Inequality in Latin America: equity, perceptions and opportunities

Inequality in Latin America: equity, perceptions and opportunities

Carlos Eduardo Vélez ERF 20th Annual Conference

Cairo, March 23 / 2014

2 Inequality in LAC

• 1. Income inequality and key determinants • 2. Perceptions of equity and fairness

• 3. Inequality of opportunity

LatAm highest Gini Coefficients (1995-2005)

Peru Venezuela

0%

20%

40%

60%

80%

100%

20 30 40 50 60

Acc

umul

ated

frac

tion

of w

orld

pop

ulat

ion

Gini Coefficient % (income inequality)

Colombia

Note: Includes 108 countries. Data obtained on 1993-1999

Mexico

Uruguay

Brazil

China

India

Ireland

Korea

Source: World Bank Indicators, 2002

LatAm highest Gini Coefficients (1995-2005) / Gasparini et al (2011)

LatAm Income inequality: trend change (2002-2010): Cornia (2012) averange LAC Gini “widespread decline in income inequality over 2002-2010”

5

Figure 3 Average regional Gini index of the distribution of household income per capita

49.848.9

51.0

52.6

53.7

52.2

50.9 50.5

46

48

50

52

54

56

early�1980s

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2009

2010

Source: IDLA dataset and SWIID3 for the period early 1980s.

In terms of yearly changes, Figure 3 shows that the regional Gini yearly increment was greater during the 1980s (0.31 Gini points) than during the 1990s (0.22); that the drops over 2002-08 (0.47), 2009 (0.41) and 2010 (0.70 points, for eight countries with available data) were more sizeable than the yearly increases of the prior two decades; and that, if the pace of decline recorded during the 2000s is maintained, it will take another three years to return to the average pre-Washington consensus level of inequality of the early 1980s.

A key feature of the trend towards rising inequality during the 1980s and 1990s was the decline of the labour share in total income and a parallel rise in the capital share. For instance, between 1980 and the late 1980s, the labour share declined by 5-6 percentage points in Argentina, Chile and Venezuela and by ten in Mexico (Sainz and Calcagno 1992). Alvaredo (2010: Table 6.7) confirms that the income share of the top one percent of taxpayers in Argentina (whose labour income accounted for less than 50 percent of the total) rose from 7 to 15 percent between 1973 and 2002, while Sanhueza and Mayer (2011) show that in Chile it rose from 7 to 14 percent between 1980 and 1990. Five structural changes help to explain this remarkable shift. First, with stagnant growth and a slowdown in job creation during the 1980s, the unemployment rate for Latin America as a whole rose from 6.2 to 10.7 per cent between 1990 and 2002 (Table 8), and so did the number of underemployed. Second, the labour market was affected by a massive shift of labour to the informal sector, where low productivity and wages are the rule. Third, formal sector wages evolved more slowly than GDP per capita, while with rare exceptions, minimum wages fell in relation to average wages. Finally, wage differentials by educational level widened (Table 2).

What factors explain the deterioration of income inequality during the 1980s and 1990s? Barring an aggravation of the structural causes of inequality mentioned at the beginning of this paper, two sets of causes are generally mentioned in the literature and are briefly reviewed hereafter: first, the ‘skill-bias technical change’ (SBTC) hypothesis; and, second, the adoption of Washington consensus policies. The main effect of the skilled-

Washington�Consensus��and�Lost�Decade�

Augmented�WashingtonConsensus� New�Policy�Approach�

LatAm Income inequality: improving trend (2002-2010): Cornia (2012) changes LAC Ginis 15 of 18 LAC countries experienced lower inequality

