F Rossi -Dissertation

University of Kent School of Economics

MASTER IN AGRICULTURAL ECONOMICS

AN ANALYSIS OF INTERREGIONAL

MIGRATION IN ITALY

Dissertation

STUDENT SUPERVISOR Fabio Rossi Dr Bill Collier

SEPTEMBER 2012

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ACKNOWLEDGMENT I would like to thank all the members of the School of Economics, in particular Prof.

Davidova who encouraged me to carry on with my studies in the most difficult

moment during the past year. Special thanks go also to my supervicor, Dr Collier,

whose advices and insightful comments made it possible the “climb to the top of the

mountain”.

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INDEX ACKNOWLEDGMENT ............................................................................................... 2

Chapter 1 – Introduction ................................................................................................ 4

Chapter 2 – Italian migration history............................................................................. 6

2.1 – Historical background ...................................................................................... 6

2.2 – The “Empirical Puzzle” .................................................................................... 8

2.3 – The “Southern Question” ............................................................................... 10

Chapter 3 – Theoretical framework: a brief literature review ..................................... 14

Chapter 4 – Empirical literature .................................................................................. 21

Chapter 5 – Data and methodology ............................................................................. 24

5.1 – Empirical framework ...................................................................................... 24

5.2 – Data................................................................................................................. 26

5.3 – Descriptive Analysis ....................................................................................... 29

5.4 – Methodology................................................................................................... 31

Chapter 6 – Empirical results and discussion .............................................................. 34

6.1 – Negative Binomial Regression ....................................................................... 34

6.2 – Some remarks and discussion ......................................................................... 37

6.3 – Limitations and extensions ............................................................................. 39

Chapter 7 – Conclusion ............................................................................................... 41

References ................................................................................................................... 43

APPENDIX ................................................................................................................. 48

Word count: 12,033

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Chapter 1 – Introduction

Migration of people across different areas has been studied as a complex

phenomenon involving mainly demographic and economic aspects (Etzo, 2008), and

is recognized to be an important mechanism through which the geographical

distribution of people changes over time (Greenwood, 1997). Italy has a long history

of internal migrations characterized by relative differences between different areas,

in particular North and South. Dualism, alongside differences in productivity and

local labour market conditions, have boosted migration and interfered in the process

of growth and convergence. Classical macroeconomic models consider migration as

an equilibrating mechanism that reduces differences among regions with respect to

key economic variables (e.g. unemployment, per capita income) (Etzo, 2008).

However, despite a history of significant structural change and intense migration

flows, the empirical evidence does not show substantial convergence between Italian

regions. This is an aspect that many researchers have for long stressed and that

remains unsolved (Capasso et al., 2011).

Italy is divided into twenty administrative regions, each charactherized by a

strong linguistic and cultural identity. Since the unification of Italy in 1860, there has

been an increasing gap between Northern and Southern regions in terms of economic

development, as well as the emergence of strong internal migration flows. Despite

significant economic differences between these two Italian areas, there has been a

marked drop in migration rates between the mid-1970s and mid-1990s, followed by a

sharp turnaround in more recent years.

Following the studies of various authors (Basile & Causi, 2005; Piras, 2010;

Etzo, 2010; Napolitano & Bonasia, 2010; Biagi et al., 2011), this dissertation

investigates the determinants of interregional migration in Italy, and consider how

migrants respond to changes in economic factors. In particular, the main interest is to

investigate the role of macroeconomic variables in determining the intensity and

direction of observed migration flows. In order to do so, we utilise an extended

Gravity model (Lee, 1966), where regional levels of GDP and unemployment are

considered alongside distance and population size as the main determinants of

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migration. Gravity models were one of the first formal models of migration and

remain the most common theoretical framework in empirical migration analysis

concerning migration flows.

The remainder of the dissertation is organized as follows. Chapter 2 introduces

the historical background of the Italian migration and gives an insight into the

“empirical puzzle” that has characterized migration between the mid-1970s and mid-

1990s, and the “Southern Question” which has not been resolved following the

unification of Italy. Chapters 3 and 4 present a selected migration literature review,

both theoretical and empirical, dealing with the determinants of internal migration

flows. Chapter 5 describes the data and methodology employed in the empirical

analyses. Chapter 6 presents the empirical results and analysis, and provides an

economic interpretation of the key findings. Finally, chapter 7 provides some

conclusive remarks.

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Chapter 2 – Italian migration history 2.1 – Historical background

Since the end of the Second World War the Italian economic system has been

transformed radically and a steady increase of the per capita income occurred

throughout the years in all Italian regions. A once large prevailing agriculture sector

has shrunk in favour of the industry and services sectors. However, the distinction

between advanced sectors (industry and services) and backward sectors (agriculture)

which characterises many dual economies, is accompanied in Italy by a severe

dichotomy between North and South, with the South chronically lagging behind

(Capasso et al., 2011).

Evidence of Italian migration is reported in Figure 2.1 and can be traced to the

period of unification which has characterised Italian economic history for over a

century. Between 1861 and 1985, it is estimated that more than 26 million italians

migrated abroad (Del Boca & Venturini, 2003). This large-scale migration away

from Italy is referred to as the “Italian diaspora” and it is considered the biggest mass

migration of contemporary times.

Figure 2.1 Italian migration abroad in thousands, 1876-1981. (Source: Golini & Birindelli, 1990 in Del Boca & Venturini, 2001.)

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Over the second half of the last century, interregional migration flows in Italy

have gone through different phases. These can be summarised in three main trends.

The first one, which dominated the 1950s and 1960s, was characterized by intense

migration flows, mainly from rural to urban areas and from South to the more

industrialised North. A considerable increase in labour demand from the big

industries, located mostly in the North-West1, triggered the migration of people

working in the rural Southern regions, acting as a “pull” factor. Similarly, but in

opposite direction, the excess of labour supply in the agricultural sector played an

important role as a “push” factor, boosting the migration from the sending regions.

From the early 1970s internal migration markedly declined, a trend which

persisted till the mid 1990s. Differentials in per capita incomes and unemployment

rates, however, were still substantially high and did not decrease during this second

period (Faini et al., 1997). In fact, during this period migration rates seemed not to be

strongly correlated with unemployment and income differentials. The main feature of

the second cycle of internal migration flows, therefore, is the mismatch between

internal migration and regional disparities. Falling migration rates despite the

presence of strong regional disparities has become known as the “empirical puzzle”

and the failure of traditional theory to explain such a phenomenon has attracted the

interest of many researchers (Etzo, 2010).

Finally, after the mid 1990s internal migration flows grew again with a

significant flow of migrants from Southern to Northern regions, as reported by the

Italian National Institute of Statistics (ISTAT). Importantly, whilst the main

geographical patterns of these flows has not changed (i.e., from South to Centre-

North), its composition reveals some interesting changes. For instance, during the

period 1950-1970, migrants leaving the Southern regions of Italy were very young

and with low educational attainment. By contrast, the young migrants of more recent

years have been shown to be more skilled and five years older (on average) than

migrants during the 1960s (Etzo, 2010).

1 The so called “industrial triangle” (i.e., Turin-Genoa-Milan)

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2.2 – The “Empirical Puzzle”

As drawn previously, the pattern of migration between the mid-1970s and mid-

1990s has been referred to as the “empirical puzzle” to indicate that, despite

increasing differentials in regional unemployment levels, mobility levels fell and

internal migration rates were surprisingly lower than in previous decades. The

reasons for this slow down in migration are still not entirely clear and various

explanations have been proposed by different studies (Biagi et al., 2011).

Attanasio et al. (1991) argue that past governments have used pensions as a

discretionary income subsidy, especially in the South, thereby increasing disposable

income in Southern households. This increase in disposable income in Southern

regions favoured by family and government support may explain the observed

decline in the propensity to migrate since any increase in net household income will

lower the expected net benefit of migration. Fachin (2007) analyses the determinants

of internal migrations during the period 1973-1996, and provides some empirical

support for this hypothesis.

Faini et al. (1997) provide a further explanation for the “empirical puzzle”,

arguing that the combination of interregional job mismatching and high mobility

costs could explain the marked fall in migration rates between the mid-1970s and

mid-1990s. Over this period, job agencies in Italy were publicly operated legal

monopolies which provided no training or information on job vacancies in other

regions (Etzo, 2010). This lack of information invariably resulted in an excessive

reliance of the unemployed on local networks when undertaking job search activities.

