intergenerational mobility and the rise and fall of

Intergenerational mobility and the rise and fall of inequality:Lessons from Latin America

Guido Neidhöfer∗

July 2, 2015

Preliminary version. Please do not quote!Comments are very welcome!

Abstract

Countries with a high level of inequality show also a high association between parents’ andchildrens’ economic outcomes; i.e. low intergenerational mobility. So far, this relationship hasbeen investigated in cross-country comparisons and for rising inequality. This study focuses oncountries where inequality has fallen in an analysis between and within countries. The laboratoryfor this exercise is Latin America: On the one hand, the region displays the world’s highest levelsof inequality and intergenerational persistence of socioeconomic status; on the other, while world-wide inequality has been rising, most Latin American countries experienced a significant decreasein inequality in the last decade. Multiple sources of data are used: i) the public opinion survey Lati-nobarometro. ii) the Socio-Economic Database for Latin America and the Caribbean. iii) WorldBank Data. iv) Harmonized micro data from several household surveys of various Latin Ameri-can countries. All data sources share the great advantage of comparability between countries andover time. One stylized finding, is that in some countries where inequality has significantly fallen,measurably higher intergenerational mobility can be observed. Moreover, changes in inequalityand changes in mobility are negatively associated. However, this relationship is not true for allof the analyzed countries. Still, the micro analysis reveals that higher inequality influences inter-generational mobility negatively in a between and within country set up. Hence, patterns whichhas been identified in the past literature to influence intergenerational mobility (e.g. economicgrowth, returns to education, public investment in human capital and other institutional factors)are evaluated, shedding light on the different developments across countries.

Keywords: Inequality, Intergenerational Mobility, Equality of Opportunity.JEL Classification: D63, I24, J62, O15

∗School of Business & Economics, Freie Universität Berlin, Boltzmannstraße 20, 14195 Berlin, Germany.([email protected])

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1 Introduction

The view of researchers and the public on inequality has been changing over the course of time.While the classical approach suggested that inequality might be beneficial for growth because of hismotivating nature (Keynes, 1920), it changed to be seen as simply part of the process of economicdevelopment with no direct causal interrelation (Kuznets, 1955). Later, economists theorized thatthe shape of the income distribution in a country has a significant impact on its growth rates (Galorand Zeira, 1993; Atkinson, 1997).1 Finally, recent empirical studies evidenced a strong associationbetween inequality and clearly detrimental patterns for a society, like higher crime, drug use andpersistent poverty (Wilkinson and Pickett, 2009). The key to understand if it is worth more or lessto care about the income distribution in a society - i.e. on (in)equality of outcomes - is probably theevaluation of (in)equality of opportunities.

Equality of opportunity is a long studied subject and mostly one of the primary goals of policymakers. Hereby, the fundamental discussion concerns the differentiation between inequality of out-comes resulting from individual efforts and inequality of resources deriving from given circumstances(Roemer, 2000).2 Recent studies on the relationship between income inequality and growth found,indeed, opposite effects when the distribution of income is determined by inequality of opportunitiesor by inequality of efforts, being negative in the first and positive in the second case (Marrero andRodríguez, 2013). Similar results have been found by authors who dedicated to the study of inequalityof educational attainments: They confirm that human capital is enhancing growth and economic de-velopment, but only conditional on the degree of educational inequality (Cuaresma et al., 2013; Sauerand Zagler, 2014). Education takes place early in life and shapes strongly individual opportunities.The choice of certain educational tracks and first educational attainments are strongly determined byparental background, the overall environment in childhood, and, generally speaking, mostly by cir-cumstances out of the influence of the individual (for a recent survey, see Heckman and Mosso, 2014).Hence, these findings can be interpreted as a further evidence for the detrimental impact of inequalityof opportunities.

Recently, the topic has been extensively debated because of a somehow alarming discovery: Incountries where inequality is on a high level, there is also a strong association between parents’ andchildrens’ economic well-being (i.e. low intergenerational mobility). The graph showing this phe-nomenon is well-known today as the Great Gatsby curve (Corak, 2013). Since the graph from Corak’swork showing the mentioned association got this name in Alan Kruger’s presidential adress, a branchof studies flourished analyzing the reasons and mechanisms behind this relationship. However, as forexample Jäntti and Jenkins (2013) point out, more research with comparable data on multiple countriesand cohorts is crucial for our understanding of the interplay between inequality and intergenerationalmobility.