12

Figure 5 Changes in Gini income by economic structure, 1990–2002 and 2002–09

1.3.6 The uniqueness of Latin America’s inequality decline during the last decade

An appreciation of the importance of the recent decline of income inequality in Latin America is offered by a comparison with the trends observed during the same period in other regions. In this regard, Table 6 confirms that during the broad period 1980-2000, the majority of the countries of Latin America experienced an increase in inequality, a trend observed also in all other regions with the exception of MENA. During this period, 73 of the 105 countries with reasonably good data (69 percent) showed an increase in income inequality. During the broad period 2000-10 (which in most cases was characterized by a faster growth than the prior two decades) inequality rises were less common than during the prior period. However, in no region except Latin America was there a clear and generalized drop in income inequality. Also sub-Saharan Africa and South East Asia show during this period a greater number of inequality decreases than inequality increases, but the tendency is less marked and widespread than in Latin America. This bifurcation of trends is difficult to explain on the basis of ‘luck’ or some supposed advantages of Latin America. Most developing regions are, in fact, as similarly heterogeneous as is Latin America: all of them comprise countries depending on commodity exports and remittances, as well as semi-industrialized nations. And all of them but the OECD benefitted from the high commodity prices, rising remittances, financial exuberance, and rapid world growth of the last decade. Nor does the inequality decline appear to have been driven by growth. Indeed, the fastest growing Asian countries (e.g., China, India and Vietnam) experienced steep rises in inequality,albeit starting from lower levels. Yet, in 2010, China’s Gini (47.0) is higher than those of Argentina, Uruguay and Venezuela, and similar to that of Mexico. It is thus difficult to argue that the improvements recorded in Latin America are due only to a favourable external environment, world growth, or ‘luck’. Other factors discussed in Section 3 (such as long-term effects of rising educational achievements, changes in economic and social policies and the consolidation of democracy) are likely to explain in part this encouraging trend.

Ͳ4

Ͳ3

Ͳ2

Ͳ1

0

1

2

3

4

1990�Ͳ 2002 2002�Ͳ 2009

Remittances�Recipients

Industrial�economies

Commodity�Exporters

Notes: The industrial economies include Argentina, Brazil, Mexico and Uruguay; commodity exporters include Bolivia, Chile, Colombia, Costa Rica, Ecuador, Peru and Venezuela; the remittances recipients are Dominican Republic, El Salvador, Guatemala, Honduras, Nicaragua, Panama and Paraguay.

Source: Author’s elaboration, based on IDLA.

7

Key reasons behind higher inequality in LAC: example: Bourguignon, Ferreira& Leite (2007) Brazil vs US micro-data based comparison (PNAD, 1999)

• Three factors accounted for most of Brazil's excessive levels of inequality

• Lower and unequally distributed endowments of

education across households (28%) •  Larger earning differentials by skill level

-“skill wage differentials” – (32%) •  Highly regressive public transfers, chiefly

retirement pensions (39%)

8

Key reasons behind higher inequality in LAC: Distribution of education tilted towards low skills

9

Key reasons behind higher inequality in LAC: Labor earning differentials too steep

10

Key reasons behind higher inequality in LAC: Bourguignon&Ferreira (2007) the incidence of pensions, Brazil vs the U.S.

0%

5%

10%

15%

20%

25%

0 20 40 60 80 100

Percentiles of the Distribution of Household Per Capita Income

Perc

enta

ge o

f Tot

al H

ouse

hold

Inc

ome

Brazil US

Source: PNAD/IBGE 1999, CPS/ADS 2000 and author's calculation

11

Key reasons behind higher inequality in LAC: Lopez&Perry (2008): Inequality of educational endowments

high income inequality, leading to the low intergenerational social mobility shown in Section II.5. It should not come as a surprise, then, that we find a significant correlation among educational and income Gini’s in the region.

Figure 10: Educational Gini in LAC countries vs. others Panel A. Gini coefficient of years of education

Panel B. Years of education: rich vs. poor

Panel C: educational and income gini correlation

00.10.20.30.40.50.60.7

LAC Africa Asia EasternEurope

Developedcountries

Argentina

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18+

years of education

30% poorest30% richest

Jamaica

Chile

Argentina

Uruguay

Panamá

Costa Rica Venezuela

Paraguay

Mexico

Colombia

Peru

Brazil Bolivia

El Salvador

Honduras

Nicaragua

Guatemala

4042444648505254565860

10 20 30 40 50 60Education gini

Inco

me

gin

i

Source: Panel A. De Ferranti et al. (2004). Panel B, Perry et al. (2006).

15

12

Key reasons behind higher inequality in LAC: Frankema (2008): rising skill premiums in LAC

6

above the trend line, the far majority ranging between 2.0 to 3.0. This outcome seems to

suggest that educated workers with the ability to carry out administrative work (reading,

writing, algebra), are scarcer in LAC’s than in NWC’s or, from the opposite angle, that blue-

collar workers in the NWC’s manufacturing sector are better educated, trained and therefore

more productive and better paid relative to white-collar employees.