Punitive housing taxation and rent controls also served to increase the costs of

geographical mobility. Indeed, Faini et al. (1997) and Cannari et al. (2000) both

report evidence that housing market differentials discouraged interregional migration

in Italy between the mid-1970s and mid-1990s.

One final explanation for the empirical puzzle comes from the significant

translocation of labour demand over this period from the North-West region to the

North-East. Technological progress and changes in industrial structures required

more qualified and specialised workers than generic workers that had been hired in

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the previous two decades. As a result, new potential migrants could not rely on the

old family networks as they did in the past (Etzo, 2010).

The “empirical puzzle” seems to come to an end during the 1990s. Indeed, as

can be seen from Figure 2.2, between the mid-1990s and 2000s, Italian South-North

migration flows rose again significantly. During this period, almost eight hundred

and fifty thousand (850,000) people moved from the South to the Centre-North. In

2000, gross migration flows reached the peak of nearly one hundred and fifty

thousand (150,000), the same level as in 1975 (Basile & Causi, 2005). Once again, a

variety of explanations for this migration turnaround have been proposed.

Figure 2.2 Change of residence from South to North 1975-2000 (Source: Basile & Causi, 2005)

First, as a result of the big financial effort that Italy made to join the European

Monetary Union, the development policies for Southern Italy were drastically

reduced (SVIMEZ, 2004) and the government support that had characterized the

1980s shrank considerably. This caused a significant decline in household disposable

income (Basile & Causi, 2005), presumably determining a change in the attitude of

family members toward the choice of migrating.

Second, the widespread use of internet among households, together with the rise

and development of private job agencies, improved the efficiency of the job

searching activities and reduced the migration risks for the potential migrants.

Increased information is fundamental for potential migrants to assess the real

differences between their region and the possible destinations (i.e., differences in

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income, in unemployment rates, cost of living, etc.), thereby facilitating a better

match between the labour demand and the labour supply (Etzo, 2010).

Third, Basile and Causi (2005) point to the success of Northern industrial

districts following the devaluation of the Italian currency in 1992. This devaluation

caused an increase in the international competitiveness of the Northern industrial

sector and in export sales, thus inducing benefits mostly in the Northern areas with

stronger industrial specialization. The economic expansion in these areas has

contributed to determine in the second half of the 1990s a revival of South-North

migration flows (Basile & Causi, 2005).

Finally, it may be also the case that this migration turnaround is also related to

external issues such as an emerging EU-wide pattern of interregional divergence,

driven by increased competition between regions in response to the new era of global

competition (Biagi et al., 2011).

2.3 – The “Southern Question”

At the time of unification in 1861, Italy was a relatively backward country

compared to the more advanced western European nations. Agriculture was the main

productive sector and 60 per cent of the labour force was still toiling on the land.

From then on the Italian economy underwent deep changes, both in its productive

capacity and structure. Italy caught up with the level of output per head of the most

advanced economies and today shares a similar economic structure and a low rate of

growth (Malanima & Zamagni, 2010).

By the end of the nineteenth century, the poorest regions in Italy were located in

the South. At that time politicians and intellectuals started to discuss the causes of the

backwardness of the South (also known as Mezzogiorno), starting the so-called

debate on the “Questione Meridionale” (“Southern Question”) as the backward South

was then becoming a central feature of the Italian economy and society (Malanima &

Zamagni, 2010). This debate remains vivid nowadays.

Many explanations for the origins of regional disparities, especially the North-

South divide, and their persistence throughout the twentieth century have been

proposed. These can be broadly summarized by two contrasting views.

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The first view, prevailing up to the 1990s, emphasises the “natural poverty” of

the South which arose from a dry climate, shortness of natural resources, low levels

of human and social capital, and a feudal heritage in the land system. The North, by

contrast, in particular the North-West, was a natural candidate for industrialization

because of a more favourable geographical position and natural endowments, more

advanced human and social capital, better transport infrastructure, improved credit

markets, and the existence of crucial manufactures (Felice, 2011).

The alternative view considers the South to be the wealthiest and more advanced

part of the Italian peninsula but exploited by the North. The Kingdom of the Two

Sicilies (the actual South without Sardinia) had larger monetary resources in 1860,

which were appropriated by the Kingdom of Piedmont-Sardinia and redirected by the

new Italian state in favour of northern infrastructures, to promote the process of

northern industrialisation, and to pay the debts of the House of Savoy, the royal

family leading the newly formed Kingdom of Italy (Felice, 2011; Aprile, 2011).2 At

this time, several Southern regions displayed important signs of modernity and

dynamism, showing a potential for industrialization similar to the North-West

(Felice, 2011). However, for many years after the unification, cutting-edge Southern

industries were dismantled or allowed to perish, due either to unfair competition

from the North, or as a consequence of explicit decisions made by the newly formed

Italian State (Aprile, 2011; De Crescenzo, 2002).3

New studies have been progressively emerging for the past few decades,

resulting in a deeper knowledge of the economy after 1861, especially with the

publication of new statistical series and analysis of the organization of economic

activity, technology, economic policy, North-South disparities and changes in

income distribution. This progress has been quite remarkable (Malanima & Zamagni,

2010).

2 Before the unification, Italy was divided into seven different territories and, according to the studies of Nitti (1993), the Kingdom of the Two Sicilies had about 65.7% of all the money circulating in the peninsula, followed by Papal States (14%), Duchy of Tuscany (12.9%), Kingdom of Piedmont-Sardinia (4%), Venice (1.9%), Lombrady (1.2), Parma and Modena (0.3%). 3 For a broad review of the industrial history in the Kingdom of the Two Sicilies, see De Crescenzo (2002) and Pedio (1977).

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Figure 2.3 Per capita GDP in North and South Italy at 1911 constant prices, 1861-2004. (Source: Daniele & Malanima, 2007).

In the light of these new statistical series, Daniele and Malanima (2007) have

suggested that disparities within the regions where much more significant than those

between regions. They highlight that the difference between the two areas as

measured by GDP per capita was not significant in 1861 and approximately only 7%

by 1891. In the subsequent decades, this figure rose substantially, reaching a peak of

44% by 1940. A process of convergence was observed between 1951 and 1973.

However, a further period of significant and sustained economic disparities occurs

between the two regions thereafter (Fig.2.3).4

In terms of agriculture, Daniele and Malanima (2007) assert that in 1891, per

capita agricultural production was 10% higher in the South than the North. By

contrast, several authors (Fenoaltea, 2003; Fenoaltea & Ciccarelli, 2010) find

evidence of a pervasive similarity across these different regions in terms of industrial

structure. Indeed, as illustrated in Figure 2.4, only by 1911 is the northern industrial

triangle of Piedmont, Liguria, and Lombardy clearly apparent.

4 See Daniele & Malanima, 2007; Malanima & Zamagni, 2010 for details.

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Figure 2.4 Indices of relative industrialization, 1871 and 1911. (Source: Fenoaltea, 2003)

Thus, at the time of unification, there were no significant differences between

the two Italian macro-areas and the view of a backward and underdeveloped South

with dramatically low levels of literacy and lack of infrastructures and natural

resources does not appear to be adequately supported by existing data or historical

documentation. The “Southern Question” debate over regional disparities seems far

from closed, not least because the extent and development of those disparities

remains highly conjectural (Fenoaltea, 2003).

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Chapter 3 – Theoretical framework: a brief literature review

The modern economics literature on migration often is traced to Lewis' seminal

work on economic development with unlimited supplies of labor5. The Lewis model

consists of a dual economy with both "capitalist" and a "non-capitalist" sectors. The

“capitalist” sector develops by drawing labour from the non-capitalist “subsistence”

sector. The existence of “surplus labour” in the subsistence sector ensures that during

an extended period, wages in the capitalist sector remain constant because the supply

of labour to the capitalist sector exceeds demand at the prevailing wage rate. The

surplus of output over wages is captured by the capitalists as profits (Kirkpatrick &

Barrientos, 2004). Although Lewis does not propose an explicit migration model

himself, his seminal contribution is to explain the mechanisms by which an unlimited

supply of labor in traditional sectors of less developed countries might be absorbed

through capital accumulation and savings in an expanding modern sector. In practice,

the capitalist sector has generally become identified with the urban economy and the

non-capitalist sector with agriculture or the rural economy. If the capitalist economy

is concentrated in the urban economy, labour transfer implies geographic movement,

both for workers and households, and hence rural-to-urban migration (Taylor &

Martin, 2002).