Theoretical models in past gave yet some important insights on the subject; starting from the sem-inal contributions by Becker and Tomes (1979) and Solon (1992)3, to macroeconomic models amongothers by Owen and Weil (1998), Maoz and Moav (1999) and Hassler et al. (2007). The models con-ceptualize the mechanisms behind the intergenerational transmission of inequality, showing that themain role is played by family endowments inherited from parents to children4 and emphasizing the

1A stimulating survey on researcher’s view on inequality can be found in Galor (2009).2The original conceptual discussion of equality of opportunity comes from Phylosophy (e.g. Dworkin). A complemen-

tary way of analysis to the one presented here by John Roemer is Fleurbaey (2008).3Extensions to Solon’s first contribution are Solon (1999, 2002, 2004, 2014).4Some allowing for genetic transmission of abilities, too.

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role of credit market constraints that limit private investment in human capital. The main hypothe-sis, is that rising inequality of income between families leads to higher inequality of investment inchildren’s human capital, and thus to less intergenerational mobility (Becker and Tomes, 1979; Solon,2004). This dynamic arises out of the assumption that parents invest in their children proportionallyto their income or earnings, with even stronger negative consequences on mobility if the propensityto invest in children’s human capital is positively associated with relative income. Furthermore, otherdirect and indirect effects of certain parental background features play an important role, like parentaleducation or cognitive abilities (e.g. the possibility to help their children doing homework and theinformational advantage about the value of education on the labor market), as well as so-called net-work and neighborhood effects. Consequently, when income becomes more unequally distributed,inequality of investment in children’s human capital rises, causing low intergenerational mobility, so-cial stratification and even higher income inequality in the following generation. One of the mayorcontrasting forces of this dynamic is public investment in human capital.

The purpose of the present study is to deepen our understanding on the relationship betweenincome inequality and intergenerational mobility. This subject has been analyzed in past by cross-country comparisons (among others Checchi et al., 1999; Corak, 2013; Brunori et al., 2013; Blanden,2013) or in within-country analyses for cases of rising inequality (Chetty et al., 2014; Blanden et al.,2014; Heineck and Riphahn, 2009). In this study the claims given by economic theory are empiri-cally verified in a cross- and within-country set up and in situations where the distribution of incomebecomes less unequal. If the dynamics that have been identified to explain lower intergenerationalmobility caused by rising inequality are valid, the relationship should work also the other way around,and falling inequality might lead eventually to higher intergenerational mobility in the next genera-tion. An extremely interesting laboratory for this exercise is Latin America: On the one hand theregion displays the world’s highest levels of inequality and intergenerational persistence of socioeco-nomic status (Lustig et al., 2013; Hertz et al., 2007). But on the other, while worldwide inequalityhas been rising, most Latin American countries experienced a significant decrease in inequality in thelast decade (Gasparini and Lustig, 2011; Gasparini et al., 2011a; The World Bank, 2013). The LatinAmerican countries where inequality has not fallen act as an useful counterfactual.

Ideally, the requirement for an empirical analysis of intergenerational mobility, is a valid mea-sure (or a good proxy) for permanent income of parents and children. Alternatively, a method tomeasure the influence of parental background on the human capital of the children should do thejob as well. Furthermore, for a cross-country comparison to make sense, the measurements must beas comparable as possible between countries. The sources of data used in this study are one of thefew to fulfill this prerequisites: First, the public opinion survey Latinobarometro, which since 1995records individual and household characteristics of a representative sample of adult respondents in 18Latin American countries, including questions about own and parental education (since 1998). Sec-ond, various harmonized household surveys for 8 Latin American countries which ask directly withretrospective questions about the highest educational attainments of parents. From these data sets, aregression-based index of intergenerational mobility is estimated, using the information given by adultrespondents about their own and their parent’s education, and constructing variables identifying therelative individual deviations from the mean educational achievements within people of the same sex,country, age and cohort. Inequality indices and other characteristics derive from the Socio-EconomicDatabase for Latin America and the Caribbean (CEDLAS and The World Bank) which includes in-formation from over 300 household surveys carried out in 24 Latin American and Caribbean countriesand represents in each period more than 97 % of the total population in the region. This data setdoes not enable to construct a similar measure for all countries, since most surveys do not comprise

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a question about parental education or socioeconomic status. However, an alternative social mobilityindex proposed by Andersen (2000), based on the schooling gap of children still living in their parentshousehold is reported every year for each country.5 All used data sources have certainly their strengthsand weaknesses, but share the great advantage of comparability between different countries and overtime. Thus, it is possible to compare the intergenerational mobility of individuals belonging to twodifferent cohorts; i.e. of people who faced high and low inequality in childhood respectively (whenparental investment is essential).

