
our measures of income inequality and NRD Results are then compared with those of a panel data
setting
241 Operationalization of key variables
The dependent variable in the present study income inequality at the county level is
measured calculating the Gini coefficient using three definitions of household income labour
autonomous and monetary income5 Labour income corresponds to the incomes obtained by all
members in the household excluding domestic service consisting of wages and salaries earnings
3 CASEN survey is conducted by the Chilean Ministry of Social Development covering topics such as education employment income and health CASEN is considered nationally representative and it is the main source for measures of inequality and poverty used for the design and evaluation of social policies in Chile 4 The six waves of CASEN for our study considered an average of 75599 households and 252081 individuals 5 The Gini coefficient is chosen because provides an overall estimate of income inequality It summarizes what proportion of the population gains what proportion of the total income The Gini coefficient can range between 0 (everyone in the population has the same income) and 1 (one person earns 100 per cent of the income in the community)
37
from independent work and self-provision of goods Autonomous income is the sum of labour
income and non-labour income (including capital income) consisting of rents interest and dividend
earnings pension healthcare benefits and other private transfers Finally monetary income is
defined as the sum of autonomous income and monetary subsidies which correspond to cash
transfers by the public sector through social programs Appendix A shows summary statistics for
the Gini coefficient of our three measures of income
The main independent variable in our study is the degree of dependence on natural resources
in each county To have an idea of the importance of each economic activity in the Chilean
economy particularly those activities related to natural resources Figure 21 shows their average
share in Chilean Gross Domestic Product (GDP) for the period 2006-17 We can observe that the
leading activities are those related to the primary sector especially mining and to the tertiary
sector where financial personal commerce restaurants and hotels services stand out The shares
of each economic activity in GDP vary significantly between Chilean regions and such
information is not available at the county level
Figure 21 Average share in GDP of economic activities (2006ndash17)
38
Leamer (1999) argues that when the main source of income is labour income (as indeed
happens for the Chilean case) using employment shares allows a better approach to measuring
dependence on natural resources Using employment data from CASEN survey we define our
measure of NRD as the employment in the primary sector (mining fishing forestry and
agriculture) as a percentage of the total employment in each county We name this variable
pss_casen where ldquopssrdquo stands for ldquoprimary sector sharerdquo We built other two proxies of NRD
using data from the ldquoServicio de Impuestos Internosrdquo (SII) which is the agency in charge of
collecting taxes in Chile The variable pss measures the percentage of employment in the primary
sector and the variable pss_firms measures the number of firms in the primary sector as a
percentage of the total number of firms in each county Appendix B shows summary statistics for
our three measures of NRD disaggregated by region
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17)
39
Figure 22 shows the evolution of our measure of inequality (using the Gini coefficient of
autonomous income) and our three potential proxies for NRD for the period 2006-2017 We
observe that both income inequality and the degree of NRD show a downward trend This seems
to support our hypothesis of a positive link between inequality and NRD however we need to
control of other sources of inequality before getting such a conclusion In what follows we use the
variable gini as our measure of income inequality capturing the Gini coefficient of autonomous
income Our measure of NRD is the variable pss_casen defined previously
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17)
Note Gini and NRD averages 2006-09-11-13-15-17 for 324 Chilean counties divided into five equal groups Source Own elaboration based on CASEN survey
40
Figure 23 shows quantile maps for income inequality (on the left) and NRD (on the right)
using the six-years average dataset6 On the one hand we observe that high levels of inequality
seem to be clustered in the Centre-South of the country where agriculture forestry and fishery are
the predominant economic activities Only isolated counties show high inequality in the Centre
(Metropolitan area where the countyrsquos capital is located) and North (Mining) areas On the other
hand our measure of NRD seems to show an opposite spatial pattern than income inequality with
high levels in the Centre and North of the country
242 Control variables
To control for county characteristics we use a set of socio-economic demographic and
institutional variables Economic factors are captured by the natural log of the mean autonomous
household income per capita (in thousands of Chilean pesos of 2017) lnincome the poverty rate
poverty the unemployment rate unemployment the percentage of the population living in rural
areas rural and the average years of education of the population over 15 years old education
Demographic factors include the proportion of the population in the labour force labour_force
and the natural log of population density (population divided by county area) lndensity
We also include the natural log of the total municipal public expenditure per capita
lnmuni_expenditure to control for municipal heterogeneity This heterogeneity is mainly related
to the capacity of municipalities to generate their own revenues In addition the richest
municipalities are in the Metropolitan region which concentrates economic power and around 40
6 After sorting a variable in ascending order quantile maps use the quantiles (quartiles quintiles deciles etc) as class breaks to divide the distribution of the variable where each class includes approximately the same number of observations (counties)
41
of the population This has basically implied a lag in the development of regions other than the
metropolitan region
The spatial distribution of our measures of income inequality and NRD displayed in Figure
23 seems to show different patterns in the North Centre and South of the country Appendix C
shows the administrative division of Chile in 15 regions and how we have grouped them in three
zones North Centre and South We consider as the ldquoCentrerdquo area that formed by the Metropolitan
region (XIII) and its two neighbouring regions V and VI Using the Centre area as our reference
we include in our analysis two dummy variables indicating whether a county is located in the North
area (regions XV I II III and IV) or South area (regions VII VIII IX XIV X XI and XII)
Appendix D shows summary statistics for the set of numeric control variables and the
correlation matrix between our measure of NRD pss_casen and the set of numeric controls
243 Methods
To assess and then consider the spatial nature of the data we need to define the set of relevant
neighbours for each country This is operationalized by building a matrix called ldquoWrdquo with a ldquo1rdquo
for neighbouring counties and a ldquo0rdquo for non-neighbouring counties We could build W using
contiguity-based (whether counties share a border or point) or geography-based (taking the
distances among the centroids of each county polygon) spatial weights Specifically we build a W
matrix considering the 5-nearest counties7 Two reasons explain the choice of k-nearest
neighbours First we cannot use a contiguity criterium because we do not have information about
all the counties and there are some geographically isolated counties Second given the significant
7 We assign a ldquo1rdquo to the five nearest counties to each county based on the distances among the polygon centroids Then W is ldquorow standardizedrdquo This facilitates the interpretation of the spatial lag of a variable as the ldquoweighted average valuerdquo of the same variable in neighbouring counties
42
differences in county areas (ldquobig countiesrdquo in northern and southern regions) using a distance-
band criterium with a not enough large distance band can lead to many ldquoislandsrdquo in extreme regions
and a multi-modal distribution for the number of neighbours
We start testing our inequality and NRD variables for spatial autocorrelation in order to
evaluate statistically the clustering patterns shown in Figure 23 Next we run an OLS regression
of inequality against NRD and our set of controls and we test the spatial autocorrelation of OLS
residuals If we cannot reject the null hypothesis of random spatial distribution we do not need
spatial models to analyse income inequality which would give contrasting evidence to previous
suggestions about the relevance of the spatial dimension of income inequality in Chile (Paredes
2013 Paredes et al 2016) If we find significant spatial autocorrelation in the OLS residuals this
justifies the use of spatial models and highlight the need to find the correct spatial structure8
If inequality in one county spillovers or influences inequality in neighbouring counties the
spatial lag of inequality should be included as an explanatory variable and we should use a spatial
autoregressive model (SAR) If some unobserved variable is the explanation for the clustering of
counties with similar inequality then this will be better captured including a spatial lag of the
errors and we should use a spatial error model (SEM) (Anselin 1988 Anselin amp Bera 1998)
Finally when our main explanatory variable or some of the controls show spatial autocorrelation
a spatial lag of the explanatory variable(s) should be included in our model
8 The existence of spatial autocorrelation violates the standard assumption of independence among observations needed for OLS regression This will result in OLS coefficients biased and inconsistent (Anselin 1988)
43
244 Spatial Model Specification
A model that includes the three forms of spatial dependence described above is called the
Cliff-Ord Model The model in its cross-sectional representation could be expressed as
119910 120582119882119910 119883120573 119882119883120574 119906 (21)
where
119906 120588119882119906 120576 (22)
119882 is our weight matrix that works as an NxN spatial lag operator9 Thus 119882119910 119882119883 and 119882119906
are the spatial lags for the dependent variable explanatory variables and the error term
respectively The parameter 120582 capture the spatial dependence in the dependent variable 120574 the
spatial dependence in the explanatory variables 120588 capture the spatial dependence in the error term
and 120598 is a vector of idiosyncratic errors For instance if ldquoyrdquo is income inequality and ldquoXrdquo a measure
of NRD the level of inequality in one county will be explained by the degree of NRD in the same
county 119883120573 the average degree of NRD in neighbouring counties 119882119883120574 the average level of
inequality in neighbouring counties 120582119882119910 and the average value of residuals in neighbouring
counties 12058811988211990610
From equations (21) and (22) the SAR and SEM models can be seen as special cases of
the Cliff-Ord representation after imposing restrictions over the spatial parameters 120582 120574 and 120588 For
the specification of the spatial panel models we follow the terminology by Croissant and Millo
9 The spatial lag is a weighted sum of the values observed at neighbouring locations 10 On the one hand the impact that income inequality in one county has over income inequality in neighbouring counties is called a ldquoglobal spilloverrdquo and it is associated with the feedback effect among neighbours (one county is its neighboursrsquo neighbourrdquo) on the other hand the influence that the degree of NRD in neighbouring counties has over inequality in one county is called a ldquolocal spilloverrdquo
44
(2018) Spatial panel models including the spatial lag of the dependent variable (SAR) the spatial
lag of the residuals (SEM) or both (SARAR) are described in Appendix E
25 Results
251 Exploratory Spatial Data Analysis (ESDA)
To analyse the significance of the spatial dimension in our data set we use the six-year
average of our variables Spatial autocorrelation is tested using the Moranrsquos I statistic11 Moranrsquos
I measures the correlation of one variable with itself in space12 Figure 24 shows the Moran scatter
plots where the standardized variable (Gini coefficient and NRD for each county) appears in the
horizontal axis against its spatial lag (average value in the 5-nearest neighbouring counties) The
Moranrsquos I (slope of the line in the Moran scatter plot) of income inequality shows a significant
positive spatial autocorrelation that is counties with high (low) inequality tend to be close to each
other
11 There are many statistics to formally test the significance of the spatial dimension in the distribution of our data The null hypothesis assumes spatial randomness which means that there is not spatial structure in the data so any spatial pattern is equally likely to occur and values in one location do not depend on values in other locations The alternative hypotheses can be the existence of positive or negative spatial autocorrelation Positive spatial autocorrelation means similar values in neighbouring locations (less variability than under spatial randomness) Negative spatial autocorrelation means dissimilar values in neighbouring locations (more variability than under spatial randomness) 12 Traditional measures of correlation such as the Pearsonrsquos coefficient measure the degree of linear correlation between two different variables Measures of spatial autocorrelation assess the correlation between the values of one variable in one location related to the values of the same variable in other neighbouring locations
45
Figure 23 Moran scatter plots for variables gini and pss_casen
Moranrsquos I is a measure of global spatial autocorrelation this means it is intended to capture
the clustering property of the entire data set To identify where are the significant hot-spots
(clusters of counties showing high income inequality) or cold-spots (clusters of counties showing
low income inequality) we need local indicators of spatial association (LISA) Using the local
Moranrsquos I (not reported) we find significant hot-spots in the South of the country (mainly
agricultural regions) and significant cold-spots in the Centre (Metropolitan area) of the country
The next step is to check whether the clustering pattern in inequality is the result of a process of
spatial dependence in the variable itself or it can be explained by other variables related to
inequality
252 Cross-sectional analysis
We start analysing differences in income inequality between counties using the six-year
average data and running an OLS regression for the model
119892119894119899119894 120573 120573 119901119904119904_119888119886119904119890119899 120573 119897119899119894119899119888119900119898119890 120573 119901119900119907119890119903119905119910 120573 119906119899119890119898119901119897119900119910119898119890119899119905 120573 119897119886119887119900119906119903_119891119900119903119888119890 120573 119890119889119906119888119886119905119894119900119899 120573 119897119899119889119890119899119904119894119905119910 120573 119903119906119903119886119897 120573 119897119899119898119906119899_119890119909119901119890119899119889119894119905119906119903119890 120573 119899119900119903119905ℎ 120573 119904119900119906119905ℎ
(23)
46
The Moran scatter plot and spatial statistical test of OLS residuals from equation (23) are
in Appendix F OLS residuals show a small but significant positive autocorrelation (Moranrsquos I =
0121) This means that income inequality continues showing a significant degree of spatial
autocorrelation after controlling for the set of covariates In addition robust Lagrange Multiplier
(LM) tests show that a spatial error model is preferred over a spatial lag model (Anselin Bera
Florax amp Yoon 1996)13 This means that income inequality tends to cluster in zones larger than a
county so the analysis should be performed on a larger scale such as provinces regions or macro
zones If the SAR model were preferred it would mean that income inequality in one county is
influenced by the level of income inequality in neighbouring counties To find the spatial structure
that best fits the clustering process of income inequality we run the full set of spatial model
specifications in a cross-sectional setting and results are shown in Table 21
Column 2 in Table 21 shows the results of our ldquoOLSrdquo model The ldquoSLXrdquo model includes
spatial dependence only through the explanatory variables The ldquoSARrdquo model includes the spatial
lag of the dependent variable as a regressor and the ldquoSEMrdquo model includes spatial dependence
through the error term The ldquoSARARrdquo model includes both the spatial lag of the response and the
errors and the ldquoSDMrdquo and ldquoSDEMrdquo are the extensions of the ldquoSARrdquo and ldquoSEMrdquo models
respectively including the spatial lag of the explanatory variables Finally a model including
spatial lags for the response errors and explanatory variables (the ldquoSARARXrdquo model) is shown in
the last column
13 Following the ldquoAnselin methodrdquo when both non robust LM tests are significant we should select between the robust LR tests From Table F1 in Appendix F only the robust LM test for the SEM model (RLMerr) appears as significant
47
Table 21
Cross-sectional Model Comparison (six-year average data)
48
Opposite to our hypothesis we observe a significant and negative coefficient for our measure
of NRD This means that counties more dependent on natural resources show lower levels of
inequality Education years population density and municipal expenditure per capita are also
negatively related to inequality On the other hand the level of income the poverty rate and the
proportion of the population living in rural areas show a positive relationship with income
inequality There is no significant influence of the unemployment rate and the proportion of the
population in the labour force In addition the SAR SEM and SARAR models show a
significantly higher average inequality in the South of the country related to the Centre area
The main finding from our cross-sectional analysis is that there is a significant and negative
relationship between inequality and NRD which is quite robust to the model specification
253 Panel Data analysis
Like the cross-sectional case we start estimating the panel without spatial effects Results
for the pooled fixed effects (FE) and random effects (RE) specifications of equation (3) are in
Appendix G Spatial models were estimated using Maximum Likelihood (ML) and Generalized
Moments (GM) and assuming that the spatial structure (W matrix) is the same for all years14
Tables 22 23 and 24 show results for the ML estimation of the SAR SEM and SARAR models
using the pooled FE and RE specifications Results for the GM estimation are in Appendix H
All our spatial models include time fixed effects In the case of the pooled and RE models they
additionally include indicator variables for those counties located in the North and South of the
country
14 In traditional (non-spatial) panels data are stacked as time series for each observational unit (county) In the case of spatial panels data are stacked as slice of cross-sections for each year in the panel
49
The main result is that the negative and significant effect of NRD on income inequality is
robust to most of the spatial panel specifications In addition the coefficient for the variable
pss_casen changes slightly among panel specifications (pooled FE and RE) but does not change
among spatial models (SAR SEM and SARAR)
Another important finding is related to the significance of the spatial dimension of income
inequality When spatial models cross-sectional or panel are compared to non-spatial models
there are no major differences in the magnitude of the coefficients or their significance This could
mean that the positive spatial autocorrelation shown by income inequality seems to be better
explained by a process of spatial heterogeneity rather than spatial dependence The practical
implication of this result is that including dummy variables for aggregated units (eg regions or
groups of regions) could be enough to control for the spatial dimension in the modelling and
analysis of income inequality
Among control variables years of education seems to be the main variable for the design of
long-term policies aimed at reducing inequality This result is in line with previous evidence for
cross-country studies (Leamer et al 1999) and specific countries (Howie amp Atakhanova 2014)
Municipal expenditure per capita also shows a significant and negative association with income
inequality in the pooled and RE spatial specifications This means that higher municipal
expenditure helps to reduce inequality between counties but its effect is more limited within
counties This result support the importance of local governments (Fleming amp Measham 2015a)
however the negative coefficient appears as evidence against the ldquoParadox of Plentyrdquo (Borge et
al 2015)
50
Table 22
ML Spatial SAR Models
Table 23
ML Spatial SEM Models
51
Table 24
ML Spatial SARAR Models
26 Discussion and conclusions
In this essay we delve deep into the sources of income inequality analysing its association
with the degree of dependence on natural resources using county-level data for the 2006ndash2017
period in Chile Given recent evidence for the Chilean economy suggesting a significant spatial
dimension we assess and incorporate explicitly the spatial structure of income inequality using
spatial methods We use cross-sectional and panel data to evaluate the significance of the spatial
dimension and we test whether NRD has a positive effect on income inequality
Contrary to what theory predicts NRD shows a significant and negative association with
income inequality This result is robust to the type of analysis (cross-sectional vs panel data) the
approach (spatial vs non-spatial) and the inclusion of different controls The negative and
significant coefficient implies that if the degree of NRD would not have experienced a 10 drop
during this period income inequality could have fallen in 2 additional points So the downward
trend in the participation of the primary sector in terms of employment in the Chilean economy
52
could be one of the main reasons explaining the high persistence in the levels of income inequality
This means that those areas that undergo a process of productive transformation mainly towards
the services sector would be facing greater problems to reduce inequality This process of
productive transformation natural or policy-driven highlights the importance of policies focused
on human capital and the role of local governments in reducing inequality
The main implication for policymakers is that a reduction in NRD does not help to reduce
inequality generating additional challenges for local and central governments in its attempt to
transform the structure of their economies to fewer dependent ones on natural resources The
finding of a significant spatial dimension suggests that defining macro zones capturing the spatial
heterogeneity in the data should be done before analysing the relationship among variables and the
design and evaluation of specific policies Particularly relevant in those areas experiencing a
reduction in NRD are migration commuting and the characteristics of the tertiary (services) sector
In addition our findings show that education and municipal expenditure could be effective policy
tools in the fight to reduce inequality in Chile
Although our results seem quite robust they do not allow us to make causal inferences about
the effect of NRD on income inequality However we could think of the following explanation to
explain the negative relationship found and the differences between geographical areas
Areas highly dependent on NR used to demand a high proportion of low-skill labour This
has change in sectors such as the mining sector in the northern area which has simultaneously
experienced an increase in activities related to the service sector such as retail restaurants
transport and housing However those services associated with more skilled labour such as the
finance sector remain concentrated in the capital region The reduction in the degree of NRD
(employment in extractive activities) implies lower labour force but more specialized with most
53
of the low-skilled labour transferred to a service sector characterized by low productivity and low
wages
Non-spatial models show that the North and South particularly the latter present
significantly higher levels of inequality This could be associated with the type of resources with
ldquopointrdquo resources such as minerals in the North and ldquodiffusedrdquo resources such as agriculture in the
South This translates into higher average incomes in the Centre and North areas and lower average
incomes in the South
The reduction in NRD implies not only a movement of the labour force from extractive
activities to manufacturing or services with the latter characterized by low productivity and low
salaries of the labour force We could also speculate that most of the high incomes move to the
central area where the economic power and ownership over firms and resources are concentrated
This would explain low inequality associated with higher average incomes in the central area and
high inequality associated with lower average incomes in the South A more in-depth analysis
capturing the mobility of wealth and labour force between counties or more aggregated areas is
needed to better understand the causal mechanism involved
Our findings open avenues for future research in different strands First studies on the causes
of income inequality should take the role of NRD into consideration which has been overlooked
so far Given that the spatial dimension of income inequality seems to be explained by a
phenomenon of spatial heterogeneity estimation strategies such as spatial regime models or
geographically weighted regression should be used (Chi amp Zhu 2019) Second the effect of NRD
on income inequality could manifest through different channels such as education fiscal transfers
and institutions We could extend our analysis to identify which of these competing channels is
the most relevant Transforming some continuous variables such as educational level to a
54
categorical variable or defining new indicator variables for instance whether a local government
shows or not an efficient performance we could classify counties in different groups and then
check whether there are differences or not in the relationship between income inequality and NRD
A third strand could be to disaggregate our measure of NRD for different industries This
would allow us to test differences among industries and to identify the sectors that promote greater
equality and which greater inequality Forth the analysis of the consequences of income inequality
on other economic and social phenomena such as efficiency economic growth and social cohesion
has a growing interest in researchers and policymakers Our findings suggest that to answer the
question of whether income inequality has a causal impact on other variables we could include a
measure of NRD as an instrument to address endogeneity issues For instance two interesting
topics for future research are the analysis of how differences in income inequality between counties
could help to explain differences in the level of efficiency of local governments and differences in
the degree of social cohesion (unrest) throughout the country Those are the issues to be addressed
in the next two essays
55
Chapter 3 The Impact of Income Inequality on the Efficiency of
Municipalities in Chile
31 Introduction
In Chile municipalities are the smallest administrative unit for which citizens choose their
local authorities playing an important role in the provision of public goods and services at the
local level Municipalities have a similar set of objectives but the level of financial resources
available to finance their activities is highly heterogeneous This could result in significant
differences in the levels of performance between municipalities Despite their importance there is
little empirical evidence about the efficiency of local governments in Chile This essay aims to
measure the technical efficiency of Chilean municipalities and to analyse how local characteristics
particularly those related to income distribution at the county level could help to explain
differences in municipal performance
Cross-country studies situate Chile as an efficient country in international comparisons about
efficiency of government spending (Herrera amp Pang 2005 Loacutepez amp Miller 2008) However
evidence for Chile at the local level is relatively sparse suggesting significant levels of
inefficiency For instance Pacheco Sanchez and Villena (2013) found a mean efficiency level of
around 70 using a sample of 309 municipalities for the period 2008-2010 This suggests that
municipalities could achieve the same level of output by reducing the usage of inputs by an average
of 30 Their study also showed that those municipalities more dependent on the central
56
government or those located in counties with lower income per capita are more efficient than their
counterparts
Most empirical research on Local Government Efficiency (LGE) has been conducted for
member countries of the Organization for Economic Cooperation and Development (OECD) of
which Chile has been a member since 2010 In the case of European countries such as Spain and
Italy which share similar characteristics such as the monetary union and levels of GDP per head
efficiency studies have been mainly motivated by budgetary constraints (Balaguer-Coll Brun-
Martos Maacuterquez-Ramos amp Prior 2019 lo Storto 2013) The Chilean context differs in three
main ways from its OECD counterparts First except for the Metropolitan Region that concentrates
most of the population Chilean regions are highly dependent on natural resources Second Chile
is also characterized by one of the highest levels of income inequality among OECD countries
which contrast with the situation of developed natural resource-rich countries such as Australia
and Norway Third although budget constraints are also a relevant issue Chilean municipalities
have experienced a sustained increase in the level of financial resources and expenditure
Another relevant distinction when we benchmark the performance of municipalities across
different countries is the type of public services they provide On the one hand in most of the
countries included Chile the main role of local governments is to provide ldquoservices to peoplerdquo
such as public education and public health On the other hand there are countries such as Australia
where local governments mainly provide ldquoservices to propertyrdquo including waste management
maintenance of local roads and the provision of community facilities such as libraries swimming
pools and parks (Dollery Wallis amp Akimov 2010 Drew Kortt amp Dollery 2015 McQuestin
Drew amp Dollery 2018)
57
Despite contextual differences Chilean municipalities seem not to perform differently from
municipalities in other developed and natural resource-rich countries where income inequality is
significantly less than in Chile (Narboacuten-Perpintildeaacute amp De Witte 2018a) This result highlights the
need to study the role of income inequality and the degree of dependence on natural resources over
LGE characteristics that have been largely overlooked in the literature
We measure and analyse differences in municipal performance using a two-stage approach
In the first stage we measure municipal efficiency using an input-oriented Data Envelopment
Analysis (DEA) to get a set of ldquoefficiency scoresrdquo In the second stage we regress efficiency scores
against our measure of income inequality controlling for a set of contextual factors describing the
economic socio-demographic and political context of each county
We use a sample of 324 municipalities for the period 2006-2017 During this period Chile
was divided into 346 counties belonging to 15 regions This period was characterized by important
external and internal shocks including the Global Financial Crisis (GFC) one of the biggest
earthquakes in Chilean history in 2010 and three municipal elections The availability of
information allows us to measure efficiency for the full period but the influence of contextual
factors is analysed using a panel of six years (2006 2009 2011 2013 2015 and 2017) for which
household income information is available
The main hypothesis tested in the second stage is whether higher levels of income inequality
are associated with lower levels of efficiency Previous evidence shows that when progress is not
evenly shared persistent within-country inequality reduces the effectiveness and efficiency of the
public sector (Ortega Sanjuaacuten amp Casquero 2017 Tandon 2005)
Income inequality has been used to control for a wide range of idiosyncratic factors
associated with historical institutional and cultural factors affecting efficiency (Greene 2016
58
Ortega et al 2017) For instance at the local level income inequality has been considered as an
indicator of economic heterogeneity in the population where higher inequality is associated with
a more heterogeneous set of conflicting demands for public services which adversely affect an
efficient provision (Ashworth Geys Heyndels amp Wille 2014 Geys amp Moesen 2009) Higher
levels of income inequality could also relate to economically privileged groups having a greater
capacity to influence the political system for their own benefit rather than that of the majority
When high inequality is persistent the feeling of frustration and disappointment in the population
could reduce not only trust and cooperation among individuals but also trust in institutions which
would negatively affect government efficiency (Boix amp Posner 1998 Coffeacute amp Geys 2005) For
instance national or local authorities could end exerting patronage and clientelism and showing
rising levels of corruption (Uslaner 2011 Uslaner amp Brown 2005)
One of the main gaps in extant literature is the need to conduct more analysis of LGE using
panel data taking into consideration endogeneity issues and controlling for unobserved
heterogeneity (Narboacuten-Perpintildeaacute amp De Witte 2018a) To address the above we set-up a panel with
time and county-specific effects and we propose the use of a measure of natural resource
dependence (NRD) as an instrument for income inequality Based on the ldquoParadox of Plentyrdquo
fiscal revenues from natural resources windfalls could be associated with an over expansion of the
public sector fostering rent-seeking and corruption and reducing local government efficiency
(Dauvin amp Guerreiro 2017 Manzano amp Rigobon 2001) In the Chilean case most of the revenues
generated by local governments included those from natural resources end up in a common fund
which benefits all municipalities The aim of this common fund is precisely to reduce inequalities
among municipalities so although we do not expect a direct impact of natural resources on LGE
we could expect an indirect effect through other indicators particularly income inequality
59
As far as we know this is the first study analysing the influence of income inequality as a
determinant of municipal efficiency in Chile Moreover this is the first study in the context of a
natural resource-rich country which specifically suggests a measure of natural resource
dependence as an instrument to correct for endogeneity bias We propose the use of the proportion
of firms in the primary sector as proxy for the degree of NRD in each county We argue that this
variable is a better proxy than using the proportion of employment in the manufacturing sector
which has been proposed in previous studies (Alesina amp La Ferrara 2002) During the period
analysed our proxy remained relatively stable and showed a significant relationship with income
inequality In addition it is less likely that it has directly affected municipal efficiency
This study adds to the literature in two other ways First the extant literature suggests that
efficiency measurement could be highly sensitive to the chosen technique as well as the selection
of inputs and outputs (Narboacuten-Perpintildeaacute amp De Witte 2018a) Inputs are usually proxied by a single
measure of total public expenditures and outputs by general proxies such as population andor the
number of businesses in each county We offer a novel approach for the selection of inputs and
outputs On the one hand we disaggregate government expenditures into four components
(operation personnel health and education) and we use the number of public schools and health
facilities in each county as a proxy for physical capital On the other hand we use four outputs
aiming to capture the wide variety of goods and services supplied by each municipality Through
this approach we aim to better describe the production function of each municipality capturing
not only the variety of inputs and outputs but also differences in size among municipalities
A third contribution relates to the measurement of LGE in the Chilean context We measure
technical and scale efficiency using a larger sample and a longer period This has empirical and
policy relevance On the one hand it helps us to select the correct DEA model and allows us to
60
determine the importance of scale inefficiencies as explanation for differences in municipal
performance On the other hand efficiency measures increase the information available for both
central and local governments to better understand the production technology that best describes
each municipality and to carry out policies to improve efficiency
We believe that our selection of inputs and outputs the use of a large dataset and the joint
analysis using cross-sectional and panel data provide a more accurate and robust analysis of
municipal efficiency Likewise knowing whether inequality has a significant influence on
municipal efficiency may provide useful insights and guidance for policymakers not only in Chile
but also for countries sharing similar characteristics
DEA results show an average level of technical efficiency (inefficiency) of around 83
(17) This means that municipalities could reduce on average a 17 the use of inputs without
reducing the outputs There are significant differences among geographic areas with the Centre
area (where the countyrsquos capital is located) displaying higher efficiency than the rest of the country
When municipal efficiency is measured under different assumptions about returns to scale results
reveal a production technology with variable returns to scales and around 75 of the
municipalities displaying scale inefficiencies However when technical efficiency is
disaggregated between pure technical efficiency and scale efficiency results show that scale
inefficiency explains a small proportion of the total municipal technical inefficiency This finding
justifies a deeper analysis of the reasons why municipalities could operate inefficiently and why
municipal performance could vary among municipalities
Efficiency scores also show a significant degree of positive spatial autocorrelation This
means that municipal efficiency shows a general clustering process with neighbouring
municipalities showing similar levels of efficiency A further analysis shows that most of the
61
spatial pattern in municipal efficiency is exogenous that is could be associated to other variables
Hence we conduct most of our regression analysis using traditional (non-spatial) methods and
leaving spatial regressions in the appendixes
Findings from cross-sectional and panel regressions support the hypothesis that municipal
performance is significantly and negatively associated with income inequality at the county level
The coefficient of income inequality is close to one which means that reductions in income
inequality ceteris paribus could be associated with increases in municipal efficiency in the same
proportion This result supports the strand of research arguing that there is not a trade-off at least
at the municipal level between equity and efficiency (Andersen amp Maibom 2020 Berg amp Ostry
2011 2017) The main policy implications are that authorities in more unequal counties would
face higher challenges to perform efficiently and policies pertaining to inequality and efficiency
should not be designed independently
The chapter is structured as follows Section 32 provides a brief literature review on related
local government efficiency Section 33 introduces the methodological background and empirical
models Section 34 presents the empirical results and discussions Section 35 concludes the
chapter
32 Related Literature
321 Measuring efficiency of local governments
Studies on measuring LGE can be grouped in those analysing the provision of single services
such as health or education and those assessing overall efficiency (lo Storto 2013) Once inputs
and outputs have been defined efficiency is measured using parametric andor non-parametric
techniques Among the former group Stochastic Frontier Analysis (SFA) seems to be preferred
62
by scholars aiming to measure efficiency and to analyse the link with environmental variables
using a single procedure (Pacheco et al 2013 Tsekeris Sotiris Tsekeris amp Papaioannou 2018)
On the non-parametric group Data Envelopment Analysis (DEA) is by far the most used technique
(Afonso amp Fernandes 2006 Balaguer-Coll amp Prior 2009 lo Storto 2013)
The selection of inputs and outputs depends not only on the aimed of the study (specific
sector vs whole measure of efficiency) but also on the role that municipalities play in different
countries There are countries such as Australia (Drew et al 2015) and Spain (Balaguer-Coll amp
Prior 2009) where local governments mainly supply services to ldquopropertyrdquo such as waste
management and road maintenance In these cases efficiency has been mainly measured using
total indicators of local government expenditure and outputs have been proxied using general
indicators such as population or number of business (Drew et al 2015) On the other hand in
countries such as Italy (lo Storto 2013) and Portugal (Afonso amp Fernandes 2008) in Europe or
Brazil (de Sousa Cribari-Neto amp Stosic 2005) and Chile (Pacheco et al 2013) in South America
municipalities mainly supply services to ldquopeoplerdquo Here in addition to financial expenditures or
revenues inputs have included the number of local government employees the number of schools
or the number of hospitals and health centres School-age population the number of students
enrolled in primary and secondary schools and the number of beds in hospitals have been
considered as outputs Based on the study of Narboacuten-Perpintildeaacute amp De Witte (2018a) a wider list of
inputs and outputs used in previous studies can be found in Appendix I
Studies from different countries show important differences in the average efficiency scores
both between and within countries These studies also differ in the samples methodologies and
variables included A summary showing the range and variability of the mean efficiency scores
founds in countries all around the world can be found in Narboacuten-Perpintildeaacute amp De Witte (2018a)
63
These authors also show that OECD natural resource-rich countries such as Australia Belgium
and Chile show similar results in terms of mean efficiency scores with LGE studies being less
frequent in Latin American countries
Measuring efficiency of local governments as decision-making units (DMU) presents many
challenges and difficulties (Borger Kerstens Moesen amp Vanneste 1994 Ravallion 2005)
Worthington and Dollery (2000) mention problems with the selection and measurement of inputs
the identification of different stakeholders the hidden characteristic of the ldquolocal government
technologyrdquo and the multidimensionality of the services provided by local governments All these
issues make difficult to identify and distinguish between outputs and outcomes with outputs
commonly proxied by general indicators such as county area or county population Because
efficiency measures are highly sensitive to the chosen technique and the selection of inputs and
outputs Narboacuten-Perpintildeaacute amp De Witte (2018a) suggest formulating different specifications and
using less general and unspecified indicators Moreover the complexity in defining outputs and
the use of general indicators make more likely that contextual factors affect municipal efficiency
322 Explaining differences in LGE
To explain differences in local government performance researchers have basically
distinguished between ldquodiscretionaryrdquo and ldquonon-discretionaryrdquo factors Discretionary factors refer
to the degree of discretion of local authorities in the selection and management of inputs and
outputs On the other hand scholars have investigated the influence on LGE of contextual factors
beyond authoritiesrsquo control These factors reflective at the environment where municipalities
operate include economic socio-demographic geographic financial political and institutional
characteristics (da Cruz amp Marques 2014 Narboacuten-Perpintildeaacute amp De Witte 2018b)
64
In general the evidence about the influence of contextual factors has delivered mixed and
country-specific results (Narboacuten-Perpintildeaacute amp De Witte 2018b) Sampaio de Sousa et al (2005)
using data for Brazilian municipalities finds that population density and urbanization rate have
strong positive effects on efficiency scores Benito et al (2010) show that lower levels of
efficiency of Spanish municipalities are associated with a greater economic level a less stable
population and a bigger size of the local government Afonso (2008) finds that per capita income
level and education are not significant factors influencing LGE of Portuguese municipalities He
also finds that municipalities in Northern areas show greater efficiency than their counterparts in
Southern areas More recently Tsekeris (2018) finds that spatial variations in efficiency in Greece
can be attributed to factors related to inter-regional market access specialization and sectoral
concentration resource-factor endowments and political factors among others Characteristics
describing each local government have also been used including municipal indebtedness (Benito
et al 2010) fiscal deficits (Sinha 2017) degree of fiscal autonomy (Boetti Piacenza amp Turati
2009) and individual characteristics of local authorities such as age gender and political ideology
Narboacuten‐Perpintildeaacute amp De Witte (2018b) conclude after analysing 63 articles that studies on the
influence of contextual factors have mostly used cross-sectional data with little attention to
endogeneity issues which makes any causal interpretation doubtful
323 The trade-off between efficiency and equity
The existence of a potential trade-off between efficiency and equity is in the core of
economic discussion (Andersen amp Maibom 2020 Berg amp Ostry 2011 Browning amp Johnson
1984 Okun 2015)15 The argument that economic growth (one of the most common efficiency
15 Redistributive policies distort incentives and lead to suboptimal outcomes and thus efficiency losses
65
measures) could be negatively affected in the search for greater equality has been translated not
only into economic policies that favour economic growth over those that reduce inequality but
also in the emphasis of scholarly research Thus theoretical and empirical research has been
mainly focussed on efficiency and policy implications of a great diversity of shocks and policies
leaving the analysis of inequality as one of measurement and mostly descriptive Additionally
empirical evidence of the trade-off is scant and inconclusive (Andersen amp Maibom 2020
Browning amp Johnson 1984)
Among economic contextual factors that could affect LGE income inequality has been
largely ignored An exception is a cross-country comparison conducted by Ortega (2017) who
analyses the role of inequality on government efficiency in developing countries He finds that
more unequal countries could have higher difficulties to achieve specific health outcomes Income
inequality has even been considered as part of the outputs to measure efficiency particularly for
the case of European and OECD countries (Afonso Schuknecht amp Tanzi 2010 Antonelli amp De
Bonis 2018)
At the local level income inequality has been mainly used as a proxy for the effect of income
heterogeneity Economic inequality could have a direct and an indirect effect on government
efficiency The direct effect poses that higher income inequality could reduce municipal efficiency
because it is associated with a more complex and competing set of public services demanded by
the population (Jottier et al 2012) The indirect effect puts the focus in the link between inequality
social capital and levels of corruption Economic diversity could reduce trust in people and
institutions when related to high and persistent levels of income inequality It could also affect the
willingness to participate in community and political groups the existence of a shared objective
by citizens and the perception of a prosperous future (Uslaner amp Brown 2005)
66
The evidence is ambiguous For instance Geys and Moesen (2009) find that income
inequality has little relation to efficiency of Flemish municipalities and Ashworth et al (2014)
find a negative relationship for the Norwegian case Findings also indicate that inequality is the
strongest determinant of trust and that trust has a greater effect on communal participation than on
political participation (Uslaner amp Brown 2005)
33 Methodology
We follow a two-stage approach widely used in this kind of analysis A DEA analysis is
conducted in the first stage to get efficiency scores for each municipality Then regression analysis
is conducted in the second stage aiming to identify contextual variables other than differences in
the management of inputs that can help to explain the heterogeneity in municipal performance
331 Chilean Municipalities and period of analysis
The territory of Chile is divided into regions and these into provinces which for purposes of
the local administration are divided into counties The local administration of each county resides
in a municipality which is administrated by a Mayor assisted by a Municipal Council16
Municipalities represent the decentralization of the central power in Chile They are autonomous
organizations with legal personality and own patrimony whose purpose is to satisfy the needs of
the local community and ensure their participation in the economic social and cultural progress of
the county Municipalities have a diversity of functions related to public health education and
social assistance among others
16 The Mayor and City Council are elected by suffrage for the citizens of the respective commune every 4 years
67
To achieve their goals two are the main sources of municipal incomes own permanent
revenues (OPR) and the Municipal Common Fund (MCF) OPR are incomes generated by the
county and they are an indicator of the self-financing capacity of each municipality OPR are not
subject to restrictions regarding their investment and they are mainly generated by territorial taxes
commercial patents and circulation permits17 The MCF is a fund that aims to redistribute
community income to ensure compliance with the purpose of the municipalities and their proper
functioning Sources to finance the MCF come from municipal revenues The distribution
mechanism of the fund is regulated by parameters such as whether municipalities generate OPR
per capita lower than the national average and the number of poor people in the commune in
relation to the number of poor people in the country
This study covers the period from 2006 to 2017 During this period Chile was divided into
15 regions 54 provinces and 346 counties18 Although the information on inputs and outputs is
available for the entire period information on contextual factors at the county level such as
household income is only available every two-three years In addition some counties are excluded
from household surveys due to their difficult access Hence we use a sample of 324 municipalities
to measure municipal efficiency for the whole period (3888 observations) However the analysis
of contextual factors is conducted for those years when household income information is available
2006 2009 2011 2013 2015 and 2017 (1944 observations)
17 The territorial tax is a tax on agricultural and non-agricultural real estate Specifically of this income only 40 is left to the municipality as its own financing while the remaining 60 is allocated to the MCF (in the case of the four largest communes- Santiago Providencia Las Condes and Vitacura-percentages are 35 and 65 respectively) Unlike the territorial tax commercial patents are regulated mainly by the municipality which chooses the rate to be charged subject to a range established by law and is responsible of their collection Finally regarding the circulation permits 375 is of municipal benefit while 645 is directed to the MCF 18 There are 346 counties managed by 345 municipalities where counties ldquoCabo de Hornosrdquo and ldquoAntaacuterticardquo are managed by the municipality of ldquoCabo de Hornosrdquo
68
332 Measuring municipal efficiency
Municipal efficiency is measured using Data Envelopment Analysis (Coelli Prasada Rao
OrsquoDonnell amp Battese 2005) This is a non-parametric approach that uses linear programming to
measure efficiency for a group of municipalities as decision making units (DMUrsquos) The main
advantage of using DEA and the reason why DEA is used for the case of Chilean municipalities
is its flexibility in handling multiple inputs and outputs without the need to specify a functional
form (Balaguer-Coll amp Prior 2009 Mikušovaacute 2015 Tigga amp Mishra 2015) Following Afonso
and Fernandes (2008) the relationship between inputs and outputs for each municipality could be
represented by the following equation
119884 119891 119883 119894 1 119899 (31)
In equation (31) 119884 is the set of outputs and 119883 the set of inputs for each of the n
municipalities Using linear programming the production frontier is constructed and a vector of
efficiency scores is obtained The frontier represents full technical efficiency mdash the point at which
the highest output occurs given specified inputs or the point at which the lowest amount of inputs
is used to produce a specified quantity of output Efficiency scores under DEA are relative
measures of efficiency They measure a municipalityrsquos efficiency against the other measured
municipalities in the sample and not a hypothetical lsquoperfect municipalityrsquo The further from the
frontier the less technically efficient a municipality is
We use an input-oriented approach because Chilean municipalities have a greater control
over the management of inputs relative to the outputs they have to manage Obtaining efficiency
scores requires an assumption about the returns to scale exhibited by each municipality When
DMUrsquos are homogeneous the CCR model (Charnes Cooper amp Rhodes 1978) which assumes
69
constant return-to-scale (CRS) is the appropriate specification The CCR model assumes full
proportionality between outputs and inputs and that DMUrsquos operate at their optimal When DMUrsquos
are highly heterogeneous as is the case with local governments in most countries it is not realistic
to assume complete proportionality between inputs and outputs nor that all DMUrsquos operate at their
optimum scale In this situation a variable returns-to-scale (VRS) or BCC model (Banker
Charnes amp Cooper 1984) is the preferred formulation
Assuming VRS imposes minimum restrictions on the efficient frontier and allows for
comparisons only among municipalities of similar scale (Coelli et al 2005 Wu Huang amp Pan
2014) This means that when we use the CCR model (assuming CRS) on a heterogeneous sample
of DMUrsquos the resulting measure of technical efficiency that we obtain is related not only to the
management of inputs but also to issues of scale19 To empirically check the validity of the VRS
assumption we measure technical efficiency under CRS VRS and non-increasing returns-to-scale
(NIRS) and we analyse the existence of scale inefficiencies This allows us to check the relevance
of scale effects as a potential explanation of differences in municipal efficiency Appendix J
shows the specification of the DEA model under VRS and how ldquototal technical efficiencyrdquo
(assuming CRS and therefore due to management and scale issues) could be disaggregated in ldquopure
technical efficiencyrdquo (under VRS and related only to management issues) and scale efficiency (due
to scale issues)
19 Assuming VRS can also lead to measurement problems such as overestimation of efficiency scores This is the case when an important proportion of DMUrsquos shows CRS so assuming VRS ignores the information about proportionality between inputs and outputs (Podinovski 2004)
70
333 Inputs and outputs used in DEA
Following the literature on local government expenditure efficiency (Afonso amp Fernandes
2008 de Sousa et al 2005 Dlouhyacute 2018 Tandon 2005 Tigga amp Mishra 2015) and trying to
reflect as well as possible the functioning of municipalities five inputs and four outputs were
selected Input and output data were obtained from the National System of Municipal Information
(SINIM in its Spanish acronym) and they are expressed in thousands of Chilean pesos of 201720
Inputs are Municipal Operational Expenditure X1 (including expenses on goods and
services social assistance investment and transfers to community organizations) Municipal
Personnel Expenditure X2 (including full time and part-time workers) Total Municipal
Expenditure in Education sector X3 Total Municipal Expenditure in Health sector X4 and the
Number of Municipal Buildings X5 (proxied by the number of public facilities in education and
health sectors)
Output variables were selected highlighting the relevance of education and health sectors
and trying to capture the wide range of local services provided by municipalities The variable
ldquoOwn Permanent Revenuesrdquo Y1 is used to capture the scale and diversity of municipal
activities21 The ldquoMonthly Average Enrolmentrdquo in municipal education establishments related to
the school-age population in each county Y2 is used as educational output As health output the
ldquoNumber of Medical Consultationsrdquo in public facilities Y3 is considered Finally the number of
community organizations Y4 is used as output reflecting the promotion of community
development by each municipality Table 31 shows the summary statistics of input and output
20 The data from SINIM database was mostly obtained using the sinimr package (Salas 2019) 21 According to SINIM database this variable aims to measure the management of the municipalityrsquos own resources with respect to the population of the commune
71
variables for the whole sample and period Inputs and outputs excepting the Monthly Average
Enrolment Y2 are measured in per capita terms using county population information from the
National Institute of Statistics (INE in its Spanish acronym)
Table 31
Descriptive statistics Inputs and Output variables used in DEA analysis
334 Regression model
Contextual factors could play an important role not only in explaining why some
municipalities operate inefficiently but also why municipal performance differs among them
These factors may affect municipal performance modifying incentives for local authorities to
operate efficiently and their capability to take advantage of economies of scale They also define
the conditions for cooperation or competition among municipalities and the citizensacute ability and
willingness to monitor local authorities (Afonso amp Fernandes 2008)
Information on income at the household level for each county was obtained from the
ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) CASEN is
conducted every two-three years being the reason why consecutive years are not considered in
72
our regression analysis The other contextual factors used as controls were obtained from different
sources including SINIM INE and the ldquoServicio de Impuestos Internosrdquo (SII)22
Our main hypothesis is whether higher levels of income inequality are associated with lower
levels of municipal efficiency To test our hypothesis the empirical model is defined as
120579 120573 119892119894119899119894 119885 120573 120575 120572 120598 (32)
Where 120579 is the vector of DEA efficiency scores 119892119894119899119894 is the Gini coefficient of each
county 120575 are year-specific effects 120572 are county-specific constants 120598 is a vector of error terms
and 119885 is a vector of controls Next we discuss the motivation for these controls
The level of purchasing power of countiesrsquo citizens is proxied by the variable log(income)
which is the natural log of the mean household income per capita in thousands of Chilean pesos of
2017 On the one hand poorer counties could display higher efficiency due to their necessity to
take care of their constraint resources (Pacheco et al 2013) On the other hand richer counties
could show higher efficiency because richer citizens exert higher monitoring over local authorities
and demand better quality public services in return for their tax payments (Afonso et al 2010)
The possibility for municipalities to take advantage of economies of scale and urbanization is
captured by three variables First the variable log(density) which correspond to the natural log of
population density Second the dummy variable reg_cap indicating whether a county is a regional
capital or not Third the variable agroland which correspond to the proportion of land for
agricultural use which is informed to the SII We expect a positive effect of log(density) but
negative for regcap and agroland
22 The SII is the institution in charge of collecting taxes in Chile
73
Socio-demographic characteristics are captured including a Dependence Index IDD IDD
corresponds to the number of people under 15 years or over 65 years per 100 people in the active
population (those people between 15 and 65 years old) A higher proportion of young and older
population could be associated with a higher demand for municipal services relating to education
and health making harder to offer public services efficiently The citizensrsquo capacity to monitor
local authorities is proxied including the variable education (average years of education for the
population older than 15 years) and the variable housing (proportion of households which are
owners of the property where they live in each county) In both cases we expect a positive
association with LGE
Among municipal characteristics the variable professional (percentage of municipal
personnel with a professional degree) is used to control for the quality of municipal services and
it is expected a positive impact The variable mcf (proportion of total municipal income coming
from the MCF) is included to capture the influence of financial dependence on the central
government A higher dependence from MCF could be associated with higher efficiency when it
is linked to more control from central government (Worthington amp Dollery 2000) However when
MCF discourages the generation of own resources and proper management of resources from the
fund a lower efficiency should be expected (Bravo 2014) In addition the dummy variable mayor
is included to capture differences among mayors supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo
political parties related to those ldquoINDEPENDENTrdquo mayors
Table 32 report summary statistics for the set of numeric contextual factors and Appendix
K the corresponding correlation matrix Despite the high correlation between income and
education variables we include both in the regression section as they capture different county
characteristics
74
Table 32
Summary Statistics Numeric Contextual Factors
Figure 31 Geographical distribution of Chilean regions and macrozones
Previous evidence on growth and convergence of Chilean regions have found that regions
tend to cluster spatially (Aroca amp Bosch 2000) Aiming to capture this regional clustering process
75
and considering the high concentration in the number of municipalities and population in the
central area we group municipalities in four ldquozonesrdquo We define as reference the ldquoCentre-Northrdquo
zone consisting of regions XIII (where the countryrsquos capital is located) and its two neighbouring
regions V and VI The ldquoNorthrdquo zone includes regions XV I II III and IV The ldquoCentre-Southrdquo
zone includes regions VII VIII and IX Finally the ldquoSouthrdquo zone embraces regions XIV X XI
and XII Figure 31 displays the regional administrative division and zones considered in this
essay
Efficiency scores (ES) are limited to have values between 0 and 1 However they are relative
measures (relative to the sample of municipalities) This implies that when a municipality is on the
frontier (ES = 1) it does not mean that potentially larger technical efficiency gains cannot be made
Hence equation 32 is estimated using OLS and censored regressions We start running cross-
sectional regressions for each of the six years Then we compare the results with those from panel
regressions Because fixed-effects panel Tobit models could be affected by the incidental
parameters problem (Henningsen 2010 2019) we use random-effects panel Tobit models
including indicator variables for years and zones Finally to deal with the potential endogeneity
problem we also use an instrumental variable approach The instrument is described next
335 The instrument
Government effectiveness and income distribution are both structural components of
economies (Ortega et al 2017 Ravallion 2005) In the search for a causal interpretation for the
influence of income inequality on municipal efficiency we need an instrument which must be
correlated with the variable to be instrumented (in our case income inequality) and uncorrelated
with the error term in the efficiency equation (32) Previous literature has used as instruments for
Gini the number of townships governments in a previous period the percentage of revenues from
76
intergovernmental transfers in a previous period and the current share of the labour force in the
manufacturing sector (Alesina amp La Ferrara 2002) Using the share of the labour force in a specific
sector is unlikely to reduce the problem of endogeneity particularly in countries where local
governments supply mostly ldquoservices to peoplerdquo and the main source of income inequality is
labour income
We propose as an instrument the proportion of firms in the primary sector (mining fishing
forestry and agriculture)
119901119904119904_119891119894119903119898119904Number of firms in the primary sector
Total number of firms (33)
On the one hand this instrument is likely to be correlated with local income inequality in
natural resource-rich countries23 On the other hand we contend that our instrument is less likely
to be correlated with the error term in the efficiency equation First the main services supplied by
Chilean municipalities are services to people (health and education) not to firms Second most of
the revenues collected by municipalities included those associated with natural resources end up
in the municipal common fund whose objective is precisely to reduce inequalities among
municipalities Third services to firms are expected to be more significant with the tertiary sector
We argue that our instrument captures natural and structural conditions which directly
influence income inequality but it does not directly affect LGE Figure 32 shows the evolution
of the annual average efficiency score and the proportion of firms in the primary secondary
(manufacturing) and tertiary (services) sectors We observe that sectors shares have remained
relatively stable with a slight reduction in the participation of the primary sector in favour of the
23 Results confirm a significant association between income inequality and the degree of dependence on natural resources at the county level
77
tertiary sector by the end of the period This is contrary to the evolution of municipal efficiency
which shows a cyclical behaviour as will be shown in the next section
Figure 32 Evolution of efficiency scores and the proportion of firms by sector
34 Results and discussion
341 DEA results
Figure 33 displays the evolution of our three measures of efficiency Overall technical
efficiency pure technical efficiency and scale efficiency are around 78 83 and 95
respectively with fluctuations over the years Therefore around three quarters of the overall
78
inefficiency is attributed to inefficiency in the management of inputs and around one quarter to
scale inefficiencies24
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE)
Returnstoscale
Figure 34 reports by zone and for the whole period the proportion of municipalities
showing CRS decreasing returns to scale (DRS) or increasing returns to scale (IRS) Most of the
municipalities operate under variable (increasing or decreasing) returns to scale which could be
explained by the high heterogeneity in size among municipalities A summary of RTS
disaggregated by year and zone is in Appendix L Policies oriented to increase efficiency usually
24 The average scale efficiency score of 95 means that municipalities could get an additional 5 of inputs saving if municipalities were able to change their size to achieve their optimal scale
79
consider amalgamation de-amalgamation or ways of cooperation among municipalities To have
a better idea about where and how feasible is the implementation of such policies Appendix M
shows maps with the administrative division of the country in its 345 municipalities and which
municipalities show CRS IRS or DRS in each of the six years of data
Figure 34 Returns to scale by zone
Based on results for the whole period (Figure 34) the North has the highest proportion of
municipalities exhibiting DRS This suggests the need to reduce the size of municipalities splitting
those ldquotoo bigrdquo creating new administrative areas or giving more autonomy to current
municipalities25 The opposite occurs in the Centre-North area where municipalities mostly
exhibit IRS This indicates the need to merge municipalities An alternative strategy to the
amalgamation process is increasing inter-municipal cooperation (Balaguer-Coll et al 2019)
25 This has been the policy followed in Chile Although two new regions were created (XV in the North and XIV in the South) the number of municipalities has not changed
80
which seems to be a more plausible option in Chile Finally evidence on scale efficiency for the
Centre-South and South areas is not clear in terms of the adequate strategy to improve efficiency
Efficiencymeasure
Although most municipalities show scale inefficiencies (Figure 34) only a small proportion
of total inefficiency is associated to scale issues (Figure 33) Together this results justify not only
the use of the BCC model (efficiency scores under VRS) to capture municipal heterogeneity but
also highlights the need to look for other factors outside the control of local authorities which
could be influencing municipal performance
Table 33
Summary efficiency scores (VRS) by zone and region
Table 33 summarizes ES under VRS using data for the six years of CASEN survey A mean
efficiency score of 83 is found for the full sample and period This means that on average
inefficient municipalities can reduce the use of inputs by 17 to get the same current output By
81
comparing average ES per zone it can be concluded that municipalities in the North Centre-North
Centre-South and South could achieve the same level of output with 17 12 18 and 23 fewer
resources respectively Results also show that one third of the municipalities present an efficiency
score equal to one
Figure 35 shows the evolution of the mean ES for the four zones over the 12 years period
A clear drop in the efficiency level is observed in 2009 (the financial crisis year) excepting for the
North area Likewise although mean efficiency returned to its pre-crisis levels in 2014-2015 a
new drop in efficiency was experienced in 2016 It also seems that the 2010 earthquake did not
generate a significant effect on municipal efficiency Figure 35 also shows that although levels
of efficiency seem to differ among zones they follow a similar trend through time with the only
exception of the North which corresponds to the mining area In addition efficiency seems to be
significantly higher in the Centre-North area This is explained by the high mean level of efficiency
in region XIII which includes the countryrsquos capital city
Figure 35 Evolution mean efficiency scores (VRS) by zone
82
To know which and where are the efficient municipalities and if they are surrounded by
municipalities with a similar level of efficiency Appendix N has maps showing the ldquoefficiency
statusrdquo of each municipality that is whether they are efficient (ES = 1) or inefficient (ES lt 1)
Additionally Appendix O shows ldquoequal intervalsrdquo maps describing the spatial distribution of ES
among municipalities for each of the six years26 Results show that efficient municipalities can be
found all through the country the ldquoefficiency statusrdquo could change from one year to another and
municipalities with similar level-status of efficiency tend to cluster in space
342 Regression results
Exploratoryspatialanalysis
DEA efficiency scores and their geographical representations seem to show that municipal
efficiency presents a spatial clustering pattern This means that municipal performance could be
influenced not only by contextual factors of the county where municipality belongs but also by the
level of efficiency of neighbouring municipalities and their characteristics To test the significance
of the spatial dimension in municipal efficiency we use a cross-section of data considering the six-
year average of efficiency scores the Gini coefficient and the set of controls
We use the Moranrsquos I indicator to test for spatial autocorrelation Moranrsquos I is a measure of
the correlation between the level of efficiency in one municipality and its ldquospatial lagrdquo that is the
average level of efficiency in neighbouring municipalities We define as the relevant neighbours
for each municipality the 5-nearest municipalities This is obtained using the distances among the
26 An equal intervals map uses the same principle as a histogram to organize the observations into categories that divide the range of the variable into equal interval bins For the equal interval classification the value range between the lower and upper bound in each bin is constant across bins but the number of observations in each bin is typically not equal
83
polygonsrsquo centroids (latitude and longitude) of each county Results confirm that municipal
efficiency show a significant level of positive spatial autocorrelation This means that
municipalities tend to have neighbouring municipalities with similar performance
The positive spatial autocorrelation shown by municipal efficiency could be due to the
performance in one municipality is influenced by the performance in neighbouring municipalities
(spatial dependence in the variable itself) or due to structural differences among regions-zones
(spatial heterogeneity) To check the source of the spatial autocorrelation we run an OLS
regression of ES against income inequality and controls and then we test OLS residuals for spatial
autocorrelation Moranrsquos I over OLS residuals although significant is barely higher than zero (see
Appendix P) This means that the spatial effect itself is not a serious issue and can be handle for
instance including zone indicators variables hence we proceed to analyse the influence of income
inequality on LGE using non-spatial regression27
Cross‐sectionalanalysis
We start reporting censored regressions for each year in our panel Efficiency scores have
been rescaled to numbers between 0 and 100 to facilitate the interpretation of the results All
regressions include dummy variables for three of the four zones in which we have grouped Chilean
regions Results are in Table 3428 Income inequality shows a negative sign in all years which is
consistent with our hypothesis that inequality is negatively related to municipal efficiency
However only in three of the six years the effect of income inequality appears as statistically
27 In the case of having incorporated the spatial dimension the two most commonly used forms are including in the model the spatial lag of the dependent variable (spatial autoregressive model SAR) or the spatial lag of the error (spatial error model SEM) Following the method suggested by Anselin et al (1996) Lagrange Multiplier (LM) tests on the OLS residuals showed that the SAR model is preferred over the SEM Moranrsquos I LM tests and spatial regressions can be found in Appendix P 28 Regression results using OLS are in Appendix Q
84
significant Only the income level displays a significant and positive influence on efficiency for
the whole period A higher population density also consistently favours municipal efficiency On
the other hand as we expected a higher IDD makes it more difficult to achieve an efficient
performance29 Consistent with results in Pacheco et al (Pacheco et al 2013) municipal
efficiency show a significant an positive association with the MCF only in the first half of our
period of analysis with the second half showing an insignificant relationship
Table 34
Cross-sectional (censored) regressions
Paneldataanalysis
Estimation results for the six-year panel are reported in Table 35 Columns (1) and (2) show
the results for the pooled and random effects censored models only controlling for zone and year
29 The set of cross-sectional regressions was also run for each zone independently Results confirm that the set of significant contextual factors differs not only among years but also among geographic areas This support previous evidence suggesting that cross-sectional analysis should be treated with caution (Narboacuten-Perpintildeaacute amp De Witte 2018b)
85
dummies Income inequality appears as non-significant Zone indicator variables confirm that
municipalities located in the Centre-South and South of the country display a lower average level
of efficiency compared to the Centre-North area Time dummies mostly show negative
coefficients This is a signal that shocks such as the GFC and the earthquake in 2010 may have
had a negative impact on efficiency but that impact was not permanent The results for the pooled
and RE models including the full set of controls are reported in columns (3) and (4) These results
show a significant negative influence of income inequality on LGE
When income inequality is instrumented by the variable pss_firms most of the coefficients
remain unchanged except for those associated with the income variables gini and log(income)
This result implies that our original model suffers for instance from the omitted variable bias
This means that LGE and income inequality are determined simultaneously by some variable not
included in our model Columns (5) and (6) show results using our instrument for income
inequality The ldquotrue coefficientrdquo of variable gini remains negative but the magnitude of the
relationship is higher The negative coefficient for gini implies on the one hand that municipalities
located in more unequal counties face more challenges to achieve an efficient management of
public resources On the other hand the coefficient in column (6) is close to one The interpretation
is that for each point of reduction in income inequality ceteris paribus LGE should increase in the
same proportion Next we discuss some of the results associated with the controls variables
Contrary to Pacheco et al (2013) income level has a positive coefficient meaning that richer
counties in terms of income per capita show higher efficiency This could be explained by higher
monitoring and increasing demands from citizen and taxpayers In addition it is expected a higher
efficiency in municipalities located in counties with a higher population density and those with a
lower proportion of land for agricultural use This result is mainly explained by municipalities
86
located in the Centre area The opposite happens with municipalities in the South implying that
they are too ldquosmallrdquo to take advantage of agglomeration economies There is also a limit for
agglomeration and scale economies which is shown by the negative coefficient of the variable
regcap although this coefficient loses its significance in the IV approaches30
Unexpectedly efficiency was found to be negatively associated with the variable education
This result is similar to the case of Czech municipalities (Šťastnaacute amp Gregor 2014) where
explanations include a weakened monitoring effect due to the fact that more educated citizens
present greater mobility and labour cost disadvantages for municipalities with better educated
labour force In Chile an additional explanation could be the relationship between education and
voter turnout Since 2012 voting is no longer mandatory in Chile This fact considerably reduced
voter turnout which in turn may have influenced the monitoring and control effect of more
educated voters For the case of variable IDD results show that local authorities in counties with
higher proportion of aging and young population (related to those in the active population) face a
greater challenge in their quest to offer public services efficiently
The influence of mcf is like that found by Pacheco et al (2013) with municipalities more
dependent on central transfers showing more efficiency31 Political influence captured by the
variable mayor did not show a significant effect This result is like other studies concluding that
the ideological position did not have a significant influence on efficiency (Benito et al 2010
Boetti et al 2009 Cordero Pedraja-Chaparro Pisaflores amp Polo 2017)
30 This negative coefficient suggests that the negative effects of agglomeration economies such as overcrowding pollution high cost of land and traffic congestion could predominate over the positive effects of scale economies in regional capitals 31 When the analysis is conducted for each zone independently MCF displays a negative influence in the North and Centre-North areas but a positive influence in the Centre-South and South areas These results confirm that on the one hand richer municipalities (those in the North and Centre-North) have less incentives for efficient management of their resources On the other hand results support the relevance of MCF helping poorer municipalities (those in the South) to reach their outcomes
87
Table 35
Panel data regressions
88
35 Conclusions
The trade-off between equity and efficiency is in the core of the economic discussion This
ldquoprinciplerdquo has been used by policymakers to prioritize the design of policies focused on economic
growth delaying those policies aimed at reducing economic inequalities This essay offers
empirical evidence of a negative relationship between inequality and efficiency that is a reduction
of income inequality could have positive effects on economic efficiency at least at the level of
local governments
We followed a traditional Two-Stage approach commonly used in the analysis of LGE We
compared cross-sectional and panel data results and we have added an instrumental variable
approach to give a causal interpretation to the link between efficiency and inequality We proposed
the use of a measure of natural resource dependence to instrumentalize the impact of income
inequality on LGE Given that our units of analysis are municipalities and not counties we argue
that our measure of NRD is correlated with income inequality and it does not have a direct
influence on LGE
We found that Chilean municipalities perform better than previous studies suggest
Municipal efficiency depicted significant levels of positive spatial autocorrelation and most of the
municipalities showed to be operating under increasing or decreasing returns to scale This would
imply that the policies generally used to improve efficiency such as amalgamation or cooperation
should be implemented observing the reality of each region and not as strategies at the national
level We also found that scale inefficiency explains a small proportion of the average total
inefficiency reason why the analysis of external factors that could affect the municipal efficiency
takes greater relevance
89
Income inequality plays an important part in explaining municipal efficiency In fact it was
found that reductions in income inequality could result in increases in municipal efficiency in a
similar proportion An unexpected finding was that the levels of education shows a negative
association with municipal performance This could be due to a low average level of education or
the existence of an omitted variable This variable could be the significant reduction in voting
turnout rates for local and national elections due to changes in the voting system during the period
of our analysis All in all our results may help to shed light on the potential consequences of
changes in contextual factors and the design of strategies aimed to increase municipal efficiency
in countries with similar characteristics to the Chilean economy For instance policies oriented to
take advantage of economies of scale can be formulated merging municipalities or establishing
networks in specific sectors such as education or health
Further work needs to be done both in measurement and in the explanation of differences in
municipal performance in Chile One area of future work will be to identify the factors that better
predict why municipalities operates under increasing decreasing or constant returns to scale
Multinomial logistic regression and the application of machine learning algorithms to SINIM data
sets appear as suitable methods for that purpose Intertemporal DEA (Drew amp Dollery 2015)
should be used to measure municipal efficiency capturing changes in total factor productivity In
addition municipalities operate under different levels of geographical authorities such as the
provincial mayor and the regional governor Hence it would be useful to know how each
municipality performs within each region-zone related to how performs to the whole country This
should be done conducting a metafrontier analysis (OrsquoDonnell Rao amp Battese 2008)
We have also identified through a cross sectional spatial exploratory analysis that on
average municipalities with similar levels of efficiency tend to cluster in space Regarding to
90
analyse the importance of contextual factors on municipal efficiency a deeper analysis should use
censored spatial models to check the significance of the spatial dimension in cross-sectional and
panel contexts Another interesting avenue for future research is associated with the negative
association found between LGE and education The significant reduction in votersacute turnout since
the law of automatic registration and voluntary voting in 2011 appears as a natural experiment to
analyse its effects on efficiency indicators such as municipal performance Incorporating variables
such as the voting turnout in each county or classifying municipalities based on individual
institutional political and economic characteristics could help to shed light on which of these
channels is the most relevant when analysing the impact of inequality on municipal efficiency
Finally we argued that an important part of the influence of income inequality over LGE
could be through its indirect effect on trust social capital and social cohesion The final essay will
delve deep in that relationship
91
Chapter 4 Social Cohesion Incivilities and Diversity
Evidence at the municipal level in Chile
41 Introduction
A deterioration in social cohesion could carry significant costs such as a reduction in
generalized trust between individuals and in institutions a society caught in a vicious circle of
inequality and citizens increasingly distanced from civic life (Uslaner 2011) A growing feeling
of frustration and discontentment can eventually translate into a social outbreak with uncertain
results This is precisely what have been happening in many countries around the world included
Chile
ldquoSocial cohesion is a state of affairs concerning both the vertical and the horizontal
interactions among members of society as characterized by a set of attitudes and norms that
includes trust a sense of belonging and the willingness to participate and help as well as their
behavioural manifestationsrdquo (Chan et al 2006) This definition highlights the multidimensionality
in the concept of social cohesion which has been measured using objective andor subjective
indicators of trust social norms solidarity willingness to participate in social and political groups
and feelings of belonging (Ariely 2014 Chan et al 2006) Multidimensionality also implies that
the impact of determinants of social cohesion such as economic and racial diversity could be
different for each of its various dimensions (Ariely 2014)
A common characteristic to all societies is that they are made up of different groups that
differ with respect to race ethnicity income religion language local identity etc The
92
Community Heterogeneity Thesis (Coffeacute amp Geys 2006) argues that individuals prefer to interact
with others that are like themselves Hence high levels of diversity particularly economic and
racial represent a complex scenario to maintain social cohesion One of the most common factors
adduced for social cohesion is income inequality with higher levels linked to lower levels of trust
(Ariely 2014 Rothstein amp Uslaner 2005)
Traditional measures of social cohesion may not be adequately capturing the deterioration
in social connections For instance measures of (lack of) trust include a strong subjective element
On the other hand proxies for social participation such as volunteering jobs or joining to social
organizations have not been supported by empirical evidence as a source of generalized social trust
(Rothstein amp Uslaner 2005) We proposed to use the rate of incivilities which we argue is a more
appropriate measure of the degree of worsening in the social context
Incivilities are those visible disorders in the public space that violate respectful social norms
and tend not to be treated as crimes by the criminal justice system There are two types of
incivilities social and physical Social incivilities include antisocial behaviours such as public
drinking noisy neighbours and fighting in public places Physical incivilities include among
others vandalism graffiti abandoned cars and garbage on the streets Because citizens and
political authorities cannot always distinguish between incivilities and crime they are usually
treated as an additional category of crime This implies that policies aimed to reduce incivilities
are generally based on punitive actions However theory and evidence on incivilities suggest that
factors explaining incivilities and crime could be different (Lewis 2017 Taylor 1999)
In Chile crime rates have shown a sustained downward trend after reaching its highest level
in 2011 On the other hand incivilities rate has shown a sustained upward trend which coincides
with the increasing victimization and feeling of insecurity in the population This has motivated
93
Chilean authorities based on the ldquoBroken Windows Theoryrdquo to propose new punitive actions (or
increase the severity of the current ones) to those who commit this type of antisocial behaviours
The ldquoBroken Windows Theoryrdquo states that higher rates of incivilities are a signal of social
disorganization which result in higher crime rates (J Q Wilson amp Kelling 1982) This is expected
to have consequences on familiesrsquo decisions such as moving away from public spaces or even
leaving their neighbourhoods
As far as we know there is no previous evidence about the potential causes of incivilities in
Chile Efforts to identify the factors explaining incivilities could help not only to reduce the risk
factors favouring violent and property crimes but also to guide interventions aimed to change
social behaviours and strengthen social cohesion in highly unequal societies Thus the main
contribution of the present study is to provide a deeper comprehension of the problem of incivilities
and how they can help to better understand the weakening of social cohesion that many
contemporary societies experience
We aim to offer the first evidence on the factors explaining the evolution and the differences
in incivilities rates in Chile We set up a panel for six years (2006 2009 2011 2013 2015 and
2017) and 324 counties (1944 observations) We start exploring the evolution and geographical
distribution of incivilities Then we investigate whether economic and racial diversity after
controlling for other socioeconomic demographic and municipal characteristics can be regarded
as key predictors of incivilities
We use the Gini coefficient to proxy economic heterogeneity and the number of new visas
granted to foreigners as proportion of the county population as proxy for racial diversity The main
hypothesis is whether economic and racial diversity have a positive association with the rate of
incivilities In addition to the arguments regarding the ldquoCommunity Heterogeneity Thesisrdquo Taylor
94
(1999) and Skogan (1986 1999 2015) argue that incivilities are caused by inequality and the lack
of informal mechanisms of social control Based on the ldquoIncivilities Thesisrdquo the patterns of
incivilities should mirror the patterns of inequality (Taylor 1999) Then higher inequality should
be associated with higher physical and social vulnerability of the population This reduces social
control and increases social disorganization which triggers antisocial or negligent behaviours
Our main result reveals a strong positive association between the rate of incivilities and the
number of new visas granted per year The relationship with income inequality although also
positive seems to be less significant These findings give strong support to the ldquoCommunity
Heterogeneity Thesisrdquo and to a lesser extent to the ldquoIncivilities Thesisrdquo When the analysis is
disaggregated geographically racial diversity shows a clear positive effect The impact of income
inequality seems to be conditional depending on the level of income showing no effect in poorer
regions Results also show that the impact of economic and racial diversity differs by type of
incivility For example income inequality shows a strong association with ldquoStreet Tradingrdquo while
racial diversity with ldquoAlcohol Consumptionrdquo Two are the main policy implications On the one
hand a national strategy to address the problems associated with foreign immigration could help
to reduce incivilities For instance a joint effort between national and local authorities to curb
immigration and its distribution throughout the country On the other hand our results show that
the relationship between incivilities and economic diversity differs depending on the region or
geographical area Hence the impact on social cohesion of policies aimed to tackle economic
inequalities should be analysed in each specific context
The rate of incivilities also shows a negative association with the level of municipal financial
autonomy This implies that municipalities can effectively carry out policies to reduce incivilities
beyond the efforts of the central government Another important finding is that our results do not
95
support the hypothesis that a higher proportion of the young population is associated with higher
rates of incivilities Hence policies aimed to reduce incivilities should be focused on the causes of
incivilities rather than the criminalization of behaviours or stigmatization of specific population
groups
The structure of the chapter is as follows Section 42 outlines the relevant literature on social
cohesion and incivilities Section 43 describes the data variables and methodology and
establishes the hypotheses of the study Section 44 contains the results and discussions Section
45 presents the main conclusions
42 Related Literature
421 The Community Heterogeneity Thesis
The idea under ldquoThe Community Heterogeneity Thesisrdquo is that if individuals prefer to
interact with others who are similar to themselves in terms of income race or ethnicity high levels
of income inequality and racial diversity facilitate a context for lower tolerance and antisocial
behaviours lowering the ldquostaterdquo of social cohesion (Alesina 2000 Coffeacute amp Geys 2006 Letki
2008) Alessina and Ferrara (2002) give support to this hypothesis arguing that individuals have a
natural aversion to heterogeneity However the most popular explanation is the principle of
homophily people prefer to interact with others who share the same ethnic heritage have the same
social status and hence share experiences and tastes (Letki 2008 McPherson Smith-Lovin amp
Cook 2001 Tolsma et al 2009) For instance Delhey and Newton (2005) find for a sample of
60 countries that income inequality and ethnicity are strongly and negatively correlated with trust
Tolsma et al (2009) using data for Dutch neighbourhoods and municipalities find that social
cohesion is negatively and consistently affected by economic deprivation but not by ethnic
96
heterogeneity These authors also conclude that the effect of neighbourhood and municipal
characteristics on social cohesion depends on residentsrsquo income and educational level
Rothstein and Uslaner (2005) give two theoretical reasons why economic and racial diversity
should be causally related to social trust a key element of social cohesion First optimism about
the future makes less sense when there is more economic inequality which generally translates into
inequality of opportunities especially in areas such as education and the labour market Second
the distribution of resources and opportunities plays a key role in establishing the belief that people
share a common destiny and have similar fundamental values In highly unequal societies people
are likely to stick with their own kind Perceptions of injustice will reinforce negative stereotypes
of other groups making social trust and accommodation more difficult
Uslaner (2002 2011) and Uslaner and Brown (2005) find that high levels of inequality are
the single major factor driving down trust in people who are different from yourself Evidence for
USA finds that inequality is the strongest determinant of generalized trust over time (Rothstein amp
Uslaner 2005) Reducing inequality and then increasing generalized trust should have positive
consequences at the individual and aggregates levels At the individual level it may lead to greater
tolerance and more acts of altruism for people of different backgrounds At the aggregate level it
may lead to greater economic growth more redistribution from the rich to the poor and less
corruption (Uslaner 2002 2013) Letki (2008) argues that when neighbourhood socio-economic
context (apart from just an individualrsquos socioeconomic status) is considered it turns out to be the
main factor triggering negative attitudes and lack of trust in out-group members
The increasing diversity caused by immigration can also reduce the conditions necessary for
social cohesion (Ariely 2014 Holtug amp Mason 2010) Christel Kesler and Irene Bloemraad
(2010) find for nineteen advanced democracies between 1981 and 2000 that increasing migration
97
generally decreases trust civic engagement and political participation The authors also find that
in more equal countries with clear policies in favour of cultural minorities the negative effects of
migration are mitigated or even reversed Letki (2008) states that deprivation and disorder tend to
be strongly correlated with racial diversity Because we propose the use of the number of disorders
or antisocial behaviours known as ldquoincivilitiesrdquo as our measure of social cohesion we describe the
literature on incivilities in the next section
422 The literature on incivilities
The study of incivilities has been a continuing concern mainly for developed countries since
the 1980s The focus has changed from individual and psychological explanations to ecological
(contextual) and social explanations (Taylor 1999) The individual approach basically considered
perceptions of incivilities as an explanatory variable of fear of crime The ecological explanation
argues that indicators of economic disadvantage (eg income levels income inequality
unemployment rate and poverty rate) are the keys to understand a process of social disorganization
and lack of informal control These economic factors lead to higher rates of inappropriate or
negligent behaviours and ultimately to higher crime rates (Blau amp Blau 1982 Messner Rosenfeld
amp Baumer 2004 Phan Orsquobrien Mendolia amp Paloyo 2017 Sampson 1986)
The negative impact of incivilities is not merely reflected in its association with crime rates
(Skogan 2015) Physical and social incivilities could worsen neighbourhoods by affecting quality
of life perception of the environment and public and private behaviours Previous research has
indicated that a higher level of incivilities is associated with health problems (Branas et al 2011
Cohen et al 2000 Hill amp Angel 2005 Ross 2011 Ross amp Mirowsky 2001) greater
victimization and fear of crime (Brunton-Smith Jackson amp Sutherland 2014 Mijanovich amp
Weitzman 2003) and multiple negative economic effects For instance incivilities could be
98
related to a reduction in commercial activity lower investment in real estate reduction in house
prices (Skogan 2015) and population instability (Hipp 2010)
To describe the state of the art in the study of incivilities and their consequences Skogan
(2015) used the concept of untidiness to characterize the research on incivilities The study of
incivilities has had multiple approaches (economic ecological and psychological) Incivilities
have also been measured using multiple sources of information (police reports surveys trained
observation) which result in different measures (perceptions vs count data) However the question
about what specific factors have the strongest effect on incivilities has been overlooked and
perceptions about incivilities have been used mainly as a predictor of crime fear of crime and
victimization
There are two types of incivilities social and physical Social incivilities are a matter of
behaviour including groups of rowdy teens public drunkenness people fighting and street hassles
Physical incivilities involve visual signs of negligence and decay such as abandoned buildings
broken streetlights trash-filled lots and graffiti (Skogan 1999 2015 Taylor 1999) Three reasons
justify the distinction between physical and social incivilities First like multiple dimensions of
social cohesion different structural and social conditions could be responsible for different types
and categories of incivilities Second punitive sanctions are expected to have a greater impact on
physical than on social incivilities since the latter are more related to behaviours rooted in citizensrsquo
culture Third physical incivilities should be more related to absolute measures of economic
disadvantage (eg poverty or unemployment rates) and social incivilities to relative indicators of
economic disadvantage (eg such as income inequality) This line of research is based on the
ldquoincivilities thesisrdquo which states that to understand the distribution of disorders it is necesary to
analyse the patterns of structured inequalities (Skogan 1986 Taylor 1999)
99
423 The ldquoIncivilities Thesisrdquo
Incivilities theories began with a focus on psychological dynamics (Garofalo 1978) moved
forward to an interest in social psychological processes (J Q Wilson amp Kelling 1982) and finally
evolved into a focus on community dynamics and outcomes (Skogan 1999) Individual and group
behaviours in tandem with ecological features have been proposed as the key factors explaining
incivilities and their posterior influence on social control quality of life and more serious crime
(J Q Wilson amp Kelling 1982)
In terms of ecological factors particularly those related to economic conditions Skogan
(1986) was the first linking the distribution of incivilities to the patterns of structural inequality If
incivilities mirror inequality structure this will have consequences in residentsrsquo health and safety
due to its levels of vulnerability In addition structured inequality associated with the proportion
of the manufacturing sector (eg when fabrics tend to move from cities to farther areas) will be
related to higher social disorganization and differences between urban and rural areas (W J
Wilson 1996) In addition a persistent feeling of relative deprivation (persistently high levels of
income inequality) could lead to fellow inhabitants of the community to commit antisocial
behaviours showing their frustration with the current economic model
The literature on incivilities posits that their causes are different from those of crime (Lewis
2017) Unlike crime analysis especially property crimes information on the location where the
incivility takes place is the same as the location where the perpetrator resides To achieve a
comprehensive understanding of the different types of incivilities it is crucial to consider
incivilities data covering an entire territory and not just specific areas (Hooghe Vanhoutte
Hardyns amp Bircan 2010) If we add to this the availability of panel data it could be possible not
100
only to identify the main determinants of incivilities but also the causal mechanism from income
inequality towards incivilities rate
In Chile citizen security crime and delinquency are among the most significant issues for
citizens based on opinion polls Existing research has found weak evidence of a significant
relationship between crime and indicators of socio-economic disadvantage such as income
inequality and unemployment rate with significant effects only on property crime (Beyer amp
Vergara 2006 Nuntildeez Rivera Villavicencio amp Molina 2003 Rivera Gutieacuterrez amp Nuacutentildeez 2009)
Crime deterrence variables such as the probability of being caught or the number of police
resources have also shown ambiguous results (Beyer amp Vergara 2006 Rivera et al 2009
Vergara 2012) Evidence at the county level shows that crime is higher in urban counties those
with a lower mean income per capita and counties located in the North of the country In addition
at least half of the crimes reported in one county are perpetrated by criminals from other counties
(Rivera et al 2009) No studies could be found about the determinants of incivilities
4 3 Methodology
431 Period of analysis and data sample
Chile is a relatively small country in Latin America with a population of 18346018
inhabitants in 2017 The country is divided into 345 municipalities with on average 53104
inhabitants (median value 18705) Municipalities are the organ of the State Administration
responsible to solve local needs Municipalities are not only the relevant political and
administrative local unit of analysis but also they represent the feeling of lsquocommunityrsquo among
the inhabitants of each municipality (Hooghe et al 2010) Our data includes many sources of
101
heterogeneity among municipalities such as indicators of economic deprivation population
density demographic characteristics and whether the county is a regional or provincial capital
We use a sample of 324 municipalities covering most of the Chilean territory for the period
2006ndash17 Data on incivilities is obtained from the ldquoCentre of Studies and Analysis of Crimerdquo
which is part of the ldquoSubsecretaria de Prevencion del Delitordquo (SPD in its Spanish acronym) of the
Chilean government32 Information on income inequality and control variables is obtained from
the ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) the
ldquoNational Institute of Statisticsrdquo (INE in its Spanish acronym) the ldquoNational Municipal
Information Systemrdquo (SINIM in its Spanish acronym) and the Immigration Department of the
Government of Chile Our panel only includes the years for which CASEN survey is available
2006 2009 2011 2013 2015 and 2017
432 Operationalisation of the response variable and exploratory analysis
Official Chilean records contain information for the total number of cases of incivilities per
year at the county level The number of cases is the sum of complains and detentions reported at
the police Our dependent variable 119894119899119888119894119907119894119897119894119905119894119890119904 correspond to the number of cases per year Due
to population differences comparisons between counties are made using the incivilities rate per
1000 population calculated as
119894119899119888119894119907_119903119886119905119890 lowast 1000 (41)
where 119894119899119888119894119907_119903119886119905119890 is the incivilities rate 119905 is the year 119894 the county and 119899 is the population of the
county per year
32 httpceadspdgovclestadisticas-delictuales
102
Figure 41 illustrates at the top the evolution of the total number (cases reported) of
incivilities and crimes at the country level for the period 2006-1733 At the bottom Figure 41
shows the evolution of the mean county rate per 1000 inhabitants We observe that both the number
of incivilities and the number of crimes has reached similar annual figures however average
county rates per 1000 population show different trends Crime rate displays a sustained fall after
reaching its pick in 2011 Incivilities average county rate which also reached a pick and posterior
drop in 2011 has recovered its upward trend since 2016 considerably exceeding the crime rate
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017
33 Crime refers to ldquocrimes of greater social connotationrdquo (DMCS in its Spanish acronym) which includes violent and property crimes
103
Chilean records classify incivilities in nine categories most of them associated with social
incivilities Summary statistics for the total and for each of the nine categories are presented in
Table 41 In addition Figure 42 shows the evolution of incivilities by category for the whole
period We see that the global trend in incivilities is mainly due to a substantial increase in ldquoStreet
Tradingrdquo and ldquoPublic Alcohol Consumptionrdquo A common element of Figures 41 and 42 is the
significant change in trend experienced by crimes and incivilities in 2011 That year the SPD
became dependent on the Ministry of Interior of the Chilean Government This event put the issue
of crime and delinquency within national priorities for the central government
Table 41
Summary statistics total count of incivilities and by category (full sample and period)
Unlike crime rates we do not expect significant cross-county spillover effects in incivilities
However the questions of where incivilities are concentrated and why they are there can be of
great interest (Skogan 2015) Figure 43 shows quantile maps for the rate of incivilities per 1000
inhabitants for the initial and final years in our panel
104
Figure 42 Evolution total number of incivilities by category
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017)
105
We observe that the range of values has increased significantly from 2006 to 2017 but the
spatial distribution remains almost unchanged On the one hand high incivilities rates in the North
could be associated with the mining activity On the other hand high rates in the Centre area
(where the countyrsquos capital is located) could be related to the higher population density and the
concentration of the economic activity34
To see how the different types of incivilities are distributed throughout the country we have
grouped those similar categories in four groups ldquoPublic Damagerdquo (ldquoPublic Disturbsrdquo ldquoPublic
Damagerdquo ldquoOtherrdquo) ldquoThreatsrdquo (ldquoPublic Fightrdquo ldquoThreatsrdquo and ldquoAnnoying Noisesrdquo) ldquoAlcohol
Consumptionrdquo (ldquoPublic Alcohol Consumptionrdquo and ldquoDrunkennessrdquo) and ldquoStreet Tradingrdquo This
distinction in groups could be relevant if we expect different patterns and different effects of
community heterogeneity on social cohesion among counties For instance we expect higher
levels of Public Damage in big urban cities Street trading is more likely in urban cities but also in
tourist areas The spatial distribution of these four groups for the six-year-average rate per 1000
inhabitants can be found in Appendix R
433 Measures of community heterogeneity and control variables
Social cohesion income inequality and racial diversity are all ldquocommunityrdquo (not individual)
characteristics Thus to understand their relationship we need aggregated data at least at the
county-municipal level With more disaggregated data like at the suburbs level the required
heterogeneity among groups of citizens is lost (Wilkinson 1999) Like Coffeacute and Geys (2006) we
use the Gini coefficient to capture economic heterogeneity However instead of a measured for
34 We also analysed the spatial distribution of crime rates In general areas with high levels of incivilities not necessarily are associated with high levels of crime This could imply that factors explaining incivilities and crime are different
106
the diversity of nationalities we use the proportion of foreign population to capture racial
heterogeneity Income data is obtained from the CASEN survey The Gini coefficient is calculated
for each county and included through the variable gini Racial heterogeneity is included through
the variable foreign which is the annual number of new VISAS granted to foreigners as a
proportion of the county population Chile has experienced a significant increase in immigration
since 2011 Immigration has been concentrated in the metropolitan region and mining regions in
the North of the country We expect a positive relationship between immigration and incivilities
although as with the relationship between immigration and crime the foundations for this
hypothesis are not strong (Hooghe et al 2010 Sampson 2008)
Economic development is another explanation for social cohesion frequently appealed to
explain trust with wealthier societies considered to exhibit higher levels of trust (Delhey amp
Newton 2005) In this study we include the natural log of the mean household income per capita
log(income) We also include the poverty rate poverty and the unemployment rate
unemployment Unlike the variable log(income) these variables are expected to be positively
associated with the number of incivilities When a relative indicator of economic heterogeneity
such as income inequality is included as determinant of social cohesion we should expect less
effect from absolute indicators of economic disadvantage such as poverty and unemployment rates
(Hooghe et al 2010 Tolsma et al 2009)
Among demographic variables the percentage of inhabitants between 10 and 24 years old is
included through the variable youth The variable women defined as the proportion of the female
population in each county is also included Variable youth is expected to have an ambiguous effect
Although young people have lower victimization and report rates they also represent the group
more likely to commit antisocial behaviours when a community has a low capacity of self-
107
regulation (eg when there is low parental supervision) The female population is associated with
a higher report of incivilities related to the male population
It is argued that crime and incivilities are essentially urban problems (Christiansen 1960
Wirth 1938) We include the variable log(density) defined as the log of population density (the
number of inhabitants divided by the area of each county in square kilometres) and a dummy
variable capital indicating whether a county is an administrative capital (provincial or regional)
Two additional variables are included to capture the level of informal social control exerted
by families living in each municipality First the variable education which is defined as the
average years of education of people over 15 years old Second the variable housing which capture
the proportion of families which are owners of their housing unit Although education and housing
are related to both the possibility of reporting and committing an incivility we expect a negative
association with the rate of incivilities
In Chile crime has been mainly a problem faced by the police and the Central Government
Administration To control for current law enforcement policies we include the variable
deterrence defined as the number of arrests as a proportion of the total number of incivilities cases
In addition municipalities can develop their own initiatives to deal with crime and incivilities
depending on their capacity to generate its own resources The level of financial autonomy from
central transfers is captured by the variable autonomy This variable is obtained from SINIM and
it is defined as the proportion of the budget revenue of each municipality that comes from its own
permanent sources of revenues A categorical variable mayor is also included This variable
indicates whether the municipality mayor is supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo political
parties (related to those ldquoINDEPENDENTrdquo mayors)
108
Table 42 presents descriptive statistics for our measures of income and racial heterogeneity
and the set of numeric control variables The Pearson correlation among these variables is shown
in Appendix S
Table 42
Summary statistics numeric explanatory variables
434 Methods
The annual count of incivilities as is characteristic for count data is highly concentrated in
a relatively small range of values In addition the distribution is right-skewed due to the presence
of important outliers (counties with a high number of incivilities) Figure 44 shows the
distribution of the six-year average number of incivilities for each of the 15 regions in Chile35 We
observe that regions differ in the number of counties in which they are divided In addition
counties within each region show important differences in the number of incivilities For instance
35 Regions are ordered from left to right and from top to bottom in the way they are geographically distributed from North to South So the northernmost region of the country is the ldquoXVrdquo and the southernmost region is the ldquoXIIrdquo The Metropolitan region (where the countryrsquos capital is located) is region ldquoXIIIrdquo in the centre of the country (see Appendix C)
109
excepting the Metropolitan region ldquoXIIIrdquo most counties in regions located in the centre of the
country (middle row in Figure 44) show a range of incivilities between 0 and 2000 The number
of incivilities is considerably lower in counties located in the northern (top row in Figure 44) and
southern (bottom row in Figure 44) regions of the country compared to regions in the centre of
the country It also seems clear from Figure 44 that the number of incivilities does not follow a
normal distribution
Figure 44 Annual average number of incivilities per county
The number of incivilities can be better described by a Poisson distribution In this case the
number of incivilities is ldquothe countrdquo and the number of incivilities per year is ldquothe rate per unit
timerdquo We are interested in modelling the average number of incivilities per year usually called 120582
as a function of a set of contextual factors to explain differences in incivilities between and within
110
counties The main characteristic of the Poisson distribution is that the mean is equal to the
variance This implies that as the mean rate for a Poisson variable increases the variance also
increases The main implication is we cannot use OLS to model 120582 as a function of the set of
contextual factors because the equal variance assumption in linear regression is violated
The rate of incivilities between counties is not directly comparable due to population
differences We expect counties with more people to have more reports of incivilities since there
are more people who could be affected To capture differences in population which is called the
exposure of our response variable 120582 it is necessary to include a term on the right side of our model
called an offset We will use the log of the county population in thousands as our offset36
Additionally similar to the case of crime data incivilities show a significant degree of
overdispersion (variance higher than the mean) suggesting that there is more variation in the
response than the Poisson model implies37 We also model and regress incivilities assuming a
Negative Binomial distribution to address overdispersion An advantage of this approach is that it
introduces a dispersion parameter in addition to 120582 which gives the model more flexibility38
Considering as the response variable the count of incivilities per year the model can be
expressed as follow
120582 119890119909119901 120573 120573 119892119894119899119894 120573 119891119900119903119890119894119892119899 119883120574 120572 120579 (42)
36 If we think of 120582 as the average number of incivilities per year then represents the number per 1000
inhabitants so that the yearly count is adjusted to be comparable across counties of different sizes Adjusting the yearly count by population is equivalent to adding 119897119900119892 1199011199001199011199061198971198861199051198941199001198991000 to the right-hand side of the regression equation 37 Without adjusting for overdispersion we use incorrect artificially small standard errors leading to artificially small p-values for model coefficients 38 The Negative Binomial model posits selecting a 120582 (average number of incivilities) for each county and then generating a count using a Poisson random variable with the selected 120582 With this approach the counts will be more dispersed than would be expected for observations based on a single Poisson variable with rate 120582
111
where 120582 is the rate of incivilities 119883 is our vector of controls 120572prime119904 are county-specific constants
and 120579prime119904 are time-specific constants Accounting for differences in county population we have
119890119909119901 120573 120573 119892119894119899119894 119883120574 120572 120579 (43)
where 119901119900119901119906119897119886119905119894119900119899 is the county population per year Hence the model to be estimated using
Maximum Likelihood Estimation (MLE) is
119897119900119892 120582 120573 120573 119892119894119899119894 119883120574 120572 120579 119897119900119892 (44)
Finally to account for different effects depending on the type of incivilities we also run
equation (44) for each of the four groups of incivilities defined in section (432)
435 Hypotheses
Based on the community heterogeneity hypothesis the relationship between social cohesion
and diversity should be stronger for lower levels of income and less educated groups of people
(Tolsma et al 2009) Hence contrary to evidence for developed and more equal countries we
expect a significant positive association for the Chilean case where more than 50 of the
population is economically vulnerable (OECD 2017)
The main hypotheses to be tested in this essay is whether the number of incivilities is
positively associated with the level of economic and racial heterogeneity at the county level We
start analysing this association for the full sample and period Next we analyse whether the
relationship between incivilities and our measures of diversity differs by geographic area (region
or zone) Finally we check whether the effect of economic and racial diversity is different
depending on the group of incivilities
112
44 Results and Discussion
Overall our results show that the rate of incivilities displays a stronger and more significant
relationship with racial diversity than with economic heterogeneity This association differs for
different geographic areas and for different types of incivilities Absolute economic indicators
except for income show a significant but small effect Increases in the average levels of income
or education and more financial autonomy for municipalities seem to be effective ways to reduce
the rate of incivilities
We estimate equation (44) assuming that the number of incivilities follows a Poisson
distribution Regional and temporal heterogeneity are captured through the inclusion of dummy
variables for five years (with 2006 as the reference year) and fourteen regional dummies (with
region XIII as the reference region) Results are reported in Table 4339 This table is structured in
two blocks of regressions pooled models in columns (1)-(4) and fixed-effects models in columns
(5)-(8)40 The first column in each block only includes economic indicators relative and absolute
trying to test which ones are more relevant and whether incivilities tend to mirror income
inequality (the ldquoincivilities thesisrdquo) The second column adds the variable foreign to account for
the effect of racial diversity (Letki 2008) The third column includes education to check whether
the association between economic and racial diversity with social cohesion changes (gets less
significant) when we control for educational level (Tolsma et al 2009) The final column in each
block corresponds to the full model specification which includes the rest of controls
39 Pooled estimations were obtained using the R command glm() and compared with results obtained from the command poisson in STATA 15 Fixed effects and random effects models were estimated using the STATA command xtpoisson Negative Binomial models were estimated using the command glmnb() in the MASS package for R and commands nbreg and xtnbreg in STATA 15 Results for Negative Binomial regressions are in Appendix T 40 We have omitted results with random effects due to the literature on panel count data models suggest that the most robust estimator is Poisson regression with Fixed Effects (Santos Silva amp Tenreyro 2010 2011)
113
Table 43
Poisson regressions
114
The positive and significant coefficient for the variable gini besides being small it becomes
insignificant in the fixed effects specification which includes the full set of controls This result
does not seem to be enough evidence to support our hypothesis that more unequal counties display
higher rates of incivilities On the other hand racial diversity through the variable foreign shows
a consistent positive association with the rate of incivilities41 Together coefficients for gini and
foreign seems to support the ldquocommunity heterogeneity thesisrdquo (Letki 2008) but not the
ldquoincivilities thesisrdquo (Skogan 1999) To check this finding we run the pooled full model
specification for each region and results are shown in Table 44 where regions have been ordered
from North to South The sign of the coefficient of the variable gini differs for different regions
Moreover the relationship is insignificant in some of the most unequal regions which are in the
South of the country (VIII IX and XIV) This result rejects the hypothesis that incivilities mirror
structural income inequalities For the variable foreign 12 out of 15 regions confirm the positive
association with the rate of incivilities42
We also run our pooled full model separately for each group of incivilities defined at the end
of section (432) Income inequality keeps its significant but small association with each group of
incivilities (see Table 45) Our measure of racial diversity shows a stronger association with
ldquoAlcohol Consumptionrdquo related to ldquoPublic Damagerdquo and ldquoThreatsrdquo The link with ldquoStreet Tradingrdquo
appears as non-significant These results support our general finding that on the one hand racial
heterogeneity exert a more significant influence on the rate of incivilities than economic
41 To get the effects of the variables gini and foreign over the rate of incivilities per 1000 inhabitants we need to exponentiate their coefficients Thus an increase in one unit in the variable gini increases the rate of incivilities per 1000 inhabitantsrdquo in 1005 times (e^0005) in the pooled full model Similarly an increase in one point in variable foreign is expected to increase the ldquorate of incivilities per 1000 inhabitantsrdquo in 10876 times (e^0084) in the full pooled model and 10471 (e^0046) times in the full Fixed Effects model 42 Given that depending on the geographic location regions share certain characteristics the model was also run clustering regions in 4 zones (see Figure 31 in chapter 3) Results by zone are in Appendix U
115
heterogeneity On the other hand results confirm our hypothesis that the effect of diversity is
different for different types of incivilities
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region
Back to our general results in Table 43 the significant and negative coefficient of the
income variable and to a lesser extent the significant and positive coefficients of poverty and
unemployment provide evidence that absolute rather than relative economic indicators may be
more important explanations of the rate of incivilities This is opposite to evidence for the analysis
116
of crime rates such as in Hoodge (2010) and support the idea that determinants of incivilities are
different from those of crime Our results are also opposite to those for Dutch municipalities where
economic indicators turned out to be more important than ethnic heterogeneity (Tolsma et al
2009) The coefficient for the variable log(income) could be interpreted as counties with an income
level under the average face higher problems of antisocial behaviours such as incivilities In
addition as the income level moves far away from its average low level the problem of incivilities
is less relevant43 In terms of policy implications only those policies that achieve a significant
increase in the average level of county income seem to be effective in reducing incivilities and
strengthening social cohesion
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group
43 We ran our model splitting the sample of counties in quintiles based on their income level The coefficient for the variable gini only showed a significant (and positive) coefficient for the second third and fourth quintile A non-significant coefficient for the first quintile could imply that other types of inequalities (eg health education) are more relevant in the poorest counties In the case of the fifth quintile (the richest group of counties) they have better facilities in terms of infrastructure police resources self-protection etc which could reduce the potential triggers of incivilities
117
The inclusion of the variable education significantly improved the goodness of fit of the
models and did not generate significant changes in the coefficients of our measures of economic
and racial diversity This result rejects the proposition that the relationship between social
cohesion and diversity becomes less strong when controlled by education (Tolsma et al 2009)
Additionally it highlights the topic of education as a relevant determinant of the rate of incivilities
and social cohesion
Among control variables there are also some important results Opposite to what we
expected the variable youth shows a negative or non-significant coefficient Although this result
could be due to the lack of ldquophysical incivilitiesrdquo in Chilean records it indicates that it is incorrect
to stereotype this group as the main responsible for high incivilities rates The significant and
negative coefficient of the variable autonomy in the fixed effects specification could also have
important policy implications It is a signal that local governments can play an important role in
reducing incivilities or complementing the efforts from the central government Another
interesting result is the significant coefficient of the variable housing The latter finding is
particularly important in the sense that a negative sign supports public policies oriented to increase
homeownership as effective ways to improve social cohesion However the small magnitude of
the coefficient that even showed the opposite sign in some model specifications could be
explained for the high level of segregation that these policies have generated in Chilean society
As mentioned in the Introduction and Literature Review so far only a few studies have
used measures of disorders or incivilities as dependent variable to explain changes in social
cohesion (Skogan 2015) In addition there is no evidence in Chile analysing the determinants of
incivilities separately from those of crimes The importance of our results on identifying the
importance of economic and racial diversity on social cohesion lies mainly in its generality An
118
important number of countries all around the world share a similar context characterized by high
levels of inequality and an explosive increase in immigration These countries are also
experiencing a worsening in social cohesion which increases the risk of a social outburst
4 5 Conclusions
The main goal of this essay was to determine whether differences in incivilities at the county
level mirror differences in income distribution and racial diversity Previous literature suggests a
positive and strong association between social cohesion and indicators of economic disadvantage
relative deprivation and racial diversity (Letki 2008 Tolsma et al 2009 Uslaner amp Brown 2005)
While not all our results were significant they showed helpful insights about how and where
economic and racial diversity are more likely to influence the rate of incivilities and social
cohesion
We used data for the period 2006ndash17 economic heterogeneity was measured through the
Gini coefficient at the county level and racial heterogeneity was proxied by the number of granted
visas to foreigners as proportion of county population We found strong evidence of a significant
and positive association between the rate of incivilities and racial diversity but not with income
inequality Contrary to previous evidence at the municipal level (Coffeacute amp Geys 2006 Tolsma et
al 2009) in general our results give support for the ldquohomophily principlerdquo and the ldquocommunity
heterogeneity hypothesisrdquo However results also showed that the effect of economic and racial
diversity varies throughout the Chilean regions and for the different types of incivilities
We also found that policies aimed at controlling the behaviour of young people did not have
strong empirical support In terms of the role that local governments may have in facing the
119
growing problem of incivilities we found evidence that efforts managed from the municipalities
can be an important complement to those from the central government
Future research should go further on the role of local authorities on incivilities and social
cohesion On the one hand municipalities could have a direct impact on social cohesion through
the implementation of programs complementary to those of central authorities oriented to reduce
incivilities and crime On the other hand social cohesion could be indirectly affected when local
authorities display an inefficient performance supplying public services to citizens or they are
recognized as corrupted institutions We suggest that policy makers from central government
should give local authorities a greater role in fighting antisocial behaviours and crime Evaluating
programs in specific municipalities could help to elucidate the causal effect of for instance higher
fiscal autonomy on the rate of incivilities
Another interesting area for future work will be to analyse how housing policies have
contributed to the phenomenon of segregation of Chilean society and in the process of weakening
social cohesion Finally our main result highlights the need of a deeper analysis of the impact that
foreign immigration is having in Chile For instance disaggregating information by country of
origin and the reasons why immigrants are arriving to the country or specific regions will surely
help to understand the impacts of immigration
120
Chapter 5 Conclusions
This thesis investigated in three essays the issue of income inequality in Chile using county-
level data for the period 2006-2017 The first essay supplied empirical evidence about the
importance of the degree of dependence on natural resources in terms of employment in explaining
cross-county differences in income inequality The second essay analysed the potential causal
effect that income inequality has on the level of technical efficiency of local governments
providing public goods and services Lastly the third essay studied the relationship between social
cohesion measured through the number of antisocial behaviours classified as ldquoincivilitiesrdquo and
the levels of income and racial heterogeneity
Findings from the first essay support the idea that the endowment of natural resources plays
a significant role in explaining income inequality in Chile However contrary to what most
theoretical and empirical evidence postulates our findings showed a robust negative association
between the two variables This means that the reduction experienced in Chile in the degree of
dependence on natural resources in terms of employment has contributed to the persistence of high
levels of income inequality The exploratory analysis indicated that income inequality shows a
general clustering process characterized by a significant and positive spatial autocorrelation
Regarding the previous evidence for Chile (Paredes et al 2016) the regression analysis confirmed
the relevance of the spatial dimension of income inequality through a process of spatial
heterogeneity giving less support to the existence of a process of spatial dependence (spillover
effect) in the variable itself
121
Essay 2 studied the potential trade-off between efficiency and equity analysing the influence
of income inequality on the efficiency of local governments at the municipal level To identify the
causal effect of income inequality on municipal efficiency we proposed the use of the proportion
of firms in the primary sector as an instrument for income inequality Findings confirmed our
hypothesis that efficiency is lower in more unequal counties This result suggests the non-existence
of the trade-off between equity and efficiency Hence policies intended to reduce inequality could
help to increase efficiency at least at the level of municipal local governments
The third essay analysed how social cohesion proxied by the rate of incivilities is associated
with the levels of economic diversity proxied by income inequality and the levels of racial
diversity proxied by the number of new visas grated as proportion of the county population
Findings gave strong support to the hypothesis that the rate of incivilities is positively related to
racial diversity and to a lesser extent to economic diversity In addition the rate of incivilities
appears negatively related to the degree of financial autonomy of municipalities This means that
local governments can effectively contribute to the reduction of incivilities which could help
reduce victimization and crime rates ultimately strengthening social cohesion
Taken together findings from essays 2 and 3 highlight the important role that income
inequality could play in other relevant economic and social dimensions These findings add to the
understanding of the potential consequences of income inequality particularly in natural resource
rich countries with persistently high levels of inequality
The present study has mainly investigated income inequality at the county level In addition
Chilean municipalities play an important role providing ldquoservices to peoplerdquo so our findings could
be applied in other highly unequal countries with a high degree of dependence on natural resources
and local governments with similar responsibilities For instance in Latin America apart from
122
Chile and Brazil there are no studies on the efficiency of local governments Other limitations are
associated with the availability of information For instance important indicators such as GDP per
capita are only available at the regional level and information of incomes is not available annually
In addition given the heterogeneity among municipalities some type of grouping of municipalities
should be performed before looking for causal relationships or conducting program evaluation
Despite these limitations we believe this study could be the basis for different strands of future
research on the topic of inequality local government efficiency and social cohesion
It was stated in chapter 2 based on the resource curse hypothesis literature there are two
elements that determine whether NR are a curse or blessing in terms of socioeconomic outcomes
First the curse is more likely in countries with weak political and governance institutions Second
different types of resources affect institutions differently with resources that are concentrated in
space so-called ldquopointrdquo resources tend to impair institutions while ldquodiffuserdquo resources do not
(Deacon 2011 Isham Woolcock Pritchett amp Busby 2005) Our results showed a negative
relationship between income inequality and our measure of natural resource dependence even after
controlling for zone fixed effects and for the level of government expenditure This result could
be interpreted as a signal that NR has a direct effect on income inequality in addition to the indirect
impact through market or institutional channels Using other potential institutional transmission
channels will shed light about the true effect that the endowment of natural resources has over
income inequality Variables that could capture these institutional channels include the level of
employment in the public sector measures of rule of law and corruption and changes in the
creation of new business in the secondary and tertiary sectors related to the primary sector
Based on results from chapter 3 most of the municipalities show scale inefficiencies One
immediate area for future work will involve using our set of contextual factors to predict the status
123
of municipalities in terms of scale inefficiencies Defining as dependent variable whether a
municipality shows constant decreasing or increasing returns to scale we could run a multinomial
logistic regression to predict municipal status For instance we would expect that a one-unit
increase in the Gini coefficient should increase the probability of scale inefficiencies (increasing
or decreasing returns to scale rather than constant returns to scale) Because the aim in this case
would be predicting a certain result in terms of returns to scale the next step should involve to
split the full sample in training and testing data sets and to use some resampling methods such as
bootstrapping This will allow us to evaluate the performance and accuracy of our model
predictions using different random samples of municipalities Results from Machine Learning
algorithms will help us to assess the generalizability of our results to other data sets
Future work should also benefit greatly by using data on different Latin American countries
to (1) compare the responsibilities of local governments (2) select a common set of inputs and
output to evaluate LGE (3) identify the relevance of scales inefficiencies in explaining differences
in performance and (4) analyse the influence of contextual characteristics over LGE Differences
in the main primary sector activity in each country such as oil in Brazil mining in Chile or Coffee
in Colombia could be responsible for differences in LGE among countries These differences could
be associated with sources of revenue management of expenditure and definitions of outputs or
contextual effects such as corrupted institutions or the delay in the development of other sectors
such as manufacturing or services
To delve deep on reasons explaining the social crisis experienced by Chilean society and
other countries one area of future work will be to analyse the relationship between diversity and
the origins of social revolutions Based on Tiruneh (2014) the three most important factors that
explain the onset of social revolutions are economic development regime type and state
124
ineffectiveness Interesting questions include whether the characteristics of Chilean context at the
end of 2019 are enough to trigger the transformation of the political and socioeconomic system
Social revolutions particularly violent revolutions are less likely in more democratic educated
and wealthy societies So it would be relevant to identify the factors explaining the violence that
has characterized the social crisis in Chile Finally the democratic regime has been maintained in
the last decades with changes between left and right governments This could imply that more
important than the regime has been the efficiency or ineffectiveness of the governments to satisfy
the needs of the population
Future work should also cover the disaggregation of information regarding foreign
population in terms of the reasons for new granted visas and the country of origin Official data
allows us to disaggregate whether the benefit is permanent (students and employees with contract)
or temporary Furthermore most of the new visas were traditionally granted to neighbouring
countries (Peru and Bolivia) a trend that has changed in the recent years to include countries such
as Colombia Venezuela and Haiti An analysis of how economic and social indicators have been
affected by changes in the composition of foreigners their reasons for immigrating to the country
and their geographical distribution have implications for economic policy at both the national and
local levels At the national level such analysis should be a key input when proposing changes to
the national immigration policy At the local level it could help define the role of municipalities
to assess the benefits and challenges of immigration These challenges are mainly related to the
provision of public goods and services such as health and education which in Chile are the
responsibility of the municipalities
The findings of this thesis suggest that policymakers should encourage policies that reduce
income inequality The key role that municipalities could play to strengthen social cohesion and
125
the increasingly important role that foreign population is acquiring in most modern societies are
also interesting avenues for future research However the picture is still incomplete and more
research is needed incorporating other dimensions of inequality This is essential if we want to
understand the reasons that could have triggered the social outbursts experienced by various
economies across the globe
126
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Acemoglu D (2002) Technical Change Inequality and the Labor Market Journal of Economic Literature 40(1) 7ndash72 httpsdoiorg1012570022051026976
Acemoglu D Aghion P amp Violante G L (2001) Deunionization Technical Change and Inequality Carnegie-Rochester Conference Series on Public Policy 55(1) 229ndash264 httpsdoiorg101016S0167-2231(01)00058-6
Acemoglu D Johnson S amp Robinson J A (2001) The Colonial Origins of Comparative Development An Empirical Investigation The American Economic Review 91(5) 1369ndash1401 httpsdoiorg101257aer9151369
Acemoglu D amp Robinson J A (2002) The Political Economy of the Kuznets Curve Review of Development Economics 6(2) 183ndash203 httpsdoiorg1011111467-936100149
Afonso A amp Fernandes S (2006) Measuring local government spending efficiency Evidence for the Lisbon region Regional Studies 40(1) 39ndash53 httpsdoiorg10108000343400500449937
Afonso A amp Fernandes S (2008) Assessing and explaining the relative efficiency of local government The Journal of Socio-Economics 37(5) 1946ndash1979 httpsdoiorg101016jsocec200703007
Afonso A Schuknecht L amp Tanzi V (2010) Income distribution determinants and public spending efficiency Journal of Economic Inequality 8(3) 367ndash389 httpsdoiorg101007s10888-010-9138-z
Akita T (2003) Decomposing regional income inequality in China and Indonesia using two-stage nested Theil decomposition method The Annals of Regional Science 37(1) 55ndash77 httpsdoiorg101007s001680200107
Alesina A (2000) Participation in heterogeneous communities The Quarterly Journal of Economics 115(3) 847ndash904 httpsdoiorg101162003355300554935
Alesina A amp La Ferrara E (2002) Who trusts others Journal of Public Economics 85(2) 207ndash234 httpsdoiorg101016S0047-2727(01)00084-6
Allcott H amp Keniston D (2014) Dutch Disease or Agglomeration The Local Economic Effects of Natural Resource Booms in Modern America (N W P N 20508 Ed) NBER Working Paper No 20508 (Vol w20508) NBER Working Paper No 20508 NBER Working Paper No 20508 httpsdoiorgNBER Working Paper No 20508
Andersen T M amp Maibom J (2020) The big trade-off between efficiency and equitymdashis it there Oxford Economic Papers 72(2) 391ndash411
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Anselin L Bera A K Florax R amp Yoon M J (1996) Simple diagnostic tests for spatial dependence Regional Science and Urban Economics 26(1) 77ndash104 httpsdoiorg1010160166-0462(95)02111-6
Antonelli M A amp De Bonis V (2018) The efficiency of social public expenditure in European countries a two-stage analysis Applied Economics 1ndash14 httpsdoiorg1010800003684620181489522
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Ariely G (2014) Does Diversity Erode Social Cohesion Conceptual and Methodological Issues Political Studies 62(3) 573ndash595 httpsdoiorg1011111467-924812068
Armstrong H amp Taylor J (2000) Regional economics and policy (3rd ed) Oxford Blackwell
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Ashworth J Geys B Heyndels B amp Wille F (2014) Competition in the political arena and local government performance Applied Economics 46(19) 2264ndash2276 httpsdoiorg101080000368462014899679
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Balaguer-Coll M T Brun-Martos M I Maacuterquez-Ramos L amp Prior D (2019) Local government efficiency determinants and spatial interdependence Applied Economics
128
51(14) 1478ndash1494 httpsdoiorg1010800003684620181527458
Balaguer-Coll M T amp Prior D (2009) Short- and long-term evaluation of efficiency and quality An application to Spanish municipalities Applied Economics 41(23) 2991ndash3002 httpsdoiorg10108000036840701351923
Banker R D Charnes A amp Cooper W W (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis Management Science 30(9) 1078ndash1092
Beine M Coulombe S amp Vermeulen W N (2015) Dutch Disease and the Mitigation Effect of Migration Evidence from Canadian Provinces The Economic Journal 125(589) 1574ndash1615 httpsdoiorg101111ecoj12171
Benito B Bastida F amp Garciacutea J A (2010) Explaining differences in efficiency an application to Spanish municipalities Applied Economics 42(4) 515ndash528 httpsdoiorg10108000036840701675560
Berg A amp Ostry J (2011) Equality and efficiency Finance amp Development 48(3) 12ndash15
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Beyer H amp Vergara R (2006) Delincuencia en Chile Determinantes y rol de las poliacuteticas puacuteblicas Instituto de Economiacutea UC
Blanco L amp Grier R (2012) Natural resource dependence and the accumulation of physical and human capital in Latin America Resources Policy 37(3) 281ndash295 httpsdoiorghttpdoiorg101016jresourpol201201005
Blau J R amp Blau P M (1982) The cost of inequality Metropolitan structure and violent crime American Sociological Review 114ndash129
Boetti L Piacenza M amp Turati G (2009) Fiscal decentralization and spending efficiency of local governments An Empirical Investigation on a Sample Of 4
Boix C amp Posner D (1998) Social capital Explaining its origins and effects on government performance British Journal Of Political Science 28 686ndash693
Borge L E Parmer P amp Torvik R (2015) Local natural resource curse JOURNAL OF PUBLIC ECONOMICS 131 101ndash114 httpsdoiorg101016jjpubeco201509002
Borger B Kerstens K Moesen W amp Vanneste J (1994) Explaining differences in productive efficiency An application to Belgian municipalities Public Choice 80(3) 339ndash358 httpsdoiorg101007BF01053225
Bourguignon F amp Morrisson C (1990) Income distribution development and foreign trade A cross-sectional analysislowast European Economic Review 34(6) 1113ndash1132 httpsdoiorghttpsdoiorg1010160014-2921(90)90071-6
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Bravo J (2014) Fondo Comuacuten Municipal y su desincentivo a la recaudacioacuten en Chile Temas de La Agenda Legislativa-Centro de Poliacuteticas Puacuteblicas UC 9(68)
Browning E K amp Johnson W R (1984) The Trade-Off between Equality and Efficiency Journal of Political Economy 92(2) 175ndash203 httpsdoiorg101086261219
Brunnschweiler C N amp Bulte E H (2008) The resource curse revisited and revised A tale of paradoxes and red herrings Journal of Environmental Economics and Management 55(3) 248ndash264 httpsdoiorghttpsdoiorg101016jjeem200708004
Brunori P Ferreira F H G amp Peragine V (2013) Inequality of opportunity income inequality and economic mobility Some international comparisons In Getting Development Right (pp 85ndash115) Springer
Brunton-Smith I Jackson J amp Sutherland A (2014) Bridging structure and perception On the neighbourhood ecology of beliefs and worries about violent crime British Journal of Criminology 54(4) 503ndash526
Bulte E H Damania R amp Deacon R T (2005) Resource intensity institutions and development World Development 33(7) 1029ndash1044 httpsdoiorg101016jworlddev200504004
Carmignani F (2013) Development outcomes resource abundance and the transmission through inequality Resource and Energy Economics 35(3) 412ndash428 httpsdoiorg101016jreseneeco201304007
Carmignani Fabrizio amp Avom D (2010) The social development effects of primary commodity export dependence Ecological Economics 70(2) 317ndash330 httpsdoiorg101016jecolecon201009003
Caselli F amp Michaels G (2013) Do Oil Windfalls Improve Living Standards Evidence from Brazil American Economic Journal Applied Economics 5(1) 208ndash238 httpsdoiorg101257app51208
Celebioglu F amp Dallrsquoerba S (2010) Spatial disparities across the regions of Turkey An exploratory spatial data analysis Annals of Regional Science 45(2) 379ndash400 httpsdoiorg101007s00168-009-0313-8
Chan J To H-P amp Chan E (2006) Reconsidering social cohesion Developing a definition and analytical framework for empirical research Social Indicators Research 75(2) 273ndash302
Charnes A Cooper W W amp Rhodes E (1978) Measuring the efficiency of decision making units European Journal of Operational Research 2(6) 429ndash444
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Chi G amp Zhu J (2019) Spatial Regression Models for the Social Sciences SAGE Publications Retrieved from httpsbooksgooglecomaubooksid=zHGkvwEACAAJ
Christiansen K O (1960) Industrialization and urbanization in relation to crime and juvenile delinquency International Review of Criminal Policy 16 3ndash8
Cingano F (2014) Trends in income inequality and its impact on economic growth (Vol 163) Paris OECD Publishing httpsdoiorg1017875jxrjncwxv6j-en
Coelli T J Prasada Rao D S OrsquoDonnell C J amp Battese G E (2005) An introduction to efficiency and productivity analysis An Introduction to Efficiency and Productivity Analysis Springer Science amp Business Media httpsdoiorg101007b136381
Coffeacute H amp Geys B (2005) Institutional Performance and Social Capital An Application to the Local Government Level Journal of Urban Affairs 27(5) 485ndash501 httpsdoiorg101111j0735-2166200500249x
Coffeacute H amp Geys B (2006) Community Heterogeneity A Burden for the Creation of Social Capital Social Science Quarterly 87(5) 1053ndash1072 httpsdoiorg101111j1540-6237200600415x
Cohen D Spear S Scribner R Kissinger P Mason K amp Wildgen J (2000) ldquo Broken windowsrdquo and the risk of gonorrhea American Journal of Public Health 90(2) 230
Corden W M amp Neary J P (1982) Booming sector and de-industrialisation in a small open economy The Economic Journal 92(368) 825ndash848
Cordero J M Pedraja-Chaparro F Pisaflores E C amp Polo C (2017) Efficiency assessment of Portuguese municipalities using a conditional nonparametric approach Journal of Productivity Analysis 48(1) 1ndash24 httpsdoiorg101007s11123-017-0500-z
Croissant Y amp Millo G (2018) Panel Data Econometrics with R John Wiley amp Sons
Cust J amp Poelhekke S (2015) The Local Economic Impacts of Natural Resource Extraction 7 251ndash268 httpsdoiorg101146annurev-resource-100814-125106
da Cruz N F amp Marques R C (2014) Revisiting the determinants of local government performance Omega 44 91ndash103 httpsdoiorg101016JOMEGA201309002
Dauvin M amp Guerreiro D (2017) The Paradox of Plenty A Meta-Analysis World Development 94 httpsdoiorg101016jworlddev201701009
de Sousa M da C S Cribari-Neto F amp Stosic B D (2005) Explaining DEA technical efficiency scores in an outlier corrected environment the case of public services in Brazilian municipalities Brazilian Review of Econometrics 25(2) 287ndash313
Deacon R T (2011) The Political Economy of the Natural Resource Curse A Survey of Theory and Evidence Foundations and Trends in Microeconomics 111-208
Delhey J amp Newton K (2005) Predicting cross-national levels of social trust global pattern or
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Dlouhyacute M (2018) Measuring Geographic Inequalities Dealing with Multiple Health Resources by Data Envelopment Analysis Frontiers in Public Health 6 53 httpsdoiorg103389fpubh201800053
Dollery B Wallis J amp Akimov A (2010) One Size Does Not Fit All The Special Case of Remote Small Local Councils in Outback Queensland Local Government Studies 36(1) 21ndash42 httpsdoiorg10108003003930903435716
Domenech J (2008) Mineral resource abundance and regional growth in Spain 1860ndash2000 Journal of International Development The Journal of the Development Studies Association 20(8) 1122ndash1135
Doran J amp Jordan D (2016) Decomposing US regional income inequality from 1969 to 2009 Applied Economics Letters 23(11) 781ndash784 httpsdoiorg1010801350485120151109030
Drew J amp Dollery B (2015) The State of Things The Dynamic Efficiency of Australian State and Territories Economic Papers A Journal of Applied Economics and Policy 34(3) 165ndash176
Drew J Kortt M amp Dollery B (2015) What Determines Efficiency in Local Government A DEA Analysis of NSW Local Government Economic Papers A Journal of Applied Economics and Policy 34(4) 243ndash256 httpsdoiorg1011111759-344112118
Easterly W (2007) Inequality does cause underdevelopment Insights from a new instrument Journal of Development Economics 84(2) 755ndash776 httpsdoiorghttpdxdoiorg101016jjdeveco200611002
Ebert L amp La Menza T (2015) Chile copper and resource revenue A holistic approach to assessing commodity dependence Resources Policy 43(Supplement C) 101ndash111 httpsdoiorghttpsdoiorg101016jresourpol201410007
ElGindi T (2017) Natural resource dependency neoliberal globalization and income inequality Are they related A longitudinal study of developing countries (1980ndash2010) Current Sociology 65(1) 21ndash53 httpsdoiorg1011770011392116632031
Engerman S L amp Sokoloff K L (1994) Factor Endowments Institutions and Differential Paths of Growth Among New World Economies National Bureau of Economic Research
Engerman S L amp Sokoloff K L (1997) Factor endowments institutions and differential paths of growth among new world economies How Latin America Fell Behind 260ndash304
Engerman S L Sokoloff K L Urquiola M amp Acemoglu D (2002) Factor Endowments Inequality and Paths of Development among New World Economies [with Comments] EconomampxedA 3(1) 41ndash109 Retrieved from httpwwwjstororgezp01libraryquteduaustable20065432
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Faumlre R Grosskopf S amp Lovell C A K (1985) The measurement of efficiency of production (Vol 6) Springer Science amp Business Media
Fleming D A amp Measham T G (2015a) Income Inequality across Australian Regions during the Mining Boom 2001-11 Australian Geographer 46(2) 203ndash216 httpsdoiorg1010800004918220151020596
Fleming D A amp Measham T G (2015b) Local economic impacts of an unconventional energy boom the coal seam gas industry in Australia Australian Journal of Agricultural and Resource Economics 59(1) 78ndash94 httpsdoiorg1011111467-848912043
Fum R M amp Hodler R (2010) Natural resources and income inequality The role of ethnic divisions Economics Letters 107(3) 360ndash363 httpsdoiorg101016jeconlet201003008
Garofalo J (1978) The fear of crime Broadening our perspective
Geys B amp Moesen W (2009) Exploring Sources of Local Government Technical Inefficiency Evidence from Flemish Municipalities Public Finance and Management 9(1) 1ndash29
Goderis B amp Malone S W (2011) Natural Resource Booms and Inequality Theory and Evidence The Scandinavian Journal of Economics 113(2) 388ndash417 httpsdoiorg101111j1467-9442201101659x
Greene W H (2016) Productivity and Efficiency Analysis (W H Greene L Khalaf R Sickles M Veall amp M-C Voia Eds) (1st ed 20) Cham Springer International Publishing httpsdoiorg101007978-3-319-23228-7
Gustafsson B amp Shi L (2002) Income inequality within and across counties in rural China 1988 and 1995 Journal of Development Economics 69(1) 179ndash204 httpsdoiorg101016S0304-3878(02)00058-5
Gylfason T amp Zoega G (2003) Inequality and Economic Growth Do Natural Resources Matter In T T Eicher S (Ed) Inequality and Growth Theory and Policy Implications (pp 255ndash292) The MIT Press
Henningsen A (2010) Estimating Censored Regression Models in R using the censReg Package R Package Vignettes Collection 5(2) 12
Henningsen A (2019) censReg Censored Regression (Tobit) Models R package version 05-30 httpscranr-projectorgpackage=censReg Retrieved from httpscranr-projectorgpackage=censReg
Herrera S amp Pang G (2005) Efficiency of Public Spending in Developing Countriesthinsp An Efficiency Frontier Approach World Bank Washington DC
Hill T D amp Angel R J (2005) Neighborhood disorder psychological distress and heavy drinking Social Science amp Medicine 61(5) 965ndash975
133
httpsdoiorghttpsdoiorg101016jsocscimed200412027
Hipp J R (2010) RESIDENT PERCEPTIONS OF CRIME AND DISORDER HOW MUCH IS ldquoBIASrdquo AND HOW MUCH IS SOCIAL ENVIRONMENT DIFFERENCES Criminology 48(2) 475ndash508 httpsdoiorg101111j1745-9125201000193x
Holtug N amp Mason A (2010) Introduction Immigration diversity and social cohesion SAGE Publications Sage UK London England
Hooghe M Vanhoutte B Hardyns W amp Bircan T (2010) Unemployment Inequality Poverty and Crime Spatial Distribution Patterns of Criminal Acts in Belgium 2001ndash06 The British Journal of Criminology 51(1) 1ndash20 httpsdoiorg101093bjcazq067
Howie P amp Atakhanova Z (2014) Resource boom and inequality Kazakhstan as a case study Resources Policy 39(1) 71ndash79 httpsdoiorg101016jresourpol201311004
Isham J Woolcock M Pritchett L amp Busby G (2005) The Varieties of Resource Experience Natural Resource Export Structures and the Political Economy of Economic Growth The World Bank Economic Review 19(2) 141ndash174 httpsdoiorg101093wberlhi010
Jottier D Ashworth J amp Heyndels B (2012) Understanding Votersrsquo Preferences How the Electoratersquos Complexity Affects Prediction Accuracy and Wishful Thinking among Politicians with Respect to Election Outcomes Kyklos 65(3) 340ndash370 httpsdoiorg101111j1467-6435201200542x
Kanbur S M R amp Venables A (2005) Spatial inequality and development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Kesler C amp Bloemraad I (2010) Does immigration erode social capital The conditional effects of immigration-generated diversity on trust membership and participation across 19 countries 1981ndash2000 Canadian Journal of Political ScienceRevue Canadienne de Science Politique 43(2) 319ndash347
Kuznets S (1955) Economic Growth and Income Inequality The American Economic Review 45(1) 1ndash28 Retrieved from httpwwwjstororgstable1811581
Lagos G amp Blanco E (2010) Mining and development in the region of Antofagasta Resources Policy 35(4) 265ndash275 httpsdoiorghttpsdoiorg101016jresourpol201007006
Leamer E E Maul H Rodriguez S amp Schott P K (1999) Does natural resource abundance increase Latin American income inequality Journal of Development Economics 59(1) 3ndash42 httpsdoiorg101016s0304-3878(99)00004-8
Leibbrandt M Finn A amp Woolard I (2012) Describing and decomposing post-apartheid income inequality in South Africa Development Southern Africa 29(1) 19ndash34 httpsdoiorg1010800376835X2012645639
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Letki N (2008) Does Diversity Erode Social Cohesion Social Capital and Race in British Neighbourhoods Political Studies 56(1) 99ndash126 httpsdoiorg101111j1467-9248200700692x
Lewis D A (2017) Fear of crime Incivility and the production of a social problem Routledge
lo Storto C (2013) Evaluating Technical Efficiency of Italian Major Municipalities A Data Envelopment Analysis model Procedia - Social and Behavioral Sciences 81 346ndash350 httpsdoiorg101016JSBSPRO201306440
Loayza N amp Rigolini J (2016) The Local Impact of Mining on Poverty and Inequality Evidence from the Commodity Boom in Peru World Development 84 219ndash234 httpsdoiorg101016jworlddev201603005
Loayza N Teran A M y amp Rigolini J (2013) Poverty Inequality and the Local Natural Resource Curse World Bank Policy Research Working Paper (6366) httpsdoiorg1015961813-9450-6366
Loacutepez R amp Miller S J (2008) Chile The Unbearable Burden of Inequality World Development 36(12) 2679ndash2695 httpsdoiorg101016jworlddev200801012
Manzano O amp Rigobon R (2001) Resource Curse or Debt Overhang National Bureau of Economic Research Working Paper Series No 8390 1 httpsdoiorghttpwwwnberorgpapersw9424bib
McPherson M Smith-Lovin L amp Cook J M (2001) Birds of a Feather Homophily in Social Networks Annual Review of Sociology 27(1) 415ndash444 httpsdoiorg101146annurevsoc271415
McQuestin D Drew J amp Dollery B (2018) Do Municipal Mergers Improve Technical Efficiency An Empirical Analysis of the 2008 Queensland Municipal Merger Program Australian Journal of Public Administration 77(3) 442ndash455 httpsdoiorg1011111467-850012286
Messner S F Rosenfeld R amp Baumer E P (2004) Dimensions of Social Capital and Rates of Criminal Homicide American Sociological Review 69(6) 882ndash903 httpsdoiorg101177000312240406900607
Michaels G (2011) THE LONG TERM CONSEQUENCES OF RESOURCE-BASED SPECIALISATION The Economic Journal 121(551) 31ndash57 httpsdoiorg101111j1468-0297201002402x
Mijanovich T amp Weitzman B C (2003) Which ldquobroken windowsrdquo matter School neighborhood and family characteristics associated with youthsrsquo feelings of unsafety Journal of Urban Health 80(3) 400ndash415
Mikušovaacute P (2015) An Application of DEA Methodology in Efficiency Measurement of the Czech Public Universities Procedia Economics and Finance 25 569ndash578 httpsdoiorg101016S2212-5671(15)00771-6
135
Milanovic B (2016) Global inequality Harvard University Press
Millo G amp Piras G (2012) splm Spatial panel data models in R Journal of Statistical Software 47(1) 1ndash38
Murphy K M amp Topel R H (2016) Human Capital Investment Inequality and Economic Growth JOURNAL OF LABOR ECONOMICS 34(2) S99ndashS127 httpsdoiorg101086683779
Narboacuten-Perpintildeaacute I amp De Witte K (2018a) Local governmentsrsquo efficiency a systematic literature reviewmdashpart I International Transactions in Operational Research 25(2) 431ndash468 httpsdoiorg101111itor12364
Narboacuten-Perpintildeaacute I amp De Witte K (2018b) Local governmentsrsquo efficiency a systematic literature reviewmdashpart II International Transactions in Operational Research 25(4) 1107ndash1136 httpsdoiorg101111itor12389
Nuntildeez J Rivera J Villavicencio X amp Molina O (2003) Determinantes socioeconoacutemicos y demograacuteficos del crimen en Chile Estudios de Economiacutea 30(1) 55ndash85
OrsquoDonnell C J Rao D S P amp Battese G E (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios Empirical Economics 34(2) 231ndash255 httpsdoiorg101007s00181-007-0119-4
Ocampo J A (2004) Latin Americarsquos Growth and Equity Frustrations During Structural Reforms The Journal of Economic Perspectives 18(2) 67ndash88 httpsdoiorg1012570895330041371349
OECD (2014) Focus on inequality and growth OECD
OECD (2017) Howrsquos Life 2017 Life Satisfaction Oecd httpsdoiorg101787how_life-2017-en
Ohtake F (2008) Inequality in Japan Asian Economic Policy Review 3(1) 87ndash109 httpsdoiorg101111j1748-3131200800093x
Okun A M (2015) Equality and efficiency the big tradeoff Washington DC Brookings Institution Press
Ortega B Sanjuaacuten J amp Casquero A (2017) Determinants of efficiency in reducing child mortality in developing countries The role of inequality and government effectiveness Health Care Management Science 20(4) 500ndash516 httpsdoiorg101007s10729-016-9367-1
Ostry J Berg A amp Tsangarides C (2014) Redistribution inequality and growth International Monetary Fund
Pacheco F Saacutenchez R amp Villena M (2013) Eficiencia de los Gobiernos Locales y sus Determinantes Un anaacutelisis de Fronteras Estocaacutesticas en Datos de Panel para
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Municipalidades Chilenas Santiago de Chile Chile
Papyrakis E amp Raveh O (2014) An Empirical Analysis of a Regional Dutch Disease The Case of Canada Environmental and Resource Economics 58(2) 179ndash198 httpsdoiorg101007s10640-013-9698-z
Paredes D (2013) The Role of Human Capital Market Potential and Natural Amenities in Understanding Spatial Wage Disparities in Chile Spatial Economic Analysis 8(2) 154ndash175 httpsdoiorg101080174217722013774094
Paredes D Iturra V amp Lufin M (2016) A spatial decomposition of income inequality in Chile Regional Studies 50(5) 771ndash789
Phan P Van Orsquobrien M Mendolia S amp Paloyo A (2017) National pro-poor spending programmes and their effect on income inequality and poverty Evidence from Vietnam Applied Economics 49(55) 5579ndash5590 httpsdoiorg1010800003684620171313957
Podinovski V V (2004) Bridging the Gap between the Constant and Variable Returns-to-Scale Models Selective Proportionality in Data Envelopment Analysis The Journal of the Operational Research Society 55(3) 265ndash276 Retrieved from httpwwwjstororgstable4102006
Ravallion M (2005) On Measuring Aggregate Social Efficiency Economic Development and Cultural Change 53(2) 273ndash292 httpsdoiorg101086425380
Rehner J Baeza S A amp Barton J R (2014) Chilersquos resource-based export boom and its outcomes Regional specialization export stability and economic growth Geoforum 56(Supplement C) 35ndash45 httpsdoiorghttpsdoiorg101016jgeoforum201406007
Rivera J Gutieacuterrez M amp Nuacutentildeez J (2009) Caracterizacioacuten socioeconoacutemica y espacial de la criminalidad en Chile Revista CEPAL
Ross C E (2011) Collective threat trust and the sense of personal control Journal of Health and Social Behavior 52(3) 287ndash296
Ross C E amp Mirowsky J (2001) Neighborhood disadvantage disorder and health Journal of Health and Social Behavior 258ndash276
Rothstein B amp Uslaner E M (2005) All for all Equality corruption and social trust World Politics 58(1) 41ndash72
Sachs J D amp Warner A M (2001) The curse of natural resources European Economic Review 45(4ndash6) 827ndash838 httpsdoiorg101016S0014-2921(01)00125-8
Salas R (2019) sinimr Chilean Municipalities Information System Wrapper Retrieved from httpsgithubcomrobsalascosinimr
Sampson R J (1986) Crime in Cities The Effects of Formal and Informal Social Control Crime and Justice 8 271ndash311
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Sampson R J (2008) Rethinking crime and immigration Contexts 7(1) 28ndash33
Santos Silva J M C amp Tenreyro S (2010) On the existence of the maximum likelihood estimates in Poisson regression Economics Letters 107(2) 310ndash312 httpsdoiorghttpsdoiorg101016jeconlet201002020
Santos Silva J M C amp Tenreyro S (2011) poisson Some convergence issues Stata Journal 11(2) 207ndash212 Retrieved from httpwwwstata-journalcomarticlehtmlarticle=st0225
Scott Z (2009) Decentralisation local development and social cohesion an analytical review GSDRC Research Paper 5
Sinha R P (2017) Fiscal Performance Benchmarking of Indian States-A Robust Frontier Approach The Central European Review of Economics and Management 1(4) 225ndash249
Skogan W (1986) Fear of crime and neighborhood change Crime and Justice 8 203ndash229
Skogan W (1999) Measuring what matters Crime disorder and fear In Measuring what matters Proceedings from the Policing Research Institute meetings (pp 37ndash53) National Institute of Justice Washington DC
Skogan W (2015) Disorder and Decline The State of Research Journal of Research in Crime and Delinquency 52(4) 464ndash485 httpsdoiorg1011770022427815577836
Šťastnaacute L amp Gregor M (2014) Public sector efficiency in transition and beyond evidence from Czech local governments Applied Economics 47(7) 1ndash20 httpsdoiorg101080000368462014978077
Tandon A (2005) Measuring Efficiency of Macro Systems An Application to Millennium Development Goal Attainment Asian Development Review 22(2) 108ndash125
Taylor R B (1999) The incivilities thesis Theory measurement and policy Measuring What Matters 65 88
Tigga N S amp Mishra U S (2015) On Measuring Technical Efficiency of the Health System in India An Application of Data Envelopment Analysis Journal of Health Management 17(3) 285ndash298 httpsdoiorg1011770972063415589229
Tinbergen J (1975) Demographic Development and the Exhaustion of Natural Resources Population and Development Review 1(1) 23ndash32 httpsdoiorg1023071972269
Tiruneh G (2014) Social Revolutions Their Causes Patterns and Phases SAGE Open 4(3) 2158244014548845 httpsdoiorg1011772158244014548845
Tolsma J Van der Meer T amp Gesthuizen M (2009) The impact of neighbourhood and municipality characteristics on social cohesion in the Netherlands Acta Politica 44(3) 286ndash313
Tsekeris Sotiris T Tsekeris T amp Papaioannou S (2018) Regional determinants of technical efficiency evidence from the Greek economy Regional Studies [London]thinsp Carfax
138
httpsdoiorg1010800034340420171390312
Uslaner E (2002) The moral foundations of trust Cambridge University Press
Uslaner E (2011) CORRUPTION AND INEQUALITY DICE Report 9(2) 20ndash24
Uslaner E (2013) Trust and corruption revisited how and why trust and corruption shape each other Quality amp Quantity 47(6) 3603ndash3608 httpsdoiorg101007s11135-012-9742-z
Uslaner E amp Brown M (2005) Inequality trust and civic engagement American Politics Research 33(6) 868ndash894
Van der Ploeg F (2011) Natural Resources Curse or Blessing Journal of Economic Literature 49(2) 366ndash420 httpsdoiorg101257jel492366
Vergara R (2012) Crime Prevention Programs Evidence From CHILE The Developing Economies 50(1) 1ndash24
Watkins M H (1963) A staple theory of economic growth Canadian Journal of Economics and Political ScienceRevue Canadienne de Economiques et Science Politique 29(2) 141ndash158
Wilkinson R G (1999) Income inequality social cohesion and health clarifying the theorymdasha reply to Muntaner and Lynch International Journal of Health Services 29(3) 525ndash543
Wilson J Q amp Kelling G L (1982) Broken windows Atlantic Monthly 249(3) 29ndash38
Wilson W J (1996) When Work Disappears Political Science Quarterly 111(4) 567ndash595 httpsdoiorg1023072152085
Wirth L (1938) Urbanism as a Way of Life American Journal of Sociology 44(1) 1ndash24
Worthington A amp Dollery B (2000) An empirical survey of frontier efficiency measurement techniques in local government Local Government Studies 26(2) 23ndash52 httpsdoiorg10108003003930008433988
Wu P-C Huang T-H amp Pan S-C (2014) Country Performance Evaluation The DEA Model Approach Social Indicators Research 118(2) 835ndash849 httpsdoiorg101007s11205-013-0443-3
Ye X Ma L Ye K Chen J amp Xie Q (2017) Analysis of Regional Inequality from Sectoral Structure Spatial Policy and Economic Development A Case Study of Chongqing China Sustainability 9(4) 633 Retrieved from httpwwwmdpicom2071-105094633
Yue W Z Zhang Y T Ye X Y Cheng Y Q amp Leipnik M R (2014) Dynamics of Multi-Scale Intra-Provincial Regional Inequality in Zhejiang China Sustainability 6(9) 5763ndash5784 httpsdoiorg103390su6095763
139
Appendices
Appendix A Summary statistics income inequality
Table A1
Summary statistics Gini coefficients by year and zone
140
Appendix B Summary statistics for NRD measures by region
Table B1
Summary statistics NRD measures by region
141
Appendix C Regional administrative division and defined zones
Figure C1 Geographical distribution of Chilean regions and 3 zones
142
Appendix D Summary statistics numeric controls and correlation matrix
Table D1
Summary Statistics Numeric Explanatory Variables
Figure D1 Correlation matrix numeric explanatory variables
143
Appendix E Static spatial panel models
Following Millo amp Piras (2012) a model including a spatial lag of the dependent variable and
spatial autoregressive disturbances but not spatial lags for the explanatory variable(s) is called
SARAR model A static spatial SARAR panel could be expressed as
119910 120582 119868 otimes119882 119910 119883120573 119906 (E1)
where y is an 119873 1 vector of observations on the dependent variable X is a 119873 119896 matrix of
observations on the non-stochastic exogenous regressors 119868 an identity matrix of dimension 119879 otimes
is the kronecker operator 119882 is the 119873 119873 spatial weights matrix of known constants whose
diagonal elements are set to zero and 120582 the corresponding spatial parameter44
The disturbance vector is the sum of two terms
119906 120580 otimes 119868 120583 120576 (E2)
where 120580 is a 119879 1 vector of ones 119868 an 119873 119873 identity matrix 120583 is a vector of time-invariant
individual specific effects (not spatially autocorrelated) and 120576 a vector of spatially autocorrelated
innovations that follow a spatial autoregressive process of the form
120576 120588 119868 otimes119882 120576 120584 (E3)
If we assume that spatial correlation applies to both the individual effects 120583 and the remainder
error components 120576 Kapoor et al (2007) propose that the disturbance term 119906 follows a first order
spatial autoregressive process of the form
119906 120588 119868 otimes119882 119906 120576 (E4)
44 Unlike ldquotraditional panelsrdquo which are organized as different time series for each cross-sectional unit ldquospatial panelsrdquo are organized as a series of cross-sections for each year
144
where 119882 is the spatial weights matrix and 119903ℎ119900 the corresponding spatial autoregressive
parameter To further allow for the innovations to be correlated over time the innovations vector
in Equation 7 follows an error component structure
120576 120580 otimes 119868 120583 120584 (E5)
where 120583 is the vector of cross-sectional specific effects 120584 a vector of innovations that vary
both over cross-sectional units and time periods 120580 is a vector of ones and 119868 an 119873 119873 identity
matrix45
Spatial panel models are usually estimated by Maximum Likelihood or GMM46 The SAR
SEM or SARAR models could be estimated with Random or Fixed effects For instance A fixed
effect spatial lag model can be written in stacked form as
119910 120582 119868 otimes119882 119910 120580 otimes 119868 120583 119883120573 120576 (E6)
where 120582 is the spatial autoregressive coefficient 119882 a non-stochastic spatial weights matrix
120580 a column vector of ones of dimension 119879 119868 an 119873 119873 identity matrix and 120576 sim 119873 0120590 On
the other hand a fixed effects spatial error model assuming the disturbance specification by
Kapoor et al (2007) can be written as
119910 120580119879 otimes 119868119873 120583 119883120573 119906119906 120588 119868119879 otimes119882119873 119906 120576
(E7)
where 120588 is the spatial autocorrelation coefficient and 120576 is a well-behaved error term
45 In the regression implementation the specification given by equations (22) and (3) is denoted by ldquobrdquo for ldquoBaltagirdquo On the other hand the specification given by equations (23) and (31) is denoted by ldquokkprdquo for ldquoKapoor Kelejian and Pruchardquo 46 We use the R package splm which allow both types of regression procedure
145
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis
Table F1
Analysis OLS residuals Anselin Method
Figure F1 Moran scatter plot OLS residuals
146
Appendix G Linear panel data models
Table G1
Panel regressions (non-spatial)
147
Appendix H Spatial panel models (Generalized Moments (GM) estimation)
Table H1
GM Spatial Models
148
Appendix I Inputs and outputs used in DEA analysis
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018)
149
Appendix J Technical and scale efficiency
Following lo Storto (2013) under an input-oriented specification assuming VRS with n
municipalities using k inputs to produce m outputs the DEA model for a given i-th municipality
is specified with the following mathematical programming problem
119898119894119899 120579119904119906119887119895119890119888119905 119905119900 119910 119884120582 0120579119909 119883120582 01198991prime120582 1 120582 0prime
Where 119910 is the column vector of the outputs and 119909 is the column vector of the inputs
Moreover we can define X as the (k times n) input matrix and Y as the (m times n) output matrix 120579 is a
scalar (that satisfies 120579 1) more specifically it is the efficiency score that measures technical
efficiency It measures the distance between a municipality and the efficiency frontier defined as
a linear combination of the best practice observations With 120579 1 the municipality is inside the
frontier (ie it is inefficient) while 120579 1 implies that the municipality is on the frontier (ie it is
efficient) The vector 120582 is an (n times 1) vector of constants that measures the weights used to compute
the location of an inefficient municipality if it were to become efficient
The total technical efficiency 119879119864 can be decomposed into pure technical efficiency
119879119864 and scale efficiency 119878119864 where 119878119864 119879119864 119879119864 (Coelli et al 2005) To find out
whether a municipality is scale efficient and qualify the type of returns of scale a DEA model
under non-increasing returns to scale 119879119864 is implemented where 119878119864 119879119864 119879119864 Hence
the following rule can be applied (Faumlre Grosskopf amp Lovell 1985)
bull If 119878119864 1 then a municipality is scale efficient both under CRS and VRS
bull If 119878119864 1 it operates under increasing returns to scale
bull If 119878119864 1 it operates under decreasing returns to scale
150
Appendix K Correlation matrix
Figure K1 Correlation matrix contextual factors
151
Appendix L Returns to scale by year and zone
Table L1
Returns to scale (percentage of municipalities)
152
Appendix M Returns to scale by year (maps)
Figure M1 Spatial distribution of returns to scale by county per year
153
Appendix N Efficiency status by year (maps)
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year
154
Appendix O Spatial distribution efficiency scores by year (maps)
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year
155
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis
Table P1
Analysis OLS residuals Anselin Method
Figure P1 Moran scatter plot efficiency scores and OLS residuals
156
Table P2
OLS and spatial regression models for the six-year averaged data
157
Appendix Q OLS regressions for cross-sectional and panel data
Table Q1
OLS cross-sectional regression per year
158
Table Q2
OLS panel regressions Pooled random effects and instrumental variable
159
Appendix R Quantile maps incivilities rate by group (average total period)
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17)
160
Appendix S Correlation matrix numeric covariates
Figure S1 Correlation matrix numeric covariates
161
Appendix T Negative Binomial regressions
Table T1
Negative Binomial regressions
162
Appendix U Coefficients economic and racial diversity by geographical zone
Table U1
Coefficients economic and racial diversity in pooled Poisson models by geographic zone
ii
Abstract
Persistently high indicators of relative economic disadvantage such as measures of income
inequality can give rise to a feeling of discontent in the population which in turn can trigger
costly social conflicts For instance inequality has been suggested as one of the main causes of
social outburst considering recent events in many countries around the world This has generated
in extant literature an increasing number of criticisms of current political and socio-economic
models This research considers the Chilean economy which is recognised as an example of the
success of standard economic thinking however it is also well-known for its persistently high
levels of inequality an adverse indicator of economic performance This thesis contributes with
three essays to the understanding of the sources and potential consequences of income inequality
in Chile The data consider a panel of 324 Chilean counties and their corresponding municipalities
for the 2006ndash2017 period
The first essay investigates the association between income inequality and the endowment
of natural resources The Gini coefficient of each county is used as a measure of income inequality
The influence of natural resources on income inequality is captured by using the proportion of
employment in the primary sector as a proxy for the degree of dependence on natural resources in
each county Previous literature has identified a significant spatial dimension of income inequality
in Chile but this spatial dimension has been largely neglected in the domain of policy design and
implementation Thus the analysis in this essay applies spatial regression models for cross-
sectional and panel data while controlling for other socioeconomic and demographic
characteristics The main finding is that contrary to what theory predicts our measure of natural
resource dependence in terms of employment shows a robust and significant negative association
with income inequality The main implication of this empirical result is that a transformation
process towards activities less dependent on natural resources reinforces rather than reduces the
persistence of income inequality at least through the channel of employment Hence this
transformation process imposes additional challenges to central and local governments in their
goal of reducing income inequality Empirical analysis also shows a significant degree of positive
spatial autocorrelation of income inequality This means that counties with similar levels of income
iii
inequality tend to cluster in space The regression analysis confirms the importance of capturing
geographical heterogeneity in the explanation of income inequality however gives less support
to a process of spatial dependence like a spillover effect of income inequality among
neighbouring counties
Among the potential consequences of income inequality the literature highlights its
possible impacts on the efficiency in the provision of public services by local authorities however
empirical evidence is very little For this reason the second essay analyses the technical efficiency
of municipal local governments in Chile and examine if income inequality has significant impacts
on the variations in the efficiency levels across municipalities An input-oriented Data
Envelopment Analysis is used to measure municipal efficiency Results reveal that the municipal
production technology is characterized by variable returns to scale but scale inefficiencies only
explain a small proportion of total inefficiency This justify a need for analysing the influence of
variables which are beyond the control of local authorities in explaining differences in municipal
efficiency The main hypothesis tested was whether income inequality has a negative influence on
municipal efficiency whilst a measure of natural resource dependence at the county level was used
as an instrument to control for the effects of possible endogeneity issues Results showed that
changes in income inequality could be associated with changes in the municipal efficiency level
in the same magnitude but in the opposite direction This confirms that local authorities in counties
characterized by high levels of income inequality face greater challenges to achieve more efficient
performance This result suggests that policies aimed at reducing income inequality can also
increase the efficiency of local governments Our results also reveal that policies such as
amalgamation de-amalgamation or cooperation among municipalities should be designed
specifically for each region rather than as a standard national strategy
Finally the third essay analyses how social cohesion is associated with the levels of
economic and racial diversity Social cohesion is proxied using the reported number of antisocial
behaviours catalogued as incivilities Incivilities are those antisocial behaviours which violate
social norms but are not usually considered as criminal Research has documented the implications
of incivilities on human stress health public behaviour and increasing feelings of insecurity and
fear among the population Few studies have explicitly considered incivilities as a dependent
variable to identify their determinants or use them to analyse the weakening of social cohesion and
iv
the growing feeling of social unrest in contemporary societies Economic diversity is proxied using
the Gini coefficient in each county and racial diversity through the number of new visas granted
as proportion of the county population Our findings show that incivilities are strongly associated
with racial diversity and to a lesser extent with economic diversity The rate of incivilities also
shows a negative association with the level of income and a positive relationship with poverty and
unemployment rates These results give empirical support to the idea that both relative and
absolute indicators of economic deprivation play an important role in understanding the growing
problem of incivilities in highly unequal economies like Chile Results also show that the rate of
incivilities is negatively related to the degree of financial autonomy of municipalities These
findings represent promising areas for central and local governments in the implementation of
policies aimed at increasing social cohesion through the reduction of incivilities and other types of
antisocial behaviours
v
Table of Contents
Keywords i
Abstract ii
Table of Contents v
List of Figures viii
List of Tables ix
List of Abbreviations x
Statement of Original Authorship xi
Acknowledgements xii
Chapter 1 Introduction 13
Income inequality and dependence on natural resources 14
Local government efficiency and income inequality 16
Social cohesion and economic diversity 19
Contributions 21
Thesis outline 23
Chapter 2 Natural Resources Curse or Blessing Evidence on Income Inequality at the County Level in Chile 24
21 Introduction 24
22 Inequality and Natural Resources 28 221 Theoretical Framework 28
Cross-country literature 29 Single country evidence 32
222 The relevance of the spatial approach 33
23 Research problem and hypotheses 35
24 Data and Methods 36 241 Operationalization of key variables 36 242 Control variables 40 243 Methods 41 244 Spatial Model Specification 43
25 Results 44 251 Exploratory Spatial Data Analysis (ESDA) 44 252 Cross-sectional analysis 45 253 Panel Data analysis 48
26 Discussion and conclusions 51
Chapter 3 The Impact of Income Inequality on the Efficiency of Municipalities in Chile 55
vi
31 Introduction 55
32 Related Literature 61 321 Measuring efficiency of local governments 61 322 Explaining differences in LGE 63 323 The trade-off between efficiency and equity 64
33 Methodology 66 331 Chilean Municipalities and period of analysis 66 332 Measuring municipal efficiency 68 333 Inputs and outputs used in DEA 70 334 Regression model 71 335 The instrument 75
34 Results and discussion 77 341 DEA results 77
Returns to scale 78 Efficiency measure 80
342 Regression results 82 Exploratory spatial analysis 82 Cross-sectional analysis 83 Panel data analysis 84
35 Conclusions 88
Chapter 4 Social Cohesion Incivilities and Diversity Evidence at the municipal level in Chile 91
41 Introduction 91
42 Related Literature 95 421 The Community Heterogeneity Thesis 95 422 The literature on incivilities 97 423 The ldquoIncivilities Thesisrdquo 99
4 3 Methodology 100 431 Period of analysis and data sample 100 432 Operationalisation of the response variable and exploratory analysis 101 433 Measures of community heterogeneity and control variables 105 434 Methods 108 435 Hypotheses 111
44 Results and Discussion 112
4 5 Conclusions 118
Chapter 5 Conclusions 120
Bibliography 126
Appendices 139
Appendix A Summary statistics income inequality 139
Appendix B Summary statistics for NRD measures by region 140
Appendix C Regional administrative division and defined zones 141
Appendix D Summary statistics numeric controls and correlation matrix 142
vii
Appendix E Static spatial panel models 143
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis 145
Appendix G Linear panel data models 146
Appendix H Spatial panel models (Generalized Moments (GM) estimation) 147
Appendix I Inputs and outputs used in DEA analysis 148
Appendix J Technical and scale efficiency 149
Appendix K Correlation matrix 150
Appendix L Returns to scale by year and zone 151
Appendix M Returns to scale by year (maps) 152
Appendix N Efficiency status by year (maps) 153
Appendix O Spatial distribution efficiency scores by year (maps) 154
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis 155
Appendix Q OLS regressions for cross-sectional and panel data 157
Appendix R Quantile maps incivilities rate by group (average total period) 159
Appendix S Correlation matrix numeric covariates 160
Appendix T Negative Binomial regressions 161
Appendix U Coefficients economic and racial diversity by geographical zone 162
viii
List of Figures
Figure 21 Average share in GDP of economic activities (2006ndash17) 37
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17) 38
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17) 39
Figure 23 Moran scatter plots for variables gini and pss_casen 45
Figure 31 Geographical distribution of Chilean regions and macrozones 74
Figure 32 Evolution of efficiency scores and the proportion of firms by sector 77
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE) 78
Figure 34 Returns to scale by zone 79
Figure 35 Evolution mean efficiency scores (VRS) by zone 81
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017 102
Figure 42 Evolution total number of incivilities by category 104
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017) 104
Figure 44 Annual average number of incivilities per county 109
Figure C1 Geographical distribution of Chilean regions and 3 zones 141
Figure D1 Correlation matrix numeric explanatory variables 142
Figure F1 Moran scatter plot OLS residuals 145
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018) 148
Figure K1 Correlation matrix contextual factors 150
Figure M1 Spatial distribution of returns to scale by county per year 152
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year 153
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year 154
Figure P1 Moran scatter plot efficiency scores and OLS residuals 155
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17) 159
Figure S1 Correlation matrix numeric covariates 160
ix
List of Tables
Table 21 Cross-sectional Model Comparison (six-year average data) 47
Table 22 ML Spatial SAR Models 50
Table 23 ML Spatial SEM Models 50
Table 24 ML Spatial SARAR Models 51
Table 31 Descriptive statistics Inputs and Output variables used in DEA analysis 71
Table 32 Summary Statistics Numeric Contextual Factors 74
Table 33 Summary efficiency scores (VRS) by zone and region 80
Table 34 Cross-sectional (censored) regressions 84
Table 35 Panel data regressions 87
Table 41 Summary statistics total count of incivilities and by category (full sample and period) 103
Table 42 Summary statistics numeric explanatory variables 108
Table 43 Poisson regressions 113
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region 115
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group 116
Table A1 Summary statistics Gini coefficients by year and zone 139
Table B1 Summary statistics NRD measures by region 140
Table D1 Summary Statistics Numeric Explanatory Variables 142
Table F1 Analysis OLS residuals Anselin Method 145
Table G1 Panel regressions (non-spatial) 146
Table H1 GM Spatial Models 147
Table L1 Returns to scale (percentage of municipalities) 151
Table P1 Analysis OLS residuals Anselin Method 155
Table P2 OLS and spatial regression models for the six-year averaged data 156
Table Q1 OLS cross-sectional regression per year 157
Table Q2 OLS panel regressions Pooled random effects and instrumental variable 158
Table T1 Negative Binomial regressions 161
Table U1 Coefficients economic and racial diversity in pooled Poisson models by geographic zone 162
x
List of Abbreviations
Constant returns to scale CRS
Data envelopment analysis DEA
Decreasing returns to scale DRS
Efficiency scores ES
Exploratory spatial data analysis ESDA
Generalized methods of moments GMM
Gross Domestic Product GDP
Increasing returns to scale IRS
Local government efficiency LGE
Maximum likelihood ML
Municipal common fund MCF
Natural resource dependence NRD
Natural resource endowment NRE
Ordinary Least Squares OLS
Organization for Economic Cooperation and Development OECD
Own permanent revenues OPR
Resource curse hypothesis RCH
Spatial autoregressive model SAR
Spatial error model SEM
Variable returns to scale VRS
xi
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet requirements
for an award at this or any other higher education institution To the best of my knowledge and
belief the thesis contains no material previously published or written by another person except
where due reference is made
Signature QUT Verified Signature
Date _________04092020_________
xii
Acknowledgements
First I would like to thank my wife Lilian who joined me in this challenge and patiently
supported me all these years I would also like to thank our family who always supported us from
Chile I especially thank my sister Silvia who took care of our house and dog
I am also grateful to my supervisory team Dr Radhika Lahiri and Dr Vincent Hoang who
supported and guided me in the process of making this thesis a reality
I also thank the Deans of the Faculty of Economics and Business at my beloved University
of Talca Dr Arcadio Cerda and Dr Rodrigo Herrera who trusted me and supported me in this
process In the same way I would like to thank all the support of the director of the Commercial
Engineering career Mr Milton Inostroza
Finally I would like to thank the government of Chile for the financial support that made
my stay and studies possible here at the Queensland University of Technology
13
Chapter 1 Introduction
Efficiency and equity issues are often considered together in the evaluation of economic
performance While higher efficiency usually measured by growth rates of income per capita
correlates with improvements in measures of well-being the link between inequality and well-
being is less clear This is reflected not only in the type and amount of research related to efficiency
and equity but also in the role that both play in the design of the economic policy For instance
several market-oriented countries have focused primarily on economic growth trusting in a trickle-
down process where financial benefits given to the wealthy are expected to ultimately benefit the
poor However despite the growing interest in the issue of inequality there is a considerable lack
of studies about its consequences
Although some level of inequality is inevitable or even necessary for economic activity this
study is motivated by the argument that relatively high levels of inequality can be associated with
many problems such as persistent unemployment increasing fiscal expenses indebtedness and
political instability (Berg amp Ostry 2011) Inequality can also have other severe social
consequences including increased crime rates teenage pregnancy obesity and fewer
opportunities for low-income households to invest in health and education (Atkinson 2015) In
addition when the role of money and concentration of economic power undermine political
outcomes inequality of opportunities hampers social and economic mobility trust and social
cohesion In summary inequality can increase the fragility of the economic and social situation in
a country reducing economic growth and making it less inclusive and sustainable
14
A country well-known for its market-oriented economy and high level of dependence on
natural resources is Chile Chilean success in terms of economic growth contrasts with its inability
to reduce the persistently high levels of social and economic inequality particularly in the last
three decades Using data for the 2006-2017 period and considering 324 out of 345 Chilean
counties this thesis presents three essays with empirical evidence aiming to explain the
phenomenon of persistent income inequality and some of its potential consequences The first
essay aims to analyse how the evolution and variability of income inequality throughout the
country are associated with the degree of natural resource dependence The second essay studies
the relevance of income inequality in explaining cross-county differences in the performance of
local governments (municipalities) Finally the third essay explores the link between social
cohesion and community heterogeneity highlighting the importance of economic and racial
diversity
Income inequality and dependence on natural resources
The first essay explores how cross-county differences in income inequality are associated
with differences in the degree of dependence on natural resources We use the Gini coefficient in
each county as our dependent variable and the proportion of employment in the primary sector as
our measure of natural resource dependence The main hypothesis is that income inequality should
be positively related to the degree of natural resource dependence To test our hypothesis we use
a spatial econometric approach This approach is motivated by the study of Paredes Iturra and
Lufin (2016) who explore the geographic heterogeneity of income inequality in Chile finding
evidence of a significant spatial dimension
15
The theoretical and empirical literature has mostly proposed a positive link between
inequality and natural resources Although most of the evidence corresponds to cross-country