Figure 1: The “white-collar premium” in manufacturing, Latin America versus the

USA, Canada and Australia, 1905-1990

Mexico

Chile

Guatemala

Uruguay

Chile

Venezuela

Bolivia

Chile

Venezuela

Canada

Australia Argentina

AustraliaUSA

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

1900 1906 1912 1918 1924 1930 1936 1942 1948 1954 1960 1966 1972 1978 1984 1990 1996

Latin America USA, Canada, Australia Source: see appendix table A.2 Notes: Latin American countries included with at least one observation are: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Mexico, Panama, Peru, Uruguay, Venezuela.

Yet, for the earlier periods in the twentieth century the picture is unclear. A lack of pre-war

observations complicates a sound analysis. In Chile, for example, the white-collar premium

went up and down over time, but on the whole there is just a limited difference between the

first observed year, (1925: 3.01) and the last observed year (1984: 3.54). In Argentina the

white-collar premium stayed almost at par (1.71 in 1910 to 1.60 in 1985). There are reasons to

believe that differences in educational background and skills have had a milder effect on the

wage distribution earlier in the twentieth century. Productive activities in the rural and service

sectors required, in many instances, physical capacities rather than mental skills which are

less dependent on formal education. Training on the job in urban workshops or in the fields,

may have compensated for a lack of schooling much more than in the contemporary modern

urban sectors. The evidence on wage differentials presented in the next section indicates that

13

Key reasons behind higher inequality in LAC: Lopez&Perry (2008): Higher marital sorting vs OECD countries

The effect of the high degree of persistence of educational differences on low social mobility is further exacerbated by the high degree of matching among educational groups in the formation of new households. For example, Figure 12 plots the Gini coefficient against marital sorting coefficients (defined as Pearson correlation coefficients for years of schooling between husbands and wives). Two basic messages emerge from this figure. First, there is a strong relationship between the two variables. In fact, the correlation coefficient between marital sorting and the Gini coefficient is above .6. The second message is that the marital sorting coefficients in Latin America are unusually high (at least relative to those in the rest of the world), something that can be taken as a symptom of a severe social stratification problem that not only further concentrates household incomes but reinforces the observed low social mobility.

Figure 12. Gini coefficients and marital sorting by educational levels.

Source: De Ferranti et al. (2004).

III.2 Fiscal policy and income inequality

Though such differences in educational levels and returns are a major channel of the observed persistence of high income inequality levels, it is by no means the only one. Differences in access to other complementary assets may partially explain differences in returns to education among children from households located in high and low income quintiles. Indeed, previous studies have shown that the persistence of significant differences in “bundles” of assets (education, public infrastructure, institutions) across regions within countries explain the persistence (and frequent divergence) of high spatial income per capita disparities 17. Indeed, “location” appears as a major predictor of household income differences, together with education. 17 “Beyond the City: the Rural Contribution to development”, WB 2005, and “Poverty Reduction and Growth: Virtuous and Vicious Circles”, WB 2006

17

14

Key reasons behind higher inequality in LAC: … and public expenditure, transfers and taxes? / Cornia (2012) Public social expenditure increased mostly in LOC countries (left of center)

29

education and healthcare are mildly progressive or proportional (depending in the case of health on the approach to its financing), those on tertiary education are as concentratedasthedistribution of income. In turn, expenditure on social security (pensions, unemployment insurance) is only slightly less concentrated than that of private income. These are average regional data and things vary between the three main country groups in the region (Table 13:Panel B). There are also indications that the incidence of social expenditure became more progressive over time (CEPAL 2005; López-Calva and Lustig 2010). Democratization is thus showing its impact not only on labour policies but also on non-clientelistic redistributive expenditure policies.

Table 12 Average public social expenditure/GDP in LOC versus non-LOC countries

Year Social public expenditure as percentage of GDP

Total Education Health Social security Housing 1990 9.0 2.8 2.1 3.3 0.7 1996 10.9 3.4 2.4 4.0 1.0 2003 12.8 4.3 2.8 4.6 1.1 2008-9 13.3 4.3 2.9 4.6 1.4 LOC ǻ (2008/9–2003) 1.33 0.2 0.38 0.46 0.29 Non LOC ǻ (2008/9–2003) 0.48 -0.12 0.06 0.11 0.43

Notes: The data refer to the 18 countries analysed in this study, including Bolivia (on the basis of national data) that has been omitted in similar studies.