One of the main findings, is that in countries where inequality has significantly fallen, measur-ably higher intergenerational mobility can be observed, and vice versa (intergenerational mobilitydecreased with rising inequality). Moreover, changes in inequality and changes in mobility are nega-tively associated - both in levels and percentages - across countries and observing time trends. How-ever, a general trend towards higher intergenerational mobility can be observed also in countries whereinequality has not fallen. So, different patterns which has been identified in the past literature to influ-ence intergenerational mobility (e.g. returns to education and public investment in human capital) areinvestigated, too, and lead to different conclusions.

The article is structured as follows...

2 Inequality and intergenerational mobility

2.1 Theory

The theoretical literature on the relationship between inequality and intergenerational mobility buildsbasically on the fact, that parents derive utility apart from their present consumption level, also fromthe future utility of their children. Since utility depends on consumption, consumption depends onincome, and income depends on human capital, parents invest in the human capital of their children toraise their future income and thus utility. If the investment in childrens’ human capital is exclusivelyprivate, budget constraints limit the investment choices of families and lead - in presence of creditmarket imperfections - to persistence of inequality from one generation to the next; i.e. poor parentsare unable to invest in the human capital of their children who therefor are unable to afford betterincome opportunities for themselves and climb up the social ladder. This implication arise from theseminal model by Becker and Tomes (1979) and its adaptations done by Solon (1992, 2004). Latermacroeconomic models build mainly on this framework (Owen and Weil, 1998; Maoz and Moav,1999; Hassler, Mora and Zeira, 2007). As a logical consequence of the above explained mechanism,observing the dynamics of the process, we would expect raising inequality coming together with anincrease in the intergenerational persistence of wealth; i.e. lower intergenerational mobility. Thenegative relationship between inequality and intergenerational mobility should however consequentlywork also the other way around: With falling income inequality also inequality in parental investmentin childrens’ human capital should fall (ceteris paribus) and the children should experience a moreequal distribution of incomes in the society than their parents.

Figure 2.1 visualizes the above mentioned relationship. In country A inequality falls from periodt to period t+1, what drives also intergenerational mobility. Movements along the solid line displaythe expected ceteris paribus relationship between inequality and intergenerational mobility. However,it is possible to observe other situations empirically, namely the three points at the end of the dotted

5As will be discussed more in detail in section 5, this index faces serious limitations and will only be displayed for thesake of completeness.

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Intergenerational mobility in childrens‘ generation

Inequality

Low Mobility

High Mobility

Low Inequality High Inequality

𝐴𝐴𝑡𝑡

𝐴𝐴𝑡𝑡+1 𝐴𝐴′𝑡𝑡+1𝐴𝐴′′𝑡𝑡+1

𝐴𝐴′′′𝑡𝑡+1

Lower (higher) inequality → lower (higher) inequality of parental investment in childrens‘ human capital

Figure 2.1: Inequality and Intergenerational mobility

arrows A’, A” or A”’. Here, different situations can affect intergenerational mobility which go beyondprivate investment in human capital. So, for a complete study on the persistence of social inequalityit has to be taken into account, that the interplay between three institutions determines the amount ofintergenerational mobility in a society: The family, the market, and the state (Corak, 2011). First,the family, mainly because of the inheritance of endowments from parents to children, for exampletrough investments in human capital, as stated above, genetic transmission of abilities, or the heritageof certain values. On the latter, empirical research found for example an positive association betweenincome inequality and stronger work ethic (Corneo and Neher, 2013) what might therefor lead tohigher intergenerational mobility.6 Second, the market, since higher returns to investment in humancapital might act as an incentive for families to invest more and thus raise mobility (Solon, 2014).Third, the state, providing public investment in human capital for the families that cannot afford anefficient amount of it due to budget constrains (Davies et al., 2005). Additionally on this last point,Ichino et al. (2011) argue that political institutions influence strongly the degree of persistence ofsocioeconomic status in a society and are one of the main explanations of cross-country differencesin intergenerational mobility estimates. Moreover, an important aspect might be the timing of theinvestment in human capital. As pointed out among others by Heckman and Mosso (2014) investmentsare more effective at earlier ages, while interventions in adolescence have only short run effects.