comparisons there is also increasing body of research at the local level A rationale underpinning
the positive link suggested in the literature is that in natural resource-rich countries ownership is
concentrated in small groups and extraction activities require low-skilled workers (Gylfason amp
Zoega 2003 Leamer Maul Rodriguez amp Schott 1999) Another market-based argument often
labelled as the ldquoDutch Diseaserdquo proposes that natural resource windfalls could be associated with
a crowding-out effect on the manufacturing sector (Corden amp Neary 1982 Easterly 2007) This
process encourages rent-seeking behaviours discourages investment in physical and human
capital and delays the process of technology adoption and industrialization (Auty 2001 Bulte
Damania amp Deacon 2005 Gylfason amp Zoega 2003) The result could be a lower economic
growth which is the central idea under the ldquoResource Curse Hypothesisrdquo (Auty 1993 Sachs amp
Warner 2001)
An ldquoinstitutionalrdquo argument for the positive association between inequality and the
endowment of natural resources is based on the so-called ldquoParadox of Plentyrdquo (Borge Parmer amp
Torvik 2015 Dauvin amp Guerreiro 2017) The idea is that both national and local authorities have
less incentive to operate efficiently when they experience windfalls in their revenues for
instance from natural resources This could end with corrupted authorities exerting patronage
clientelism and designing public policies to favour specific groups of the population (Uslaner amp
Brown 2005) Evidence also suggests that the final effect of natural resource booms on income
inequality will depend on authoritiesrsquo capacity to manage these additional resources the extent of
commuting and migration among regions and the potential increase in the demand for non-tradable
16
goods which are intensive in unskilled workers (Aroca amp Atienza 2011 Cust amp Poelhekke 2015
Fleming amp Measham 2015b Howie amp Atakhanova 2014 Michaels 2011)
Contrary to most theoretical and empirical evidence we find that income inequality shows
a robust and significant negative association with our proxy for natural resource dependence This
result suggests that the process of transformation to an economy less dependent on natural
resources could have exacerbated rather than alleviated the persistence of income inequality The
decrease in the participation of the primary sector in employment in favour mainly of the tertiary
sector highlights the importance of the latter to explain the current high levels of inequality and its
future evolution Another important result is that spatial linear models show practically the same
results as traditional linear models This could be interpreted as the spatial dimension previously
found in income inequality is not the result of spatial dependence in the variable itself for instance
due to a process of spillover among counties Hence the usually found positive spatial
autocorrelation of income inequality (similar levels in neighbouring counties) could be explained
by spatial patterns in other variables or to the spatial heterogeneity that characterizes the Chilean
economy
Local government efficiency and income inequality
Essay 2 delves deep into the potential trade-off between efficiency and equity We measure
the efficiency of Chilean municipalities which correspond to the organizations in charge of
managing each county Municipal efficiency is understood as ldquotechnical efficiencyrdquo that is the
possibility that each municipality has reached the same level of outputs with less use of inputs
Then we analyse how income inequality controlling for other contextual factors such as
socioeconomic demographic geographical and political characteristics may help to explain
17
differences in municipal performance Our main hypothesis is that municipal efficiency is
inversely associated with income inequality Moreover we seek a causal interpretation of this
relationship
Municipal performance could be influenced by income inequality in direct and indirect ways
In a direct sense income inequality is used to capture the degree of heterogeneity and complexity
in the demand for public services that citizens exert over local authorities Hence higher levels of
income inequality should be associated with a more complex set of public services and therefore
with lower levels of municipal efficiency (Jottier Ashworth amp Heyndels 2012) Furthermore
when high levels of inequality exist the richest groups can exert a higher influence over local
authorities resulting in low quality and quantity of services for most of the population Among
indirect effects high and persistent inequality could be the source of corrupted institutions and
local authorities favouring themselves or specific groups This undermines citizensrsquo participation
in civic activities and their willingness to monitor municipal performance (Uslaner amp Brown
2005) Additionally the potential benefits of decentralization on the way local governments
deliver public services will be limited when the context is characterized by corrupted politicians
and a limited administrative and financial capacity (Scott 2009)
We measure municipal efficiency using an input-oriented Data Envelopment Analysis
(DEA) to obtain efficiency scores for our sample of 324 municipalities in each year from 2006 to
2017 Then we study the influence on municipal efficiency of income inequality and our set of
contextual factors using a panel of six years corresponding to those years for which household
income information is available 2006 2009 2011 2013 2015 and 2017 Our dependent variable
is the set of efficiency scores which are relative measures of efficiency They are relative to the
18
municipalities included in the sample and they do not imply that higher technical efficiency gains
cannot be achieved Thus we use both cross-sectional and panel censored regression models To
tackle endogeneity issues and suggest a causal interpretation we also propose using the proportion
of firms in the primary sector as an instrument for income inequality
We find an average efficiency score of 83 meaning that Chilean municipalities could
reduce the use of inputs by 17 without reducing their outputs We also measure municipal
efficiency under different assumptions related to returns to scale This allows us to disaggregate
technical efficiency to assess whether inefficiencies are due to management issues (pure technical
efficiency) or scale issues (scale efficiency) Although the results show that most municipalities
operate under increasing or decreasing returns to scale scale inefficiencies only explain a small
proportion of total municipal inefficiencies This highlights the need to look for contextual factors
outside the control of local authorities to explain differences in municipal performance
Geographical representations of our results in terms of returns to scale and efficiency scores
show some spatial clustering process among municipalities Spatial statistics tests confirm that
efficiency scores show a significant positive spatial autocorrelation This means that neighbouring
municipalities tend to show similar levels of efficiency This similar performance could be due to
a process of spatial dependence (eg efficiency spillovers among neighbouring municipalities) or
due to the existence of structural-geographical similarities (spatial heterogeneity) To assess the
spatial dimension in municipal efficiency abstracting from temporal fluctuations we use a cross-
section of data consisting of the six-year average for the variables in our panel After running a
regression of efficiency scores against the set of controls the analysis of OLS residuals shows that
the spatial autocorrelation is almost completely removed This means that the spatial pattern in
19
municipal efficiency can be explained (controlled) by other variables such as regional indicator
variables rather than efficiency itself Given this result we proceed to study the influence of
income inequality on municipal efficiency using traditional (non-spatial) regression analysis
In contrast to literature that emphasizes an equity-efficiency trade-off (Andersen amp Maibom
2020 Berg amp Ostry 2011 Browning amp Johnson 1984 Okun 2015) that is greater equality leads
to lower efficiency we find that municipal efficiency is inversely associated with income
inequality This implies that more equal counties are also those with higher municipal efficiency
Furthermore the coefficient of income inequality is close to one when we use the instrumental
variable approach This means that a reduction in income inequality ceteris paribus should be
associated with an increase in the same magnitude in municipal efficiency This result has strong
policy implications The non-existence of the trade-off suggests that there is more to be gained by
targeting policies towards the reduction of inequality than conventional theories suggest For
instance these policies may help increase the levels of efficiency and well-being at least at the
municipal level
Social cohesion and economic diversity
The third essay studies the relationship between the degree of social cohesion and diversity
in Chile Extant literature has argued that one of the main factors influencing social cohesion is
the degree of economic and ethnic-racial diversity within a society This diversity erodes social
cohesion reducing trust and corrupting institutions (Letki 2008 Rothstein amp Uslaner 2005
Tolsma Van der Meer amp Gesthuizen 2009 Uslaner 2011 2013)
To measure social cohesion scholars have traditionally used measures of social capital trust
or feelings of insecurity (Ariely 2014 Chan To amp Chan 2006 Letki 2008) We suggest the use
20
of the rate of incivilities per 1000 inhabitants as a proxy for social cohesion Incivilities correspond
to those antisocial behaviours (eg groups of rowdy teens and public drunkenness) or visible
neighbourhood conditions (eg graffiti and abandoned buildings) that tend not to be treated as
crime Using the rate of incivilities is arguably a more objective and reliable measure of social
cohesion particularly in countries where institutions of order and security are among the most
trusted An increase in the rate of incivilities rather than changes in crime rates should better
capture the worsening in social cohesion experienced in countries such as Chile where crime rates
are not growing but social conflicts are on the rise Thus the main hypothesis in this essay is that
the rate of incivilities (social cohesion) should be positively (negatively) associated with economic
and racial diversity
Using panel count data models we start analysing how differences in incivilities rates
between and within counties are associated with differences in indicators of relative and absolute
economic disadvantage We use the Gini coefficient of each county as our measure of economic
diversity Although we find a significant and positive association between the rate of incivilities
and the level of income inequality the magnitude of the link seems to be small Among absolute
indicators of economic disadvantage only the level of income shows a strong effect Next we
include our measure of racial diversity We use the number of new visas granted to foreigners as
a proportion of the county population Results show a significant and strong positive association
between the rate of incivilities and racial diversity
To check the robustness of our results we analyse the impact of our measures of economic
and racial diversity running our models separately for each Chilean region and clustering them
geographically We also split the total number of incivilities in four categories to see which type
21
of incivilities show the greatest association with our measures of diversity In general results
support the ldquocommunity heterogeneity hypothesisrdquo that higher community heterogeneity is
associated with higher rates of incivilities (Alesina amp La Ferrara 2002 Letki 2008 Tolsma et al
2009) However results do not support the ldquoincivilities thesisrdquo that the distribution of incivilities
tends to mirror the distribution of income inequality (Skogan 1999 Taylor 1999)
Three results stand out among the set of control variables First the level of education shows
and independent and significant negative association with the rate of incivilities This is in contrast
to previous studies where education acts mainly as a moderator of the effect of economic and racial
diversity on social cohesion (Tolsma et al 2009) The results also show that there is no significant
relationship between the rate of incivilities and the proportion of young population This is relevant
because policies aimed to reduce incivilities usually put the focus on specific groups such as young
people which are linked to physical and social incivilities when social control is weakened
Finally the degree of financial municipal autonomy also shows a significant negative association
with the rate of incivilities This result suggests that municipalities can contribute independently
or together with the central government to reduce incivilities and strengthen social cohesion
Contributions
The three essays in this thesis provide several important insights into the analysis of the
causes and consequences of income inequality particularly in the context of Chile ndash a typical
resource rich economy with persistently high levels of income inequality
Essay 1 advances the understanding of the relationship between income inequality and
natural resources in Chile extending the empirical analysis from the regional level to the county
level In addition the geographic heterogeneity of income inequality is explored with the inclusion
22
of alternative sources of spatial dependence as a potential dimension of the causal relationship
between income inequality and natural resources This essay demonstrates the relevance of natural
resources in explaining the persistence of income inequality even after controlling for other
socioeconomics and institutional factors Findings from this study have potential contribution not
only in the design of policies aimed to reduce income inequality but also in addressing the current
developmental bias between the metropolitan region and the rest of the country
Essay 2 is one of the first studies that undertake a longitudinal analysis of the effects of
income inequality on the efficiency of municipal governments in Chile To capture the role of the
municipal governments in the provision to local people of public services such as education and
health we specify several inputs and outputs in our efficiency model which is different from the
conventional specification in the existing literature For example the number of medical
consultations in public health facilities and the number of enrolled students in public schools are
used as outputs instead of general indicators such as county population Our empirical analysis
also utilises a larger sample of municipalities and covers a much longer period spanning from 2006
to 2017 This essay also investigates the contextual factors beyond the control of local authorities
that can explain variations in the efficiency of municipal governments across the country
Empirical findings from Essay 2 help us increase our understanding of the production
technology of municipalities the sources of inefficiencies and specifically the impact of income
inequality on the performance of local authorities The results deliver two main policy
implications First municipal inefficiencies in the provision of public goods and services differ
across Chilean municipalities In addition efficiency levels show some degree of spatial
autocorrelation This implies that policies such as amalgamation or cooperation among
23
municipalities could have effects beyond the municipalities involved which must be considered
Second the causal effect that income inequality has on municipal efficiency provides another
dimension into the design and implementation of development policies
Essay 3 explores for the first time the effects of economic and racial diversity on social
cohesion in Chile This essay considers incivilities as manifestation of social cohesion and
investigates as extant literature suggests whether indicators of relative economic disadvantage
such as income inequality are among the main factors driving social disorganization and social
unrest Empirical findings suggest that on the one hand economic heterogeneity captured by the
Gini coefficient has a disparate effect both in terms of magnitude and significance on the rate of
incivilities across the country On the other hand the impact of racial heterogeneity appears to be
stronger more significant and of a similar magnitude throughout the country Results also provide
new insights into the design of national policies addressing social disorders particularly those
policies focussed on specific groups of the population and the role of local authorities Overall the
findings provide an opportunity to advance the understanding of the process of weakening in the
social cohesion experienced in Chile and the conflicts that have risen from this process
Thesis outline
The remainder of the thesis is organized as follows Chapter 2 presents essay 1 examining
the association between income inequality and the degree of dependence on natural resources
Chapter 3 presents essay 2 which looks for a causal relationship between municipal efficiency and
income inequality Chapter 4 presents essay 3 analysing the relationship between social cohesion
and economic and racial diversity Finally Chapter 5 presents some concluding remarks
24
Chapter 2 Natural Resources Curse or Blessing Evidence on
Income Inequality at the County Level in Chile
21 Introduction
A phenomenon of increasing inequality of incomes and wealth in recent decades has been
documented by leading scholars and international organizations such as the International Monetary
Fund (Berg amp Ostry 2017 Ostry Berg amp Tsangarides 2014) and the Organization for Economic
Cooperation and Development (Cingano 2014) These efforts have placed the topic of inequality
at the top of the current economic debate recognizing inequality as a determinant not only of
economic growth but also of human development They also have highlighted the necessity for
more research on the drivers of inequality and mechanisms through which it manifests aiming to
design effective policies in reducing economic and social inequalities
Various factors have been analysed as the sources of high and increasing levels of inequality
Among the most significant factors are the levels of income at initial stages of economic
development (Kuznets 1955) Globalization (Milanovic 2016) skill-biased technological change
(Tinbergen 1975) investment in human capital (Murphy amp Topel 2016) institutions
redistributive policy and country-specific characteristics (Acemoglu 1995 2002 Acemoglu
Aghion amp Violante 2001 Acemoglu Johnson amp Robinson 2001) Our focus in this essay is on
the importance that the natural resource endowment (NRE) or lack thereof can play in the
determination of income disparities
25
This essay studies the patterns and evolution of income inequality in the context of a natural
resource-rich country Using the case of the Chilean economy we aim to understand and
disentangle how a phenomenon of high- and persistent-income inequality is related to the
endowment of natural resources that a country owns Chile is an interesting case to study because
despite showing a successful history of economic growth inequality among individuals and among
aggregated spatial units has shown a strong persistence (Paredes et al 2016) Furthermore Chile
has remained among the most unequal countries in the world1
Theory and empirical evidence do not establish a clear link between income inequality and
NRE In addition NRE has received considerably less attention (Auty 2001 ElGindi 2017) and
most of the evidence has been focused on cross-country comparisons For instance NRE can
influence inequality by determining its initial levels (Engerman amp Sokoloff 1994 1997
Engerman Sokoloff Urquiola amp Acemoglu 2002) shaping the evolution of institutions
(Acemoglu 2002) make the educational system less intellectually challenging and moulding the
structure of economic activity (Leamer et al 1999) So studying how cross-county differences in
NRE are associated with the distribution of income within a country has theoretical empirical and
policy implications
In this study we offer empirical evidence on the relationship between income inequality and
the endowment of natural resources using data at the county level in Chile for the period 2006-
2017 Income inequality is measured by the Gini coefficient The importance of NRE is proxied
using a measure of natural resource dependence (NRD) defined as the percentage of the total
1 A 2014 OECD report on income inequality (httpwwwoecdorgsocialincome-distribution-databasehtm) showed Chile as the country with the highest Gini coefficient of disposable income among OECD countries OECD also indicates Chile as the country with the widest gap between the richest 10 percent and the poorest 10 percent of countryrsquos population among its 34 members (OECD 2014)
26
employment in each county corresponding to the primary sector (agriculture forestry fishing and
mining)
The main hypothesis to be tested is whether income inequality is positively associated with
the degree of NRD The transmission mechanisms through which natural resources could influence
socioeconomic outcomes could be based on the market or institutions The market-based approach
argues that natural resource booms could be associated with an appreciation of the real exchange
rate and a crowding out effect over other more productive economic activities such as
manufacturing It could also delay the adoption of new technologies and reduce incentives to invest
in physical and human capital (Gylfason amp Zoega 2003) Based on the ldquoResource Curse
Hypothesisrdquo (RCH) natural resources could be a curse when the political and institutional
framework is weak and natural resources are concentrated in space such as oil and minerals
(Deacon 2011) 2 Among institutional channels a higher NRD or natural resource booms could
be associated with rent seeking misallocation of labour and entrepreneurial talent institutional
and political decline or even violent conflicts For instance the ldquoParadox of Plentyrdquo sustains that
windfalls of revenues as a consequence of resource booms could be related to a lack of incentives
to perform efficiently corruption patronage and local authorities favouring their voters or being
captured by the richest groups (Dauvin amp Guerreiro 2017) Hence a higher NRD or natural
resource booms could be the explanation not only for low levels of growth in regions more
dependent on natural resources but also it could be the root of income disparities
2 There is a wide strand of research on the Resource Curse Hypothesis however the evidence so far is not conclusive Evidence in favour of RCH has been mainly found in developing resource rich countries (Auty 1993 2001 Badeeb Lean amp Clark 2017 Blanco amp Grier 2012 Borge et al 2015 Brunnschweiler amp Bulte 2008 Sachs amp Warner 2001 Van der Ploeg 2011)
27
To test our hypothesis that is whether the levels of income inequality across counties are
positively associated with the degree of NRD we use a spatial econometric approach We use this
approach because attributes such as income inequality in one region may not be independent of
attributes in neighbouring regions (Armstrong amp Taylor 2000) This process of spatial dependence
invalidates the use of traditional (non-spatial) approaches
This study seeks to make two contributions to research First previous empirical evidence
shows a significant spatial dimension of income inequality in Chile (Paredes et al 2016)
However this dimension has been barely explored with most studies limiting the degree of
disaggregation to a regional scale (Aroca amp Bosch 2000) We use a spatial approach which makes
it possible to model and test the significance of the spatial dimension in the analysis of income
inequality and its relationship with other variables Second previous research for the Chilean
economy linking inequality with NRE has been mainly focused on explaining differences between
regions or the importance and effects of the mining-copper sector (Aroca amp Atienza 2011 Ebert
amp La Menza 2015 Lagos amp Blanco 2010 Rehner Baeza amp Barton 2014) We extend this
analysis using data for local economies Identifying and quantifying the impact of NRE on income
inequality at the county level is likely to be more informative for policies aiming to address the
current developmental bias between the metropolitan region and the rest of the country Moreover
the analysis of the role of natural resources in conjunction with other potential sources of inequality
may shed lights in understanding the persistence of the high levels of inequality observed in the
Chilean economy All in all this study could contribute to the design of policies that
simultaneously help reduce inequality increase efficiency and promote sustainable and inclusive
growth
28
Our main finding shows that after controlling for other potential sources of income
inequality such as educational level demographic characteristics and the level of public
government expenditure the degree of dependence on natural resources has a significant effect on
income inequality However contrary to our expectations the effect is negative This result
suggests that the natural or policy-driven process of transformation from primary and extractive
activities to manufacturing and service sectors imposes additional challenges to central and local
authorities aiming to reduce income inequality
In section 22 we review the literature on the relationship between income inequality and
natural resources In section 23 we establish our research problem and main hypothesis Section
24 describes our data and methods and section 25 the empirical results We finish with section
26 discussing our main results concluding and proposing avenues for future research
22 Inequality and Natural Resources
221 Theoretical Framework
Explanations for income inequality can be associated with individual institutional political
and contextual characteristics Individual characteristics include age gender and mainly the level
of education and skills of the population in the labour force For instance globalization and
technological change lead firms to increase the demand for skilled labour deepening income
inequality between skilled and unskilled workers (Atkinson 2015 Milanovic 2016 Tinbergen
1975) Among institutional characteristics labour unions collective bargaining and the minimum
wage have been suggested as explanations of income inequality (Acemoglu Aghion et al 2001
Atkinson 2015) Policy design associated with market regulation progressive taxation and
redistribution can also impact the levels and patterns of inequality
29
A key factor in understanding the levels and differences in income distribution within a
country may be its endowment of natural resources NRE shapes the structure of the economy
(Leamer et al 1999) it is associated with the creation of institutions that define the political
culture and it can also influence the performance of other sectors (Watkins 1963) In addition
NRE determines initial conditions market competition ownership over resources rent seeking
and the geographical concentration of the population and economic activity
Cross‐countryliterature
Bourguignon and Morrison (1990) introduce one of the earliest theoretical frameworks
describing the relationship between inequality and NRE They develop a small open economy
model where income distribution is a function of NRE ownership structure and trade protection
Giving cross-sectional evidence for a group of developing countries they conclude that the impact
of NRE particularly mineral resources and land depends on the number and size of the firms
whether they are public or private and the level of protection A higher concentration of production
in a few private firms a big share of production oriented to foreign instead of domestic markets
and protection increasing the relative price of scarce resources are some of the reasons explaining
why some countries are less egalitarian than others
NRE could also influence the evolution and levels of inequality by determining the initial
distribution of incomes This is known as the ldquoEngerman-Sokoloff Hypothesisrdquo (Engerman amp
Sokoloff 1997 Engerman et al 2002) In addition Leamer (1999) proposes that inequality and
development paths in each economy are a function of its economic structure which in turn depends
on ldquofundamentalsrdquo and ldquosymptomsrdquo On the one hand ldquofundamentalsrdquo refer to resource
endowment production structure closeness to markets and governments interventions On the
30
other hand ldquosymptomsrdquo are related to institutions employment structure and net export structure
Using this conceptual framework Leamer argues that natural Resource-Rich Countries (RRC) can
experience a higher level of inequality because can have a ldquodumbbell educational systemrdquo
ownership is concentrated in small groups and extraction activities require low-skilled workers
This implies fewer incentives to educate citizens until very late in the development process
resulting in human capital not prepared to take advantage of the process of technological progress
and delaying the emergence of more efficient and competitive sectors such as manufacturing and
services
Using 1980 and 1990 data for a group of countries classified according to land abundance
Leamer (1999) provides evidence showing that on the one hand land-scarce countries concentrate
their production and employment in sectors that promote equality such as capital-intensive
manufacturing chemical or machinery On the other hand countries abundant in natural resources
concentrate their production trade or employment in sectors that promote income inequality such
as the production of food beverages extraction activities or forestry
Gylfason and Zoega (2003) using a framework based on standard growth models also
proposed a positive relationship between NRE and inequality They assume that workers can work
in the primary sector or in the manufacturing (including services) sector In addition wage income
is equally distributed in the manufacturing sector but unequally in the primary sector (because of
initial distribution competition rent seeking etc) Therefore inequality will be greater when a
bigger proportion of labour is dedicated to extraction activities in the primary sector This
phenomenon is further amplified because of lower incentives to invest in physical and human
capital to adopt new technologies and to increase the share of the manufacturing sector
31
Diverse mechanisms explaining the link between NRE and inequality have been proposed
arguing that NRE determines simultaneously economic growth and inequality (Gylfason amp Zoega
2003) NRE could impact economic growth through the real exchange rate and the crowding-out
effect on manufacturing (ldquoDutch Diseaserdquo) reducing incentives to invest in physical and human
capital (Easterly 2007) and influencing the processes of technology adoption industrialization
and diversification of the economy in a manner that is less conducive to growth (Bulte et al 2005)
These potential explanations related to the called ldquoResource Curse Hypothesisrdquo do not have strong
empirical support (Auty 2001 Bulte et al 2005)
NRE may also influence economic growth through the quality of institutions (Acemoglu
1995 Acemoglu Aghion et al 2001 Acemoglu amp Robinson 2002 Engerman amp Sokoloff 1997
Engerman et al 2002) the concentration of ownership political power and rent-seeking NRE
acts by shaping institutional context and social infrastructure a phenomenon that is stronger when
resources are spatially concentrated such as minerals and plantations (Bulte et al 2005) NRE
could also have a significant effect on social cohesion and instability spreading its influence like
a disease (Brunori Ferreira amp Peragine 2013 Kanbur amp Venables 2005 Milanovic 2016
Ocampo 2004)
Considering a non-tradable sector intensive in unskilled workers Goderis and Malone
(2011) develop a model where the natural resources sector experiences an exogenous gift of
resource income They analyse the impact over income inequality of resource booms proxied by
changes in a commodity price index They conclude that inequality decreases in the short run but
increases after the initial reduction
32
Fum and Hodler (2010) show that natural resources increase inequality but this is
conditional on the level of ethnical polarization of society Carmignani (2013 2010) confirms this
positive relationship using different measures of dependence and abundance and goes further
arguing that inequality constitutes an indirect channel through which NRE affects human
development
Singlecountryevidence
Most of the studies about the relationship between inequality and NRE derive from cross-
country analyses Evidence for specific countries has been mainly based on case studies Howie
and Atakhanova (2014) based on the model of Goderis and Malone (2011) find for the case of
Kazakhstan that income and consumption inequality decreased significantly after booms in the oil-
and-gas sector because of resource booms increase the demand for non-tradable goods which are
intensive in unskilled workers The results depend on the level of rurality institutional quality
education levels and public spending on health and education Fleming and Measham (2015b
2015a) evaluate the impact of booms in the mining and oil sectors in Australia They find that a
boom in the mining sector increases income inequality due to commuting and migration among
regions This phenomenon can be exacerbated when the demanding access to natural resource
revenues is associated with the creation of more local administrative units (counties provinces and
even regions) but the government capacity is not simultaneously improved (Cust amp Poelhekke
2015 Michaels 2011) Furthermore the benefits that a region can receive in the form of fiscal
transfers can be more than compensated by the loses due to city-to-mine commuting such as the
case of mining regions in Chile (Aroca amp Atienza 2011)
33
Other studies at the local level have analysed the impact of the mining sector in Peru (Aragoacuten
amp Rud 2013 Loayza amp Rigolini 2016 Loayza Teran amp Rigolini 2013) Spain (Domenech
2008) and Canada (Papyrakis amp Raveh 2014) and the effects of oil windfalls in Brazil (Caselli amp
Michaels 2013)
In summary there is a wide range of potential mechanisms through which NRE could
influence income inequality Although most of them seem to suggest a positive relationship others
such as commuting and increased within-county demand for non-tradable goods and services
could lead to a negative association This highlights the need to know the sign of this association
in the Chilean economy where the trend shows a reduction in the degree of NRD After controlling
for other factors a positive link would support the argument that the reduction in the degree of
NRD has been relevant in the reduction experienced by income inequality in the same period
However a negative link would support the position that the reduction in NRD has contributed to
explain the persistence of income inequality and its slow reduction
222 The relevance of the spatial approach
Inequalities within countries are still the most important form of inequality from the political
point of view (Milanovic 2016) People from a geographic area within a country are influenced
and care most about their status relative to the people in other areas in the same country The
influence among regions involves multiple aspects (eg economic political and environmental)
These potential interactions have been traditionally ignored assuming independence among
observations related to different regions Moreover neglecting the process of spatial interaction in
key indicators of the economic and social performance of a country may mislead the design of the
public policy
34
The spatial dimension could play a significant role in understanding the distribution of
income within a country One strand of efforts aiming to capture the geographic heterogeneity of
inequality has been focussed on decomposing general indicators such as the Gini coefficient or the
Theil Index Evidence for different countries including the US (Doran amp Jordan 2016) China
(Akita 2003 Gustafsson amp Shi 2002 Ye Ma Ye Chen amp Xie 2017 Yue Zhang Ye Cheng
amp Leipnik 2014) Japan (Ohtake 2008) South Africa (Leibbrandt Finn amp Woolard 2012) and
Chile (Paredes et al 2016) shows that regional inequality is sensitive to the geographic scale of
analysis These studies also show a significant spatial component in the explanation of inequality
of income expenditure or gross domestic product for each country
Another strand explicitly uses exploratory spatial data analysis (ESDA) and spatial
econometrics ESDA has been used to provide new insights about the nature of regional disparities
of incomes and growth rates (Celebioglu amp Dallrsquoerba 2010 Yue et al 2014) Spatial econometric
models aim to assess and address the nature of the spatial effects These effects could be the result
of ldquospatial heterogeneityrdquo that is different relationships in distinct locations or ldquospatial
dependencerdquo which implies cross-sectional interactions (spillover effects) among units from
distinct but near locations
Spatial spillovers have been analysed to study both positive and negative spatial correlation
among less resource-abundant counties and resource-abundant counties On the one hand less
resource-abundant counties may experience positive spillovers because their industries supply
more goods and services to meet the increasing regional demand They can also be benefited from
positive agglomeration externalities and higher investment in private and public infrastructure
(Allcott amp Keniston 2014 Michaels 2011) On the other hand negative spillovers could be the
35
result of a high degree of interregional migration that limits the rise in wages and higher local
prices due to the increase in the share of the non-tradable sector In addition local governments
could have a limited capacity to translate the revenues from resource booms into effective public
policies promoting a sustained local development (Beine Coulombe amp Vermeulen 2015 Caselli
amp Michaels 2013 Papyrakis amp Raveh 2014)
23 Research problem and hypotheses
We can conclude from our overview of the literature that the theoretical and empirical
evidence about the link between inequality and natural resources is inconclusive This does not
make clear whether the process of reduction in the degree of dependence on natural resources
such as that experienced by the Chilean economy helps to explain the sustained but slow reduction
in income inequality or its high persistence
The research question guiding this study relates to how the natural resource endowment
determines the paths and structure of income inequality in natural resource-rich countries Using
the case of Chile the main hypotheses to be tested is whether a higher degree of dependence on
natural resources is associated with higher levels of income inequality To do that we use data at
the county level and we explicitly include the spatial dimension Our aim is to arrive at a more
comprehensive understanding of the drivers and transmission mechanisms explaining the
evolution and patterns shown by income inequality In addition we test whether the spatial
dimension plays a significant role in explaining differences in income distribution in Chile
36
24 Data and Methods
We use county-level data for the years 2006 2009 2011 2013 2015 and 2017 The reason
for not using contiguous years is that income data at the household level are only available every
two-three years from the Chilean National Socioeconomic Characterization Survey (CASEN in its
Spanish acronym)3 For the period 2006-2017 the Chilean administrative division considers 15
regions 54 provinces and 346 counties Data on income are available for 324 counties and six
years resulting in a panel with 1944 observations4
We start evaluating the spatial dimension in our data and analysing the link between
inequality and NRD using a cross-sectional setting To this end we use the ldquosix-year averagerdquo
(2006 2009 2011 2013 2015 2017) for our variables given the low time variability showed by
our measures of income inequality and NRD Results are then compared with those of a panel data
setting
241 Operationalization of key variables
The dependent variable in the present study income inequality at the county level is
measured calculating the Gini coefficient using three definitions of household income labour
autonomous and monetary income5 Labour income corresponds to the incomes obtained by all
members in the household excluding domestic service consisting of wages and salaries earnings
3 CASEN survey is conducted by the Chilean Ministry of Social Development covering topics such as education employment income and health CASEN is considered nationally representative and it is the main source for measures of inequality and poverty used for the design and evaluation of social policies in Chile 4 The six waves of CASEN for our study considered an average of 75599 households and 252081 individuals 5 The Gini coefficient is chosen because provides an overall estimate of income inequality It summarizes what proportion of the population gains what proportion of the total income The Gini coefficient can range between 0 (everyone in the population has the same income) and 1 (one person earns 100 per cent of the income in the community)
37
from independent work and self-provision of goods Autonomous income is the sum of labour
income and non-labour income (including capital income) consisting of rents interest and dividend
earnings pension healthcare benefits and other private transfers Finally monetary income is
defined as the sum of autonomous income and monetary subsidies which correspond to cash
transfers by the public sector through social programs Appendix A shows summary statistics for
the Gini coefficient of our three measures of income
The main independent variable in our study is the degree of dependence on natural resources
in each county To have an idea of the importance of each economic activity in the Chilean
economy particularly those activities related to natural resources Figure 21 shows their average
share in Chilean Gross Domestic Product (GDP) for the period 2006-17 We can observe that the
leading activities are those related to the primary sector especially mining and to the tertiary
sector where financial personal commerce restaurants and hotels services stand out The shares
of each economic activity in GDP vary significantly between Chilean regions and such
information is not available at the county level
Figure 21 Average share in GDP of economic activities (2006ndash17)
38
Leamer (1999) argues that when the main source of income is labour income (as indeed
happens for the Chilean case) using employment shares allows a better approach to measuring
dependence on natural resources Using employment data from CASEN survey we define our
measure of NRD as the employment in the primary sector (mining fishing forestry and
agriculture) as a percentage of the total employment in each county We name this variable
pss_casen where ldquopssrdquo stands for ldquoprimary sector sharerdquo We built other two proxies of NRD
using data from the ldquoServicio de Impuestos Internosrdquo (SII) which is the agency in charge of
collecting taxes in Chile The variable pss measures the percentage of employment in the primary
sector and the variable pss_firms measures the number of firms in the primary sector as a
percentage of the total number of firms in each county Appendix B shows summary statistics for
our three measures of NRD disaggregated by region
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17)
39
Figure 22 shows the evolution of our measure of inequality (using the Gini coefficient of
autonomous income) and our three potential proxies for NRD for the period 2006-2017 We
observe that both income inequality and the degree of NRD show a downward trend This seems
to support our hypothesis of a positive link between inequality and NRD however we need to
control of other sources of inequality before getting such a conclusion In what follows we use the
variable gini as our measure of income inequality capturing the Gini coefficient of autonomous
income Our measure of NRD is the variable pss_casen defined previously
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17)
Note Gini and NRD averages 2006-09-11-13-15-17 for 324 Chilean counties divided into five equal groups Source Own elaboration based on CASEN survey
40
Figure 23 shows quantile maps for income inequality (on the left) and NRD (on the right)
using the six-years average dataset6 On the one hand we observe that high levels of inequality
seem to be clustered in the Centre-South of the country where agriculture forestry and fishery are
the predominant economic activities Only isolated counties show high inequality in the Centre
(Metropolitan area where the countyrsquos capital is located) and North (Mining) areas On the other
hand our measure of NRD seems to show an opposite spatial pattern than income inequality with
high levels in the Centre and North of the country
242 Control variables
To control for county characteristics we use a set of socio-economic demographic and
institutional variables Economic factors are captured by the natural log of the mean autonomous
household income per capita (in thousands of Chilean pesos of 2017) lnincome the poverty rate
poverty the unemployment rate unemployment the percentage of the population living in rural
areas rural and the average years of education of the population over 15 years old education
Demographic factors include the proportion of the population in the labour force labour_force
and the natural log of population density (population divided by county area) lndensity
We also include the natural log of the total municipal public expenditure per capita
lnmuni_expenditure to control for municipal heterogeneity This heterogeneity is mainly related
to the capacity of municipalities to generate their own revenues In addition the richest
municipalities are in the Metropolitan region which concentrates economic power and around 40
6 After sorting a variable in ascending order quantile maps use the quantiles (quartiles quintiles deciles etc) as class breaks to divide the distribution of the variable where each class includes approximately the same number of observations (counties)
41
of the population This has basically implied a lag in the development of regions other than the
metropolitan region
The spatial distribution of our measures of income inequality and NRD displayed in Figure
23 seems to show different patterns in the North Centre and South of the country Appendix C
shows the administrative division of Chile in 15 regions and how we have grouped them in three
zones North Centre and South We consider as the ldquoCentrerdquo area that formed by the Metropolitan
region (XIII) and its two neighbouring regions V and VI Using the Centre area as our reference
we include in our analysis two dummy variables indicating whether a county is located in the North
area (regions XV I II III and IV) or South area (regions VII VIII IX XIV X XI and XII)
Appendix D shows summary statistics for the set of numeric control variables and the
correlation matrix between our measure of NRD pss_casen and the set of numeric controls
243 Methods
To assess and then consider the spatial nature of the data we need to define the set of relevant
neighbours for each country This is operationalized by building a matrix called ldquoWrdquo with a ldquo1rdquo
for neighbouring counties and a ldquo0rdquo for non-neighbouring counties We could build W using
contiguity-based (whether counties share a border or point) or geography-based (taking the
distances among the centroids of each county polygon) spatial weights Specifically we build a W
matrix considering the 5-nearest counties7 Two reasons explain the choice of k-nearest
neighbours First we cannot use a contiguity criterium because we do not have information about
all the counties and there are some geographically isolated counties Second given the significant
7 We assign a ldquo1rdquo to the five nearest counties to each county based on the distances among the polygon centroids Then W is ldquorow standardizedrdquo This facilitates the interpretation of the spatial lag of a variable as the ldquoweighted average valuerdquo of the same variable in neighbouring counties
42
differences in county areas (ldquobig countiesrdquo in northern and southern regions) using a distance-
band criterium with a not enough large distance band can lead to many ldquoislandsrdquo in extreme regions
and a multi-modal distribution for the number of neighbours
We start testing our inequality and NRD variables for spatial autocorrelation in order to
evaluate statistically the clustering patterns shown in Figure 23 Next we run an OLS regression
of inequality against NRD and our set of controls and we test the spatial autocorrelation of OLS
residuals If we cannot reject the null hypothesis of random spatial distribution we do not need
spatial models to analyse income inequality which would give contrasting evidence to previous
suggestions about the relevance of the spatial dimension of income inequality in Chile (Paredes
2013 Paredes et al 2016) If we find significant spatial autocorrelation in the OLS residuals this
justifies the use of spatial models and highlight the need to find the correct spatial structure8
If inequality in one county spillovers or influences inequality in neighbouring counties the
spatial lag of inequality should be included as an explanatory variable and we should use a spatial
autoregressive model (SAR) If some unobserved variable is the explanation for the clustering of
counties with similar inequality then this will be better captured including a spatial lag of the
errors and we should use a spatial error model (SEM) (Anselin 1988 Anselin amp Bera 1998)
Finally when our main explanatory variable or some of the controls show spatial autocorrelation
a spatial lag of the explanatory variable(s) should be included in our model
8 The existence of spatial autocorrelation violates the standard assumption of independence among observations needed for OLS regression This will result in OLS coefficients biased and inconsistent (Anselin 1988)
43
244 Spatial Model Specification
A model that includes the three forms of spatial dependence described above is called the
Cliff-Ord Model The model in its cross-sectional representation could be expressed as
119910 120582119882119910 119883120573 119882119883120574 119906 (21)
where
119906 120588119882119906 120576 (22)
119882 is our weight matrix that works as an NxN spatial lag operator9 Thus 119882119910 119882119883 and 119882119906
are the spatial lags for the dependent variable explanatory variables and the error term
respectively The parameter 120582 capture the spatial dependence in the dependent variable 120574 the
spatial dependence in the explanatory variables 120588 capture the spatial dependence in the error term
and 120598 is a vector of idiosyncratic errors For instance if ldquoyrdquo is income inequality and ldquoXrdquo a measure
of NRD the level of inequality in one county will be explained by the degree of NRD in the same
county 119883120573 the average degree of NRD in neighbouring counties 119882119883120574 the average level of
inequality in neighbouring counties 120582119882119910 and the average value of residuals in neighbouring
counties 12058811988211990610
From equations (21) and (22) the SAR and SEM models can be seen as special cases of
the Cliff-Ord representation after imposing restrictions over the spatial parameters 120582 120574 and 120588 For
the specification of the spatial panel models we follow the terminology by Croissant and Millo
9 The spatial lag is a weighted sum of the values observed at neighbouring locations 10 On the one hand the impact that income inequality in one county has over income inequality in neighbouring counties is called a ldquoglobal spilloverrdquo and it is associated with the feedback effect among neighbours (one county is its neighboursrsquo neighbourrdquo) on the other hand the influence that the degree of NRD in neighbouring counties has over inequality in one county is called a ldquolocal spilloverrdquo
44
(2018) Spatial panel models including the spatial lag of the dependent variable (SAR) the spatial
lag of the residuals (SEM) or both (SARAR) are described in Appendix E
25 Results
251 Exploratory Spatial Data Analysis (ESDA)
To analyse the significance of the spatial dimension in our data set we use the six-year
average of our variables Spatial autocorrelation is tested using the Moranrsquos I statistic11 Moranrsquos
I measures the correlation of one variable with itself in space12 Figure 24 shows the Moran scatter
plots where the standardized variable (Gini coefficient and NRD for each county) appears in the
horizontal axis against its spatial lag (average value in the 5-nearest neighbouring counties) The
Moranrsquos I (slope of the line in the Moran scatter plot) of income inequality shows a significant
positive spatial autocorrelation that is counties with high (low) inequality tend to be close to each
other
11 There are many statistics to formally test the significance of the spatial dimension in the distribution of our data The null hypothesis assumes spatial randomness which means that there is not spatial structure in the data so any spatial pattern is equally likely to occur and values in one location do not depend on values in other locations The alternative hypotheses can be the existence of positive or negative spatial autocorrelation Positive spatial autocorrelation means similar values in neighbouring locations (less variability than under spatial randomness) Negative spatial autocorrelation means dissimilar values in neighbouring locations (more variability than under spatial randomness) 12 Traditional measures of correlation such as the Pearsonrsquos coefficient measure the degree of linear correlation between two different variables Measures of spatial autocorrelation assess the correlation between the values of one variable in one location related to the values of the same variable in other neighbouring locations
45
Figure 23 Moran scatter plots for variables gini and pss_casen
Moranrsquos I is a measure of global spatial autocorrelation this means it is intended to capture
the clustering property of the entire data set To identify where are the significant hot-spots
(clusters of counties showing high income inequality) or cold-spots (clusters of counties showing
low income inequality) we need local indicators of spatial association (LISA) Using the local
Moranrsquos I (not reported) we find significant hot-spots in the South of the country (mainly
agricultural regions) and significant cold-spots in the Centre (Metropolitan area) of the country
The next step is to check whether the clustering pattern in inequality is the result of a process of
spatial dependence in the variable itself or it can be explained by other variables related to
inequality
252 Cross-sectional analysis
We start analysing differences in income inequality between counties using the six-year
average data and running an OLS regression for the model
119892119894119899119894 120573 120573 119901119904119904_119888119886119904119890119899 120573 119897119899119894119899119888119900119898119890 120573 119901119900119907119890119903119905119910 120573 119906119899119890119898119901119897119900119910119898119890119899119905 120573 119897119886119887119900119906119903_119891119900119903119888119890 120573 119890119889119906119888119886119905119894119900119899 120573 119897119899119889119890119899119904119894119905119910 120573 119903119906119903119886119897 120573 119897119899119898119906119899_119890119909119901119890119899119889119894119905119906119903119890 120573 119899119900119903119905ℎ 120573 119904119900119906119905ℎ
(23)
46
The Moran scatter plot and spatial statistical test of OLS residuals from equation (23) are
in Appendix F OLS residuals show a small but significant positive autocorrelation (Moranrsquos I =
0121) This means that income inequality continues showing a significant degree of spatial
autocorrelation after controlling for the set of covariates In addition robust Lagrange Multiplier
(LM) tests show that a spatial error model is preferred over a spatial lag model (Anselin Bera
Florax amp Yoon 1996)13 This means that income inequality tends to cluster in zones larger than a
county so the analysis should be performed on a larger scale such as provinces regions or macro
zones If the SAR model were preferred it would mean that income inequality in one county is
influenced by the level of income inequality in neighbouring counties To find the spatial structure
that best fits the clustering process of income inequality we run the full set of spatial model
specifications in a cross-sectional setting and results are shown in Table 21
Column 2 in Table 21 shows the results of our ldquoOLSrdquo model The ldquoSLXrdquo model includes
spatial dependence only through the explanatory variables The ldquoSARrdquo model includes the spatial
lag of the dependent variable as a regressor and the ldquoSEMrdquo model includes spatial dependence
through the error term The ldquoSARARrdquo model includes both the spatial lag of the response and the
errors and the ldquoSDMrdquo and ldquoSDEMrdquo are the extensions of the ldquoSARrdquo and ldquoSEMrdquo models
respectively including the spatial lag of the explanatory variables Finally a model including
spatial lags for the response errors and explanatory variables (the ldquoSARARXrdquo model) is shown in
the last column
13 Following the ldquoAnselin methodrdquo when both non robust LM tests are significant we should select between the robust LR tests From Table F1 in Appendix F only the robust LM test for the SEM model (RLMerr) appears as significant
47
Table 21
Cross-sectional Model Comparison (six-year average data)
48
Opposite to our hypothesis we observe a significant and negative coefficient for our measure
of NRD This means that counties more dependent on natural resources show lower levels of
inequality Education years population density and municipal expenditure per capita are also
negatively related to inequality On the other hand the level of income the poverty rate and the
proportion of the population living in rural areas show a positive relationship with income
inequality There is no significant influence of the unemployment rate and the proportion of the
population in the labour force In addition the SAR SEM and SARAR models show a
significantly higher average inequality in the South of the country related to the Centre area
The main finding from our cross-sectional analysis is that there is a significant and negative
relationship between inequality and NRD which is quite robust to the model specification
253 Panel Data analysis
Like the cross-sectional case we start estimating the panel without spatial effects Results
for the pooled fixed effects (FE) and random effects (RE) specifications of equation (3) are in
Appendix G Spatial models were estimated using Maximum Likelihood (ML) and Generalized
Moments (GM) and assuming that the spatial structure (W matrix) is the same for all years14
Tables 22 23 and 24 show results for the ML estimation of the SAR SEM and SARAR models
using the pooled FE and RE specifications Results for the GM estimation are in Appendix H
All our spatial models include time fixed effects In the case of the pooled and RE models they
additionally include indicator variables for those counties located in the North and South of the
country
14 In traditional (non-spatial) panels data are stacked as time series for each observational unit (county) In the case of spatial panels data are stacked as slice of cross-sections for each year in the panel
49
The main result is that the negative and significant effect of NRD on income inequality is
robust to most of the spatial panel specifications In addition the coefficient for the variable
pss_casen changes slightly among panel specifications (pooled FE and RE) but does not change
among spatial models (SAR SEM and SARAR)
Another important finding is related to the significance of the spatial dimension of income
inequality When spatial models cross-sectional or panel are compared to non-spatial models
there are no major differences in the magnitude of the coefficients or their significance This could
mean that the positive spatial autocorrelation shown by income inequality seems to be better
explained by a process of spatial heterogeneity rather than spatial dependence The practical
implication of this result is that including dummy variables for aggregated units (eg regions or
groups of regions) could be enough to control for the spatial dimension in the modelling and
analysis of income inequality
Among control variables years of education seems to be the main variable for the design of
long-term policies aimed at reducing inequality This result is in line with previous evidence for
cross-country studies (Leamer et al 1999) and specific countries (Howie amp Atakhanova 2014)
Municipal expenditure per capita also shows a significant and negative association with income
inequality in the pooled and RE spatial specifications This means that higher municipal
expenditure helps to reduce inequality between counties but its effect is more limited within
counties This result support the importance of local governments (Fleming amp Measham 2015a)
however the negative coefficient appears as evidence against the ldquoParadox of Plentyrdquo (Borge et
al 2015)
50
Table 22
ML Spatial SAR Models
Table 23
ML Spatial SEM Models
51
Table 24
ML Spatial SARAR Models
26 Discussion and conclusions
In this essay we delve deep into the sources of income inequality analysing its association
with the degree of dependence on natural resources using county-level data for the 2006ndash2017
period in Chile Given recent evidence for the Chilean economy suggesting a significant spatial
dimension we assess and incorporate explicitly the spatial structure of income inequality using
spatial methods We use cross-sectional and panel data to evaluate the significance of the spatial
dimension and we test whether NRD has a positive effect on income inequality
Contrary to what theory predicts NRD shows a significant and negative association with
income inequality This result is robust to the type of analysis (cross-sectional vs panel data) the
approach (spatial vs non-spatial) and the inclusion of different controls The negative and
significant coefficient implies that if the degree of NRD would not have experienced a 10 drop
during this period income inequality could have fallen in 2 additional points So the downward
trend in the participation of the primary sector in terms of employment in the Chilean economy
52
could be one of the main reasons explaining the high persistence in the levels of income inequality
This means that those areas that undergo a process of productive transformation mainly towards
the services sector would be facing greater problems to reduce inequality This process of
productive transformation natural or policy-driven highlights the importance of policies focused
on human capital and the role of local governments in reducing inequality
The main implication for policymakers is that a reduction in NRD does not help to reduce
inequality generating additional challenges for local and central governments in its attempt to
transform the structure of their economies to fewer dependent ones on natural resources The
finding of a significant spatial dimension suggests that defining macro zones capturing the spatial
heterogeneity in the data should be done before analysing the relationship among variables and the
design and evaluation of specific policies Particularly relevant in those areas experiencing a
reduction in NRD are migration commuting and the characteristics of the tertiary (services) sector
In addition our findings show that education and municipal expenditure could be effective policy
tools in the fight to reduce inequality in Chile
Although our results seem quite robust they do not allow us to make causal inferences about
the effect of NRD on income inequality However we could think of the following explanation to
explain the negative relationship found and the differences between geographical areas
Areas highly dependent on NR used to demand a high proportion of low-skill labour This
has change in sectors such as the mining sector in the northern area which has simultaneously
experienced an increase in activities related to the service sector such as retail restaurants
transport and housing However those services associated with more skilled labour such as the
finance sector remain concentrated in the capital region The reduction in the degree of NRD
(employment in extractive activities) implies lower labour force but more specialized with most
53
of the low-skilled labour transferred to a service sector characterized by low productivity and low
wages
Non-spatial models show that the North and South particularly the latter present
significantly higher levels of inequality This could be associated with the type of resources with
ldquopointrdquo resources such as minerals in the North and ldquodiffusedrdquo resources such as agriculture in the
South This translates into higher average incomes in the Centre and North areas and lower average
incomes in the South
The reduction in NRD implies not only a movement of the labour force from extractive
activities to manufacturing or services with the latter characterized by low productivity and low
salaries of the labour force We could also speculate that most of the high incomes move to the
central area where the economic power and ownership over firms and resources are concentrated
This would explain low inequality associated with higher average incomes in the central area and
high inequality associated with lower average incomes in the South A more in-depth analysis
capturing the mobility of wealth and labour force between counties or more aggregated areas is
needed to better understand the causal mechanism involved
Our findings open avenues for future research in different strands First studies on the causes
of income inequality should take the role of NRD into consideration which has been overlooked
so far Given that the spatial dimension of income inequality seems to be explained by a
phenomenon of spatial heterogeneity estimation strategies such as spatial regime models or
geographically weighted regression should be used (Chi amp Zhu 2019) Second the effect of NRD
on income inequality could manifest through different channels such as education fiscal transfers
and institutions We could extend our analysis to identify which of these competing channels is
the most relevant Transforming some continuous variables such as educational level to a
54
categorical variable or defining new indicator variables for instance whether a local government
shows or not an efficient performance we could classify counties in different groups and then
check whether there are differences or not in the relationship between income inequality and NRD
A third strand could be to disaggregate our measure of NRD for different industries This
would allow us to test differences among industries and to identify the sectors that promote greater
equality and which greater inequality Forth the analysis of the consequences of income inequality
on other economic and social phenomena such as efficiency economic growth and social cohesion
has a growing interest in researchers and policymakers Our findings suggest that to answer the
question of whether income inequality has a causal impact on other variables we could include a
measure of NRD as an instrument to address endogeneity issues For instance two interesting
topics for future research are the analysis of how differences in income inequality between counties
could help to explain differences in the level of efficiency of local governments and differences in
the degree of social cohesion (unrest) throughout the country Those are the issues to be addressed
in the next two essays
55
Chapter 3 The Impact of Income Inequality on the Efficiency of
Municipalities in Chile
31 Introduction
In Chile municipalities are the smallest administrative unit for which citizens choose their
local authorities playing an important role in the provision of public goods and services at the
local level Municipalities have a similar set of objectives but the level of financial resources
available to finance their activities is highly heterogeneous This could result in significant
differences in the levels of performance between municipalities Despite their importance there is
little empirical evidence about the efficiency of local governments in Chile This essay aims to
measure the technical efficiency of Chilean municipalities and to analyse how local characteristics
particularly those related to income distribution at the county level could help to explain
differences in municipal performance
Cross-country studies situate Chile as an efficient country in international comparisons about
efficiency of government spending (Herrera amp Pang 2005 Loacutepez amp Miller 2008) However
evidence for Chile at the local level is relatively sparse suggesting significant levels of
inefficiency For instance Pacheco Sanchez and Villena (2013) found a mean efficiency level of
around 70 using a sample of 309 municipalities for the period 2008-2010 This suggests that
municipalities could achieve the same level of output by reducing the usage of inputs by an average
of 30 Their study also showed that those municipalities more dependent on the central
56
government or those located in counties with lower income per capita are more efficient than their
counterparts
Most empirical research on Local Government Efficiency (LGE) has been conducted for
member countries of the Organization for Economic Cooperation and Development (OECD) of
which Chile has been a member since 2010 In the case of European countries such as Spain and
Italy which share similar characteristics such as the monetary union and levels of GDP per head
efficiency studies have been mainly motivated by budgetary constraints (Balaguer-Coll Brun-
Martos Maacuterquez-Ramos amp Prior 2019 lo Storto 2013) The Chilean context differs in three
main ways from its OECD counterparts First except for the Metropolitan Region that concentrates
most of the population Chilean regions are highly dependent on natural resources Second Chile
is also characterized by one of the highest levels of income inequality among OECD countries
which contrast with the situation of developed natural resource-rich countries such as Australia
and Norway Third although budget constraints are also a relevant issue Chilean municipalities
have experienced a sustained increase in the level of financial resources and expenditure
Another relevant distinction when we benchmark the performance of municipalities across
different countries is the type of public services they provide On the one hand in most of the
countries included Chile the main role of local governments is to provide ldquoservices to peoplerdquo
such as public education and public health On the other hand there are countries such as Australia
where local governments mainly provide ldquoservices to propertyrdquo including waste management
maintenance of local roads and the provision of community facilities such as libraries swimming
pools and parks (Dollery Wallis amp Akimov 2010 Drew Kortt amp Dollery 2015 McQuestin
Drew amp Dollery 2018)
57
Despite contextual differences Chilean municipalities seem not to perform differently from
municipalities in other developed and natural resource-rich countries where income inequality is
significantly less than in Chile (Narboacuten-Perpintildeaacute amp De Witte 2018a) This result highlights the
need to study the role of income inequality and the degree of dependence on natural resources over
LGE characteristics that have been largely overlooked in the literature
We measure and analyse differences in municipal performance using a two-stage approach
In the first stage we measure municipal efficiency using an input-oriented Data Envelopment
Analysis (DEA) to get a set of ldquoefficiency scoresrdquo In the second stage we regress efficiency scores
against our measure of income inequality controlling for a set of contextual factors describing the
economic socio-demographic and political context of each county
We use a sample of 324 municipalities for the period 2006-2017 During this period Chile
was divided into 346 counties belonging to 15 regions This period was characterized by important
external and internal shocks including the Global Financial Crisis (GFC) one of the biggest
earthquakes in Chilean history in 2010 and three municipal elections The availability of
information allows us to measure efficiency for the full period but the influence of contextual
factors is analysed using a panel of six years (2006 2009 2011 2013 2015 and 2017) for which
household income information is available
The main hypothesis tested in the second stage is whether higher levels of income inequality
are associated with lower levels of efficiency Previous evidence shows that when progress is not
evenly shared persistent within-country inequality reduces the effectiveness and efficiency of the
public sector (Ortega Sanjuaacuten amp Casquero 2017 Tandon 2005)
Income inequality has been used to control for a wide range of idiosyncratic factors
associated with historical institutional and cultural factors affecting efficiency (Greene 2016
58
Ortega et al 2017) For instance at the local level income inequality has been considered as an
indicator of economic heterogeneity in the population where higher inequality is associated with
a more heterogeneous set of conflicting demands for public services which adversely affect an
efficient provision (Ashworth Geys Heyndels amp Wille 2014 Geys amp Moesen 2009) Higher
levels of income inequality could also relate to economically privileged groups having a greater
capacity to influence the political system for their own benefit rather than that of the majority
When high inequality is persistent the feeling of frustration and disappointment in the population
could reduce not only trust and cooperation among individuals but also trust in institutions which
would negatively affect government efficiency (Boix amp Posner 1998 Coffeacute amp Geys 2005) For
instance national or local authorities could end exerting patronage and clientelism and showing
rising levels of corruption (Uslaner 2011 Uslaner amp Brown 2005)
One of the main gaps in extant literature is the need to conduct more analysis of LGE using
panel data taking into consideration endogeneity issues and controlling for unobserved
heterogeneity (Narboacuten-Perpintildeaacute amp De Witte 2018a) To address the above we set-up a panel with
time and county-specific effects and we propose the use of a measure of natural resource
dependence (NRD) as an instrument for income inequality Based on the ldquoParadox of Plentyrdquo
fiscal revenues from natural resources windfalls could be associated with an over expansion of the
public sector fostering rent-seeking and corruption and reducing local government efficiency
(Dauvin amp Guerreiro 2017 Manzano amp Rigobon 2001) In the Chilean case most of the revenues
generated by local governments included those from natural resources end up in a common fund
which benefits all municipalities The aim of this common fund is precisely to reduce inequalities
among municipalities so although we do not expect a direct impact of natural resources on LGE
we could expect an indirect effect through other indicators particularly income inequality
59
As far as we know this is the first study analysing the influence of income inequality as a
determinant of municipal efficiency in Chile Moreover this is the first study in the context of a
natural resource-rich country which specifically suggests a measure of natural resource
dependence as an instrument to correct for endogeneity bias We propose the use of the proportion
of firms in the primary sector as proxy for the degree of NRD in each county We argue that this
variable is a better proxy than using the proportion of employment in the manufacturing sector
which has been proposed in previous studies (Alesina amp La Ferrara 2002) During the period
analysed our proxy remained relatively stable and showed a significant relationship with income
inequality In addition it is less likely that it has directly affected municipal efficiency
This study adds to the literature in two other ways First the extant literature suggests that
efficiency measurement could be highly sensitive to the chosen technique as well as the selection
of inputs and outputs (Narboacuten-Perpintildeaacute amp De Witte 2018a) Inputs are usually proxied by a single
measure of total public expenditures and outputs by general proxies such as population andor the
number of businesses in each county We offer a novel approach for the selection of inputs and
outputs On the one hand we disaggregate government expenditures into four components
(operation personnel health and education) and we use the number of public schools and health
facilities in each county as a proxy for physical capital On the other hand we use four outputs
aiming to capture the wide variety of goods and services supplied by each municipality Through
this approach we aim to better describe the production function of each municipality capturing
not only the variety of inputs and outputs but also differences in size among municipalities
A third contribution relates to the measurement of LGE in the Chilean context We measure
technical and scale efficiency using a larger sample and a longer period This has empirical and
policy relevance On the one hand it helps us to select the correct DEA model and allows us to
60
determine the importance of scale inefficiencies as explanation for differences in municipal
performance On the other hand efficiency measures increase the information available for both
central and local governments to better understand the production technology that best describes
each municipality and to carry out policies to improve efficiency
We believe that our selection of inputs and outputs the use of a large dataset and the joint
analysis using cross-sectional and panel data provide a more accurate and robust analysis of
municipal efficiency Likewise knowing whether inequality has a significant influence on
municipal efficiency may provide useful insights and guidance for policymakers not only in Chile
but also for countries sharing similar characteristics
DEA results show an average level of technical efficiency (inefficiency) of around 83
(17) This means that municipalities could reduce on average a 17 the use of inputs without
reducing the outputs There are significant differences among geographic areas with the Centre
area (where the countyrsquos capital is located) displaying higher efficiency than the rest of the country
When municipal efficiency is measured under different assumptions about returns to scale results
reveal a production technology with variable returns to scales and around 75 of the
municipalities displaying scale inefficiencies However when technical efficiency is
disaggregated between pure technical efficiency and scale efficiency results show that scale
inefficiency explains a small proportion of the total municipal technical inefficiency This finding
justifies a deeper analysis of the reasons why municipalities could operate inefficiently and why
municipal performance could vary among municipalities
Efficiency scores also show a significant degree of positive spatial autocorrelation This
means that municipal efficiency shows a general clustering process with neighbouring
municipalities showing similar levels of efficiency A further analysis shows that most of the
61
spatial pattern in municipal efficiency is exogenous that is could be associated to other variables
Hence we conduct most of our regression analysis using traditional (non-spatial) methods and
leaving spatial regressions in the appendixes
Findings from cross-sectional and panel regressions support the hypothesis that municipal
performance is significantly and negatively associated with income inequality at the county level
The coefficient of income inequality is close to one which means that reductions in income
inequality ceteris paribus could be associated with increases in municipal efficiency in the same
proportion This result supports the strand of research arguing that there is not a trade-off at least
at the municipal level between equity and efficiency (Andersen amp Maibom 2020 Berg amp Ostry
2011 2017) The main policy implications are that authorities in more unequal counties would
face higher challenges to perform efficiently and policies pertaining to inequality and efficiency
should not be designed independently
The chapter is structured as follows Section 32 provides a brief literature review on related
local government efficiency Section 33 introduces the methodological background and empirical
models Section 34 presents the empirical results and discussions Section 35 concludes the
chapter
32 Related Literature
321 Measuring efficiency of local governments
Studies on measuring LGE can be grouped in those analysing the provision of single services
such as health or education and those assessing overall efficiency (lo Storto 2013) Once inputs
and outputs have been defined efficiency is measured using parametric andor non-parametric
techniques Among the former group Stochastic Frontier Analysis (SFA) seems to be preferred
62
by scholars aiming to measure efficiency and to analyse the link with environmental variables
using a single procedure (Pacheco et al 2013 Tsekeris Sotiris Tsekeris amp Papaioannou 2018)
On the non-parametric group Data Envelopment Analysis (DEA) is by far the most used technique
(Afonso amp Fernandes 2006 Balaguer-Coll amp Prior 2009 lo Storto 2013)
The selection of inputs and outputs depends not only on the aimed of the study (specific
sector vs whole measure of efficiency) but also on the role that municipalities play in different
countries There are countries such as Australia (Drew et al 2015) and Spain (Balaguer-Coll amp
Prior 2009) where local governments mainly supply services to ldquopropertyrdquo such as waste
management and road maintenance In these cases efficiency has been mainly measured using
total indicators of local government expenditure and outputs have been proxied using general
indicators such as population or number of business (Drew et al 2015) On the other hand in
countries such as Italy (lo Storto 2013) and Portugal (Afonso amp Fernandes 2008) in Europe or
Brazil (de Sousa Cribari-Neto amp Stosic 2005) and Chile (Pacheco et al 2013) in South America
municipalities mainly supply services to ldquopeoplerdquo Here in addition to financial expenditures or
revenues inputs have included the number of local government employees the number of schools
or the number of hospitals and health centres School-age population the number of students
enrolled in primary and secondary schools and the number of beds in hospitals have been
considered as outputs Based on the study of Narboacuten-Perpintildeaacute amp De Witte (2018a) a wider list of
inputs and outputs used in previous studies can be found in Appendix I
Studies from different countries show important differences in the average efficiency scores
both between and within countries These studies also differ in the samples methodologies and
variables included A summary showing the range and variability of the mean efficiency scores
founds in countries all around the world can be found in Narboacuten-Perpintildeaacute amp De Witte (2018a)
63
These authors also show that OECD natural resource-rich countries such as Australia Belgium
and Chile show similar results in terms of mean efficiency scores with LGE studies being less
frequent in Latin American countries
Measuring efficiency of local governments as decision-making units (DMU) presents many
challenges and difficulties (Borger Kerstens Moesen amp Vanneste 1994 Ravallion 2005)
Worthington and Dollery (2000) mention problems with the selection and measurement of inputs
the identification of different stakeholders the hidden characteristic of the ldquolocal government
technologyrdquo and the multidimensionality of the services provided by local governments All these
issues make difficult to identify and distinguish between outputs and outcomes with outputs
commonly proxied by general indicators such as county area or county population Because
efficiency measures are highly sensitive to the chosen technique and the selection of inputs and
outputs Narboacuten-Perpintildeaacute amp De Witte (2018a) suggest formulating different specifications and
using less general and unspecified indicators Moreover the complexity in defining outputs and
the use of general indicators make more likely that contextual factors affect municipal efficiency
322 Explaining differences in LGE
To explain differences in local government performance researchers have basically
distinguished between ldquodiscretionaryrdquo and ldquonon-discretionaryrdquo factors Discretionary factors refer
to the degree of discretion of local authorities in the selection and management of inputs and
outputs On the other hand scholars have investigated the influence on LGE of contextual factors
beyond authoritiesrsquo control These factors reflective at the environment where municipalities
operate include economic socio-demographic geographic financial political and institutional
characteristics (da Cruz amp Marques 2014 Narboacuten-Perpintildeaacute amp De Witte 2018b)
64
In general the evidence about the influence of contextual factors has delivered mixed and
country-specific results (Narboacuten-Perpintildeaacute amp De Witte 2018b) Sampaio de Sousa et al (2005)
using data for Brazilian municipalities finds that population density and urbanization rate have
strong positive effects on efficiency scores Benito et al (2010) show that lower levels of
efficiency of Spanish municipalities are associated with a greater economic level a less stable
population and a bigger size of the local government Afonso (2008) finds that per capita income
level and education are not significant factors influencing LGE of Portuguese municipalities He
also finds that municipalities in Northern areas show greater efficiency than their counterparts in
Southern areas More recently Tsekeris (2018) finds that spatial variations in efficiency in Greece
can be attributed to factors related to inter-regional market access specialization and sectoral
concentration resource-factor endowments and political factors among others Characteristics
describing each local government have also been used including municipal indebtedness (Benito
et al 2010) fiscal deficits (Sinha 2017) degree of fiscal autonomy (Boetti Piacenza amp Turati
2009) and individual characteristics of local authorities such as age gender and political ideology
Narboacuten‐Perpintildeaacute amp De Witte (2018b) conclude after analysing 63 articles that studies on the
influence of contextual factors have mostly used cross-sectional data with little attention to
endogeneity issues which makes any causal interpretation doubtful
323 The trade-off between efficiency and equity
The existence of a potential trade-off between efficiency and equity is in the core of
economic discussion (Andersen amp Maibom 2020 Berg amp Ostry 2011 Browning amp Johnson
1984 Okun 2015)15 The argument that economic growth (one of the most common efficiency
15 Redistributive policies distort incentives and lead to suboptimal outcomes and thus efficiency losses
65
measures) could be negatively affected in the search for greater equality has been translated not
only into economic policies that favour economic growth over those that reduce inequality but
also in the emphasis of scholarly research Thus theoretical and empirical research has been
mainly focussed on efficiency and policy implications of a great diversity of shocks and policies
leaving the analysis of inequality as one of measurement and mostly descriptive Additionally
empirical evidence of the trade-off is scant and inconclusive (Andersen amp Maibom 2020
Browning amp Johnson 1984)
Among economic contextual factors that could affect LGE income inequality has been
largely ignored An exception is a cross-country comparison conducted by Ortega (2017) who
analyses the role of inequality on government efficiency in developing countries He finds that
more unequal countries could have higher difficulties to achieve specific health outcomes Income
inequality has even been considered as part of the outputs to measure efficiency particularly for
the case of European and OECD countries (Afonso Schuknecht amp Tanzi 2010 Antonelli amp De
Bonis 2018)
At the local level income inequality has been mainly used as a proxy for the effect of income
heterogeneity Economic inequality could have a direct and an indirect effect on government
efficiency The direct effect poses that higher income inequality could reduce municipal efficiency
because it is associated with a more complex and competing set of public services demanded by
the population (Jottier et al 2012) The indirect effect puts the focus in the link between inequality
social capital and levels of corruption Economic diversity could reduce trust in people and
institutions when related to high and persistent levels of income inequality It could also affect the
willingness to participate in community and political groups the existence of a shared objective
by citizens and the perception of a prosperous future (Uslaner amp Brown 2005)
66
The evidence is ambiguous For instance Geys and Moesen (2009) find that income
inequality has little relation to efficiency of Flemish municipalities and Ashworth et al (2014)
find a negative relationship for the Norwegian case Findings also indicate that inequality is the
strongest determinant of trust and that trust has a greater effect on communal participation than on
political participation (Uslaner amp Brown 2005)
33 Methodology
We follow a two-stage approach widely used in this kind of analysis A DEA analysis is
conducted in the first stage to get efficiency scores for each municipality Then regression analysis
is conducted in the second stage aiming to identify contextual variables other than differences in
the management of inputs that can help to explain the heterogeneity in municipal performance
331 Chilean Municipalities and period of analysis
The territory of Chile is divided into regions and these into provinces which for purposes of
the local administration are divided into counties The local administration of each county resides
in a municipality which is administrated by a Mayor assisted by a Municipal Council16
Municipalities represent the decentralization of the central power in Chile They are autonomous
organizations with legal personality and own patrimony whose purpose is to satisfy the needs of
the local community and ensure their participation in the economic social and cultural progress of
the county Municipalities have a diversity of functions related to public health education and
social assistance among others
16 The Mayor and City Council are elected by suffrage for the citizens of the respective commune every 4 years
67
To achieve their goals two are the main sources of municipal incomes own permanent
revenues (OPR) and the Municipal Common Fund (MCF) OPR are incomes generated by the
county and they are an indicator of the self-financing capacity of each municipality OPR are not
subject to restrictions regarding their investment and they are mainly generated by territorial taxes
commercial patents and circulation permits17 The MCF is a fund that aims to redistribute
community income to ensure compliance with the purpose of the municipalities and their proper
functioning Sources to finance the MCF come from municipal revenues The distribution
mechanism of the fund is regulated by parameters such as whether municipalities generate OPR
per capita lower than the national average and the number of poor people in the commune in
relation to the number of poor people in the country
This study covers the period from 2006 to 2017 During this period Chile was divided into
15 regions 54 provinces and 346 counties18 Although the information on inputs and outputs is
available for the entire period information on contextual factors at the county level such as
household income is only available every two-three years In addition some counties are excluded
from household surveys due to their difficult access Hence we use a sample of 324 municipalities
to measure municipal efficiency for the whole period (3888 observations) However the analysis
of contextual factors is conducted for those years when household income information is available
2006 2009 2011 2013 2015 and 2017 (1944 observations)
17 The territorial tax is a tax on agricultural and non-agricultural real estate Specifically of this income only 40 is left to the municipality as its own financing while the remaining 60 is allocated to the MCF (in the case of the four largest communes- Santiago Providencia Las Condes and Vitacura-percentages are 35 and 65 respectively) Unlike the territorial tax commercial patents are regulated mainly by the municipality which chooses the rate to be charged subject to a range established by law and is responsible of their collection Finally regarding the circulation permits 375 is of municipal benefit while 645 is directed to the MCF 18 There are 346 counties managed by 345 municipalities where counties ldquoCabo de Hornosrdquo and ldquoAntaacuterticardquo are managed by the municipality of ldquoCabo de Hornosrdquo
68
332 Measuring municipal efficiency
Municipal efficiency is measured using Data Envelopment Analysis (Coelli Prasada Rao
OrsquoDonnell amp Battese 2005) This is a non-parametric approach that uses linear programming to
measure efficiency for a group of municipalities as decision making units (DMUrsquos) The main
advantage of using DEA and the reason why DEA is used for the case of Chilean municipalities
is its flexibility in handling multiple inputs and outputs without the need to specify a functional
form (Balaguer-Coll amp Prior 2009 Mikušovaacute 2015 Tigga amp Mishra 2015) Following Afonso
and Fernandes (2008) the relationship between inputs and outputs for each municipality could be
represented by the following equation
119884 119891 119883 119894 1 119899 (31)
In equation (31) 119884 is the set of outputs and 119883 the set of inputs for each of the n
municipalities Using linear programming the production frontier is constructed and a vector of
efficiency scores is obtained The frontier represents full technical efficiency mdash the point at which
the highest output occurs given specified inputs or the point at which the lowest amount of inputs
is used to produce a specified quantity of output Efficiency scores under DEA are relative
measures of efficiency They measure a municipalityrsquos efficiency against the other measured
municipalities in the sample and not a hypothetical lsquoperfect municipalityrsquo The further from the
frontier the less technically efficient a municipality is
We use an input-oriented approach because Chilean municipalities have a greater control
over the management of inputs relative to the outputs they have to manage Obtaining efficiency
scores requires an assumption about the returns to scale exhibited by each municipality When
DMUrsquos are homogeneous the CCR model (Charnes Cooper amp Rhodes 1978) which assumes
69
constant return-to-scale (CRS) is the appropriate specification The CCR model assumes full
proportionality between outputs and inputs and that DMUrsquos operate at their optimal When DMUrsquos
are highly heterogeneous as is the case with local governments in most countries it is not realistic
to assume complete proportionality between inputs and outputs nor that all DMUrsquos operate at their
optimum scale In this situation a variable returns-to-scale (VRS) or BCC model (Banker
Charnes amp Cooper 1984) is the preferred formulation
Assuming VRS imposes minimum restrictions on the efficient frontier and allows for
comparisons only among municipalities of similar scale (Coelli et al 2005 Wu Huang amp Pan
2014) This means that when we use the CCR model (assuming CRS) on a heterogeneous sample
of DMUrsquos the resulting measure of technical efficiency that we obtain is related not only to the
management of inputs but also to issues of scale19 To empirically check the validity of the VRS
assumption we measure technical efficiency under CRS VRS and non-increasing returns-to-scale
(NIRS) and we analyse the existence of scale inefficiencies This allows us to check the relevance
of scale effects as a potential explanation of differences in municipal efficiency Appendix J
shows the specification of the DEA model under VRS and how ldquototal technical efficiencyrdquo
(assuming CRS and therefore due to management and scale issues) could be disaggregated in ldquopure
technical efficiencyrdquo (under VRS and related only to management issues) and scale efficiency (due
to scale issues)
19 Assuming VRS can also lead to measurement problems such as overestimation of efficiency scores This is the case when an important proportion of DMUrsquos shows CRS so assuming VRS ignores the information about proportionality between inputs and outputs (Podinovski 2004)
70
333 Inputs and outputs used in DEA
Following the literature on local government expenditure efficiency (Afonso amp Fernandes
2008 de Sousa et al 2005 Dlouhyacute 2018 Tandon 2005 Tigga amp Mishra 2015) and trying to
reflect as well as possible the functioning of municipalities five inputs and four outputs were
selected Input and output data were obtained from the National System of Municipal Information
(SINIM in its Spanish acronym) and they are expressed in thousands of Chilean pesos of 201720
Inputs are Municipal Operational Expenditure X1 (including expenses on goods and
services social assistance investment and transfers to community organizations) Municipal
Personnel Expenditure X2 (including full time and part-time workers) Total Municipal
Expenditure in Education sector X3 Total Municipal Expenditure in Health sector X4 and the
Number of Municipal Buildings X5 (proxied by the number of public facilities in education and
health sectors)
Output variables were selected highlighting the relevance of education and health sectors
and trying to capture the wide range of local services provided by municipalities The variable
ldquoOwn Permanent Revenuesrdquo Y1 is used to capture the scale and diversity of municipal
activities21 The ldquoMonthly Average Enrolmentrdquo in municipal education establishments related to
the school-age population in each county Y2 is used as educational output As health output the
ldquoNumber of Medical Consultationsrdquo in public facilities Y3 is considered Finally the number of
community organizations Y4 is used as output reflecting the promotion of community
development by each municipality Table 31 shows the summary statistics of input and output
20 The data from SINIM database was mostly obtained using the sinimr package (Salas 2019) 21 According to SINIM database this variable aims to measure the management of the municipalityrsquos own resources with respect to the population of the commune
71
variables for the whole sample and period Inputs and outputs excepting the Monthly Average
Enrolment Y2 are measured in per capita terms using county population information from the
National Institute of Statistics (INE in its Spanish acronym)
Table 31
Descriptive statistics Inputs and Output variables used in DEA analysis
334 Regression model
Contextual factors could play an important role not only in explaining why some
municipalities operate inefficiently but also why municipal performance differs among them
These factors may affect municipal performance modifying incentives for local authorities to
operate efficiently and their capability to take advantage of economies of scale They also define
the conditions for cooperation or competition among municipalities and the citizensacute ability and
willingness to monitor local authorities (Afonso amp Fernandes 2008)
Information on income at the household level for each county was obtained from the
ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) CASEN is
conducted every two-three years being the reason why consecutive years are not considered in
72
our regression analysis The other contextual factors used as controls were obtained from different
sources including SINIM INE and the ldquoServicio de Impuestos Internosrdquo (SII)22
Our main hypothesis is whether higher levels of income inequality are associated with lower
levels of municipal efficiency To test our hypothesis the empirical model is defined as
120579 120573 119892119894119899119894 119885 120573 120575 120572 120598 (32)
Where 120579 is the vector of DEA efficiency scores 119892119894119899119894 is the Gini coefficient of each
county 120575 are year-specific effects 120572 are county-specific constants 120598 is a vector of error terms
and 119885 is a vector of controls Next we discuss the motivation for these controls
The level of purchasing power of countiesrsquo citizens is proxied by the variable log(income)
which is the natural log of the mean household income per capita in thousands of Chilean pesos of
2017 On the one hand poorer counties could display higher efficiency due to their necessity to
take care of their constraint resources (Pacheco et al 2013) On the other hand richer counties
could show higher efficiency because richer citizens exert higher monitoring over local authorities
and demand better quality public services in return for their tax payments (Afonso et al 2010)
The possibility for municipalities to take advantage of economies of scale and urbanization is
captured by three variables First the variable log(density) which correspond to the natural log of
population density Second the dummy variable reg_cap indicating whether a county is a regional
capital or not Third the variable agroland which correspond to the proportion of land for
agricultural use which is informed to the SII We expect a positive effect of log(density) but
negative for regcap and agroland
22 The SII is the institution in charge of collecting taxes in Chile
73
Socio-demographic characteristics are captured including a Dependence Index IDD IDD
corresponds to the number of people under 15 years or over 65 years per 100 people in the active
population (those people between 15 and 65 years old) A higher proportion of young and older
population could be associated with a higher demand for municipal services relating to education
and health making harder to offer public services efficiently The citizensrsquo capacity to monitor
local authorities is proxied including the variable education (average years of education for the
population older than 15 years) and the variable housing (proportion of households which are
owners of the property where they live in each county) In both cases we expect a positive
association with LGE
Among municipal characteristics the variable professional (percentage of municipal
personnel with a professional degree) is used to control for the quality of municipal services and
it is expected a positive impact The variable mcf (proportion of total municipal income coming
from the MCF) is included to capture the influence of financial dependence on the central
government A higher dependence from MCF could be associated with higher efficiency when it
is linked to more control from central government (Worthington amp Dollery 2000) However when
MCF discourages the generation of own resources and proper management of resources from the
fund a lower efficiency should be expected (Bravo 2014) In addition the dummy variable mayor
is included to capture differences among mayors supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo
political parties related to those ldquoINDEPENDENTrdquo mayors
Table 32 report summary statistics for the set of numeric contextual factors and Appendix
K the corresponding correlation matrix Despite the high correlation between income and
education variables we include both in the regression section as they capture different county
characteristics
74
Table 32
Summary Statistics Numeric Contextual Factors
Figure 31 Geographical distribution of Chilean regions and macrozones
Previous evidence on growth and convergence of Chilean regions have found that regions
tend to cluster spatially (Aroca amp Bosch 2000) Aiming to capture this regional clustering process
75
and considering the high concentration in the number of municipalities and population in the
central area we group municipalities in four ldquozonesrdquo We define as reference the ldquoCentre-Northrdquo
zone consisting of regions XIII (where the countryrsquos capital is located) and its two neighbouring
regions V and VI The ldquoNorthrdquo zone includes regions XV I II III and IV The ldquoCentre-Southrdquo
zone includes regions VII VIII and IX Finally the ldquoSouthrdquo zone embraces regions XIV X XI
and XII Figure 31 displays the regional administrative division and zones considered in this
essay
Efficiency scores (ES) are limited to have values between 0 and 1 However they are relative
measures (relative to the sample of municipalities) This implies that when a municipality is on the
frontier (ES = 1) it does not mean that potentially larger technical efficiency gains cannot be made
Hence equation 32 is estimated using OLS and censored regressions We start running cross-
sectional regressions for each of the six years Then we compare the results with those from panel
regressions Because fixed-effects panel Tobit models could be affected by the incidental
parameters problem (Henningsen 2010 2019) we use random-effects panel Tobit models
including indicator variables for years and zones Finally to deal with the potential endogeneity
problem we also use an instrumental variable approach The instrument is described next
335 The instrument
Government effectiveness and income distribution are both structural components of
economies (Ortega et al 2017 Ravallion 2005) In the search for a causal interpretation for the
influence of income inequality on municipal efficiency we need an instrument which must be
correlated with the variable to be instrumented (in our case income inequality) and uncorrelated
with the error term in the efficiency equation (32) Previous literature has used as instruments for
Gini the number of townships governments in a previous period the percentage of revenues from
76
intergovernmental transfers in a previous period and the current share of the labour force in the
manufacturing sector (Alesina amp La Ferrara 2002) Using the share of the labour force in a specific
sector is unlikely to reduce the problem of endogeneity particularly in countries where local
governments supply mostly ldquoservices to peoplerdquo and the main source of income inequality is
labour income
We propose as an instrument the proportion of firms in the primary sector (mining fishing
forestry and agriculture)
119901119904119904_119891119894119903119898119904Number of firms in the primary sector
Total number of firms (33)
On the one hand this instrument is likely to be correlated with local income inequality in
natural resource-rich countries23 On the other hand we contend that our instrument is less likely
to be correlated with the error term in the efficiency equation First the main services supplied by
Chilean municipalities are services to people (health and education) not to firms Second most of
the revenues collected by municipalities included those associated with natural resources end up
in the municipal common fund whose objective is precisely to reduce inequalities among
municipalities Third services to firms are expected to be more significant with the tertiary sector
We argue that our instrument captures natural and structural conditions which directly
influence income inequality but it does not directly affect LGE Figure 32 shows the evolution
of the annual average efficiency score and the proportion of firms in the primary secondary
(manufacturing) and tertiary (services) sectors We observe that sectors shares have remained
relatively stable with a slight reduction in the participation of the primary sector in favour of the
23 Results confirm a significant association between income inequality and the degree of dependence on natural resources at the county level
77
tertiary sector by the end of the period This is contrary to the evolution of municipal efficiency
which shows a cyclical behaviour as will be shown in the next section
Figure 32 Evolution of efficiency scores and the proportion of firms by sector
34 Results and discussion
341 DEA results
Figure 33 displays the evolution of our three measures of efficiency Overall technical
efficiency pure technical efficiency and scale efficiency are around 78 83 and 95
respectively with fluctuations over the years Therefore around three quarters of the overall
78
inefficiency is attributed to inefficiency in the management of inputs and around one quarter to
scale inefficiencies24
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE)
Returnstoscale
Figure 34 reports by zone and for the whole period the proportion of municipalities
showing CRS decreasing returns to scale (DRS) or increasing returns to scale (IRS) Most of the
municipalities operate under variable (increasing or decreasing) returns to scale which could be
explained by the high heterogeneity in size among municipalities A summary of RTS
disaggregated by year and zone is in Appendix L Policies oriented to increase efficiency usually
24 The average scale efficiency score of 95 means that municipalities could get an additional 5 of inputs saving if municipalities were able to change their size to achieve their optimal scale
79
consider amalgamation de-amalgamation or ways of cooperation among municipalities To have
a better idea about where and how feasible is the implementation of such policies Appendix M
shows maps with the administrative division of the country in its 345 municipalities and which
municipalities show CRS IRS or DRS in each of the six years of data
Figure 34 Returns to scale by zone
Based on results for the whole period (Figure 34) the North has the highest proportion of
municipalities exhibiting DRS This suggests the need to reduce the size of municipalities splitting
those ldquotoo bigrdquo creating new administrative areas or giving more autonomy to current
municipalities25 The opposite occurs in the Centre-North area where municipalities mostly
exhibit IRS This indicates the need to merge municipalities An alternative strategy to the
amalgamation process is increasing inter-municipal cooperation (Balaguer-Coll et al 2019)
25 This has been the policy followed in Chile Although two new regions were created (XV in the North and XIV in the South) the number of municipalities has not changed
80
which seems to be a more plausible option in Chile Finally evidence on scale efficiency for the
Centre-South and South areas is not clear in terms of the adequate strategy to improve efficiency
Efficiencymeasure
Although most municipalities show scale inefficiencies (Figure 34) only a small proportion
of total inefficiency is associated to scale issues (Figure 33) Together this results justify not only
the use of the BCC model (efficiency scores under VRS) to capture municipal heterogeneity but
also highlights the need to look for other factors outside the control of local authorities which
could be influencing municipal performance
Table 33
Summary efficiency scores (VRS) by zone and region
Table 33 summarizes ES under VRS using data for the six years of CASEN survey A mean
efficiency score of 83 is found for the full sample and period This means that on average
inefficient municipalities can reduce the use of inputs by 17 to get the same current output By
81
comparing average ES per zone it can be concluded that municipalities in the North Centre-North
Centre-South and South could achieve the same level of output with 17 12 18 and 23 fewer
resources respectively Results also show that one third of the municipalities present an efficiency
score equal to one
Figure 35 shows the evolution of the mean ES for the four zones over the 12 years period
A clear drop in the efficiency level is observed in 2009 (the financial crisis year) excepting for the
North area Likewise although mean efficiency returned to its pre-crisis levels in 2014-2015 a
new drop in efficiency was experienced in 2016 It also seems that the 2010 earthquake did not
generate a significant effect on municipal efficiency Figure 35 also shows that although levels
of efficiency seem to differ among zones they follow a similar trend through time with the only
exception of the North which corresponds to the mining area In addition efficiency seems to be
significantly higher in the Centre-North area This is explained by the high mean level of efficiency
in region XIII which includes the countryrsquos capital city
Figure 35 Evolution mean efficiency scores (VRS) by zone
82
To know which and where are the efficient municipalities and if they are surrounded by
municipalities with a similar level of efficiency Appendix N has maps showing the ldquoefficiency
statusrdquo of each municipality that is whether they are efficient (ES = 1) or inefficient (ES lt 1)
Additionally Appendix O shows ldquoequal intervalsrdquo maps describing the spatial distribution of ES
among municipalities for each of the six years26 Results show that efficient municipalities can be
found all through the country the ldquoefficiency statusrdquo could change from one year to another and
municipalities with similar level-status of efficiency tend to cluster in space
342 Regression results
Exploratoryspatialanalysis
DEA efficiency scores and their geographical representations seem to show that municipal
efficiency presents a spatial clustering pattern This means that municipal performance could be
influenced not only by contextual factors of the county where municipality belongs but also by the
level of efficiency of neighbouring municipalities and their characteristics To test the significance
of the spatial dimension in municipal efficiency we use a cross-section of data considering the six-
year average of efficiency scores the Gini coefficient and the set of controls
We use the Moranrsquos I indicator to test for spatial autocorrelation Moranrsquos I is a measure of
the correlation between the level of efficiency in one municipality and its ldquospatial lagrdquo that is the
average level of efficiency in neighbouring municipalities We define as the relevant neighbours
for each municipality the 5-nearest municipalities This is obtained using the distances among the
26 An equal intervals map uses the same principle as a histogram to organize the observations into categories that divide the range of the variable into equal interval bins For the equal interval classification the value range between the lower and upper bound in each bin is constant across bins but the number of observations in each bin is typically not equal
83
polygonsrsquo centroids (latitude and longitude) of each county Results confirm that municipal
efficiency show a significant level of positive spatial autocorrelation This means that
municipalities tend to have neighbouring municipalities with similar performance
The positive spatial autocorrelation shown by municipal efficiency could be due to the
performance in one municipality is influenced by the performance in neighbouring municipalities
(spatial dependence in the variable itself) or due to structural differences among regions-zones
(spatial heterogeneity) To check the source of the spatial autocorrelation we run an OLS
regression of ES against income inequality and controls and then we test OLS residuals for spatial
autocorrelation Moranrsquos I over OLS residuals although significant is barely higher than zero (see
Appendix P) This means that the spatial effect itself is not a serious issue and can be handle for
instance including zone indicators variables hence we proceed to analyse the influence of income
inequality on LGE using non-spatial regression27
Cross‐sectionalanalysis
We start reporting censored regressions for each year in our panel Efficiency scores have
been rescaled to numbers between 0 and 100 to facilitate the interpretation of the results All
regressions include dummy variables for three of the four zones in which we have grouped Chilean
regions Results are in Table 3428 Income inequality shows a negative sign in all years which is
consistent with our hypothesis that inequality is negatively related to municipal efficiency
However only in three of the six years the effect of income inequality appears as statistically
27 In the case of having incorporated the spatial dimension the two most commonly used forms are including in the model the spatial lag of the dependent variable (spatial autoregressive model SAR) or the spatial lag of the error (spatial error model SEM) Following the method suggested by Anselin et al (1996) Lagrange Multiplier (LM) tests on the OLS residuals showed that the SAR model is preferred over the SEM Moranrsquos I LM tests and spatial regressions can be found in Appendix P 28 Regression results using OLS are in Appendix Q
84
significant Only the income level displays a significant and positive influence on efficiency for
the whole period A higher population density also consistently favours municipal efficiency On
the other hand as we expected a higher IDD makes it more difficult to achieve an efficient
performance29 Consistent with results in Pacheco et al (Pacheco et al 2013) municipal
efficiency show a significant an positive association with the MCF only in the first half of our
period of analysis with the second half showing an insignificant relationship
Table 34
Cross-sectional (censored) regressions
Paneldataanalysis
Estimation results for the six-year panel are reported in Table 35 Columns (1) and (2) show
the results for the pooled and random effects censored models only controlling for zone and year
29 The set of cross-sectional regressions was also run for each zone independently Results confirm that the set of significant contextual factors differs not only among years but also among geographic areas This support previous evidence suggesting that cross-sectional analysis should be treated with caution (Narboacuten-Perpintildeaacute amp De Witte 2018b)
85
dummies Income inequality appears as non-significant Zone indicator variables confirm that
municipalities located in the Centre-South and South of the country display a lower average level
of efficiency compared to the Centre-North area Time dummies mostly show negative
coefficients This is a signal that shocks such as the GFC and the earthquake in 2010 may have
had a negative impact on efficiency but that impact was not permanent The results for the pooled
and RE models including the full set of controls are reported in columns (3) and (4) These results
show a significant negative influence of income inequality on LGE
When income inequality is instrumented by the variable pss_firms most of the coefficients
remain unchanged except for those associated with the income variables gini and log(income)
This result implies that our original model suffers for instance from the omitted variable bias
This means that LGE and income inequality are determined simultaneously by some variable not
included in our model Columns (5) and (6) show results using our instrument for income
inequality The ldquotrue coefficientrdquo of variable gini remains negative but the magnitude of the
relationship is higher The negative coefficient for gini implies on the one hand that municipalities
located in more unequal counties face more challenges to achieve an efficient management of
public resources On the other hand the coefficient in column (6) is close to one The interpretation
is that for each point of reduction in income inequality ceteris paribus LGE should increase in the
same proportion Next we discuss some of the results associated with the controls variables
Contrary to Pacheco et al (2013) income level has a positive coefficient meaning that richer
counties in terms of income per capita show higher efficiency This could be explained by higher
monitoring and increasing demands from citizen and taxpayers In addition it is expected a higher
efficiency in municipalities located in counties with a higher population density and those with a
lower proportion of land for agricultural use This result is mainly explained by municipalities
86
located in the Centre area The opposite happens with municipalities in the South implying that
they are too ldquosmallrdquo to take advantage of agglomeration economies There is also a limit for
agglomeration and scale economies which is shown by the negative coefficient of the variable
regcap although this coefficient loses its significance in the IV approaches30
Unexpectedly efficiency was found to be negatively associated with the variable education
This result is similar to the case of Czech municipalities (Šťastnaacute amp Gregor 2014) where
explanations include a weakened monitoring effect due to the fact that more educated citizens
present greater mobility and labour cost disadvantages for municipalities with better educated
labour force In Chile an additional explanation could be the relationship between education and
voter turnout Since 2012 voting is no longer mandatory in Chile This fact considerably reduced
voter turnout which in turn may have influenced the monitoring and control effect of more
educated voters For the case of variable IDD results show that local authorities in counties with
higher proportion of aging and young population (related to those in the active population) face a
greater challenge in their quest to offer public services efficiently
The influence of mcf is like that found by Pacheco et al (2013) with municipalities more
dependent on central transfers showing more efficiency31 Political influence captured by the
variable mayor did not show a significant effect This result is like other studies concluding that
the ideological position did not have a significant influence on efficiency (Benito et al 2010
Boetti et al 2009 Cordero Pedraja-Chaparro Pisaflores amp Polo 2017)
30 This negative coefficient suggests that the negative effects of agglomeration economies such as overcrowding pollution high cost of land and traffic congestion could predominate over the positive effects of scale economies in regional capitals 31 When the analysis is conducted for each zone independently MCF displays a negative influence in the North and Centre-North areas but a positive influence in the Centre-South and South areas These results confirm that on the one hand richer municipalities (those in the North and Centre-North) have less incentives for efficient management of their resources On the other hand results support the relevance of MCF helping poorer municipalities (those in the South) to reach their outcomes
87
Table 35
Panel data regressions
88
35 Conclusions
The trade-off between equity and efficiency is in the core of the economic discussion This
ldquoprinciplerdquo has been used by policymakers to prioritize the design of policies focused on economic
growth delaying those policies aimed at reducing economic inequalities This essay offers
empirical evidence of a negative relationship between inequality and efficiency that is a reduction
of income inequality could have positive effects on economic efficiency at least at the level of
local governments
We followed a traditional Two-Stage approach commonly used in the analysis of LGE We
compared cross-sectional and panel data results and we have added an instrumental variable
approach to give a causal interpretation to the link between efficiency and inequality We proposed
the use of a measure of natural resource dependence to instrumentalize the impact of income
inequality on LGE Given that our units of analysis are municipalities and not counties we argue
that our measure of NRD is correlated with income inequality and it does not have a direct
influence on LGE
We found that Chilean municipalities perform better than previous studies suggest
Municipal efficiency depicted significant levels of positive spatial autocorrelation and most of the
municipalities showed to be operating under increasing or decreasing returns to scale This would
imply that the policies generally used to improve efficiency such as amalgamation or cooperation
should be implemented observing the reality of each region and not as strategies at the national
level We also found that scale inefficiency explains a small proportion of the average total
inefficiency reason why the analysis of external factors that could affect the municipal efficiency
takes greater relevance
89
Income inequality plays an important part in explaining municipal efficiency In fact it was
found that reductions in income inequality could result in increases in municipal efficiency in a
similar proportion An unexpected finding was that the levels of education shows a negative
association with municipal performance This could be due to a low average level of education or
the existence of an omitted variable This variable could be the significant reduction in voting
turnout rates for local and national elections due to changes in the voting system during the period
of our analysis All in all our results may help to shed light on the potential consequences of
changes in contextual factors and the design of strategies aimed to increase municipal efficiency
in countries with similar characteristics to the Chilean economy For instance policies oriented to
take advantage of economies of scale can be formulated merging municipalities or establishing
networks in specific sectors such as education or health
Further work needs to be done both in measurement and in the explanation of differences in
municipal performance in Chile One area of future work will be to identify the factors that better
predict why municipalities operates under increasing decreasing or constant returns to scale
Multinomial logistic regression and the application of machine learning algorithms to SINIM data
sets appear as suitable methods for that purpose Intertemporal DEA (Drew amp Dollery 2015)
should be used to measure municipal efficiency capturing changes in total factor productivity In
addition municipalities operate under different levels of geographical authorities such as the
provincial mayor and the regional governor Hence it would be useful to know how each
municipality performs within each region-zone related to how performs to the whole country This
should be done conducting a metafrontier analysis (OrsquoDonnell Rao amp Battese 2008)
We have also identified through a cross sectional spatial exploratory analysis that on
average municipalities with similar levels of efficiency tend to cluster in space Regarding to
90
analyse the importance of contextual factors on municipal efficiency a deeper analysis should use
censored spatial models to check the significance of the spatial dimension in cross-sectional and
panel contexts Another interesting avenue for future research is associated with the negative
association found between LGE and education The significant reduction in votersacute turnout since
the law of automatic registration and voluntary voting in 2011 appears as a natural experiment to
analyse its effects on efficiency indicators such as municipal performance Incorporating variables
such as the voting turnout in each county or classifying municipalities based on individual
institutional political and economic characteristics could help to shed light on which of these
channels is the most relevant when analysing the impact of inequality on municipal efficiency
Finally we argued that an important part of the influence of income inequality over LGE
could be through its indirect effect on trust social capital and social cohesion The final essay will
delve deep in that relationship
91
Chapter 4 Social Cohesion Incivilities and Diversity
Evidence at the municipal level in Chile
41 Introduction
A deterioration in social cohesion could carry significant costs such as a reduction in
generalized trust between individuals and in institutions a society caught in a vicious circle of
inequality and citizens increasingly distanced from civic life (Uslaner 2011) A growing feeling
of frustration and discontentment can eventually translate into a social outbreak with uncertain
results This is precisely what have been happening in many countries around the world included
Chile
ldquoSocial cohesion is a state of affairs concerning both the vertical and the horizontal
interactions among members of society as characterized by a set of attitudes and norms that
includes trust a sense of belonging and the willingness to participate and help as well as their
behavioural manifestationsrdquo (Chan et al 2006) This definition highlights the multidimensionality
in the concept of social cohesion which has been measured using objective andor subjective
indicators of trust social norms solidarity willingness to participate in social and political groups
and feelings of belonging (Ariely 2014 Chan et al 2006) Multidimensionality also implies that
the impact of determinants of social cohesion such as economic and racial diversity could be
different for each of its various dimensions (Ariely 2014)
A common characteristic to all societies is that they are made up of different groups that
differ with respect to race ethnicity income religion language local identity etc The
92
Community Heterogeneity Thesis (Coffeacute amp Geys 2006) argues that individuals prefer to interact
with others that are like themselves Hence high levels of diversity particularly economic and
racial represent a complex scenario to maintain social cohesion One of the most common factors
adduced for social cohesion is income inequality with higher levels linked to lower levels of trust
(Ariely 2014 Rothstein amp Uslaner 2005)
Traditional measures of social cohesion may not be adequately capturing the deterioration
in social connections For instance measures of (lack of) trust include a strong subjective element
On the other hand proxies for social participation such as volunteering jobs or joining to social
organizations have not been supported by empirical evidence as a source of generalized social trust
(Rothstein amp Uslaner 2005) We proposed to use the rate of incivilities which we argue is a more
appropriate measure of the degree of worsening in the social context
Incivilities are those visible disorders in the public space that violate respectful social norms
and tend not to be treated as crimes by the criminal justice system There are two types of
incivilities social and physical Social incivilities include antisocial behaviours such as public
drinking noisy neighbours and fighting in public places Physical incivilities include among
others vandalism graffiti abandoned cars and garbage on the streets Because citizens and
political authorities cannot always distinguish between incivilities and crime they are usually
treated as an additional category of crime This implies that policies aimed to reduce incivilities
are generally based on punitive actions However theory and evidence on incivilities suggest that
factors explaining incivilities and crime could be different (Lewis 2017 Taylor 1999)
In Chile crime rates have shown a sustained downward trend after reaching its highest level
in 2011 On the other hand incivilities rate has shown a sustained upward trend which coincides
with the increasing victimization and feeling of insecurity in the population This has motivated
93
Chilean authorities based on the ldquoBroken Windows Theoryrdquo to propose new punitive actions (or
increase the severity of the current ones) to those who commit this type of antisocial behaviours
The ldquoBroken Windows Theoryrdquo states that higher rates of incivilities are a signal of social
disorganization which result in higher crime rates (J Q Wilson amp Kelling 1982) This is expected
to have consequences on familiesrsquo decisions such as moving away from public spaces or even
leaving their neighbourhoods
As far as we know there is no previous evidence about the potential causes of incivilities in
Chile Efforts to identify the factors explaining incivilities could help not only to reduce the risk
factors favouring violent and property crimes but also to guide interventions aimed to change
social behaviours and strengthen social cohesion in highly unequal societies Thus the main
contribution of the present study is to provide a deeper comprehension of the problem of incivilities
and how they can help to better understand the weakening of social cohesion that many
contemporary societies experience
We aim to offer the first evidence on the factors explaining the evolution and the differences
in incivilities rates in Chile We set up a panel for six years (2006 2009 2011 2013 2015 and
2017) and 324 counties (1944 observations) We start exploring the evolution and geographical
distribution of incivilities Then we investigate whether economic and racial diversity after
controlling for other socioeconomic demographic and municipal characteristics can be regarded
as key predictors of incivilities
We use the Gini coefficient to proxy economic heterogeneity and the number of new visas
granted to foreigners as proportion of the county population as proxy for racial diversity The main
hypothesis is whether economic and racial diversity have a positive association with the rate of
incivilities In addition to the arguments regarding the ldquoCommunity Heterogeneity Thesisrdquo Taylor
94
(1999) and Skogan (1986 1999 2015) argue that incivilities are caused by inequality and the lack
of informal mechanisms of social control Based on the ldquoIncivilities Thesisrdquo the patterns of
incivilities should mirror the patterns of inequality (Taylor 1999) Then higher inequality should
be associated with higher physical and social vulnerability of the population This reduces social
control and increases social disorganization which triggers antisocial or negligent behaviours
Our main result reveals a strong positive association between the rate of incivilities and the
number of new visas granted per year The relationship with income inequality although also
positive seems to be less significant These findings give strong support to the ldquoCommunity
Heterogeneity Thesisrdquo and to a lesser extent to the ldquoIncivilities Thesisrdquo When the analysis is
disaggregated geographically racial diversity shows a clear positive effect The impact of income
inequality seems to be conditional depending on the level of income showing no effect in poorer
regions Results also show that the impact of economic and racial diversity differs by type of
incivility For example income inequality shows a strong association with ldquoStreet Tradingrdquo while
racial diversity with ldquoAlcohol Consumptionrdquo Two are the main policy implications On the one
hand a national strategy to address the problems associated with foreign immigration could help
to reduce incivilities For instance a joint effort between national and local authorities to curb
immigration and its distribution throughout the country On the other hand our results show that
the relationship between incivilities and economic diversity differs depending on the region or
geographical area Hence the impact on social cohesion of policies aimed to tackle economic
inequalities should be analysed in each specific context
The rate of incivilities also shows a negative association with the level of municipal financial
autonomy This implies that municipalities can effectively carry out policies to reduce incivilities
beyond the efforts of the central government Another important finding is that our results do not
95
support the hypothesis that a higher proportion of the young population is associated with higher
rates of incivilities Hence policies aimed to reduce incivilities should be focused on the causes of
incivilities rather than the criminalization of behaviours or stigmatization of specific population
groups
The structure of the chapter is as follows Section 42 outlines the relevant literature on social
cohesion and incivilities Section 43 describes the data variables and methodology and
establishes the hypotheses of the study Section 44 contains the results and discussions Section
45 presents the main conclusions
42 Related Literature
421 The Community Heterogeneity Thesis
The idea under ldquoThe Community Heterogeneity Thesisrdquo is that if individuals prefer to
interact with others who are similar to themselves in terms of income race or ethnicity high levels
of income inequality and racial diversity facilitate a context for lower tolerance and antisocial
behaviours lowering the ldquostaterdquo of social cohesion (Alesina 2000 Coffeacute amp Geys 2006 Letki
2008) Alessina and Ferrara (2002) give support to this hypothesis arguing that individuals have a
natural aversion to heterogeneity However the most popular explanation is the principle of
homophily people prefer to interact with others who share the same ethnic heritage have the same
social status and hence share experiences and tastes (Letki 2008 McPherson Smith-Lovin amp
Cook 2001 Tolsma et al 2009) For instance Delhey and Newton (2005) find for a sample of
60 countries that income inequality and ethnicity are strongly and negatively correlated with trust
Tolsma et al (2009) using data for Dutch neighbourhoods and municipalities find that social
cohesion is negatively and consistently affected by economic deprivation but not by ethnic
96
heterogeneity These authors also conclude that the effect of neighbourhood and municipal
characteristics on social cohesion depends on residentsrsquo income and educational level
Rothstein and Uslaner (2005) give two theoretical reasons why economic and racial diversity
should be causally related to social trust a key element of social cohesion First optimism about
the future makes less sense when there is more economic inequality which generally translates into
inequality of opportunities especially in areas such as education and the labour market Second
the distribution of resources and opportunities plays a key role in establishing the belief that people
share a common destiny and have similar fundamental values In highly unequal societies people
are likely to stick with their own kind Perceptions of injustice will reinforce negative stereotypes
of other groups making social trust and accommodation more difficult
Uslaner (2002 2011) and Uslaner and Brown (2005) find that high levels of inequality are
the single major factor driving down trust in people who are different from yourself Evidence for
USA finds that inequality is the strongest determinant of generalized trust over time (Rothstein amp
Uslaner 2005) Reducing inequality and then increasing generalized trust should have positive
consequences at the individual and aggregates levels At the individual level it may lead to greater
tolerance and more acts of altruism for people of different backgrounds At the aggregate level it
may lead to greater economic growth more redistribution from the rich to the poor and less
corruption (Uslaner 2002 2013) Letki (2008) argues that when neighbourhood socio-economic
context (apart from just an individualrsquos socioeconomic status) is considered it turns out to be the
main factor triggering negative attitudes and lack of trust in out-group members
The increasing diversity caused by immigration can also reduce the conditions necessary for
social cohesion (Ariely 2014 Holtug amp Mason 2010) Christel Kesler and Irene Bloemraad
(2010) find for nineteen advanced democracies between 1981 and 2000 that increasing migration
97
generally decreases trust civic engagement and political participation The authors also find that
in more equal countries with clear policies in favour of cultural minorities the negative effects of
migration are mitigated or even reversed Letki (2008) states that deprivation and disorder tend to
be strongly correlated with racial diversity Because we propose the use of the number of disorders
or antisocial behaviours known as ldquoincivilitiesrdquo as our measure of social cohesion we describe the
literature on incivilities in the next section
422 The literature on incivilities
The study of incivilities has been a continuing concern mainly for developed countries since
the 1980s The focus has changed from individual and psychological explanations to ecological
(contextual) and social explanations (Taylor 1999) The individual approach basically considered
perceptions of incivilities as an explanatory variable of fear of crime The ecological explanation
argues that indicators of economic disadvantage (eg income levels income inequality
unemployment rate and poverty rate) are the keys to understand a process of social disorganization
and lack of informal control These economic factors lead to higher rates of inappropriate or
negligent behaviours and ultimately to higher crime rates (Blau amp Blau 1982 Messner Rosenfeld
amp Baumer 2004 Phan Orsquobrien Mendolia amp Paloyo 2017 Sampson 1986)
The negative impact of incivilities is not merely reflected in its association with crime rates
(Skogan 2015) Physical and social incivilities could worsen neighbourhoods by affecting quality
of life perception of the environment and public and private behaviours Previous research has
indicated that a higher level of incivilities is associated with health problems (Branas et al 2011
Cohen et al 2000 Hill amp Angel 2005 Ross 2011 Ross amp Mirowsky 2001) greater
victimization and fear of crime (Brunton-Smith Jackson amp Sutherland 2014 Mijanovich amp
Weitzman 2003) and multiple negative economic effects For instance incivilities could be
98
related to a reduction in commercial activity lower investment in real estate reduction in house
prices (Skogan 2015) and population instability (Hipp 2010)
To describe the state of the art in the study of incivilities and their consequences Skogan
(2015) used the concept of untidiness to characterize the research on incivilities The study of
incivilities has had multiple approaches (economic ecological and psychological) Incivilities
have also been measured using multiple sources of information (police reports surveys trained
observation) which result in different measures (perceptions vs count data) However the question
about what specific factors have the strongest effect on incivilities has been overlooked and
perceptions about incivilities have been used mainly as a predictor of crime fear of crime and
victimization
There are two types of incivilities social and physical Social incivilities are a matter of
behaviour including groups of rowdy teens public drunkenness people fighting and street hassles
Physical incivilities involve visual signs of negligence and decay such as abandoned buildings
broken streetlights trash-filled lots and graffiti (Skogan 1999 2015 Taylor 1999) Three reasons
justify the distinction between physical and social incivilities First like multiple dimensions of
social cohesion different structural and social conditions could be responsible for different types
and categories of incivilities Second punitive sanctions are expected to have a greater impact on
physical than on social incivilities since the latter are more related to behaviours rooted in citizensrsquo
culture Third physical incivilities should be more related to absolute measures of economic
disadvantage (eg poverty or unemployment rates) and social incivilities to relative indicators of
economic disadvantage (eg such as income inequality) This line of research is based on the
ldquoincivilities thesisrdquo which states that to understand the distribution of disorders it is necesary to
analyse the patterns of structured inequalities (Skogan 1986 Taylor 1999)
99
423 The ldquoIncivilities Thesisrdquo
Incivilities theories began with a focus on psychological dynamics (Garofalo 1978) moved
forward to an interest in social psychological processes (J Q Wilson amp Kelling 1982) and finally
evolved into a focus on community dynamics and outcomes (Skogan 1999) Individual and group
behaviours in tandem with ecological features have been proposed as the key factors explaining
incivilities and their posterior influence on social control quality of life and more serious crime
(J Q Wilson amp Kelling 1982)
In terms of ecological factors particularly those related to economic conditions Skogan
(1986) was the first linking the distribution of incivilities to the patterns of structural inequality If
incivilities mirror inequality structure this will have consequences in residentsrsquo health and safety
due to its levels of vulnerability In addition structured inequality associated with the proportion
of the manufacturing sector (eg when fabrics tend to move from cities to farther areas) will be
related to higher social disorganization and differences between urban and rural areas (W J
Wilson 1996) In addition a persistent feeling of relative deprivation (persistently high levels of
income inequality) could lead to fellow inhabitants of the community to commit antisocial
behaviours showing their frustration with the current economic model
The literature on incivilities posits that their causes are different from those of crime (Lewis
2017) Unlike crime analysis especially property crimes information on the location where the
incivility takes place is the same as the location where the perpetrator resides To achieve a
comprehensive understanding of the different types of incivilities it is crucial to consider
incivilities data covering an entire territory and not just specific areas (Hooghe Vanhoutte
Hardyns amp Bircan 2010) If we add to this the availability of panel data it could be possible not
100
only to identify the main determinants of incivilities but also the causal mechanism from income
inequality towards incivilities rate
In Chile citizen security crime and delinquency are among the most significant issues for
citizens based on opinion polls Existing research has found weak evidence of a significant
relationship between crime and indicators of socio-economic disadvantage such as income
inequality and unemployment rate with significant effects only on property crime (Beyer amp
Vergara 2006 Nuntildeez Rivera Villavicencio amp Molina 2003 Rivera Gutieacuterrez amp Nuacutentildeez 2009)
Crime deterrence variables such as the probability of being caught or the number of police
resources have also shown ambiguous results (Beyer amp Vergara 2006 Rivera et al 2009
Vergara 2012) Evidence at the county level shows that crime is higher in urban counties those
with a lower mean income per capita and counties located in the North of the country In addition
at least half of the crimes reported in one county are perpetrated by criminals from other counties
(Rivera et al 2009) No studies could be found about the determinants of incivilities
4 3 Methodology
431 Period of analysis and data sample
Chile is a relatively small country in Latin America with a population of 18346018
inhabitants in 2017 The country is divided into 345 municipalities with on average 53104
inhabitants (median value 18705) Municipalities are the organ of the State Administration
responsible to solve local needs Municipalities are not only the relevant political and
administrative local unit of analysis but also they represent the feeling of lsquocommunityrsquo among
the inhabitants of each municipality (Hooghe et al 2010) Our data includes many sources of
101
heterogeneity among municipalities such as indicators of economic deprivation population
density demographic characteristics and whether the county is a regional or provincial capital
We use a sample of 324 municipalities covering most of the Chilean territory for the period
2006ndash17 Data on incivilities is obtained from the ldquoCentre of Studies and Analysis of Crimerdquo
which is part of the ldquoSubsecretaria de Prevencion del Delitordquo (SPD in its Spanish acronym) of the
Chilean government32 Information on income inequality and control variables is obtained from
the ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) the
ldquoNational Institute of Statisticsrdquo (INE in its Spanish acronym) the ldquoNational Municipal
Information Systemrdquo (SINIM in its Spanish acronym) and the Immigration Department of the
Government of Chile Our panel only includes the years for which CASEN survey is available
2006 2009 2011 2013 2015 and 2017
432 Operationalisation of the response variable and exploratory analysis
Official Chilean records contain information for the total number of cases of incivilities per
year at the county level The number of cases is the sum of complains and detentions reported at
the police Our dependent variable 119894119899119888119894119907119894119897119894119905119894119890119904 correspond to the number of cases per year Due
to population differences comparisons between counties are made using the incivilities rate per
1000 population calculated as
119894119899119888119894119907_119903119886119905119890 lowast 1000 (41)
where 119894119899119888119894119907_119903119886119905119890 is the incivilities rate 119905 is the year 119894 the county and 119899 is the population of the
county per year
32 httpceadspdgovclestadisticas-delictuales
102
Figure 41 illustrates at the top the evolution of the total number (cases reported) of
incivilities and crimes at the country level for the period 2006-1733 At the bottom Figure 41
shows the evolution of the mean county rate per 1000 inhabitants We observe that both the number
of incivilities and the number of crimes has reached similar annual figures however average
county rates per 1000 population show different trends Crime rate displays a sustained fall after
reaching its pick in 2011 Incivilities average county rate which also reached a pick and posterior
drop in 2011 has recovered its upward trend since 2016 considerably exceeding the crime rate
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017
33 Crime refers to ldquocrimes of greater social connotationrdquo (DMCS in its Spanish acronym) which includes violent and property crimes
103
Chilean records classify incivilities in nine categories most of them associated with social
incivilities Summary statistics for the total and for each of the nine categories are presented in
Table 41 In addition Figure 42 shows the evolution of incivilities by category for the whole
period We see that the global trend in incivilities is mainly due to a substantial increase in ldquoStreet
Tradingrdquo and ldquoPublic Alcohol Consumptionrdquo A common element of Figures 41 and 42 is the
significant change in trend experienced by crimes and incivilities in 2011 That year the SPD
became dependent on the Ministry of Interior of the Chilean Government This event put the issue
of crime and delinquency within national priorities for the central government
Table 41
Summary statistics total count of incivilities and by category (full sample and period)
Unlike crime rates we do not expect significant cross-county spillover effects in incivilities
However the questions of where incivilities are concentrated and why they are there can be of
great interest (Skogan 2015) Figure 43 shows quantile maps for the rate of incivilities per 1000
inhabitants for the initial and final years in our panel
104
Figure 42 Evolution total number of incivilities by category
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017)
105
We observe that the range of values has increased significantly from 2006 to 2017 but the
spatial distribution remains almost unchanged On the one hand high incivilities rates in the North
could be associated with the mining activity On the other hand high rates in the Centre area
(where the countyrsquos capital is located) could be related to the higher population density and the
concentration of the economic activity34
To see how the different types of incivilities are distributed throughout the country we have
grouped those similar categories in four groups ldquoPublic Damagerdquo (ldquoPublic Disturbsrdquo ldquoPublic
Damagerdquo ldquoOtherrdquo) ldquoThreatsrdquo (ldquoPublic Fightrdquo ldquoThreatsrdquo and ldquoAnnoying Noisesrdquo) ldquoAlcohol
Consumptionrdquo (ldquoPublic Alcohol Consumptionrdquo and ldquoDrunkennessrdquo) and ldquoStreet Tradingrdquo This
distinction in groups could be relevant if we expect different patterns and different effects of
community heterogeneity on social cohesion among counties For instance we expect higher
levels of Public Damage in big urban cities Street trading is more likely in urban cities but also in
tourist areas The spatial distribution of these four groups for the six-year-average rate per 1000
inhabitants can be found in Appendix R
433 Measures of community heterogeneity and control variables
Social cohesion income inequality and racial diversity are all ldquocommunityrdquo (not individual)
characteristics Thus to understand their relationship we need aggregated data at least at the
county-municipal level With more disaggregated data like at the suburbs level the required
heterogeneity among groups of citizens is lost (Wilkinson 1999) Like Coffeacute and Geys (2006) we
use the Gini coefficient to capture economic heterogeneity However instead of a measured for
34 We also analysed the spatial distribution of crime rates In general areas with high levels of incivilities not necessarily are associated with high levels of crime This could imply that factors explaining incivilities and crime are different
106
the diversity of nationalities we use the proportion of foreign population to capture racial
heterogeneity Income data is obtained from the CASEN survey The Gini coefficient is calculated
for each county and included through the variable gini Racial heterogeneity is included through
the variable foreign which is the annual number of new VISAS granted to foreigners as a
proportion of the county population Chile has experienced a significant increase in immigration
since 2011 Immigration has been concentrated in the metropolitan region and mining regions in
the North of the country We expect a positive relationship between immigration and incivilities
although as with the relationship between immigration and crime the foundations for this
hypothesis are not strong (Hooghe et al 2010 Sampson 2008)
Economic development is another explanation for social cohesion frequently appealed to
explain trust with wealthier societies considered to exhibit higher levels of trust (Delhey amp
Newton 2005) In this study we include the natural log of the mean household income per capita
log(income) We also include the poverty rate poverty and the unemployment rate
unemployment Unlike the variable log(income) these variables are expected to be positively
associated with the number of incivilities When a relative indicator of economic heterogeneity
such as income inequality is included as determinant of social cohesion we should expect less
effect from absolute indicators of economic disadvantage such as poverty and unemployment rates
(Hooghe et al 2010 Tolsma et al 2009)
Among demographic variables the percentage of inhabitants between 10 and 24 years old is
included through the variable youth The variable women defined as the proportion of the female
population in each county is also included Variable youth is expected to have an ambiguous effect
Although young people have lower victimization and report rates they also represent the group
more likely to commit antisocial behaviours when a community has a low capacity of self-
107
regulation (eg when there is low parental supervision) The female population is associated with
a higher report of incivilities related to the male population
It is argued that crime and incivilities are essentially urban problems (Christiansen 1960
Wirth 1938) We include the variable log(density) defined as the log of population density (the
number of inhabitants divided by the area of each county in square kilometres) and a dummy
variable capital indicating whether a county is an administrative capital (provincial or regional)
Two additional variables are included to capture the level of informal social control exerted
by families living in each municipality First the variable education which is defined as the
average years of education of people over 15 years old Second the variable housing which capture
the proportion of families which are owners of their housing unit Although education and housing
are related to both the possibility of reporting and committing an incivility we expect a negative
association with the rate of incivilities
In Chile crime has been mainly a problem faced by the police and the Central Government
Administration To control for current law enforcement policies we include the variable
deterrence defined as the number of arrests as a proportion of the total number of incivilities cases
In addition municipalities can develop their own initiatives to deal with crime and incivilities
depending on their capacity to generate its own resources The level of financial autonomy from
central transfers is captured by the variable autonomy This variable is obtained from SINIM and
it is defined as the proportion of the budget revenue of each municipality that comes from its own
permanent sources of revenues A categorical variable mayor is also included This variable
indicates whether the municipality mayor is supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo political
parties (related to those ldquoINDEPENDENTrdquo mayors)
108
Table 42 presents descriptive statistics for our measures of income and racial heterogeneity
and the set of numeric control variables The Pearson correlation among these variables is shown
in Appendix S
Table 42
Summary statistics numeric explanatory variables
434 Methods
The annual count of incivilities as is characteristic for count data is highly concentrated in
a relatively small range of values In addition the distribution is right-skewed due to the presence
of important outliers (counties with a high number of incivilities) Figure 44 shows the
distribution of the six-year average number of incivilities for each of the 15 regions in Chile35 We
observe that regions differ in the number of counties in which they are divided In addition
counties within each region show important differences in the number of incivilities For instance
35 Regions are ordered from left to right and from top to bottom in the way they are geographically distributed from North to South So the northernmost region of the country is the ldquoXVrdquo and the southernmost region is the ldquoXIIrdquo The Metropolitan region (where the countryrsquos capital is located) is region ldquoXIIIrdquo in the centre of the country (see Appendix C)
109
excepting the Metropolitan region ldquoXIIIrdquo most counties in regions located in the centre of the
country (middle row in Figure 44) show a range of incivilities between 0 and 2000 The number
of incivilities is considerably lower in counties located in the northern (top row in Figure 44) and
southern (bottom row in Figure 44) regions of the country compared to regions in the centre of
the country It also seems clear from Figure 44 that the number of incivilities does not follow a
normal distribution
Figure 44 Annual average number of incivilities per county
The number of incivilities can be better described by a Poisson distribution In this case the
number of incivilities is ldquothe countrdquo and the number of incivilities per year is ldquothe rate per unit
timerdquo We are interested in modelling the average number of incivilities per year usually called 120582
as a function of a set of contextual factors to explain differences in incivilities between and within
110
counties The main characteristic of the Poisson distribution is that the mean is equal to the
variance This implies that as the mean rate for a Poisson variable increases the variance also
increases The main implication is we cannot use OLS to model 120582 as a function of the set of
contextual factors because the equal variance assumption in linear regression is violated
The rate of incivilities between counties is not directly comparable due to population
differences We expect counties with more people to have more reports of incivilities since there
are more people who could be affected To capture differences in population which is called the
exposure of our response variable 120582 it is necessary to include a term on the right side of our model
called an offset We will use the log of the county population in thousands as our offset36
Additionally similar to the case of crime data incivilities show a significant degree of
overdispersion (variance higher than the mean) suggesting that there is more variation in the
response than the Poisson model implies37 We also model and regress incivilities assuming a
Negative Binomial distribution to address overdispersion An advantage of this approach is that it
introduces a dispersion parameter in addition to 120582 which gives the model more flexibility38
Considering as the response variable the count of incivilities per year the model can be
expressed as follow
120582 119890119909119901 120573 120573 119892119894119899119894 120573 119891119900119903119890119894119892119899 119883120574 120572 120579 (42)
36 If we think of 120582 as the average number of incivilities per year then represents the number per 1000
inhabitants so that the yearly count is adjusted to be comparable across counties of different sizes Adjusting the yearly count by population is equivalent to adding 119897119900119892 1199011199001199011199061198971198861199051198941199001198991000 to the right-hand side of the regression equation 37 Without adjusting for overdispersion we use incorrect artificially small standard errors leading to artificially small p-values for model coefficients 38 The Negative Binomial model posits selecting a 120582 (average number of incivilities) for each county and then generating a count using a Poisson random variable with the selected 120582 With this approach the counts will be more dispersed than would be expected for observations based on a single Poisson variable with rate 120582
111
where 120582 is the rate of incivilities 119883 is our vector of controls 120572prime119904 are county-specific constants
and 120579prime119904 are time-specific constants Accounting for differences in county population we have
119890119909119901 120573 120573 119892119894119899119894 119883120574 120572 120579 (43)
where 119901119900119901119906119897119886119905119894119900119899 is the county population per year Hence the model to be estimated using
Maximum Likelihood Estimation (MLE) is
119897119900119892 120582 120573 120573 119892119894119899119894 119883120574 120572 120579 119897119900119892 (44)
Finally to account for different effects depending on the type of incivilities we also run
equation (44) for each of the four groups of incivilities defined in section (432)
435 Hypotheses
Based on the community heterogeneity hypothesis the relationship between social cohesion
and diversity should be stronger for lower levels of income and less educated groups of people
(Tolsma et al 2009) Hence contrary to evidence for developed and more equal countries we
expect a significant positive association for the Chilean case where more than 50 of the
population is economically vulnerable (OECD 2017)
The main hypotheses to be tested in this essay is whether the number of incivilities is
positively associated with the level of economic and racial heterogeneity at the county level We
start analysing this association for the full sample and period Next we analyse whether the
relationship between incivilities and our measures of diversity differs by geographic area (region
or zone) Finally we check whether the effect of economic and racial diversity is different
depending on the group of incivilities
112
44 Results and Discussion
Overall our results show that the rate of incivilities displays a stronger and more significant
relationship with racial diversity than with economic heterogeneity This association differs for
different geographic areas and for different types of incivilities Absolute economic indicators
except for income show a significant but small effect Increases in the average levels of income
or education and more financial autonomy for municipalities seem to be effective ways to reduce
the rate of incivilities
We estimate equation (44) assuming that the number of incivilities follows a Poisson
distribution Regional and temporal heterogeneity are captured through the inclusion of dummy
variables for five years (with 2006 as the reference year) and fourteen regional dummies (with
region XIII as the reference region) Results are reported in Table 4339 This table is structured in
two blocks of regressions pooled models in columns (1)-(4) and fixed-effects models in columns
(5)-(8)40 The first column in each block only includes economic indicators relative and absolute
trying to test which ones are more relevant and whether incivilities tend to mirror income
inequality (the ldquoincivilities thesisrdquo) The second column adds the variable foreign to account for
the effect of racial diversity (Letki 2008) The third column includes education to check whether
the association between economic and racial diversity with social cohesion changes (gets less
significant) when we control for educational level (Tolsma et al 2009) The final column in each
block corresponds to the full model specification which includes the rest of controls
39 Pooled estimations were obtained using the R command glm() and compared with results obtained from the command poisson in STATA 15 Fixed effects and random effects models were estimated using the STATA command xtpoisson Negative Binomial models were estimated using the command glmnb() in the MASS package for R and commands nbreg and xtnbreg in STATA 15 Results for Negative Binomial regressions are in Appendix T 40 We have omitted results with random effects due to the literature on panel count data models suggest that the most robust estimator is Poisson regression with Fixed Effects (Santos Silva amp Tenreyro 2010 2011)
113
Table 43
Poisson regressions
114
The positive and significant coefficient for the variable gini besides being small it becomes
insignificant in the fixed effects specification which includes the full set of controls This result
does not seem to be enough evidence to support our hypothesis that more unequal counties display
higher rates of incivilities On the other hand racial diversity through the variable foreign shows
a consistent positive association with the rate of incivilities41 Together coefficients for gini and
foreign seems to support the ldquocommunity heterogeneity thesisrdquo (Letki 2008) but not the
ldquoincivilities thesisrdquo (Skogan 1999) To check this finding we run the pooled full model
specification for each region and results are shown in Table 44 where regions have been ordered
from North to South The sign of the coefficient of the variable gini differs for different regions
Moreover the relationship is insignificant in some of the most unequal regions which are in the
South of the country (VIII IX and XIV) This result rejects the hypothesis that incivilities mirror
structural income inequalities For the variable foreign 12 out of 15 regions confirm the positive
association with the rate of incivilities42
We also run our pooled full model separately for each group of incivilities defined at the end
of section (432) Income inequality keeps its significant but small association with each group of
incivilities (see Table 45) Our measure of racial diversity shows a stronger association with
ldquoAlcohol Consumptionrdquo related to ldquoPublic Damagerdquo and ldquoThreatsrdquo The link with ldquoStreet Tradingrdquo
appears as non-significant These results support our general finding that on the one hand racial
heterogeneity exert a more significant influence on the rate of incivilities than economic
41 To get the effects of the variables gini and foreign over the rate of incivilities per 1000 inhabitants we need to exponentiate their coefficients Thus an increase in one unit in the variable gini increases the rate of incivilities per 1000 inhabitantsrdquo in 1005 times (e^0005) in the pooled full model Similarly an increase in one point in variable foreign is expected to increase the ldquorate of incivilities per 1000 inhabitantsrdquo in 10876 times (e^0084) in the full pooled model and 10471 (e^0046) times in the full Fixed Effects model 42 Given that depending on the geographic location regions share certain characteristics the model was also run clustering regions in 4 zones (see Figure 31 in chapter 3) Results by zone are in Appendix U
115
heterogeneity On the other hand results confirm our hypothesis that the effect of diversity is
different for different types of incivilities
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region
Back to our general results in Table 43 the significant and negative coefficient of the
income variable and to a lesser extent the significant and positive coefficients of poverty and
unemployment provide evidence that absolute rather than relative economic indicators may be
more important explanations of the rate of incivilities This is opposite to evidence for the analysis
116
of crime rates such as in Hoodge (2010) and support the idea that determinants of incivilities are
different from those of crime Our results are also opposite to those for Dutch municipalities where
economic indicators turned out to be more important than ethnic heterogeneity (Tolsma et al
2009) The coefficient for the variable log(income) could be interpreted as counties with an income
level under the average face higher problems of antisocial behaviours such as incivilities In
addition as the income level moves far away from its average low level the problem of incivilities
is less relevant43 In terms of policy implications only those policies that achieve a significant
increase in the average level of county income seem to be effective in reducing incivilities and
strengthening social cohesion
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group
43 We ran our model splitting the sample of counties in quintiles based on their income level The coefficient for the variable gini only showed a significant (and positive) coefficient for the second third and fourth quintile A non-significant coefficient for the first quintile could imply that other types of inequalities (eg health education) are more relevant in the poorest counties In the case of the fifth quintile (the richest group of counties) they have better facilities in terms of infrastructure police resources self-protection etc which could reduce the potential triggers of incivilities
117
The inclusion of the variable education significantly improved the goodness of fit of the
models and did not generate significant changes in the coefficients of our measures of economic
and racial diversity This result rejects the proposition that the relationship between social
cohesion and diversity becomes less strong when controlled by education (Tolsma et al 2009)
Additionally it highlights the topic of education as a relevant determinant of the rate of incivilities
and social cohesion
Among control variables there are also some important results Opposite to what we
expected the variable youth shows a negative or non-significant coefficient Although this result
could be due to the lack of ldquophysical incivilitiesrdquo in Chilean records it indicates that it is incorrect
to stereotype this group as the main responsible for high incivilities rates The significant and
negative coefficient of the variable autonomy in the fixed effects specification could also have
important policy implications It is a signal that local governments can play an important role in
reducing incivilities or complementing the efforts from the central government Another
interesting result is the significant coefficient of the variable housing The latter finding is
particularly important in the sense that a negative sign supports public policies oriented to increase
homeownership as effective ways to improve social cohesion However the small magnitude of
the coefficient that even showed the opposite sign in some model specifications could be
explained for the high level of segregation that these policies have generated in Chilean society
As mentioned in the Introduction and Literature Review so far only a few studies have
used measures of disorders or incivilities as dependent variable to explain changes in social
cohesion (Skogan 2015) In addition there is no evidence in Chile analysing the determinants of
incivilities separately from those of crimes The importance of our results on identifying the
importance of economic and racial diversity on social cohesion lies mainly in its generality An
118
important number of countries all around the world share a similar context characterized by high
levels of inequality and an explosive increase in immigration These countries are also
experiencing a worsening in social cohesion which increases the risk of a social outburst
4 5 Conclusions
The main goal of this essay was to determine whether differences in incivilities at the county
level mirror differences in income distribution and racial diversity Previous literature suggests a
positive and strong association between social cohesion and indicators of economic disadvantage
relative deprivation and racial diversity (Letki 2008 Tolsma et al 2009 Uslaner amp Brown 2005)
While not all our results were significant they showed helpful insights about how and where
economic and racial diversity are more likely to influence the rate of incivilities and social
cohesion
We used data for the period 2006ndash17 economic heterogeneity was measured through the
Gini coefficient at the county level and racial heterogeneity was proxied by the number of granted
visas to foreigners as proportion of county population We found strong evidence of a significant
and positive association between the rate of incivilities and racial diversity but not with income
inequality Contrary to previous evidence at the municipal level (Coffeacute amp Geys 2006 Tolsma et
al 2009) in general our results give support for the ldquohomophily principlerdquo and the ldquocommunity
heterogeneity hypothesisrdquo However results also showed that the effect of economic and racial
diversity varies throughout the Chilean regions and for the different types of incivilities
We also found that policies aimed at controlling the behaviour of young people did not have
strong empirical support In terms of the role that local governments may have in facing the
119
growing problem of incivilities we found evidence that efforts managed from the municipalities
can be an important complement to those from the central government
Future research should go further on the role of local authorities on incivilities and social
cohesion On the one hand municipalities could have a direct impact on social cohesion through
the implementation of programs complementary to those of central authorities oriented to reduce
incivilities and crime On the other hand social cohesion could be indirectly affected when local
authorities display an inefficient performance supplying public services to citizens or they are
recognized as corrupted institutions We suggest that policy makers from central government
should give local authorities a greater role in fighting antisocial behaviours and crime Evaluating
programs in specific municipalities could help to elucidate the causal effect of for instance higher
fiscal autonomy on the rate of incivilities
Another interesting area for future work will be to analyse how housing policies have
contributed to the phenomenon of segregation of Chilean society and in the process of weakening
social cohesion Finally our main result highlights the need of a deeper analysis of the impact that
foreign immigration is having in Chile For instance disaggregating information by country of
origin and the reasons why immigrants are arriving to the country or specific regions will surely
help to understand the impacts of immigration
120
Chapter 5 Conclusions
This thesis investigated in three essays the issue of income inequality in Chile using county-
level data for the period 2006-2017 The first essay supplied empirical evidence about the
importance of the degree of dependence on natural resources in terms of employment in explaining
cross-county differences in income inequality The second essay analysed the potential causal
effect that income inequality has on the level of technical efficiency of local governments
providing public goods and services Lastly the third essay studied the relationship between social
cohesion measured through the number of antisocial behaviours classified as ldquoincivilitiesrdquo and
the levels of income and racial heterogeneity
Findings from the first essay support the idea that the endowment of natural resources plays
a significant role in explaining income inequality in Chile However contrary to what most
theoretical and empirical evidence postulates our findings showed a robust negative association
between the two variables This means that the reduction experienced in Chile in the degree of
dependence on natural resources in terms of employment has contributed to the persistence of high
levels of income inequality The exploratory analysis indicated that income inequality shows a
general clustering process characterized by a significant and positive spatial autocorrelation
Regarding the previous evidence for Chile (Paredes et al 2016) the regression analysis confirmed
the relevance of the spatial dimension of income inequality through a process of spatial
heterogeneity giving less support to the existence of a process of spatial dependence (spillover
effect) in the variable itself
121
Essay 2 studied the potential trade-off between efficiency and equity analysing the influence
of income inequality on the efficiency of local governments at the municipal level To identify the
causal effect of income inequality on municipal efficiency we proposed the use of the proportion
of firms in the primary sector as an instrument for income inequality Findings confirmed our
hypothesis that efficiency is lower in more unequal counties This result suggests the non-existence
of the trade-off between equity and efficiency Hence policies intended to reduce inequality could
help to increase efficiency at least at the level of municipal local governments
The third essay analysed how social cohesion proxied by the rate of incivilities is associated
with the levels of economic diversity proxied by income inequality and the levels of racial
diversity proxied by the number of new visas grated as proportion of the county population
Findings gave strong support to the hypothesis that the rate of incivilities is positively related to
racial diversity and to a lesser extent to economic diversity In addition the rate of incivilities
appears negatively related to the degree of financial autonomy of municipalities This means that
local governments can effectively contribute to the reduction of incivilities which could help
reduce victimization and crime rates ultimately strengthening social cohesion
Taken together findings from essays 2 and 3 highlight the important role that income
inequality could play in other relevant economic and social dimensions These findings add to the
understanding of the potential consequences of income inequality particularly in natural resource
rich countries with persistently high levels of inequality
The present study has mainly investigated income inequality at the county level In addition
Chilean municipalities play an important role providing ldquoservices to peoplerdquo so our findings could
be applied in other highly unequal countries with a high degree of dependence on natural resources
and local governments with similar responsibilities For instance in Latin America apart from
122
Chile and Brazil there are no studies on the efficiency of local governments Other limitations are
associated with the availability of information For instance important indicators such as GDP per
capita are only available at the regional level and information of incomes is not available annually
In addition given the heterogeneity among municipalities some type of grouping of municipalities
should be performed before looking for causal relationships or conducting program evaluation
Despite these limitations we believe this study could be the basis for different strands of future
research on the topic of inequality local government efficiency and social cohesion
It was stated in chapter 2 based on the resource curse hypothesis literature there are two
elements that determine whether NR are a curse or blessing in terms of socioeconomic outcomes
First the curse is more likely in countries with weak political and governance institutions Second
different types of resources affect institutions differently with resources that are concentrated in
space so-called ldquopointrdquo resources tend to impair institutions while ldquodiffuserdquo resources do not
(Deacon 2011 Isham Woolcock Pritchett amp Busby 2005) Our results showed a negative
relationship between income inequality and our measure of natural resource dependence even after
controlling for zone fixed effects and for the level of government expenditure This result could
be interpreted as a signal that NR has a direct effect on income inequality in addition to the indirect
impact through market or institutional channels Using other potential institutional transmission
channels will shed light about the true effect that the endowment of natural resources has over
income inequality Variables that could capture these institutional channels include the level of
employment in the public sector measures of rule of law and corruption and changes in the
creation of new business in the secondary and tertiary sectors related to the primary sector
Based on results from chapter 3 most of the municipalities show scale inefficiencies One
immediate area for future work will involve using our set of contextual factors to predict the status
123
of municipalities in terms of scale inefficiencies Defining as dependent variable whether a
municipality shows constant decreasing or increasing returns to scale we could run a multinomial
logistic regression to predict municipal status For instance we would expect that a one-unit
increase in the Gini coefficient should increase the probability of scale inefficiencies (increasing
or decreasing returns to scale rather than constant returns to scale) Because the aim in this case
would be predicting a certain result in terms of returns to scale the next step should involve to
split the full sample in training and testing data sets and to use some resampling methods such as
bootstrapping This will allow us to evaluate the performance and accuracy of our model
predictions using different random samples of municipalities Results from Machine Learning
algorithms will help us to assess the generalizability of our results to other data sets
Future work should also benefit greatly by using data on different Latin American countries
to (1) compare the responsibilities of local governments (2) select a common set of inputs and
output to evaluate LGE (3) identify the relevance of scales inefficiencies in explaining differences
in performance and (4) analyse the influence of contextual characteristics over LGE Differences
in the main primary sector activity in each country such as oil in Brazil mining in Chile or Coffee
in Colombia could be responsible for differences in LGE among countries These differences could
be associated with sources of revenue management of expenditure and definitions of outputs or
contextual effects such as corrupted institutions or the delay in the development of other sectors
such as manufacturing or services
To delve deep on reasons explaining the social crisis experienced by Chilean society and
other countries one area of future work will be to analyse the relationship between diversity and
the origins of social revolutions Based on Tiruneh (2014) the three most important factors that
explain the onset of social revolutions are economic development regime type and state
124
ineffectiveness Interesting questions include whether the characteristics of Chilean context at the
end of 2019 are enough to trigger the transformation of the political and socioeconomic system
Social revolutions particularly violent revolutions are less likely in more democratic educated
and wealthy societies So it would be relevant to identify the factors explaining the violence that
has characterized the social crisis in Chile Finally the democratic regime has been maintained in
the last decades with changes between left and right governments This could imply that more
important than the regime has been the efficiency or ineffectiveness of the governments to satisfy
the needs of the population
Future work should also cover the disaggregation of information regarding foreign
population in terms of the reasons for new granted visas and the country of origin Official data
allows us to disaggregate whether the benefit is permanent (students and employees with contract)
or temporary Furthermore most of the new visas were traditionally granted to neighbouring
countries (Peru and Bolivia) a trend that has changed in the recent years to include countries such
as Colombia Venezuela and Haiti An analysis of how economic and social indicators have been
affected by changes in the composition of foreigners their reasons for immigrating to the country
and their geographical distribution have implications for economic policy at both the national and
local levels At the national level such analysis should be a key input when proposing changes to
the national immigration policy At the local level it could help define the role of municipalities
to assess the benefits and challenges of immigration These challenges are mainly related to the
provision of public goods and services such as health and education which in Chile are the
responsibility of the municipalities
The findings of this thesis suggest that policymakers should encourage policies that reduce
income inequality The key role that municipalities could play to strengthen social cohesion and
125
the increasingly important role that foreign population is acquiring in most modern societies are
also interesting avenues for future research However the picture is still incomplete and more
research is needed incorporating other dimensions of inequality This is essential if we want to
understand the reasons that could have triggered the social outbursts experienced by various
economies across the globe
126
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Acemoglu D (2002) Technical Change Inequality and the Labor Market Journal of Economic Literature 40(1) 7ndash72 httpsdoiorg1012570022051026976
Acemoglu D Aghion P amp Violante G L (2001) Deunionization Technical Change and Inequality Carnegie-Rochester Conference Series on Public Policy 55(1) 229ndash264 httpsdoiorg101016S0167-2231(01)00058-6
Acemoglu D Johnson S amp Robinson J A (2001) The Colonial Origins of Comparative Development An Empirical Investigation The American Economic Review 91(5) 1369ndash1401 httpsdoiorg101257aer9151369
Acemoglu D amp Robinson J A (2002) The Political Economy of the Kuznets Curve Review of Development Economics 6(2) 183ndash203 httpsdoiorg1011111467-936100149
Afonso A amp Fernandes S (2006) Measuring local government spending efficiency Evidence for the Lisbon region Regional Studies 40(1) 39ndash53 httpsdoiorg10108000343400500449937
Afonso A amp Fernandes S (2008) Assessing and explaining the relative efficiency of local government The Journal of Socio-Economics 37(5) 1946ndash1979 httpsdoiorg101016jsocec200703007
Afonso A Schuknecht L amp Tanzi V (2010) Income distribution determinants and public spending efficiency Journal of Economic Inequality 8(3) 367ndash389 httpsdoiorg101007s10888-010-9138-z
Akita T (2003) Decomposing regional income inequality in China and Indonesia using two-stage nested Theil decomposition method The Annals of Regional Science 37(1) 55ndash77 httpsdoiorg101007s001680200107
Alesina A (2000) Participation in heterogeneous communities The Quarterly Journal of Economics 115(3) 847ndash904 httpsdoiorg101162003355300554935
Alesina A amp La Ferrara E (2002) Who trusts others Journal of Public Economics 85(2) 207ndash234 httpsdoiorg101016S0047-2727(01)00084-6
Allcott H amp Keniston D (2014) Dutch Disease or Agglomeration The Local Economic Effects of Natural Resource Booms in Modern America (N W P N 20508 Ed) NBER Working Paper No 20508 (Vol w20508) NBER Working Paper No 20508 NBER Working Paper No 20508 httpsdoiorgNBER Working Paper No 20508
Andersen T M amp Maibom J (2020) The big trade-off between efficiency and equitymdashis it there Oxford Economic Papers 72(2) 391ndash411
127
Anselin L (1988) Spatial econometrics methods and models (Vol 4) DordrechtBoston Kluwer Academic Publishers
Anselin L amp Bera A K (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics Statistics Textbooks and Monographs (Vol 155)
Anselin L Bera A K Florax R amp Yoon M J (1996) Simple diagnostic tests for spatial dependence Regional Science and Urban Economics 26(1) 77ndash104 httpsdoiorg1010160166-0462(95)02111-6
Antonelli M A amp De Bonis V (2018) The efficiency of social public expenditure in European countries a two-stage analysis Applied Economics 1ndash14 httpsdoiorg1010800003684620181489522
Aragoacuten F M amp Rud J P (2013) Natural Resources and Local Communities Evidence from a Peruvian Gold Mine American Economic Journal Economic Policy 5(2) 1ndash25 httpsdoiorg101257pol521
Ariely G (2014) Does Diversity Erode Social Cohesion Conceptual and Methodological Issues Political Studies 62(3) 573ndash595 httpsdoiorg1011111467-924812068
Armstrong H amp Taylor J (2000) Regional economics and policy (3rd ed) Oxford Blackwell
Aroca P amp Atienza M (2011) Economic implications of long distance commuting in the Chilean mining industry Resources Policy 36(3) 196ndash203 httpsdoiorg101016jresourpol201103004
Aroca P amp Bosch M (2000) Crecimiento convergencia y espacio en las regiones chilenas 1960 - 1998 Estudios de Economiacutea 27 199ndash224 Retrieved from httprepositoriouchileclbitstreamhandle2250127853Patricio_Arocapdfsequence=1
Ashworth J Geys B Heyndels B amp Wille F (2014) Competition in the political arena and local government performance Applied Economics 46(19) 2264ndash2276 httpsdoiorg101080000368462014899679
Atkinson A B (2015) Inequality What Can Be Done Harvard University Press
Auty R (1993) Sustaining development in mineral economies the resource curse thesis London [ua] Routledge
Auty R (2001) Resource abundance and economic development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Badeeb R A Lean H H amp Clark J (2017) The evolution of the natural resource curse thesis A critical literature survey Resources Policy 51 123ndash134 httpsdoiorg101016jresourpol201610015
Balaguer-Coll M T Brun-Martos M I Maacuterquez-Ramos L amp Prior D (2019) Local government efficiency determinants and spatial interdependence Applied Economics
128
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Balaguer-Coll M T amp Prior D (2009) Short- and long-term evaluation of efficiency and quality An application to Spanish municipalities Applied Economics 41(23) 2991ndash3002 httpsdoiorg10108000036840701351923
Banker R D Charnes A amp Cooper W W (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis Management Science 30(9) 1078ndash1092
Beine M Coulombe S amp Vermeulen W N (2015) Dutch Disease and the Mitigation Effect of Migration Evidence from Canadian Provinces The Economic Journal 125(589) 1574ndash1615 httpsdoiorg101111ecoj12171
Benito B Bastida F amp Garciacutea J A (2010) Explaining differences in efficiency an application to Spanish municipalities Applied Economics 42(4) 515ndash528 httpsdoiorg10108000036840701675560
Berg A amp Ostry J (2011) Equality and efficiency Finance amp Development 48(3) 12ndash15
Berg A amp Ostry J (2017) Inequality and Unsustainable Growth Two Sides of the Same Coin IMF ECONOMIC REVIEW 65(4) 792ndash815 httpsdoiorg101057s41308-017-0030-8
Beyer H amp Vergara R (2006) Delincuencia en Chile Determinantes y rol de las poliacuteticas puacuteblicas Instituto de Economiacutea UC
Blanco L amp Grier R (2012) Natural resource dependence and the accumulation of physical and human capital in Latin America Resources Policy 37(3) 281ndash295 httpsdoiorghttpdoiorg101016jresourpol201201005
Blau J R amp Blau P M (1982) The cost of inequality Metropolitan structure and violent crime American Sociological Review 114ndash129
Boetti L Piacenza M amp Turati G (2009) Fiscal decentralization and spending efficiency of local governments An Empirical Investigation on a Sample Of 4
Boix C amp Posner D (1998) Social capital Explaining its origins and effects on government performance British Journal Of Political Science 28 686ndash693
Borge L E Parmer P amp Torvik R (2015) Local natural resource curse JOURNAL OF PUBLIC ECONOMICS 131 101ndash114 httpsdoiorg101016jjpubeco201509002
Borger B Kerstens K Moesen W amp Vanneste J (1994) Explaining differences in productive efficiency An application to Belgian municipalities Public Choice 80(3) 339ndash358 httpsdoiorg101007BF01053225
Bourguignon F amp Morrisson C (1990) Income distribution development and foreign trade A cross-sectional analysislowast European Economic Review 34(6) 1113ndash1132 httpsdoiorghttpsdoiorg1010160014-2921(90)90071-6
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Branas C C Cheney R A MacDonald J M Tam V W Jackson T D amp Ten Have T R (2011) A difference-in-differences analysis of health safety and greening vacant urban space American Journal of Epidemiology 174(11) 1296ndash1306
Bravo J (2014) Fondo Comuacuten Municipal y su desincentivo a la recaudacioacuten en Chile Temas de La Agenda Legislativa-Centro de Poliacuteticas Puacuteblicas UC 9(68)
Browning E K amp Johnson W R (1984) The Trade-Off between Equality and Efficiency Journal of Political Economy 92(2) 175ndash203 httpsdoiorg101086261219
Brunnschweiler C N amp Bulte E H (2008) The resource curse revisited and revised A tale of paradoxes and red herrings Journal of Environmental Economics and Management 55(3) 248ndash264 httpsdoiorghttpsdoiorg101016jjeem200708004
Brunori P Ferreira F H G amp Peragine V (2013) Inequality of opportunity income inequality and economic mobility Some international comparisons In Getting Development Right (pp 85ndash115) Springer
Brunton-Smith I Jackson J amp Sutherland A (2014) Bridging structure and perception On the neighbourhood ecology of beliefs and worries about violent crime British Journal of Criminology 54(4) 503ndash526
Bulte E H Damania R amp Deacon R T (2005) Resource intensity institutions and development World Development 33(7) 1029ndash1044 httpsdoiorg101016jworlddev200504004
Carmignani F (2013) Development outcomes resource abundance and the transmission through inequality Resource and Energy Economics 35(3) 412ndash428 httpsdoiorg101016jreseneeco201304007
Carmignani Fabrizio amp Avom D (2010) The social development effects of primary commodity export dependence Ecological Economics 70(2) 317ndash330 httpsdoiorg101016jecolecon201009003
Caselli F amp Michaels G (2013) Do Oil Windfalls Improve Living Standards Evidence from Brazil American Economic Journal Applied Economics 5(1) 208ndash238 httpsdoiorg101257app51208
Celebioglu F amp Dallrsquoerba S (2010) Spatial disparities across the regions of Turkey An exploratory spatial data analysis Annals of Regional Science 45(2) 379ndash400 httpsdoiorg101007s00168-009-0313-8
Chan J To H-P amp Chan E (2006) Reconsidering social cohesion Developing a definition and analytical framework for empirical research Social Indicators Research 75(2) 273ndash302
Charnes A Cooper W W amp Rhodes E (1978) Measuring the efficiency of decision making units European Journal of Operational Research 2(6) 429ndash444
130
Chi G amp Zhu J (2019) Spatial Regression Models for the Social Sciences SAGE Publications Retrieved from httpsbooksgooglecomaubooksid=zHGkvwEACAAJ
Christiansen K O (1960) Industrialization and urbanization in relation to crime and juvenile delinquency International Review of Criminal Policy 16 3ndash8
Cingano F (2014) Trends in income inequality and its impact on economic growth (Vol 163) Paris OECD Publishing httpsdoiorg1017875jxrjncwxv6j-en
Coelli T J Prasada Rao D S OrsquoDonnell C J amp Battese G E (2005) An introduction to efficiency and productivity analysis An Introduction to Efficiency and Productivity Analysis Springer Science amp Business Media httpsdoiorg101007b136381
Coffeacute H amp Geys B (2005) Institutional Performance and Social Capital An Application to the Local Government Level Journal of Urban Affairs 27(5) 485ndash501 httpsdoiorg101111j0735-2166200500249x
Coffeacute H amp Geys B (2006) Community Heterogeneity A Burden for the Creation of Social Capital Social Science Quarterly 87(5) 1053ndash1072 httpsdoiorg101111j1540-6237200600415x
Cohen D Spear S Scribner R Kissinger P Mason K amp Wildgen J (2000) ldquo Broken windowsrdquo and the risk of gonorrhea American Journal of Public Health 90(2) 230
Corden W M amp Neary J P (1982) Booming sector and de-industrialisation in a small open economy The Economic Journal 92(368) 825ndash848
Cordero J M Pedraja-Chaparro F Pisaflores E C amp Polo C (2017) Efficiency assessment of Portuguese municipalities using a conditional nonparametric approach Journal of Productivity Analysis 48(1) 1ndash24 httpsdoiorg101007s11123-017-0500-z
Croissant Y amp Millo G (2018) Panel Data Econometrics with R John Wiley amp Sons
Cust J amp Poelhekke S (2015) The Local Economic Impacts of Natural Resource Extraction 7 251ndash268 httpsdoiorg101146annurev-resource-100814-125106
da Cruz N F amp Marques R C (2014) Revisiting the determinants of local government performance Omega 44 91ndash103 httpsdoiorg101016JOMEGA201309002
Dauvin M amp Guerreiro D (2017) The Paradox of Plenty A Meta-Analysis World Development 94 httpsdoiorg101016jworlddev201701009
de Sousa M da C S Cribari-Neto F amp Stosic B D (2005) Explaining DEA technical efficiency scores in an outlier corrected environment the case of public services in Brazilian municipalities Brazilian Review of Econometrics 25(2) 287ndash313
Deacon R T (2011) The Political Economy of the Natural Resource Curse A Survey of Theory and Evidence Foundations and Trends in Microeconomics 111-208
Delhey J amp Newton K (2005) Predicting cross-national levels of social trust global pattern or
131
Nordic exceptionalism European Sociological Review 21(4) 311ndash327
Dlouhyacute M (2018) Measuring Geographic Inequalities Dealing with Multiple Health Resources by Data Envelopment Analysis Frontiers in Public Health 6 53 httpsdoiorg103389fpubh201800053
Dollery B Wallis J amp Akimov A (2010) One Size Does Not Fit All The Special Case of Remote Small Local Councils in Outback Queensland Local Government Studies 36(1) 21ndash42 httpsdoiorg10108003003930903435716
Domenech J (2008) Mineral resource abundance and regional growth in Spain 1860ndash2000 Journal of International Development The Journal of the Development Studies Association 20(8) 1122ndash1135
Doran J amp Jordan D (2016) Decomposing US regional income inequality from 1969 to 2009 Applied Economics Letters 23(11) 781ndash784 httpsdoiorg1010801350485120151109030
Drew J amp Dollery B (2015) The State of Things The Dynamic Efficiency of Australian State and Territories Economic Papers A Journal of Applied Economics and Policy 34(3) 165ndash176
Drew J Kortt M amp Dollery B (2015) What Determines Efficiency in Local Government A DEA Analysis of NSW Local Government Economic Papers A Journal of Applied Economics and Policy 34(4) 243ndash256 httpsdoiorg1011111759-344112118
Easterly W (2007) Inequality does cause underdevelopment Insights from a new instrument Journal of Development Economics 84(2) 755ndash776 httpsdoiorghttpdxdoiorg101016jjdeveco200611002
Ebert L amp La Menza T (2015) Chile copper and resource revenue A holistic approach to assessing commodity dependence Resources Policy 43(Supplement C) 101ndash111 httpsdoiorghttpsdoiorg101016jresourpol201410007
ElGindi T (2017) Natural resource dependency neoliberal globalization and income inequality Are they related A longitudinal study of developing countries (1980ndash2010) Current Sociology 65(1) 21ndash53 httpsdoiorg1011770011392116632031
Engerman S L amp Sokoloff K L (1994) Factor Endowments Institutions and Differential Paths of Growth Among New World Economies National Bureau of Economic Research
Engerman S L amp Sokoloff K L (1997) Factor endowments institutions and differential paths of growth among new world economies How Latin America Fell Behind 260ndash304
Engerman S L Sokoloff K L Urquiola M amp Acemoglu D (2002) Factor Endowments Inequality and Paths of Development among New World Economies [with Comments] EconomampxedA 3(1) 41ndash109 Retrieved from httpwwwjstororgezp01libraryquteduaustable20065432
132
Faumlre R Grosskopf S amp Lovell C A K (1985) The measurement of efficiency of production (Vol 6) Springer Science amp Business Media
Fleming D A amp Measham T G (2015a) Income Inequality across Australian Regions during the Mining Boom 2001-11 Australian Geographer 46(2) 203ndash216 httpsdoiorg1010800004918220151020596
Fleming D A amp Measham T G (2015b) Local economic impacts of an unconventional energy boom the coal seam gas industry in Australia Australian Journal of Agricultural and Resource Economics 59(1) 78ndash94 httpsdoiorg1011111467-848912043
Fum R M amp Hodler R (2010) Natural resources and income inequality The role of ethnic divisions Economics Letters 107(3) 360ndash363 httpsdoiorg101016jeconlet201003008