The neoclassical theory predicts a positive role of migration in the convergence

process. People migrate from low capital intensity to high capital intensity regions,

reducing the capital intensity in the destination region and increasing it in the sending

region. The latter will grow faster than the former and migration flows will continue

till the convergence process finishes (Etzo, 2008). The dominant assumptions of the

neoclassical model are full employment, market clearing and perfect competition.

According to the neoclassical analysis, rural-to-urban migration exerts upward

pressure on wages and on the marginal value product of labour in rural areas, while

putting downward pressure on urban wages, assuming that wages adjust to ensure

that both rural and urban labour markets clear (Taylor & Martin, 2002). Thus, the

5 See Lewis (1954) for details.

15

most basic neoclassical model of the labour market asserts the convergence of wages

across sectors. Furthermore, assuming full employment of labour in both rural and

urban sectors, and minimal transactions costs, inter-sectoral wage differentials should

be the primary factors driving rural out-migration. (Taylor & Martin, 2002).

However, dispite the assumptions of perfect competition, homogeneity of

workers and market-clearing equilibrium outcomes, there are clearly differences

among the wages of individuals. In fact, in reality one can observe rigid wages,

persistent inter-firm wage differentials, layoffs and large-scale unemployment, all

facts that appear to challenge the validity of the model and its assumptions. In order

to reconcile theory and empirical facts, the core model of the neo-classical paradigm

has been amended and modified in various ways.

An alternative model which attempts to overcome the limitation of the

neoclassical analysis is the Harris-Todaro model. In contrast with neoclassical

model, the Harris-Todaro model (hereafter H-T model) does not assume full

employment and is thus able to explain the continuation of rural-to-urban migration,

even in presence of rising urban unemployment (Etzo, 2008). Harris and Todaro

(1970) propose a modification of the neoclassical framework and permit

imperfections in the labour market in the context of internal rural-to-urban migration,

thus relaxing the strict assumptions found in the basic model. They assert that the

unemployment rate and wage differentials between rural and urban sectors to be the

key elements of migration. Each potential rural-to-urban migrant decides whether or

not to move to the city based on an expected-income maximization objective.

The H-T model diverges from the usual full employment, flexible wage-price

models of economic analysis, where the achievement of a full employment

equilibrium is assured through appropriate wage and price adjustments (Harris &

Todaro, 1970). In H-T model, expected urban income at a given location is the

product of the wage and the probability to find a job (proxied by employment rate).

Expected rural income is calculated similarly. Individuals are assumed to migrate if

their discounted future stream of urban-rural expected income differentials exceeds

migration costs (Bojnec & Dries, 2005).

The H-T model can be formalized as

where pu(t) is the probability of urban employment at time

given employment, the term

rural sector and r is the discounted rate. If the

values holds, then rural

assumed to remain in the rural labour market.

The H-T framework implies that rural

urban unemployment can be economically rational if expected urban income exceeds

expected rural income. This is the power of the H

continuation and, frequently, acceleration of rural

of high and rising urban unemployment (Taylor & Martin, 2002).

higher wage in a different location may not be enough to encourage migration if it is

not coupled with a low unemployment rate.

probability of obtaining a j

lower in urban than in rural areas, where the conditional wage is low but the

likelihood of employment is high. Conversely, high rural unemployment will make a

given expected urban wage more conducive

Martin, 2002).

The above model is based on a certain number of restrictive assumptions and is

not without criticism. The most restrictive assumption of the model is that the

expected income is the only factor to affect the

any other form of influence that may shape potential migrants' decisions, and also

their potential impacts on rural economies, appears unduly restrictive. As a result, the

Harris-Todaro model is not able to explain why,

satisfied (i.e., urban-

only some individuals migrate whilst others do not.

potential migrants to be risk neutral, such that they are

expected rural income and an uncertain expected urban income of the same

magnitude. The validity of this assumption is questionable, since poor migrants are

likely to be risk averse and require a significantly greater expecte

is the probability of urban employment at time t, wu denotes urban wages

given employment, the term c represents the migration costs, wr

is the discounted rate. If the above relationship of discounted

values holds, then rural-urban migration will be induced. Otherwise, individuals are

assumed to remain in the rural labour market.

T framework implies that rural-urban migration in a context of high

nt can be economically rational if expected urban income exceeds

expected rural income. This is the power of the H-T model: its ability to explain the

continuation and, frequently, acceleration of rural-to-urban migration in the presence

urban unemployment (Taylor & Martin, 2002).


not coupled with a low unemployment rate. A high urban wage coupled with a low

probability of obtaining a job at that wage may result in an expected wage that is



given expected urban wage more conducive to promoting migration (Taylor &



expected income is the only factor to affect the decision to migrate. The omission of



Todaro model is not able to explain why, even when its basic condition is

-rural expected income differentials exceeds migration costs)

only some individuals migrate whilst others do not. Moreover, the model assumes

potential migrants to be risk neutral, such that they are indifferent between a certain



likely to be risk averse and require a significantly greater expecte

16

denotes urban wages

is the wage in the

above relationship of discounted

urban migration will be induced. Otherwise, individuals are

urban migration in a context of high

nt can be economically rational if expected urban income exceeds

T model: its ability to explain the

urban migration in the presence

urban unemployment (Taylor & Martin, 2002). More generally, a


A high urban wage coupled with a low

ob at that wage may result in an expected wage that is



to promoting migration (Taylor &



decision to migrate. The omission of



even when its basic condition is

rural expected income differentials exceeds migration costs),

Moreover, the model assumes

indifferent between a certain



likely to be risk averse and require a significantly greater expected urban income in

order to migrate. It is in this regard that the migration literature has evolved to

integrate the Harris-Todaro expected utility maximisation approach with a further

approach, namely that of human capital (e.g., Sjaastad, 1962;

emphasizes the role of personal characteristics (Etzo, 2008).

Human Capital Theory addresses the heterogeneous nature of the labour market,

relaxing the basic model assumption of homogeneity. The theory seeks to explain

wage differentials as a co

an individual’s marginal productivity. In fact, it is well documented that the earnings

of more educated people are almost always well above the average (Becker, 1964).

Human capital analysis expla

with better education and training. That is, individuals who invest money and time

gain skills that improves their human capital and eventually their productivity.

Sjaastad (1962) develops a microecon

is modelled explicitly as an investment in human capital. In this framework the

migration decision in the interregional migration context is explained on the basis

that each individual decides to move to a particular

total benefits to moving is greater than the present value of the cost of moving (Etzo,

2008). Thus, the decision to migrate is based on a cost

that the individual is rational and maximises

be represented by the following expression:

where i denotes the origin region and

benefits and C the total cost related to the respective region,

T is the lifetime period. If the net present value of migration (NPVM) is positive,

then the migration takes place. In this model, the benefits are represented by the

income earned by migrant in the two alternative regions, which

of personal human capital stocks. It is also important to point out the non monetary

nature of some migration costs, such as the psychic costs of leaving the origin region

(Etzo, 2008).

It is in this regard that the migration literature has evolved to

Todaro expected utility maximisation approach with a further

approach, namely that of human capital (e.g., Sjaastad, 1962; Becker, 19

emphasizes the role of personal characteristics (Etzo, 2008).



wage differentials as a consequence of different human capital stocks that determine



Human capital analysis explains this as the increased productivity of those workers



Sjaastad (1962) develops a microeconomic model where the migration decision



that each individual decides to move to a particular region if the present value of the


2008). Thus, the decision to migrate is based on a cost-benefit analysis and, assumes

that the individual is rational and maximises his/her utility function. The model can

be represented by the following expression:

denotes the origin region and j the destination region, B

the total cost related to the respective region, r is the discount rate and

is the lifetime period. If the net present value of migration (NPVM) is positive,


income earned by migrant in the two alternative regions, which in turn is a function



17

It is in this regard that the migration literature has evolved to

Todaro expected utility maximisation approach with a further

Becker, 1964) which



nsequence of different human capital stocks that determine



ins this as the increased productivity of those workers



omic model where the migration decision



region if the present value of the


benefit analysis and, assumes

his/her utility function. The model can

B denotes the total

is the discount rate and

is the lifetime period. If the net present value of migration (NPVM) is positive,


in turn is a function



This strand of literature predicts that young in

older ones because they can obtain greater returns to investments in migration over a

longer period of time. Moreover, individuals who have not yet invested in

themselves would have an incentive to migrate, and this partl

young migrate more than the old (Becker, 1964). Thus, age is significant in

influencing migration and must be considered in interpreting earnings differentials

over space and among occupations (Sjaastad, 1962).