6Furthermore, Corneo (2013) develops a model where the transmission of values towards a stronger work ethic dependson the characteristics of the labor market, as well as on the amplitude of the welfare state. Indeed, he empirically finds anassociation between lower intergenerational (occupation) mobility, and the generosity of unemployment benefits.

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2.2 Empirical Evidence

[INCLUDE REVIEW OF EMPIRICAL STUDIES]

2.3 Income inequality and social mobility in Latin America

1. Evidence on inequality in Latin America

(a) Gasparini and Lustig (2011); Gasparini et al. (2011b); Alvaredo and Gasparini (2015)

(b) Lustig et al. (2013)

2. Evidence on intergenerational mobility in Latin America

(a) Hertz et al. (2007)

(b) Behrman et al. (2001); Dahan and Gaviria (2001); Gaviria et al. (2007); Binder and Woodruff(2002); Azevedo and Bouillon (2010)

(c) Torche (2014)

3 Data & Descriptive

The sources of data used in this study are one of the few to fulfill this prerequisites: First, the publicopinion survey Latinobarometro, which since 1995 records individual and household characteristicsof a representative sample of adult respondents in 18 Latin American countries, including questionsabout own and parental education (since 1998). Second, various harmonized household surveys for8 Latin American countries which ask directly with retrospective questions about the highest edu-cational attainments of parents. From these data sets, a regression-based index of intergenerationalmobility is estimated, using the information given by adult respondents about their own and theirparent’s education, and constructing variables identifying the relative individual deviations from themean educational achievements within people of the same sex, country, age and cohort. Inequalityindices and other characteristics derive from the Socio-Economic Database for Latin America andthe Caribbean (CEDLAS and The World Bank) which includes information from over 300 householdsurveys carried out in 24 Latin American and Caribbean countries and represents in each period morethan 97 % of the total population in the region. This data set does not enable to construct a similarmeasure for all countries, since most surveys do not comprise a question about parental education orsocioeconomic status. However, an alternative social mobility index proposed by Andersen (2000),based on the schooling gap of children still living in their parents household is reported every year foreach country. All used data sources have certainly their strengths and weaknesses, but share the greatadvantage of comparability between different countries and over time. Thus, it is possible to comparethe intergenerational mobility of individuals belonging to two different cohorts; i.e. of people whofaced high and low inequality in childhood respectively (when parental investment is essential).

[INCLUDE DESCRIPTIVE ANALYSIS HERE]

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(a) Inequality

(b) Intergenerational mobility

Figure 3.1: Trends in Latin America7

4 Measurement

Intergenerational mobility measures the degree of persistence in socioeconomic status (and inequal-ity) from parents (p) to children (c) in a society

Yc = α +β ·Yp + ε

Measurement Intergenerational mobility InequalitySEDLAC Social Mobility Index (Andersen, 2004) Gini of disposable hh income

age intervals (13-19) and (20-25)

Latinobarometro Intergenerational persistence of human capital

y = Years of education

Time Frame Benchmarks CohortsHigh inequality: 1998 1980 - 1987

Lower inequality: 2006 1988 - 1995

Individuals aged between 11 and 18 years in benchmark year.

4.1 Social Mobility Index (Andersen, 2004)

Individual i in family jSchoolingGapi j = γX j +δZi

where X j are household income and parental education, and Zi individual control variables, likeage and sex. Trough a Fields (1996) decomposition the weight of single factors (w j, wi) is obtained.Then, the Social Mobility Inex by Andersen (2004) is:

SMI = 1−w j ; ε[Lowmobility(0) , Highmobility(1)]

Advantages:

• Makes full use of available data

• Comparable across countries and over time

• Includes nearly all children and young adults in data

Limitations:

• Not representative: Only children and young adults still living with their parents

• Schooling gap maybe not a good proxy for future outcomes

4.2 Intergenerational persistence of human capital

A comparable measure of intergenerational mobility across different countries and over different timeperiods is obtained through a normalization of parents’ and childrens’ outcomes in the estimation ofthe following equation:

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y∗c = α +β · y∗p + γ · y∗p ·C+ζ ·C+δXc + εc (4.1)

Where y∗c = (Yc − Y ai jc )/Y ai j

c and y∗p = (Yp − Y i jp )/Y i j

p , beingYc,p : Years of education of child, parentsY ai j

c : Mean of Yc for people aged a in country i belonging to cohort jY i j

p : Mean of Yp for people in country i belonging to cohort jXc is a matrix of controls for sex, age (polynomial), city size and C a dummy variable being 1 for

individuals born in cohort 1988-1995 and 0 for the cohort 1980-1987.