Garofalo J (1978) The fear of crime Broadening our perspective
Geys B amp Moesen W (2009) Exploring Sources of Local Government Technical Inefficiency Evidence from Flemish Municipalities Public Finance and Management 9(1) 1ndash29
Goderis B amp Malone S W (2011) Natural Resource Booms and Inequality Theory and Evidence The Scandinavian Journal of Economics 113(2) 388ndash417 httpsdoiorg101111j1467-9442201101659x
Greene W H (2016) Productivity and Efficiency Analysis (W H Greene L Khalaf R Sickles M Veall amp M-C Voia Eds) (1st ed 20) Cham Springer International Publishing httpsdoiorg101007978-3-319-23228-7
Gustafsson B amp Shi L (2002) Income inequality within and across counties in rural China 1988 and 1995 Journal of Development Economics 69(1) 179ndash204 httpsdoiorg101016S0304-3878(02)00058-5
Gylfason T amp Zoega G (2003) Inequality and Economic Growth Do Natural Resources Matter In T T Eicher S (Ed) Inequality and Growth Theory and Policy Implications (pp 255ndash292) The MIT Press
Henningsen A (2010) Estimating Censored Regression Models in R using the censReg Package R Package Vignettes Collection 5(2) 12
Henningsen A (2019) censReg Censored Regression (Tobit) Models R package version 05-30 httpscranr-projectorgpackage=censReg Retrieved from httpscranr-projectorgpackage=censReg
Herrera S amp Pang G (2005) Efficiency of Public Spending in Developing Countriesthinsp An Efficiency Frontier Approach World Bank Washington DC
Hill T D amp Angel R J (2005) Neighborhood disorder psychological distress and heavy drinking Social Science amp Medicine 61(5) 965ndash975
133
httpsdoiorghttpsdoiorg101016jsocscimed200412027
Hipp J R (2010) RESIDENT PERCEPTIONS OF CRIME AND DISORDER HOW MUCH IS ldquoBIASrdquo AND HOW MUCH IS SOCIAL ENVIRONMENT DIFFERENCES Criminology 48(2) 475ndash508 httpsdoiorg101111j1745-9125201000193x
Holtug N amp Mason A (2010) Introduction Immigration diversity and social cohesion SAGE Publications Sage UK London England
Hooghe M Vanhoutte B Hardyns W amp Bircan T (2010) Unemployment Inequality Poverty and Crime Spatial Distribution Patterns of Criminal Acts in Belgium 2001ndash06 The British Journal of Criminology 51(1) 1ndash20 httpsdoiorg101093bjcazq067
Howie P amp Atakhanova Z (2014) Resource boom and inequality Kazakhstan as a case study Resources Policy 39(1) 71ndash79 httpsdoiorg101016jresourpol201311004
Isham J Woolcock M Pritchett L amp Busby G (2005) The Varieties of Resource Experience Natural Resource Export Structures and the Political Economy of Economic Growth The World Bank Economic Review 19(2) 141ndash174 httpsdoiorg101093wberlhi010
Jottier D Ashworth J amp Heyndels B (2012) Understanding Votersrsquo Preferences How the Electoratersquos Complexity Affects Prediction Accuracy and Wishful Thinking among Politicians with Respect to Election Outcomes Kyklos 65(3) 340ndash370 httpsdoiorg101111j1467-6435201200542x
Kanbur S M R amp Venables A (2005) Spatial inequality and development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Kesler C amp Bloemraad I (2010) Does immigration erode social capital The conditional effects of immigration-generated diversity on trust membership and participation across 19 countries 1981ndash2000 Canadian Journal of Political ScienceRevue Canadienne de Science Politique 43(2) 319ndash347
Kuznets S (1955) Economic Growth and Income Inequality The American Economic Review 45(1) 1ndash28 Retrieved from httpwwwjstororgstable1811581
Lagos G amp Blanco E (2010) Mining and development in the region of Antofagasta Resources Policy 35(4) 265ndash275 httpsdoiorghttpsdoiorg101016jresourpol201007006
Leamer E E Maul H Rodriguez S amp Schott P K (1999) Does natural resource abundance increase Latin American income inequality Journal of Development Economics 59(1) 3ndash42 httpsdoiorg101016s0304-3878(99)00004-8
Leibbrandt M Finn A amp Woolard I (2012) Describing and decomposing post-apartheid income inequality in South Africa Development Southern Africa 29(1) 19ndash34 httpsdoiorg1010800376835X2012645639
134
Letki N (2008) Does Diversity Erode Social Cohesion Social Capital and Race in British Neighbourhoods Political Studies 56(1) 99ndash126 httpsdoiorg101111j1467-9248200700692x
Lewis D A (2017) Fear of crime Incivility and the production of a social problem Routledge
lo Storto C (2013) Evaluating Technical Efficiency of Italian Major Municipalities A Data Envelopment Analysis model Procedia - Social and Behavioral Sciences 81 346ndash350 httpsdoiorg101016JSBSPRO201306440
Loayza N amp Rigolini J (2016) The Local Impact of Mining on Poverty and Inequality Evidence from the Commodity Boom in Peru World Development 84 219ndash234 httpsdoiorg101016jworlddev201603005
Loayza N Teran A M y amp Rigolini J (2013) Poverty Inequality and the Local Natural Resource Curse World Bank Policy Research Working Paper (6366) httpsdoiorg1015961813-9450-6366
Loacutepez R amp Miller S J (2008) Chile The Unbearable Burden of Inequality World Development 36(12) 2679ndash2695 httpsdoiorg101016jworlddev200801012
Manzano O amp Rigobon R (2001) Resource Curse or Debt Overhang National Bureau of Economic Research Working Paper Series No 8390 1 httpsdoiorghttpwwwnberorgpapersw9424bib
McPherson M Smith-Lovin L amp Cook J M (2001) Birds of a Feather Homophily in Social Networks Annual Review of Sociology 27(1) 415ndash444 httpsdoiorg101146annurevsoc271415
McQuestin D Drew J amp Dollery B (2018) Do Municipal Mergers Improve Technical Efficiency An Empirical Analysis of the 2008 Queensland Municipal Merger Program Australian Journal of Public Administration 77(3) 442ndash455 httpsdoiorg1011111467-850012286
Messner S F Rosenfeld R amp Baumer E P (2004) Dimensions of Social Capital and Rates of Criminal Homicide American Sociological Review 69(6) 882ndash903 httpsdoiorg101177000312240406900607
Michaels G (2011) THE LONG TERM CONSEQUENCES OF RESOURCE-BASED SPECIALISATION The Economic Journal 121(551) 31ndash57 httpsdoiorg101111j1468-0297201002402x
Mijanovich T amp Weitzman B C (2003) Which ldquobroken windowsrdquo matter School neighborhood and family characteristics associated with youthsrsquo feelings of unsafety Journal of Urban Health 80(3) 400ndash415
Mikušovaacute P (2015) An Application of DEA Methodology in Efficiency Measurement of the Czech Public Universities Procedia Economics and Finance 25 569ndash578 httpsdoiorg101016S2212-5671(15)00771-6
135
Milanovic B (2016) Global inequality Harvard University Press
Millo G amp Piras G (2012) splm Spatial panel data models in R Journal of Statistical Software 47(1) 1ndash38
Murphy K M amp Topel R H (2016) Human Capital Investment Inequality and Economic Growth JOURNAL OF LABOR ECONOMICS 34(2) S99ndashS127 httpsdoiorg101086683779
Narboacuten-Perpintildeaacute I amp De Witte K (2018a) Local governmentsrsquo efficiency a systematic literature reviewmdashpart I International Transactions in Operational Research 25(2) 431ndash468 httpsdoiorg101111itor12364
Narboacuten-Perpintildeaacute I amp De Witte K (2018b) Local governmentsrsquo efficiency a systematic literature reviewmdashpart II International Transactions in Operational Research 25(4) 1107ndash1136 httpsdoiorg101111itor12389
Nuntildeez J Rivera J Villavicencio X amp Molina O (2003) Determinantes socioeconoacutemicos y demograacuteficos del crimen en Chile Estudios de Economiacutea 30(1) 55ndash85
OrsquoDonnell C J Rao D S P amp Battese G E (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios Empirical Economics 34(2) 231ndash255 httpsdoiorg101007s00181-007-0119-4
Ocampo J A (2004) Latin Americarsquos Growth and Equity Frustrations During Structural Reforms The Journal of Economic Perspectives 18(2) 67ndash88 httpsdoiorg1012570895330041371349
OECD (2014) Focus on inequality and growth OECD
OECD (2017) Howrsquos Life 2017 Life Satisfaction Oecd httpsdoiorg101787how_life-2017-en
Ohtake F (2008) Inequality in Japan Asian Economic Policy Review 3(1) 87ndash109 httpsdoiorg101111j1748-3131200800093x
Okun A M (2015) Equality and efficiency the big tradeoff Washington DC Brookings Institution Press
Ortega B Sanjuaacuten J amp Casquero A (2017) Determinants of efficiency in reducing child mortality in developing countries The role of inequality and government effectiveness Health Care Management Science 20(4) 500ndash516 httpsdoiorg101007s10729-016-9367-1
Ostry J Berg A amp Tsangarides C (2014) Redistribution inequality and growth International Monetary Fund
Pacheco F Saacutenchez R amp Villena M (2013) Eficiencia de los Gobiernos Locales y sus Determinantes Un anaacutelisis de Fronteras Estocaacutesticas en Datos de Panel para
136
Municipalidades Chilenas Santiago de Chile Chile
Papyrakis E amp Raveh O (2014) An Empirical Analysis of a Regional Dutch Disease The Case of Canada Environmental and Resource Economics 58(2) 179ndash198 httpsdoiorg101007s10640-013-9698-z
Paredes D (2013) The Role of Human Capital Market Potential and Natural Amenities in Understanding Spatial Wage Disparities in Chile Spatial Economic Analysis 8(2) 154ndash175 httpsdoiorg101080174217722013774094
Paredes D Iturra V amp Lufin M (2016) A spatial decomposition of income inequality in Chile Regional Studies 50(5) 771ndash789
Phan P Van Orsquobrien M Mendolia S amp Paloyo A (2017) National pro-poor spending programmes and their effect on income inequality and poverty Evidence from Vietnam Applied Economics 49(55) 5579ndash5590 httpsdoiorg1010800003684620171313957
Podinovski V V (2004) Bridging the Gap between the Constant and Variable Returns-to-Scale Models Selective Proportionality in Data Envelopment Analysis The Journal of the Operational Research Society 55(3) 265ndash276 Retrieved from httpwwwjstororgstable4102006
Ravallion M (2005) On Measuring Aggregate Social Efficiency Economic Development and Cultural Change 53(2) 273ndash292 httpsdoiorg101086425380
Rehner J Baeza S A amp Barton J R (2014) Chilersquos resource-based export boom and its outcomes Regional specialization export stability and economic growth Geoforum 56(Supplement C) 35ndash45 httpsdoiorghttpsdoiorg101016jgeoforum201406007
Rivera J Gutieacuterrez M amp Nuacutentildeez J (2009) Caracterizacioacuten socioeconoacutemica y espacial de la criminalidad en Chile Revista CEPAL
Ross C E (2011) Collective threat trust and the sense of personal control Journal of Health and Social Behavior 52(3) 287ndash296
Ross C E amp Mirowsky J (2001) Neighborhood disadvantage disorder and health Journal of Health and Social Behavior 258ndash276
Rothstein B amp Uslaner E M (2005) All for all Equality corruption and social trust World Politics 58(1) 41ndash72
Sachs J D amp Warner A M (2001) The curse of natural resources European Economic Review 45(4ndash6) 827ndash838 httpsdoiorg101016S0014-2921(01)00125-8
Salas R (2019) sinimr Chilean Municipalities Information System Wrapper Retrieved from httpsgithubcomrobsalascosinimr
Sampson R J (1986) Crime in Cities The Effects of Formal and Informal Social Control Crime and Justice 8 271ndash311
137
Sampson R J (2008) Rethinking crime and immigration Contexts 7(1) 28ndash33
Santos Silva J M C amp Tenreyro S (2010) On the existence of the maximum likelihood estimates in Poisson regression Economics Letters 107(2) 310ndash312 httpsdoiorghttpsdoiorg101016jeconlet201002020
Santos Silva J M C amp Tenreyro S (2011) poisson Some convergence issues Stata Journal 11(2) 207ndash212 Retrieved from httpwwwstata-journalcomarticlehtmlarticle=st0225
Scott Z (2009) Decentralisation local development and social cohesion an analytical review GSDRC Research Paper 5
Sinha R P (2017) Fiscal Performance Benchmarking of Indian States-A Robust Frontier Approach The Central European Review of Economics and Management 1(4) 225ndash249
Skogan W (1986) Fear of crime and neighborhood change Crime and Justice 8 203ndash229
Skogan W (1999) Measuring what matters Crime disorder and fear In Measuring what matters Proceedings from the Policing Research Institute meetings (pp 37ndash53) National Institute of Justice Washington DC
Skogan W (2015) Disorder and Decline The State of Research Journal of Research in Crime and Delinquency 52(4) 464ndash485 httpsdoiorg1011770022427815577836
Šťastnaacute L amp Gregor M (2014) Public sector efficiency in transition and beyond evidence from Czech local governments Applied Economics 47(7) 1ndash20 httpsdoiorg101080000368462014978077
Tandon A (2005) Measuring Efficiency of Macro Systems An Application to Millennium Development Goal Attainment Asian Development Review 22(2) 108ndash125
Taylor R B (1999) The incivilities thesis Theory measurement and policy Measuring What Matters 65 88
Tigga N S amp Mishra U S (2015) On Measuring Technical Efficiency of the Health System in India An Application of Data Envelopment Analysis Journal of Health Management 17(3) 285ndash298 httpsdoiorg1011770972063415589229
Tinbergen J (1975) Demographic Development and the Exhaustion of Natural Resources Population and Development Review 1(1) 23ndash32 httpsdoiorg1023071972269
Tiruneh G (2014) Social Revolutions Their Causes Patterns and Phases SAGE Open 4(3) 2158244014548845 httpsdoiorg1011772158244014548845
Tolsma J Van der Meer T amp Gesthuizen M (2009) The impact of neighbourhood and municipality characteristics on social cohesion in the Netherlands Acta Politica 44(3) 286ndash313
Tsekeris Sotiris T Tsekeris T amp Papaioannou S (2018) Regional determinants of technical efficiency evidence from the Greek economy Regional Studies [London]thinsp Carfax
138
httpsdoiorg1010800034340420171390312
Uslaner E (2002) The moral foundations of trust Cambridge University Press
Uslaner E (2011) CORRUPTION AND INEQUALITY DICE Report 9(2) 20ndash24
Uslaner E (2013) Trust and corruption revisited how and why trust and corruption shape each other Quality amp Quantity 47(6) 3603ndash3608 httpsdoiorg101007s11135-012-9742-z
Uslaner E amp Brown M (2005) Inequality trust and civic engagement American Politics Research 33(6) 868ndash894
Van der Ploeg F (2011) Natural Resources Curse or Blessing Journal of Economic Literature 49(2) 366ndash420 httpsdoiorg101257jel492366
Vergara R (2012) Crime Prevention Programs Evidence From CHILE The Developing Economies 50(1) 1ndash24
Watkins M H (1963) A staple theory of economic growth Canadian Journal of Economics and Political ScienceRevue Canadienne de Economiques et Science Politique 29(2) 141ndash158
Wilkinson R G (1999) Income inequality social cohesion and health clarifying the theorymdasha reply to Muntaner and Lynch International Journal of Health Services 29(3) 525ndash543
Wilson J Q amp Kelling G L (1982) Broken windows Atlantic Monthly 249(3) 29ndash38
Wilson W J (1996) When Work Disappears Political Science Quarterly 111(4) 567ndash595 httpsdoiorg1023072152085
Wirth L (1938) Urbanism as a Way of Life American Journal of Sociology 44(1) 1ndash24
Worthington A amp Dollery B (2000) An empirical survey of frontier efficiency measurement techniques in local government Local Government Studies 26(2) 23ndash52 httpsdoiorg10108003003930008433988
Wu P-C Huang T-H amp Pan S-C (2014) Country Performance Evaluation The DEA Model Approach Social Indicators Research 118(2) 835ndash849 httpsdoiorg101007s11205-013-0443-3
Ye X Ma L Ye K Chen J amp Xie Q (2017) Analysis of Regional Inequality from Sectoral Structure Spatial Policy and Economic Development A Case Study of Chongqing China Sustainability 9(4) 633 Retrieved from httpwwwmdpicom2071-105094633
Yue W Z Zhang Y T Ye X Y Cheng Y Q amp Leipnik M R (2014) Dynamics of Multi-Scale Intra-Provincial Regional Inequality in Zhejiang China Sustainability 6(9) 5763ndash5784 httpsdoiorg103390su6095763
139
Appendices
Appendix A Summary statistics income inequality
Table A1
Summary statistics Gini coefficients by year and zone
140
Appendix B Summary statistics for NRD measures by region
Table B1
Summary statistics NRD measures by region
141
Appendix C Regional administrative division and defined zones
Figure C1 Geographical distribution of Chilean regions and 3 zones
142
Appendix D Summary statistics numeric controls and correlation matrix
Table D1
Summary Statistics Numeric Explanatory Variables
Figure D1 Correlation matrix numeric explanatory variables
143
Appendix E Static spatial panel models
Following Millo amp Piras (2012) a model including a spatial lag of the dependent variable and
spatial autoregressive disturbances but not spatial lags for the explanatory variable(s) is called
SARAR model A static spatial SARAR panel could be expressed as
119910 120582 119868 otimes119882 119910 119883120573 119906 (E1)
where y is an 119873 1 vector of observations on the dependent variable X is a 119873 119896 matrix of
observations on the non-stochastic exogenous regressors 119868 an identity matrix of dimension 119879 otimes
is the kronecker operator 119882 is the 119873 119873 spatial weights matrix of known constants whose
diagonal elements are set to zero and 120582 the corresponding spatial parameter44
The disturbance vector is the sum of two terms
119906 120580 otimes 119868 120583 120576 (E2)
where 120580 is a 119879 1 vector of ones 119868 an 119873 119873 identity matrix 120583 is a vector of time-invariant
individual specific effects (not spatially autocorrelated) and 120576 a vector of spatially autocorrelated
innovations that follow a spatial autoregressive process of the form
120576 120588 119868 otimes119882 120576 120584 (E3)
If we assume that spatial correlation applies to both the individual effects 120583 and the remainder
error components 120576 Kapoor et al (2007) propose that the disturbance term 119906 follows a first order
spatial autoregressive process of the form
119906 120588 119868 otimes119882 119906 120576 (E4)
44 Unlike ldquotraditional panelsrdquo which are organized as different time series for each cross-sectional unit ldquospatial panelsrdquo are organized as a series of cross-sections for each year
144
where 119882 is the spatial weights matrix and 119903ℎ119900 the corresponding spatial autoregressive
parameter To further allow for the innovations to be correlated over time the innovations vector
in Equation 7 follows an error component structure
120576 120580 otimes 119868 120583 120584 (E5)
where 120583 is the vector of cross-sectional specific effects 120584 a vector of innovations that vary
both over cross-sectional units and time periods 120580 is a vector of ones and 119868 an 119873 119873 identity
matrix45
Spatial panel models are usually estimated by Maximum Likelihood or GMM46 The SAR
SEM or SARAR models could be estimated with Random or Fixed effects For instance A fixed
effect spatial lag model can be written in stacked form as
119910 120582 119868 otimes119882 119910 120580 otimes 119868 120583 119883120573 120576 (E6)
where 120582 is the spatial autoregressive coefficient 119882 a non-stochastic spatial weights matrix
120580 a column vector of ones of dimension 119879 119868 an 119873 119873 identity matrix and 120576 sim 119873 0120590 On
the other hand a fixed effects spatial error model assuming the disturbance specification by
Kapoor et al (2007) can be written as
119910 120580119879 otimes 119868119873 120583 119883120573 119906119906 120588 119868119879 otimes119882119873 119906 120576
(E7)
where 120588 is the spatial autocorrelation coefficient and 120576 is a well-behaved error term
45 In the regression implementation the specification given by equations (22) and (3) is denoted by ldquobrdquo for ldquoBaltagirdquo On the other hand the specification given by equations (23) and (31) is denoted by ldquokkprdquo for ldquoKapoor Kelejian and Pruchardquo 46 We use the R package splm which allow both types of regression procedure
145
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis
Table F1
Analysis OLS residuals Anselin Method
Figure F1 Moran scatter plot OLS residuals
146
Appendix G Linear panel data models
Table G1
Panel regressions (non-spatial)
147
Appendix H Spatial panel models (Generalized Moments (GM) estimation)
Table H1
GM Spatial Models
148
Appendix I Inputs and outputs used in DEA analysis
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018)
149
Appendix J Technical and scale efficiency
Following lo Storto (2013) under an input-oriented specification assuming VRS with n
municipalities using k inputs to produce m outputs the DEA model for a given i-th municipality
is specified with the following mathematical programming problem
119898119894119899 120579119904119906119887119895119890119888119905 119905119900 119910 119884120582 0120579119909 119883120582 01198991prime120582 1 120582 0prime
Where 119910 is the column vector of the outputs and 119909 is the column vector of the inputs
Moreover we can define X as the (k times n) input matrix and Y as the (m times n) output matrix 120579 is a
scalar (that satisfies 120579 1) more specifically it is the efficiency score that measures technical
efficiency It measures the distance between a municipality and the efficiency frontier defined as
a linear combination of the best practice observations With 120579 1 the municipality is inside the
frontier (ie it is inefficient) while 120579 1 implies that the municipality is on the frontier (ie it is
efficient) The vector 120582 is an (n times 1) vector of constants that measures the weights used to compute
the location of an inefficient municipality if it were to become efficient
The total technical efficiency 119879119864 can be decomposed into pure technical efficiency
119879119864 and scale efficiency 119878119864 where 119878119864 119879119864 119879119864 (Coelli et al 2005) To find out
whether a municipality is scale efficient and qualify the type of returns of scale a DEA model
under non-increasing returns to scale 119879119864 is implemented where 119878119864 119879119864 119879119864 Hence
the following rule can be applied (Faumlre Grosskopf amp Lovell 1985)
bull If 119878119864 1 then a municipality is scale efficient both under CRS and VRS
bull If 119878119864 1 it operates under increasing returns to scale
bull If 119878119864 1 it operates under decreasing returns to scale
150
Appendix K Correlation matrix
Figure K1 Correlation matrix contextual factors
151
Appendix L Returns to scale by year and zone
Table L1
Returns to scale (percentage of municipalities)
152
Appendix M Returns to scale by year (maps)
Figure M1 Spatial distribution of returns to scale by county per year
153
Appendix N Efficiency status by year (maps)
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year
154
Appendix O Spatial distribution efficiency scores by year (maps)
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year
155
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis
Table P1
Analysis OLS residuals Anselin Method
Figure P1 Moran scatter plot efficiency scores and OLS residuals
156
Table P2
OLS and spatial regression models for the six-year averaged data
157
Appendix Q OLS regressions for cross-sectional and panel data
Table Q1
OLS cross-sectional regression per year
158
Table Q2
OLS panel regressions Pooled random effects and instrumental variable
159
Appendix R Quantile maps incivilities rate by group (average total period)
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17)
160
Appendix S Correlation matrix numeric covariates
Figure S1 Correlation matrix numeric covariates
161
Appendix T Negative Binomial regressions
Table T1
Negative Binomial regressions
162
Appendix U Coefficients economic and racial diversity by geographical zone
Table U1
Coefficients economic and racial diversity in pooled Poisson models by geographic zone
iii
inequality tend to cluster in space The regression analysis confirms the importance of capturing
geographical heterogeneity in the explanation of income inequality however gives less support
to a process of spatial dependence like a spillover effect of income inequality among
neighbouring counties
Among the potential consequences of income inequality the literature highlights its
possible impacts on the efficiency in the provision of public services by local authorities however
empirical evidence is very little For this reason the second essay analyses the technical efficiency
of municipal local governments in Chile and examine if income inequality has significant impacts
on the variations in the efficiency levels across municipalities An input-oriented Data
Envelopment Analysis is used to measure municipal efficiency Results reveal that the municipal
production technology is characterized by variable returns to scale but scale inefficiencies only
explain a small proportion of total inefficiency This justify a need for analysing the influence of
variables which are beyond the control of local authorities in explaining differences in municipal
efficiency The main hypothesis tested was whether income inequality has a negative influence on
municipal efficiency whilst a measure of natural resource dependence at the county level was used
as an instrument to control for the effects of possible endogeneity issues Results showed that
changes in income inequality could be associated with changes in the municipal efficiency level
in the same magnitude but in the opposite direction This confirms that local authorities in counties
characterized by high levels of income inequality face greater challenges to achieve more efficient
performance This result suggests that policies aimed at reducing income inequality can also
increase the efficiency of local governments Our results also reveal that policies such as
amalgamation de-amalgamation or cooperation among municipalities should be designed
specifically for each region rather than as a standard national strategy
Finally the third essay analyses how social cohesion is associated with the levels of
economic and racial diversity Social cohesion is proxied using the reported number of antisocial
behaviours catalogued as incivilities Incivilities are those antisocial behaviours which violate
social norms but are not usually considered as criminal Research has documented the implications
of incivilities on human stress health public behaviour and increasing feelings of insecurity and
fear among the population Few studies have explicitly considered incivilities as a dependent
variable to identify their determinants or use them to analyse the weakening of social cohesion and
iv
the growing feeling of social unrest in contemporary societies Economic diversity is proxied using
the Gini coefficient in each county and racial diversity through the number of new visas granted
as proportion of the county population Our findings show that incivilities are strongly associated
with racial diversity and to a lesser extent with economic diversity The rate of incivilities also
shows a negative association with the level of income and a positive relationship with poverty and
unemployment rates These results give empirical support to the idea that both relative and
absolute indicators of economic deprivation play an important role in understanding the growing
problem of incivilities in highly unequal economies like Chile Results also show that the rate of
incivilities is negatively related to the degree of financial autonomy of municipalities These
findings represent promising areas for central and local governments in the implementation of
policies aimed at increasing social cohesion through the reduction of incivilities and other types of
antisocial behaviours
v
Table of Contents
Keywords i
Abstract ii
Table of Contents v
List of Figures viii
List of Tables ix
List of Abbreviations x
Statement of Original Authorship xi
Acknowledgements xii
Chapter 1 Introduction 13
Income inequality and dependence on natural resources 14
Local government efficiency and income inequality 16
Social cohesion and economic diversity 19
Contributions 21
Thesis outline 23
Chapter 2 Natural Resources Curse or Blessing Evidence on Income Inequality at the County Level in Chile 24
21 Introduction 24
22 Inequality and Natural Resources 28 221 Theoretical Framework 28
Cross-country literature 29 Single country evidence 32
222 The relevance of the spatial approach 33
23 Research problem and hypotheses 35
24 Data and Methods 36 241 Operationalization of key variables 36 242 Control variables 40 243 Methods 41 244 Spatial Model Specification 43
25 Results 44 251 Exploratory Spatial Data Analysis (ESDA) 44 252 Cross-sectional analysis 45 253 Panel Data analysis 48
26 Discussion and conclusions 51
Chapter 3 The Impact of Income Inequality on the Efficiency of Municipalities in Chile 55
vi
31 Introduction 55
32 Related Literature 61 321 Measuring efficiency of local governments 61 322 Explaining differences in LGE 63 323 The trade-off between efficiency and equity 64
33 Methodology 66 331 Chilean Municipalities and period of analysis 66 332 Measuring municipal efficiency 68 333 Inputs and outputs used in DEA 70 334 Regression model 71 335 The instrument 75
34 Results and discussion 77 341 DEA results 77
Returns to scale 78 Efficiency measure 80
342 Regression results 82 Exploratory spatial analysis 82 Cross-sectional analysis 83 Panel data analysis 84
35 Conclusions 88
Chapter 4 Social Cohesion Incivilities and Diversity Evidence at the municipal level in Chile 91
41 Introduction 91
42 Related Literature 95 421 The Community Heterogeneity Thesis 95 422 The literature on incivilities 97 423 The ldquoIncivilities Thesisrdquo 99
4 3 Methodology 100 431 Period of analysis and data sample 100 432 Operationalisation of the response variable and exploratory analysis 101 433 Measures of community heterogeneity and control variables 105 434 Methods 108 435 Hypotheses 111
44 Results and Discussion 112
4 5 Conclusions 118
Chapter 5 Conclusions 120
Bibliography 126
Appendices 139
Appendix A Summary statistics income inequality 139
Appendix B Summary statistics for NRD measures by region 140
Appendix C Regional administrative division and defined zones 141
Appendix D Summary statistics numeric controls and correlation matrix 142
vii
Appendix E Static spatial panel models 143
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis 145
Appendix G Linear panel data models 146
Appendix H Spatial panel models (Generalized Moments (GM) estimation) 147
Appendix I Inputs and outputs used in DEA analysis 148
Appendix J Technical and scale efficiency 149
Appendix K Correlation matrix 150
Appendix L Returns to scale by year and zone 151
Appendix M Returns to scale by year (maps) 152
Appendix N Efficiency status by year (maps) 153
Appendix O Spatial distribution efficiency scores by year (maps) 154
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis 155
Appendix Q OLS regressions for cross-sectional and panel data 157
Appendix R Quantile maps incivilities rate by group (average total period) 159
Appendix S Correlation matrix numeric covariates 160
Appendix T Negative Binomial regressions 161
Appendix U Coefficients economic and racial diversity by geographical zone 162
viii
List of Figures
Figure 21 Average share in GDP of economic activities (2006ndash17) 37
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17) 38
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17) 39
Figure 23 Moran scatter plots for variables gini and pss_casen 45
Figure 31 Geographical distribution of Chilean regions and macrozones 74
Figure 32 Evolution of efficiency scores and the proportion of firms by sector 77
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE) 78
Figure 34 Returns to scale by zone 79
Figure 35 Evolution mean efficiency scores (VRS) by zone 81
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017 102
Figure 42 Evolution total number of incivilities by category 104
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017) 104
Figure 44 Annual average number of incivilities per county 109
Figure C1 Geographical distribution of Chilean regions and 3 zones 141
Figure D1 Correlation matrix numeric explanatory variables 142
Figure F1 Moran scatter plot OLS residuals 145
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018) 148
Figure K1 Correlation matrix contextual factors 150
Figure M1 Spatial distribution of returns to scale by county per year 152
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year 153
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year 154
Figure P1 Moran scatter plot efficiency scores and OLS residuals 155
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17) 159
Figure S1 Correlation matrix numeric covariates 160
ix
List of Tables
Table 21 Cross-sectional Model Comparison (six-year average data) 47
Table 22 ML Spatial SAR Models 50
Table 23 ML Spatial SEM Models 50
Table 24 ML Spatial SARAR Models 51
Table 31 Descriptive statistics Inputs and Output variables used in DEA analysis 71
Table 32 Summary Statistics Numeric Contextual Factors 74
Table 33 Summary efficiency scores (VRS) by zone and region 80
Table 34 Cross-sectional (censored) regressions 84
Table 35 Panel data regressions 87
Table 41 Summary statistics total count of incivilities and by category (full sample and period) 103
Table 42 Summary statistics numeric explanatory variables 108
Table 43 Poisson regressions 113
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region 115
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group 116
Table A1 Summary statistics Gini coefficients by year and zone 139
Table B1 Summary statistics NRD measures by region 140
Table D1 Summary Statistics Numeric Explanatory Variables 142
Table F1 Analysis OLS residuals Anselin Method 145
Table G1 Panel regressions (non-spatial) 146
Table H1 GM Spatial Models 147
Table L1 Returns to scale (percentage of municipalities) 151
Table P1 Analysis OLS residuals Anselin Method 155
Table P2 OLS and spatial regression models for the six-year averaged data 156
Table Q1 OLS cross-sectional regression per year 157
Table Q2 OLS panel regressions Pooled random effects and instrumental variable 158
Table T1 Negative Binomial regressions 161
Table U1 Coefficients economic and racial diversity in pooled Poisson models by geographic zone 162
x
List of Abbreviations
Constant returns to scale CRS
Data envelopment analysis DEA
Decreasing returns to scale DRS
Efficiency scores ES
Exploratory spatial data analysis ESDA
Generalized methods of moments GMM
Gross Domestic Product GDP
Increasing returns to scale IRS
Local government efficiency LGE
Maximum likelihood ML
Municipal common fund MCF
Natural resource dependence NRD
Natural resource endowment NRE
Ordinary Least Squares OLS
Organization for Economic Cooperation and Development OECD
Own permanent revenues OPR
Resource curse hypothesis RCH
Spatial autoregressive model SAR
Spatial error model SEM
Variable returns to scale VRS
xi
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet requirements
for an award at this or any other higher education institution To the best of my knowledge and
belief the thesis contains no material previously published or written by another person except
where due reference is made
Signature QUT Verified Signature
Date _________04092020_________
xii
Acknowledgements
First I would like to thank my wife Lilian who joined me in this challenge and patiently
supported me all these years I would also like to thank our family who always supported us from
Chile I especially thank my sister Silvia who took care of our house and dog
I am also grateful to my supervisory team Dr Radhika Lahiri and Dr Vincent Hoang who
supported and guided me in the process of making this thesis a reality
I also thank the Deans of the Faculty of Economics and Business at my beloved University
of Talca Dr Arcadio Cerda and Dr Rodrigo Herrera who trusted me and supported me in this
process In the same way I would like to thank all the support of the director of the Commercial
Engineering career Mr Milton Inostroza
Finally I would like to thank the government of Chile for the financial support that made
my stay and studies possible here at the Queensland University of Technology
13
Chapter 1 Introduction
Efficiency and equity issues are often considered together in the evaluation of economic
performance While higher efficiency usually measured by growth rates of income per capita
correlates with improvements in measures of well-being the link between inequality and well-
being is less clear This is reflected not only in the type and amount of research related to efficiency
and equity but also in the role that both play in the design of the economic policy For instance
several market-oriented countries have focused primarily on economic growth trusting in a trickle-
down process where financial benefits given to the wealthy are expected to ultimately benefit the
poor However despite the growing interest in the issue of inequality there is a considerable lack
of studies about its consequences
Although some level of inequality is inevitable or even necessary for economic activity this
study is motivated by the argument that relatively high levels of inequality can be associated with
many problems such as persistent unemployment increasing fiscal expenses indebtedness and
political instability (Berg amp Ostry 2011) Inequality can also have other severe social
consequences including increased crime rates teenage pregnancy obesity and fewer
opportunities for low-income households to invest in health and education (Atkinson 2015) In
addition when the role of money and concentration of economic power undermine political
outcomes inequality of opportunities hampers social and economic mobility trust and social
cohesion In summary inequality can increase the fragility of the economic and social situation in
a country reducing economic growth and making it less inclusive and sustainable
14
A country well-known for its market-oriented economy and high level of dependence on
natural resources is Chile Chilean success in terms of economic growth contrasts with its inability
to reduce the persistently high levels of social and economic inequality particularly in the last
three decades Using data for the 2006-2017 period and considering 324 out of 345 Chilean
counties this thesis presents three essays with empirical evidence aiming to explain the
phenomenon of persistent income inequality and some of its potential consequences The first
essay aims to analyse how the evolution and variability of income inequality throughout the
country are associated with the degree of natural resource dependence The second essay studies
the relevance of income inequality in explaining cross-county differences in the performance of
local governments (municipalities) Finally the third essay explores the link between social
cohesion and community heterogeneity highlighting the importance of economic and racial
diversity
Income inequality and dependence on natural resources
The first essay explores how cross-county differences in income inequality are associated
with differences in the degree of dependence on natural resources We use the Gini coefficient in
each county as our dependent variable and the proportion of employment in the primary sector as
our measure of natural resource dependence The main hypothesis is that income inequality should
be positively related to the degree of natural resource dependence To test our hypothesis we use
a spatial econometric approach This approach is motivated by the study of Paredes Iturra and
Lufin (2016) who explore the geographic heterogeneity of income inequality in Chile finding
evidence of a significant spatial dimension
15
The theoretical and empirical literature has mostly proposed a positive link between
inequality and natural resources Although most of the evidence corresponds to cross-country
comparisons there is also increasing body of research at the local level A rationale underpinning
the positive link suggested in the literature is that in natural resource-rich countries ownership is
concentrated in small groups and extraction activities require low-skilled workers (Gylfason amp
Zoega 2003 Leamer Maul Rodriguez amp Schott 1999) Another market-based argument often
labelled as the ldquoDutch Diseaserdquo proposes that natural resource windfalls could be associated with
a crowding-out effect on the manufacturing sector (Corden amp Neary 1982 Easterly 2007) This
process encourages rent-seeking behaviours discourages investment in physical and human
capital and delays the process of technology adoption and industrialization (Auty 2001 Bulte
Damania amp Deacon 2005 Gylfason amp Zoega 2003) The result could be a lower economic
growth which is the central idea under the ldquoResource Curse Hypothesisrdquo (Auty 1993 Sachs amp
Warner 2001)
An ldquoinstitutionalrdquo argument for the positive association between inequality and the
endowment of natural resources is based on the so-called ldquoParadox of Plentyrdquo (Borge Parmer amp
Torvik 2015 Dauvin amp Guerreiro 2017) The idea is that both national and local authorities have
less incentive to operate efficiently when they experience windfalls in their revenues for
instance from natural resources This could end with corrupted authorities exerting patronage
clientelism and designing public policies to favour specific groups of the population (Uslaner amp
Brown 2005) Evidence also suggests that the final effect of natural resource booms on income
inequality will depend on authoritiesrsquo capacity to manage these additional resources the extent of
commuting and migration among regions and the potential increase in the demand for non-tradable
16
goods which are intensive in unskilled workers (Aroca amp Atienza 2011 Cust amp Poelhekke 2015
Fleming amp Measham 2015b Howie amp Atakhanova 2014 Michaels 2011)
Contrary to most theoretical and empirical evidence we find that income inequality shows
a robust and significant negative association with our proxy for natural resource dependence This
result suggests that the process of transformation to an economy less dependent on natural
resources could have exacerbated rather than alleviated the persistence of income inequality The
decrease in the participation of the primary sector in employment in favour mainly of the tertiary
sector highlights the importance of the latter to explain the current high levels of inequality and its
future evolution Another important result is that spatial linear models show practically the same
results as traditional linear models This could be interpreted as the spatial dimension previously
found in income inequality is not the result of spatial dependence in the variable itself for instance
due to a process of spillover among counties Hence the usually found positive spatial
autocorrelation of income inequality (similar levels in neighbouring counties) could be explained
by spatial patterns in other variables or to the spatial heterogeneity that characterizes the Chilean
economy
Local government efficiency and income inequality
Essay 2 delves deep into the potential trade-off between efficiency and equity We measure
the efficiency of Chilean municipalities which correspond to the organizations in charge of
managing each county Municipal efficiency is understood as ldquotechnical efficiencyrdquo that is the
possibility that each municipality has reached the same level of outputs with less use of inputs
Then we analyse how income inequality controlling for other contextual factors such as
socioeconomic demographic geographical and political characteristics may help to explain
17
differences in municipal performance Our main hypothesis is that municipal efficiency is
inversely associated with income inequality Moreover we seek a causal interpretation of this
relationship
Municipal performance could be influenced by income inequality in direct and indirect ways
In a direct sense income inequality is used to capture the degree of heterogeneity and complexity
in the demand for public services that citizens exert over local authorities Hence higher levels of
income inequality should be associated with a more complex set of public services and therefore
with lower levels of municipal efficiency (Jottier Ashworth amp Heyndels 2012) Furthermore
when high levels of inequality exist the richest groups can exert a higher influence over local
authorities resulting in low quality and quantity of services for most of the population Among
indirect effects high and persistent inequality could be the source of corrupted institutions and
local authorities favouring themselves or specific groups This undermines citizensrsquo participation
in civic activities and their willingness to monitor municipal performance (Uslaner amp Brown
2005) Additionally the potential benefits of decentralization on the way local governments
deliver public services will be limited when the context is characterized by corrupted politicians
and a limited administrative and financial capacity (Scott 2009)
We measure municipal efficiency using an input-oriented Data Envelopment Analysis
(DEA) to obtain efficiency scores for our sample of 324 municipalities in each year from 2006 to
2017 Then we study the influence on municipal efficiency of income inequality and our set of
contextual factors using a panel of six years corresponding to those years for which household
income information is available 2006 2009 2011 2013 2015 and 2017 Our dependent variable
is the set of efficiency scores which are relative measures of efficiency They are relative to the
18
municipalities included in the sample and they do not imply that higher technical efficiency gains
cannot be achieved Thus we use both cross-sectional and panel censored regression models To
tackle endogeneity issues and suggest a causal interpretation we also propose using the proportion
of firms in the primary sector as an instrument for income inequality
We find an average efficiency score of 83 meaning that Chilean municipalities could
reduce the use of inputs by 17 without reducing their outputs We also measure municipal
efficiency under different assumptions related to returns to scale This allows us to disaggregate
technical efficiency to assess whether inefficiencies are due to management issues (pure technical
efficiency) or scale issues (scale efficiency) Although the results show that most municipalities
operate under increasing or decreasing returns to scale scale inefficiencies only explain a small
proportion of total municipal inefficiencies This highlights the need to look for contextual factors
outside the control of local authorities to explain differences in municipal performance
Geographical representations of our results in terms of returns to scale and efficiency scores
show some spatial clustering process among municipalities Spatial statistics tests confirm that
efficiency scores show a significant positive spatial autocorrelation This means that neighbouring
municipalities tend to show similar levels of efficiency This similar performance could be due to
a process of spatial dependence (eg efficiency spillovers among neighbouring municipalities) or
due to the existence of structural-geographical similarities (spatial heterogeneity) To assess the
spatial dimension in municipal efficiency abstracting from temporal fluctuations we use a cross-
section of data consisting of the six-year average for the variables in our panel After running a
regression of efficiency scores against the set of controls the analysis of OLS residuals shows that
the spatial autocorrelation is almost completely removed This means that the spatial pattern in
19
municipal efficiency can be explained (controlled) by other variables such as regional indicator
variables rather than efficiency itself Given this result we proceed to study the influence of
income inequality on municipal efficiency using traditional (non-spatial) regression analysis
In contrast to literature that emphasizes an equity-efficiency trade-off (Andersen amp Maibom
2020 Berg amp Ostry 2011 Browning amp Johnson 1984 Okun 2015) that is greater equality leads
to lower efficiency we find that municipal efficiency is inversely associated with income
inequality This implies that more equal counties are also those with higher municipal efficiency
Furthermore the coefficient of income inequality is close to one when we use the instrumental
variable approach This means that a reduction in income inequality ceteris paribus should be
associated with an increase in the same magnitude in municipal efficiency This result has strong
policy implications The non-existence of the trade-off suggests that there is more to be gained by
targeting policies towards the reduction of inequality than conventional theories suggest For
instance these policies may help increase the levels of efficiency and well-being at least at the
municipal level
Social cohesion and economic diversity
The third essay studies the relationship between the degree of social cohesion and diversity
in Chile Extant literature has argued that one of the main factors influencing social cohesion is
the degree of economic and ethnic-racial diversity within a society This diversity erodes social
cohesion reducing trust and corrupting institutions (Letki 2008 Rothstein amp Uslaner 2005
Tolsma Van der Meer amp Gesthuizen 2009 Uslaner 2011 2013)
To measure social cohesion scholars have traditionally used measures of social capital trust
or feelings of insecurity (Ariely 2014 Chan To amp Chan 2006 Letki 2008) We suggest the use
20
of the rate of incivilities per 1000 inhabitants as a proxy for social cohesion Incivilities correspond
to those antisocial behaviours (eg groups of rowdy teens and public drunkenness) or visible
neighbourhood conditions (eg graffiti and abandoned buildings) that tend not to be treated as
crime Using the rate of incivilities is arguably a more objective and reliable measure of social
cohesion particularly in countries where institutions of order and security are among the most
trusted An increase in the rate of incivilities rather than changes in crime rates should better
capture the worsening in social cohesion experienced in countries such as Chile where crime rates
are not growing but social conflicts are on the rise Thus the main hypothesis in this essay is that
the rate of incivilities (social cohesion) should be positively (negatively) associated with economic
and racial diversity
Using panel count data models we start analysing how differences in incivilities rates
between and within counties are associated with differences in indicators of relative and absolute
economic disadvantage We use the Gini coefficient of each county as our measure of economic
diversity Although we find a significant and positive association between the rate of incivilities
and the level of income inequality the magnitude of the link seems to be small Among absolute
indicators of economic disadvantage only the level of income shows a strong effect Next we
include our measure of racial diversity We use the number of new visas granted to foreigners as
a proportion of the county population Results show a significant and strong positive association
between the rate of incivilities and racial diversity
To check the robustness of our results we analyse the impact of our measures of economic
and racial diversity running our models separately for each Chilean region and clustering them
geographically We also split the total number of incivilities in four categories to see which type
21
of incivilities show the greatest association with our measures of diversity In general results
support the ldquocommunity heterogeneity hypothesisrdquo that higher community heterogeneity is
associated with higher rates of incivilities (Alesina amp La Ferrara 2002 Letki 2008 Tolsma et al
2009) However results do not support the ldquoincivilities thesisrdquo that the distribution of incivilities
tends to mirror the distribution of income inequality (Skogan 1999 Taylor 1999)
Three results stand out among the set of control variables First the level of education shows
and independent and significant negative association with the rate of incivilities This is in contrast
to previous studies where education acts mainly as a moderator of the effect of economic and racial
diversity on social cohesion (Tolsma et al 2009) The results also show that there is no significant
relationship between the rate of incivilities and the proportion of young population This is relevant
because policies aimed to reduce incivilities usually put the focus on specific groups such as young
people which are linked to physical and social incivilities when social control is weakened
Finally the degree of financial municipal autonomy also shows a significant negative association
with the rate of incivilities This result suggests that municipalities can contribute independently
or together with the central government to reduce incivilities and strengthen social cohesion
Contributions
The three essays in this thesis provide several important insights into the analysis of the
causes and consequences of income inequality particularly in the context of Chile ndash a typical
resource rich economy with persistently high levels of income inequality
Essay 1 advances the understanding of the relationship between income inequality and
natural resources in Chile extending the empirical analysis from the regional level to the county
level In addition the geographic heterogeneity of income inequality is explored with the inclusion
22
of alternative sources of spatial dependence as a potential dimension of the causal relationship
between income inequality and natural resources This essay demonstrates the relevance of natural
resources in explaining the persistence of income inequality even after controlling for other
socioeconomics and institutional factors Findings from this study have potential contribution not
only in the design of policies aimed to reduce income inequality but also in addressing the current
developmental bias between the metropolitan region and the rest of the country
Essay 2 is one of the first studies that undertake a longitudinal analysis of the effects of
income inequality on the efficiency of municipal governments in Chile To capture the role of the
municipal governments in the provision to local people of public services such as education and
health we specify several inputs and outputs in our efficiency model which is different from the
conventional specification in the existing literature For example the number of medical
consultations in public health facilities and the number of enrolled students in public schools are
used as outputs instead of general indicators such as county population Our empirical analysis
also utilises a larger sample of municipalities and covers a much longer period spanning from 2006
to 2017 This essay also investigates the contextual factors beyond the control of local authorities
that can explain variations in the efficiency of municipal governments across the country
Empirical findings from Essay 2 help us increase our understanding of the production
technology of municipalities the sources of inefficiencies and specifically the impact of income
inequality on the performance of local authorities The results deliver two main policy
implications First municipal inefficiencies in the provision of public goods and services differ
across Chilean municipalities In addition efficiency levels show some degree of spatial
autocorrelation This implies that policies such as amalgamation or cooperation among
23
municipalities could have effects beyond the municipalities involved which must be considered
Second the causal effect that income inequality has on municipal efficiency provides another
dimension into the design and implementation of development policies
Essay 3 explores for the first time the effects of economic and racial diversity on social
cohesion in Chile This essay considers incivilities as manifestation of social cohesion and
investigates as extant literature suggests whether indicators of relative economic disadvantage
such as income inequality are among the main factors driving social disorganization and social
unrest Empirical findings suggest that on the one hand economic heterogeneity captured by the
Gini coefficient has a disparate effect both in terms of magnitude and significance on the rate of
incivilities across the country On the other hand the impact of racial heterogeneity appears to be
stronger more significant and of a similar magnitude throughout the country Results also provide
new insights into the design of national policies addressing social disorders particularly those
policies focussed on specific groups of the population and the role of local authorities Overall the
findings provide an opportunity to advance the understanding of the process of weakening in the
social cohesion experienced in Chile and the conflicts that have risen from this process
Thesis outline
The remainder of the thesis is organized as follows Chapter 2 presents essay 1 examining
the association between income inequality and the degree of dependence on natural resources
Chapter 3 presents essay 2 which looks for a causal relationship between municipal efficiency and
income inequality Chapter 4 presents essay 3 analysing the relationship between social cohesion
and economic and racial diversity Finally Chapter 5 presents some concluding remarks
24
Chapter 2 Natural Resources Curse or Blessing Evidence on
Income Inequality at the County Level in Chile
21 Introduction
A phenomenon of increasing inequality of incomes and wealth in recent decades has been
documented by leading scholars and international organizations such as the International Monetary
Fund (Berg amp Ostry 2017 Ostry Berg amp Tsangarides 2014) and the Organization for Economic
Cooperation and Development (Cingano 2014) These efforts have placed the topic of inequality
at the top of the current economic debate recognizing inequality as a determinant not only of
economic growth but also of human development They also have highlighted the necessity for
more research on the drivers of inequality and mechanisms through which it manifests aiming to
design effective policies in reducing economic and social inequalities
Various factors have been analysed as the sources of high and increasing levels of inequality
Among the most significant factors are the levels of income at initial stages of economic
development (Kuznets 1955) Globalization (Milanovic 2016) skill-biased technological change
(Tinbergen 1975) investment in human capital (Murphy amp Topel 2016) institutions
redistributive policy and country-specific characteristics (Acemoglu 1995 2002 Acemoglu
Aghion amp Violante 2001 Acemoglu Johnson amp Robinson 2001) Our focus in this essay is on
the importance that the natural resource endowment (NRE) or lack thereof can play in the
determination of income disparities
25
This essay studies the patterns and evolution of income inequality in the context of a natural
resource-rich country Using the case of the Chilean economy we aim to understand and
disentangle how a phenomenon of high- and persistent-income inequality is related to the
endowment of natural resources that a country owns Chile is an interesting case to study because
despite showing a successful history of economic growth inequality among individuals and among
aggregated spatial units has shown a strong persistence (Paredes et al 2016) Furthermore Chile
has remained among the most unequal countries in the world1
Theory and empirical evidence do not establish a clear link between income inequality and
NRE In addition NRE has received considerably less attention (Auty 2001 ElGindi 2017) and
most of the evidence has been focused on cross-country comparisons For instance NRE can
influence inequality by determining its initial levels (Engerman amp Sokoloff 1994 1997
Engerman Sokoloff Urquiola amp Acemoglu 2002) shaping the evolution of institutions
(Acemoglu 2002) make the educational system less intellectually challenging and moulding the
structure of economic activity (Leamer et al 1999) So studying how cross-county differences in
NRE are associated with the distribution of income within a country has theoretical empirical and
policy implications
In this study we offer empirical evidence on the relationship between income inequality and
the endowment of natural resources using data at the county level in Chile for the period 2006-
2017 Income inequality is measured by the Gini coefficient The importance of NRE is proxied
using a measure of natural resource dependence (NRD) defined as the percentage of the total
1 A 2014 OECD report on income inequality (httpwwwoecdorgsocialincome-distribution-databasehtm) showed Chile as the country with the highest Gini coefficient of disposable income among OECD countries OECD also indicates Chile as the country with the widest gap between the richest 10 percent and the poorest 10 percent of countryrsquos population among its 34 members (OECD 2014)
26
employment in each county corresponding to the primary sector (agriculture forestry fishing and
mining)
The main hypothesis to be tested is whether income inequality is positively associated with
the degree of NRD The transmission mechanisms through which natural resources could influence
socioeconomic outcomes could be based on the market or institutions The market-based approach
argues that natural resource booms could be associated with an appreciation of the real exchange
rate and a crowding out effect over other more productive economic activities such as
manufacturing It could also delay the adoption of new technologies and reduce incentives to invest
in physical and human capital (Gylfason amp Zoega 2003) Based on the ldquoResource Curse
Hypothesisrdquo (RCH) natural resources could be a curse when the political and institutional
framework is weak and natural resources are concentrated in space such as oil and minerals
(Deacon 2011) 2 Among institutional channels a higher NRD or natural resource booms could
be associated with rent seeking misallocation of labour and entrepreneurial talent institutional
and political decline or even violent conflicts For instance the ldquoParadox of Plentyrdquo sustains that
windfalls of revenues as a consequence of resource booms could be related to a lack of incentives
to perform efficiently corruption patronage and local authorities favouring their voters or being
captured by the richest groups (Dauvin amp Guerreiro 2017) Hence a higher NRD or natural
resource booms could be the explanation not only for low levels of growth in regions more
dependent on natural resources but also it could be the root of income disparities
2 There is a wide strand of research on the Resource Curse Hypothesis however the evidence so far is not conclusive Evidence in favour of RCH has been mainly found in developing resource rich countries (Auty 1993 2001 Badeeb Lean amp Clark 2017 Blanco amp Grier 2012 Borge et al 2015 Brunnschweiler amp Bulte 2008 Sachs amp Warner 2001 Van der Ploeg 2011)
27
To test our hypothesis that is whether the levels of income inequality across counties are
positively associated with the degree of NRD we use a spatial econometric approach We use this
approach because attributes such as income inequality in one region may not be independent of
attributes in neighbouring regions (Armstrong amp Taylor 2000) This process of spatial dependence
invalidates the use of traditional (non-spatial) approaches
This study seeks to make two contributions to research First previous empirical evidence
shows a significant spatial dimension of income inequality in Chile (Paredes et al 2016)
However this dimension has been barely explored with most studies limiting the degree of
disaggregation to a regional scale (Aroca amp Bosch 2000) We use a spatial approach which makes
it possible to model and test the significance of the spatial dimension in the analysis of income
inequality and its relationship with other variables Second previous research for the Chilean
economy linking inequality with NRE has been mainly focused on explaining differences between
regions or the importance and effects of the mining-copper sector (Aroca amp Atienza 2011 Ebert
amp La Menza 2015 Lagos amp Blanco 2010 Rehner Baeza amp Barton 2014) We extend this
analysis using data for local economies Identifying and quantifying the impact of NRE on income
inequality at the county level is likely to be more informative for policies aiming to address the
current developmental bias between the metropolitan region and the rest of the country Moreover
the analysis of the role of natural resources in conjunction with other potential sources of inequality
may shed lights in understanding the persistence of the high levels of inequality observed in the
Chilean economy All in all this study could contribute to the design of policies that
simultaneously help reduce inequality increase efficiency and promote sustainable and inclusive
growth
28
Our main finding shows that after controlling for other potential sources of income
inequality such as educational level demographic characteristics and the level of public
government expenditure the degree of dependence on natural resources has a significant effect on
income inequality However contrary to our expectations the effect is negative This result
suggests that the natural or policy-driven process of transformation from primary and extractive
activities to manufacturing and service sectors imposes additional challenges to central and local
authorities aiming to reduce income inequality
In section 22 we review the literature on the relationship between income inequality and
natural resources In section 23 we establish our research problem and main hypothesis Section
24 describes our data and methods and section 25 the empirical results We finish with section
26 discussing our main results concluding and proposing avenues for future research
22 Inequality and Natural Resources
221 Theoretical Framework
Explanations for income inequality can be associated with individual institutional political
and contextual characteristics Individual characteristics include age gender and mainly the level
of education and skills of the population in the labour force For instance globalization and
technological change lead firms to increase the demand for skilled labour deepening income
inequality between skilled and unskilled workers (Atkinson 2015 Milanovic 2016 Tinbergen
1975) Among institutional characteristics labour unions collective bargaining and the minimum
wage have been suggested as explanations of income inequality (Acemoglu Aghion et al 2001
Atkinson 2015) Policy design associated with market regulation progressive taxation and
redistribution can also impact the levels and patterns of inequality
29
A key factor in understanding the levels and differences in income distribution within a
country may be its endowment of natural resources NRE shapes the structure of the economy
(Leamer et al 1999) it is associated with the creation of institutions that define the political
culture and it can also influence the performance of other sectors (Watkins 1963) In addition
NRE determines initial conditions market competition ownership over resources rent seeking
and the geographical concentration of the population and economic activity
Cross‐countryliterature
Bourguignon and Morrison (1990) introduce one of the earliest theoretical frameworks
describing the relationship between inequality and NRE They develop a small open economy
model where income distribution is a function of NRE ownership structure and trade protection
Giving cross-sectional evidence for a group of developing countries they conclude that the impact
of NRE particularly mineral resources and land depends on the number and size of the firms
whether they are public or private and the level of protection A higher concentration of production
in a few private firms a big share of production oriented to foreign instead of domestic markets
and protection increasing the relative price of scarce resources are some of the reasons explaining
why some countries are less egalitarian than others
NRE could also influence the evolution and levels of inequality by determining the initial
distribution of incomes This is known as the ldquoEngerman-Sokoloff Hypothesisrdquo (Engerman amp
Sokoloff 1997 Engerman et al 2002) In addition Leamer (1999) proposes that inequality and
development paths in each economy are a function of its economic structure which in turn depends
on ldquofundamentalsrdquo and ldquosymptomsrdquo On the one hand ldquofundamentalsrdquo refer to resource
endowment production structure closeness to markets and governments interventions On the
30
other hand ldquosymptomsrdquo are related to institutions employment structure and net export structure
Using this conceptual framework Leamer argues that natural Resource-Rich Countries (RRC) can
experience a higher level of inequality because can have a ldquodumbbell educational systemrdquo
ownership is concentrated in small groups and extraction activities require low-skilled workers
This implies fewer incentives to educate citizens until very late in the development process
resulting in human capital not prepared to take advantage of the process of technological progress
and delaying the emergence of more efficient and competitive sectors such as manufacturing and
services
Using 1980 and 1990 data for a group of countries classified according to land abundance
Leamer (1999) provides evidence showing that on the one hand land-scarce countries concentrate
their production and employment in sectors that promote equality such as capital-intensive
manufacturing chemical or machinery On the other hand countries abundant in natural resources
concentrate their production trade or employment in sectors that promote income inequality such
as the production of food beverages extraction activities or forestry
Gylfason and Zoega (2003) using a framework based on standard growth models also
proposed a positive relationship between NRE and inequality They assume that workers can work
in the primary sector or in the manufacturing (including services) sector In addition wage income
is equally distributed in the manufacturing sector but unequally in the primary sector (because of
initial distribution competition rent seeking etc) Therefore inequality will be greater when a
bigger proportion of labour is dedicated to extraction activities in the primary sector This
phenomenon is further amplified because of lower incentives to invest in physical and human
capital to adopt new technologies and to increase the share of the manufacturing sector
31
Diverse mechanisms explaining the link between NRE and inequality have been proposed
arguing that NRE determines simultaneously economic growth and inequality (Gylfason amp Zoega
2003) NRE could impact economic growth through the real exchange rate and the crowding-out
effect on manufacturing (ldquoDutch Diseaserdquo) reducing incentives to invest in physical and human
capital (Easterly 2007) and influencing the processes of technology adoption industrialization
and diversification of the economy in a manner that is less conducive to growth (Bulte et al 2005)
These potential explanations related to the called ldquoResource Curse Hypothesisrdquo do not have strong
empirical support (Auty 2001 Bulte et al 2005)
NRE may also influence economic growth through the quality of institutions (Acemoglu
1995 Acemoglu Aghion et al 2001 Acemoglu amp Robinson 2002 Engerman amp Sokoloff 1997
Engerman et al 2002) the concentration of ownership political power and rent-seeking NRE
acts by shaping institutional context and social infrastructure a phenomenon that is stronger when
resources are spatially concentrated such as minerals and plantations (Bulte et al 2005) NRE
could also have a significant effect on social cohesion and instability spreading its influence like
a disease (Brunori Ferreira amp Peragine 2013 Kanbur amp Venables 2005 Milanovic 2016
Ocampo 2004)
Considering a non-tradable sector intensive in unskilled workers Goderis and Malone
(2011) develop a model where the natural resources sector experiences an exogenous gift of
resource income They analyse the impact over income inequality of resource booms proxied by
changes in a commodity price index They conclude that inequality decreases in the short run but
increases after the initial reduction
32
Fum and Hodler (2010) show that natural resources increase inequality but this is
conditional on the level of ethnical polarization of society Carmignani (2013 2010) confirms this
positive relationship using different measures of dependence and abundance and goes further
arguing that inequality constitutes an indirect channel through which NRE affects human
development
Singlecountryevidence
Most of the studies about the relationship between inequality and NRE derive from cross-
country analyses Evidence for specific countries has been mainly based on case studies Howie
and Atakhanova (2014) based on the model of Goderis and Malone (2011) find for the case of
Kazakhstan that income and consumption inequality decreased significantly after booms in the oil-
and-gas sector because of resource booms increase the demand for non-tradable goods which are
intensive in unskilled workers The results depend on the level of rurality institutional quality
education levels and public spending on health and education Fleming and Measham (2015b
2015a) evaluate the impact of booms in the mining and oil sectors in Australia They find that a
boom in the mining sector increases income inequality due to commuting and migration among
regions This phenomenon can be exacerbated when the demanding access to natural resource
revenues is associated with the creation of more local administrative units (counties provinces and
even regions) but the government capacity is not simultaneously improved (Cust amp Poelhekke
2015 Michaels 2011) Furthermore the benefits that a region can receive in the form of fiscal
transfers can be more than compensated by the loses due to city-to-mine commuting such as the
case of mining regions in Chile (Aroca amp Atienza 2011)
33
Other studies at the local level have analysed the impact of the mining sector in Peru (Aragoacuten
amp Rud 2013 Loayza amp Rigolini 2016 Loayza Teran amp Rigolini 2013) Spain (Domenech
2008) and Canada (Papyrakis amp Raveh 2014) and the effects of oil windfalls in Brazil (Caselli amp
Michaels 2013)
In summary there is a wide range of potential mechanisms through which NRE could
influence income inequality Although most of them seem to suggest a positive relationship others
such as commuting and increased within-county demand for non-tradable goods and services
could lead to a negative association This highlights the need to know the sign of this association
in the Chilean economy where the trend shows a reduction in the degree of NRD After controlling
for other factors a positive link would support the argument that the reduction in the degree of
NRD has been relevant in the reduction experienced by income inequality in the same period
However a negative link would support the position that the reduction in NRD has contributed to
explain the persistence of income inequality and its slow reduction
222 The relevance of the spatial approach
Inequalities within countries are still the most important form of inequality from the political
point of view (Milanovic 2016) People from a geographic area within a country are influenced
and care most about their status relative to the people in other areas in the same country The
influence among regions involves multiple aspects (eg economic political and environmental)
These potential interactions have been traditionally ignored assuming independence among
observations related to different regions Moreover neglecting the process of spatial interaction in
key indicators of the economic and social performance of a country may mislead the design of the
public policy
34
The spatial dimension could play a significant role in understanding the distribution of
income within a country One strand of efforts aiming to capture the geographic heterogeneity of
inequality has been focussed on decomposing general indicators such as the Gini coefficient or the
Theil Index Evidence for different countries including the US (Doran amp Jordan 2016) China
(Akita 2003 Gustafsson amp Shi 2002 Ye Ma Ye Chen amp Xie 2017 Yue Zhang Ye Cheng
amp Leipnik 2014) Japan (Ohtake 2008) South Africa (Leibbrandt Finn amp Woolard 2012) and
Chile (Paredes et al 2016) shows that regional inequality is sensitive to the geographic scale of
analysis These studies also show a significant spatial component in the explanation of inequality
of income expenditure or gross domestic product for each country
Another strand explicitly uses exploratory spatial data analysis (ESDA) and spatial
econometrics ESDA has been used to provide new insights about the nature of regional disparities
of incomes and growth rates (Celebioglu amp Dallrsquoerba 2010 Yue et al 2014) Spatial econometric
models aim to assess and address the nature of the spatial effects These effects could be the result
of ldquospatial heterogeneityrdquo that is different relationships in distinct locations or ldquospatial
dependencerdquo which implies cross-sectional interactions (spillover effects) among units from
distinct but near locations
Spatial spillovers have been analysed to study both positive and negative spatial correlation
among less resource-abundant counties and resource-abundant counties On the one hand less
resource-abundant counties may experience positive spillovers because their industries supply
more goods and services to meet the increasing regional demand They can also be benefited from
positive agglomeration externalities and higher investment in private and public infrastructure
(Allcott amp Keniston 2014 Michaels 2011) On the other hand negative spillovers could be the
35
result of a high degree of interregional migration that limits the rise in wages and higher local
prices due to the increase in the share of the non-tradable sector In addition local governments
could have a limited capacity to translate the revenues from resource booms into effective public
policies promoting a sustained local development (Beine Coulombe amp Vermeulen 2015 Caselli
amp Michaels 2013 Papyrakis amp Raveh 2014)
23 Research problem and hypotheses
We can conclude from our overview of the literature that the theoretical and empirical
evidence about the link between inequality and natural resources is inconclusive This does not
make clear whether the process of reduction in the degree of dependence on natural resources
such as that experienced by the Chilean economy helps to explain the sustained but slow reduction
in income inequality or its high persistence
The research question guiding this study relates to how the natural resource endowment
determines the paths and structure of income inequality in natural resource-rich countries Using
the case of Chile the main hypotheses to be tested is whether a higher degree of dependence on
natural resources is associated with higher levels of income inequality To do that we use data at
the county level and we explicitly include the spatial dimension Our aim is to arrive at a more
comprehensive understanding of the drivers and transmission mechanisms explaining the
evolution and patterns shown by income inequality In addition we test whether the spatial
dimension plays a significant role in explaining differences in income distribution in Chile
36
24 Data and Methods
We use county-level data for the years 2006 2009 2011 2013 2015 and 2017 The reason
for not using contiguous years is that income data at the household level are only available every
two-three years from the Chilean National Socioeconomic Characterization Survey (CASEN in its
Spanish acronym)3 For the period 2006-2017 the Chilean administrative division considers 15
regions 54 provinces and 346 counties Data on income are available for 324 counties and six
years resulting in a panel with 1944 observations4
We start evaluating the spatial dimension in our data and analysing the link between
inequality and NRD using a cross-sectional setting To this end we use the ldquosix-year averagerdquo
(2006 2009 2011 2013 2015 2017) for our variables given the low time variability showed by
our measures of income inequality and NRD Results are then compared with those of a panel data
setting
241 Operationalization of key variables
The dependent variable in the present study income inequality at the county level is
measured calculating the Gini coefficient using three definitions of household income labour
autonomous and monetary income5 Labour income corresponds to the incomes obtained by all
members in the household excluding domestic service consisting of wages and salaries earnings
3 CASEN survey is conducted by the Chilean Ministry of Social Development covering topics such as education employment income and health CASEN is considered nationally representative and it is the main source for measures of inequality and poverty used for the design and evaluation of social policies in Chile 4 The six waves of CASEN for our study considered an average of 75599 households and 252081 individuals 5 The Gini coefficient is chosen because provides an overall estimate of income inequality It summarizes what proportion of the population gains what proportion of the total income The Gini coefficient can range between 0 (everyone in the population has the same income) and 1 (one person earns 100 per cent of the income in the community)
37
from independent work and self-provision of goods Autonomous income is the sum of labour
income and non-labour income (including capital income) consisting of rents interest and dividend
earnings pension healthcare benefits and other private transfers Finally monetary income is
defined as the sum of autonomous income and monetary subsidies which correspond to cash
transfers by the public sector through social programs Appendix A shows summary statistics for
the Gini coefficient of our three measures of income
The main independent variable in our study is the degree of dependence on natural resources
in each county To have an idea of the importance of each economic activity in the Chilean
economy particularly those activities related to natural resources Figure 21 shows their average
share in Chilean Gross Domestic Product (GDP) for the period 2006-17 We can observe that the
leading activities are those related to the primary sector especially mining and to the tertiary
sector where financial personal commerce restaurants and hotels services stand out The shares
of each economic activity in GDP vary significantly between Chilean regions and such
information is not available at the county level
Figure 21 Average share in GDP of economic activities (2006ndash17)
38
Leamer (1999) argues that when the main source of income is labour income (as indeed
happens for the Chilean case) using employment shares allows a better approach to measuring
dependence on natural resources Using employment data from CASEN survey we define our
measure of NRD as the employment in the primary sector (mining fishing forestry and
agriculture) as a percentage of the total employment in each county We name this variable
pss_casen where ldquopssrdquo stands for ldquoprimary sector sharerdquo We built other two proxies of NRD
using data from the ldquoServicio de Impuestos Internosrdquo (SII) which is the agency in charge of
collecting taxes in Chile The variable pss measures the percentage of employment in the primary
sector and the variable pss_firms measures the number of firms in the primary sector as a
percentage of the total number of firms in each county Appendix B shows summary statistics for
our three measures of NRD disaggregated by region
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17)
39
Figure 22 shows the evolution of our measure of inequality (using the Gini coefficient of
autonomous income) and our three potential proxies for NRD for the period 2006-2017 We
observe that both income inequality and the degree of NRD show a downward trend This seems
to support our hypothesis of a positive link between inequality and NRD however we need to
control of other sources of inequality before getting such a conclusion In what follows we use the
variable gini as our measure of income inequality capturing the Gini coefficient of autonomous
income Our measure of NRD is the variable pss_casen defined previously
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17)
Note Gini and NRD averages 2006-09-11-13-15-17 for 324 Chilean counties divided into five equal groups Source Own elaboration based on CASEN survey
40
Figure 23 shows quantile maps for income inequality (on the left) and NRD (on the right)
using the six-years average dataset6 On the one hand we observe that high levels of inequality
seem to be clustered in the Centre-South of the country where agriculture forestry and fishery are
the predominant economic activities Only isolated counties show high inequality in the Centre
(Metropolitan area where the countyrsquos capital is located) and North (Mining) areas On the other
hand our measure of NRD seems to show an opposite spatial pattern than income inequality with
high levels in the Centre and North of the country
242 Control variables
To control for county characteristics we use a set of socio-economic demographic and
institutional variables Economic factors are captured by the natural log of the mean autonomous
household income per capita (in thousands of Chilean pesos of 2017) lnincome the poverty rate
poverty the unemployment rate unemployment the percentage of the population living in rural
areas rural and the average years of education of the population over 15 years old education
Demographic factors include the proportion of the population in the labour force labour_force
and the natural log of population density (population divided by county area) lndensity
We also include the natural log of the total municipal public expenditure per capita
lnmuni_expenditure to control for municipal heterogeneity This heterogeneity is mainly related
to the capacity of municipalities to generate their own revenues In addition the richest
municipalities are in the Metropolitan region which concentrates economic power and around 40
6 After sorting a variable in ascending order quantile maps use the quantiles (quartiles quintiles deciles etc) as class breaks to divide the distribution of the variable where each class includes approximately the same number of observations (counties)
41
of the population This has basically implied a lag in the development of regions other than the
metropolitan region
The spatial distribution of our measures of income inequality and NRD displayed in Figure
23 seems to show different patterns in the North Centre and South of the country Appendix C
shows the administrative division of Chile in 15 regions and how we have grouped them in three
zones North Centre and South We consider as the ldquoCentrerdquo area that formed by the Metropolitan
region (XIII) and its two neighbouring regions V and VI Using the Centre area as our reference
we include in our analysis two dummy variables indicating whether a county is located in the North
area (regions XV I II III and IV) or South area (regions VII VIII IX XIV X XI and XII)
Appendix D shows summary statistics for the set of numeric control variables and the
correlation matrix between our measure of NRD pss_casen and the set of numeric controls
243 Methods
To assess and then consider the spatial nature of the data we need to define the set of relevant
neighbours for each country This is operationalized by building a matrix called ldquoWrdquo with a ldquo1rdquo
for neighbouring counties and a ldquo0rdquo for non-neighbouring counties We could build W using
contiguity-based (whether counties share a border or point) or geography-based (taking the
distances among the centroids of each county polygon) spatial weights Specifically we build a W
matrix considering the 5-nearest counties7 Two reasons explain the choice of k-nearest
neighbours First we cannot use a contiguity criterium because we do not have information about
all the counties and there are some geographically isolated counties Second given the significant
7 We assign a ldquo1rdquo to the five nearest counties to each county based on the distances among the polygon centroids Then W is ldquorow standardizedrdquo This facilitates the interpretation of the spatial lag of a variable as the ldquoweighted average valuerdquo of the same variable in neighbouring counties
42
differences in county areas (ldquobig countiesrdquo in northern and southern regions) using a distance-
band criterium with a not enough large distance band can lead to many ldquoislandsrdquo in extreme regions
and a multi-modal distribution for the number of neighbours
We start testing our inequality and NRD variables for spatial autocorrelation in order to
evaluate statistically the clustering patterns shown in Figure 23 Next we run an OLS regression
of inequality against NRD and our set of controls and we test the spatial autocorrelation of OLS
residuals If we cannot reject the null hypothesis of random spatial distribution we do not need
spatial models to analyse income inequality which would give contrasting evidence to previous
suggestions about the relevance of the spatial dimension of income inequality in Chile (Paredes
2013 Paredes et al 2016) If we find significant spatial autocorrelation in the OLS residuals this
justifies the use of spatial models and highlight the need to find the correct spatial structure8
If inequality in one county spillovers or influences inequality in neighbouring counties the
spatial lag of inequality should be included as an explanatory variable and we should use a spatial
autoregressive model (SAR) If some unobserved variable is the explanation for the clustering of
counties with similar inequality then this will be better captured including a spatial lag of the
errors and we should use a spatial error model (SEM) (Anselin 1988 Anselin amp Bera 1998)
Finally when our main explanatory variable or some of the controls show spatial autocorrelation
a spatial lag of the explanatory variable(s) should be included in our model
8 The existence of spatial autocorrelation violates the standard assumption of independence among observations needed for OLS regression This will result in OLS coefficients biased and inconsistent (Anselin 1988)
43
244 Spatial Model Specification
A model that includes the three forms of spatial dependence described above is called the
Cliff-Ord Model The model in its cross-sectional representation could be expressed as
119910 120582119882119910 119883120573 119882119883120574 119906 (21)
where
119906 120588119882119906 120576 (22)
119882 is our weight matrix that works as an NxN spatial lag operator9 Thus 119882119910 119882119883 and 119882119906
are the spatial lags for the dependent variable explanatory variables and the error term
respectively The parameter 120582 capture the spatial dependence in the dependent variable 120574 the
spatial dependence in the explanatory variables 120588 capture the spatial dependence in the error term
and 120598 is a vector of idiosyncratic errors For instance if ldquoyrdquo is income inequality and ldquoXrdquo a measure
of NRD the level of inequality in one county will be explained by the degree of NRD in the same
county 119883120573 the average degree of NRD in neighbouring counties 119882119883120574 the average level of
inequality in neighbouring counties 120582119882119910 and the average value of residuals in neighbouring
counties 12058811988211990610
From equations (21) and (22) the SAR and SEM models can be seen as special cases of
the Cliff-Ord representation after imposing restrictions over the spatial parameters 120582 120574 and 120588 For
the specification of the spatial panel models we follow the terminology by Croissant and Millo
9 The spatial lag is a weighted sum of the values observed at neighbouring locations 10 On the one hand the impact that income inequality in one county has over income inequality in neighbouring counties is called a ldquoglobal spilloverrdquo and it is associated with the feedback effect among neighbours (one county is its neighboursrsquo neighbourrdquo) on the other hand the influence that the degree of NRD in neighbouring counties has over inequality in one county is called a ldquolocal spilloverrdquo
44
(2018) Spatial panel models including the spatial lag of the dependent variable (SAR) the spatial
lag of the residuals (SEM) or both (SARAR) are described in Appendix E
25 Results
251 Exploratory Spatial Data Analysis (ESDA)
To analyse the significance of the spatial dimension in our data set we use the six-year
average of our variables Spatial autocorrelation is tested using the Moranrsquos I statistic11 Moranrsquos
I measures the correlation of one variable with itself in space12 Figure 24 shows the Moran scatter
plots where the standardized variable (Gini coefficient and NRD for each county) appears in the
horizontal axis against its spatial lag (average value in the 5-nearest neighbouring counties) The
Moranrsquos I (slope of the line in the Moran scatter plot) of income inequality shows a significant
positive spatial autocorrelation that is counties with high (low) inequality tend to be close to each
other
11 There are many statistics to formally test the significance of the spatial dimension in the distribution of our data The null hypothesis assumes spatial randomness which means that there is not spatial structure in the data so any spatial pattern is equally likely to occur and values in one location do not depend on values in other locations The alternative hypotheses can be the existence of positive or negative spatial autocorrelation Positive spatial autocorrelation means similar values in neighbouring locations (less variability than under spatial randomness) Negative spatial autocorrelation means dissimilar values in neighbouring locations (more variability than under spatial randomness) 12 Traditional measures of correlation such as the Pearsonrsquos coefficient measure the degree of linear correlation between two different variables Measures of spatial autocorrelation assess the correlation between the values of one variable in one location related to the values of the same variable in other neighbouring locations
45
Figure 23 Moran scatter plots for variables gini and pss_casen
Moranrsquos I is a measure of global spatial autocorrelation this means it is intended to capture
the clustering property of the entire data set To identify where are the significant hot-spots
(clusters of counties showing high income inequality) or cold-spots (clusters of counties showing
low income inequality) we need local indicators of spatial association (LISA) Using the local
Moranrsquos I (not reported) we find significant hot-spots in the South of the country (mainly
agricultural regions) and significant cold-spots in the Centre (Metropolitan area) of the country
The next step is to check whether the clustering pattern in inequality is the result of a process of
spatial dependence in the variable itself or it can be explained by other variables related to
inequality
252 Cross-sectional analysis
We start analysing differences in income inequality between counties using the six-year
average data and running an OLS regression for the model
119892119894119899119894 120573 120573 119901119904119904_119888119886119904119890119899 120573 119897119899119894119899119888119900119898119890 120573 119901119900119907119890119903119905119910 120573 119906119899119890119898119901119897119900119910119898119890119899119905 120573 119897119886119887119900119906119903_119891119900119903119888119890 120573 119890119889119906119888119886119905119894119900119899 120573 119897119899119889119890119899119904119894119905119910 120573 119903119906119903119886119897 120573 119897119899119898119906119899_119890119909119901119890119899119889119894119905119906119903119890 120573 119899119900119903119905ℎ 120573 119904119900119906119905ℎ
(23)
46
The Moran scatter plot and spatial statistical test of OLS residuals from equation (23) are
in Appendix F OLS residuals show a small but significant positive autocorrelation (Moranrsquos I =
0121) This means that income inequality continues showing a significant degree of spatial
autocorrelation after controlling for the set of covariates In addition robust Lagrange Multiplier
(LM) tests show that a spatial error model is preferred over a spatial lag model (Anselin Bera
Florax amp Yoon 1996)13 This means that income inequality tends to cluster in zones larger than a
county so the analysis should be performed on a larger scale such as provinces regions or macro
zones If the SAR model were preferred it would mean that income inequality in one county is
influenced by the level of income inequality in neighbouring counties To find the spatial structure
that best fits the clustering process of income inequality we run the full set of spatial model
specifications in a cross-sectional setting and results are shown in Table 21
Column 2 in Table 21 shows the results of our ldquoOLSrdquo model The ldquoSLXrdquo model includes
spatial dependence only through the explanatory variables The ldquoSARrdquo model includes the spatial
lag of the dependent variable as a regressor and the ldquoSEMrdquo model includes spatial dependence
through the error term The ldquoSARARrdquo model includes both the spatial lag of the response and the
errors and the ldquoSDMrdquo and ldquoSDEMrdquo are the extensions of the ldquoSARrdquo and ldquoSEMrdquo models
respectively including the spatial lag of the explanatory variables Finally a model including
spatial lags for the response errors and explanatory variables (the ldquoSARARXrdquo model) is shown in
the last column
13 Following the ldquoAnselin methodrdquo when both non robust LM tests are significant we should select between the robust LR tests From Table F1 in Appendix F only the robust LM test for the SEM model (RLMerr) appears as significant
47
Table 21
Cross-sectional Model Comparison (six-year average data)
48
Opposite to our hypothesis we observe a significant and negative coefficient for our measure
of NRD This means that counties more dependent on natural resources show lower levels of
inequality Education years population density and municipal expenditure per capita are also
negatively related to inequality On the other hand the level of income the poverty rate and the
proportion of the population living in rural areas show a positive relationship with income
inequality There is no significant influence of the unemployment rate and the proportion of the
population in the labour force In addition the SAR SEM and SARAR models show a
significantly higher average inequality in the South of the country related to the Centre area
The main finding from our cross-sectional analysis is that there is a significant and negative
relationship between inequality and NRD which is quite robust to the model specification
253 Panel Data analysis
Like the cross-sectional case we start estimating the panel without spatial effects Results
for the pooled fixed effects (FE) and random effects (RE) specifications of equation (3) are in
Appendix G Spatial models were estimated using Maximum Likelihood (ML) and Generalized
Moments (GM) and assuming that the spatial structure (W matrix) is the same for all years14
Tables 22 23 and 24 show results for the ML estimation of the SAR SEM and SARAR models
using the pooled FE and RE specifications Results for the GM estimation are in Appendix H
All our spatial models include time fixed effects In the case of the pooled and RE models they
additionally include indicator variables for those counties located in the North and South of the
country
14 In traditional (non-spatial) panels data are stacked as time series for each observational unit (county) In the case of spatial panels data are stacked as slice of cross-sections for each year in the panel
49
The main result is that the negative and significant effect of NRD on income inequality is
robust to most of the spatial panel specifications In addition the coefficient for the variable
pss_casen changes slightly among panel specifications (pooled FE and RE) but does not change
among spatial models (SAR SEM and SARAR)
Another important finding is related to the significance of the spatial dimension of income
inequality When spatial models cross-sectional or panel are compared to non-spatial models
there are no major differences in the magnitude of the coefficients or their significance This could
mean that the positive spatial autocorrelation shown by income inequality seems to be better
explained by a process of spatial heterogeneity rather than spatial dependence The practical
implication of this result is that including dummy variables for aggregated units (eg regions or
groups of regions) could be enough to control for the spatial dimension in the modelling and
analysis of income inequality
Among control variables years of education seems to be the main variable for the design of
long-term policies aimed at reducing inequality This result is in line with previous evidence for
cross-country studies (Leamer et al 1999) and specific countries (Howie amp Atakhanova 2014)
Municipal expenditure per capita also shows a significant and negative association with income
inequality in the pooled and RE spatial specifications This means that higher municipal
expenditure helps to reduce inequality between counties but its effect is more limited within
counties This result support the importance of local governments (Fleming amp Measham 2015a)
however the negative coefficient appears as evidence against the ldquoParadox of Plentyrdquo (Borge et
al 2015)
50
Table 22
ML Spatial SAR Models
Table 23
ML Spatial SEM Models
51
Table 24
ML Spatial SARAR Models
26 Discussion and conclusions
In this essay we delve deep into the sources of income inequality analysing its association
with the degree of dependence on natural resources using county-level data for the 2006ndash2017
period in Chile Given recent evidence for the Chilean economy suggesting a significant spatial
dimension we assess and incorporate explicitly the spatial structure of income inequality using
spatial methods We use cross-sectional and panel data to evaluate the significance of the spatial
dimension and we test whether NRD has a positive effect on income inequality
Contrary to what theory predicts NRD shows a significant and negative association with
income inequality This result is robust to the type of analysis (cross-sectional vs panel data) the
approach (spatial vs non-spatial) and the inclusion of different controls The negative and
significant coefficient implies that if the degree of NRD would not have experienced a 10 drop
during this period income inequality could have fallen in 2 additional points So the downward
trend in the participation of the primary sector in terms of employment in the Chilean economy
52
could be one of the main reasons explaining the high persistence in the levels of income inequality
This means that those areas that undergo a process of productive transformation mainly towards
the services sector would be facing greater problems to reduce inequality This process of
productive transformation natural or policy-driven highlights the importance of policies focused
on human capital and the role of local governments in reducing inequality
The main implication for policymakers is that a reduction in NRD does not help to reduce
inequality generating additional challenges for local and central governments in its attempt to
transform the structure of their economies to fewer dependent ones on natural resources The
finding of a significant spatial dimension suggests that defining macro zones capturing the spatial
heterogeneity in the data should be done before analysing the relationship among variables and the
design and evaluation of specific policies Particularly relevant in those areas experiencing a
reduction in NRD are migration commuting and the characteristics of the tertiary (services) sector
In addition our findings show that education and municipal expenditure could be effective policy
tools in the fight to reduce inequality in Chile
Although our results seem quite robust they do not allow us to make causal inferences about
the effect of NRD on income inequality However we could think of the following explanation to
explain the negative relationship found and the differences between geographical areas
Areas highly dependent on NR used to demand a high proportion of low-skill labour This
has change in sectors such as the mining sector in the northern area which has simultaneously
experienced an increase in activities related to the service sector such as retail restaurants
transport and housing However those services associated with more skilled labour such as the
finance sector remain concentrated in the capital region The reduction in the degree of NRD
(employment in extractive activities) implies lower labour force but more specialized with most
53
of the low-skilled labour transferred to a service sector characterized by low productivity and low
wages
Non-spatial models show that the North and South particularly the latter present
significantly higher levels of inequality This could be associated with the type of resources with
ldquopointrdquo resources such as minerals in the North and ldquodiffusedrdquo resources such as agriculture in the
South This translates into higher average incomes in the Centre and North areas and lower average
incomes in the South
The reduction in NRD implies not only a movement of the labour force from extractive
activities to manufacturing or services with the latter characterized by low productivity and low
salaries of the labour force We could also speculate that most of the high incomes move to the
central area where the economic power and ownership over firms and resources are concentrated
This would explain low inequality associated with higher average incomes in the central area and
high inequality associated with lower average incomes in the South A more in-depth analysis
capturing the mobility of wealth and labour force between counties or more aggregated areas is
needed to better understand the causal mechanism involved
Our findings open avenues for future research in different strands First studies on the causes
of income inequality should take the role of NRD into consideration which has been overlooked
so far Given that the spatial dimension of income inequality seems to be explained by a
phenomenon of spatial heterogeneity estimation strategies such as spatial regime models or
geographically weighted regression should be used (Chi amp Zhu 2019) Second the effect of NRD
on income inequality could manifest through different channels such as education fiscal transfers
and institutions We could extend our analysis to identify which of these competing channels is
the most relevant Transforming some continuous variables such as educational level to a
54
categorical variable or defining new indicator variables for instance whether a local government
shows or not an efficient performance we could classify counties in different groups and then
check whether there are differences or not in the relationship between income inequality and NRD
A third strand could be to disaggregate our measure of NRD for different industries This
would allow us to test differences among industries and to identify the sectors that promote greater
equality and which greater inequality Forth the analysis of the consequences of income inequality
on other economic and social phenomena such as efficiency economic growth and social cohesion
has a growing interest in researchers and policymakers Our findings suggest that to answer the
question of whether income inequality has a causal impact on other variables we could include a
measure of NRD as an instrument to address endogeneity issues For instance two interesting
topics for future research are the analysis of how differences in income inequality between counties
could help to explain differences in the level of efficiency of local governments and differences in
the degree of social cohesion (unrest) throughout the country Those are the issues to be addressed
in the next two essays
55
Chapter 3 The Impact of Income Inequality on the Efficiency of
Municipalities in Chile
31 Introduction
In Chile municipalities are the smallest administrative unit for which citizens choose their
local authorities playing an important role in the provision of public goods and services at the
local level Municipalities have a similar set of objectives but the level of financial resources
available to finance their activities is highly heterogeneous This could result in significant
differences in the levels of performance between municipalities Despite their importance there is
little empirical evidence about the efficiency of local governments in Chile This essay aims to
measure the technical efficiency of Chilean municipalities and to analyse how local characteristics
particularly those related to income distribution at the county level could help to explain
differences in municipal performance
Cross-country studies situate Chile as an efficient country in international comparisons about
efficiency of government spending (Herrera amp Pang 2005 Loacutepez amp Miller 2008) However
evidence for Chile at the local level is relatively sparse suggesting significant levels of
inefficiency For instance Pacheco Sanchez and Villena (2013) found a mean efficiency level of
around 70 using a sample of 309 municipalities for the period 2008-2010 This suggests that
municipalities could achieve the same level of output by reducing the usage of inputs by an average
of 30 Their study also showed that those municipalities more dependent on the central
56
government or those located in counties with lower income per capita are more efficient than their
counterparts
Most empirical research on Local Government Efficiency (LGE) has been conducted for
member countries of the Organization for Economic Cooperation and Development (OECD) of
which Chile has been a member since 2010 In the case of European countries such as Spain and
Italy which share similar characteristics such as the monetary union and levels of GDP per head
efficiency studies have been mainly motivated by budgetary constraints (Balaguer-Coll Brun-
Martos Maacuterquez-Ramos amp Prior 2019 lo Storto 2013) The Chilean context differs in three
main ways from its OECD counterparts First except for the Metropolitan Region that concentrates
most of the population Chilean regions are highly dependent on natural resources Second Chile
is also characterized by one of the highest levels of income inequality among OECD countries
which contrast with the situation of developed natural resource-rich countries such as Australia
and Norway Third although budget constraints are also a relevant issue Chilean municipalities
have experienced a sustained increase in the level of financial resources and expenditure
Another relevant distinction when we benchmark the performance of municipalities across
different countries is the type of public services they provide On the one hand in most of the
countries included Chile the main role of local governments is to provide ldquoservices to peoplerdquo
such as public education and public health On the other hand there are countries such as Australia
where local governments mainly provide ldquoservices to propertyrdquo including waste management
maintenance of local roads and the provision of community facilities such as libraries swimming
pools and parks (Dollery Wallis amp Akimov 2010 Drew Kortt amp Dollery 2015 McQuestin
Drew amp Dollery 2018)
57
Despite contextual differences Chilean municipalities seem not to perform differently from
municipalities in other developed and natural resource-rich countries where income inequality is
significantly less than in Chile (Narboacuten-Perpintildeaacute amp De Witte 2018a) This result highlights the
need to study the role of income inequality and the degree of dependence on natural resources over
LGE characteristics that have been largely overlooked in the literature
We measure and analyse differences in municipal performance using a two-stage approach
In the first stage we measure municipal efficiency using an input-oriented Data Envelopment
Analysis (DEA) to get a set of ldquoefficiency scoresrdquo In the second stage we regress efficiency scores
against our measure of income inequality controlling for a set of contextual factors describing the
economic socio-demographic and political context of each county
We use a sample of 324 municipalities for the period 2006-2017 During this period Chile
was divided into 346 counties belonging to 15 regions This period was characterized by important
external and internal shocks including the Global Financial Crisis (GFC) one of the biggest
earthquakes in Chilean history in 2010 and three municipal elections The availability of
information allows us to measure efficiency for the full period but the influence of contextual
factors is analysed using a panel of six years (2006 2009 2011 2013 2015 and 2017) for which
household income information is available
The main hypothesis tested in the second stage is whether higher levels of income inequality
are associated with lower levels of efficiency Previous evidence shows that when progress is not
evenly shared persistent within-country inequality reduces the effectiveness and efficiency of the
public sector (Ortega Sanjuaacuten amp Casquero 2017 Tandon 2005)
Income inequality has been used to control for a wide range of idiosyncratic factors
associated with historical institutional and cultural factors affecting efficiency (Greene 2016
58
Ortega et al 2017) For instance at the local level income inequality has been considered as an
indicator of economic heterogeneity in the population where higher inequality is associated with
a more heterogeneous set of conflicting demands for public services which adversely affect an
efficient provision (Ashworth Geys Heyndels amp Wille 2014 Geys amp Moesen 2009) Higher
levels of income inequality could also relate to economically privileged groups having a greater
capacity to influence the political system for their own benefit rather than that of the majority
When high inequality is persistent the feeling of frustration and disappointment in the population
could reduce not only trust and cooperation among individuals but also trust in institutions which
would negatively affect government efficiency (Boix amp Posner 1998 Coffeacute amp Geys 2005) For
instance national or local authorities could end exerting patronage and clientelism and showing
rising levels of corruption (Uslaner 2011 Uslaner amp Brown 2005)
One of the main gaps in extant literature is the need to conduct more analysis of LGE using
panel data taking into consideration endogeneity issues and controlling for unobserved
heterogeneity (Narboacuten-Perpintildeaacute amp De Witte 2018a) To address the above we set-up a panel with
time and county-specific effects and we propose the use of a measure of natural resource
dependence (NRD) as an instrument for income inequality Based on the ldquoParadox of Plentyrdquo
fiscal revenues from natural resources windfalls could be associated with an over expansion of the
public sector fostering rent-seeking and corruption and reducing local government efficiency
(Dauvin amp Guerreiro 2017 Manzano amp Rigobon 2001) In the Chilean case most of the revenues
generated by local governments included those from natural resources end up in a common fund
which benefits all municipalities The aim of this common fund is precisely to reduce inequalities
among municipalities so although we do not expect a direct impact of natural resources on LGE
we could expect an indirect effect through other indicators particularly income inequality
59
As far as we know this is the first study analysing the influence of income inequality as a
determinant of municipal efficiency in Chile Moreover this is the first study in the context of a
natural resource-rich country which specifically suggests a measure of natural resource
dependence as an instrument to correct for endogeneity bias We propose the use of the proportion
of firms in the primary sector as proxy for the degree of NRD in each county We argue that this
variable is a better proxy than using the proportion of employment in the manufacturing sector
which has been proposed in previous studies (Alesina amp La Ferrara 2002) During the period
analysed our proxy remained relatively stable and showed a significant relationship with income
inequality In addition it is less likely that it has directly affected municipal efficiency
This study adds to the literature in two other ways First the extant literature suggests that
efficiency measurement could be highly sensitive to the chosen technique as well as the selection
of inputs and outputs (Narboacuten-Perpintildeaacute amp De Witte 2018a) Inputs are usually proxied by a single
measure of total public expenditures and outputs by general proxies such as population andor the
number of businesses in each county We offer a novel approach for the selection of inputs and
outputs On the one hand we disaggregate government expenditures into four components
(operation personnel health and education) and we use the number of public schools and health
facilities in each county as a proxy for physical capital On the other hand we use four outputs
aiming to capture the wide variety of goods and services supplied by each municipality Through
this approach we aim to better describe the production function of each municipality capturing
not only the variety of inputs and outputs but also differences in size among municipalities
A third contribution relates to the measurement of LGE in the Chilean context We measure
technical and scale efficiency using a larger sample and a longer period This has empirical and
policy relevance On the one hand it helps us to select the correct DEA model and allows us to
60
determine the importance of scale inefficiencies as explanation for differences in municipal
performance On the other hand efficiency measures increase the information available for both
central and local governments to better understand the production technology that best describes
each municipality and to carry out policies to improve efficiency
We believe that our selection of inputs and outputs the use of a large dataset and the joint
analysis using cross-sectional and panel data provide a more accurate and robust analysis of
municipal efficiency Likewise knowing whether inequality has a significant influence on
municipal efficiency may provide useful insights and guidance for policymakers not only in Chile
but also for countries sharing similar characteristics
DEA results show an average level of technical efficiency (inefficiency) of around 83
(17) This means that municipalities could reduce on average a 17 the use of inputs without
reducing the outputs There are significant differences among geographic areas with the Centre
area (where the countyrsquos capital is located) displaying higher efficiency than the rest of the country
When municipal efficiency is measured under different assumptions about returns to scale results
reveal a production technology with variable returns to scales and around 75 of the
municipalities displaying scale inefficiencies However when technical efficiency is
disaggregated between pure technical efficiency and scale efficiency results show that scale
inefficiency explains a small proportion of the total municipal technical inefficiency This finding
justifies a deeper analysis of the reasons why municipalities could operate inefficiently and why
municipal performance could vary among municipalities
Efficiency scores also show a significant degree of positive spatial autocorrelation This
means that municipal efficiency shows a general clustering process with neighbouring
municipalities showing similar levels of efficiency A further analysis shows that most of the
61
spatial pattern in municipal efficiency is exogenous that is could be associated to other variables
Hence we conduct most of our regression analysis using traditional (non-spatial) methods and
leaving spatial regressions in the appendixes
Findings from cross-sectional and panel regressions support the hypothesis that municipal
performance is significantly and negatively associated with income inequality at the county level
The coefficient of income inequality is close to one which means that reductions in income
inequality ceteris paribus could be associated with increases in municipal efficiency in the same
proportion This result supports the strand of research arguing that there is not a trade-off at least
at the municipal level between equity and efficiency (Andersen amp Maibom 2020 Berg amp Ostry
2011 2017) The main policy implications are that authorities in more unequal counties would
face higher challenges to perform efficiently and policies pertaining to inequality and efficiency
should not be designed independently
The chapter is structured as follows Section 32 provides a brief literature review on related
local government efficiency Section 33 introduces the methodological background and empirical
models Section 34 presents the empirical results and discussions Section 35 concludes the
chapter
32 Related Literature
321 Measuring efficiency of local governments
Studies on measuring LGE can be grouped in those analysing the provision of single services
such as health or education and those assessing overall efficiency (lo Storto 2013) Once inputs
and outputs have been defined efficiency is measured using parametric andor non-parametric
techniques Among the former group Stochastic Frontier Analysis (SFA) seems to be preferred
62
by scholars aiming to measure efficiency and to analyse the link with environmental variables
using a single procedure (Pacheco et al 2013 Tsekeris Sotiris Tsekeris amp Papaioannou 2018)
On the non-parametric group Data Envelopment Analysis (DEA) is by far the most used technique
(Afonso amp Fernandes 2006 Balaguer-Coll amp Prior 2009 lo Storto 2013)
The selection of inputs and outputs depends not only on the aimed of the study (specific
sector vs whole measure of efficiency) but also on the role that municipalities play in different
countries There are countries such as Australia (Drew et al 2015) and Spain (Balaguer-Coll amp
Prior 2009) where local governments mainly supply services to ldquopropertyrdquo such as waste
management and road maintenance In these cases efficiency has been mainly measured using
total indicators of local government expenditure and outputs have been proxied using general
indicators such as population or number of business (Drew et al 2015) On the other hand in
countries such as Italy (lo Storto 2013) and Portugal (Afonso amp Fernandes 2008) in Europe or
Brazil (de Sousa Cribari-Neto amp Stosic 2005) and Chile (Pacheco et al 2013) in South America
municipalities mainly supply services to ldquopeoplerdquo Here in addition to financial expenditures or
revenues inputs have included the number of local government employees the number of schools
or the number of hospitals and health centres School-age population the number of students
enrolled in primary and secondary schools and the number of beds in hospitals have been
considered as outputs Based on the study of Narboacuten-Perpintildeaacute amp De Witte (2018a) a wider list of
inputs and outputs used in previous studies can be found in Appendix I
Studies from different countries show important differences in the average efficiency scores
both between and within countries These studies also differ in the samples methodologies and
variables included A summary showing the range and variability of the mean efficiency scores
founds in countries all around the world can be found in Narboacuten-Perpintildeaacute amp De Witte (2018a)
63
These authors also show that OECD natural resource-rich countries such as Australia Belgium
and Chile show similar results in terms of mean efficiency scores with LGE studies being less
frequent in Latin American countries
Measuring efficiency of local governments as decision-making units (DMU) presents many
challenges and difficulties (Borger Kerstens Moesen amp Vanneste 1994 Ravallion 2005)
Worthington and Dollery (2000) mention problems with the selection and measurement of inputs
the identification of different stakeholders the hidden characteristic of the ldquolocal government
technologyrdquo and the multidimensionality of the services provided by local governments All these
issues make difficult to identify and distinguish between outputs and outcomes with outputs
commonly proxied by general indicators such as county area or county population Because
efficiency measures are highly sensitive to the chosen technique and the selection of inputs and
outputs Narboacuten-Perpintildeaacute amp De Witte (2018a) suggest formulating different specifications and
using less general and unspecified indicators Moreover the complexity in defining outputs and
the use of general indicators make more likely that contextual factors affect municipal efficiency
322 Explaining differences in LGE
To explain differences in local government performance researchers have basically
distinguished between ldquodiscretionaryrdquo and ldquonon-discretionaryrdquo factors Discretionary factors refer
to the degree of discretion of local authorities in the selection and management of inputs and
outputs On the other hand scholars have investigated the influence on LGE of contextual factors
beyond authoritiesrsquo control These factors reflective at the environment where municipalities
operate include economic socio-demographic geographic financial political and institutional
characteristics (da Cruz amp Marques 2014 Narboacuten-Perpintildeaacute amp De Witte 2018b)
64
In general the evidence about the influence of contextual factors has delivered mixed and
country-specific results (Narboacuten-Perpintildeaacute amp De Witte 2018b) Sampaio de Sousa et al (2005)
using data for Brazilian municipalities finds that population density and urbanization rate have
strong positive effects on efficiency scores Benito et al (2010) show that lower levels of
efficiency of Spanish municipalities are associated with a greater economic level a less stable
population and a bigger size of the local government Afonso (2008) finds that per capita income
level and education are not significant factors influencing LGE of Portuguese municipalities He
also finds that municipalities in Northern areas show greater efficiency than their counterparts in
Southern areas More recently Tsekeris (2018) finds that spatial variations in efficiency in Greece
can be attributed to factors related to inter-regional market access specialization and sectoral
concentration resource-factor endowments and political factors among others Characteristics
describing each local government have also been used including municipal indebtedness (Benito
et al 2010) fiscal deficits (Sinha 2017) degree of fiscal autonomy (Boetti Piacenza amp Turati
2009) and individual characteristics of local authorities such as age gender and political ideology
Narboacuten‐Perpintildeaacute amp De Witte (2018b) conclude after analysing 63 articles that studies on the
influence of contextual factors have mostly used cross-sectional data with little attention to
endogeneity issues which makes any causal interpretation doubtful
323 The trade-off between efficiency and equity
The existence of a potential trade-off between efficiency and equity is in the core of
economic discussion (Andersen amp Maibom 2020 Berg amp Ostry 2011 Browning amp Johnson
1984 Okun 2015)15 The argument that economic growth (one of the most common efficiency
15 Redistributive policies distort incentives and lead to suboptimal outcomes and thus efficiency losses
65
measures) could be negatively affected in the search for greater equality has been translated not
only into economic policies that favour economic growth over those that reduce inequality but
also in the emphasis of scholarly research Thus theoretical and empirical research has been
mainly focussed on efficiency and policy implications of a great diversity of shocks and policies
leaving the analysis of inequality as one of measurement and mostly descriptive Additionally
empirical evidence of the trade-off is scant and inconclusive (Andersen amp Maibom 2020
Browning amp Johnson 1984)
Among economic contextual factors that could affect LGE income inequality has been
largely ignored An exception is a cross-country comparison conducted by Ortega (2017) who
analyses the role of inequality on government efficiency in developing countries He finds that
more unequal countries could have higher difficulties to achieve specific health outcomes Income
inequality has even been considered as part of the outputs to measure efficiency particularly for
the case of European and OECD countries (Afonso Schuknecht amp Tanzi 2010 Antonelli amp De
Bonis 2018)
At the local level income inequality has been mainly used as a proxy for the effect of income
heterogeneity Economic inequality could have a direct and an indirect effect on government
efficiency The direct effect poses that higher income inequality could reduce municipal efficiency
because it is associated with a more complex and competing set of public services demanded by
the population (Jottier et al 2012) The indirect effect puts the focus in the link between inequality
social capital and levels of corruption Economic diversity could reduce trust in people and
institutions when related to high and persistent levels of income inequality It could also affect the
willingness to participate in community and political groups the existence of a shared objective
by citizens and the perception of a prosperous future (Uslaner amp Brown 2005)
66
The evidence is ambiguous For instance Geys and Moesen (2009) find that income
inequality has little relation to efficiency of Flemish municipalities and Ashworth et al (2014)
find a negative relationship for the Norwegian case Findings also indicate that inequality is the
strongest determinant of trust and that trust has a greater effect on communal participation than on
political participation (Uslaner amp Brown 2005)
33 Methodology
We follow a two-stage approach widely used in this kind of analysis A DEA analysis is
conducted in the first stage to get efficiency scores for each municipality Then regression analysis
is conducted in the second stage aiming to identify contextual variables other than differences in
the management of inputs that can help to explain the heterogeneity in municipal performance
331 Chilean Municipalities and period of analysis
The territory of Chile is divided into regions and these into provinces which for purposes of
the local administration are divided into counties The local administration of each county resides
in a municipality which is administrated by a Mayor assisted by a Municipal Council16
Municipalities represent the decentralization of the central power in Chile They are autonomous
organizations with legal personality and own patrimony whose purpose is to satisfy the needs of
the local community and ensure their participation in the economic social and cultural progress of
the county Municipalities have a diversity of functions related to public health education and
social assistance among others
16 The Mayor and City Council are elected by suffrage for the citizens of the respective commune every 4 years
67
To achieve their goals two are the main sources of municipal incomes own permanent
revenues (OPR) and the Municipal Common Fund (MCF) OPR are incomes generated by the
county and they are an indicator of the self-financing capacity of each municipality OPR are not
subject to restrictions regarding their investment and they are mainly generated by territorial taxes
commercial patents and circulation permits17 The MCF is a fund that aims to redistribute
community income to ensure compliance with the purpose of the municipalities and their proper
functioning Sources to finance the MCF come from municipal revenues The distribution
mechanism of the fund is regulated by parameters such as whether municipalities generate OPR
per capita lower than the national average and the number of poor people in the commune in
relation to the number of poor people in the country
This study covers the period from 2006 to 2017 During this period Chile was divided into
15 regions 54 provinces and 346 counties18 Although the information on inputs and outputs is
available for the entire period information on contextual factors at the county level such as
household income is only available every two-three years In addition some counties are excluded
from household surveys due to their difficult access Hence we use a sample of 324 municipalities
to measure municipal efficiency for the whole period (3888 observations) However the analysis
of contextual factors is conducted for those years when household income information is available
2006 2009 2011 2013 2015 and 2017 (1944 observations)
17 The territorial tax is a tax on agricultural and non-agricultural real estate Specifically of this income only 40 is left to the municipality as its own financing while the remaining 60 is allocated to the MCF (in the case of the four largest communes- Santiago Providencia Las Condes and Vitacura-percentages are 35 and 65 respectively) Unlike the territorial tax commercial patents are regulated mainly by the municipality which chooses the rate to be charged subject to a range established by law and is responsible of their collection Finally regarding the circulation permits 375 is of municipal benefit while 645 is directed to the MCF 18 There are 346 counties managed by 345 municipalities where counties ldquoCabo de Hornosrdquo and ldquoAntaacuterticardquo are managed by the municipality of ldquoCabo de Hornosrdquo
68
332 Measuring municipal efficiency
Municipal efficiency is measured using Data Envelopment Analysis (Coelli Prasada Rao
OrsquoDonnell amp Battese 2005) This is a non-parametric approach that uses linear programming to
measure efficiency for a group of municipalities as decision making units (DMUrsquos) The main
advantage of using DEA and the reason why DEA is used for the case of Chilean municipalities
is its flexibility in handling multiple inputs and outputs without the need to specify a functional
form (Balaguer-Coll amp Prior 2009 Mikušovaacute 2015 Tigga amp Mishra 2015) Following Afonso
and Fernandes (2008) the relationship between inputs and outputs for each municipality could be
represented by the following equation
119884 119891 119883 119894 1 119899 (31)
In equation (31) 119884 is the set of outputs and 119883 the set of inputs for each of the n
municipalities Using linear programming the production frontier is constructed and a vector of
efficiency scores is obtained The frontier represents full technical efficiency mdash the point at which
the highest output occurs given specified inputs or the point at which the lowest amount of inputs
is used to produce a specified quantity of output Efficiency scores under DEA are relative
measures of efficiency They measure a municipalityrsquos efficiency against the other measured
municipalities in the sample and not a hypothetical lsquoperfect municipalityrsquo The further from the
frontier the less technically efficient a municipality is
We use an input-oriented approach because Chilean municipalities have a greater control
over the management of inputs relative to the outputs they have to manage Obtaining efficiency
scores requires an assumption about the returns to scale exhibited by each municipality When
DMUrsquos are homogeneous the CCR model (Charnes Cooper amp Rhodes 1978) which assumes
69
constant return-to-scale (CRS) is the appropriate specification The CCR model assumes full
proportionality between outputs and inputs and that DMUrsquos operate at their optimal When DMUrsquos
are highly heterogeneous as is the case with local governments in most countries it is not realistic
to assume complete proportionality between inputs and outputs nor that all DMUrsquos operate at their
optimum scale In this situation a variable returns-to-scale (VRS) or BCC model (Banker
Charnes amp Cooper 1984) is the preferred formulation
Assuming VRS imposes minimum restrictions on the efficient frontier and allows for
comparisons only among municipalities of similar scale (Coelli et al 2005 Wu Huang amp Pan
2014) This means that when we use the CCR model (assuming CRS) on a heterogeneous sample
of DMUrsquos the resulting measure of technical efficiency that we obtain is related not only to the
management of inputs but also to issues of scale19 To empirically check the validity of the VRS
assumption we measure technical efficiency under CRS VRS and non-increasing returns-to-scale
(NIRS) and we analyse the existence of scale inefficiencies This allows us to check the relevance
of scale effects as a potential explanation of differences in municipal efficiency Appendix J
shows the specification of the DEA model under VRS and how ldquototal technical efficiencyrdquo
(assuming CRS and therefore due to management and scale issues) could be disaggregated in ldquopure
technical efficiencyrdquo (under VRS and related only to management issues) and scale efficiency (due
to scale issues)
19 Assuming VRS can also lead to measurement problems such as overestimation of efficiency scores This is the case when an important proportion of DMUrsquos shows CRS so assuming VRS ignores the information about proportionality between inputs and outputs (Podinovski 2004)
70
333 Inputs and outputs used in DEA
Following the literature on local government expenditure efficiency (Afonso amp Fernandes
2008 de Sousa et al 2005 Dlouhyacute 2018 Tandon 2005 Tigga amp Mishra 2015) and trying to
reflect as well as possible the functioning of municipalities five inputs and four outputs were
selected Input and output data were obtained from the National System of Municipal Information
(SINIM in its Spanish acronym) and they are expressed in thousands of Chilean pesos of 201720
Inputs are Municipal Operational Expenditure X1 (including expenses on goods and
services social assistance investment and transfers to community organizations) Municipal
Personnel Expenditure X2 (including full time and part-time workers) Total Municipal
Expenditure in Education sector X3 Total Municipal Expenditure in Health sector X4 and the
Number of Municipal Buildings X5 (proxied by the number of public facilities in education and
health sectors)
Output variables were selected highlighting the relevance of education and health sectors
and trying to capture the wide range of local services provided by municipalities The variable
ldquoOwn Permanent Revenuesrdquo Y1 is used to capture the scale and diversity of municipal
activities21 The ldquoMonthly Average Enrolmentrdquo in municipal education establishments related to
the school-age population in each county Y2 is used as educational output As health output the
ldquoNumber of Medical Consultationsrdquo in public facilities Y3 is considered Finally the number of
community organizations Y4 is used as output reflecting the promotion of community
development by each municipality Table 31 shows the summary statistics of input and output
20 The data from SINIM database was mostly obtained using the sinimr package (Salas 2019) 21 According to SINIM database this variable aims to measure the management of the municipalityrsquos own resources with respect to the population of the commune
71
variables for the whole sample and period Inputs and outputs excepting the Monthly Average
Enrolment Y2 are measured in per capita terms using county population information from the
National Institute of Statistics (INE in its Spanish acronym)
Table 31
Descriptive statistics Inputs and Output variables used in DEA analysis
334 Regression model
Contextual factors could play an important role not only in explaining why some
municipalities operate inefficiently but also why municipal performance differs among them
These factors may affect municipal performance modifying incentives for local authorities to
operate efficiently and their capability to take advantage of economies of scale They also define
the conditions for cooperation or competition among municipalities and the citizensacute ability and
willingness to monitor local authorities (Afonso amp Fernandes 2008)
Information on income at the household level for each county was obtained from the
ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) CASEN is
conducted every two-three years being the reason why consecutive years are not considered in
72
our regression analysis The other contextual factors used as controls were obtained from different
sources including SINIM INE and the ldquoServicio de Impuestos Internosrdquo (SII)22
Our main hypothesis is whether higher levels of income inequality are associated with lower
levels of municipal efficiency To test our hypothesis the empirical model is defined as
120579 120573 119892119894119899119894 119885 120573 120575 120572 120598 (32)
Where 120579 is the vector of DEA efficiency scores 119892119894119899119894 is the Gini coefficient of each
county 120575 are year-specific effects 120572 are county-specific constants 120598 is a vector of error terms
and 119885 is a vector of controls Next we discuss the motivation for these controls
The level of purchasing power of countiesrsquo citizens is proxied by the variable log(income)
which is the natural log of the mean household income per capita in thousands of Chilean pesos of
2017 On the one hand poorer counties could display higher efficiency due to their necessity to
take care of their constraint resources (Pacheco et al 2013) On the other hand richer counties
could show higher efficiency because richer citizens exert higher monitoring over local authorities
and demand better quality public services in return for their tax payments (Afonso et al 2010)
The possibility for municipalities to take advantage of economies of scale and urbanization is
captured by three variables First the variable log(density) which correspond to the natural log of
population density Second the dummy variable reg_cap indicating whether a county is a regional
capital or not Third the variable agroland which correspond to the proportion of land for
agricultural use which is informed to the SII We expect a positive effect of log(density) but
negative for regcap and agroland
22 The SII is the institution in charge of collecting taxes in Chile
73
Socio-demographic characteristics are captured including a Dependence Index IDD IDD
corresponds to the number of people under 15 years or over 65 years per 100 people in the active
population (those people between 15 and 65 years old) A higher proportion of young and older
population could be associated with a higher demand for municipal services relating to education
and health making harder to offer public services efficiently The citizensrsquo capacity to monitor
local authorities is proxied including the variable education (average years of education for the
population older than 15 years) and the variable housing (proportion of households which are
owners of the property where they live in each county) In both cases we expect a positive
association with LGE
Among municipal characteristics the variable professional (percentage of municipal
personnel with a professional degree) is used to control for the quality of municipal services and
it is expected a positive impact The variable mcf (proportion of total municipal income coming
from the MCF) is included to capture the influence of financial dependence on the central
government A higher dependence from MCF could be associated with higher efficiency when it
is linked to more control from central government (Worthington amp Dollery 2000) However when
MCF discourages the generation of own resources and proper management of resources from the
fund a lower efficiency should be expected (Bravo 2014) In addition the dummy variable mayor
is included to capture differences among mayors supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo
political parties related to those ldquoINDEPENDENTrdquo mayors
Table 32 report summary statistics for the set of numeric contextual factors and Appendix
K the corresponding correlation matrix Despite the high correlation between income and
education variables we include both in the regression section as they capture different county
characteristics
74
Table 32
Summary Statistics Numeric Contextual Factors
Figure 31 Geographical distribution of Chilean regions and macrozones
Previous evidence on growth and convergence of Chilean regions have found that regions
tend to cluster spatially (Aroca amp Bosch 2000) Aiming to capture this regional clustering process
75
and considering the high concentration in the number of municipalities and population in the
central area we group municipalities in four ldquozonesrdquo We define as reference the ldquoCentre-Northrdquo
zone consisting of regions XIII (where the countryrsquos capital is located) and its two neighbouring
regions V and VI The ldquoNorthrdquo zone includes regions XV I II III and IV The ldquoCentre-Southrdquo
zone includes regions VII VIII and IX Finally the ldquoSouthrdquo zone embraces regions XIV X XI
and XII Figure 31 displays the regional administrative division and zones considered in this
essay
Efficiency scores (ES) are limited to have values between 0 and 1 However they are relative
measures (relative to the sample of municipalities) This implies that when a municipality is on the
frontier (ES = 1) it does not mean that potentially larger technical efficiency gains cannot be made
Hence equation 32 is estimated using OLS and censored regressions We start running cross-
sectional regressions for each of the six years Then we compare the results with those from panel
regressions Because fixed-effects panel Tobit models could be affected by the incidental
parameters problem (Henningsen 2010 2019) we use random-effects panel Tobit models
including indicator variables for years and zones Finally to deal with the potential endogeneity
problem we also use an instrumental variable approach The instrument is described next
335 The instrument
Government effectiveness and income distribution are both structural components of
economies (Ortega et al 2017 Ravallion 2005) In the search for a causal interpretation for the
influence of income inequality on municipal efficiency we need an instrument which must be
correlated with the variable to be instrumented (in our case income inequality) and uncorrelated
with the error term in the efficiency equation (32) Previous literature has used as instruments for
Gini the number of townships governments in a previous period the percentage of revenues from
76
intergovernmental transfers in a previous period and the current share of the labour force in the
manufacturing sector (Alesina amp La Ferrara 2002) Using the share of the labour force in a specific
sector is unlikely to reduce the problem of endogeneity particularly in countries where local
governments supply mostly ldquoservices to peoplerdquo and the main source of income inequality is
labour income
We propose as an instrument the proportion of firms in the primary sector (mining fishing
forestry and agriculture)
119901119904119904_119891119894119903119898119904Number of firms in the primary sector
Total number of firms (33)
On the one hand this instrument is likely to be correlated with local income inequality in
natural resource-rich countries23 On the other hand we contend that our instrument is less likely
to be correlated with the error term in the efficiency equation First the main services supplied by
Chilean municipalities are services to people (health and education) not to firms Second most of
the revenues collected by municipalities included those associated with natural resources end up
in the municipal common fund whose objective is precisely to reduce inequalities among
municipalities Third services to firms are expected to be more significant with the tertiary sector
We argue that our instrument captures natural and structural conditions which directly
influence income inequality but it does not directly affect LGE Figure 32 shows the evolution
of the annual average efficiency score and the proportion of firms in the primary secondary
(manufacturing) and tertiary (services) sectors We observe that sectors shares have remained
relatively stable with a slight reduction in the participation of the primary sector in favour of the
23 Results confirm a significant association between income inequality and the degree of dependence on natural resources at the county level
77
tertiary sector by the end of the period This is contrary to the evolution of municipal efficiency
which shows a cyclical behaviour as will be shown in the next section
Figure 32 Evolution of efficiency scores and the proportion of firms by sector
34 Results and discussion
341 DEA results
Figure 33 displays the evolution of our three measures of efficiency Overall technical
efficiency pure technical efficiency and scale efficiency are around 78 83 and 95
respectively with fluctuations over the years Therefore around three quarters of the overall
78
inefficiency is attributed to inefficiency in the management of inputs and around one quarter to
scale inefficiencies24
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE)
Returnstoscale
Figure 34 reports by zone and for the whole period the proportion of municipalities
showing CRS decreasing returns to scale (DRS) or increasing returns to scale (IRS) Most of the
municipalities operate under variable (increasing or decreasing) returns to scale which could be
explained by the high heterogeneity in size among municipalities A summary of RTS
disaggregated by year and zone is in Appendix L Policies oriented to increase efficiency usually
24 The average scale efficiency score of 95 means that municipalities could get an additional 5 of inputs saving if municipalities were able to change their size to achieve their optimal scale
79
consider amalgamation de-amalgamation or ways of cooperation among municipalities To have
a better idea about where and how feasible is the implementation of such policies Appendix M
shows maps with the administrative division of the country in its 345 municipalities and which
municipalities show CRS IRS or DRS in each of the six years of data
Figure 34 Returns to scale by zone
Based on results for the whole period (Figure 34) the North has the highest proportion of
municipalities exhibiting DRS This suggests the need to reduce the size of municipalities splitting
those ldquotoo bigrdquo creating new administrative areas or giving more autonomy to current
municipalities25 The opposite occurs in the Centre-North area where municipalities mostly
exhibit IRS This indicates the need to merge municipalities An alternative strategy to the
amalgamation process is increasing inter-municipal cooperation (Balaguer-Coll et al 2019)
25 This has been the policy followed in Chile Although two new regions were created (XV in the North and XIV in the South) the number of municipalities has not changed
80
which seems to be a more plausible option in Chile Finally evidence on scale efficiency for the
Centre-South and South areas is not clear in terms of the adequate strategy to improve efficiency
Efficiencymeasure
Although most municipalities show scale inefficiencies (Figure 34) only a small proportion
of total inefficiency is associated to scale issues (Figure 33) Together this results justify not only
the use of the BCC model (efficiency scores under VRS) to capture municipal heterogeneity but
also highlights the need to look for other factors outside the control of local authorities which
could be influencing municipal performance
Table 33
Summary efficiency scores (VRS) by zone and region
Table 33 summarizes ES under VRS using data for the six years of CASEN survey A mean
efficiency score of 83 is found for the full sample and period This means that on average
inefficient municipalities can reduce the use of inputs by 17 to get the same current output By
81
comparing average ES per zone it can be concluded that municipalities in the North Centre-North
Centre-South and South could achieve the same level of output with 17 12 18 and 23 fewer
resources respectively Results also show that one third of the municipalities present an efficiency
score equal to one
Figure 35 shows the evolution of the mean ES for the four zones over the 12 years period
A clear drop in the efficiency level is observed in 2009 (the financial crisis year) excepting for the
North area Likewise although mean efficiency returned to its pre-crisis levels in 2014-2015 a
new drop in efficiency was experienced in 2016 It also seems that the 2010 earthquake did not
generate a significant effect on municipal efficiency Figure 35 also shows that although levels
of efficiency seem to differ among zones they follow a similar trend through time with the only
exception of the North which corresponds to the mining area In addition efficiency seems to be
significantly higher in the Centre-North area This is explained by the high mean level of efficiency
in region XIII which includes the countryrsquos capital city
Figure 35 Evolution mean efficiency scores (VRS) by zone
82
To know which and where are the efficient municipalities and if they are surrounded by
municipalities with a similar level of efficiency Appendix N has maps showing the ldquoefficiency
statusrdquo of each municipality that is whether they are efficient (ES = 1) or inefficient (ES lt 1)
Additionally Appendix O shows ldquoequal intervalsrdquo maps describing the spatial distribution of ES
among municipalities for each of the six years26 Results show that efficient municipalities can be
found all through the country the ldquoefficiency statusrdquo could change from one year to another and
municipalities with similar level-status of efficiency tend to cluster in space
342 Regression results
Exploratoryspatialanalysis
DEA efficiency scores and their geographical representations seem to show that municipal
efficiency presents a spatial clustering pattern This means that municipal performance could be
influenced not only by contextual factors of the county where municipality belongs but also by the
level of efficiency of neighbouring municipalities and their characteristics To test the significance
of the spatial dimension in municipal efficiency we use a cross-section of data considering the six-
year average of efficiency scores the Gini coefficient and the set of controls
We use the Moranrsquos I indicator to test for spatial autocorrelation Moranrsquos I is a measure of
the correlation between the level of efficiency in one municipality and its ldquospatial lagrdquo that is the
average level of efficiency in neighbouring municipalities We define as the relevant neighbours
for each municipality the 5-nearest municipalities This is obtained using the distances among the
26 An equal intervals map uses the same principle as a histogram to organize the observations into categories that divide the range of the variable into equal interval bins For the equal interval classification the value range between the lower and upper bound in each bin is constant across bins but the number of observations in each bin is typically not equal
83
polygonsrsquo centroids (latitude and longitude) of each county Results confirm that municipal
efficiency show a significant level of positive spatial autocorrelation This means that
municipalities tend to have neighbouring municipalities with similar performance
The positive spatial autocorrelation shown by municipal efficiency could be due to the
performance in one municipality is influenced by the performance in neighbouring municipalities
(spatial dependence in the variable itself) or due to structural differences among regions-zones
(spatial heterogeneity) To check the source of the spatial autocorrelation we run an OLS
regression of ES against income inequality and controls and then we test OLS residuals for spatial
autocorrelation Moranrsquos I over OLS residuals although significant is barely higher than zero (see
Appendix P) This means that the spatial effect itself is not a serious issue and can be handle for
instance including zone indicators variables hence we proceed to analyse the influence of income
inequality on LGE using non-spatial regression27
Cross‐sectionalanalysis
We start reporting censored regressions for each year in our panel Efficiency scores have
been rescaled to numbers between 0 and 100 to facilitate the interpretation of the results All
regressions include dummy variables for three of the four zones in which we have grouped Chilean
regions Results are in Table 3428 Income inequality shows a negative sign in all years which is
consistent with our hypothesis that inequality is negatively related to municipal efficiency
However only in three of the six years the effect of income inequality appears as statistically
27 In the case of having incorporated the spatial dimension the two most commonly used forms are including in the model the spatial lag of the dependent variable (spatial autoregressive model SAR) or the spatial lag of the error (spatial error model SEM) Following the method suggested by Anselin et al (1996) Lagrange Multiplier (LM) tests on the OLS residuals showed that the SAR model is preferred over the SEM Moranrsquos I LM tests and spatial regressions can be found in Appendix P 28 Regression results using OLS are in Appendix Q
84
significant Only the income level displays a significant and positive influence on efficiency for
the whole period A higher population density also consistently favours municipal efficiency On
the other hand as we expected a higher IDD makes it more difficult to achieve an efficient
performance29 Consistent with results in Pacheco et al (Pacheco et al 2013) municipal
efficiency show a significant an positive association with the MCF only in the first half of our
period of analysis with the second half showing an insignificant relationship
Table 34
Cross-sectional (censored) regressions
Paneldataanalysis
Estimation results for the six-year panel are reported in Table 35 Columns (1) and (2) show
the results for the pooled and random effects censored models only controlling for zone and year
29 The set of cross-sectional regressions was also run for each zone independently Results confirm that the set of significant contextual factors differs not only among years but also among geographic areas This support previous evidence suggesting that cross-sectional analysis should be treated with caution (Narboacuten-Perpintildeaacute amp De Witte 2018b)
85
dummies Income inequality appears as non-significant Zone indicator variables confirm that
municipalities located in the Centre-South and South of the country display a lower average level
of efficiency compared to the Centre-North area Time dummies mostly show negative
coefficients This is a signal that shocks such as the GFC and the earthquake in 2010 may have
had a negative impact on efficiency but that impact was not permanent The results for the pooled
and RE models including the full set of controls are reported in columns (3) and (4) These results
show a significant negative influence of income inequality on LGE
When income inequality is instrumented by the variable pss_firms most of the coefficients
remain unchanged except for those associated with the income variables gini and log(income)
This result implies that our original model suffers for instance from the omitted variable bias
This means that LGE and income inequality are determined simultaneously by some variable not
included in our model Columns (5) and (6) show results using our instrument for income
inequality The ldquotrue coefficientrdquo of variable gini remains negative but the magnitude of the
relationship is higher The negative coefficient for gini implies on the one hand that municipalities
located in more unequal counties face more challenges to achieve an efficient management of
public resources On the other hand the coefficient in column (6) is close to one The interpretation
is that for each point of reduction in income inequality ceteris paribus LGE should increase in the
same proportion Next we discuss some of the results associated with the controls variables
Contrary to Pacheco et al (2013) income level has a positive coefficient meaning that richer
counties in terms of income per capita show higher efficiency This could be explained by higher
monitoring and increasing demands from citizen and taxpayers In addition it is expected a higher
efficiency in municipalities located in counties with a higher population density and those with a
lower proportion of land for agricultural use This result is mainly explained by municipalities
86
located in the Centre area The opposite happens with municipalities in the South implying that
they are too ldquosmallrdquo to take advantage of agglomeration economies There is also a limit for
agglomeration and scale economies which is shown by the negative coefficient of the variable
regcap although this coefficient loses its significance in the IV approaches30
Unexpectedly efficiency was found to be negatively associated with the variable education
This result is similar to the case of Czech municipalities (Šťastnaacute amp Gregor 2014) where
explanations include a weakened monitoring effect due to the fact that more educated citizens
present greater mobility and labour cost disadvantages for municipalities with better educated
labour force In Chile an additional explanation could be the relationship between education and
voter turnout Since 2012 voting is no longer mandatory in Chile This fact considerably reduced
voter turnout which in turn may have influenced the monitoring and control effect of more
educated voters For the case of variable IDD results show that local authorities in counties with
higher proportion of aging and young population (related to those in the active population) face a
greater challenge in their quest to offer public services efficiently
The influence of mcf is like that found by Pacheco et al (2013) with municipalities more
dependent on central transfers showing more efficiency31 Political influence captured by the
variable mayor did not show a significant effect This result is like other studies concluding that
the ideological position did not have a significant influence on efficiency (Benito et al 2010
Boetti et al 2009 Cordero Pedraja-Chaparro Pisaflores amp Polo 2017)
30 This negative coefficient suggests that the negative effects of agglomeration economies such as overcrowding pollution high cost of land and traffic congestion could predominate over the positive effects of scale economies in regional capitals 31 When the analysis is conducted for each zone independently MCF displays a negative influence in the North and Centre-North areas but a positive influence in the Centre-South and South areas These results confirm that on the one hand richer municipalities (those in the North and Centre-North) have less incentives for efficient management of their resources On the other hand results support the relevance of MCF helping poorer municipalities (those in the South) to reach their outcomes
87
Table 35
Panel data regressions
88
35 Conclusions
The trade-off between equity and efficiency is in the core of the economic discussion This
ldquoprinciplerdquo has been used by policymakers to prioritize the design of policies focused on economic
growth delaying those policies aimed at reducing economic inequalities This essay offers
empirical evidence of a negative relationship between inequality and efficiency that is a reduction
of income inequality could have positive effects on economic efficiency at least at the level of
local governments
We followed a traditional Two-Stage approach commonly used in the analysis of LGE We
compared cross-sectional and panel data results and we have added an instrumental variable
approach to give a causal interpretation to the link between efficiency and inequality We proposed
the use of a measure of natural resource dependence to instrumentalize the impact of income
inequality on LGE Given that our units of analysis are municipalities and not counties we argue
that our measure of NRD is correlated with income inequality and it does not have a direct
influence on LGE
We found that Chilean municipalities perform better than previous studies suggest
Municipal efficiency depicted significant levels of positive spatial autocorrelation and most of the
municipalities showed to be operating under increasing or decreasing returns to scale This would
imply that the policies generally used to improve efficiency such as amalgamation or cooperation
should be implemented observing the reality of each region and not as strategies at the national
level We also found that scale inefficiency explains a small proportion of the average total
inefficiency reason why the analysis of external factors that could affect the municipal efficiency
takes greater relevance
89
Income inequality plays an important part in explaining municipal efficiency In fact it was
found that reductions in income inequality could result in increases in municipal efficiency in a
similar proportion An unexpected finding was that the levels of education shows a negative
association with municipal performance This could be due to a low average level of education or
the existence of an omitted variable This variable could be the significant reduction in voting
turnout rates for local and national elections due to changes in the voting system during the period
of our analysis All in all our results may help to shed light on the potential consequences of
changes in contextual factors and the design of strategies aimed to increase municipal efficiency
in countries with similar characteristics to the Chilean economy For instance policies oriented to
take advantage of economies of scale can be formulated merging municipalities or establishing
networks in specific sectors such as education or health
Further work needs to be done both in measurement and in the explanation of differences in
municipal performance in Chile One area of future work will be to identify the factors that better
predict why municipalities operates under increasing decreasing or constant returns to scale
Multinomial logistic regression and the application of machine learning algorithms to SINIM data
sets appear as suitable methods for that purpose Intertemporal DEA (Drew amp Dollery 2015)
should be used to measure municipal efficiency capturing changes in total factor productivity In
addition municipalities operate under different levels of geographical authorities such as the
provincial mayor and the regional governor Hence it would be useful to know how each
municipality performs within each region-zone related to how performs to the whole country This
should be done conducting a metafrontier analysis (OrsquoDonnell Rao amp Battese 2008)
We have also identified through a cross sectional spatial exploratory analysis that on
average municipalities with similar levels of efficiency tend to cluster in space Regarding to
90
analyse the importance of contextual factors on municipal efficiency a deeper analysis should use
censored spatial models to check the significance of the spatial dimension in cross-sectional and
panel contexts Another interesting avenue for future research is associated with the negative
association found between LGE and education The significant reduction in votersacute turnout since
the law of automatic registration and voluntary voting in 2011 appears as a natural experiment to
analyse its effects on efficiency indicators such as municipal performance Incorporating variables
such as the voting turnout in each county or classifying municipalities based on individual
institutional political and economic characteristics could help to shed light on which of these
channels is the most relevant when analysing the impact of inequality on municipal efficiency
Finally we argued that an important part of the influence of income inequality over LGE
could be through its indirect effect on trust social capital and social cohesion The final essay will
delve deep in that relationship
91
Chapter 4 Social Cohesion Incivilities and Diversity
Evidence at the municipal level in Chile
41 Introduction
A deterioration in social cohesion could carry significant costs such as a reduction in
generalized trust between individuals and in institutions a society caught in a vicious circle of
inequality and citizens increasingly distanced from civic life (Uslaner 2011) A growing feeling
of frustration and discontentment can eventually translate into a social outbreak with uncertain
results This is precisely what have been happening in many countries around the world included
Chile
ldquoSocial cohesion is a state of affairs concerning both the vertical and the horizontal
interactions among members of society as characterized by a set of attitudes and norms that
includes trust a sense of belonging and the willingness to participate and help as well as their
behavioural manifestationsrdquo (Chan et al 2006) This definition highlights the multidimensionality
in the concept of social cohesion which has been measured using objective andor subjective
indicators of trust social norms solidarity willingness to participate in social and political groups
and feelings of belonging (Ariely 2014 Chan et al 2006) Multidimensionality also implies that
the impact of determinants of social cohesion such as economic and racial diversity could be
different for each of its various dimensions (Ariely 2014)
A common characteristic to all societies is that they are made up of different groups that
differ with respect to race ethnicity income religion language local identity etc The
92
Community Heterogeneity Thesis (Coffeacute amp Geys 2006) argues that individuals prefer to interact
with others that are like themselves Hence high levels of diversity particularly economic and
racial represent a complex scenario to maintain social cohesion One of the most common factors
adduced for social cohesion is income inequality with higher levels linked to lower levels of trust
(Ariely 2014 Rothstein amp Uslaner 2005)
Traditional measures of social cohesion may not be adequately capturing the deterioration
in social connections For instance measures of (lack of) trust include a strong subjective element
On the other hand proxies for social participation such as volunteering jobs or joining to social
organizations have not been supported by empirical evidence as a source of generalized social trust
(Rothstein amp Uslaner 2005) We proposed to use the rate of incivilities which we argue is a more
appropriate measure of the degree of worsening in the social context
Incivilities are those visible disorders in the public space that violate respectful social norms
and tend not to be treated as crimes by the criminal justice system There are two types of
incivilities social and physical Social incivilities include antisocial behaviours such as public
drinking noisy neighbours and fighting in public places Physical incivilities include among
others vandalism graffiti abandoned cars and garbage on the streets Because citizens and
political authorities cannot always distinguish between incivilities and crime they are usually
treated as an additional category of crime This implies that policies aimed to reduce incivilities
are generally based on punitive actions However theory and evidence on incivilities suggest that
factors explaining incivilities and crime could be different (Lewis 2017 Taylor 1999)
In Chile crime rates have shown a sustained downward trend after reaching its highest level
in 2011 On the other hand incivilities rate has shown a sustained upward trend which coincides
with the increasing victimization and feeling of insecurity in the population This has motivated
93
Chilean authorities based on the ldquoBroken Windows Theoryrdquo to propose new punitive actions (or
increase the severity of the current ones) to those who commit this type of antisocial behaviours
The ldquoBroken Windows Theoryrdquo states that higher rates of incivilities are a signal of social
disorganization which result in higher crime rates (J Q Wilson amp Kelling 1982) This is expected
to have consequences on familiesrsquo decisions such as moving away from public spaces or even
leaving their neighbourhoods
As far as we know there is no previous evidence about the potential causes of incivilities in
Chile Efforts to identify the factors explaining incivilities could help not only to reduce the risk
factors favouring violent and property crimes but also to guide interventions aimed to change
social behaviours and strengthen social cohesion in highly unequal societies Thus the main
contribution of the present study is to provide a deeper comprehension of the problem of incivilities
and how they can help to better understand the weakening of social cohesion that many
contemporary societies experience
We aim to offer the first evidence on the factors explaining the evolution and the differences
in incivilities rates in Chile We set up a panel for six years (2006 2009 2011 2013 2015 and
2017) and 324 counties (1944 observations) We start exploring the evolution and geographical
distribution of incivilities Then we investigate whether economic and racial diversity after
controlling for other socioeconomic demographic and municipal characteristics can be regarded
as key predictors of incivilities
We use the Gini coefficient to proxy economic heterogeneity and the number of new visas
granted to foreigners as proportion of the county population as proxy for racial diversity The main
hypothesis is whether economic and racial diversity have a positive association with the rate of
incivilities In addition to the arguments regarding the ldquoCommunity Heterogeneity Thesisrdquo Taylor
94
(1999) and Skogan (1986 1999 2015) argue that incivilities are caused by inequality and the lack
of informal mechanisms of social control Based on the ldquoIncivilities Thesisrdquo the patterns of
incivilities should mirror the patterns of inequality (Taylor 1999) Then higher inequality should
be associated with higher physical and social vulnerability of the population This reduces social
control and increases social disorganization which triggers antisocial or negligent behaviours
Our main result reveals a strong positive association between the rate of incivilities and the
number of new visas granted per year The relationship with income inequality although also
positive seems to be less significant These findings give strong support to the ldquoCommunity
Heterogeneity Thesisrdquo and to a lesser extent to the ldquoIncivilities Thesisrdquo When the analysis is
disaggregated geographically racial diversity shows a clear positive effect The impact of income
inequality seems to be conditional depending on the level of income showing no effect in poorer
regions Results also show that the impact of economic and racial diversity differs by type of
incivility For example income inequality shows a strong association with ldquoStreet Tradingrdquo while
racial diversity with ldquoAlcohol Consumptionrdquo Two are the main policy implications On the one
hand a national strategy to address the problems associated with foreign immigration could help
to reduce incivilities For instance a joint effort between national and local authorities to curb
immigration and its distribution throughout the country On the other hand our results show that
the relationship between incivilities and economic diversity differs depending on the region or
geographical area Hence the impact on social cohesion of policies aimed to tackle economic
inequalities should be analysed in each specific context
The rate of incivilities also shows a negative association with the level of municipal financial
autonomy This implies that municipalities can effectively carry out policies to reduce incivilities
beyond the efforts of the central government Another important finding is that our results do not
95
support the hypothesis that a higher proportion of the young population is associated with higher
rates of incivilities Hence policies aimed to reduce incivilities should be focused on the causes of
incivilities rather than the criminalization of behaviours or stigmatization of specific population
groups
The structure of the chapter is as follows Section 42 outlines the relevant literature on social
cohesion and incivilities Section 43 describes the data variables and methodology and
establishes the hypotheses of the study Section 44 contains the results and discussions Section
45 presents the main conclusions
42 Related Literature
421 The Community Heterogeneity Thesis
The idea under ldquoThe Community Heterogeneity Thesisrdquo is that if individuals prefer to
interact with others who are similar to themselves in terms of income race or ethnicity high levels
of income inequality and racial diversity facilitate a context for lower tolerance and antisocial
behaviours lowering the ldquostaterdquo of social cohesion (Alesina 2000 Coffeacute amp Geys 2006 Letki
2008) Alessina and Ferrara (2002) give support to this hypothesis arguing that individuals have a
natural aversion to heterogeneity However the most popular explanation is the principle of
homophily people prefer to interact with others who share the same ethnic heritage have the same
social status and hence share experiences and tastes (Letki 2008 McPherson Smith-Lovin amp
Cook 2001 Tolsma et al 2009) For instance Delhey and Newton (2005) find for a sample of
60 countries that income inequality and ethnicity are strongly and negatively correlated with trust
Tolsma et al (2009) using data for Dutch neighbourhoods and municipalities find that social
cohesion is negatively and consistently affected by economic deprivation but not by ethnic
96
heterogeneity These authors also conclude that the effect of neighbourhood and municipal
characteristics on social cohesion depends on residentsrsquo income and educational level
Rothstein and Uslaner (2005) give two theoretical reasons why economic and racial diversity
should be causally related to social trust a key element of social cohesion First optimism about
the future makes less sense when there is more economic inequality which generally translates into
inequality of opportunities especially in areas such as education and the labour market Second
the distribution of resources and opportunities plays a key role in establishing the belief that people
share a common destiny and have similar fundamental values In highly unequal societies people
are likely to stick with their own kind Perceptions of injustice will reinforce negative stereotypes
of other groups making social trust and accommodation more difficult
Uslaner (2002 2011) and Uslaner and Brown (2005) find that high levels of inequality are
the single major factor driving down trust in people who are different from yourself Evidence for
USA finds that inequality is the strongest determinant of generalized trust over time (Rothstein amp
Uslaner 2005) Reducing inequality and then increasing generalized trust should have positive
consequences at the individual and aggregates levels At the individual level it may lead to greater
tolerance and more acts of altruism for people of different backgrounds At the aggregate level it
may lead to greater economic growth more redistribution from the rich to the poor and less
corruption (Uslaner 2002 2013) Letki (2008) argues that when neighbourhood socio-economic
context (apart from just an individualrsquos socioeconomic status) is considered it turns out to be the
main factor triggering negative attitudes and lack of trust in out-group members
The increasing diversity caused by immigration can also reduce the conditions necessary for
social cohesion (Ariely 2014 Holtug amp Mason 2010) Christel Kesler and Irene Bloemraad
(2010) find for nineteen advanced democracies between 1981 and 2000 that increasing migration
97
generally decreases trust civic engagement and political participation The authors also find that
in more equal countries with clear policies in favour of cultural minorities the negative effects of
migration are mitigated or even reversed Letki (2008) states that deprivation and disorder tend to
be strongly correlated with racial diversity Because we propose the use of the number of disorders
or antisocial behaviours known as ldquoincivilitiesrdquo as our measure of social cohesion we describe the
literature on incivilities in the next section
422 The literature on incivilities
The study of incivilities has been a continuing concern mainly for developed countries since
the 1980s The focus has changed from individual and psychological explanations to ecological
(contextual) and social explanations (Taylor 1999) The individual approach basically considered
perceptions of incivilities as an explanatory variable of fear of crime The ecological explanation
argues that indicators of economic disadvantage (eg income levels income inequality
unemployment rate and poverty rate) are the keys to understand a process of social disorganization
and lack of informal control These economic factors lead to higher rates of inappropriate or
negligent behaviours and ultimately to higher crime rates (Blau amp Blau 1982 Messner Rosenfeld
amp Baumer 2004 Phan Orsquobrien Mendolia amp Paloyo 2017 Sampson 1986)
The negative impact of incivilities is not merely reflected in its association with crime rates
(Skogan 2015) Physical and social incivilities could worsen neighbourhoods by affecting quality
of life perception of the environment and public and private behaviours Previous research has
indicated that a higher level of incivilities is associated with health problems (Branas et al 2011
Cohen et al 2000 Hill amp Angel 2005 Ross 2011 Ross amp Mirowsky 2001) greater
victimization and fear of crime (Brunton-Smith Jackson amp Sutherland 2014 Mijanovich amp
Weitzman 2003) and multiple negative economic effects For instance incivilities could be
98
related to a reduction in commercial activity lower investment in real estate reduction in house
prices (Skogan 2015) and population instability (Hipp 2010)
To describe the state of the art in the study of incivilities and their consequences Skogan
(2015) used the concept of untidiness to characterize the research on incivilities The study of
incivilities has had multiple approaches (economic ecological and psychological) Incivilities
have also been measured using multiple sources of information (police reports surveys trained
observation) which result in different measures (perceptions vs count data) However the question
about what specific factors have the strongest effect on incivilities has been overlooked and
perceptions about incivilities have been used mainly as a predictor of crime fear of crime and
victimization
There are two types of incivilities social and physical Social incivilities are a matter of
behaviour including groups of rowdy teens public drunkenness people fighting and street hassles
Physical incivilities involve visual signs of negligence and decay such as abandoned buildings
broken streetlights trash-filled lots and graffiti (Skogan 1999 2015 Taylor 1999) Three reasons
justify the distinction between physical and social incivilities First like multiple dimensions of
social cohesion different structural and social conditions could be responsible for different types
and categories of incivilities Second punitive sanctions are expected to have a greater impact on
physical than on social incivilities since the latter are more related to behaviours rooted in citizensrsquo
culture Third physical incivilities should be more related to absolute measures of economic
disadvantage (eg poverty or unemployment rates) and social incivilities to relative indicators of
economic disadvantage (eg such as income inequality) This line of research is based on the
ldquoincivilities thesisrdquo which states that to understand the distribution of disorders it is necesary to
analyse the patterns of structured inequalities (Skogan 1986 Taylor 1999)
99
423 The ldquoIncivilities Thesisrdquo
Incivilities theories began with a focus on psychological dynamics (Garofalo 1978) moved
forward to an interest in social psychological processes (J Q Wilson amp Kelling 1982) and finally
evolved into a focus on community dynamics and outcomes (Skogan 1999) Individual and group
behaviours in tandem with ecological features have been proposed as the key factors explaining
incivilities and their posterior influence on social control quality of life and more serious crime
(J Q Wilson amp Kelling 1982)
In terms of ecological factors particularly those related to economic conditions Skogan
(1986) was the first linking the distribution of incivilities to the patterns of structural inequality If
incivilities mirror inequality structure this will have consequences in residentsrsquo health and safety
due to its levels of vulnerability In addition structured inequality associated with the proportion
of the manufacturing sector (eg when fabrics tend to move from cities to farther areas) will be
related to higher social disorganization and differences between urban and rural areas (W J
Wilson 1996) In addition a persistent feeling of relative deprivation (persistently high levels of
income inequality) could lead to fellow inhabitants of the community to commit antisocial
behaviours showing their frustration with the current economic model
The literature on incivilities posits that their causes are different from those of crime (Lewis
2017) Unlike crime analysis especially property crimes information on the location where the
incivility takes place is the same as the location where the perpetrator resides To achieve a
comprehensive understanding of the different types of incivilities it is crucial to consider
incivilities data covering an entire territory and not just specific areas (Hooghe Vanhoutte
Hardyns amp Bircan 2010) If we add to this the availability of panel data it could be possible not
100
only to identify the main determinants of incivilities but also the causal mechanism from income
inequality towards incivilities rate
In Chile citizen security crime and delinquency are among the most significant issues for
citizens based on opinion polls Existing research has found weak evidence of a significant
relationship between crime and indicators of socio-economic disadvantage such as income
inequality and unemployment rate with significant effects only on property crime (Beyer amp
Vergara 2006 Nuntildeez Rivera Villavicencio amp Molina 2003 Rivera Gutieacuterrez amp Nuacutentildeez 2009)
Crime deterrence variables such as the probability of being caught or the number of police
resources have also shown ambiguous results (Beyer amp Vergara 2006 Rivera et al 2009
Vergara 2012) Evidence at the county level shows that crime is higher in urban counties those
with a lower mean income per capita and counties located in the North of the country In addition
at least half of the crimes reported in one county are perpetrated by criminals from other counties
(Rivera et al 2009) No studies could be found about the determinants of incivilities
4 3 Methodology
431 Period of analysis and data sample
Chile is a relatively small country in Latin America with a population of 18346018
inhabitants in 2017 The country is divided into 345 municipalities with on average 53104
inhabitants (median value 18705) Municipalities are the organ of the State Administration
responsible to solve local needs Municipalities are not only the relevant political and
administrative local unit of analysis but also they represent the feeling of lsquocommunityrsquo among
the inhabitants of each municipality (Hooghe et al 2010) Our data includes many sources of
101
heterogeneity among municipalities such as indicators of economic deprivation population
density demographic characteristics and whether the county is a regional or provincial capital
We use a sample of 324 municipalities covering most of the Chilean territory for the period
2006ndash17 Data on incivilities is obtained from the ldquoCentre of Studies and Analysis of Crimerdquo
which is part of the ldquoSubsecretaria de Prevencion del Delitordquo (SPD in its Spanish acronym) of the
Chilean government32 Information on income inequality and control variables is obtained from
the ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) the
ldquoNational Institute of Statisticsrdquo (INE in its Spanish acronym) the ldquoNational Municipal
Information Systemrdquo (SINIM in its Spanish acronym) and the Immigration Department of the
Government of Chile Our panel only includes the years for which CASEN survey is available
2006 2009 2011 2013 2015 and 2017
432 Operationalisation of the response variable and exploratory analysis
Official Chilean records contain information for the total number of cases of incivilities per
year at the county level The number of cases is the sum of complains and detentions reported at
the police Our dependent variable 119894119899119888119894119907119894119897119894119905119894119890119904 correspond to the number of cases per year Due
to population differences comparisons between counties are made using the incivilities rate per
1000 population calculated as
119894119899119888119894119907_119903119886119905119890 lowast 1000 (41)
where 119894119899119888119894119907_119903119886119905119890 is the incivilities rate 119905 is the year 119894 the county and 119899 is the population of the
county per year
32 httpceadspdgovclestadisticas-delictuales
102
Figure 41 illustrates at the top the evolution of the total number (cases reported) of
incivilities and crimes at the country level for the period 2006-1733 At the bottom Figure 41
shows the evolution of the mean county rate per 1000 inhabitants We observe that both the number
of incivilities and the number of crimes has reached similar annual figures however average
county rates per 1000 population show different trends Crime rate displays a sustained fall after
reaching its pick in 2011 Incivilities average county rate which also reached a pick and posterior
drop in 2011 has recovered its upward trend since 2016 considerably exceeding the crime rate
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017
33 Crime refers to ldquocrimes of greater social connotationrdquo (DMCS in its Spanish acronym) which includes violent and property crimes
103
Chilean records classify incivilities in nine categories most of them associated with social
incivilities Summary statistics for the total and for each of the nine categories are presented in
Table 41 In addition Figure 42 shows the evolution of incivilities by category for the whole
period We see that the global trend in incivilities is mainly due to a substantial increase in ldquoStreet
Tradingrdquo and ldquoPublic Alcohol Consumptionrdquo A common element of Figures 41 and 42 is the
significant change in trend experienced by crimes and incivilities in 2011 That year the SPD
became dependent on the Ministry of Interior of the Chilean Government This event put the issue
of crime and delinquency within national priorities for the central government
Table 41
Summary statistics total count of incivilities and by category (full sample and period)
Unlike crime rates we do not expect significant cross-county spillover effects in incivilities
However the questions of where incivilities are concentrated and why they are there can be of
great interest (Skogan 2015) Figure 43 shows quantile maps for the rate of incivilities per 1000
inhabitants for the initial and final years in our panel
104
Figure 42 Evolution total number of incivilities by category
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017)
105
We observe that the range of values has increased significantly from 2006 to 2017 but the
spatial distribution remains almost unchanged On the one hand high incivilities rates in the North
could be associated with the mining activity On the other hand high rates in the Centre area
(where the countyrsquos capital is located) could be related to the higher population density and the
concentration of the economic activity34
To see how the different types of incivilities are distributed throughout the country we have
grouped those similar categories in four groups ldquoPublic Damagerdquo (ldquoPublic Disturbsrdquo ldquoPublic
Damagerdquo ldquoOtherrdquo) ldquoThreatsrdquo (ldquoPublic Fightrdquo ldquoThreatsrdquo and ldquoAnnoying Noisesrdquo) ldquoAlcohol
Consumptionrdquo (ldquoPublic Alcohol Consumptionrdquo and ldquoDrunkennessrdquo) and ldquoStreet Tradingrdquo This
distinction in groups could be relevant if we expect different patterns and different effects of
community heterogeneity on social cohesion among counties For instance we expect higher
levels of Public Damage in big urban cities Street trading is more likely in urban cities but also in
tourist areas The spatial distribution of these four groups for the six-year-average rate per 1000
inhabitants can be found in Appendix R
433 Measures of community heterogeneity and control variables
Social cohesion income inequality and racial diversity are all ldquocommunityrdquo (not individual)
characteristics Thus to understand their relationship we need aggregated data at least at the
county-municipal level With more disaggregated data like at the suburbs level the required
heterogeneity among groups of citizens is lost (Wilkinson 1999) Like Coffeacute and Geys (2006) we
use the Gini coefficient to capture economic heterogeneity However instead of a measured for
34 We also analysed the spatial distribution of crime rates In general areas with high levels of incivilities not necessarily are associated with high levels of crime This could imply that factors explaining incivilities and crime are different
106
the diversity of nationalities we use the proportion of foreign population to capture racial
heterogeneity Income data is obtained from the CASEN survey The Gini coefficient is calculated
for each county and included through the variable gini Racial heterogeneity is included through
the variable foreign which is the annual number of new VISAS granted to foreigners as a
proportion of the county population Chile has experienced a significant increase in immigration
since 2011 Immigration has been concentrated in the metropolitan region and mining regions in
the North of the country We expect a positive relationship between immigration and incivilities
although as with the relationship between immigration and crime the foundations for this
hypothesis are not strong (Hooghe et al 2010 Sampson 2008)
Economic development is another explanation for social cohesion frequently appealed to
explain trust with wealthier societies considered to exhibit higher levels of trust (Delhey amp
Newton 2005) In this study we include the natural log of the mean household income per capita
log(income) We also include the poverty rate poverty and the unemployment rate
unemployment Unlike the variable log(income) these variables are expected to be positively
associated with the number of incivilities When a relative indicator of economic heterogeneity
such as income inequality is included as determinant of social cohesion we should expect less
effect from absolute indicators of economic disadvantage such as poverty and unemployment rates
(Hooghe et al 2010 Tolsma et al 2009)
Among demographic variables the percentage of inhabitants between 10 and 24 years old is
included through the variable youth The variable women defined as the proportion of the female
population in each county is also included Variable youth is expected to have an ambiguous effect
Although young people have lower victimization and report rates they also represent the group
more likely to commit antisocial behaviours when a community has a low capacity of self-
107
regulation (eg when there is low parental supervision) The female population is associated with
a higher report of incivilities related to the male population
It is argued that crime and incivilities are essentially urban problems (Christiansen 1960
Wirth 1938) We include the variable log(density) defined as the log of population density (the
number of inhabitants divided by the area of each county in square kilometres) and a dummy
variable capital indicating whether a county is an administrative capital (provincial or regional)
Two additional variables are included to capture the level of informal social control exerted
by families living in each municipality First the variable education which is defined as the
average years of education of people over 15 years old Second the variable housing which capture
the proportion of families which are owners of their housing unit Although education and housing
are related to both the possibility of reporting and committing an incivility we expect a negative
association with the rate of incivilities
In Chile crime has been mainly a problem faced by the police and the Central Government
Administration To control for current law enforcement policies we include the variable
deterrence defined as the number of arrests as a proportion of the total number of incivilities cases
In addition municipalities can develop their own initiatives to deal with crime and incivilities
depending on their capacity to generate its own resources The level of financial autonomy from
central transfers is captured by the variable autonomy This variable is obtained from SINIM and
it is defined as the proportion of the budget revenue of each municipality that comes from its own
permanent sources of revenues A categorical variable mayor is also included This variable
indicates whether the municipality mayor is supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo political
parties (related to those ldquoINDEPENDENTrdquo mayors)
108
Table 42 presents descriptive statistics for our measures of income and racial heterogeneity
and the set of numeric control variables The Pearson correlation among these variables is shown
in Appendix S
Table 42
Summary statistics numeric explanatory variables
434 Methods
The annual count of incivilities as is characteristic for count data is highly concentrated in
a relatively small range of values In addition the distribution is right-skewed due to the presence
of important outliers (counties with a high number of incivilities) Figure 44 shows the
distribution of the six-year average number of incivilities for each of the 15 regions in Chile35 We
observe that regions differ in the number of counties in which they are divided In addition
counties within each region show important differences in the number of incivilities For instance
35 Regions are ordered from left to right and from top to bottom in the way they are geographically distributed from North to South So the northernmost region of the country is the ldquoXVrdquo and the southernmost region is the ldquoXIIrdquo The Metropolitan region (where the countryrsquos capital is located) is region ldquoXIIIrdquo in the centre of the country (see Appendix C)
109
excepting the Metropolitan region ldquoXIIIrdquo most counties in regions located in the centre of the
country (middle row in Figure 44) show a range of incivilities between 0 and 2000 The number
of incivilities is considerably lower in counties located in the northern (top row in Figure 44) and
southern (bottom row in Figure 44) regions of the country compared to regions in the centre of
the country It also seems clear from Figure 44 that the number of incivilities does not follow a
normal distribution
Figure 44 Annual average number of incivilities per county
The number of incivilities can be better described by a Poisson distribution In this case the
number of incivilities is ldquothe countrdquo and the number of incivilities per year is ldquothe rate per unit
timerdquo We are interested in modelling the average number of incivilities per year usually called 120582
as a function of a set of contextual factors to explain differences in incivilities between and within
110
counties The main characteristic of the Poisson distribution is that the mean is equal to the
variance This implies that as the mean rate for a Poisson variable increases the variance also
increases The main implication is we cannot use OLS to model 120582 as a function of the set of
contextual factors because the equal variance assumption in linear regression is violated
The rate of incivilities between counties is not directly comparable due to population
differences We expect counties with more people to have more reports of incivilities since there
are more people who could be affected To capture differences in population which is called the
exposure of our response variable 120582 it is necessary to include a term on the right side of our model
called an offset We will use the log of the county population in thousands as our offset36
Additionally similar to the case of crime data incivilities show a significant degree of
overdispersion (variance higher than the mean) suggesting that there is more variation in the
response than the Poisson model implies37 We also model and regress incivilities assuming a
Negative Binomial distribution to address overdispersion An advantage of this approach is that it
introduces a dispersion parameter in addition to 120582 which gives the model more flexibility38
Considering as the response variable the count of incivilities per year the model can be
expressed as follow
120582 119890119909119901 120573 120573 119892119894119899119894 120573 119891119900119903119890119894119892119899 119883120574 120572 120579 (42)
36 If we think of 120582 as the average number of incivilities per year then represents the number per 1000
inhabitants so that the yearly count is adjusted to be comparable across counties of different sizes Adjusting the yearly count by population is equivalent to adding 119897119900119892 1199011199001199011199061198971198861199051198941199001198991000 to the right-hand side of the regression equation 37 Without adjusting for overdispersion we use incorrect artificially small standard errors leading to artificially small p-values for model coefficients 38 The Negative Binomial model posits selecting a 120582 (average number of incivilities) for each county and then generating a count using a Poisson random variable with the selected 120582 With this approach the counts will be more dispersed than would be expected for observations based on a single Poisson variable with rate 120582
111
where 120582 is the rate of incivilities 119883 is our vector of controls 120572prime119904 are county-specific constants
and 120579prime119904 are time-specific constants Accounting for differences in county population we have
119890119909119901 120573 120573 119892119894119899119894 119883120574 120572 120579 (43)
where 119901119900119901119906119897119886119905119894119900119899 is the county population per year Hence the model to be estimated using