Source: Author’s elaboration on the basis of the ECLAC database Cepalstat and national data for 2009,

Table 13 Incidence of government expenditure by quintile (18 countries, 1997-2004)

and concentration coefficients of the public expenditure by three country groups

(Panel A) Shares of public social expenditure

by sector and income quintile Expenditure

sector

(Panel B) Concentration coefficients of public

social expenditure I

Quintile II

Quintile III

Quintile IV

Quintile V

Quintile Group 1 Group 2 Group 3

7.4 6.5 6.3 5.9 5.6 Education -0.067 0.116 -0.138 5.1 4.7 4.2 4.0 3.7 Health 0.074 -0.073 -0.192 2.0 2.8 4.3 6.3 16.5 Socialsecurity 0.504 0.568 0.349 3.3 2.1 1.6 1.3 1.1 Socialassist. -0.089 -0.154 -0.484 0.8 0.9 1.1 1.4 0.9 Housing 0.206 0.067 -0.026

19.6 17.0 17.5 18.9 27.8 Total 0.143 0.042 0.044

Note: Group 1 includes Bolivia, El Salvador, Guatemala, Honduras, Ecuador, Nicaragua, Paraguay, and Peru;

Group 2 includes: Colombia, Dominican Republic, Mexico, Panama, and Venezuela; Group 3 includes: Argentina, Brazil, Chile, Costa Rica and Uruguay. Source: Elaboration on CEPAL (2007).

A key dilemma in this area concerns the expenditure on social security. As shown by Table 13, the latter is only slightly progressive, as it mainly covers the better paid formal sector workers with stable employment. This raises the question of how best can government expand coverage, whether by extending the formal sector or by setting up solidarity-based, non-contributory universal basic benefits (such as minimum pensions) to informal sector workers, their families and uninsured elderly. Both approaches were adopted in recent years,although the latter was more common, as explained hereafter.

15

Key reasons behind higher inequality in LAC: … and public expenditure, transfers and taxes? / Cornia (2012) which lower inequality of secondary income + subsidies (in kind)

29











sector



Quintile II

Quintile III

Quintile IV

Quintile V



19.6 17.0 17.5 18.9 27.8 Total 0.143 0.042 0.044




29











sector



Quintile II

Quintile III

Quintile IV

Quintile V



19.6 17.0 17.5 18.9 27.8 Total 0.143 0.042 0.044




16

Key reasons behind higher inequality in LAC: … and public expenditure, transfers and taxes? / Lopez&Perry (2008) Latin America vs Europe: inequality differences in secondary income

All these said, high asset inequality does not have to necessarily translate into high disposable income inequality unless taxes and transfers do not have significant corrective effects. In this regard, it may worth looking at the role played by the government in Latin America and compare it with some countries like the Europeans that are well known for having inequality as a policy concern.

Figure 13. Disposable and Market income in Latin America and Europe

Market Income

Disposable IncomePanel A. Latin America Panel B. Europe

Panel C. Latin America Panel D. Europe

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0.15

0.25

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0.55

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Arge

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Chile

Colom

bia

Mexic

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Peru

LAC

6

0.15

0.25

0.35

0.45

0.55

0.65

Arge

ntina

Brazi

l

Chile

Colom

bia

Mexic

o

Peru

LAC

6

Source: Goñi, Lopez, and Serven (2008)

In a recent paper, Goñi, Lopez and Serven (2008), elaborating on a topic highlighted in Perry et al (2006), argued that whereas in Latin America the distribution of market income (i.e. the income before taxes and government transfers and thus a measure that is largely determined by market rewards to the private assets and efforts of individuals, and by the underlying distribution of those private assets) and disposable income (i.e. the income after government cash benefits such as pensions, unemployment insurance, and social assistance transfers have been received and direct taxes have been paid) are very similar, in Europe this is not the case. Figure 13 (taken from Goñi, Lopez and Serven, 2006) reports the value of the gini coefficient of the distributions of disposable income (Panels A and B) and market income (Panels C and D) in Latin America (Panels A and

18

secondary

primary

17

Key reasons behind higher inequality in LAC: … and public expenditure, transfers and taxes? / Lopez&Perry (2008) Latin America vs Europe: impact of taxes vs transfers

between the average Gini coefficients of market and disposable income across European countries, about two-thirds (10 percentage points) are due to transfers.