The importance of location

the migration decision. Lee (1966) in his classic paper “A theory of migration”

conceptualises migration as involving a set of factors at origin and destination, and

the links between them. In Lee’s mo

relative population size are the main determinants of the migration process, which is

the result of “push” and “pull” factors discounted by the distance between the areas.

In fact, in every area there are m

or attract people to it, and there are others which tend to repel them (Lee, 1966).

Thus, the characteristics of the origin act to “push” an individual into migration,

while the attributes of the destina

Moreover, between every two locations there are some intervening obstacles which

impose a cost on migration either directly (e.g. removal cost, cost of seeking a job or

home) or indirectly, such as the amou

destination. It is clear that in Lee’s model, the distance represents an important factor

that can affect the migration process. Indeed, a greater distance results in stonger

barriers which may limit migration

history or culture between origin and destination) (Champion

theoretical framework developed by Lee (1966) can be formalized in the following

basic Gravity model (Lowry, 1966):

where the number of people

on the population size in each region (

between the two regions (

This strand of literature predicts that young individuals will be more mobile than



themselves would have an incentive to migrate, and this partly explains why the



over space and among occupations (Sjaastad, 1962).

The importance of location and distance can also be considered in the context of



the links between them. In Lee’s model, the distance between two locations and their


result of “push” and “pull” factors discounted by the distance between the areas.

In fact, in every area there are many factors which act to hold people within the area



while the attributes of the destination “pull” the migrant to a particular location.



home) or indirectly, such as the amount of information available about the area of



barriers which may limit migration (e.g. national borders, differences in language,

history or culture between origin and destination) (Champion


basic Gravity model (Lowry, 1966):

number of people Mij moving from region i to region j

on the population size in each region (Pi, Pj) and negatively on the physical distance

between the two regions (dij).

18

dividuals will be more mobile than



y explains why the



and distance can also be considered in the context of



distance between two locations and their


result of “push” and “pull” factors discounted by the distance between the areas.

any factors which act to hold people within the area



tion “pull” the migrant to a particular location.



nt of information available about the area of



(e.g. national borders, differences in language,

history or culture between origin and destination) (Champion et al., 1998). The


j depends positively

and negatively on the physical distance

19

From the previous paragraphs, we can see how migration theory has evolved

from the neoclassical model, based on “simple” wage-driven decisions, to Harris-

Todaro and Human Capital models with an increasing degree of complexity. More

recent studies of migration phenomena have led to the emergence of the New

Economics of Labour Migration (NELM), which has added explanatory power to the

neoclassical model by integrating the decision to migrate made by individuals in a

household decision-making process, in a context where the collective decision-

making not only maximizes the expected income but also minimizes risks related to

different market imperfections.

In the NELM model, pioneered by Stark and Bloom (1985), migrants are viewed

as financial intermediaries who provide their families with financial resources and

income insurance, such that migration can be seen as an opportunity to diversify risk

for the family. The argument here is that, in the presence of uncertainty of income,

migration may occur even in the absence of significant wage and unemployment

differentials (Daveri & Faini, 1999), since households may reduce total income risk

by having some of their working members relocated. The fundamental aspect of the

NELM is that migration is not solely the decision of an individual, but rather the

joint decision of members within a household. The idea that migration decisions are

taken by families rather than by a single individual is also emphasized by Mincer

(1977).

Migration may also occur in response to income inequality and relative

deprivation in origin regions, or be the consequence of asymmetric information. In

the first case, individuals are concerned about their relative social status and

migration may improve their social rankings at home. Indeed, NELM suggests that

people and households migrate not only to improve income in absolute terms, but

also to increase income relative to other households. Stark and Taylor (1989) have

argued that the decision to migrate is positively correlated with inequality in the

sending regions, and negatively correlated with inequality in the destination regions.

Under asymmetric information, low productivity workers may decide to migrate if

employers in the receiving areas are uninformed about individual workers’

productivity. In this case, employers cannot distinguish high-ability workers from

low-ability workers and the outcome is a pooling equilibrium where each worker is

20

paid according to the mean productivity rather than his unknown marginal

productivity (Daveri & Faini, 1999).

A further development of household models of migration is the dynamic

approach of networks models. Migration in these models is dynamic in the sense that

migration costs may be reduced by the increased information from previous migrants

(Filiztekin & Gokhan, 2008). The basic idea of these models is that migrants create

networks in the destination regions, which reduce both migration costs and risks for

new migrants, thereby influencing the expected income gains and uncertainty

associated with migration, and inducing future migration from the same origin

regions (Etzo, 2008).

In the context of these theoretical contributions, we can generally distinguish

between two approaches to the study of migration: a microeconomic and a

macroeconomic approach. The former focuses on the migration unit (e.g. single

individual or family) and related decision-making process. In this case, we need to

take into account those individual characteristics that might affect the decision to

migrate, such as age, gender and educational attainments. Macro approaches, instead,

focus on migration flows with respect to the spatial context and related aggregate

variables. Thus, we need to consider those factors associated with area of origin and

area of destination that affect the degree of attractiveness of a location. Since

aggregate migration flows represent the outcome of the underlying individual or

household decision-making process, the two approaches can be considered as

complementary (Etzo, 2008).

21

Chapter 4 – Empirical literature

The majority of empirical studies are based on aggregate-level analysis where

the decisions of migrants are summarized into migration flows across geographical

areas. Theoretical literature and empirical evidence suggest that interregional

migration is primarily driven by a disequilibrium mechanism in which migration is

mainly a response to spatial differences in economic factors such as wages and

employment opportunities.

Biagi et al. (2011) investigate the differences between long distance and short

distance migration within Italy. In the case of short distance migration, the authors

find that individuals give more weight to quality of life and amenities differences. By

contrast, in the case of long distance flows, economic/labour market variables

appears to be the key factors. Indeed, the results of their study show that people tend

to migrate to provinces with higher GDP per capita and lower unemployment rates.

The presence of the 20-39 age group is also important in attracting long distance

migrants. This is consistent with the finding that in many countries this is the age

group which is most mobile in response to wage signals and can obtain greater

returns to investments in migration, as suggested by Human Capital Theory.

Piras (2010) finds that relative per capita GDP and relative unemployment rates

were the most important drivers for internal migration in Italy between 1970 and

2002. However, other studies of Italian migration do not report a significant

influence of unemployment on mobility (Daveri & Faini, 1999; Fachin, 2007).

Daveri and Faini (1999) use aggregate data from the regions of Southern Italy to

study migration decisions taken by risk-averse households. They compare the out-

migration flows as shares of population from eight districts in Southern Italy into two

broad categories of destinations: abroad (outside Italy) and domestic (Northern Italy),

and find evidence to support their hypothesis that risk is a significant determinant of

the decision to migrate. They also report real wages to exert a negative effect on

internal migration while the unemployment rate is found to have no effect at all.

Similar results are reported by Fachin (2007) who finds that income growth in the

22

origin region is a significant driving force of migration, while unemployment rates

have only weak effects.

Basile and Causi (2005) analyse the determinants of net migration rates in the

Italian provinces in the period when the internal migration flows were in the

declining phase (1991-1995) and the period when the internal migration flows

increased (1996-2000). For the first period, their analyses reveal that net migration

was only weakly influenced by macroeconomic variables such as unemployment and

income. In the second period, however, migration behaviour appears more consistent

with traditional theories in which economic variables play a crucial role in explaining

internal migration. Indeed, the authors find a statistically significant negative effect

on migration for the unemployment rate and a positive and significant effect for

income.