Maximum Likelihood Estimation (MLE) is
119897119900119892 120582 120573 120573 119892119894119899119894 119883120574 120572 120579 119897119900119892 (44)
Finally to account for different effects depending on the type of incivilities we also run
equation (44) for each of the four groups of incivilities defined in section (432)
435 Hypotheses
Based on the community heterogeneity hypothesis the relationship between social cohesion
and diversity should be stronger for lower levels of income and less educated groups of people
(Tolsma et al 2009) Hence contrary to evidence for developed and more equal countries we
expect a significant positive association for the Chilean case where more than 50 of the
population is economically vulnerable (OECD 2017)
The main hypotheses to be tested in this essay is whether the number of incivilities is
positively associated with the level of economic and racial heterogeneity at the county level We
start analysing this association for the full sample and period Next we analyse whether the
relationship between incivilities and our measures of diversity differs by geographic area (region
or zone) Finally we check whether the effect of economic and racial diversity is different
depending on the group of incivilities
112
44 Results and Discussion
Overall our results show that the rate of incivilities displays a stronger and more significant
relationship with racial diversity than with economic heterogeneity This association differs for
different geographic areas and for different types of incivilities Absolute economic indicators
except for income show a significant but small effect Increases in the average levels of income
or education and more financial autonomy for municipalities seem to be effective ways to reduce
the rate of incivilities
We estimate equation (44) assuming that the number of incivilities follows a Poisson
distribution Regional and temporal heterogeneity are captured through the inclusion of dummy
variables for five years (with 2006 as the reference year) and fourteen regional dummies (with
region XIII as the reference region) Results are reported in Table 4339 This table is structured in
two blocks of regressions pooled models in columns (1)-(4) and fixed-effects models in columns
(5)-(8)40 The first column in each block only includes economic indicators relative and absolute
trying to test which ones are more relevant and whether incivilities tend to mirror income
inequality (the ldquoincivilities thesisrdquo) The second column adds the variable foreign to account for
the effect of racial diversity (Letki 2008) The third column includes education to check whether
the association between economic and racial diversity with social cohesion changes (gets less
significant) when we control for educational level (Tolsma et al 2009) The final column in each
block corresponds to the full model specification which includes the rest of controls
39 Pooled estimations were obtained using the R command glm() and compared with results obtained from the command poisson in STATA 15 Fixed effects and random effects models were estimated using the STATA command xtpoisson Negative Binomial models were estimated using the command glmnb() in the MASS package for R and commands nbreg and xtnbreg in STATA 15 Results for Negative Binomial regressions are in Appendix T 40 We have omitted results with random effects due to the literature on panel count data models suggest that the most robust estimator is Poisson regression with Fixed Effects (Santos Silva amp Tenreyro 2010 2011)
113
Table 43
Poisson regressions
114
The positive and significant coefficient for the variable gini besides being small it becomes
insignificant in the fixed effects specification which includes the full set of controls This result
does not seem to be enough evidence to support our hypothesis that more unequal counties display
higher rates of incivilities On the other hand racial diversity through the variable foreign shows
a consistent positive association with the rate of incivilities41 Together coefficients for gini and
foreign seems to support the ldquocommunity heterogeneity thesisrdquo (Letki 2008) but not the
ldquoincivilities thesisrdquo (Skogan 1999) To check this finding we run the pooled full model
specification for each region and results are shown in Table 44 where regions have been ordered
from North to South The sign of the coefficient of the variable gini differs for different regions
Moreover the relationship is insignificant in some of the most unequal regions which are in the
South of the country (VIII IX and XIV) This result rejects the hypothesis that incivilities mirror
structural income inequalities For the variable foreign 12 out of 15 regions confirm the positive
association with the rate of incivilities42
We also run our pooled full model separately for each group of incivilities defined at the end
of section (432) Income inequality keeps its significant but small association with each group of
incivilities (see Table 45) Our measure of racial diversity shows a stronger association with
ldquoAlcohol Consumptionrdquo related to ldquoPublic Damagerdquo and ldquoThreatsrdquo The link with ldquoStreet Tradingrdquo
appears as non-significant These results support our general finding that on the one hand racial
heterogeneity exert a more significant influence on the rate of incivilities than economic
41 To get the effects of the variables gini and foreign over the rate of incivilities per 1000 inhabitants we need to exponentiate their coefficients Thus an increase in one unit in the variable gini increases the rate of incivilities per 1000 inhabitantsrdquo in 1005 times (e^0005) in the pooled full model Similarly an increase in one point in variable foreign is expected to increase the ldquorate of incivilities per 1000 inhabitantsrdquo in 10876 times (e^0084) in the full pooled model and 10471 (e^0046) times in the full Fixed Effects model 42 Given that depending on the geographic location regions share certain characteristics the model was also run clustering regions in 4 zones (see Figure 31 in chapter 3) Results by zone are in Appendix U
115
heterogeneity On the other hand results confirm our hypothesis that the effect of diversity is
different for different types of incivilities
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region
Back to our general results in Table 43 the significant and negative coefficient of the
income variable and to a lesser extent the significant and positive coefficients of poverty and
unemployment provide evidence that absolute rather than relative economic indicators may be
more important explanations of the rate of incivilities This is opposite to evidence for the analysis
116
of crime rates such as in Hoodge (2010) and support the idea that determinants of incivilities are
different from those of crime Our results are also opposite to those for Dutch municipalities where
economic indicators turned out to be more important than ethnic heterogeneity (Tolsma et al
2009) The coefficient for the variable log(income) could be interpreted as counties with an income
level under the average face higher problems of antisocial behaviours such as incivilities In
addition as the income level moves far away from its average low level the problem of incivilities
is less relevant43 In terms of policy implications only those policies that achieve a significant
increase in the average level of county income seem to be effective in reducing incivilities and
strengthening social cohesion
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group
43 We ran our model splitting the sample of counties in quintiles based on their income level The coefficient for the variable gini only showed a significant (and positive) coefficient for the second third and fourth quintile A non-significant coefficient for the first quintile could imply that other types of inequalities (eg health education) are more relevant in the poorest counties In the case of the fifth quintile (the richest group of counties) they have better facilities in terms of infrastructure police resources self-protection etc which could reduce the potential triggers of incivilities
117
The inclusion of the variable education significantly improved the goodness of fit of the
models and did not generate significant changes in the coefficients of our measures of economic
and racial diversity This result rejects the proposition that the relationship between social
cohesion and diversity becomes less strong when controlled by education (Tolsma et al 2009)
Additionally it highlights the topic of education as a relevant determinant of the rate of incivilities
and social cohesion
Among control variables there are also some important results Opposite to what we
expected the variable youth shows a negative or non-significant coefficient Although this result
could be due to the lack of ldquophysical incivilitiesrdquo in Chilean records it indicates that it is incorrect
to stereotype this group as the main responsible for high incivilities rates The significant and
negative coefficient of the variable autonomy in the fixed effects specification could also have
important policy implications It is a signal that local governments can play an important role in
reducing incivilities or complementing the efforts from the central government Another
interesting result is the significant coefficient of the variable housing The latter finding is
particularly important in the sense that a negative sign supports public policies oriented to increase
homeownership as effective ways to improve social cohesion However the small magnitude of
the coefficient that even showed the opposite sign in some model specifications could be
explained for the high level of segregation that these policies have generated in Chilean society
As mentioned in the Introduction and Literature Review so far only a few studies have
used measures of disorders or incivilities as dependent variable to explain changes in social
cohesion (Skogan 2015) In addition there is no evidence in Chile analysing the determinants of
incivilities separately from those of crimes The importance of our results on identifying the
importance of economic and racial diversity on social cohesion lies mainly in its generality An
118
important number of countries all around the world share a similar context characterized by high
levels of inequality and an explosive increase in immigration These countries are also
experiencing a worsening in social cohesion which increases the risk of a social outburst
4 5 Conclusions
The main goal of this essay was to determine whether differences in incivilities at the county
level mirror differences in income distribution and racial diversity Previous literature suggests a
positive and strong association between social cohesion and indicators of economic disadvantage
relative deprivation and racial diversity (Letki 2008 Tolsma et al 2009 Uslaner amp Brown 2005)
While not all our results were significant they showed helpful insights about how and where
economic and racial diversity are more likely to influence the rate of incivilities and social
cohesion
We used data for the period 2006ndash17 economic heterogeneity was measured through the
Gini coefficient at the county level and racial heterogeneity was proxied by the number of granted
visas to foreigners as proportion of county population We found strong evidence of a significant
and positive association between the rate of incivilities and racial diversity but not with income
inequality Contrary to previous evidence at the municipal level (Coffeacute amp Geys 2006 Tolsma et
al 2009) in general our results give support for the ldquohomophily principlerdquo and the ldquocommunity
heterogeneity hypothesisrdquo However results also showed that the effect of economic and racial
diversity varies throughout the Chilean regions and for the different types of incivilities
We also found that policies aimed at controlling the behaviour of young people did not have
strong empirical support In terms of the role that local governments may have in facing the
119
growing problem of incivilities we found evidence that efforts managed from the municipalities
can be an important complement to those from the central government
Future research should go further on the role of local authorities on incivilities and social
cohesion On the one hand municipalities could have a direct impact on social cohesion through
the implementation of programs complementary to those of central authorities oriented to reduce
incivilities and crime On the other hand social cohesion could be indirectly affected when local
authorities display an inefficient performance supplying public services to citizens or they are
recognized as corrupted institutions We suggest that policy makers from central government
should give local authorities a greater role in fighting antisocial behaviours and crime Evaluating
programs in specific municipalities could help to elucidate the causal effect of for instance higher
fiscal autonomy on the rate of incivilities
Another interesting area for future work will be to analyse how housing policies have
contributed to the phenomenon of segregation of Chilean society and in the process of weakening
social cohesion Finally our main result highlights the need of a deeper analysis of the impact that
foreign immigration is having in Chile For instance disaggregating information by country of
origin and the reasons why immigrants are arriving to the country or specific regions will surely
help to understand the impacts of immigration
120
Chapter 5 Conclusions
This thesis investigated in three essays the issue of income inequality in Chile using county-
level data for the period 2006-2017 The first essay supplied empirical evidence about the
importance of the degree of dependence on natural resources in terms of employment in explaining
cross-county differences in income inequality The second essay analysed the potential causal
effect that income inequality has on the level of technical efficiency of local governments
providing public goods and services Lastly the third essay studied the relationship between social
cohesion measured through the number of antisocial behaviours classified as ldquoincivilitiesrdquo and
the levels of income and racial heterogeneity
Findings from the first essay support the idea that the endowment of natural resources plays
a significant role in explaining income inequality in Chile However contrary to what most
theoretical and empirical evidence postulates our findings showed a robust negative association
between the two variables This means that the reduction experienced in Chile in the degree of
dependence on natural resources in terms of employment has contributed to the persistence of high
levels of income inequality The exploratory analysis indicated that income inequality shows a
general clustering process characterized by a significant and positive spatial autocorrelation
Regarding the previous evidence for Chile (Paredes et al 2016) the regression analysis confirmed
the relevance of the spatial dimension of income inequality through a process of spatial
heterogeneity giving less support to the existence of a process of spatial dependence (spillover
effect) in the variable itself
121
Essay 2 studied the potential trade-off between efficiency and equity analysing the influence
of income inequality on the efficiency of local governments at the municipal level To identify the
causal effect of income inequality on municipal efficiency we proposed the use of the proportion
of firms in the primary sector as an instrument for income inequality Findings confirmed our
hypothesis that efficiency is lower in more unequal counties This result suggests the non-existence
of the trade-off between equity and efficiency Hence policies intended to reduce inequality could
help to increase efficiency at least at the level of municipal local governments
The third essay analysed how social cohesion proxied by the rate of incivilities is associated
with the levels of economic diversity proxied by income inequality and the levels of racial
diversity proxied by the number of new visas grated as proportion of the county population
Findings gave strong support to the hypothesis that the rate of incivilities is positively related to
racial diversity and to a lesser extent to economic diversity In addition the rate of incivilities
appears negatively related to the degree of financial autonomy of municipalities This means that
local governments can effectively contribute to the reduction of incivilities which could help
reduce victimization and crime rates ultimately strengthening social cohesion
Taken together findings from essays 2 and 3 highlight the important role that income
inequality could play in other relevant economic and social dimensions These findings add to the
understanding of the potential consequences of income inequality particularly in natural resource
rich countries with persistently high levels of inequality
The present study has mainly investigated income inequality at the county level In addition
Chilean municipalities play an important role providing ldquoservices to peoplerdquo so our findings could
be applied in other highly unequal countries with a high degree of dependence on natural resources
and local governments with similar responsibilities For instance in Latin America apart from
122
Chile and Brazil there are no studies on the efficiency of local governments Other limitations are
associated with the availability of information For instance important indicators such as GDP per
capita are only available at the regional level and information of incomes is not available annually
In addition given the heterogeneity among municipalities some type of grouping of municipalities
should be performed before looking for causal relationships or conducting program evaluation
Despite these limitations we believe this study could be the basis for different strands of future
research on the topic of inequality local government efficiency and social cohesion
It was stated in chapter 2 based on the resource curse hypothesis literature there are two
elements that determine whether NR are a curse or blessing in terms of socioeconomic outcomes
First the curse is more likely in countries with weak political and governance institutions Second
different types of resources affect institutions differently with resources that are concentrated in
space so-called ldquopointrdquo resources tend to impair institutions while ldquodiffuserdquo resources do not
(Deacon 2011 Isham Woolcock Pritchett amp Busby 2005) Our results showed a negative
relationship between income inequality and our measure of natural resource dependence even after
controlling for zone fixed effects and for the level of government expenditure This result could
be interpreted as a signal that NR has a direct effect on income inequality in addition to the indirect
impact through market or institutional channels Using other potential institutional transmission
channels will shed light about the true effect that the endowment of natural resources has over
income inequality Variables that could capture these institutional channels include the level of
employment in the public sector measures of rule of law and corruption and changes in the
creation of new business in the secondary and tertiary sectors related to the primary sector
Based on results from chapter 3 most of the municipalities show scale inefficiencies One
immediate area for future work will involve using our set of contextual factors to predict the status
123
of municipalities in terms of scale inefficiencies Defining as dependent variable whether a
municipality shows constant decreasing or increasing returns to scale we could run a multinomial
logistic regression to predict municipal status For instance we would expect that a one-unit
increase in the Gini coefficient should increase the probability of scale inefficiencies (increasing
or decreasing returns to scale rather than constant returns to scale) Because the aim in this case
would be predicting a certain result in terms of returns to scale the next step should involve to
split the full sample in training and testing data sets and to use some resampling methods such as
bootstrapping This will allow us to evaluate the performance and accuracy of our model
predictions using different random samples of municipalities Results from Machine Learning
algorithms will help us to assess the generalizability of our results to other data sets
Future work should also benefit greatly by using data on different Latin American countries
to (1) compare the responsibilities of local governments (2) select a common set of inputs and
output to evaluate LGE (3) identify the relevance of scales inefficiencies in explaining differences
in performance and (4) analyse the influence of contextual characteristics over LGE Differences
in the main primary sector activity in each country such as oil in Brazil mining in Chile or Coffee
in Colombia could be responsible for differences in LGE among countries These differences could
be associated with sources of revenue management of expenditure and definitions of outputs or
contextual effects such as corrupted institutions or the delay in the development of other sectors
such as manufacturing or services
To delve deep on reasons explaining the social crisis experienced by Chilean society and
other countries one area of future work will be to analyse the relationship between diversity and
the origins of social revolutions Based on Tiruneh (2014) the three most important factors that
explain the onset of social revolutions are economic development regime type and state
124
ineffectiveness Interesting questions include whether the characteristics of Chilean context at the
end of 2019 are enough to trigger the transformation of the political and socioeconomic system
Social revolutions particularly violent revolutions are less likely in more democratic educated
and wealthy societies So it would be relevant to identify the factors explaining the violence that
has characterized the social crisis in Chile Finally the democratic regime has been maintained in
the last decades with changes between left and right governments This could imply that more
important than the regime has been the efficiency or ineffectiveness of the governments to satisfy
the needs of the population
Future work should also cover the disaggregation of information regarding foreign
population in terms of the reasons for new granted visas and the country of origin Official data
allows us to disaggregate whether the benefit is permanent (students and employees with contract)
or temporary Furthermore most of the new visas were traditionally granted to neighbouring
countries (Peru and Bolivia) a trend that has changed in the recent years to include countries such
as Colombia Venezuela and Haiti An analysis of how economic and social indicators have been
affected by changes in the composition of foreigners their reasons for immigrating to the country
and their geographical distribution have implications for economic policy at both the national and
local levels At the national level such analysis should be a key input when proposing changes to
the national immigration policy At the local level it could help define the role of municipalities
to assess the benefits and challenges of immigration These challenges are mainly related to the
provision of public goods and services such as health and education which in Chile are the
responsibility of the municipalities
The findings of this thesis suggest that policymakers should encourage policies that reduce
income inequality The key role that municipalities could play to strengthen social cohesion and
125
the increasingly important role that foreign population is acquiring in most modern societies are
also interesting avenues for future research However the picture is still incomplete and more
research is needed incorporating other dimensions of inequality This is essential if we want to
understand the reasons that could have triggered the social outbursts experienced by various
economies across the globe
126
Bibliography
Acemoglu D (1995) Reward structures and the allocation of talent European Economic Review 39(1) 17ndash33 httpsdoiorghttpsdoiorg1010160014-2921(94)00014-Q
Acemoglu D (2002) Technical Change Inequality and the Labor Market Journal of Economic Literature 40(1) 7ndash72 httpsdoiorg1012570022051026976
Acemoglu D Aghion P amp Violante G L (2001) Deunionization Technical Change and Inequality Carnegie-Rochester Conference Series on Public Policy 55(1) 229ndash264 httpsdoiorg101016S0167-2231(01)00058-6
Acemoglu D Johnson S amp Robinson J A (2001) The Colonial Origins of Comparative Development An Empirical Investigation The American Economic Review 91(5) 1369ndash1401 httpsdoiorg101257aer9151369
Acemoglu D amp Robinson J A (2002) The Political Economy of the Kuznets Curve Review of Development Economics 6(2) 183ndash203 httpsdoiorg1011111467-936100149
Afonso A amp Fernandes S (2006) Measuring local government spending efficiency Evidence for the Lisbon region Regional Studies 40(1) 39ndash53 httpsdoiorg10108000343400500449937
Afonso A amp Fernandes S (2008) Assessing and explaining the relative efficiency of local government The Journal of Socio-Economics 37(5) 1946ndash1979 httpsdoiorg101016jsocec200703007
Afonso A Schuknecht L amp Tanzi V (2010) Income distribution determinants and public spending efficiency Journal of Economic Inequality 8(3) 367ndash389 httpsdoiorg101007s10888-010-9138-z
Akita T (2003) Decomposing regional income inequality in China and Indonesia using two-stage nested Theil decomposition method The Annals of Regional Science 37(1) 55ndash77 httpsdoiorg101007s001680200107
Alesina A (2000) Participation in heterogeneous communities The Quarterly Journal of Economics 115(3) 847ndash904 httpsdoiorg101162003355300554935
Alesina A amp La Ferrara E (2002) Who trusts others Journal of Public Economics 85(2) 207ndash234 httpsdoiorg101016S0047-2727(01)00084-6
Allcott H amp Keniston D (2014) Dutch Disease or Agglomeration The Local Economic Effects of Natural Resource Booms in Modern America (N W P N 20508 Ed) NBER Working Paper No 20508 (Vol w20508) NBER Working Paper No 20508 NBER Working Paper No 20508 httpsdoiorgNBER Working Paper No 20508
Andersen T M amp Maibom J (2020) The big trade-off between efficiency and equitymdashis it there Oxford Economic Papers 72(2) 391ndash411
127
Anselin L (1988) Spatial econometrics methods and models (Vol 4) DordrechtBoston Kluwer Academic Publishers
Anselin L amp Bera A K (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics Statistics Textbooks and Monographs (Vol 155)
Anselin L Bera A K Florax R amp Yoon M J (1996) Simple diagnostic tests for spatial dependence Regional Science and Urban Economics 26(1) 77ndash104 httpsdoiorg1010160166-0462(95)02111-6
Antonelli M A amp De Bonis V (2018) The efficiency of social public expenditure in European countries a two-stage analysis Applied Economics 1ndash14 httpsdoiorg1010800003684620181489522
Aragoacuten F M amp Rud J P (2013) Natural Resources and Local Communities Evidence from a Peruvian Gold Mine American Economic Journal Economic Policy 5(2) 1ndash25 httpsdoiorg101257pol521
Ariely G (2014) Does Diversity Erode Social Cohesion Conceptual and Methodological Issues Political Studies 62(3) 573ndash595 httpsdoiorg1011111467-924812068
Armstrong H amp Taylor J (2000) Regional economics and policy (3rd ed) Oxford Blackwell
Aroca P amp Atienza M (2011) Economic implications of long distance commuting in the Chilean mining industry Resources Policy 36(3) 196ndash203 httpsdoiorg101016jresourpol201103004
Aroca P amp Bosch M (2000) Crecimiento convergencia y espacio en las regiones chilenas 1960 - 1998 Estudios de Economiacutea 27 199ndash224 Retrieved from httprepositoriouchileclbitstreamhandle2250127853Patricio_Arocapdfsequence=1
Ashworth J Geys B Heyndels B amp Wille F (2014) Competition in the political arena and local government performance Applied Economics 46(19) 2264ndash2276 httpsdoiorg101080000368462014899679
Atkinson A B (2015) Inequality What Can Be Done Harvard University Press
Auty R (1993) Sustaining development in mineral economies the resource curse thesis London [ua] Routledge
Auty R (2001) Resource abundance and economic development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Badeeb R A Lean H H amp Clark J (2017) The evolution of the natural resource curse thesis A critical literature survey Resources Policy 51 123ndash134 httpsdoiorg101016jresourpol201610015
Balaguer-Coll M T Brun-Martos M I Maacuterquez-Ramos L amp Prior D (2019) Local government efficiency determinants and spatial interdependence Applied Economics
128
51(14) 1478ndash1494 httpsdoiorg1010800003684620181527458
Balaguer-Coll M T amp Prior D (2009) Short- and long-term evaluation of efficiency and quality An application to Spanish municipalities Applied Economics 41(23) 2991ndash3002 httpsdoiorg10108000036840701351923
Banker R D Charnes A amp Cooper W W (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis Management Science 30(9) 1078ndash1092
Beine M Coulombe S amp Vermeulen W N (2015) Dutch Disease and the Mitigation Effect of Migration Evidence from Canadian Provinces The Economic Journal 125(589) 1574ndash1615 httpsdoiorg101111ecoj12171
Benito B Bastida F amp Garciacutea J A (2010) Explaining differences in efficiency an application to Spanish municipalities Applied Economics 42(4) 515ndash528 httpsdoiorg10108000036840701675560
Berg A amp Ostry J (2011) Equality and efficiency Finance amp Development 48(3) 12ndash15
Berg A amp Ostry J (2017) Inequality and Unsustainable Growth Two Sides of the Same Coin IMF ECONOMIC REVIEW 65(4) 792ndash815 httpsdoiorg101057s41308-017-0030-8
Beyer H amp Vergara R (2006) Delincuencia en Chile Determinantes y rol de las poliacuteticas puacuteblicas Instituto de Economiacutea UC
Blanco L amp Grier R (2012) Natural resource dependence and the accumulation of physical and human capital in Latin America Resources Policy 37(3) 281ndash295 httpsdoiorghttpdoiorg101016jresourpol201201005
Blau J R amp Blau P M (1982) The cost of inequality Metropolitan structure and violent crime American Sociological Review 114ndash129
Boetti L Piacenza M amp Turati G (2009) Fiscal decentralization and spending efficiency of local governments An Empirical Investigation on a Sample Of 4
Boix C amp Posner D (1998) Social capital Explaining its origins and effects on government performance British Journal Of Political Science 28 686ndash693
Borge L E Parmer P amp Torvik R (2015) Local natural resource curse JOURNAL OF PUBLIC ECONOMICS 131 101ndash114 httpsdoiorg101016jjpubeco201509002
Borger B Kerstens K Moesen W amp Vanneste J (1994) Explaining differences in productive efficiency An application to Belgian municipalities Public Choice 80(3) 339ndash358 httpsdoiorg101007BF01053225
Bourguignon F amp Morrisson C (1990) Income distribution development and foreign trade A cross-sectional analysislowast European Economic Review 34(6) 1113ndash1132 httpsdoiorghttpsdoiorg1010160014-2921(90)90071-6
129
Branas C C Cheney R A MacDonald J M Tam V W Jackson T D amp Ten Have T R (2011) A difference-in-differences analysis of health safety and greening vacant urban space American Journal of Epidemiology 174(11) 1296ndash1306
Bravo J (2014) Fondo Comuacuten Municipal y su desincentivo a la recaudacioacuten en Chile Temas de La Agenda Legislativa-Centro de Poliacuteticas Puacuteblicas UC 9(68)
Browning E K amp Johnson W R (1984) The Trade-Off between Equality and Efficiency Journal of Political Economy 92(2) 175ndash203 httpsdoiorg101086261219
Brunnschweiler C N amp Bulte E H (2008) The resource curse revisited and revised A tale of paradoxes and red herrings Journal of Environmental Economics and Management 55(3) 248ndash264 httpsdoiorghttpsdoiorg101016jjeem200708004
Brunori P Ferreira F H G amp Peragine V (2013) Inequality of opportunity income inequality and economic mobility Some international comparisons In Getting Development Right (pp 85ndash115) Springer
Brunton-Smith I Jackson J amp Sutherland A (2014) Bridging structure and perception On the neighbourhood ecology of beliefs and worries about violent crime British Journal of Criminology 54(4) 503ndash526
Bulte E H Damania R amp Deacon R T (2005) Resource intensity institutions and development World Development 33(7) 1029ndash1044 httpsdoiorg101016jworlddev200504004
Carmignani F (2013) Development outcomes resource abundance and the transmission through inequality Resource and Energy Economics 35(3) 412ndash428 httpsdoiorg101016jreseneeco201304007
Carmignani Fabrizio amp Avom D (2010) The social development effects of primary commodity export dependence Ecological Economics 70(2) 317ndash330 httpsdoiorg101016jecolecon201009003
Caselli F amp Michaels G (2013) Do Oil Windfalls Improve Living Standards Evidence from Brazil American Economic Journal Applied Economics 5(1) 208ndash238 httpsdoiorg101257app51208
Celebioglu F amp Dallrsquoerba S (2010) Spatial disparities across the regions of Turkey An exploratory spatial data analysis Annals of Regional Science 45(2) 379ndash400 httpsdoiorg101007s00168-009-0313-8
Chan J To H-P amp Chan E (2006) Reconsidering social cohesion Developing a definition and analytical framework for empirical research Social Indicators Research 75(2) 273ndash302
Charnes A Cooper W W amp Rhodes E (1978) Measuring the efficiency of decision making units European Journal of Operational Research 2(6) 429ndash444
130
Chi G amp Zhu J (2019) Spatial Regression Models for the Social Sciences SAGE Publications Retrieved from httpsbooksgooglecomaubooksid=zHGkvwEACAAJ
Christiansen K O (1960) Industrialization and urbanization in relation to crime and juvenile delinquency International Review of Criminal Policy 16 3ndash8
Cingano F (2014) Trends in income inequality and its impact on economic growth (Vol 163) Paris OECD Publishing httpsdoiorg1017875jxrjncwxv6j-en
Coelli T J Prasada Rao D S OrsquoDonnell C J amp Battese G E (2005) An introduction to efficiency and productivity analysis An Introduction to Efficiency and Productivity Analysis Springer Science amp Business Media httpsdoiorg101007b136381
Coffeacute H amp Geys B (2005) Institutional Performance and Social Capital An Application to the Local Government Level Journal of Urban Affairs 27(5) 485ndash501 httpsdoiorg101111j0735-2166200500249x
Coffeacute H amp Geys B (2006) Community Heterogeneity A Burden for the Creation of Social Capital Social Science Quarterly 87(5) 1053ndash1072 httpsdoiorg101111j1540-6237200600415x
Cohen D Spear S Scribner R Kissinger P Mason K amp Wildgen J (2000) ldquo Broken windowsrdquo and the risk of gonorrhea American Journal of Public Health 90(2) 230
Corden W M amp Neary J P (1982) Booming sector and de-industrialisation in a small open economy The Economic Journal 92(368) 825ndash848
Cordero J M Pedraja-Chaparro F Pisaflores E C amp Polo C (2017) Efficiency assessment of Portuguese municipalities using a conditional nonparametric approach Journal of Productivity Analysis 48(1) 1ndash24 httpsdoiorg101007s11123-017-0500-z
Croissant Y amp Millo G (2018) Panel Data Econometrics with R John Wiley amp Sons
Cust J amp Poelhekke S (2015) The Local Economic Impacts of Natural Resource Extraction 7 251ndash268 httpsdoiorg101146annurev-resource-100814-125106
da Cruz N F amp Marques R C (2014) Revisiting the determinants of local government performance Omega 44 91ndash103 httpsdoiorg101016JOMEGA201309002
Dauvin M amp Guerreiro D (2017) The Paradox of Plenty A Meta-Analysis World Development 94 httpsdoiorg101016jworlddev201701009
de Sousa M da C S Cribari-Neto F amp Stosic B D (2005) Explaining DEA technical efficiency scores in an outlier corrected environment the case of public services in Brazilian municipalities Brazilian Review of Econometrics 25(2) 287ndash313
Deacon R T (2011) The Political Economy of the Natural Resource Curse A Survey of Theory and Evidence Foundations and Trends in Microeconomics 111-208
Delhey J amp Newton K (2005) Predicting cross-national levels of social trust global pattern or
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Nordic exceptionalism European Sociological Review 21(4) 311ndash327
Dlouhyacute M (2018) Measuring Geographic Inequalities Dealing with Multiple Health Resources by Data Envelopment Analysis Frontiers in Public Health 6 53 httpsdoiorg103389fpubh201800053
Dollery B Wallis J amp Akimov A (2010) One Size Does Not Fit All The Special Case of Remote Small Local Councils in Outback Queensland Local Government Studies 36(1) 21ndash42 httpsdoiorg10108003003930903435716
Domenech J (2008) Mineral resource abundance and regional growth in Spain 1860ndash2000 Journal of International Development The Journal of the Development Studies Association 20(8) 1122ndash1135
Doran J amp Jordan D (2016) Decomposing US regional income inequality from 1969 to 2009 Applied Economics Letters 23(11) 781ndash784 httpsdoiorg1010801350485120151109030
Drew J amp Dollery B (2015) The State of Things The Dynamic Efficiency of Australian State and Territories Economic Papers A Journal of Applied Economics and Policy 34(3) 165ndash176
Drew J Kortt M amp Dollery B (2015) What Determines Efficiency in Local Government A DEA Analysis of NSW Local Government Economic Papers A Journal of Applied Economics and Policy 34(4) 243ndash256 httpsdoiorg1011111759-344112118
Easterly W (2007) Inequality does cause underdevelopment Insights from a new instrument Journal of Development Economics 84(2) 755ndash776 httpsdoiorghttpdxdoiorg101016jjdeveco200611002
Ebert L amp La Menza T (2015) Chile copper and resource revenue A holistic approach to assessing commodity dependence Resources Policy 43(Supplement C) 101ndash111 httpsdoiorghttpsdoiorg101016jresourpol201410007
ElGindi T (2017) Natural resource dependency neoliberal globalization and income inequality Are they related A longitudinal study of developing countries (1980ndash2010) Current Sociology 65(1) 21ndash53 httpsdoiorg1011770011392116632031
Engerman S L amp Sokoloff K L (1994) Factor Endowments Institutions and Differential Paths of Growth Among New World Economies National Bureau of Economic Research
Engerman S L amp Sokoloff K L (1997) Factor endowments institutions and differential paths of growth among new world economies How Latin America Fell Behind 260ndash304
Engerman S L Sokoloff K L Urquiola M amp Acemoglu D (2002) Factor Endowments Inequality and Paths of Development among New World Economies [with Comments] EconomampxedA 3(1) 41ndash109 Retrieved from httpwwwjstororgezp01libraryquteduaustable20065432
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Faumlre R Grosskopf S amp Lovell C A K (1985) The measurement of efficiency of production (Vol 6) Springer Science amp Business Media
Fleming D A amp Measham T G (2015a) Income Inequality across Australian Regions during the Mining Boom 2001-11 Australian Geographer 46(2) 203ndash216 httpsdoiorg1010800004918220151020596
Fleming D A amp Measham T G (2015b) Local economic impacts of an unconventional energy boom the coal seam gas industry in Australia Australian Journal of Agricultural and Resource Economics 59(1) 78ndash94 httpsdoiorg1011111467-848912043
Fum R M amp Hodler R (2010) Natural resources and income inequality The role of ethnic divisions Economics Letters 107(3) 360ndash363 httpsdoiorg101016jeconlet201003008
Garofalo J (1978) The fear of crime Broadening our perspective
Geys B amp Moesen W (2009) Exploring Sources of Local Government Technical Inefficiency Evidence from Flemish Municipalities Public Finance and Management 9(1) 1ndash29
Goderis B amp Malone S W (2011) Natural Resource Booms and Inequality Theory and Evidence The Scandinavian Journal of Economics 113(2) 388ndash417 httpsdoiorg101111j1467-9442201101659x
Greene W H (2016) Productivity and Efficiency Analysis (W H Greene L Khalaf R Sickles M Veall amp M-C Voia Eds) (1st ed 20) Cham Springer International Publishing httpsdoiorg101007978-3-319-23228-7
Gustafsson B amp Shi L (2002) Income inequality within and across counties in rural China 1988 and 1995 Journal of Development Economics 69(1) 179ndash204 httpsdoiorg101016S0304-3878(02)00058-5
Gylfason T amp Zoega G (2003) Inequality and Economic Growth Do Natural Resources Matter In T T Eicher S (Ed) Inequality and Growth Theory and Policy Implications (pp 255ndash292) The MIT Press
Henningsen A (2010) Estimating Censored Regression Models in R using the censReg Package R Package Vignettes Collection 5(2) 12
Henningsen A (2019) censReg Censored Regression (Tobit) Models R package version 05-30 httpscranr-projectorgpackage=censReg Retrieved from httpscranr-projectorgpackage=censReg
Herrera S amp Pang G (2005) Efficiency of Public Spending in Developing Countriesthinsp An Efficiency Frontier Approach World Bank Washington DC
Hill T D amp Angel R J (2005) Neighborhood disorder psychological distress and heavy drinking Social Science amp Medicine 61(5) 965ndash975
133
httpsdoiorghttpsdoiorg101016jsocscimed200412027
Hipp J R (2010) RESIDENT PERCEPTIONS OF CRIME AND DISORDER HOW MUCH IS ldquoBIASrdquo AND HOW MUCH IS SOCIAL ENVIRONMENT DIFFERENCES Criminology 48(2) 475ndash508 httpsdoiorg101111j1745-9125201000193x
Holtug N amp Mason A (2010) Introduction Immigration diversity and social cohesion SAGE Publications Sage UK London England
Hooghe M Vanhoutte B Hardyns W amp Bircan T (2010) Unemployment Inequality Poverty and Crime Spatial Distribution Patterns of Criminal Acts in Belgium 2001ndash06 The British Journal of Criminology 51(1) 1ndash20 httpsdoiorg101093bjcazq067
Howie P amp Atakhanova Z (2014) Resource boom and inequality Kazakhstan as a case study Resources Policy 39(1) 71ndash79 httpsdoiorg101016jresourpol201311004
Isham J Woolcock M Pritchett L amp Busby G (2005) The Varieties of Resource Experience Natural Resource Export Structures and the Political Economy of Economic Growth The World Bank Economic Review 19(2) 141ndash174 httpsdoiorg101093wberlhi010
Jottier D Ashworth J amp Heyndels B (2012) Understanding Votersrsquo Preferences How the Electoratersquos Complexity Affects Prediction Accuracy and Wishful Thinking among Politicians with Respect to Election Outcomes Kyklos 65(3) 340ndash370 httpsdoiorg101111j1467-6435201200542x
Kanbur S M R amp Venables A (2005) Spatial inequality and development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Kesler C amp Bloemraad I (2010) Does immigration erode social capital The conditional effects of immigration-generated diversity on trust membership and participation across 19 countries 1981ndash2000 Canadian Journal of Political ScienceRevue Canadienne de Science Politique 43(2) 319ndash347
Kuznets S (1955) Economic Growth and Income Inequality The American Economic Review 45(1) 1ndash28 Retrieved from httpwwwjstororgstable1811581
Lagos G amp Blanco E (2010) Mining and development in the region of Antofagasta Resources Policy 35(4) 265ndash275 httpsdoiorghttpsdoiorg101016jresourpol201007006
Leamer E E Maul H Rodriguez S amp Schott P K (1999) Does natural resource abundance increase Latin American income inequality Journal of Development Economics 59(1) 3ndash42 httpsdoiorg101016s0304-3878(99)00004-8
Leibbrandt M Finn A amp Woolard I (2012) Describing and decomposing post-apartheid income inequality in South Africa Development Southern Africa 29(1) 19ndash34 httpsdoiorg1010800376835X2012645639
134
Letki N (2008) Does Diversity Erode Social Cohesion Social Capital and Race in British Neighbourhoods Political Studies 56(1) 99ndash126 httpsdoiorg101111j1467-9248200700692x
Lewis D A (2017) Fear of crime Incivility and the production of a social problem Routledge
lo Storto C (2013) Evaluating Technical Efficiency of Italian Major Municipalities A Data Envelopment Analysis model Procedia - Social and Behavioral Sciences 81 346ndash350 httpsdoiorg101016JSBSPRO201306440
Loayza N amp Rigolini J (2016) The Local Impact of Mining on Poverty and Inequality Evidence from the Commodity Boom in Peru World Development 84 219ndash234 httpsdoiorg101016jworlddev201603005
Loayza N Teran A M y amp Rigolini J (2013) Poverty Inequality and the Local Natural Resource Curse World Bank Policy Research Working Paper (6366) httpsdoiorg1015961813-9450-6366
Loacutepez R amp Miller S J (2008) Chile The Unbearable Burden of Inequality World Development 36(12) 2679ndash2695 httpsdoiorg101016jworlddev200801012
Manzano O amp Rigobon R (2001) Resource Curse or Debt Overhang National Bureau of Economic Research Working Paper Series No 8390 1 httpsdoiorghttpwwwnberorgpapersw9424bib
McPherson M Smith-Lovin L amp Cook J M (2001) Birds of a Feather Homophily in Social Networks Annual Review of Sociology 27(1) 415ndash444 httpsdoiorg101146annurevsoc271415
McQuestin D Drew J amp Dollery B (2018) Do Municipal Mergers Improve Technical Efficiency An Empirical Analysis of the 2008 Queensland Municipal Merger Program Australian Journal of Public Administration 77(3) 442ndash455 httpsdoiorg1011111467-850012286
Messner S F Rosenfeld R amp Baumer E P (2004) Dimensions of Social Capital and Rates of Criminal Homicide American Sociological Review 69(6) 882ndash903 httpsdoiorg101177000312240406900607
Michaels G (2011) THE LONG TERM CONSEQUENCES OF RESOURCE-BASED SPECIALISATION The Economic Journal 121(551) 31ndash57 httpsdoiorg101111j1468-0297201002402x
Mijanovich T amp Weitzman B C (2003) Which ldquobroken windowsrdquo matter School neighborhood and family characteristics associated with youthsrsquo feelings of unsafety Journal of Urban Health 80(3) 400ndash415
Mikušovaacute P (2015) An Application of DEA Methodology in Efficiency Measurement of the Czech Public Universities Procedia Economics and Finance 25 569ndash578 httpsdoiorg101016S2212-5671(15)00771-6
135
Milanovic B (2016) Global inequality Harvard University Press
Millo G amp Piras G (2012) splm Spatial panel data models in R Journal of Statistical Software 47(1) 1ndash38
Murphy K M amp Topel R H (2016) Human Capital Investment Inequality and Economic Growth JOURNAL OF LABOR ECONOMICS 34(2) S99ndashS127 httpsdoiorg101086683779
Narboacuten-Perpintildeaacute I amp De Witte K (2018a) Local governmentsrsquo efficiency a systematic literature reviewmdashpart I International Transactions in Operational Research 25(2) 431ndash468 httpsdoiorg101111itor12364
Narboacuten-Perpintildeaacute I amp De Witte K (2018b) Local governmentsrsquo efficiency a systematic literature reviewmdashpart II International Transactions in Operational Research 25(4) 1107ndash1136 httpsdoiorg101111itor12389
Nuntildeez J Rivera J Villavicencio X amp Molina O (2003) Determinantes socioeconoacutemicos y demograacuteficos del crimen en Chile Estudios de Economiacutea 30(1) 55ndash85
OrsquoDonnell C J Rao D S P amp Battese G E (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios Empirical Economics 34(2) 231ndash255 httpsdoiorg101007s00181-007-0119-4
Ocampo J A (2004) Latin Americarsquos Growth and Equity Frustrations During Structural Reforms The Journal of Economic Perspectives 18(2) 67ndash88 httpsdoiorg1012570895330041371349
OECD (2014) Focus on inequality and growth OECD
OECD (2017) Howrsquos Life 2017 Life Satisfaction Oecd httpsdoiorg101787how_life-2017-en
Ohtake F (2008) Inequality in Japan Asian Economic Policy Review 3(1) 87ndash109 httpsdoiorg101111j1748-3131200800093x
Okun A M (2015) Equality and efficiency the big tradeoff Washington DC Brookings Institution Press
Ortega B Sanjuaacuten J amp Casquero A (2017) Determinants of efficiency in reducing child mortality in developing countries The role of inequality and government effectiveness Health Care Management Science 20(4) 500ndash516 httpsdoiorg101007s10729-016-9367-1
Ostry J Berg A amp Tsangarides C (2014) Redistribution inequality and growth International Monetary Fund
Pacheco F Saacutenchez R amp Villena M (2013) Eficiencia de los Gobiernos Locales y sus Determinantes Un anaacutelisis de Fronteras Estocaacutesticas en Datos de Panel para
136
Municipalidades Chilenas Santiago de Chile Chile
Papyrakis E amp Raveh O (2014) An Empirical Analysis of a Regional Dutch Disease The Case of Canada Environmental and Resource Economics 58(2) 179ndash198 httpsdoiorg101007s10640-013-9698-z
Paredes D (2013) The Role of Human Capital Market Potential and Natural Amenities in Understanding Spatial Wage Disparities in Chile Spatial Economic Analysis 8(2) 154ndash175 httpsdoiorg101080174217722013774094
Paredes D Iturra V amp Lufin M (2016) A spatial decomposition of income inequality in Chile Regional Studies 50(5) 771ndash789
Phan P Van Orsquobrien M Mendolia S amp Paloyo A (2017) National pro-poor spending programmes and their effect on income inequality and poverty Evidence from Vietnam Applied Economics 49(55) 5579ndash5590 httpsdoiorg1010800003684620171313957
Podinovski V V (2004) Bridging the Gap between the Constant and Variable Returns-to-Scale Models Selective Proportionality in Data Envelopment Analysis The Journal of the Operational Research Society 55(3) 265ndash276 Retrieved from httpwwwjstororgstable4102006
Ravallion M (2005) On Measuring Aggregate Social Efficiency Economic Development and Cultural Change 53(2) 273ndash292 httpsdoiorg101086425380
Rehner J Baeza S A amp Barton J R (2014) Chilersquos resource-based export boom and its outcomes Regional specialization export stability and economic growth Geoforum 56(Supplement C) 35ndash45 httpsdoiorghttpsdoiorg101016jgeoforum201406007
Rivera J Gutieacuterrez M amp Nuacutentildeez J (2009) Caracterizacioacuten socioeconoacutemica y espacial de la criminalidad en Chile Revista CEPAL
Ross C E (2011) Collective threat trust and the sense of personal control Journal of Health and Social Behavior 52(3) 287ndash296
Ross C E amp Mirowsky J (2001) Neighborhood disadvantage disorder and health Journal of Health and Social Behavior 258ndash276
Rothstein B amp Uslaner E M (2005) All for all Equality corruption and social trust World Politics 58(1) 41ndash72
Sachs J D amp Warner A M (2001) The curse of natural resources European Economic Review 45(4ndash6) 827ndash838 httpsdoiorg101016S0014-2921(01)00125-8
Salas R (2019) sinimr Chilean Municipalities Information System Wrapper Retrieved from httpsgithubcomrobsalascosinimr
Sampson R J (1986) Crime in Cities The Effects of Formal and Informal Social Control Crime and Justice 8 271ndash311
137
Sampson R J (2008) Rethinking crime and immigration Contexts 7(1) 28ndash33
Santos Silva J M C amp Tenreyro S (2010) On the existence of the maximum likelihood estimates in Poisson regression Economics Letters 107(2) 310ndash312 httpsdoiorghttpsdoiorg101016jeconlet201002020
Santos Silva J M C amp Tenreyro S (2011) poisson Some convergence issues Stata Journal 11(2) 207ndash212 Retrieved from httpwwwstata-journalcomarticlehtmlarticle=st0225
Scott Z (2009) Decentralisation local development and social cohesion an analytical review GSDRC Research Paper 5
Sinha R P (2017) Fiscal Performance Benchmarking of Indian States-A Robust Frontier Approach The Central European Review of Economics and Management 1(4) 225ndash249
Skogan W (1986) Fear of crime and neighborhood change Crime and Justice 8 203ndash229
Skogan W (1999) Measuring what matters Crime disorder and fear In Measuring what matters Proceedings from the Policing Research Institute meetings (pp 37ndash53) National Institute of Justice Washington DC
Skogan W (2015) Disorder and Decline The State of Research Journal of Research in Crime and Delinquency 52(4) 464ndash485 httpsdoiorg1011770022427815577836
Šťastnaacute L amp Gregor M (2014) Public sector efficiency in transition and beyond evidence from Czech local governments Applied Economics 47(7) 1ndash20 httpsdoiorg101080000368462014978077
Tandon A (2005) Measuring Efficiency of Macro Systems An Application to Millennium Development Goal Attainment Asian Development Review 22(2) 108ndash125
Taylor R B (1999) The incivilities thesis Theory measurement and policy Measuring What Matters 65 88
Tigga N S amp Mishra U S (2015) On Measuring Technical Efficiency of the Health System in India An Application of Data Envelopment Analysis Journal of Health Management 17(3) 285ndash298 httpsdoiorg1011770972063415589229
Tinbergen J (1975) Demographic Development and the Exhaustion of Natural Resources Population and Development Review 1(1) 23ndash32 httpsdoiorg1023071972269
Tiruneh G (2014) Social Revolutions Their Causes Patterns and Phases SAGE Open 4(3) 2158244014548845 httpsdoiorg1011772158244014548845
Tolsma J Van der Meer T amp Gesthuizen M (2009) The impact of neighbourhood and municipality characteristics on social cohesion in the Netherlands Acta Politica 44(3) 286ndash313
Tsekeris Sotiris T Tsekeris T amp Papaioannou S (2018) Regional determinants of technical efficiency evidence from the Greek economy Regional Studies [London]thinsp Carfax
138
httpsdoiorg1010800034340420171390312
Uslaner E (2002) The moral foundations of trust Cambridge University Press
Uslaner E (2011) CORRUPTION AND INEQUALITY DICE Report 9(2) 20ndash24
Uslaner E (2013) Trust and corruption revisited how and why trust and corruption shape each other Quality amp Quantity 47(6) 3603ndash3608 httpsdoiorg101007s11135-012-9742-z
Uslaner E amp Brown M (2005) Inequality trust and civic engagement American Politics Research 33(6) 868ndash894
Van der Ploeg F (2011) Natural Resources Curse or Blessing Journal of Economic Literature 49(2) 366ndash420 httpsdoiorg101257jel492366
Vergara R (2012) Crime Prevention Programs Evidence From CHILE The Developing Economies 50(1) 1ndash24
Watkins M H (1963) A staple theory of economic growth Canadian Journal of Economics and Political ScienceRevue Canadienne de Economiques et Science Politique 29(2) 141ndash158
Wilkinson R G (1999) Income inequality social cohesion and health clarifying the theorymdasha reply to Muntaner and Lynch International Journal of Health Services 29(3) 525ndash543
Wilson J Q amp Kelling G L (1982) Broken windows Atlantic Monthly 249(3) 29ndash38
Wilson W J (1996) When Work Disappears Political Science Quarterly 111(4) 567ndash595 httpsdoiorg1023072152085
Wirth L (1938) Urbanism as a Way of Life American Journal of Sociology 44(1) 1ndash24
Worthington A amp Dollery B (2000) An empirical survey of frontier efficiency measurement techniques in local government Local Government Studies 26(2) 23ndash52 httpsdoiorg10108003003930008433988
Wu P-C Huang T-H amp Pan S-C (2014) Country Performance Evaluation The DEA Model Approach Social Indicators Research 118(2) 835ndash849 httpsdoiorg101007s11205-013-0443-3
Ye X Ma L Ye K Chen J amp Xie Q (2017) Analysis of Regional Inequality from Sectoral Structure Spatial Policy and Economic Development A Case Study of Chongqing China Sustainability 9(4) 633 Retrieved from httpwwwmdpicom2071-105094633
Yue W Z Zhang Y T Ye X Y Cheng Y Q amp Leipnik M R (2014) Dynamics of Multi-Scale Intra-Provincial Regional Inequality in Zhejiang China Sustainability 6(9) 5763ndash5784 httpsdoiorg103390su6095763
139
Appendices
Appendix A Summary statistics income inequality
Table A1
Summary statistics Gini coefficients by year and zone
140
Appendix B Summary statistics for NRD measures by region
Table B1
Summary statistics NRD measures by region
141
Appendix C Regional administrative division and defined zones
Figure C1 Geographical distribution of Chilean regions and 3 zones
142
Appendix D Summary statistics numeric controls and correlation matrix
Table D1
Summary Statistics Numeric Explanatory Variables
Figure D1 Correlation matrix numeric explanatory variables
143
Appendix E Static spatial panel models
Following Millo amp Piras (2012) a model including a spatial lag of the dependent variable and
spatial autoregressive disturbances but not spatial lags for the explanatory variable(s) is called
SARAR model A static spatial SARAR panel could be expressed as
119910 120582 119868 otimes119882 119910 119883120573 119906 (E1)
where y is an 119873 1 vector of observations on the dependent variable X is a 119873 119896 matrix of
observations on the non-stochastic exogenous regressors 119868 an identity matrix of dimension 119879 otimes
is the kronecker operator 119882 is the 119873 119873 spatial weights matrix of known constants whose
diagonal elements are set to zero and 120582 the corresponding spatial parameter44
The disturbance vector is the sum of two terms
119906 120580 otimes 119868 120583 120576 (E2)
where 120580 is a 119879 1 vector of ones 119868 an 119873 119873 identity matrix 120583 is a vector of time-invariant
individual specific effects (not spatially autocorrelated) and 120576 a vector of spatially autocorrelated
innovations that follow a spatial autoregressive process of the form
120576 120588 119868 otimes119882 120576 120584 (E3)
If we assume that spatial correlation applies to both the individual effects 120583 and the remainder
error components 120576 Kapoor et al (2007) propose that the disturbance term 119906 follows a first order
spatial autoregressive process of the form
119906 120588 119868 otimes119882 119906 120576 (E4)
44 Unlike ldquotraditional panelsrdquo which are organized as different time series for each cross-sectional unit ldquospatial panelsrdquo are organized as a series of cross-sections for each year
144
where 119882 is the spatial weights matrix and 119903ℎ119900 the corresponding spatial autoregressive
parameter To further allow for the innovations to be correlated over time the innovations vector
in Equation 7 follows an error component structure
120576 120580 otimes 119868 120583 120584 (E5)
where 120583 is the vector of cross-sectional specific effects 120584 a vector of innovations that vary
both over cross-sectional units and time periods 120580 is a vector of ones and 119868 an 119873 119873 identity
matrix45
Spatial panel models are usually estimated by Maximum Likelihood or GMM46 The SAR
SEM or SARAR models could be estimated with Random or Fixed effects For instance A fixed
effect spatial lag model can be written in stacked form as
119910 120582 119868 otimes119882 119910 120580 otimes 119868 120583 119883120573 120576 (E6)
where 120582 is the spatial autoregressive coefficient 119882 a non-stochastic spatial weights matrix
120580 a column vector of ones of dimension 119879 119868 an 119873 119873 identity matrix and 120576 sim 119873 0120590 On
the other hand a fixed effects spatial error model assuming the disturbance specification by
Kapoor et al (2007) can be written as
119910 120580119879 otimes 119868119873 120583 119883120573 119906119906 120588 119868119879 otimes119882119873 119906 120576
(E7)
where 120588 is the spatial autocorrelation coefficient and 120576 is a well-behaved error term
45 In the regression implementation the specification given by equations (22) and (3) is denoted by ldquobrdquo for ldquoBaltagirdquo On the other hand the specification given by equations (23) and (31) is denoted by ldquokkprdquo for ldquoKapoor Kelejian and Pruchardquo 46 We use the R package splm which allow both types of regression procedure
145
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis
Table F1
Analysis OLS residuals Anselin Method
Figure F1 Moran scatter plot OLS residuals
146
Appendix G Linear panel data models
Table G1
Panel regressions (non-spatial)
147
Appendix H Spatial panel models (Generalized Moments (GM) estimation)
Table H1
GM Spatial Models
148
Appendix I Inputs and outputs used in DEA analysis
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018)
149
Appendix J Technical and scale efficiency
Following lo Storto (2013) under an input-oriented specification assuming VRS with n
municipalities using k inputs to produce m outputs the DEA model for a given i-th municipality
is specified with the following mathematical programming problem
119898119894119899 120579119904119906119887119895119890119888119905 119905119900 119910 119884120582 0120579119909 119883120582 01198991prime120582 1 120582 0prime
Where 119910 is the column vector of the outputs and 119909 is the column vector of the inputs
Moreover we can define X as the (k times n) input matrix and Y as the (m times n) output matrix 120579 is a
scalar (that satisfies 120579 1) more specifically it is the efficiency score that measures technical
efficiency It measures the distance between a municipality and the efficiency frontier defined as
a linear combination of the best practice observations With 120579 1 the municipality is inside the
frontier (ie it is inefficient) while 120579 1 implies that the municipality is on the frontier (ie it is
efficient) The vector 120582 is an (n times 1) vector of constants that measures the weights used to compute
the location of an inefficient municipality if it were to become efficient
The total technical efficiency 119879119864 can be decomposed into pure technical efficiency
119879119864 and scale efficiency 119878119864 where 119878119864 119879119864 119879119864 (Coelli et al 2005) To find out
whether a municipality is scale efficient and qualify the type of returns of scale a DEA model
under non-increasing returns to scale 119879119864 is implemented where 119878119864 119879119864 119879119864 Hence
the following rule can be applied (Faumlre Grosskopf amp Lovell 1985)
bull If 119878119864 1 then a municipality is scale efficient both under CRS and VRS
bull If 119878119864 1 it operates under increasing returns to scale
bull If 119878119864 1 it operates under decreasing returns to scale
150
Appendix K Correlation matrix
Figure K1 Correlation matrix contextual factors
151
Appendix L Returns to scale by year and zone
Table L1
Returns to scale (percentage of municipalities)
152
Appendix M Returns to scale by year (maps)
Figure M1 Spatial distribution of returns to scale by county per year
153
Appendix N Efficiency status by year (maps)
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year
154
Appendix O Spatial distribution efficiency scores by year (maps)
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year
155
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis
Table P1
Analysis OLS residuals Anselin Method
Figure P1 Moran scatter plot efficiency scores and OLS residuals
156
Table P2
OLS and spatial regression models for the six-year averaged data
157
Appendix Q OLS regressions for cross-sectional and panel data
Table Q1
OLS cross-sectional regression per year
158
Table Q2
OLS panel regressions Pooled random effects and instrumental variable
159
Appendix R Quantile maps incivilities rate by group (average total period)
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17)
160
Appendix S Correlation matrix numeric covariates
Figure S1 Correlation matrix numeric covariates
161
Appendix T Negative Binomial regressions
Table T1
Negative Binomial regressions
162
Appendix U Coefficients economic and racial diversity by geographical zone
Table U1
Coefficients economic and racial diversity in pooled Poisson models by geographic zone
iv
the growing feeling of social unrest in contemporary societies Economic diversity is proxied using
the Gini coefficient in each county and racial diversity through the number of new visas granted
as proportion of the county population Our findings show that incivilities are strongly associated
with racial diversity and to a lesser extent with economic diversity The rate of incivilities also
shows a negative association with the level of income and a positive relationship with poverty and
unemployment rates These results give empirical support to the idea that both relative and
absolute indicators of economic deprivation play an important role in understanding the growing
problem of incivilities in highly unequal economies like Chile Results also show that the rate of
incivilities is negatively related to the degree of financial autonomy of municipalities These
findings represent promising areas for central and local governments in the implementation of
policies aimed at increasing social cohesion through the reduction of incivilities and other types of
antisocial behaviours
v
Table of Contents
Keywords i
Abstract ii
Table of Contents v
List of Figures viii
List of Tables ix
List of Abbreviations x
Statement of Original Authorship xi
Acknowledgements xii
Chapter 1 Introduction 13
Income inequality and dependence on natural resources 14
Local government efficiency and income inequality 16
Social cohesion and economic diversity 19
Contributions 21
Thesis outline 23
Chapter 2 Natural Resources Curse or Blessing Evidence on Income Inequality at the County Level in Chile 24
21 Introduction 24
22 Inequality and Natural Resources 28 221 Theoretical Framework 28
Cross-country literature 29 Single country evidence 32
222 The relevance of the spatial approach 33
23 Research problem and hypotheses 35
24 Data and Methods 36 241 Operationalization of key variables 36 242 Control variables 40 243 Methods 41 244 Spatial Model Specification 43
25 Results 44 251 Exploratory Spatial Data Analysis (ESDA) 44 252 Cross-sectional analysis 45 253 Panel Data analysis 48
26 Discussion and conclusions 51
Chapter 3 The Impact of Income Inequality on the Efficiency of Municipalities in Chile 55
vi
31 Introduction 55
32 Related Literature 61 321 Measuring efficiency of local governments 61 322 Explaining differences in LGE 63 323 The trade-off between efficiency and equity 64
33 Methodology 66 331 Chilean Municipalities and period of analysis 66 332 Measuring municipal efficiency 68 333 Inputs and outputs used in DEA 70 334 Regression model 71 335 The instrument 75
34 Results and discussion 77 341 DEA results 77
Returns to scale 78 Efficiency measure 80
342 Regression results 82 Exploratory spatial analysis 82 Cross-sectional analysis 83 Panel data analysis 84
35 Conclusions 88
Chapter 4 Social Cohesion Incivilities and Diversity Evidence at the municipal level in Chile 91
41 Introduction 91
42 Related Literature 95 421 The Community Heterogeneity Thesis 95 422 The literature on incivilities 97 423 The ldquoIncivilities Thesisrdquo 99
4 3 Methodology 100 431 Period of analysis and data sample 100 432 Operationalisation of the response variable and exploratory analysis 101 433 Measures of community heterogeneity and control variables 105 434 Methods 108 435 Hypotheses 111
44 Results and Discussion 112
4 5 Conclusions 118
Chapter 5 Conclusions 120
Bibliography 126
Appendices 139
Appendix A Summary statistics income inequality 139
Appendix B Summary statistics for NRD measures by region 140
Appendix C Regional administrative division and defined zones 141
Appendix D Summary statistics numeric controls and correlation matrix 142
vii
Appendix E Static spatial panel models 143
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis 145
Appendix G Linear panel data models 146
Appendix H Spatial panel models (Generalized Moments (GM) estimation) 147
Appendix I Inputs and outputs used in DEA analysis 148
Appendix J Technical and scale efficiency 149
Appendix K Correlation matrix 150
Appendix L Returns to scale by year and zone 151
Appendix M Returns to scale by year (maps) 152
Appendix N Efficiency status by year (maps) 153
Appendix O Spatial distribution efficiency scores by year (maps) 154
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis 155
Appendix Q OLS regressions for cross-sectional and panel data 157
Appendix R Quantile maps incivilities rate by group (average total period) 159
Appendix S Correlation matrix numeric covariates 160
Appendix T Negative Binomial regressions 161
Appendix U Coefficients economic and racial diversity by geographical zone 162
viii
List of Figures
Figure 21 Average share in GDP of economic activities (2006ndash17) 37
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17) 38
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17) 39
Figure 23 Moran scatter plots for variables gini and pss_casen 45
Figure 31 Geographical distribution of Chilean regions and macrozones 74
Figure 32 Evolution of efficiency scores and the proportion of firms by sector 77
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE) 78
Figure 34 Returns to scale by zone 79
Figure 35 Evolution mean efficiency scores (VRS) by zone 81
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017 102
Figure 42 Evolution total number of incivilities by category 104
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017) 104
Figure 44 Annual average number of incivilities per county 109
Figure C1 Geographical distribution of Chilean regions and 3 zones 141
Figure D1 Correlation matrix numeric explanatory variables 142
Figure F1 Moran scatter plot OLS residuals 145
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018) 148
Figure K1 Correlation matrix contextual factors 150
Figure M1 Spatial distribution of returns to scale by county per year 152
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year 153
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year 154
Figure P1 Moran scatter plot efficiency scores and OLS residuals 155
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17) 159
Figure S1 Correlation matrix numeric covariates 160
ix
List of Tables
Table 21 Cross-sectional Model Comparison (six-year average data) 47
Table 22 ML Spatial SAR Models 50
Table 23 ML Spatial SEM Models 50
Table 24 ML Spatial SARAR Models 51
Table 31 Descriptive statistics Inputs and Output variables used in DEA analysis 71
Table 32 Summary Statistics Numeric Contextual Factors 74
Table 33 Summary efficiency scores (VRS) by zone and region 80
Table 34 Cross-sectional (censored) regressions 84
Table 35 Panel data regressions 87
Table 41 Summary statistics total count of incivilities and by category (full sample and period) 103
Table 42 Summary statistics numeric explanatory variables 108
Table 43 Poisson regressions 113
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region 115
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group 116
Table A1 Summary statistics Gini coefficients by year and zone 139
Table B1 Summary statistics NRD measures by region 140
Table D1 Summary Statistics Numeric Explanatory Variables 142
Table F1 Analysis OLS residuals Anselin Method 145
Table G1 Panel regressions (non-spatial) 146
Table H1 GM Spatial Models 147
Table L1 Returns to scale (percentage of municipalities) 151
Table P1 Analysis OLS residuals Anselin Method 155
Table P2 OLS and spatial regression models for the six-year averaged data 156
Table Q1 OLS cross-sectional regression per year 157
Table Q2 OLS panel regressions Pooled random effects and instrumental variable 158
Table T1 Negative Binomial regressions 161
Table U1 Coefficients economic and racial diversity in pooled Poisson models by geographic zone 162
x
List of Abbreviations
Constant returns to scale CRS
Data envelopment analysis DEA
Decreasing returns to scale DRS
Efficiency scores ES
Exploratory spatial data analysis ESDA
Generalized methods of moments GMM
Gross Domestic Product GDP
Increasing returns to scale IRS
Local government efficiency LGE
Maximum likelihood ML
Municipal common fund MCF
Natural resource dependence NRD
Natural resource endowment NRE
Ordinary Least Squares OLS
Organization for Economic Cooperation and Development OECD
Own permanent revenues OPR
Resource curse hypothesis RCH
Spatial autoregressive model SAR
Spatial error model SEM
Variable returns to scale VRS
xi
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet requirements
for an award at this or any other higher education institution To the best of my knowledge and
belief the thesis contains no material previously published or written by another person except
where due reference is made
Signature QUT Verified Signature
Date _________04092020_________
xii
Acknowledgements
First I would like to thank my wife Lilian who joined me in this challenge and patiently
supported me all these years I would also like to thank our family who always supported us from
Chile I especially thank my sister Silvia who took care of our house and dog
I am also grateful to my supervisory team Dr Radhika Lahiri and Dr Vincent Hoang who
supported and guided me in the process of making this thesis a reality
I also thank the Deans of the Faculty of Economics and Business at my beloved University
of Talca Dr Arcadio Cerda and Dr Rodrigo Herrera who trusted me and supported me in this
process In the same way I would like to thank all the support of the director of the Commercial
Engineering career Mr Milton Inostroza
Finally I would like to thank the government of Chile for the financial support that made
my stay and studies possible here at the Queensland University of Technology
13
Chapter 1 Introduction
Efficiency and equity issues are often considered together in the evaluation of economic
performance While higher efficiency usually measured by growth rates of income per capita
correlates with improvements in measures of well-being the link between inequality and well-
being is less clear This is reflected not only in the type and amount of research related to efficiency
and equity but also in the role that both play in the design of the economic policy For instance
several market-oriented countries have focused primarily on economic growth trusting in a trickle-
down process where financial benefits given to the wealthy are expected to ultimately benefit the
poor However despite the growing interest in the issue of inequality there is a considerable lack
of studies about its consequences
Although some level of inequality is inevitable or even necessary for economic activity this
study is motivated by the argument that relatively high levels of inequality can be associated with
many problems such as persistent unemployment increasing fiscal expenses indebtedness and
political instability (Berg amp Ostry 2011) Inequality can also have other severe social
consequences including increased crime rates teenage pregnancy obesity and fewer
opportunities for low-income households to invest in health and education (Atkinson 2015) In
addition when the role of money and concentration of economic power undermine political
outcomes inequality of opportunities hampers social and economic mobility trust and social
cohesion In summary inequality can increase the fragility of the economic and social situation in
a country reducing economic growth and making it less inclusive and sustainable
14
A country well-known for its market-oriented economy and high level of dependence on
natural resources is Chile Chilean success in terms of economic growth contrasts with its inability
to reduce the persistently high levels of social and economic inequality particularly in the last
three decades Using data for the 2006-2017 period and considering 324 out of 345 Chilean
counties this thesis presents three essays with empirical evidence aiming to explain the
phenomenon of persistent income inequality and some of its potential consequences The first
essay aims to analyse how the evolution and variability of income inequality throughout the
country are associated with the degree of natural resource dependence The second essay studies
the relevance of income inequality in explaining cross-county differences in the performance of
local governments (municipalities) Finally the third essay explores the link between social
cohesion and community heterogeneity highlighting the importance of economic and racial
diversity
Income inequality and dependence on natural resources
The first essay explores how cross-county differences in income inequality are associated
with differences in the degree of dependence on natural resources We use the Gini coefficient in
each county as our dependent variable and the proportion of employment in the primary sector as
our measure of natural resource dependence The main hypothesis is that income inequality should
be positively related to the degree of natural resource dependence To test our hypothesis we use
a spatial econometric approach This approach is motivated by the study of Paredes Iturra and
Lufin (2016) who explore the geographic heterogeneity of income inequality in Chile finding
evidence of a significant spatial dimension
15
The theoretical and empirical literature has mostly proposed a positive link between
inequality and natural resources Although most of the evidence corresponds to cross-country
comparisons there is also increasing body of research at the local level A rationale underpinning
the positive link suggested in the literature is that in natural resource-rich countries ownership is
concentrated in small groups and extraction activities require low-skilled workers (Gylfason amp
Zoega 2003 Leamer Maul Rodriguez amp Schott 1999) Another market-based argument often
labelled as the ldquoDutch Diseaserdquo proposes that natural resource windfalls could be associated with
a crowding-out effect on the manufacturing sector (Corden amp Neary 1982 Easterly 2007) This
process encourages rent-seeking behaviours discourages investment in physical and human
capital and delays the process of technology adoption and industrialization (Auty 2001 Bulte
Damania amp Deacon 2005 Gylfason amp Zoega 2003) The result could be a lower economic
growth which is the central idea under the ldquoResource Curse Hypothesisrdquo (Auty 1993 Sachs amp
Warner 2001)
An ldquoinstitutionalrdquo argument for the positive association between inequality and the
endowment of natural resources is based on the so-called ldquoParadox of Plentyrdquo (Borge Parmer amp
Torvik 2015 Dauvin amp Guerreiro 2017) The idea is that both national and local authorities have
less incentive to operate efficiently when they experience windfalls in their revenues for
instance from natural resources This could end with corrupted authorities exerting patronage
clientelism and designing public policies to favour specific groups of the population (Uslaner amp
Brown 2005) Evidence also suggests that the final effect of natural resource booms on income
inequality will depend on authoritiesrsquo capacity to manage these additional resources the extent of
commuting and migration among regions and the potential increase in the demand for non-tradable
16
goods which are intensive in unskilled workers (Aroca amp Atienza 2011 Cust amp Poelhekke 2015
Fleming amp Measham 2015b Howie amp Atakhanova 2014 Michaels 2011)
Contrary to most theoretical and empirical evidence we find that income inequality shows
a robust and significant negative association with our proxy for natural resource dependence This
result suggests that the process of transformation to an economy less dependent on natural
resources could have exacerbated rather than alleviated the persistence of income inequality The
decrease in the participation of the primary sector in employment in favour mainly of the tertiary
sector highlights the importance of the latter to explain the current high levels of inequality and its
future evolution Another important result is that spatial linear models show practically the same
results as traditional linear models This could be interpreted as the spatial dimension previously
found in income inequality is not the result of spatial dependence in the variable itself for instance
due to a process of spillover among counties Hence the usually found positive spatial
autocorrelation of income inequality (similar levels in neighbouring counties) could be explained
by spatial patterns in other variables or to the spatial heterogeneity that characterizes the Chilean
economy
Local government efficiency and income inequality
Essay 2 delves deep into the potential trade-off between efficiency and equity We measure
the efficiency of Chilean municipalities which correspond to the organizations in charge of
managing each county Municipal efficiency is understood as ldquotechnical efficiencyrdquo that is the
possibility that each municipality has reached the same level of outputs with less use of inputs
Then we analyse how income inequality controlling for other contextual factors such as
socioeconomic demographic geographical and political characteristics may help to explain
17
differences in municipal performance Our main hypothesis is that municipal efficiency is
inversely associated with income inequality Moreover we seek a causal interpretation of this
relationship
Municipal performance could be influenced by income inequality in direct and indirect ways
In a direct sense income inequality is used to capture the degree of heterogeneity and complexity
in the demand for public services that citizens exert over local authorities Hence higher levels of
income inequality should be associated with a more complex set of public services and therefore
with lower levels of municipal efficiency (Jottier Ashworth amp Heyndels 2012) Furthermore
when high levels of inequality exist the richest groups can exert a higher influence over local
authorities resulting in low quality and quantity of services for most of the population Among
indirect effects high and persistent inequality could be the source of corrupted institutions and
local authorities favouring themselves or specific groups This undermines citizensrsquo participation
in civic activities and their willingness to monitor municipal performance (Uslaner amp Brown
2005) Additionally the potential benefits of decentralization on the way local governments
deliver public services will be limited when the context is characterized by corrupted politicians
and a limited administrative and financial capacity (Scott 2009)
We measure municipal efficiency using an input-oriented Data Envelopment Analysis
(DEA) to obtain efficiency scores for our sample of 324 municipalities in each year from 2006 to
2017 Then we study the influence on municipal efficiency of income inequality and our set of
contextual factors using a panel of six years corresponding to those years for which household
income information is available 2006 2009 2011 2013 2015 and 2017 Our dependent variable
is the set of efficiency scores which are relative measures of efficiency They are relative to the
18
municipalities included in the sample and they do not imply that higher technical efficiency gains
cannot be achieved Thus we use both cross-sectional and panel censored regression models To
tackle endogeneity issues and suggest a causal interpretation we also propose using the proportion
of firms in the primary sector as an instrument for income inequality
We find an average efficiency score of 83 meaning that Chilean municipalities could
reduce the use of inputs by 17 without reducing their outputs We also measure municipal
efficiency under different assumptions related to returns to scale This allows us to disaggregate
technical efficiency to assess whether inefficiencies are due to management issues (pure technical
efficiency) or scale issues (scale efficiency) Although the results show that most municipalities
operate under increasing or decreasing returns to scale scale inefficiencies only explain a small
proportion of total municipal inefficiencies This highlights the need to look for contextual factors
outside the control of local authorities to explain differences in municipal performance
Geographical representations of our results in terms of returns to scale and efficiency scores
show some spatial clustering process among municipalities Spatial statistics tests confirm that
efficiency scores show a significant positive spatial autocorrelation This means that neighbouring
municipalities tend to show similar levels of efficiency This similar performance could be due to
a process of spatial dependence (eg efficiency spillovers among neighbouring municipalities) or
due to the existence of structural-geographical similarities (spatial heterogeneity) To assess the
spatial dimension in municipal efficiency abstracting from temporal fluctuations we use a cross-
section of data consisting of the six-year average for the variables in our panel After running a
regression of efficiency scores against the set of controls the analysis of OLS residuals shows that
the spatial autocorrelation is almost completely removed This means that the spatial pattern in
19
municipal efficiency can be explained (controlled) by other variables such as regional indicator
variables rather than efficiency itself Given this result we proceed to study the influence of
income inequality on municipal efficiency using traditional (non-spatial) regression analysis
In contrast to literature that emphasizes an equity-efficiency trade-off (Andersen amp Maibom
2020 Berg amp Ostry 2011 Browning amp Johnson 1984 Okun 2015) that is greater equality leads
to lower efficiency we find that municipal efficiency is inversely associated with income
inequality This implies that more equal counties are also those with higher municipal efficiency
Furthermore the coefficient of income inequality is close to one when we use the instrumental
variable approach This means that a reduction in income inequality ceteris paribus should be
associated with an increase in the same magnitude in municipal efficiency This result has strong
policy implications The non-existence of the trade-off suggests that there is more to be gained by
targeting policies towards the reduction of inequality than conventional theories suggest For
instance these policies may help increase the levels of efficiency and well-being at least at the
municipal level
Social cohesion and economic diversity
The third essay studies the relationship between the degree of social cohesion and diversity
in Chile Extant literature has argued that one of the main factors influencing social cohesion is
the degree of economic and ethnic-racial diversity within a society This diversity erodes social
cohesion reducing trust and corrupting institutions (Letki 2008 Rothstein amp Uslaner 2005
Tolsma Van der Meer amp Gesthuizen 2009 Uslaner 2011 2013)
To measure social cohesion scholars have traditionally used measures of social capital trust
or feelings of insecurity (Ariely 2014 Chan To amp Chan 2006 Letki 2008) We suggest the use
20
of the rate of incivilities per 1000 inhabitants as a proxy for social cohesion Incivilities correspond
to those antisocial behaviours (eg groups of rowdy teens and public drunkenness) or visible
neighbourhood conditions (eg graffiti and abandoned buildings) that tend not to be treated as
crime Using the rate of incivilities is arguably a more objective and reliable measure of social
cohesion particularly in countries where institutions of order and security are among the most
trusted An increase in the rate of incivilities rather than changes in crime rates should better
capture the worsening in social cohesion experienced in countries such as Chile where crime rates
are not growing but social conflicts are on the rise Thus the main hypothesis in this essay is that
the rate of incivilities (social cohesion) should be positively (negatively) associated with economic
and racial diversity
Using panel count data models we start analysing how differences in incivilities rates
between and within counties are associated with differences in indicators of relative and absolute
economic disadvantage We use the Gini coefficient of each county as our measure of economic
diversity Although we find a significant and positive association between the rate of incivilities
and the level of income inequality the magnitude of the link seems to be small Among absolute
indicators of economic disadvantage only the level of income shows a strong effect Next we
include our measure of racial diversity We use the number of new visas granted to foreigners as
a proportion of the county population Results show a significant and strong positive association
between the rate of incivilities and racial diversity
To check the robustness of our results we analyse the impact of our measures of economic
and racial diversity running our models separately for each Chilean region and clustering them
geographically We also split the total number of incivilities in four categories to see which type
21
of incivilities show the greatest association with our measures of diversity In general results
support the ldquocommunity heterogeneity hypothesisrdquo that higher community heterogeneity is
associated with higher rates of incivilities (Alesina amp La Ferrara 2002 Letki 2008 Tolsma et al
2009) However results do not support the ldquoincivilities thesisrdquo that the distribution of incivilities
tends to mirror the distribution of income inequality (Skogan 1999 Taylor 1999)
Three results stand out among the set of control variables First the level of education shows
and independent and significant negative association with the rate of incivilities This is in contrast
to previous studies where education acts mainly as a moderator of the effect of economic and racial
diversity on social cohesion (Tolsma et al 2009) The results also show that there is no significant
relationship between the rate of incivilities and the proportion of young population This is relevant
because policies aimed to reduce incivilities usually put the focus on specific groups such as young
people which are linked to physical and social incivilities when social control is weakened
Finally the degree of financial municipal autonomy also shows a significant negative association
with the rate of incivilities This result suggests that municipalities can contribute independently
or together with the central government to reduce incivilities and strengthen social cohesion
Contributions
The three essays in this thesis provide several important insights into the analysis of the
causes and consequences of income inequality particularly in the context of Chile ndash a typical
resource rich economy with persistently high levels of income inequality
Essay 1 advances the understanding of the relationship between income inequality and
natural resources in Chile extending the empirical analysis from the regional level to the county
level In addition the geographic heterogeneity of income inequality is explored with the inclusion
22
of alternative sources of spatial dependence as a potential dimension of the causal relationship
between income inequality and natural resources This essay demonstrates the relevance of natural
resources in explaining the persistence of income inequality even after controlling for other
socioeconomics and institutional factors Findings from this study have potential contribution not
only in the design of policies aimed to reduce income inequality but also in addressing the current
developmental bias between the metropolitan region and the rest of the country
Essay 2 is one of the first studies that undertake a longitudinal analysis of the effects of
income inequality on the efficiency of municipal governments in Chile To capture the role of the
municipal governments in the provision to local people of public services such as education and
health we specify several inputs and outputs in our efficiency model which is different from the
conventional specification in the existing literature For example the number of medical
consultations in public health facilities and the number of enrolled students in public schools are
used as outputs instead of general indicators such as county population Our empirical analysis
also utilises a larger sample of municipalities and covers a much longer period spanning from 2006
to 2017 This essay also investigates the contextual factors beyond the control of local authorities
that can explain variations in the efficiency of municipal governments across the country
Empirical findings from Essay 2 help us increase our understanding of the production
technology of municipalities the sources of inefficiencies and specifically the impact of income
inequality on the performance of local authorities The results deliver two main policy
implications First municipal inefficiencies in the provision of public goods and services differ
across Chilean municipalities In addition efficiency levels show some degree of spatial
autocorrelation This implies that policies such as amalgamation or cooperation among
23
municipalities could have effects beyond the municipalities involved which must be considered
Second the causal effect that income inequality has on municipal efficiency provides another
dimension into the design and implementation of development policies
Essay 3 explores for the first time the effects of economic and racial diversity on social
cohesion in Chile This essay considers incivilities as manifestation of social cohesion and
investigates as extant literature suggests whether indicators of relative economic disadvantage
such as income inequality are among the main factors driving social disorganization and social
unrest Empirical findings suggest that on the one hand economic heterogeneity captured by the
Gini coefficient has a disparate effect both in terms of magnitude and significance on the rate of
incivilities across the country On the other hand the impact of racial heterogeneity appears to be
stronger more significant and of a similar magnitude throughout the country Results also provide
new insights into the design of national policies addressing social disorders particularly those
policies focussed on specific groups of the population and the role of local authorities Overall the
findings provide an opportunity to advance the understanding of the process of weakening in the
social cohesion experienced in Chile and the conflicts that have risen from this process
Thesis outline
The remainder of the thesis is organized as follows Chapter 2 presents essay 1 examining
the association between income inequality and the degree of dependence on natural resources
Chapter 3 presents essay 2 which looks for a causal relationship between municipal efficiency and
income inequality Chapter 4 presents essay 3 analysing the relationship between social cohesion
and economic and racial diversity Finally Chapter 5 presents some concluding remarks
24
Chapter 2 Natural Resources Curse or Blessing Evidence on
Income Inequality at the County Level in Chile
21 Introduction
A phenomenon of increasing inequality of incomes and wealth in recent decades has been
documented by leading scholars and international organizations such as the International Monetary
Fund (Berg amp Ostry 2017 Ostry Berg amp Tsangarides 2014) and the Organization for Economic
Cooperation and Development (Cingano 2014) These efforts have placed the topic of inequality
at the top of the current economic debate recognizing inequality as a determinant not only of
economic growth but also of human development They also have highlighted the necessity for
more research on the drivers of inequality and mechanisms through which it manifests aiming to
design effective policies in reducing economic and social inequalities
Various factors have been analysed as the sources of high and increasing levels of inequality
Among the most significant factors are the levels of income at initial stages of economic
development (Kuznets 1955) Globalization (Milanovic 2016) skill-biased technological change
(Tinbergen 1975) investment in human capital (Murphy amp Topel 2016) institutions
redistributive policy and country-specific characteristics (Acemoglu 1995 2002 Acemoglu
Aghion amp Violante 2001 Acemoglu Johnson amp Robinson 2001) Our focus in this essay is on
the importance that the natural resource endowment (NRE) or lack thereof can play in the
determination of income disparities
25
This essay studies the patterns and evolution of income inequality in the context of a natural
resource-rich country Using the case of the Chilean economy we aim to understand and
disentangle how a phenomenon of high- and persistent-income inequality is related to the
endowment of natural resources that a country owns Chile is an interesting case to study because
despite showing a successful history of economic growth inequality among individuals and among
aggregated spatial units has shown a strong persistence (Paredes et al 2016) Furthermore Chile
has remained among the most unequal countries in the world1
Theory and empirical evidence do not establish a clear link between income inequality and
NRE In addition NRE has received considerably less attention (Auty 2001 ElGindi 2017) and
most of the evidence has been focused on cross-country comparisons For instance NRE can
influence inequality by determining its initial levels (Engerman amp Sokoloff 1994 1997
Engerman Sokoloff Urquiola amp Acemoglu 2002) shaping the evolution of institutions
(Acemoglu 2002) make the educational system less intellectually challenging and moulding the
structure of economic activity (Leamer et al 1999) So studying how cross-county differences in
NRE are associated with the distribution of income within a country has theoretical empirical and
policy implications
In this study we offer empirical evidence on the relationship between income inequality and
the endowment of natural resources using data at the county level in Chile for the period 2006-
2017 Income inequality is measured by the Gini coefficient The importance of NRE is proxied
using a measure of natural resource dependence (NRD) defined as the percentage of the total
1 A 2014 OECD report on income inequality (httpwwwoecdorgsocialincome-distribution-databasehtm) showed Chile as the country with the highest Gini coefficient of disposable income among OECD countries OECD also indicates Chile as the country with the widest gap between the richest 10 percent and the poorest 10 percent of countryrsquos population among its 34 members (OECD 2014)
26
employment in each county corresponding to the primary sector (agriculture forestry fishing and
mining)
The main hypothesis to be tested is whether income inequality is positively associated with
the degree of NRD The transmission mechanisms through which natural resources could influence
socioeconomic outcomes could be based on the market or institutions The market-based approach
argues that natural resource booms could be associated with an appreciation of the real exchange
rate and a crowding out effect over other more productive economic activities such as
manufacturing It could also delay the adoption of new technologies and reduce incentives to invest
in physical and human capital (Gylfason amp Zoega 2003) Based on the ldquoResource Curse
Hypothesisrdquo (RCH) natural resources could be a curse when the political and institutional
framework is weak and natural resources are concentrated in space such as oil and minerals
(Deacon 2011) 2 Among institutional channels a higher NRD or natural resource booms could
be associated with rent seeking misallocation of labour and entrepreneurial talent institutional
and political decline or even violent conflicts For instance the ldquoParadox of Plentyrdquo sustains that
windfalls of revenues as a consequence of resource booms could be related to a lack of incentives
to perform efficiently corruption patronage and local authorities favouring their voters or being
captured by the richest groups (Dauvin amp Guerreiro 2017) Hence a higher NRD or natural
resource booms could be the explanation not only for low levels of growth in regions more
dependent on natural resources but also it could be the root of income disparities
2 There is a wide strand of research on the Resource Curse Hypothesis however the evidence so far is not conclusive Evidence in favour of RCH has been mainly found in developing resource rich countries (Auty 1993 2001 Badeeb Lean amp Clark 2017 Blanco amp Grier 2012 Borge et al 2015 Brunnschweiler amp Bulte 2008 Sachs amp Warner 2001 Van der Ploeg 2011)
27
To test our hypothesis that is whether the levels of income inequality across counties are
positively associated with the degree of NRD we use a spatial econometric approach We use this
approach because attributes such as income inequality in one region may not be independent of
attributes in neighbouring regions (Armstrong amp Taylor 2000) This process of spatial dependence
invalidates the use of traditional (non-spatial) approaches
This study seeks to make two contributions to research First previous empirical evidence
shows a significant spatial dimension of income inequality in Chile (Paredes et al 2016)
However this dimension has been barely explored with most studies limiting the degree of
disaggregation to a regional scale (Aroca amp Bosch 2000) We use a spatial approach which makes
it possible to model and test the significance of the spatial dimension in the analysis of income
inequality and its relationship with other variables Second previous research for the Chilean
economy linking inequality with NRE has been mainly focused on explaining differences between
regions or the importance and effects of the mining-copper sector (Aroca amp Atienza 2011 Ebert
amp La Menza 2015 Lagos amp Blanco 2010 Rehner Baeza amp Barton 2014) We extend this
analysis using data for local economies Identifying and quantifying the impact of NRE on income
inequality at the county level is likely to be more informative for policies aiming to address the
current developmental bias between the metropolitan region and the rest of the country Moreover
the analysis of the role of natural resources in conjunction with other potential sources of inequality
may shed lights in understanding the persistence of the high levels of inequality observed in the
Chilean economy All in all this study could contribute to the design of policies that
simultaneously help reduce inequality increase efficiency and promote sustainable and inclusive
growth
28
Our main finding shows that after controlling for other potential sources of income
inequality such as educational level demographic characteristics and the level of public
government expenditure the degree of dependence on natural resources has a significant effect on
income inequality However contrary to our expectations the effect is negative This result
suggests that the natural or policy-driven process of transformation from primary and extractive
activities to manufacturing and service sectors imposes additional challenges to central and local
authorities aiming to reduce income inequality
In section 22 we review the literature on the relationship between income inequality and
natural resources In section 23 we establish our research problem and main hypothesis Section
24 describes our data and methods and section 25 the empirical results We finish with section
26 discussing our main results concluding and proposing avenues for future research
22 Inequality and Natural Resources
221 Theoretical Framework
Explanations for income inequality can be associated with individual institutional political
and contextual characteristics Individual characteristics include age gender and mainly the level
of education and skills of the population in the labour force For instance globalization and
technological change lead firms to increase the demand for skilled labour deepening income
inequality between skilled and unskilled workers (Atkinson 2015 Milanovic 2016 Tinbergen
1975) Among institutional characteristics labour unions collective bargaining and the minimum
wage have been suggested as explanations of income inequality (Acemoglu Aghion et al 2001
Atkinson 2015) Policy design associated with market regulation progressive taxation and
redistribution can also impact the levels and patterns of inequality
29
A key factor in understanding the levels and differences in income distribution within a
country may be its endowment of natural resources NRE shapes the structure of the economy
(Leamer et al 1999) it is associated with the creation of institutions that define the political
culture and it can also influence the performance of other sectors (Watkins 1963) In addition
NRE determines initial conditions market competition ownership over resources rent seeking
and the geographical concentration of the population and economic activity
Cross‐countryliterature
Bourguignon and Morrison (1990) introduce one of the earliest theoretical frameworks
describing the relationship between inequality and NRE They develop a small open economy
model where income distribution is a function of NRE ownership structure and trade protection
Giving cross-sectional evidence for a group of developing countries they conclude that the impact
of NRE particularly mineral resources and land depends on the number and size of the firms
whether they are public or private and the level of protection A higher concentration of production
in a few private firms a big share of production oriented to foreign instead of domestic markets
and protection increasing the relative price of scarce resources are some of the reasons explaining
why some countries are less egalitarian than others
NRE could also influence the evolution and levels of inequality by determining the initial
distribution of incomes This is known as the ldquoEngerman-Sokoloff Hypothesisrdquo (Engerman amp
Sokoloff 1997 Engerman et al 2002) In addition Leamer (1999) proposes that inequality and
development paths in each economy are a function of its economic structure which in turn depends
on ldquofundamentalsrdquo and ldquosymptomsrdquo On the one hand ldquofundamentalsrdquo refer to resource
endowment production structure closeness to markets and governments interventions On the
30
other hand ldquosymptomsrdquo are related to institutions employment structure and net export structure
Using this conceptual framework Leamer argues that natural Resource-Rich Countries (RRC) can
experience a higher level of inequality because can have a ldquodumbbell educational systemrdquo
ownership is concentrated in small groups and extraction activities require low-skilled workers
This implies fewer incentives to educate citizens until very late in the development process
resulting in human capital not prepared to take advantage of the process of technological progress
and delaying the emergence of more efficient and competitive sectors such as manufacturing and
services
Using 1980 and 1990 data for a group of countries classified according to land abundance
Leamer (1999) provides evidence showing that on the one hand land-scarce countries concentrate
their production and employment in sectors that promote equality such as capital-intensive
manufacturing chemical or machinery On the other hand countries abundant in natural resources
concentrate their production trade or employment in sectors that promote income inequality such
as the production of food beverages extraction activities or forestry
Gylfason and Zoega (2003) using a framework based on standard growth models also
proposed a positive relationship between NRE and inequality They assume that workers can work
in the primary sector or in the manufacturing (including services) sector In addition wage income
is equally distributed in the manufacturing sector but unequally in the primary sector (because of
initial distribution competition rent seeking etc) Therefore inequality will be greater when a
bigger proportion of labour is dedicated to extraction activities in the primary sector This
phenomenon is further amplified because of lower incentives to invest in physical and human
capital to adopt new technologies and to increase the share of the manufacturing sector
31
Diverse mechanisms explaining the link between NRE and inequality have been proposed
arguing that NRE determines simultaneously economic growth and inequality (Gylfason amp Zoega
2003) NRE could impact economic growth through the real exchange rate and the crowding-out
effect on manufacturing (ldquoDutch Diseaserdquo) reducing incentives to invest in physical and human
capital (Easterly 2007) and influencing the processes of technology adoption industrialization
and diversification of the economy in a manner that is less conducive to growth (Bulte et al 2005)
These potential explanations related to the called ldquoResource Curse Hypothesisrdquo do not have strong
empirical support (Auty 2001 Bulte et al 2005)
NRE may also influence economic growth through the quality of institutions (Acemoglu
1995 Acemoglu Aghion et al 2001 Acemoglu amp Robinson 2002 Engerman amp Sokoloff 1997
Engerman et al 2002) the concentration of ownership political power and rent-seeking NRE
acts by shaping institutional context and social infrastructure a phenomenon that is stronger when
resources are spatially concentrated such as minerals and plantations (Bulte et al 2005) NRE
could also have a significant effect on social cohesion and instability spreading its influence like
a disease (Brunori Ferreira amp Peragine 2013 Kanbur amp Venables 2005 Milanovic 2016
Ocampo 2004)
Considering a non-tradable sector intensive in unskilled workers Goderis and Malone
(2011) develop a model where the natural resources sector experiences an exogenous gift of
resource income They analyse the impact over income inequality of resource booms proxied by
changes in a commodity price index They conclude that inequality decreases in the short run but
increases after the initial reduction
32
Fum and Hodler (2010) show that natural resources increase inequality but this is
conditional on the level of ethnical polarization of society Carmignani (2013 2010) confirms this
positive relationship using different measures of dependence and abundance and goes further
arguing that inequality constitutes an indirect channel through which NRE affects human
development
Singlecountryevidence
Most of the studies about the relationship between inequality and NRE derive from cross-
country analyses Evidence for specific countries has been mainly based on case studies Howie
and Atakhanova (2014) based on the model of Goderis and Malone (2011) find for the case of
Kazakhstan that income and consumption inequality decreased significantly after booms in the oil-
and-gas sector because of resource booms increase the demand for non-tradable goods which are
intensive in unskilled workers The results depend on the level of rurality institutional quality
education levels and public spending on health and education Fleming and Measham (2015b
2015a) evaluate the impact of booms in the mining and oil sectors in Australia They find that a
boom in the mining sector increases income inequality due to commuting and migration among
regions This phenomenon can be exacerbated when the demanding access to natural resource
revenues is associated with the creation of more local administrative units (counties provinces and
even regions) but the government capacity is not simultaneously improved (Cust amp Poelhekke
2015 Michaels 2011) Furthermore the benefits that a region can receive in the form of fiscal
transfers can be more than compensated by the loses due to city-to-mine commuting such as the
case of mining regions in Chile (Aroca amp Atienza 2011)
33
Other studies at the local level have analysed the impact of the mining sector in Peru (Aragoacuten
amp Rud 2013 Loayza amp Rigolini 2016 Loayza Teran amp Rigolini 2013) Spain (Domenech
2008) and Canada (Papyrakis amp Raveh 2014) and the effects of oil windfalls in Brazil (Caselli amp
Michaels 2013)
In summary there is a wide range of potential mechanisms through which NRE could
influence income inequality Although most of them seem to suggest a positive relationship others
such as commuting and increased within-county demand for non-tradable goods and services
could lead to a negative association This highlights the need to know the sign of this association
in the Chilean economy where the trend shows a reduction in the degree of NRD After controlling
for other factors a positive link would support the argument that the reduction in the degree of
NRD has been relevant in the reduction experienced by income inequality in the same period
However a negative link would support the position that the reduction in NRD has contributed to
explain the persistence of income inequality and its slow reduction
222 The relevance of the spatial approach
Inequalities within countries are still the most important form of inequality from the political
point of view (Milanovic 2016) People from a geographic area within a country are influenced
and care most about their status relative to the people in other areas in the same country The
influence among regions involves multiple aspects (eg economic political and environmental)
These potential interactions have been traditionally ignored assuming independence among
observations related to different regions Moreover neglecting the process of spatial interaction in
key indicators of the economic and social performance of a country may mislead the design of the
public policy
34
The spatial dimension could play a significant role in understanding the distribution of
income within a country One strand of efforts aiming to capture the geographic heterogeneity of
inequality has been focussed on decomposing general indicators such as the Gini coefficient or the
Theil Index Evidence for different countries including the US (Doran amp Jordan 2016) China
(Akita 2003 Gustafsson amp Shi 2002 Ye Ma Ye Chen amp Xie 2017 Yue Zhang Ye Cheng
amp Leipnik 2014) Japan (Ohtake 2008) South Africa (Leibbrandt Finn amp Woolard 2012) and
Chile (Paredes et al 2016) shows that regional inequality is sensitive to the geographic scale of
analysis These studies also show a significant spatial component in the explanation of inequality
of income expenditure or gross domestic product for each country
Another strand explicitly uses exploratory spatial data analysis (ESDA) and spatial
econometrics ESDA has been used to provide new insights about the nature of regional disparities
of incomes and growth rates (Celebioglu amp Dallrsquoerba 2010 Yue et al 2014) Spatial econometric
models aim to assess and address the nature of the spatial effects These effects could be the result
of ldquospatial heterogeneityrdquo that is different relationships in distinct locations or ldquospatial
dependencerdquo which implies cross-sectional interactions (spillover effects) among units from
distinct but near locations
Spatial spillovers have been analysed to study both positive and negative spatial correlation
among less resource-abundant counties and resource-abundant counties On the one hand less
resource-abundant counties may experience positive spillovers because their industries supply
more goods and services to meet the increasing regional demand They can also be benefited from
positive agglomeration externalities and higher investment in private and public infrastructure
(Allcott amp Keniston 2014 Michaels 2011) On the other hand negative spillovers could be the
35
result of a high degree of interregional migration that limits the rise in wages and higher local
prices due to the increase in the share of the non-tradable sector In addition local governments
could have a limited capacity to translate the revenues from resource booms into effective public
policies promoting a sustained local development (Beine Coulombe amp Vermeulen 2015 Caselli
amp Michaels 2013 Papyrakis amp Raveh 2014)
23 Research problem and hypotheses
We can conclude from our overview of the literature that the theoretical and empirical
evidence about the link between inequality and natural resources is inconclusive This does not
make clear whether the process of reduction in the degree of dependence on natural resources
such as that experienced by the Chilean economy helps to explain the sustained but slow reduction
in income inequality or its high persistence
The research question guiding this study relates to how the natural resource endowment
determines the paths and structure of income inequality in natural resource-rich countries Using
the case of Chile the main hypotheses to be tested is whether a higher degree of dependence on
natural resources is associated with higher levels of income inequality To do that we use data at
the county level and we explicitly include the spatial dimension Our aim is to arrive at a more
comprehensive understanding of the drivers and transmission mechanisms explaining the
evolution and patterns shown by income inequality In addition we test whether the spatial
dimension plays a significant role in explaining differences in income distribution in Chile
36
24 Data and Methods
We use county-level data for the years 2006 2009 2011 2013 2015 and 2017 The reason
for not using contiguous years is that income data at the household level are only available every
two-three years from the Chilean National Socioeconomic Characterization Survey (CASEN in its
Spanish acronym)3 For the period 2006-2017 the Chilean administrative division considers 15
regions 54 provinces and 346 counties Data on income are available for 324 counties and six
years resulting in a panel with 1944 observations4
We start evaluating the spatial dimension in our data and analysing the link between
inequality and NRD using a cross-sectional setting To this end we use the ldquosix-year averagerdquo
(2006 2009 2011 2013 2015 2017) for our variables given the low time variability showed by
our measures of income inequality and NRD Results are then compared with those of a panel data
setting
241 Operationalization of key variables
The dependent variable in the present study income inequality at the county level is
measured calculating the Gini coefficient using three definitions of household income labour
autonomous and monetary income5 Labour income corresponds to the incomes obtained by all
members in the household excluding domestic service consisting of wages and salaries earnings
3 CASEN survey is conducted by the Chilean Ministry of Social Development covering topics such as education employment income and health CASEN is considered nationally representative and it is the main source for measures of inequality and poverty used for the design and evaluation of social policies in Chile 4 The six waves of CASEN for our study considered an average of 75599 households and 252081 individuals 5 The Gini coefficient is chosen because provides an overall estimate of income inequality It summarizes what proportion of the population gains what proportion of the total income The Gini coefficient can range between 0 (everyone in the population has the same income) and 1 (one person earns 100 per cent of the income in the community)
37
from independent work and self-provision of goods Autonomous income is the sum of labour
income and non-labour income (including capital income) consisting of rents interest and dividend
earnings pension healthcare benefits and other private transfers Finally monetary income is
defined as the sum of autonomous income and monetary subsidies which correspond to cash
transfers by the public sector through social programs Appendix A shows summary statistics for
the Gini coefficient of our three measures of income
The main independent variable in our study is the degree of dependence on natural resources
in each county To have an idea of the importance of each economic activity in the Chilean
economy particularly those activities related to natural resources Figure 21 shows their average
share in Chilean Gross Domestic Product (GDP) for the period 2006-17 We can observe that the
leading activities are those related to the primary sector especially mining and to the tertiary
sector where financial personal commerce restaurants and hotels services stand out The shares
of each economic activity in GDP vary significantly between Chilean regions and such
information is not available at the county level
Figure 21 Average share in GDP of economic activities (2006ndash17)
38
Leamer (1999) argues that when the main source of income is labour income (as indeed
happens for the Chilean case) using employment shares allows a better approach to measuring
dependence on natural resources Using employment data from CASEN survey we define our
measure of NRD as the employment in the primary sector (mining fishing forestry and
agriculture) as a percentage of the total employment in each county We name this variable
pss_casen where ldquopssrdquo stands for ldquoprimary sector sharerdquo We built other two proxies of NRD
using data from the ldquoServicio de Impuestos Internosrdquo (SII) which is the agency in charge of
collecting taxes in Chile The variable pss measures the percentage of employment in the primary
sector and the variable pss_firms measures the number of firms in the primary sector as a
percentage of the total number of firms in each county Appendix B shows summary statistics for
our three measures of NRD disaggregated by region
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17)
39
Figure 22 shows the evolution of our measure of inequality (using the Gini coefficient of
autonomous income) and our three potential proxies for NRD for the period 2006-2017 We
observe that both income inequality and the degree of NRD show a downward trend This seems
to support our hypothesis of a positive link between inequality and NRD however we need to
control of other sources of inequality before getting such a conclusion In what follows we use the
variable gini as our measure of income inequality capturing the Gini coefficient of autonomous
income Our measure of NRD is the variable pss_casen defined previously
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17)
Note Gini and NRD averages 2006-09-11-13-15-17 for 324 Chilean counties divided into five equal groups Source Own elaboration based on CASEN survey
40
Figure 23 shows quantile maps for income inequality (on the left) and NRD (on the right)
using the six-years average dataset6 On the one hand we observe that high levels of inequality
seem to be clustered in the Centre-South of the country where agriculture forestry and fishery are
the predominant economic activities Only isolated counties show high inequality in the Centre
(Metropolitan area where the countyrsquos capital is located) and North (Mining) areas On the other
hand our measure of NRD seems to show an opposite spatial pattern than income inequality with
high levels in the Centre and North of the country
242 Control variables
To control for county characteristics we use a set of socio-economic demographic and
institutional variables Economic factors are captured by the natural log of the mean autonomous
household income per capita (in thousands of Chilean pesos of 2017) lnincome the poverty rate
poverty the unemployment rate unemployment the percentage of the population living in rural
areas rural and the average years of education of the population over 15 years old education
Demographic factors include the proportion of the population in the labour force labour_force
and the natural log of population density (population divided by county area) lndensity
We also include the natural log of the total municipal public expenditure per capita
lnmuni_expenditure to control for municipal heterogeneity This heterogeneity is mainly related
to the capacity of municipalities to generate their own revenues In addition the richest
municipalities are in the Metropolitan region which concentrates economic power and around 40
6 After sorting a variable in ascending order quantile maps use the quantiles (quartiles quintiles deciles etc) as class breaks to divide the distribution of the variable where each class includes approximately the same number of observations (counties)
41
of the population This has basically implied a lag in the development of regions other than the
metropolitan region
The spatial distribution of our measures of income inequality and NRD displayed in Figure
23 seems to show different patterns in the North Centre and South of the country Appendix C
shows the administrative division of Chile in 15 regions and how we have grouped them in three
zones North Centre and South We consider as the ldquoCentrerdquo area that formed by the Metropolitan
region (XIII) and its two neighbouring regions V and VI Using the Centre area as our reference
we include in our analysis two dummy variables indicating whether a county is located in the North
area (regions XV I II III and IV) or South area (regions VII VIII IX XIV X XI and XII)
Appendix D shows summary statistics for the set of numeric control variables and the
correlation matrix between our measure of NRD pss_casen and the set of numeric controls
243 Methods
To assess and then consider the spatial nature of the data we need to define the set of relevant
neighbours for each country This is operationalized by building a matrix called ldquoWrdquo with a ldquo1rdquo
for neighbouring counties and a ldquo0rdquo for non-neighbouring counties We could build W using
contiguity-based (whether counties share a border or point) or geography-based (taking the
distances among the centroids of each county polygon) spatial weights Specifically we build a W
matrix considering the 5-nearest counties7 Two reasons explain the choice of k-nearest
neighbours First we cannot use a contiguity criterium because we do not have information about
all the counties and there are some geographically isolated counties Second given the significant
7 We assign a ldquo1rdquo to the five nearest counties to each county based on the distances among the polygon centroids Then W is ldquorow standardizedrdquo This facilitates the interpretation of the spatial lag of a variable as the ldquoweighted average valuerdquo of the same variable in neighbouring counties
42
differences in county areas (ldquobig countiesrdquo in northern and southern regions) using a distance-
band criterium with a not enough large distance band can lead to many ldquoislandsrdquo in extreme regions
and a multi-modal distribution for the number of neighbours
We start testing our inequality and NRD variables for spatial autocorrelation in order to
evaluate statistically the clustering patterns shown in Figure 23 Next we run an OLS regression
of inequality against NRD and our set of controls and we test the spatial autocorrelation of OLS
residuals If we cannot reject the null hypothesis of random spatial distribution we do not need
spatial models to analyse income inequality which would give contrasting evidence to previous
suggestions about the relevance of the spatial dimension of income inequality in Chile (Paredes
2013 Paredes et al 2016) If we find significant spatial autocorrelation in the OLS residuals this
justifies the use of spatial models and highlight the need to find the correct spatial structure8
If inequality in one county spillovers or influences inequality in neighbouring counties the
spatial lag of inequality should be included as an explanatory variable and we should use a spatial
autoregressive model (SAR) If some unobserved variable is the explanation for the clustering of
counties with similar inequality then this will be better captured including a spatial lag of the
errors and we should use a spatial error model (SEM) (Anselin 1988 Anselin amp Bera 1998)
Finally when our main explanatory variable or some of the controls show spatial autocorrelation
a spatial lag of the explanatory variable(s) should be included in our model
8 The existence of spatial autocorrelation violates the standard assumption of independence among observations needed for OLS regression This will result in OLS coefficients biased and inconsistent (Anselin 1988)
43
244 Spatial Model Specification
A model that includes the three forms of spatial dependence described above is called the
Cliff-Ord Model The model in its cross-sectional representation could be expressed as
119910 120582119882119910 119883120573 119882119883120574 119906 (21)
where
119906 120588119882119906 120576 (22)
119882 is our weight matrix that works as an NxN spatial lag operator9 Thus 119882119910 119882119883 and 119882119906
are the spatial lags for the dependent variable explanatory variables and the error term
respectively The parameter 120582 capture the spatial dependence in the dependent variable 120574 the
spatial dependence in the explanatory variables 120588 capture the spatial dependence in the error term
and 120598 is a vector of idiosyncratic errors For instance if ldquoyrdquo is income inequality and ldquoXrdquo a measure
of NRD the level of inequality in one county will be explained by the degree of NRD in the same
county 119883120573 the average degree of NRD in neighbouring counties 119882119883120574 the average level of
inequality in neighbouring counties 120582119882119910 and the average value of residuals in neighbouring
counties 12058811988211990610
From equations (21) and (22) the SAR and SEM models can be seen as special cases of
the Cliff-Ord representation after imposing restrictions over the spatial parameters 120582 120574 and 120588 For
the specification of the spatial panel models we follow the terminology by Croissant and Millo
9 The spatial lag is a weighted sum of the values observed at neighbouring locations 10 On the one hand the impact that income inequality in one county has over income inequality in neighbouring counties is called a ldquoglobal spilloverrdquo and it is associated with the feedback effect among neighbours (one county is its neighboursrsquo neighbourrdquo) on the other hand the influence that the degree of NRD in neighbouring counties has over inequality in one county is called a ldquolocal spilloverrdquo
44
(2018) Spatial panel models including the spatial lag of the dependent variable (SAR) the spatial
lag of the residuals (SEM) or both (SARAR) are described in Appendix E
25 Results
251 Exploratory Spatial Data Analysis (ESDA)
To analyse the significance of the spatial dimension in our data set we use the six-year
average of our variables Spatial autocorrelation is tested using the Moranrsquos I statistic11 Moranrsquos
I measures the correlation of one variable with itself in space12 Figure 24 shows the Moran scatter
plots where the standardized variable (Gini coefficient and NRD for each county) appears in the
horizontal axis against its spatial lag (average value in the 5-nearest neighbouring counties) The
Moranrsquos I (slope of the line in the Moran scatter plot) of income inequality shows a significant
positive spatial autocorrelation that is counties with high (low) inequality tend to be close to each
other
11 There are many statistics to formally test the significance of the spatial dimension in the distribution of our data The null hypothesis assumes spatial randomness which means that there is not spatial structure in the data so any spatial pattern is equally likely to occur and values in one location do not depend on values in other locations The alternative hypotheses can be the existence of positive or negative spatial autocorrelation Positive spatial autocorrelation means similar values in neighbouring locations (less variability than under spatial randomness) Negative spatial autocorrelation means dissimilar values in neighbouring locations (more variability than under spatial randomness) 12 Traditional measures of correlation such as the Pearsonrsquos coefficient measure the degree of linear correlation between two different variables Measures of spatial autocorrelation assess the correlation between the values of one variable in one location related to the values of the same variable in other neighbouring locations
45
Figure 23 Moran scatter plots for variables gini and pss_casen
Moranrsquos I is a measure of global spatial autocorrelation this means it is intended to capture
the clustering property of the entire data set To identify where are the significant hot-spots
(clusters of counties showing high income inequality) or cold-spots (clusters of counties showing
low income inequality) we need local indicators of spatial association (LISA) Using the local
Moranrsquos I (not reported) we find significant hot-spots in the South of the country (mainly
agricultural regions) and significant cold-spots in the Centre (Metropolitan area) of the country
The next step is to check whether the clustering pattern in inequality is the result of a process of
spatial dependence in the variable itself or it can be explained by other variables related to
inequality
252 Cross-sectional analysis
We start analysing differences in income inequality between counties using the six-year
average data and running an OLS regression for the model
119892119894119899119894 120573 120573 119901119904119904_119888119886119904119890119899 120573 119897119899119894119899119888119900119898119890 120573 119901119900119907119890119903119905119910 120573 119906119899119890119898119901119897119900119910119898119890119899119905 120573 119897119886119887119900119906119903_119891119900119903119888119890 120573 119890119889119906119888119886119905119894119900119899 120573 119897119899119889119890119899119904119894119905119910 120573 119903119906119903119886119897 120573 119897119899119898119906119899_119890119909119901119890119899119889119894119905119906119903119890 120573 119899119900119903119905ℎ 120573 119904119900119906119905ℎ
(23)
46
The Moran scatter plot and spatial statistical test of OLS residuals from equation (23) are
in Appendix F OLS residuals show a small but significant positive autocorrelation (Moranrsquos I =
0121) This means that income inequality continues showing a significant degree of spatial
autocorrelation after controlling for the set of covariates In addition robust Lagrange Multiplier
(LM) tests show that a spatial error model is preferred over a spatial lag model (Anselin Bera
Florax amp Yoon 1996)13 This means that income inequality tends to cluster in zones larger than a
county so the analysis should be performed on a larger scale such as provinces regions or macro
zones If the SAR model were preferred it would mean that income inequality in one county is
influenced by the level of income inequality in neighbouring counties To find the spatial structure
that best fits the clustering process of income inequality we run the full set of spatial model
specifications in a cross-sectional setting and results are shown in Table 21
Column 2 in Table 21 shows the results of our ldquoOLSrdquo model The ldquoSLXrdquo model includes
spatial dependence only through the explanatory variables The ldquoSARrdquo model includes the spatial
lag of the dependent variable as a regressor and the ldquoSEMrdquo model includes spatial dependence
through the error term The ldquoSARARrdquo model includes both the spatial lag of the response and the
errors and the ldquoSDMrdquo and ldquoSDEMrdquo are the extensions of the ldquoSARrdquo and ldquoSEMrdquo models
respectively including the spatial lag of the explanatory variables Finally a model including
spatial lags for the response errors and explanatory variables (the ldquoSARARXrdquo model) is shown in
the last column
13 Following the ldquoAnselin methodrdquo when both non robust LM tests are significant we should select between the robust LR tests From Table F1 in Appendix F only the robust LM test for the SEM model (RLMerr) appears as significant
47
Table 21
Cross-sectional Model Comparison (six-year average data)
48
Opposite to our hypothesis we observe a significant and negative coefficient for our measure
of NRD This means that counties more dependent on natural resources show lower levels of
inequality Education years population density and municipal expenditure per capita are also
negatively related to inequality On the other hand the level of income the poverty rate and the
proportion of the population living in rural areas show a positive relationship with income
inequality There is no significant influence of the unemployment rate and the proportion of the
population in the labour force In addition the SAR SEM and SARAR models show a
significantly higher average inequality in the South of the country related to the Centre area
The main finding from our cross-sectional analysis is that there is a significant and negative
relationship between inequality and NRD which is quite robust to the model specification
253 Panel Data analysis
Like the cross-sectional case we start estimating the panel without spatial effects Results
for the pooled fixed effects (FE) and random effects (RE) specifications of equation (3) are in
Appendix G Spatial models were estimated using Maximum Likelihood (ML) and Generalized
Moments (GM) and assuming that the spatial structure (W matrix) is the same for all years14
Tables 22 23 and 24 show results for the ML estimation of the SAR SEM and SARAR models
using the pooled FE and RE specifications Results for the GM estimation are in Appendix H
All our spatial models include time fixed effects In the case of the pooled and RE models they
additionally include indicator variables for those counties located in the North and South of the
country
14 In traditional (non-spatial) panels data are stacked as time series for each observational unit (county) In the case of spatial panels data are stacked as slice of cross-sections for each year in the panel
49
The main result is that the negative and significant effect of NRD on income inequality is
robust to most of the spatial panel specifications In addition the coefficient for the variable
pss_casen changes slightly among panel specifications (pooled FE and RE) but does not change
among spatial models (SAR SEM and SARAR)
Another important finding is related to the significance of the spatial dimension of income
inequality When spatial models cross-sectional or panel are compared to non-spatial models
there are no major differences in the magnitude of the coefficients or their significance This could
mean that the positive spatial autocorrelation shown by income inequality seems to be better
explained by a process of spatial heterogeneity rather than spatial dependence The practical
implication of this result is that including dummy variables for aggregated units (eg regions or
groups of regions) could be enough to control for the spatial dimension in the modelling and
analysis of income inequality
Among control variables years of education seems to be the main variable for the design of
long-term policies aimed at reducing inequality This result is in line with previous evidence for
cross-country studies (Leamer et al 1999) and specific countries (Howie amp Atakhanova 2014)
Municipal expenditure per capita also shows a significant and negative association with income
inequality in the pooled and RE spatial specifications This means that higher municipal
expenditure helps to reduce inequality between counties but its effect is more limited within
counties This result support the importance of local governments (Fleming amp Measham 2015a)
however the negative coefficient appears as evidence against the ldquoParadox of Plentyrdquo (Borge et
al 2015)
50
Table 22
ML Spatial SAR Models
Table 23
ML Spatial SEM Models
51
Table 24
ML Spatial SARAR Models
26 Discussion and conclusions
In this essay we delve deep into the sources of income inequality analysing its association
with the degree of dependence on natural resources using county-level data for the 2006ndash2017
period in Chile Given recent evidence for the Chilean economy suggesting a significant spatial
dimension we assess and incorporate explicitly the spatial structure of income inequality using
spatial methods We use cross-sectional and panel data to evaluate the significance of the spatial
dimension and we test whether NRD has a positive effect on income inequality
Contrary to what theory predicts NRD shows a significant and negative association with
income inequality This result is robust to the type of analysis (cross-sectional vs panel data) the
approach (spatial vs non-spatial) and the inclusion of different controls The negative and
significant coefficient implies that if the degree of NRD would not have experienced a 10 drop
during this period income inequality could have fallen in 2 additional points So the downward
trend in the participation of the primary sector in terms of employment in the Chilean economy
52
could be one of the main reasons explaining the high persistence in the levels of income inequality
This means that those areas that undergo a process of productive transformation mainly towards
the services sector would be facing greater problems to reduce inequality This process of
productive transformation natural or policy-driven highlights the importance of policies focused
on human capital and the role of local governments in reducing inequality
The main implication for policymakers is that a reduction in NRD does not help to reduce
inequality generating additional challenges for local and central governments in its attempt to
transform the structure of their economies to fewer dependent ones on natural resources The
finding of a significant spatial dimension suggests that defining macro zones capturing the spatial
heterogeneity in the data should be done before analysing the relationship among variables and the
design and evaluation of specific policies Particularly relevant in those areas experiencing a
reduction in NRD are migration commuting and the characteristics of the tertiary (services) sector
In addition our findings show that education and municipal expenditure could be effective policy
tools in the fight to reduce inequality in Chile
Although our results seem quite robust they do not allow us to make causal inferences about
the effect of NRD on income inequality However we could think of the following explanation to
explain the negative relationship found and the differences between geographical areas
Areas highly dependent on NR used to demand a high proportion of low-skill labour This
has change in sectors such as the mining sector in the northern area which has simultaneously
experienced an increase in activities related to the service sector such as retail restaurants
transport and housing However those services associated with more skilled labour such as the
finance sector remain concentrated in the capital region The reduction in the degree of NRD
(employment in extractive activities) implies lower labour force but more specialized with most
53
of the low-skilled labour transferred to a service sector characterized by low productivity and low
wages
Non-spatial models show that the North and South particularly the latter present
significantly higher levels of inequality This could be associated with the type of resources with
ldquopointrdquo resources such as minerals in the North and ldquodiffusedrdquo resources such as agriculture in the
South This translates into higher average incomes in the Centre and North areas and lower average
incomes in the South
The reduction in NRD implies not only a movement of the labour force from extractive
activities to manufacturing or services with the latter characterized by low productivity and low
salaries of the labour force We could also speculate that most of the high incomes move to the
central area where the economic power and ownership over firms and resources are concentrated
This would explain low inequality associated with higher average incomes in the central area and
high inequality associated with lower average incomes in the South A more in-depth analysis
capturing the mobility of wealth and labour force between counties or more aggregated areas is
needed to better understand the causal mechanism involved
Our findings open avenues for future research in different strands First studies on the causes
of income inequality should take the role of NRD into consideration which has been overlooked
so far Given that the spatial dimension of income inequality seems to be explained by a
phenomenon of spatial heterogeneity estimation strategies such as spatial regime models or
geographically weighted regression should be used (Chi amp Zhu 2019) Second the effect of NRD
on income inequality could manifest through different channels such as education fiscal transfers
and institutions We could extend our analysis to identify which of these competing channels is
the most relevant Transforming some continuous variables such as educational level to a
54
categorical variable or defining new indicator variables for instance whether a local government
shows or not an efficient performance we could classify counties in different groups and then
check whether there are differences or not in the relationship between income inequality and NRD
A third strand could be to disaggregate our measure of NRD for different industries This
would allow us to test differences among industries and to identify the sectors that promote greater
equality and which greater inequality Forth the analysis of the consequences of income inequality
on other economic and social phenomena such as efficiency economic growth and social cohesion
has a growing interest in researchers and policymakers Our findings suggest that to answer the
question of whether income inequality has a causal impact on other variables we could include a
measure of NRD as an instrument to address endogeneity issues For instance two interesting
topics for future research are the analysis of how differences in income inequality between counties
could help to explain differences in the level of efficiency of local governments and differences in
the degree of social cohesion (unrest) throughout the country Those are the issues to be addressed
in the next two essays
55
Chapter 3 The Impact of Income Inequality on the Efficiency of
Municipalities in Chile
31 Introduction
In Chile municipalities are the smallest administrative unit for which citizens choose their
local authorities playing an important role in the provision of public goods and services at the
local level Municipalities have a similar set of objectives but the level of financial resources
available to finance their activities is highly heterogeneous This could result in significant
differences in the levels of performance between municipalities Despite their importance there is
little empirical evidence about the efficiency of local governments in Chile This essay aims to
measure the technical efficiency of Chilean municipalities and to analyse how local characteristics
particularly those related to income distribution at the county level could help to explain
differences in municipal performance
Cross-country studies situate Chile as an efficient country in international comparisons about
efficiency of government spending (Herrera amp Pang 2005 Loacutepez amp Miller 2008) However
evidence for Chile at the local level is relatively sparse suggesting significant levels of
inefficiency For instance Pacheco Sanchez and Villena (2013) found a mean efficiency level of
around 70 using a sample of 309 municipalities for the period 2008-2010 This suggests that
municipalities could achieve the same level of output by reducing the usage of inputs by an average
of 30 Their study also showed that those municipalities more dependent on the central
56
government or those located in counties with lower income per capita are more efficient than their
counterparts
Most empirical research on Local Government Efficiency (LGE) has been conducted for
member countries of the Organization for Economic Cooperation and Development (OECD) of
which Chile has been a member since 2010 In the case of European countries such as Spain and
Italy which share similar characteristics such as the monetary union and levels of GDP per head
efficiency studies have been mainly motivated by budgetary constraints (Balaguer-Coll Brun-
Martos Maacuterquez-Ramos amp Prior 2019 lo Storto 2013) The Chilean context differs in three
main ways from its OECD counterparts First except for the Metropolitan Region that concentrates
most of the population Chilean regions are highly dependent on natural resources Second Chile
is also characterized by one of the highest levels of income inequality among OECD countries
which contrast with the situation of developed natural resource-rich countries such as Australia
and Norway Third although budget constraints are also a relevant issue Chilean municipalities
have experienced a sustained increase in the level of financial resources and expenditure
Another relevant distinction when we benchmark the performance of municipalities across
different countries is the type of public services they provide On the one hand in most of the
countries included Chile the main role of local governments is to provide ldquoservices to peoplerdquo
such as public education and public health On the other hand there are countries such as Australia
where local governments mainly provide ldquoservices to propertyrdquo including waste management
maintenance of local roads and the provision of community facilities such as libraries swimming
pools and parks (Dollery Wallis amp Akimov 2010 Drew Kortt amp Dollery 2015 McQuestin
Drew amp Dollery 2018)
57
Despite contextual differences Chilean municipalities seem not to perform differently from
municipalities in other developed and natural resource-rich countries where income inequality is
significantly less than in Chile (Narboacuten-Perpintildeaacute amp De Witte 2018a) This result highlights the
need to study the role of income inequality and the degree of dependence on natural resources over
LGE characteristics that have been largely overlooked in the literature
We measure and analyse differences in municipal performance using a two-stage approach
In the first stage we measure municipal efficiency using an input-oriented Data Envelopment
Analysis (DEA) to get a set of ldquoefficiency scoresrdquo In the second stage we regress efficiency scores
against our measure of income inequality controlling for a set of contextual factors describing the
economic socio-demographic and political context of each county
We use a sample of 324 municipalities for the period 2006-2017 During this period Chile
was divided into 346 counties belonging to 15 regions This period was characterized by important
external and internal shocks including the Global Financial Crisis (GFC) one of the biggest
earthquakes in Chilean history in 2010 and three municipal elections The availability of
information allows us to measure efficiency for the full period but the influence of contextual
factors is analysed using a panel of six years (2006 2009 2011 2013 2015 and 2017) for which
household income information is available
The main hypothesis tested in the second stage is whether higher levels of income inequality
are associated with lower levels of efficiency Previous evidence shows that when progress is not
evenly shared persistent within-country inequality reduces the effectiveness and efficiency of the
public sector (Ortega Sanjuaacuten amp Casquero 2017 Tandon 2005)
Income inequality has been used to control for a wide range of idiosyncratic factors
associated with historical institutional and cultural factors affecting efficiency (Greene 2016
58
Ortega et al 2017) For instance at the local level income inequality has been considered as an
indicator of economic heterogeneity in the population where higher inequality is associated with
a more heterogeneous set of conflicting demands for public services which adversely affect an
efficient provision (Ashworth Geys Heyndels amp Wille 2014 Geys amp Moesen 2009) Higher
levels of income inequality could also relate to economically privileged groups having a greater
capacity to influence the political system for their own benefit rather than that of the majority
When high inequality is persistent the feeling of frustration and disappointment in the population
could reduce not only trust and cooperation among individuals but also trust in institutions which
would negatively affect government efficiency (Boix amp Posner 1998 Coffeacute amp Geys 2005) For
instance national or local authorities could end exerting patronage and clientelism and showing
rising levels of corruption (Uslaner 2011 Uslaner amp Brown 2005)
One of the main gaps in extant literature is the need to conduct more analysis of LGE using
panel data taking into consideration endogeneity issues and controlling for unobserved
heterogeneity (Narboacuten-Perpintildeaacute amp De Witte 2018a) To address the above we set-up a panel with
time and county-specific effects and we propose the use of a measure of natural resource
dependence (NRD) as an instrument for income inequality Based on the ldquoParadox of Plentyrdquo
fiscal revenues from natural resources windfalls could be associated with an over expansion of the
public sector fostering rent-seeking and corruption and reducing local government efficiency
(Dauvin amp Guerreiro 2017 Manzano amp Rigobon 2001) In the Chilean case most of the revenues
generated by local governments included those from natural resources end up in a common fund
which benefits all municipalities The aim of this common fund is precisely to reduce inequalities
among municipalities so although we do not expect a direct impact of natural resources on LGE
we could expect an indirect effect through other indicators particularly income inequality
59
As far as we know this is the first study analysing the influence of income inequality as a
determinant of municipal efficiency in Chile Moreover this is the first study in the context of a
natural resource-rich country which specifically suggests a measure of natural resource
dependence as an instrument to correct for endogeneity bias We propose the use of the proportion
of firms in the primary sector as proxy for the degree of NRD in each county We argue that this
variable is a better proxy than using the proportion of employment in the manufacturing sector
which has been proposed in previous studies (Alesina amp La Ferrara 2002) During the period
analysed our proxy remained relatively stable and showed a significant relationship with income
inequality In addition it is less likely that it has directly affected municipal efficiency
This study adds to the literature in two other ways First the extant literature suggests that
efficiency measurement could be highly sensitive to the chosen technique as well as the selection
of inputs and outputs (Narboacuten-Perpintildeaacute amp De Witte 2018a) Inputs are usually proxied by a single
measure of total public expenditures and outputs by general proxies such as population andor the
number of businesses in each county We offer a novel approach for the selection of inputs and
outputs On the one hand we disaggregate government expenditures into four components
(operation personnel health and education) and we use the number of public schools and health
facilities in each county as a proxy for physical capital On the other hand we use four outputs
aiming to capture the wide variety of goods and services supplied by each municipality Through
this approach we aim to better describe the production function of each municipality capturing
not only the variety of inputs and outputs but also differences in size among municipalities
A third contribution relates to the measurement of LGE in the Chilean context We measure
technical and scale efficiency using a larger sample and a longer period This has empirical and
policy relevance On the one hand it helps us to select the correct DEA model and allows us to
60
determine the importance of scale inefficiencies as explanation for differences in municipal
performance On the other hand efficiency measures increase the information available for both
central and local governments to better understand the production technology that best describes
each municipality and to carry out policies to improve efficiency
We believe that our selection of inputs and outputs the use of a large dataset and the joint
analysis using cross-sectional and panel data provide a more accurate and robust analysis of
municipal efficiency Likewise knowing whether inequality has a significant influence on
municipal efficiency may provide useful insights and guidance for policymakers not only in Chile
but also for countries sharing similar characteristics
DEA results show an average level of technical efficiency (inefficiency) of around 83
(17) This means that municipalities could reduce on average a 17 the use of inputs without
reducing the outputs There are significant differences among geographic areas with the Centre
area (where the countyrsquos capital is located) displaying higher efficiency than the rest of the country
When municipal efficiency is measured under different assumptions about returns to scale results
reveal a production technology with variable returns to scales and around 75 of the
municipalities displaying scale inefficiencies However when technical efficiency is
disaggregated between pure technical efficiency and scale efficiency results show that scale
inefficiency explains a small proportion of the total municipal technical inefficiency This finding
justifies a deeper analysis of the reasons why municipalities could operate inefficiently and why
municipal performance could vary among municipalities
Efficiency scores also show a significant degree of positive spatial autocorrelation This
means that municipal efficiency shows a general clustering process with neighbouring
municipalities showing similar levels of efficiency A further analysis shows that most of the
61
spatial pattern in municipal efficiency is exogenous that is could be associated to other variables
Hence we conduct most of our regression analysis using traditional (non-spatial) methods and
leaving spatial regressions in the appendixes
Findings from cross-sectional and panel regressions support the hypothesis that municipal
performance is significantly and negatively associated with income inequality at the county level
The coefficient of income inequality is close to one which means that reductions in income
inequality ceteris paribus could be associated with increases in municipal efficiency in the same
proportion This result supports the strand of research arguing that there is not a trade-off at least
at the municipal level between equity and efficiency (Andersen amp Maibom 2020 Berg amp Ostry
2011 2017) The main policy implications are that authorities in more unequal counties would
face higher challenges to perform efficiently and policies pertaining to inequality and efficiency
should not be designed independently
The chapter is structured as follows Section 32 provides a brief literature review on related
local government efficiency Section 33 introduces the methodological background and empirical
models Section 34 presents the empirical results and discussions Section 35 concludes the
chapter
32 Related Literature
321 Measuring efficiency of local governments
Studies on measuring LGE can be grouped in those analysing the provision of single services
such as health or education and those assessing overall efficiency (lo Storto 2013) Once inputs
and outputs have been defined efficiency is measured using parametric andor non-parametric
techniques Among the former group Stochastic Frontier Analysis (SFA) seems to be preferred
62
by scholars aiming to measure efficiency and to analyse the link with environmental variables
using a single procedure (Pacheco et al 2013 Tsekeris Sotiris Tsekeris amp Papaioannou 2018)
On the non-parametric group Data Envelopment Analysis (DEA) is by far the most used technique
(Afonso amp Fernandes 2006 Balaguer-Coll amp Prior 2009 lo Storto 2013)
The selection of inputs and outputs depends not only on the aimed of the study (specific
sector vs whole measure of efficiency) but also on the role that municipalities play in different
countries There are countries such as Australia (Drew et al 2015) and Spain (Balaguer-Coll amp
Prior 2009) where local governments mainly supply services to ldquopropertyrdquo such as waste
management and road maintenance In these cases efficiency has been mainly measured using
total indicators of local government expenditure and outputs have been proxied using general
indicators such as population or number of business (Drew et al 2015) On the other hand in
countries such as Italy (lo Storto 2013) and Portugal (Afonso amp Fernandes 2008) in Europe or
Brazil (de Sousa Cribari-Neto amp Stosic 2005) and Chile (Pacheco et al 2013) in South America
municipalities mainly supply services to ldquopeoplerdquo Here in addition to financial expenditures or
revenues inputs have included the number of local government employees the number of schools
or the number of hospitals and health centres School-age population the number of students
enrolled in primary and secondary schools and the number of beds in hospitals have been
considered as outputs Based on the study of Narboacuten-Perpintildeaacute amp De Witte (2018a) a wider list of
inputs and outputs used in previous studies can be found in Appendix I
Studies from different countries show important differences in the average efficiency scores
both between and within countries These studies also differ in the samples methodologies and
variables included A summary showing the range and variability of the mean efficiency scores
founds in countries all around the world can be found in Narboacuten-Perpintildeaacute amp De Witte (2018a)
63
These authors also show that OECD natural resource-rich countries such as Australia Belgium
and Chile show similar results in terms of mean efficiency scores with LGE studies being less
frequent in Latin American countries
Measuring efficiency of local governments as decision-making units (DMU) presents many
challenges and difficulties (Borger Kerstens Moesen amp Vanneste 1994 Ravallion 2005)
Worthington and Dollery (2000) mention problems with the selection and measurement of inputs
the identification of different stakeholders the hidden characteristic of the ldquolocal government
technologyrdquo and the multidimensionality of the services provided by local governments All these
issues make difficult to identify and distinguish between outputs and outcomes with outputs
commonly proxied by general indicators such as county area or county population Because
efficiency measures are highly sensitive to the chosen technique and the selection of inputs and
outputs Narboacuten-Perpintildeaacute amp De Witte (2018a) suggest formulating different specifications and
using less general and unspecified indicators Moreover the complexity in defining outputs and
the use of general indicators make more likely that contextual factors affect municipal efficiency
322 Explaining differences in LGE
To explain differences in local government performance researchers have basically
distinguished between ldquodiscretionaryrdquo and ldquonon-discretionaryrdquo factors Discretionary factors refer
to the degree of discretion of local authorities in the selection and management of inputs and
outputs On the other hand scholars have investigated the influence on LGE of contextual factors
beyond authoritiesrsquo control These factors reflective at the environment where municipalities
operate include economic socio-demographic geographic financial political and institutional
characteristics (da Cruz amp Marques 2014 Narboacuten-Perpintildeaacute amp De Witte 2018b)
64
In general the evidence about the influence of contextual factors has delivered mixed and
country-specific results (Narboacuten-Perpintildeaacute amp De Witte 2018b) Sampaio de Sousa et al (2005)
using data for Brazilian municipalities finds that population density and urbanization rate have
strong positive effects on efficiency scores Benito et al (2010) show that lower levels of
efficiency of Spanish municipalities are associated with a greater economic level a less stable
population and a bigger size of the local government Afonso (2008) finds that per capita income
level and education are not significant factors influencing LGE of Portuguese municipalities He
also finds that municipalities in Northern areas show greater efficiency than their counterparts in
Southern areas More recently Tsekeris (2018) finds that spatial variations in efficiency in Greece
can be attributed to factors related to inter-regional market access specialization and sectoral
concentration resource-factor endowments and political factors among others Characteristics
describing each local government have also been used including municipal indebtedness (Benito
et al 2010) fiscal deficits (Sinha 2017) degree of fiscal autonomy (Boetti Piacenza amp Turati
2009) and individual characteristics of local authorities such as age gender and political ideology
Narboacuten‐Perpintildeaacute amp De Witte (2018b) conclude after analysing 63 articles that studies on the
influence of contextual factors have mostly used cross-sectional data with little attention to
endogeneity issues which makes any causal interpretation doubtful
323 The trade-off between efficiency and equity
The existence of a potential trade-off between efficiency and equity is in the core of
economic discussion (Andersen amp Maibom 2020 Berg amp Ostry 2011 Browning amp Johnson
1984 Okun 2015)15 The argument that economic growth (one of the most common efficiency
15 Redistributive policies distort incentives and lead to suboptimal outcomes and thus efficiency losses
65
measures) could be negatively affected in the search for greater equality has been translated not
only into economic policies that favour economic growth over those that reduce inequality but
also in the emphasis of scholarly research Thus theoretical and empirical research has been
mainly focussed on efficiency and policy implications of a great diversity of shocks and policies
leaving the analysis of inequality as one of measurement and mostly descriptive Additionally
empirical evidence of the trade-off is scant and inconclusive (Andersen amp Maibom 2020
Browning amp Johnson 1984)
Among economic contextual factors that could affect LGE income inequality has been
largely ignored An exception is a cross-country comparison conducted by Ortega (2017) who
analyses the role of inequality on government efficiency in developing countries He finds that
more unequal countries could have higher difficulties to achieve specific health outcomes Income
inequality has even been considered as part of the outputs to measure efficiency particularly for
the case of European and OECD countries (Afonso Schuknecht amp Tanzi 2010 Antonelli amp De
Bonis 2018)
At the local level income inequality has been mainly used as a proxy for the effect of income
heterogeneity Economic inequality could have a direct and an indirect effect on government
efficiency The direct effect poses that higher income inequality could reduce municipal efficiency
because it is associated with a more complex and competing set of public services demanded by
the population (Jottier et al 2012) The indirect effect puts the focus in the link between inequality
social capital and levels of corruption Economic diversity could reduce trust in people and
institutions when related to high and persistent levels of income inequality It could also affect the
willingness to participate in community and political groups the existence of a shared objective
by citizens and the perception of a prosperous future (Uslaner amp Brown 2005)
66
The evidence is ambiguous For instance Geys and Moesen (2009) find that income
inequality has little relation to efficiency of Flemish municipalities and Ashworth et al (2014)
find a negative relationship for the Norwegian case Findings also indicate that inequality is the
strongest determinant of trust and that trust has a greater effect on communal participation than on
political participation (Uslaner amp Brown 2005)
33 Methodology
We follow a two-stage approach widely used in this kind of analysis A DEA analysis is
conducted in the first stage to get efficiency scores for each municipality Then regression analysis
is conducted in the second stage aiming to identify contextual variables other than differences in
the management of inputs that can help to explain the heterogeneity in municipal performance
331 Chilean Municipalities and period of analysis
The territory of Chile is divided into regions and these into provinces which for purposes of
the local administration are divided into counties The local administration of each county resides
in a municipality which is administrated by a Mayor assisted by a Municipal Council16
Municipalities represent the decentralization of the central power in Chile They are autonomous
organizations with legal personality and own patrimony whose purpose is to satisfy the needs of
the local community and ensure their participation in the economic social and cultural progress of
the county Municipalities have a diversity of functions related to public health education and
social assistance among others
16 The Mayor and City Council are elected by suffrage for the citizens of the respective commune every 4 years
67
To achieve their goals two are the main sources of municipal incomes own permanent
revenues (OPR) and the Municipal Common Fund (MCF) OPR are incomes generated by the
county and they are an indicator of the self-financing capacity of each municipality OPR are not
subject to restrictions regarding their investment and they are mainly generated by territorial taxes
commercial patents and circulation permits17 The MCF is a fund that aims to redistribute
community income to ensure compliance with the purpose of the municipalities and their proper
functioning Sources to finance the MCF come from municipal revenues The distribution
mechanism of the fund is regulated by parameters such as whether municipalities generate OPR
per capita lower than the national average and the number of poor people in the commune in
relation to the number of poor people in the country
This study covers the period from 2006 to 2017 During this period Chile was divided into
15 regions 54 provinces and 346 counties18 Although the information on inputs and outputs is
available for the entire period information on contextual factors at the county level such as
household income is only available every two-three years In addition some counties are excluded
from household surveys due to their difficult access Hence we use a sample of 324 municipalities
to measure municipal efficiency for the whole period (3888 observations) However the analysis
of contextual factors is conducted for those years when household income information is available
2006 2009 2011 2013 2015 and 2017 (1944 observations)
17 The territorial tax is a tax on agricultural and non-agricultural real estate Specifically of this income only 40 is left to the municipality as its own financing while the remaining 60 is allocated to the MCF (in the case of the four largest communes- Santiago Providencia Las Condes and Vitacura-percentages are 35 and 65 respectively) Unlike the territorial tax commercial patents are regulated mainly by the municipality which chooses the rate to be charged subject to a range established by law and is responsible of their collection Finally regarding the circulation permits 375 is of municipal benefit while 645 is directed to the MCF 18 There are 346 counties managed by 345 municipalities where counties ldquoCabo de Hornosrdquo and ldquoAntaacuterticardquo are managed by the municipality of ldquoCabo de Hornosrdquo
68
332 Measuring municipal efficiency
Municipal efficiency is measured using Data Envelopment Analysis (Coelli Prasada Rao
OrsquoDonnell amp Battese 2005) This is a non-parametric approach that uses linear programming to
measure efficiency for a group of municipalities as decision making units (DMUrsquos) The main
advantage of using DEA and the reason why DEA is used for the case of Chilean municipalities
is its flexibility in handling multiple inputs and outputs without the need to specify a functional
form (Balaguer-Coll amp Prior 2009 Mikušovaacute 2015 Tigga amp Mishra 2015) Following Afonso
and Fernandes (2008) the relationship between inputs and outputs for each municipality could be
represented by the following equation
119884 119891 119883 119894 1 119899 (31)
In equation (31) 119884 is the set of outputs and 119883 the set of inputs for each of the n
municipalities Using linear programming the production frontier is constructed and a vector of
efficiency scores is obtained The frontier represents full technical efficiency mdash the point at which
the highest output occurs given specified inputs or the point at which the lowest amount of inputs
is used to produce a specified quantity of output Efficiency scores under DEA are relative
measures of efficiency They measure a municipalityrsquos efficiency against the other measured
municipalities in the sample and not a hypothetical lsquoperfect municipalityrsquo The further from the
frontier the less technically efficient a municipality is
We use an input-oriented approach because Chilean municipalities have a greater control
over the management of inputs relative to the outputs they have to manage Obtaining efficiency
scores requires an assumption about the returns to scale exhibited by each municipality When
DMUrsquos are homogeneous the CCR model (Charnes Cooper amp Rhodes 1978) which assumes
69
constant return-to-scale (CRS) is the appropriate specification The CCR model assumes full
proportionality between outputs and inputs and that DMUrsquos operate at their optimal When DMUrsquos
are highly heterogeneous as is the case with local governments in most countries it is not realistic
to assume complete proportionality between inputs and outputs nor that all DMUrsquos operate at their
optimum scale In this situation a variable returns-to-scale (VRS) or BCC model (Banker
Charnes amp Cooper 1984) is the preferred formulation
Assuming VRS imposes minimum restrictions on the efficient frontier and allows for
comparisons only among municipalities of similar scale (Coelli et al 2005 Wu Huang amp Pan
2014) This means that when we use the CCR model (assuming CRS) on a heterogeneous sample
of DMUrsquos the resulting measure of technical efficiency that we obtain is related not only to the
management of inputs but also to issues of scale19 To empirically check the validity of the VRS
assumption we measure technical efficiency under CRS VRS and non-increasing returns-to-scale
(NIRS) and we analyse the existence of scale inefficiencies This allows us to check the relevance
of scale effects as a potential explanation of differences in municipal efficiency Appendix J
shows the specification of the DEA model under VRS and how ldquototal technical efficiencyrdquo
(assuming CRS and therefore due to management and scale issues) could be disaggregated in ldquopure
technical efficiencyrdquo (under VRS and related only to management issues) and scale efficiency (due
to scale issues)
19 Assuming VRS can also lead to measurement problems such as overestimation of efficiency scores This is the case when an important proportion of DMUrsquos shows CRS so assuming VRS ignores the information about proportionality between inputs and outputs (Podinovski 2004)
70
333 Inputs and outputs used in DEA
Following the literature on local government expenditure efficiency (Afonso amp Fernandes
2008 de Sousa et al 2005 Dlouhyacute 2018 Tandon 2005 Tigga amp Mishra 2015) and trying to
reflect as well as possible the functioning of municipalities five inputs and four outputs were
selected Input and output data were obtained from the National System of Municipal Information
(SINIM in its Spanish acronym) and they are expressed in thousands of Chilean pesos of 201720
Inputs are Municipal Operational Expenditure X1 (including expenses on goods and
services social assistance investment and transfers to community organizations) Municipal
Personnel Expenditure X2 (including full time and part-time workers) Total Municipal
Expenditure in Education sector X3 Total Municipal Expenditure in Health sector X4 and the
Number of Municipal Buildings X5 (proxied by the number of public facilities in education and
health sectors)
Output variables were selected highlighting the relevance of education and health sectors
and trying to capture the wide range of local services provided by municipalities The variable
ldquoOwn Permanent Revenuesrdquo Y1 is used to capture the scale and diversity of municipal
activities21 The ldquoMonthly Average Enrolmentrdquo in municipal education establishments related to
the school-age population in each county Y2 is used as educational output As health output the
ldquoNumber of Medical Consultationsrdquo in public facilities Y3 is considered Finally the number of
community organizations Y4 is used as output reflecting the promotion of community
development by each municipality Table 31 shows the summary statistics of input and output
20 The data from SINIM database was mostly obtained using the sinimr package (Salas 2019) 21 According to SINIM database this variable aims to measure the management of the municipalityrsquos own resources with respect to the population of the commune
71
variables for the whole sample and period Inputs and outputs excepting the Monthly Average
Enrolment Y2 are measured in per capita terms using county population information from the
National Institute of Statistics (INE in its Spanish acronym)
Table 31
Descriptive statistics Inputs and Output variables used in DEA analysis
334 Regression model
Contextual factors could play an important role not only in explaining why some
municipalities operate inefficiently but also why municipal performance differs among them
These factors may affect municipal performance modifying incentives for local authorities to
operate efficiently and their capability to take advantage of economies of scale They also define
the conditions for cooperation or competition among municipalities and the citizensacute ability and
willingness to monitor local authorities (Afonso amp Fernandes 2008)
Information on income at the household level for each county was obtained from the
ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) CASEN is
conducted every two-three years being the reason why consecutive years are not considered in
72
our regression analysis The other contextual factors used as controls were obtained from different
sources including SINIM INE and the ldquoServicio de Impuestos Internosrdquo (SII)22
Our main hypothesis is whether higher levels of income inequality are associated with lower
levels of municipal efficiency To test our hypothesis the empirical model is defined as
120579 120573 119892119894119899119894 119885 120573 120575 120572 120598 (32)
Where 120579 is the vector of DEA efficiency scores 119892119894119899119894 is the Gini coefficient of each
county 120575 are year-specific effects 120572 are county-specific constants 120598 is a vector of error terms
and 119885 is a vector of controls Next we discuss the motivation for these controls
The level of purchasing power of countiesrsquo citizens is proxied by the variable log(income)
which is the natural log of the mean household income per capita in thousands of Chilean pesos of
2017 On the one hand poorer counties could display higher efficiency due to their necessity to
take care of their constraint resources (Pacheco et al 2013) On the other hand richer counties
could show higher efficiency because richer citizens exert higher monitoring over local authorities
and demand better quality public services in return for their tax payments (Afonso et al 2010)
The possibility for municipalities to take advantage of economies of scale and urbanization is
captured by three variables First the variable log(density) which correspond to the natural log of
population density Second the dummy variable reg_cap indicating whether a county is a regional
capital or not Third the variable agroland which correspond to the proportion of land for
agricultural use which is informed to the SII We expect a positive effect of log(density) but
negative for regcap and agroland
22 The SII is the institution in charge of collecting taxes in Chile
73
Socio-demographic characteristics are captured including a Dependence Index IDD IDD
corresponds to the number of people under 15 years or over 65 years per 100 people in the active
population (those people between 15 and 65 years old) A higher proportion of young and older
population could be associated with a higher demand for municipal services relating to education
and health making harder to offer public services efficiently The citizensrsquo capacity to monitor
local authorities is proxied including the variable education (average years of education for the
population older than 15 years) and the variable housing (proportion of households which are
owners of the property where they live in each county) In both cases we expect a positive
association with LGE
Among municipal characteristics the variable professional (percentage of municipal
personnel with a professional degree) is used to control for the quality of municipal services and
it is expected a positive impact The variable mcf (proportion of total municipal income coming
from the MCF) is included to capture the influence of financial dependence on the central
government A higher dependence from MCF could be associated with higher efficiency when it
is linked to more control from central government (Worthington amp Dollery 2000) However when
MCF discourages the generation of own resources and proper management of resources from the
fund a lower efficiency should be expected (Bravo 2014) In addition the dummy variable mayor
is included to capture differences among mayors supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo
political parties related to those ldquoINDEPENDENTrdquo mayors
Table 32 report summary statistics for the set of numeric contextual factors and Appendix
K the corresponding correlation matrix Despite the high correlation between income and
education variables we include both in the regression section as they capture different county
characteristics
74
Table 32
Summary Statistics Numeric Contextual Factors
Figure 31 Geographical distribution of Chilean regions and macrozones
Previous evidence on growth and convergence of Chilean regions have found that regions
tend to cluster spatially (Aroca amp Bosch 2000) Aiming to capture this regional clustering process
75
and considering the high concentration in the number of municipalities and population in the
central area we group municipalities in four ldquozonesrdquo We define as reference the ldquoCentre-Northrdquo
zone consisting of regions XIII (where the countryrsquos capital is located) and its two neighbouring
regions V and VI The ldquoNorthrdquo zone includes regions XV I II III and IV The ldquoCentre-Southrdquo
zone includes regions VII VIII and IX Finally the ldquoSouthrdquo zone embraces regions XIV X XI
and XII Figure 31 displays the regional administrative division and zones considered in this
essay
Efficiency scores (ES) are limited to have values between 0 and 1 However they are relative
measures (relative to the sample of municipalities) This implies that when a municipality is on the
frontier (ES = 1) it does not mean that potentially larger technical efficiency gains cannot be made
Hence equation 32 is estimated using OLS and censored regressions We start running cross-
sectional regressions for each of the six years Then we compare the results with those from panel
regressions Because fixed-effects panel Tobit models could be affected by the incidental
parameters problem (Henningsen 2010 2019) we use random-effects panel Tobit models
including indicator variables for years and zones Finally to deal with the potential endogeneity
problem we also use an instrumental variable approach The instrument is described next
335 The instrument
Government effectiveness and income distribution are both structural components of
economies (Ortega et al 2017 Ravallion 2005) In the search for a causal interpretation for the
influence of income inequality on municipal efficiency we need an instrument which must be
correlated with the variable to be instrumented (in our case income inequality) and uncorrelated
with the error term in the efficiency equation (32) Previous literature has used as instruments for
Gini the number of townships governments in a previous period the percentage of revenues from
76
intergovernmental transfers in a previous period and the current share of the labour force in the
manufacturing sector (Alesina amp La Ferrara 2002) Using the share of the labour force in a specific
sector is unlikely to reduce the problem of endogeneity particularly in countries where local
governments supply mostly ldquoservices to peoplerdquo and the main source of income inequality is
labour income
We propose as an instrument the proportion of firms in the primary sector (mining fishing
forestry and agriculture)
119901119904119904_119891119894119903119898119904Number of firms in the primary sector
Total number of firms (33)
On the one hand this instrument is likely to be correlated with local income inequality in
natural resource-rich countries23 On the other hand we contend that our instrument is less likely
to be correlated with the error term in the efficiency equation First the main services supplied by
Chilean municipalities are services to people (health and education) not to firms Second most of
the revenues collected by municipalities included those associated with natural resources end up
in the municipal common fund whose objective is precisely to reduce inequalities among
municipalities Third services to firms are expected to be more significant with the tertiary sector
We argue that our instrument captures natural and structural conditions which directly
influence income inequality but it does not directly affect LGE Figure 32 shows the evolution
of the annual average efficiency score and the proportion of firms in the primary secondary
(manufacturing) and tertiary (services) sectors We observe that sectors shares have remained
relatively stable with a slight reduction in the participation of the primary sector in favour of the
23 Results confirm a significant association between income inequality and the degree of dependence on natural resources at the county level
77
tertiary sector by the end of the period This is contrary to the evolution of municipal efficiency
which shows a cyclical behaviour as will be shown in the next section
Figure 32 Evolution of efficiency scores and the proportion of firms by sector
34 Results and discussion
341 DEA results
Figure 33 displays the evolution of our three measures of efficiency Overall technical
efficiency pure technical efficiency and scale efficiency are around 78 83 and 95
respectively with fluctuations over the years Therefore around three quarters of the overall
78
inefficiency is attributed to inefficiency in the management of inputs and around one quarter to
scale inefficiencies24
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE)
Returnstoscale
Figure 34 reports by zone and for the whole period the proportion of municipalities
showing CRS decreasing returns to scale (DRS) or increasing returns to scale (IRS) Most of the
municipalities operate under variable (increasing or decreasing) returns to scale which could be
explained by the high heterogeneity in size among municipalities A summary of RTS
disaggregated by year and zone is in Appendix L Policies oriented to increase efficiency usually
24 The average scale efficiency score of 95 means that municipalities could get an additional 5 of inputs saving if municipalities were able to change their size to achieve their optimal scale
79
consider amalgamation de-amalgamation or ways of cooperation among municipalities To have
a better idea about where and how feasible is the implementation of such policies Appendix M
shows maps with the administrative division of the country in its 345 municipalities and which
municipalities show CRS IRS or DRS in each of the six years of data
Figure 34 Returns to scale by zone
Based on results for the whole period (Figure 34) the North has the highest proportion of
municipalities exhibiting DRS This suggests the need to reduce the size of municipalities splitting
those ldquotoo bigrdquo creating new administrative areas or giving more autonomy to current
municipalities25 The opposite occurs in the Centre-North area where municipalities mostly
exhibit IRS This indicates the need to merge municipalities An alternative strategy to the
amalgamation process is increasing inter-municipal cooperation (Balaguer-Coll et al 2019)
25 This has been the policy followed in Chile Although two new regions were created (XV in the North and XIV in the South) the number of municipalities has not changed
80
which seems to be a more plausible option in Chile Finally evidence on scale efficiency for the
Centre-South and South areas is not clear in terms of the adequate strategy to improve efficiency
Efficiencymeasure
Although most municipalities show scale inefficiencies (Figure 34) only a small proportion
of total inefficiency is associated to scale issues (Figure 33) Together this results justify not only
the use of the BCC model (efficiency scores under VRS) to capture municipal heterogeneity but
also highlights the need to look for other factors outside the control of local authorities which
could be influencing municipal performance
Table 33
Summary efficiency scores (VRS) by zone and region
Table 33 summarizes ES under VRS using data for the six years of CASEN survey A mean
efficiency score of 83 is found for the full sample and period This means that on average
inefficient municipalities can reduce the use of inputs by 17 to get the same current output By
81
comparing average ES per zone it can be concluded that municipalities in the North Centre-North
Centre-South and South could achieve the same level of output with 17 12 18 and 23 fewer
resources respectively Results also show that one third of the municipalities present an efficiency
score equal to one
Figure 35 shows the evolution of the mean ES for the four zones over the 12 years period
A clear drop in the efficiency level is observed in 2009 (the financial crisis year) excepting for the
North area Likewise although mean efficiency returned to its pre-crisis levels in 2014-2015 a
new drop in efficiency was experienced in 2016 It also seems that the 2010 earthquake did not
generate a significant effect on municipal efficiency Figure 35 also shows that although levels
of efficiency seem to differ among zones they follow a similar trend through time with the only
exception of the North which corresponds to the mining area In addition efficiency seems to be
significantly higher in the Centre-North area This is explained by the high mean level of efficiency
in region XIII which includes the countryrsquos capital city
Figure 35 Evolution mean efficiency scores (VRS) by zone
82
To know which and where are the efficient municipalities and if they are surrounded by
municipalities with a similar level of efficiency Appendix N has maps showing the ldquoefficiency
statusrdquo of each municipality that is whether they are efficient (ES = 1) or inefficient (ES lt 1)
Additionally Appendix O shows ldquoequal intervalsrdquo maps describing the spatial distribution of ES
among municipalities for each of the six years26 Results show that efficient municipalities can be
found all through the country the ldquoefficiency statusrdquo could change from one year to another and
municipalities with similar level-status of efficiency tend to cluster in space
342 Regression results
Exploratoryspatialanalysis
DEA efficiency scores and their geographical representations seem to show that municipal
efficiency presents a spatial clustering pattern This means that municipal performance could be
influenced not only by contextual factors of the county where municipality belongs but also by the
level of efficiency of neighbouring municipalities and their characteristics To test the significance
of the spatial dimension in municipal efficiency we use a cross-section of data considering the six-
year average of efficiency scores the Gini coefficient and the set of controls
We use the Moranrsquos I indicator to test for spatial autocorrelation Moranrsquos I is a measure of
the correlation between the level of efficiency in one municipality and its ldquospatial lagrdquo that is the
average level of efficiency in neighbouring municipalities We define as the relevant neighbours
for each municipality the 5-nearest municipalities This is obtained using the distances among the
26 An equal intervals map uses the same principle as a histogram to organize the observations into categories that divide the range of the variable into equal interval bins For the equal interval classification the value range between the lower and upper bound in each bin is constant across bins but the number of observations in each bin is typically not equal
83
polygonsrsquo centroids (latitude and longitude) of each county Results confirm that municipal
efficiency show a significant level of positive spatial autocorrelation This means that
municipalities tend to have neighbouring municipalities with similar performance
The positive spatial autocorrelation shown by municipal efficiency could be due to the
performance in one municipality is influenced by the performance in neighbouring municipalities
(spatial dependence in the variable itself) or due to structural differences among regions-zones
(spatial heterogeneity) To check the source of the spatial autocorrelation we run an OLS
regression of ES against income inequality and controls and then we test OLS residuals for spatial
autocorrelation Moranrsquos I over OLS residuals although significant is barely higher than zero (see
Appendix P) This means that the spatial effect itself is not a serious issue and can be handle for
instance including zone indicators variables hence we proceed to analyse the influence of income
inequality on LGE using non-spatial regression27
Cross‐sectionalanalysis
We start reporting censored regressions for each year in our panel Efficiency scores have
been rescaled to numbers between 0 and 100 to facilitate the interpretation of the results All
regressions include dummy variables for three of the four zones in which we have grouped Chilean
regions Results are in Table 3428 Income inequality shows a negative sign in all years which is
consistent with our hypothesis that inequality is negatively related to municipal efficiency
However only in three of the six years the effect of income inequality appears as statistically
27 In the case of having incorporated the spatial dimension the two most commonly used forms are including in the model the spatial lag of the dependent variable (spatial autoregressive model SAR) or the spatial lag of the error (spatial error model SEM) Following the method suggested by Anselin et al (1996) Lagrange Multiplier (LM) tests on the OLS residuals showed that the SAR model is preferred over the SEM Moranrsquos I LM tests and spatial regressions can be found in Appendix P 28 Regression results using OLS are in Appendix Q
84
significant Only the income level displays a significant and positive influence on efficiency for
the whole period A higher population density also consistently favours municipal efficiency On
the other hand as we expected a higher IDD makes it more difficult to achieve an efficient
performance29 Consistent with results in Pacheco et al (Pacheco et al 2013) municipal
efficiency show a significant an positive association with the MCF only in the first half of our
period of analysis with the second half showing an insignificant relationship
Table 34
Cross-sectional (censored) regressions
Paneldataanalysis
Estimation results for the six-year panel are reported in Table 35 Columns (1) and (2) show
the results for the pooled and random effects censored models only controlling for zone and year
29 The set of cross-sectional regressions was also run for each zone independently Results confirm that the set of significant contextual factors differs not only among years but also among geographic areas This support previous evidence suggesting that cross-sectional analysis should be treated with caution (Narboacuten-Perpintildeaacute amp De Witte 2018b)
85
dummies Income inequality appears as non-significant Zone indicator variables confirm that
municipalities located in the Centre-South and South of the country display a lower average level
of efficiency compared to the Centre-North area Time dummies mostly show negative
coefficients This is a signal that shocks such as the GFC and the earthquake in 2010 may have
had a negative impact on efficiency but that impact was not permanent The results for the pooled
and RE models including the full set of controls are reported in columns (3) and (4) These results
show a significant negative influence of income inequality on LGE
When income inequality is instrumented by the variable pss_firms most of the coefficients
remain unchanged except for those associated with the income variables gini and log(income)
This result implies that our original model suffers for instance from the omitted variable bias
This means that LGE and income inequality are determined simultaneously by some variable not
included in our model Columns (5) and (6) show results using our instrument for income
inequality The ldquotrue coefficientrdquo of variable gini remains negative but the magnitude of the
relationship is higher The negative coefficient for gini implies on the one hand that municipalities
located in more unequal counties face more challenges to achieve an efficient management of
public resources On the other hand the coefficient in column (6) is close to one The interpretation
is that for each point of reduction in income inequality ceteris paribus LGE should increase in the
same proportion Next we discuss some of the results associated with the controls variables
Contrary to Pacheco et al (2013) income level has a positive coefficient meaning that richer
counties in terms of income per capita show higher efficiency This could be explained by higher
monitoring and increasing demands from citizen and taxpayers In addition it is expected a higher
efficiency in municipalities located in counties with a higher population density and those with a
lower proportion of land for agricultural use This result is mainly explained by municipalities
86
located in the Centre area The opposite happens with municipalities in the South implying that
they are too ldquosmallrdquo to take advantage of agglomeration economies There is also a limit for
agglomeration and scale economies which is shown by the negative coefficient of the variable
regcap although this coefficient loses its significance in the IV approaches30
Unexpectedly efficiency was found to be negatively associated with the variable education
This result is similar to the case of Czech municipalities (Šťastnaacute amp Gregor 2014) where
explanations include a weakened monitoring effect due to the fact that more educated citizens
present greater mobility and labour cost disadvantages for municipalities with better educated
labour force In Chile an additional explanation could be the relationship between education and
voter turnout Since 2012 voting is no longer mandatory in Chile This fact considerably reduced
voter turnout which in turn may have influenced the monitoring and control effect of more
educated voters For the case of variable IDD results show that local authorities in counties with
higher proportion of aging and young population (related to those in the active population) face a
greater challenge in their quest to offer public services efficiently
The influence of mcf is like that found by Pacheco et al (2013) with municipalities more
dependent on central transfers showing more efficiency31 Political influence captured by the
variable mayor did not show a significant effect This result is like other studies concluding that
the ideological position did not have a significant influence on efficiency (Benito et al 2010
Boetti et al 2009 Cordero Pedraja-Chaparro Pisaflores amp Polo 2017)
30 This negative coefficient suggests that the negative effects of agglomeration economies such as overcrowding pollution high cost of land and traffic congestion could predominate over the positive effects of scale economies in regional capitals 31 When the analysis is conducted for each zone independently MCF displays a negative influence in the North and Centre-North areas but a positive influence in the Centre-South and South areas These results confirm that on the one hand richer municipalities (those in the North and Centre-North) have less incentives for efficient management of their resources On the other hand results support the relevance of MCF helping poorer municipalities (those in the South) to reach their outcomes
87
Table 35
Panel data regressions
88
35 Conclusions
The trade-off between equity and efficiency is in the core of the economic discussion This
ldquoprinciplerdquo has been used by policymakers to prioritize the design of policies focused on economic
growth delaying those policies aimed at reducing economic inequalities This essay offers
empirical evidence of a negative relationship between inequality and efficiency that is a reduction
of income inequality could have positive effects on economic efficiency at least at the level of
local governments
We followed a traditional Two-Stage approach commonly used in the analysis of LGE We
compared cross-sectional and panel data results and we have added an instrumental variable
approach to give a causal interpretation to the link between efficiency and inequality We proposed
the use of a measure of natural resource dependence to instrumentalize the impact of income
inequality on LGE Given that our units of analysis are municipalities and not counties we argue
that our measure of NRD is correlated with income inequality and it does not have a direct
influence on LGE
We found that Chilean municipalities perform better than previous studies suggest
Municipal efficiency depicted significant levels of positive spatial autocorrelation and most of the
municipalities showed to be operating under increasing or decreasing returns to scale This would
imply that the policies generally used to improve efficiency such as amalgamation or cooperation
should be implemented observing the reality of each region and not as strategies at the national
level We also found that scale inefficiency explains a small proportion of the average total
inefficiency reason why the analysis of external factors that could affect the municipal efficiency
takes greater relevance
89
Income inequality plays an important part in explaining municipal efficiency In fact it was
found that reductions in income inequality could result in increases in municipal efficiency in a
similar proportion An unexpected finding was that the levels of education shows a negative
association with municipal performance This could be due to a low average level of education or
the existence of an omitted variable This variable could be the significant reduction in voting
turnout rates for local and national elections due to changes in the voting system during the period
of our analysis All in all our results may help to shed light on the potential consequences of
changes in contextual factors and the design of strategies aimed to increase municipal efficiency
in countries with similar characteristics to the Chilean economy For instance policies oriented to
take advantage of economies of scale can be formulated merging municipalities or establishing
networks in specific sectors such as education or health
Further work needs to be done both in measurement and in the explanation of differences in
municipal performance in Chile One area of future work will be to identify the factors that better