Figure 14. The role of taxes and transfers in Europe and Latin America

Difference between Gini coefficients of gross income and market incomePanel A. Latin America Panel B. Europe

Panel C. Latin America Panel D. EuropeDifference between Gini coefficients of disposable income and gross income

-0.16

-0.12

-0.08

-0.04

0.00

Austr

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Denm

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Fran

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Irelan

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EU

RO 15

-0.16

-0.12

-0.08

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-0.16

-0.12

-0.08

-0.04

0.00

Arge

ntina

Braz

il

Chile

Colom

bia

Mex

ico Peru

LAC

6

-0.16

-0.12

-0.08

-0.04

0.00

Arge

ntina

Braz

il

Chile

Colom

bia

Mex

ico Peru

LAC

6

Source: Goñi, Lopez, and Serven (2008)

Such low levels of income redistribution through the State may be a reflection of high levels of State capture, and this in turn a reflection of high inequality levels as discussed below. But the fact that Europe was able to “break” with a history of high inequality during the twentieth century as indicated in Figure 15 below, suggest that such an event is not an impossibility going forward in Latin America.

20

transfers

taxes

Still some skepticism about inequality progress in LA •  Inequality in Latin America: breaking with history - World

Bank Report / 2003

•  Inequality in Latin America: Better, But Still Terrible – WSJ / 2013

• Yes but syndrome (inequality high and contained, but encouraging middle class growth) - Economist / 2014

• Summary: Inequality still too high, but progress being made

19

Inequality of opportunity in LAC: Burroni etal (2013), Lower bound of IOR (hhld per capita income and circumstances)

27

Figure 3: Inequality of economic opportunity and the level of development

Figure 4: Inequality of opportunity and income inequality

Income Inequality (mean logarithmic deviation)

20

Inequality of opportunity in LAC: Burroni etal (2013), strongly associated with low social mobility

28

Figure 5: Inequality of opportunity and intergenerational mobility

Figure 6: Inequality of opportunity and the intergenerational correlation of education

21

Perceptions of equity and fairness in LAC: Gaviria (2013), pessimist & optimist: contrast in perceptions of past and future mobility

22

Perceptions of equity and fairness in LAC: Gaviria (2013), pessimist & optimist: contrast in perceptions of past and future mobility

the Latinobarómetro survey. When respondents were asked whether they facebetter opportunities to improve their lives than their parents did a generationbefore, 55 percent answered that they face better or much better opportunities,18 percent stated that the opportunities were the same, and the rest said thatthey were worse or much worse. Individuals were also asked a similar questionabout future opportunities for their children. In this case, 58 percent felt theopportunities would be much better or better, 18 percent thought they wouldbe the same, and 24 percent indicated they would be worse or much worse.

The 2000 round of Latinobarómetro included several direct questions aboutsocial justice (specifically, about the distribution of opportunities and the rootcauses of poverty). Respondents were asked whether all fellow citizens hadthe same opportunities to stop being poor and whether poverty is due to lackof effort or to circumstances beyond effort and ability. As shown in table 5,74 percent stated that opportunities are not distributed equally, and 64 percentconsidered that poverty is caused by circumstances beyond individual skillsand personal efforts.

Three Latinobarómetro surveys further asked whether connections are keyfor socioeconomic success and whether hard work guarantees being successful.Table 5 presents the results for 1998, 2000, and 2002. Over 70 percent agree thatconnections are important and more than half believe that hard work guaranteessuccess. Percentages are stable throughout. Inside the Latin American region,

7 0 E C O N O M I A , Fall 2007

T A B L E 5 . Perceptions of Social JusticePercent

Survey year

Survey question 2002 2000 1998 1996

Opportunities to escape povertyAll have equal opportunities . . . 25.9 . . . . . .All do not have equal opportunities . . . 74.1 . . . . . .

Causes of povertyLack of effort 36.5External circumstances 63.6

Success depends on connectionsYes 68.62 71.5 71.3 76.4No 31.38 28.5 28.7 23.6

Hard work does not guarantee successYes 58.11 53.8 54.9 55.6No 41.89 46.2 45.1 44.4

Source: Latinobarómetro (various years).. . . Not applicable.