Napolitano and Bonasia (2010) analyse internal migration flows in Italy in the

periods 1985-1995 and 1995-2006. Despite a substantial increase in regional

differentials in terms of unemployment rates and real per capita income in both

decades, migration flows in the two periods were characterized by different trends.

The authors investigate this divergent trend and consider the role of wage (proxied

by GDP per capita) and unemployment differentials, alongside house prices, and

externalities such as population density, carbon dioxide emissions and juvenile

delinquency. They find that house price differentials affected migration flows in the

first decade (characterized by falling migration) but there was no role for wages.

Conversely, in the second decade, wage differentials are found to be important

whereas unemployment and housing price differentials are not. These results

illustrate the complexity of the internal migration process in Italy and the limitations

of the H-T framework which does not permit dynamic considerations as captured in

the NELM.

Using panel data on gross migration flows between Italian regions, Etzo (2010)

analyses the role of macroeconomic determinants during the period 1996-2002. The

author distinguishes between origin and destination regions, investigating the impact

of the same macroeconomic variables in the two groups of regions. The empirical

results reveal per capita GDP and the unemployment rate to be the main economic

23

determinants. However, migrants do not respond to the unemployment rate in

destination regions. The outcome of the analysis shows that the geographical

reallocation of migrants is the result of the interaction of distance together with

“push” and “pull” forces.

Another Mediterranean country with a long record of migration is Spain. The

equilibrating role of internal migration in Spain has been investigated in various

studies. Bentolila and Dolado (1991) focus on interregional migration in the 1980s.

They analyse aggregate migration flows and find that both an increase in a region's

relative wage and a fall in its relative unemployment rate cause very little increase in

net migration to that region. Antolin & Bover (1997) and Devillanova & Garcia-

Fontes (1998) report similarly.

Regarding interregional migration, Germany is also of special interest since

there is evidence of structural differences between West and East Germany, which

results in a German “empirical puzzle” similar to the Italian case. During the 1990s,

East-West migratory movements did not fully react to regional labour market signals

as expected and, similarly to the Italian South-North migration between mid-1970s

and mid-1990s, the level of German East-West migration flows was substantially

low, despite the existence of large regional labour market disparities.6 Alecke et al.

(2009) analyse the relationship between regional disparities in labour market

variables and interregional migration flows in Germany following re-unification and

identify a clear role for regional differences in the real wage and unemployment rate

as key drivers of internal migration. Similar findings are reported by Parikh & Van

Leuvensteijn (2003) and Mitze & Reinkowski (2010).

6 Huge income transfers (politically driven) and the possibility of high East-West commuting are the likely explanations for this German ”empirical puzzle” (Alecke et al., 2009).

Chapter 5

5.1 – Empirical framework

For the analysis of

introduced in chapter 3. Recall, its basic formulation is:

which can be linearized by expressing all the variables in logarithmic form:

where the dependent variable is the gross migration from region

Pj are respectively the origin and destination region’s population,

between the main city of origin region and the main city of destination region. The

expected signs are positive for

of both the origin and destination regions is expected to affect migration positively

while distance should discourage migration. The distance is commonly used as a

proxy for all costs that, directly or indirectly, might affect migration decisions, such

as transportation costs, information costs, and psychological costs.

The basic Gravity model has been criticized for not addressing the causes of

migration and for not taking

(Fan, 2005). It is possible to reduce this weakness by adding variables that represent

the socioeconomic conditions of the origin and the destination (Lowry, 1966).

Accordingly, we extend the Gravity mo

regional labour markets and income levels.

Regional GDP per capita is used as a proxy for income or regional development

to account for the overall level of prosperity of each region. Regional unemployment

rates account for labour market conditions and the probability of finding job.

According to economic theory, per capita GDP is expected to be inversely correlated

with migration flows in the sending region. Conversely, an increase in per capita

GDP should “pull” migrants in the destination region. The unemployment rate is

Chapter 5 – Data and methodology

Empirical framework

For the analysis of internal migration in Italy, we adopt the Gravity model

introduced in chapter 3. Recall, its basic formulation is:


the dependent variable is the gross migration from region i

are respectively the origin and destination region’s population,


cted signs are positive for a1 and a2 and negative for a3. That is, population size



or all costs that, directly or indirectly, might affect migration decisions, such



migration and for not taking into account the decision-making process of migrants



Accordingly, we extend the Gravity model framework to account for differences in

regional labour markets and income levels.



account for labour market conditions and the probability of finding job.



migrants in the destination region. The unemployment rate is

24

Data and methodology

internal migration in Italy, we adopt the Gravity model


i to region j, Pi and

are respectively the origin and destination region’s population, dij is the distance


. That is, population size



or all costs that, directly or indirectly, might affect migration decisions, such



making process of migrants



del framework to account for differences in



account for labour market conditions and the probability of finding job.



migrants in the destination region. The unemployment rate is

25

expected to have a positive effect in the sending region and a negative effect in the

destination region.

In the simple gravity model, distance is used to proxy travel costs associated

with migration between regions. In this context, Cartesian co-ordinates are often used

to determine the spatial distance between locations. However, alternative measures

of travel cost that capture travel time and mode of transport may also be considered.

For example, travel time can capture the quality of the transport network and the

differential effects of road, rail and maritime transportation, the latter of which is

particularly relevant for the Italian regions of Sardegna and Sicilia which represent

the largest and most important islands in the Mediterranean Sea.

Finally, as discussed earlier, migrants may also consider amenity-related

characteristics that make a region more or less attractive relative to other regions.

Accordinlgy, we adopt the approach of Etzo (2010) and utilise a composite index

that controls for regional differences in infrastructure endowment.

Thus, the empirical specification for our extended gravity model takes the form:

migr = a0 + a1 popo + a2 popd + a3 dist + a4 gdpo + a5 gdpd + a6 unempo + a7

unempd + a8 infrao + a9 infrad + εit

A description of these variables is reported in Table 5.1. Descriptive statistics

are reported in Table 5.2. In particular, Table 5.2 reports the mean values for GDP

per capita (20,639 Euros), unemployment rate (8.8%) and infrastructures index

(92.6), as well as distance and travel time.

However, these figures are very different with regards to macroareas. Indeed,

Table A1 of the Appendix reports the minimum, maximum and mean values of the

variables used for the empirical analysis, distinguished between Centre-North (C-N)

and South (S). This allows us to make a comparison between the two macroareas.

The mean values show the existing substantial gap in GDP per capita (approx.

24,000 Euros in C-N and 15,000 in S), unemployment rate (approx. 5% in C-N and

15% in S), level of infrastructures (approx. 106 in C-N and 72 in S).

26

Variables Definitions

migr Interregional migration flows from region i to region j

popo Population in sending region

popd Population in destination region

gdpo Per capita GDP in origin region

gdpd Per capita GDP in destination region

unempo Unemployment rate in origin region

unempd Unemployment rate in destination region

infrao Infrastructure index for sending region

infrad Infrastructure index for receiving region

trtm Railroad travel time in hours between capital in region i and capital in region j

rdtm Road travel time in hours between capital in region i and capital in region j

dist Road distance in kilometers between capital in region i and capital in region j

εit Stochastic error term

Table 5.1 Variables descriptions

Variable Mean Std. Dev. Min Max

Interr Migrarion Flows 864 1319.8 1 9225

Population 2,853,366 2,256,302 119,410 9,071,124

GDP per capita 20,639 5,063.1 13,438 28,067

Unemployment Rate 8.8 5.6 2.5 22

Infrastructures Index 92.6 33.5 43.3 183.8

Road distance in Km 622.6 349.2 115 1659

Road Travel time in hours 7.15 4.17 1.35 17.25

Train Travel time in hours 7.16 4.59 0.37 24.21

Table 5.2 Summary statistics

5.2 – Data

The data come from the Italian National Institute of Statistics (ISTAT). Data on

gross migration flows are derived from the “Migratory movements of resident

population” published by the Italian National Institute of Statistics (ISTAT, 2006) for

the years 2001 and 2002. The vector representing the dependent variable is obtained

using the matrix of interregional movements. Since we have 20 Italian regions, the

27

matrix has a dimension of 20x20 with null values in the diagonal.7 Thus, we have

380 observations on gross migration flows for each year (2001 and 2002). Migration

flows indicate the number of people that, during each year, have changed their

official residency from one region to another (ISTAT, 2006). Gross migration allows

us to identify those “push” and “pull” factors that affect migration in origin and

destination regions.