4.3 Effect of inequality and other patterns

To estimate the association between inequality and mobility, the following equation is estimated:

yci j = α +β · yp

i j + γ · ypi j ·Q j +δXi j + τQ j +ϑ j + εi j (4.2)

being Q a matrix of specific country features experienced in childhood or adolescence (age inter-vals: 0-6, 6-12, 12-18) and ϑ j country fixed effects.

5 Results

[INCLUDE RESULTS HERE]

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Table 1: Intergenerational mobility in Latin America - Own vs. parental years of education

(1) (2)Ineq.1998 Ineq.2006

Argentina 0.237∗∗∗ (0.0060) 0.224∗∗∗ (0.0189)

Bolivia 0.228∗∗∗ (0.0136) 0.217∗∗∗ (0.0095)

Brazil 0.259∗∗∗ (0.0092) 0.241∗∗∗ (0.0079)

Chile 0.228∗∗∗ (0.0120) 0.198∗∗∗ (0.0266)

Colombia 0.257∗∗∗ (0.0122) 0.222∗∗∗ (0.0089)

Costa Rica 0.293∗∗∗ (0.0160) 0.220∗∗∗ (0.0139)

Dominican Rep. 0.256∗∗∗ (0.0168) 0.240∗∗∗ (0.0203)

Ecuador 0.299∗∗∗ (0.0085) 0.325∗∗∗ (0.0154)

El Salvador 0.263∗∗∗ (0.0121) 0.235∗∗∗ (0.0142)

Guatemala 0.388∗∗∗ (0.0123) 0.308∗∗∗ (0.0154)

Honduras 0.335∗∗∗ (0.0167) 0.390∗∗∗ (0.0120)

Mexico 0.196∗∗∗ (0.0080) 0.194∗∗∗ (0.0160)

Nicaragua 0.270∗∗∗ (0.0080) 0.306∗∗∗ (0.0154)

Panama 0.328∗∗∗ (0.0099) 0.348∗∗∗ (0.0387)

Paraguay 0.232∗∗∗ (0.0109) 0.107∗∗∗ (0.0234)

Peru 0.256∗∗∗ (0.0082) 0.284∗∗∗ (0.0212)

Uruguay 0.302∗∗∗ (0.0110) 0.299∗∗∗ (0.0199)

Venezuela 0.200∗∗∗ (0.0102) 0.158∗∗∗ (0.0131)

Demographic controls Yes Yes

Country fixed effects Yes Yes

City size Yes Yes

Observations 47240 16918R2 0.236 0.222Outcome variables measured as relative distance from the mean by age, sex, country and cohort.

Year of Birth: Ineq.1998 = 1980-1987; Ineq.2006 = 1988-1995.

Data: Latinobarometro 1998-2013. Statistical significance level * 0.1 ** 0.05 *** 0.01.

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Table 2: Inequality and other determinants of intergenerational mobility

(1) (2) (3) (4) (5) (6) (7) (8)

Parental Education 0.164∗∗∗ 0.164∗∗∗ 0.266∗∗∗ 0.095 -0.372∗ -0.372∗ 0.067 0.019(0.0354) (0.0355) (0.0517) (0.1315) (0.2067) (0.2065) (0.2220) (0.1857)

Parental Education*gini_0_6 0.184∗∗∗ 0.184∗∗∗ 0.122 0.329 1.069∗∗∗ 1.067∗∗∗ 0.457 0.422∗

(0.0661) (0.0664) (0.0824) (0.1990) (0.3669) (0.3666) (0.3629) (0.2099)

Parental Education*gdp_pc_0_6 -0.000∗∗∗ -0.000∗ -0.000∗∗∗ -0.000(0.0000) (0.0000) (0.0000) (0.0000)

Parental Education*starting_age 0.008 -0.002(0.0149) (0.0104)

Demographic controls Yes Yes Yes Yes Yes Yes Yes Yes

City size Yes Yes Yes Yes Yes Yes Yes Yes

Country fixed effects No Yes Yes Yes No Yes Yes Yes

Economic growth No No Yes Yes No No Yes Yes

Public Education No No No Yes No No No Yes

Observations 34744 34744 18786 5909 66398 66398 66398 22592R2 0.210 0.211 0.221 0.221 0.234 0.234 0.235 0.163N_clust 198 198 143 88 43 43 43 25Data: Latinobarometro 1998-2013 (1)-(4), Harmonized household surveys (5)-(8).