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Perceptions of equity and fairness in LAC: Gaviria (2013), pessimist & optimist: : contrast in perceptions of past and future mobility

With respect to the chances for getting out of poverty, respondents from LatinAmerica are less optimistic than people from Asia and member countries ofthe Organization for Economic Cooperation and Development (OECD), butmore optimistic than respondents from Africa, eastern Europe, and the UnitedStates. With regard to the causes of poverty, the United States registers thestrongest belief in lack of effort, followed by Asia, the OECD countries, LatinAmerica, Africa, and eastern Europe. Finally, when asked about whether hardwork guarantees success, Latin Americans are the most pessimistic of all thegroups. In sum, at the international level, Latin Americans are either weaklyoptimistic or pessimistic in their social justice outlook, as measured by theirperceptions of the opportunities to escape poverty, the causes of poverty, andthe relationship between hard work and success.

Political Preferences, Social Mobility, and Equality

This section describes the evidence on the demand for redistribution andother political preferences in Latin America and investigates its individual-level correlates. The intention is to empirically examine, for the case of LatinAmerica, a model of political preferences based on two main premises:people expect individual effort and skills to be rewarded by society, but they

7 4 E C O N O M I A , Fall 2007

T A B L E 7 . International Perceptions of Social Justice, 1994–99Percent

Latin Eastern OECD United Survey question America Europe countries Asia States Africa Total

Opportunities to escape povertyPeople have opportunities 41.7 25.7 44.7 49.9 27.3 40.0 38.2People have very few opportunities 58.3 74.3 55.3 50.1 72.7 60.0 61.8

Causes of povertyLack of effort 31.2 21.7 33.7 34.8 60.0 28.1 34.9External circumstances 66.8 78.3 66.3 64.7 40.0 71.3 64.6

Success depends on connectionsYes 61.5 65.0 65.2 73.2 80.5 82.1 71.2No 38.5 35.0 34.8 26.8 19.5 17.9 28.8

Hard work does not guarantee successYesNo

Source: World Values Survey, various years.OECD = Organization for Economic Cooperation and Development.


With respect to the chances for getting out of poverty, respondents from LatinAmerica are less optimistic than people from Asia and member countries ofthe Organization for Economic Cooperation and Development (OECD), butmore optimistic than respondents from Africa, eastern Europe, and the UnitedStates. With regard to the causes of poverty, the United States registers thestrongest belief in lack of effort, followed by Asia, the OECD countries, LatinAmerica, Africa, and eastern Europe. Finally, when asked about whether hardwork guarantees success, Latin Americans are the most pessimistic of all thegroups. In sum, at the international level, Latin Americans are either weaklyoptimistic or pessimistic in their social justice outlook, as measured by theirperceptions of the opportunities to escape poverty, the causes of poverty, andthe relationship between hard work and success.

Political Preferences, Social Mobility, and Equality

This section describes the evidence on the demand for redistribution andother political preferences in Latin America and investigates its individual-level correlates. The intention is to empirically examine, for the case of LatinAmerica, a model of political preferences based on two main premises:people expect individual effort and skills to be rewarded by society, but they

7 4 E C O N O M I A , Fall 2007

T A B L E 7 . International Perceptions of Social Justice, 1994–99Percent

Latin Eastern OECD United Survey question America Europe countries Asia States Africa Total

Opportunities to escape povertyPeople have opportunities 41.7 25.7 44.7 49.9 27.3 40.0 38.2People have very few opportunities 58.3 74.3 55.3 50.1 72.7 60.0 61.8

Causes of povertyLack of effort 31.2 21.7 33.7 34.8 60.0 28.1 34.9External circumstances 66.8 78.3 66.3 64.7 40.0 71.3 64.6

Success depends on connectionsYes 61.5 65.0 65.2 73.2 80.5 82.1 71.2No 38.5 35.0 34.8 26.8 19.5 17.9 28.8

Hard work does not guarantee successYesNo

Source: World Values Survey, various years.OECD = Organization for Economic Cooperation and Development.


24


Preferences for redistribution, support for the market economy and optimism about future mobility: •  Respondent believes that reducing the differences between the rich and the

poor is one of the main responsibilities of the state? •  73 percent answered “of course it is,” 17 percent said “maybe yes,” 6

percent responded “maybe not,” and 4 percent answered “of course not.” •  Respondent considers the market economy to be the most convenient for

their country? •  17 percent declared themselves to be very much in agreement, 40

percent in agreement, 29 percent in disagreement, and 14 percent very much in disagreement.