predict why municipalities operates under increasing decreasing or constant returns to scale
Multinomial logistic regression and the application of machine learning algorithms to SINIM data
sets appear as suitable methods for that purpose Intertemporal DEA (Drew amp Dollery 2015)
should be used to measure municipal efficiency capturing changes in total factor productivity In
addition municipalities operate under different levels of geographical authorities such as the
provincial mayor and the regional governor Hence it would be useful to know how each
municipality performs within each region-zone related to how performs to the whole country This
should be done conducting a metafrontier analysis (OrsquoDonnell Rao amp Battese 2008)
We have also identified through a cross sectional spatial exploratory analysis that on
average municipalities with similar levels of efficiency tend to cluster in space Regarding to
90
analyse the importance of contextual factors on municipal efficiency a deeper analysis should use
censored spatial models to check the significance of the spatial dimension in cross-sectional and
panel contexts Another interesting avenue for future research is associated with the negative
association found between LGE and education The significant reduction in votersacute turnout since
the law of automatic registration and voluntary voting in 2011 appears as a natural experiment to
analyse its effects on efficiency indicators such as municipal performance Incorporating variables
such as the voting turnout in each county or classifying municipalities based on individual
institutional political and economic characteristics could help to shed light on which of these
channels is the most relevant when analysing the impact of inequality on municipal efficiency
Finally we argued that an important part of the influence of income inequality over LGE
could be through its indirect effect on trust social capital and social cohesion The final essay will
delve deep in that relationship
91
Chapter 4 Social Cohesion Incivilities and Diversity
Evidence at the municipal level in Chile
41 Introduction
A deterioration in social cohesion could carry significant costs such as a reduction in
generalized trust between individuals and in institutions a society caught in a vicious circle of
inequality and citizens increasingly distanced from civic life (Uslaner 2011) A growing feeling
of frustration and discontentment can eventually translate into a social outbreak with uncertain
results This is precisely what have been happening in many countries around the world included
Chile
ldquoSocial cohesion is a state of affairs concerning both the vertical and the horizontal
interactions among members of society as characterized by a set of attitudes and norms that
includes trust a sense of belonging and the willingness to participate and help as well as their
behavioural manifestationsrdquo (Chan et al 2006) This definition highlights the multidimensionality
in the concept of social cohesion which has been measured using objective andor subjective
indicators of trust social norms solidarity willingness to participate in social and political groups
and feelings of belonging (Ariely 2014 Chan et al 2006) Multidimensionality also implies that
the impact of determinants of social cohesion such as economic and racial diversity could be
different for each of its various dimensions (Ariely 2014)
A common characteristic to all societies is that they are made up of different groups that
differ with respect to race ethnicity income religion language local identity etc The
92
Community Heterogeneity Thesis (Coffeacute amp Geys 2006) argues that individuals prefer to interact
with others that are like themselves Hence high levels of diversity particularly economic and
racial represent a complex scenario to maintain social cohesion One of the most common factors
adduced for social cohesion is income inequality with higher levels linked to lower levels of trust
(Ariely 2014 Rothstein amp Uslaner 2005)
Traditional measures of social cohesion may not be adequately capturing the deterioration
in social connections For instance measures of (lack of) trust include a strong subjective element
On the other hand proxies for social participation such as volunteering jobs or joining to social
organizations have not been supported by empirical evidence as a source of generalized social trust
(Rothstein amp Uslaner 2005) We proposed to use the rate of incivilities which we argue is a more
appropriate measure of the degree of worsening in the social context
Incivilities are those visible disorders in the public space that violate respectful social norms
and tend not to be treated as crimes by the criminal justice system There are two types of
incivilities social and physical Social incivilities include antisocial behaviours such as public
drinking noisy neighbours and fighting in public places Physical incivilities include among
others vandalism graffiti abandoned cars and garbage on the streets Because citizens and
political authorities cannot always distinguish between incivilities and crime they are usually
treated as an additional category of crime This implies that policies aimed to reduce incivilities
are generally based on punitive actions However theory and evidence on incivilities suggest that
factors explaining incivilities and crime could be different (Lewis 2017 Taylor 1999)
In Chile crime rates have shown a sustained downward trend after reaching its highest level
in 2011 On the other hand incivilities rate has shown a sustained upward trend which coincides
with the increasing victimization and feeling of insecurity in the population This has motivated
93
Chilean authorities based on the ldquoBroken Windows Theoryrdquo to propose new punitive actions (or
increase the severity of the current ones) to those who commit this type of antisocial behaviours
The ldquoBroken Windows Theoryrdquo states that higher rates of incivilities are a signal of social
disorganization which result in higher crime rates (J Q Wilson amp Kelling 1982) This is expected
to have consequences on familiesrsquo decisions such as moving away from public spaces or even
leaving their neighbourhoods
As far as we know there is no previous evidence about the potential causes of incivilities in
Chile Efforts to identify the factors explaining incivilities could help not only to reduce the risk
factors favouring violent and property crimes but also to guide interventions aimed to change
social behaviours and strengthen social cohesion in highly unequal societies Thus the main
contribution of the present study is to provide a deeper comprehension of the problem of incivilities
and how they can help to better understand the weakening of social cohesion that many
contemporary societies experience
We aim to offer the first evidence on the factors explaining the evolution and the differences
in incivilities rates in Chile We set up a panel for six years (2006 2009 2011 2013 2015 and
2017) and 324 counties (1944 observations) We start exploring the evolution and geographical
distribution of incivilities Then we investigate whether economic and racial diversity after
controlling for other socioeconomic demographic and municipal characteristics can be regarded
as key predictors of incivilities
We use the Gini coefficient to proxy economic heterogeneity and the number of new visas
granted to foreigners as proportion of the county population as proxy for racial diversity The main
hypothesis is whether economic and racial diversity have a positive association with the rate of
incivilities In addition to the arguments regarding the ldquoCommunity Heterogeneity Thesisrdquo Taylor
94
(1999) and Skogan (1986 1999 2015) argue that incivilities are caused by inequality and the lack
of informal mechanisms of social control Based on the ldquoIncivilities Thesisrdquo the patterns of
incivilities should mirror the patterns of inequality (Taylor 1999) Then higher inequality should
be associated with higher physical and social vulnerability of the population This reduces social
control and increases social disorganization which triggers antisocial or negligent behaviours
Our main result reveals a strong positive association between the rate of incivilities and the
number of new visas granted per year The relationship with income inequality although also
positive seems to be less significant These findings give strong support to the ldquoCommunity
Heterogeneity Thesisrdquo and to a lesser extent to the ldquoIncivilities Thesisrdquo When the analysis is
disaggregated geographically racial diversity shows a clear positive effect The impact of income
inequality seems to be conditional depending on the level of income showing no effect in poorer
regions Results also show that the impact of economic and racial diversity differs by type of
incivility For example income inequality shows a strong association with ldquoStreet Tradingrdquo while
racial diversity with ldquoAlcohol Consumptionrdquo Two are the main policy implications On the one
hand a national strategy to address the problems associated with foreign immigration could help
to reduce incivilities For instance a joint effort between national and local authorities to curb
immigration and its distribution throughout the country On the other hand our results show that
the relationship between incivilities and economic diversity differs depending on the region or
geographical area Hence the impact on social cohesion of policies aimed to tackle economic
inequalities should be analysed in each specific context
The rate of incivilities also shows a negative association with the level of municipal financial
autonomy This implies that municipalities can effectively carry out policies to reduce incivilities
beyond the efforts of the central government Another important finding is that our results do not
95
support the hypothesis that a higher proportion of the young population is associated with higher
rates of incivilities Hence policies aimed to reduce incivilities should be focused on the causes of
incivilities rather than the criminalization of behaviours or stigmatization of specific population
groups
The structure of the chapter is as follows Section 42 outlines the relevant literature on social
cohesion and incivilities Section 43 describes the data variables and methodology and
establishes the hypotheses of the study Section 44 contains the results and discussions Section
45 presents the main conclusions
42 Related Literature
421 The Community Heterogeneity Thesis
The idea under ldquoThe Community Heterogeneity Thesisrdquo is that if individuals prefer to
interact with others who are similar to themselves in terms of income race or ethnicity high levels
of income inequality and racial diversity facilitate a context for lower tolerance and antisocial
behaviours lowering the ldquostaterdquo of social cohesion (Alesina 2000 Coffeacute amp Geys 2006 Letki
2008) Alessina and Ferrara (2002) give support to this hypothesis arguing that individuals have a
natural aversion to heterogeneity However the most popular explanation is the principle of
homophily people prefer to interact with others who share the same ethnic heritage have the same
social status and hence share experiences and tastes (Letki 2008 McPherson Smith-Lovin amp
Cook 2001 Tolsma et al 2009) For instance Delhey and Newton (2005) find for a sample of
60 countries that income inequality and ethnicity are strongly and negatively correlated with trust
Tolsma et al (2009) using data for Dutch neighbourhoods and municipalities find that social
cohesion is negatively and consistently affected by economic deprivation but not by ethnic
96
heterogeneity These authors also conclude that the effect of neighbourhood and municipal
characteristics on social cohesion depends on residentsrsquo income and educational level
Rothstein and Uslaner (2005) give two theoretical reasons why economic and racial diversity
should be causally related to social trust a key element of social cohesion First optimism about
the future makes less sense when there is more economic inequality which generally translates into
inequality of opportunities especially in areas such as education and the labour market Second
the distribution of resources and opportunities plays a key role in establishing the belief that people
share a common destiny and have similar fundamental values In highly unequal societies people
are likely to stick with their own kind Perceptions of injustice will reinforce negative stereotypes
of other groups making social trust and accommodation more difficult
Uslaner (2002 2011) and Uslaner and Brown (2005) find that high levels of inequality are
the single major factor driving down trust in people who are different from yourself Evidence for
USA finds that inequality is the strongest determinant of generalized trust over time (Rothstein amp
Uslaner 2005) Reducing inequality and then increasing generalized trust should have positive
consequences at the individual and aggregates levels At the individual level it may lead to greater
tolerance and more acts of altruism for people of different backgrounds At the aggregate level it
may lead to greater economic growth more redistribution from the rich to the poor and less
corruption (Uslaner 2002 2013) Letki (2008) argues that when neighbourhood socio-economic
context (apart from just an individualrsquos socioeconomic status) is considered it turns out to be the
main factor triggering negative attitudes and lack of trust in out-group members
The increasing diversity caused by immigration can also reduce the conditions necessary for
social cohesion (Ariely 2014 Holtug amp Mason 2010) Christel Kesler and Irene Bloemraad
(2010) find for nineteen advanced democracies between 1981 and 2000 that increasing migration
97
generally decreases trust civic engagement and political participation The authors also find that
in more equal countries with clear policies in favour of cultural minorities the negative effects of
migration are mitigated or even reversed Letki (2008) states that deprivation and disorder tend to
be strongly correlated with racial diversity Because we propose the use of the number of disorders
or antisocial behaviours known as ldquoincivilitiesrdquo as our measure of social cohesion we describe the
literature on incivilities in the next section
422 The literature on incivilities
The study of incivilities has been a continuing concern mainly for developed countries since
the 1980s The focus has changed from individual and psychological explanations to ecological
(contextual) and social explanations (Taylor 1999) The individual approach basically considered
perceptions of incivilities as an explanatory variable of fear of crime The ecological explanation
argues that indicators of economic disadvantage (eg income levels income inequality
unemployment rate and poverty rate) are the keys to understand a process of social disorganization
and lack of informal control These economic factors lead to higher rates of inappropriate or
negligent behaviours and ultimately to higher crime rates (Blau amp Blau 1982 Messner Rosenfeld
amp Baumer 2004 Phan Orsquobrien Mendolia amp Paloyo 2017 Sampson 1986)
The negative impact of incivilities is not merely reflected in its association with crime rates
(Skogan 2015) Physical and social incivilities could worsen neighbourhoods by affecting quality
of life perception of the environment and public and private behaviours Previous research has
indicated that a higher level of incivilities is associated with health problems (Branas et al 2011
Cohen et al 2000 Hill amp Angel 2005 Ross 2011 Ross amp Mirowsky 2001) greater
victimization and fear of crime (Brunton-Smith Jackson amp Sutherland 2014 Mijanovich amp
Weitzman 2003) and multiple negative economic effects For instance incivilities could be
98
related to a reduction in commercial activity lower investment in real estate reduction in house
prices (Skogan 2015) and population instability (Hipp 2010)
To describe the state of the art in the study of incivilities and their consequences Skogan
(2015) used the concept of untidiness to characterize the research on incivilities The study of
incivilities has had multiple approaches (economic ecological and psychological) Incivilities
have also been measured using multiple sources of information (police reports surveys trained
observation) which result in different measures (perceptions vs count data) However the question
about what specific factors have the strongest effect on incivilities has been overlooked and
perceptions about incivilities have been used mainly as a predictor of crime fear of crime and
victimization
There are two types of incivilities social and physical Social incivilities are a matter of
behaviour including groups of rowdy teens public drunkenness people fighting and street hassles
Physical incivilities involve visual signs of negligence and decay such as abandoned buildings
broken streetlights trash-filled lots and graffiti (Skogan 1999 2015 Taylor 1999) Three reasons
justify the distinction between physical and social incivilities First like multiple dimensions of
social cohesion different structural and social conditions could be responsible for different types
and categories of incivilities Second punitive sanctions are expected to have a greater impact on
physical than on social incivilities since the latter are more related to behaviours rooted in citizensrsquo
culture Third physical incivilities should be more related to absolute measures of economic
disadvantage (eg poverty or unemployment rates) and social incivilities to relative indicators of
economic disadvantage (eg such as income inequality) This line of research is based on the
ldquoincivilities thesisrdquo which states that to understand the distribution of disorders it is necesary to
analyse the patterns of structured inequalities (Skogan 1986 Taylor 1999)
99
423 The ldquoIncivilities Thesisrdquo
Incivilities theories began with a focus on psychological dynamics (Garofalo 1978) moved
forward to an interest in social psychological processes (J Q Wilson amp Kelling 1982) and finally
evolved into a focus on community dynamics and outcomes (Skogan 1999) Individual and group
behaviours in tandem with ecological features have been proposed as the key factors explaining
incivilities and their posterior influence on social control quality of life and more serious crime
(J Q Wilson amp Kelling 1982)
In terms of ecological factors particularly those related to economic conditions Skogan
(1986) was the first linking the distribution of incivilities to the patterns of structural inequality If
incivilities mirror inequality structure this will have consequences in residentsrsquo health and safety
due to its levels of vulnerability In addition structured inequality associated with the proportion
of the manufacturing sector (eg when fabrics tend to move from cities to farther areas) will be
related to higher social disorganization and differences between urban and rural areas (W J
Wilson 1996) In addition a persistent feeling of relative deprivation (persistently high levels of
income inequality) could lead to fellow inhabitants of the community to commit antisocial
behaviours showing their frustration with the current economic model
The literature on incivilities posits that their causes are different from those of crime (Lewis
2017) Unlike crime analysis especially property crimes information on the location where the
incivility takes place is the same as the location where the perpetrator resides To achieve a
comprehensive understanding of the different types of incivilities it is crucial to consider
incivilities data covering an entire territory and not just specific areas (Hooghe Vanhoutte
Hardyns amp Bircan 2010) If we add to this the availability of panel data it could be possible not
100
only to identify the main determinants of incivilities but also the causal mechanism from income
inequality towards incivilities rate
In Chile citizen security crime and delinquency are among the most significant issues for
citizens based on opinion polls Existing research has found weak evidence of a significant
relationship between crime and indicators of socio-economic disadvantage such as income
inequality and unemployment rate with significant effects only on property crime (Beyer amp
Vergara 2006 Nuntildeez Rivera Villavicencio amp Molina 2003 Rivera Gutieacuterrez amp Nuacutentildeez 2009)
Crime deterrence variables such as the probability of being caught or the number of police
resources have also shown ambiguous results (Beyer amp Vergara 2006 Rivera et al 2009
Vergara 2012) Evidence at the county level shows that crime is higher in urban counties those
with a lower mean income per capita and counties located in the North of the country In addition
at least half of the crimes reported in one county are perpetrated by criminals from other counties
(Rivera et al 2009) No studies could be found about the determinants of incivilities
4 3 Methodology
431 Period of analysis and data sample
Chile is a relatively small country in Latin America with a population of 18346018
inhabitants in 2017 The country is divided into 345 municipalities with on average 53104
inhabitants (median value 18705) Municipalities are the organ of the State Administration
responsible to solve local needs Municipalities are not only the relevant political and
administrative local unit of analysis but also they represent the feeling of lsquocommunityrsquo among
the inhabitants of each municipality (Hooghe et al 2010) Our data includes many sources of
101
heterogeneity among municipalities such as indicators of economic deprivation population
density demographic characteristics and whether the county is a regional or provincial capital
We use a sample of 324 municipalities covering most of the Chilean territory for the period
2006ndash17 Data on incivilities is obtained from the ldquoCentre of Studies and Analysis of Crimerdquo
which is part of the ldquoSubsecretaria de Prevencion del Delitordquo (SPD in its Spanish acronym) of the
Chilean government32 Information on income inequality and control variables is obtained from
the ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) the
ldquoNational Institute of Statisticsrdquo (INE in its Spanish acronym) the ldquoNational Municipal
Information Systemrdquo (SINIM in its Spanish acronym) and the Immigration Department of the
Government of Chile Our panel only includes the years for which CASEN survey is available
2006 2009 2011 2013 2015 and 2017
432 Operationalisation of the response variable and exploratory analysis
Official Chilean records contain information for the total number of cases of incivilities per
year at the county level The number of cases is the sum of complains and detentions reported at
the police Our dependent variable 119894119899119888119894119907119894119897119894119905119894119890119904 correspond to the number of cases per year Due
to population differences comparisons between counties are made using the incivilities rate per
1000 population calculated as
119894119899119888119894119907_119903119886119905119890 lowast 1000 (41)
where 119894119899119888119894119907_119903119886119905119890 is the incivilities rate 119905 is the year 119894 the county and 119899 is the population of the
county per year
32 httpceadspdgovclestadisticas-delictuales
102
Figure 41 illustrates at the top the evolution of the total number (cases reported) of
incivilities and crimes at the country level for the period 2006-1733 At the bottom Figure 41
shows the evolution of the mean county rate per 1000 inhabitants We observe that both the number
of incivilities and the number of crimes has reached similar annual figures however average
county rates per 1000 population show different trends Crime rate displays a sustained fall after
reaching its pick in 2011 Incivilities average county rate which also reached a pick and posterior
drop in 2011 has recovered its upward trend since 2016 considerably exceeding the crime rate
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017
33 Crime refers to ldquocrimes of greater social connotationrdquo (DMCS in its Spanish acronym) which includes violent and property crimes
103
Chilean records classify incivilities in nine categories most of them associated with social
incivilities Summary statistics for the total and for each of the nine categories are presented in
Table 41 In addition Figure 42 shows the evolution of incivilities by category for the whole
period We see that the global trend in incivilities is mainly due to a substantial increase in ldquoStreet
Tradingrdquo and ldquoPublic Alcohol Consumptionrdquo A common element of Figures 41 and 42 is the
significant change in trend experienced by crimes and incivilities in 2011 That year the SPD
became dependent on the Ministry of Interior of the Chilean Government This event put the issue
of crime and delinquency within national priorities for the central government
Table 41
Summary statistics total count of incivilities and by category (full sample and period)
Unlike crime rates we do not expect significant cross-county spillover effects in incivilities
However the questions of where incivilities are concentrated and why they are there can be of
great interest (Skogan 2015) Figure 43 shows quantile maps for the rate of incivilities per 1000
inhabitants for the initial and final years in our panel
104
Figure 42 Evolution total number of incivilities by category
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017)
105
We observe that the range of values has increased significantly from 2006 to 2017 but the
spatial distribution remains almost unchanged On the one hand high incivilities rates in the North
could be associated with the mining activity On the other hand high rates in the Centre area
(where the countyrsquos capital is located) could be related to the higher population density and the
concentration of the economic activity34
To see how the different types of incivilities are distributed throughout the country we have
grouped those similar categories in four groups ldquoPublic Damagerdquo (ldquoPublic Disturbsrdquo ldquoPublic
Damagerdquo ldquoOtherrdquo) ldquoThreatsrdquo (ldquoPublic Fightrdquo ldquoThreatsrdquo and ldquoAnnoying Noisesrdquo) ldquoAlcohol
Consumptionrdquo (ldquoPublic Alcohol Consumptionrdquo and ldquoDrunkennessrdquo) and ldquoStreet Tradingrdquo This
distinction in groups could be relevant if we expect different patterns and different effects of
community heterogeneity on social cohesion among counties For instance we expect higher
levels of Public Damage in big urban cities Street trading is more likely in urban cities but also in
tourist areas The spatial distribution of these four groups for the six-year-average rate per 1000
inhabitants can be found in Appendix R
433 Measures of community heterogeneity and control variables
Social cohesion income inequality and racial diversity are all ldquocommunityrdquo (not individual)
characteristics Thus to understand their relationship we need aggregated data at least at the
county-municipal level With more disaggregated data like at the suburbs level the required
heterogeneity among groups of citizens is lost (Wilkinson 1999) Like Coffeacute and Geys (2006) we
use the Gini coefficient to capture economic heterogeneity However instead of a measured for
34 We also analysed the spatial distribution of crime rates In general areas with high levels of incivilities not necessarily are associated with high levels of crime This could imply that factors explaining incivilities and crime are different
106
the diversity of nationalities we use the proportion of foreign population to capture racial
heterogeneity Income data is obtained from the CASEN survey The Gini coefficient is calculated
for each county and included through the variable gini Racial heterogeneity is included through
the variable foreign which is the annual number of new VISAS granted to foreigners as a
proportion of the county population Chile has experienced a significant increase in immigration
since 2011 Immigration has been concentrated in the metropolitan region and mining regions in
the North of the country We expect a positive relationship between immigration and incivilities
although as with the relationship between immigration and crime the foundations for this
hypothesis are not strong (Hooghe et al 2010 Sampson 2008)
Economic development is another explanation for social cohesion frequently appealed to
explain trust with wealthier societies considered to exhibit higher levels of trust (Delhey amp
Newton 2005) In this study we include the natural log of the mean household income per capita
log(income) We also include the poverty rate poverty and the unemployment rate
unemployment Unlike the variable log(income) these variables are expected to be positively
associated with the number of incivilities When a relative indicator of economic heterogeneity
such as income inequality is included as determinant of social cohesion we should expect less
effect from absolute indicators of economic disadvantage such as poverty and unemployment rates
(Hooghe et al 2010 Tolsma et al 2009)
Among demographic variables the percentage of inhabitants between 10 and 24 years old is
included through the variable youth The variable women defined as the proportion of the female
population in each county is also included Variable youth is expected to have an ambiguous effect
Although young people have lower victimization and report rates they also represent the group
more likely to commit antisocial behaviours when a community has a low capacity of self-
107
regulation (eg when there is low parental supervision) The female population is associated with
a higher report of incivilities related to the male population
It is argued that crime and incivilities are essentially urban problems (Christiansen 1960
Wirth 1938) We include the variable log(density) defined as the log of population density (the
number of inhabitants divided by the area of each county in square kilometres) and a dummy
variable capital indicating whether a county is an administrative capital (provincial or regional)
Two additional variables are included to capture the level of informal social control exerted
by families living in each municipality First the variable education which is defined as the
average years of education of people over 15 years old Second the variable housing which capture
the proportion of families which are owners of their housing unit Although education and housing
are related to both the possibility of reporting and committing an incivility we expect a negative
association with the rate of incivilities
In Chile crime has been mainly a problem faced by the police and the Central Government
Administration To control for current law enforcement policies we include the variable
deterrence defined as the number of arrests as a proportion of the total number of incivilities cases
In addition municipalities can develop their own initiatives to deal with crime and incivilities
depending on their capacity to generate its own resources The level of financial autonomy from
central transfers is captured by the variable autonomy This variable is obtained from SINIM and
it is defined as the proportion of the budget revenue of each municipality that comes from its own
permanent sources of revenues A categorical variable mayor is also included This variable
indicates whether the municipality mayor is supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo political
parties (related to those ldquoINDEPENDENTrdquo mayors)
108
Table 42 presents descriptive statistics for our measures of income and racial heterogeneity
and the set of numeric control variables The Pearson correlation among these variables is shown
in Appendix S
Table 42
Summary statistics numeric explanatory variables
434 Methods
The annual count of incivilities as is characteristic for count data is highly concentrated in
a relatively small range of values In addition the distribution is right-skewed due to the presence
of important outliers (counties with a high number of incivilities) Figure 44 shows the
distribution of the six-year average number of incivilities for each of the 15 regions in Chile35 We
observe that regions differ in the number of counties in which they are divided In addition
counties within each region show important differences in the number of incivilities For instance
35 Regions are ordered from left to right and from top to bottom in the way they are geographically distributed from North to South So the northernmost region of the country is the ldquoXVrdquo and the southernmost region is the ldquoXIIrdquo The Metropolitan region (where the countryrsquos capital is located) is region ldquoXIIIrdquo in the centre of the country (see Appendix C)
109
excepting the Metropolitan region ldquoXIIIrdquo most counties in regions located in the centre of the
country (middle row in Figure 44) show a range of incivilities between 0 and 2000 The number
of incivilities is considerably lower in counties located in the northern (top row in Figure 44) and
southern (bottom row in Figure 44) regions of the country compared to regions in the centre of
the country It also seems clear from Figure 44 that the number of incivilities does not follow a
normal distribution
Figure 44 Annual average number of incivilities per county
The number of incivilities can be better described by a Poisson distribution In this case the
number of incivilities is ldquothe countrdquo and the number of incivilities per year is ldquothe rate per unit
timerdquo We are interested in modelling the average number of incivilities per year usually called 120582
as a function of a set of contextual factors to explain differences in incivilities between and within
110
counties The main characteristic of the Poisson distribution is that the mean is equal to the
variance This implies that as the mean rate for a Poisson variable increases the variance also
increases The main implication is we cannot use OLS to model 120582 as a function of the set of
contextual factors because the equal variance assumption in linear regression is violated
The rate of incivilities between counties is not directly comparable due to population
differences We expect counties with more people to have more reports of incivilities since there
are more people who could be affected To capture differences in population which is called the
exposure of our response variable 120582 it is necessary to include a term on the right side of our model
called an offset We will use the log of the county population in thousands as our offset36
Additionally similar to the case of crime data incivilities show a significant degree of
overdispersion (variance higher than the mean) suggesting that there is more variation in the
response than the Poisson model implies37 We also model and regress incivilities assuming a
Negative Binomial distribution to address overdispersion An advantage of this approach is that it
introduces a dispersion parameter in addition to 120582 which gives the model more flexibility38
Considering as the response variable the count of incivilities per year the model can be
expressed as follow
120582 119890119909119901 120573 120573 119892119894119899119894 120573 119891119900119903119890119894119892119899 119883120574 120572 120579 (42)
36 If we think of 120582 as the average number of incivilities per year then represents the number per 1000
inhabitants so that the yearly count is adjusted to be comparable across counties of different sizes Adjusting the yearly count by population is equivalent to adding 119897119900119892 1199011199001199011199061198971198861199051198941199001198991000 to the right-hand side of the regression equation 37 Without adjusting for overdispersion we use incorrect artificially small standard errors leading to artificially small p-values for model coefficients 38 The Negative Binomial model posits selecting a 120582 (average number of incivilities) for each county and then generating a count using a Poisson random variable with the selected 120582 With this approach the counts will be more dispersed than would be expected for observations based on a single Poisson variable with rate 120582
111
where 120582 is the rate of incivilities 119883 is our vector of controls 120572prime119904 are county-specific constants
and 120579prime119904 are time-specific constants Accounting for differences in county population we have
119890119909119901 120573 120573 119892119894119899119894 119883120574 120572 120579 (43)
where 119901119900119901119906119897119886119905119894119900119899 is the county population per year Hence the model to be estimated using
Maximum Likelihood Estimation (MLE) is
119897119900119892 120582 120573 120573 119892119894119899119894 119883120574 120572 120579 119897119900119892 (44)
Finally to account for different effects depending on the type of incivilities we also run
equation (44) for each of the four groups of incivilities defined in section (432)
435 Hypotheses
Based on the community heterogeneity hypothesis the relationship between social cohesion
and diversity should be stronger for lower levels of income and less educated groups of people
(Tolsma et al 2009) Hence contrary to evidence for developed and more equal countries we
expect a significant positive association for the Chilean case where more than 50 of the
population is economically vulnerable (OECD 2017)
The main hypotheses to be tested in this essay is whether the number of incivilities is
positively associated with the level of economic and racial heterogeneity at the county level We
start analysing this association for the full sample and period Next we analyse whether the
relationship between incivilities and our measures of diversity differs by geographic area (region
or zone) Finally we check whether the effect of economic and racial diversity is different
depending on the group of incivilities
112
44 Results and Discussion
Overall our results show that the rate of incivilities displays a stronger and more significant
relationship with racial diversity than with economic heterogeneity This association differs for
different geographic areas and for different types of incivilities Absolute economic indicators
except for income show a significant but small effect Increases in the average levels of income
or education and more financial autonomy for municipalities seem to be effective ways to reduce
the rate of incivilities
We estimate equation (44) assuming that the number of incivilities follows a Poisson
distribution Regional and temporal heterogeneity are captured through the inclusion of dummy
variables for five years (with 2006 as the reference year) and fourteen regional dummies (with
region XIII as the reference region) Results are reported in Table 4339 This table is structured in
two blocks of regressions pooled models in columns (1)-(4) and fixed-effects models in columns
(5)-(8)40 The first column in each block only includes economic indicators relative and absolute
trying to test which ones are more relevant and whether incivilities tend to mirror income
inequality (the ldquoincivilities thesisrdquo) The second column adds the variable foreign to account for
the effect of racial diversity (Letki 2008) The third column includes education to check whether
the association between economic and racial diversity with social cohesion changes (gets less
significant) when we control for educational level (Tolsma et al 2009) The final column in each
block corresponds to the full model specification which includes the rest of controls
39 Pooled estimations were obtained using the R command glm() and compared with results obtained from the command poisson in STATA 15 Fixed effects and random effects models were estimated using the STATA command xtpoisson Negative Binomial models were estimated using the command glmnb() in the MASS package for R and commands nbreg and xtnbreg in STATA 15 Results for Negative Binomial regressions are in Appendix T 40 We have omitted results with random effects due to the literature on panel count data models suggest that the most robust estimator is Poisson regression with Fixed Effects (Santos Silva amp Tenreyro 2010 2011)
113
Table 43
Poisson regressions
114
The positive and significant coefficient for the variable gini besides being small it becomes
insignificant in the fixed effects specification which includes the full set of controls This result
does not seem to be enough evidence to support our hypothesis that more unequal counties display
higher rates of incivilities On the other hand racial diversity through the variable foreign shows
a consistent positive association with the rate of incivilities41 Together coefficients for gini and
foreign seems to support the ldquocommunity heterogeneity thesisrdquo (Letki 2008) but not the
ldquoincivilities thesisrdquo (Skogan 1999) To check this finding we run the pooled full model
specification for each region and results are shown in Table 44 where regions have been ordered
from North to South The sign of the coefficient of the variable gini differs for different regions
Moreover the relationship is insignificant in some of the most unequal regions which are in the
South of the country (VIII IX and XIV) This result rejects the hypothesis that incivilities mirror
structural income inequalities For the variable foreign 12 out of 15 regions confirm the positive
association with the rate of incivilities42
We also run our pooled full model separately for each group of incivilities defined at the end
of section (432) Income inequality keeps its significant but small association with each group of
incivilities (see Table 45) Our measure of racial diversity shows a stronger association with
ldquoAlcohol Consumptionrdquo related to ldquoPublic Damagerdquo and ldquoThreatsrdquo The link with ldquoStreet Tradingrdquo
appears as non-significant These results support our general finding that on the one hand racial
heterogeneity exert a more significant influence on the rate of incivilities than economic
41 To get the effects of the variables gini and foreign over the rate of incivilities per 1000 inhabitants we need to exponentiate their coefficients Thus an increase in one unit in the variable gini increases the rate of incivilities per 1000 inhabitantsrdquo in 1005 times (e^0005) in the pooled full model Similarly an increase in one point in variable foreign is expected to increase the ldquorate of incivilities per 1000 inhabitantsrdquo in 10876 times (e^0084) in the full pooled model and 10471 (e^0046) times in the full Fixed Effects model 42 Given that depending on the geographic location regions share certain characteristics the model was also run clustering regions in 4 zones (see Figure 31 in chapter 3) Results by zone are in Appendix U
115
heterogeneity On the other hand results confirm our hypothesis that the effect of diversity is
different for different types of incivilities
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region
Back to our general results in Table 43 the significant and negative coefficient of the
income variable and to a lesser extent the significant and positive coefficients of poverty and
unemployment provide evidence that absolute rather than relative economic indicators may be
more important explanations of the rate of incivilities This is opposite to evidence for the analysis
116
of crime rates such as in Hoodge (2010) and support the idea that determinants of incivilities are
different from those of crime Our results are also opposite to those for Dutch municipalities where
economic indicators turned out to be more important than ethnic heterogeneity (Tolsma et al
2009) The coefficient for the variable log(income) could be interpreted as counties with an income
level under the average face higher problems of antisocial behaviours such as incivilities In
addition as the income level moves far away from its average low level the problem of incivilities
is less relevant43 In terms of policy implications only those policies that achieve a significant
increase in the average level of county income seem to be effective in reducing incivilities and
strengthening social cohesion
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group
43 We ran our model splitting the sample of counties in quintiles based on their income level The coefficient for the variable gini only showed a significant (and positive) coefficient for the second third and fourth quintile A non-significant coefficient for the first quintile could imply that other types of inequalities (eg health education) are more relevant in the poorest counties In the case of the fifth quintile (the richest group of counties) they have better facilities in terms of infrastructure police resources self-protection etc which could reduce the potential triggers of incivilities
117
The inclusion of the variable education significantly improved the goodness of fit of the
models and did not generate significant changes in the coefficients of our measures of economic
and racial diversity This result rejects the proposition that the relationship between social
cohesion and diversity becomes less strong when controlled by education (Tolsma et al 2009)
Additionally it highlights the topic of education as a relevant determinant of the rate of incivilities
and social cohesion
Among control variables there are also some important results Opposite to what we
expected the variable youth shows a negative or non-significant coefficient Although this result
could be due to the lack of ldquophysical incivilitiesrdquo in Chilean records it indicates that it is incorrect
to stereotype this group as the main responsible for high incivilities rates The significant and
negative coefficient of the variable autonomy in the fixed effects specification could also have
important policy implications It is a signal that local governments can play an important role in
reducing incivilities or complementing the efforts from the central government Another
interesting result is the significant coefficient of the variable housing The latter finding is
particularly important in the sense that a negative sign supports public policies oriented to increase
homeownership as effective ways to improve social cohesion However the small magnitude of
the coefficient that even showed the opposite sign in some model specifications could be
explained for the high level of segregation that these policies have generated in Chilean society
As mentioned in the Introduction and Literature Review so far only a few studies have
used measures of disorders or incivilities as dependent variable to explain changes in social
cohesion (Skogan 2015) In addition there is no evidence in Chile analysing the determinants of
incivilities separately from those of crimes The importance of our results on identifying the
importance of economic and racial diversity on social cohesion lies mainly in its generality An
118
important number of countries all around the world share a similar context characterized by high
levels of inequality and an explosive increase in immigration These countries are also
experiencing a worsening in social cohesion which increases the risk of a social outburst
4 5 Conclusions
The main goal of this essay was to determine whether differences in incivilities at the county
level mirror differences in income distribution and racial diversity Previous literature suggests a
positive and strong association between social cohesion and indicators of economic disadvantage
relative deprivation and racial diversity (Letki 2008 Tolsma et al 2009 Uslaner amp Brown 2005)
While not all our results were significant they showed helpful insights about how and where
economic and racial diversity are more likely to influence the rate of incivilities and social
cohesion
We used data for the period 2006ndash17 economic heterogeneity was measured through the
Gini coefficient at the county level and racial heterogeneity was proxied by the number of granted
visas to foreigners as proportion of county population We found strong evidence of a significant
and positive association between the rate of incivilities and racial diversity but not with income
inequality Contrary to previous evidence at the municipal level (Coffeacute amp Geys 2006 Tolsma et
al 2009) in general our results give support for the ldquohomophily principlerdquo and the ldquocommunity
heterogeneity hypothesisrdquo However results also showed that the effect of economic and racial
diversity varies throughout the Chilean regions and for the different types of incivilities
We also found that policies aimed at controlling the behaviour of young people did not have
strong empirical support In terms of the role that local governments may have in facing the
119
growing problem of incivilities we found evidence that efforts managed from the municipalities
can be an important complement to those from the central government
Future research should go further on the role of local authorities on incivilities and social
cohesion On the one hand municipalities could have a direct impact on social cohesion through
the implementation of programs complementary to those of central authorities oriented to reduce
incivilities and crime On the other hand social cohesion could be indirectly affected when local
authorities display an inefficient performance supplying public services to citizens or they are
recognized as corrupted institutions We suggest that policy makers from central government
should give local authorities a greater role in fighting antisocial behaviours and crime Evaluating
programs in specific municipalities could help to elucidate the causal effect of for instance higher
fiscal autonomy on the rate of incivilities
Another interesting area for future work will be to analyse how housing policies have
contributed to the phenomenon of segregation of Chilean society and in the process of weakening
social cohesion Finally our main result highlights the need of a deeper analysis of the impact that
foreign immigration is having in Chile For instance disaggregating information by country of
origin and the reasons why immigrants are arriving to the country or specific regions will surely
help to understand the impacts of immigration
120
Chapter 5 Conclusions
This thesis investigated in three essays the issue of income inequality in Chile using county-
level data for the period 2006-2017 The first essay supplied empirical evidence about the
importance of the degree of dependence on natural resources in terms of employment in explaining
cross-county differences in income inequality The second essay analysed the potential causal
effect that income inequality has on the level of technical efficiency of local governments
providing public goods and services Lastly the third essay studied the relationship between social
cohesion measured through the number of antisocial behaviours classified as ldquoincivilitiesrdquo and
the levels of income and racial heterogeneity
Findings from the first essay support the idea that the endowment of natural resources plays
a significant role in explaining income inequality in Chile However contrary to what most
theoretical and empirical evidence postulates our findings showed a robust negative association
between the two variables This means that the reduction experienced in Chile in the degree of
dependence on natural resources in terms of employment has contributed to the persistence of high
levels of income inequality The exploratory analysis indicated that income inequality shows a
general clustering process characterized by a significant and positive spatial autocorrelation
Regarding the previous evidence for Chile (Paredes et al 2016) the regression analysis confirmed
the relevance of the spatial dimension of income inequality through a process of spatial
heterogeneity giving less support to the existence of a process of spatial dependence (spillover
effect) in the variable itself
121
Essay 2 studied the potential trade-off between efficiency and equity analysing the influence
of income inequality on the efficiency of local governments at the municipal level To identify the
causal effect of income inequality on municipal efficiency we proposed the use of the proportion
of firms in the primary sector as an instrument for income inequality Findings confirmed our
hypothesis that efficiency is lower in more unequal counties This result suggests the non-existence
of the trade-off between equity and efficiency Hence policies intended to reduce inequality could
help to increase efficiency at least at the level of municipal local governments
The third essay analysed how social cohesion proxied by the rate of incivilities is associated
with the levels of economic diversity proxied by income inequality and the levels of racial
diversity proxied by the number of new visas grated as proportion of the county population
Findings gave strong support to the hypothesis that the rate of incivilities is positively related to
racial diversity and to a lesser extent to economic diversity In addition the rate of incivilities
appears negatively related to the degree of financial autonomy of municipalities This means that
local governments can effectively contribute to the reduction of incivilities which could help
reduce victimization and crime rates ultimately strengthening social cohesion
Taken together findings from essays 2 and 3 highlight the important role that income
inequality could play in other relevant economic and social dimensions These findings add to the
understanding of the potential consequences of income inequality particularly in natural resource
rich countries with persistently high levels of inequality
The present study has mainly investigated income inequality at the county level In addition
Chilean municipalities play an important role providing ldquoservices to peoplerdquo so our findings could
be applied in other highly unequal countries with a high degree of dependence on natural resources
and local governments with similar responsibilities For instance in Latin America apart from
122
Chile and Brazil there are no studies on the efficiency of local governments Other limitations are
associated with the availability of information For instance important indicators such as GDP per
capita are only available at the regional level and information of incomes is not available annually
In addition given the heterogeneity among municipalities some type of grouping of municipalities
should be performed before looking for causal relationships or conducting program evaluation
Despite these limitations we believe this study could be the basis for different strands of future
research on the topic of inequality local government efficiency and social cohesion
It was stated in chapter 2 based on the resource curse hypothesis literature there are two
elements that determine whether NR are a curse or blessing in terms of socioeconomic outcomes
First the curse is more likely in countries with weak political and governance institutions Second
different types of resources affect institutions differently with resources that are concentrated in
space so-called ldquopointrdquo resources tend to impair institutions while ldquodiffuserdquo resources do not
(Deacon 2011 Isham Woolcock Pritchett amp Busby 2005) Our results showed a negative
relationship between income inequality and our measure of natural resource dependence even after
controlling for zone fixed effects and for the level of government expenditure This result could
be interpreted as a signal that NR has a direct effect on income inequality in addition to the indirect
impact through market or institutional channels Using other potential institutional transmission
channels will shed light about the true effect that the endowment of natural resources has over
income inequality Variables that could capture these institutional channels include the level of
employment in the public sector measures of rule of law and corruption and changes in the
creation of new business in the secondary and tertiary sectors related to the primary sector
Based on results from chapter 3 most of the municipalities show scale inefficiencies One
immediate area for future work will involve using our set of contextual factors to predict the status
123
of municipalities in terms of scale inefficiencies Defining as dependent variable whether a
municipality shows constant decreasing or increasing returns to scale we could run a multinomial
logistic regression to predict municipal status For instance we would expect that a one-unit
increase in the Gini coefficient should increase the probability of scale inefficiencies (increasing
or decreasing returns to scale rather than constant returns to scale) Because the aim in this case
would be predicting a certain result in terms of returns to scale the next step should involve to
split the full sample in training and testing data sets and to use some resampling methods such as
bootstrapping This will allow us to evaluate the performance and accuracy of our model
predictions using different random samples of municipalities Results from Machine Learning
algorithms will help us to assess the generalizability of our results to other data sets
Future work should also benefit greatly by using data on different Latin American countries
to (1) compare the responsibilities of local governments (2) select a common set of inputs and
output to evaluate LGE (3) identify the relevance of scales inefficiencies in explaining differences
in performance and (4) analyse the influence of contextual characteristics over LGE Differences
in the main primary sector activity in each country such as oil in Brazil mining in Chile or Coffee
in Colombia could be responsible for differences in LGE among countries These differences could
be associated with sources of revenue management of expenditure and definitions of outputs or
contextual effects such as corrupted institutions or the delay in the development of other sectors
such as manufacturing or services
To delve deep on reasons explaining the social crisis experienced by Chilean society and
other countries one area of future work will be to analyse the relationship between diversity and
the origins of social revolutions Based on Tiruneh (2014) the three most important factors that
explain the onset of social revolutions are economic development regime type and state
124
ineffectiveness Interesting questions include whether the characteristics of Chilean context at the
end of 2019 are enough to trigger the transformation of the political and socioeconomic system
Social revolutions particularly violent revolutions are less likely in more democratic educated
and wealthy societies So it would be relevant to identify the factors explaining the violence that
has characterized the social crisis in Chile Finally the democratic regime has been maintained in
the last decades with changes between left and right governments This could imply that more
important than the regime has been the efficiency or ineffectiveness of the governments to satisfy
the needs of the population
Future work should also cover the disaggregation of information regarding foreign
population in terms of the reasons for new granted visas and the country of origin Official data
allows us to disaggregate whether the benefit is permanent (students and employees with contract)
or temporary Furthermore most of the new visas were traditionally granted to neighbouring
countries (Peru and Bolivia) a trend that has changed in the recent years to include countries such
as Colombia Venezuela and Haiti An analysis of how economic and social indicators have been
affected by changes in the composition of foreigners their reasons for immigrating to the country
and their geographical distribution have implications for economic policy at both the national and
local levels At the national level such analysis should be a key input when proposing changes to
the national immigration policy At the local level it could help define the role of municipalities
to assess the benefits and challenges of immigration These challenges are mainly related to the
provision of public goods and services such as health and education which in Chile are the
responsibility of the municipalities
The findings of this thesis suggest that policymakers should encourage policies that reduce
income inequality The key role that municipalities could play to strengthen social cohesion and
125
the increasingly important role that foreign population is acquiring in most modern societies are
also interesting avenues for future research However the picture is still incomplete and more
research is needed incorporating other dimensions of inequality This is essential if we want to
understand the reasons that could have triggered the social outbursts experienced by various
economies across the globe
126
Bibliography
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Acemoglu D (2002) Technical Change Inequality and the Labor Market Journal of Economic Literature 40(1) 7ndash72 httpsdoiorg1012570022051026976
Acemoglu D Aghion P amp Violante G L (2001) Deunionization Technical Change and Inequality Carnegie-Rochester Conference Series on Public Policy 55(1) 229ndash264 httpsdoiorg101016S0167-2231(01)00058-6
Acemoglu D Johnson S amp Robinson J A (2001) The Colonial Origins of Comparative Development An Empirical Investigation The American Economic Review 91(5) 1369ndash1401 httpsdoiorg101257aer9151369
Acemoglu D amp Robinson J A (2002) The Political Economy of the Kuznets Curve Review of Development Economics 6(2) 183ndash203 httpsdoiorg1011111467-936100149
Afonso A amp Fernandes S (2006) Measuring local government spending efficiency Evidence for the Lisbon region Regional Studies 40(1) 39ndash53 httpsdoiorg10108000343400500449937
Afonso A amp Fernandes S (2008) Assessing and explaining the relative efficiency of local government The Journal of Socio-Economics 37(5) 1946ndash1979 httpsdoiorg101016jsocec200703007
Afonso A Schuknecht L amp Tanzi V (2010) Income distribution determinants and public spending efficiency Journal of Economic Inequality 8(3) 367ndash389 httpsdoiorg101007s10888-010-9138-z
Akita T (2003) Decomposing regional income inequality in China and Indonesia using two-stage nested Theil decomposition method The Annals of Regional Science 37(1) 55ndash77 httpsdoiorg101007s001680200107
Alesina A (2000) Participation in heterogeneous communities The Quarterly Journal of Economics 115(3) 847ndash904 httpsdoiorg101162003355300554935
Alesina A amp La Ferrara E (2002) Who trusts others Journal of Public Economics 85(2) 207ndash234 httpsdoiorg101016S0047-2727(01)00084-6
Allcott H amp Keniston D (2014) Dutch Disease or Agglomeration The Local Economic Effects of Natural Resource Booms in Modern America (N W P N 20508 Ed) NBER Working Paper No 20508 (Vol w20508) NBER Working Paper No 20508 NBER Working Paper No 20508 httpsdoiorgNBER Working Paper No 20508
Andersen T M amp Maibom J (2020) The big trade-off between efficiency and equitymdashis it there Oxford Economic Papers 72(2) 391ndash411
127
Anselin L (1988) Spatial econometrics methods and models (Vol 4) DordrechtBoston Kluwer Academic Publishers
Anselin L amp Bera A K (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics Statistics Textbooks and Monographs (Vol 155)
Anselin L Bera A K Florax R amp Yoon M J (1996) Simple diagnostic tests for spatial dependence Regional Science and Urban Economics 26(1) 77ndash104 httpsdoiorg1010160166-0462(95)02111-6
Antonelli M A amp De Bonis V (2018) The efficiency of social public expenditure in European countries a two-stage analysis Applied Economics 1ndash14 httpsdoiorg1010800003684620181489522
Aragoacuten F M amp Rud J P (2013) Natural Resources and Local Communities Evidence from a Peruvian Gold Mine American Economic Journal Economic Policy 5(2) 1ndash25 httpsdoiorg101257pol521
Ariely G (2014) Does Diversity Erode Social Cohesion Conceptual and Methodological Issues Political Studies 62(3) 573ndash595 httpsdoiorg1011111467-924812068
Armstrong H amp Taylor J (2000) Regional economics and policy (3rd ed) Oxford Blackwell
Aroca P amp Atienza M (2011) Economic implications of long distance commuting in the Chilean mining industry Resources Policy 36(3) 196ndash203 httpsdoiorg101016jresourpol201103004
Aroca P amp Bosch M (2000) Crecimiento convergencia y espacio en las regiones chilenas 1960 - 1998 Estudios de Economiacutea 27 199ndash224 Retrieved from httprepositoriouchileclbitstreamhandle2250127853Patricio_Arocapdfsequence=1
Ashworth J Geys B Heyndels B amp Wille F (2014) Competition in the political arena and local government performance Applied Economics 46(19) 2264ndash2276 httpsdoiorg101080000368462014899679
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Auty R (1993) Sustaining development in mineral economies the resource curse thesis London [ua] Routledge
Auty R (2001) Resource abundance and economic development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Badeeb R A Lean H H amp Clark J (2017) The evolution of the natural resource curse thesis A critical literature survey Resources Policy 51 123ndash134 httpsdoiorg101016jresourpol201610015
Balaguer-Coll M T Brun-Martos M I Maacuterquez-Ramos L amp Prior D (2019) Local government efficiency determinants and spatial interdependence Applied Economics
128
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Balaguer-Coll M T amp Prior D (2009) Short- and long-term evaluation of efficiency and quality An application to Spanish municipalities Applied Economics 41(23) 2991ndash3002 httpsdoiorg10108000036840701351923
Banker R D Charnes A amp Cooper W W (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis Management Science 30(9) 1078ndash1092
Beine M Coulombe S amp Vermeulen W N (2015) Dutch Disease and the Mitigation Effect of Migration Evidence from Canadian Provinces The Economic Journal 125(589) 1574ndash1615 httpsdoiorg101111ecoj12171
Benito B Bastida F amp Garciacutea J A (2010) Explaining differences in efficiency an application to Spanish municipalities Applied Economics 42(4) 515ndash528 httpsdoiorg10108000036840701675560
Berg A amp Ostry J (2011) Equality and efficiency Finance amp Development 48(3) 12ndash15
Berg A amp Ostry J (2017) Inequality and Unsustainable Growth Two Sides of the Same Coin IMF ECONOMIC REVIEW 65(4) 792ndash815 httpsdoiorg101057s41308-017-0030-8
Beyer H amp Vergara R (2006) Delincuencia en Chile Determinantes y rol de las poliacuteticas puacuteblicas Instituto de Economiacutea UC
Blanco L amp Grier R (2012) Natural resource dependence and the accumulation of physical and human capital in Latin America Resources Policy 37(3) 281ndash295 httpsdoiorghttpdoiorg101016jresourpol201201005
Blau J R amp Blau P M (1982) The cost of inequality Metropolitan structure and violent crime American Sociological Review 114ndash129
Boetti L Piacenza M amp Turati G (2009) Fiscal decentralization and spending efficiency of local governments An Empirical Investigation on a Sample Of 4
Boix C amp Posner D (1998) Social capital Explaining its origins and effects on government performance British Journal Of Political Science 28 686ndash693
Borge L E Parmer P amp Torvik R (2015) Local natural resource curse JOURNAL OF PUBLIC ECONOMICS 131 101ndash114 httpsdoiorg101016jjpubeco201509002
Borger B Kerstens K Moesen W amp Vanneste J (1994) Explaining differences in productive efficiency An application to Belgian municipalities Public Choice 80(3) 339ndash358 httpsdoiorg101007BF01053225
Bourguignon F amp Morrisson C (1990) Income distribution development and foreign trade A cross-sectional analysislowast European Economic Review 34(6) 1113ndash1132 httpsdoiorghttpsdoiorg1010160014-2921(90)90071-6
129
Branas C C Cheney R A MacDonald J M Tam V W Jackson T D amp Ten Have T R (2011) A difference-in-differences analysis of health safety and greening vacant urban space American Journal of Epidemiology 174(11) 1296ndash1306
Bravo J (2014) Fondo Comuacuten Municipal y su desincentivo a la recaudacioacuten en Chile Temas de La Agenda Legislativa-Centro de Poliacuteticas Puacuteblicas UC 9(68)
Browning E K amp Johnson W R (1984) The Trade-Off between Equality and Efficiency Journal of Political Economy 92(2) 175ndash203 httpsdoiorg101086261219
Brunnschweiler C N amp Bulte E H (2008) The resource curse revisited and revised A tale of paradoxes and red herrings Journal of Environmental Economics and Management 55(3) 248ndash264 httpsdoiorghttpsdoiorg101016jjeem200708004
Brunori P Ferreira F H G amp Peragine V (2013) Inequality of opportunity income inequality and economic mobility Some international comparisons In Getting Development Right (pp 85ndash115) Springer
Brunton-Smith I Jackson J amp Sutherland A (2014) Bridging structure and perception On the neighbourhood ecology of beliefs and worries about violent crime British Journal of Criminology 54(4) 503ndash526
Bulte E H Damania R amp Deacon R T (2005) Resource intensity institutions and development World Development 33(7) 1029ndash1044 httpsdoiorg101016jworlddev200504004
Carmignani F (2013) Development outcomes resource abundance and the transmission through inequality Resource and Energy Economics 35(3) 412ndash428 httpsdoiorg101016jreseneeco201304007
Carmignani Fabrizio amp Avom D (2010) The social development effects of primary commodity export dependence Ecological Economics 70(2) 317ndash330 httpsdoiorg101016jecolecon201009003
Caselli F amp Michaels G (2013) Do Oil Windfalls Improve Living Standards Evidence from Brazil American Economic Journal Applied Economics 5(1) 208ndash238 httpsdoiorg101257app51208
Celebioglu F amp Dallrsquoerba S (2010) Spatial disparities across the regions of Turkey An exploratory spatial data analysis Annals of Regional Science 45(2) 379ndash400 httpsdoiorg101007s00168-009-0313-8
Chan J To H-P amp Chan E (2006) Reconsidering social cohesion Developing a definition and analytical framework for empirical research Social Indicators Research 75(2) 273ndash302
Charnes A Cooper W W amp Rhodes E (1978) Measuring the efficiency of decision making units European Journal of Operational Research 2(6) 429ndash444
130
Chi G amp Zhu J (2019) Spatial Regression Models for the Social Sciences SAGE Publications Retrieved from httpsbooksgooglecomaubooksid=zHGkvwEACAAJ
Christiansen K O (1960) Industrialization and urbanization in relation to crime and juvenile delinquency International Review of Criminal Policy 16 3ndash8
Cingano F (2014) Trends in income inequality and its impact on economic growth (Vol 163) Paris OECD Publishing httpsdoiorg1017875jxrjncwxv6j-en
Coelli T J Prasada Rao D S OrsquoDonnell C J amp Battese G E (2005) An introduction to efficiency and productivity analysis An Introduction to Efficiency and Productivity Analysis Springer Science amp Business Media httpsdoiorg101007b136381
Coffeacute H amp Geys B (2005) Institutional Performance and Social Capital An Application to the Local Government Level Journal of Urban Affairs 27(5) 485ndash501 httpsdoiorg101111j0735-2166200500249x
Coffeacute H amp Geys B (2006) Community Heterogeneity A Burden for the Creation of Social Capital Social Science Quarterly 87(5) 1053ndash1072 httpsdoiorg101111j1540-6237200600415x
Cohen D Spear S Scribner R Kissinger P Mason K amp Wildgen J (2000) ldquo Broken windowsrdquo and the risk of gonorrhea American Journal of Public Health 90(2) 230
Corden W M amp Neary J P (1982) Booming sector and de-industrialisation in a small open economy The Economic Journal 92(368) 825ndash848
Cordero J M Pedraja-Chaparro F Pisaflores E C amp Polo C (2017) Efficiency assessment of Portuguese municipalities using a conditional nonparametric approach Journal of Productivity Analysis 48(1) 1ndash24 httpsdoiorg101007s11123-017-0500-z
Croissant Y amp Millo G (2018) Panel Data Econometrics with R John Wiley amp Sons
Cust J amp Poelhekke S (2015) The Local Economic Impacts of Natural Resource Extraction 7 251ndash268 httpsdoiorg101146annurev-resource-100814-125106
da Cruz N F amp Marques R C (2014) Revisiting the determinants of local government performance Omega 44 91ndash103 httpsdoiorg101016JOMEGA201309002
Dauvin M amp Guerreiro D (2017) The Paradox of Plenty A Meta-Analysis World Development 94 httpsdoiorg101016jworlddev201701009
de Sousa M da C S Cribari-Neto F amp Stosic B D (2005) Explaining DEA technical efficiency scores in an outlier corrected environment the case of public services in Brazilian municipalities Brazilian Review of Econometrics 25(2) 287ndash313
Deacon R T (2011) The Political Economy of the Natural Resource Curse A Survey of Theory and Evidence Foundations and Trends in Microeconomics 111-208
Delhey J amp Newton K (2005) Predicting cross-national levels of social trust global pattern or
131
Nordic exceptionalism European Sociological Review 21(4) 311ndash327
Dlouhyacute M (2018) Measuring Geographic Inequalities Dealing with Multiple Health Resources by Data Envelopment Analysis Frontiers in Public Health 6 53 httpsdoiorg103389fpubh201800053
Dollery B Wallis J amp Akimov A (2010) One Size Does Not Fit All The Special Case of Remote Small Local Councils in Outback Queensland Local Government Studies 36(1) 21ndash42 httpsdoiorg10108003003930903435716
Domenech J (2008) Mineral resource abundance and regional growth in Spain 1860ndash2000 Journal of International Development The Journal of the Development Studies Association 20(8) 1122ndash1135
Doran J amp Jordan D (2016) Decomposing US regional income inequality from 1969 to 2009 Applied Economics Letters 23(11) 781ndash784 httpsdoiorg1010801350485120151109030
Drew J amp Dollery B (2015) The State of Things The Dynamic Efficiency of Australian State and Territories Economic Papers A Journal of Applied Economics and Policy 34(3) 165ndash176
Drew J Kortt M amp Dollery B (2015) What Determines Efficiency in Local Government A DEA Analysis of NSW Local Government Economic Papers A Journal of Applied Economics and Policy 34(4) 243ndash256 httpsdoiorg1011111759-344112118
Easterly W (2007) Inequality does cause underdevelopment Insights from a new instrument Journal of Development Economics 84(2) 755ndash776 httpsdoiorghttpdxdoiorg101016jjdeveco200611002
Ebert L amp La Menza T (2015) Chile copper and resource revenue A holistic approach to assessing commodity dependence Resources Policy 43(Supplement C) 101ndash111 httpsdoiorghttpsdoiorg101016jresourpol201410007
ElGindi T (2017) Natural resource dependency neoliberal globalization and income inequality Are they related A longitudinal study of developing countries (1980ndash2010) Current Sociology 65(1) 21ndash53 httpsdoiorg1011770011392116632031
Engerman S L amp Sokoloff K L (1994) Factor Endowments Institutions and Differential Paths of Growth Among New World Economies National Bureau of Economic Research
Engerman S L amp Sokoloff K L (1997) Factor endowments institutions and differential paths of growth among new world economies How Latin America Fell Behind 260ndash304
Engerman S L Sokoloff K L Urquiola M amp Acemoglu D (2002) Factor Endowments Inequality and Paths of Development among New World Economies [with Comments] EconomampxedA 3(1) 41ndash109 Retrieved from httpwwwjstororgezp01libraryquteduaustable20065432
132
Faumlre R Grosskopf S amp Lovell C A K (1985) The measurement of efficiency of production (Vol 6) Springer Science amp Business Media
Fleming D A amp Measham T G (2015a) Income Inequality across Australian Regions during the Mining Boom 2001-11 Australian Geographer 46(2) 203ndash216 httpsdoiorg1010800004918220151020596
Fleming D A amp Measham T G (2015b) Local economic impacts of an unconventional energy boom the coal seam gas industry in Australia Australian Journal of Agricultural and Resource Economics 59(1) 78ndash94 httpsdoiorg1011111467-848912043
Fum R M amp Hodler R (2010) Natural resources and income inequality The role of ethnic divisions Economics Letters 107(3) 360ndash363 httpsdoiorg101016jeconlet201003008
Garofalo J (1978) The fear of crime Broadening our perspective
Geys B amp Moesen W (2009) Exploring Sources of Local Government Technical Inefficiency Evidence from Flemish Municipalities Public Finance and Management 9(1) 1ndash29
Goderis B amp Malone S W (2011) Natural Resource Booms and Inequality Theory and Evidence The Scandinavian Journal of Economics 113(2) 388ndash417 httpsdoiorg101111j1467-9442201101659x
Greene W H (2016) Productivity and Efficiency Analysis (W H Greene L Khalaf R Sickles M Veall amp M-C Voia Eds) (1st ed 20) Cham Springer International Publishing httpsdoiorg101007978-3-319-23228-7
Gustafsson B amp Shi L (2002) Income inequality within and across counties in rural China 1988 and 1995 Journal of Development Economics 69(1) 179ndash204 httpsdoiorg101016S0304-3878(02)00058-5
Gylfason T amp Zoega G (2003) Inequality and Economic Growth Do Natural Resources Matter In T T Eicher S (Ed) Inequality and Growth Theory and Policy Implications (pp 255ndash292) The MIT Press
Henningsen A (2010) Estimating Censored Regression Models in R using the censReg Package R Package Vignettes Collection 5(2) 12
Henningsen A (2019) censReg Censored Regression (Tobit) Models R package version 05-30 httpscranr-projectorgpackage=censReg Retrieved from httpscranr-projectorgpackage=censReg
Herrera S amp Pang G (2005) Efficiency of Public Spending in Developing Countriesthinsp An Efficiency Frontier Approach World Bank Washington DC
Hill T D amp Angel R J (2005) Neighborhood disorder psychological distress and heavy drinking Social Science amp Medicine 61(5) 965ndash975
133
httpsdoiorghttpsdoiorg101016jsocscimed200412027
Hipp J R (2010) RESIDENT PERCEPTIONS OF CRIME AND DISORDER HOW MUCH IS ldquoBIASrdquo AND HOW MUCH IS SOCIAL ENVIRONMENT DIFFERENCES Criminology 48(2) 475ndash508 httpsdoiorg101111j1745-9125201000193x
Holtug N amp Mason A (2010) Introduction Immigration diversity and social cohesion SAGE Publications Sage UK London England
Hooghe M Vanhoutte B Hardyns W amp Bircan T (2010) Unemployment Inequality Poverty and Crime Spatial Distribution Patterns of Criminal Acts in Belgium 2001ndash06 The British Journal of Criminology 51(1) 1ndash20 httpsdoiorg101093bjcazq067
Howie P amp Atakhanova Z (2014) Resource boom and inequality Kazakhstan as a case study Resources Policy 39(1) 71ndash79 httpsdoiorg101016jresourpol201311004
Isham J Woolcock M Pritchett L amp Busby G (2005) The Varieties of Resource Experience Natural Resource Export Structures and the Political Economy of Economic Growth The World Bank Economic Review 19(2) 141ndash174 httpsdoiorg101093wberlhi010
Jottier D Ashworth J amp Heyndels B (2012) Understanding Votersrsquo Preferences How the Electoratersquos Complexity Affects Prediction Accuracy and Wishful Thinking among Politicians with Respect to Election Outcomes Kyklos 65(3) 340ndash370 httpsdoiorg101111j1467-6435201200542x
Kanbur S M R amp Venables A (2005) Spatial inequality and development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Kesler C amp Bloemraad I (2010) Does immigration erode social capital The conditional effects of immigration-generated diversity on trust membership and participation across 19 countries 1981ndash2000 Canadian Journal of Political ScienceRevue Canadienne de Science Politique 43(2) 319ndash347
Kuznets S (1955) Economic Growth and Income Inequality The American Economic Review 45(1) 1ndash28 Retrieved from httpwwwjstororgstable1811581
Lagos G amp Blanco E (2010) Mining and development in the region of Antofagasta Resources Policy 35(4) 265ndash275 httpsdoiorghttpsdoiorg101016jresourpol201007006
Leamer E E Maul H Rodriguez S amp Schott P K (1999) Does natural resource abundance increase Latin American income inequality Journal of Development Economics 59(1) 3ndash42 httpsdoiorg101016s0304-3878(99)00004-8
Leibbrandt M Finn A amp Woolard I (2012) Describing and decomposing post-apartheid income inequality in South Africa Development Southern Africa 29(1) 19ndash34 httpsdoiorg1010800376835X2012645639
134
Letki N (2008) Does Diversity Erode Social Cohesion Social Capital and Race in British Neighbourhoods Political Studies 56(1) 99ndash126 httpsdoiorg101111j1467-9248200700692x
Lewis D A (2017) Fear of crime Incivility and the production of a social problem Routledge
lo Storto C (2013) Evaluating Technical Efficiency of Italian Major Municipalities A Data Envelopment Analysis model Procedia - Social and Behavioral Sciences 81 346ndash350 httpsdoiorg101016JSBSPRO201306440
Loayza N amp Rigolini J (2016) The Local Impact of Mining on Poverty and Inequality Evidence from the Commodity Boom in Peru World Development 84 219ndash234 httpsdoiorg101016jworlddev201603005
Loayza N Teran A M y amp Rigolini J (2013) Poverty Inequality and the Local Natural Resource Curse World Bank Policy Research Working Paper (6366) httpsdoiorg1015961813-9450-6366
Loacutepez R amp Miller S J (2008) Chile The Unbearable Burden of Inequality World Development 36(12) 2679ndash2695 httpsdoiorg101016jworlddev200801012
Manzano O amp Rigobon R (2001) Resource Curse or Debt Overhang National Bureau of Economic Research Working Paper Series No 8390 1 httpsdoiorghttpwwwnberorgpapersw9424bib
McPherson M Smith-Lovin L amp Cook J M (2001) Birds of a Feather Homophily in Social Networks Annual Review of Sociology 27(1) 415ndash444 httpsdoiorg101146annurevsoc271415
McQuestin D Drew J amp Dollery B (2018) Do Municipal Mergers Improve Technical Efficiency An Empirical Analysis of the 2008 Queensland Municipal Merger Program Australian Journal of Public Administration 77(3) 442ndash455 httpsdoiorg1011111467-850012286
Messner S F Rosenfeld R amp Baumer E P (2004) Dimensions of Social Capital and Rates of Criminal Homicide American Sociological Review 69(6) 882ndash903 httpsdoiorg101177000312240406900607
Michaels G (2011) THE LONG TERM CONSEQUENCES OF RESOURCE-BASED SPECIALISATION The Economic Journal 121(551) 31ndash57 httpsdoiorg101111j1468-0297201002402x
Mijanovich T amp Weitzman B C (2003) Which ldquobroken windowsrdquo matter School neighborhood and family characteristics associated with youthsrsquo feelings of unsafety Journal of Urban Health 80(3) 400ndash415
Mikušovaacute P (2015) An Application of DEA Methodology in Efficiency Measurement of the Czech Public Universities Procedia Economics and Finance 25 569ndash578 httpsdoiorg101016S2212-5671(15)00771-6
135
Milanovic B (2016) Global inequality Harvard University Press
Millo G amp Piras G (2012) splm Spatial panel data models in R Journal of Statistical Software 47(1) 1ndash38
Murphy K M amp Topel R H (2016) Human Capital Investment Inequality and Economic Growth JOURNAL OF LABOR ECONOMICS 34(2) S99ndashS127 httpsdoiorg101086683779
Narboacuten-Perpintildeaacute I amp De Witte K (2018a) Local governmentsrsquo efficiency a systematic literature reviewmdashpart I International Transactions in Operational Research 25(2) 431ndash468 httpsdoiorg101111itor12364
Narboacuten-Perpintildeaacute I amp De Witte K (2018b) Local governmentsrsquo efficiency a systematic literature reviewmdashpart II International Transactions in Operational Research 25(4) 1107ndash1136 httpsdoiorg101111itor12389
Nuntildeez J Rivera J Villavicencio X amp Molina O (2003) Determinantes socioeconoacutemicos y demograacuteficos del crimen en Chile Estudios de Economiacutea 30(1) 55ndash85
OrsquoDonnell C J Rao D S P amp Battese G E (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios Empirical Economics 34(2) 231ndash255 httpsdoiorg101007s00181-007-0119-4
Ocampo J A (2004) Latin Americarsquos Growth and Equity Frustrations During Structural Reforms The Journal of Economic Perspectives 18(2) 67ndash88 httpsdoiorg1012570895330041371349
OECD (2014) Focus on inequality and growth OECD
OECD (2017) Howrsquos Life 2017 Life Satisfaction Oecd httpsdoiorg101787how_life-2017-en
Ohtake F (2008) Inequality in Japan Asian Economic Policy Review 3(1) 87ndash109 httpsdoiorg101111j1748-3131200800093x
Okun A M (2015) Equality and efficiency the big tradeoff Washington DC Brookings Institution Press
Ortega B Sanjuaacuten J amp Casquero A (2017) Determinants of efficiency in reducing child mortality in developing countries The role of inequality and government effectiveness Health Care Management Science 20(4) 500ndash516 httpsdoiorg101007s10729-016-9367-1
Ostry J Berg A amp Tsangarides C (2014) Redistribution inequality and growth International Monetary Fund
Pacheco F Saacutenchez R amp Villena M (2013) Eficiencia de los Gobiernos Locales y sus Determinantes Un anaacutelisis de Fronteras Estocaacutesticas en Datos de Panel para
136
Municipalidades Chilenas Santiago de Chile Chile
Papyrakis E amp Raveh O (2014) An Empirical Analysis of a Regional Dutch Disease The Case of Canada Environmental and Resource Economics 58(2) 179ndash198 httpsdoiorg101007s10640-013-9698-z
Paredes D (2013) The Role of Human Capital Market Potential and Natural Amenities in Understanding Spatial Wage Disparities in Chile Spatial Economic Analysis 8(2) 154ndash175 httpsdoiorg101080174217722013774094
Paredes D Iturra V amp Lufin M (2016) A spatial decomposition of income inequality in Chile Regional Studies 50(5) 771ndash789
Phan P Van Orsquobrien M Mendolia S amp Paloyo A (2017) National pro-poor spending programmes and their effect on income inequality and poverty Evidence from Vietnam Applied Economics 49(55) 5579ndash5590 httpsdoiorg1010800003684620171313957
Podinovski V V (2004) Bridging the Gap between the Constant and Variable Returns-to-Scale Models Selective Proportionality in Data Envelopment Analysis The Journal of the Operational Research Society 55(3) 265ndash276 Retrieved from httpwwwjstororgstable4102006
Ravallion M (2005) On Measuring Aggregate Social Efficiency Economic Development and Cultural Change 53(2) 273ndash292 httpsdoiorg101086425380
Rehner J Baeza S A amp Barton J R (2014) Chilersquos resource-based export boom and its outcomes Regional specialization export stability and economic growth Geoforum 56(Supplement C) 35ndash45 httpsdoiorghttpsdoiorg101016jgeoforum201406007
Rivera J Gutieacuterrez M amp Nuacutentildeez J (2009) Caracterizacioacuten socioeconoacutemica y espacial de la criminalidad en Chile Revista CEPAL
Ross C E (2011) Collective threat trust and the sense of personal control Journal of Health and Social Behavior 52(3) 287ndash296
Ross C E amp Mirowsky J (2001) Neighborhood disadvantage disorder and health Journal of Health and Social Behavior 258ndash276
Rothstein B amp Uslaner E M (2005) All for all Equality corruption and social trust World Politics 58(1) 41ndash72
Sachs J D amp Warner A M (2001) The curse of natural resources European Economic Review 45(4ndash6) 827ndash838 httpsdoiorg101016S0014-2921(01)00125-8
Salas R (2019) sinimr Chilean Municipalities Information System Wrapper Retrieved from httpsgithubcomrobsalascosinimr
Sampson R J (1986) Crime in Cities The Effects of Formal and Informal Social Control Crime and Justice 8 271ndash311
137
Sampson R J (2008) Rethinking crime and immigration Contexts 7(1) 28ndash33
Santos Silva J M C amp Tenreyro S (2010) On the existence of the maximum likelihood estimates in Poisson regression Economics Letters 107(2) 310ndash312 httpsdoiorghttpsdoiorg101016jeconlet201002020
Santos Silva J M C amp Tenreyro S (2011) poisson Some convergence issues Stata Journal 11(2) 207ndash212 Retrieved from httpwwwstata-journalcomarticlehtmlarticle=st0225
Scott Z (2009) Decentralisation local development and social cohesion an analytical review GSDRC Research Paper 5
Sinha R P (2017) Fiscal Performance Benchmarking of Indian States-A Robust Frontier Approach The Central European Review of Economics and Management 1(4) 225ndash249
Skogan W (1986) Fear of crime and neighborhood change Crime and Justice 8 203ndash229
Skogan W (1999) Measuring what matters Crime disorder and fear In Measuring what matters Proceedings from the Policing Research Institute meetings (pp 37ndash53) National Institute of Justice Washington DC
Skogan W (2015) Disorder and Decline The State of Research Journal of Research in Crime and Delinquency 52(4) 464ndash485 httpsdoiorg1011770022427815577836
Šťastnaacute L amp Gregor M (2014) Public sector efficiency in transition and beyond evidence from Czech local governments Applied Economics 47(7) 1ndash20 httpsdoiorg101080000368462014978077
Tandon A (2005) Measuring Efficiency of Macro Systems An Application to Millennium Development Goal Attainment Asian Development Review 22(2) 108ndash125
Taylor R B (1999) The incivilities thesis Theory measurement and policy Measuring What Matters 65 88
Tigga N S amp Mishra U S (2015) On Measuring Technical Efficiency of the Health System in India An Application of Data Envelopment Analysis Journal of Health Management 17(3) 285ndash298 httpsdoiorg1011770972063415589229
Tinbergen J (1975) Demographic Development and the Exhaustion of Natural Resources Population and Development Review 1(1) 23ndash32 httpsdoiorg1023071972269
Tiruneh G (2014) Social Revolutions Their Causes Patterns and Phases SAGE Open 4(3) 2158244014548845 httpsdoiorg1011772158244014548845
Tolsma J Van der Meer T amp Gesthuizen M (2009) The impact of neighbourhood and municipality characteristics on social cohesion in the Netherlands Acta Politica 44(3) 286ndash313
Tsekeris Sotiris T Tsekeris T amp Papaioannou S (2018) Regional determinants of technical efficiency evidence from the Greek economy Regional Studies [London]thinsp Carfax
138
httpsdoiorg1010800034340420171390312
Uslaner E (2002) The moral foundations of trust Cambridge University Press
Uslaner E (2011) CORRUPTION AND INEQUALITY DICE Report 9(2) 20ndash24
Uslaner E (2013) Trust and corruption revisited how and why trust and corruption shape each other Quality amp Quantity 47(6) 3603ndash3608 httpsdoiorg101007s11135-012-9742-z
Uslaner E amp Brown M (2005) Inequality trust and civic engagement American Politics Research 33(6) 868ndash894
Van der Ploeg F (2011) Natural Resources Curse or Blessing Journal of Economic Literature 49(2) 366ndash420 httpsdoiorg101257jel492366
Vergara R (2012) Crime Prevention Programs Evidence From CHILE The Developing Economies 50(1) 1ndash24
Watkins M H (1963) A staple theory of economic growth Canadian Journal of Economics and Political ScienceRevue Canadienne de Economiques et Science Politique 29(2) 141ndash158
Wilkinson R G (1999) Income inequality social cohesion and health clarifying the theorymdasha reply to Muntaner and Lynch International Journal of Health Services 29(3) 525ndash543
Wilson J Q amp Kelling G L (1982) Broken windows Atlantic Monthly 249(3) 29ndash38
Wilson W J (1996) When Work Disappears Political Science Quarterly 111(4) 567ndash595 httpsdoiorg1023072152085
Wirth L (1938) Urbanism as a Way of Life American Journal of Sociology 44(1) 1ndash24
Worthington A amp Dollery B (2000) An empirical survey of frontier efficiency measurement techniques in local government Local Government Studies 26(2) 23ndash52 httpsdoiorg10108003003930008433988
Wu P-C Huang T-H amp Pan S-C (2014) Country Performance Evaluation The DEA Model Approach Social Indicators Research 118(2) 835ndash849 httpsdoiorg101007s11205-013-0443-3
Ye X Ma L Ye K Chen J amp Xie Q (2017) Analysis of Regional Inequality from Sectoral Structure Spatial Policy and Economic Development A Case Study of Chongqing China Sustainability 9(4) 633 Retrieved from httpwwwmdpicom2071-105094633
Yue W Z Zhang Y T Ye X Y Cheng Y Q amp Leipnik M R (2014) Dynamics of Multi-Scale Intra-Provincial Regional Inequality in Zhejiang China Sustainability 6(9) 5763ndash5784 httpsdoiorg103390su6095763
139
Appendices
Appendix A Summary statistics income inequality
Table A1
Summary statistics Gini coefficients by year and zone
140
Appendix B Summary statistics for NRD measures by region
Table B1
Summary statistics NRD measures by region
141
Appendix C Regional administrative division and defined zones
Figure C1 Geographical distribution of Chilean regions and 3 zones
142
Appendix D Summary statistics numeric controls and correlation matrix
Table D1
Summary Statistics Numeric Explanatory Variables
Figure D1 Correlation matrix numeric explanatory variables
143
Appendix E Static spatial panel models
Following Millo amp Piras (2012) a model including a spatial lag of the dependent variable and
spatial autoregressive disturbances but not spatial lags for the explanatory variable(s) is called
SARAR model A static spatial SARAR panel could be expressed as
119910 120582 119868 otimes119882 119910 119883120573 119906 (E1)
where y is an 119873 1 vector of observations on the dependent variable X is a 119873 119896 matrix of
observations on the non-stochastic exogenous regressors 119868 an identity matrix of dimension 119879 otimes
is the kronecker operator 119882 is the 119873 119873 spatial weights matrix of known constants whose
diagonal elements are set to zero and 120582 the corresponding spatial parameter44
The disturbance vector is the sum of two terms
119906 120580 otimes 119868 120583 120576 (E2)
where 120580 is a 119879 1 vector of ones 119868 an 119873 119873 identity matrix 120583 is a vector of time-invariant
individual specific effects (not spatially autocorrelated) and 120576 a vector of spatially autocorrelated
innovations that follow a spatial autoregressive process of the form
120576 120588 119868 otimes119882 120576 120584 (E3)
If we assume that spatial correlation applies to both the individual effects 120583 and the remainder
error components 120576 Kapoor et al (2007) propose that the disturbance term 119906 follows a first order
spatial autoregressive process of the form
119906 120588 119868 otimes119882 119906 120576 (E4)
44 Unlike ldquotraditional panelsrdquo which are organized as different time series for each cross-sectional unit ldquospatial panelsrdquo are organized as a series of cross-sections for each year
144
where 119882 is the spatial weights matrix and 119903ℎ119900 the corresponding spatial autoregressive
parameter To further allow for the innovations to be correlated over time the innovations vector
in Equation 7 follows an error component structure
120576 120580 otimes 119868 120583 120584 (E5)
where 120583 is the vector of cross-sectional specific effects 120584 a vector of innovations that vary
both over cross-sectional units and time periods 120580 is a vector of ones and 119868 an 119873 119873 identity
matrix45
Spatial panel models are usually estimated by Maximum Likelihood or GMM46 The SAR
SEM or SARAR models could be estimated with Random or Fixed effects For instance A fixed
effect spatial lag model can be written in stacked form as
119910 120582 119868 otimes119882 119910 120580 otimes 119868 120583 119883120573 120576 (E6)
where 120582 is the spatial autoregressive coefficient 119882 a non-stochastic spatial weights matrix
120580 a column vector of ones of dimension 119879 119868 an 119873 119873 identity matrix and 120576 sim 119873 0120590 On
the other hand a fixed effects spatial error model assuming the disturbance specification by
Kapoor et al (2007) can be written as
119910 120580119879 otimes 119868119873 120583 119883120573 119906119906 120588 119868119879 otimes119882119873 119906 120576
(E7)
where 120588 is the spatial autocorrelation coefficient and 120576 is a well-behaved error term
45 In the regression implementation the specification given by equations (22) and (3) is denoted by ldquobrdquo for ldquoBaltagirdquo On the other hand the specification given by equations (23) and (31) is denoted by ldquokkprdquo for ldquoKapoor Kelejian and Pruchardquo 46 We use the R package splm which allow both types of regression procedure
145
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis
Table F1
Analysis OLS residuals Anselin Method
Figure F1 Moran scatter plot OLS residuals
146
Appendix G Linear panel data models
Table G1
Panel regressions (non-spatial)
147
Appendix H Spatial panel models (Generalized Moments (GM) estimation)
Table H1
GM Spatial Models
148
Appendix I Inputs and outputs used in DEA analysis
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018)
149
Appendix J Technical and scale efficiency
Following lo Storto (2013) under an input-oriented specification assuming VRS with n
municipalities using k inputs to produce m outputs the DEA model for a given i-th municipality
is specified with the following mathematical programming problem
119898119894119899 120579119904119906119887119895119890119888119905 119905119900 119910 119884120582 0120579119909 119883120582 01198991prime120582 1 120582 0prime
Where 119910 is the column vector of the outputs and 119909 is the column vector of the inputs
Moreover we can define X as the (k times n) input matrix and Y as the (m times n) output matrix 120579 is a
scalar (that satisfies 120579 1) more specifically it is the efficiency score that measures technical
efficiency It measures the distance between a municipality and the efficiency frontier defined as
a linear combination of the best practice observations With 120579 1 the municipality is inside the
frontier (ie it is inefficient) while 120579 1 implies that the municipality is on the frontier (ie it is
efficient) The vector 120582 is an (n times 1) vector of constants that measures the weights used to compute
the location of an inefficient municipality if it were to become efficient
The total technical efficiency 119879119864 can be decomposed into pure technical efficiency
119879119864 and scale efficiency 119878119864 where 119878119864 119879119864 119879119864 (Coelli et al 2005) To find out
whether a municipality is scale efficient and qualify the type of returns of scale a DEA model
under non-increasing returns to scale 119879119864 is implemented where 119878119864 119879119864 119879119864 Hence
the following rule can be applied (Faumlre Grosskopf amp Lovell 1985)
bull If 119878119864 1 then a municipality is scale efficient both under CRS and VRS
bull If 119878119864 1 it operates under increasing returns to scale
bull If 119878119864 1 it operates under decreasing returns to scale
150
Appendix K Correlation matrix
Figure K1 Correlation matrix contextual factors
151
Appendix L Returns to scale by year and zone
Table L1
Returns to scale (percentage of municipalities)
152
Appendix M Returns to scale by year (maps)
Figure M1 Spatial distribution of returns to scale by county per year
153
Appendix N Efficiency status by year (maps)
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year
154
Appendix O Spatial distribution efficiency scores by year (maps)
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year
155
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis
Table P1
Analysis OLS residuals Anselin Method
Figure P1 Moran scatter plot efficiency scores and OLS residuals
156
Table P2
OLS and spatial regression models for the six-year averaged data
157
Appendix Q OLS regressions for cross-sectional and panel data
Table Q1
OLS cross-sectional regression per year
158
Table Q2
OLS panel regressions Pooled random effects and instrumental variable
159
Appendix R Quantile maps incivilities rate by group (average total period)
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17)
160
Appendix S Correlation matrix numeric covariates
Figure S1 Correlation matrix numeric covariates
161
Appendix T Negative Binomial regressions
Table T1
Negative Binomial regressions
162
Appendix U Coefficients economic and racial diversity by geographical zone
Table U1
Coefficients economic and racial diversity in pooled Poisson models by geographic zone
v
Table of Contents
Keywords i
Abstract ii
Table of Contents v
List of Figures viii
List of Tables ix
List of Abbreviations x
Statement of Original Authorship xi
Acknowledgements xii
Chapter 1 Introduction 13
Income inequality and dependence on natural resources 14
Local government efficiency and income inequality 16
Social cohesion and economic diversity 19
Contributions 21
Thesis outline 23
Chapter 2 Natural Resources Curse or Blessing Evidence on Income Inequality at the County Level in Chile 24
21 Introduction 24
22 Inequality and Natural Resources 28 221 Theoretical Framework 28
Cross-country literature 29 Single country evidence 32
222 The relevance of the spatial approach 33
23 Research problem and hypotheses 35
24 Data and Methods 36 241 Operationalization of key variables 36 242 Control variables 40 243 Methods 41 244 Spatial Model Specification 43
25 Results 44 251 Exploratory Spatial Data Analysis (ESDA) 44 252 Cross-sectional analysis 45 253 Panel Data analysis 48
26 Discussion and conclusions 51
Chapter 3 The Impact of Income Inequality on the Efficiency of Municipalities in Chile 55
vi
31 Introduction 55
32 Related Literature 61 321 Measuring efficiency of local governments 61 322 Explaining differences in LGE 63 323 The trade-off between efficiency and equity 64
33 Methodology 66 331 Chilean Municipalities and period of analysis 66 332 Measuring municipal efficiency 68 333 Inputs and outputs used in DEA 70 334 Regression model 71 335 The instrument 75
34 Results and discussion 77 341 DEA results 77
Returns to scale 78 Efficiency measure 80
342 Regression results 82 Exploratory spatial analysis 82 Cross-sectional analysis 83 Panel data analysis 84
35 Conclusions 88
Chapter 4 Social Cohesion Incivilities and Diversity Evidence at the municipal level in Chile 91
41 Introduction 91
42 Related Literature 95 421 The Community Heterogeneity Thesis 95 422 The literature on incivilities 97 423 The ldquoIncivilities Thesisrdquo 99
4 3 Methodology 100 431 Period of analysis and data sample 100 432 Operationalisation of the response variable and exploratory analysis 101 433 Measures of community heterogeneity and control variables 105 434 Methods 108 435 Hypotheses 111
44 Results and Discussion 112
4 5 Conclusions 118
Chapter 5 Conclusions 120
Bibliography 126
Appendices 139
Appendix A Summary statistics income inequality 139
Appendix B Summary statistics for NRD measures by region 140
Appendix C Regional administrative division and defined zones 141
Appendix D Summary statistics numeric controls and correlation matrix 142
vii
Appendix E Static spatial panel models 143
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis 145
Appendix G Linear panel data models 146
Appendix H Spatial panel models (Generalized Moments (GM) estimation) 147
Appendix I Inputs and outputs used in DEA analysis 148
Appendix J Technical and scale efficiency 149
Appendix K Correlation matrix 150
Appendix L Returns to scale by year and zone 151
Appendix M Returns to scale by year (maps) 152
Appendix N Efficiency status by year (maps) 153
Appendix O Spatial distribution efficiency scores by year (maps) 154
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis 155
Appendix Q OLS regressions for cross-sectional and panel data 157
Appendix R Quantile maps incivilities rate by group (average total period) 159
Appendix S Correlation matrix numeric covariates 160
Appendix T Negative Binomial regressions 161
Appendix U Coefficients economic and racial diversity by geographical zone 162
viii
List of Figures
Figure 21 Average share in GDP of economic activities (2006ndash17) 37
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17) 38
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17) 39
Figure 23 Moran scatter plots for variables gini and pss_casen 45
Figure 31 Geographical distribution of Chilean regions and macrozones 74
Figure 32 Evolution of efficiency scores and the proportion of firms by sector 77
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE) 78
Figure 34 Returns to scale by zone 79
Figure 35 Evolution mean efficiency scores (VRS) by zone 81
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017 102
Figure 42 Evolution total number of incivilities by category 104
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017) 104
Figure 44 Annual average number of incivilities per county 109
Figure C1 Geographical distribution of Chilean regions and 3 zones 141
Figure D1 Correlation matrix numeric explanatory variables 142
Figure F1 Moran scatter plot OLS residuals 145
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018) 148
Figure K1 Correlation matrix contextual factors 150
Figure M1 Spatial distribution of returns to scale by county per year 152
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year 153
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year 154
Figure P1 Moran scatter plot efficiency scores and OLS residuals 155
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17) 159
Figure S1 Correlation matrix numeric covariates 160
ix
List of Tables
Table 21 Cross-sectional Model Comparison (six-year average data) 47
Table 22 ML Spatial SAR Models 50
Table 23 ML Spatial SEM Models 50
Table 24 ML Spatial SARAR Models 51
Table 31 Descriptive statistics Inputs and Output variables used in DEA analysis 71
Table 32 Summary Statistics Numeric Contextual Factors 74
Table 33 Summary efficiency scores (VRS) by zone and region 80
Table 34 Cross-sectional (censored) regressions 84
Table 35 Panel data regressions 87
Table 41 Summary statistics total count of incivilities and by category (full sample and period) 103
Table 42 Summary statistics numeric explanatory variables 108
Table 43 Poisson regressions 113
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region 115
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group 116
Table A1 Summary statistics Gini coefficients by year and zone 139
Table B1 Summary statistics NRD measures by region 140
Table D1 Summary Statistics Numeric Explanatory Variables 142
Table F1 Analysis OLS residuals Anselin Method 145
Table G1 Panel regressions (non-spatial) 146
Table H1 GM Spatial Models 147
Table L1 Returns to scale (percentage of municipalities) 151
Table P1 Analysis OLS residuals Anselin Method 155
Table P2 OLS and spatial regression models for the six-year averaged data 156
Table Q1 OLS cross-sectional regression per year 157
Table Q2 OLS panel regressions Pooled random effects and instrumental variable 158
Table T1 Negative Binomial regressions 161
Table U1 Coefficients economic and racial diversity in pooled Poisson models by geographic zone 162
x
List of Abbreviations
Constant returns to scale CRS
Data envelopment analysis DEA
Decreasing returns to scale DRS
Efficiency scores ES
Exploratory spatial data analysis ESDA
Generalized methods of moments GMM
Gross Domestic Product GDP
Increasing returns to scale IRS
Local government efficiency LGE
Maximum likelihood ML
Municipal common fund MCF
Natural resource dependence NRD
Natural resource endowment NRE
Ordinary Least Squares OLS
Organization for Economic Cooperation and Development OECD
Own permanent revenues OPR
Resource curse hypothesis RCH
Spatial autoregressive model SAR
Spatial error model SEM
Variable returns to scale VRS
xi
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet requirements
for an award at this or any other higher education institution To the best of my knowledge and
belief the thesis contains no material previously published or written by another person except
where due reference is made
Signature QUT Verified Signature
Date _________04092020_________
xii
Acknowledgements
First I would like to thank my wife Lilian who joined me in this challenge and patiently
supported me all these years I would also like to thank our family who always supported us from
Chile I especially thank my sister Silvia who took care of our house and dog
I am also grateful to my supervisory team Dr Radhika Lahiri and Dr Vincent Hoang who
supported and guided me in the process of making this thesis a reality
I also thank the Deans of the Faculty of Economics and Business at my beloved University
of Talca Dr Arcadio Cerda and Dr Rodrigo Herrera who trusted me and supported me in this
process In the same way I would like to thank all the support of the director of the Commercial
Engineering career Mr Milton Inostroza
Finally I would like to thank the government of Chile for the financial support that made
my stay and studies possible here at the Queensland University of Technology
13
Chapter 1 Introduction
Efficiency and equity issues are often considered together in the evaluation of economic
performance While higher efficiency usually measured by growth rates of income per capita
correlates with improvements in measures of well-being the link between inequality and well-
being is less clear This is reflected not only in the type and amount of research related to efficiency
and equity but also in the role that both play in the design of the economic policy For instance
several market-oriented countries have focused primarily on economic growth trusting in a trickle-
down process where financial benefits given to the wealthy are expected to ultimately benefit the
poor However despite the growing interest in the issue of inequality there is a considerable lack
of studies about its consequences
Although some level of inequality is inevitable or even necessary for economic activity this
study is motivated by the argument that relatively high levels of inequality can be associated with
many problems such as persistent unemployment increasing fiscal expenses indebtedness and
political instability (Berg amp Ostry 2011) Inequality can also have other severe social
consequences including increased crime rates teenage pregnancy obesity and fewer
opportunities for low-income households to invest in health and education (Atkinson 2015) In
addition when the role of money and concentration of economic power undermine political
outcomes inequality of opportunities hampers social and economic mobility trust and social
cohesion In summary inequality can increase the fragility of the economic and social situation in
a country reducing economic growth and making it less inclusive and sustainable
14
A country well-known for its market-oriented economy and high level of dependence on
natural resources is Chile Chilean success in terms of economic growth contrasts with its inability
to reduce the persistently high levels of social and economic inequality particularly in the last
three decades Using data for the 2006-2017 period and considering 324 out of 345 Chilean
counties this thesis presents three essays with empirical evidence aiming to explain the
phenomenon of persistent income inequality and some of its potential consequences The first
essay aims to analyse how the evolution and variability of income inequality throughout the
country are associated with the degree of natural resource dependence The second essay studies
the relevance of income inequality in explaining cross-county differences in the performance of
local governments (municipalities) Finally the third essay explores the link between social
cohesion and community heterogeneity highlighting the importance of economic and racial
diversity
Income inequality and dependence on natural resources
The first essay explores how cross-county differences in income inequality are associated
with differences in the degree of dependence on natural resources We use the Gini coefficient in
each county as our dependent variable and the proportion of employment in the primary sector as
our measure of natural resource dependence The main hypothesis is that income inequality should
be positively related to the degree of natural resource dependence To test our hypothesis we use
a spatial econometric approach This approach is motivated by the study of Paredes Iturra and
Lufin (2016) who explore the geographic heterogeneity of income inequality in Chile finding
evidence of a significant spatial dimension
15
The theoretical and empirical literature has mostly proposed a positive link between
inequality and natural resources Although most of the evidence corresponds to cross-country
comparisons there is also increasing body of research at the local level A rationale underpinning
the positive link suggested in the literature is that in natural resource-rich countries ownership is
concentrated in small groups and extraction activities require low-skilled workers (Gylfason amp
Zoega 2003 Leamer Maul Rodriguez amp Schott 1999) Another market-based argument often
labelled as the ldquoDutch Diseaserdquo proposes that natural resource windfalls could be associated with
a crowding-out effect on the manufacturing sector (Corden amp Neary 1982 Easterly 2007) This
process encourages rent-seeking behaviours discourages investment in physical and human
capital and delays the process of technology adoption and industrialization (Auty 2001 Bulte
Damania amp Deacon 2005 Gylfason amp Zoega 2003) The result could be a lower economic
growth which is the central idea under the ldquoResource Curse Hypothesisrdquo (Auty 1993 Sachs amp
Warner 2001)
An ldquoinstitutionalrdquo argument for the positive association between inequality and the
endowment of natural resources is based on the so-called ldquoParadox of Plentyrdquo (Borge Parmer amp
Torvik 2015 Dauvin amp Guerreiro 2017) The idea is that both national and local authorities have
less incentive to operate efficiently when they experience windfalls in their revenues for
instance from natural resources This could end with corrupted authorities exerting patronage
clientelism and designing public policies to favour specific groups of the population (Uslaner amp
Brown 2005) Evidence also suggests that the final effect of natural resource booms on income
inequality will depend on authoritiesrsquo capacity to manage these additional resources the extent of
commuting and migration among regions and the potential increase in the demand for non-tradable
16
goods which are intensive in unskilled workers (Aroca amp Atienza 2011 Cust amp Poelhekke 2015
Fleming amp Measham 2015b Howie amp Atakhanova 2014 Michaels 2011)
Contrary to most theoretical and empirical evidence we find that income inequality shows
a robust and significant negative association with our proxy for natural resource dependence This
result suggests that the process of transformation to an economy less dependent on natural
resources could have exacerbated rather than alleviated the persistence of income inequality The
decrease in the participation of the primary sector in employment in favour mainly of the tertiary
sector highlights the importance of the latter to explain the current high levels of inequality and its
future evolution Another important result is that spatial linear models show practically the same
results as traditional linear models This could be interpreted as the spatial dimension previously
found in income inequality is not the result of spatial dependence in the variable itself for instance
due to a process of spillover among counties Hence the usually found positive spatial
autocorrelation of income inequality (similar levels in neighbouring counties) could be explained
by spatial patterns in other variables or to the spatial heterogeneity that characterizes the Chilean
economy
Local government efficiency and income inequality
Essay 2 delves deep into the potential trade-off between efficiency and equity We measure
the efficiency of Chilean municipalities which correspond to the organizations in charge of
managing each county Municipal efficiency is understood as ldquotechnical efficiencyrdquo that is the
possibility that each municipality has reached the same level of outputs with less use of inputs
Then we analyse how income inequality controlling for other contextual factors such as
socioeconomic demographic geographical and political characteristics may help to explain
17
differences in municipal performance Our main hypothesis is that municipal efficiency is
inversely associated with income inequality Moreover we seek a causal interpretation of this
relationship
Municipal performance could be influenced by income inequality in direct and indirect ways
In a direct sense income inequality is used to capture the degree of heterogeneity and complexity
in the demand for public services that citizens exert over local authorities Hence higher levels of
income inequality should be associated with a more complex set of public services and therefore
with lower levels of municipal efficiency (Jottier Ashworth amp Heyndels 2012) Furthermore
when high levels of inequality exist the richest groups can exert a higher influence over local
authorities resulting in low quality and quantity of services for most of the population Among
indirect effects high and persistent inequality could be the source of corrupted institutions and
local authorities favouring themselves or specific groups This undermines citizensrsquo participation
in civic activities and their willingness to monitor municipal performance (Uslaner amp Brown
2005) Additionally the potential benefits of decentralization on the way local governments
deliver public services will be limited when the context is characterized by corrupted politicians
and a limited administrative and financial capacity (Scott 2009)
We measure municipal efficiency using an input-oriented Data Envelopment Analysis
(DEA) to obtain efficiency scores for our sample of 324 municipalities in each year from 2006 to
2017 Then we study the influence on municipal efficiency of income inequality and our set of
contextual factors using a panel of six years corresponding to those years for which household
income information is available 2006 2009 2011 2013 2015 and 2017 Our dependent variable
is the set of efficiency scores which are relative measures of efficiency They are relative to the
18
municipalities included in the sample and they do not imply that higher technical efficiency gains
cannot be achieved Thus we use both cross-sectional and panel censored regression models To
tackle endogeneity issues and suggest a causal interpretation we also propose using the proportion
of firms in the primary sector as an instrument for income inequality
We find an average efficiency score of 83 meaning that Chilean municipalities could
reduce the use of inputs by 17 without reducing their outputs We also measure municipal
efficiency under different assumptions related to returns to scale This allows us to disaggregate
technical efficiency to assess whether inefficiencies are due to management issues (pure technical
efficiency) or scale issues (scale efficiency) Although the results show that most municipalities
operate under increasing or decreasing returns to scale scale inefficiencies only explain a small
proportion of total municipal inefficiencies This highlights the need to look for contextual factors
outside the control of local authorities to explain differences in municipal performance
Geographical representations of our results in terms of returns to scale and efficiency scores
show some spatial clustering process among municipalities Spatial statistics tests confirm that
efficiency scores show a significant positive spatial autocorrelation This means that neighbouring
municipalities tend to show similar levels of efficiency This similar performance could be due to
a process of spatial dependence (eg efficiency spillovers among neighbouring municipalities) or
due to the existence of structural-geographical similarities (spatial heterogeneity) To assess the
spatial dimension in municipal efficiency abstracting from temporal fluctuations we use a cross-
section of data consisting of the six-year average for the variables in our panel After running a
regression of efficiency scores against the set of controls the analysis of OLS residuals shows that
the spatial autocorrelation is almost completely removed This means that the spatial pattern in
19
municipal efficiency can be explained (controlled) by other variables such as regional indicator
variables rather than efficiency itself Given this result we proceed to study the influence of
income inequality on municipal efficiency using traditional (non-spatial) regression analysis
In contrast to literature that emphasizes an equity-efficiency trade-off (Andersen amp Maibom
2020 Berg amp Ostry 2011 Browning amp Johnson 1984 Okun 2015) that is greater equality leads
to lower efficiency we find that municipal efficiency is inversely associated with income
inequality This implies that more equal counties are also those with higher municipal efficiency
Furthermore the coefficient of income inequality is close to one when we use the instrumental
variable approach This means that a reduction in income inequality ceteris paribus should be
associated with an increase in the same magnitude in municipal efficiency This result has strong
policy implications The non-existence of the trade-off suggests that there is more to be gained by
targeting policies towards the reduction of inequality than conventional theories suggest For
instance these policies may help increase the levels of efficiency and well-being at least at the
municipal level
Social cohesion and economic diversity
The third essay studies the relationship between the degree of social cohesion and diversity
in Chile Extant literature has argued that one of the main factors influencing social cohesion is
the degree of economic and ethnic-racial diversity within a society This diversity erodes social
cohesion reducing trust and corrupting institutions (Letki 2008 Rothstein amp Uslaner 2005
Tolsma Van der Meer amp Gesthuizen 2009 Uslaner 2011 2013)
To measure social cohesion scholars have traditionally used measures of social capital trust
or feelings of insecurity (Ariely 2014 Chan To amp Chan 2006 Letki 2008) We suggest the use
20
of the rate of incivilities per 1000 inhabitants as a proxy for social cohesion Incivilities correspond
to those antisocial behaviours (eg groups of rowdy teens and public drunkenness) or visible
neighbourhood conditions (eg graffiti and abandoned buildings) that tend not to be treated as
crime Using the rate of incivilities is arguably a more objective and reliable measure of social
cohesion particularly in countries where institutions of order and security are among the most
trusted An increase in the rate of incivilities rather than changes in crime rates should better
capture the worsening in social cohesion experienced in countries such as Chile where crime rates
are not growing but social conflicts are on the rise Thus the main hypothesis in this essay is that
the rate of incivilities (social cohesion) should be positively (negatively) associated with economic
and racial diversity
Using panel count data models we start analysing how differences in incivilities rates
between and within counties are associated with differences in indicators of relative and absolute
economic disadvantage We use the Gini coefficient of each county as our measure of economic
diversity Although we find a significant and positive association between the rate of incivilities
and the level of income inequality the magnitude of the link seems to be small Among absolute
indicators of economic disadvantage only the level of income shows a strong effect Next we
include our measure of racial diversity We use the number of new visas granted to foreigners as
a proportion of the county population Results show a significant and strong positive association
between the rate of incivilities and racial diversity
To check the robustness of our results we analyse the impact of our measures of economic
and racial diversity running our models separately for each Chilean region and clustering them
geographically We also split the total number of incivilities in four categories to see which type
21
of incivilities show the greatest association with our measures of diversity In general results
support the ldquocommunity heterogeneity hypothesisrdquo that higher community heterogeneity is
associated with higher rates of incivilities (Alesina amp La Ferrara 2002 Letki 2008 Tolsma et al
2009) However results do not support the ldquoincivilities thesisrdquo that the distribution of incivilities
tends to mirror the distribution of income inequality (Skogan 1999 Taylor 1999)
Three results stand out among the set of control variables First the level of education shows
and independent and significant negative association with the rate of incivilities This is in contrast
to previous studies where education acts mainly as a moderator of the effect of economic and racial
diversity on social cohesion (Tolsma et al 2009) The results also show that there is no significant
relationship between the rate of incivilities and the proportion of young population This is relevant
because policies aimed to reduce incivilities usually put the focus on specific groups such as young
people which are linked to physical and social incivilities when social control is weakened
Finally the degree of financial municipal autonomy also shows a significant negative association
with the rate of incivilities This result suggests that municipalities can contribute independently
or together with the central government to reduce incivilities and strengthen social cohesion
Contributions
The three essays in this thesis provide several important insights into the analysis of the
causes and consequences of income inequality particularly in the context of Chile ndash a typical
resource rich economy with persistently high levels of income inequality
Essay 1 advances the understanding of the relationship between income inequality and
natural resources in Chile extending the empirical analysis from the regional level to the county
level In addition the geographic heterogeneity of income inequality is explored with the inclusion
22
of alternative sources of spatial dependence as a potential dimension of the causal relationship
between income inequality and natural resources This essay demonstrates the relevance of natural
resources in explaining the persistence of income inequality even after controlling for other
socioeconomics and institutional factors Findings from this study have potential contribution not
only in the design of policies aimed to reduce income inequality but also in addressing the current
developmental bias between the metropolitan region and the rest of the country
Essay 2 is one of the first studies that undertake a longitudinal analysis of the effects of
income inequality on the efficiency of municipal governments in Chile To capture the role of the
municipal governments in the provision to local people of public services such as education and
health we specify several inputs and outputs in our efficiency model which is different from the
conventional specification in the existing literature For example the number of medical
consultations in public health facilities and the number of enrolled students in public schools are
used as outputs instead of general indicators such as county population Our empirical analysis
also utilises a larger sample of municipalities and covers a much longer period spanning from 2006
to 2017 This essay also investigates the contextual factors beyond the control of local authorities
that can explain variations in the efficiency of municipal governments across the country
Empirical findings from Essay 2 help us increase our understanding of the production
technology of municipalities the sources of inefficiencies and specifically the impact of income
inequality on the performance of local authorities The results deliver two main policy
implications First municipal inefficiencies in the provision of public goods and services differ
across Chilean municipalities In addition efficiency levels show some degree of spatial
autocorrelation This implies that policies such as amalgamation or cooperation among
23
municipalities could have effects beyond the municipalities involved which must be considered
Second the causal effect that income inequality has on municipal efficiency provides another
dimension into the design and implementation of development policies
Essay 3 explores for the first time the effects of economic and racial diversity on social
cohesion in Chile This essay considers incivilities as manifestation of social cohesion and
investigates as extant literature suggests whether indicators of relative economic disadvantage
such as income inequality are among the main factors driving social disorganization and social
unrest Empirical findings suggest that on the one hand economic heterogeneity captured by the
Gini coefficient has a disparate effect both in terms of magnitude and significance on the rate of
incivilities across the country On the other hand the impact of racial heterogeneity appears to be
stronger more significant and of a similar magnitude throughout the country Results also provide
new insights into the design of national policies addressing social disorders particularly those
policies focussed on specific groups of the population and the role of local authorities Overall the
findings provide an opportunity to advance the understanding of the process of weakening in the
social cohesion experienced in Chile and the conflicts that have risen from this process
Thesis outline
The remainder of the thesis is organized as follows Chapter 2 presents essay 1 examining
the association between income inequality and the degree of dependence on natural resources
Chapter 3 presents essay 2 which looks for a causal relationship between municipal efficiency and
income inequality Chapter 4 presents essay 3 analysing the relationship between social cohesion
and economic and racial diversity Finally Chapter 5 presents some concluding remarks
24
Chapter 2 Natural Resources Curse or Blessing Evidence on
Income Inequality at the County Level in Chile
21 Introduction
A phenomenon of increasing inequality of incomes and wealth in recent decades has been
documented by leading scholars and international organizations such as the International Monetary
Fund (Berg amp Ostry 2017 Ostry Berg amp Tsangarides 2014) and the Organization for Economic
Cooperation and Development (Cingano 2014) These efforts have placed the topic of inequality
at the top of the current economic debate recognizing inequality as a determinant not only of
economic growth but also of human development They also have highlighted the necessity for
more research on the drivers of inequality and mechanisms through which it manifests aiming to
design effective policies in reducing economic and social inequalities
Various factors have been analysed as the sources of high and increasing levels of inequality
Among the most significant factors are the levels of income at initial stages of economic
development (Kuznets 1955) Globalization (Milanovic 2016) skill-biased technological change
(Tinbergen 1975) investment in human capital (Murphy amp Topel 2016) institutions
redistributive policy and country-specific characteristics (Acemoglu 1995 2002 Acemoglu
Aghion amp Violante 2001 Acemoglu Johnson amp Robinson 2001) Our focus in this essay is on
the importance that the natural resource endowment (NRE) or lack thereof can play in the
determination of income disparities
25
This essay studies the patterns and evolution of income inequality in the context of a natural
resource-rich country Using the case of the Chilean economy we aim to understand and
disentangle how a phenomenon of high- and persistent-income inequality is related to the
endowment of natural resources that a country owns Chile is an interesting case to study because
despite showing a successful history of economic growth inequality among individuals and among
aggregated spatial units has shown a strong persistence (Paredes et al 2016) Furthermore Chile
has remained among the most unequal countries in the world1
Theory and empirical evidence do not establish a clear link between income inequality and
NRE In addition NRE has received considerably less attention (Auty 2001 ElGindi 2017) and
most of the evidence has been focused on cross-country comparisons For instance NRE can
influence inequality by determining its initial levels (Engerman amp Sokoloff 1994 1997
Engerman Sokoloff Urquiola amp Acemoglu 2002) shaping the evolution of institutions
(Acemoglu 2002) make the educational system less intellectually challenging and moulding the
structure of economic activity (Leamer et al 1999) So studying how cross-county differences in
NRE are associated with the distribution of income within a country has theoretical empirical and
policy implications
In this study we offer empirical evidence on the relationship between income inequality and
the endowment of natural resources using data at the county level in Chile for the period 2006-
2017 Income inequality is measured by the Gini coefficient The importance of NRE is proxied
using a measure of natural resource dependence (NRD) defined as the percentage of the total
1 A 2014 OECD report on income inequality (httpwwwoecdorgsocialincome-distribution-databasehtm) showed Chile as the country with the highest Gini coefficient of disposable income among OECD countries OECD also indicates Chile as the country with the widest gap between the richest 10 percent and the poorest 10 percent of countryrsquos population among its 34 members (OECD 2014)
26
employment in each county corresponding to the primary sector (agriculture forestry fishing and
mining)
The main hypothesis to be tested is whether income inequality is positively associated with
the degree of NRD The transmission mechanisms through which natural resources could influence
socioeconomic outcomes could be based on the market or institutions The market-based approach
argues that natural resource booms could be associated with an appreciation of the real exchange
rate and a crowding out effect over other more productive economic activities such as
manufacturing It could also delay the adoption of new technologies and reduce incentives to invest
in physical and human capital (Gylfason amp Zoega 2003) Based on the ldquoResource Curse
Hypothesisrdquo (RCH) natural resources could be a curse when the political and institutional
framework is weak and natural resources are concentrated in space such as oil and minerals
(Deacon 2011) 2 Among institutional channels a higher NRD or natural resource booms could
be associated with rent seeking misallocation of labour and entrepreneurial talent institutional
and political decline or even violent conflicts For instance the ldquoParadox of Plentyrdquo sustains that
windfalls of revenues as a consequence of resource booms could be related to a lack of incentives
to perform efficiently corruption patronage and local authorities favouring their voters or being
captured by the richest groups (Dauvin amp Guerreiro 2017) Hence a higher NRD or natural
resource booms could be the explanation not only for low levels of growth in regions more
dependent on natural resources but also it could be the root of income disparities
2 There is a wide strand of research on the Resource Curse Hypothesis however the evidence so far is not conclusive Evidence in favour of RCH has been mainly found in developing resource rich countries (Auty 1993 2001 Badeeb Lean amp Clark 2017 Blanco amp Grier 2012 Borge et al 2015 Brunnschweiler amp Bulte 2008 Sachs amp Warner 2001 Van der Ploeg 2011)
27
To test our hypothesis that is whether the levels of income inequality across counties are
positively associated with the degree of NRD we use a spatial econometric approach We use this
approach because attributes such as income inequality in one region may not be independent of
attributes in neighbouring regions (Armstrong amp Taylor 2000) This process of spatial dependence
invalidates the use of traditional (non-spatial) approaches
This study seeks to make two contributions to research First previous empirical evidence
shows a significant spatial dimension of income inequality in Chile (Paredes et al 2016)
However this dimension has been barely explored with most studies limiting the degree of
disaggregation to a regional scale (Aroca amp Bosch 2000) We use a spatial approach which makes
it possible to model and test the significance of the spatial dimension in the analysis of income
inequality and its relationship with other variables Second previous research for the Chilean
economy linking inequality with NRE has been mainly focused on explaining differences between
regions or the importance and effects of the mining-copper sector (Aroca amp Atienza 2011 Ebert
amp La Menza 2015 Lagos amp Blanco 2010 Rehner Baeza amp Barton 2014) We extend this
analysis using data for local economies Identifying and quantifying the impact of NRE on income
inequality at the county level is likely to be more informative for policies aiming to address the
current developmental bias between the metropolitan region and the rest of the country Moreover
the analysis of the role of natural resources in conjunction with other potential sources of inequality
may shed lights in understanding the persistence of the high levels of inequality observed in the
Chilean economy All in all this study could contribute to the design of policies that
simultaneously help reduce inequality increase efficiency and promote sustainable and inclusive
growth
28
Our main finding shows that after controlling for other potential sources of income
inequality such as educational level demographic characteristics and the level of public
government expenditure the degree of dependence on natural resources has a significant effect on
income inequality However contrary to our expectations the effect is negative This result
suggests that the natural or policy-driven process of transformation from primary and extractive
activities to manufacturing and service sectors imposes additional challenges to central and local
authorities aiming to reduce income inequality
In section 22 we review the literature on the relationship between income inequality and
natural resources In section 23 we establish our research problem and main hypothesis Section
24 describes our data and methods and section 25 the empirical results We finish with section
26 discussing our main results concluding and proposing avenues for future research
22 Inequality and Natural Resources
221 Theoretical Framework
Explanations for income inequality can be associated with individual institutional political
and contextual characteristics Individual characteristics include age gender and mainly the level
of education and skills of the population in the labour force For instance globalization and
technological change lead firms to increase the demand for skilled labour deepening income
inequality between skilled and unskilled workers (Atkinson 2015 Milanovic 2016 Tinbergen
1975) Among institutional characteristics labour unions collective bargaining and the minimum
wage have been suggested as explanations of income inequality (Acemoglu Aghion et al 2001
Atkinson 2015) Policy design associated with market regulation progressive taxation and
redistribution can also impact the levels and patterns of inequality
29
A key factor in understanding the levels and differences in income distribution within a
country may be its endowment of natural resources NRE shapes the structure of the economy
(Leamer et al 1999) it is associated with the creation of institutions that define the political
culture and it can also influence the performance of other sectors (Watkins 1963) In addition
NRE determines initial conditions market competition ownership over resources rent seeking
and the geographical concentration of the population and economic activity
Cross‐countryliterature
Bourguignon and Morrison (1990) introduce one of the earliest theoretical frameworks
describing the relationship between inequality and NRE They develop a small open economy
model where income distribution is a function of NRE ownership structure and trade protection
Giving cross-sectional evidence for a group of developing countries they conclude that the impact
of NRE particularly mineral resources and land depends on the number and size of the firms
whether they are public or private and the level of protection A higher concentration of production
in a few private firms a big share of production oriented to foreign instead of domestic markets
and protection increasing the relative price of scarce resources are some of the reasons explaining
why some countries are less egalitarian than others
NRE could also influence the evolution and levels of inequality by determining the initial
distribution of incomes This is known as the ldquoEngerman-Sokoloff Hypothesisrdquo (Engerman amp
Sokoloff 1997 Engerman et al 2002) In addition Leamer (1999) proposes that inequality and
development paths in each economy are a function of its economic structure which in turn depends
on ldquofundamentalsrdquo and ldquosymptomsrdquo On the one hand ldquofundamentalsrdquo refer to resource
endowment production structure closeness to markets and governments interventions On the
30
other hand ldquosymptomsrdquo are related to institutions employment structure and net export structure
Using this conceptual framework Leamer argues that natural Resource-Rich Countries (RRC) can
experience a higher level of inequality because can have a ldquodumbbell educational systemrdquo
ownership is concentrated in small groups and extraction activities require low-skilled workers
This implies fewer incentives to educate citizens until very late in the development process
resulting in human capital not prepared to take advantage of the process of technological progress
and delaying the emergence of more efficient and competitive sectors such as manufacturing and
services
Using 1980 and 1990 data for a group of countries classified according to land abundance
Leamer (1999) provides evidence showing that on the one hand land-scarce countries concentrate
their production and employment in sectors that promote equality such as capital-intensive
manufacturing chemical or machinery On the other hand countries abundant in natural resources
concentrate their production trade or employment in sectors that promote income inequality such
as the production of food beverages extraction activities or forestry
Gylfason and Zoega (2003) using a framework based on standard growth models also
proposed a positive relationship between NRE and inequality They assume that workers can work
in the primary sector or in the manufacturing (including services) sector In addition wage income
is equally distributed in the manufacturing sector but unequally in the primary sector (because of
initial distribution competition rent seeking etc) Therefore inequality will be greater when a
bigger proportion of labour is dedicated to extraction activities in the primary sector This
phenomenon is further amplified because of lower incentives to invest in physical and human
capital to adopt new technologies and to increase the share of the manufacturing sector
31
Diverse mechanisms explaining the link between NRE and inequality have been proposed
arguing that NRE determines simultaneously economic growth and inequality (Gylfason amp Zoega
2003) NRE could impact economic growth through the real exchange rate and the crowding-out
effect on manufacturing (ldquoDutch Diseaserdquo) reducing incentives to invest in physical and human
capital (Easterly 2007) and influencing the processes of technology adoption industrialization
and diversification of the economy in a manner that is less conducive to growth (Bulte et al 2005)
These potential explanations related to the called ldquoResource Curse Hypothesisrdquo do not have strong
empirical support (Auty 2001 Bulte et al 2005)
NRE may also influence economic growth through the quality of institutions (Acemoglu
1995 Acemoglu Aghion et al 2001 Acemoglu amp Robinson 2002 Engerman amp Sokoloff 1997
Engerman et al 2002) the concentration of ownership political power and rent-seeking NRE
acts by shaping institutional context and social infrastructure a phenomenon that is stronger when
resources are spatially concentrated such as minerals and plantations (Bulte et al 2005) NRE
could also have a significant effect on social cohesion and instability spreading its influence like
a disease (Brunori Ferreira amp Peragine 2013 Kanbur amp Venables 2005 Milanovic 2016
Ocampo 2004)
Considering a non-tradable sector intensive in unskilled workers Goderis and Malone
(2011) develop a model where the natural resources sector experiences an exogenous gift of
resource income They analyse the impact over income inequality of resource booms proxied by
changes in a commodity price index They conclude that inequality decreases in the short run but
increases after the initial reduction
32
Fum and Hodler (2010) show that natural resources increase inequality but this is
conditional on the level of ethnical polarization of society Carmignani (2013 2010) confirms this
positive relationship using different measures of dependence and abundance and goes further
arguing that inequality constitutes an indirect channel through which NRE affects human
development
Singlecountryevidence
Most of the studies about the relationship between inequality and NRE derive from cross-
country analyses Evidence for specific countries has been mainly based on case studies Howie
and Atakhanova (2014) based on the model of Goderis and Malone (2011) find for the case of
Kazakhstan that income and consumption inequality decreased significantly after booms in the oil-
and-gas sector because of resource booms increase the demand for non-tradable goods which are
intensive in unskilled workers The results depend on the level of rurality institutional quality
education levels and public spending on health and education Fleming and Measham (2015b
2015a) evaluate the impact of booms in the mining and oil sectors in Australia They find that a
boom in the mining sector increases income inequality due to commuting and migration among
regions This phenomenon can be exacerbated when the demanding access to natural resource
revenues is associated with the creation of more local administrative units (counties provinces and
even regions) but the government capacity is not simultaneously improved (Cust amp Poelhekke
2015 Michaels 2011) Furthermore the benefits that a region can receive in the form of fiscal
transfers can be more than compensated by the loses due to city-to-mine commuting such as the
case of mining regions in Chile (Aroca amp Atienza 2011)
33
Other studies at the local level have analysed the impact of the mining sector in Peru (Aragoacuten
amp Rud 2013 Loayza amp Rigolini 2016 Loayza Teran amp Rigolini 2013) Spain (Domenech
2008) and Canada (Papyrakis amp Raveh 2014) and the effects of oil windfalls in Brazil (Caselli amp
Michaels 2013)
In summary there is a wide range of potential mechanisms through which NRE could
influence income inequality Although most of them seem to suggest a positive relationship others
such as commuting and increased within-county demand for non-tradable goods and services
could lead to a negative association This highlights the need to know the sign of this association
in the Chilean economy where the trend shows a reduction in the degree of NRD After controlling
for other factors a positive link would support the argument that the reduction in the degree of
NRD has been relevant in the reduction experienced by income inequality in the same period
However a negative link would support the position that the reduction in NRD has contributed to
explain the persistence of income inequality and its slow reduction
222 The relevance of the spatial approach
Inequalities within countries are still the most important form of inequality from the political
point of view (Milanovic 2016) People from a geographic area within a country are influenced
and care most about their status relative to the people in other areas in the same country The
influence among regions involves multiple aspects (eg economic political and environmental)
These potential interactions have been traditionally ignored assuming independence among
observations related to different regions Moreover neglecting the process of spatial interaction in
key indicators of the economic and social performance of a country may mislead the design of the
public policy
34
The spatial dimension could play a significant role in understanding the distribution of
income within a country One strand of efforts aiming to capture the geographic heterogeneity of
inequality has been focussed on decomposing general indicators such as the Gini coefficient or the
Theil Index Evidence for different countries including the US (Doran amp Jordan 2016) China
(Akita 2003 Gustafsson amp Shi 2002 Ye Ma Ye Chen amp Xie 2017 Yue Zhang Ye Cheng
amp Leipnik 2014) Japan (Ohtake 2008) South Africa (Leibbrandt Finn amp Woolard 2012) and
Chile (Paredes et al 2016) shows that regional inequality is sensitive to the geographic scale of
analysis These studies also show a significant spatial component in the explanation of inequality
of income expenditure or gross domestic product for each country
Another strand explicitly uses exploratory spatial data analysis (ESDA) and spatial
econometrics ESDA has been used to provide new insights about the nature of regional disparities
of incomes and growth rates (Celebioglu amp Dallrsquoerba 2010 Yue et al 2014) Spatial econometric
models aim to assess and address the nature of the spatial effects These effects could be the result
of ldquospatial heterogeneityrdquo that is different relationships in distinct locations or ldquospatial
dependencerdquo which implies cross-sectional interactions (spillover effects) among units from
distinct but near locations
Spatial spillovers have been analysed to study both positive and negative spatial correlation
among less resource-abundant counties and resource-abundant counties On the one hand less
resource-abundant counties may experience positive spillovers because their industries supply
more goods and services to meet the increasing regional demand They can also be benefited from
positive agglomeration externalities and higher investment in private and public infrastructure
(Allcott amp Keniston 2014 Michaels 2011) On the other hand negative spillovers could be the
35
result of a high degree of interregional migration that limits the rise in wages and higher local
prices due to the increase in the share of the non-tradable sector In addition local governments
could have a limited capacity to translate the revenues from resource booms into effective public
policies promoting a sustained local development (Beine Coulombe amp Vermeulen 2015 Caselli
amp Michaels 2013 Papyrakis amp Raveh 2014)
23 Research problem and hypotheses
We can conclude from our overview of the literature that the theoretical and empirical
evidence about the link between inequality and natural resources is inconclusive This does not
make clear whether the process of reduction in the degree of dependence on natural resources
such as that experienced by the Chilean economy helps to explain the sustained but slow reduction
in income inequality or its high persistence
The research question guiding this study relates to how the natural resource endowment
determines the paths and structure of income inequality in natural resource-rich countries Using
the case of Chile the main hypotheses to be tested is whether a higher degree of dependence on
natural resources is associated with higher levels of income inequality To do that we use data at
the county level and we explicitly include the spatial dimension Our aim is to arrive at a more
comprehensive understanding of the drivers and transmission mechanisms explaining the
evolution and patterns shown by income inequality In addition we test whether the spatial
dimension plays a significant role in explaining differences in income distribution in Chile
36
24 Data and Methods
We use county-level data for the years 2006 2009 2011 2013 2015 and 2017 The reason
for not using contiguous years is that income data at the household level are only available every
two-three years from the Chilean National Socioeconomic Characterization Survey (CASEN in its
Spanish acronym)3 For the period 2006-2017 the Chilean administrative division considers 15
regions 54 provinces and 346 counties Data on income are available for 324 counties and six
years resulting in a panel with 1944 observations4
We start evaluating the spatial dimension in our data and analysing the link between
inequality and NRD using a cross-sectional setting To this end we use the ldquosix-year averagerdquo
(2006 2009 2011 2013 2015 2017) for our variables given the low time variability showed by
our measures of income inequality and NRD Results are then compared with those of a panel data
setting
241 Operationalization of key variables
The dependent variable in the present study income inequality at the county level is
measured calculating the Gini coefficient using three definitions of household income labour
autonomous and monetary income5 Labour income corresponds to the incomes obtained by all
members in the household excluding domestic service consisting of wages and salaries earnings
3 CASEN survey is conducted by the Chilean Ministry of Social Development covering topics such as education employment income and health CASEN is considered nationally representative and it is the main source for measures of inequality and poverty used for the design and evaluation of social policies in Chile 4 The six waves of CASEN for our study considered an average of 75599 households and 252081 individuals 5 The Gini coefficient is chosen because provides an overall estimate of income inequality It summarizes what proportion of the population gains what proportion of the total income The Gini coefficient can range between 0 (everyone in the population has the same income) and 1 (one person earns 100 per cent of the income in the community)
37
from independent work and self-provision of goods Autonomous income is the sum of labour
income and non-labour income (including capital income) consisting of rents interest and dividend
earnings pension healthcare benefits and other private transfers Finally monetary income is
defined as the sum of autonomous income and monetary subsidies which correspond to cash
transfers by the public sector through social programs Appendix A shows summary statistics for
the Gini coefficient of our three measures of income
The main independent variable in our study is the degree of dependence on natural resources
in each county To have an idea of the importance of each economic activity in the Chilean
economy particularly those activities related to natural resources Figure 21 shows their average
share in Chilean Gross Domestic Product (GDP) for the period 2006-17 We can observe that the
leading activities are those related to the primary sector especially mining and to the tertiary
sector where financial personal commerce restaurants and hotels services stand out The shares
of each economic activity in GDP vary significantly between Chilean regions and such
information is not available at the county level
Figure 21 Average share in GDP of economic activities (2006ndash17)
38
Leamer (1999) argues that when the main source of income is labour income (as indeed
happens for the Chilean case) using employment shares allows a better approach to measuring
dependence on natural resources Using employment data from CASEN survey we define our
measure of NRD as the employment in the primary sector (mining fishing forestry and
agriculture) as a percentage of the total employment in each county We name this variable
pss_casen where ldquopssrdquo stands for ldquoprimary sector sharerdquo We built other two proxies of NRD
using data from the ldquoServicio de Impuestos Internosrdquo (SII) which is the agency in charge of
collecting taxes in Chile The variable pss measures the percentage of employment in the primary
sector and the variable pss_firms measures the number of firms in the primary sector as a
percentage of the total number of firms in each county Appendix B shows summary statistics for
our three measures of NRD disaggregated by region
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17)
39
Figure 22 shows the evolution of our measure of inequality (using the Gini coefficient of
autonomous income) and our three potential proxies for NRD for the period 2006-2017 We
observe that both income inequality and the degree of NRD show a downward trend This seems
to support our hypothesis of a positive link between inequality and NRD however we need to
control of other sources of inequality before getting such a conclusion In what follows we use the
variable gini as our measure of income inequality capturing the Gini coefficient of autonomous
income Our measure of NRD is the variable pss_casen defined previously
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17)
Note Gini and NRD averages 2006-09-11-13-15-17 for 324 Chilean counties divided into five equal groups Source Own elaboration based on CASEN survey
40
Figure 23 shows quantile maps for income inequality (on the left) and NRD (on the right)
using the six-years average dataset6 On the one hand we observe that high levels of inequality
seem to be clustered in the Centre-South of the country where agriculture forestry and fishery are
the predominant economic activities Only isolated counties show high inequality in the Centre
(Metropolitan area where the countyrsquos capital is located) and North (Mining) areas On the other
hand our measure of NRD seems to show an opposite spatial pattern than income inequality with
high levels in the Centre and North of the country
242 Control variables
To control for county characteristics we use a set of socio-economic demographic and
institutional variables Economic factors are captured by the natural log of the mean autonomous
household income per capita (in thousands of Chilean pesos of 2017) lnincome the poverty rate
poverty the unemployment rate unemployment the percentage of the population living in rural
areas rural and the average years of education of the population over 15 years old education
Demographic factors include the proportion of the population in the labour force labour_force
and the natural log of population density (population divided by county area) lndensity
We also include the natural log of the total municipal public expenditure per capita
lnmuni_expenditure to control for municipal heterogeneity This heterogeneity is mainly related
to the capacity of municipalities to generate their own revenues In addition the richest
municipalities are in the Metropolitan region which concentrates economic power and around 40
6 After sorting a variable in ascending order quantile maps use the quantiles (quartiles quintiles deciles etc) as class breaks to divide the distribution of the variable where each class includes approximately the same number of observations (counties)
41
of the population This has basically implied a lag in the development of regions other than the
metropolitan region
The spatial distribution of our measures of income inequality and NRD displayed in Figure
23 seems to show different patterns in the North Centre and South of the country Appendix C
shows the administrative division of Chile in 15 regions and how we have grouped them in three
zones North Centre and South We consider as the ldquoCentrerdquo area that formed by the Metropolitan
region (XIII) and its two neighbouring regions V and VI Using the Centre area as our reference
we include in our analysis two dummy variables indicating whether a county is located in the North
area (regions XV I II III and IV) or South area (regions VII VIII IX XIV X XI and XII)
Appendix D shows summary statistics for the set of numeric control variables and the
correlation matrix between our measure of NRD pss_casen and the set of numeric controls
243 Methods
To assess and then consider the spatial nature of the data we need to define the set of relevant
neighbours for each country This is operationalized by building a matrix called ldquoWrdquo with a ldquo1rdquo
for neighbouring counties and a ldquo0rdquo for non-neighbouring counties We could build W using
contiguity-based (whether counties share a border or point) or geography-based (taking the
distances among the centroids of each county polygon) spatial weights Specifically we build a W
matrix considering the 5-nearest counties7 Two reasons explain the choice of k-nearest
neighbours First we cannot use a contiguity criterium because we do not have information about
all the counties and there are some geographically isolated counties Second given the significant
7 We assign a ldquo1rdquo to the five nearest counties to each county based on the distances among the polygon centroids Then W is ldquorow standardizedrdquo This facilitates the interpretation of the spatial lag of a variable as the ldquoweighted average valuerdquo of the same variable in neighbouring counties
42
differences in county areas (ldquobig countiesrdquo in northern and southern regions) using a distance-
band criterium with a not enough large distance band can lead to many ldquoislandsrdquo in extreme regions
and a multi-modal distribution for the number of neighbours
We start testing our inequality and NRD variables for spatial autocorrelation in order to
evaluate statistically the clustering patterns shown in Figure 23 Next we run an OLS regression
of inequality against NRD and our set of controls and we test the spatial autocorrelation of OLS
residuals If we cannot reject the null hypothesis of random spatial distribution we do not need
spatial models to analyse income inequality which would give contrasting evidence to previous
suggestions about the relevance of the spatial dimension of income inequality in Chile (Paredes
2013 Paredes et al 2016) If we find significant spatial autocorrelation in the OLS residuals this
justifies the use of spatial models and highlight the need to find the correct spatial structure8
If inequality in one county spillovers or influences inequality in neighbouring counties the
spatial lag of inequality should be included as an explanatory variable and we should use a spatial
autoregressive model (SAR) If some unobserved variable is the explanation for the clustering of
counties with similar inequality then this will be better captured including a spatial lag of the
errors and we should use a spatial error model (SEM) (Anselin 1988 Anselin amp Bera 1998)
Finally when our main explanatory variable or some of the controls show spatial autocorrelation
a spatial lag of the explanatory variable(s) should be included in our model
8 The existence of spatial autocorrelation violates the standard assumption of independence among observations needed for OLS regression This will result in OLS coefficients biased and inconsistent (Anselin 1988)
43
244 Spatial Model Specification
A model that includes the three forms of spatial dependence described above is called the
Cliff-Ord Model The model in its cross-sectional representation could be expressed as
119910 120582119882119910 119883120573 119882119883120574 119906 (21)
where
119906 120588119882119906 120576 (22)
119882 is our weight matrix that works as an NxN spatial lag operator9 Thus 119882119910 119882119883 and 119882119906
are the spatial lags for the dependent variable explanatory variables and the error term
respectively The parameter 120582 capture the spatial dependence in the dependent variable 120574 the
spatial dependence in the explanatory variables 120588 capture the spatial dependence in the error term
and 120598 is a vector of idiosyncratic errors For instance if ldquoyrdquo is income inequality and ldquoXrdquo a measure
of NRD the level of inequality in one county will be explained by the degree of NRD in the same
county 119883120573 the average degree of NRD in neighbouring counties 119882119883120574 the average level of
inequality in neighbouring counties 120582119882119910 and the average value of residuals in neighbouring
counties 12058811988211990610
From equations (21) and (22) the SAR and SEM models can be seen as special cases of
the Cliff-Ord representation after imposing restrictions over the spatial parameters 120582 120574 and 120588 For
the specification of the spatial panel models we follow the terminology by Croissant and Millo
9 The spatial lag is a weighted sum of the values observed at neighbouring locations 10 On the one hand the impact that income inequality in one county has over income inequality in neighbouring counties is called a ldquoglobal spilloverrdquo and it is associated with the feedback effect among neighbours (one county is its neighboursrsquo neighbourrdquo) on the other hand the influence that the degree of NRD in neighbouring counties has over inequality in one county is called a ldquolocal spilloverrdquo
44
(2018) Spatial panel models including the spatial lag of the dependent variable (SAR) the spatial
lag of the residuals (SEM) or both (SARAR) are described in Appendix E
25 Results
251 Exploratory Spatial Data Analysis (ESDA)
To analyse the significance of the spatial dimension in our data set we use the six-year
average of our variables Spatial autocorrelation is tested using the Moranrsquos I statistic11 Moranrsquos
I measures the correlation of one variable with itself in space12 Figure 24 shows the Moran scatter
plots where the standardized variable (Gini coefficient and NRD for each county) appears in the
horizontal axis against its spatial lag (average value in the 5-nearest neighbouring counties) The
Moranrsquos I (slope of the line in the Moran scatter plot) of income inequality shows a significant
positive spatial autocorrelation that is counties with high (low) inequality tend to be close to each
other
11 There are many statistics to formally test the significance of the spatial dimension in the distribution of our data The null hypothesis assumes spatial randomness which means that there is not spatial structure in the data so any spatial pattern is equally likely to occur and values in one location do not depend on values in other locations The alternative hypotheses can be the existence of positive or negative spatial autocorrelation Positive spatial autocorrelation means similar values in neighbouring locations (less variability than under spatial randomness) Negative spatial autocorrelation means dissimilar values in neighbouring locations (more variability than under spatial randomness) 12 Traditional measures of correlation such as the Pearsonrsquos coefficient measure the degree of linear correlation between two different variables Measures of spatial autocorrelation assess the correlation between the values of one variable in one location related to the values of the same variable in other neighbouring locations
45
Figure 23 Moran scatter plots for variables gini and pss_casen
Moranrsquos I is a measure of global spatial autocorrelation this means it is intended to capture
the clustering property of the entire data set To identify where are the significant hot-spots
(clusters of counties showing high income inequality) or cold-spots (clusters of counties showing
low income inequality) we need local indicators of spatial association (LISA) Using the local
Moranrsquos I (not reported) we find significant hot-spots in the South of the country (mainly
agricultural regions) and significant cold-spots in the Centre (Metropolitan area) of the country
The next step is to check whether the clustering pattern in inequality is the result of a process of
spatial dependence in the variable itself or it can be explained by other variables related to
inequality
252 Cross-sectional analysis
We start analysing differences in income inequality between counties using the six-year
average data and running an OLS regression for the model
119892119894119899119894 120573 120573 119901119904119904_119888119886119904119890119899 120573 119897119899119894119899119888119900119898119890 120573 119901119900119907119890119903119905119910 120573 119906119899119890119898119901119897119900119910119898119890119899119905 120573 119897119886119887119900119906119903_119891119900119903119888119890 120573 119890119889119906119888119886119905119894119900119899 120573 119897119899119889119890119899119904119894119905119910 120573 119903119906119903119886119897 120573 119897119899119898119906119899_119890119909119901119890119899119889119894119905119906119903119890 120573 119899119900119903119905ℎ 120573 119904119900119906119905ℎ
(23)
46
The Moran scatter plot and spatial statistical test of OLS residuals from equation (23) are
in Appendix F OLS residuals show a small but significant positive autocorrelation (Moranrsquos I =
0121) This means that income inequality continues showing a significant degree of spatial
autocorrelation after controlling for the set of covariates In addition robust Lagrange Multiplier
(LM) tests show that a spatial error model is preferred over a spatial lag model (Anselin Bera
Florax amp Yoon 1996)13 This means that income inequality tends to cluster in zones larger than a
county so the analysis should be performed on a larger scale such as provinces regions or macro
zones If the SAR model were preferred it would mean that income inequality in one county is
influenced by the level of income inequality in neighbouring counties To find the spatial structure
that best fits the clustering process of income inequality we run the full set of spatial model
specifications in a cross-sectional setting and results are shown in Table 21
Column 2 in Table 21 shows the results of our ldquoOLSrdquo model The ldquoSLXrdquo model includes
spatial dependence only through the explanatory variables The ldquoSARrdquo model includes the spatial
lag of the dependent variable as a regressor and the ldquoSEMrdquo model includes spatial dependence
through the error term The ldquoSARARrdquo model includes both the spatial lag of the response and the
errors and the ldquoSDMrdquo and ldquoSDEMrdquo are the extensions of the ldquoSARrdquo and ldquoSEMrdquo models
respectively including the spatial lag of the explanatory variables Finally a model including
spatial lags for the response errors and explanatory variables (the ldquoSARARXrdquo model) is shown in
the last column
13 Following the ldquoAnselin methodrdquo when both non robust LM tests are significant we should select between the robust LR tests From Table F1 in Appendix F only the robust LM test for the SEM model (RLMerr) appears as significant
47
Table 21
Cross-sectional Model Comparison (six-year average data)
48
Opposite to our hypothesis we observe a significant and negative coefficient for our measure
of NRD This means that counties more dependent on natural resources show lower levels of
inequality Education years population density and municipal expenditure per capita are also
negatively related to inequality On the other hand the level of income the poverty rate and the
proportion of the population living in rural areas show a positive relationship with income
inequality There is no significant influence of the unemployment rate and the proportion of the
population in the labour force In addition the SAR SEM and SARAR models show a
significantly higher average inequality in the South of the country related to the Centre area
The main finding from our cross-sectional analysis is that there is a significant and negative
relationship between inequality and NRD which is quite robust to the model specification
253 Panel Data analysis
Like the cross-sectional case we start estimating the panel without spatial effects Results
for the pooled fixed effects (FE) and random effects (RE) specifications of equation (3) are in
Appendix G Spatial models were estimated using Maximum Likelihood (ML) and Generalized
Moments (GM) and assuming that the spatial structure (W matrix) is the same for all years14
Tables 22 23 and 24 show results for the ML estimation of the SAR SEM and SARAR models
using the pooled FE and RE specifications Results for the GM estimation are in Appendix H
All our spatial models include time fixed effects In the case of the pooled and RE models they
additionally include indicator variables for those counties located in the North and South of the
country
14 In traditional (non-spatial) panels data are stacked as time series for each observational unit (county) In the case of spatial panels data are stacked as slice of cross-sections for each year in the panel
49
The main result is that the negative and significant effect of NRD on income inequality is
robust to most of the spatial panel specifications In addition the coefficient for the variable
pss_casen changes slightly among panel specifications (pooled FE and RE) but does not change
among spatial models (SAR SEM and SARAR)
Another important finding is related to the significance of the spatial dimension of income
inequality When spatial models cross-sectional or panel are compared to non-spatial models
there are no major differences in the magnitude of the coefficients or their significance This could
mean that the positive spatial autocorrelation shown by income inequality seems to be better
explained by a process of spatial heterogeneity rather than spatial dependence The practical
implication of this result is that including dummy variables for aggregated units (eg regions or
groups of regions) could be enough to control for the spatial dimension in the modelling and
analysis of income inequality
Among control variables years of education seems to be the main variable for the design of
long-term policies aimed at reducing inequality This result is in line with previous evidence for
cross-country studies (Leamer et al 1999) and specific countries (Howie amp Atakhanova 2014)
Municipal expenditure per capita also shows a significant and negative association with income
inequality in the pooled and RE spatial specifications This means that higher municipal
expenditure helps to reduce inequality between counties but its effect is more limited within
counties This result support the importance of local governments (Fleming amp Measham 2015a)
however the negative coefficient appears as evidence against the ldquoParadox of Plentyrdquo (Borge et
al 2015)
50
Table 22
ML Spatial SAR Models
Table 23
ML Spatial SEM Models
51
Table 24
ML Spatial SARAR Models
26 Discussion and conclusions
In this essay we delve deep into the sources of income inequality analysing its association
with the degree of dependence on natural resources using county-level data for the 2006ndash2017
period in Chile Given recent evidence for the Chilean economy suggesting a significant spatial
dimension we assess and incorporate explicitly the spatial structure of income inequality using
spatial methods We use cross-sectional and panel data to evaluate the significance of the spatial
dimension and we test whether NRD has a positive effect on income inequality
Contrary to what theory predicts NRD shows a significant and negative association with
income inequality This result is robust to the type of analysis (cross-sectional vs panel data) the
approach (spatial vs non-spatial) and the inclusion of different controls The negative and
significant coefficient implies that if the degree of NRD would not have experienced a 10 drop
during this period income inequality could have fallen in 2 additional points So the downward
trend in the participation of the primary sector in terms of employment in the Chilean economy
52
could be one of the main reasons explaining the high persistence in the levels of income inequality
This means that those areas that undergo a process of productive transformation mainly towards
the services sector would be facing greater problems to reduce inequality This process of
productive transformation natural or policy-driven highlights the importance of policies focused
on human capital and the role of local governments in reducing inequality
The main implication for policymakers is that a reduction in NRD does not help to reduce
inequality generating additional challenges for local and central governments in its attempt to
transform the structure of their economies to fewer dependent ones on natural resources The
finding of a significant spatial dimension suggests that defining macro zones capturing the spatial
heterogeneity in the data should be done before analysing the relationship among variables and the
design and evaluation of specific policies Particularly relevant in those areas experiencing a
reduction in NRD are migration commuting and the characteristics of the tertiary (services) sector
In addition our findings show that education and municipal expenditure could be effective policy
tools in the fight to reduce inequality in Chile
Although our results seem quite robust they do not allow us to make causal inferences about
the effect of NRD on income inequality However we could think of the following explanation to
explain the negative relationship found and the differences between geographical areas
Areas highly dependent on NR used to demand a high proportion of low-skill labour This
has change in sectors such as the mining sector in the northern area which has simultaneously
experienced an increase in activities related to the service sector such as retail restaurants
transport and housing However those services associated with more skilled labour such as the
finance sector remain concentrated in the capital region The reduction in the degree of NRD
(employment in extractive activities) implies lower labour force but more specialized with most
53
of the low-skilled labour transferred to a service sector characterized by low productivity and low
wages
Non-spatial models show that the North and South particularly the latter present
significantly higher levels of inequality This could be associated with the type of resources with
ldquopointrdquo resources such as minerals in the North and ldquodiffusedrdquo resources such as agriculture in the
South This translates into higher average incomes in the Centre and North areas and lower average
incomes in the South
The reduction in NRD implies not only a movement of the labour force from extractive
activities to manufacturing or services with the latter characterized by low productivity and low
salaries of the labour force We could also speculate that most of the high incomes move to the
central area where the economic power and ownership over firms and resources are concentrated
This would explain low inequality associated with higher average incomes in the central area and
high inequality associated with lower average incomes in the South A more in-depth analysis
capturing the mobility of wealth and labour force between counties or more aggregated areas is
needed to better understand the causal mechanism involved
Our findings open avenues for future research in different strands First studies on the causes
of income inequality should take the role of NRD into consideration which has been overlooked
so far Given that the spatial dimension of income inequality seems to be explained by a
phenomenon of spatial heterogeneity estimation strategies such as spatial regime models or
geographically weighted regression should be used (Chi amp Zhu 2019) Second the effect of NRD
on income inequality could manifest through different channels such as education fiscal transfers
and institutions We could extend our analysis to identify which of these competing channels is
the most relevant Transforming some continuous variables such as educational level to a
54
categorical variable or defining new indicator variables for instance whether a local government
shows or not an efficient performance we could classify counties in different groups and then
check whether there are differences or not in the relationship between income inequality and NRD
A third strand could be to disaggregate our measure of NRD for different industries This
would allow us to test differences among industries and to identify the sectors that promote greater
equality and which greater inequality Forth the analysis of the consequences of income inequality
on other economic and social phenomena such as efficiency economic growth and social cohesion
has a growing interest in researchers and policymakers Our findings suggest that to answer the
question of whether income inequality has a causal impact on other variables we could include a
measure of NRD as an instrument to address endogeneity issues For instance two interesting
topics for future research are the analysis of how differences in income inequality between counties
could help to explain differences in the level of efficiency of local governments and differences in
the degree of social cohesion (unrest) throughout the country Those are the issues to be addressed
in the next two essays
55
Chapter 3 The Impact of Income Inequality on the Efficiency of
Municipalities in Chile
31 Introduction
In Chile municipalities are the smallest administrative unit for which citizens choose their
local authorities playing an important role in the provision of public goods and services at the
local level Municipalities have a similar set of objectives but the level of financial resources
available to finance their activities is highly heterogeneous This could result in significant
differences in the levels of performance between municipalities Despite their importance there is
little empirical evidence about the efficiency of local governments in Chile This essay aims to
measure the technical efficiency of Chilean municipalities and to analyse how local characteristics
particularly those related to income distribution at the county level could help to explain
differences in municipal performance
Cross-country studies situate Chile as an efficient country in international comparisons about
efficiency of government spending (Herrera amp Pang 2005 Loacutepez amp Miller 2008) However
evidence for Chile at the local level is relatively sparse suggesting significant levels of
inefficiency For instance Pacheco Sanchez and Villena (2013) found a mean efficiency level of
around 70 using a sample of 309 municipalities for the period 2008-2010 This suggests that
municipalities could achieve the same level of output by reducing the usage of inputs by an average
of 30 Their study also showed that those municipalities more dependent on the central
56
government or those located in counties with lower income per capita are more efficient than their
counterparts
Most empirical research on Local Government Efficiency (LGE) has been conducted for
member countries of the Organization for Economic Cooperation and Development (OECD) of
which Chile has been a member since 2010 In the case of European countries such as Spain and
Italy which share similar characteristics such as the monetary union and levels of GDP per head
efficiency studies have been mainly motivated by budgetary constraints (Balaguer-Coll Brun-
Martos Maacuterquez-Ramos amp Prior 2019 lo Storto 2013) The Chilean context differs in three
main ways from its OECD counterparts First except for the Metropolitan Region that concentrates
most of the population Chilean regions are highly dependent on natural resources Second Chile
is also characterized by one of the highest levels of income inequality among OECD countries
which contrast with the situation of developed natural resource-rich countries such as Australia
and Norway Third although budget constraints are also a relevant issue Chilean municipalities
have experienced a sustained increase in the level of financial resources and expenditure
Another relevant distinction when we benchmark the performance of municipalities across
different countries is the type of public services they provide On the one hand in most of the
countries included Chile the main role of local governments is to provide ldquoservices to peoplerdquo
such as public education and public health On the other hand there are countries such as Australia
where local governments mainly provide ldquoservices to propertyrdquo including waste management
maintenance of local roads and the provision of community facilities such as libraries swimming
pools and parks (Dollery Wallis amp Akimov 2010 Drew Kortt amp Dollery 2015 McQuestin
Drew amp Dollery 2018)
57
Despite contextual differences Chilean municipalities seem not to perform differently from
municipalities in other developed and natural resource-rich countries where income inequality is
significantly less than in Chile (Narboacuten-Perpintildeaacute amp De Witte 2018a) This result highlights the
need to study the role of income inequality and the degree of dependence on natural resources over
LGE characteristics that have been largely overlooked in the literature
We measure and analyse differences in municipal performance using a two-stage approach
In the first stage we measure municipal efficiency using an input-oriented Data Envelopment
Analysis (DEA) to get a set of ldquoefficiency scoresrdquo In the second stage we regress efficiency scores
against our measure of income inequality controlling for a set of contextual factors describing the
economic socio-demographic and political context of each county
We use a sample of 324 municipalities for the period 2006-2017 During this period Chile
was divided into 346 counties belonging to 15 regions This period was characterized by important
external and internal shocks including the Global Financial Crisis (GFC) one of the biggest
earthquakes in Chilean history in 2010 and three municipal elections The availability of
information allows us to measure efficiency for the full period but the influence of contextual
factors is analysed using a panel of six years (2006 2009 2011 2013 2015 and 2017) for which
household income information is available
The main hypothesis tested in the second stage is whether higher levels of income inequality
are associated with lower levels of efficiency Previous evidence shows that when progress is not
evenly shared persistent within-country inequality reduces the effectiveness and efficiency of the
public sector (Ortega Sanjuaacuten amp Casquero 2017 Tandon 2005)
Income inequality has been used to control for a wide range of idiosyncratic factors
associated with historical institutional and cultural factors affecting efficiency (Greene 2016
58
Ortega et al 2017) For instance at the local level income inequality has been considered as an
indicator of economic heterogeneity in the population where higher inequality is associated with
a more heterogeneous set of conflicting demands for public services which adversely affect an
efficient provision (Ashworth Geys Heyndels amp Wille 2014 Geys amp Moesen 2009) Higher
levels of income inequality could also relate to economically privileged groups having a greater
capacity to influence the political system for their own benefit rather than that of the majority
When high inequality is persistent the feeling of frustration and disappointment in the population
could reduce not only trust and cooperation among individuals but also trust in institutions which
would negatively affect government efficiency (Boix amp Posner 1998 Coffeacute amp Geys 2005) For
instance national or local authorities could end exerting patronage and clientelism and showing
rising levels of corruption (Uslaner 2011 Uslaner amp Brown 2005)
One of the main gaps in extant literature is the need to conduct more analysis of LGE using
panel data taking into consideration endogeneity issues and controlling for unobserved
heterogeneity (Narboacuten-Perpintildeaacute amp De Witte 2018a) To address the above we set-up a panel with
time and county-specific effects and we propose the use of a measure of natural resource
dependence (NRD) as an instrument for income inequality Based on the ldquoParadox of Plentyrdquo
fiscal revenues from natural resources windfalls could be associated with an over expansion of the
public sector fostering rent-seeking and corruption and reducing local government efficiency
(Dauvin amp Guerreiro 2017 Manzano amp Rigobon 2001) In the Chilean case most of the revenues
generated by local governments included those from natural resources end up in a common fund
which benefits all municipalities The aim of this common fund is precisely to reduce inequalities
among municipalities so although we do not expect a direct impact of natural resources on LGE
we could expect an indirect effect through other indicators particularly income inequality
59
As far as we know this is the first study analysing the influence of income inequality as a
determinant of municipal efficiency in Chile Moreover this is the first study in the context of a
natural resource-rich country which specifically suggests a measure of natural resource
dependence as an instrument to correct for endogeneity bias We propose the use of the proportion
of firms in the primary sector as proxy for the degree of NRD in each county We argue that this
variable is a better proxy than using the proportion of employment in the manufacturing sector
which has been proposed in previous studies (Alesina amp La Ferrara 2002) During the period
analysed our proxy remained relatively stable and showed a significant relationship with income
inequality In addition it is less likely that it has directly affected municipal efficiency
This study adds to the literature in two other ways First the extant literature suggests that
efficiency measurement could be highly sensitive to the chosen technique as well as the selection
of inputs and outputs (Narboacuten-Perpintildeaacute amp De Witte 2018a) Inputs are usually proxied by a single
measure of total public expenditures and outputs by general proxies such as population andor the
number of businesses in each county We offer a novel approach for the selection of inputs and
outputs On the one hand we disaggregate government expenditures into four components
(operation personnel health and education) and we use the number of public schools and health
facilities in each county as a proxy for physical capital On the other hand we use four outputs
aiming to capture the wide variety of goods and services supplied by each municipality Through
this approach we aim to better describe the production function of each municipality capturing
not only the variety of inputs and outputs but also differences in size among municipalities
A third contribution relates to the measurement of LGE in the Chilean context We measure
technical and scale efficiency using a larger sample and a longer period This has empirical and
policy relevance On the one hand it helps us to select the correct DEA model and allows us to
60
determine the importance of scale inefficiencies as explanation for differences in municipal
performance On the other hand efficiency measures increase the information available for both
central and local governments to better understand the production technology that best describes
each municipality and to carry out policies to improve efficiency
We believe that our selection of inputs and outputs the use of a large dataset and the joint
analysis using cross-sectional and panel data provide a more accurate and robust analysis of
municipal efficiency Likewise knowing whether inequality has a significant influence on
municipal efficiency may provide useful insights and guidance for policymakers not only in Chile
but also for countries sharing similar characteristics
DEA results show an average level of technical efficiency (inefficiency) of around 83
(17) This means that municipalities could reduce on average a 17 the use of inputs without
reducing the outputs There are significant differences among geographic areas with the Centre
area (where the countyrsquos capital is located) displaying higher efficiency than the rest of the country
When municipal efficiency is measured under different assumptions about returns to scale results
reveal a production technology with variable returns to scales and around 75 of the
municipalities displaying scale inefficiencies However when technical efficiency is
disaggregated between pure technical efficiency and scale efficiency results show that scale
inefficiency explains a small proportion of the total municipal technical inefficiency This finding
justifies a deeper analysis of the reasons why municipalities could operate inefficiently and why
municipal performance could vary among municipalities
Efficiency scores also show a significant degree of positive spatial autocorrelation This
means that municipal efficiency shows a general clustering process with neighbouring
municipalities showing similar levels of efficiency A further analysis shows that most of the
61
spatial pattern in municipal efficiency is exogenous that is could be associated to other variables
Hence we conduct most of our regression analysis using traditional (non-spatial) methods and
leaving spatial regressions in the appendixes
Findings from cross-sectional and panel regressions support the hypothesis that municipal
performance is significantly and negatively associated with income inequality at the county level
The coefficient of income inequality is close to one which means that reductions in income
inequality ceteris paribus could be associated with increases in municipal efficiency in the same
proportion This result supports the strand of research arguing that there is not a trade-off at least
at the municipal level between equity and efficiency (Andersen amp Maibom 2020 Berg amp Ostry
2011 2017) The main policy implications are that authorities in more unequal counties would
face higher challenges to perform efficiently and policies pertaining to inequality and efficiency
should not be designed independently
The chapter is structured as follows Section 32 provides a brief literature review on related
local government efficiency Section 33 introduces the methodological background and empirical
models Section 34 presents the empirical results and discussions Section 35 concludes the
chapter
32 Related Literature
321 Measuring efficiency of local governments
Studies on measuring LGE can be grouped in those analysing the provision of single services
such as health or education and those assessing overall efficiency (lo Storto 2013) Once inputs
and outputs have been defined efficiency is measured using parametric andor non-parametric
techniques Among the former group Stochastic Frontier Analysis (SFA) seems to be preferred
62
by scholars aiming to measure efficiency and to analyse the link with environmental variables
using a single procedure (Pacheco et al 2013 Tsekeris Sotiris Tsekeris amp Papaioannou 2018)
On the non-parametric group Data Envelopment Analysis (DEA) is by far the most used technique
(Afonso amp Fernandes 2006 Balaguer-Coll amp Prior 2009 lo Storto 2013)
The selection of inputs and outputs depends not only on the aimed of the study (specific
sector vs whole measure of efficiency) but also on the role that municipalities play in different
countries There are countries such as Australia (Drew et al 2015) and Spain (Balaguer-Coll amp
Prior 2009) where local governments mainly supply services to ldquopropertyrdquo such as waste
management and road maintenance In these cases efficiency has been mainly measured using
total indicators of local government expenditure and outputs have been proxied using general
indicators such as population or number of business (Drew et al 2015) On the other hand in
countries such as Italy (lo Storto 2013) and Portugal (Afonso amp Fernandes 2008) in Europe or
Brazil (de Sousa Cribari-Neto amp Stosic 2005) and Chile (Pacheco et al 2013) in South America
municipalities mainly supply services to ldquopeoplerdquo Here in addition to financial expenditures or
revenues inputs have included the number of local government employees the number of schools
or the number of hospitals and health centres School-age population the number of students
enrolled in primary and secondary schools and the number of beds in hospitals have been
considered as outputs Based on the study of Narboacuten-Perpintildeaacute amp De Witte (2018a) a wider list of
inputs and outputs used in previous studies can be found in Appendix I
Studies from different countries show important differences in the average efficiency scores
both between and within countries These studies also differ in the samples methodologies and
variables included A summary showing the range and variability of the mean efficiency scores
founds in countries all around the world can be found in Narboacuten-Perpintildeaacute amp De Witte (2018a)
63
These authors also show that OECD natural resource-rich countries such as Australia Belgium
and Chile show similar results in terms of mean efficiency scores with LGE studies being less
frequent in Latin American countries
Measuring efficiency of local governments as decision-making units (DMU) presents many
challenges and difficulties (Borger Kerstens Moesen amp Vanneste 1994 Ravallion 2005)
Worthington and Dollery (2000) mention problems with the selection and measurement of inputs
the identification of different stakeholders the hidden characteristic of the ldquolocal government
technologyrdquo and the multidimensionality of the services provided by local governments All these
issues make difficult to identify and distinguish between outputs and outcomes with outputs
commonly proxied by general indicators such as county area or county population Because
efficiency measures are highly sensitive to the chosen technique and the selection of inputs and
outputs Narboacuten-Perpintildeaacute amp De Witte (2018a) suggest formulating different specifications and
using less general and unspecified indicators Moreover the complexity in defining outputs and
the use of general indicators make more likely that contextual factors affect municipal efficiency
322 Explaining differences in LGE
To explain differences in local government performance researchers have basically
distinguished between ldquodiscretionaryrdquo and ldquonon-discretionaryrdquo factors Discretionary factors refer
to the degree of discretion of local authorities in the selection and management of inputs and
outputs On the other hand scholars have investigated the influence on LGE of contextual factors
beyond authoritiesrsquo control These factors reflective at the environment where municipalities
operate include economic socio-demographic geographic financial political and institutional
characteristics (da Cruz amp Marques 2014 Narboacuten-Perpintildeaacute amp De Witte 2018b)
64
In general the evidence about the influence of contextual factors has delivered mixed and
country-specific results (Narboacuten-Perpintildeaacute amp De Witte 2018b) Sampaio de Sousa et al (2005)
using data for Brazilian municipalities finds that population density and urbanization rate have
strong positive effects on efficiency scores Benito et al (2010) show that lower levels of
efficiency of Spanish municipalities are associated with a greater economic level a less stable
population and a bigger size of the local government Afonso (2008) finds that per capita income
level and education are not significant factors influencing LGE of Portuguese municipalities He
also finds that municipalities in Northern areas show greater efficiency than their counterparts in
Southern areas More recently Tsekeris (2018) finds that spatial variations in efficiency in Greece
can be attributed to factors related to inter-regional market access specialization and sectoral
concentration resource-factor endowments and political factors among others Characteristics
describing each local government have also been used including municipal indebtedness (Benito
et al 2010) fiscal deficits (Sinha 2017) degree of fiscal autonomy (Boetti Piacenza amp Turati
2009) and individual characteristics of local authorities such as age gender and political ideology
Narboacuten‐Perpintildeaacute amp De Witte (2018b) conclude after analysing 63 articles that studies on the
influence of contextual factors have mostly used cross-sectional data with little attention to
endogeneity issues which makes any causal interpretation doubtful
323 The trade-off between efficiency and equity
The existence of a potential trade-off between efficiency and equity is in the core of
economic discussion (Andersen amp Maibom 2020 Berg amp Ostry 2011 Browning amp Johnson
1984 Okun 2015)15 The argument that economic growth (one of the most common efficiency
15 Redistributive policies distort incentives and lead to suboptimal outcomes and thus efficiency losses
65
measures) could be negatively affected in the search for greater equality has been translated not
only into economic policies that favour economic growth over those that reduce inequality but
also in the emphasis of scholarly research Thus theoretical and empirical research has been
mainly focussed on efficiency and policy implications of a great diversity of shocks and policies
leaving the analysis of inequality as one of measurement and mostly descriptive Additionally
empirical evidence of the trade-off is scant and inconclusive (Andersen amp Maibom 2020
Browning amp Johnson 1984)
Among economic contextual factors that could affect LGE income inequality has been
largely ignored An exception is a cross-country comparison conducted by Ortega (2017) who
analyses the role of inequality on government efficiency in developing countries He finds that
more unequal countries could have higher difficulties to achieve specific health outcomes Income
inequality has even been considered as part of the outputs to measure efficiency particularly for
the case of European and OECD countries (Afonso Schuknecht amp Tanzi 2010 Antonelli amp De
Bonis 2018)
At the local level income inequality has been mainly used as a proxy for the effect of income
heterogeneity Economic inequality could have a direct and an indirect effect on government
efficiency The direct effect poses that higher income inequality could reduce municipal efficiency
because it is associated with a more complex and competing set of public services demanded by
the population (Jottier et al 2012) The indirect effect puts the focus in the link between inequality
social capital and levels of corruption Economic diversity could reduce trust in people and
institutions when related to high and persistent levels of income inequality It could also affect the
willingness to participate in community and political groups the existence of a shared objective
by citizens and the perception of a prosperous future (Uslaner amp Brown 2005)
66
The evidence is ambiguous For instance Geys and Moesen (2009) find that income
inequality has little relation to efficiency of Flemish municipalities and Ashworth et al (2014)
find a negative relationship for the Norwegian case Findings also indicate that inequality is the
strongest determinant of trust and that trust has a greater effect on communal participation than on
political participation (Uslaner amp Brown 2005)
33 Methodology
We follow a two-stage approach widely used in this kind of analysis A DEA analysis is
conducted in the first stage to get efficiency scores for each municipality Then regression analysis
is conducted in the second stage aiming to identify contextual variables other than differences in
the management of inputs that can help to explain the heterogeneity in municipal performance
331 Chilean Municipalities and period of analysis
The territory of Chile is divided into regions and these into provinces which for purposes of
the local administration are divided into counties The local administration of each county resides
in a municipality which is administrated by a Mayor assisted by a Municipal Council16