Cluster adjusted s.e. by country and birthyear. Statistical significance level * 0.1 ** 0.05 *** 0.01.


(1) (2) (3) (4) (5) (6) (7) (8)

Parental Education 0.197∗∗∗ 0.197∗∗∗ 0.269∗∗∗ 0.271∗∗∗ -0.356∗∗∗ -0.356∗∗∗ -0.229 0.095(0.0318) (0.0318) (0.0383) (0.0441) (0.1220) (0.1224) (0.1451) (0.1626)

Parental Education*gini_6_12 0.114∗ 0.113∗ 0.106 0.105 1.056∗∗∗ 1.056∗∗∗ 0.903∗∗∗ 0.439(0.0607) (0.0608) (0.0690) (0.0856) (0.2156) (0.2164) (0.2380) (0.2769)

Parental Education*gdp_pc_6_12 -0.000∗∗∗ -0.000∗∗∗ -0.000∗∗ -0.000∗∗∗

(0.0000) (0.0000) (0.0000) (0.0000)

Parental Education*total_pub_educ_gdp_6_12 -0.001 -0.018∗∗

(0.0045) (0.0084)








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(1) (2) (3) (4) (5) (6) (7) (8)

Parental Education 0.176∗∗∗ 0.175∗∗∗ 0.284∗∗∗ 0.298∗∗∗ -0.290∗ -0.294∗ -0.167 -0.425∗∗∗

(0.0333) (0.0333) (0.0335) (0.0368) (0.1523) (0.1522) (0.1624) (0.1397)

Parental Education*gini_12_18 0.163∗∗ 0.162∗∗ 0.051 0.111 0.962∗∗∗ 0.968∗∗∗ 0.841∗∗∗ 1.422∗∗∗

(0.0646) (0.0646) (0.0620) (0.0745) (0.2708) (0.2707) (0.2759) (0.2273)

Parental Education*gdp_pc_12_18 -0.000∗∗∗ -0.000∗∗∗ -0.000∗∗∗ -0.000∗∗∗

(0.0000) (0.0000) (0.0000) (0.0000)

Parental Education*total_pub_educ_gdp_12_18 -0.015∗∗∗ -0.026∗∗∗

(0.0042) (0.0077)








Table 5: Inequality and other determinants of intergenerational mobility (Restricted)

(1) (2) (3) (4) (5) (6) (7) (8)

Parental Education -0.085 -0.082 0.750∗∗ 0.535 -0.372∗ -0.372∗ 0.067 0.019(0.1072) (0.1075) (0.3189) (0.4550) (0.2067) (0.2065) (0.2220) (0.1857)

Parental Education*gini_0_6 0.603∗∗∗ 0.598∗∗∗ -0.585 -0.148 1.069∗∗∗ 1.067∗∗∗ 0.457 0.422∗

(0.1838) (0.1844) (0.4893) (0.5696) (0.3669) (0.3666) (0.3629) (0.2099)

Parental Education*gdp_pc_0_6 -0.000∗∗∗ -0.000 -0.000∗∗∗ -0.000(0.0000) (0.0000) (0.0000) (0.0000)

Parental Education*starting_age -0.012 -0.002(0.0145) (0.0104)








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(1) (2) (3) (4) (5) (6) (7) (8)

Parental Education -0.050 -0.051 0.183∗ 0.100 -0.356∗∗∗ -0.356∗∗∗ -0.229 0.095(0.1174) (0.1175) (0.1043) (0.1633) (0.1220) (0.1224) (0.1451) (0.1626)

Parental Education*gini_6_12 0.553∗∗∗ 0.554∗∗∗ 0.331∗∗ 0.441 1.056∗∗∗ 1.056∗∗∗ 0.903∗∗∗ 0.439(0.2042) (0.2044) (0.1640) (0.2744) (0.2156) (0.2164) (0.2380) (0.2769)

Parental Education*gdp_pc_6_12 -0.000∗∗∗ -0.000∗∗∗ -0.000∗∗ -0.000∗∗∗

(0.0000) (0.0000) (0.0000) (0.0000)

Parental Education*total_pub_educ_gdp_6_12 0.007 -0.018∗∗

(0.0064) (0.0084)









(1) (2) (3) (4) (5) (6) (7) (8)