•  Expectations of future mobility are quite optimistic: 55 percent of individuals surveyed expect their children to have a higher socioeconomic status than themselves, while only 9 percent expect a lower level for their children.

25


group 1. Additionally, individuals in group 1 are more optimistic about thefuture than those in groups 2 and 3.

The experiences of past mobility and expectations for future mobility are veryindependent. Correlation between these two variables is slight and negative, onaverage.18 The idea of reversion to the mean appears to be widespread in the

6 8 E C O N O M I A , Fall 2007

18. The negative correlation of past and future mobility is significant and substantial(greater than 0.3) for Mexico and Venezuela.

B. Future mobility

0%

10%

20%

30%

40%

50%Percent

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9

A. Past mobility

0%

10%

20%

30%

40%

50%

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9

Percent

Source: Latinobarómetro (2000).

F I G U R E 5 . Perceptions of Past and Future Mobility


26

Inequality of opportunity for children: HOI indicators provide some reason for optimism

•  Human Opportunity Index -HOI- developed by the World Bank (2005) is based on the principle of equality of opportunities (Romer) and measures whether children have equitable access to human development opportunities (7, education, basic housing, etc), independently of exogenous circumstances (ethnicity, parents education, gender, location, hh income)

•  HOI is basically a coverage rate C penalized by inequality of opportunity D

IOH = C – P = C (1-D)

•  Hence, HOI rewards human development that reduces the most undesirable inequities –inequality due to unfavorable circumstances-

27 Inequality of opportunity for children: HOI indicators provide some reason for optimism Molinas et al (2010), wide range of HOI values

40 50 60 70 80 90 100

Honduras

Nicaragua

El Salvador

Guatemala

Peru

Panama

Paraguay

Dominican Republic

Brazil

Colombia

Ecuador

Jamaica

Argentina

Venezuel

Costa

a

Rica

Mexico

Uruguay

Chile

HOI 2010

Figure 2.1: The 2010 Human Opportunity Index for LAC.

Source: Author’s calculations based on household surveys

28 Inequality of opportunity for children: HOI indicators provide some reason for optimism Molinas et al (2010), HOI growth 1995-2010 : 1% per year, scale effect twice the importance of equalization

Growth Rates by Indicators, Dimensions, and Overall Human Opportunity Index

CountrySixth grade on

timeSchool

Attendance Education Water Electricity Sanitation HousingOverall

HOIArgentina -0.17 -0.02 -0.10 0.32 0.10 1.21 0.54 0.22Brazil 1.53 0.81 1.17 2.02 1.24 1.86 1.70 1.44Chile 0.81 0.11 0.46 1.07 0.70 2.01 1.26 0.86Colombia 1.82 0.61 1.21 0.24 0.63 1.83 0.90 1.06Costa Rica 0.61 0.75 0.68 0.24 0.51 1.59 0.78 0.73Dominican Republic 1.87 -0.06 0.91 0.97 1.59 1.41 1.32 1.11Ecuador 1.35 0.62 0.98 3.98 0.91 0.84 1.91 1.45El Salvador 1.60 0.92 1.26 0.02 1.92 0.23 0.73 0.99Guatemala 1.31 1.11 1.21 1.35 1.62 1.55 1.51 1.36Honduras 1.73 1.30 1.52 0.81 0.57 -0.93 0.15 0.83Jamaica 0.52 0.10 0.31 -0.86 1.75 -0.09 0.27 0.29Mexico 1.66 0.60 1.13 4.08 0.74 2.24 2.35 1.74Nicaragua 1.48 1.24 1.36 0.42 0.73 4.46 1.87 1.61Panama 0.48 0.32 0.40 0.62 0.79 0.32 0.58 0.49Paraguay 1.21 0.12 0.67 2.25 1.24 1.13 1.56 1.11Peru 2.24 0.30 1.27 0.50 1.67 2.36 1.51 1.39Uruguay 1.40 -0.43 0.48 2.15 0.35 0.33 0.94 0.71Venezuela 1.13 0.25 0.69 0.07 0.05 0.52 0.21 0.45