Municipalities represent the decentralization of the central power in Chile They are autonomous
organizations with legal personality and own patrimony whose purpose is to satisfy the needs of
the local community and ensure their participation in the economic social and cultural progress of
the county Municipalities have a diversity of functions related to public health education and
social assistance among others
16 The Mayor and City Council are elected by suffrage for the citizens of the respective commune every 4 years
67
To achieve their goals two are the main sources of municipal incomes own permanent
revenues (OPR) and the Municipal Common Fund (MCF) OPR are incomes generated by the
county and they are an indicator of the self-financing capacity of each municipality OPR are not
subject to restrictions regarding their investment and they are mainly generated by territorial taxes
commercial patents and circulation permits17 The MCF is a fund that aims to redistribute
community income to ensure compliance with the purpose of the municipalities and their proper
functioning Sources to finance the MCF come from municipal revenues The distribution
mechanism of the fund is regulated by parameters such as whether municipalities generate OPR
per capita lower than the national average and the number of poor people in the commune in
relation to the number of poor people in the country
This study covers the period from 2006 to 2017 During this period Chile was divided into
15 regions 54 provinces and 346 counties18 Although the information on inputs and outputs is
available for the entire period information on contextual factors at the county level such as
household income is only available every two-three years In addition some counties are excluded
from household surveys due to their difficult access Hence we use a sample of 324 municipalities
to measure municipal efficiency for the whole period (3888 observations) However the analysis
of contextual factors is conducted for those years when household income information is available
2006 2009 2011 2013 2015 and 2017 (1944 observations)
17 The territorial tax is a tax on agricultural and non-agricultural real estate Specifically of this income only 40 is left to the municipality as its own financing while the remaining 60 is allocated to the MCF (in the case of the four largest communes- Santiago Providencia Las Condes and Vitacura-percentages are 35 and 65 respectively) Unlike the territorial tax commercial patents are regulated mainly by the municipality which chooses the rate to be charged subject to a range established by law and is responsible of their collection Finally regarding the circulation permits 375 is of municipal benefit while 645 is directed to the MCF 18 There are 346 counties managed by 345 municipalities where counties ldquoCabo de Hornosrdquo and ldquoAntaacuterticardquo are managed by the municipality of ldquoCabo de Hornosrdquo
68
332 Measuring municipal efficiency
Municipal efficiency is measured using Data Envelopment Analysis (Coelli Prasada Rao
OrsquoDonnell amp Battese 2005) This is a non-parametric approach that uses linear programming to
measure efficiency for a group of municipalities as decision making units (DMUrsquos) The main
advantage of using DEA and the reason why DEA is used for the case of Chilean municipalities
is its flexibility in handling multiple inputs and outputs without the need to specify a functional
form (Balaguer-Coll amp Prior 2009 Mikušovaacute 2015 Tigga amp Mishra 2015) Following Afonso
and Fernandes (2008) the relationship between inputs and outputs for each municipality could be
represented by the following equation
119884 119891 119883 119894 1 119899 (31)
In equation (31) 119884 is the set of outputs and 119883 the set of inputs for each of the n
municipalities Using linear programming the production frontier is constructed and a vector of
efficiency scores is obtained The frontier represents full technical efficiency mdash the point at which
the highest output occurs given specified inputs or the point at which the lowest amount of inputs
is used to produce a specified quantity of output Efficiency scores under DEA are relative
measures of efficiency They measure a municipalityrsquos efficiency against the other measured
municipalities in the sample and not a hypothetical lsquoperfect municipalityrsquo The further from the
frontier the less technically efficient a municipality is
We use an input-oriented approach because Chilean municipalities have a greater control
over the management of inputs relative to the outputs they have to manage Obtaining efficiency
scores requires an assumption about the returns to scale exhibited by each municipality When
DMUrsquos are homogeneous the CCR model (Charnes Cooper amp Rhodes 1978) which assumes
69
constant return-to-scale (CRS) is the appropriate specification The CCR model assumes full
proportionality between outputs and inputs and that DMUrsquos operate at their optimal When DMUrsquos
are highly heterogeneous as is the case with local governments in most countries it is not realistic
to assume complete proportionality between inputs and outputs nor that all DMUrsquos operate at their
optimum scale In this situation a variable returns-to-scale (VRS) or BCC model (Banker
Charnes amp Cooper 1984) is the preferred formulation
Assuming VRS imposes minimum restrictions on the efficient frontier and allows for
comparisons only among municipalities of similar scale (Coelli et al 2005 Wu Huang amp Pan
2014) This means that when we use the CCR model (assuming CRS) on a heterogeneous sample
of DMUrsquos the resulting measure of technical efficiency that we obtain is related not only to the
management of inputs but also to issues of scale19 To empirically check the validity of the VRS
assumption we measure technical efficiency under CRS VRS and non-increasing returns-to-scale
(NIRS) and we analyse the existence of scale inefficiencies This allows us to check the relevance
of scale effects as a potential explanation of differences in municipal efficiency Appendix J
shows the specification of the DEA model under VRS and how ldquototal technical efficiencyrdquo
(assuming CRS and therefore due to management and scale issues) could be disaggregated in ldquopure
technical efficiencyrdquo (under VRS and related only to management issues) and scale efficiency (due
to scale issues)
19 Assuming VRS can also lead to measurement problems such as overestimation of efficiency scores This is the case when an important proportion of DMUrsquos shows CRS so assuming VRS ignores the information about proportionality between inputs and outputs (Podinovski 2004)
70
333 Inputs and outputs used in DEA
Following the literature on local government expenditure efficiency (Afonso amp Fernandes
2008 de Sousa et al 2005 Dlouhyacute 2018 Tandon 2005 Tigga amp Mishra 2015) and trying to
reflect as well as possible the functioning of municipalities five inputs and four outputs were
selected Input and output data were obtained from the National System of Municipal Information
(SINIM in its Spanish acronym) and they are expressed in thousands of Chilean pesos of 201720
Inputs are Municipal Operational Expenditure X1 (including expenses on goods and
services social assistance investment and transfers to community organizations) Municipal
Personnel Expenditure X2 (including full time and part-time workers) Total Municipal
Expenditure in Education sector X3 Total Municipal Expenditure in Health sector X4 and the
Number of Municipal Buildings X5 (proxied by the number of public facilities in education and
health sectors)
Output variables were selected highlighting the relevance of education and health sectors
and trying to capture the wide range of local services provided by municipalities The variable
ldquoOwn Permanent Revenuesrdquo Y1 is used to capture the scale and diversity of municipal
activities21 The ldquoMonthly Average Enrolmentrdquo in municipal education establishments related to
the school-age population in each county Y2 is used as educational output As health output the
ldquoNumber of Medical Consultationsrdquo in public facilities Y3 is considered Finally the number of
community organizations Y4 is used as output reflecting the promotion of community
development by each municipality Table 31 shows the summary statistics of input and output
20 The data from SINIM database was mostly obtained using the sinimr package (Salas 2019) 21 According to SINIM database this variable aims to measure the management of the municipalityrsquos own resources with respect to the population of the commune
71
variables for the whole sample and period Inputs and outputs excepting the Monthly Average
Enrolment Y2 are measured in per capita terms using county population information from the
National Institute of Statistics (INE in its Spanish acronym)
Table 31
Descriptive statistics Inputs and Output variables used in DEA analysis
334 Regression model
Contextual factors could play an important role not only in explaining why some
municipalities operate inefficiently but also why municipal performance differs among them
These factors may affect municipal performance modifying incentives for local authorities to
operate efficiently and their capability to take advantage of economies of scale They also define
the conditions for cooperation or competition among municipalities and the citizensacute ability and
willingness to monitor local authorities (Afonso amp Fernandes 2008)
Information on income at the household level for each county was obtained from the
ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) CASEN is
conducted every two-three years being the reason why consecutive years are not considered in
72
our regression analysis The other contextual factors used as controls were obtained from different
sources including SINIM INE and the ldquoServicio de Impuestos Internosrdquo (SII)22
Our main hypothesis is whether higher levels of income inequality are associated with lower
levels of municipal efficiency To test our hypothesis the empirical model is defined as
120579 120573 119892119894119899119894 119885 120573 120575 120572 120598 (32)
Where 120579 is the vector of DEA efficiency scores 119892119894119899119894 is the Gini coefficient of each
county 120575 are year-specific effects 120572 are county-specific constants 120598 is a vector of error terms
and 119885 is a vector of controls Next we discuss the motivation for these controls
The level of purchasing power of countiesrsquo citizens is proxied by the variable log(income)
which is the natural log of the mean household income per capita in thousands of Chilean pesos of
2017 On the one hand poorer counties could display higher efficiency due to their necessity to
take care of their constraint resources (Pacheco et al 2013) On the other hand richer counties
could show higher efficiency because richer citizens exert higher monitoring over local authorities
and demand better quality public services in return for their tax payments (Afonso et al 2010)
The possibility for municipalities to take advantage of economies of scale and urbanization is
captured by three variables First the variable log(density) which correspond to the natural log of
population density Second the dummy variable reg_cap indicating whether a county is a regional
capital or not Third the variable agroland which correspond to the proportion of land for
agricultural use which is informed to the SII We expect a positive effect of log(density) but
negative for regcap and agroland
22 The SII is the institution in charge of collecting taxes in Chile
73
Socio-demographic characteristics are captured including a Dependence Index IDD IDD
corresponds to the number of people under 15 years or over 65 years per 100 people in the active
population (those people between 15 and 65 years old) A higher proportion of young and older
population could be associated with a higher demand for municipal services relating to education
and health making harder to offer public services efficiently The citizensrsquo capacity to monitor
local authorities is proxied including the variable education (average years of education for the
population older than 15 years) and the variable housing (proportion of households which are
owners of the property where they live in each county) In both cases we expect a positive
association with LGE
Among municipal characteristics the variable professional (percentage of municipal
personnel with a professional degree) is used to control for the quality of municipal services and
it is expected a positive impact The variable mcf (proportion of total municipal income coming
from the MCF) is included to capture the influence of financial dependence on the central
government A higher dependence from MCF could be associated with higher efficiency when it
is linked to more control from central government (Worthington amp Dollery 2000) However when
MCF discourages the generation of own resources and proper management of resources from the
fund a lower efficiency should be expected (Bravo 2014) In addition the dummy variable mayor
is included to capture differences among mayors supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo
political parties related to those ldquoINDEPENDENTrdquo mayors
Table 32 report summary statistics for the set of numeric contextual factors and Appendix
K the corresponding correlation matrix Despite the high correlation between income and
education variables we include both in the regression section as they capture different county
characteristics
74
Table 32
Summary Statistics Numeric Contextual Factors
Figure 31 Geographical distribution of Chilean regions and macrozones
Previous evidence on growth and convergence of Chilean regions have found that regions
tend to cluster spatially (Aroca amp Bosch 2000) Aiming to capture this regional clustering process
75
and considering the high concentration in the number of municipalities and population in the
central area we group municipalities in four ldquozonesrdquo We define as reference the ldquoCentre-Northrdquo
zone consisting of regions XIII (where the countryrsquos capital is located) and its two neighbouring
regions V and VI The ldquoNorthrdquo zone includes regions XV I II III and IV The ldquoCentre-Southrdquo
zone includes regions VII VIII and IX Finally the ldquoSouthrdquo zone embraces regions XIV X XI
and XII Figure 31 displays the regional administrative division and zones considered in this
essay
Efficiency scores (ES) are limited to have values between 0 and 1 However they are relative
measures (relative to the sample of municipalities) This implies that when a municipality is on the
frontier (ES = 1) it does not mean that potentially larger technical efficiency gains cannot be made
Hence equation 32 is estimated using OLS and censored regressions We start running cross-
sectional regressions for each of the six years Then we compare the results with those from panel
regressions Because fixed-effects panel Tobit models could be affected by the incidental
parameters problem (Henningsen 2010 2019) we use random-effects panel Tobit models
including indicator variables for years and zones Finally to deal with the potential endogeneity
problem we also use an instrumental variable approach The instrument is described next
335 The instrument
Government effectiveness and income distribution are both structural components of
economies (Ortega et al 2017 Ravallion 2005) In the search for a causal interpretation for the
influence of income inequality on municipal efficiency we need an instrument which must be
correlated with the variable to be instrumented (in our case income inequality) and uncorrelated
with the error term in the efficiency equation (32) Previous literature has used as instruments for
Gini the number of townships governments in a previous period the percentage of revenues from
76
intergovernmental transfers in a previous period and the current share of the labour force in the
manufacturing sector (Alesina amp La Ferrara 2002) Using the share of the labour force in a specific
sector is unlikely to reduce the problem of endogeneity particularly in countries where local
governments supply mostly ldquoservices to peoplerdquo and the main source of income inequality is
labour income
We propose as an instrument the proportion of firms in the primary sector (mining fishing
forestry and agriculture)
119901119904119904_119891119894119903119898119904Number of firms in the primary sector
Total number of firms (33)
On the one hand this instrument is likely to be correlated with local income inequality in
natural resource-rich countries23 On the other hand we contend that our instrument is less likely
to be correlated with the error term in the efficiency equation First the main services supplied by
Chilean municipalities are services to people (health and education) not to firms Second most of
the revenues collected by municipalities included those associated with natural resources end up
in the municipal common fund whose objective is precisely to reduce inequalities among
municipalities Third services to firms are expected to be more significant with the tertiary sector
We argue that our instrument captures natural and structural conditions which directly
influence income inequality but it does not directly affect LGE Figure 32 shows the evolution
of the annual average efficiency score and the proportion of firms in the primary secondary
(manufacturing) and tertiary (services) sectors We observe that sectors shares have remained
relatively stable with a slight reduction in the participation of the primary sector in favour of the
23 Results confirm a significant association between income inequality and the degree of dependence on natural resources at the county level
77
tertiary sector by the end of the period This is contrary to the evolution of municipal efficiency
which shows a cyclical behaviour as will be shown in the next section
Figure 32 Evolution of efficiency scores and the proportion of firms by sector
34 Results and discussion
341 DEA results
Figure 33 displays the evolution of our three measures of efficiency Overall technical
efficiency pure technical efficiency and scale efficiency are around 78 83 and 95
respectively with fluctuations over the years Therefore around three quarters of the overall
78
inefficiency is attributed to inefficiency in the management of inputs and around one quarter to
scale inefficiencies24
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE)
Returnstoscale
Figure 34 reports by zone and for the whole period the proportion of municipalities
showing CRS decreasing returns to scale (DRS) or increasing returns to scale (IRS) Most of the
municipalities operate under variable (increasing or decreasing) returns to scale which could be
explained by the high heterogeneity in size among municipalities A summary of RTS
disaggregated by year and zone is in Appendix L Policies oriented to increase efficiency usually
24 The average scale efficiency score of 95 means that municipalities could get an additional 5 of inputs saving if municipalities were able to change their size to achieve their optimal scale
79
consider amalgamation de-amalgamation or ways of cooperation among municipalities To have
a better idea about where and how feasible is the implementation of such policies Appendix M
shows maps with the administrative division of the country in its 345 municipalities and which
municipalities show CRS IRS or DRS in each of the six years of data
Figure 34 Returns to scale by zone
Based on results for the whole period (Figure 34) the North has the highest proportion of
municipalities exhibiting DRS This suggests the need to reduce the size of municipalities splitting
those ldquotoo bigrdquo creating new administrative areas or giving more autonomy to current
municipalities25 The opposite occurs in the Centre-North area where municipalities mostly
exhibit IRS This indicates the need to merge municipalities An alternative strategy to the
amalgamation process is increasing inter-municipal cooperation (Balaguer-Coll et al 2019)
25 This has been the policy followed in Chile Although two new regions were created (XV in the North and XIV in the South) the number of municipalities has not changed
80
which seems to be a more plausible option in Chile Finally evidence on scale efficiency for the
Centre-South and South areas is not clear in terms of the adequate strategy to improve efficiency
Efficiencymeasure
Although most municipalities show scale inefficiencies (Figure 34) only a small proportion
of total inefficiency is associated to scale issues (Figure 33) Together this results justify not only
the use of the BCC model (efficiency scores under VRS) to capture municipal heterogeneity but
also highlights the need to look for other factors outside the control of local authorities which
could be influencing municipal performance
Table 33
Summary efficiency scores (VRS) by zone and region
Table 33 summarizes ES under VRS using data for the six years of CASEN survey A mean
efficiency score of 83 is found for the full sample and period This means that on average
inefficient municipalities can reduce the use of inputs by 17 to get the same current output By
81
comparing average ES per zone it can be concluded that municipalities in the North Centre-North
Centre-South and South could achieve the same level of output with 17 12 18 and 23 fewer
resources respectively Results also show that one third of the municipalities present an efficiency
score equal to one
Figure 35 shows the evolution of the mean ES for the four zones over the 12 years period
A clear drop in the efficiency level is observed in 2009 (the financial crisis year) excepting for the
North area Likewise although mean efficiency returned to its pre-crisis levels in 2014-2015 a
new drop in efficiency was experienced in 2016 It also seems that the 2010 earthquake did not
generate a significant effect on municipal efficiency Figure 35 also shows that although levels
of efficiency seem to differ among zones they follow a similar trend through time with the only
exception of the North which corresponds to the mining area In addition efficiency seems to be
significantly higher in the Centre-North area This is explained by the high mean level of efficiency
in region XIII which includes the countryrsquos capital city
Figure 35 Evolution mean efficiency scores (VRS) by zone
82
To know which and where are the efficient municipalities and if they are surrounded by
municipalities with a similar level of efficiency Appendix N has maps showing the ldquoefficiency
statusrdquo of each municipality that is whether they are efficient (ES = 1) or inefficient (ES lt 1)
Additionally Appendix O shows ldquoequal intervalsrdquo maps describing the spatial distribution of ES
among municipalities for each of the six years26 Results show that efficient municipalities can be
found all through the country the ldquoefficiency statusrdquo could change from one year to another and
municipalities with similar level-status of efficiency tend to cluster in space
342 Regression results
Exploratoryspatialanalysis
DEA efficiency scores and their geographical representations seem to show that municipal
efficiency presents a spatial clustering pattern This means that municipal performance could be
influenced not only by contextual factors of the county where municipality belongs but also by the
level of efficiency of neighbouring municipalities and their characteristics To test the significance
of the spatial dimension in municipal efficiency we use a cross-section of data considering the six-
year average of efficiency scores the Gini coefficient and the set of controls
We use the Moranrsquos I indicator to test for spatial autocorrelation Moranrsquos I is a measure of
the correlation between the level of efficiency in one municipality and its ldquospatial lagrdquo that is the
average level of efficiency in neighbouring municipalities We define as the relevant neighbours
for each municipality the 5-nearest municipalities This is obtained using the distances among the
26 An equal intervals map uses the same principle as a histogram to organize the observations into categories that divide the range of the variable into equal interval bins For the equal interval classification the value range between the lower and upper bound in each bin is constant across bins but the number of observations in each bin is typically not equal
83
polygonsrsquo centroids (latitude and longitude) of each county Results confirm that municipal
efficiency show a significant level of positive spatial autocorrelation This means that
municipalities tend to have neighbouring municipalities with similar performance
The positive spatial autocorrelation shown by municipal efficiency could be due to the
performance in one municipality is influenced by the performance in neighbouring municipalities
(spatial dependence in the variable itself) or due to structural differences among regions-zones
(spatial heterogeneity) To check the source of the spatial autocorrelation we run an OLS
regression of ES against income inequality and controls and then we test OLS residuals for spatial
autocorrelation Moranrsquos I over OLS residuals although significant is barely higher than zero (see
Appendix P) This means that the spatial effect itself is not a serious issue and can be handle for
instance including zone indicators variables hence we proceed to analyse the influence of income
inequality on LGE using non-spatial regression27
Cross‐sectionalanalysis
We start reporting censored regressions for each year in our panel Efficiency scores have
been rescaled to numbers between 0 and 100 to facilitate the interpretation of the results All
regressions include dummy variables for three of the four zones in which we have grouped Chilean
regions Results are in Table 3428 Income inequality shows a negative sign in all years which is
consistent with our hypothesis that inequality is negatively related to municipal efficiency
However only in three of the six years the effect of income inequality appears as statistically
27 In the case of having incorporated the spatial dimension the two most commonly used forms are including in the model the spatial lag of the dependent variable (spatial autoregressive model SAR) or the spatial lag of the error (spatial error model SEM) Following the method suggested by Anselin et al (1996) Lagrange Multiplier (LM) tests on the OLS residuals showed that the SAR model is preferred over the SEM Moranrsquos I LM tests and spatial regressions can be found in Appendix P 28 Regression results using OLS are in Appendix Q
84
significant Only the income level displays a significant and positive influence on efficiency for
the whole period A higher population density also consistently favours municipal efficiency On
the other hand as we expected a higher IDD makes it more difficult to achieve an efficient
performance29 Consistent with results in Pacheco et al (Pacheco et al 2013) municipal
efficiency show a significant an positive association with the MCF only in the first half of our
period of analysis with the second half showing an insignificant relationship
Table 34
Cross-sectional (censored) regressions
Paneldataanalysis
Estimation results for the six-year panel are reported in Table 35 Columns (1) and (2) show
the results for the pooled and random effects censored models only controlling for zone and year
29 The set of cross-sectional regressions was also run for each zone independently Results confirm that the set of significant contextual factors differs not only among years but also among geographic areas This support previous evidence suggesting that cross-sectional analysis should be treated with caution (Narboacuten-Perpintildeaacute amp De Witte 2018b)
85
dummies Income inequality appears as non-significant Zone indicator variables confirm that
municipalities located in the Centre-South and South of the country display a lower average level
of efficiency compared to the Centre-North area Time dummies mostly show negative
coefficients This is a signal that shocks such as the GFC and the earthquake in 2010 may have
had a negative impact on efficiency but that impact was not permanent The results for the pooled
and RE models including the full set of controls are reported in columns (3) and (4) These results
show a significant negative influence of income inequality on LGE
When income inequality is instrumented by the variable pss_firms most of the coefficients
remain unchanged except for those associated with the income variables gini and log(income)
This result implies that our original model suffers for instance from the omitted variable bias
This means that LGE and income inequality are determined simultaneously by some variable not
included in our model Columns (5) and (6) show results using our instrument for income
inequality The ldquotrue coefficientrdquo of variable gini remains negative but the magnitude of the
relationship is higher The negative coefficient for gini implies on the one hand that municipalities
located in more unequal counties face more challenges to achieve an efficient management of
public resources On the other hand the coefficient in column (6) is close to one The interpretation
is that for each point of reduction in income inequality ceteris paribus LGE should increase in the
same proportion Next we discuss some of the results associated with the controls variables
Contrary to Pacheco et al (2013) income level has a positive coefficient meaning that richer
counties in terms of income per capita show higher efficiency This could be explained by higher
monitoring and increasing demands from citizen and taxpayers In addition it is expected a higher
efficiency in municipalities located in counties with a higher population density and those with a
lower proportion of land for agricultural use This result is mainly explained by municipalities
86
located in the Centre area The opposite happens with municipalities in the South implying that
they are too ldquosmallrdquo to take advantage of agglomeration economies There is also a limit for
agglomeration and scale economies which is shown by the negative coefficient of the variable
regcap although this coefficient loses its significance in the IV approaches30
Unexpectedly efficiency was found to be negatively associated with the variable education
This result is similar to the case of Czech municipalities (Šťastnaacute amp Gregor 2014) where
explanations include a weakened monitoring effect due to the fact that more educated citizens
present greater mobility and labour cost disadvantages for municipalities with better educated
labour force In Chile an additional explanation could be the relationship between education and
voter turnout Since 2012 voting is no longer mandatory in Chile This fact considerably reduced
voter turnout which in turn may have influenced the monitoring and control effect of more
educated voters For the case of variable IDD results show that local authorities in counties with
higher proportion of aging and young population (related to those in the active population) face a
greater challenge in their quest to offer public services efficiently
The influence of mcf is like that found by Pacheco et al (2013) with municipalities more
dependent on central transfers showing more efficiency31 Political influence captured by the
variable mayor did not show a significant effect This result is like other studies concluding that
the ideological position did not have a significant influence on efficiency (Benito et al 2010
Boetti et al 2009 Cordero Pedraja-Chaparro Pisaflores amp Polo 2017)
30 This negative coefficient suggests that the negative effects of agglomeration economies such as overcrowding pollution high cost of land and traffic congestion could predominate over the positive effects of scale economies in regional capitals 31 When the analysis is conducted for each zone independently MCF displays a negative influence in the North and Centre-North areas but a positive influence in the Centre-South and South areas These results confirm that on the one hand richer municipalities (those in the North and Centre-North) have less incentives for efficient management of their resources On the other hand results support the relevance of MCF helping poorer municipalities (those in the South) to reach their outcomes
87
Table 35
Panel data regressions
88
35 Conclusions
The trade-off between equity and efficiency is in the core of the economic discussion This
ldquoprinciplerdquo has been used by policymakers to prioritize the design of policies focused on economic
growth delaying those policies aimed at reducing economic inequalities This essay offers
empirical evidence of a negative relationship between inequality and efficiency that is a reduction
of income inequality could have positive effects on economic efficiency at least at the level of
local governments
We followed a traditional Two-Stage approach commonly used in the analysis of LGE We
compared cross-sectional and panel data results and we have added an instrumental variable
approach to give a causal interpretation to the link between efficiency and inequality We proposed
the use of a measure of natural resource dependence to instrumentalize the impact of income
inequality on LGE Given that our units of analysis are municipalities and not counties we argue
that our measure of NRD is correlated with income inequality and it does not have a direct
influence on LGE
We found that Chilean municipalities perform better than previous studies suggest
Municipal efficiency depicted significant levels of positive spatial autocorrelation and most of the
municipalities showed to be operating under increasing or decreasing returns to scale This would
imply that the policies generally used to improve efficiency such as amalgamation or cooperation
should be implemented observing the reality of each region and not as strategies at the national
level We also found that scale inefficiency explains a small proportion of the average total
inefficiency reason why the analysis of external factors that could affect the municipal efficiency
takes greater relevance
89
Income inequality plays an important part in explaining municipal efficiency In fact it was
found that reductions in income inequality could result in increases in municipal efficiency in a
similar proportion An unexpected finding was that the levels of education shows a negative
association with municipal performance This could be due to a low average level of education or
the existence of an omitted variable This variable could be the significant reduction in voting
turnout rates for local and national elections due to changes in the voting system during the period
of our analysis All in all our results may help to shed light on the potential consequences of
changes in contextual factors and the design of strategies aimed to increase municipal efficiency
in countries with similar characteristics to the Chilean economy For instance policies oriented to
take advantage of economies of scale can be formulated merging municipalities or establishing
networks in specific sectors such as education or health
Further work needs to be done both in measurement and in the explanation of differences in
municipal performance in Chile One area of future work will be to identify the factors that better
predict why municipalities operates under increasing decreasing or constant returns to scale
Multinomial logistic regression and the application of machine learning algorithms to SINIM data
sets appear as suitable methods for that purpose Intertemporal DEA (Drew amp Dollery 2015)
should be used to measure municipal efficiency capturing changes in total factor productivity In
addition municipalities operate under different levels of geographical authorities such as the
provincial mayor and the regional governor Hence it would be useful to know how each
municipality performs within each region-zone related to how performs to the whole country This
should be done conducting a metafrontier analysis (OrsquoDonnell Rao amp Battese 2008)
We have also identified through a cross sectional spatial exploratory analysis that on
average municipalities with similar levels of efficiency tend to cluster in space Regarding to
90
analyse the importance of contextual factors on municipal efficiency a deeper analysis should use
censored spatial models to check the significance of the spatial dimension in cross-sectional and
panel contexts Another interesting avenue for future research is associated with the negative
association found between LGE and education The significant reduction in votersacute turnout since
the law of automatic registration and voluntary voting in 2011 appears as a natural experiment to
analyse its effects on efficiency indicators such as municipal performance Incorporating variables
such as the voting turnout in each county or classifying municipalities based on individual
institutional political and economic characteristics could help to shed light on which of these
channels is the most relevant when analysing the impact of inequality on municipal efficiency
Finally we argued that an important part of the influence of income inequality over LGE
could be through its indirect effect on trust social capital and social cohesion The final essay will
delve deep in that relationship
91
Chapter 4 Social Cohesion Incivilities and Diversity
Evidence at the municipal level in Chile
41 Introduction
A deterioration in social cohesion could carry significant costs such as a reduction in
generalized trust between individuals and in institutions a society caught in a vicious circle of
inequality and citizens increasingly distanced from civic life (Uslaner 2011) A growing feeling
of frustration and discontentment can eventually translate into a social outbreak with uncertain
results This is precisely what have been happening in many countries around the world included
Chile
ldquoSocial cohesion is a state of affairs concerning both the vertical and the horizontal
interactions among members of society as characterized by a set of attitudes and norms that
includes trust a sense of belonging and the willingness to participate and help as well as their
behavioural manifestationsrdquo (Chan et al 2006) This definition highlights the multidimensionality
in the concept of social cohesion which has been measured using objective andor subjective
indicators of trust social norms solidarity willingness to participate in social and political groups
and feelings of belonging (Ariely 2014 Chan et al 2006) Multidimensionality also implies that
the impact of determinants of social cohesion such as economic and racial diversity could be
different for each of its various dimensions (Ariely 2014)
A common characteristic to all societies is that they are made up of different groups that
differ with respect to race ethnicity income religion language local identity etc The
92
Community Heterogeneity Thesis (Coffeacute amp Geys 2006) argues that individuals prefer to interact
with others that are like themselves Hence high levels of diversity particularly economic and
racial represent a complex scenario to maintain social cohesion One of the most common factors
adduced for social cohesion is income inequality with higher levels linked to lower levels of trust
(Ariely 2014 Rothstein amp Uslaner 2005)
Traditional measures of social cohesion may not be adequately capturing the deterioration
in social connections For instance measures of (lack of) trust include a strong subjective element
On the other hand proxies for social participation such as volunteering jobs or joining to social
organizations have not been supported by empirical evidence as a source of generalized social trust
(Rothstein amp Uslaner 2005) We proposed to use the rate of incivilities which we argue is a more
appropriate measure of the degree of worsening in the social context
Incivilities are those visible disorders in the public space that violate respectful social norms
and tend not to be treated as crimes by the criminal justice system There are two types of
incivilities social and physical Social incivilities include antisocial behaviours such as public
drinking noisy neighbours and fighting in public places Physical incivilities include among
others vandalism graffiti abandoned cars and garbage on the streets Because citizens and
political authorities cannot always distinguish between incivilities and crime they are usually
treated as an additional category of crime This implies that policies aimed to reduce incivilities
are generally based on punitive actions However theory and evidence on incivilities suggest that
factors explaining incivilities and crime could be different (Lewis 2017 Taylor 1999)
In Chile crime rates have shown a sustained downward trend after reaching its highest level
in 2011 On the other hand incivilities rate has shown a sustained upward trend which coincides
with the increasing victimization and feeling of insecurity in the population This has motivated
93
Chilean authorities based on the ldquoBroken Windows Theoryrdquo to propose new punitive actions (or
increase the severity of the current ones) to those who commit this type of antisocial behaviours
The ldquoBroken Windows Theoryrdquo states that higher rates of incivilities are a signal of social
disorganization which result in higher crime rates (J Q Wilson amp Kelling 1982) This is expected
to have consequences on familiesrsquo decisions such as moving away from public spaces or even
leaving their neighbourhoods
As far as we know there is no previous evidence about the potential causes of incivilities in
Chile Efforts to identify the factors explaining incivilities could help not only to reduce the risk
factors favouring violent and property crimes but also to guide interventions aimed to change
social behaviours and strengthen social cohesion in highly unequal societies Thus the main
contribution of the present study is to provide a deeper comprehension of the problem of incivilities
and how they can help to better understand the weakening of social cohesion that many
contemporary societies experience
We aim to offer the first evidence on the factors explaining the evolution and the differences
in incivilities rates in Chile We set up a panel for six years (2006 2009 2011 2013 2015 and
2017) and 324 counties (1944 observations) We start exploring the evolution and geographical
distribution of incivilities Then we investigate whether economic and racial diversity after
controlling for other socioeconomic demographic and municipal characteristics can be regarded
as key predictors of incivilities
We use the Gini coefficient to proxy economic heterogeneity and the number of new visas
granted to foreigners as proportion of the county population as proxy for racial diversity The main
hypothesis is whether economic and racial diversity have a positive association with the rate of
incivilities In addition to the arguments regarding the ldquoCommunity Heterogeneity Thesisrdquo Taylor
94
(1999) and Skogan (1986 1999 2015) argue that incivilities are caused by inequality and the lack
of informal mechanisms of social control Based on the ldquoIncivilities Thesisrdquo the patterns of
incivilities should mirror the patterns of inequality (Taylor 1999) Then higher inequality should
be associated with higher physical and social vulnerability of the population This reduces social
control and increases social disorganization which triggers antisocial or negligent behaviours
Our main result reveals a strong positive association between the rate of incivilities and the
number of new visas granted per year The relationship with income inequality although also
positive seems to be less significant These findings give strong support to the ldquoCommunity
Heterogeneity Thesisrdquo and to a lesser extent to the ldquoIncivilities Thesisrdquo When the analysis is
disaggregated geographically racial diversity shows a clear positive effect The impact of income
inequality seems to be conditional depending on the level of income showing no effect in poorer
regions Results also show that the impact of economic and racial diversity differs by type of
incivility For example income inequality shows a strong association with ldquoStreet Tradingrdquo while
racial diversity with ldquoAlcohol Consumptionrdquo Two are the main policy implications On the one
hand a national strategy to address the problems associated with foreign immigration could help
to reduce incivilities For instance a joint effort between national and local authorities to curb
immigration and its distribution throughout the country On the other hand our results show that
the relationship between incivilities and economic diversity differs depending on the region or
geographical area Hence the impact on social cohesion of policies aimed to tackle economic
inequalities should be analysed in each specific context
The rate of incivilities also shows a negative association with the level of municipal financial
autonomy This implies that municipalities can effectively carry out policies to reduce incivilities
beyond the efforts of the central government Another important finding is that our results do not
95
support the hypothesis that a higher proportion of the young population is associated with higher
rates of incivilities Hence policies aimed to reduce incivilities should be focused on the causes of
incivilities rather than the criminalization of behaviours or stigmatization of specific population
groups
The structure of the chapter is as follows Section 42 outlines the relevant literature on social
cohesion and incivilities Section 43 describes the data variables and methodology and
establishes the hypotheses of the study Section 44 contains the results and discussions Section
45 presents the main conclusions
42 Related Literature
421 The Community Heterogeneity Thesis
The idea under ldquoThe Community Heterogeneity Thesisrdquo is that if individuals prefer to
interact with others who are similar to themselves in terms of income race or ethnicity high levels
of income inequality and racial diversity facilitate a context for lower tolerance and antisocial
behaviours lowering the ldquostaterdquo of social cohesion (Alesina 2000 Coffeacute amp Geys 2006 Letki
2008) Alessina and Ferrara (2002) give support to this hypothesis arguing that individuals have a
natural aversion to heterogeneity However the most popular explanation is the principle of
homophily people prefer to interact with others who share the same ethnic heritage have the same
social status and hence share experiences and tastes (Letki 2008 McPherson Smith-Lovin amp
Cook 2001 Tolsma et al 2009) For instance Delhey and Newton (2005) find for a sample of
60 countries that income inequality and ethnicity are strongly and negatively correlated with trust
Tolsma et al (2009) using data for Dutch neighbourhoods and municipalities find that social
cohesion is negatively and consistently affected by economic deprivation but not by ethnic
96
heterogeneity These authors also conclude that the effect of neighbourhood and municipal
characteristics on social cohesion depends on residentsrsquo income and educational level
Rothstein and Uslaner (2005) give two theoretical reasons why economic and racial diversity
should be causally related to social trust a key element of social cohesion First optimism about
the future makes less sense when there is more economic inequality which generally translates into
inequality of opportunities especially in areas such as education and the labour market Second
the distribution of resources and opportunities plays a key role in establishing the belief that people
share a common destiny and have similar fundamental values In highly unequal societies people
are likely to stick with their own kind Perceptions of injustice will reinforce negative stereotypes
of other groups making social trust and accommodation more difficult
Uslaner (2002 2011) and Uslaner and Brown (2005) find that high levels of inequality are
the single major factor driving down trust in people who are different from yourself Evidence for
USA finds that inequality is the strongest determinant of generalized trust over time (Rothstein amp
Uslaner 2005) Reducing inequality and then increasing generalized trust should have positive
consequences at the individual and aggregates levels At the individual level it may lead to greater
tolerance and more acts of altruism for people of different backgrounds At the aggregate level it
may lead to greater economic growth more redistribution from the rich to the poor and less
corruption (Uslaner 2002 2013) Letki (2008) argues that when neighbourhood socio-economic
context (apart from just an individualrsquos socioeconomic status) is considered it turns out to be the
main factor triggering negative attitudes and lack of trust in out-group members
The increasing diversity caused by immigration can also reduce the conditions necessary for
social cohesion (Ariely 2014 Holtug amp Mason 2010) Christel Kesler and Irene Bloemraad
(2010) find for nineteen advanced democracies between 1981 and 2000 that increasing migration
97
generally decreases trust civic engagement and political participation The authors also find that
in more equal countries with clear policies in favour of cultural minorities the negative effects of
migration are mitigated or even reversed Letki (2008) states that deprivation and disorder tend to
be strongly correlated with racial diversity Because we propose the use of the number of disorders
or antisocial behaviours known as ldquoincivilitiesrdquo as our measure of social cohesion we describe the
literature on incivilities in the next section
422 The literature on incivilities
The study of incivilities has been a continuing concern mainly for developed countries since
the 1980s The focus has changed from individual and psychological explanations to ecological
(contextual) and social explanations (Taylor 1999) The individual approach basically considered
perceptions of incivilities as an explanatory variable of fear of crime The ecological explanation
argues that indicators of economic disadvantage (eg income levels income inequality
unemployment rate and poverty rate) are the keys to understand a process of social disorganization
and lack of informal control These economic factors lead to higher rates of inappropriate or
negligent behaviours and ultimately to higher crime rates (Blau amp Blau 1982 Messner Rosenfeld
amp Baumer 2004 Phan Orsquobrien Mendolia amp Paloyo 2017 Sampson 1986)
The negative impact of incivilities is not merely reflected in its association with crime rates
(Skogan 2015) Physical and social incivilities could worsen neighbourhoods by affecting quality
of life perception of the environment and public and private behaviours Previous research has
indicated that a higher level of incivilities is associated with health problems (Branas et al 2011
Cohen et al 2000 Hill amp Angel 2005 Ross 2011 Ross amp Mirowsky 2001) greater
victimization and fear of crime (Brunton-Smith Jackson amp Sutherland 2014 Mijanovich amp
Weitzman 2003) and multiple negative economic effects For instance incivilities could be
98
related to a reduction in commercial activity lower investment in real estate reduction in house
prices (Skogan 2015) and population instability (Hipp 2010)
To describe the state of the art in the study of incivilities and their consequences Skogan
(2015) used the concept of untidiness to characterize the research on incivilities The study of
incivilities has had multiple approaches (economic ecological and psychological) Incivilities
have also been measured using multiple sources of information (police reports surveys trained
observation) which result in different measures (perceptions vs count data) However the question
about what specific factors have the strongest effect on incivilities has been overlooked and
perceptions about incivilities have been used mainly as a predictor of crime fear of crime and
victimization
There are two types of incivilities social and physical Social incivilities are a matter of
behaviour including groups of rowdy teens public drunkenness people fighting and street hassles
Physical incivilities involve visual signs of negligence and decay such as abandoned buildings
broken streetlights trash-filled lots and graffiti (Skogan 1999 2015 Taylor 1999) Three reasons
justify the distinction between physical and social incivilities First like multiple dimensions of
social cohesion different structural and social conditions could be responsible for different types
and categories of incivilities Second punitive sanctions are expected to have a greater impact on
physical than on social incivilities since the latter are more related to behaviours rooted in citizensrsquo
culture Third physical incivilities should be more related to absolute measures of economic
disadvantage (eg poverty or unemployment rates) and social incivilities to relative indicators of
economic disadvantage (eg such as income inequality) This line of research is based on the
ldquoincivilities thesisrdquo which states that to understand the distribution of disorders it is necesary to
analyse the patterns of structured inequalities (Skogan 1986 Taylor 1999)
99
423 The ldquoIncivilities Thesisrdquo
Incivilities theories began with a focus on psychological dynamics (Garofalo 1978) moved
forward to an interest in social psychological processes (J Q Wilson amp Kelling 1982) and finally
evolved into a focus on community dynamics and outcomes (Skogan 1999) Individual and group
behaviours in tandem with ecological features have been proposed as the key factors explaining
incivilities and their posterior influence on social control quality of life and more serious crime
(J Q Wilson amp Kelling 1982)
In terms of ecological factors particularly those related to economic conditions Skogan
(1986) was the first linking the distribution of incivilities to the patterns of structural inequality If
incivilities mirror inequality structure this will have consequences in residentsrsquo health and safety
due to its levels of vulnerability In addition structured inequality associated with the proportion
of the manufacturing sector (eg when fabrics tend to move from cities to farther areas) will be
related to higher social disorganization and differences between urban and rural areas (W J
Wilson 1996) In addition a persistent feeling of relative deprivation (persistently high levels of
income inequality) could lead to fellow inhabitants of the community to commit antisocial
behaviours showing their frustration with the current economic model
The literature on incivilities posits that their causes are different from those of crime (Lewis
2017) Unlike crime analysis especially property crimes information on the location where the
incivility takes place is the same as the location where the perpetrator resides To achieve a
comprehensive understanding of the different types of incivilities it is crucial to consider
incivilities data covering an entire territory and not just specific areas (Hooghe Vanhoutte
Hardyns amp Bircan 2010) If we add to this the availability of panel data it could be possible not
100
only to identify the main determinants of incivilities but also the causal mechanism from income
inequality towards incivilities rate
In Chile citizen security crime and delinquency are among the most significant issues for
citizens based on opinion polls Existing research has found weak evidence of a significant
relationship between crime and indicators of socio-economic disadvantage such as income
inequality and unemployment rate with significant effects only on property crime (Beyer amp
Vergara 2006 Nuntildeez Rivera Villavicencio amp Molina 2003 Rivera Gutieacuterrez amp Nuacutentildeez 2009)
Crime deterrence variables such as the probability of being caught or the number of police
resources have also shown ambiguous results (Beyer amp Vergara 2006 Rivera et al 2009
Vergara 2012) Evidence at the county level shows that crime is higher in urban counties those
with a lower mean income per capita and counties located in the North of the country In addition
at least half of the crimes reported in one county are perpetrated by criminals from other counties
(Rivera et al 2009) No studies could be found about the determinants of incivilities
4 3 Methodology
431 Period of analysis and data sample
Chile is a relatively small country in Latin America with a population of 18346018
inhabitants in 2017 The country is divided into 345 municipalities with on average 53104
inhabitants (median value 18705) Municipalities are the organ of the State Administration
responsible to solve local needs Municipalities are not only the relevant political and
administrative local unit of analysis but also they represent the feeling of lsquocommunityrsquo among
the inhabitants of each municipality (Hooghe et al 2010) Our data includes many sources of
101
heterogeneity among municipalities such as indicators of economic deprivation population
density demographic characteristics and whether the county is a regional or provincial capital
We use a sample of 324 municipalities covering most of the Chilean territory for the period
2006ndash17 Data on incivilities is obtained from the ldquoCentre of Studies and Analysis of Crimerdquo
which is part of the ldquoSubsecretaria de Prevencion del Delitordquo (SPD in its Spanish acronym) of the
Chilean government32 Information on income inequality and control variables is obtained from
the ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) the
ldquoNational Institute of Statisticsrdquo (INE in its Spanish acronym) the ldquoNational Municipal
Information Systemrdquo (SINIM in its Spanish acronym) and the Immigration Department of the
Government of Chile Our panel only includes the years for which CASEN survey is available
2006 2009 2011 2013 2015 and 2017
432 Operationalisation of the response variable and exploratory analysis
Official Chilean records contain information for the total number of cases of incivilities per
year at the county level The number of cases is the sum of complains and detentions reported at
the police Our dependent variable 119894119899119888119894119907119894119897119894119905119894119890119904 correspond to the number of cases per year Due
to population differences comparisons between counties are made using the incivilities rate per
1000 population calculated as
119894119899119888119894119907_119903119886119905119890 lowast 1000 (41)
where 119894119899119888119894119907_119903119886119905119890 is the incivilities rate 119905 is the year 119894 the county and 119899 is the population of the
county per year
32 httpceadspdgovclestadisticas-delictuales
102
Figure 41 illustrates at the top the evolution of the total number (cases reported) of
incivilities and crimes at the country level for the period 2006-1733 At the bottom Figure 41
shows the evolution of the mean county rate per 1000 inhabitants We observe that both the number
of incivilities and the number of crimes has reached similar annual figures however average
county rates per 1000 population show different trends Crime rate displays a sustained fall after
reaching its pick in 2011 Incivilities average county rate which also reached a pick and posterior
drop in 2011 has recovered its upward trend since 2016 considerably exceeding the crime rate
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017
33 Crime refers to ldquocrimes of greater social connotationrdquo (DMCS in its Spanish acronym) which includes violent and property crimes
103
Chilean records classify incivilities in nine categories most of them associated with social
incivilities Summary statistics for the total and for each of the nine categories are presented in
Table 41 In addition Figure 42 shows the evolution of incivilities by category for the whole
period We see that the global trend in incivilities is mainly due to a substantial increase in ldquoStreet
Tradingrdquo and ldquoPublic Alcohol Consumptionrdquo A common element of Figures 41 and 42 is the
significant change in trend experienced by crimes and incivilities in 2011 That year the SPD
became dependent on the Ministry of Interior of the Chilean Government This event put the issue
of crime and delinquency within national priorities for the central government
Table 41
Summary statistics total count of incivilities and by category (full sample and period)
Unlike crime rates we do not expect significant cross-county spillover effects in incivilities
However the questions of where incivilities are concentrated and why they are there can be of
great interest (Skogan 2015) Figure 43 shows quantile maps for the rate of incivilities per 1000
inhabitants for the initial and final years in our panel
104
Figure 42 Evolution total number of incivilities by category
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017)
105
We observe that the range of values has increased significantly from 2006 to 2017 but the
spatial distribution remains almost unchanged On the one hand high incivilities rates in the North
could be associated with the mining activity On the other hand high rates in the Centre area
(where the countyrsquos capital is located) could be related to the higher population density and the
concentration of the economic activity34
To see how the different types of incivilities are distributed throughout the country we have
grouped those similar categories in four groups ldquoPublic Damagerdquo (ldquoPublic Disturbsrdquo ldquoPublic
Damagerdquo ldquoOtherrdquo) ldquoThreatsrdquo (ldquoPublic Fightrdquo ldquoThreatsrdquo and ldquoAnnoying Noisesrdquo) ldquoAlcohol
Consumptionrdquo (ldquoPublic Alcohol Consumptionrdquo and ldquoDrunkennessrdquo) and ldquoStreet Tradingrdquo This
distinction in groups could be relevant if we expect different patterns and different effects of
community heterogeneity on social cohesion among counties For instance we expect higher
levels of Public Damage in big urban cities Street trading is more likely in urban cities but also in
tourist areas The spatial distribution of these four groups for the six-year-average rate per 1000
inhabitants can be found in Appendix R
433 Measures of community heterogeneity and control variables
Social cohesion income inequality and racial diversity are all ldquocommunityrdquo (not individual)
characteristics Thus to understand their relationship we need aggregated data at least at the
county-municipal level With more disaggregated data like at the suburbs level the required
heterogeneity among groups of citizens is lost (Wilkinson 1999) Like Coffeacute and Geys (2006) we
use the Gini coefficient to capture economic heterogeneity However instead of a measured for
34 We also analysed the spatial distribution of crime rates In general areas with high levels of incivilities not necessarily are associated with high levels of crime This could imply that factors explaining incivilities and crime are different
106
the diversity of nationalities we use the proportion of foreign population to capture racial
heterogeneity Income data is obtained from the CASEN survey The Gini coefficient is calculated
for each county and included through the variable gini Racial heterogeneity is included through
the variable foreign which is the annual number of new VISAS granted to foreigners as a
proportion of the county population Chile has experienced a significant increase in immigration
since 2011 Immigration has been concentrated in the metropolitan region and mining regions in
the North of the country We expect a positive relationship between immigration and incivilities
although as with the relationship between immigration and crime the foundations for this
hypothesis are not strong (Hooghe et al 2010 Sampson 2008)
Economic development is another explanation for social cohesion frequently appealed to
explain trust with wealthier societies considered to exhibit higher levels of trust (Delhey amp
Newton 2005) In this study we include the natural log of the mean household income per capita
log(income) We also include the poverty rate poverty and the unemployment rate
unemployment Unlike the variable log(income) these variables are expected to be positively
associated with the number of incivilities When a relative indicator of economic heterogeneity
such as income inequality is included as determinant of social cohesion we should expect less
effect from absolute indicators of economic disadvantage such as poverty and unemployment rates
(Hooghe et al 2010 Tolsma et al 2009)
Among demographic variables the percentage of inhabitants between 10 and 24 years old is
included through the variable youth The variable women defined as the proportion of the female
population in each county is also included Variable youth is expected to have an ambiguous effect
Although young people have lower victimization and report rates they also represent the group
more likely to commit antisocial behaviours when a community has a low capacity of self-
107
regulation (eg when there is low parental supervision) The female population is associated with
a higher report of incivilities related to the male population
It is argued that crime and incivilities are essentially urban problems (Christiansen 1960
Wirth 1938) We include the variable log(density) defined as the log of population density (the
number of inhabitants divided by the area of each county in square kilometres) and a dummy
variable capital indicating whether a county is an administrative capital (provincial or regional)
Two additional variables are included to capture the level of informal social control exerted
by families living in each municipality First the variable education which is defined as the
average years of education of people over 15 years old Second the variable housing which capture
the proportion of families which are owners of their housing unit Although education and housing
are related to both the possibility of reporting and committing an incivility we expect a negative
association with the rate of incivilities
In Chile crime has been mainly a problem faced by the police and the Central Government
Administration To control for current law enforcement policies we include the variable
deterrence defined as the number of arrests as a proportion of the total number of incivilities cases
In addition municipalities can develop their own initiatives to deal with crime and incivilities
depending on their capacity to generate its own resources The level of financial autonomy from
central transfers is captured by the variable autonomy This variable is obtained from SINIM and
it is defined as the proportion of the budget revenue of each municipality that comes from its own
permanent sources of revenues A categorical variable mayor is also included This variable
indicates whether the municipality mayor is supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo political
parties (related to those ldquoINDEPENDENTrdquo mayors)
108
Table 42 presents descriptive statistics for our measures of income and racial heterogeneity
and the set of numeric control variables The Pearson correlation among these variables is shown
in Appendix S
Table 42
Summary statistics numeric explanatory variables
434 Methods
The annual count of incivilities as is characteristic for count data is highly concentrated in
a relatively small range of values In addition the distribution is right-skewed due to the presence
of important outliers (counties with a high number of incivilities) Figure 44 shows the
distribution of the six-year average number of incivilities for each of the 15 regions in Chile35 We
observe that regions differ in the number of counties in which they are divided In addition
counties within each region show important differences in the number of incivilities For instance
35 Regions are ordered from left to right and from top to bottom in the way they are geographically distributed from North to South So the northernmost region of the country is the ldquoXVrdquo and the southernmost region is the ldquoXIIrdquo The Metropolitan region (where the countryrsquos capital is located) is region ldquoXIIIrdquo in the centre of the country (see Appendix C)
109
excepting the Metropolitan region ldquoXIIIrdquo most counties in regions located in the centre of the
country (middle row in Figure 44) show a range of incivilities between 0 and 2000 The number
of incivilities is considerably lower in counties located in the northern (top row in Figure 44) and
southern (bottom row in Figure 44) regions of the country compared to regions in the centre of
the country It also seems clear from Figure 44 that the number of incivilities does not follow a
normal distribution
Figure 44 Annual average number of incivilities per county
The number of incivilities can be better described by a Poisson distribution In this case the
number of incivilities is ldquothe countrdquo and the number of incivilities per year is ldquothe rate per unit
timerdquo We are interested in modelling the average number of incivilities per year usually called 120582
as a function of a set of contextual factors to explain differences in incivilities between and within
110
counties The main characteristic of the Poisson distribution is that the mean is equal to the
variance This implies that as the mean rate for a Poisson variable increases the variance also
increases The main implication is we cannot use OLS to model 120582 as a function of the set of
contextual factors because the equal variance assumption in linear regression is violated
The rate of incivilities between counties is not directly comparable due to population
differences We expect counties with more people to have more reports of incivilities since there
are more people who could be affected To capture differences in population which is called the
exposure of our response variable 120582 it is necessary to include a term on the right side of our model
called an offset We will use the log of the county population in thousands as our offset36
Additionally similar to the case of crime data incivilities show a significant degree of
overdispersion (variance higher than the mean) suggesting that there is more variation in the
response than the Poisson model implies37 We also model and regress incivilities assuming a
Negative Binomial distribution to address overdispersion An advantage of this approach is that it
introduces a dispersion parameter in addition to 120582 which gives the model more flexibility38
Considering as the response variable the count of incivilities per year the model can be
expressed as follow
120582 119890119909119901 120573 120573 119892119894119899119894 120573 119891119900119903119890119894119892119899 119883120574 120572 120579 (42)
36 If we think of 120582 as the average number of incivilities per year then represents the number per 1000
inhabitants so that the yearly count is adjusted to be comparable across counties of different sizes Adjusting the yearly count by population is equivalent to adding 119897119900119892 1199011199001199011199061198971198861199051198941199001198991000 to the right-hand side of the regression equation 37 Without adjusting for overdispersion we use incorrect artificially small standard errors leading to artificially small p-values for model coefficients 38 The Negative Binomial model posits selecting a 120582 (average number of incivilities) for each county and then generating a count using a Poisson random variable with the selected 120582 With this approach the counts will be more dispersed than would be expected for observations based on a single Poisson variable with rate 120582
111
where 120582 is the rate of incivilities 119883 is our vector of controls 120572prime119904 are county-specific constants
and 120579prime119904 are time-specific constants Accounting for differences in county population we have
119890119909119901 120573 120573 119892119894119899119894 119883120574 120572 120579 (43)
where 119901119900119901119906119897119886119905119894119900119899 is the county population per year Hence the model to be estimated using
Maximum Likelihood Estimation (MLE) is
119897119900119892 120582 120573 120573 119892119894119899119894 119883120574 120572 120579 119897119900119892 (44)
Finally to account for different effects depending on the type of incivilities we also run
equation (44) for each of the four groups of incivilities defined in section (432)
435 Hypotheses
Based on the community heterogeneity hypothesis the relationship between social cohesion
and diversity should be stronger for lower levels of income and less educated groups of people
(Tolsma et al 2009) Hence contrary to evidence for developed and more equal countries we
expect a significant positive association for the Chilean case where more than 50 of the
population is economically vulnerable (OECD 2017)
The main hypotheses to be tested in this essay is whether the number of incivilities is
positively associated with the level of economic and racial heterogeneity at the county level We
start analysing this association for the full sample and period Next we analyse whether the
relationship between incivilities and our measures of diversity differs by geographic area (region
or zone) Finally we check whether the effect of economic and racial diversity is different
depending on the group of incivilities
112
44 Results and Discussion
Overall our results show that the rate of incivilities displays a stronger and more significant
relationship with racial diversity than with economic heterogeneity This association differs for
different geographic areas and for different types of incivilities Absolute economic indicators
except for income show a significant but small effect Increases in the average levels of income
or education and more financial autonomy for municipalities seem to be effective ways to reduce
the rate of incivilities
We estimate equation (44) assuming that the number of incivilities follows a Poisson
distribution Regional and temporal heterogeneity are captured through the inclusion of dummy
variables for five years (with 2006 as the reference year) and fourteen regional dummies (with
region XIII as the reference region) Results are reported in Table 4339 This table is structured in
two blocks of regressions pooled models in columns (1)-(4) and fixed-effects models in columns
(5)-(8)40 The first column in each block only includes economic indicators relative and absolute
trying to test which ones are more relevant and whether incivilities tend to mirror income
inequality (the ldquoincivilities thesisrdquo) The second column adds the variable foreign to account for
the effect of racial diversity (Letki 2008) The third column includes education to check whether
the association between economic and racial diversity with social cohesion changes (gets less
significant) when we control for educational level (Tolsma et al 2009) The final column in each
block corresponds to the full model specification which includes the rest of controls
39 Pooled estimations were obtained using the R command glm() and compared with results obtained from the command poisson in STATA 15 Fixed effects and random effects models were estimated using the STATA command xtpoisson Negative Binomial models were estimated using the command glmnb() in the MASS package for R and commands nbreg and xtnbreg in STATA 15 Results for Negative Binomial regressions are in Appendix T 40 We have omitted results with random effects due to the literature on panel count data models suggest that the most robust estimator is Poisson regression with Fixed Effects (Santos Silva amp Tenreyro 2010 2011)
113
Table 43
Poisson regressions
114
The positive and significant coefficient for the variable gini besides being small it becomes
insignificant in the fixed effects specification which includes the full set of controls This result
does not seem to be enough evidence to support our hypothesis that more unequal counties display
higher rates of incivilities On the other hand racial diversity through the variable foreign shows
a consistent positive association with the rate of incivilities41 Together coefficients for gini and
foreign seems to support the ldquocommunity heterogeneity thesisrdquo (Letki 2008) but not the
ldquoincivilities thesisrdquo (Skogan 1999) To check this finding we run the pooled full model
specification for each region and results are shown in Table 44 where regions have been ordered
from North to South The sign of the coefficient of the variable gini differs for different regions
Moreover the relationship is insignificant in some of the most unequal regions which are in the
South of the country (VIII IX and XIV) This result rejects the hypothesis that incivilities mirror
structural income inequalities For the variable foreign 12 out of 15 regions confirm the positive
association with the rate of incivilities42
We also run our pooled full model separately for each group of incivilities defined at the end
of section (432) Income inequality keeps its significant but small association with each group of
incivilities (see Table 45) Our measure of racial diversity shows a stronger association with
ldquoAlcohol Consumptionrdquo related to ldquoPublic Damagerdquo and ldquoThreatsrdquo The link with ldquoStreet Tradingrdquo
appears as non-significant These results support our general finding that on the one hand racial
heterogeneity exert a more significant influence on the rate of incivilities than economic
41 To get the effects of the variables gini and foreign over the rate of incivilities per 1000 inhabitants we need to exponentiate their coefficients Thus an increase in one unit in the variable gini increases the rate of incivilities per 1000 inhabitantsrdquo in 1005 times (e^0005) in the pooled full model Similarly an increase in one point in variable foreign is expected to increase the ldquorate of incivilities per 1000 inhabitantsrdquo in 10876 times (e^0084) in the full pooled model and 10471 (e^0046) times in the full Fixed Effects model 42 Given that depending on the geographic location regions share certain characteristics the model was also run clustering regions in 4 zones (see Figure 31 in chapter 3) Results by zone are in Appendix U
115
heterogeneity On the other hand results confirm our hypothesis that the effect of diversity is
different for different types of incivilities
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region
Back to our general results in Table 43 the significant and negative coefficient of the
income variable and to a lesser extent the significant and positive coefficients of poverty and
unemployment provide evidence that absolute rather than relative economic indicators may be
more important explanations of the rate of incivilities This is opposite to evidence for the analysis
116
of crime rates such as in Hoodge (2010) and support the idea that determinants of incivilities are
different from those of crime Our results are also opposite to those for Dutch municipalities where
economic indicators turned out to be more important than ethnic heterogeneity (Tolsma et al
2009) The coefficient for the variable log(income) could be interpreted as counties with an income
level under the average face higher problems of antisocial behaviours such as incivilities In
addition as the income level moves far away from its average low level the problem of incivilities
is less relevant43 In terms of policy implications only those policies that achieve a significant
increase in the average level of county income seem to be effective in reducing incivilities and
strengthening social cohesion
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group
43 We ran our model splitting the sample of counties in quintiles based on their income level The coefficient for the variable gini only showed a significant (and positive) coefficient for the second third and fourth quintile A non-significant coefficient for the first quintile could imply that other types of inequalities (eg health education) are more relevant in the poorest counties In the case of the fifth quintile (the richest group of counties) they have better facilities in terms of infrastructure police resources self-protection etc which could reduce the potential triggers of incivilities
117
The inclusion of the variable education significantly improved the goodness of fit of the
models and did not generate significant changes in the coefficients of our measures of economic
and racial diversity This result rejects the proposition that the relationship between social
cohesion and diversity becomes less strong when controlled by education (Tolsma et al 2009)
Additionally it highlights the topic of education as a relevant determinant of the rate of incivilities
and social cohesion
Among control variables there are also some important results Opposite to what we
expected the variable youth shows a negative or non-significant coefficient Although this result
could be due to the lack of ldquophysical incivilitiesrdquo in Chilean records it indicates that it is incorrect
to stereotype this group as the main responsible for high incivilities rates The significant and
negative coefficient of the variable autonomy in the fixed effects specification could also have
important policy implications It is a signal that local governments can play an important role in
reducing incivilities or complementing the efforts from the central government Another
interesting result is the significant coefficient of the variable housing The latter finding is
particularly important in the sense that a negative sign supports public policies oriented to increase
homeownership as effective ways to improve social cohesion However the small magnitude of
the coefficient that even showed the opposite sign in some model specifications could be
explained for the high level of segregation that these policies have generated in Chilean society
As mentioned in the Introduction and Literature Review so far only a few studies have
used measures of disorders or incivilities as dependent variable to explain changes in social
cohesion (Skogan 2015) In addition there is no evidence in Chile analysing the determinants of
incivilities separately from those of crimes The importance of our results on identifying the
importance of economic and racial diversity on social cohesion lies mainly in its generality An
118
important number of countries all around the world share a similar context characterized by high
levels of inequality and an explosive increase in immigration These countries are also
experiencing a worsening in social cohesion which increases the risk of a social outburst
4 5 Conclusions
The main goal of this essay was to determine whether differences in incivilities at the county
level mirror differences in income distribution and racial diversity Previous literature suggests a
positive and strong association between social cohesion and indicators of economic disadvantage
relative deprivation and racial diversity (Letki 2008 Tolsma et al 2009 Uslaner amp Brown 2005)
While not all our results were significant they showed helpful insights about how and where
economic and racial diversity are more likely to influence the rate of incivilities and social
cohesion
We used data for the period 2006ndash17 economic heterogeneity was measured through the
Gini coefficient at the county level and racial heterogeneity was proxied by the number of granted
visas to foreigners as proportion of county population We found strong evidence of a significant
and positive association between the rate of incivilities and racial diversity but not with income
inequality Contrary to previous evidence at the municipal level (Coffeacute amp Geys 2006 Tolsma et
al 2009) in general our results give support for the ldquohomophily principlerdquo and the ldquocommunity
heterogeneity hypothesisrdquo However results also showed that the effect of economic and racial
diversity varies throughout the Chilean regions and for the different types of incivilities
We also found that policies aimed at controlling the behaviour of young people did not have
strong empirical support In terms of the role that local governments may have in facing the
119
growing problem of incivilities we found evidence that efforts managed from the municipalities
can be an important complement to those from the central government
Future research should go further on the role of local authorities on incivilities and social
cohesion On the one hand municipalities could have a direct impact on social cohesion through
the implementation of programs complementary to those of central authorities oriented to reduce
incivilities and crime On the other hand social cohesion could be indirectly affected when local
authorities display an inefficient performance supplying public services to citizens or they are
recognized as corrupted institutions We suggest that policy makers from central government
should give local authorities a greater role in fighting antisocial behaviours and crime Evaluating
programs in specific municipalities could help to elucidate the causal effect of for instance higher
fiscal autonomy on the rate of incivilities
Another interesting area for future work will be to analyse how housing policies have
contributed to the phenomenon of segregation of Chilean society and in the process of weakening
social cohesion Finally our main result highlights the need of a deeper analysis of the impact that
foreign immigration is having in Chile For instance disaggregating information by country of
origin and the reasons why immigrants are arriving to the country or specific regions will surely
help to understand the impacts of immigration
120
Chapter 5 Conclusions
This thesis investigated in three essays the issue of income inequality in Chile using county-
level data for the period 2006-2017 The first essay supplied empirical evidence about the
importance of the degree of dependence on natural resources in terms of employment in explaining
cross-county differences in income inequality The second essay analysed the potential causal
effect that income inequality has on the level of technical efficiency of local governments
providing public goods and services Lastly the third essay studied the relationship between social
cohesion measured through the number of antisocial behaviours classified as ldquoincivilitiesrdquo and
the levels of income and racial heterogeneity
Findings from the first essay support the idea that the endowment of natural resources plays
a significant role in explaining income inequality in Chile However contrary to what most
theoretical and empirical evidence postulates our findings showed a robust negative association
between the two variables This means that the reduction experienced in Chile in the degree of
dependence on natural resources in terms of employment has contributed to the persistence of high
levels of income inequality The exploratory analysis indicated that income inequality shows a
general clustering process characterized by a significant and positive spatial autocorrelation
Regarding the previous evidence for Chile (Paredes et al 2016) the regression analysis confirmed
the relevance of the spatial dimension of income inequality through a process of spatial
heterogeneity giving less support to the existence of a process of spatial dependence (spillover
effect) in the variable itself
121
Essay 2 studied the potential trade-off between efficiency and equity analysing the influence
of income inequality on the efficiency of local governments at the municipal level To identify the
causal effect of income inequality on municipal efficiency we proposed the use of the proportion
of firms in the primary sector as an instrument for income inequality Findings confirmed our
hypothesis that efficiency is lower in more unequal counties This result suggests the non-existence
of the trade-off between equity and efficiency Hence policies intended to reduce inequality could
help to increase efficiency at least at the level of municipal local governments
The third essay analysed how social cohesion proxied by the rate of incivilities is associated
with the levels of economic diversity proxied by income inequality and the levels of racial
diversity proxied by the number of new visas grated as proportion of the county population
Findings gave strong support to the hypothesis that the rate of incivilities is positively related to
racial diversity and to a lesser extent to economic diversity In addition the rate of incivilities
appears negatively related to the degree of financial autonomy of municipalities This means that
local governments can effectively contribute to the reduction of incivilities which could help
reduce victimization and crime rates ultimately strengthening social cohesion
Taken together findings from essays 2 and 3 highlight the important role that income
inequality could play in other relevant economic and social dimensions These findings add to the
understanding of the potential consequences of income inequality particularly in natural resource
rich countries with persistently high levels of inequality
The present study has mainly investigated income inequality at the county level In addition
Chilean municipalities play an important role providing ldquoservices to peoplerdquo so our findings could
be applied in other highly unequal countries with a high degree of dependence on natural resources
and local governments with similar responsibilities For instance in Latin America apart from
122
Chile and Brazil there are no studies on the efficiency of local governments Other limitations are
associated with the availability of information For instance important indicators such as GDP per
capita are only available at the regional level and information of incomes is not available annually
In addition given the heterogeneity among municipalities some type of grouping of municipalities
should be performed before looking for causal relationships or conducting program evaluation
Despite these limitations we believe this study could be the basis for different strands of future
research on the topic of inequality local government efficiency and social cohesion
It was stated in chapter 2 based on the resource curse hypothesis literature there are two
elements that determine whether NR are a curse or blessing in terms of socioeconomic outcomes
First the curse is more likely in countries with weak political and governance institutions Second
different types of resources affect institutions differently with resources that are concentrated in
space so-called ldquopointrdquo resources tend to impair institutions while ldquodiffuserdquo resources do not
(Deacon 2011 Isham Woolcock Pritchett amp Busby 2005) Our results showed a negative
relationship between income inequality and our measure of natural resource dependence even after
controlling for zone fixed effects and for the level of government expenditure This result could
be interpreted as a signal that NR has a direct effect on income inequality in addition to the indirect
impact through market or institutional channels Using other potential institutional transmission
channels will shed light about the true effect that the endowment of natural resources has over
income inequality Variables that could capture these institutional channels include the level of
employment in the public sector measures of rule of law and corruption and changes in the
creation of new business in the secondary and tertiary sectors related to the primary sector
Based on results from chapter 3 most of the municipalities show scale inefficiencies One
immediate area for future work will involve using our set of contextual factors to predict the status
123
of municipalities in terms of scale inefficiencies Defining as dependent variable whether a
municipality shows constant decreasing or increasing returns to scale we could run a multinomial
logistic regression to predict municipal status For instance we would expect that a one-unit
increase in the Gini coefficient should increase the probability of scale inefficiencies (increasing
or decreasing returns to scale rather than constant returns to scale) Because the aim in this case
would be predicting a certain result in terms of returns to scale the next step should involve to
split the full sample in training and testing data sets and to use some resampling methods such as
bootstrapping This will allow us to evaluate the performance and accuracy of our model
predictions using different random samples of municipalities Results from Machine Learning
algorithms will help us to assess the generalizability of our results to other data sets
Future work should also benefit greatly by using data on different Latin American countries
to (1) compare the responsibilities of local governments (2) select a common set of inputs and
output to evaluate LGE (3) identify the relevance of scales inefficiencies in explaining differences
in performance and (4) analyse the influence of contextual characteristics over LGE Differences
in the main primary sector activity in each country such as oil in Brazil mining in Chile or Coffee
in Colombia could be responsible for differences in LGE among countries These differences could
be associated with sources of revenue management of expenditure and definitions of outputs or
contextual effects such as corrupted institutions or the delay in the development of other sectors
such as manufacturing or services
To delve deep on reasons explaining the social crisis experienced by Chilean society and
other countries one area of future work will be to analyse the relationship between diversity and
the origins of social revolutions Based on Tiruneh (2014) the three most important factors that
explain the onset of social revolutions are economic development regime type and state
124
ineffectiveness Interesting questions include whether the characteristics of Chilean context at the
end of 2019 are enough to trigger the transformation of the political and socioeconomic system
Social revolutions particularly violent revolutions are less likely in more democratic educated
and wealthy societies So it would be relevant to identify the factors explaining the violence that
has characterized the social crisis in Chile Finally the democratic regime has been maintained in
the last decades with changes between left and right governments This could imply that more
important than the regime has been the efficiency or ineffectiveness of the governments to satisfy
the needs of the population
Future work should also cover the disaggregation of information regarding foreign
population in terms of the reasons for new granted visas and the country of origin Official data
allows us to disaggregate whether the benefit is permanent (students and employees with contract)
or temporary Furthermore most of the new visas were traditionally granted to neighbouring
countries (Peru and Bolivia) a trend that has changed in the recent years to include countries such
as Colombia Venezuela and Haiti An analysis of how economic and social indicators have been
affected by changes in the composition of foreigners their reasons for immigrating to the country
and their geographical distribution have implications for economic policy at both the national and
local levels At the national level such analysis should be a key input when proposing changes to
the national immigration policy At the local level it could help define the role of municipalities
to assess the benefits and challenges of immigration These challenges are mainly related to the
provision of public goods and services such as health and education which in Chile are the
responsibility of the municipalities
The findings of this thesis suggest that policymakers should encourage policies that reduce
income inequality The key role that municipalities could play to strengthen social cohesion and
125
the increasingly important role that foreign population is acquiring in most modern societies are
also interesting avenues for future research However the picture is still incomplete and more
research is needed incorporating other dimensions of inequality This is essential if we want to
understand the reasons that could have triggered the social outbursts experienced by various
economies across the globe
126
Bibliography
Acemoglu D (1995) Reward structures and the allocation of talent European Economic Review 39(1) 17ndash33 httpsdoiorghttpsdoiorg1010160014-2921(94)00014-Q
Acemoglu D (2002) Technical Change Inequality and the Labor Market Journal of Economic Literature 40(1) 7ndash72 httpsdoiorg1012570022051026976
Acemoglu D Aghion P amp Violante G L (2001) Deunionization Technical Change and Inequality Carnegie-Rochester Conference Series on Public Policy 55(1) 229ndash264 httpsdoiorg101016S0167-2231(01)00058-6
Acemoglu D Johnson S amp Robinson J A (2001) The Colonial Origins of Comparative Development An Empirical Investigation The American Economic Review 91(5) 1369ndash1401 httpsdoiorg101257aer9151369
Acemoglu D amp Robinson J A (2002) The Political Economy of the Kuznets Curve Review of Development Economics 6(2) 183ndash203 httpsdoiorg1011111467-936100149
Afonso A amp Fernandes S (2006) Measuring local government spending efficiency Evidence for the Lisbon region Regional Studies 40(1) 39ndash53 httpsdoiorg10108000343400500449937
Afonso A amp Fernandes S (2008) Assessing and explaining the relative efficiency of local government The Journal of Socio-Economics 37(5) 1946ndash1979 httpsdoiorg101016jsocec200703007
Afonso A Schuknecht L amp Tanzi V (2010) Income distribution determinants and public spending efficiency Journal of Economic Inequality 8(3) 367ndash389 httpsdoiorg101007s10888-010-9138-z
Akita T (2003) Decomposing regional income inequality in China and Indonesia using two-stage nested Theil decomposition method The Annals of Regional Science 37(1) 55ndash77 httpsdoiorg101007s001680200107
Alesina A (2000) Participation in heterogeneous communities The Quarterly Journal of Economics 115(3) 847ndash904 httpsdoiorg101162003355300554935
Alesina A amp La Ferrara E (2002) Who trusts others Journal of Public Economics 85(2) 207ndash234 httpsdoiorg101016S0047-2727(01)00084-6
Allcott H amp Keniston D (2014) Dutch Disease or Agglomeration The Local Economic Effects of Natural Resource Booms in Modern America (N W P N 20508 Ed) NBER Working Paper No 20508 (Vol w20508) NBER Working Paper No 20508 NBER Working Paper No 20508 httpsdoiorgNBER Working Paper No 20508
Andersen T M amp Maibom J (2020) The big trade-off between efficiency and equitymdashis it there Oxford Economic Papers 72(2) 391ndash411
127
Anselin L (1988) Spatial econometrics methods and models (Vol 4) DordrechtBoston Kluwer Academic Publishers
Anselin L amp Bera A K (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics Statistics Textbooks and Monographs (Vol 155)
Anselin L Bera A K Florax R amp Yoon M J (1996) Simple diagnostic tests for spatial dependence Regional Science and Urban Economics 26(1) 77ndash104 httpsdoiorg1010160166-0462(95)02111-6
Antonelli M A amp De Bonis V (2018) The efficiency of social public expenditure in European countries a two-stage analysis Applied Economics 1ndash14 httpsdoiorg1010800003684620181489522
Aragoacuten F M amp Rud J P (2013) Natural Resources and Local Communities Evidence from a Peruvian Gold Mine American Economic Journal Economic Policy 5(2) 1ndash25 httpsdoiorg101257pol521
Ariely G (2014) Does Diversity Erode Social Cohesion Conceptual and Methodological Issues Political Studies 62(3) 573ndash595 httpsdoiorg1011111467-924812068
Armstrong H amp Taylor J (2000) Regional economics and policy (3rd ed) Oxford Blackwell
Aroca P amp Atienza M (2011) Economic implications of long distance commuting in the Chilean mining industry Resources Policy 36(3) 196ndash203 httpsdoiorg101016jresourpol201103004
Aroca P amp Bosch M (2000) Crecimiento convergencia y espacio en las regiones chilenas 1960 - 1998 Estudios de Economiacutea 27 199ndash224 Retrieved from httprepositoriouchileclbitstreamhandle2250127853Patricio_Arocapdfsequence=1
Ashworth J Geys B Heyndels B amp Wille F (2014) Competition in the political arena and local government performance Applied Economics 46(19) 2264ndash2276 httpsdoiorg101080000368462014899679
Atkinson A B (2015) Inequality What Can Be Done Harvard University Press
Auty R (1993) Sustaining development in mineral economies the resource curse thesis London [ua] Routledge
Auty R (2001) Resource abundance and economic development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Badeeb R A Lean H H amp Clark J (2017) The evolution of the natural resource curse thesis A critical literature survey Resources Policy 51 123ndash134 httpsdoiorg101016jresourpol201610015
Balaguer-Coll M T Brun-Martos M I Maacuterquez-Ramos L amp Prior D (2019) Local government efficiency determinants and spatial interdependence Applied Economics
128
51(14) 1478ndash1494 httpsdoiorg1010800003684620181527458
Balaguer-Coll M T amp Prior D (2009) Short- and long-term evaluation of efficiency and quality An application to Spanish municipalities Applied Economics 41(23) 2991ndash3002 httpsdoiorg10108000036840701351923
Banker R D Charnes A amp Cooper W W (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis Management Science 30(9) 1078ndash1092
Beine M Coulombe S amp Vermeulen W N (2015) Dutch Disease and the Mitigation Effect of Migration Evidence from Canadian Provinces The Economic Journal 125(589) 1574ndash1615 httpsdoiorg101111ecoj12171
Benito B Bastida F amp Garciacutea J A (2010) Explaining differences in efficiency an application to Spanish municipalities Applied Economics 42(4) 515ndash528 httpsdoiorg10108000036840701675560
Berg A amp Ostry J (2011) Equality and efficiency Finance amp Development 48(3) 12ndash15
Berg A amp Ostry J (2017) Inequality and Unsustainable Growth Two Sides of the Same Coin IMF ECONOMIC REVIEW 65(4) 792ndash815 httpsdoiorg101057s41308-017-0030-8
Beyer H amp Vergara R (2006) Delincuencia en Chile Determinantes y rol de las poliacuteticas puacuteblicas Instituto de Economiacutea UC
Blanco L amp Grier R (2012) Natural resource dependence and the accumulation of physical and human capital in Latin America Resources Policy 37(3) 281ndash295 httpsdoiorghttpdoiorg101016jresourpol201201005
Blau J R amp Blau P M (1982) The cost of inequality Metropolitan structure and violent crime American Sociological Review 114ndash129
Boetti L Piacenza M amp Turati G (2009) Fiscal decentralization and spending efficiency of local governments An Empirical Investigation on a Sample Of 4
Boix C amp Posner D (1998) Social capital Explaining its origins and effects on government performance British Journal Of Political Science 28 686ndash693
Borge L E Parmer P amp Torvik R (2015) Local natural resource curse JOURNAL OF PUBLIC ECONOMICS 131 101ndash114 httpsdoiorg101016jjpubeco201509002
Borger B Kerstens K Moesen W amp Vanneste J (1994) Explaining differences in productive efficiency An application to Belgian municipalities Public Choice 80(3) 339ndash358 httpsdoiorg101007BF01053225
Bourguignon F amp Morrisson C (1990) Income distribution development and foreign trade A cross-sectional analysislowast European Economic Review 34(6) 1113ndash1132 httpsdoiorghttpsdoiorg1010160014-2921(90)90071-6
129
Branas C C Cheney R A MacDonald J M Tam V W Jackson T D amp Ten Have T R (2011) A difference-in-differences analysis of health safety and greening vacant urban space American Journal of Epidemiology 174(11) 1296ndash1306
Bravo J (2014) Fondo Comuacuten Municipal y su desincentivo a la recaudacioacuten en Chile Temas de La Agenda Legislativa-Centro de Poliacuteticas Puacuteblicas UC 9(68)
Browning E K amp Johnson W R (1984) The Trade-Off between Equality and Efficiency Journal of Political Economy 92(2) 175ndash203 httpsdoiorg101086261219
Brunnschweiler C N amp Bulte E H (2008) The resource curse revisited and revised A tale of paradoxes and red herrings Journal of Environmental Economics and Management 55(3) 248ndash264 httpsdoiorghttpsdoiorg101016jjeem200708004
Brunori P Ferreira F H G amp Peragine V (2013) Inequality of opportunity income inequality and economic mobility Some international comparisons In Getting Development Right (pp 85ndash115) Springer
Brunton-Smith I Jackson J amp Sutherland A (2014) Bridging structure and perception On the neighbourhood ecology of beliefs and worries about violent crime British Journal of Criminology 54(4) 503ndash526
Bulte E H Damania R amp Deacon R T (2005) Resource intensity institutions and development World Development 33(7) 1029ndash1044 httpsdoiorg101016jworlddev200504004
Carmignani F (2013) Development outcomes resource abundance and the transmission through inequality Resource and Energy Economics 35(3) 412ndash428 httpsdoiorg101016jreseneeco201304007
Carmignani Fabrizio amp Avom D (2010) The social development effects of primary commodity export dependence Ecological Economics 70(2) 317ndash330 httpsdoiorg101016jecolecon201009003
Caselli F amp Michaels G (2013) Do Oil Windfalls Improve Living Standards Evidence from Brazil American Economic Journal Applied Economics 5(1) 208ndash238 httpsdoiorg101257app51208
Celebioglu F amp Dallrsquoerba S (2010) Spatial disparities across the regions of Turkey An exploratory spatial data analysis Annals of Regional Science 45(2) 379ndash400 httpsdoiorg101007s00168-009-0313-8
Chan J To H-P amp Chan E (2006) Reconsidering social cohesion Developing a definition and analytical framework for empirical research Social Indicators Research 75(2) 273ndash302
Charnes A Cooper W W amp Rhodes E (1978) Measuring the efficiency of decision making units European Journal of Operational Research 2(6) 429ndash444
130
Chi G amp Zhu J (2019) Spatial Regression Models for the Social Sciences SAGE Publications Retrieved from httpsbooksgooglecomaubooksid=zHGkvwEACAAJ
Christiansen K O (1960) Industrialization and urbanization in relation to crime and juvenile delinquency International Review of Criminal Policy 16 3ndash8
Cingano F (2014) Trends in income inequality and its impact on economic growth (Vol 163) Paris OECD Publishing httpsdoiorg1017875jxrjncwxv6j-en
Coelli T J Prasada Rao D S OrsquoDonnell C J amp Battese G E (2005) An introduction to efficiency and productivity analysis An Introduction to Efficiency and Productivity Analysis Springer Science amp Business Media httpsdoiorg101007b136381
Coffeacute H amp Geys B (2005) Institutional Performance and Social Capital An Application to the Local Government Level Journal of Urban Affairs 27(5) 485ndash501 httpsdoiorg101111j0735-2166200500249x
Coffeacute H amp Geys B (2006) Community Heterogeneity A Burden for the Creation of Social Capital Social Science Quarterly 87(5) 1053ndash1072 httpsdoiorg101111j1540-6237200600415x
Cohen D Spear S Scribner R Kissinger P Mason K amp Wildgen J (2000) ldquo Broken windowsrdquo and the risk of gonorrhea American Journal of Public Health 90(2) 230
Corden W M amp Neary J P (1982) Booming sector and de-industrialisation in a small open economy The Economic Journal 92(368) 825ndash848
Cordero J M Pedraja-Chaparro F Pisaflores E C amp Polo C (2017) Efficiency assessment of Portuguese municipalities using a conditional nonparametric approach Journal of Productivity Analysis 48(1) 1ndash24 httpsdoiorg101007s11123-017-0500-z
Croissant Y amp Millo G (2018) Panel Data Econometrics with R John Wiley amp Sons
Cust J amp Poelhekke S (2015) The Local Economic Impacts of Natural Resource Extraction 7 251ndash268 httpsdoiorg101146annurev-resource-100814-125106
da Cruz N F amp Marques R C (2014) Revisiting the determinants of local government performance Omega 44 91ndash103 httpsdoiorg101016JOMEGA201309002
Dauvin M amp Guerreiro D (2017) The Paradox of Plenty A Meta-Analysis World Development 94 httpsdoiorg101016jworlddev201701009
de Sousa M da C S Cribari-Neto F amp Stosic B D (2005) Explaining DEA technical efficiency scores in an outlier corrected environment the case of public services in Brazilian municipalities Brazilian Review of Econometrics 25(2) 287ndash313
Deacon R T (2011) The Political Economy of the Natural Resource Curse A Survey of Theory and Evidence Foundations and Trends in Microeconomics 111-208
Delhey J amp Newton K (2005) Predicting cross-national levels of social trust global pattern or
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Nordic exceptionalism European Sociological Review 21(4) 311ndash327
Dlouhyacute M (2018) Measuring Geographic Inequalities Dealing with Multiple Health Resources by Data Envelopment Analysis Frontiers in Public Health 6 53 httpsdoiorg103389fpubh201800053
Dollery B Wallis J amp Akimov A (2010) One Size Does Not Fit All The Special Case of Remote Small Local Councils in Outback Queensland Local Government Studies 36(1) 21ndash42 httpsdoiorg10108003003930903435716
Domenech J (2008) Mineral resource abundance and regional growth in Spain 1860ndash2000 Journal of International Development The Journal of the Development Studies Association 20(8) 1122ndash1135
Doran J amp Jordan D (2016) Decomposing US regional income inequality from 1969 to 2009 Applied Economics Letters 23(11) 781ndash784 httpsdoiorg1010801350485120151109030
Drew J amp Dollery B (2015) The State of Things The Dynamic Efficiency of Australian State and Territories Economic Papers A Journal of Applied Economics and Policy 34(3) 165ndash176
Drew J Kortt M amp Dollery B (2015) What Determines Efficiency in Local Government A DEA Analysis of NSW Local Government Economic Papers A Journal of Applied Economics and Policy 34(4) 243ndash256 httpsdoiorg1011111759-344112118
Easterly W (2007) Inequality does cause underdevelopment Insights from a new instrument Journal of Development Economics 84(2) 755ndash776 httpsdoiorghttpdxdoiorg101016jjdeveco200611002
Ebert L amp La Menza T (2015) Chile copper and resource revenue A holistic approach to assessing commodity dependence Resources Policy 43(Supplement C) 101ndash111 httpsdoiorghttpsdoiorg101016jresourpol201410007
ElGindi T (2017) Natural resource dependency neoliberal globalization and income inequality Are they related A longitudinal study of developing countries (1980ndash2010) Current Sociology 65(1) 21ndash53 httpsdoiorg1011770011392116632031
Engerman S L amp Sokoloff K L (1994) Factor Endowments Institutions and Differential Paths of Growth Among New World Economies National Bureau of Economic Research
Engerman S L amp Sokoloff K L (1997) Factor endowments institutions and differential paths of growth among new world economies How Latin America Fell Behind 260ndash304
Engerman S L Sokoloff K L Urquiola M amp Acemoglu D (2002) Factor Endowments Inequality and Paths of Development among New World Economies [with Comments] EconomampxedA 3(1) 41ndash109 Retrieved from httpwwwjstororgezp01libraryquteduaustable20065432
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Faumlre R Grosskopf S amp Lovell C A K (1985) The measurement of efficiency of production (Vol 6) Springer Science amp Business Media
Fleming D A amp Measham T G (2015a) Income Inequality across Australian Regions during the Mining Boom 2001-11 Australian Geographer 46(2) 203ndash216 httpsdoiorg1010800004918220151020596
Fleming D A amp Measham T G (2015b) Local economic impacts of an unconventional energy boom the coal seam gas industry in Australia Australian Journal of Agricultural and Resource Economics 59(1) 78ndash94 httpsdoiorg1011111467-848912043
Fum R M amp Hodler R (2010) Natural resources and income inequality The role of ethnic divisions Economics Letters 107(3) 360ndash363 httpsdoiorg101016jeconlet201003008
Garofalo J (1978) The fear of crime Broadening our perspective
Geys B amp Moesen W (2009) Exploring Sources of Local Government Technical Inefficiency Evidence from Flemish Municipalities Public Finance and Management 9(1) 1ndash29
Goderis B amp Malone S W (2011) Natural Resource Booms and Inequality Theory and Evidence The Scandinavian Journal of Economics 113(2) 388ndash417 httpsdoiorg101111j1467-9442201101659x
Greene W H (2016) Productivity and Efficiency Analysis (W H Greene L Khalaf R Sickles M Veall amp M-C Voia Eds) (1st ed 20) Cham Springer International Publishing httpsdoiorg101007978-3-319-23228-7
Gustafsson B amp Shi L (2002) Income inequality within and across counties in rural China 1988 and 1995 Journal of Development Economics 69(1) 179ndash204 httpsdoiorg101016S0304-3878(02)00058-5
Gylfason T amp Zoega G (2003) Inequality and Economic Growth Do Natural Resources Matter In T T Eicher S (Ed) Inequality and Growth Theory and Policy Implications (pp 255ndash292) The MIT Press
Henningsen A (2010) Estimating Censored Regression Models in R using the censReg Package R Package Vignettes Collection 5(2) 12
Henningsen A (2019) censReg Censored Regression (Tobit) Models R package version 05-30 httpscranr-projectorgpackage=censReg Retrieved from httpscranr-projectorgpackage=censReg
Herrera S amp Pang G (2005) Efficiency of Public Spending in Developing Countriesthinsp An Efficiency Frontier Approach World Bank Washington DC
Hill T D amp Angel R J (2005) Neighborhood disorder psychological distress and heavy drinking Social Science amp Medicine 61(5) 965ndash975
133
httpsdoiorghttpsdoiorg101016jsocscimed200412027
Hipp J R (2010) RESIDENT PERCEPTIONS OF CRIME AND DISORDER HOW MUCH IS ldquoBIASrdquo AND HOW MUCH IS SOCIAL ENVIRONMENT DIFFERENCES Criminology 48(2) 475ndash508 httpsdoiorg101111j1745-9125201000193x
Holtug N amp Mason A (2010) Introduction Immigration diversity and social cohesion SAGE Publications Sage UK London England
Hooghe M Vanhoutte B Hardyns W amp Bircan T (2010) Unemployment Inequality Poverty and Crime Spatial Distribution Patterns of Criminal Acts in Belgium 2001ndash06 The British Journal of Criminology 51(1) 1ndash20 httpsdoiorg101093bjcazq067
Howie P amp Atakhanova Z (2014) Resource boom and inequality Kazakhstan as a case study Resources Policy 39(1) 71ndash79 httpsdoiorg101016jresourpol201311004
Isham J Woolcock M Pritchett L amp Busby G (2005) The Varieties of Resource Experience Natural Resource Export Structures and the Political Economy of Economic Growth The World Bank Economic Review 19(2) 141ndash174 httpsdoiorg101093wberlhi010
Jottier D Ashworth J amp Heyndels B (2012) Understanding Votersrsquo Preferences How the Electoratersquos Complexity Affects Prediction Accuracy and Wishful Thinking among Politicians with Respect to Election Outcomes Kyklos 65(3) 340ndash370 httpsdoiorg101111j1467-6435201200542x
Kanbur S M R amp Venables A (2005) Spatial inequality and development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Kesler C amp Bloemraad I (2010) Does immigration erode social capital The conditional effects of immigration-generated diversity on trust membership and participation across 19 countries 1981ndash2000 Canadian Journal of Political ScienceRevue Canadienne de Science Politique 43(2) 319ndash347
Kuznets S (1955) Economic Growth and Income Inequality The American Economic Review 45(1) 1ndash28 Retrieved from httpwwwjstororgstable1811581
Lagos G amp Blanco E (2010) Mining and development in the region of Antofagasta Resources Policy 35(4) 265ndash275 httpsdoiorghttpsdoiorg101016jresourpol201007006
Leamer E E Maul H Rodriguez S amp Schott P K (1999) Does natural resource abundance increase Latin American income inequality Journal of Development Economics 59(1) 3ndash42 httpsdoiorg101016s0304-3878(99)00004-8
Leibbrandt M Finn A amp Woolard I (2012) Describing and decomposing post-apartheid income inequality in South Africa Development Southern Africa 29(1) 19ndash34 httpsdoiorg1010800376835X2012645639
134
Letki N (2008) Does Diversity Erode Social Cohesion Social Capital and Race in British Neighbourhoods Political Studies 56(1) 99ndash126 httpsdoiorg101111j1467-9248200700692x
Lewis D A (2017) Fear of crime Incivility and the production of a social problem Routledge
lo Storto C (2013) Evaluating Technical Efficiency of Italian Major Municipalities A Data Envelopment Analysis model Procedia - Social and Behavioral Sciences 81 346ndash350 httpsdoiorg101016JSBSPRO201306440
Loayza N amp Rigolini J (2016) The Local Impact of Mining on Poverty and Inequality Evidence from the Commodity Boom in Peru World Development 84 219ndash234 httpsdoiorg101016jworlddev201603005
Loayza N Teran A M y amp Rigolini J (2013) Poverty Inequality and the Local Natural Resource Curse World Bank Policy Research Working Paper (6366) httpsdoiorg1015961813-9450-6366
Loacutepez R amp Miller S J (2008) Chile The Unbearable Burden of Inequality World Development 36(12) 2679ndash2695 httpsdoiorg101016jworlddev200801012
Manzano O amp Rigobon R (2001) Resource Curse or Debt Overhang National Bureau of Economic Research Working Paper Series No 8390 1 httpsdoiorghttpwwwnberorgpapersw9424bib
McPherson M Smith-Lovin L amp Cook J M (2001) Birds of a Feather Homophily in Social Networks Annual Review of Sociology 27(1) 415ndash444 httpsdoiorg101146annurevsoc271415
McQuestin D Drew J amp Dollery B (2018) Do Municipal Mergers Improve Technical Efficiency An Empirical Analysis of the 2008 Queensland Municipal Merger Program Australian Journal of Public Administration 77(3) 442ndash455 httpsdoiorg1011111467-850012286
Messner S F Rosenfeld R amp Baumer E P (2004) Dimensions of Social Capital and Rates of Criminal Homicide American Sociological Review 69(6) 882ndash903 httpsdoiorg101177000312240406900607
Michaels G (2011) THE LONG TERM CONSEQUENCES OF RESOURCE-BASED SPECIALISATION The Economic Journal 121(551) 31ndash57 httpsdoiorg101111j1468-0297201002402x
Mijanovich T amp Weitzman B C (2003) Which ldquobroken windowsrdquo matter School neighborhood and family characteristics associated with youthsrsquo feelings of unsafety Journal of Urban Health 80(3) 400ndash415
Mikušovaacute P (2015) An Application of DEA Methodology in Efficiency Measurement of the Czech Public Universities Procedia Economics and Finance 25 569ndash578 httpsdoiorg101016S2212-5671(15)00771-6
135
Milanovic B (2016) Global inequality Harvard University Press
Millo G amp Piras G (2012) splm Spatial panel data models in R Journal of Statistical Software 47(1) 1ndash38
Murphy K M amp Topel R H (2016) Human Capital Investment Inequality and Economic Growth JOURNAL OF LABOR ECONOMICS 34(2) S99ndashS127 httpsdoiorg101086683779
Narboacuten-Perpintildeaacute I amp De Witte K (2018a) Local governmentsrsquo efficiency a systematic literature reviewmdashpart I International Transactions in Operational Research 25(2) 431ndash468 httpsdoiorg101111itor12364
Narboacuten-Perpintildeaacute I amp De Witte K (2018b) Local governmentsrsquo efficiency a systematic literature reviewmdashpart II International Transactions in Operational Research 25(4) 1107ndash1136 httpsdoiorg101111itor12389
Nuntildeez J Rivera J Villavicencio X amp Molina O (2003) Determinantes socioeconoacutemicos y demograacuteficos del crimen en Chile Estudios de Economiacutea 30(1) 55ndash85
OrsquoDonnell C J Rao D S P amp Battese G E (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios Empirical Economics 34(2) 231ndash255 httpsdoiorg101007s00181-007-0119-4
Ocampo J A (2004) Latin Americarsquos Growth and Equity Frustrations During Structural Reforms The Journal of Economic Perspectives 18(2) 67ndash88 httpsdoiorg1012570895330041371349
OECD (2014) Focus on inequality and growth OECD
OECD (2017) Howrsquos Life 2017 Life Satisfaction Oecd httpsdoiorg101787how_life-2017-en
Ohtake F (2008) Inequality in Japan Asian Economic Policy Review 3(1) 87ndash109 httpsdoiorg101111j1748-3131200800093x
Okun A M (2015) Equality and efficiency the big tradeoff Washington DC Brookings Institution Press
Ortega B Sanjuaacuten J amp Casquero A (2017) Determinants of efficiency in reducing child mortality in developing countries The role of inequality and government effectiveness Health Care Management Science 20(4) 500ndash516 httpsdoiorg101007s10729-016-9367-1
Ostry J Berg A amp Tsangarides C (2014) Redistribution inequality and growth International Monetary Fund
Pacheco F Saacutenchez R amp Villena M (2013) Eficiencia de los Gobiernos Locales y sus Determinantes Un anaacutelisis de Fronteras Estocaacutesticas en Datos de Panel para
136
Municipalidades Chilenas Santiago de Chile Chile
Papyrakis E amp Raveh O (2014) An Empirical Analysis of a Regional Dutch Disease The Case of Canada Environmental and Resource Economics 58(2) 179ndash198 httpsdoiorg101007s10640-013-9698-z
Paredes D (2013) The Role of Human Capital Market Potential and Natural Amenities in Understanding Spatial Wage Disparities in Chile Spatial Economic Analysis 8(2) 154ndash175 httpsdoiorg101080174217722013774094
Paredes D Iturra V amp Lufin M (2016) A spatial decomposition of income inequality in Chile Regional Studies 50(5) 771ndash789
Phan P Van Orsquobrien M Mendolia S amp Paloyo A (2017) National pro-poor spending programmes and their effect on income inequality and poverty Evidence from Vietnam Applied Economics 49(55) 5579ndash5590 httpsdoiorg1010800003684620171313957
Podinovski V V (2004) Bridging the Gap between the Constant and Variable Returns-to-Scale Models Selective Proportionality in Data Envelopment Analysis The Journal of the Operational Research Society 55(3) 265ndash276 Retrieved from httpwwwjstororgstable4102006
Ravallion M (2005) On Measuring Aggregate Social Efficiency Economic Development and Cultural Change 53(2) 273ndash292 httpsdoiorg101086425380
Rehner J Baeza S A amp Barton J R (2014) Chilersquos resource-based export boom and its outcomes Regional specialization export stability and economic growth Geoforum 56(Supplement C) 35ndash45 httpsdoiorghttpsdoiorg101016jgeoforum201406007
Rivera J Gutieacuterrez M amp Nuacutentildeez J (2009) Caracterizacioacuten socioeconoacutemica y espacial de la criminalidad en Chile Revista CEPAL
Ross C E (2011) Collective threat trust and the sense of personal control Journal of Health and Social Behavior 52(3) 287ndash296
Ross C E amp Mirowsky J (2001) Neighborhood disadvantage disorder and health Journal of Health and Social Behavior 258ndash276
Rothstein B amp Uslaner E M (2005) All for all Equality corruption and social trust World Politics 58(1) 41ndash72
Sachs J D amp Warner A M (2001) The curse of natural resources European Economic Review 45(4ndash6) 827ndash838 httpsdoiorg101016S0014-2921(01)00125-8
Salas R (2019) sinimr Chilean Municipalities Information System Wrapper Retrieved from httpsgithubcomrobsalascosinimr
Sampson R J (1986) Crime in Cities The Effects of Formal and Informal Social Control Crime and Justice 8 271ndash311
137
Sampson R J (2008) Rethinking crime and immigration Contexts 7(1) 28ndash33
Santos Silva J M C amp Tenreyro S (2010) On the existence of the maximum likelihood estimates in Poisson regression Economics Letters 107(2) 310ndash312 httpsdoiorghttpsdoiorg101016jeconlet201002020
Santos Silva J M C amp Tenreyro S (2011) poisson Some convergence issues Stata Journal 11(2) 207ndash212 Retrieved from httpwwwstata-journalcomarticlehtmlarticle=st0225
Scott Z (2009) Decentralisation local development and social cohesion an analytical review GSDRC Research Paper 5
Sinha R P (2017) Fiscal Performance Benchmarking of Indian States-A Robust Frontier Approach The Central European Review of Economics and Management 1(4) 225ndash249
Skogan W (1986) Fear of crime and neighborhood change Crime and Justice 8 203ndash229
Skogan W (1999) Measuring what matters Crime disorder and fear In Measuring what matters Proceedings from the Policing Research Institute meetings (pp 37ndash53) National Institute of Justice Washington DC
Skogan W (2015) Disorder and Decline The State of Research Journal of Research in Crime and Delinquency 52(4) 464ndash485 httpsdoiorg1011770022427815577836
Šťastnaacute L amp Gregor M (2014) Public sector efficiency in transition and beyond evidence from Czech local governments Applied Economics 47(7) 1ndash20 httpsdoiorg101080000368462014978077
Tandon A (2005) Measuring Efficiency of Macro Systems An Application to Millennium Development Goal Attainment Asian Development Review 22(2) 108ndash125
Taylor R B (1999) The incivilities thesis Theory measurement and policy Measuring What Matters 65 88
Tigga N S amp Mishra U S (2015) On Measuring Technical Efficiency of the Health System in India An Application of Data Envelopment Analysis Journal of Health Management 17(3) 285ndash298 httpsdoiorg1011770972063415589229
Tinbergen J (1975) Demographic Development and the Exhaustion of Natural Resources Population and Development Review 1(1) 23ndash32 httpsdoiorg1023071972269
Tiruneh G (2014) Social Revolutions Their Causes Patterns and Phases SAGE Open 4(3) 2158244014548845 httpsdoiorg1011772158244014548845
Tolsma J Van der Meer T amp Gesthuizen M (2009) The impact of neighbourhood and municipality characteristics on social cohesion in the Netherlands Acta Politica 44(3) 286ndash313
Tsekeris Sotiris T Tsekeris T amp Papaioannou S (2018) Regional determinants of technical efficiency evidence from the Greek economy Regional Studies [London]thinsp Carfax
138
httpsdoiorg1010800034340420171390312
Uslaner E (2002) The moral foundations of trust Cambridge University Press
Uslaner E (2011) CORRUPTION AND INEQUALITY DICE Report 9(2) 20ndash24
Uslaner E (2013) Trust and corruption revisited how and why trust and corruption shape each other Quality amp Quantity 47(6) 3603ndash3608 httpsdoiorg101007s11135-012-9742-z
Uslaner E amp Brown M (2005) Inequality trust and civic engagement American Politics Research 33(6) 868ndash894
Van der Ploeg F (2011) Natural Resources Curse or Blessing Journal of Economic Literature 49(2) 366ndash420 httpsdoiorg101257jel492366
Vergara R (2012) Crime Prevention Programs Evidence From CHILE The Developing Economies 50(1) 1ndash24
Watkins M H (1963) A staple theory of economic growth Canadian Journal of Economics and Political ScienceRevue Canadienne de Economiques et Science Politique 29(2) 141ndash158
Wilkinson R G (1999) Income inequality social cohesion and health clarifying the theorymdasha reply to Muntaner and Lynch International Journal of Health Services 29(3) 525ndash543
Wilson J Q amp Kelling G L (1982) Broken windows Atlantic Monthly 249(3) 29ndash38
Wilson W J (1996) When Work Disappears Political Science Quarterly 111(4) 567ndash595 httpsdoiorg1023072152085
Wirth L (1938) Urbanism as a Way of Life American Journal of Sociology 44(1) 1ndash24
Worthington A amp Dollery B (2000) An empirical survey of frontier efficiency measurement techniques in local government Local Government Studies 26(2) 23ndash52 httpsdoiorg10108003003930008433988
Wu P-C Huang T-H amp Pan S-C (2014) Country Performance Evaluation The DEA Model Approach Social Indicators Research 118(2) 835ndash849 httpsdoiorg101007s11205-013-0443-3
Ye X Ma L Ye K Chen J amp Xie Q (2017) Analysis of Regional Inequality from Sectoral Structure Spatial Policy and Economic Development A Case Study of Chongqing China Sustainability 9(4) 633 Retrieved from httpwwwmdpicom2071-105094633
Yue W Z Zhang Y T Ye X Y Cheng Y Q amp Leipnik M R (2014) Dynamics of Multi-Scale Intra-Provincial Regional Inequality in Zhejiang China Sustainability 6(9) 5763ndash5784 httpsdoiorg103390su6095763
139
Appendices
Appendix A Summary statistics income inequality
Table A1
Summary statistics Gini coefficients by year and zone
140
Appendix B Summary statistics for NRD measures by region
Table B1
Summary statistics NRD measures by region
141
Appendix C Regional administrative division and defined zones
Figure C1 Geographical distribution of Chilean regions and 3 zones
142
Appendix D Summary statistics numeric controls and correlation matrix
Table D1
Summary Statistics Numeric Explanatory Variables
Figure D1 Correlation matrix numeric explanatory variables
143
Appendix E Static spatial panel models
Following Millo amp Piras (2012) a model including a spatial lag of the dependent variable and
spatial autoregressive disturbances but not spatial lags for the explanatory variable(s) is called
SARAR model A static spatial SARAR panel could be expressed as
119910 120582 119868 otimes119882 119910 119883120573 119906 (E1)
where y is an 119873 1 vector of observations on the dependent variable X is a 119873 119896 matrix of
observations on the non-stochastic exogenous regressors 119868 an identity matrix of dimension 119879 otimes
is the kronecker operator 119882 is the 119873 119873 spatial weights matrix of known constants whose
diagonal elements are set to zero and 120582 the corresponding spatial parameter44
The disturbance vector is the sum of two terms
119906 120580 otimes 119868 120583 120576 (E2)
where 120580 is a 119879 1 vector of ones 119868 an 119873 119873 identity matrix 120583 is a vector of time-invariant
individual specific effects (not spatially autocorrelated) and 120576 a vector of spatially autocorrelated
innovations that follow a spatial autoregressive process of the form
120576 120588 119868 otimes119882 120576 120584 (E3)
If we assume that spatial correlation applies to both the individual effects 120583 and the remainder
error components 120576 Kapoor et al (2007) propose that the disturbance term 119906 follows a first order
spatial autoregressive process of the form
119906 120588 119868 otimes119882 119906 120576 (E4)
44 Unlike ldquotraditional panelsrdquo which are organized as different time series for each cross-sectional unit ldquospatial panelsrdquo are organized as a series of cross-sections for each year
144
where 119882 is the spatial weights matrix and 119903ℎ119900 the corresponding spatial autoregressive
parameter To further allow for the innovations to be correlated over time the innovations vector
in Equation 7 follows an error component structure
120576 120580 otimes 119868 120583 120584 (E5)
where 120583 is the vector of cross-sectional specific effects 120584 a vector of innovations that vary
both over cross-sectional units and time periods 120580 is a vector of ones and 119868 an 119873 119873 identity
matrix45
Spatial panel models are usually estimated by Maximum Likelihood or GMM46 The SAR
SEM or SARAR models could be estimated with Random or Fixed effects For instance A fixed
effect spatial lag model can be written in stacked form as
119910 120582 119868 otimes119882 119910 120580 otimes 119868 120583 119883120573 120576 (E6)
where 120582 is the spatial autoregressive coefficient 119882 a non-stochastic spatial weights matrix
120580 a column vector of ones of dimension 119879 119868 an 119873 119873 identity matrix and 120576 sim 119873 0120590 On
the other hand a fixed effects spatial error model assuming the disturbance specification by
Kapoor et al (2007) can be written as
119910 120580119879 otimes 119868119873 120583 119883120573 119906119906 120588 119868119879 otimes119882119873 119906 120576
(E7)
where 120588 is the spatial autocorrelation coefficient and 120576 is a well-behaved error term
45 In the regression implementation the specification given by equations (22) and (3) is denoted by ldquobrdquo for ldquoBaltagirdquo On the other hand the specification given by equations (23) and (31) is denoted by ldquokkprdquo for ldquoKapoor Kelejian and Pruchardquo 46 We use the R package splm which allow both types of regression procedure
145
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis
Table F1
Analysis OLS residuals Anselin Method
Figure F1 Moran scatter plot OLS residuals
146
Appendix G Linear panel data models
Table G1
Panel regressions (non-spatial)
147
Appendix H Spatial panel models (Generalized Moments (GM) estimation)
Table H1
GM Spatial Models
148
Appendix I Inputs and outputs used in DEA analysis
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018)
149
Appendix J Technical and scale efficiency
Following lo Storto (2013) under an input-oriented specification assuming VRS with n
municipalities using k inputs to produce m outputs the DEA model for a given i-th municipality
is specified with the following mathematical programming problem
119898119894119899 120579119904119906119887119895119890119888119905 119905119900 119910 119884120582 0120579119909 119883120582 01198991prime120582 1 120582 0prime
Where 119910 is the column vector of the outputs and 119909 is the column vector of the inputs
Moreover we can define X as the (k times n) input matrix and Y as the (m times n) output matrix 120579 is a
scalar (that satisfies 120579 1) more specifically it is the efficiency score that measures technical
efficiency It measures the distance between a municipality and the efficiency frontier defined as
a linear combination of the best practice observations With 120579 1 the municipality is inside the
frontier (ie it is inefficient) while 120579 1 implies that the municipality is on the frontier (ie it is
efficient) The vector 120582 is an (n times 1) vector of constants that measures the weights used to compute
the location of an inefficient municipality if it were to become efficient
The total technical efficiency 119879119864 can be decomposed into pure technical efficiency
119879119864 and scale efficiency 119878119864 where 119878119864 119879119864 119879119864 (Coelli et al 2005) To find out
whether a municipality is scale efficient and qualify the type of returns of scale a DEA model
under non-increasing returns to scale 119879119864 is implemented where 119878119864 119879119864 119879119864 Hence
the following rule can be applied (Faumlre Grosskopf amp Lovell 1985)
bull If 119878119864 1 then a municipality is scale efficient both under CRS and VRS
bull If 119878119864 1 it operates under increasing returns to scale
bull If 119878119864 1 it operates under decreasing returns to scale
150
Appendix K Correlation matrix
Figure K1 Correlation matrix contextual factors
151
Appendix L Returns to scale by year and zone
Table L1
Returns to scale (percentage of municipalities)
152
Appendix M Returns to scale by year (maps)
Figure M1 Spatial distribution of returns to scale by county per year
153
Appendix N Efficiency status by year (maps)
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year
154
Appendix O Spatial distribution efficiency scores by year (maps)
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year
155
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis
Table P1
Analysis OLS residuals Anselin Method
Figure P1 Moran scatter plot efficiency scores and OLS residuals
156
Table P2
OLS and spatial regression models for the six-year averaged data
157
Appendix Q OLS regressions for cross-sectional and panel data
Table Q1
OLS cross-sectional regression per year
158
Table Q2
OLS panel regressions Pooled random effects and instrumental variable
159
Appendix R Quantile maps incivilities rate by group (average total period)
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17)
160
Appendix S Correlation matrix numeric covariates
Figure S1 Correlation matrix numeric covariates
161
Appendix T Negative Binomial regressions
Table T1
Negative Binomial regressions
162
Appendix U Coefficients economic and racial diversity by geographical zone
Table U1
Coefficients economic and racial diversity in pooled Poisson models by geographic zone
vi
31 Introduction 55
32 Related Literature 61 321 Measuring efficiency of local governments 61 322 Explaining differences in LGE 63 323 The trade-off between efficiency and equity 64
33 Methodology 66 331 Chilean Municipalities and period of analysis 66 332 Measuring municipal efficiency 68 333 Inputs and outputs used in DEA 70 334 Regression model 71 335 The instrument 75
34 Results and discussion 77 341 DEA results 77
Returns to scale 78 Efficiency measure 80
342 Regression results 82 Exploratory spatial analysis 82 Cross-sectional analysis 83 Panel data analysis 84
35 Conclusions 88
Chapter 4 Social Cohesion Incivilities and Diversity Evidence at the municipal level in Chile 91
41 Introduction 91
42 Related Literature 95 421 The Community Heterogeneity Thesis 95 422 The literature on incivilities 97 423 The ldquoIncivilities Thesisrdquo 99
4 3 Methodology 100 431 Period of analysis and data sample 100 432 Operationalisation of the response variable and exploratory analysis 101 433 Measures of community heterogeneity and control variables 105 434 Methods 108 435 Hypotheses 111
44 Results and Discussion 112
4 5 Conclusions 118
Chapter 5 Conclusions 120
Bibliography 126
Appendices 139
Appendix A Summary statistics income inequality 139
Appendix B Summary statistics for NRD measures by region 140
Appendix C Regional administrative division and defined zones 141
Appendix D Summary statistics numeric controls and correlation matrix 142
vii
Appendix E Static spatial panel models 143
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis 145
Appendix G Linear panel data models 146
Appendix H Spatial panel models (Generalized Moments (GM) estimation) 147
Appendix I Inputs and outputs used in DEA analysis 148
Appendix J Technical and scale efficiency 149
Appendix K Correlation matrix 150
Appendix L Returns to scale by year and zone 151
Appendix M Returns to scale by year (maps) 152
Appendix N Efficiency status by year (maps) 153
Appendix O Spatial distribution efficiency scores by year (maps) 154
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis 155
Appendix Q OLS regressions for cross-sectional and panel data 157
Appendix R Quantile maps incivilities rate by group (average total period) 159
Appendix S Correlation matrix numeric covariates 160
Appendix T Negative Binomial regressions 161
Appendix U Coefficients economic and racial diversity by geographical zone 162
viii
List of Figures
Figure 21 Average share in GDP of economic activities (2006ndash17) 37
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17) 38
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17) 39
Figure 23 Moran scatter plots for variables gini and pss_casen 45
Figure 31 Geographical distribution of Chilean regions and macrozones 74
Figure 32 Evolution of efficiency scores and the proportion of firms by sector 77
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE) 78
Figure 34 Returns to scale by zone 79
Figure 35 Evolution mean efficiency scores (VRS) by zone 81
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017 102
Figure 42 Evolution total number of incivilities by category 104
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017) 104
Figure 44 Annual average number of incivilities per county 109
Figure C1 Geographical distribution of Chilean regions and 3 zones 141
Figure D1 Correlation matrix numeric explanatory variables 142
Figure F1 Moran scatter plot OLS residuals 145
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018) 148
Figure K1 Correlation matrix contextual factors 150
Figure M1 Spatial distribution of returns to scale by county per year 152
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year 153
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year 154
Figure P1 Moran scatter plot efficiency scores and OLS residuals 155
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17) 159
Figure S1 Correlation matrix numeric covariates 160
ix
List of Tables
Table 21 Cross-sectional Model Comparison (six-year average data) 47
Table 22 ML Spatial SAR Models 50
Table 23 ML Spatial SEM Models 50
Table 24 ML Spatial SARAR Models 51
Table 31 Descriptive statistics Inputs and Output variables used in DEA analysis 71
Table 32 Summary Statistics Numeric Contextual Factors 74
Table 33 Summary efficiency scores (VRS) by zone and region 80
Table 34 Cross-sectional (censored) regressions 84
Table 35 Panel data regressions 87
Table 41 Summary statistics total count of incivilities and by category (full sample and period) 103
Table 42 Summary statistics numeric explanatory variables 108
Table 43 Poisson regressions 113
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region 115
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group 116
Table A1 Summary statistics Gini coefficients by year and zone 139
Table B1 Summary statistics NRD measures by region 140
Table D1 Summary Statistics Numeric Explanatory Variables 142
Table F1 Analysis OLS residuals Anselin Method 145
Table G1 Panel regressions (non-spatial) 146
Table H1 GM Spatial Models 147
Table L1 Returns to scale (percentage of municipalities) 151
Table P1 Analysis OLS residuals Anselin Method 155
Table P2 OLS and spatial regression models for the six-year averaged data 156
Table Q1 OLS cross-sectional regression per year 157
Table Q2 OLS panel regressions Pooled random effects and instrumental variable 158
Table T1 Negative Binomial regressions 161
Table U1 Coefficients economic and racial diversity in pooled Poisson models by geographic zone 162
x
List of Abbreviations
Constant returns to scale CRS
Data envelopment analysis DEA
Decreasing returns to scale DRS
Efficiency scores ES
Exploratory spatial data analysis ESDA
Generalized methods of moments GMM
Gross Domestic Product GDP
Increasing returns to scale IRS
Local government efficiency LGE
Maximum likelihood ML
Municipal common fund MCF
Natural resource dependence NRD
Natural resource endowment NRE
Ordinary Least Squares OLS
Organization for Economic Cooperation and Development OECD
Own permanent revenues OPR
Resource curse hypothesis RCH
Spatial autoregressive model SAR
Spatial error model SEM
Variable returns to scale VRS
xi
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet requirements
for an award at this or any other higher education institution To the best of my knowledge and
belief the thesis contains no material previously published or written by another person except
where due reference is made
Signature QUT Verified Signature
Date _________04092020_________
xii
Acknowledgements
First I would like to thank my wife Lilian who joined me in this challenge and patiently
supported me all these years I would also like to thank our family who always supported us from
Chile I especially thank my sister Silvia who took care of our house and dog
I am also grateful to my supervisory team Dr Radhika Lahiri and Dr Vincent Hoang who
supported and guided me in the process of making this thesis a reality
I also thank the Deans of the Faculty of Economics and Business at my beloved University
of Talca Dr Arcadio Cerda and Dr Rodrigo Herrera who trusted me and supported me in this
process In the same way I would like to thank all the support of the director of the Commercial
Engineering career Mr Milton Inostroza
Finally I would like to thank the government of Chile for the financial support that made
my stay and studies possible here at the Queensland University of Technology
13
Chapter 1 Introduction
Efficiency and equity issues are often considered together in the evaluation of economic
performance While higher efficiency usually measured by growth rates of income per capita
correlates with improvements in measures of well-being the link between inequality and well-
being is less clear This is reflected not only in the type and amount of research related to efficiency
and equity but also in the role that both play in the design of the economic policy For instance
several market-oriented countries have focused primarily on economic growth trusting in a trickle-
down process where financial benefits given to the wealthy are expected to ultimately benefit the
poor However despite the growing interest in the issue of inequality there is a considerable lack
of studies about its consequences
Although some level of inequality is inevitable or even necessary for economic activity this
study is motivated by the argument that relatively high levels of inequality can be associated with
many problems such as persistent unemployment increasing fiscal expenses indebtedness and
political instability (Berg amp Ostry 2011) Inequality can also have other severe social
consequences including increased crime rates teenage pregnancy obesity and fewer
opportunities for low-income households to invest in health and education (Atkinson 2015) In
addition when the role of money and concentration of economic power undermine political
outcomes inequality of opportunities hampers social and economic mobility trust and social
cohesion In summary inequality can increase the fragility of the economic and social situation in
a country reducing economic growth and making it less inclusive and sustainable
14
A country well-known for its market-oriented economy and high level of dependence on
natural resources is Chile Chilean success in terms of economic growth contrasts with its inability
to reduce the persistently high levels of social and economic inequality particularly in the last
three decades Using data for the 2006-2017 period and considering 324 out of 345 Chilean
counties this thesis presents three essays with empirical evidence aiming to explain the
phenomenon of persistent income inequality and some of its potential consequences The first
essay aims to analyse how the evolution and variability of income inequality throughout the
country are associated with the degree of natural resource dependence The second essay studies
the relevance of income inequality in explaining cross-county differences in the performance of
local governments (municipalities) Finally the third essay explores the link between social
cohesion and community heterogeneity highlighting the importance of economic and racial
diversity
Income inequality and dependence on natural resources
The first essay explores how cross-county differences in income inequality are associated
with differences in the degree of dependence on natural resources We use the Gini coefficient in
each county as our dependent variable and the proportion of employment in the primary sector as
our measure of natural resource dependence The main hypothesis is that income inequality should
be positively related to the degree of natural resource dependence To test our hypothesis we use
a spatial econometric approach This approach is motivated by the study of Paredes Iturra and
Lufin (2016) who explore the geographic heterogeneity of income inequality in Chile finding
evidence of a significant spatial dimension
15
The theoretical and empirical literature has mostly proposed a positive link between
inequality and natural resources Although most of the evidence corresponds to cross-country
comparisons there is also increasing body of research at the local level A rationale underpinning
the positive link suggested in the literature is that in natural resource-rich countries ownership is
concentrated in small groups and extraction activities require low-skilled workers (Gylfason amp
Zoega 2003 Leamer Maul Rodriguez amp Schott 1999) Another market-based argument often
labelled as the ldquoDutch Diseaserdquo proposes that natural resource windfalls could be associated with
a crowding-out effect on the manufacturing sector (Corden amp Neary 1982 Easterly 2007) This
process encourages rent-seeking behaviours discourages investment in physical and human
capital and delays the process of technology adoption and industrialization (Auty 2001 Bulte
Damania amp Deacon 2005 Gylfason amp Zoega 2003) The result could be a lower economic
growth which is the central idea under the ldquoResource Curse Hypothesisrdquo (Auty 1993 Sachs amp
Warner 2001)
An ldquoinstitutionalrdquo argument for the positive association between inequality and the
endowment of natural resources is based on the so-called ldquoParadox of Plentyrdquo (Borge Parmer amp
Torvik 2015 Dauvin amp Guerreiro 2017) The idea is that both national and local authorities have
less incentive to operate efficiently when they experience windfalls in their revenues for
instance from natural resources This could end with corrupted authorities exerting patronage
clientelism and designing public policies to favour specific groups of the population (Uslaner amp
Brown 2005) Evidence also suggests that the final effect of natural resource booms on income
inequality will depend on authoritiesrsquo capacity to manage these additional resources the extent of
commuting and migration among regions and the potential increase in the demand for non-tradable
16
goods which are intensive in unskilled workers (Aroca amp Atienza 2011 Cust amp Poelhekke 2015
Fleming amp Measham 2015b Howie amp Atakhanova 2014 Michaels 2011)
Contrary to most theoretical and empirical evidence we find that income inequality shows
a robust and significant negative association with our proxy for natural resource dependence This
result suggests that the process of transformation to an economy less dependent on natural
resources could have exacerbated rather than alleviated the persistence of income inequality The
decrease in the participation of the primary sector in employment in favour mainly of the tertiary
sector highlights the importance of the latter to explain the current high levels of inequality and its
future evolution Another important result is that spatial linear models show practically the same
results as traditional linear models This could be interpreted as the spatial dimension previously
found in income inequality is not the result of spatial dependence in the variable itself for instance
due to a process of spillover among counties Hence the usually found positive spatial
autocorrelation of income inequality (similar levels in neighbouring counties) could be explained
by spatial patterns in other variables or to the spatial heterogeneity that characterizes the Chilean
economy
Local government efficiency and income inequality
Essay 2 delves deep into the potential trade-off between efficiency and equity We measure
the efficiency of Chilean municipalities which correspond to the organizations in charge of
managing each county Municipal efficiency is understood as ldquotechnical efficiencyrdquo that is the
possibility that each municipality has reached the same level of outputs with less use of inputs
Then we analyse how income inequality controlling for other contextual factors such as
socioeconomic demographic geographical and political characteristics may help to explain
17
differences in municipal performance Our main hypothesis is that municipal efficiency is
inversely associated with income inequality Moreover we seek a causal interpretation of this
relationship
Municipal performance could be influenced by income inequality in direct and indirect ways
In a direct sense income inequality is used to capture the degree of heterogeneity and complexity
in the demand for public services that citizens exert over local authorities Hence higher levels of
income inequality should be associated with a more complex set of public services and therefore
with lower levels of municipal efficiency (Jottier Ashworth amp Heyndels 2012) Furthermore
when high levels of inequality exist the richest groups can exert a higher influence over local
authorities resulting in low quality and quantity of services for most of the population Among
indirect effects high and persistent inequality could be the source of corrupted institutions and
local authorities favouring themselves or specific groups This undermines citizensrsquo participation
in civic activities and their willingness to monitor municipal performance (Uslaner amp Brown
2005) Additionally the potential benefits of decentralization on the way local governments
deliver public services will be limited when the context is characterized by corrupted politicians
and a limited administrative and financial capacity (Scott 2009)
We measure municipal efficiency using an input-oriented Data Envelopment Analysis
(DEA) to obtain efficiency scores for our sample of 324 municipalities in each year from 2006 to
2017 Then we study the influence on municipal efficiency of income inequality and our set of
contextual factors using a panel of six years corresponding to those years for which household
income information is available 2006 2009 2011 2013 2015 and 2017 Our dependent variable
is the set of efficiency scores which are relative measures of efficiency They are relative to the
18
municipalities included in the sample and they do not imply that higher technical efficiency gains
cannot be achieved Thus we use both cross-sectional and panel censored regression models To
tackle endogeneity issues and suggest a causal interpretation we also propose using the proportion
of firms in the primary sector as an instrument for income inequality
We find an average efficiency score of 83 meaning that Chilean municipalities could
reduce the use of inputs by 17 without reducing their outputs We also measure municipal
efficiency under different assumptions related to returns to scale This allows us to disaggregate
technical efficiency to assess whether inefficiencies are due to management issues (pure technical
efficiency) or scale issues (scale efficiency) Although the results show that most municipalities
operate under increasing or decreasing returns to scale scale inefficiencies only explain a small
proportion of total municipal inefficiencies This highlights the need to look for contextual factors
outside the control of local authorities to explain differences in municipal performance
Geographical representations of our results in terms of returns to scale and efficiency scores
show some spatial clustering process among municipalities Spatial statistics tests confirm that
efficiency scores show a significant positive spatial autocorrelation This means that neighbouring
municipalities tend to show similar levels of efficiency This similar performance could be due to
a process of spatial dependence (eg efficiency spillovers among neighbouring municipalities) or
due to the existence of structural-geographical similarities (spatial heterogeneity) To assess the
spatial dimension in municipal efficiency abstracting from temporal fluctuations we use a cross-
section of data consisting of the six-year average for the variables in our panel After running a
regression of efficiency scores against the set of controls the analysis of OLS residuals shows that
the spatial autocorrelation is almost completely removed This means that the spatial pattern in
19
municipal efficiency can be explained (controlled) by other variables such as regional indicator
variables rather than efficiency itself Given this result we proceed to study the influence of
income inequality on municipal efficiency using traditional (non-spatial) regression analysis
In contrast to literature that emphasizes an equity-efficiency trade-off (Andersen amp Maibom
2020 Berg amp Ostry 2011 Browning amp Johnson 1984 Okun 2015) that is greater equality leads
to lower efficiency we find that municipal efficiency is inversely associated with income
inequality This implies that more equal counties are also those with higher municipal efficiency
Furthermore the coefficient of income inequality is close to one when we use the instrumental
variable approach This means that a reduction in income inequality ceteris paribus should be
associated with an increase in the same magnitude in municipal efficiency This result has strong
policy implications The non-existence of the trade-off suggests that there is more to be gained by
targeting policies towards the reduction of inequality than conventional theories suggest For
instance these policies may help increase the levels of efficiency and well-being at least at the
municipal level
Social cohesion and economic diversity
The third essay studies the relationship between the degree of social cohesion and diversity
in Chile Extant literature has argued that one of the main factors influencing social cohesion is
the degree of economic and ethnic-racial diversity within a society This diversity erodes social
cohesion reducing trust and corrupting institutions (Letki 2008 Rothstein amp Uslaner 2005
Tolsma Van der Meer amp Gesthuizen 2009 Uslaner 2011 2013)
To measure social cohesion scholars have traditionally used measures of social capital trust
or feelings of insecurity (Ariely 2014 Chan To amp Chan 2006 Letki 2008) We suggest the use
20
of the rate of incivilities per 1000 inhabitants as a proxy for social cohesion Incivilities correspond
to those antisocial behaviours (eg groups of rowdy teens and public drunkenness) or visible
neighbourhood conditions (eg graffiti and abandoned buildings) that tend not to be treated as
crime Using the rate of incivilities is arguably a more objective and reliable measure of social
cohesion particularly in countries where institutions of order and security are among the most
trusted An increase in the rate of incivilities rather than changes in crime rates should better
capture the worsening in social cohesion experienced in countries such as Chile where crime rates
are not growing but social conflicts are on the rise Thus the main hypothesis in this essay is that
the rate of incivilities (social cohesion) should be positively (negatively) associated with economic
and racial diversity
Using panel count data models we start analysing how differences in incivilities rates
between and within counties are associated with differences in indicators of relative and absolute
economic disadvantage We use the Gini coefficient of each county as our measure of economic
diversity Although we find a significant and positive association between the rate of incivilities
and the level of income inequality the magnitude of the link seems to be small Among absolute
indicators of economic disadvantage only the level of income shows a strong effect Next we
include our measure of racial diversity We use the number of new visas granted to foreigners as
a proportion of the county population Results show a significant and strong positive association
between the rate of incivilities and racial diversity
To check the robustness of our results we analyse the impact of our measures of economic
and racial diversity running our models separately for each Chilean region and clustering them
geographically We also split the total number of incivilities in four categories to see which type
21
of incivilities show the greatest association with our measures of diversity In general results
support the ldquocommunity heterogeneity hypothesisrdquo that higher community heterogeneity is
associated with higher rates of incivilities (Alesina amp La Ferrara 2002 Letki 2008 Tolsma et al
2009) However results do not support the ldquoincivilities thesisrdquo that the distribution of incivilities
tends to mirror the distribution of income inequality (Skogan 1999 Taylor 1999)
Three results stand out among the set of control variables First the level of education shows
and independent and significant negative association with the rate of incivilities This is in contrast
to previous studies where education acts mainly as a moderator of the effect of economic and racial
diversity on social cohesion (Tolsma et al 2009) The results also show that there is no significant
relationship between the rate of incivilities and the proportion of young population This is relevant
because policies aimed to reduce incivilities usually put the focus on specific groups such as young
people which are linked to physical and social incivilities when social control is weakened
Finally the degree of financial municipal autonomy also shows a significant negative association
with the rate of incivilities This result suggests that municipalities can contribute independently
or together with the central government to reduce incivilities and strengthen social cohesion
Contributions
The three essays in this thesis provide several important insights into the analysis of the
causes and consequences of income inequality particularly in the context of Chile ndash a typical
resource rich economy with persistently high levels of income inequality
Essay 1 advances the understanding of the relationship between income inequality and
natural resources in Chile extending the empirical analysis from the regional level to the county
level In addition the geographic heterogeneity of income inequality is explored with the inclusion
22
of alternative sources of spatial dependence as a potential dimension of the causal relationship
between income inequality and natural resources This essay demonstrates the relevance of natural
resources in explaining the persistence of income inequality even after controlling for other
socioeconomics and institutional factors Findings from this study have potential contribution not
only in the design of policies aimed to reduce income inequality but also in addressing the current
developmental bias between the metropolitan region and the rest of the country
Essay 2 is one of the first studies that undertake a longitudinal analysis of the effects of
income inequality on the efficiency of municipal governments in Chile To capture the role of the
municipal governments in the provision to local people of public services such as education and
health we specify several inputs and outputs in our efficiency model which is different from the
conventional specification in the existing literature For example the number of medical
consultations in public health facilities and the number of enrolled students in public schools are
used as outputs instead of general indicators such as county population Our empirical analysis
also utilises a larger sample of municipalities and covers a much longer period spanning from 2006
to 2017 This essay also investigates the contextual factors beyond the control of local authorities
that can explain variations in the efficiency of municipal governments across the country
Empirical findings from Essay 2 help us increase our understanding of the production
technology of municipalities the sources of inefficiencies and specifically the impact of income
inequality on the performance of local authorities The results deliver two main policy
implications First municipal inefficiencies in the provision of public goods and services differ
across Chilean municipalities In addition efficiency levels show some degree of spatial
autocorrelation This implies that policies such as amalgamation or cooperation among
23
municipalities could have effects beyond the municipalities involved which must be considered
Second the causal effect that income inequality has on municipal efficiency provides another
dimension into the design and implementation of development policies
Essay 3 explores for the first time the effects of economic and racial diversity on social
cohesion in Chile This essay considers incivilities as manifestation of social cohesion and
investigates as extant literature suggests whether indicators of relative economic disadvantage
such as income inequality are among the main factors driving social disorganization and social
unrest Empirical findings suggest that on the one hand economic heterogeneity captured by the
Gini coefficient has a disparate effect both in terms of magnitude and significance on the rate of
incivilities across the country On the other hand the impact of racial heterogeneity appears to be
stronger more significant and of a similar magnitude throughout the country Results also provide
new insights into the design of national policies addressing social disorders particularly those
policies focussed on specific groups of the population and the role of local authorities Overall the
findings provide an opportunity to advance the understanding of the process of weakening in the
social cohesion experienced in Chile and the conflicts that have risen from this process
Thesis outline
The remainder of the thesis is organized as follows Chapter 2 presents essay 1 examining
the association between income inequality and the degree of dependence on natural resources
Chapter 3 presents essay 2 which looks for a causal relationship between municipal efficiency and
income inequality Chapter 4 presents essay 3 analysing the relationship between social cohesion
and economic and racial diversity Finally Chapter 5 presents some concluding remarks
24
Chapter 2 Natural Resources Curse or Blessing Evidence on
Income Inequality at the County Level in Chile
21 Introduction
A phenomenon of increasing inequality of incomes and wealth in recent decades has been
documented by leading scholars and international organizations such as the International Monetary
Fund (Berg amp Ostry 2017 Ostry Berg amp Tsangarides 2014) and the Organization for Economic
Cooperation and Development (Cingano 2014) These efforts have placed the topic of inequality
at the top of the current economic debate recognizing inequality as a determinant not only of
economic growth but also of human development They also have highlighted the necessity for
more research on the drivers of inequality and mechanisms through which it manifests aiming to
design effective policies in reducing economic and social inequalities
Various factors have been analysed as the sources of high and increasing levels of inequality
Among the most significant factors are the levels of income at initial stages of economic
development (Kuznets 1955) Globalization (Milanovic 2016) skill-biased technological change
(Tinbergen 1975) investment in human capital (Murphy amp Topel 2016) institutions
redistributive policy and country-specific characteristics (Acemoglu 1995 2002 Acemoglu
Aghion amp Violante 2001 Acemoglu Johnson amp Robinson 2001) Our focus in this essay is on
the importance that the natural resource endowment (NRE) or lack thereof can play in the
determination of income disparities
25
This essay studies the patterns and evolution of income inequality in the context of a natural
resource-rich country Using the case of the Chilean economy we aim to understand and
disentangle how a phenomenon of high- and persistent-income inequality is related to the
endowment of natural resources that a country owns Chile is an interesting case to study because
despite showing a successful history of economic growth inequality among individuals and among
aggregated spatial units has shown a strong persistence (Paredes et al 2016) Furthermore Chile
has remained among the most unequal countries in the world1
Theory and empirical evidence do not establish a clear link between income inequality and
NRE In addition NRE has received considerably less attention (Auty 2001 ElGindi 2017) and
most of the evidence has been focused on cross-country comparisons For instance NRE can
influence inequality by determining its initial levels (Engerman amp Sokoloff 1994 1997
Engerman Sokoloff Urquiola amp Acemoglu 2002) shaping the evolution of institutions
(Acemoglu 2002) make the educational system less intellectually challenging and moulding the
structure of economic activity (Leamer et al 1999) So studying how cross-county differences in
NRE are associated with the distribution of income within a country has theoretical empirical and
policy implications
In this study we offer empirical evidence on the relationship between income inequality and
the endowment of natural resources using data at the county level in Chile for the period 2006-
2017 Income inequality is measured by the Gini coefficient The importance of NRE is proxied
using a measure of natural resource dependence (NRD) defined as the percentage of the total
1 A 2014 OECD report on income inequality (httpwwwoecdorgsocialincome-distribution-databasehtm) showed Chile as the country with the highest Gini coefficient of disposable income among OECD countries OECD also indicates Chile as the country with the widest gap between the richest 10 percent and the poorest 10 percent of countryrsquos population among its 34 members (OECD 2014)
26
employment in each county corresponding to the primary sector (agriculture forestry fishing and
mining)
The main hypothesis to be tested is whether income inequality is positively associated with
the degree of NRD The transmission mechanisms through which natural resources could influence
socioeconomic outcomes could be based on the market or institutions The market-based approach
argues that natural resource booms could be associated with an appreciation of the real exchange
rate and a crowding out effect over other more productive economic activities such as
manufacturing It could also delay the adoption of new technologies and reduce incentives to invest
in physical and human capital (Gylfason amp Zoega 2003) Based on the ldquoResource Curse
Hypothesisrdquo (RCH) natural resources could be a curse when the political and institutional
framework is weak and natural resources are concentrated in space such as oil and minerals
(Deacon 2011) 2 Among institutional channels a higher NRD or natural resource booms could
be associated with rent seeking misallocation of labour and entrepreneurial talent institutional
and political decline or even violent conflicts For instance the ldquoParadox of Plentyrdquo sustains that
windfalls of revenues as a consequence of resource booms could be related to a lack of incentives
to perform efficiently corruption patronage and local authorities favouring their voters or being
captured by the richest groups (Dauvin amp Guerreiro 2017) Hence a higher NRD or natural
resource booms could be the explanation not only for low levels of growth in regions more
dependent on natural resources but also it could be the root of income disparities
2 There is a wide strand of research on the Resource Curse Hypothesis however the evidence so far is not conclusive Evidence in favour of RCH has been mainly found in developing resource rich countries (Auty 1993 2001 Badeeb Lean amp Clark 2017 Blanco amp Grier 2012 Borge et al 2015 Brunnschweiler amp Bulte 2008 Sachs amp Warner 2001 Van der Ploeg 2011)
27
To test our hypothesis that is whether the levels of income inequality across counties are
positively associated with the degree of NRD we use a spatial econometric approach We use this
approach because attributes such as income inequality in one region may not be independent of
attributes in neighbouring regions (Armstrong amp Taylor 2000) This process of spatial dependence
invalidates the use of traditional (non-spatial) approaches
This study seeks to make two contributions to research First previous empirical evidence
shows a significant spatial dimension of income inequality in Chile (Paredes et al 2016)
However this dimension has been barely explored with most studies limiting the degree of
disaggregation to a regional scale (Aroca amp Bosch 2000) We use a spatial approach which makes
it possible to model and test the significance of the spatial dimension in the analysis of income
inequality and its relationship with other variables Second previous research for the Chilean
economy linking inequality with NRE has been mainly focused on explaining differences between
regions or the importance and effects of the mining-copper sector (Aroca amp Atienza 2011 Ebert
amp La Menza 2015 Lagos amp Blanco 2010 Rehner Baeza amp Barton 2014) We extend this
analysis using data for local economies Identifying and quantifying the impact of NRE on income
inequality at the county level is likely to be more informative for policies aiming to address the
current developmental bias between the metropolitan region and the rest of the country Moreover
the analysis of the role of natural resources in conjunction with other potential sources of inequality
may shed lights in understanding the persistence of the high levels of inequality observed in the
Chilean economy All in all this study could contribute to the design of policies that
simultaneously help reduce inequality increase efficiency and promote sustainable and inclusive
growth
28
Our main finding shows that after controlling for other potential sources of income
inequality such as educational level demographic characteristics and the level of public
government expenditure the degree of dependence on natural resources has a significant effect on
income inequality However contrary to our expectations the effect is negative This result
suggests that the natural or policy-driven process of transformation from primary and extractive
activities to manufacturing and service sectors imposes additional challenges to central and local
authorities aiming to reduce income inequality
In section 22 we review the literature on the relationship between income inequality and
natural resources In section 23 we establish our research problem and main hypothesis Section
24 describes our data and methods and section 25 the empirical results We finish with section
26 discussing our main results concluding and proposing avenues for future research
22 Inequality and Natural Resources
221 Theoretical Framework
Explanations for income inequality can be associated with individual institutional political
and contextual characteristics Individual characteristics include age gender and mainly the level
of education and skills of the population in the labour force For instance globalization and
technological change lead firms to increase the demand for skilled labour deepening income
inequality between skilled and unskilled workers (Atkinson 2015 Milanovic 2016 Tinbergen
1975) Among institutional characteristics labour unions collective bargaining and the minimum
wage have been suggested as explanations of income inequality (Acemoglu Aghion et al 2001
Atkinson 2015) Policy design associated with market regulation progressive taxation and
redistribution can also impact the levels and patterns of inequality
29
A key factor in understanding the levels and differences in income distribution within a
country may be its endowment of natural resources NRE shapes the structure of the economy
(Leamer et al 1999) it is associated with the creation of institutions that define the political
culture and it can also influence the performance of other sectors (Watkins 1963) In addition
NRE determines initial conditions market competition ownership over resources rent seeking
and the geographical concentration of the population and economic activity
Cross‐countryliterature
Bourguignon and Morrison (1990) introduce one of the earliest theoretical frameworks
describing the relationship between inequality and NRE They develop a small open economy
model where income distribution is a function of NRE ownership structure and trade protection
Giving cross-sectional evidence for a group of developing countries they conclude that the impact
of NRE particularly mineral resources and land depends on the number and size of the firms
whether they are public or private and the level of protection A higher concentration of production
in a few private firms a big share of production oriented to foreign instead of domestic markets
and protection increasing the relative price of scarce resources are some of the reasons explaining
why some countries are less egalitarian than others
NRE could also influence the evolution and levels of inequality by determining the initial
distribution of incomes This is known as the ldquoEngerman-Sokoloff Hypothesisrdquo (Engerman amp
Sokoloff 1997 Engerman et al 2002) In addition Leamer (1999) proposes that inequality and
development paths in each economy are a function of its economic structure which in turn depends
on ldquofundamentalsrdquo and ldquosymptomsrdquo On the one hand ldquofundamentalsrdquo refer to resource
endowment production structure closeness to markets and governments interventions On the
30
other hand ldquosymptomsrdquo are related to institutions employment structure and net export structure
Using this conceptual framework Leamer argues that natural Resource-Rich Countries (RRC) can
experience a higher level of inequality because can have a ldquodumbbell educational systemrdquo
ownership is concentrated in small groups and extraction activities require low-skilled workers
This implies fewer incentives to educate citizens until very late in the development process
resulting in human capital not prepared to take advantage of the process of technological progress
and delaying the emergence of more efficient and competitive sectors such as manufacturing and
services
Using 1980 and 1990 data for a group of countries classified according to land abundance
Leamer (1999) provides evidence showing that on the one hand land-scarce countries concentrate
their production and employment in sectors that promote equality such as capital-intensive
manufacturing chemical or machinery On the other hand countries abundant in natural resources
concentrate their production trade or employment in sectors that promote income inequality such
as the production of food beverages extraction activities or forestry
Gylfason and Zoega (2003) using a framework based on standard growth models also
proposed a positive relationship between NRE and inequality They assume that workers can work
in the primary sector or in the manufacturing (including services) sector In addition wage income
is equally distributed in the manufacturing sector but unequally in the primary sector (because of
initial distribution competition rent seeking etc) Therefore inequality will be greater when a
bigger proportion of labour is dedicated to extraction activities in the primary sector This
phenomenon is further amplified because of lower incentives to invest in physical and human
capital to adopt new technologies and to increase the share of the manufacturing sector
31
Diverse mechanisms explaining the link between NRE and inequality have been proposed
arguing that NRE determines simultaneously economic growth and inequality (Gylfason amp Zoega
2003) NRE could impact economic growth through the real exchange rate and the crowding-out
effect on manufacturing (ldquoDutch Diseaserdquo) reducing incentives to invest in physical and human
capital (Easterly 2007) and influencing the processes of technology adoption industrialization
and diversification of the economy in a manner that is less conducive to growth (Bulte et al 2005)
These potential explanations related to the called ldquoResource Curse Hypothesisrdquo do not have strong
empirical support (Auty 2001 Bulte et al 2005)
NRE may also influence economic growth through the quality of institutions (Acemoglu
1995 Acemoglu Aghion et al 2001 Acemoglu amp Robinson 2002 Engerman amp Sokoloff 1997
Engerman et al 2002) the concentration of ownership political power and rent-seeking NRE
acts by shaping institutional context and social infrastructure a phenomenon that is stronger when
resources are spatially concentrated such as minerals and plantations (Bulte et al 2005) NRE
could also have a significant effect on social cohesion and instability spreading its influence like
a disease (Brunori Ferreira amp Peragine 2013 Kanbur amp Venables 2005 Milanovic 2016
Ocampo 2004)
Considering a non-tradable sector intensive in unskilled workers Goderis and Malone
(2011) develop a model where the natural resources sector experiences an exogenous gift of
resource income They analyse the impact over income inequality of resource booms proxied by
changes in a commodity price index They conclude that inequality decreases in the short run but
increases after the initial reduction
32
Fum and Hodler (2010) show that natural resources increase inequality but this is
conditional on the level of ethnical polarization of society Carmignani (2013 2010) confirms this
positive relationship using different measures of dependence and abundance and goes further
arguing that inequality constitutes an indirect channel through which NRE affects human
development
Singlecountryevidence
Most of the studies about the relationship between inequality and NRE derive from cross-
country analyses Evidence for specific countries has been mainly based on case studies Howie
and Atakhanova (2014) based on the model of Goderis and Malone (2011) find for the case of
Kazakhstan that income and consumption inequality decreased significantly after booms in the oil-
and-gas sector because of resource booms increase the demand for non-tradable goods which are
intensive in unskilled workers The results depend on the level of rurality institutional quality
education levels and public spending on health and education Fleming and Measham (2015b
2015a) evaluate the impact of booms in the mining and oil sectors in Australia They find that a
boom in the mining sector increases income inequality due to commuting and migration among
regions This phenomenon can be exacerbated when the demanding access to natural resource
revenues is associated with the creation of more local administrative units (counties provinces and
even regions) but the government capacity is not simultaneously improved (Cust amp Poelhekke
2015 Michaels 2011) Furthermore the benefits that a region can receive in the form of fiscal
transfers can be more than compensated by the loses due to city-to-mine commuting such as the
case of mining regions in Chile (Aroca amp Atienza 2011)
33
Other studies at the local level have analysed the impact of the mining sector in Peru (Aragoacuten
amp Rud 2013 Loayza amp Rigolini 2016 Loayza Teran amp Rigolini 2013) Spain (Domenech
2008) and Canada (Papyrakis amp Raveh 2014) and the effects of oil windfalls in Brazil (Caselli amp
Michaels 2013)
In summary there is a wide range of potential mechanisms through which NRE could
influence income inequality Although most of them seem to suggest a positive relationship others
such as commuting and increased within-county demand for non-tradable goods and services
could lead to a negative association This highlights the need to know the sign of this association
in the Chilean economy where the trend shows a reduction in the degree of NRD After controlling
for other factors a positive link would support the argument that the reduction in the degree of
NRD has been relevant in the reduction experienced by income inequality in the same period
However a negative link would support the position that the reduction in NRD has contributed to
explain the persistence of income inequality and its slow reduction
222 The relevance of the spatial approach
Inequalities within countries are still the most important form of inequality from the political
point of view (Milanovic 2016) People from a geographic area within a country are influenced
and care most about their status relative to the people in other areas in the same country The
influence among regions involves multiple aspects (eg economic political and environmental)
These potential interactions have been traditionally ignored assuming independence among
observations related to different regions Moreover neglecting the process of spatial interaction in
key indicators of the economic and social performance of a country may mislead the design of the
public policy
34
The spatial dimension could play a significant role in understanding the distribution of
income within a country One strand of efforts aiming to capture the geographic heterogeneity of
inequality has been focussed on decomposing general indicators such as the Gini coefficient or the
Theil Index Evidence for different countries including the US (Doran amp Jordan 2016) China
(Akita 2003 Gustafsson amp Shi 2002 Ye Ma Ye Chen amp Xie 2017 Yue Zhang Ye Cheng
amp Leipnik 2014) Japan (Ohtake 2008) South Africa (Leibbrandt Finn amp Woolard 2012) and
Chile (Paredes et al 2016) shows that regional inequality is sensitive to the geographic scale of
analysis These studies also show a significant spatial component in the explanation of inequality
of income expenditure or gross domestic product for each country
Another strand explicitly uses exploratory spatial data analysis (ESDA) and spatial
econometrics ESDA has been used to provide new insights about the nature of regional disparities
of incomes and growth rates (Celebioglu amp Dallrsquoerba 2010 Yue et al 2014) Spatial econometric
models aim to assess and address the nature of the spatial effects These effects could be the result
of ldquospatial heterogeneityrdquo that is different relationships in distinct locations or ldquospatial
dependencerdquo which implies cross-sectional interactions (spillover effects) among units from
distinct but near locations
Spatial spillovers have been analysed to study both positive and negative spatial correlation
among less resource-abundant counties and resource-abundant counties On the one hand less
resource-abundant counties may experience positive spillovers because their industries supply
more goods and services to meet the increasing regional demand They can also be benefited from
positive agglomeration externalities and higher investment in private and public infrastructure
(Allcott amp Keniston 2014 Michaels 2011) On the other hand negative spillovers could be the
35
result of a high degree of interregional migration that limits the rise in wages and higher local
prices due to the increase in the share of the non-tradable sector In addition local governments
could have a limited capacity to translate the revenues from resource booms into effective public
policies promoting a sustained local development (Beine Coulombe amp Vermeulen 2015 Caselli
amp Michaels 2013 Papyrakis amp Raveh 2014)
23 Research problem and hypotheses
We can conclude from our overview of the literature that the theoretical and empirical
evidence about the link between inequality and natural resources is inconclusive This does not
make clear whether the process of reduction in the degree of dependence on natural resources
such as that experienced by the Chilean economy helps to explain the sustained but slow reduction
in income inequality or its high persistence
The research question guiding this study relates to how the natural resource endowment
determines the paths and structure of income inequality in natural resource-rich countries Using
the case of Chile the main hypotheses to be tested is whether a higher degree of dependence on
natural resources is associated with higher levels of income inequality To do that we use data at
the county level and we explicitly include the spatial dimension Our aim is to arrive at a more
comprehensive understanding of the drivers and transmission mechanisms explaining the
evolution and patterns shown by income inequality In addition we test whether the spatial
dimension plays a significant role in explaining differences in income distribution in Chile
36
24 Data and Methods
We use county-level data for the years 2006 2009 2011 2013 2015 and 2017 The reason
for not using contiguous years is that income data at the household level are only available every
two-three years from the Chilean National Socioeconomic Characterization Survey (CASEN in its
Spanish acronym)3 For the period 2006-2017 the Chilean administrative division considers 15
regions 54 provinces and 346 counties Data on income are available for 324 counties and six
years resulting in a panel with 1944 observations4
We start evaluating the spatial dimension in our data and analysing the link between
inequality and NRD using a cross-sectional setting To this end we use the ldquosix-year averagerdquo
(2006 2009 2011 2013 2015 2017) for our variables given the low time variability showed by
our measures of income inequality and NRD Results are then compared with those of a panel data
setting
241 Operationalization of key variables
The dependent variable in the present study income inequality at the county level is
measured calculating the Gini coefficient using three definitions of household income labour
autonomous and monetary income5 Labour income corresponds to the incomes obtained by all
members in the household excluding domestic service consisting of wages and salaries earnings
3 CASEN survey is conducted by the Chilean Ministry of Social Development covering topics such as education employment income and health CASEN is considered nationally representative and it is the main source for measures of inequality and poverty used for the design and evaluation of social policies in Chile 4 The six waves of CASEN for our study considered an average of 75599 households and 252081 individuals 5 The Gini coefficient is chosen because provides an overall estimate of income inequality It summarizes what proportion of the population gains what proportion of the total income The Gini coefficient can range between 0 (everyone in the population has the same income) and 1 (one person earns 100 per cent of the income in the community)
37
from independent work and self-provision of goods Autonomous income is the sum of labour
income and non-labour income (including capital income) consisting of rents interest and dividend
earnings pension healthcare benefits and other private transfers Finally monetary income is
defined as the sum of autonomous income and monetary subsidies which correspond to cash
transfers by the public sector through social programs Appendix A shows summary statistics for
the Gini coefficient of our three measures of income
The main independent variable in our study is the degree of dependence on natural resources
in each county To have an idea of the importance of each economic activity in the Chilean
economy particularly those activities related to natural resources Figure 21 shows their average
share in Chilean Gross Domestic Product (GDP) for the period 2006-17 We can observe that the
leading activities are those related to the primary sector especially mining and to the tertiary
sector where financial personal commerce restaurants and hotels services stand out The shares
of each economic activity in GDP vary significantly between Chilean regions and such
information is not available at the county level
Figure 21 Average share in GDP of economic activities (2006ndash17)
38
Leamer (1999) argues that when the main source of income is labour income (as indeed
happens for the Chilean case) using employment shares allows a better approach to measuring
dependence on natural resources Using employment data from CASEN survey we define our
measure of NRD as the employment in the primary sector (mining fishing forestry and
agriculture) as a percentage of the total employment in each county We name this variable
pss_casen where ldquopssrdquo stands for ldquoprimary sector sharerdquo We built other two proxies of NRD
using data from the ldquoServicio de Impuestos Internosrdquo (SII) which is the agency in charge of
collecting taxes in Chile The variable pss measures the percentage of employment in the primary
sector and the variable pss_firms measures the number of firms in the primary sector as a
percentage of the total number of firms in each county Appendix B shows summary statistics for
our three measures of NRD disaggregated by region
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17)
39
Figure 22 shows the evolution of our measure of inequality (using the Gini coefficient of
autonomous income) and our three potential proxies for NRD for the period 2006-2017 We
observe that both income inequality and the degree of NRD show a downward trend This seems
to support our hypothesis of a positive link between inequality and NRD however we need to
control of other sources of inequality before getting such a conclusion In what follows we use the
variable gini as our measure of income inequality capturing the Gini coefficient of autonomous
income Our measure of NRD is the variable pss_casen defined previously
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17)
Note Gini and NRD averages 2006-09-11-13-15-17 for 324 Chilean counties divided into five equal groups Source Own elaboration based on CASEN survey
40
Figure 23 shows quantile maps for income inequality (on the left) and NRD (on the right)
using the six-years average dataset6 On the one hand we observe that high levels of inequality
seem to be clustered in the Centre-South of the country where agriculture forestry and fishery are
the predominant economic activities Only isolated counties show high inequality in the Centre
(Metropolitan area where the countyrsquos capital is located) and North (Mining) areas On the other
hand our measure of NRD seems to show an opposite spatial pattern than income inequality with
high levels in the Centre and North of the country
242 Control variables
To control for county characteristics we use a set of socio-economic demographic and
institutional variables Economic factors are captured by the natural log of the mean autonomous
household income per capita (in thousands of Chilean pesos of 2017) lnincome the poverty rate
poverty the unemployment rate unemployment the percentage of the population living in rural
areas rural and the average years of education of the population over 15 years old education
Demographic factors include the proportion of the population in the labour force labour_force
and the natural log of population density (population divided by county area) lndensity
We also include the natural log of the total municipal public expenditure per capita
lnmuni_expenditure to control for municipal heterogeneity This heterogeneity is mainly related
to the capacity of municipalities to generate their own revenues In addition the richest
municipalities are in the Metropolitan region which concentrates economic power and around 40
6 After sorting a variable in ascending order quantile maps use the quantiles (quartiles quintiles deciles etc) as class breaks to divide the distribution of the variable where each class includes approximately the same number of observations (counties)
41
of the population This has basically implied a lag in the development of regions other than the
metropolitan region
The spatial distribution of our measures of income inequality and NRD displayed in Figure
23 seems to show different patterns in the North Centre and South of the country Appendix C
shows the administrative division of Chile in 15 regions and how we have grouped them in three
zones North Centre and South We consider as the ldquoCentrerdquo area that formed by the Metropolitan
region (XIII) and its two neighbouring regions V and VI Using the Centre area as our reference
we include in our analysis two dummy variables indicating whether a county is located in the North
area (regions XV I II III and IV) or South area (regions VII VIII IX XIV X XI and XII)
Appendix D shows summary statistics for the set of numeric control variables and the
correlation matrix between our measure of NRD pss_casen and the set of numeric controls
243 Methods
To assess and then consider the spatial nature of the data we need to define the set of relevant
neighbours for each country This is operationalized by building a matrix called ldquoWrdquo with a ldquo1rdquo
for neighbouring counties and a ldquo0rdquo for non-neighbouring counties We could build W using
contiguity-based (whether counties share a border or point) or geography-based (taking the
distances among the centroids of each county polygon) spatial weights Specifically we build a W
matrix considering the 5-nearest counties7 Two reasons explain the choice of k-nearest
neighbours First we cannot use a contiguity criterium because we do not have information about
all the counties and there are some geographically isolated counties Second given the significant
7 We assign a ldquo1rdquo to the five nearest counties to each county based on the distances among the polygon centroids Then W is ldquorow standardizedrdquo This facilitates the interpretation of the spatial lag of a variable as the ldquoweighted average valuerdquo of the same variable in neighbouring counties
42
differences in county areas (ldquobig countiesrdquo in northern and southern regions) using a distance-
band criterium with a not enough large distance band can lead to many ldquoislandsrdquo in extreme regions
and a multi-modal distribution for the number of neighbours
We start testing our inequality and NRD variables for spatial autocorrelation in order to
evaluate statistically the clustering patterns shown in Figure 23 Next we run an OLS regression
of inequality against NRD and our set of controls and we test the spatial autocorrelation of OLS
residuals If we cannot reject the null hypothesis of random spatial distribution we do not need
spatial models to analyse income inequality which would give contrasting evidence to previous
suggestions about the relevance of the spatial dimension of income inequality in Chile (Paredes
2013 Paredes et al 2016) If we find significant spatial autocorrelation in the OLS residuals this
justifies the use of spatial models and highlight the need to find the correct spatial structure8
If inequality in one county spillovers or influences inequality in neighbouring counties the
spatial lag of inequality should be included as an explanatory variable and we should use a spatial
autoregressive model (SAR) If some unobserved variable is the explanation for the clustering of
counties with similar inequality then this will be better captured including a spatial lag of the
errors and we should use a spatial error model (SEM) (Anselin 1988 Anselin amp Bera 1998)
Finally when our main explanatory variable or some of the controls show spatial autocorrelation
a spatial lag of the explanatory variable(s) should be included in our model
8 The existence of spatial autocorrelation violates the standard assumption of independence among observations needed for OLS regression This will result in OLS coefficients biased and inconsistent (Anselin 1988)
43
244 Spatial Model Specification
A model that includes the three forms of spatial dependence described above is called the
Cliff-Ord Model The model in its cross-sectional representation could be expressed as
119910 120582119882119910 119883120573 119882119883120574 119906 (21)
where
119906 120588119882119906 120576 (22)
119882 is our weight matrix that works as an NxN spatial lag operator9 Thus 119882119910 119882119883 and 119882119906
are the spatial lags for the dependent variable explanatory variables and the error term
respectively The parameter 120582 capture the spatial dependence in the dependent variable 120574 the
spatial dependence in the explanatory variables 120588 capture the spatial dependence in the error term
and 120598 is a vector of idiosyncratic errors For instance if ldquoyrdquo is income inequality and ldquoXrdquo a measure
of NRD the level of inequality in one county will be explained by the degree of NRD in the same
county 119883120573 the average degree of NRD in neighbouring counties 119882119883120574 the average level of
inequality in neighbouring counties 120582119882119910 and the average value of residuals in neighbouring
counties 12058811988211990610
From equations (21) and (22) the SAR and SEM models can be seen as special cases of
the Cliff-Ord representation after imposing restrictions over the spatial parameters 120582 120574 and 120588 For
the specification of the spatial panel models we follow the terminology by Croissant and Millo
9 The spatial lag is a weighted sum of the values observed at neighbouring locations 10 On the one hand the impact that income inequality in one county has over income inequality in neighbouring counties is called a ldquoglobal spilloverrdquo and it is associated with the feedback effect among neighbours (one county is its neighboursrsquo neighbourrdquo) on the other hand the influence that the degree of NRD in neighbouring counties has over inequality in one county is called a ldquolocal spilloverrdquo
44
(2018) Spatial panel models including the spatial lag of the dependent variable (SAR) the spatial
lag of the residuals (SEM) or both (SARAR) are described in Appendix E
25 Results
251 Exploratory Spatial Data Analysis (ESDA)
To analyse the significance of the spatial dimension in our data set we use the six-year
average of our variables Spatial autocorrelation is tested using the Moranrsquos I statistic11 Moranrsquos
I measures the correlation of one variable with itself in space12 Figure 24 shows the Moran scatter
plots where the standardized variable (Gini coefficient and NRD for each county) appears in the
horizontal axis against its spatial lag (average value in the 5-nearest neighbouring counties) The
Moranrsquos I (slope of the line in the Moran scatter plot) of income inequality shows a significant
positive spatial autocorrelation that is counties with high (low) inequality tend to be close to each
other
11 There are many statistics to formally test the significance of the spatial dimension in the distribution of our data The null hypothesis assumes spatial randomness which means that there is not spatial structure in the data so any spatial pattern is equally likely to occur and values in one location do not depend on values in other locations The alternative hypotheses can be the existence of positive or negative spatial autocorrelation Positive spatial autocorrelation means similar values in neighbouring locations (less variability than under spatial randomness) Negative spatial autocorrelation means dissimilar values in neighbouring locations (more variability than under spatial randomness) 12 Traditional measures of correlation such as the Pearsonrsquos coefficient measure the degree of linear correlation between two different variables Measures of spatial autocorrelation assess the correlation between the values of one variable in one location related to the values of the same variable in other neighbouring locations
45
Figure 23 Moran scatter plots for variables gini and pss_casen
Moranrsquos I is a measure of global spatial autocorrelation this means it is intended to capture
the clustering property of the entire data set To identify where are the significant hot-spots
(clusters of counties showing high income inequality) or cold-spots (clusters of counties showing
low income inequality) we need local indicators of spatial association (LISA) Using the local
Moranrsquos I (not reported) we find significant hot-spots in the South of the country (mainly
agricultural regions) and significant cold-spots in the Centre (Metropolitan area) of the country
The next step is to check whether the clustering pattern in inequality is the result of a process of
spatial dependence in the variable itself or it can be explained by other variables related to
inequality
252 Cross-sectional analysis
We start analysing differences in income inequality between counties using the six-year
average data and running an OLS regression for the model
119892119894119899119894 120573 120573 119901119904119904_119888119886119904119890119899 120573 119897119899119894119899119888119900119898119890 120573 119901119900119907119890119903119905119910 120573 119906119899119890119898119901119897119900119910119898119890119899119905 120573 119897119886119887119900119906119903_119891119900119903119888119890 120573 119890119889119906119888119886119905119894119900119899 120573 119897119899119889119890119899119904119894119905119910 120573 119903119906119903119886119897 120573 119897119899119898119906119899_119890119909119901119890119899119889119894119905119906119903119890 120573 119899119900119903119905ℎ 120573 119904119900119906119905ℎ
(23)
46
The Moran scatter plot and spatial statistical test of OLS residuals from equation (23) are
in Appendix F OLS residuals show a small but significant positive autocorrelation (Moranrsquos I =
0121) This means that income inequality continues showing a significant degree of spatial
autocorrelation after controlling for the set of covariates In addition robust Lagrange Multiplier
(LM) tests show that a spatial error model is preferred over a spatial lag model (Anselin Bera