Data on regional population, real per capita GDP and the unemployment rate

also come from ISTAT. In particular, regional population come from “Annuario

statistico italiano” (Istat, 2011), regional GDP from “Conti economici regionali”

(Istat, 2010) and regional unemployment rate from “Rilevazione sulle forze di

lavoro” (Istat, 2011). Population size is expressed as the annual average number of

people living (domicile) in each region. The unemployment rate is the ratio between

the unemployed males and females (aged 15 years and more) and the total labour

force (Istat, 2011).

The infrastructure index is provided by Istituto G. Tagliacarne (2001) and is

reported in Table A2 of the Appendix. The index includes all the main infrastructures

and considers both quantitative and qualitative aspects.8 Following Etzo (2010), this

index is computed as the ratio between the endowment measure and the demand

measure (expressed by the population and the region’s geographical extension).

Finally, road travel time and distance between Italian regions were obtained

using the Michelin Route Planner based on the Global Positioning System (GPS).

The capital of each region is chosen as the representative location, since in Italy

regional capitals are also those cities with the largest population within the regional

borders. Railroad travel times have been collected from Trenitalia who are the

primary train operator in Italy and owned by the Italian Government. Maritime

measures between Sardegna and Sicilia consider both overall distance and travel time

across the sea.

7 The Italian regions can be grouped into three macroareas (North, Centre and South). Piemonte, Val D’Aosta, Lombardia, Liguria, Trentino-Alto Adige, Veneto, Friuli Venezia Giulia and Emilia Romagna (North); Toscana, Umbria, Marche and Lazio (Centre); Abruzzo, Molise, Campania, Puglia, Basilicata, Calabria, Sicilia and Sardegna (South plus Islands). 8 Road network, railroad, seaports, airports, power plants, communication networks, banks, amenities, educational and cultural centres, health centres.

28

29

5.3 – Descriptive Analysis

Table 5.3 reports the population of each region, as well as regional GDP per

capita, unemployment rates and volumes of in-, out- and net-migration for 2001-

2002. Among the regions, Emilia-Romagna and Lombardia are the strongest gainers

in migration with high positive net migration. Lombardia accounts for about 16% of

Italy’s population and 17% of interregional in-migration in 2001. With regards to

Emilia-Romagna, these figures are 7% and 12% respectively.

Regions displaying negative net migration are all located in South. Campania

which accounts for approximately 10% of the Italian population and 14% of

interregional out-migration in 2001 is by far the leading donor region. Sicily and

Puglia are the next two largest donor regions, as further evidenced by their large

volumes of out-migration. A clearer pattern of interregional migration is illustrated in

Figure 5.1, which maps the data across the twenty Italian regions. It is clear that the

geographical patterns of interregional migration follow the direction from South to

Centre-North.

Figure 5.1 Gainer-loser regions and patterns of the largest out-migration flows (2001-2002). Regional GDP per capita (year 2001). (Source: own calculation).

30

Table 5.4 reports the 21 largest out-migration flows with the corresponding

region of origin and destination. The data reinforce what is evident in Figure 5.1,

namely, that interregional migration flows originate substantially from Southern

regions (Sicilia, Campania, Puglia, Calabria) and the main regions of destination are

located in the Centre-North (Lombardia, Emilia-Romagna, Lazio).

Region of origin Region of destination Out-migration

Sicilia (South) Lombardia (North) 9225 Sicilia (South) Lombardia (North) 9037

Campania (South) Emilia-Romagna (North) 8953 Campania (South) Emilia-Romagna (North) 8882 Campania (South) Lombardia (North) 8591 Campania (South) Lombardia (North) 8204 Campania (South) Lazio (Centre) 6956 Campania (South) Lazio (Centre) 6718

Puglia (South) Lombardia (North) 6013 Puglia (South) Lombardia (North) 5693 Sicilia (South) Emilia-Romagna (North) 5601

Lombardia (North) Piemonte (North) 5595 Piemonte (North) Lombardia (North) 5538

Lombardia (North) Emilia-Romagna (North) 5341 Lombardia (North) Piemonte (North) 5322

Calabria (South) Lombardia (North) 5308 Puglia (South) Emilia-Romagna (North) 5171

Calabria (South) Lombardia (North) 5117 Puglia (South) Emilia-Romagna (North) 5117

Lombardia (North) Emilia-Romagna (North) 5107 Sicilia (South) Emilia-Romagna (North) 4941

Table 5.4 The first 21 largest out-migration flows in 2001-2002. (Source: own calculations)

The disparity in economic development among the regions appears to be

correlated with the geographical patterns of interregional migration. Sending regions

are relatively poor, and most of the destination regions are economically more

developed with markedly lower unemployment rates. Using GDP per capita as an

indicator of regional economic development, Table 5.3 reveals Lombardia to be the

most developed region (27,929 Euros in 2001 and 28,067 Euros in 2002), closely

followed by other Northern regions. Southern regions hold the lowest levels of GDP

per capita, within the range 13,000-19,000 Euros (Fig.5.1).

Figure 5.2 reveals that a similar story can be told regarding unemployment.

Southern regions show high levels of unemployment rates. Among them, Sicily

31

holds the highest rate (22.0% in 2001 and 20.6% in 2002). The opposite is true for

the Northern regions, with Emilia-Romagna leading the ranking (3.2% in 2001 and

2.5% in 2002), closely followed by Trentino-Alto Adige and Lombardia.

Figure 5.2 Regional unemployment rate, year 2001. (Source: own calculation).

5.4 – Methodology

We utilise an econometric approach that takes into account that migration flows

are best considered as a count process that captures the number of times an event

occurs. Employing Ordinary Least Squares (OLS) in this context is unlikely to be

appropriate since the dependent variable can only take nonnegative integer values (0,

1, 2, ..., n). The truncated nature of count data implies that count data cannot have a

normal distribution such that OLS estimation is likely to be inconsistent.

Consequently, models that employ a probability distribution that can account for the

non-normal nature of count data are required. Two such models are the Poisson

Rergression Model and the Negative Binomial Regression Model.

The most popular specification of count data is the Poisson regression model, in

which the dependent variable is assumed to take integer values with probability

where Yi is the count variable,

particular count value,

count and can be related to a set of regressors:

A characteristic of the Poisson distribu

variance (equidispersion assumption):

This assumption is restrictive and is often violated in those applications where the

variance is found to be greater than the mean, with the consequence that standard

errors of the parameter estimates will be biased downwards (Wooldridge, 2009). This

problem, known as overdispersion, motivates alternatives to the Poisson Regression

Model which generalize the Poisson process in a variety of ways.

Descriptive analysis of our depende

variance to be greater than the mean.

generalized version of the Poisson regression model, namely the Negative Binomial

Regression Model. This version of the Poisson Model per

adjust the variance independently of the mean. More precisely, count data are now

assumed to be generated by a Poisson process

where

in which exp(εi) is drawn from a gamma distribution,

variance αβ2. 9 Variance = 1741951; Mean = 863.9



is the count variable, yi is a strictly nonnegative number and represents a

particular count value, λi is the sole parameter representing the expected value of the

count and can be related to a set of regressors:

A characteristic of the Poisson distribution is that its mean is equal to its

variance (equidispersion assumption):



he parameter estimates will be biased downwards (Wooldridge, 2009). This


Model which generalize the Poisson process in a variety of ways.

Descriptive analysis of our dependent variable (migration flows) reveals the

variance to be greater than the mean.9 Accordingly, we proceed to estimate a


Regression Model. This version of the Poisson Model permits a second parameter to


assumed to be generated by a Poisson process

is drawn from a gamma distribution, Gamma(α,β)

Variance = 1741951; Mean = 863.9

32



is a strictly nonnegative number and represents a

is the sole parameter representing the expected value of the

tion is that its mean is equal to its



he parameter estimates will be biased downwards (Wooldridge, 2009). This


nt variable (migration flows) reveals the

Accordingly, we proceed to estimate a


mits a second parameter to


Gamma(α,β), with mean αβ and

33

In the Negative Binomial model, the expected value of the count variable

remains λi = xi β and its variance becomes (λi + λi2α). That is, the variance is inflated

in order to address the overdispersion. The parameter α is called the overdispersion

parameter and the larger α, the greater the overdispersion. Therefore, if the

overdispersion parameter is significantly greater than zero, the data are over

dispersed and are better estimated using a Negative Binomial model than a Poisson

model. Notably, if the overdispersion parameter equals zero, the Negative Binomial

model reduces to the Poisson model. It is in this sense that we can consider the

Negative Binomial Model as a generalization of the Poisson Model.