Parental Education 0.178∗ 0.177∗ 0.635∗∗∗ 0.448∗∗∗ -0.290∗ -0.294∗ -0.167 -0.425∗∗∗

(0.1069) (0.1066) (0.1208) (0.1583) (0.1523) (0.1522) (0.1624) (0.1397)

Parental Education*gini_12_18 0.176 0.177 -0.391∗∗ -0.052 0.962∗∗∗ 0.968∗∗∗ 0.841∗∗∗ 1.422∗∗∗

(0.1825) (0.1819) (0.1809) (0.2427) (0.2708) (0.2707) (0.2759) (0.2273)

Parental Education*gdp_pc_12_18 -0.000∗∗∗ -0.000∗∗∗ -0.000∗∗∗ -0.000∗∗∗

(0.0000) (0.0000) (0.0000) (0.0000)

Parental Education*total_pub_educ_gdp_12_18 -0.009 -0.026∗∗∗

(0.0070) (0.0077)








6 Conclusion

This study analyzed the relationship between inequality and intergenerational mobility, making useof comparable micro-data on 18 Latin American countries. Hereby, the influence of different institu-tional and country specific factors on intergenerational mobility has been verified. First, the degree

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of intergenerational mobility is estimated for each country and two different cohorts - which expe-rienced high and lower inequality in childhood respectively - are compared. Then, the associationof inequality in three crucial points of the lifecycle is evaluated: in early childhood, at primary andsecondary school age. Here, I control for parental education and different institutional and countryspecific factors to check if the degree of inequality is still of significant relevance.

The main result, is that high inequality is negatively associated with intergenerational mobility.The effect is significant between and within countries, seems however to be driven by economic growthmeasured by GDP per capita. Public investment in human capital, measured by public educationexpenditures, do not outweigh the effect of inequality but has, however a significant and positive effecton intergenerational mobility. There seem to be a strong role played by the institutional environment,or other differences between countries, which will be further analyzed.

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Table 8: Estimations without normalization of own and parental education - Cohort 1980 - 1987

(1) (2) (3) (4) (5) (6) (7) (8) (9)no restrict. age>17 age>18 age>19 age>20 age>21 age>22 age>23 age>24

Argentina 0.279∗∗∗ 0.279∗∗∗ 0.284∗∗∗ 0.277∗∗∗ 0.273∗∗∗ 0.279∗∗∗ 0.288∗∗∗ 0.288∗∗∗ 0.279∗∗∗

(0.0074) (0.0074) (0.0069) (0.0075) (0.0074) (0.0095) (0.0097) (0.0132) (0.0116)

Bolivia 0.345∗∗∗ 0.344∗∗∗ 0.371∗∗∗ 0.385∗∗∗ 0.403∗∗∗ 0.418∗∗∗ 0.441∗∗∗ 0.435∗∗∗ 0.435∗∗∗

(0.0201) (0.0202) (0.0172) (0.0162) (0.0153) (0.0185) (0.0170) (0.0151) (0.0200)

Brazil 0.336∗∗∗ 0.355∗∗∗ 0.370∗∗∗ 0.379∗∗∗ 0.387∗∗∗ 0.394∗∗∗ 0.402∗∗∗ 0.418∗∗∗ 0.422∗∗∗

(0.0149) (0.0126) (0.0147) (0.0139) (0.0135) (0.0128) (0.0152) (0.0140) (0.0103)

Chile 0.255∗∗∗ 0.254∗∗∗ 0.268∗∗∗ 0.273∗∗∗ 0.284∗∗∗ 0.289∗∗∗ 0.304∗∗∗ 0.309∗∗∗ 0.305∗∗∗

(0.0133) (0.0132) (0.0133) (0.0130) (0.0144) (0.0130) (0.0103) (0.0088) (0.0167)

Colombia 0.349∗∗∗ 0.349∗∗∗ 0.350∗∗∗ 0.354∗∗∗ 0.356∗∗∗ 0.354∗∗∗ 0.344∗∗∗ 0.334∗∗∗ 0.326∗∗∗

(0.0179) (0.0179) (0.0179) (0.0198) (0.0238) (0.0250) (0.0269) (0.0283) (0.0324)

Costa Rica 0.340∗∗∗ 0.339∗∗∗ 0.349∗∗∗ 0.350∗∗∗ 0.342∗∗∗ 0.340∗∗∗ 0.342∗∗∗ 0.341∗∗∗ 0.358∗∗∗