Florax amp Yoon 1996)13 This means that income inequality tends to cluster in zones larger than a
county so the analysis should be performed on a larger scale such as provinces regions or macro
zones If the SAR model were preferred it would mean that income inequality in one county is
influenced by the level of income inequality in neighbouring counties To find the spatial structure
that best fits the clustering process of income inequality we run the full set of spatial model
specifications in a cross-sectional setting and results are shown in Table 21
Column 2 in Table 21 shows the results of our ldquoOLSrdquo model The ldquoSLXrdquo model includes
spatial dependence only through the explanatory variables The ldquoSARrdquo model includes the spatial
lag of the dependent variable as a regressor and the ldquoSEMrdquo model includes spatial dependence
through the error term The ldquoSARARrdquo model includes both the spatial lag of the response and the
errors and the ldquoSDMrdquo and ldquoSDEMrdquo are the extensions of the ldquoSARrdquo and ldquoSEMrdquo models
respectively including the spatial lag of the explanatory variables Finally a model including
spatial lags for the response errors and explanatory variables (the ldquoSARARXrdquo model) is shown in
the last column
13 Following the ldquoAnselin methodrdquo when both non robust LM tests are significant we should select between the robust LR tests From Table F1 in Appendix F only the robust LM test for the SEM model (RLMerr) appears as significant
47
Table 21
Cross-sectional Model Comparison (six-year average data)
48
Opposite to our hypothesis we observe a significant and negative coefficient for our measure
of NRD This means that counties more dependent on natural resources show lower levels of
inequality Education years population density and municipal expenditure per capita are also
negatively related to inequality On the other hand the level of income the poverty rate and the
proportion of the population living in rural areas show a positive relationship with income
inequality There is no significant influence of the unemployment rate and the proportion of the
population in the labour force In addition the SAR SEM and SARAR models show a
significantly higher average inequality in the South of the country related to the Centre area
The main finding from our cross-sectional analysis is that there is a significant and negative
relationship between inequality and NRD which is quite robust to the model specification
253 Panel Data analysis
Like the cross-sectional case we start estimating the panel without spatial effects Results
for the pooled fixed effects (FE) and random effects (RE) specifications of equation (3) are in
Appendix G Spatial models were estimated using Maximum Likelihood (ML) and Generalized
Moments (GM) and assuming that the spatial structure (W matrix) is the same for all years14
Tables 22 23 and 24 show results for the ML estimation of the SAR SEM and SARAR models
using the pooled FE and RE specifications Results for the GM estimation are in Appendix H
All our spatial models include time fixed effects In the case of the pooled and RE models they
additionally include indicator variables for those counties located in the North and South of the
country
14 In traditional (non-spatial) panels data are stacked as time series for each observational unit (county) In the case of spatial panels data are stacked as slice of cross-sections for each year in the panel
49
The main result is that the negative and significant effect of NRD on income inequality is
robust to most of the spatial panel specifications In addition the coefficient for the variable
pss_casen changes slightly among panel specifications (pooled FE and RE) but does not change
among spatial models (SAR SEM and SARAR)
Another important finding is related to the significance of the spatial dimension of income
inequality When spatial models cross-sectional or panel are compared to non-spatial models
there are no major differences in the magnitude of the coefficients or their significance This could
mean that the positive spatial autocorrelation shown by income inequality seems to be better
explained by a process of spatial heterogeneity rather than spatial dependence The practical
implication of this result is that including dummy variables for aggregated units (eg regions or
groups of regions) could be enough to control for the spatial dimension in the modelling and
analysis of income inequality
Among control variables years of education seems to be the main variable for the design of
long-term policies aimed at reducing inequality This result is in line with previous evidence for
cross-country studies (Leamer et al 1999) and specific countries (Howie amp Atakhanova 2014)
Municipal expenditure per capita also shows a significant and negative association with income
inequality in the pooled and RE spatial specifications This means that higher municipal
expenditure helps to reduce inequality between counties but its effect is more limited within
counties This result support the importance of local governments (Fleming amp Measham 2015a)
however the negative coefficient appears as evidence against the ldquoParadox of Plentyrdquo (Borge et
al 2015)
50
Table 22
ML Spatial SAR Models
Table 23
ML Spatial SEM Models
51
Table 24
ML Spatial SARAR Models
26 Discussion and conclusions
In this essay we delve deep into the sources of income inequality analysing its association
with the degree of dependence on natural resources using county-level data for the 2006ndash2017
period in Chile Given recent evidence for the Chilean economy suggesting a significant spatial
dimension we assess and incorporate explicitly the spatial structure of income inequality using
spatial methods We use cross-sectional and panel data to evaluate the significance of the spatial
dimension and we test whether NRD has a positive effect on income inequality
Contrary to what theory predicts NRD shows a significant and negative association with
income inequality This result is robust to the type of analysis (cross-sectional vs panel data) the
approach (spatial vs non-spatial) and the inclusion of different controls The negative and
significant coefficient implies that if the degree of NRD would not have experienced a 10 drop
during this period income inequality could have fallen in 2 additional points So the downward
trend in the participation of the primary sector in terms of employment in the Chilean economy
52
could be one of the main reasons explaining the high persistence in the levels of income inequality
This means that those areas that undergo a process of productive transformation mainly towards
the services sector would be facing greater problems to reduce inequality This process of
productive transformation natural or policy-driven highlights the importance of policies focused
on human capital and the role of local governments in reducing inequality
The main implication for policymakers is that a reduction in NRD does not help to reduce
inequality generating additional challenges for local and central governments in its attempt to
transform the structure of their economies to fewer dependent ones on natural resources The
finding of a significant spatial dimension suggests that defining macro zones capturing the spatial
heterogeneity in the data should be done before analysing the relationship among variables and the
design and evaluation of specific policies Particularly relevant in those areas experiencing a
reduction in NRD are migration commuting and the characteristics of the tertiary (services) sector
In addition our findings show that education and municipal expenditure could be effective policy
tools in the fight to reduce inequality in Chile
Although our results seem quite robust they do not allow us to make causal inferences about
the effect of NRD on income inequality However we could think of the following explanation to
explain the negative relationship found and the differences between geographical areas
Areas highly dependent on NR used to demand a high proportion of low-skill labour This
has change in sectors such as the mining sector in the northern area which has simultaneously
experienced an increase in activities related to the service sector such as retail restaurants
transport and housing However those services associated with more skilled labour such as the
finance sector remain concentrated in the capital region The reduction in the degree of NRD
(employment in extractive activities) implies lower labour force but more specialized with most
53
of the low-skilled labour transferred to a service sector characterized by low productivity and low
wages
Non-spatial models show that the North and South particularly the latter present
significantly higher levels of inequality This could be associated with the type of resources with
ldquopointrdquo resources such as minerals in the North and ldquodiffusedrdquo resources such as agriculture in the
South This translates into higher average incomes in the Centre and North areas and lower average
incomes in the South
The reduction in NRD implies not only a movement of the labour force from extractive
activities to manufacturing or services with the latter characterized by low productivity and low
salaries of the labour force We could also speculate that most of the high incomes move to the
central area where the economic power and ownership over firms and resources are concentrated
This would explain low inequality associated with higher average incomes in the central area and
high inequality associated with lower average incomes in the South A more in-depth analysis
capturing the mobility of wealth and labour force between counties or more aggregated areas is
needed to better understand the causal mechanism involved
Our findings open avenues for future research in different strands First studies on the causes
of income inequality should take the role of NRD into consideration which has been overlooked
so far Given that the spatial dimension of income inequality seems to be explained by a
phenomenon of spatial heterogeneity estimation strategies such as spatial regime models or
geographically weighted regression should be used (Chi amp Zhu 2019) Second the effect of NRD
on income inequality could manifest through different channels such as education fiscal transfers
and institutions We could extend our analysis to identify which of these competing channels is
the most relevant Transforming some continuous variables such as educational level to a
54
categorical variable or defining new indicator variables for instance whether a local government
shows or not an efficient performance we could classify counties in different groups and then
check whether there are differences or not in the relationship between income inequality and NRD
A third strand could be to disaggregate our measure of NRD for different industries This
would allow us to test differences among industries and to identify the sectors that promote greater
equality and which greater inequality Forth the analysis of the consequences of income inequality
on other economic and social phenomena such as efficiency economic growth and social cohesion
has a growing interest in researchers and policymakers Our findings suggest that to answer the
question of whether income inequality has a causal impact on other variables we could include a
measure of NRD as an instrument to address endogeneity issues For instance two interesting
topics for future research are the analysis of how differences in income inequality between counties
could help to explain differences in the level of efficiency of local governments and differences in
the degree of social cohesion (unrest) throughout the country Those are the issues to be addressed
in the next two essays
55
Chapter 3 The Impact of Income Inequality on the Efficiency of
Municipalities in Chile
31 Introduction
In Chile municipalities are the smallest administrative unit for which citizens choose their
local authorities playing an important role in the provision of public goods and services at the
local level Municipalities have a similar set of objectives but the level of financial resources
available to finance their activities is highly heterogeneous This could result in significant
differences in the levels of performance between municipalities Despite their importance there is
little empirical evidence about the efficiency of local governments in Chile This essay aims to
measure the technical efficiency of Chilean municipalities and to analyse how local characteristics
particularly those related to income distribution at the county level could help to explain
differences in municipal performance
Cross-country studies situate Chile as an efficient country in international comparisons about
efficiency of government spending (Herrera amp Pang 2005 Loacutepez amp Miller 2008) However
evidence for Chile at the local level is relatively sparse suggesting significant levels of
inefficiency For instance Pacheco Sanchez and Villena (2013) found a mean efficiency level of
around 70 using a sample of 309 municipalities for the period 2008-2010 This suggests that
municipalities could achieve the same level of output by reducing the usage of inputs by an average
of 30 Their study also showed that those municipalities more dependent on the central
56
government or those located in counties with lower income per capita are more efficient than their
counterparts
Most empirical research on Local Government Efficiency (LGE) has been conducted for
member countries of the Organization for Economic Cooperation and Development (OECD) of
which Chile has been a member since 2010 In the case of European countries such as Spain and
Italy which share similar characteristics such as the monetary union and levels of GDP per head
efficiency studies have been mainly motivated by budgetary constraints (Balaguer-Coll Brun-
Martos Maacuterquez-Ramos amp Prior 2019 lo Storto 2013) The Chilean context differs in three
main ways from its OECD counterparts First except for the Metropolitan Region that concentrates
most of the population Chilean regions are highly dependent on natural resources Second Chile
is also characterized by one of the highest levels of income inequality among OECD countries
which contrast with the situation of developed natural resource-rich countries such as Australia
and Norway Third although budget constraints are also a relevant issue Chilean municipalities
have experienced a sustained increase in the level of financial resources and expenditure
Another relevant distinction when we benchmark the performance of municipalities across
different countries is the type of public services they provide On the one hand in most of the
countries included Chile the main role of local governments is to provide ldquoservices to peoplerdquo
such as public education and public health On the other hand there are countries such as Australia
where local governments mainly provide ldquoservices to propertyrdquo including waste management
maintenance of local roads and the provision of community facilities such as libraries swimming
pools and parks (Dollery Wallis amp Akimov 2010 Drew Kortt amp Dollery 2015 McQuestin
Drew amp Dollery 2018)
57
Despite contextual differences Chilean municipalities seem not to perform differently from
municipalities in other developed and natural resource-rich countries where income inequality is
significantly less than in Chile (Narboacuten-Perpintildeaacute amp De Witte 2018a) This result highlights the
need to study the role of income inequality and the degree of dependence on natural resources over
LGE characteristics that have been largely overlooked in the literature
We measure and analyse differences in municipal performance using a two-stage approach
In the first stage we measure municipal efficiency using an input-oriented Data Envelopment
Analysis (DEA) to get a set of ldquoefficiency scoresrdquo In the second stage we regress efficiency scores
against our measure of income inequality controlling for a set of contextual factors describing the
economic socio-demographic and political context of each county
We use a sample of 324 municipalities for the period 2006-2017 During this period Chile
was divided into 346 counties belonging to 15 regions This period was characterized by important
external and internal shocks including the Global Financial Crisis (GFC) one of the biggest
earthquakes in Chilean history in 2010 and three municipal elections The availability of
information allows us to measure efficiency for the full period but the influence of contextual
factors is analysed using a panel of six years (2006 2009 2011 2013 2015 and 2017) for which
household income information is available
The main hypothesis tested in the second stage is whether higher levels of income inequality
are associated with lower levels of efficiency Previous evidence shows that when progress is not
evenly shared persistent within-country inequality reduces the effectiveness and efficiency of the
public sector (Ortega Sanjuaacuten amp Casquero 2017 Tandon 2005)
Income inequality has been used to control for a wide range of idiosyncratic factors
associated with historical institutional and cultural factors affecting efficiency (Greene 2016
58
Ortega et al 2017) For instance at the local level income inequality has been considered as an
indicator of economic heterogeneity in the population where higher inequality is associated with
a more heterogeneous set of conflicting demands for public services which adversely affect an
efficient provision (Ashworth Geys Heyndels amp Wille 2014 Geys amp Moesen 2009) Higher
levels of income inequality could also relate to economically privileged groups having a greater
capacity to influence the political system for their own benefit rather than that of the majority
When high inequality is persistent the feeling of frustration and disappointment in the population
could reduce not only trust and cooperation among individuals but also trust in institutions which
would negatively affect government efficiency (Boix amp Posner 1998 Coffeacute amp Geys 2005) For
instance national or local authorities could end exerting patronage and clientelism and showing
rising levels of corruption (Uslaner 2011 Uslaner amp Brown 2005)
One of the main gaps in extant literature is the need to conduct more analysis of LGE using
panel data taking into consideration endogeneity issues and controlling for unobserved
heterogeneity (Narboacuten-Perpintildeaacute amp De Witte 2018a) To address the above we set-up a panel with
time and county-specific effects and we propose the use of a measure of natural resource
dependence (NRD) as an instrument for income inequality Based on the ldquoParadox of Plentyrdquo
fiscal revenues from natural resources windfalls could be associated with an over expansion of the
public sector fostering rent-seeking and corruption and reducing local government efficiency
(Dauvin amp Guerreiro 2017 Manzano amp Rigobon 2001) In the Chilean case most of the revenues
generated by local governments included those from natural resources end up in a common fund
which benefits all municipalities The aim of this common fund is precisely to reduce inequalities
among municipalities so although we do not expect a direct impact of natural resources on LGE
we could expect an indirect effect through other indicators particularly income inequality
59
As far as we know this is the first study analysing the influence of income inequality as a
determinant of municipal efficiency in Chile Moreover this is the first study in the context of a
natural resource-rich country which specifically suggests a measure of natural resource
dependence as an instrument to correct for endogeneity bias We propose the use of the proportion
of firms in the primary sector as proxy for the degree of NRD in each county We argue that this
variable is a better proxy than using the proportion of employment in the manufacturing sector
which has been proposed in previous studies (Alesina amp La Ferrara 2002) During the period
analysed our proxy remained relatively stable and showed a significant relationship with income
inequality In addition it is less likely that it has directly affected municipal efficiency
This study adds to the literature in two other ways First the extant literature suggests that
efficiency measurement could be highly sensitive to the chosen technique as well as the selection
of inputs and outputs (Narboacuten-Perpintildeaacute amp De Witte 2018a) Inputs are usually proxied by a single
measure of total public expenditures and outputs by general proxies such as population andor the
number of businesses in each county We offer a novel approach for the selection of inputs and
outputs On the one hand we disaggregate government expenditures into four components
(operation personnel health and education) and we use the number of public schools and health
facilities in each county as a proxy for physical capital On the other hand we use four outputs
aiming to capture the wide variety of goods and services supplied by each municipality Through
this approach we aim to better describe the production function of each municipality capturing
not only the variety of inputs and outputs but also differences in size among municipalities
A third contribution relates to the measurement of LGE in the Chilean context We measure
technical and scale efficiency using a larger sample and a longer period This has empirical and
policy relevance On the one hand it helps us to select the correct DEA model and allows us to
60
determine the importance of scale inefficiencies as explanation for differences in municipal
performance On the other hand efficiency measures increase the information available for both
central and local governments to better understand the production technology that best describes
each municipality and to carry out policies to improve efficiency
We believe that our selection of inputs and outputs the use of a large dataset and the joint
analysis using cross-sectional and panel data provide a more accurate and robust analysis of
municipal efficiency Likewise knowing whether inequality has a significant influence on
municipal efficiency may provide useful insights and guidance for policymakers not only in Chile
but also for countries sharing similar characteristics
DEA results show an average level of technical efficiency (inefficiency) of around 83
(17) This means that municipalities could reduce on average a 17 the use of inputs without
reducing the outputs There are significant differences among geographic areas with the Centre
area (where the countyrsquos capital is located) displaying higher efficiency than the rest of the country
When municipal efficiency is measured under different assumptions about returns to scale results
reveal a production technology with variable returns to scales and around 75 of the
municipalities displaying scale inefficiencies However when technical efficiency is
disaggregated between pure technical efficiency and scale efficiency results show that scale
inefficiency explains a small proportion of the total municipal technical inefficiency This finding
justifies a deeper analysis of the reasons why municipalities could operate inefficiently and why
municipal performance could vary among municipalities
Efficiency scores also show a significant degree of positive spatial autocorrelation This
means that municipal efficiency shows a general clustering process with neighbouring
municipalities showing similar levels of efficiency A further analysis shows that most of the
61
spatial pattern in municipal efficiency is exogenous that is could be associated to other variables
Hence we conduct most of our regression analysis using traditional (non-spatial) methods and
leaving spatial regressions in the appendixes
Findings from cross-sectional and panel regressions support the hypothesis that municipal
performance is significantly and negatively associated with income inequality at the county level
The coefficient of income inequality is close to one which means that reductions in income
inequality ceteris paribus could be associated with increases in municipal efficiency in the same
proportion This result supports the strand of research arguing that there is not a trade-off at least
at the municipal level between equity and efficiency (Andersen amp Maibom 2020 Berg amp Ostry
2011 2017) The main policy implications are that authorities in more unequal counties would
face higher challenges to perform efficiently and policies pertaining to inequality and efficiency
should not be designed independently
The chapter is structured as follows Section 32 provides a brief literature review on related
local government efficiency Section 33 introduces the methodological background and empirical
models Section 34 presents the empirical results and discussions Section 35 concludes the
chapter
32 Related Literature
321 Measuring efficiency of local governments
Studies on measuring LGE can be grouped in those analysing the provision of single services
such as health or education and those assessing overall efficiency (lo Storto 2013) Once inputs
and outputs have been defined efficiency is measured using parametric andor non-parametric
techniques Among the former group Stochastic Frontier Analysis (SFA) seems to be preferred
62
by scholars aiming to measure efficiency and to analyse the link with environmental variables
using a single procedure (Pacheco et al 2013 Tsekeris Sotiris Tsekeris amp Papaioannou 2018)
On the non-parametric group Data Envelopment Analysis (DEA) is by far the most used technique
(Afonso amp Fernandes 2006 Balaguer-Coll amp Prior 2009 lo Storto 2013)
The selection of inputs and outputs depends not only on the aimed of the study (specific
sector vs whole measure of efficiency) but also on the role that municipalities play in different
countries There are countries such as Australia (Drew et al 2015) and Spain (Balaguer-Coll amp
Prior 2009) where local governments mainly supply services to ldquopropertyrdquo such as waste
management and road maintenance In these cases efficiency has been mainly measured using
total indicators of local government expenditure and outputs have been proxied using general
indicators such as population or number of business (Drew et al 2015) On the other hand in
countries such as Italy (lo Storto 2013) and Portugal (Afonso amp Fernandes 2008) in Europe or
Brazil (de Sousa Cribari-Neto amp Stosic 2005) and Chile (Pacheco et al 2013) in South America
municipalities mainly supply services to ldquopeoplerdquo Here in addition to financial expenditures or
revenues inputs have included the number of local government employees the number of schools
or the number of hospitals and health centres School-age population the number of students
enrolled in primary and secondary schools and the number of beds in hospitals have been
considered as outputs Based on the study of Narboacuten-Perpintildeaacute amp De Witte (2018a) a wider list of
inputs and outputs used in previous studies can be found in Appendix I
Studies from different countries show important differences in the average efficiency scores
both between and within countries These studies also differ in the samples methodologies and
variables included A summary showing the range and variability of the mean efficiency scores
founds in countries all around the world can be found in Narboacuten-Perpintildeaacute amp De Witte (2018a)
63
These authors also show that OECD natural resource-rich countries such as Australia Belgium
and Chile show similar results in terms of mean efficiency scores with LGE studies being less
frequent in Latin American countries
Measuring efficiency of local governments as decision-making units (DMU) presents many
challenges and difficulties (Borger Kerstens Moesen amp Vanneste 1994 Ravallion 2005)
Worthington and Dollery (2000) mention problems with the selection and measurement of inputs
the identification of different stakeholders the hidden characteristic of the ldquolocal government
technologyrdquo and the multidimensionality of the services provided by local governments All these
issues make difficult to identify and distinguish between outputs and outcomes with outputs
commonly proxied by general indicators such as county area or county population Because
efficiency measures are highly sensitive to the chosen technique and the selection of inputs and
outputs Narboacuten-Perpintildeaacute amp De Witte (2018a) suggest formulating different specifications and
using less general and unspecified indicators Moreover the complexity in defining outputs and
the use of general indicators make more likely that contextual factors affect municipal efficiency
322 Explaining differences in LGE
To explain differences in local government performance researchers have basically
distinguished between ldquodiscretionaryrdquo and ldquonon-discretionaryrdquo factors Discretionary factors refer
to the degree of discretion of local authorities in the selection and management of inputs and
outputs On the other hand scholars have investigated the influence on LGE of contextual factors
beyond authoritiesrsquo control These factors reflective at the environment where municipalities
operate include economic socio-demographic geographic financial political and institutional
characteristics (da Cruz amp Marques 2014 Narboacuten-Perpintildeaacute amp De Witte 2018b)
64
In general the evidence about the influence of contextual factors has delivered mixed and
country-specific results (Narboacuten-Perpintildeaacute amp De Witte 2018b) Sampaio de Sousa et al (2005)
using data for Brazilian municipalities finds that population density and urbanization rate have
strong positive effects on efficiency scores Benito et al (2010) show that lower levels of
efficiency of Spanish municipalities are associated with a greater economic level a less stable
population and a bigger size of the local government Afonso (2008) finds that per capita income
level and education are not significant factors influencing LGE of Portuguese municipalities He
also finds that municipalities in Northern areas show greater efficiency than their counterparts in
Southern areas More recently Tsekeris (2018) finds that spatial variations in efficiency in Greece
can be attributed to factors related to inter-regional market access specialization and sectoral
concentration resource-factor endowments and political factors among others Characteristics
describing each local government have also been used including municipal indebtedness (Benito
et al 2010) fiscal deficits (Sinha 2017) degree of fiscal autonomy (Boetti Piacenza amp Turati
2009) and individual characteristics of local authorities such as age gender and political ideology
Narboacuten‐Perpintildeaacute amp De Witte (2018b) conclude after analysing 63 articles that studies on the
influence of contextual factors have mostly used cross-sectional data with little attention to
endogeneity issues which makes any causal interpretation doubtful
323 The trade-off between efficiency and equity
The existence of a potential trade-off between efficiency and equity is in the core of
economic discussion (Andersen amp Maibom 2020 Berg amp Ostry 2011 Browning amp Johnson
1984 Okun 2015)15 The argument that economic growth (one of the most common efficiency
15 Redistributive policies distort incentives and lead to suboptimal outcomes and thus efficiency losses
65
measures) could be negatively affected in the search for greater equality has been translated not
only into economic policies that favour economic growth over those that reduce inequality but
also in the emphasis of scholarly research Thus theoretical and empirical research has been
mainly focussed on efficiency and policy implications of a great diversity of shocks and policies
leaving the analysis of inequality as one of measurement and mostly descriptive Additionally
empirical evidence of the trade-off is scant and inconclusive (Andersen amp Maibom 2020
Browning amp Johnson 1984)
Among economic contextual factors that could affect LGE income inequality has been
largely ignored An exception is a cross-country comparison conducted by Ortega (2017) who
analyses the role of inequality on government efficiency in developing countries He finds that
more unequal countries could have higher difficulties to achieve specific health outcomes Income
inequality has even been considered as part of the outputs to measure efficiency particularly for
the case of European and OECD countries (Afonso Schuknecht amp Tanzi 2010 Antonelli amp De
Bonis 2018)
At the local level income inequality has been mainly used as a proxy for the effect of income
heterogeneity Economic inequality could have a direct and an indirect effect on government
efficiency The direct effect poses that higher income inequality could reduce municipal efficiency
because it is associated with a more complex and competing set of public services demanded by
the population (Jottier et al 2012) The indirect effect puts the focus in the link between inequality
social capital and levels of corruption Economic diversity could reduce trust in people and
institutions when related to high and persistent levels of income inequality It could also affect the
willingness to participate in community and political groups the existence of a shared objective
by citizens and the perception of a prosperous future (Uslaner amp Brown 2005)
66
The evidence is ambiguous For instance Geys and Moesen (2009) find that income
inequality has little relation to efficiency of Flemish municipalities and Ashworth et al (2014)
find a negative relationship for the Norwegian case Findings also indicate that inequality is the
strongest determinant of trust and that trust has a greater effect on communal participation than on
political participation (Uslaner amp Brown 2005)
33 Methodology
We follow a two-stage approach widely used in this kind of analysis A DEA analysis is
conducted in the first stage to get efficiency scores for each municipality Then regression analysis
is conducted in the second stage aiming to identify contextual variables other than differences in
the management of inputs that can help to explain the heterogeneity in municipal performance
331 Chilean Municipalities and period of analysis
The territory of Chile is divided into regions and these into provinces which for purposes of
the local administration are divided into counties The local administration of each county resides
in a municipality which is administrated by a Mayor assisted by a Municipal Council16
Municipalities represent the decentralization of the central power in Chile They are autonomous
organizations with legal personality and own patrimony whose purpose is to satisfy the needs of
the local community and ensure their participation in the economic social and cultural progress of
the county Municipalities have a diversity of functions related to public health education and
social assistance among others
16 The Mayor and City Council are elected by suffrage for the citizens of the respective commune every 4 years
67
To achieve their goals two are the main sources of municipal incomes own permanent
revenues (OPR) and the Municipal Common Fund (MCF) OPR are incomes generated by the
county and they are an indicator of the self-financing capacity of each municipality OPR are not
subject to restrictions regarding their investment and they are mainly generated by territorial taxes
commercial patents and circulation permits17 The MCF is a fund that aims to redistribute
community income to ensure compliance with the purpose of the municipalities and their proper
functioning Sources to finance the MCF come from municipal revenues The distribution
mechanism of the fund is regulated by parameters such as whether municipalities generate OPR
per capita lower than the national average and the number of poor people in the commune in
relation to the number of poor people in the country
This study covers the period from 2006 to 2017 During this period Chile was divided into
15 regions 54 provinces and 346 counties18 Although the information on inputs and outputs is
available for the entire period information on contextual factors at the county level such as
household income is only available every two-three years In addition some counties are excluded
from household surveys due to their difficult access Hence we use a sample of 324 municipalities
to measure municipal efficiency for the whole period (3888 observations) However the analysis
of contextual factors is conducted for those years when household income information is available
2006 2009 2011 2013 2015 and 2017 (1944 observations)
17 The territorial tax is a tax on agricultural and non-agricultural real estate Specifically of this income only 40 is left to the municipality as its own financing while the remaining 60 is allocated to the MCF (in the case of the four largest communes- Santiago Providencia Las Condes and Vitacura-percentages are 35 and 65 respectively) Unlike the territorial tax commercial patents are regulated mainly by the municipality which chooses the rate to be charged subject to a range established by law and is responsible of their collection Finally regarding the circulation permits 375 is of municipal benefit while 645 is directed to the MCF 18 There are 346 counties managed by 345 municipalities where counties ldquoCabo de Hornosrdquo and ldquoAntaacuterticardquo are managed by the municipality of ldquoCabo de Hornosrdquo
68
332 Measuring municipal efficiency
Municipal efficiency is measured using Data Envelopment Analysis (Coelli Prasada Rao
OrsquoDonnell amp Battese 2005) This is a non-parametric approach that uses linear programming to
measure efficiency for a group of municipalities as decision making units (DMUrsquos) The main
advantage of using DEA and the reason why DEA is used for the case of Chilean municipalities
is its flexibility in handling multiple inputs and outputs without the need to specify a functional
form (Balaguer-Coll amp Prior 2009 Mikušovaacute 2015 Tigga amp Mishra 2015) Following Afonso
and Fernandes (2008) the relationship between inputs and outputs for each municipality could be
represented by the following equation
119884 119891 119883 119894 1 119899 (31)
In equation (31) 119884 is the set of outputs and 119883 the set of inputs for each of the n
municipalities Using linear programming the production frontier is constructed and a vector of
efficiency scores is obtained The frontier represents full technical efficiency mdash the point at which
the highest output occurs given specified inputs or the point at which the lowest amount of inputs
is used to produce a specified quantity of output Efficiency scores under DEA are relative
measures of efficiency They measure a municipalityrsquos efficiency against the other measured
municipalities in the sample and not a hypothetical lsquoperfect municipalityrsquo The further from the
frontier the less technically efficient a municipality is
We use an input-oriented approach because Chilean municipalities have a greater control
over the management of inputs relative to the outputs they have to manage Obtaining efficiency
scores requires an assumption about the returns to scale exhibited by each municipality When
DMUrsquos are homogeneous the CCR model (Charnes Cooper amp Rhodes 1978) which assumes
69
constant return-to-scale (CRS) is the appropriate specification The CCR model assumes full
proportionality between outputs and inputs and that DMUrsquos operate at their optimal When DMUrsquos
are highly heterogeneous as is the case with local governments in most countries it is not realistic
to assume complete proportionality between inputs and outputs nor that all DMUrsquos operate at their
optimum scale In this situation a variable returns-to-scale (VRS) or BCC model (Banker
Charnes amp Cooper 1984) is the preferred formulation
Assuming VRS imposes minimum restrictions on the efficient frontier and allows for
comparisons only among municipalities of similar scale (Coelli et al 2005 Wu Huang amp Pan
2014) This means that when we use the CCR model (assuming CRS) on a heterogeneous sample
of DMUrsquos the resulting measure of technical efficiency that we obtain is related not only to the
management of inputs but also to issues of scale19 To empirically check the validity of the VRS
assumption we measure technical efficiency under CRS VRS and non-increasing returns-to-scale
(NIRS) and we analyse the existence of scale inefficiencies This allows us to check the relevance
of scale effects as a potential explanation of differences in municipal efficiency Appendix J
shows the specification of the DEA model under VRS and how ldquototal technical efficiencyrdquo
(assuming CRS and therefore due to management and scale issues) could be disaggregated in ldquopure
technical efficiencyrdquo (under VRS and related only to management issues) and scale efficiency (due
to scale issues)
19 Assuming VRS can also lead to measurement problems such as overestimation of efficiency scores This is the case when an important proportion of DMUrsquos shows CRS so assuming VRS ignores the information about proportionality between inputs and outputs (Podinovski 2004)
70
333 Inputs and outputs used in DEA
Following the literature on local government expenditure efficiency (Afonso amp Fernandes
2008 de Sousa et al 2005 Dlouhyacute 2018 Tandon 2005 Tigga amp Mishra 2015) and trying to
reflect as well as possible the functioning of municipalities five inputs and four outputs were
selected Input and output data were obtained from the National System of Municipal Information
(SINIM in its Spanish acronym) and they are expressed in thousands of Chilean pesos of 201720
Inputs are Municipal Operational Expenditure X1 (including expenses on goods and
services social assistance investment and transfers to community organizations) Municipal
Personnel Expenditure X2 (including full time and part-time workers) Total Municipal
Expenditure in Education sector X3 Total Municipal Expenditure in Health sector X4 and the
Number of Municipal Buildings X5 (proxied by the number of public facilities in education and
health sectors)
Output variables were selected highlighting the relevance of education and health sectors
and trying to capture the wide range of local services provided by municipalities The variable
ldquoOwn Permanent Revenuesrdquo Y1 is used to capture the scale and diversity of municipal
activities21 The ldquoMonthly Average Enrolmentrdquo in municipal education establishments related to
the school-age population in each county Y2 is used as educational output As health output the
ldquoNumber of Medical Consultationsrdquo in public facilities Y3 is considered Finally the number of
community organizations Y4 is used as output reflecting the promotion of community
development by each municipality Table 31 shows the summary statistics of input and output
20 The data from SINIM database was mostly obtained using the sinimr package (Salas 2019) 21 According to SINIM database this variable aims to measure the management of the municipalityrsquos own resources with respect to the population of the commune
71
variables for the whole sample and period Inputs and outputs excepting the Monthly Average
Enrolment Y2 are measured in per capita terms using county population information from the
National Institute of Statistics (INE in its Spanish acronym)
Table 31
Descriptive statistics Inputs and Output variables used in DEA analysis
334 Regression model
Contextual factors could play an important role not only in explaining why some
municipalities operate inefficiently but also why municipal performance differs among them
These factors may affect municipal performance modifying incentives for local authorities to
operate efficiently and their capability to take advantage of economies of scale They also define
the conditions for cooperation or competition among municipalities and the citizensacute ability and
willingness to monitor local authorities (Afonso amp Fernandes 2008)
Information on income at the household level for each county was obtained from the
ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) CASEN is
conducted every two-three years being the reason why consecutive years are not considered in
72
our regression analysis The other contextual factors used as controls were obtained from different
sources including SINIM INE and the ldquoServicio de Impuestos Internosrdquo (SII)22
Our main hypothesis is whether higher levels of income inequality are associated with lower
levels of municipal efficiency To test our hypothesis the empirical model is defined as
120579 120573 119892119894119899119894 119885 120573 120575 120572 120598 (32)
Where 120579 is the vector of DEA efficiency scores 119892119894119899119894 is the Gini coefficient of each
county 120575 are year-specific effects 120572 are county-specific constants 120598 is a vector of error terms
and 119885 is a vector of controls Next we discuss the motivation for these controls
The level of purchasing power of countiesrsquo citizens is proxied by the variable log(income)
which is the natural log of the mean household income per capita in thousands of Chilean pesos of
2017 On the one hand poorer counties could display higher efficiency due to their necessity to
take care of their constraint resources (Pacheco et al 2013) On the other hand richer counties
could show higher efficiency because richer citizens exert higher monitoring over local authorities
and demand better quality public services in return for their tax payments (Afonso et al 2010)
The possibility for municipalities to take advantage of economies of scale and urbanization is
captured by three variables First the variable log(density) which correspond to the natural log of
population density Second the dummy variable reg_cap indicating whether a county is a regional
capital or not Third the variable agroland which correspond to the proportion of land for
agricultural use which is informed to the SII We expect a positive effect of log(density) but
negative for regcap and agroland
22 The SII is the institution in charge of collecting taxes in Chile
73
Socio-demographic characteristics are captured including a Dependence Index IDD IDD
corresponds to the number of people under 15 years or over 65 years per 100 people in the active
population (those people between 15 and 65 years old) A higher proportion of young and older
population could be associated with a higher demand for municipal services relating to education
and health making harder to offer public services efficiently The citizensrsquo capacity to monitor
local authorities is proxied including the variable education (average years of education for the
population older than 15 years) and the variable housing (proportion of households which are
owners of the property where they live in each county) In both cases we expect a positive
association with LGE
Among municipal characteristics the variable professional (percentage of municipal
personnel with a professional degree) is used to control for the quality of municipal services and
it is expected a positive impact The variable mcf (proportion of total municipal income coming
from the MCF) is included to capture the influence of financial dependence on the central
government A higher dependence from MCF could be associated with higher efficiency when it
is linked to more control from central government (Worthington amp Dollery 2000) However when
MCF discourages the generation of own resources and proper management of resources from the
fund a lower efficiency should be expected (Bravo 2014) In addition the dummy variable mayor
is included to capture differences among mayors supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo
political parties related to those ldquoINDEPENDENTrdquo mayors
Table 32 report summary statistics for the set of numeric contextual factors and Appendix
K the corresponding correlation matrix Despite the high correlation between income and
education variables we include both in the regression section as they capture different county
characteristics
74
Table 32
Summary Statistics Numeric Contextual Factors
Figure 31 Geographical distribution of Chilean regions and macrozones
Previous evidence on growth and convergence of Chilean regions have found that regions
tend to cluster spatially (Aroca amp Bosch 2000) Aiming to capture this regional clustering process
75
and considering the high concentration in the number of municipalities and population in the
central area we group municipalities in four ldquozonesrdquo We define as reference the ldquoCentre-Northrdquo
zone consisting of regions XIII (where the countryrsquos capital is located) and its two neighbouring
regions V and VI The ldquoNorthrdquo zone includes regions XV I II III and IV The ldquoCentre-Southrdquo
zone includes regions VII VIII and IX Finally the ldquoSouthrdquo zone embraces regions XIV X XI
and XII Figure 31 displays the regional administrative division and zones considered in this
essay
Efficiency scores (ES) are limited to have values between 0 and 1 However they are relative
measures (relative to the sample of municipalities) This implies that when a municipality is on the
frontier (ES = 1) it does not mean that potentially larger technical efficiency gains cannot be made
Hence equation 32 is estimated using OLS and censored regressions We start running cross-
sectional regressions for each of the six years Then we compare the results with those from panel
regressions Because fixed-effects panel Tobit models could be affected by the incidental
parameters problem (Henningsen 2010 2019) we use random-effects panel Tobit models
including indicator variables for years and zones Finally to deal with the potential endogeneity
problem we also use an instrumental variable approach The instrument is described next
335 The instrument
Government effectiveness and income distribution are both structural components of
economies (Ortega et al 2017 Ravallion 2005) In the search for a causal interpretation for the
influence of income inequality on municipal efficiency we need an instrument which must be
correlated with the variable to be instrumented (in our case income inequality) and uncorrelated
with the error term in the efficiency equation (32) Previous literature has used as instruments for
Gini the number of townships governments in a previous period the percentage of revenues from
76
intergovernmental transfers in a previous period and the current share of the labour force in the
manufacturing sector (Alesina amp La Ferrara 2002) Using the share of the labour force in a specific
sector is unlikely to reduce the problem of endogeneity particularly in countries where local
governments supply mostly ldquoservices to peoplerdquo and the main source of income inequality is
labour income
We propose as an instrument the proportion of firms in the primary sector (mining fishing
forestry and agriculture)
119901119904119904_119891119894119903119898119904Number of firms in the primary sector
Total number of firms (33)
On the one hand this instrument is likely to be correlated with local income inequality in
natural resource-rich countries23 On the other hand we contend that our instrument is less likely
to be correlated with the error term in the efficiency equation First the main services supplied by
Chilean municipalities are services to people (health and education) not to firms Second most of
the revenues collected by municipalities included those associated with natural resources end up
in the municipal common fund whose objective is precisely to reduce inequalities among
municipalities Third services to firms are expected to be more significant with the tertiary sector
We argue that our instrument captures natural and structural conditions which directly
influence income inequality but it does not directly affect LGE Figure 32 shows the evolution
of the annual average efficiency score and the proportion of firms in the primary secondary
(manufacturing) and tertiary (services) sectors We observe that sectors shares have remained
relatively stable with a slight reduction in the participation of the primary sector in favour of the
23 Results confirm a significant association between income inequality and the degree of dependence on natural resources at the county level
77
tertiary sector by the end of the period This is contrary to the evolution of municipal efficiency
which shows a cyclical behaviour as will be shown in the next section
Figure 32 Evolution of efficiency scores and the proportion of firms by sector
34 Results and discussion
341 DEA results
Figure 33 displays the evolution of our three measures of efficiency Overall technical
efficiency pure technical efficiency and scale efficiency are around 78 83 and 95
respectively with fluctuations over the years Therefore around three quarters of the overall
78
inefficiency is attributed to inefficiency in the management of inputs and around one quarter to
scale inefficiencies24
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE)
Returnstoscale
Figure 34 reports by zone and for the whole period the proportion of municipalities
showing CRS decreasing returns to scale (DRS) or increasing returns to scale (IRS) Most of the
municipalities operate under variable (increasing or decreasing) returns to scale which could be
explained by the high heterogeneity in size among municipalities A summary of RTS
disaggregated by year and zone is in Appendix L Policies oriented to increase efficiency usually
24 The average scale efficiency score of 95 means that municipalities could get an additional 5 of inputs saving if municipalities were able to change their size to achieve their optimal scale
79
consider amalgamation de-amalgamation or ways of cooperation among municipalities To have
a better idea about where and how feasible is the implementation of such policies Appendix M
shows maps with the administrative division of the country in its 345 municipalities and which
municipalities show CRS IRS or DRS in each of the six years of data
Figure 34 Returns to scale by zone
Based on results for the whole period (Figure 34) the North has the highest proportion of
municipalities exhibiting DRS This suggests the need to reduce the size of municipalities splitting
those ldquotoo bigrdquo creating new administrative areas or giving more autonomy to current
municipalities25 The opposite occurs in the Centre-North area where municipalities mostly
exhibit IRS This indicates the need to merge municipalities An alternative strategy to the
amalgamation process is increasing inter-municipal cooperation (Balaguer-Coll et al 2019)
25 This has been the policy followed in Chile Although two new regions were created (XV in the North and XIV in the South) the number of municipalities has not changed
80
which seems to be a more plausible option in Chile Finally evidence on scale efficiency for the
Centre-South and South areas is not clear in terms of the adequate strategy to improve efficiency
Efficiencymeasure
Although most municipalities show scale inefficiencies (Figure 34) only a small proportion
of total inefficiency is associated to scale issues (Figure 33) Together this results justify not only
the use of the BCC model (efficiency scores under VRS) to capture municipal heterogeneity but
also highlights the need to look for other factors outside the control of local authorities which
could be influencing municipal performance
Table 33
Summary efficiency scores (VRS) by zone and region
Table 33 summarizes ES under VRS using data for the six years of CASEN survey A mean
efficiency score of 83 is found for the full sample and period This means that on average
inefficient municipalities can reduce the use of inputs by 17 to get the same current output By
81
comparing average ES per zone it can be concluded that municipalities in the North Centre-North
Centre-South and South could achieve the same level of output with 17 12 18 and 23 fewer
resources respectively Results also show that one third of the municipalities present an efficiency
score equal to one
Figure 35 shows the evolution of the mean ES for the four zones over the 12 years period
A clear drop in the efficiency level is observed in 2009 (the financial crisis year) excepting for the
North area Likewise although mean efficiency returned to its pre-crisis levels in 2014-2015 a
new drop in efficiency was experienced in 2016 It also seems that the 2010 earthquake did not
generate a significant effect on municipal efficiency Figure 35 also shows that although levels
of efficiency seem to differ among zones they follow a similar trend through time with the only
exception of the North which corresponds to the mining area In addition efficiency seems to be
significantly higher in the Centre-North area This is explained by the high mean level of efficiency
in region XIII which includes the countryrsquos capital city
Figure 35 Evolution mean efficiency scores (VRS) by zone
82
To know which and where are the efficient municipalities and if they are surrounded by
municipalities with a similar level of efficiency Appendix N has maps showing the ldquoefficiency
statusrdquo of each municipality that is whether they are efficient (ES = 1) or inefficient (ES lt 1)
Additionally Appendix O shows ldquoequal intervalsrdquo maps describing the spatial distribution of ES
among municipalities for each of the six years26 Results show that efficient municipalities can be
found all through the country the ldquoefficiency statusrdquo could change from one year to another and
municipalities with similar level-status of efficiency tend to cluster in space
342 Regression results
Exploratoryspatialanalysis
DEA efficiency scores and their geographical representations seem to show that municipal
efficiency presents a spatial clustering pattern This means that municipal performance could be
influenced not only by contextual factors of the county where municipality belongs but also by the
level of efficiency of neighbouring municipalities and their characteristics To test the significance
of the spatial dimension in municipal efficiency we use a cross-section of data considering the six-
year average of efficiency scores the Gini coefficient and the set of controls
We use the Moranrsquos I indicator to test for spatial autocorrelation Moranrsquos I is a measure of
the correlation between the level of efficiency in one municipality and its ldquospatial lagrdquo that is the
average level of efficiency in neighbouring municipalities We define as the relevant neighbours
for each municipality the 5-nearest municipalities This is obtained using the distances among the
26 An equal intervals map uses the same principle as a histogram to organize the observations into categories that divide the range of the variable into equal interval bins For the equal interval classification the value range between the lower and upper bound in each bin is constant across bins but the number of observations in each bin is typically not equal
83
polygonsrsquo centroids (latitude and longitude) of each county Results confirm that municipal
efficiency show a significant level of positive spatial autocorrelation This means that
municipalities tend to have neighbouring municipalities with similar performance
The positive spatial autocorrelation shown by municipal efficiency could be due to the
performance in one municipality is influenced by the performance in neighbouring municipalities
(spatial dependence in the variable itself) or due to structural differences among regions-zones
(spatial heterogeneity) To check the source of the spatial autocorrelation we run an OLS
regression of ES against income inequality and controls and then we test OLS residuals for spatial
autocorrelation Moranrsquos I over OLS residuals although significant is barely higher than zero (see
Appendix P) This means that the spatial effect itself is not a serious issue and can be handle for
instance including zone indicators variables hence we proceed to analyse the influence of income
inequality on LGE using non-spatial regression27
Cross‐sectionalanalysis
We start reporting censored regressions for each year in our panel Efficiency scores have
been rescaled to numbers between 0 and 100 to facilitate the interpretation of the results All
regressions include dummy variables for three of the four zones in which we have grouped Chilean
regions Results are in Table 3428 Income inequality shows a negative sign in all years which is
consistent with our hypothesis that inequality is negatively related to municipal efficiency
However only in three of the six years the effect of income inequality appears as statistically
27 In the case of having incorporated the spatial dimension the two most commonly used forms are including in the model the spatial lag of the dependent variable (spatial autoregressive model SAR) or the spatial lag of the error (spatial error model SEM) Following the method suggested by Anselin et al (1996) Lagrange Multiplier (LM) tests on the OLS residuals showed that the SAR model is preferred over the SEM Moranrsquos I LM tests and spatial regressions can be found in Appendix P 28 Regression results using OLS are in Appendix Q
84
significant Only the income level displays a significant and positive influence on efficiency for
the whole period A higher population density also consistently favours municipal efficiency On
the other hand as we expected a higher IDD makes it more difficult to achieve an efficient
performance29 Consistent with results in Pacheco et al (Pacheco et al 2013) municipal
efficiency show a significant an positive association with the MCF only in the first half of our
period of analysis with the second half showing an insignificant relationship
Table 34
Cross-sectional (censored) regressions
Paneldataanalysis
Estimation results for the six-year panel are reported in Table 35 Columns (1) and (2) show
the results for the pooled and random effects censored models only controlling for zone and year
29 The set of cross-sectional regressions was also run for each zone independently Results confirm that the set of significant contextual factors differs not only among years but also among geographic areas This support previous evidence suggesting that cross-sectional analysis should be treated with caution (Narboacuten-Perpintildeaacute amp De Witte 2018b)
85
dummies Income inequality appears as non-significant Zone indicator variables confirm that
municipalities located in the Centre-South and South of the country display a lower average level
of efficiency compared to the Centre-North area Time dummies mostly show negative
coefficients This is a signal that shocks such as the GFC and the earthquake in 2010 may have
had a negative impact on efficiency but that impact was not permanent The results for the pooled
and RE models including the full set of controls are reported in columns (3) and (4) These results
show a significant negative influence of income inequality on LGE
When income inequality is instrumented by the variable pss_firms most of the coefficients
remain unchanged except for those associated with the income variables gini and log(income)
This result implies that our original model suffers for instance from the omitted variable bias
This means that LGE and income inequality are determined simultaneously by some variable not
included in our model Columns (5) and (6) show results using our instrument for income
inequality The ldquotrue coefficientrdquo of variable gini remains negative but the magnitude of the
relationship is higher The negative coefficient for gini implies on the one hand that municipalities
located in more unequal counties face more challenges to achieve an efficient management of
public resources On the other hand the coefficient in column (6) is close to one The interpretation
is that for each point of reduction in income inequality ceteris paribus LGE should increase in the
same proportion Next we discuss some of the results associated with the controls variables
Contrary to Pacheco et al (2013) income level has a positive coefficient meaning that richer
counties in terms of income per capita show higher efficiency This could be explained by higher
monitoring and increasing demands from citizen and taxpayers In addition it is expected a higher
efficiency in municipalities located in counties with a higher population density and those with a
lower proportion of land for agricultural use This result is mainly explained by municipalities
86
located in the Centre area The opposite happens with municipalities in the South implying that
they are too ldquosmallrdquo to take advantage of agglomeration economies There is also a limit for
agglomeration and scale economies which is shown by the negative coefficient of the variable
regcap although this coefficient loses its significance in the IV approaches30
Unexpectedly efficiency was found to be negatively associated with the variable education
This result is similar to the case of Czech municipalities (Šťastnaacute amp Gregor 2014) where
explanations include a weakened monitoring effect due to the fact that more educated citizens
present greater mobility and labour cost disadvantages for municipalities with better educated
labour force In Chile an additional explanation could be the relationship between education and
voter turnout Since 2012 voting is no longer mandatory in Chile This fact considerably reduced
voter turnout which in turn may have influenced the monitoring and control effect of more
educated voters For the case of variable IDD results show that local authorities in counties with
higher proportion of aging and young population (related to those in the active population) face a
greater challenge in their quest to offer public services efficiently
The influence of mcf is like that found by Pacheco et al (2013) with municipalities more
dependent on central transfers showing more efficiency31 Political influence captured by the
variable mayor did not show a significant effect This result is like other studies concluding that
the ideological position did not have a significant influence on efficiency (Benito et al 2010
Boetti et al 2009 Cordero Pedraja-Chaparro Pisaflores amp Polo 2017)
30 This negative coefficient suggests that the negative effects of agglomeration economies such as overcrowding pollution high cost of land and traffic congestion could predominate over the positive effects of scale economies in regional capitals 31 When the analysis is conducted for each zone independently MCF displays a negative influence in the North and Centre-North areas but a positive influence in the Centre-South and South areas These results confirm that on the one hand richer municipalities (those in the North and Centre-North) have less incentives for efficient management of their resources On the other hand results support the relevance of MCF helping poorer municipalities (those in the South) to reach their outcomes
87
Table 35
Panel data regressions
88
35 Conclusions
The trade-off between equity and efficiency is in the core of the economic discussion This
ldquoprinciplerdquo has been used by policymakers to prioritize the design of policies focused on economic
growth delaying those policies aimed at reducing economic inequalities This essay offers
empirical evidence of a negative relationship between inequality and efficiency that is a reduction
of income inequality could have positive effects on economic efficiency at least at the level of
local governments
We followed a traditional Two-Stage approach commonly used in the analysis of LGE We
compared cross-sectional and panel data results and we have added an instrumental variable
approach to give a causal interpretation to the link between efficiency and inequality We proposed
the use of a measure of natural resource dependence to instrumentalize the impact of income
inequality on LGE Given that our units of analysis are municipalities and not counties we argue
that our measure of NRD is correlated with income inequality and it does not have a direct
influence on LGE
We found that Chilean municipalities perform better than previous studies suggest
Municipal efficiency depicted significant levels of positive spatial autocorrelation and most of the
municipalities showed to be operating under increasing or decreasing returns to scale This would
imply that the policies generally used to improve efficiency such as amalgamation or cooperation
should be implemented observing the reality of each region and not as strategies at the national
level We also found that scale inefficiency explains a small proportion of the average total
inefficiency reason why the analysis of external factors that could affect the municipal efficiency
takes greater relevance
89
Income inequality plays an important part in explaining municipal efficiency In fact it was
found that reductions in income inequality could result in increases in municipal efficiency in a
similar proportion An unexpected finding was that the levels of education shows a negative
association with municipal performance This could be due to a low average level of education or
the existence of an omitted variable This variable could be the significant reduction in voting
turnout rates for local and national elections due to changes in the voting system during the period
of our analysis All in all our results may help to shed light on the potential consequences of
changes in contextual factors and the design of strategies aimed to increase municipal efficiency
in countries with similar characteristics to the Chilean economy For instance policies oriented to
take advantage of economies of scale can be formulated merging municipalities or establishing
networks in specific sectors such as education or health
Further work needs to be done both in measurement and in the explanation of differences in
municipal performance in Chile One area of future work will be to identify the factors that better
predict why municipalities operates under increasing decreasing or constant returns to scale
Multinomial logistic regression and the application of machine learning algorithms to SINIM data
sets appear as suitable methods for that purpose Intertemporal DEA (Drew amp Dollery 2015)
should be used to measure municipal efficiency capturing changes in total factor productivity In
addition municipalities operate under different levels of geographical authorities such as the
provincial mayor and the regional governor Hence it would be useful to know how each
municipality performs within each region-zone related to how performs to the whole country This
should be done conducting a metafrontier analysis (OrsquoDonnell Rao amp Battese 2008)
We have also identified through a cross sectional spatial exploratory analysis that on
average municipalities with similar levels of efficiency tend to cluster in space Regarding to
90
analyse the importance of contextual factors on municipal efficiency a deeper analysis should use
censored spatial models to check the significance of the spatial dimension in cross-sectional and
panel contexts Another interesting avenue for future research is associated with the negative
association found between LGE and education The significant reduction in votersacute turnout since
the law of automatic registration and voluntary voting in 2011 appears as a natural experiment to
analyse its effects on efficiency indicators such as municipal performance Incorporating variables
such as the voting turnout in each county or classifying municipalities based on individual
institutional political and economic characteristics could help to shed light on which of these
channels is the most relevant when analysing the impact of inequality on municipal efficiency
Finally we argued that an important part of the influence of income inequality over LGE
could be through its indirect effect on trust social capital and social cohesion The final essay will
delve deep in that relationship
91
Chapter 4 Social Cohesion Incivilities and Diversity
Evidence at the municipal level in Chile
41 Introduction
A deterioration in social cohesion could carry significant costs such as a reduction in
generalized trust between individuals and in institutions a society caught in a vicious circle of
inequality and citizens increasingly distanced from civic life (Uslaner 2011) A growing feeling
of frustration and discontentment can eventually translate into a social outbreak with uncertain
results This is precisely what have been happening in many countries around the world included
Chile
ldquoSocial cohesion is a state of affairs concerning both the vertical and the horizontal
interactions among members of society as characterized by a set of attitudes and norms that
includes trust a sense of belonging and the willingness to participate and help as well as their
behavioural manifestationsrdquo (Chan et al 2006) This definition highlights the multidimensionality
in the concept of social cohesion which has been measured using objective andor subjective
indicators of trust social norms solidarity willingness to participate in social and political groups
and feelings of belonging (Ariely 2014 Chan et al 2006) Multidimensionality also implies that
the impact of determinants of social cohesion such as economic and racial diversity could be
different for each of its various dimensions (Ariely 2014)
A common characteristic to all societies is that they are made up of different groups that
differ with respect to race ethnicity income religion language local identity etc The
92
Community Heterogeneity Thesis (Coffeacute amp Geys 2006) argues that individuals prefer to interact
with others that are like themselves Hence high levels of diversity particularly economic and
racial represent a complex scenario to maintain social cohesion One of the most common factors
adduced for social cohesion is income inequality with higher levels linked to lower levels of trust
(Ariely 2014 Rothstein amp Uslaner 2005)
Traditional measures of social cohesion may not be adequately capturing the deterioration
in social connections For instance measures of (lack of) trust include a strong subjective element
On the other hand proxies for social participation such as volunteering jobs or joining to social
organizations have not been supported by empirical evidence as a source of generalized social trust
(Rothstein amp Uslaner 2005) We proposed to use the rate of incivilities which we argue is a more
appropriate measure of the degree of worsening in the social context
Incivilities are those visible disorders in the public space that violate respectful social norms
and tend not to be treated as crimes by the criminal justice system There are two types of
incivilities social and physical Social incivilities include antisocial behaviours such as public
drinking noisy neighbours and fighting in public places Physical incivilities include among
others vandalism graffiti abandoned cars and garbage on the streets Because citizens and
political authorities cannot always distinguish between incivilities and crime they are usually
treated as an additional category of crime This implies that policies aimed to reduce incivilities
are generally based on punitive actions However theory and evidence on incivilities suggest that
factors explaining incivilities and crime could be different (Lewis 2017 Taylor 1999)
In Chile crime rates have shown a sustained downward trend after reaching its highest level
in 2011 On the other hand incivilities rate has shown a sustained upward trend which coincides
with the increasing victimization and feeling of insecurity in the population This has motivated
93
Chilean authorities based on the ldquoBroken Windows Theoryrdquo to propose new punitive actions (or
increase the severity of the current ones) to those who commit this type of antisocial behaviours
The ldquoBroken Windows Theoryrdquo states that higher rates of incivilities are a signal of social
disorganization which result in higher crime rates (J Q Wilson amp Kelling 1982) This is expected
to have consequences on familiesrsquo decisions such as moving away from public spaces or even
leaving their neighbourhoods
As far as we know there is no previous evidence about the potential causes of incivilities in
Chile Efforts to identify the factors explaining incivilities could help not only to reduce the risk
factors favouring violent and property crimes but also to guide interventions aimed to change
social behaviours and strengthen social cohesion in highly unequal societies Thus the main
contribution of the present study is to provide a deeper comprehension of the problem of incivilities
and how they can help to better understand the weakening of social cohesion that many
contemporary societies experience
We aim to offer the first evidence on the factors explaining the evolution and the differences
in incivilities rates in Chile We set up a panel for six years (2006 2009 2011 2013 2015 and
2017) and 324 counties (1944 observations) We start exploring the evolution and geographical
distribution of incivilities Then we investigate whether economic and racial diversity after
controlling for other socioeconomic demographic and municipal characteristics can be regarded
as key predictors of incivilities
We use the Gini coefficient to proxy economic heterogeneity and the number of new visas
granted to foreigners as proportion of the county population as proxy for racial diversity The main
hypothesis is whether economic and racial diversity have a positive association with the rate of
incivilities In addition to the arguments regarding the ldquoCommunity Heterogeneity Thesisrdquo Taylor
94
(1999) and Skogan (1986 1999 2015) argue that incivilities are caused by inequality and the lack
of informal mechanisms of social control Based on the ldquoIncivilities Thesisrdquo the patterns of
incivilities should mirror the patterns of inequality (Taylor 1999) Then higher inequality should
be associated with higher physical and social vulnerability of the population This reduces social
control and increases social disorganization which triggers antisocial or negligent behaviours
Our main result reveals a strong positive association between the rate of incivilities and the
number of new visas granted per year The relationship with income inequality although also
positive seems to be less significant These findings give strong support to the ldquoCommunity
Heterogeneity Thesisrdquo and to a lesser extent to the ldquoIncivilities Thesisrdquo When the analysis is
disaggregated geographically racial diversity shows a clear positive effect The impact of income
inequality seems to be conditional depending on the level of income showing no effect in poorer
regions Results also show that the impact of economic and racial diversity differs by type of
incivility For example income inequality shows a strong association with ldquoStreet Tradingrdquo while
racial diversity with ldquoAlcohol Consumptionrdquo Two are the main policy implications On the one
hand a national strategy to address the problems associated with foreign immigration could help
to reduce incivilities For instance a joint effort between national and local authorities to curb
immigration and its distribution throughout the country On the other hand our results show that
the relationship between incivilities and economic diversity differs depending on the region or
geographical area Hence the impact on social cohesion of policies aimed to tackle economic
inequalities should be analysed in each specific context
The rate of incivilities also shows a negative association with the level of municipal financial
autonomy This implies that municipalities can effectively carry out policies to reduce incivilities
beyond the efforts of the central government Another important finding is that our results do not
95
support the hypothesis that a higher proportion of the young population is associated with higher
rates of incivilities Hence policies aimed to reduce incivilities should be focused on the causes of
incivilities rather than the criminalization of behaviours or stigmatization of specific population
groups
The structure of the chapter is as follows Section 42 outlines the relevant literature on social
cohesion and incivilities Section 43 describes the data variables and methodology and
establishes the hypotheses of the study Section 44 contains the results and discussions Section
45 presents the main conclusions
42 Related Literature
421 The Community Heterogeneity Thesis
The idea under ldquoThe Community Heterogeneity Thesisrdquo is that if individuals prefer to
interact with others who are similar to themselves in terms of income race or ethnicity high levels
of income inequality and racial diversity facilitate a context for lower tolerance and antisocial
behaviours lowering the ldquostaterdquo of social cohesion (Alesina 2000 Coffeacute amp Geys 2006 Letki
2008) Alessina and Ferrara (2002) give support to this hypothesis arguing that individuals have a
natural aversion to heterogeneity However the most popular explanation is the principle of
homophily people prefer to interact with others who share the same ethnic heritage have the same
social status and hence share experiences and tastes (Letki 2008 McPherson Smith-Lovin amp
Cook 2001 Tolsma et al 2009) For instance Delhey and Newton (2005) find for a sample of
60 countries that income inequality and ethnicity are strongly and negatively correlated with trust
Tolsma et al (2009) using data for Dutch neighbourhoods and municipalities find that social
cohesion is negatively and consistently affected by economic deprivation but not by ethnic
96
heterogeneity These authors also conclude that the effect of neighbourhood and municipal
characteristics on social cohesion depends on residentsrsquo income and educational level
Rothstein and Uslaner (2005) give two theoretical reasons why economic and racial diversity
should be causally related to social trust a key element of social cohesion First optimism about
the future makes less sense when there is more economic inequality which generally translates into
inequality of opportunities especially in areas such as education and the labour market Second
the distribution of resources and opportunities plays a key role in establishing the belief that people
share a common destiny and have similar fundamental values In highly unequal societies people
are likely to stick with their own kind Perceptions of injustice will reinforce negative stereotypes
of other groups making social trust and accommodation more difficult
Uslaner (2002 2011) and Uslaner and Brown (2005) find that high levels of inequality are
the single major factor driving down trust in people who are different from yourself Evidence for
USA finds that inequality is the strongest determinant of generalized trust over time (Rothstein amp
Uslaner 2005) Reducing inequality and then increasing generalized trust should have positive
consequences at the individual and aggregates levels At the individual level it may lead to greater
tolerance and more acts of altruism for people of different backgrounds At the aggregate level it
may lead to greater economic growth more redistribution from the rich to the poor and less
corruption (Uslaner 2002 2013) Letki (2008) argues that when neighbourhood socio-economic
context (apart from just an individualrsquos socioeconomic status) is considered it turns out to be the
main factor triggering negative attitudes and lack of trust in out-group members
The increasing diversity caused by immigration can also reduce the conditions necessary for
social cohesion (Ariely 2014 Holtug amp Mason 2010) Christel Kesler and Irene Bloemraad
(2010) find for nineteen advanced democracies between 1981 and 2000 that increasing migration
97
generally decreases trust civic engagement and political participation The authors also find that
in more equal countries with clear policies in favour of cultural minorities the negative effects of
migration are mitigated or even reversed Letki (2008) states that deprivation and disorder tend to
be strongly correlated with racial diversity Because we propose the use of the number of disorders
or antisocial behaviours known as ldquoincivilitiesrdquo as our measure of social cohesion we describe the
literature on incivilities in the next section
422 The literature on incivilities
The study of incivilities has been a continuing concern mainly for developed countries since
the 1980s The focus has changed from individual and psychological explanations to ecological
(contextual) and social explanations (Taylor 1999) The individual approach basically considered
perceptions of incivilities as an explanatory variable of fear of crime The ecological explanation
argues that indicators of economic disadvantage (eg income levels income inequality
unemployment rate and poverty rate) are the keys to understand a process of social disorganization
and lack of informal control These economic factors lead to higher rates of inappropriate or
negligent behaviours and ultimately to higher crime rates (Blau amp Blau 1982 Messner Rosenfeld
amp Baumer 2004 Phan Orsquobrien Mendolia amp Paloyo 2017 Sampson 1986)
The negative impact of incivilities is not merely reflected in its association with crime rates
(Skogan 2015) Physical and social incivilities could worsen neighbourhoods by affecting quality
of life perception of the environment and public and private behaviours Previous research has
indicated that a higher level of incivilities is associated with health problems (Branas et al 2011
Cohen et al 2000 Hill amp Angel 2005 Ross 2011 Ross amp Mirowsky 2001) greater
victimization and fear of crime (Brunton-Smith Jackson amp Sutherland 2014 Mijanovich amp
Weitzman 2003) and multiple negative economic effects For instance incivilities could be
98
related to a reduction in commercial activity lower investment in real estate reduction in house
prices (Skogan 2015) and population instability (Hipp 2010)
To describe the state of the art in the study of incivilities and their consequences Skogan
(2015) used the concept of untidiness to characterize the research on incivilities The study of
incivilities has had multiple approaches (economic ecological and psychological) Incivilities
have also been measured using multiple sources of information (police reports surveys trained
observation) which result in different measures (perceptions vs count data) However the question
about what specific factors have the strongest effect on incivilities has been overlooked and
perceptions about incivilities have been used mainly as a predictor of crime fear of crime and
victimization
There are two types of incivilities social and physical Social incivilities are a matter of
behaviour including groups of rowdy teens public drunkenness people fighting and street hassles
Physical incivilities involve visual signs of negligence and decay such as abandoned buildings
broken streetlights trash-filled lots and graffiti (Skogan 1999 2015 Taylor 1999) Three reasons
justify the distinction between physical and social incivilities First like multiple dimensions of
social cohesion different structural and social conditions could be responsible for different types
and categories of incivilities Second punitive sanctions are expected to have a greater impact on
physical than on social incivilities since the latter are more related to behaviours rooted in citizensrsquo
culture Third physical incivilities should be more related to absolute measures of economic
disadvantage (eg poverty or unemployment rates) and social incivilities to relative indicators of
economic disadvantage (eg such as income inequality) This line of research is based on the
ldquoincivilities thesisrdquo which states that to understand the distribution of disorders it is necesary to
analyse the patterns of structured inequalities (Skogan 1986 Taylor 1999)
99
423 The ldquoIncivilities Thesisrdquo
Incivilities theories began with a focus on psychological dynamics (Garofalo 1978) moved
forward to an interest in social psychological processes (J Q Wilson amp Kelling 1982) and finally
evolved into a focus on community dynamics and outcomes (Skogan 1999) Individual and group
behaviours in tandem with ecological features have been proposed as the key factors explaining
incivilities and their posterior influence on social control quality of life and more serious crime
(J Q Wilson amp Kelling 1982)
In terms of ecological factors particularly those related to economic conditions Skogan
(1986) was the first linking the distribution of incivilities to the patterns of structural inequality If
incivilities mirror inequality structure this will have consequences in residentsrsquo health and safety
due to its levels of vulnerability In addition structured inequality associated with the proportion
of the manufacturing sector (eg when fabrics tend to move from cities to farther areas) will be
related to higher social disorganization and differences between urban and rural areas (W J
Wilson 1996) In addition a persistent feeling of relative deprivation (persistently high levels of
income inequality) could lead to fellow inhabitants of the community to commit antisocial
behaviours showing their frustration with the current economic model
The literature on incivilities posits that their causes are different from those of crime (Lewis
2017) Unlike crime analysis especially property crimes information on the location where the
incivility takes place is the same as the location where the perpetrator resides To achieve a
comprehensive understanding of the different types of incivilities it is crucial to consider
incivilities data covering an entire territory and not just specific areas (Hooghe Vanhoutte
Hardyns amp Bircan 2010) If we add to this the availability of panel data it could be possible not
100
only to identify the main determinants of incivilities but also the causal mechanism from income
inequality towards incivilities rate
In Chile citizen security crime and delinquency are among the most significant issues for
citizens based on opinion polls Existing research has found weak evidence of a significant
relationship between crime and indicators of socio-economic disadvantage such as income
inequality and unemployment rate with significant effects only on property crime (Beyer amp
Vergara 2006 Nuntildeez Rivera Villavicencio amp Molina 2003 Rivera Gutieacuterrez amp Nuacutentildeez 2009)
Crime deterrence variables such as the probability of being caught or the number of police
resources have also shown ambiguous results (Beyer amp Vergara 2006 Rivera et al 2009
Vergara 2012) Evidence at the county level shows that crime is higher in urban counties those
with a lower mean income per capita and counties located in the North of the country In addition
at least half of the crimes reported in one county are perpetrated by criminals from other counties
(Rivera et al 2009) No studies could be found about the determinants of incivilities
4 3 Methodology
431 Period of analysis and data sample
Chile is a relatively small country in Latin America with a population of 18346018
inhabitants in 2017 The country is divided into 345 municipalities with on average 53104
inhabitants (median value 18705) Municipalities are the organ of the State Administration
responsible to solve local needs Municipalities are not only the relevant political and
administrative local unit of analysis but also they represent the feeling of lsquocommunityrsquo among
the inhabitants of each municipality (Hooghe et al 2010) Our data includes many sources of
101
heterogeneity among municipalities such as indicators of economic deprivation population
density demographic characteristics and whether the county is a regional or provincial capital
We use a sample of 324 municipalities covering most of the Chilean territory for the period
2006ndash17 Data on incivilities is obtained from the ldquoCentre of Studies and Analysis of Crimerdquo
which is part of the ldquoSubsecretaria de Prevencion del Delitordquo (SPD in its Spanish acronym) of the
Chilean government32 Information on income inequality and control variables is obtained from
the ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) the
ldquoNational Institute of Statisticsrdquo (INE in its Spanish acronym) the ldquoNational Municipal
Information Systemrdquo (SINIM in its Spanish acronym) and the Immigration Department of the
Government of Chile Our panel only includes the years for which CASEN survey is available
2006 2009 2011 2013 2015 and 2017
432 Operationalisation of the response variable and exploratory analysis
Official Chilean records contain information for the total number of cases of incivilities per
year at the county level The number of cases is the sum of complains and detentions reported at
the police Our dependent variable 119894119899119888119894119907119894119897119894119905119894119890119904 correspond to the number of cases per year Due
to population differences comparisons between counties are made using the incivilities rate per
1000 population calculated as
119894119899119888119894119907_119903119886119905119890 lowast 1000 (41)
where 119894119899119888119894119907_119903119886119905119890 is the incivilities rate 119905 is the year 119894 the county and 119899 is the population of the
county per year
32 httpceadspdgovclestadisticas-delictuales
102
Figure 41 illustrates at the top the evolution of the total number (cases reported) of
incivilities and crimes at the country level for the period 2006-1733 At the bottom Figure 41
shows the evolution of the mean county rate per 1000 inhabitants We observe that both the number
of incivilities and the number of crimes has reached similar annual figures however average
county rates per 1000 population show different trends Crime rate displays a sustained fall after
reaching its pick in 2011 Incivilities average county rate which also reached a pick and posterior
drop in 2011 has recovered its upward trend since 2016 considerably exceeding the crime rate
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017
33 Crime refers to ldquocrimes of greater social connotationrdquo (DMCS in its Spanish acronym) which includes violent and property crimes
103
Chilean records classify incivilities in nine categories most of them associated with social
incivilities Summary statistics for the total and for each of the nine categories are presented in
Table 41 In addition Figure 42 shows the evolution of incivilities by category for the whole
period We see that the global trend in incivilities is mainly due to a substantial increase in ldquoStreet
Tradingrdquo and ldquoPublic Alcohol Consumptionrdquo A common element of Figures 41 and 42 is the
significant change in trend experienced by crimes and incivilities in 2011 That year the SPD
became dependent on the Ministry of Interior of the Chilean Government This event put the issue
of crime and delinquency within national priorities for the central government
Table 41
Summary statistics total count of incivilities and by category (full sample and period)
Unlike crime rates we do not expect significant cross-county spillover effects in incivilities
However the questions of where incivilities are concentrated and why they are there can be of
great interest (Skogan 2015) Figure 43 shows quantile maps for the rate of incivilities per 1000
inhabitants for the initial and final years in our panel
104
Figure 42 Evolution total number of incivilities by category
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017)
105
We observe that the range of values has increased significantly from 2006 to 2017 but the
spatial distribution remains almost unchanged On the one hand high incivilities rates in the North
could be associated with the mining activity On the other hand high rates in the Centre area
(where the countyrsquos capital is located) could be related to the higher population density and the
concentration of the economic activity34
To see how the different types of incivilities are distributed throughout the country we have
grouped those similar categories in four groups ldquoPublic Damagerdquo (ldquoPublic Disturbsrdquo ldquoPublic
Damagerdquo ldquoOtherrdquo) ldquoThreatsrdquo (ldquoPublic Fightrdquo ldquoThreatsrdquo and ldquoAnnoying Noisesrdquo) ldquoAlcohol
Consumptionrdquo (ldquoPublic Alcohol Consumptionrdquo and ldquoDrunkennessrdquo) and ldquoStreet Tradingrdquo This
distinction in groups could be relevant if we expect different patterns and different effects of
community heterogeneity on social cohesion among counties For instance we expect higher
levels of Public Damage in big urban cities Street trading is more likely in urban cities but also in
tourist areas The spatial distribution of these four groups for the six-year-average rate per 1000
inhabitants can be found in Appendix R
433 Measures of community heterogeneity and control variables
Social cohesion income inequality and racial diversity are all ldquocommunityrdquo (not individual)
characteristics Thus to understand their relationship we need aggregated data at least at the
county-municipal level With more disaggregated data like at the suburbs level the required
heterogeneity among groups of citizens is lost (Wilkinson 1999) Like Coffeacute and Geys (2006) we
use the Gini coefficient to capture economic heterogeneity However instead of a measured for
34 We also analysed the spatial distribution of crime rates In general areas with high levels of incivilities not necessarily are associated with high levels of crime This could imply that factors explaining incivilities and crime are different
106
the diversity of nationalities we use the proportion of foreign population to capture racial
heterogeneity Income data is obtained from the CASEN survey The Gini coefficient is calculated
for each county and included through the variable gini Racial heterogeneity is included through
the variable foreign which is the annual number of new VISAS granted to foreigners as a
proportion of the county population Chile has experienced a significant increase in immigration
since 2011 Immigration has been concentrated in the metropolitan region and mining regions in
the North of the country We expect a positive relationship between immigration and incivilities
although as with the relationship between immigration and crime the foundations for this
hypothesis are not strong (Hooghe et al 2010 Sampson 2008)
Economic development is another explanation for social cohesion frequently appealed to
explain trust with wealthier societies considered to exhibit higher levels of trust (Delhey amp
Newton 2005) In this study we include the natural log of the mean household income per capita
log(income) We also include the poverty rate poverty and the unemployment rate
unemployment Unlike the variable log(income) these variables are expected to be positively
associated with the number of incivilities When a relative indicator of economic heterogeneity
such as income inequality is included as determinant of social cohesion we should expect less
effect from absolute indicators of economic disadvantage such as poverty and unemployment rates
(Hooghe et al 2010 Tolsma et al 2009)
Among demographic variables the percentage of inhabitants between 10 and 24 years old is
included through the variable youth The variable women defined as the proportion of the female
population in each county is also included Variable youth is expected to have an ambiguous effect
Although young people have lower victimization and report rates they also represent the group
more likely to commit antisocial behaviours when a community has a low capacity of self-
107
regulation (eg when there is low parental supervision) The female population is associated with
a higher report of incivilities related to the male population
It is argued that crime and incivilities are essentially urban problems (Christiansen 1960
Wirth 1938) We include the variable log(density) defined as the log of population density (the
number of inhabitants divided by the area of each county in square kilometres) and a dummy
variable capital indicating whether a county is an administrative capital (provincial or regional)
Two additional variables are included to capture the level of informal social control exerted
by families living in each municipality First the variable education which is defined as the
average years of education of people over 15 years old Second the variable housing which capture
the proportion of families which are owners of their housing unit Although education and housing
are related to both the possibility of reporting and committing an incivility we expect a negative
association with the rate of incivilities
In Chile crime has been mainly a problem faced by the police and the Central Government
Administration To control for current law enforcement policies we include the variable
deterrence defined as the number of arrests as a proportion of the total number of incivilities cases
In addition municipalities can develop their own initiatives to deal with crime and incivilities
depending on their capacity to generate its own resources The level of financial autonomy from
central transfers is captured by the variable autonomy This variable is obtained from SINIM and
it is defined as the proportion of the budget revenue of each municipality that comes from its own
permanent sources of revenues A categorical variable mayor is also included This variable
indicates whether the municipality mayor is supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo political
parties (related to those ldquoINDEPENDENTrdquo mayors)
108
Table 42 presents descriptive statistics for our measures of income and racial heterogeneity
and the set of numeric control variables The Pearson correlation among these variables is shown
in Appendix S
Table 42
Summary statistics numeric explanatory variables
434 Methods
The annual count of incivilities as is characteristic for count data is highly concentrated in
a relatively small range of values In addition the distribution is right-skewed due to the presence
of important outliers (counties with a high number of incivilities) Figure 44 shows the
distribution of the six-year average number of incivilities for each of the 15 regions in Chile35 We
observe that regions differ in the number of counties in which they are divided In addition
counties within each region show important differences in the number of incivilities For instance
35 Regions are ordered from left to right and from top to bottom in the way they are geographically distributed from North to South So the northernmost region of the country is the ldquoXVrdquo and the southernmost region is the ldquoXIIrdquo The Metropolitan region (where the countryrsquos capital is located) is region ldquoXIIIrdquo in the centre of the country (see Appendix C)
109
excepting the Metropolitan region ldquoXIIIrdquo most counties in regions located in the centre of the
country (middle row in Figure 44) show a range of incivilities between 0 and 2000 The number
of incivilities is considerably lower in counties located in the northern (top row in Figure 44) and
southern (bottom row in Figure 44) regions of the country compared to regions in the centre of
the country It also seems clear from Figure 44 that the number of incivilities does not follow a
normal distribution
Figure 44 Annual average number of incivilities per county
The number of incivilities can be better described by a Poisson distribution In this case the
number of incivilities is ldquothe countrdquo and the number of incivilities per year is ldquothe rate per unit
timerdquo We are interested in modelling the average number of incivilities per year usually called 120582
as a function of a set of contextual factors to explain differences in incivilities between and within
110
counties The main characteristic of the Poisson distribution is that the mean is equal to the
variance This implies that as the mean rate for a Poisson variable increases the variance also
increases The main implication is we cannot use OLS to model 120582 as a function of the set of
contextual factors because the equal variance assumption in linear regression is violated
The rate of incivilities between counties is not directly comparable due to population
differences We expect counties with more people to have more reports of incivilities since there
are more people who could be affected To capture differences in population which is called the
exposure of our response variable 120582 it is necessary to include a term on the right side of our model
called an offset We will use the log of the county population in thousands as our offset36
Additionally similar to the case of crime data incivilities show a significant degree of
overdispersion (variance higher than the mean) suggesting that there is more variation in the
response than the Poisson model implies37 We also model and regress incivilities assuming a
Negative Binomial distribution to address overdispersion An advantage of this approach is that it
introduces a dispersion parameter in addition to 120582 which gives the model more flexibility38
Considering as the response variable the count of incivilities per year the model can be
expressed as follow
120582 119890119909119901 120573 120573 119892119894119899119894 120573 119891119900119903119890119894119892119899 119883120574 120572 120579 (42)
36 If we think of 120582 as the average number of incivilities per year then represents the number per 1000
inhabitants so that the yearly count is adjusted to be comparable across counties of different sizes Adjusting the yearly count by population is equivalent to adding 119897119900119892 1199011199001199011199061198971198861199051198941199001198991000 to the right-hand side of the regression equation 37 Without adjusting for overdispersion we use incorrect artificially small standard errors leading to artificially small p-values for model coefficients 38 The Negative Binomial model posits selecting a 120582 (average number of incivilities) for each county and then generating a count using a Poisson random variable with the selected 120582 With this approach the counts will be more dispersed than would be expected for observations based on a single Poisson variable with rate 120582
111
where 120582 is the rate of incivilities 119883 is our vector of controls 120572prime119904 are county-specific constants
and 120579prime119904 are time-specific constants Accounting for differences in county population we have
119890119909119901 120573 120573 119892119894119899119894 119883120574 120572 120579 (43)
where 119901119900119901119906119897119886119905119894119900119899 is the county population per year Hence the model to be estimated using
Maximum Likelihood Estimation (MLE) is
119897119900119892 120582 120573 120573 119892119894119899119894 119883120574 120572 120579 119897119900119892 (44)
Finally to account for different effects depending on the type of incivilities we also run
equation (44) for each of the four groups of incivilities defined in section (432)
435 Hypotheses
Based on the community heterogeneity hypothesis the relationship between social cohesion
and diversity should be stronger for lower levels of income and less educated groups of people
(Tolsma et al 2009) Hence contrary to evidence for developed and more equal countries we
expect a significant positive association for the Chilean case where more than 50 of the
population is economically vulnerable (OECD 2017)
The main hypotheses to be tested in this essay is whether the number of incivilities is
positively associated with the level of economic and racial heterogeneity at the county level We
start analysing this association for the full sample and period Next we analyse whether the
relationship between incivilities and our measures of diversity differs by geographic area (region
or zone) Finally we check whether the effect of economic and racial diversity is different
depending on the group of incivilities
112
44 Results and Discussion
Overall our results show that the rate of incivilities displays a stronger and more significant
relationship with racial diversity than with economic heterogeneity This association differs for
different geographic areas and for different types of incivilities Absolute economic indicators
except for income show a significant but small effect Increases in the average levels of income
or education and more financial autonomy for municipalities seem to be effective ways to reduce
the rate of incivilities
We estimate equation (44) assuming that the number of incivilities follows a Poisson
distribution Regional and temporal heterogeneity are captured through the inclusion of dummy
variables for five years (with 2006 as the reference year) and fourteen regional dummies (with
region XIII as the reference region) Results are reported in Table 4339 This table is structured in
two blocks of regressions pooled models in columns (1)-(4) and fixed-effects models in columns
(5)-(8)40 The first column in each block only includes economic indicators relative and absolute
trying to test which ones are more relevant and whether incivilities tend to mirror income
inequality (the ldquoincivilities thesisrdquo) The second column adds the variable foreign to account for
the effect of racial diversity (Letki 2008) The third column includes education to check whether
the association between economic and racial diversity with social cohesion changes (gets less
significant) when we control for educational level (Tolsma et al 2009) The final column in each
block corresponds to the full model specification which includes the rest of controls
39 Pooled estimations were obtained using the R command glm() and compared with results obtained from the command poisson in STATA 15 Fixed effects and random effects models were estimated using the STATA command xtpoisson Negative Binomial models were estimated using the command glmnb() in the MASS package for R and commands nbreg and xtnbreg in STATA 15 Results for Negative Binomial regressions are in Appendix T 40 We have omitted results with random effects due to the literature on panel count data models suggest that the most robust estimator is Poisson regression with Fixed Effects (Santos Silva amp Tenreyro 2010 2011)
113
Table 43
Poisson regressions
114
The positive and significant coefficient for the variable gini besides being small it becomes
insignificant in the fixed effects specification which includes the full set of controls This result
does not seem to be enough evidence to support our hypothesis that more unequal counties display
higher rates of incivilities On the other hand racial diversity through the variable foreign shows
a consistent positive association with the rate of incivilities41 Together coefficients for gini and
foreign seems to support the ldquocommunity heterogeneity thesisrdquo (Letki 2008) but not the
ldquoincivilities thesisrdquo (Skogan 1999) To check this finding we run the pooled full model
specification for each region and results are shown in Table 44 where regions have been ordered
from North to South The sign of the coefficient of the variable gini differs for different regions
Moreover the relationship is insignificant in some of the most unequal regions which are in the
South of the country (VIII IX and XIV) This result rejects the hypothesis that incivilities mirror
structural income inequalities For the variable foreign 12 out of 15 regions confirm the positive
association with the rate of incivilities42
We also run our pooled full model separately for each group of incivilities defined at the end
of section (432) Income inequality keeps its significant but small association with each group of
incivilities (see Table 45) Our measure of racial diversity shows a stronger association with
ldquoAlcohol Consumptionrdquo related to ldquoPublic Damagerdquo and ldquoThreatsrdquo The link with ldquoStreet Tradingrdquo
appears as non-significant These results support our general finding that on the one hand racial
heterogeneity exert a more significant influence on the rate of incivilities than economic
41 To get the effects of the variables gini and foreign over the rate of incivilities per 1000 inhabitants we need to exponentiate their coefficients Thus an increase in one unit in the variable gini increases the rate of incivilities per 1000 inhabitantsrdquo in 1005 times (e^0005) in the pooled full model Similarly an increase in one point in variable foreign is expected to increase the ldquorate of incivilities per 1000 inhabitantsrdquo in 10876 times (e^0084) in the full pooled model and 10471 (e^0046) times in the full Fixed Effects model 42 Given that depending on the geographic location regions share certain characteristics the model was also run clustering regions in 4 zones (see Figure 31 in chapter 3) Results by zone are in Appendix U
115
heterogeneity On the other hand results confirm our hypothesis that the effect of diversity is
different for different types of incivilities
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region
Back to our general results in Table 43 the significant and negative coefficient of the
income variable and to a lesser extent the significant and positive coefficients of poverty and
unemployment provide evidence that absolute rather than relative economic indicators may be
more important explanations of the rate of incivilities This is opposite to evidence for the analysis
116
of crime rates such as in Hoodge (2010) and support the idea that determinants of incivilities are
different from those of crime Our results are also opposite to those for Dutch municipalities where
economic indicators turned out to be more important than ethnic heterogeneity (Tolsma et al
2009) The coefficient for the variable log(income) could be interpreted as counties with an income
level under the average face higher problems of antisocial behaviours such as incivilities In
addition as the income level moves far away from its average low level the problem of incivilities
is less relevant43 In terms of policy implications only those policies that achieve a significant
increase in the average level of county income seem to be effective in reducing incivilities and
strengthening social cohesion
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group
43 We ran our model splitting the sample of counties in quintiles based on their income level The coefficient for the variable gini only showed a significant (and positive) coefficient for the second third and fourth quintile A non-significant coefficient for the first quintile could imply that other types of inequalities (eg health education) are more relevant in the poorest counties In the case of the fifth quintile (the richest group of counties) they have better facilities in terms of infrastructure police resources self-protection etc which could reduce the potential triggers of incivilities
117
The inclusion of the variable education significantly improved the goodness of fit of the
models and did not generate significant changes in the coefficients of our measures of economic
and racial diversity This result rejects the proposition that the relationship between social
cohesion and diversity becomes less strong when controlled by education (Tolsma et al 2009)
Additionally it highlights the topic of education as a relevant determinant of the rate of incivilities
and social cohesion
Among control variables there are also some important results Opposite to what we
expected the variable youth shows a negative or non-significant coefficient Although this result
could be due to the lack of ldquophysical incivilitiesrdquo in Chilean records it indicates that it is incorrect
to stereotype this group as the main responsible for high incivilities rates The significant and
negative coefficient of the variable autonomy in the fixed effects specification could also have
important policy implications It is a signal that local governments can play an important role in
reducing incivilities or complementing the efforts from the central government Another
interesting result is the significant coefficient of the variable housing The latter finding is
particularly important in the sense that a negative sign supports public policies oriented to increase
homeownership as effective ways to improve social cohesion However the small magnitude of
the coefficient that even showed the opposite sign in some model specifications could be
explained for the high level of segregation that these policies have generated in Chilean society
As mentioned in the Introduction and Literature Review so far only a few studies have
used measures of disorders or incivilities as dependent variable to explain changes in social
cohesion (Skogan 2015) In addition there is no evidence in Chile analysing the determinants of
incivilities separately from those of crimes The importance of our results on identifying the
importance of economic and racial diversity on social cohesion lies mainly in its generality An
118
important number of countries all around the world share a similar context characterized by high
levels of inequality and an explosive increase in immigration These countries are also
experiencing a worsening in social cohesion which increases the risk of a social outburst
4 5 Conclusions
The main goal of this essay was to determine whether differences in incivilities at the county
level mirror differences in income distribution and racial diversity Previous literature suggests a
positive and strong association between social cohesion and indicators of economic disadvantage
relative deprivation and racial diversity (Letki 2008 Tolsma et al 2009 Uslaner amp Brown 2005)
While not all our results were significant they showed helpful insights about how and where
economic and racial diversity are more likely to influence the rate of incivilities and social
cohesion
We used data for the period 2006ndash17 economic heterogeneity was measured through the
Gini coefficient at the county level and racial heterogeneity was proxied by the number of granted
visas to foreigners as proportion of county population We found strong evidence of a significant
and positive association between the rate of incivilities and racial diversity but not with income
inequality Contrary to previous evidence at the municipal level (Coffeacute amp Geys 2006 Tolsma et
al 2009) in general our results give support for the ldquohomophily principlerdquo and the ldquocommunity
heterogeneity hypothesisrdquo However results also showed that the effect of economic and racial
diversity varies throughout the Chilean regions and for the different types of incivilities
We also found that policies aimed at controlling the behaviour of young people did not have
strong empirical support In terms of the role that local governments may have in facing the
119
growing problem of incivilities we found evidence that efforts managed from the municipalities
can be an important complement to those from the central government
Future research should go further on the role of local authorities on incivilities and social
cohesion On the one hand municipalities could have a direct impact on social cohesion through
the implementation of programs complementary to those of central authorities oriented to reduce
incivilities and crime On the other hand social cohesion could be indirectly affected when local
authorities display an inefficient performance supplying public services to citizens or they are
recognized as corrupted institutions We suggest that policy makers from central government
should give local authorities a greater role in fighting antisocial behaviours and crime Evaluating
programs in specific municipalities could help to elucidate the causal effect of for instance higher
fiscal autonomy on the rate of incivilities
Another interesting area for future work will be to analyse how housing policies have
contributed to the phenomenon of segregation of Chilean society and in the process of weakening
social cohesion Finally our main result highlights the need of a deeper analysis of the impact that
foreign immigration is having in Chile For instance disaggregating information by country of
origin and the reasons why immigrants are arriving to the country or specific regions will surely
help to understand the impacts of immigration
120
Chapter 5 Conclusions
This thesis investigated in three essays the issue of income inequality in Chile using county-
level data for the period 2006-2017 The first essay supplied empirical evidence about the
importance of the degree of dependence on natural resources in terms of employment in explaining
cross-county differences in income inequality The second essay analysed the potential causal
effect that income inequality has on the level of technical efficiency of local governments
providing public goods and services Lastly the third essay studied the relationship between social
cohesion measured through the number of antisocial behaviours classified as ldquoincivilitiesrdquo and
the levels of income and racial heterogeneity
Findings from the first essay support the idea that the endowment of natural resources plays
a significant role in explaining income inequality in Chile However contrary to what most
theoretical and empirical evidence postulates our findings showed a robust negative association
between the two variables This means that the reduction experienced in Chile in the degree of
dependence on natural resources in terms of employment has contributed to the persistence of high
levels of income inequality The exploratory analysis indicated that income inequality shows a
general clustering process characterized by a significant and positive spatial autocorrelation
Regarding the previous evidence for Chile (Paredes et al 2016) the regression analysis confirmed
the relevance of the spatial dimension of income inequality through a process of spatial
heterogeneity giving less support to the existence of a process of spatial dependence (spillover
effect) in the variable itself
121
Essay 2 studied the potential trade-off between efficiency and equity analysing the influence
of income inequality on the efficiency of local governments at the municipal level To identify the
causal effect of income inequality on municipal efficiency we proposed the use of the proportion
of firms in the primary sector as an instrument for income inequality Findings confirmed our
hypothesis that efficiency is lower in more unequal counties This result suggests the non-existence
of the trade-off between equity and efficiency Hence policies intended to reduce inequality could
help to increase efficiency at least at the level of municipal local governments
The third essay analysed how social cohesion proxied by the rate of incivilities is associated
with the levels of economic diversity proxied by income inequality and the levels of racial
diversity proxied by the number of new visas grated as proportion of the county population
Findings gave strong support to the hypothesis that the rate of incivilities is positively related to
racial diversity and to a lesser extent to economic diversity In addition the rate of incivilities
appears negatively related to the degree of financial autonomy of municipalities This means that
local governments can effectively contribute to the reduction of incivilities which could help
reduce victimization and crime rates ultimately strengthening social cohesion
Taken together findings from essays 2 and 3 highlight the important role that income
inequality could play in other relevant economic and social dimensions These findings add to the
understanding of the potential consequences of income inequality particularly in natural resource
rich countries with persistently high levels of inequality
The present study has mainly investigated income inequality at the county level In addition
Chilean municipalities play an important role providing ldquoservices to peoplerdquo so our findings could
be applied in other highly unequal countries with a high degree of dependence on natural resources
and local governments with similar responsibilities For instance in Latin America apart from
122
Chile and Brazil there are no studies on the efficiency of local governments Other limitations are
associated with the availability of information For instance important indicators such as GDP per
capita are only available at the regional level and information of incomes is not available annually
In addition given the heterogeneity among municipalities some type of grouping of municipalities
should be performed before looking for causal relationships or conducting program evaluation
Despite these limitations we believe this study could be the basis for different strands of future
research on the topic of inequality local government efficiency and social cohesion
It was stated in chapter 2 based on the resource curse hypothesis literature there are two
elements that determine whether NR are a curse or blessing in terms of socioeconomic outcomes
First the curse is more likely in countries with weak political and governance institutions Second
different types of resources affect institutions differently with resources that are concentrated in
space so-called ldquopointrdquo resources tend to impair institutions while ldquodiffuserdquo resources do not
(Deacon 2011 Isham Woolcock Pritchett amp Busby 2005) Our results showed a negative
relationship between income inequality and our measure of natural resource dependence even after
controlling for zone fixed effects and for the level of government expenditure This result could
be interpreted as a signal that NR has a direct effect on income inequality in addition to the indirect
impact through market or institutional channels Using other potential institutional transmission
channels will shed light about the true effect that the endowment of natural resources has over
income inequality Variables that could capture these institutional channels include the level of
employment in the public sector measures of rule of law and corruption and changes in the
creation of new business in the secondary and tertiary sectors related to the primary sector
Based on results from chapter 3 most of the municipalities show scale inefficiencies One
immediate area for future work will involve using our set of contextual factors to predict the status
123
of municipalities in terms of scale inefficiencies Defining as dependent variable whether a
municipality shows constant decreasing or increasing returns to scale we could run a multinomial
logistic regression to predict municipal status For instance we would expect that a one-unit
increase in the Gini coefficient should increase the probability of scale inefficiencies (increasing
or decreasing returns to scale rather than constant returns to scale) Because the aim in this case
would be predicting a certain result in terms of returns to scale the next step should involve to
split the full sample in training and testing data sets and to use some resampling methods such as
bootstrapping This will allow us to evaluate the performance and accuracy of our model
predictions using different random samples of municipalities Results from Machine Learning
algorithms will help us to assess the generalizability of our results to other data sets
Future work should also benefit greatly by using data on different Latin American countries
to (1) compare the responsibilities of local governments (2) select a common set of inputs and
output to evaluate LGE (3) identify the relevance of scales inefficiencies in explaining differences
in performance and (4) analyse the influence of contextual characteristics over LGE Differences
in the main primary sector activity in each country such as oil in Brazil mining in Chile or Coffee
in Colombia could be responsible for differences in LGE among countries These differences could
be associated with sources of revenue management of expenditure and definitions of outputs or
contextual effects such as corrupted institutions or the delay in the development of other sectors
such as manufacturing or services
To delve deep on reasons explaining the social crisis experienced by Chilean society and
other countries one area of future work will be to analyse the relationship between diversity and
the origins of social revolutions Based on Tiruneh (2014) the three most important factors that
explain the onset of social revolutions are economic development regime type and state
124
ineffectiveness Interesting questions include whether the characteristics of Chilean context at the
end of 2019 are enough to trigger the transformation of the political and socioeconomic system
Social revolutions particularly violent revolutions are less likely in more democratic educated
and wealthy societies So it would be relevant to identify the factors explaining the violence that
has characterized the social crisis in Chile Finally the democratic regime has been maintained in
the last decades with changes between left and right governments This could imply that more
important than the regime has been the efficiency or ineffectiveness of the governments to satisfy
the needs of the population
Future work should also cover the disaggregation of information regarding foreign
population in terms of the reasons for new granted visas and the country of origin Official data
allows us to disaggregate whether the benefit is permanent (students and employees with contract)
or temporary Furthermore most of the new visas were traditionally granted to neighbouring
countries (Peru and Bolivia) a trend that has changed in the recent years to include countries such
as Colombia Venezuela and Haiti An analysis of how economic and social indicators have been
affected by changes in the composition of foreigners their reasons for immigrating to the country
and their geographical distribution have implications for economic policy at both the national and
local levels At the national level such analysis should be a key input when proposing changes to
the national immigration policy At the local level it could help define the role of municipalities
to assess the benefits and challenges of immigration These challenges are mainly related to the
provision of public goods and services such as health and education which in Chile are the
responsibility of the municipalities
The findings of this thesis suggest that policymakers should encourage policies that reduce
income inequality The key role that municipalities could play to strengthen social cohesion and
125
the increasingly important role that foreign population is acquiring in most modern societies are
also interesting avenues for future research However the picture is still incomplete and more
research is needed incorporating other dimensions of inequality This is essential if we want to
understand the reasons that could have triggered the social outbursts experienced by various
economies across the globe
126
Bibliography
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Acemoglu D (2002) Technical Change Inequality and the Labor Market Journal of Economic Literature 40(1) 7ndash72 httpsdoiorg1012570022051026976
Acemoglu D Aghion P amp Violante G L (2001) Deunionization Technical Change and Inequality Carnegie-Rochester Conference Series on Public Policy 55(1) 229ndash264 httpsdoiorg101016S0167-2231(01)00058-6
Acemoglu D Johnson S amp Robinson J A (2001) The Colonial Origins of Comparative Development An Empirical Investigation The American Economic Review 91(5) 1369ndash1401 httpsdoiorg101257aer9151369
Acemoglu D amp Robinson J A (2002) The Political Economy of the Kuznets Curve Review of Development Economics 6(2) 183ndash203 httpsdoiorg1011111467-936100149
Afonso A amp Fernandes S (2006) Measuring local government spending efficiency Evidence for the Lisbon region Regional Studies 40(1) 39ndash53 httpsdoiorg10108000343400500449937
Afonso A amp Fernandes S (2008) Assessing and explaining the relative efficiency of local government The Journal of Socio-Economics 37(5) 1946ndash1979 httpsdoiorg101016jsocec200703007
Afonso A Schuknecht L amp Tanzi V (2010) Income distribution determinants and public spending efficiency Journal of Economic Inequality 8(3) 367ndash389 httpsdoiorg101007s10888-010-9138-z
Akita T (2003) Decomposing regional income inequality in China and Indonesia using two-stage nested Theil decomposition method The Annals of Regional Science 37(1) 55ndash77 httpsdoiorg101007s001680200107
Alesina A (2000) Participation in heterogeneous communities The Quarterly Journal of Economics 115(3) 847ndash904 httpsdoiorg101162003355300554935
Alesina A amp La Ferrara E (2002) Who trusts others Journal of Public Economics 85(2) 207ndash234 httpsdoiorg101016S0047-2727(01)00084-6
Allcott H amp Keniston D (2014) Dutch Disease or Agglomeration The Local Economic Effects of Natural Resource Booms in Modern America (N W P N 20508 Ed) NBER Working Paper No 20508 (Vol w20508) NBER Working Paper No 20508 NBER Working Paper No 20508 httpsdoiorgNBER Working Paper No 20508
Andersen T M amp Maibom J (2020) The big trade-off between efficiency and equitymdashis it there Oxford Economic Papers 72(2) 391ndash411
127
Anselin L (1988) Spatial econometrics methods and models (Vol 4) DordrechtBoston Kluwer Academic Publishers
Anselin L amp Bera A K (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics Statistics Textbooks and Monographs (Vol 155)
Anselin L Bera A K Florax R amp Yoon M J (1996) Simple diagnostic tests for spatial dependence Regional Science and Urban Economics 26(1) 77ndash104 httpsdoiorg1010160166-0462(95)02111-6
Antonelli M A amp De Bonis V (2018) The efficiency of social public expenditure in European countries a two-stage analysis Applied Economics 1ndash14 httpsdoiorg1010800003684620181489522
Aragoacuten F M amp Rud J P (2013) Natural Resources and Local Communities Evidence from a Peruvian Gold Mine American Economic Journal Economic Policy 5(2) 1ndash25 httpsdoiorg101257pol521
Ariely G (2014) Does Diversity Erode Social Cohesion Conceptual and Methodological Issues Political Studies 62(3) 573ndash595 httpsdoiorg1011111467-924812068
Armstrong H amp Taylor J (2000) Regional economics and policy (3rd ed) Oxford Blackwell
Aroca P amp Atienza M (2011) Economic implications of long distance commuting in the Chilean mining industry Resources Policy 36(3) 196ndash203 httpsdoiorg101016jresourpol201103004
Aroca P amp Bosch M (2000) Crecimiento convergencia y espacio en las regiones chilenas 1960 - 1998 Estudios de Economiacutea 27 199ndash224 Retrieved from httprepositoriouchileclbitstreamhandle2250127853Patricio_Arocapdfsequence=1
Ashworth J Geys B Heyndels B amp Wille F (2014) Competition in the political arena and local government performance Applied Economics 46(19) 2264ndash2276 httpsdoiorg101080000368462014899679
Atkinson A B (2015) Inequality What Can Be Done Harvard University Press
Auty R (1993) Sustaining development in mineral economies the resource curse thesis London [ua] Routledge
Auty R (2001) Resource abundance and economic development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Badeeb R A Lean H H amp Clark J (2017) The evolution of the natural resource curse thesis A critical literature survey Resources Policy 51 123ndash134 httpsdoiorg101016jresourpol201610015
Balaguer-Coll M T Brun-Martos M I Maacuterquez-Ramos L amp Prior D (2019) Local government efficiency determinants and spatial interdependence Applied Economics
128
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Balaguer-Coll M T amp Prior D (2009) Short- and long-term evaluation of efficiency and quality An application to Spanish municipalities Applied Economics 41(23) 2991ndash3002 httpsdoiorg10108000036840701351923
Banker R D Charnes A amp Cooper W W (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis Management Science 30(9) 1078ndash1092
Beine M Coulombe S amp Vermeulen W N (2015) Dutch Disease and the Mitigation Effect of Migration Evidence from Canadian Provinces The Economic Journal 125(589) 1574ndash1615 httpsdoiorg101111ecoj12171
Benito B Bastida F amp Garciacutea J A (2010) Explaining differences in efficiency an application to Spanish municipalities Applied Economics 42(4) 515ndash528 httpsdoiorg10108000036840701675560
Berg A amp Ostry J (2011) Equality and efficiency Finance amp Development 48(3) 12ndash15
Berg A amp Ostry J (2017) Inequality and Unsustainable Growth Two Sides of the Same Coin IMF ECONOMIC REVIEW 65(4) 792ndash815 httpsdoiorg101057s41308-017-0030-8
Beyer H amp Vergara R (2006) Delincuencia en Chile Determinantes y rol de las poliacuteticas puacuteblicas Instituto de Economiacutea UC
Blanco L amp Grier R (2012) Natural resource dependence and the accumulation of physical and human capital in Latin America Resources Policy 37(3) 281ndash295 httpsdoiorghttpdoiorg101016jresourpol201201005
Blau J R amp Blau P M (1982) The cost of inequality Metropolitan structure and violent crime American Sociological Review 114ndash129
Boetti L Piacenza M amp Turati G (2009) Fiscal decentralization and spending efficiency of local governments An Empirical Investigation on a Sample Of 4
Boix C amp Posner D (1998) Social capital Explaining its origins and effects on government performance British Journal Of Political Science 28 686ndash693
Borge L E Parmer P amp Torvik R (2015) Local natural resource curse JOURNAL OF PUBLIC ECONOMICS 131 101ndash114 httpsdoiorg101016jjpubeco201509002
Borger B Kerstens K Moesen W amp Vanneste J (1994) Explaining differences in productive efficiency An application to Belgian municipalities Public Choice 80(3) 339ndash358 httpsdoiorg101007BF01053225
Bourguignon F amp Morrisson C (1990) Income distribution development and foreign trade A cross-sectional analysislowast European Economic Review 34(6) 1113ndash1132 httpsdoiorghttpsdoiorg1010160014-2921(90)90071-6
129
Branas C C Cheney R A MacDonald J M Tam V W Jackson T D amp Ten Have T R (2011) A difference-in-differences analysis of health safety and greening vacant urban space American Journal of Epidemiology 174(11) 1296ndash1306
Bravo J (2014) Fondo Comuacuten Municipal y su desincentivo a la recaudacioacuten en Chile Temas de La Agenda Legislativa-Centro de Poliacuteticas Puacuteblicas UC 9(68)
Browning E K amp Johnson W R (1984) The Trade-Off between Equality and Efficiency Journal of Political Economy 92(2) 175ndash203 httpsdoiorg101086261219
Brunnschweiler C N amp Bulte E H (2008) The resource curse revisited and revised A tale of paradoxes and red herrings Journal of Environmental Economics and Management 55(3) 248ndash264 httpsdoiorghttpsdoiorg101016jjeem200708004
Brunori P Ferreira F H G amp Peragine V (2013) Inequality of opportunity income inequality and economic mobility Some international comparisons In Getting Development Right (pp 85ndash115) Springer
Brunton-Smith I Jackson J amp Sutherland A (2014) Bridging structure and perception On the neighbourhood ecology of beliefs and worries about violent crime British Journal of Criminology 54(4) 503ndash526
Bulte E H Damania R amp Deacon R T (2005) Resource intensity institutions and development World Development 33(7) 1029ndash1044 httpsdoiorg101016jworlddev200504004
Carmignani F (2013) Development outcomes resource abundance and the transmission through inequality Resource and Energy Economics 35(3) 412ndash428 httpsdoiorg101016jreseneeco201304007
Carmignani Fabrizio amp Avom D (2010) The social development effects of primary commodity export dependence Ecological Economics 70(2) 317ndash330 httpsdoiorg101016jecolecon201009003
Caselli F amp Michaels G (2013) Do Oil Windfalls Improve Living Standards Evidence from Brazil American Economic Journal Applied Economics 5(1) 208ndash238 httpsdoiorg101257app51208
Celebioglu F amp Dallrsquoerba S (2010) Spatial disparities across the regions of Turkey An exploratory spatial data analysis Annals of Regional Science 45(2) 379ndash400 httpsdoiorg101007s00168-009-0313-8
Chan J To H-P amp Chan E (2006) Reconsidering social cohesion Developing a definition and analytical framework for empirical research Social Indicators Research 75(2) 273ndash302
Charnes A Cooper W W amp Rhodes E (1978) Measuring the efficiency of decision making units European Journal of Operational Research 2(6) 429ndash444
130
Chi G amp Zhu J (2019) Spatial Regression Models for the Social Sciences SAGE Publications Retrieved from httpsbooksgooglecomaubooksid=zHGkvwEACAAJ
Christiansen K O (1960) Industrialization and urbanization in relation to crime and juvenile delinquency International Review of Criminal Policy 16 3ndash8
Cingano F (2014) Trends in income inequality and its impact on economic growth (Vol 163) Paris OECD Publishing httpsdoiorg1017875jxrjncwxv6j-en
Coelli T J Prasada Rao D S OrsquoDonnell C J amp Battese G E (2005) An introduction to efficiency and productivity analysis An Introduction to Efficiency and Productivity Analysis Springer Science amp Business Media httpsdoiorg101007b136381
Coffeacute H amp Geys B (2005) Institutional Performance and Social Capital An Application to the Local Government Level Journal of Urban Affairs 27(5) 485ndash501 httpsdoiorg101111j0735-2166200500249x
Coffeacute H amp Geys B (2006) Community Heterogeneity A Burden for the Creation of Social Capital Social Science Quarterly 87(5) 1053ndash1072 httpsdoiorg101111j1540-6237200600415x
Cohen D Spear S Scribner R Kissinger P Mason K amp Wildgen J (2000) ldquo Broken windowsrdquo and the risk of gonorrhea American Journal of Public Health 90(2) 230
Corden W M amp Neary J P (1982) Booming sector and de-industrialisation in a small open economy The Economic Journal 92(368) 825ndash848
Cordero J M Pedraja-Chaparro F Pisaflores E C amp Polo C (2017) Efficiency assessment of Portuguese municipalities using a conditional nonparametric approach Journal of Productivity Analysis 48(1) 1ndash24 httpsdoiorg101007s11123-017-0500-z
Croissant Y amp Millo G (2018) Panel Data Econometrics with R John Wiley amp Sons
Cust J amp Poelhekke S (2015) The Local Economic Impacts of Natural Resource Extraction 7 251ndash268 httpsdoiorg101146annurev-resource-100814-125106
da Cruz N F amp Marques R C (2014) Revisiting the determinants of local government performance Omega 44 91ndash103 httpsdoiorg101016JOMEGA201309002
Dauvin M amp Guerreiro D (2017) The Paradox of Plenty A Meta-Analysis World Development 94 httpsdoiorg101016jworlddev201701009
de Sousa M da C S Cribari-Neto F amp Stosic B D (2005) Explaining DEA technical efficiency scores in an outlier corrected environment the case of public services in Brazilian municipalities Brazilian Review of Econometrics 25(2) 287ndash313
Deacon R T (2011) The Political Economy of the Natural Resource Curse A Survey of Theory and Evidence Foundations and Trends in Microeconomics 111-208
Delhey J amp Newton K (2005) Predicting cross-national levels of social trust global pattern or
131
Nordic exceptionalism European Sociological Review 21(4) 311ndash327
Dlouhyacute M (2018) Measuring Geographic Inequalities Dealing with Multiple Health Resources by Data Envelopment Analysis Frontiers in Public Health 6 53 httpsdoiorg103389fpubh201800053
Dollery B Wallis J amp Akimov A (2010) One Size Does Not Fit All The Special Case of Remote Small Local Councils in Outback Queensland Local Government Studies 36(1) 21ndash42 httpsdoiorg10108003003930903435716
Domenech J (2008) Mineral resource abundance and regional growth in Spain 1860ndash2000 Journal of International Development The Journal of the Development Studies Association 20(8) 1122ndash1135
Doran J amp Jordan D (2016) Decomposing US regional income inequality from 1969 to 2009 Applied Economics Letters 23(11) 781ndash784 httpsdoiorg1010801350485120151109030
Drew J amp Dollery B (2015) The State of Things The Dynamic Efficiency of Australian State and Territories Economic Papers A Journal of Applied Economics and Policy 34(3) 165ndash176
Drew J Kortt M amp Dollery B (2015) What Determines Efficiency in Local Government A DEA Analysis of NSW Local Government Economic Papers A Journal of Applied Economics and Policy 34(4) 243ndash256 httpsdoiorg1011111759-344112118
Easterly W (2007) Inequality does cause underdevelopment Insights from a new instrument Journal of Development Economics 84(2) 755ndash776 httpsdoiorghttpdxdoiorg101016jjdeveco200611002
Ebert L amp La Menza T (2015) Chile copper and resource revenue A holistic approach to assessing commodity dependence Resources Policy 43(Supplement C) 101ndash111 httpsdoiorghttpsdoiorg101016jresourpol201410007
ElGindi T (2017) Natural resource dependency neoliberal globalization and income inequality Are they related A longitudinal study of developing countries (1980ndash2010) Current Sociology 65(1) 21ndash53 httpsdoiorg1011770011392116632031
Engerman S L amp Sokoloff K L (1994) Factor Endowments Institutions and Differential Paths of Growth Among New World Economies National Bureau of Economic Research
Engerman S L amp Sokoloff K L (1997) Factor endowments institutions and differential paths of growth among new world economies How Latin America Fell Behind 260ndash304
Engerman S L Sokoloff K L Urquiola M amp Acemoglu D (2002) Factor Endowments Inequality and Paths of Development among New World Economies [with Comments] EconomampxedA 3(1) 41ndash109 Retrieved from httpwwwjstororgezp01libraryquteduaustable20065432
132
Faumlre R Grosskopf S amp Lovell C A K (1985) The measurement of efficiency of production (Vol 6) Springer Science amp Business Media
Fleming D A amp Measham T G (2015a) Income Inequality across Australian Regions during the Mining Boom 2001-11 Australian Geographer 46(2) 203ndash216 httpsdoiorg1010800004918220151020596
Fleming D A amp Measham T G (2015b) Local economic impacts of an unconventional energy boom the coal seam gas industry in Australia Australian Journal of Agricultural and Resource Economics 59(1) 78ndash94 httpsdoiorg1011111467-848912043
Fum R M amp Hodler R (2010) Natural resources and income inequality The role of ethnic divisions Economics Letters 107(3) 360ndash363 httpsdoiorg101016jeconlet201003008
Garofalo J (1978) The fear of crime Broadening our perspective
Geys B amp Moesen W (2009) Exploring Sources of Local Government Technical Inefficiency Evidence from Flemish Municipalities Public Finance and Management 9(1) 1ndash29
Goderis B amp Malone S W (2011) Natural Resource Booms and Inequality Theory and Evidence The Scandinavian Journal of Economics 113(2) 388ndash417 httpsdoiorg101111j1467-9442201101659x
Greene W H (2016) Productivity and Efficiency Analysis (W H Greene L Khalaf R Sickles M Veall amp M-C Voia Eds) (1st ed 20) Cham Springer International Publishing httpsdoiorg101007978-3-319-23228-7
Gustafsson B amp Shi L (2002) Income inequality within and across counties in rural China 1988 and 1995 Journal of Development Economics 69(1) 179ndash204 httpsdoiorg101016S0304-3878(02)00058-5
Gylfason T amp Zoega G (2003) Inequality and Economic Growth Do Natural Resources Matter In T T Eicher S (Ed) Inequality and Growth Theory and Policy Implications (pp 255ndash292) The MIT Press
Henningsen A (2010) Estimating Censored Regression Models in R using the censReg Package R Package Vignettes Collection 5(2) 12
Henningsen A (2019) censReg Censored Regression (Tobit) Models R package version 05-30 httpscranr-projectorgpackage=censReg Retrieved from httpscranr-projectorgpackage=censReg
Herrera S amp Pang G (2005) Efficiency of Public Spending in Developing Countriesthinsp An Efficiency Frontier Approach World Bank Washington DC
Hill T D amp Angel R J (2005) Neighborhood disorder psychological distress and heavy drinking Social Science amp Medicine 61(5) 965ndash975
133
httpsdoiorghttpsdoiorg101016jsocscimed200412027
Hipp J R (2010) RESIDENT PERCEPTIONS OF CRIME AND DISORDER HOW MUCH IS ldquoBIASrdquo AND HOW MUCH IS SOCIAL ENVIRONMENT DIFFERENCES Criminology 48(2) 475ndash508 httpsdoiorg101111j1745-9125201000193x
Holtug N amp Mason A (2010) Introduction Immigration diversity and social cohesion SAGE Publications Sage UK London England
Hooghe M Vanhoutte B Hardyns W amp Bircan T (2010) Unemployment Inequality Poverty and Crime Spatial Distribution Patterns of Criminal Acts in Belgium 2001ndash06 The British Journal of Criminology 51(1) 1ndash20 httpsdoiorg101093bjcazq067
Howie P amp Atakhanova Z (2014) Resource boom and inequality Kazakhstan as a case study Resources Policy 39(1) 71ndash79 httpsdoiorg101016jresourpol201311004
Isham J Woolcock M Pritchett L amp Busby G (2005) The Varieties of Resource Experience Natural Resource Export Structures and the Political Economy of Economic Growth The World Bank Economic Review 19(2) 141ndash174 httpsdoiorg101093wberlhi010
Jottier D Ashworth J amp Heyndels B (2012) Understanding Votersrsquo Preferences How the Electoratersquos Complexity Affects Prediction Accuracy and Wishful Thinking among Politicians with Respect to Election Outcomes Kyklos 65(3) 340ndash370 httpsdoiorg101111j1467-6435201200542x
Kanbur S M R amp Venables A (2005) Spatial inequality and development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Kesler C amp Bloemraad I (2010) Does immigration erode social capital The conditional effects of immigration-generated diversity on trust membership and participation across 19 countries 1981ndash2000 Canadian Journal of Political ScienceRevue Canadienne de Science Politique 43(2) 319ndash347
Kuznets S (1955) Economic Growth and Income Inequality The American Economic Review 45(1) 1ndash28 Retrieved from httpwwwjstororgstable1811581
Lagos G amp Blanco E (2010) Mining and development in the region of Antofagasta Resources Policy 35(4) 265ndash275 httpsdoiorghttpsdoiorg101016jresourpol201007006
Leamer E E Maul H Rodriguez S amp Schott P K (1999) Does natural resource abundance increase Latin American income inequality Journal of Development Economics 59(1) 3ndash42 httpsdoiorg101016s0304-3878(99)00004-8
Leibbrandt M Finn A amp Woolard I (2012) Describing and decomposing post-apartheid income inequality in South Africa Development Southern Africa 29(1) 19ndash34 httpsdoiorg1010800376835X2012645639
134
Letki N (2008) Does Diversity Erode Social Cohesion Social Capital and Race in British Neighbourhoods Political Studies 56(1) 99ndash126 httpsdoiorg101111j1467-9248200700692x
Lewis D A (2017) Fear of crime Incivility and the production of a social problem Routledge
lo Storto C (2013) Evaluating Technical Efficiency of Italian Major Municipalities A Data Envelopment Analysis model Procedia - Social and Behavioral Sciences 81 346ndash350 httpsdoiorg101016JSBSPRO201306440
Loayza N amp Rigolini J (2016) The Local Impact of Mining on Poverty and Inequality Evidence from the Commodity Boom in Peru World Development 84 219ndash234 httpsdoiorg101016jworlddev201603005
Loayza N Teran A M y amp Rigolini J (2013) Poverty Inequality and the Local Natural Resource Curse World Bank Policy Research Working Paper (6366) httpsdoiorg1015961813-9450-6366
Loacutepez R amp Miller S J (2008) Chile The Unbearable Burden of Inequality World Development 36(12) 2679ndash2695 httpsdoiorg101016jworlddev200801012
Manzano O amp Rigobon R (2001) Resource Curse or Debt Overhang National Bureau of Economic Research Working Paper Series No 8390 1 httpsdoiorghttpwwwnberorgpapersw9424bib
McPherson M Smith-Lovin L amp Cook J M (2001) Birds of a Feather Homophily in Social Networks Annual Review of Sociology 27(1) 415ndash444 httpsdoiorg101146annurevsoc271415
McQuestin D Drew J amp Dollery B (2018) Do Municipal Mergers Improve Technical Efficiency An Empirical Analysis of the 2008 Queensland Municipal Merger Program Australian Journal of Public Administration 77(3) 442ndash455 httpsdoiorg1011111467-850012286
Messner S F Rosenfeld R amp Baumer E P (2004) Dimensions of Social Capital and Rates of Criminal Homicide American Sociological Review 69(6) 882ndash903 httpsdoiorg101177000312240406900607
Michaels G (2011) THE LONG TERM CONSEQUENCES OF RESOURCE-BASED SPECIALISATION The Economic Journal 121(551) 31ndash57 httpsdoiorg101111j1468-0297201002402x
Mijanovich T amp Weitzman B C (2003) Which ldquobroken windowsrdquo matter School neighborhood and family characteristics associated with youthsrsquo feelings of unsafety Journal of Urban Health 80(3) 400ndash415
Mikušovaacute P (2015) An Application of DEA Methodology in Efficiency Measurement of the Czech Public Universities Procedia Economics and Finance 25 569ndash578 httpsdoiorg101016S2212-5671(15)00771-6
135
Milanovic B (2016) Global inequality Harvard University Press
Millo G amp Piras G (2012) splm Spatial panel data models in R Journal of Statistical Software 47(1) 1ndash38
Murphy K M amp Topel R H (2016) Human Capital Investment Inequality and Economic Growth JOURNAL OF LABOR ECONOMICS 34(2) S99ndashS127 httpsdoiorg101086683779
Narboacuten-Perpintildeaacute I amp De Witte K (2018a) Local governmentsrsquo efficiency a systematic literature reviewmdashpart I International Transactions in Operational Research 25(2) 431ndash468 httpsdoiorg101111itor12364
Narboacuten-Perpintildeaacute I amp De Witte K (2018b) Local governmentsrsquo efficiency a systematic literature reviewmdashpart II International Transactions in Operational Research 25(4) 1107ndash1136 httpsdoiorg101111itor12389
Nuntildeez J Rivera J Villavicencio X amp Molina O (2003) Determinantes socioeconoacutemicos y demograacuteficos del crimen en Chile Estudios de Economiacutea 30(1) 55ndash85
OrsquoDonnell C J Rao D S P amp Battese G E (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios Empirical Economics 34(2) 231ndash255 httpsdoiorg101007s00181-007-0119-4
Ocampo J A (2004) Latin Americarsquos Growth and Equity Frustrations During Structural Reforms The Journal of Economic Perspectives 18(2) 67ndash88 httpsdoiorg1012570895330041371349
OECD (2014) Focus on inequality and growth OECD
OECD (2017) Howrsquos Life 2017 Life Satisfaction Oecd httpsdoiorg101787how_life-2017-en
Ohtake F (2008) Inequality in Japan Asian Economic Policy Review 3(1) 87ndash109 httpsdoiorg101111j1748-3131200800093x
Okun A M (2015) Equality and efficiency the big tradeoff Washington DC Brookings Institution Press
Ortega B Sanjuaacuten J amp Casquero A (2017) Determinants of efficiency in reducing child mortality in developing countries The role of inequality and government effectiveness Health Care Management Science 20(4) 500ndash516 httpsdoiorg101007s10729-016-9367-1
Ostry J Berg A amp Tsangarides C (2014) Redistribution inequality and growth International Monetary Fund
Pacheco F Saacutenchez R amp Villena M (2013) Eficiencia de los Gobiernos Locales y sus Determinantes Un anaacutelisis de Fronteras Estocaacutesticas en Datos de Panel para
136
Municipalidades Chilenas Santiago de Chile Chile
Papyrakis E amp Raveh O (2014) An Empirical Analysis of a Regional Dutch Disease The Case of Canada Environmental and Resource Economics 58(2) 179ndash198 httpsdoiorg101007s10640-013-9698-z
Paredes D (2013) The Role of Human Capital Market Potential and Natural Amenities in Understanding Spatial Wage Disparities in Chile Spatial Economic Analysis 8(2) 154ndash175 httpsdoiorg101080174217722013774094
Paredes D Iturra V amp Lufin M (2016) A spatial decomposition of income inequality in Chile Regional Studies 50(5) 771ndash789
Phan P Van Orsquobrien M Mendolia S amp Paloyo A (2017) National pro-poor spending programmes and their effect on income inequality and poverty Evidence from Vietnam Applied Economics 49(55) 5579ndash5590 httpsdoiorg1010800003684620171313957
Podinovski V V (2004) Bridging the Gap between the Constant and Variable Returns-to-Scale Models Selective Proportionality in Data Envelopment Analysis The Journal of the Operational Research Society 55(3) 265ndash276 Retrieved from httpwwwjstororgstable4102006
Ravallion M (2005) On Measuring Aggregate Social Efficiency Economic Development and Cultural Change 53(2) 273ndash292 httpsdoiorg101086425380
Rehner J Baeza S A amp Barton J R (2014) Chilersquos resource-based export boom and its outcomes Regional specialization export stability and economic growth Geoforum 56(Supplement C) 35ndash45 httpsdoiorghttpsdoiorg101016jgeoforum201406007
Rivera J Gutieacuterrez M amp Nuacutentildeez J (2009) Caracterizacioacuten socioeconoacutemica y espacial de la criminalidad en Chile Revista CEPAL
Ross C E (2011) Collective threat trust and the sense of personal control Journal of Health and Social Behavior 52(3) 287ndash296
Ross C E amp Mirowsky J (2001) Neighborhood disadvantage disorder and health Journal of Health and Social Behavior 258ndash276
Rothstein B amp Uslaner E M (2005) All for all Equality corruption and social trust World Politics 58(1) 41ndash72
Sachs J D amp Warner A M (2001) The curse of natural resources European Economic Review 45(4ndash6) 827ndash838 httpsdoiorg101016S0014-2921(01)00125-8
Salas R (2019) sinimr Chilean Municipalities Information System Wrapper Retrieved from httpsgithubcomrobsalascosinimr
Sampson R J (1986) Crime in Cities The Effects of Formal and Informal Social Control Crime and Justice 8 271ndash311
137
Sampson R J (2008) Rethinking crime and immigration Contexts 7(1) 28ndash33
Santos Silva J M C amp Tenreyro S (2010) On the existence of the maximum likelihood estimates in Poisson regression Economics Letters 107(2) 310ndash312 httpsdoiorghttpsdoiorg101016jeconlet201002020
Santos Silva J M C amp Tenreyro S (2011) poisson Some convergence issues Stata Journal 11(2) 207ndash212 Retrieved from httpwwwstata-journalcomarticlehtmlarticle=st0225
Scott Z (2009) Decentralisation local development and social cohesion an analytical review GSDRC Research Paper 5
Sinha R P (2017) Fiscal Performance Benchmarking of Indian States-A Robust Frontier Approach The Central European Review of Economics and Management 1(4) 225ndash249
Skogan W (1986) Fear of crime and neighborhood change Crime and Justice 8 203ndash229
Skogan W (1999) Measuring what matters Crime disorder and fear In Measuring what matters Proceedings from the Policing Research Institute meetings (pp 37ndash53) National Institute of Justice Washington DC
Skogan W (2015) Disorder and Decline The State of Research Journal of Research in Crime and Delinquency 52(4) 464ndash485 httpsdoiorg1011770022427815577836
Šťastnaacute L amp Gregor M (2014) Public sector efficiency in transition and beyond evidence from Czech local governments Applied Economics 47(7) 1ndash20 httpsdoiorg101080000368462014978077
Tandon A (2005) Measuring Efficiency of Macro Systems An Application to Millennium Development Goal Attainment Asian Development Review 22(2) 108ndash125
Taylor R B (1999) The incivilities thesis Theory measurement and policy Measuring What Matters 65 88
Tigga N S amp Mishra U S (2015) On Measuring Technical Efficiency of the Health System in India An Application of Data Envelopment Analysis Journal of Health Management 17(3) 285ndash298 httpsdoiorg1011770972063415589229
Tinbergen J (1975) Demographic Development and the Exhaustion of Natural Resources Population and Development Review 1(1) 23ndash32 httpsdoiorg1023071972269
Tiruneh G (2014) Social Revolutions Their Causes Patterns and Phases SAGE Open 4(3) 2158244014548845 httpsdoiorg1011772158244014548845
Tolsma J Van der Meer T amp Gesthuizen M (2009) The impact of neighbourhood and municipality characteristics on social cohesion in the Netherlands Acta Politica 44(3) 286ndash313
Tsekeris Sotiris T Tsekeris T amp Papaioannou S (2018) Regional determinants of technical efficiency evidence from the Greek economy Regional Studies [London]thinsp Carfax
138
httpsdoiorg1010800034340420171390312
Uslaner E (2002) The moral foundations of trust Cambridge University Press
Uslaner E (2011) CORRUPTION AND INEQUALITY DICE Report 9(2) 20ndash24
Uslaner E (2013) Trust and corruption revisited how and why trust and corruption shape each other Quality amp Quantity 47(6) 3603ndash3608 httpsdoiorg101007s11135-012-9742-z
Uslaner E amp Brown M (2005) Inequality trust and civic engagement American Politics Research 33(6) 868ndash894
Van der Ploeg F (2011) Natural Resources Curse or Blessing Journal of Economic Literature 49(2) 366ndash420 httpsdoiorg101257jel492366
Vergara R (2012) Crime Prevention Programs Evidence From CHILE The Developing Economies 50(1) 1ndash24
Watkins M H (1963) A staple theory of economic growth Canadian Journal of Economics and Political ScienceRevue Canadienne de Economiques et Science Politique 29(2) 141ndash158
Wilkinson R G (1999) Income inequality social cohesion and health clarifying the theorymdasha reply to Muntaner and Lynch International Journal of Health Services 29(3) 525ndash543
Wilson J Q amp Kelling G L (1982) Broken windows Atlantic Monthly 249(3) 29ndash38
Wilson W J (1996) When Work Disappears Political Science Quarterly 111(4) 567ndash595 httpsdoiorg1023072152085
Wirth L (1938) Urbanism as a Way of Life American Journal of Sociology 44(1) 1ndash24
Worthington A amp Dollery B (2000) An empirical survey of frontier efficiency measurement techniques in local government Local Government Studies 26(2) 23ndash52 httpsdoiorg10108003003930008433988
Wu P-C Huang T-H amp Pan S-C (2014) Country Performance Evaluation The DEA Model Approach Social Indicators Research 118(2) 835ndash849 httpsdoiorg101007s11205-013-0443-3
Ye X Ma L Ye K Chen J amp Xie Q (2017) Analysis of Regional Inequality from Sectoral Structure Spatial Policy and Economic Development A Case Study of Chongqing China Sustainability 9(4) 633 Retrieved from httpwwwmdpicom2071-105094633
Yue W Z Zhang Y T Ye X Y Cheng Y Q amp Leipnik M R (2014) Dynamics of Multi-Scale Intra-Provincial Regional Inequality in Zhejiang China Sustainability 6(9) 5763ndash5784 httpsdoiorg103390su6095763
139
Appendices
Appendix A Summary statistics income inequality
Table A1
Summary statistics Gini coefficients by year and zone
140
Appendix B Summary statistics for NRD measures by region
Table B1
Summary statistics NRD measures by region
141
Appendix C Regional administrative division and defined zones
Figure C1 Geographical distribution of Chilean regions and 3 zones
142
Appendix D Summary statistics numeric controls and correlation matrix
Table D1
Summary Statistics Numeric Explanatory Variables
Figure D1 Correlation matrix numeric explanatory variables
143
Appendix E Static spatial panel models
Following Millo amp Piras (2012) a model including a spatial lag of the dependent variable and
spatial autoregressive disturbances but not spatial lags for the explanatory variable(s) is called
SARAR model A static spatial SARAR panel could be expressed as
119910 120582 119868 otimes119882 119910 119883120573 119906 (E1)
where y is an 119873 1 vector of observations on the dependent variable X is a 119873 119896 matrix of
observations on the non-stochastic exogenous regressors 119868 an identity matrix of dimension 119879 otimes
is the kronecker operator 119882 is the 119873 119873 spatial weights matrix of known constants whose
diagonal elements are set to zero and 120582 the corresponding spatial parameter44
The disturbance vector is the sum of two terms
119906 120580 otimes 119868 120583 120576 (E2)
where 120580 is a 119879 1 vector of ones 119868 an 119873 119873 identity matrix 120583 is a vector of time-invariant
individual specific effects (not spatially autocorrelated) and 120576 a vector of spatially autocorrelated
innovations that follow a spatial autoregressive process of the form
120576 120588 119868 otimes119882 120576 120584 (E3)
If we assume that spatial correlation applies to both the individual effects 120583 and the remainder
error components 120576 Kapoor et al (2007) propose that the disturbance term 119906 follows a first order
spatial autoregressive process of the form
119906 120588 119868 otimes119882 119906 120576 (E4)
44 Unlike ldquotraditional panelsrdquo which are organized as different time series for each cross-sectional unit ldquospatial panelsrdquo are organized as a series of cross-sections for each year
144
where 119882 is the spatial weights matrix and 119903ℎ119900 the corresponding spatial autoregressive
parameter To further allow for the innovations to be correlated over time the innovations vector
in Equation 7 follows an error component structure
120576 120580 otimes 119868 120583 120584 (E5)
where 120583 is the vector of cross-sectional specific effects 120584 a vector of innovations that vary
both over cross-sectional units and time periods 120580 is a vector of ones and 119868 an 119873 119873 identity
matrix45
Spatial panel models are usually estimated by Maximum Likelihood or GMM46 The SAR
SEM or SARAR models could be estimated with Random or Fixed effects For instance A fixed
effect spatial lag model can be written in stacked form as
119910 120582 119868 otimes119882 119910 120580 otimes 119868 120583 119883120573 120576 (E6)
where 120582 is the spatial autoregressive coefficient 119882 a non-stochastic spatial weights matrix
120580 a column vector of ones of dimension 119879 119868 an 119873 119873 identity matrix and 120576 sim 119873 0120590 On
the other hand a fixed effects spatial error model assuming the disturbance specification by
Kapoor et al (2007) can be written as
119910 120580119879 otimes 119868119873 120583 119883120573 119906119906 120588 119868119879 otimes119882119873 119906 120576
(E7)
where 120588 is the spatial autocorrelation coefficient and 120576 is a well-behaved error term
45 In the regression implementation the specification given by equations (22) and (3) is denoted by ldquobrdquo for ldquoBaltagirdquo On the other hand the specification given by equations (23) and (31) is denoted by ldquokkprdquo for ldquoKapoor Kelejian and Pruchardquo 46 We use the R package splm which allow both types of regression procedure
145
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis
Table F1
Analysis OLS residuals Anselin Method
Figure F1 Moran scatter plot OLS residuals
146
Appendix G Linear panel data models
Table G1
Panel regressions (non-spatial)
147
Appendix H Spatial panel models (Generalized Moments (GM) estimation)
Table H1
GM Spatial Models
148
Appendix I Inputs and outputs used in DEA analysis
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018)
149
Appendix J Technical and scale efficiency
Following lo Storto (2013) under an input-oriented specification assuming VRS with n
municipalities using k inputs to produce m outputs the DEA model for a given i-th municipality
is specified with the following mathematical programming problem
119898119894119899 120579119904119906119887119895119890119888119905 119905119900 119910 119884120582 0120579119909 119883120582 01198991prime120582 1 120582 0prime
Where 119910 is the column vector of the outputs and 119909 is the column vector of the inputs
Moreover we can define X as the (k times n) input matrix and Y as the (m times n) output matrix 120579 is a
scalar (that satisfies 120579 1) more specifically it is the efficiency score that measures technical
efficiency It measures the distance between a municipality and the efficiency frontier defined as
a linear combination of the best practice observations With 120579 1 the municipality is inside the
frontier (ie it is inefficient) while 120579 1 implies that the municipality is on the frontier (ie it is
efficient) The vector 120582 is an (n times 1) vector of constants that measures the weights used to compute
the location of an inefficient municipality if it were to become efficient
The total technical efficiency 119879119864 can be decomposed into pure technical efficiency
119879119864 and scale efficiency 119878119864 where 119878119864 119879119864 119879119864 (Coelli et al 2005) To find out
whether a municipality is scale efficient and qualify the type of returns of scale a DEA model
under non-increasing returns to scale 119879119864 is implemented where 119878119864 119879119864 119879119864 Hence
the following rule can be applied (Faumlre Grosskopf amp Lovell 1985)
bull If 119878119864 1 then a municipality is scale efficient both under CRS and VRS
bull If 119878119864 1 it operates under increasing returns to scale
bull If 119878119864 1 it operates under decreasing returns to scale
150
Appendix K Correlation matrix
Figure K1 Correlation matrix contextual factors
151
Appendix L Returns to scale by year and zone
Table L1
Returns to scale (percentage of municipalities)
152
Appendix M Returns to scale by year (maps)
Figure M1 Spatial distribution of returns to scale by county per year
153
Appendix N Efficiency status by year (maps)
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year
154
Appendix O Spatial distribution efficiency scores by year (maps)
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year
155
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis
Table P1
Analysis OLS residuals Anselin Method
Figure P1 Moran scatter plot efficiency scores and OLS residuals
156
Table P2
OLS and spatial regression models for the six-year averaged data
157
Appendix Q OLS regressions for cross-sectional and panel data
Table Q1
OLS cross-sectional regression per year
158
Table Q2
OLS panel regressions Pooled random effects and instrumental variable
159
Appendix R Quantile maps incivilities rate by group (average total period)
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17)
160
Appendix S Correlation matrix numeric covariates
Figure S1 Correlation matrix numeric covariates
161
Appendix T Negative Binomial regressions
Table T1
Negative Binomial regressions
162
Appendix U Coefficients economic and racial diversity by geographical zone
Table U1
Coefficients economic and racial diversity in pooled Poisson models by geographic zone
vii
Appendix E Static spatial panel models 143
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis 145
Appendix G Linear panel data models 146
Appendix H Spatial panel models (Generalized Moments (GM) estimation) 147
Appendix I Inputs and outputs used in DEA analysis 148
Appendix J Technical and scale efficiency 149
Appendix K Correlation matrix 150
Appendix L Returns to scale by year and zone 151
Appendix M Returns to scale by year (maps) 152
Appendix N Efficiency status by year (maps) 153
Appendix O Spatial distribution efficiency scores by year (maps) 154
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis 155
Appendix Q OLS regressions for cross-sectional and panel data 157
Appendix R Quantile maps incivilities rate by group (average total period) 159
Appendix S Correlation matrix numeric covariates 160
Appendix T Negative Binomial regressions 161
Appendix U Coefficients economic and racial diversity by geographical zone 162
viii
List of Figures
Figure 21 Average share in GDP of economic activities (2006ndash17) 37
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17) 38
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17) 39
Figure 23 Moran scatter plots for variables gini and pss_casen 45
Figure 31 Geographical distribution of Chilean regions and macrozones 74
Figure 32 Evolution of efficiency scores and the proportion of firms by sector 77
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE) 78
Figure 34 Returns to scale by zone 79
Figure 35 Evolution mean efficiency scores (VRS) by zone 81
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017 102
Figure 42 Evolution total number of incivilities by category 104
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017) 104
Figure 44 Annual average number of incivilities per county 109
Figure C1 Geographical distribution of Chilean regions and 3 zones 141
Figure D1 Correlation matrix numeric explanatory variables 142
Figure F1 Moran scatter plot OLS residuals 145
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018) 148
Figure K1 Correlation matrix contextual factors 150
Figure M1 Spatial distribution of returns to scale by county per year 152
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year 153
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year 154
Figure P1 Moran scatter plot efficiency scores and OLS residuals 155
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17) 159
Figure S1 Correlation matrix numeric covariates 160
ix
List of Tables
Table 21 Cross-sectional Model Comparison (six-year average data) 47
Table 22 ML Spatial SAR Models 50
Table 23 ML Spatial SEM Models 50
Table 24 ML Spatial SARAR Models 51
Table 31 Descriptive statistics Inputs and Output variables used in DEA analysis 71
Table 32 Summary Statistics Numeric Contextual Factors 74
Table 33 Summary efficiency scores (VRS) by zone and region 80
Table 34 Cross-sectional (censored) regressions 84
Table 35 Panel data regressions 87
Table 41 Summary statistics total count of incivilities and by category (full sample and period) 103
Table 42 Summary statistics numeric explanatory variables 108
Table 43 Poisson regressions 113
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region 115
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group 116
Table A1 Summary statistics Gini coefficients by year and zone 139
Table B1 Summary statistics NRD measures by region 140
Table D1 Summary Statistics Numeric Explanatory Variables 142
Table F1 Analysis OLS residuals Anselin Method 145
Table G1 Panel regressions (non-spatial) 146
Table H1 GM Spatial Models 147
Table L1 Returns to scale (percentage of municipalities) 151
Table P1 Analysis OLS residuals Anselin Method 155
Table P2 OLS and spatial regression models for the six-year averaged data 156
Table Q1 OLS cross-sectional regression per year 157
Table Q2 OLS panel regressions Pooled random effects and instrumental variable 158
Table T1 Negative Binomial regressions 161
Table U1 Coefficients economic and racial diversity in pooled Poisson models by geographic zone 162
x
List of Abbreviations
Constant returns to scale CRS
Data envelopment analysis DEA
Decreasing returns to scale DRS
Efficiency scores ES
Exploratory spatial data analysis ESDA
Generalized methods of moments GMM
Gross Domestic Product GDP
Increasing returns to scale IRS
Local government efficiency LGE
Maximum likelihood ML
Municipal common fund MCF
Natural resource dependence NRD
Natural resource endowment NRE
Ordinary Least Squares OLS
Organization for Economic Cooperation and Development OECD
Own permanent revenues OPR
Resource curse hypothesis RCH
Spatial autoregressive model SAR
Spatial error model SEM
Variable returns to scale VRS
xi
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet requirements
for an award at this or any other higher education institution To the best of my knowledge and
belief the thesis contains no material previously published or written by another person except
where due reference is made
Signature QUT Verified Signature
Date _________04092020_________
xii
Acknowledgements
First I would like to thank my wife Lilian who joined me in this challenge and patiently
supported me all these years I would also like to thank our family who always supported us from
Chile I especially thank my sister Silvia who took care of our house and dog
I am also grateful to my supervisory team Dr Radhika Lahiri and Dr Vincent Hoang who
supported and guided me in the process of making this thesis a reality
I also thank the Deans of the Faculty of Economics and Business at my beloved University
of Talca Dr Arcadio Cerda and Dr Rodrigo Herrera who trusted me and supported me in this
process In the same way I would like to thank all the support of the director of the Commercial
Engineering career Mr Milton Inostroza
Finally I would like to thank the government of Chile for the financial support that made
my stay and studies possible here at the Queensland University of Technology
13
Chapter 1 Introduction
Efficiency and equity issues are often considered together in the evaluation of economic
performance While higher efficiency usually measured by growth rates of income per capita
correlates with improvements in measures of well-being the link between inequality and well-
being is less clear This is reflected not only in the type and amount of research related to efficiency
and equity but also in the role that both play in the design of the economic policy For instance
several market-oriented countries have focused primarily on economic growth trusting in a trickle-
down process where financial benefits given to the wealthy are expected to ultimately benefit the
poor However despite the growing interest in the issue of inequality there is a considerable lack
of studies about its consequences
Although some level of inequality is inevitable or even necessary for economic activity this
study is motivated by the argument that relatively high levels of inequality can be associated with
many problems such as persistent unemployment increasing fiscal expenses indebtedness and
political instability (Berg amp Ostry 2011) Inequality can also have other severe social
consequences including increased crime rates teenage pregnancy obesity and fewer
opportunities for low-income households to invest in health and education (Atkinson 2015) In
addition when the role of money and concentration of economic power undermine political
outcomes inequality of opportunities hampers social and economic mobility trust and social
cohesion In summary inequality can increase the fragility of the economic and social situation in
a country reducing economic growth and making it less inclusive and sustainable
14
A country well-known for its market-oriented economy and high level of dependence on
natural resources is Chile Chilean success in terms of economic growth contrasts with its inability
to reduce the persistently high levels of social and economic inequality particularly in the last
three decades Using data for the 2006-2017 period and considering 324 out of 345 Chilean
counties this thesis presents three essays with empirical evidence aiming to explain the
phenomenon of persistent income inequality and some of its potential consequences The first
essay aims to analyse how the evolution and variability of income inequality throughout the
country are associated with the degree of natural resource dependence The second essay studies
the relevance of income inequality in explaining cross-county differences in the performance of
local governments (municipalities) Finally the third essay explores the link between social
cohesion and community heterogeneity highlighting the importance of economic and racial
diversity
Income inequality and dependence on natural resources
The first essay explores how cross-county differences in income inequality are associated
with differences in the degree of dependence on natural resources We use the Gini coefficient in
each county as our dependent variable and the proportion of employment in the primary sector as
our measure of natural resource dependence The main hypothesis is that income inequality should
be positively related to the degree of natural resource dependence To test our hypothesis we use
a spatial econometric approach This approach is motivated by the study of Paredes Iturra and
Lufin (2016) who explore the geographic heterogeneity of income inequality in Chile finding
evidence of a significant spatial dimension
15
The theoretical and empirical literature has mostly proposed a positive link between
inequality and natural resources Although most of the evidence corresponds to cross-country
comparisons there is also increasing body of research at the local level A rationale underpinning
the positive link suggested in the literature is that in natural resource-rich countries ownership is
concentrated in small groups and extraction activities require low-skilled workers (Gylfason amp
Zoega 2003 Leamer Maul Rodriguez amp Schott 1999) Another market-based argument often
labelled as the ldquoDutch Diseaserdquo proposes that natural resource windfalls could be associated with
a crowding-out effect on the manufacturing sector (Corden amp Neary 1982 Easterly 2007) This
process encourages rent-seeking behaviours discourages investment in physical and human
capital and delays the process of technology adoption and industrialization (Auty 2001 Bulte
Damania amp Deacon 2005 Gylfason amp Zoega 2003) The result could be a lower economic
growth which is the central idea under the ldquoResource Curse Hypothesisrdquo (Auty 1993 Sachs amp
Warner 2001)
An ldquoinstitutionalrdquo argument for the positive association between inequality and the
endowment of natural resources is based on the so-called ldquoParadox of Plentyrdquo (Borge Parmer amp
Torvik 2015 Dauvin amp Guerreiro 2017) The idea is that both national and local authorities have
less incentive to operate efficiently when they experience windfalls in their revenues for
instance from natural resources This could end with corrupted authorities exerting patronage
clientelism and designing public policies to favour specific groups of the population (Uslaner amp
Brown 2005) Evidence also suggests that the final effect of natural resource booms on income
inequality will depend on authoritiesrsquo capacity to manage these additional resources the extent of
commuting and migration among regions and the potential increase in the demand for non-tradable
16
goods which are intensive in unskilled workers (Aroca amp Atienza 2011 Cust amp Poelhekke 2015
Fleming amp Measham 2015b Howie amp Atakhanova 2014 Michaels 2011)
Contrary to most theoretical and empirical evidence we find that income inequality shows
a robust and significant negative association with our proxy for natural resource dependence This
result suggests that the process of transformation to an economy less dependent on natural
resources could have exacerbated rather than alleviated the persistence of income inequality The
decrease in the participation of the primary sector in employment in favour mainly of the tertiary
sector highlights the importance of the latter to explain the current high levels of inequality and its
future evolution Another important result is that spatial linear models show practically the same
results as traditional linear models This could be interpreted as the spatial dimension previously
found in income inequality is not the result of spatial dependence in the variable itself for instance
due to a process of spillover among counties Hence the usually found positive spatial
autocorrelation of income inequality (similar levels in neighbouring counties) could be explained
by spatial patterns in other variables or to the spatial heterogeneity that characterizes the Chilean
economy
Local government efficiency and income inequality
Essay 2 delves deep into the potential trade-off between efficiency and equity We measure
the efficiency of Chilean municipalities which correspond to the organizations in charge of
managing each county Municipal efficiency is understood as ldquotechnical efficiencyrdquo that is the
possibility that each municipality has reached the same level of outputs with less use of inputs
Then we analyse how income inequality controlling for other contextual factors such as
socioeconomic demographic geographical and political characteristics may help to explain
17
differences in municipal performance Our main hypothesis is that municipal efficiency is
inversely associated with income inequality Moreover we seek a causal interpretation of this
relationship
Municipal performance could be influenced by income inequality in direct and indirect ways
In a direct sense income inequality is used to capture the degree of heterogeneity and complexity
in the demand for public services that citizens exert over local authorities Hence higher levels of
income inequality should be associated with a more complex set of public services and therefore
with lower levels of municipal efficiency (Jottier Ashworth amp Heyndels 2012) Furthermore
when high levels of inequality exist the richest groups can exert a higher influence over local
authorities resulting in low quality and quantity of services for most of the population Among
indirect effects high and persistent inequality could be the source of corrupted institutions and
local authorities favouring themselves or specific groups This undermines citizensrsquo participation
in civic activities and their willingness to monitor municipal performance (Uslaner amp Brown
2005) Additionally the potential benefits of decentralization on the way local governments
deliver public services will be limited when the context is characterized by corrupted politicians
and a limited administrative and financial capacity (Scott 2009)
We measure municipal efficiency using an input-oriented Data Envelopment Analysis
(DEA) to obtain efficiency scores for our sample of 324 municipalities in each year from 2006 to
2017 Then we study the influence on municipal efficiency of income inequality and our set of
contextual factors using a panel of six years corresponding to those years for which household
income information is available 2006 2009 2011 2013 2015 and 2017 Our dependent variable
is the set of efficiency scores which are relative measures of efficiency They are relative to the
18
municipalities included in the sample and they do not imply that higher technical efficiency gains
cannot be achieved Thus we use both cross-sectional and panel censored regression models To
tackle endogeneity issues and suggest a causal interpretation we also propose using the proportion
of firms in the primary sector as an instrument for income inequality
We find an average efficiency score of 83 meaning that Chilean municipalities could
reduce the use of inputs by 17 without reducing their outputs We also measure municipal
efficiency under different assumptions related to returns to scale This allows us to disaggregate
technical efficiency to assess whether inefficiencies are due to management issues (pure technical
efficiency) or scale issues (scale efficiency) Although the results show that most municipalities
operate under increasing or decreasing returns to scale scale inefficiencies only explain a small
proportion of total municipal inefficiencies This highlights the need to look for contextual factors
outside the control of local authorities to explain differences in municipal performance
Geographical representations of our results in terms of returns to scale and efficiency scores
show some spatial clustering process among municipalities Spatial statistics tests confirm that
efficiency scores show a significant positive spatial autocorrelation This means that neighbouring
municipalities tend to show similar levels of efficiency This similar performance could be due to
a process of spatial dependence (eg efficiency spillovers among neighbouring municipalities) or
due to the existence of structural-geographical similarities (spatial heterogeneity) To assess the
spatial dimension in municipal efficiency abstracting from temporal fluctuations we use a cross-
section of data consisting of the six-year average for the variables in our panel After running a
regression of efficiency scores against the set of controls the analysis of OLS residuals shows that
the spatial autocorrelation is almost completely removed This means that the spatial pattern in
19
municipal efficiency can be explained (controlled) by other variables such as regional indicator
variables rather than efficiency itself Given this result we proceed to study the influence of
income inequality on municipal efficiency using traditional (non-spatial) regression analysis
In contrast to literature that emphasizes an equity-efficiency trade-off (Andersen amp Maibom
2020 Berg amp Ostry 2011 Browning amp Johnson 1984 Okun 2015) that is greater equality leads
to lower efficiency we find that municipal efficiency is inversely associated with income
inequality This implies that more equal counties are also those with higher municipal efficiency
Furthermore the coefficient of income inequality is close to one when we use the instrumental
variable approach This means that a reduction in income inequality ceteris paribus should be
associated with an increase in the same magnitude in municipal efficiency This result has strong
policy implications The non-existence of the trade-off suggests that there is more to be gained by
targeting policies towards the reduction of inequality than conventional theories suggest For
instance these policies may help increase the levels of efficiency and well-being at least at the
municipal level
Social cohesion and economic diversity
The third essay studies the relationship between the degree of social cohesion and diversity
in Chile Extant literature has argued that one of the main factors influencing social cohesion is
the degree of economic and ethnic-racial diversity within a society This diversity erodes social
cohesion reducing trust and corrupting institutions (Letki 2008 Rothstein amp Uslaner 2005
Tolsma Van der Meer amp Gesthuizen 2009 Uslaner 2011 2013)
To measure social cohesion scholars have traditionally used measures of social capital trust
or feelings of insecurity (Ariely 2014 Chan To amp Chan 2006 Letki 2008) We suggest the use
20
of the rate of incivilities per 1000 inhabitants as a proxy for social cohesion Incivilities correspond
to those antisocial behaviours (eg groups of rowdy teens and public drunkenness) or visible
neighbourhood conditions (eg graffiti and abandoned buildings) that tend not to be treated as
crime Using the rate of incivilities is arguably a more objective and reliable measure of social
cohesion particularly in countries where institutions of order and security are among the most
trusted An increase in the rate of incivilities rather than changes in crime rates should better
capture the worsening in social cohesion experienced in countries such as Chile where crime rates
are not growing but social conflicts are on the rise Thus the main hypothesis in this essay is that
the rate of incivilities (social cohesion) should be positively (negatively) associated with economic
and racial diversity
Using panel count data models we start analysing how differences in incivilities rates
between and within counties are associated with differences in indicators of relative and absolute
economic disadvantage We use the Gini coefficient of each county as our measure of economic
diversity Although we find a significant and positive association between the rate of incivilities
and the level of income inequality the magnitude of the link seems to be small Among absolute
indicators of economic disadvantage only the level of income shows a strong effect Next we
include our measure of racial diversity We use the number of new visas granted to foreigners as
a proportion of the county population Results show a significant and strong positive association
between the rate of incivilities and racial diversity
To check the robustness of our results we analyse the impact of our measures of economic
and racial diversity running our models separately for each Chilean region and clustering them
geographically We also split the total number of incivilities in four categories to see which type
21
of incivilities show the greatest association with our measures of diversity In general results
support the ldquocommunity heterogeneity hypothesisrdquo that higher community heterogeneity is
associated with higher rates of incivilities (Alesina amp La Ferrara 2002 Letki 2008 Tolsma et al
2009) However results do not support the ldquoincivilities thesisrdquo that the distribution of incivilities
tends to mirror the distribution of income inequality (Skogan 1999 Taylor 1999)
Three results stand out among the set of control variables First the level of education shows
and independent and significant negative association with the rate of incivilities This is in contrast
to previous studies where education acts mainly as a moderator of the effect of economic and racial
diversity on social cohesion (Tolsma et al 2009) The results also show that there is no significant
relationship between the rate of incivilities and the proportion of young population This is relevant
because policies aimed to reduce incivilities usually put the focus on specific groups such as young
people which are linked to physical and social incivilities when social control is weakened
Finally the degree of financial municipal autonomy also shows a significant negative association
with the rate of incivilities This result suggests that municipalities can contribute independently
or together with the central government to reduce incivilities and strengthen social cohesion
Contributions
The three essays in this thesis provide several important insights into the analysis of the
causes and consequences of income inequality particularly in the context of Chile ndash a typical
resource rich economy with persistently high levels of income inequality
Essay 1 advances the understanding of the relationship between income inequality and
natural resources in Chile extending the empirical analysis from the regional level to the county
level In addition the geographic heterogeneity of income inequality is explored with the inclusion
22
of alternative sources of spatial dependence as a potential dimension of the causal relationship
between income inequality and natural resources This essay demonstrates the relevance of natural
resources in explaining the persistence of income inequality even after controlling for other
socioeconomics and institutional factors Findings from this study have potential contribution not
only in the design of policies aimed to reduce income inequality but also in addressing the current
developmental bias between the metropolitan region and the rest of the country
Essay 2 is one of the first studies that undertake a longitudinal analysis of the effects of
income inequality on the efficiency of municipal governments in Chile To capture the role of the
municipal governments in the provision to local people of public services such as education and
health we specify several inputs and outputs in our efficiency model which is different from the
conventional specification in the existing literature For example the number of medical
consultations in public health facilities and the number of enrolled students in public schools are
used as outputs instead of general indicators such as county population Our empirical analysis
also utilises a larger sample of municipalities and covers a much longer period spanning from 2006
to 2017 This essay also investigates the contextual factors beyond the control of local authorities
that can explain variations in the efficiency of municipal governments across the country
Empirical findings from Essay 2 help us increase our understanding of the production
technology of municipalities the sources of inefficiencies and specifically the impact of income
inequality on the performance of local authorities The results deliver two main policy
implications First municipal inefficiencies in the provision of public goods and services differ
across Chilean municipalities In addition efficiency levels show some degree of spatial
autocorrelation This implies that policies such as amalgamation or cooperation among
23
municipalities could have effects beyond the municipalities involved which must be considered
Second the causal effect that income inequality has on municipal efficiency provides another
dimension into the design and implementation of development policies
Essay 3 explores for the first time the effects of economic and racial diversity on social
cohesion in Chile This essay considers incivilities as manifestation of social cohesion and
investigates as extant literature suggests whether indicators of relative economic disadvantage
such as income inequality are among the main factors driving social disorganization and social
unrest Empirical findings suggest that on the one hand economic heterogeneity captured by the
Gini coefficient has a disparate effect both in terms of magnitude and significance on the rate of
incivilities across the country On the other hand the impact of racial heterogeneity appears to be
stronger more significant and of a similar magnitude throughout the country Results also provide
new insights into the design of national policies addressing social disorders particularly those
policies focussed on specific groups of the population and the role of local authorities Overall the
findings provide an opportunity to advance the understanding of the process of weakening in the
social cohesion experienced in Chile and the conflicts that have risen from this process
Thesis outline
The remainder of the thesis is organized as follows Chapter 2 presents essay 1 examining
the association between income inequality and the degree of dependence on natural resources
Chapter 3 presents essay 2 which looks for a causal relationship between municipal efficiency and
income inequality Chapter 4 presents essay 3 analysing the relationship between social cohesion
and economic and racial diversity Finally Chapter 5 presents some concluding remarks
24
Chapter 2 Natural Resources Curse or Blessing Evidence on
Income Inequality at the County Level in Chile
21 Introduction
A phenomenon of increasing inequality of incomes and wealth in recent decades has been
documented by leading scholars and international organizations such as the International Monetary
Fund (Berg amp Ostry 2017 Ostry Berg amp Tsangarides 2014) and the Organization for Economic
Cooperation and Development (Cingano 2014) These efforts have placed the topic of inequality
at the top of the current economic debate recognizing inequality as a determinant not only of
economic growth but also of human development They also have highlighted the necessity for
more research on the drivers of inequality and mechanisms through which it manifests aiming to
design effective policies in reducing economic and social inequalities
Various factors have been analysed as the sources of high and increasing levels of inequality
Among the most significant factors are the levels of income at initial stages of economic
development (Kuznets 1955) Globalization (Milanovic 2016) skill-biased technological change
(Tinbergen 1975) investment in human capital (Murphy amp Topel 2016) institutions
redistributive policy and country-specific characteristics (Acemoglu 1995 2002 Acemoglu
Aghion amp Violante 2001 Acemoglu Johnson amp Robinson 2001) Our focus in this essay is on
the importance that the natural resource endowment (NRE) or lack thereof can play in the
determination of income disparities
25
This essay studies the patterns and evolution of income inequality in the context of a natural
resource-rich country Using the case of the Chilean economy we aim to understand and
disentangle how a phenomenon of high- and persistent-income inequality is related to the
endowment of natural resources that a country owns Chile is an interesting case to study because
despite showing a successful history of economic growth inequality among individuals and among
aggregated spatial units has shown a strong persistence (Paredes et al 2016) Furthermore Chile
has remained among the most unequal countries in the world1
Theory and empirical evidence do not establish a clear link between income inequality and
NRE In addition NRE has received considerably less attention (Auty 2001 ElGindi 2017) and
most of the evidence has been focused on cross-country comparisons For instance NRE can
influence inequality by determining its initial levels (Engerman amp Sokoloff 1994 1997
Engerman Sokoloff Urquiola amp Acemoglu 2002) shaping the evolution of institutions
(Acemoglu 2002) make the educational system less intellectually challenging and moulding the
structure of economic activity (Leamer et al 1999) So studying how cross-county differences in
NRE are associated with the distribution of income within a country has theoretical empirical and
policy implications
In this study we offer empirical evidence on the relationship between income inequality and
the endowment of natural resources using data at the county level in Chile for the period 2006-
2017 Income inequality is measured by the Gini coefficient The importance of NRE is proxied
using a measure of natural resource dependence (NRD) defined as the percentage of the total
1 A 2014 OECD report on income inequality (httpwwwoecdorgsocialincome-distribution-databasehtm) showed Chile as the country with the highest Gini coefficient of disposable income among OECD countries OECD also indicates Chile as the country with the widest gap between the richest 10 percent and the poorest 10 percent of countryrsquos population among its 34 members (OECD 2014)
26
employment in each county corresponding to the primary sector (agriculture forestry fishing and
mining)
The main hypothesis to be tested is whether income inequality is positively associated with
the degree of NRD The transmission mechanisms through which natural resources could influence
socioeconomic outcomes could be based on the market or institutions The market-based approach
argues that natural resource booms could be associated with an appreciation of the real exchange
rate and a crowding out effect over other more productive economic activities such as
manufacturing It could also delay the adoption of new technologies and reduce incentives to invest
in physical and human capital (Gylfason amp Zoega 2003) Based on the ldquoResource Curse
Hypothesisrdquo (RCH) natural resources could be a curse when the political and institutional
framework is weak and natural resources are concentrated in space such as oil and minerals
(Deacon 2011) 2 Among institutional channels a higher NRD or natural resource booms could
be associated with rent seeking misallocation of labour and entrepreneurial talent institutional
and political decline or even violent conflicts For instance the ldquoParadox of Plentyrdquo sustains that
windfalls of revenues as a consequence of resource booms could be related to a lack of incentives
to perform efficiently corruption patronage and local authorities favouring their voters or being
captured by the richest groups (Dauvin amp Guerreiro 2017) Hence a higher NRD or natural
resource booms could be the explanation not only for low levels of growth in regions more
dependent on natural resources but also it could be the root of income disparities
2 There is a wide strand of research on the Resource Curse Hypothesis however the evidence so far is not conclusive Evidence in favour of RCH has been mainly found in developing resource rich countries (Auty 1993 2001 Badeeb Lean amp Clark 2017 Blanco amp Grier 2012 Borge et al 2015 Brunnschweiler amp Bulte 2008 Sachs amp Warner 2001 Van der Ploeg 2011)
27
To test our hypothesis that is whether the levels of income inequality across counties are
positively associated with the degree of NRD we use a spatial econometric approach We use this
approach because attributes such as income inequality in one region may not be independent of
attributes in neighbouring regions (Armstrong amp Taylor 2000) This process of spatial dependence
invalidates the use of traditional (non-spatial) approaches
This study seeks to make two contributions to research First previous empirical evidence
shows a significant spatial dimension of income inequality in Chile (Paredes et al 2016)
However this dimension has been barely explored with most studies limiting the degree of
disaggregation to a regional scale (Aroca amp Bosch 2000) We use a spatial approach which makes