34

Chapter 6 – Empirical results and discussion

6.1 – Negative Binomial Regression

The empirical analysis is based on the Negative Binomial regression. The

Negative Binomial regression models the logarithm of the expected count variable as

a function of the regressors. All regressors are in logarithms, thus allowing for a

direct interpretation of the results in terms of elasticities. Using the descriptive

information given in the previous chapter, we begin the analysis of the econometric

results presented in Table 6.1 by considering first the basic Gravity model (Model

A).

All three coefficients in Model A are statistically significant and have the

expected signs, as suggested by migration theory. The positive sign for both origin

and destination population indicates that an increase in population size acts both as a

“push” and a “pull” factor, resulting in more people to leave but also more migrants

from other regions. This also explains that the more populated regions experience the

highest migration flows. The origin population appears to be slightly stronger in

magnitude than the destination population, thus determining a negative net effect.

Distance between regions is identified as a deterrent to interregional migration

implying that factors such as travel costs and uncertainty regarding the destination

region impact negatively on internal migration flows between regions.

The basic Gravity model considers only the impacts of population and distance,

and as such fails to consider the effects of regional economic disparities.

Accordingly, Model B, considers also the impact of GDP per capita in origin and

destination regions. GDP per capita appears statistically significant for both sending

and receiving regions and shows, respectively, a negative and a positive sign. This

result is consistent with economic theory and suggests that migrants move mostly

from less to more economically developed regions.

Model C utilises an alternative macroeconomic indicator, namely the

unemployment rate, to capture observable economic disparities between regions. The

coefficients for unemployment rate are statistically significant and appear to be

35

correctly signed. An increase in the unemployment rate in the origin region causes an

increase in migration flows, whereas an increase in the unemployment rate in the

destination region causes a decrease in migration flows, ceteris paribus.

Table 6.1 Negative Binomial estimation

Variable Model A Model B Model C Model D

Origin Population 0.971*** 0.956*** 0.929*** 0.965***

Destination Population 0.932*** 0.933*** 0.952*** 0.907***

Distance in km - 0.322*** - 0.351*** - 0.365*** - 0.362***

Origin GDP per capita - 0.625*** 0.579*

Dest GDP per capita 0.527*** 0.297

Origin Unemployment rate 0.307*** 0.493***

Dest Unemployment rate - 0.186*** - 0.052

Origin Infrastructure index - 0.212*

Dest Infrastructure index 0.159

Constant - 19.484*** - 18.153*** - 19.170*** - 28.135***

Log of the dispersion parameter alpha - 0.749*** - 0.870*** - 0.887*** - 0.901***

Obs 760 760 760 760

Pseudo R2 0.1032 0.1117 0.1129 0.1140

Log likelihood - 5217.743 - 5168.013 - 5161.030 - 5154.694

Note: Dependent variable = interregional migration flows. All independent variables are in natural log. Likelihood ratio (LR) test: Reject H0 of equidispersion (p = 0.000) in all four models. Legend: *significant at the 0.05 level; **significant at the 0.01 level; ***significant at 0.001

Finally, Model D extends the Gravity framework to consider both

macroeconomics indicators and the role of amenities as proxied by an infrastracture

index for each region. The coefficients for population and distance remain

36

statistically significant and with the expected signs. However, the inclusion of both

the unemployment rate and GDP per capita leads to results which are rather difficult

to interpret in the light of migration and economic theory, but which are most likely

the consequence of multicollinearity between the macroeconomic variables as

revealed in Table A3 of the Appendix. The results reveal unemployment in the origin

region to exert a strong positive impact on migration flows. A 1% increase in the

unemployment rate of the origin (sending) region results in an increase of migration

flows of approximately 0.5%, ceteris paribus. On the other hand, unemployment rate

and GDP per capita do not exert any pulling effect in the destination region, as their

estimated coefficients are not statistically significant. That is, migrants decide to

leave if an increase in unemployment rate in their region occurs, regardless the

economic and labour market conditions in the destination region. This can be

explained by the fact that the strongest migration flows are from the poorer Southern

areas to the wealthier Centre-North, in a context of high differentials in terms of

unemployment rate and GDP per capita between the two macroareas. This would

explain why migrants do not react to unemployment rate and per capita GDP

variations in the destination regions.

Notably, origin GDP per capita has a positive sign in this latter specification

suggesting the higher is the income in the origin region, the larger are observed

migration flows. A 1% increase in GDP per capita in the sending region determines

an increase of migration flows by approximately 0.6%, ceteris paribus. Again, this

might be explained by the presence of high differentials between South and Centre-

North. Moreover, migration flows from South to Centre-North involve mostly young

unemployed migrants (Fig.A1 of the Appendix). Therefore, an increase in GDP per

capita would encourage people, especially young and financially supported by their

family, to take the risk of migration. It might also be argued that more disposal

income could help to finance the costs of moving, particularly in the presence of

widening gap between Southern regions and the rest of Italy (Faini et al., 1997; Etzo,

2010).10

10 A widening gap between the economic development of South and Centre-North has also been signalled in the 2005 report on the Southern Italian economy, published by the Association for the Development of Industry in the Mezzogiorno (Svimez, 2005).

37

In comparison, the coefficients for the infrastructure index in origin and

destination regions have the expected signs. However, only infrastructure in the

origin region is statistically significant at conventional levels. This implies that those

origin regions with higher level of infrastructures experience lower migration flows.

Increasing the infrastructures index in the origin (sending) region by 1% reduces

migration flows by approximately 0.2%, ceteris paribus.

In terms of model diagnostics, the Negative Binomial regression does not have a

measure of goodness of fit analogous to the R2 measure of Ordinary Least Squares

(OLS) estimation. However, the Pseudo R2 statistic can be used as one such measure.

Adding the macroeconomic variables and infrastructure index for sending and

receiving regions to the standard gravity model increases the Pseudo R2 from 0.1032

to 0.1140, indicating some improvement in the model fit. The overall value of the

Pseudo R2 appears low but this is not uncommon for models using Maximum

Likelihood Estimation. Finally, we perform a formal test of the null hypothesis of

equidispersion (α=0) against the alternative of overdispersion utilising a likelihood

ratio (LR) test. The outcome for each model reveals the presence of significant

overdispersion. Thus, our preference for the Negative Binomial Regression Model

over the Poisson Regression Model is supported.

As discussed in section 5.1, travel time may be a more appropriate measure of

travel cost than a simple measure based on Cartesian distance. Accordingly, we test

the robustness of our results further and re-estimate each of the four models using

travel time as a proxy for distance between regions. The results are qualitatively and

quantitatively similar to those presented and discussed above. Accordingly, we do

not discuss them further here though the resutls are reported in Table A4 of the

Appendix.

6.2 – Some remarks and discussion

Our analysis confirms some of the findings from previous studies (Basile &

Causi, 2005; Etzo, 2010; Napolitano & Bonasia, 2010). First, the results show that

unemployment rate plays a crucial role in determining interregional migration flows

in 2001-2002, thereby confirming the end of the “empirical puzzle” which

characterized internal migration up until 1994. Second, the gravity model approach is

38

relevant and effective for describing and explaining internal migration in Italy,

although the interpretation of different model specifications is not always

straightforward and suggests an avenue for further research. Finally, the analysis

represents a snapshot of the Italian economic situation in 2001-2002 at regional level.

It is evident that Italy is still characterized by relevant differences between South and

Centre-North, not only in terms of GDP per capita and unemployment rate, but also

in the level of infrastructure endowment. This huge disparity between the two

macroareas explains why most of the spatial patterns of internal migration in Italy

follows the direction from South to Centre-North.