LAC Average 1.25 0.5 0.87 1.12 0.95 1.27 1.12 0.99Source: Author's calculations based on household surveys

29 Inequality of opportunity for children: HOI indicators: identifying unequalizing characteristics Velez & Torres (2014), ex. Colombia Shapley decomposition

30 Inequality of opportunity for children: HOI indicators provide some reason for optimism Molinas et al (2010), Benchmarking LAC vs Europe Figure 3.1: HOI Sanitation

388

1213141416

1920

2829

363738

4774

81

25

81921

4445

586970

92

0 20 40 60 80 100

ParaguayNicaragua

JamaicaHonduras

Dominican RepublicCosta Rica

PanamaGuatemalaEl Salvador

BoliviaEcuador

PeruBrazil

MexicoUruguay

ArgentinaChile

Venezuela

KenyaVietnam

Kyrgyz RepublicSouth Africa

RomaniaHungaryPortugalGreece

France**USA*Spain

HOI (%)

LAC

NO

N-L

AC

Access to Sanitation(Public connection only)

Sources: LAC: CEDLAS Data, Non-LAC: IPUMS Census Data

LAC EUROPE

31 Inequality of opportunity for children: HOI indicators provide some reason for optimism Burroni et al (2013), Benchmarking LAC vs Africa

29

Figure 7: The Human Opportunity Index in Africa and Latin America

Figure 8: The Human Opportunity and Development Indices

32 Inequality in LAC Conclusion

• Still very high income inequality, but improving in the last decade.

•  Inequality of opportunity (income) is also the highest across world regions.

•  Latinamericans consider unfair the current income distribution and favor redistributive policies, BUT support for the market economy and are optimists about future social mobility.

•  The steady improvement of the HOI for children, provides some reason for optimism. Nevertheless LAC is still very much behind OECD countries.

33 Inequality in LAC Conclusion

Advantages of the Human Opportunity Index HOI •  Identifies most unfair opportunity gaps by cohorts (early childhood,

children, adolescent)

•  Identifies crucial circumstances to be compensated to improve access to human development opportunities of the most disadvantaged (Sector specific).

•  Provide timely feedback to policy makers by sector. The indicator is sensitive to contemporaneous government programs for early childhood, children and adolescent.

•  Help political concensus building: allow political coalitions to concentrate on the most undesirable determinants of inequality of opportunities, to fullfill the optimistic views of Latin Americans about future social mobility

34

Inequality in LAC

Thanks!

35 Inequality in LAC References

Bourguignon, Ferreira and Leite. 2007. Beyond Oaxaca–Blinder: Accounting for differences in household income distributions, Journal of Income Inequality

Burroni, Ferreira and Peragine. 2013. Inequality of opportunity and economic mobility: Some international comparisons. IZA Working paper.

Barros, R., F. Ferreira, J. Molinas and J. Saavedra. 2008. Measuring Inequality of Opportunities in Latin America and the Caribbean. Washington D.C.: The World Bank.

Cornia. 2012. Inequality trends and their determinants: Latin America 1990-2010. UNU-WIDER Working Paper 2012/09.

Di Ferranti et al. 2004. Inequality in Latin America: breaking with history. World Bank. Ferreira and Melendez. 2012. Desigualdad de Resultados y Oportunidades en Colombia,

1997-2010. CEDE Working Paper. Frankema. 2008. Wage inequality in twentieth century Latin America: a comparative perspective.

Groningen Growth and Development Centre. Gaviria. 2013. Social Mobility and Preferences for Redistribution in Latin America. Economia Gasparini Cruces and Tornarolli, 2011. "Recent Trends In Income Inequality In Latin America,"

Journal of LACEA Economia. Lopez and Perry. 2008. Inequality in Latin America: determinants and consequences. Policy

Research Working Paper 4504. World Bank. Molinas et al. 2010. Do our children have a chance? 2010 Human Opportunity report on LAC.

World Bank. Velez Azevedo and Posso. 2010. Oportunidades para los niños colombianos: tendencias y

diferencias regionales. 1997-2008. Velez and Torres. 2014. Desigualdad de oportunidades entre los niños colombianos: avances y

retos del desarrollo humano en la última década. Working Paper. Escuela de Gobierno. Universidad de los Andes.

World Bank. 2006. World Development Report: Equity and Development.

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