it possible to model and test the significance of the spatial dimension in the analysis of income
inequality and its relationship with other variables Second previous research for the Chilean
economy linking inequality with NRE has been mainly focused on explaining differences between
regions or the importance and effects of the mining-copper sector (Aroca amp Atienza 2011 Ebert
amp La Menza 2015 Lagos amp Blanco 2010 Rehner Baeza amp Barton 2014) We extend this
analysis using data for local economies Identifying and quantifying the impact of NRE on income
inequality at the county level is likely to be more informative for policies aiming to address the
current developmental bias between the metropolitan region and the rest of the country Moreover
the analysis of the role of natural resources in conjunction with other potential sources of inequality
may shed lights in understanding the persistence of the high levels of inequality observed in the
Chilean economy All in all this study could contribute to the design of policies that
simultaneously help reduce inequality increase efficiency and promote sustainable and inclusive
growth
28
Our main finding shows that after controlling for other potential sources of income
inequality such as educational level demographic characteristics and the level of public
government expenditure the degree of dependence on natural resources has a significant effect on
income inequality However contrary to our expectations the effect is negative This result
suggests that the natural or policy-driven process of transformation from primary and extractive
activities to manufacturing and service sectors imposes additional challenges to central and local
authorities aiming to reduce income inequality
In section 22 we review the literature on the relationship between income inequality and
natural resources In section 23 we establish our research problem and main hypothesis Section
24 describes our data and methods and section 25 the empirical results We finish with section
26 discussing our main results concluding and proposing avenues for future research
22 Inequality and Natural Resources
221 Theoretical Framework
Explanations for income inequality can be associated with individual institutional political
and contextual characteristics Individual characteristics include age gender and mainly the level
of education and skills of the population in the labour force For instance globalization and
technological change lead firms to increase the demand for skilled labour deepening income
inequality between skilled and unskilled workers (Atkinson 2015 Milanovic 2016 Tinbergen
1975) Among institutional characteristics labour unions collective bargaining and the minimum
wage have been suggested as explanations of income inequality (Acemoglu Aghion et al 2001
Atkinson 2015) Policy design associated with market regulation progressive taxation and
redistribution can also impact the levels and patterns of inequality
29
A key factor in understanding the levels and differences in income distribution within a
country may be its endowment of natural resources NRE shapes the structure of the economy
(Leamer et al 1999) it is associated with the creation of institutions that define the political
culture and it can also influence the performance of other sectors (Watkins 1963) In addition
NRE determines initial conditions market competition ownership over resources rent seeking
and the geographical concentration of the population and economic activity
Cross‐countryliterature
Bourguignon and Morrison (1990) introduce one of the earliest theoretical frameworks
describing the relationship between inequality and NRE They develop a small open economy
model where income distribution is a function of NRE ownership structure and trade protection
Giving cross-sectional evidence for a group of developing countries they conclude that the impact
of NRE particularly mineral resources and land depends on the number and size of the firms
whether they are public or private and the level of protection A higher concentration of production
in a few private firms a big share of production oriented to foreign instead of domestic markets
and protection increasing the relative price of scarce resources are some of the reasons explaining
why some countries are less egalitarian than others
NRE could also influence the evolution and levels of inequality by determining the initial
distribution of incomes This is known as the ldquoEngerman-Sokoloff Hypothesisrdquo (Engerman amp
Sokoloff 1997 Engerman et al 2002) In addition Leamer (1999) proposes that inequality and
development paths in each economy are a function of its economic structure which in turn depends
on ldquofundamentalsrdquo and ldquosymptomsrdquo On the one hand ldquofundamentalsrdquo refer to resource
endowment production structure closeness to markets and governments interventions On the
30
other hand ldquosymptomsrdquo are related to institutions employment structure and net export structure
Using this conceptual framework Leamer argues that natural Resource-Rich Countries (RRC) can
experience a higher level of inequality because can have a ldquodumbbell educational systemrdquo
ownership is concentrated in small groups and extraction activities require low-skilled workers
This implies fewer incentives to educate citizens until very late in the development process
resulting in human capital not prepared to take advantage of the process of technological progress
and delaying the emergence of more efficient and competitive sectors such as manufacturing and
services
Using 1980 and 1990 data for a group of countries classified according to land abundance
Leamer (1999) provides evidence showing that on the one hand land-scarce countries concentrate
their production and employment in sectors that promote equality such as capital-intensive
manufacturing chemical or machinery On the other hand countries abundant in natural resources
concentrate their production trade or employment in sectors that promote income inequality such
as the production of food beverages extraction activities or forestry
Gylfason and Zoega (2003) using a framework based on standard growth models also
proposed a positive relationship between NRE and inequality They assume that workers can work
in the primary sector or in the manufacturing (including services) sector In addition wage income
is equally distributed in the manufacturing sector but unequally in the primary sector (because of
initial distribution competition rent seeking etc) Therefore inequality will be greater when a
bigger proportion of labour is dedicated to extraction activities in the primary sector This
phenomenon is further amplified because of lower incentives to invest in physical and human
capital to adopt new technologies and to increase the share of the manufacturing sector
31
Diverse mechanisms explaining the link between NRE and inequality have been proposed
arguing that NRE determines simultaneously economic growth and inequality (Gylfason amp Zoega
2003) NRE could impact economic growth through the real exchange rate and the crowding-out
effect on manufacturing (ldquoDutch Diseaserdquo) reducing incentives to invest in physical and human
capital (Easterly 2007) and influencing the processes of technology adoption industrialization
and diversification of the economy in a manner that is less conducive to growth (Bulte et al 2005)
These potential explanations related to the called ldquoResource Curse Hypothesisrdquo do not have strong
empirical support (Auty 2001 Bulte et al 2005)
NRE may also influence economic growth through the quality of institutions (Acemoglu
1995 Acemoglu Aghion et al 2001 Acemoglu amp Robinson 2002 Engerman amp Sokoloff 1997
Engerman et al 2002) the concentration of ownership political power and rent-seeking NRE
acts by shaping institutional context and social infrastructure a phenomenon that is stronger when
resources are spatially concentrated such as minerals and plantations (Bulte et al 2005) NRE
could also have a significant effect on social cohesion and instability spreading its influence like
a disease (Brunori Ferreira amp Peragine 2013 Kanbur amp Venables 2005 Milanovic 2016
Ocampo 2004)
Considering a non-tradable sector intensive in unskilled workers Goderis and Malone
(2011) develop a model where the natural resources sector experiences an exogenous gift of
resource income They analyse the impact over income inequality of resource booms proxied by
changes in a commodity price index They conclude that inequality decreases in the short run but
increases after the initial reduction
32
Fum and Hodler (2010) show that natural resources increase inequality but this is
conditional on the level of ethnical polarization of society Carmignani (2013 2010) confirms this
positive relationship using different measures of dependence and abundance and goes further
arguing that inequality constitutes an indirect channel through which NRE affects human
development
Singlecountryevidence
Most of the studies about the relationship between inequality and NRE derive from cross-
country analyses Evidence for specific countries has been mainly based on case studies Howie
and Atakhanova (2014) based on the model of Goderis and Malone (2011) find for the case of
Kazakhstan that income and consumption inequality decreased significantly after booms in the oil-
and-gas sector because of resource booms increase the demand for non-tradable goods which are
intensive in unskilled workers The results depend on the level of rurality institutional quality
education levels and public spending on health and education Fleming and Measham (2015b
2015a) evaluate the impact of booms in the mining and oil sectors in Australia They find that a
boom in the mining sector increases income inequality due to commuting and migration among
regions This phenomenon can be exacerbated when the demanding access to natural resource
revenues is associated with the creation of more local administrative units (counties provinces and
even regions) but the government capacity is not simultaneously improved (Cust amp Poelhekke
2015 Michaels 2011) Furthermore the benefits that a region can receive in the form of fiscal
transfers can be more than compensated by the loses due to city-to-mine commuting such as the
case of mining regions in Chile (Aroca amp Atienza 2011)
33
Other studies at the local level have analysed the impact of the mining sector in Peru (Aragoacuten
amp Rud 2013 Loayza amp Rigolini 2016 Loayza Teran amp Rigolini 2013) Spain (Domenech
2008) and Canada (Papyrakis amp Raveh 2014) and the effects of oil windfalls in Brazil (Caselli amp
Michaels 2013)
In summary there is a wide range of potential mechanisms through which NRE could
influence income inequality Although most of them seem to suggest a positive relationship others
such as commuting and increased within-county demand for non-tradable goods and services
could lead to a negative association This highlights the need to know the sign of this association
in the Chilean economy where the trend shows a reduction in the degree of NRD After controlling
for other factors a positive link would support the argument that the reduction in the degree of
NRD has been relevant in the reduction experienced by income inequality in the same period
However a negative link would support the position that the reduction in NRD has contributed to
explain the persistence of income inequality and its slow reduction
222 The relevance of the spatial approach
Inequalities within countries are still the most important form of inequality from the political
point of view (Milanovic 2016) People from a geographic area within a country are influenced
and care most about their status relative to the people in other areas in the same country The
influence among regions involves multiple aspects (eg economic political and environmental)
These potential interactions have been traditionally ignored assuming independence among
observations related to different regions Moreover neglecting the process of spatial interaction in
key indicators of the economic and social performance of a country may mislead the design of the
public policy
34
The spatial dimension could play a significant role in understanding the distribution of
income within a country One strand of efforts aiming to capture the geographic heterogeneity of
inequality has been focussed on decomposing general indicators such as the Gini coefficient or the
Theil Index Evidence for different countries including the US (Doran amp Jordan 2016) China
(Akita 2003 Gustafsson amp Shi 2002 Ye Ma Ye Chen amp Xie 2017 Yue Zhang Ye Cheng
amp Leipnik 2014) Japan (Ohtake 2008) South Africa (Leibbrandt Finn amp Woolard 2012) and
Chile (Paredes et al 2016) shows that regional inequality is sensitive to the geographic scale of
analysis These studies also show a significant spatial component in the explanation of inequality
of income expenditure or gross domestic product for each country
Another strand explicitly uses exploratory spatial data analysis (ESDA) and spatial
econometrics ESDA has been used to provide new insights about the nature of regional disparities
of incomes and growth rates (Celebioglu amp Dallrsquoerba 2010 Yue et al 2014) Spatial econometric
models aim to assess and address the nature of the spatial effects These effects could be the result
of ldquospatial heterogeneityrdquo that is different relationships in distinct locations or ldquospatial
dependencerdquo which implies cross-sectional interactions (spillover effects) among units from
distinct but near locations
Spatial spillovers have been analysed to study both positive and negative spatial correlation
among less resource-abundant counties and resource-abundant counties On the one hand less
resource-abundant counties may experience positive spillovers because their industries supply
more goods and services to meet the increasing regional demand They can also be benefited from
positive agglomeration externalities and higher investment in private and public infrastructure
(Allcott amp Keniston 2014 Michaels 2011) On the other hand negative spillovers could be the
35
result of a high degree of interregional migration that limits the rise in wages and higher local
prices due to the increase in the share of the non-tradable sector In addition local governments
could have a limited capacity to translate the revenues from resource booms into effective public
policies promoting a sustained local development (Beine Coulombe amp Vermeulen 2015 Caselli
amp Michaels 2013 Papyrakis amp Raveh 2014)
23 Research problem and hypotheses
We can conclude from our overview of the literature that the theoretical and empirical
evidence about the link between inequality and natural resources is inconclusive This does not
make clear whether the process of reduction in the degree of dependence on natural resources
such as that experienced by the Chilean economy helps to explain the sustained but slow reduction
in income inequality or its high persistence
The research question guiding this study relates to how the natural resource endowment
determines the paths and structure of income inequality in natural resource-rich countries Using
the case of Chile the main hypotheses to be tested is whether a higher degree of dependence on
natural resources is associated with higher levels of income inequality To do that we use data at
the county level and we explicitly include the spatial dimension Our aim is to arrive at a more
comprehensive understanding of the drivers and transmission mechanisms explaining the
evolution and patterns shown by income inequality In addition we test whether the spatial
dimension plays a significant role in explaining differences in income distribution in Chile
36
24 Data and Methods
We use county-level data for the years 2006 2009 2011 2013 2015 and 2017 The reason
for not using contiguous years is that income data at the household level are only available every
two-three years from the Chilean National Socioeconomic Characterization Survey (CASEN in its
Spanish acronym)3 For the period 2006-2017 the Chilean administrative division considers 15
regions 54 provinces and 346 counties Data on income are available for 324 counties and six
years resulting in a panel with 1944 observations4
We start evaluating the spatial dimension in our data and analysing the link between
inequality and NRD using a cross-sectional setting To this end we use the ldquosix-year averagerdquo
(2006 2009 2011 2013 2015 2017) for our variables given the low time variability showed by
our measures of income inequality and NRD Results are then compared with those of a panel data
setting
241 Operationalization of key variables
The dependent variable in the present study income inequality at the county level is
measured calculating the Gini coefficient using three definitions of household income labour
autonomous and monetary income5 Labour income corresponds to the incomes obtained by all
members in the household excluding domestic service consisting of wages and salaries earnings
3 CASEN survey is conducted by the Chilean Ministry of Social Development covering topics such as education employment income and health CASEN is considered nationally representative and it is the main source for measures of inequality and poverty used for the design and evaluation of social policies in Chile 4 The six waves of CASEN for our study considered an average of 75599 households and 252081 individuals 5 The Gini coefficient is chosen because provides an overall estimate of income inequality It summarizes what proportion of the population gains what proportion of the total income The Gini coefficient can range between 0 (everyone in the population has the same income) and 1 (one person earns 100 per cent of the income in the community)
37
from independent work and self-provision of goods Autonomous income is the sum of labour
income and non-labour income (including capital income) consisting of rents interest and dividend
earnings pension healthcare benefits and other private transfers Finally monetary income is
defined as the sum of autonomous income and monetary subsidies which correspond to cash
transfers by the public sector through social programs Appendix A shows summary statistics for
the Gini coefficient of our three measures of income
The main independent variable in our study is the degree of dependence on natural resources
in each county To have an idea of the importance of each economic activity in the Chilean
economy particularly those activities related to natural resources Figure 21 shows their average
share in Chilean Gross Domestic Product (GDP) for the period 2006-17 We can observe that the
leading activities are those related to the primary sector especially mining and to the tertiary
sector where financial personal commerce restaurants and hotels services stand out The shares
of each economic activity in GDP vary significantly between Chilean regions and such
information is not available at the county level
Figure 21 Average share in GDP of economic activities (2006ndash17)
38
Leamer (1999) argues that when the main source of income is labour income (as indeed
happens for the Chilean case) using employment shares allows a better approach to measuring
dependence on natural resources Using employment data from CASEN survey we define our
measure of NRD as the employment in the primary sector (mining fishing forestry and
agriculture) as a percentage of the total employment in each county We name this variable
pss_casen where ldquopssrdquo stands for ldquoprimary sector sharerdquo We built other two proxies of NRD
using data from the ldquoServicio de Impuestos Internosrdquo (SII) which is the agency in charge of
collecting taxes in Chile The variable pss measures the percentage of employment in the primary
sector and the variable pss_firms measures the number of firms in the primary sector as a
percentage of the total number of firms in each county Appendix B shows summary statistics for
our three measures of NRD disaggregated by region
Figure 22 Evolution of Gini coefficient and measures of NRD (2006ndash17)
39
Figure 22 shows the evolution of our measure of inequality (using the Gini coefficient of
autonomous income) and our three potential proxies for NRD for the period 2006-2017 We
observe that both income inequality and the degree of NRD show a downward trend This seems
to support our hypothesis of a positive link between inequality and NRD however we need to
control of other sources of inequality before getting such a conclusion In what follows we use the
variable gini as our measure of income inequality capturing the Gini coefficient of autonomous
income Our measure of NRD is the variable pss_casen defined previously
Figure 23 Spatial distribution of Gini coefficient and NRD (2006ndash17)
Note Gini and NRD averages 2006-09-11-13-15-17 for 324 Chilean counties divided into five equal groups Source Own elaboration based on CASEN survey
40
Figure 23 shows quantile maps for income inequality (on the left) and NRD (on the right)
using the six-years average dataset6 On the one hand we observe that high levels of inequality
seem to be clustered in the Centre-South of the country where agriculture forestry and fishery are
the predominant economic activities Only isolated counties show high inequality in the Centre
(Metropolitan area where the countyrsquos capital is located) and North (Mining) areas On the other
hand our measure of NRD seems to show an opposite spatial pattern than income inequality with
high levels in the Centre and North of the country
242 Control variables
To control for county characteristics we use a set of socio-economic demographic and
institutional variables Economic factors are captured by the natural log of the mean autonomous
household income per capita (in thousands of Chilean pesos of 2017) lnincome the poverty rate
poverty the unemployment rate unemployment the percentage of the population living in rural
areas rural and the average years of education of the population over 15 years old education
Demographic factors include the proportion of the population in the labour force labour_force
and the natural log of population density (population divided by county area) lndensity
We also include the natural log of the total municipal public expenditure per capita
lnmuni_expenditure to control for municipal heterogeneity This heterogeneity is mainly related
to the capacity of municipalities to generate their own revenues In addition the richest
municipalities are in the Metropolitan region which concentrates economic power and around 40
6 After sorting a variable in ascending order quantile maps use the quantiles (quartiles quintiles deciles etc) as class breaks to divide the distribution of the variable where each class includes approximately the same number of observations (counties)
41
of the population This has basically implied a lag in the development of regions other than the
metropolitan region
The spatial distribution of our measures of income inequality and NRD displayed in Figure
23 seems to show different patterns in the North Centre and South of the country Appendix C
shows the administrative division of Chile in 15 regions and how we have grouped them in three
zones North Centre and South We consider as the ldquoCentrerdquo area that formed by the Metropolitan
region (XIII) and its two neighbouring regions V and VI Using the Centre area as our reference
we include in our analysis two dummy variables indicating whether a county is located in the North
area (regions XV I II III and IV) or South area (regions VII VIII IX XIV X XI and XII)
Appendix D shows summary statistics for the set of numeric control variables and the
correlation matrix between our measure of NRD pss_casen and the set of numeric controls
243 Methods
To assess and then consider the spatial nature of the data we need to define the set of relevant
neighbours for each country This is operationalized by building a matrix called ldquoWrdquo with a ldquo1rdquo
for neighbouring counties and a ldquo0rdquo for non-neighbouring counties We could build W using
contiguity-based (whether counties share a border or point) or geography-based (taking the
distances among the centroids of each county polygon) spatial weights Specifically we build a W
matrix considering the 5-nearest counties7 Two reasons explain the choice of k-nearest
neighbours First we cannot use a contiguity criterium because we do not have information about
all the counties and there are some geographically isolated counties Second given the significant
7 We assign a ldquo1rdquo to the five nearest counties to each county based on the distances among the polygon centroids Then W is ldquorow standardizedrdquo This facilitates the interpretation of the spatial lag of a variable as the ldquoweighted average valuerdquo of the same variable in neighbouring counties
42
differences in county areas (ldquobig countiesrdquo in northern and southern regions) using a distance-
band criterium with a not enough large distance band can lead to many ldquoislandsrdquo in extreme regions
and a multi-modal distribution for the number of neighbours
We start testing our inequality and NRD variables for spatial autocorrelation in order to
evaluate statistically the clustering patterns shown in Figure 23 Next we run an OLS regression
of inequality against NRD and our set of controls and we test the spatial autocorrelation of OLS
residuals If we cannot reject the null hypothesis of random spatial distribution we do not need
spatial models to analyse income inequality which would give contrasting evidence to previous
suggestions about the relevance of the spatial dimension of income inequality in Chile (Paredes
2013 Paredes et al 2016) If we find significant spatial autocorrelation in the OLS residuals this
justifies the use of spatial models and highlight the need to find the correct spatial structure8
If inequality in one county spillovers or influences inequality in neighbouring counties the
spatial lag of inequality should be included as an explanatory variable and we should use a spatial
autoregressive model (SAR) If some unobserved variable is the explanation for the clustering of
counties with similar inequality then this will be better captured including a spatial lag of the
errors and we should use a spatial error model (SEM) (Anselin 1988 Anselin amp Bera 1998)
Finally when our main explanatory variable or some of the controls show spatial autocorrelation
a spatial lag of the explanatory variable(s) should be included in our model
8 The existence of spatial autocorrelation violates the standard assumption of independence among observations needed for OLS regression This will result in OLS coefficients biased and inconsistent (Anselin 1988)
43
244 Spatial Model Specification
A model that includes the three forms of spatial dependence described above is called the
Cliff-Ord Model The model in its cross-sectional representation could be expressed as
119910 120582119882119910 119883120573 119882119883120574 119906 (21)
where
119906 120588119882119906 120576 (22)
119882 is our weight matrix that works as an NxN spatial lag operator9 Thus 119882119910 119882119883 and 119882119906
are the spatial lags for the dependent variable explanatory variables and the error term
respectively The parameter 120582 capture the spatial dependence in the dependent variable 120574 the
spatial dependence in the explanatory variables 120588 capture the spatial dependence in the error term
and 120598 is a vector of idiosyncratic errors For instance if ldquoyrdquo is income inequality and ldquoXrdquo a measure
of NRD the level of inequality in one county will be explained by the degree of NRD in the same
county 119883120573 the average degree of NRD in neighbouring counties 119882119883120574 the average level of
inequality in neighbouring counties 120582119882119910 and the average value of residuals in neighbouring
counties 12058811988211990610
From equations (21) and (22) the SAR and SEM models can be seen as special cases of
the Cliff-Ord representation after imposing restrictions over the spatial parameters 120582 120574 and 120588 For
the specification of the spatial panel models we follow the terminology by Croissant and Millo
9 The spatial lag is a weighted sum of the values observed at neighbouring locations 10 On the one hand the impact that income inequality in one county has over income inequality in neighbouring counties is called a ldquoglobal spilloverrdquo and it is associated with the feedback effect among neighbours (one county is its neighboursrsquo neighbourrdquo) on the other hand the influence that the degree of NRD in neighbouring counties has over inequality in one county is called a ldquolocal spilloverrdquo
44
(2018) Spatial panel models including the spatial lag of the dependent variable (SAR) the spatial
lag of the residuals (SEM) or both (SARAR) are described in Appendix E
25 Results
251 Exploratory Spatial Data Analysis (ESDA)
To analyse the significance of the spatial dimension in our data set we use the six-year
average of our variables Spatial autocorrelation is tested using the Moranrsquos I statistic11 Moranrsquos
I measures the correlation of one variable with itself in space12 Figure 24 shows the Moran scatter
plots where the standardized variable (Gini coefficient and NRD for each county) appears in the
horizontal axis against its spatial lag (average value in the 5-nearest neighbouring counties) The
Moranrsquos I (slope of the line in the Moran scatter plot) of income inequality shows a significant
positive spatial autocorrelation that is counties with high (low) inequality tend to be close to each
other
11 There are many statistics to formally test the significance of the spatial dimension in the distribution of our data The null hypothesis assumes spatial randomness which means that there is not spatial structure in the data so any spatial pattern is equally likely to occur and values in one location do not depend on values in other locations The alternative hypotheses can be the existence of positive or negative spatial autocorrelation Positive spatial autocorrelation means similar values in neighbouring locations (less variability than under spatial randomness) Negative spatial autocorrelation means dissimilar values in neighbouring locations (more variability than under spatial randomness) 12 Traditional measures of correlation such as the Pearsonrsquos coefficient measure the degree of linear correlation between two different variables Measures of spatial autocorrelation assess the correlation between the values of one variable in one location related to the values of the same variable in other neighbouring locations
45
Figure 23 Moran scatter plots for variables gini and pss_casen
Moranrsquos I is a measure of global spatial autocorrelation this means it is intended to capture
the clustering property of the entire data set To identify where are the significant hot-spots
(clusters of counties showing high income inequality) or cold-spots (clusters of counties showing
low income inequality) we need local indicators of spatial association (LISA) Using the local
Moranrsquos I (not reported) we find significant hot-spots in the South of the country (mainly
agricultural regions) and significant cold-spots in the Centre (Metropolitan area) of the country
The next step is to check whether the clustering pattern in inequality is the result of a process of
spatial dependence in the variable itself or it can be explained by other variables related to
inequality
252 Cross-sectional analysis
We start analysing differences in income inequality between counties using the six-year
average data and running an OLS regression for the model
119892119894119899119894 120573 120573 119901119904119904_119888119886119904119890119899 120573 119897119899119894119899119888119900119898119890 120573 119901119900119907119890119903119905119910 120573 119906119899119890119898119901119897119900119910119898119890119899119905 120573 119897119886119887119900119906119903_119891119900119903119888119890 120573 119890119889119906119888119886119905119894119900119899 120573 119897119899119889119890119899119904119894119905119910 120573 119903119906119903119886119897 120573 119897119899119898119906119899_119890119909119901119890119899119889119894119905119906119903119890 120573 119899119900119903119905ℎ 120573 119904119900119906119905ℎ
(23)
46
The Moran scatter plot and spatial statistical test of OLS residuals from equation (23) are
in Appendix F OLS residuals show a small but significant positive autocorrelation (Moranrsquos I =
0121) This means that income inequality continues showing a significant degree of spatial
autocorrelation after controlling for the set of covariates In addition robust Lagrange Multiplier
(LM) tests show that a spatial error model is preferred over a spatial lag model (Anselin Bera
Florax amp Yoon 1996)13 This means that income inequality tends to cluster in zones larger than a
county so the analysis should be performed on a larger scale such as provinces regions or macro
zones If the SAR model were preferred it would mean that income inequality in one county is
influenced by the level of income inequality in neighbouring counties To find the spatial structure
that best fits the clustering process of income inequality we run the full set of spatial model
specifications in a cross-sectional setting and results are shown in Table 21
Column 2 in Table 21 shows the results of our ldquoOLSrdquo model The ldquoSLXrdquo model includes
spatial dependence only through the explanatory variables The ldquoSARrdquo model includes the spatial
lag of the dependent variable as a regressor and the ldquoSEMrdquo model includes spatial dependence
through the error term The ldquoSARARrdquo model includes both the spatial lag of the response and the
errors and the ldquoSDMrdquo and ldquoSDEMrdquo are the extensions of the ldquoSARrdquo and ldquoSEMrdquo models
respectively including the spatial lag of the explanatory variables Finally a model including
spatial lags for the response errors and explanatory variables (the ldquoSARARXrdquo model) is shown in
the last column
13 Following the ldquoAnselin methodrdquo when both non robust LM tests are significant we should select between the robust LR tests From Table F1 in Appendix F only the robust LM test for the SEM model (RLMerr) appears as significant
47
Table 21
Cross-sectional Model Comparison (six-year average data)
48
Opposite to our hypothesis we observe a significant and negative coefficient for our measure
of NRD This means that counties more dependent on natural resources show lower levels of
inequality Education years population density and municipal expenditure per capita are also
negatively related to inequality On the other hand the level of income the poverty rate and the
proportion of the population living in rural areas show a positive relationship with income
inequality There is no significant influence of the unemployment rate and the proportion of the
population in the labour force In addition the SAR SEM and SARAR models show a
significantly higher average inequality in the South of the country related to the Centre area
The main finding from our cross-sectional analysis is that there is a significant and negative
relationship between inequality and NRD which is quite robust to the model specification
253 Panel Data analysis
Like the cross-sectional case we start estimating the panel without spatial effects Results
for the pooled fixed effects (FE) and random effects (RE) specifications of equation (3) are in
Appendix G Spatial models were estimated using Maximum Likelihood (ML) and Generalized
Moments (GM) and assuming that the spatial structure (W matrix) is the same for all years14
Tables 22 23 and 24 show results for the ML estimation of the SAR SEM and SARAR models
using the pooled FE and RE specifications Results for the GM estimation are in Appendix H
All our spatial models include time fixed effects In the case of the pooled and RE models they
additionally include indicator variables for those counties located in the North and South of the
country
14 In traditional (non-spatial) panels data are stacked as time series for each observational unit (county) In the case of spatial panels data are stacked as slice of cross-sections for each year in the panel
49
The main result is that the negative and significant effect of NRD on income inequality is
robust to most of the spatial panel specifications In addition the coefficient for the variable
pss_casen changes slightly among panel specifications (pooled FE and RE) but does not change
among spatial models (SAR SEM and SARAR)
Another important finding is related to the significance of the spatial dimension of income
inequality When spatial models cross-sectional or panel are compared to non-spatial models
there are no major differences in the magnitude of the coefficients or their significance This could
mean that the positive spatial autocorrelation shown by income inequality seems to be better
explained by a process of spatial heterogeneity rather than spatial dependence The practical
implication of this result is that including dummy variables for aggregated units (eg regions or
groups of regions) could be enough to control for the spatial dimension in the modelling and
analysis of income inequality
Among control variables years of education seems to be the main variable for the design of
long-term policies aimed at reducing inequality This result is in line with previous evidence for
cross-country studies (Leamer et al 1999) and specific countries (Howie amp Atakhanova 2014)
Municipal expenditure per capita also shows a significant and negative association with income
inequality in the pooled and RE spatial specifications This means that higher municipal
expenditure helps to reduce inequality between counties but its effect is more limited within
counties This result support the importance of local governments (Fleming amp Measham 2015a)
however the negative coefficient appears as evidence against the ldquoParadox of Plentyrdquo (Borge et
al 2015)
50
Table 22
ML Spatial SAR Models
Table 23
ML Spatial SEM Models
51
Table 24
ML Spatial SARAR Models
26 Discussion and conclusions
In this essay we delve deep into the sources of income inequality analysing its association
with the degree of dependence on natural resources using county-level data for the 2006ndash2017
period in Chile Given recent evidence for the Chilean economy suggesting a significant spatial
dimension we assess and incorporate explicitly the spatial structure of income inequality using
spatial methods We use cross-sectional and panel data to evaluate the significance of the spatial
dimension and we test whether NRD has a positive effect on income inequality
Contrary to what theory predicts NRD shows a significant and negative association with
income inequality This result is robust to the type of analysis (cross-sectional vs panel data) the
approach (spatial vs non-spatial) and the inclusion of different controls The negative and
significant coefficient implies that if the degree of NRD would not have experienced a 10 drop
during this period income inequality could have fallen in 2 additional points So the downward
trend in the participation of the primary sector in terms of employment in the Chilean economy
52
could be one of the main reasons explaining the high persistence in the levels of income inequality
This means that those areas that undergo a process of productive transformation mainly towards
the services sector would be facing greater problems to reduce inequality This process of
productive transformation natural or policy-driven highlights the importance of policies focused
on human capital and the role of local governments in reducing inequality
The main implication for policymakers is that a reduction in NRD does not help to reduce
inequality generating additional challenges for local and central governments in its attempt to
transform the structure of their economies to fewer dependent ones on natural resources The
finding of a significant spatial dimension suggests that defining macro zones capturing the spatial
heterogeneity in the data should be done before analysing the relationship among variables and the
design and evaluation of specific policies Particularly relevant in those areas experiencing a
reduction in NRD are migration commuting and the characteristics of the tertiary (services) sector
In addition our findings show that education and municipal expenditure could be effective policy
tools in the fight to reduce inequality in Chile
Although our results seem quite robust they do not allow us to make causal inferences about
the effect of NRD on income inequality However we could think of the following explanation to
explain the negative relationship found and the differences between geographical areas
Areas highly dependent on NR used to demand a high proportion of low-skill labour This
has change in sectors such as the mining sector in the northern area which has simultaneously
experienced an increase in activities related to the service sector such as retail restaurants
transport and housing However those services associated with more skilled labour such as the
finance sector remain concentrated in the capital region The reduction in the degree of NRD
(employment in extractive activities) implies lower labour force but more specialized with most
53
of the low-skilled labour transferred to a service sector characterized by low productivity and low
wages
Non-spatial models show that the North and South particularly the latter present
significantly higher levels of inequality This could be associated with the type of resources with
ldquopointrdquo resources such as minerals in the North and ldquodiffusedrdquo resources such as agriculture in the
South This translates into higher average incomes in the Centre and North areas and lower average
incomes in the South
The reduction in NRD implies not only a movement of the labour force from extractive
activities to manufacturing or services with the latter characterized by low productivity and low
salaries of the labour force We could also speculate that most of the high incomes move to the
central area where the economic power and ownership over firms and resources are concentrated
This would explain low inequality associated with higher average incomes in the central area and
high inequality associated with lower average incomes in the South A more in-depth analysis
capturing the mobility of wealth and labour force between counties or more aggregated areas is
needed to better understand the causal mechanism involved
Our findings open avenues for future research in different strands First studies on the causes
of income inequality should take the role of NRD into consideration which has been overlooked
so far Given that the spatial dimension of income inequality seems to be explained by a
phenomenon of spatial heterogeneity estimation strategies such as spatial regime models or
geographically weighted regression should be used (Chi amp Zhu 2019) Second the effect of NRD
on income inequality could manifest through different channels such as education fiscal transfers
and institutions We could extend our analysis to identify which of these competing channels is
the most relevant Transforming some continuous variables such as educational level to a
54
categorical variable or defining new indicator variables for instance whether a local government
shows or not an efficient performance we could classify counties in different groups and then
check whether there are differences or not in the relationship between income inequality and NRD
A third strand could be to disaggregate our measure of NRD for different industries This
would allow us to test differences among industries and to identify the sectors that promote greater
equality and which greater inequality Forth the analysis of the consequences of income inequality
on other economic and social phenomena such as efficiency economic growth and social cohesion
has a growing interest in researchers and policymakers Our findings suggest that to answer the
question of whether income inequality has a causal impact on other variables we could include a
measure of NRD as an instrument to address endogeneity issues For instance two interesting
topics for future research are the analysis of how differences in income inequality between counties
could help to explain differences in the level of efficiency of local governments and differences in
the degree of social cohesion (unrest) throughout the country Those are the issues to be addressed
in the next two essays
55
Chapter 3 The Impact of Income Inequality on the Efficiency of
Municipalities in Chile
31 Introduction
In Chile municipalities are the smallest administrative unit for which citizens choose their
local authorities playing an important role in the provision of public goods and services at the
local level Municipalities have a similar set of objectives but the level of financial resources
available to finance their activities is highly heterogeneous This could result in significant
differences in the levels of performance between municipalities Despite their importance there is
little empirical evidence about the efficiency of local governments in Chile This essay aims to
measure the technical efficiency of Chilean municipalities and to analyse how local characteristics
particularly those related to income distribution at the county level could help to explain
differences in municipal performance
Cross-country studies situate Chile as an efficient country in international comparisons about
efficiency of government spending (Herrera amp Pang 2005 Loacutepez amp Miller 2008) However
evidence for Chile at the local level is relatively sparse suggesting significant levels of
inefficiency For instance Pacheco Sanchez and Villena (2013) found a mean efficiency level of
around 70 using a sample of 309 municipalities for the period 2008-2010 This suggests that
municipalities could achieve the same level of output by reducing the usage of inputs by an average
of 30 Their study also showed that those municipalities more dependent on the central
56
government or those located in counties with lower income per capita are more efficient than their
counterparts
Most empirical research on Local Government Efficiency (LGE) has been conducted for
member countries of the Organization for Economic Cooperation and Development (OECD) of
which Chile has been a member since 2010 In the case of European countries such as Spain and
Italy which share similar characteristics such as the monetary union and levels of GDP per head
efficiency studies have been mainly motivated by budgetary constraints (Balaguer-Coll Brun-
Martos Maacuterquez-Ramos amp Prior 2019 lo Storto 2013) The Chilean context differs in three
main ways from its OECD counterparts First except for the Metropolitan Region that concentrates
most of the population Chilean regions are highly dependent on natural resources Second Chile
is also characterized by one of the highest levels of income inequality among OECD countries
which contrast with the situation of developed natural resource-rich countries such as Australia
and Norway Third although budget constraints are also a relevant issue Chilean municipalities
have experienced a sustained increase in the level of financial resources and expenditure
Another relevant distinction when we benchmark the performance of municipalities across
different countries is the type of public services they provide On the one hand in most of the
countries included Chile the main role of local governments is to provide ldquoservices to peoplerdquo
such as public education and public health On the other hand there are countries such as Australia
where local governments mainly provide ldquoservices to propertyrdquo including waste management
maintenance of local roads and the provision of community facilities such as libraries swimming
pools and parks (Dollery Wallis amp Akimov 2010 Drew Kortt amp Dollery 2015 McQuestin
Drew amp Dollery 2018)
57
Despite contextual differences Chilean municipalities seem not to perform differently from
municipalities in other developed and natural resource-rich countries where income inequality is
significantly less than in Chile (Narboacuten-Perpintildeaacute amp De Witte 2018a) This result highlights the
need to study the role of income inequality and the degree of dependence on natural resources over
LGE characteristics that have been largely overlooked in the literature
We measure and analyse differences in municipal performance using a two-stage approach
In the first stage we measure municipal efficiency using an input-oriented Data Envelopment
Analysis (DEA) to get a set of ldquoefficiency scoresrdquo In the second stage we regress efficiency scores
against our measure of income inequality controlling for a set of contextual factors describing the
economic socio-demographic and political context of each county
We use a sample of 324 municipalities for the period 2006-2017 During this period Chile
was divided into 346 counties belonging to 15 regions This period was characterized by important
external and internal shocks including the Global Financial Crisis (GFC) one of the biggest
earthquakes in Chilean history in 2010 and three municipal elections The availability of
information allows us to measure efficiency for the full period but the influence of contextual
factors is analysed using a panel of six years (2006 2009 2011 2013 2015 and 2017) for which
household income information is available
The main hypothesis tested in the second stage is whether higher levels of income inequality
are associated with lower levels of efficiency Previous evidence shows that when progress is not
evenly shared persistent within-country inequality reduces the effectiveness and efficiency of the
public sector (Ortega Sanjuaacuten amp Casquero 2017 Tandon 2005)
Income inequality has been used to control for a wide range of idiosyncratic factors
associated with historical institutional and cultural factors affecting efficiency (Greene 2016
58
Ortega et al 2017) For instance at the local level income inequality has been considered as an
indicator of economic heterogeneity in the population where higher inequality is associated with
a more heterogeneous set of conflicting demands for public services which adversely affect an
efficient provision (Ashworth Geys Heyndels amp Wille 2014 Geys amp Moesen 2009) Higher
levels of income inequality could also relate to economically privileged groups having a greater
capacity to influence the political system for their own benefit rather than that of the majority
When high inequality is persistent the feeling of frustration and disappointment in the population
could reduce not only trust and cooperation among individuals but also trust in institutions which
would negatively affect government efficiency (Boix amp Posner 1998 Coffeacute amp Geys 2005) For
instance national or local authorities could end exerting patronage and clientelism and showing
rising levels of corruption (Uslaner 2011 Uslaner amp Brown 2005)
One of the main gaps in extant literature is the need to conduct more analysis of LGE using
panel data taking into consideration endogeneity issues and controlling for unobserved
heterogeneity (Narboacuten-Perpintildeaacute amp De Witte 2018a) To address the above we set-up a panel with
time and county-specific effects and we propose the use of a measure of natural resource
dependence (NRD) as an instrument for income inequality Based on the ldquoParadox of Plentyrdquo
fiscal revenues from natural resources windfalls could be associated with an over expansion of the
public sector fostering rent-seeking and corruption and reducing local government efficiency
(Dauvin amp Guerreiro 2017 Manzano amp Rigobon 2001) In the Chilean case most of the revenues
generated by local governments included those from natural resources end up in a common fund
which benefits all municipalities The aim of this common fund is precisely to reduce inequalities
among municipalities so although we do not expect a direct impact of natural resources on LGE
we could expect an indirect effect through other indicators particularly income inequality
59
As far as we know this is the first study analysing the influence of income inequality as a
determinant of municipal efficiency in Chile Moreover this is the first study in the context of a
natural resource-rich country which specifically suggests a measure of natural resource
dependence as an instrument to correct for endogeneity bias We propose the use of the proportion
of firms in the primary sector as proxy for the degree of NRD in each county We argue that this
variable is a better proxy than using the proportion of employment in the manufacturing sector
which has been proposed in previous studies (Alesina amp La Ferrara 2002) During the period
analysed our proxy remained relatively stable and showed a significant relationship with income
inequality In addition it is less likely that it has directly affected municipal efficiency
This study adds to the literature in two other ways First the extant literature suggests that
efficiency measurement could be highly sensitive to the chosen technique as well as the selection
of inputs and outputs (Narboacuten-Perpintildeaacute amp De Witte 2018a) Inputs are usually proxied by a single
measure of total public expenditures and outputs by general proxies such as population andor the
number of businesses in each county We offer a novel approach for the selection of inputs and
outputs On the one hand we disaggregate government expenditures into four components
(operation personnel health and education) and we use the number of public schools and health
facilities in each county as a proxy for physical capital On the other hand we use four outputs
aiming to capture the wide variety of goods and services supplied by each municipality Through
this approach we aim to better describe the production function of each municipality capturing
not only the variety of inputs and outputs but also differences in size among municipalities
A third contribution relates to the measurement of LGE in the Chilean context We measure
technical and scale efficiency using a larger sample and a longer period This has empirical and
policy relevance On the one hand it helps us to select the correct DEA model and allows us to
60
determine the importance of scale inefficiencies as explanation for differences in municipal
performance On the other hand efficiency measures increase the information available for both
central and local governments to better understand the production technology that best describes
each municipality and to carry out policies to improve efficiency
We believe that our selection of inputs and outputs the use of a large dataset and the joint
analysis using cross-sectional and panel data provide a more accurate and robust analysis of
municipal efficiency Likewise knowing whether inequality has a significant influence on
municipal efficiency may provide useful insights and guidance for policymakers not only in Chile
but also for countries sharing similar characteristics
DEA results show an average level of technical efficiency (inefficiency) of around 83
(17) This means that municipalities could reduce on average a 17 the use of inputs without
reducing the outputs There are significant differences among geographic areas with the Centre
area (where the countyrsquos capital is located) displaying higher efficiency than the rest of the country
When municipal efficiency is measured under different assumptions about returns to scale results
reveal a production technology with variable returns to scales and around 75 of the
municipalities displaying scale inefficiencies However when technical efficiency is
disaggregated between pure technical efficiency and scale efficiency results show that scale
inefficiency explains a small proportion of the total municipal technical inefficiency This finding
justifies a deeper analysis of the reasons why municipalities could operate inefficiently and why
municipal performance could vary among municipalities
Efficiency scores also show a significant degree of positive spatial autocorrelation This
means that municipal efficiency shows a general clustering process with neighbouring
municipalities showing similar levels of efficiency A further analysis shows that most of the
61
spatial pattern in municipal efficiency is exogenous that is could be associated to other variables
Hence we conduct most of our regression analysis using traditional (non-spatial) methods and
leaving spatial regressions in the appendixes
Findings from cross-sectional and panel regressions support the hypothesis that municipal
performance is significantly and negatively associated with income inequality at the county level
The coefficient of income inequality is close to one which means that reductions in income
inequality ceteris paribus could be associated with increases in municipal efficiency in the same
proportion This result supports the strand of research arguing that there is not a trade-off at least
at the municipal level between equity and efficiency (Andersen amp Maibom 2020 Berg amp Ostry
2011 2017) The main policy implications are that authorities in more unequal counties would
face higher challenges to perform efficiently and policies pertaining to inequality and efficiency
should not be designed independently
The chapter is structured as follows Section 32 provides a brief literature review on related
local government efficiency Section 33 introduces the methodological background and empirical
models Section 34 presents the empirical results and discussions Section 35 concludes the
chapter
32 Related Literature
321 Measuring efficiency of local governments
Studies on measuring LGE can be grouped in those analysing the provision of single services
such as health or education and those assessing overall efficiency (lo Storto 2013) Once inputs
and outputs have been defined efficiency is measured using parametric andor non-parametric
techniques Among the former group Stochastic Frontier Analysis (SFA) seems to be preferred
62
by scholars aiming to measure efficiency and to analyse the link with environmental variables
using a single procedure (Pacheco et al 2013 Tsekeris Sotiris Tsekeris amp Papaioannou 2018)
On the non-parametric group Data Envelopment Analysis (DEA) is by far the most used technique
(Afonso amp Fernandes 2006 Balaguer-Coll amp Prior 2009 lo Storto 2013)
The selection of inputs and outputs depends not only on the aimed of the study (specific
sector vs whole measure of efficiency) but also on the role that municipalities play in different
countries There are countries such as Australia (Drew et al 2015) and Spain (Balaguer-Coll amp
Prior 2009) where local governments mainly supply services to ldquopropertyrdquo such as waste
management and road maintenance In these cases efficiency has been mainly measured using
total indicators of local government expenditure and outputs have been proxied using general
indicators such as population or number of business (Drew et al 2015) On the other hand in
countries such as Italy (lo Storto 2013) and Portugal (Afonso amp Fernandes 2008) in Europe or
Brazil (de Sousa Cribari-Neto amp Stosic 2005) and Chile (Pacheco et al 2013) in South America
municipalities mainly supply services to ldquopeoplerdquo Here in addition to financial expenditures or
revenues inputs have included the number of local government employees the number of schools
or the number of hospitals and health centres School-age population the number of students
enrolled in primary and secondary schools and the number of beds in hospitals have been
considered as outputs Based on the study of Narboacuten-Perpintildeaacute amp De Witte (2018a) a wider list of
inputs and outputs used in previous studies can be found in Appendix I
Studies from different countries show important differences in the average efficiency scores
both between and within countries These studies also differ in the samples methodologies and
variables included A summary showing the range and variability of the mean efficiency scores
founds in countries all around the world can be found in Narboacuten-Perpintildeaacute amp De Witte (2018a)
63
These authors also show that OECD natural resource-rich countries such as Australia Belgium
and Chile show similar results in terms of mean efficiency scores with LGE studies being less
frequent in Latin American countries
Measuring efficiency of local governments as decision-making units (DMU) presents many
challenges and difficulties (Borger Kerstens Moesen amp Vanneste 1994 Ravallion 2005)
Worthington and Dollery (2000) mention problems with the selection and measurement of inputs
the identification of different stakeholders the hidden characteristic of the ldquolocal government
technologyrdquo and the multidimensionality of the services provided by local governments All these
issues make difficult to identify and distinguish between outputs and outcomes with outputs
commonly proxied by general indicators such as county area or county population Because
efficiency measures are highly sensitive to the chosen technique and the selection of inputs and
outputs Narboacuten-Perpintildeaacute amp De Witte (2018a) suggest formulating different specifications and
using less general and unspecified indicators Moreover the complexity in defining outputs and
the use of general indicators make more likely that contextual factors affect municipal efficiency
322 Explaining differences in LGE
To explain differences in local government performance researchers have basically
distinguished between ldquodiscretionaryrdquo and ldquonon-discretionaryrdquo factors Discretionary factors refer
to the degree of discretion of local authorities in the selection and management of inputs and
outputs On the other hand scholars have investigated the influence on LGE of contextual factors
beyond authoritiesrsquo control These factors reflective at the environment where municipalities
operate include economic socio-demographic geographic financial political and institutional
characteristics (da Cruz amp Marques 2014 Narboacuten-Perpintildeaacute amp De Witte 2018b)
64
In general the evidence about the influence of contextual factors has delivered mixed and
country-specific results (Narboacuten-Perpintildeaacute amp De Witte 2018b) Sampaio de Sousa et al (2005)
using data for Brazilian municipalities finds that population density and urbanization rate have
strong positive effects on efficiency scores Benito et al (2010) show that lower levels of
efficiency of Spanish municipalities are associated with a greater economic level a less stable
population and a bigger size of the local government Afonso (2008) finds that per capita income
level and education are not significant factors influencing LGE of Portuguese municipalities He
also finds that municipalities in Northern areas show greater efficiency than their counterparts in
Southern areas More recently Tsekeris (2018) finds that spatial variations in efficiency in Greece
can be attributed to factors related to inter-regional market access specialization and sectoral
concentration resource-factor endowments and political factors among others Characteristics
describing each local government have also been used including municipal indebtedness (Benito
et al 2010) fiscal deficits (Sinha 2017) degree of fiscal autonomy (Boetti Piacenza amp Turati
2009) and individual characteristics of local authorities such as age gender and political ideology
Narboacuten‐Perpintildeaacute amp De Witte (2018b) conclude after analysing 63 articles that studies on the
influence of contextual factors have mostly used cross-sectional data with little attention to
endogeneity issues which makes any causal interpretation doubtful
323 The trade-off between efficiency and equity
The existence of a potential trade-off between efficiency and equity is in the core of
economic discussion (Andersen amp Maibom 2020 Berg amp Ostry 2011 Browning amp Johnson
1984 Okun 2015)15 The argument that economic growth (one of the most common efficiency
15 Redistributive policies distort incentives and lead to suboptimal outcomes and thus efficiency losses
65
measures) could be negatively affected in the search for greater equality has been translated not
only into economic policies that favour economic growth over those that reduce inequality but
also in the emphasis of scholarly research Thus theoretical and empirical research has been
mainly focussed on efficiency and policy implications of a great diversity of shocks and policies
leaving the analysis of inequality as one of measurement and mostly descriptive Additionally
empirical evidence of the trade-off is scant and inconclusive (Andersen amp Maibom 2020
Browning amp Johnson 1984)
Among economic contextual factors that could affect LGE income inequality has been
largely ignored An exception is a cross-country comparison conducted by Ortega (2017) who
analyses the role of inequality on government efficiency in developing countries He finds that
more unequal countries could have higher difficulties to achieve specific health outcomes Income
inequality has even been considered as part of the outputs to measure efficiency particularly for
the case of European and OECD countries (Afonso Schuknecht amp Tanzi 2010 Antonelli amp De
Bonis 2018)
At the local level income inequality has been mainly used as a proxy for the effect of income
heterogeneity Economic inequality could have a direct and an indirect effect on government
efficiency The direct effect poses that higher income inequality could reduce municipal efficiency
because it is associated with a more complex and competing set of public services demanded by
the population (Jottier et al 2012) The indirect effect puts the focus in the link between inequality
social capital and levels of corruption Economic diversity could reduce trust in people and
institutions when related to high and persistent levels of income inequality It could also affect the
willingness to participate in community and political groups the existence of a shared objective
by citizens and the perception of a prosperous future (Uslaner amp Brown 2005)
66
The evidence is ambiguous For instance Geys and Moesen (2009) find that income
inequality has little relation to efficiency of Flemish municipalities and Ashworth et al (2014)
find a negative relationship for the Norwegian case Findings also indicate that inequality is the
strongest determinant of trust and that trust has a greater effect on communal participation than on
political participation (Uslaner amp Brown 2005)
33 Methodology
We follow a two-stage approach widely used in this kind of analysis A DEA analysis is
conducted in the first stage to get efficiency scores for each municipality Then regression analysis
is conducted in the second stage aiming to identify contextual variables other than differences in
the management of inputs that can help to explain the heterogeneity in municipal performance
331 Chilean Municipalities and period of analysis
The territory of Chile is divided into regions and these into provinces which for purposes of
the local administration are divided into counties The local administration of each county resides
in a municipality which is administrated by a Mayor assisted by a Municipal Council16
Municipalities represent the decentralization of the central power in Chile They are autonomous
organizations with legal personality and own patrimony whose purpose is to satisfy the needs of
the local community and ensure their participation in the economic social and cultural progress of
the county Municipalities have a diversity of functions related to public health education and
social assistance among others
16 The Mayor and City Council are elected by suffrage for the citizens of the respective commune every 4 years
67
To achieve their goals two are the main sources of municipal incomes own permanent
revenues (OPR) and the Municipal Common Fund (MCF) OPR are incomes generated by the
county and they are an indicator of the self-financing capacity of each municipality OPR are not
subject to restrictions regarding their investment and they are mainly generated by territorial taxes
commercial patents and circulation permits17 The MCF is a fund that aims to redistribute
community income to ensure compliance with the purpose of the municipalities and their proper
functioning Sources to finance the MCF come from municipal revenues The distribution
mechanism of the fund is regulated by parameters such as whether municipalities generate OPR
per capita lower than the national average and the number of poor people in the commune in
relation to the number of poor people in the country
This study covers the period from 2006 to 2017 During this period Chile was divided into
15 regions 54 provinces and 346 counties18 Although the information on inputs and outputs is
available for the entire period information on contextual factors at the county level such as
household income is only available every two-three years In addition some counties are excluded
from household surveys due to their difficult access Hence we use a sample of 324 municipalities
to measure municipal efficiency for the whole period (3888 observations) However the analysis
of contextual factors is conducted for those years when household income information is available
2006 2009 2011 2013 2015 and 2017 (1944 observations)
17 The territorial tax is a tax on agricultural and non-agricultural real estate Specifically of this income only 40 is left to the municipality as its own financing while the remaining 60 is allocated to the MCF (in the case of the four largest communes- Santiago Providencia Las Condes and Vitacura-percentages are 35 and 65 respectively) Unlike the territorial tax commercial patents are regulated mainly by the municipality which chooses the rate to be charged subject to a range established by law and is responsible of their collection Finally regarding the circulation permits 375 is of municipal benefit while 645 is directed to the MCF 18 There are 346 counties managed by 345 municipalities where counties ldquoCabo de Hornosrdquo and ldquoAntaacuterticardquo are managed by the municipality of ldquoCabo de Hornosrdquo
68
332 Measuring municipal efficiency
Municipal efficiency is measured using Data Envelopment Analysis (Coelli Prasada Rao
OrsquoDonnell amp Battese 2005) This is a non-parametric approach that uses linear programming to
measure efficiency for a group of municipalities as decision making units (DMUrsquos) The main
advantage of using DEA and the reason why DEA is used for the case of Chilean municipalities
is its flexibility in handling multiple inputs and outputs without the need to specify a functional
form (Balaguer-Coll amp Prior 2009 Mikušovaacute 2015 Tigga amp Mishra 2015) Following Afonso
and Fernandes (2008) the relationship between inputs and outputs for each municipality could be
represented by the following equation
119884 119891 119883 119894 1 119899 (31)
In equation (31) 119884 is the set of outputs and 119883 the set of inputs for each of the n
municipalities Using linear programming the production frontier is constructed and a vector of
efficiency scores is obtained The frontier represents full technical efficiency mdash the point at which
the highest output occurs given specified inputs or the point at which the lowest amount of inputs
is used to produce a specified quantity of output Efficiency scores under DEA are relative
measures of efficiency They measure a municipalityrsquos efficiency against the other measured
municipalities in the sample and not a hypothetical lsquoperfect municipalityrsquo The further from the
frontier the less technically efficient a municipality is
We use an input-oriented approach because Chilean municipalities have a greater control
over the management of inputs relative to the outputs they have to manage Obtaining efficiency
scores requires an assumption about the returns to scale exhibited by each municipality When
DMUrsquos are homogeneous the CCR model (Charnes Cooper amp Rhodes 1978) which assumes
69
constant return-to-scale (CRS) is the appropriate specification The CCR model assumes full
proportionality between outputs and inputs and that DMUrsquos operate at their optimal When DMUrsquos
are highly heterogeneous as is the case with local governments in most countries it is not realistic
to assume complete proportionality between inputs and outputs nor that all DMUrsquos operate at their
optimum scale In this situation a variable returns-to-scale (VRS) or BCC model (Banker
Charnes amp Cooper 1984) is the preferred formulation
Assuming VRS imposes minimum restrictions on the efficient frontier and allows for
comparisons only among municipalities of similar scale (Coelli et al 2005 Wu Huang amp Pan
2014) This means that when we use the CCR model (assuming CRS) on a heterogeneous sample
of DMUrsquos the resulting measure of technical efficiency that we obtain is related not only to the
management of inputs but also to issues of scale19 To empirically check the validity of the VRS
assumption we measure technical efficiency under CRS VRS and non-increasing returns-to-scale
(NIRS) and we analyse the existence of scale inefficiencies This allows us to check the relevance
of scale effects as a potential explanation of differences in municipal efficiency Appendix J
shows the specification of the DEA model under VRS and how ldquototal technical efficiencyrdquo
(assuming CRS and therefore due to management and scale issues) could be disaggregated in ldquopure
technical efficiencyrdquo (under VRS and related only to management issues) and scale efficiency (due
to scale issues)
19 Assuming VRS can also lead to measurement problems such as overestimation of efficiency scores This is the case when an important proportion of DMUrsquos shows CRS so assuming VRS ignores the information about proportionality between inputs and outputs (Podinovski 2004)
70
333 Inputs and outputs used in DEA
Following the literature on local government expenditure efficiency (Afonso amp Fernandes
2008 de Sousa et al 2005 Dlouhyacute 2018 Tandon 2005 Tigga amp Mishra 2015) and trying to
reflect as well as possible the functioning of municipalities five inputs and four outputs were
selected Input and output data were obtained from the National System of Municipal Information
(SINIM in its Spanish acronym) and they are expressed in thousands of Chilean pesos of 201720
Inputs are Municipal Operational Expenditure X1 (including expenses on goods and
services social assistance investment and transfers to community organizations) Municipal
Personnel Expenditure X2 (including full time and part-time workers) Total Municipal
Expenditure in Education sector X3 Total Municipal Expenditure in Health sector X4 and the
Number of Municipal Buildings X5 (proxied by the number of public facilities in education and
health sectors)
Output variables were selected highlighting the relevance of education and health sectors
and trying to capture the wide range of local services provided by municipalities The variable
ldquoOwn Permanent Revenuesrdquo Y1 is used to capture the scale and diversity of municipal
activities21 The ldquoMonthly Average Enrolmentrdquo in municipal education establishments related to
the school-age population in each county Y2 is used as educational output As health output the
ldquoNumber of Medical Consultationsrdquo in public facilities Y3 is considered Finally the number of
community organizations Y4 is used as output reflecting the promotion of community
development by each municipality Table 31 shows the summary statistics of input and output
20 The data from SINIM database was mostly obtained using the sinimr package (Salas 2019) 21 According to SINIM database this variable aims to measure the management of the municipalityrsquos own resources with respect to the population of the commune
71
variables for the whole sample and period Inputs and outputs excepting the Monthly Average
Enrolment Y2 are measured in per capita terms using county population information from the
National Institute of Statistics (INE in its Spanish acronym)
Table 31
Descriptive statistics Inputs and Output variables used in DEA analysis
334 Regression model
Contextual factors could play an important role not only in explaining why some
municipalities operate inefficiently but also why municipal performance differs among them
These factors may affect municipal performance modifying incentives for local authorities to
operate efficiently and their capability to take advantage of economies of scale They also define
the conditions for cooperation or competition among municipalities and the citizensacute ability and
willingness to monitor local authorities (Afonso amp Fernandes 2008)
Information on income at the household level for each county was obtained from the
ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) CASEN is
conducted every two-three years being the reason why consecutive years are not considered in
72
our regression analysis The other contextual factors used as controls were obtained from different
sources including SINIM INE and the ldquoServicio de Impuestos Internosrdquo (SII)22
Our main hypothesis is whether higher levels of income inequality are associated with lower
levels of municipal efficiency To test our hypothesis the empirical model is defined as
120579 120573 119892119894119899119894 119885 120573 120575 120572 120598 (32)
Where 120579 is the vector of DEA efficiency scores 119892119894119899119894 is the Gini coefficient of each
county 120575 are year-specific effects 120572 are county-specific constants 120598 is a vector of error terms
and 119885 is a vector of controls Next we discuss the motivation for these controls
The level of purchasing power of countiesrsquo citizens is proxied by the variable log(income)
which is the natural log of the mean household income per capita in thousands of Chilean pesos of
2017 On the one hand poorer counties could display higher efficiency due to their necessity to
take care of their constraint resources (Pacheco et al 2013) On the other hand richer counties
could show higher efficiency because richer citizens exert higher monitoring over local authorities
and demand better quality public services in return for their tax payments (Afonso et al 2010)
The possibility for municipalities to take advantage of economies of scale and urbanization is
captured by three variables First the variable log(density) which correspond to the natural log of
population density Second the dummy variable reg_cap indicating whether a county is a regional
capital or not Third the variable agroland which correspond to the proportion of land for
agricultural use which is informed to the SII We expect a positive effect of log(density) but
negative for regcap and agroland
22 The SII is the institution in charge of collecting taxes in Chile
73
Socio-demographic characteristics are captured including a Dependence Index IDD IDD
corresponds to the number of people under 15 years or over 65 years per 100 people in the active
population (those people between 15 and 65 years old) A higher proportion of young and older
population could be associated with a higher demand for municipal services relating to education
and health making harder to offer public services efficiently The citizensrsquo capacity to monitor
local authorities is proxied including the variable education (average years of education for the
population older than 15 years) and the variable housing (proportion of households which are
owners of the property where they live in each county) In both cases we expect a positive
association with LGE
Among municipal characteristics the variable professional (percentage of municipal
personnel with a professional degree) is used to control for the quality of municipal services and
it is expected a positive impact The variable mcf (proportion of total municipal income coming
from the MCF) is included to capture the influence of financial dependence on the central
government A higher dependence from MCF could be associated with higher efficiency when it
is linked to more control from central government (Worthington amp Dollery 2000) However when
MCF discourages the generation of own resources and proper management of resources from the
fund a lower efficiency should be expected (Bravo 2014) In addition the dummy variable mayor
is included to capture differences among mayors supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo
political parties related to those ldquoINDEPENDENTrdquo mayors
Table 32 report summary statistics for the set of numeric contextual factors and Appendix
K the corresponding correlation matrix Despite the high correlation between income and
education variables we include both in the regression section as they capture different county
characteristics
74
Table 32
Summary Statistics Numeric Contextual Factors
Figure 31 Geographical distribution of Chilean regions and macrozones
Previous evidence on growth and convergence of Chilean regions have found that regions
tend to cluster spatially (Aroca amp Bosch 2000) Aiming to capture this regional clustering process
75
and considering the high concentration in the number of municipalities and population in the
central area we group municipalities in four ldquozonesrdquo We define as reference the ldquoCentre-Northrdquo
zone consisting of regions XIII (where the countryrsquos capital is located) and its two neighbouring
regions V and VI The ldquoNorthrdquo zone includes regions XV I II III and IV The ldquoCentre-Southrdquo
zone includes regions VII VIII and IX Finally the ldquoSouthrdquo zone embraces regions XIV X XI
and XII Figure 31 displays the regional administrative division and zones considered in this
essay
Efficiency scores (ES) are limited to have values between 0 and 1 However they are relative
measures (relative to the sample of municipalities) This implies that when a municipality is on the
frontier (ES = 1) it does not mean that potentially larger technical efficiency gains cannot be made
Hence equation 32 is estimated using OLS and censored regressions We start running cross-
sectional regressions for each of the six years Then we compare the results with those from panel
regressions Because fixed-effects panel Tobit models could be affected by the incidental
parameters problem (Henningsen 2010 2019) we use random-effects panel Tobit models
including indicator variables for years and zones Finally to deal with the potential endogeneity
problem we also use an instrumental variable approach The instrument is described next
335 The instrument
Government effectiveness and income distribution are both structural components of
economies (Ortega et al 2017 Ravallion 2005) In the search for a causal interpretation for the
influence of income inequality on municipal efficiency we need an instrument which must be
correlated with the variable to be instrumented (in our case income inequality) and uncorrelated
with the error term in the efficiency equation (32) Previous literature has used as instruments for
Gini the number of townships governments in a previous period the percentage of revenues from
76
intergovernmental transfers in a previous period and the current share of the labour force in the
manufacturing sector (Alesina amp La Ferrara 2002) Using the share of the labour force in a specific
sector is unlikely to reduce the problem of endogeneity particularly in countries where local
governments supply mostly ldquoservices to peoplerdquo and the main source of income inequality is
labour income
We propose as an instrument the proportion of firms in the primary sector (mining fishing
forestry and agriculture)
119901119904119904_119891119894119903119898119904Number of firms in the primary sector
Total number of firms (33)
On the one hand this instrument is likely to be correlated with local income inequality in
natural resource-rich countries23 On the other hand we contend that our instrument is less likely
to be correlated with the error term in the efficiency equation First the main services supplied by
Chilean municipalities are services to people (health and education) not to firms Second most of
the revenues collected by municipalities included those associated with natural resources end up
in the municipal common fund whose objective is precisely to reduce inequalities among
municipalities Third services to firms are expected to be more significant with the tertiary sector
We argue that our instrument captures natural and structural conditions which directly
influence income inequality but it does not directly affect LGE Figure 32 shows the evolution
of the annual average efficiency score and the proportion of firms in the primary secondary
(manufacturing) and tertiary (services) sectors We observe that sectors shares have remained
relatively stable with a slight reduction in the participation of the primary sector in favour of the
23 Results confirm a significant association between income inequality and the degree of dependence on natural resources at the county level
77
tertiary sector by the end of the period This is contrary to the evolution of municipal efficiency
which shows a cyclical behaviour as will be shown in the next section
Figure 32 Evolution of efficiency scores and the proportion of firms by sector
34 Results and discussion
341 DEA results
Figure 33 displays the evolution of our three measures of efficiency Overall technical
efficiency pure technical efficiency and scale efficiency are around 78 83 and 95
respectively with fluctuations over the years Therefore around three quarters of the overall
78
inefficiency is attributed to inefficiency in the management of inputs and around one quarter to
scale inefficiencies24
Figure 33 Evolution technical efficiency (TE) pure technical efficiency (PTE) and scale efficiency (SE)
Returnstoscale
Figure 34 reports by zone and for the whole period the proportion of municipalities
showing CRS decreasing returns to scale (DRS) or increasing returns to scale (IRS) Most of the
municipalities operate under variable (increasing or decreasing) returns to scale which could be
explained by the high heterogeneity in size among municipalities A summary of RTS
disaggregated by year and zone is in Appendix L Policies oriented to increase efficiency usually
24 The average scale efficiency score of 95 means that municipalities could get an additional 5 of inputs saving if municipalities were able to change their size to achieve their optimal scale
79
consider amalgamation de-amalgamation or ways of cooperation among municipalities To have
a better idea about where and how feasible is the implementation of such policies Appendix M
shows maps with the administrative division of the country in its 345 municipalities and which
municipalities show CRS IRS or DRS in each of the six years of data
Figure 34 Returns to scale by zone
Based on results for the whole period (Figure 34) the North has the highest proportion of
municipalities exhibiting DRS This suggests the need to reduce the size of municipalities splitting
those ldquotoo bigrdquo creating new administrative areas or giving more autonomy to current
municipalities25 The opposite occurs in the Centre-North area where municipalities mostly
exhibit IRS This indicates the need to merge municipalities An alternative strategy to the
amalgamation process is increasing inter-municipal cooperation (Balaguer-Coll et al 2019)
25 This has been the policy followed in Chile Although two new regions were created (XV in the North and XIV in the South) the number of municipalities has not changed
80
which seems to be a more plausible option in Chile Finally evidence on scale efficiency for the
Centre-South and South areas is not clear in terms of the adequate strategy to improve efficiency
Efficiencymeasure
Although most municipalities show scale inefficiencies (Figure 34) only a small proportion
of total inefficiency is associated to scale issues (Figure 33) Together this results justify not only
the use of the BCC model (efficiency scores under VRS) to capture municipal heterogeneity but
also highlights the need to look for other factors outside the control of local authorities which
could be influencing municipal performance
Table 33
Summary efficiency scores (VRS) by zone and region
Table 33 summarizes ES under VRS using data for the six years of CASEN survey A mean
efficiency score of 83 is found for the full sample and period This means that on average
inefficient municipalities can reduce the use of inputs by 17 to get the same current output By
81
comparing average ES per zone it can be concluded that municipalities in the North Centre-North
Centre-South and South could achieve the same level of output with 17 12 18 and 23 fewer
resources respectively Results also show that one third of the municipalities present an efficiency
score equal to one
Figure 35 shows the evolution of the mean ES for the four zones over the 12 years period
A clear drop in the efficiency level is observed in 2009 (the financial crisis year) excepting for the
North area Likewise although mean efficiency returned to its pre-crisis levels in 2014-2015 a
new drop in efficiency was experienced in 2016 It also seems that the 2010 earthquake did not
generate a significant effect on municipal efficiency Figure 35 also shows that although levels
of efficiency seem to differ among zones they follow a similar trend through time with the only
exception of the North which corresponds to the mining area In addition efficiency seems to be
significantly higher in the Centre-North area This is explained by the high mean level of efficiency
in region XIII which includes the countryrsquos capital city
Figure 35 Evolution mean efficiency scores (VRS) by zone
82
To know which and where are the efficient municipalities and if they are surrounded by
municipalities with a similar level of efficiency Appendix N has maps showing the ldquoefficiency
statusrdquo of each municipality that is whether they are efficient (ES = 1) or inefficient (ES lt 1)
Additionally Appendix O shows ldquoequal intervalsrdquo maps describing the spatial distribution of ES
among municipalities for each of the six years26 Results show that efficient municipalities can be
found all through the country the ldquoefficiency statusrdquo could change from one year to another and
municipalities with similar level-status of efficiency tend to cluster in space
342 Regression results
Exploratoryspatialanalysis
DEA efficiency scores and their geographical representations seem to show that municipal
efficiency presents a spatial clustering pattern This means that municipal performance could be
influenced not only by contextual factors of the county where municipality belongs but also by the
level of efficiency of neighbouring municipalities and their characteristics To test the significance
of the spatial dimension in municipal efficiency we use a cross-section of data considering the six-
year average of efficiency scores the Gini coefficient and the set of controls
We use the Moranrsquos I indicator to test for spatial autocorrelation Moranrsquos I is a measure of
the correlation between the level of efficiency in one municipality and its ldquospatial lagrdquo that is the
average level of efficiency in neighbouring municipalities We define as the relevant neighbours
for each municipality the 5-nearest municipalities This is obtained using the distances among the
26 An equal intervals map uses the same principle as a histogram to organize the observations into categories that divide the range of the variable into equal interval bins For the equal interval classification the value range between the lower and upper bound in each bin is constant across bins but the number of observations in each bin is typically not equal
83
polygonsrsquo centroids (latitude and longitude) of each county Results confirm that municipal
efficiency show a significant level of positive spatial autocorrelation This means that
municipalities tend to have neighbouring municipalities with similar performance
The positive spatial autocorrelation shown by municipal efficiency could be due to the
performance in one municipality is influenced by the performance in neighbouring municipalities
(spatial dependence in the variable itself) or due to structural differences among regions-zones
(spatial heterogeneity) To check the source of the spatial autocorrelation we run an OLS
regression of ES against income inequality and controls and then we test OLS residuals for spatial
autocorrelation Moranrsquos I over OLS residuals although significant is barely higher than zero (see
Appendix P) This means that the spatial effect itself is not a serious issue and can be handle for
instance including zone indicators variables hence we proceed to analyse the influence of income
inequality on LGE using non-spatial regression27
Cross‐sectionalanalysis
We start reporting censored regressions for each year in our panel Efficiency scores have
been rescaled to numbers between 0 and 100 to facilitate the interpretation of the results All
regressions include dummy variables for three of the four zones in which we have grouped Chilean
regions Results are in Table 3428 Income inequality shows a negative sign in all years which is
consistent with our hypothesis that inequality is negatively related to municipal efficiency
However only in three of the six years the effect of income inequality appears as statistically
27 In the case of having incorporated the spatial dimension the two most commonly used forms are including in the model the spatial lag of the dependent variable (spatial autoregressive model SAR) or the spatial lag of the error (spatial error model SEM) Following the method suggested by Anselin et al (1996) Lagrange Multiplier (LM) tests on the OLS residuals showed that the SAR model is preferred over the SEM Moranrsquos I LM tests and spatial regressions can be found in Appendix P 28 Regression results using OLS are in Appendix Q
84
significant Only the income level displays a significant and positive influence on efficiency for
the whole period A higher population density also consistently favours municipal efficiency On
the other hand as we expected a higher IDD makes it more difficult to achieve an efficient
performance29 Consistent with results in Pacheco et al (Pacheco et al 2013) municipal
efficiency show a significant an positive association with the MCF only in the first half of our
period of analysis with the second half showing an insignificant relationship
Table 34
Cross-sectional (censored) regressions
Paneldataanalysis
Estimation results for the six-year panel are reported in Table 35 Columns (1) and (2) show
the results for the pooled and random effects censored models only controlling for zone and year
29 The set of cross-sectional regressions was also run for each zone independently Results confirm that the set of significant contextual factors differs not only among years but also among geographic areas This support previous evidence suggesting that cross-sectional analysis should be treated with caution (Narboacuten-Perpintildeaacute amp De Witte 2018b)
85
dummies Income inequality appears as non-significant Zone indicator variables confirm that
municipalities located in the Centre-South and South of the country display a lower average level
of efficiency compared to the Centre-North area Time dummies mostly show negative
coefficients This is a signal that shocks such as the GFC and the earthquake in 2010 may have
had a negative impact on efficiency but that impact was not permanent The results for the pooled
and RE models including the full set of controls are reported in columns (3) and (4) These results
show a significant negative influence of income inequality on LGE
When income inequality is instrumented by the variable pss_firms most of the coefficients
remain unchanged except for those associated with the income variables gini and log(income)
This result implies that our original model suffers for instance from the omitted variable bias
This means that LGE and income inequality are determined simultaneously by some variable not
included in our model Columns (5) and (6) show results using our instrument for income
inequality The ldquotrue coefficientrdquo of variable gini remains negative but the magnitude of the
relationship is higher The negative coefficient for gini implies on the one hand that municipalities
located in more unequal counties face more challenges to achieve an efficient management of
public resources On the other hand the coefficient in column (6) is close to one The interpretation
is that for each point of reduction in income inequality ceteris paribus LGE should increase in the
same proportion Next we discuss some of the results associated with the controls variables
Contrary to Pacheco et al (2013) income level has a positive coefficient meaning that richer
counties in terms of income per capita show higher efficiency This could be explained by higher
monitoring and increasing demands from citizen and taxpayers In addition it is expected a higher
efficiency in municipalities located in counties with a higher population density and those with a
lower proportion of land for agricultural use This result is mainly explained by municipalities
86
located in the Centre area The opposite happens with municipalities in the South implying that
they are too ldquosmallrdquo to take advantage of agglomeration economies There is also a limit for
agglomeration and scale economies which is shown by the negative coefficient of the variable
regcap although this coefficient loses its significance in the IV approaches30
Unexpectedly efficiency was found to be negatively associated with the variable education
This result is similar to the case of Czech municipalities (Šťastnaacute amp Gregor 2014) where
explanations include a weakened monitoring effect due to the fact that more educated citizens
present greater mobility and labour cost disadvantages for municipalities with better educated
labour force In Chile an additional explanation could be the relationship between education and
voter turnout Since 2012 voting is no longer mandatory in Chile This fact considerably reduced
voter turnout which in turn may have influenced the monitoring and control effect of more
educated voters For the case of variable IDD results show that local authorities in counties with
higher proportion of aging and young population (related to those in the active population) face a
greater challenge in their quest to offer public services efficiently
The influence of mcf is like that found by Pacheco et al (2013) with municipalities more
dependent on central transfers showing more efficiency31 Political influence captured by the
variable mayor did not show a significant effect This result is like other studies concluding that
the ideological position did not have a significant influence on efficiency (Benito et al 2010
Boetti et al 2009 Cordero Pedraja-Chaparro Pisaflores amp Polo 2017)
30 This negative coefficient suggests that the negative effects of agglomeration economies such as overcrowding pollution high cost of land and traffic congestion could predominate over the positive effects of scale economies in regional capitals 31 When the analysis is conducted for each zone independently MCF displays a negative influence in the North and Centre-North areas but a positive influence in the Centre-South and South areas These results confirm that on the one hand richer municipalities (those in the North and Centre-North) have less incentives for efficient management of their resources On the other hand results support the relevance of MCF helping poorer municipalities (those in the South) to reach their outcomes
87
Table 35
Panel data regressions
88
35 Conclusions
The trade-off between equity and efficiency is in the core of the economic discussion This
ldquoprinciplerdquo has been used by policymakers to prioritize the design of policies focused on economic
growth delaying those policies aimed at reducing economic inequalities This essay offers
empirical evidence of a negative relationship between inequality and efficiency that is a reduction
of income inequality could have positive effects on economic efficiency at least at the level of
local governments
We followed a traditional Two-Stage approach commonly used in the analysis of LGE We
compared cross-sectional and panel data results and we have added an instrumental variable
approach to give a causal interpretation to the link between efficiency and inequality We proposed
the use of a measure of natural resource dependence to instrumentalize the impact of income
inequality on LGE Given that our units of analysis are municipalities and not counties we argue
that our measure of NRD is correlated with income inequality and it does not have a direct
influence on LGE
We found that Chilean municipalities perform better than previous studies suggest
Municipal efficiency depicted significant levels of positive spatial autocorrelation and most of the
municipalities showed to be operating under increasing or decreasing returns to scale This would
imply that the policies generally used to improve efficiency such as amalgamation or cooperation
should be implemented observing the reality of each region and not as strategies at the national
level We also found that scale inefficiency explains a small proportion of the average total
inefficiency reason why the analysis of external factors that could affect the municipal efficiency
takes greater relevance
89
Income inequality plays an important part in explaining municipal efficiency In fact it was
found that reductions in income inequality could result in increases in municipal efficiency in a
similar proportion An unexpected finding was that the levels of education shows a negative
association with municipal performance This could be due to a low average level of education or
the existence of an omitted variable This variable could be the significant reduction in voting
turnout rates for local and national elections due to changes in the voting system during the period
of our analysis All in all our results may help to shed light on the potential consequences of
changes in contextual factors and the design of strategies aimed to increase municipal efficiency
in countries with similar characteristics to the Chilean economy For instance policies oriented to
take advantage of economies of scale can be formulated merging municipalities or establishing
networks in specific sectors such as education or health
Further work needs to be done both in measurement and in the explanation of differences in
municipal performance in Chile One area of future work will be to identify the factors that better
predict why municipalities operates under increasing decreasing or constant returns to scale
Multinomial logistic regression and the application of machine learning algorithms to SINIM data
sets appear as suitable methods for that purpose Intertemporal DEA (Drew amp Dollery 2015)
should be used to measure municipal efficiency capturing changes in total factor productivity In
addition municipalities operate under different levels of geographical authorities such as the
provincial mayor and the regional governor Hence it would be useful to know how each
municipality performs within each region-zone related to how performs to the whole country This
should be done conducting a metafrontier analysis (OrsquoDonnell Rao amp Battese 2008)
We have also identified through a cross sectional spatial exploratory analysis that on
average municipalities with similar levels of efficiency tend to cluster in space Regarding to
90
analyse the importance of contextual factors on municipal efficiency a deeper analysis should use
censored spatial models to check the significance of the spatial dimension in cross-sectional and
panel contexts Another interesting avenue for future research is associated with the negative
association found between LGE and education The significant reduction in votersacute turnout since
the law of automatic registration and voluntary voting in 2011 appears as a natural experiment to
analyse its effects on efficiency indicators such as municipal performance Incorporating variables
such as the voting turnout in each county or classifying municipalities based on individual
institutional political and economic characteristics could help to shed light on which of these
channels is the most relevant when analysing the impact of inequality on municipal efficiency
Finally we argued that an important part of the influence of income inequality over LGE
could be through its indirect effect on trust social capital and social cohesion The final essay will
delve deep in that relationship
91
Chapter 4 Social Cohesion Incivilities and Diversity
Evidence at the municipal level in Chile
41 Introduction
A deterioration in social cohesion could carry significant costs such as a reduction in
generalized trust between individuals and in institutions a society caught in a vicious circle of
inequality and citizens increasingly distanced from civic life (Uslaner 2011) A growing feeling
of frustration and discontentment can eventually translate into a social outbreak with uncertain
results This is precisely what have been happening in many countries around the world included
Chile
ldquoSocial cohesion is a state of affairs concerning both the vertical and the horizontal
interactions among members of society as characterized by a set of attitudes and norms that
includes trust a sense of belonging and the willingness to participate and help as well as their
behavioural manifestationsrdquo (Chan et al 2006) This definition highlights the multidimensionality
in the concept of social cohesion which has been measured using objective andor subjective
indicators of trust social norms solidarity willingness to participate in social and political groups
and feelings of belonging (Ariely 2014 Chan et al 2006) Multidimensionality also implies that
the impact of determinants of social cohesion such as economic and racial diversity could be
different for each of its various dimensions (Ariely 2014)
A common characteristic to all societies is that they are made up of different groups that
differ with respect to race ethnicity income religion language local identity etc The
92
Community Heterogeneity Thesis (Coffeacute amp Geys 2006) argues that individuals prefer to interact
with others that are like themselves Hence high levels of diversity particularly economic and
racial represent a complex scenario to maintain social cohesion One of the most common factors
adduced for social cohesion is income inequality with higher levels linked to lower levels of trust
(Ariely 2014 Rothstein amp Uslaner 2005)
Traditional measures of social cohesion may not be adequately capturing the deterioration
in social connections For instance measures of (lack of) trust include a strong subjective element
On the other hand proxies for social participation such as volunteering jobs or joining to social
organizations have not been supported by empirical evidence as a source of generalized social trust
(Rothstein amp Uslaner 2005) We proposed to use the rate of incivilities which we argue is a more
appropriate measure of the degree of worsening in the social context
Incivilities are those visible disorders in the public space that violate respectful social norms
and tend not to be treated as crimes by the criminal justice system There are two types of
incivilities social and physical Social incivilities include antisocial behaviours such as public
drinking noisy neighbours and fighting in public places Physical incivilities include among
others vandalism graffiti abandoned cars and garbage on the streets Because citizens and
political authorities cannot always distinguish between incivilities and crime they are usually
treated as an additional category of crime This implies that policies aimed to reduce incivilities
are generally based on punitive actions However theory and evidence on incivilities suggest that
factors explaining incivilities and crime could be different (Lewis 2017 Taylor 1999)
In Chile crime rates have shown a sustained downward trend after reaching its highest level
in 2011 On the other hand incivilities rate has shown a sustained upward trend which coincides
with the increasing victimization and feeling of insecurity in the population This has motivated
93
Chilean authorities based on the ldquoBroken Windows Theoryrdquo to propose new punitive actions (or
increase the severity of the current ones) to those who commit this type of antisocial behaviours
The ldquoBroken Windows Theoryrdquo states that higher rates of incivilities are a signal of social
disorganization which result in higher crime rates (J Q Wilson amp Kelling 1982) This is expected
to have consequences on familiesrsquo decisions such as moving away from public spaces or even
leaving their neighbourhoods
As far as we know there is no previous evidence about the potential causes of incivilities in
Chile Efforts to identify the factors explaining incivilities could help not only to reduce the risk
factors favouring violent and property crimes but also to guide interventions aimed to change
social behaviours and strengthen social cohesion in highly unequal societies Thus the main
contribution of the present study is to provide a deeper comprehension of the problem of incivilities
and how they can help to better understand the weakening of social cohesion that many
contemporary societies experience
We aim to offer the first evidence on the factors explaining the evolution and the differences
in incivilities rates in Chile We set up a panel for six years (2006 2009 2011 2013 2015 and
2017) and 324 counties (1944 observations) We start exploring the evolution and geographical
distribution of incivilities Then we investigate whether economic and racial diversity after
controlling for other socioeconomic demographic and municipal characteristics can be regarded
as key predictors of incivilities
We use the Gini coefficient to proxy economic heterogeneity and the number of new visas
granted to foreigners as proportion of the county population as proxy for racial diversity The main
hypothesis is whether economic and racial diversity have a positive association with the rate of
incivilities In addition to the arguments regarding the ldquoCommunity Heterogeneity Thesisrdquo Taylor
94
(1999) and Skogan (1986 1999 2015) argue that incivilities are caused by inequality and the lack
of informal mechanisms of social control Based on the ldquoIncivilities Thesisrdquo the patterns of
incivilities should mirror the patterns of inequality (Taylor 1999) Then higher inequality should
be associated with higher physical and social vulnerability of the population This reduces social
control and increases social disorganization which triggers antisocial or negligent behaviours
Our main result reveals a strong positive association between the rate of incivilities and the
number of new visas granted per year The relationship with income inequality although also
positive seems to be less significant These findings give strong support to the ldquoCommunity
Heterogeneity Thesisrdquo and to a lesser extent to the ldquoIncivilities Thesisrdquo When the analysis is
disaggregated geographically racial diversity shows a clear positive effect The impact of income
inequality seems to be conditional depending on the level of income showing no effect in poorer
regions Results also show that the impact of economic and racial diversity differs by type of
incivility For example income inequality shows a strong association with ldquoStreet Tradingrdquo while
racial diversity with ldquoAlcohol Consumptionrdquo Two are the main policy implications On the one
hand a national strategy to address the problems associated with foreign immigration could help
to reduce incivilities For instance a joint effort between national and local authorities to curb
immigration and its distribution throughout the country On the other hand our results show that
the relationship between incivilities and economic diversity differs depending on the region or
geographical area Hence the impact on social cohesion of policies aimed to tackle economic
inequalities should be analysed in each specific context
The rate of incivilities also shows a negative association with the level of municipal financial
autonomy This implies that municipalities can effectively carry out policies to reduce incivilities
beyond the efforts of the central government Another important finding is that our results do not
95
support the hypothesis that a higher proportion of the young population is associated with higher
rates of incivilities Hence policies aimed to reduce incivilities should be focused on the causes of
incivilities rather than the criminalization of behaviours or stigmatization of specific population
groups
The structure of the chapter is as follows Section 42 outlines the relevant literature on social
cohesion and incivilities Section 43 describes the data variables and methodology and
establishes the hypotheses of the study Section 44 contains the results and discussions Section
45 presents the main conclusions
42 Related Literature
421 The Community Heterogeneity Thesis
The idea under ldquoThe Community Heterogeneity Thesisrdquo is that if individuals prefer to
interact with others who are similar to themselves in terms of income race or ethnicity high levels
of income inequality and racial diversity facilitate a context for lower tolerance and antisocial
behaviours lowering the ldquostaterdquo of social cohesion (Alesina 2000 Coffeacute amp Geys 2006 Letki
2008) Alessina and Ferrara (2002) give support to this hypothesis arguing that individuals have a
natural aversion to heterogeneity However the most popular explanation is the principle of
homophily people prefer to interact with others who share the same ethnic heritage have the same
social status and hence share experiences and tastes (Letki 2008 McPherson Smith-Lovin amp
Cook 2001 Tolsma et al 2009) For instance Delhey and Newton (2005) find for a sample of
60 countries that income inequality and ethnicity are strongly and negatively correlated with trust
Tolsma et al (2009) using data for Dutch neighbourhoods and municipalities find that social
cohesion is negatively and consistently affected by economic deprivation but not by ethnic
96
heterogeneity These authors also conclude that the effect of neighbourhood and municipal
characteristics on social cohesion depends on residentsrsquo income and educational level
Rothstein and Uslaner (2005) give two theoretical reasons why economic and racial diversity
should be causally related to social trust a key element of social cohesion First optimism about
the future makes less sense when there is more economic inequality which generally translates into
inequality of opportunities especially in areas such as education and the labour market Second
the distribution of resources and opportunities plays a key role in establishing the belief that people
share a common destiny and have similar fundamental values In highly unequal societies people
are likely to stick with their own kind Perceptions of injustice will reinforce negative stereotypes
of other groups making social trust and accommodation more difficult
Uslaner (2002 2011) and Uslaner and Brown (2005) find that high levels of inequality are
the single major factor driving down trust in people who are different from yourself Evidence for
USA finds that inequality is the strongest determinant of generalized trust over time (Rothstein amp
Uslaner 2005) Reducing inequality and then increasing generalized trust should have positive
consequences at the individual and aggregates levels At the individual level it may lead to greater
tolerance and more acts of altruism for people of different backgrounds At the aggregate level it
may lead to greater economic growth more redistribution from the rich to the poor and less
corruption (Uslaner 2002 2013) Letki (2008) argues that when neighbourhood socio-economic
context (apart from just an individualrsquos socioeconomic status) is considered it turns out to be the
main factor triggering negative attitudes and lack of trust in out-group members
The increasing diversity caused by immigration can also reduce the conditions necessary for
social cohesion (Ariely 2014 Holtug amp Mason 2010) Christel Kesler and Irene Bloemraad
(2010) find for nineteen advanced democracies between 1981 and 2000 that increasing migration
97
generally decreases trust civic engagement and political participation The authors also find that
in more equal countries with clear policies in favour of cultural minorities the negative effects of
migration are mitigated or even reversed Letki (2008) states that deprivation and disorder tend to
be strongly correlated with racial diversity Because we propose the use of the number of disorders
or antisocial behaviours known as ldquoincivilitiesrdquo as our measure of social cohesion we describe the
literature on incivilities in the next section
422 The literature on incivilities
The study of incivilities has been a continuing concern mainly for developed countries since
the 1980s The focus has changed from individual and psychological explanations to ecological
(contextual) and social explanations (Taylor 1999) The individual approach basically considered
perceptions of incivilities as an explanatory variable of fear of crime The ecological explanation
argues that indicators of economic disadvantage (eg income levels income inequality
unemployment rate and poverty rate) are the keys to understand a process of social disorganization
and lack of informal control These economic factors lead to higher rates of inappropriate or
negligent behaviours and ultimately to higher crime rates (Blau amp Blau 1982 Messner Rosenfeld
amp Baumer 2004 Phan Orsquobrien Mendolia amp Paloyo 2017 Sampson 1986)
The negative impact of incivilities is not merely reflected in its association with crime rates
(Skogan 2015) Physical and social incivilities could worsen neighbourhoods by affecting quality
of life perception of the environment and public and private behaviours Previous research has
indicated that a higher level of incivilities is associated with health problems (Branas et al 2011
Cohen et al 2000 Hill amp Angel 2005 Ross 2011 Ross amp Mirowsky 2001) greater
victimization and fear of crime (Brunton-Smith Jackson amp Sutherland 2014 Mijanovich amp
Weitzman 2003) and multiple negative economic effects For instance incivilities could be
98
related to a reduction in commercial activity lower investment in real estate reduction in house
prices (Skogan 2015) and population instability (Hipp 2010)
To describe the state of the art in the study of incivilities and their consequences Skogan
(2015) used the concept of untidiness to characterize the research on incivilities The study of
incivilities has had multiple approaches (economic ecological and psychological) Incivilities
have also been measured using multiple sources of information (police reports surveys trained
observation) which result in different measures (perceptions vs count data) However the question
about what specific factors have the strongest effect on incivilities has been overlooked and
perceptions about incivilities have been used mainly as a predictor of crime fear of crime and
victimization
There are two types of incivilities social and physical Social incivilities are a matter of
behaviour including groups of rowdy teens public drunkenness people fighting and street hassles
Physical incivilities involve visual signs of negligence and decay such as abandoned buildings
broken streetlights trash-filled lots and graffiti (Skogan 1999 2015 Taylor 1999) Three reasons
justify the distinction between physical and social incivilities First like multiple dimensions of
social cohesion different structural and social conditions could be responsible for different types
and categories of incivilities Second punitive sanctions are expected to have a greater impact on
physical than on social incivilities since the latter are more related to behaviours rooted in citizensrsquo
culture Third physical incivilities should be more related to absolute measures of economic
disadvantage (eg poverty or unemployment rates) and social incivilities to relative indicators of
economic disadvantage (eg such as income inequality) This line of research is based on the
ldquoincivilities thesisrdquo which states that to understand the distribution of disorders it is necesary to
analyse the patterns of structured inequalities (Skogan 1986 Taylor 1999)
99
423 The ldquoIncivilities Thesisrdquo
Incivilities theories began with a focus on psychological dynamics (Garofalo 1978) moved
forward to an interest in social psychological processes (J Q Wilson amp Kelling 1982) and finally
evolved into a focus on community dynamics and outcomes (Skogan 1999) Individual and group
behaviours in tandem with ecological features have been proposed as the key factors explaining
incivilities and their posterior influence on social control quality of life and more serious crime
(J Q Wilson amp Kelling 1982)
In terms of ecological factors particularly those related to economic conditions Skogan
(1986) was the first linking the distribution of incivilities to the patterns of structural inequality If
incivilities mirror inequality structure this will have consequences in residentsrsquo health and safety
due to its levels of vulnerability In addition structured inequality associated with the proportion
of the manufacturing sector (eg when fabrics tend to move from cities to farther areas) will be
related to higher social disorganization and differences between urban and rural areas (W J
Wilson 1996) In addition a persistent feeling of relative deprivation (persistently high levels of
income inequality) could lead to fellow inhabitants of the community to commit antisocial
behaviours showing their frustration with the current economic model
The literature on incivilities posits that their causes are different from those of crime (Lewis
2017) Unlike crime analysis especially property crimes information on the location where the
incivility takes place is the same as the location where the perpetrator resides To achieve a
comprehensive understanding of the different types of incivilities it is crucial to consider
incivilities data covering an entire territory and not just specific areas (Hooghe Vanhoutte
Hardyns amp Bircan 2010) If we add to this the availability of panel data it could be possible not
100
only to identify the main determinants of incivilities but also the causal mechanism from income
inequality towards incivilities rate
In Chile citizen security crime and delinquency are among the most significant issues for
citizens based on opinion polls Existing research has found weak evidence of a significant
relationship between crime and indicators of socio-economic disadvantage such as income
inequality and unemployment rate with significant effects only on property crime (Beyer amp
Vergara 2006 Nuntildeez Rivera Villavicencio amp Molina 2003 Rivera Gutieacuterrez amp Nuacutentildeez 2009)
Crime deterrence variables such as the probability of being caught or the number of police
resources have also shown ambiguous results (Beyer amp Vergara 2006 Rivera et al 2009
Vergara 2012) Evidence at the county level shows that crime is higher in urban counties those
with a lower mean income per capita and counties located in the North of the country In addition
at least half of the crimes reported in one county are perpetrated by criminals from other counties
(Rivera et al 2009) No studies could be found about the determinants of incivilities
4 3 Methodology
431 Period of analysis and data sample
Chile is a relatively small country in Latin America with a population of 18346018
inhabitants in 2017 The country is divided into 345 municipalities with on average 53104
inhabitants (median value 18705) Municipalities are the organ of the State Administration
responsible to solve local needs Municipalities are not only the relevant political and
administrative local unit of analysis but also they represent the feeling of lsquocommunityrsquo among
the inhabitants of each municipality (Hooghe et al 2010) Our data includes many sources of
101
heterogeneity among municipalities such as indicators of economic deprivation population
density demographic characteristics and whether the county is a regional or provincial capital
We use a sample of 324 municipalities covering most of the Chilean territory for the period
2006ndash17 Data on incivilities is obtained from the ldquoCentre of Studies and Analysis of Crimerdquo
which is part of the ldquoSubsecretaria de Prevencion del Delitordquo (SPD in its Spanish acronym) of the
Chilean government32 Information on income inequality and control variables is obtained from
the ldquoNational Socioeconomic Characterization Surveyrdquo (CASEN in its Spanish acronym) the
ldquoNational Institute of Statisticsrdquo (INE in its Spanish acronym) the ldquoNational Municipal
Information Systemrdquo (SINIM in its Spanish acronym) and the Immigration Department of the
Government of Chile Our panel only includes the years for which CASEN survey is available
2006 2009 2011 2013 2015 and 2017
432 Operationalisation of the response variable and exploratory analysis
Official Chilean records contain information for the total number of cases of incivilities per
year at the county level The number of cases is the sum of complains and detentions reported at
the police Our dependent variable 119894119899119888119894119907119894119897119894119905119894119890119904 correspond to the number of cases per year Due
to population differences comparisons between counties are made using the incivilities rate per
1000 population calculated as
119894119899119888119894119907_119903119886119905119890 lowast 1000 (41)
where 119894119899119888119894119907_119903119886119905119890 is the incivilities rate 119905 is the year 119894 the county and 119899 is the population of the
county per year
32 httpceadspdgovclestadisticas-delictuales
102
Figure 41 illustrates at the top the evolution of the total number (cases reported) of
incivilities and crimes at the country level for the period 2006-1733 At the bottom Figure 41
shows the evolution of the mean county rate per 1000 inhabitants We observe that both the number
of incivilities and the number of crimes has reached similar annual figures however average
county rates per 1000 population show different trends Crime rate displays a sustained fall after
reaching its pick in 2011 Incivilities average county rate which also reached a pick and posterior
drop in 2011 has recovered its upward trend since 2016 considerably exceeding the crime rate
Figure 41 Evolution number and rates of incivilities and crime (DMCS) in Chile 2006-2017
33 Crime refers to ldquocrimes of greater social connotationrdquo (DMCS in its Spanish acronym) which includes violent and property crimes
103
Chilean records classify incivilities in nine categories most of them associated with social
incivilities Summary statistics for the total and for each of the nine categories are presented in
Table 41 In addition Figure 42 shows the evolution of incivilities by category for the whole
period We see that the global trend in incivilities is mainly due to a substantial increase in ldquoStreet
Tradingrdquo and ldquoPublic Alcohol Consumptionrdquo A common element of Figures 41 and 42 is the
significant change in trend experienced by crimes and incivilities in 2011 That year the SPD
became dependent on the Ministry of Interior of the Chilean Government This event put the issue
of crime and delinquency within national priorities for the central government
Table 41
Summary statistics total count of incivilities and by category (full sample and period)
Unlike crime rates we do not expect significant cross-county spillover effects in incivilities
However the questions of where incivilities are concentrated and why they are there can be of
great interest (Skogan 2015) Figure 43 shows quantile maps for the rate of incivilities per 1000
inhabitants for the initial and final years in our panel
104
Figure 42 Evolution total number of incivilities by category
Figure 43 Spatial distribution of incivilities rate per 1000 inhabitants (2006 vs 2017)
105
We observe that the range of values has increased significantly from 2006 to 2017 but the
spatial distribution remains almost unchanged On the one hand high incivilities rates in the North
could be associated with the mining activity On the other hand high rates in the Centre area
(where the countyrsquos capital is located) could be related to the higher population density and the
concentration of the economic activity34
To see how the different types of incivilities are distributed throughout the country we have
grouped those similar categories in four groups ldquoPublic Damagerdquo (ldquoPublic Disturbsrdquo ldquoPublic
Damagerdquo ldquoOtherrdquo) ldquoThreatsrdquo (ldquoPublic Fightrdquo ldquoThreatsrdquo and ldquoAnnoying Noisesrdquo) ldquoAlcohol
Consumptionrdquo (ldquoPublic Alcohol Consumptionrdquo and ldquoDrunkennessrdquo) and ldquoStreet Tradingrdquo This
distinction in groups could be relevant if we expect different patterns and different effects of
community heterogeneity on social cohesion among counties For instance we expect higher
levels of Public Damage in big urban cities Street trading is more likely in urban cities but also in
tourist areas The spatial distribution of these four groups for the six-year-average rate per 1000
inhabitants can be found in Appendix R
433 Measures of community heterogeneity and control variables
Social cohesion income inequality and racial diversity are all ldquocommunityrdquo (not individual)
characteristics Thus to understand their relationship we need aggregated data at least at the
county-municipal level With more disaggregated data like at the suburbs level the required
heterogeneity among groups of citizens is lost (Wilkinson 1999) Like Coffeacute and Geys (2006) we
use the Gini coefficient to capture economic heterogeneity However instead of a measured for
34 We also analysed the spatial distribution of crime rates In general areas with high levels of incivilities not necessarily are associated with high levels of crime This could imply that factors explaining incivilities and crime are different
106
the diversity of nationalities we use the proportion of foreign population to capture racial
heterogeneity Income data is obtained from the CASEN survey The Gini coefficient is calculated
for each county and included through the variable gini Racial heterogeneity is included through
the variable foreign which is the annual number of new VISAS granted to foreigners as a
proportion of the county population Chile has experienced a significant increase in immigration
since 2011 Immigration has been concentrated in the metropolitan region and mining regions in
the North of the country We expect a positive relationship between immigration and incivilities
although as with the relationship between immigration and crime the foundations for this
hypothesis are not strong (Hooghe et al 2010 Sampson 2008)
Economic development is another explanation for social cohesion frequently appealed to
explain trust with wealthier societies considered to exhibit higher levels of trust (Delhey amp
Newton 2005) In this study we include the natural log of the mean household income per capita
log(income) We also include the poverty rate poverty and the unemployment rate
unemployment Unlike the variable log(income) these variables are expected to be positively
associated with the number of incivilities When a relative indicator of economic heterogeneity
such as income inequality is included as determinant of social cohesion we should expect less
effect from absolute indicators of economic disadvantage such as poverty and unemployment rates
(Hooghe et al 2010 Tolsma et al 2009)
Among demographic variables the percentage of inhabitants between 10 and 24 years old is
included through the variable youth The variable women defined as the proportion of the female
population in each county is also included Variable youth is expected to have an ambiguous effect
Although young people have lower victimization and report rates they also represent the group
more likely to commit antisocial behaviours when a community has a low capacity of self-
107
regulation (eg when there is low parental supervision) The female population is associated with
a higher report of incivilities related to the male population
It is argued that crime and incivilities are essentially urban problems (Christiansen 1960
Wirth 1938) We include the variable log(density) defined as the log of population density (the
number of inhabitants divided by the area of each county in square kilometres) and a dummy
variable capital indicating whether a county is an administrative capital (provincial or regional)
Two additional variables are included to capture the level of informal social control exerted
by families living in each municipality First the variable education which is defined as the
average years of education of people over 15 years old Second the variable housing which capture
the proportion of families which are owners of their housing unit Although education and housing
are related to both the possibility of reporting and committing an incivility we expect a negative
association with the rate of incivilities
In Chile crime has been mainly a problem faced by the police and the Central Government
Administration To control for current law enforcement policies we include the variable
deterrence defined as the number of arrests as a proportion of the total number of incivilities cases
In addition municipalities can develop their own initiatives to deal with crime and incivilities
depending on their capacity to generate its own resources The level of financial autonomy from
central transfers is captured by the variable autonomy This variable is obtained from SINIM and
it is defined as the proportion of the budget revenue of each municipality that comes from its own
permanent sources of revenues A categorical variable mayor is also included This variable
indicates whether the municipality mayor is supported by the ldquoLEFTrdquo or the ldquoRIGHTrdquo political
parties (related to those ldquoINDEPENDENTrdquo mayors)
108
Table 42 presents descriptive statistics for our measures of income and racial heterogeneity
and the set of numeric control variables The Pearson correlation among these variables is shown
in Appendix S
Table 42
Summary statistics numeric explanatory variables
434 Methods
The annual count of incivilities as is characteristic for count data is highly concentrated in
a relatively small range of values In addition the distribution is right-skewed due to the presence
of important outliers (counties with a high number of incivilities) Figure 44 shows the
distribution of the six-year average number of incivilities for each of the 15 regions in Chile35 We
observe that regions differ in the number of counties in which they are divided In addition
counties within each region show important differences in the number of incivilities For instance
35 Regions are ordered from left to right and from top to bottom in the way they are geographically distributed from North to South So the northernmost region of the country is the ldquoXVrdquo and the southernmost region is the ldquoXIIrdquo The Metropolitan region (where the countryrsquos capital is located) is region ldquoXIIIrdquo in the centre of the country (see Appendix C)
109
excepting the Metropolitan region ldquoXIIIrdquo most counties in regions located in the centre of the
country (middle row in Figure 44) show a range of incivilities between 0 and 2000 The number
of incivilities is considerably lower in counties located in the northern (top row in Figure 44) and
southern (bottom row in Figure 44) regions of the country compared to regions in the centre of
the country It also seems clear from Figure 44 that the number of incivilities does not follow a
normal distribution
Figure 44 Annual average number of incivilities per county
The number of incivilities can be better described by a Poisson distribution In this case the
number of incivilities is ldquothe countrdquo and the number of incivilities per year is ldquothe rate per unit
timerdquo We are interested in modelling the average number of incivilities per year usually called 120582
as a function of a set of contextual factors to explain differences in incivilities between and within
110
counties The main characteristic of the Poisson distribution is that the mean is equal to the
variance This implies that as the mean rate for a Poisson variable increases the variance also
increases The main implication is we cannot use OLS to model 120582 as a function of the set of
contextual factors because the equal variance assumption in linear regression is violated
The rate of incivilities between counties is not directly comparable due to population
differences We expect counties with more people to have more reports of incivilities since there
are more people who could be affected To capture differences in population which is called the
exposure of our response variable 120582 it is necessary to include a term on the right side of our model
called an offset We will use the log of the county population in thousands as our offset36
Additionally similar to the case of crime data incivilities show a significant degree of
overdispersion (variance higher than the mean) suggesting that there is more variation in the
response than the Poisson model implies37 We also model and regress incivilities assuming a
Negative Binomial distribution to address overdispersion An advantage of this approach is that it
introduces a dispersion parameter in addition to 120582 which gives the model more flexibility38
Considering as the response variable the count of incivilities per year the model can be
expressed as follow
120582 119890119909119901 120573 120573 119892119894119899119894 120573 119891119900119903119890119894119892119899 119883120574 120572 120579 (42)
36 If we think of 120582 as the average number of incivilities per year then represents the number per 1000
inhabitants so that the yearly count is adjusted to be comparable across counties of different sizes Adjusting the yearly count by population is equivalent to adding 119897119900119892 1199011199001199011199061198971198861199051198941199001198991000 to the right-hand side of the regression equation 37 Without adjusting for overdispersion we use incorrect artificially small standard errors leading to artificially small p-values for model coefficients 38 The Negative Binomial model posits selecting a 120582 (average number of incivilities) for each county and then generating a count using a Poisson random variable with the selected 120582 With this approach the counts will be more dispersed than would be expected for observations based on a single Poisson variable with rate 120582
111
where 120582 is the rate of incivilities 119883 is our vector of controls 120572prime119904 are county-specific constants
and 120579prime119904 are time-specific constants Accounting for differences in county population we have
119890119909119901 120573 120573 119892119894119899119894 119883120574 120572 120579 (43)
where 119901119900119901119906119897119886119905119894119900119899 is the county population per year Hence the model to be estimated using
Maximum Likelihood Estimation (MLE) is
119897119900119892 120582 120573 120573 119892119894119899119894 119883120574 120572 120579 119897119900119892 (44)
Finally to account for different effects depending on the type of incivilities we also run
equation (44) for each of the four groups of incivilities defined in section (432)
435 Hypotheses
Based on the community heterogeneity hypothesis the relationship between social cohesion
and diversity should be stronger for lower levels of income and less educated groups of people
(Tolsma et al 2009) Hence contrary to evidence for developed and more equal countries we
expect a significant positive association for the Chilean case where more than 50 of the
population is economically vulnerable (OECD 2017)
The main hypotheses to be tested in this essay is whether the number of incivilities is
positively associated with the level of economic and racial heterogeneity at the county level We
start analysing this association for the full sample and period Next we analyse whether the
relationship between incivilities and our measures of diversity differs by geographic area (region
or zone) Finally we check whether the effect of economic and racial diversity is different
depending on the group of incivilities
112
44 Results and Discussion
Overall our results show that the rate of incivilities displays a stronger and more significant
relationship with racial diversity than with economic heterogeneity This association differs for
different geographic areas and for different types of incivilities Absolute economic indicators
except for income show a significant but small effect Increases in the average levels of income
or education and more financial autonomy for municipalities seem to be effective ways to reduce
the rate of incivilities
We estimate equation (44) assuming that the number of incivilities follows a Poisson
distribution Regional and temporal heterogeneity are captured through the inclusion of dummy
variables for five years (with 2006 as the reference year) and fourteen regional dummies (with
region XIII as the reference region) Results are reported in Table 4339 This table is structured in
two blocks of regressions pooled models in columns (1)-(4) and fixed-effects models in columns
(5)-(8)40 The first column in each block only includes economic indicators relative and absolute
trying to test which ones are more relevant and whether incivilities tend to mirror income
inequality (the ldquoincivilities thesisrdquo) The second column adds the variable foreign to account for
the effect of racial diversity (Letki 2008) The third column includes education to check whether
the association between economic and racial diversity with social cohesion changes (gets less
significant) when we control for educational level (Tolsma et al 2009) The final column in each
block corresponds to the full model specification which includes the rest of controls
39 Pooled estimations were obtained using the R command glm() and compared with results obtained from the command poisson in STATA 15 Fixed effects and random effects models were estimated using the STATA command xtpoisson Negative Binomial models were estimated using the command glmnb() in the MASS package for R and commands nbreg and xtnbreg in STATA 15 Results for Negative Binomial regressions are in Appendix T 40 We have omitted results with random effects due to the literature on panel count data models suggest that the most robust estimator is Poisson regression with Fixed Effects (Santos Silva amp Tenreyro 2010 2011)
113
Table 43
Poisson regressions
114
The positive and significant coefficient for the variable gini besides being small it becomes
insignificant in the fixed effects specification which includes the full set of controls This result
does not seem to be enough evidence to support our hypothesis that more unequal counties display
higher rates of incivilities On the other hand racial diversity through the variable foreign shows
a consistent positive association with the rate of incivilities41 Together coefficients for gini and
foreign seems to support the ldquocommunity heterogeneity thesisrdquo (Letki 2008) but not the
ldquoincivilities thesisrdquo (Skogan 1999) To check this finding we run the pooled full model
specification for each region and results are shown in Table 44 where regions have been ordered
from North to South The sign of the coefficient of the variable gini differs for different regions
Moreover the relationship is insignificant in some of the most unequal regions which are in the
South of the country (VIII IX and XIV) This result rejects the hypothesis that incivilities mirror
structural income inequalities For the variable foreign 12 out of 15 regions confirm the positive
association with the rate of incivilities42
We also run our pooled full model separately for each group of incivilities defined at the end
of section (432) Income inequality keeps its significant but small association with each group of
incivilities (see Table 45) Our measure of racial diversity shows a stronger association with
ldquoAlcohol Consumptionrdquo related to ldquoPublic Damagerdquo and ldquoThreatsrdquo The link with ldquoStreet Tradingrdquo
appears as non-significant These results support our general finding that on the one hand racial
heterogeneity exert a more significant influence on the rate of incivilities than economic
41 To get the effects of the variables gini and foreign over the rate of incivilities per 1000 inhabitants we need to exponentiate their coefficients Thus an increase in one unit in the variable gini increases the rate of incivilities per 1000 inhabitantsrdquo in 1005 times (e^0005) in the pooled full model Similarly an increase in one point in variable foreign is expected to increase the ldquorate of incivilities per 1000 inhabitantsrdquo in 10876 times (e^0084) in the full pooled model and 10471 (e^0046) times in the full Fixed Effects model 42 Given that depending on the geographic location regions share certain characteristics the model was also run clustering regions in 4 zones (see Figure 31 in chapter 3) Results by zone are in Appendix U
115
heterogeneity On the other hand results confirm our hypothesis that the effect of diversity is
different for different types of incivilities
Table 44 Coefficients economic and racial diversity in pooled Poisson models by region
Back to our general results in Table 43 the significant and negative coefficient of the
income variable and to a lesser extent the significant and positive coefficients of poverty and
unemployment provide evidence that absolute rather than relative economic indicators may be
more important explanations of the rate of incivilities This is opposite to evidence for the analysis
116
of crime rates such as in Hoodge (2010) and support the idea that determinants of incivilities are
different from those of crime Our results are also opposite to those for Dutch municipalities where
economic indicators turned out to be more important than ethnic heterogeneity (Tolsma et al
2009) The coefficient for the variable log(income) could be interpreted as counties with an income
level under the average face higher problems of antisocial behaviours such as incivilities In
addition as the income level moves far away from its average low level the problem of incivilities
is less relevant43 In terms of policy implications only those policies that achieve a significant
increase in the average level of county income seem to be effective in reducing incivilities and
strengthening social cohesion
Table 45 Coefficients economic and racial diversity in pooled Poisson model by incivility group
43 We ran our model splitting the sample of counties in quintiles based on their income level The coefficient for the variable gini only showed a significant (and positive) coefficient for the second third and fourth quintile A non-significant coefficient for the first quintile could imply that other types of inequalities (eg health education) are more relevant in the poorest counties In the case of the fifth quintile (the richest group of counties) they have better facilities in terms of infrastructure police resources self-protection etc which could reduce the potential triggers of incivilities
117
The inclusion of the variable education significantly improved the goodness of fit of the
models and did not generate significant changes in the coefficients of our measures of economic
and racial diversity This result rejects the proposition that the relationship between social
cohesion and diversity becomes less strong when controlled by education (Tolsma et al 2009)
Additionally it highlights the topic of education as a relevant determinant of the rate of incivilities
and social cohesion
Among control variables there are also some important results Opposite to what we
expected the variable youth shows a negative or non-significant coefficient Although this result
could be due to the lack of ldquophysical incivilitiesrdquo in Chilean records it indicates that it is incorrect
to stereotype this group as the main responsible for high incivilities rates The significant and
negative coefficient of the variable autonomy in the fixed effects specification could also have
important policy implications It is a signal that local governments can play an important role in
reducing incivilities or complementing the efforts from the central government Another
interesting result is the significant coefficient of the variable housing The latter finding is
particularly important in the sense that a negative sign supports public policies oriented to increase
homeownership as effective ways to improve social cohesion However the small magnitude of
the coefficient that even showed the opposite sign in some model specifications could be
explained for the high level of segregation that these policies have generated in Chilean society
As mentioned in the Introduction and Literature Review so far only a few studies have
used measures of disorders or incivilities as dependent variable to explain changes in social
cohesion (Skogan 2015) In addition there is no evidence in Chile analysing the determinants of
incivilities separately from those of crimes The importance of our results on identifying the
importance of economic and racial diversity on social cohesion lies mainly in its generality An
118
important number of countries all around the world share a similar context characterized by high
levels of inequality and an explosive increase in immigration These countries are also
experiencing a worsening in social cohesion which increases the risk of a social outburst
4 5 Conclusions
The main goal of this essay was to determine whether differences in incivilities at the county
level mirror differences in income distribution and racial diversity Previous literature suggests a
positive and strong association between social cohesion and indicators of economic disadvantage
relative deprivation and racial diversity (Letki 2008 Tolsma et al 2009 Uslaner amp Brown 2005)
While not all our results were significant they showed helpful insights about how and where
economic and racial diversity are more likely to influence the rate of incivilities and social
cohesion
We used data for the period 2006ndash17 economic heterogeneity was measured through the
Gini coefficient at the county level and racial heterogeneity was proxied by the number of granted
visas to foreigners as proportion of county population We found strong evidence of a significant
and positive association between the rate of incivilities and racial diversity but not with income
inequality Contrary to previous evidence at the municipal level (Coffeacute amp Geys 2006 Tolsma et
al 2009) in general our results give support for the ldquohomophily principlerdquo and the ldquocommunity
heterogeneity hypothesisrdquo However results also showed that the effect of economic and racial
diversity varies throughout the Chilean regions and for the different types of incivilities
We also found that policies aimed at controlling the behaviour of young people did not have
strong empirical support In terms of the role that local governments may have in facing the
119
growing problem of incivilities we found evidence that efforts managed from the municipalities
can be an important complement to those from the central government
Future research should go further on the role of local authorities on incivilities and social
cohesion On the one hand municipalities could have a direct impact on social cohesion through
the implementation of programs complementary to those of central authorities oriented to reduce
incivilities and crime On the other hand social cohesion could be indirectly affected when local
authorities display an inefficient performance supplying public services to citizens or they are
recognized as corrupted institutions We suggest that policy makers from central government
should give local authorities a greater role in fighting antisocial behaviours and crime Evaluating
programs in specific municipalities could help to elucidate the causal effect of for instance higher
fiscal autonomy on the rate of incivilities
Another interesting area for future work will be to analyse how housing policies have
contributed to the phenomenon of segregation of Chilean society and in the process of weakening
social cohesion Finally our main result highlights the need of a deeper analysis of the impact that
foreign immigration is having in Chile For instance disaggregating information by country of
origin and the reasons why immigrants are arriving to the country or specific regions will surely
help to understand the impacts of immigration
120
Chapter 5 Conclusions
This thesis investigated in three essays the issue of income inequality in Chile using county-
level data for the period 2006-2017 The first essay supplied empirical evidence about the
importance of the degree of dependence on natural resources in terms of employment in explaining
cross-county differences in income inequality The second essay analysed the potential causal
effect that income inequality has on the level of technical efficiency of local governments
providing public goods and services Lastly the third essay studied the relationship between social
cohesion measured through the number of antisocial behaviours classified as ldquoincivilitiesrdquo and
the levels of income and racial heterogeneity
Findings from the first essay support the idea that the endowment of natural resources plays
a significant role in explaining income inequality in Chile However contrary to what most
theoretical and empirical evidence postulates our findings showed a robust negative association
between the two variables This means that the reduction experienced in Chile in the degree of
dependence on natural resources in terms of employment has contributed to the persistence of high
levels of income inequality The exploratory analysis indicated that income inequality shows a
general clustering process characterized by a significant and positive spatial autocorrelation
Regarding the previous evidence for Chile (Paredes et al 2016) the regression analysis confirmed
the relevance of the spatial dimension of income inequality through a process of spatial
heterogeneity giving less support to the existence of a process of spatial dependence (spillover
effect) in the variable itself
121
Essay 2 studied the potential trade-off between efficiency and equity analysing the influence
of income inequality on the efficiency of local governments at the municipal level To identify the
causal effect of income inequality on municipal efficiency we proposed the use of the proportion
of firms in the primary sector as an instrument for income inequality Findings confirmed our
hypothesis that efficiency is lower in more unequal counties This result suggests the non-existence
of the trade-off between equity and efficiency Hence policies intended to reduce inequality could
help to increase efficiency at least at the level of municipal local governments
The third essay analysed how social cohesion proxied by the rate of incivilities is associated
with the levels of economic diversity proxied by income inequality and the levels of racial
diversity proxied by the number of new visas grated as proportion of the county population
Findings gave strong support to the hypothesis that the rate of incivilities is positively related to
racial diversity and to a lesser extent to economic diversity In addition the rate of incivilities
appears negatively related to the degree of financial autonomy of municipalities This means that
local governments can effectively contribute to the reduction of incivilities which could help
reduce victimization and crime rates ultimately strengthening social cohesion
Taken together findings from essays 2 and 3 highlight the important role that income
inequality could play in other relevant economic and social dimensions These findings add to the
understanding of the potential consequences of income inequality particularly in natural resource
rich countries with persistently high levels of inequality
The present study has mainly investigated income inequality at the county level In addition
Chilean municipalities play an important role providing ldquoservices to peoplerdquo so our findings could
be applied in other highly unequal countries with a high degree of dependence on natural resources
and local governments with similar responsibilities For instance in Latin America apart from
122
Chile and Brazil there are no studies on the efficiency of local governments Other limitations are
associated with the availability of information For instance important indicators such as GDP per
capita are only available at the regional level and information of incomes is not available annually
In addition given the heterogeneity among municipalities some type of grouping of municipalities
should be performed before looking for causal relationships or conducting program evaluation
Despite these limitations we believe this study could be the basis for different strands of future
research on the topic of inequality local government efficiency and social cohesion
It was stated in chapter 2 based on the resource curse hypothesis literature there are two
elements that determine whether NR are a curse or blessing in terms of socioeconomic outcomes
First the curse is more likely in countries with weak political and governance institutions Second
different types of resources affect institutions differently with resources that are concentrated in
space so-called ldquopointrdquo resources tend to impair institutions while ldquodiffuserdquo resources do not
(Deacon 2011 Isham Woolcock Pritchett amp Busby 2005) Our results showed a negative
relationship between income inequality and our measure of natural resource dependence even after
controlling for zone fixed effects and for the level of government expenditure This result could
be interpreted as a signal that NR has a direct effect on income inequality in addition to the indirect
impact through market or institutional channels Using other potential institutional transmission
channels will shed light about the true effect that the endowment of natural resources has over
income inequality Variables that could capture these institutional channels include the level of
employment in the public sector measures of rule of law and corruption and changes in the
creation of new business in the secondary and tertiary sectors related to the primary sector
Based on results from chapter 3 most of the municipalities show scale inefficiencies One
immediate area for future work will involve using our set of contextual factors to predict the status
123
of municipalities in terms of scale inefficiencies Defining as dependent variable whether a
municipality shows constant decreasing or increasing returns to scale we could run a multinomial
logistic regression to predict municipal status For instance we would expect that a one-unit
increase in the Gini coefficient should increase the probability of scale inefficiencies (increasing
or decreasing returns to scale rather than constant returns to scale) Because the aim in this case
would be predicting a certain result in terms of returns to scale the next step should involve to
split the full sample in training and testing data sets and to use some resampling methods such as
bootstrapping This will allow us to evaluate the performance and accuracy of our model
predictions using different random samples of municipalities Results from Machine Learning
algorithms will help us to assess the generalizability of our results to other data sets
Future work should also benefit greatly by using data on different Latin American countries
to (1) compare the responsibilities of local governments (2) select a common set of inputs and
output to evaluate LGE (3) identify the relevance of scales inefficiencies in explaining differences
in performance and (4) analyse the influence of contextual characteristics over LGE Differences
in the main primary sector activity in each country such as oil in Brazil mining in Chile or Coffee
in Colombia could be responsible for differences in LGE among countries These differences could
be associated with sources of revenue management of expenditure and definitions of outputs or
contextual effects such as corrupted institutions or the delay in the development of other sectors
such as manufacturing or services
To delve deep on reasons explaining the social crisis experienced by Chilean society and
other countries one area of future work will be to analyse the relationship between diversity and
the origins of social revolutions Based on Tiruneh (2014) the three most important factors that
explain the onset of social revolutions are economic development regime type and state
124
ineffectiveness Interesting questions include whether the characteristics of Chilean context at the
end of 2019 are enough to trigger the transformation of the political and socioeconomic system
Social revolutions particularly violent revolutions are less likely in more democratic educated
and wealthy societies So it would be relevant to identify the factors explaining the violence that
has characterized the social crisis in Chile Finally the democratic regime has been maintained in
the last decades with changes between left and right governments This could imply that more
important than the regime has been the efficiency or ineffectiveness of the governments to satisfy
the needs of the population
Future work should also cover the disaggregation of information regarding foreign
population in terms of the reasons for new granted visas and the country of origin Official data
allows us to disaggregate whether the benefit is permanent (students and employees with contract)
or temporary Furthermore most of the new visas were traditionally granted to neighbouring
countries (Peru and Bolivia) a trend that has changed in the recent years to include countries such
as Colombia Venezuela and Haiti An analysis of how economic and social indicators have been
affected by changes in the composition of foreigners their reasons for immigrating to the country
and their geographical distribution have implications for economic policy at both the national and
local levels At the national level such analysis should be a key input when proposing changes to
the national immigration policy At the local level it could help define the role of municipalities
to assess the benefits and challenges of immigration These challenges are mainly related to the
provision of public goods and services such as health and education which in Chile are the
responsibility of the municipalities
The findings of this thesis suggest that policymakers should encourage policies that reduce
income inequality The key role that municipalities could play to strengthen social cohesion and
125
the increasingly important role that foreign population is acquiring in most modern societies are
also interesting avenues for future research However the picture is still incomplete and more
research is needed incorporating other dimensions of inequality This is essential if we want to
understand the reasons that could have triggered the social outbursts experienced by various
economies across the globe
126
Bibliography
Acemoglu D (1995) Reward structures and the allocation of talent European Economic Review 39(1) 17ndash33 httpsdoiorghttpsdoiorg1010160014-2921(94)00014-Q
Acemoglu D (2002) Technical Change Inequality and the Labor Market Journal of Economic Literature 40(1) 7ndash72 httpsdoiorg1012570022051026976
Acemoglu D Aghion P amp Violante G L (2001) Deunionization Technical Change and Inequality Carnegie-Rochester Conference Series on Public Policy 55(1) 229ndash264 httpsdoiorg101016S0167-2231(01)00058-6
Acemoglu D Johnson S amp Robinson J A (2001) The Colonial Origins of Comparative Development An Empirical Investigation The American Economic Review 91(5) 1369ndash1401 httpsdoiorg101257aer9151369
Acemoglu D amp Robinson J A (2002) The Political Economy of the Kuznets Curve Review of Development Economics 6(2) 183ndash203 httpsdoiorg1011111467-936100149
Afonso A amp Fernandes S (2006) Measuring local government spending efficiency Evidence for the Lisbon region Regional Studies 40(1) 39ndash53 httpsdoiorg10108000343400500449937
Afonso A amp Fernandes S (2008) Assessing and explaining the relative efficiency of local government The Journal of Socio-Economics 37(5) 1946ndash1979 httpsdoiorg101016jsocec200703007
Afonso A Schuknecht L amp Tanzi V (2010) Income distribution determinants and public spending efficiency Journal of Economic Inequality 8(3) 367ndash389 httpsdoiorg101007s10888-010-9138-z
Akita T (2003) Decomposing regional income inequality in China and Indonesia using two-stage nested Theil decomposition method The Annals of Regional Science 37(1) 55ndash77 httpsdoiorg101007s001680200107
Alesina A (2000) Participation in heterogeneous communities The Quarterly Journal of Economics 115(3) 847ndash904 httpsdoiorg101162003355300554935
Alesina A amp La Ferrara E (2002) Who trusts others Journal of Public Economics 85(2) 207ndash234 httpsdoiorg101016S0047-2727(01)00084-6
Allcott H amp Keniston D (2014) Dutch Disease or Agglomeration The Local Economic Effects of Natural Resource Booms in Modern America (N W P N 20508 Ed) NBER Working Paper No 20508 (Vol w20508) NBER Working Paper No 20508 NBER Working Paper No 20508 httpsdoiorgNBER Working Paper No 20508
Andersen T M amp Maibom J (2020) The big trade-off between efficiency and equitymdashis it there Oxford Economic Papers 72(2) 391ndash411
127
Anselin L (1988) Spatial econometrics methods and models (Vol 4) DordrechtBoston Kluwer Academic Publishers
Anselin L amp Bera A K (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics Statistics Textbooks and Monographs (Vol 155)
Anselin L Bera A K Florax R amp Yoon M J (1996) Simple diagnostic tests for spatial dependence Regional Science and Urban Economics 26(1) 77ndash104 httpsdoiorg1010160166-0462(95)02111-6
Antonelli M A amp De Bonis V (2018) The efficiency of social public expenditure in European countries a two-stage analysis Applied Economics 1ndash14 httpsdoiorg1010800003684620181489522
Aragoacuten F M amp Rud J P (2013) Natural Resources and Local Communities Evidence from a Peruvian Gold Mine American Economic Journal Economic Policy 5(2) 1ndash25 httpsdoiorg101257pol521
Ariely G (2014) Does Diversity Erode Social Cohesion Conceptual and Methodological Issues Political Studies 62(3) 573ndash595 httpsdoiorg1011111467-924812068
Armstrong H amp Taylor J (2000) Regional economics and policy (3rd ed) Oxford Blackwell
Aroca P amp Atienza M (2011) Economic implications of long distance commuting in the Chilean mining industry Resources Policy 36(3) 196ndash203 httpsdoiorg101016jresourpol201103004
Aroca P amp Bosch M (2000) Crecimiento convergencia y espacio en las regiones chilenas 1960 - 1998 Estudios de Economiacutea 27 199ndash224 Retrieved from httprepositoriouchileclbitstreamhandle2250127853Patricio_Arocapdfsequence=1
Ashworth J Geys B Heyndels B amp Wille F (2014) Competition in the political arena and local government performance Applied Economics 46(19) 2264ndash2276 httpsdoiorg101080000368462014899679
Atkinson A B (2015) Inequality What Can Be Done Harvard University Press
Auty R (1993) Sustaining development in mineral economies the resource curse thesis London [ua] Routledge
Auty R (2001) Resource abundance and economic development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Badeeb R A Lean H H amp Clark J (2017) The evolution of the natural resource curse thesis A critical literature survey Resources Policy 51 123ndash134 httpsdoiorg101016jresourpol201610015
Balaguer-Coll M T Brun-Martos M I Maacuterquez-Ramos L amp Prior D (2019) Local government efficiency determinants and spatial interdependence Applied Economics
128
51(14) 1478ndash1494 httpsdoiorg1010800003684620181527458
Balaguer-Coll M T amp Prior D (2009) Short- and long-term evaluation of efficiency and quality An application to Spanish municipalities Applied Economics 41(23) 2991ndash3002 httpsdoiorg10108000036840701351923
Banker R D Charnes A amp Cooper W W (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis Management Science 30(9) 1078ndash1092
Beine M Coulombe S amp Vermeulen W N (2015) Dutch Disease and the Mitigation Effect of Migration Evidence from Canadian Provinces The Economic Journal 125(589) 1574ndash1615 httpsdoiorg101111ecoj12171
Benito B Bastida F amp Garciacutea J A (2010) Explaining differences in efficiency an application to Spanish municipalities Applied Economics 42(4) 515ndash528 httpsdoiorg10108000036840701675560
Berg A amp Ostry J (2011) Equality and efficiency Finance amp Development 48(3) 12ndash15
Berg A amp Ostry J (2017) Inequality and Unsustainable Growth Two Sides of the Same Coin IMF ECONOMIC REVIEW 65(4) 792ndash815 httpsdoiorg101057s41308-017-0030-8
Beyer H amp Vergara R (2006) Delincuencia en Chile Determinantes y rol de las poliacuteticas puacuteblicas Instituto de Economiacutea UC
Blanco L amp Grier R (2012) Natural resource dependence and the accumulation of physical and human capital in Latin America Resources Policy 37(3) 281ndash295 httpsdoiorghttpdoiorg101016jresourpol201201005
Blau J R amp Blau P M (1982) The cost of inequality Metropolitan structure and violent crime American Sociological Review 114ndash129
Boetti L Piacenza M amp Turati G (2009) Fiscal decentralization and spending efficiency of local governments An Empirical Investigation on a Sample Of 4
Boix C amp Posner D (1998) Social capital Explaining its origins and effects on government performance British Journal Of Political Science 28 686ndash693
Borge L E Parmer P amp Torvik R (2015) Local natural resource curse JOURNAL OF PUBLIC ECONOMICS 131 101ndash114 httpsdoiorg101016jjpubeco201509002
Borger B Kerstens K Moesen W amp Vanneste J (1994) Explaining differences in productive efficiency An application to Belgian municipalities Public Choice 80(3) 339ndash358 httpsdoiorg101007BF01053225
Bourguignon F amp Morrisson C (1990) Income distribution development and foreign trade A cross-sectional analysislowast European Economic Review 34(6) 1113ndash1132 httpsdoiorghttpsdoiorg1010160014-2921(90)90071-6
129
Branas C C Cheney R A MacDonald J M Tam V W Jackson T D amp Ten Have T R (2011) A difference-in-differences analysis of health safety and greening vacant urban space American Journal of Epidemiology 174(11) 1296ndash1306
Bravo J (2014) Fondo Comuacuten Municipal y su desincentivo a la recaudacioacuten en Chile Temas de La Agenda Legislativa-Centro de Poliacuteticas Puacuteblicas UC 9(68)
Browning E K amp Johnson W R (1984) The Trade-Off between Equality and Efficiency Journal of Political Economy 92(2) 175ndash203 httpsdoiorg101086261219
Brunnschweiler C N amp Bulte E H (2008) The resource curse revisited and revised A tale of paradoxes and red herrings Journal of Environmental Economics and Management 55(3) 248ndash264 httpsdoiorghttpsdoiorg101016jjeem200708004
Brunori P Ferreira F H G amp Peragine V (2013) Inequality of opportunity income inequality and economic mobility Some international comparisons In Getting Development Right (pp 85ndash115) Springer
Brunton-Smith I Jackson J amp Sutherland A (2014) Bridging structure and perception On the neighbourhood ecology of beliefs and worries about violent crime British Journal of Criminology 54(4) 503ndash526
Bulte E H Damania R amp Deacon R T (2005) Resource intensity institutions and development World Development 33(7) 1029ndash1044 httpsdoiorg101016jworlddev200504004
Carmignani F (2013) Development outcomes resource abundance and the transmission through inequality Resource and Energy Economics 35(3) 412ndash428 httpsdoiorg101016jreseneeco201304007
Carmignani Fabrizio amp Avom D (2010) The social development effects of primary commodity export dependence Ecological Economics 70(2) 317ndash330 httpsdoiorg101016jecolecon201009003
Caselli F amp Michaels G (2013) Do Oil Windfalls Improve Living Standards Evidence from Brazil American Economic Journal Applied Economics 5(1) 208ndash238 httpsdoiorg101257app51208
Celebioglu F amp Dallrsquoerba S (2010) Spatial disparities across the regions of Turkey An exploratory spatial data analysis Annals of Regional Science 45(2) 379ndash400 httpsdoiorg101007s00168-009-0313-8
Chan J To H-P amp Chan E (2006) Reconsidering social cohesion Developing a definition and analytical framework for empirical research Social Indicators Research 75(2) 273ndash302
Charnes A Cooper W W amp Rhodes E (1978) Measuring the efficiency of decision making units European Journal of Operational Research 2(6) 429ndash444
130
Chi G amp Zhu J (2019) Spatial Regression Models for the Social Sciences SAGE Publications Retrieved from httpsbooksgooglecomaubooksid=zHGkvwEACAAJ
Christiansen K O (1960) Industrialization and urbanization in relation to crime and juvenile delinquency International Review of Criminal Policy 16 3ndash8
Cingano F (2014) Trends in income inequality and its impact on economic growth (Vol 163) Paris OECD Publishing httpsdoiorg1017875jxrjncwxv6j-en
Coelli T J Prasada Rao D S OrsquoDonnell C J amp Battese G E (2005) An introduction to efficiency and productivity analysis An Introduction to Efficiency and Productivity Analysis Springer Science amp Business Media httpsdoiorg101007b136381
Coffeacute H amp Geys B (2005) Institutional Performance and Social Capital An Application to the Local Government Level Journal of Urban Affairs 27(5) 485ndash501 httpsdoiorg101111j0735-2166200500249x
Coffeacute H amp Geys B (2006) Community Heterogeneity A Burden for the Creation of Social Capital Social Science Quarterly 87(5) 1053ndash1072 httpsdoiorg101111j1540-6237200600415x
Cohen D Spear S Scribner R Kissinger P Mason K amp Wildgen J (2000) ldquo Broken windowsrdquo and the risk of gonorrhea American Journal of Public Health 90(2) 230
Corden W M amp Neary J P (1982) Booming sector and de-industrialisation in a small open economy The Economic Journal 92(368) 825ndash848
Cordero J M Pedraja-Chaparro F Pisaflores E C amp Polo C (2017) Efficiency assessment of Portuguese municipalities using a conditional nonparametric approach Journal of Productivity Analysis 48(1) 1ndash24 httpsdoiorg101007s11123-017-0500-z
Croissant Y amp Millo G (2018) Panel Data Econometrics with R John Wiley amp Sons
Cust J amp Poelhekke S (2015) The Local Economic Impacts of Natural Resource Extraction 7 251ndash268 httpsdoiorg101146annurev-resource-100814-125106
da Cruz N F amp Marques R C (2014) Revisiting the determinants of local government performance Omega 44 91ndash103 httpsdoiorg101016JOMEGA201309002
Dauvin M amp Guerreiro D (2017) The Paradox of Plenty A Meta-Analysis World Development 94 httpsdoiorg101016jworlddev201701009
de Sousa M da C S Cribari-Neto F amp Stosic B D (2005) Explaining DEA technical efficiency scores in an outlier corrected environment the case of public services in Brazilian municipalities Brazilian Review of Econometrics 25(2) 287ndash313
Deacon R T (2011) The Political Economy of the Natural Resource Curse A Survey of Theory and Evidence Foundations and Trends in Microeconomics 111-208
Delhey J amp Newton K (2005) Predicting cross-national levels of social trust global pattern or
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Nordic exceptionalism European Sociological Review 21(4) 311ndash327
Dlouhyacute M (2018) Measuring Geographic Inequalities Dealing with Multiple Health Resources by Data Envelopment Analysis Frontiers in Public Health 6 53 httpsdoiorg103389fpubh201800053
Dollery B Wallis J amp Akimov A (2010) One Size Does Not Fit All The Special Case of Remote Small Local Councils in Outback Queensland Local Government Studies 36(1) 21ndash42 httpsdoiorg10108003003930903435716
Domenech J (2008) Mineral resource abundance and regional growth in Spain 1860ndash2000 Journal of International Development The Journal of the Development Studies Association 20(8) 1122ndash1135
Doran J amp Jordan D (2016) Decomposing US regional income inequality from 1969 to 2009 Applied Economics Letters 23(11) 781ndash784 httpsdoiorg1010801350485120151109030
Drew J amp Dollery B (2015) The State of Things The Dynamic Efficiency of Australian State and Territories Economic Papers A Journal of Applied Economics and Policy 34(3) 165ndash176
Drew J Kortt M amp Dollery B (2015) What Determines Efficiency in Local Government A DEA Analysis of NSW Local Government Economic Papers A Journal of Applied Economics and Policy 34(4) 243ndash256 httpsdoiorg1011111759-344112118
Easterly W (2007) Inequality does cause underdevelopment Insights from a new instrument Journal of Development Economics 84(2) 755ndash776 httpsdoiorghttpdxdoiorg101016jjdeveco200611002
Ebert L amp La Menza T (2015) Chile copper and resource revenue A holistic approach to assessing commodity dependence Resources Policy 43(Supplement C) 101ndash111 httpsdoiorghttpsdoiorg101016jresourpol201410007
ElGindi T (2017) Natural resource dependency neoliberal globalization and income inequality Are they related A longitudinal study of developing countries (1980ndash2010) Current Sociology 65(1) 21ndash53 httpsdoiorg1011770011392116632031
Engerman S L amp Sokoloff K L (1994) Factor Endowments Institutions and Differential Paths of Growth Among New World Economies National Bureau of Economic Research
Engerman S L amp Sokoloff K L (1997) Factor endowments institutions and differential paths of growth among new world economies How Latin America Fell Behind 260ndash304
Engerman S L Sokoloff K L Urquiola M amp Acemoglu D (2002) Factor Endowments Inequality and Paths of Development among New World Economies [with Comments] EconomampxedA 3(1) 41ndash109 Retrieved from httpwwwjstororgezp01libraryquteduaustable20065432
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Faumlre R Grosskopf S amp Lovell C A K (1985) The measurement of efficiency of production (Vol 6) Springer Science amp Business Media
Fleming D A amp Measham T G (2015a) Income Inequality across Australian Regions during the Mining Boom 2001-11 Australian Geographer 46(2) 203ndash216 httpsdoiorg1010800004918220151020596
Fleming D A amp Measham T G (2015b) Local economic impacts of an unconventional energy boom the coal seam gas industry in Australia Australian Journal of Agricultural and Resource Economics 59(1) 78ndash94 httpsdoiorg1011111467-848912043
Fum R M amp Hodler R (2010) Natural resources and income inequality The role of ethnic divisions Economics Letters 107(3) 360ndash363 httpsdoiorg101016jeconlet201003008
Garofalo J (1978) The fear of crime Broadening our perspective
Geys B amp Moesen W (2009) Exploring Sources of Local Government Technical Inefficiency Evidence from Flemish Municipalities Public Finance and Management 9(1) 1ndash29
Goderis B amp Malone S W (2011) Natural Resource Booms and Inequality Theory and Evidence The Scandinavian Journal of Economics 113(2) 388ndash417 httpsdoiorg101111j1467-9442201101659x
Greene W H (2016) Productivity and Efficiency Analysis (W H Greene L Khalaf R Sickles M Veall amp M-C Voia Eds) (1st ed 20) Cham Springer International Publishing httpsdoiorg101007978-3-319-23228-7
Gustafsson B amp Shi L (2002) Income inequality within and across counties in rural China 1988 and 1995 Journal of Development Economics 69(1) 179ndash204 httpsdoiorg101016S0304-3878(02)00058-5
Gylfason T amp Zoega G (2003) Inequality and Economic Growth Do Natural Resources Matter In T T Eicher S (Ed) Inequality and Growth Theory and Policy Implications (pp 255ndash292) The MIT Press
Henningsen A (2010) Estimating Censored Regression Models in R using the censReg Package R Package Vignettes Collection 5(2) 12
Henningsen A (2019) censReg Censored Regression (Tobit) Models R package version 05-30 httpscranr-projectorgpackage=censReg Retrieved from httpscranr-projectorgpackage=censReg
Herrera S amp Pang G (2005) Efficiency of Public Spending in Developing Countriesthinsp An Efficiency Frontier Approach World Bank Washington DC
Hill T D amp Angel R J (2005) Neighborhood disorder psychological distress and heavy drinking Social Science amp Medicine 61(5) 965ndash975
133
httpsdoiorghttpsdoiorg101016jsocscimed200412027
Hipp J R (2010) RESIDENT PERCEPTIONS OF CRIME AND DISORDER HOW MUCH IS ldquoBIASrdquo AND HOW MUCH IS SOCIAL ENVIRONMENT DIFFERENCES Criminology 48(2) 475ndash508 httpsdoiorg101111j1745-9125201000193x
Holtug N amp Mason A (2010) Introduction Immigration diversity and social cohesion SAGE Publications Sage UK London England
Hooghe M Vanhoutte B Hardyns W amp Bircan T (2010) Unemployment Inequality Poverty and Crime Spatial Distribution Patterns of Criminal Acts in Belgium 2001ndash06 The British Journal of Criminology 51(1) 1ndash20 httpsdoiorg101093bjcazq067
Howie P amp Atakhanova Z (2014) Resource boom and inequality Kazakhstan as a case study Resources Policy 39(1) 71ndash79 httpsdoiorg101016jresourpol201311004
Isham J Woolcock M Pritchett L amp Busby G (2005) The Varieties of Resource Experience Natural Resource Export Structures and the Political Economy of Economic Growth The World Bank Economic Review 19(2) 141ndash174 httpsdoiorg101093wberlhi010
Jottier D Ashworth J amp Heyndels B (2012) Understanding Votersrsquo Preferences How the Electoratersquos Complexity Affects Prediction Accuracy and Wishful Thinking among Politicians with Respect to Election Outcomes Kyklos 65(3) 340ndash370 httpsdoiorg101111j1467-6435201200542x
Kanbur S M R amp Venables A (2005) Spatial inequality and development (R World Institute for Development Economics Ed) Oxford Oxford University Press
Kesler C amp Bloemraad I (2010) Does immigration erode social capital The conditional effects of immigration-generated diversity on trust membership and participation across 19 countries 1981ndash2000 Canadian Journal of Political ScienceRevue Canadienne de Science Politique 43(2) 319ndash347
Kuznets S (1955) Economic Growth and Income Inequality The American Economic Review 45(1) 1ndash28 Retrieved from httpwwwjstororgstable1811581
Lagos G amp Blanco E (2010) Mining and development in the region of Antofagasta Resources Policy 35(4) 265ndash275 httpsdoiorghttpsdoiorg101016jresourpol201007006
Leamer E E Maul H Rodriguez S amp Schott P K (1999) Does natural resource abundance increase Latin American income inequality Journal of Development Economics 59(1) 3ndash42 httpsdoiorg101016s0304-3878(99)00004-8
Leibbrandt M Finn A amp Woolard I (2012) Describing and decomposing post-apartheid income inequality in South Africa Development Southern Africa 29(1) 19ndash34 httpsdoiorg1010800376835X2012645639
134
Letki N (2008) Does Diversity Erode Social Cohesion Social Capital and Race in British Neighbourhoods Political Studies 56(1) 99ndash126 httpsdoiorg101111j1467-9248200700692x
Lewis D A (2017) Fear of crime Incivility and the production of a social problem Routledge
lo Storto C (2013) Evaluating Technical Efficiency of Italian Major Municipalities A Data Envelopment Analysis model Procedia - Social and Behavioral Sciences 81 346ndash350 httpsdoiorg101016JSBSPRO201306440
Loayza N amp Rigolini J (2016) The Local Impact of Mining on Poverty and Inequality Evidence from the Commodity Boom in Peru World Development 84 219ndash234 httpsdoiorg101016jworlddev201603005
Loayza N Teran A M y amp Rigolini J (2013) Poverty Inequality and the Local Natural Resource Curse World Bank Policy Research Working Paper (6366) httpsdoiorg1015961813-9450-6366
Loacutepez R amp Miller S J (2008) Chile The Unbearable Burden of Inequality World Development 36(12) 2679ndash2695 httpsdoiorg101016jworlddev200801012
Manzano O amp Rigobon R (2001) Resource Curse or Debt Overhang National Bureau of Economic Research Working Paper Series No 8390 1 httpsdoiorghttpwwwnberorgpapersw9424bib
McPherson M Smith-Lovin L amp Cook J M (2001) Birds of a Feather Homophily in Social Networks Annual Review of Sociology 27(1) 415ndash444 httpsdoiorg101146annurevsoc271415
McQuestin D Drew J amp Dollery B (2018) Do Municipal Mergers Improve Technical Efficiency An Empirical Analysis of the 2008 Queensland Municipal Merger Program Australian Journal of Public Administration 77(3) 442ndash455 httpsdoiorg1011111467-850012286
Messner S F Rosenfeld R amp Baumer E P (2004) Dimensions of Social Capital and Rates of Criminal Homicide American Sociological Review 69(6) 882ndash903 httpsdoiorg101177000312240406900607
Michaels G (2011) THE LONG TERM CONSEQUENCES OF RESOURCE-BASED SPECIALISATION The Economic Journal 121(551) 31ndash57 httpsdoiorg101111j1468-0297201002402x
Mijanovich T amp Weitzman B C (2003) Which ldquobroken windowsrdquo matter School neighborhood and family characteristics associated with youthsrsquo feelings of unsafety Journal of Urban Health 80(3) 400ndash415
Mikušovaacute P (2015) An Application of DEA Methodology in Efficiency Measurement of the Czech Public Universities Procedia Economics and Finance 25 569ndash578 httpsdoiorg101016S2212-5671(15)00771-6
135
Milanovic B (2016) Global inequality Harvard University Press
Millo G amp Piras G (2012) splm Spatial panel data models in R Journal of Statistical Software 47(1) 1ndash38
Murphy K M amp Topel R H (2016) Human Capital Investment Inequality and Economic Growth JOURNAL OF LABOR ECONOMICS 34(2) S99ndashS127 httpsdoiorg101086683779
Narboacuten-Perpintildeaacute I amp De Witte K (2018a) Local governmentsrsquo efficiency a systematic literature reviewmdashpart I International Transactions in Operational Research 25(2) 431ndash468 httpsdoiorg101111itor12364
Narboacuten-Perpintildeaacute I amp De Witte K (2018b) Local governmentsrsquo efficiency a systematic literature reviewmdashpart II International Transactions in Operational Research 25(4) 1107ndash1136 httpsdoiorg101111itor12389
Nuntildeez J Rivera J Villavicencio X amp Molina O (2003) Determinantes socioeconoacutemicos y demograacuteficos del crimen en Chile Estudios de Economiacutea 30(1) 55ndash85
OrsquoDonnell C J Rao D S P amp Battese G E (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios Empirical Economics 34(2) 231ndash255 httpsdoiorg101007s00181-007-0119-4
Ocampo J A (2004) Latin Americarsquos Growth and Equity Frustrations During Structural Reforms The Journal of Economic Perspectives 18(2) 67ndash88 httpsdoiorg1012570895330041371349
OECD (2014) Focus on inequality and growth OECD
OECD (2017) Howrsquos Life 2017 Life Satisfaction Oecd httpsdoiorg101787how_life-2017-en
Ohtake F (2008) Inequality in Japan Asian Economic Policy Review 3(1) 87ndash109 httpsdoiorg101111j1748-3131200800093x
Okun A M (2015) Equality and efficiency the big tradeoff Washington DC Brookings Institution Press
Ortega B Sanjuaacuten J amp Casquero A (2017) Determinants of efficiency in reducing child mortality in developing countries The role of inequality and government effectiveness Health Care Management Science 20(4) 500ndash516 httpsdoiorg101007s10729-016-9367-1
Ostry J Berg A amp Tsangarides C (2014) Redistribution inequality and growth International Monetary Fund
Pacheco F Saacutenchez R amp Villena M (2013) Eficiencia de los Gobiernos Locales y sus Determinantes Un anaacutelisis de Fronteras Estocaacutesticas en Datos de Panel para
136
Municipalidades Chilenas Santiago de Chile Chile
Papyrakis E amp Raveh O (2014) An Empirical Analysis of a Regional Dutch Disease The Case of Canada Environmental and Resource Economics 58(2) 179ndash198 httpsdoiorg101007s10640-013-9698-z
Paredes D (2013) The Role of Human Capital Market Potential and Natural Amenities in Understanding Spatial Wage Disparities in Chile Spatial Economic Analysis 8(2) 154ndash175 httpsdoiorg101080174217722013774094
Paredes D Iturra V amp Lufin M (2016) A spatial decomposition of income inequality in Chile Regional Studies 50(5) 771ndash789
Phan P Van Orsquobrien M Mendolia S amp Paloyo A (2017) National pro-poor spending programmes and their effect on income inequality and poverty Evidence from Vietnam Applied Economics 49(55) 5579ndash5590 httpsdoiorg1010800003684620171313957
Podinovski V V (2004) Bridging the Gap between the Constant and Variable Returns-to-Scale Models Selective Proportionality in Data Envelopment Analysis The Journal of the Operational Research Society 55(3) 265ndash276 Retrieved from httpwwwjstororgstable4102006
Ravallion M (2005) On Measuring Aggregate Social Efficiency Economic Development and Cultural Change 53(2) 273ndash292 httpsdoiorg101086425380
Rehner J Baeza S A amp Barton J R (2014) Chilersquos resource-based export boom and its outcomes Regional specialization export stability and economic growth Geoforum 56(Supplement C) 35ndash45 httpsdoiorghttpsdoiorg101016jgeoforum201406007
Rivera J Gutieacuterrez M amp Nuacutentildeez J (2009) Caracterizacioacuten socioeconoacutemica y espacial de la criminalidad en Chile Revista CEPAL
Ross C E (2011) Collective threat trust and the sense of personal control Journal of Health and Social Behavior 52(3) 287ndash296
Ross C E amp Mirowsky J (2001) Neighborhood disadvantage disorder and health Journal of Health and Social Behavior 258ndash276
Rothstein B amp Uslaner E M (2005) All for all Equality corruption and social trust World Politics 58(1) 41ndash72
Sachs J D amp Warner A M (2001) The curse of natural resources European Economic Review 45(4ndash6) 827ndash838 httpsdoiorg101016S0014-2921(01)00125-8
Salas R (2019) sinimr Chilean Municipalities Information System Wrapper Retrieved from httpsgithubcomrobsalascosinimr
Sampson R J (1986) Crime in Cities The Effects of Formal and Informal Social Control Crime and Justice 8 271ndash311
137
Sampson R J (2008) Rethinking crime and immigration Contexts 7(1) 28ndash33
Santos Silva J M C amp Tenreyro S (2010) On the existence of the maximum likelihood estimates in Poisson regression Economics Letters 107(2) 310ndash312 httpsdoiorghttpsdoiorg101016jeconlet201002020
Santos Silva J M C amp Tenreyro S (2011) poisson Some convergence issues Stata Journal 11(2) 207ndash212 Retrieved from httpwwwstata-journalcomarticlehtmlarticle=st0225
Scott Z (2009) Decentralisation local development and social cohesion an analytical review GSDRC Research Paper 5
Sinha R P (2017) Fiscal Performance Benchmarking of Indian States-A Robust Frontier Approach The Central European Review of Economics and Management 1(4) 225ndash249
Skogan W (1986) Fear of crime and neighborhood change Crime and Justice 8 203ndash229
Skogan W (1999) Measuring what matters Crime disorder and fear In Measuring what matters Proceedings from the Policing Research Institute meetings (pp 37ndash53) National Institute of Justice Washington DC
Skogan W (2015) Disorder and Decline The State of Research Journal of Research in Crime and Delinquency 52(4) 464ndash485 httpsdoiorg1011770022427815577836
Šťastnaacute L amp Gregor M (2014) Public sector efficiency in transition and beyond evidence from Czech local governments Applied Economics 47(7) 1ndash20 httpsdoiorg101080000368462014978077
Tandon A (2005) Measuring Efficiency of Macro Systems An Application to Millennium Development Goal Attainment Asian Development Review 22(2) 108ndash125
Taylor R B (1999) The incivilities thesis Theory measurement and policy Measuring What Matters 65 88
Tigga N S amp Mishra U S (2015) On Measuring Technical Efficiency of the Health System in India An Application of Data Envelopment Analysis Journal of Health Management 17(3) 285ndash298 httpsdoiorg1011770972063415589229
Tinbergen J (1975) Demographic Development and the Exhaustion of Natural Resources Population and Development Review 1(1) 23ndash32 httpsdoiorg1023071972269
Tiruneh G (2014) Social Revolutions Their Causes Patterns and Phases SAGE Open 4(3) 2158244014548845 httpsdoiorg1011772158244014548845
Tolsma J Van der Meer T amp Gesthuizen M (2009) The impact of neighbourhood and municipality characteristics on social cohesion in the Netherlands Acta Politica 44(3) 286ndash313
Tsekeris Sotiris T Tsekeris T amp Papaioannou S (2018) Regional determinants of technical efficiency evidence from the Greek economy Regional Studies [London]thinsp Carfax
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Uslaner E (2002) The moral foundations of trust Cambridge University Press
Uslaner E (2011) CORRUPTION AND INEQUALITY DICE Report 9(2) 20ndash24
Uslaner E (2013) Trust and corruption revisited how and why trust and corruption shape each other Quality amp Quantity 47(6) 3603ndash3608 httpsdoiorg101007s11135-012-9742-z
Uslaner E amp Brown M (2005) Inequality trust and civic engagement American Politics Research 33(6) 868ndash894
Van der Ploeg F (2011) Natural Resources Curse or Blessing Journal of Economic Literature 49(2) 366ndash420 httpsdoiorg101257jel492366
Vergara R (2012) Crime Prevention Programs Evidence From CHILE The Developing Economies 50(1) 1ndash24
Watkins M H (1963) A staple theory of economic growth Canadian Journal of Economics and Political ScienceRevue Canadienne de Economiques et Science Politique 29(2) 141ndash158
Wilkinson R G (1999) Income inequality social cohesion and health clarifying the theorymdasha reply to Muntaner and Lynch International Journal of Health Services 29(3) 525ndash543
Wilson J Q amp Kelling G L (1982) Broken windows Atlantic Monthly 249(3) 29ndash38
Wilson W J (1996) When Work Disappears Political Science Quarterly 111(4) 567ndash595 httpsdoiorg1023072152085
Wirth L (1938) Urbanism as a Way of Life American Journal of Sociology 44(1) 1ndash24
Worthington A amp Dollery B (2000) An empirical survey of frontier efficiency measurement techniques in local government Local Government Studies 26(2) 23ndash52 httpsdoiorg10108003003930008433988
Wu P-C Huang T-H amp Pan S-C (2014) Country Performance Evaluation The DEA Model Approach Social Indicators Research 118(2) 835ndash849 httpsdoiorg101007s11205-013-0443-3
Ye X Ma L Ye K Chen J amp Xie Q (2017) Analysis of Regional Inequality from Sectoral Structure Spatial Policy and Economic Development A Case Study of Chongqing China Sustainability 9(4) 633 Retrieved from httpwwwmdpicom2071-105094633
Yue W Z Zhang Y T Ye X Y Cheng Y Q amp Leipnik M R (2014) Dynamics of Multi-Scale Intra-Provincial Regional Inequality in Zhejiang China Sustainability 6(9) 5763ndash5784 httpsdoiorg103390su6095763
139
Appendices
Appendix A Summary statistics income inequality
Table A1
Summary statistics Gini coefficients by year and zone
140
Appendix B Summary statistics for NRD measures by region
Table B1
Summary statistics NRD measures by region
141
Appendix C Regional administrative division and defined zones
Figure C1 Geographical distribution of Chilean regions and 3 zones
142
Appendix D Summary statistics numeric controls and correlation matrix
Table D1
Summary Statistics Numeric Explanatory Variables
Figure D1 Correlation matrix numeric explanatory variables
143
Appendix E Static spatial panel models
Following Millo amp Piras (2012) a model including a spatial lag of the dependent variable and
spatial autoregressive disturbances but not spatial lags for the explanatory variable(s) is called
SARAR model A static spatial SARAR panel could be expressed as
119910 120582 119868 otimes119882 119910 119883120573 119906 (E1)
where y is an 119873 1 vector of observations on the dependent variable X is a 119873 119896 matrix of
observations on the non-stochastic exogenous regressors 119868 an identity matrix of dimension 119879 otimes
is the kronecker operator 119882 is the 119873 119873 spatial weights matrix of known constants whose
diagonal elements are set to zero and 120582 the corresponding spatial parameter44
The disturbance vector is the sum of two terms
119906 120580 otimes 119868 120583 120576 (E2)
where 120580 is a 119879 1 vector of ones 119868 an 119873 119873 identity matrix 120583 is a vector of time-invariant
individual specific effects (not spatially autocorrelated) and 120576 a vector of spatially autocorrelated
innovations that follow a spatial autoregressive process of the form
120576 120588 119868 otimes119882 120576 120584 (E3)
If we assume that spatial correlation applies to both the individual effects 120583 and the remainder
error components 120576 Kapoor et al (2007) propose that the disturbance term 119906 follows a first order
spatial autoregressive process of the form
119906 120588 119868 otimes119882 119906 120576 (E4)
44 Unlike ldquotraditional panelsrdquo which are organized as different time series for each cross-sectional unit ldquospatial panelsrdquo are organized as a series of cross-sections for each year
144
where 119882 is the spatial weights matrix and 119903ℎ119900 the corresponding spatial autoregressive
parameter To further allow for the innovations to be correlated over time the innovations vector
in Equation 7 follows an error component structure
120576 120580 otimes 119868 120583 120584 (E5)
where 120583 is the vector of cross-sectional specific effects 120584 a vector of innovations that vary
both over cross-sectional units and time periods 120580 is a vector of ones and 119868 an 119873 119873 identity
matrix45
Spatial panel models are usually estimated by Maximum Likelihood or GMM46 The SAR
SEM or SARAR models could be estimated with Random or Fixed effects For instance A fixed
effect spatial lag model can be written in stacked form as
119910 120582 119868 otimes119882 119910 120580 otimes 119868 120583 119883120573 120576 (E6)
where 120582 is the spatial autoregressive coefficient 119882 a non-stochastic spatial weights matrix
120580 a column vector of ones of dimension 119879 119868 an 119873 119873 identity matrix and 120576 sim 119873 0120590 On
the other hand a fixed effects spatial error model assuming the disturbance specification by
Kapoor et al (2007) can be written as
119910 120580119879 otimes 119868119873 120583 119883120573 119906119906 120588 119868119879 otimes119882119873 119906 120576
(E7)
where 120588 is the spatial autocorrelation coefficient and 120576 is a well-behaved error term
45 In the regression implementation the specification given by equations (22) and (3) is denoted by ldquobrdquo for ldquoBaltagirdquo On the other hand the specification given by equations (23) and (31) is denoted by ldquokkprdquo for ldquoKapoor Kelejian and Pruchardquo 46 We use the R package splm which allow both types of regression procedure
145
Appendix F Analysis OLS residuals cross-sectional (six-year average) analysis
Table F1
Analysis OLS residuals Anselin Method
Figure F1 Moran scatter plot OLS residuals
146
Appendix G Linear panel data models
Table G1
Panel regressions (non-spatial)
147
Appendix H Spatial panel models (Generalized Moments (GM) estimation)
Table H1
GM Spatial Models
148
Appendix I Inputs and outputs used in DEA analysis
Figure I1 Examples of inputs and outputs used to measure LGE (based on Narboacuten-Perpintildeaacute amp De Witte 2018)
149
Appendix J Technical and scale efficiency
Following lo Storto (2013) under an input-oriented specification assuming VRS with n
municipalities using k inputs to produce m outputs the DEA model for a given i-th municipality
is specified with the following mathematical programming problem
119898119894119899 120579119904119906119887119895119890119888119905 119905119900 119910 119884120582 0120579119909 119883120582 01198991prime120582 1 120582 0prime
Where 119910 is the column vector of the outputs and 119909 is the column vector of the inputs
Moreover we can define X as the (k times n) input matrix and Y as the (m times n) output matrix 120579 is a
scalar (that satisfies 120579 1) more specifically it is the efficiency score that measures technical
efficiency It measures the distance between a municipality and the efficiency frontier defined as
a linear combination of the best practice observations With 120579 1 the municipality is inside the
frontier (ie it is inefficient) while 120579 1 implies that the municipality is on the frontier (ie it is
efficient) The vector 120582 is an (n times 1) vector of constants that measures the weights used to compute
the location of an inefficient municipality if it were to become efficient
The total technical efficiency 119879119864 can be decomposed into pure technical efficiency
119879119864 and scale efficiency 119878119864 where 119878119864 119879119864 119879119864 (Coelli et al 2005) To find out
whether a municipality is scale efficient and qualify the type of returns of scale a DEA model
under non-increasing returns to scale 119879119864 is implemented where 119878119864 119879119864 119879119864 Hence
the following rule can be applied (Faumlre Grosskopf amp Lovell 1985)
bull If 119878119864 1 then a municipality is scale efficient both under CRS and VRS
bull If 119878119864 1 it operates under increasing returns to scale
bull If 119878119864 1 it operates under decreasing returns to scale
150
Appendix K Correlation matrix
Figure K1 Correlation matrix contextual factors
151
Appendix L Returns to scale by year and zone
Table L1
Returns to scale (percentage of municipalities)
152
Appendix M Returns to scale by year (maps)
Figure M1 Spatial distribution of returns to scale by county per year
153
Appendix N Efficiency status by year (maps)
Figure N1 Spatial distribution of efficient (E es = 1) and inefficient (I es lt 1) counties per year
154
Appendix O Spatial distribution efficiency scores by year (maps)
FigureO1 Custom breaks maps of efficiency scores (VRS) by county per year
155
Appendix P Analysis OLS residuals cross-sectional (six-year average) analysis
Table P1
Analysis OLS residuals Anselin Method
Figure P1 Moran scatter plot efficiency scores and OLS residuals
156
Table P2
OLS and spatial regression models for the six-year averaged data
157
Appendix Q OLS regressions for cross-sectional and panel data
Table Q1
OLS cross-sectional regression per year
158
Table Q2
OLS panel regressions Pooled random effects and instrumental variable
159
Appendix R Quantile maps incivilities rate by group (average total period)
Figure R1 Spatial distribution of incivilities by group (Average rate per 1000 inhabitants 2006-09-11-13-15-17)
160
Appendix S Correlation matrix numeric covariates
Figure S1 Correlation matrix numeric covariates
161
Appendix T Negative Binomial regressions
Table T1
Negative Binomial regressions
162
Appendix U Coefficients economic and racial diversity by geographical zone
Table U1
Coefficients economic and racial diversity in pooled Poisson models by geographic zone