In the presence of such dramatic economic disparities, it has often been

remarked that one way to reduce that gap and reverse the negative trend in the

economy of Southern Italy is to promote local development (SVIMEZ, 2002, 2004,

2007, 2011). This can be achieved by implementing a selective fiscal policy, funding

research centres, promoting technological innovation, giving incentives to private

companies that invest in the most disadvantaged areas, and providing those necessary

infrastructures that Southern regions are disperately in need. Obviously, all this

requires conspicuous financial resources and the success of a radical change in the

Southern economic system will depend primarily by the capacity of making a good

use of the endowments that already exist. To this purpose, it is also essential to

attract and keep the best human resources, reducing the phenomenon of “brain drain”

which might have detrimental effects not only for local labour market performances

but also for prospective local growth. However, financial and human resources are

not enough without a long-term vision and a strong political willing towards an

internal cohesion policy. Despite the most relevant recommendations, in the past ten

years there has been a substantial reduction in funds assigned to public investments

and a lack of strategic development policies. It has been recorded a general economic

impoverishment of Southern regions with an increasing number of unemployed

people. To some extent, this pertains also the rest of Italy, particularly following the

severe global recession that occurred in 2008-2009 and, then, the European financial

crisis.

39

6.3 – Limitations and extensions

The analysis highlights the role of regional disparity in determining the patterns

of interregional migration flows in Italy, and we have found important relationships

between migration flows and macroeconomic variables. Regional GDP per capita,

the unemployment rate and level of infrastructure in sending regions are shown to be

key determinants of migration flows in Italy, alongside the classical gravity

consideration of population and distance. Nonetheless, the empirical analysis is not

without limitation.

A first limitation is represented by the aggregate nature of the gravity model.

Indeed, while the gravity model is very successful in explaining the choice outcome

of a large number of individuals (e.g. migration flows), it is less likely to be able to

explain the decision choices of individuals or households at the microeconomic level

since it cannot sufficiently capture the heterogeneity of these groups or their

decision-making processes.

A second limitation concerns data availability. We utilise pooled cross-section

data for the years 2001 and 2002. Unfortunately, there are no such data for more

recent years. Moreover, the empirical analysis focuses only on a limited set of

macroeconomic indicators, in addition to those most commonly utilised within the

gravity model framework. Thus, our analysis may suffer from a lack of information

both in terms of explanatory variables for consideration and, in particular, any

dynamic effects of regional disparities on interregional migration behaviour.

Several steps can be made to improve model specification and the empirical

analyses. As mentioned, increased data coverage across a longer time frame would

permit one to estimate a dynamic version of the extended gravity model and test the

presence of migration networks effects, since present migration flows can be affected

by past migration flows. This could be achieved by including a lagged dependent

variable representing the number of migrants from the origin to the destination

region in the previous period.

Moreover, the empirical framework could be extended to consider additional

explanatory variables which relate to the stock of human and social capital, as well as

the quality of life available across different regions. Specifically, one could consider

40

the individual characteristics of migrants such as age, educational attainment, marital

status and employment status to analyse potential “brain drain” effects on

interregional migration flows.

Finally, it would be interesting to analyse our model in the context of migration

flows between the two core macroareas (i.e. South to Centre-North migration) and

also by excluding migration flows within the two macroareas and between close-by

regions. This may help to emphasize the role played by the main macroeconomic

variables in determining migration streams.

41

Chapter 7 – Conclusion

In this dissertation we have investigated the main determinants of interregional

migration flows in Italy in 2001-2002. The geographical patterns of internal

migration in Italy suggest that gravity variables (e.g. population and distance) are

important as well as macroeconomic variables, which enable us to take into account

regional economic disparities. Following previous studies in migration (Basile &

Causi, 2005; Fan, 2005; Etzo, 2010; Napolitano & Bonasia, 2010; Biagi et al., 2011),

different specifications of the gravity model have been estimated. The analysis uses

gross migration flows between each pair of Italian regions, which allows us to

identify the effects of our explanatory variables both in sending and receiving

regions. However, the analysis suffers from a limitation in the data (available only

for the years 2001 and 2002) and ignores the fact that migration can be strongly

affected by past migration flows. Moreover, other important variables are excluded,

particularly those related to human capital (e.g. age, education).

The gravity variables have the expected signs and are statistically significant in

all models’ specifications, whereas the coefficients for the macroeconomic variables

show unclear results. In fact, both their signs and significance are not consistent in

the different estimations. The origin unemployment rate appears to be the only

variable highly significant and consistent, acting as a strong push factor with an high

effect on migration flows. Considering that the strongest migration flows are

between Southern and Northern regions, the important role played by unemployment

rate can be connected to the dramatic gap in unemployment rate differentials

between South and Centre-North.

The empirical analysis demonstrates that models based on gravity principles are

appropriate for describing internal migration in Italy. However, it shows also that the

explanation of migration phenomenon is not straightforward for the Italian case.

What emerges clearly is the huge gap in terms of unemployment rate and GDP per

capita between the two macroareas of the country, causing an increase in

interregional migration flows.

42

In order to address the persistent regional economic disparities between South

and Centre-North Italy, it has been stressed the importance of promoting regional

development by implementing a selective fiscal policy, funding research centres,

promoting technological innovation, and providing those necessary infrastructures

that Southern regions are disperately in need. Despite the advice of many political

and academic observers to act promtly and effectively, South Italy has long been

waiting for the right policies to be put in place. Yet, the above mentioned measures

and investments seem far from being realized, especially in a context aggravated by

the harsh financial and economic crisis that all Europe has been experiencing for the

last few years.

43

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APPENDIX

Centre-North South

Variables Mean Min Max Mean Min Max

Interr Migration flows 866 1 5,595 368 7 1,187

Population 3,044,776 119,410 9,071,124 2,566,250 320,757 5,713,244

GDP per capita 24,400 20,321 28,067 14,997 13,438 18,353

Unemployment rate 4.9 2.5 10.4 14.6 8.6 22

Infrastructures Index 106.4 46.2 183.8 71.9 43.3 96.6

Road distances in Km 370 115 750 516 128 1027

Road travel time in hours 4.04 1.35 7.56 7.27 2.04 16.27

Train travel time in hours 3.73 0.37 9 8.74 2.05 18.03

Table A1. Summary statistics distinguished between C-N and S

Regions Index (Italy=100)

Piemonte 89.2 Valle d’Aosta 46.2

Lombardia 120.3 Trentino Alto Adige 62.7

Veneto 115.9 Friuli Venezia Giulia 118.6

Liguria 183.8 Emilia Romagna 107.2

Toscana 117.1 Umbria 81.8 Marche 92.5 Lazio 142.0

Abruzzo 78.5 Molise 54.3

Campania 96.6 Puglia 81.6

Basilicata 43.3 Calabria 78.0 Sicilia 86.2

Sardegna 57.0

Italia 100

Table A2. Ranking list of regions according to the infrastructure index 1997-2000.

49

Figure A1. Interregional migration rates in Italy by gender and age, 2002 (Source: Istat, in Etzo, 2008)

50

Table A4. Negative Binomial estimation with travel time

Variable Model A Model B Model C Model D

Origin Population 0.938*** 0.918*** 0.884*** 0.929***

Destination Population 0.893*** 0.887*** 0.900*** 0.862***

Travel time in hours - 0.301*** - 0.342*** - 0.370*** - 0.378***

Origin GDP per capita - 0.715*** 0.723**

Dest GDP per capita 0.429*** 0.394

Origin Unemployment rate 0.361*** 0.598***

Dest Unemployment rate - 0.128** 0.041

Origin Infrastructure index - 0.264**

Dest Infrastructure index 0.117

Constant - 19.927*** - 16.691*** - 19.628*** - 30.920***

Log of the dispersion parameter alpha - 0.742*** - 0.860*** - 0.883*** - 0.902***

Obs 760 760 760 760

Pseudo R2 0.1026 0.1110 0.1127 0.1141

Log likelihood - 5220.749 - 5171.978 - 5162.172 - 5154.081

Note: Dependent variable = interregional migration flows. All independent variables are in natural log. Likelihood ratio (LR) test: Reject H0 of equidispersion (p = 0.000) in all four models. Legend: *significant at the 0.05 level; **significant at the 0.01 level; ***significant at 0.001

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