(0.0154) (0.0153) (0.0162) (0.0177) (0.0184) (0.0189) (0.0176) (0.0179) (0.0266)

Dominican Rep. 0.328∗∗∗ 0.328∗∗∗ 0.328∗∗∗ 0.330∗∗∗ 0.336∗∗∗ 0.338∗∗∗ 0.344∗∗∗ 0.353∗∗∗ 0.361∗∗∗

(0.0194) (0.0194) (0.0198) (0.0191) (0.0180) (0.0201) (0.0218) (0.0195) (0.0192)

Ecuador 0.398∗∗∗ 0.397∗∗∗ 0.414∗∗∗ 0.430∗∗∗ 0.450∗∗∗ 0.458∗∗∗ 0.480∗∗∗ 0.486∗∗∗ 0.508∗∗∗

(0.0116) (0.0115) (0.0113) (0.0148) (0.0154) (0.0146) (0.0168) (0.0177) (0.0191)

El Salvador 0.430∗∗∗ 0.429∗∗∗ 0.445∗∗∗ 0.457∗∗∗ 0.455∗∗∗ 0.457∗∗∗ 0.455∗∗∗ 0.452∗∗∗ 0.450∗∗∗

(0.0210) (0.0210) (0.0206) (0.0211) (0.0250) (0.0302) (0.0297) (0.0285) (0.0351)

Guatemala 0.566∗∗∗ 0.564∗∗∗ 0.559∗∗∗ 0.564∗∗∗ 0.568∗∗∗ 0.561∗∗∗ 0.568∗∗∗ 0.561∗∗∗ 0.546∗∗∗

(0.0093) (0.0094) (0.0089) (0.0122) (0.0104) (0.0139) (0.0113) (0.0142) (0.0158)

Honduras 0.490∗∗∗ 0.488∗∗∗ 0.496∗∗∗ 0.492∗∗∗ 0.497∗∗∗ 0.501∗∗∗ 0.505∗∗∗ 0.514∗∗∗ 0.498∗∗∗

(0.0176) (0.0177) (0.0147) (0.0144) (0.0175) (0.0189) (0.0207) (0.0224) (0.0315)

Mexico 0.223∗∗∗ 0.222∗∗∗ 0.234∗∗∗ 0.258∗∗∗ 0.269∗∗∗ 0.296∗∗∗ 0.315∗∗∗ 0.332∗∗∗ 0.337∗∗∗

(0.0116) (0.0117) (0.0138) (0.0131) (0.0144) (0.0176) (0.0158) (0.0159) (0.0235)

Nicaragua 0.377∗∗∗ 0.382∗∗∗ 0.389∗∗∗ 0.391∗∗∗ 0.385∗∗∗ 0.385∗∗∗ 0.386∗∗∗ 0.380∗∗∗ 0.395∗∗∗

(0.0123) (0.0126) (0.0107) (0.0123) (0.0151) (0.0191) (0.0225) (0.0231) (0.0197)

Panama 0.412∗∗∗ 0.411∗∗∗ 0.422∗∗∗ 0.427∗∗∗ 0.433∗∗∗ 0.438∗∗∗ 0.442∗∗∗ 0.436∗∗∗ 0.415∗∗∗

(0.0123) (0.0123) (0.0135) (0.0123) (0.0141) (0.0168) (0.0163) (0.0172) (0.0247)

Paraguay 0.334∗∗∗ 0.333∗∗∗ 0.340∗∗∗ 0.341∗∗∗ 0.345∗∗∗ 0.351∗∗∗ 0.359∗∗∗ 0.353∗∗∗ 0.346∗∗∗

(0.0146) (0.0147) (0.0166) (0.0188) (0.0191) (0.0204) (0.0214) (0.0215) (0.0244)

Peru 0.317∗∗∗ 0.316∗∗∗ 0.331∗∗∗ 0.342∗∗∗ 0.353∗∗∗ 0.362∗∗∗ 0.370∗∗∗ 0.377∗∗∗ 0.389∗∗∗

(0.0105) (0.0106) (0.0115) (0.0120) (0.0118) (0.0142) (0.0154) (0.0151) (0.0168)

Uruguay 0.344∗∗∗ 0.343∗∗∗ 0.372∗∗∗ 0.384∗∗∗ 0.398∗∗∗ 0.412∗∗∗ 0.408∗∗∗ 0.413∗∗∗ 0.399∗∗∗

(0.0115) (0.0114) (0.0107) (0.0115) (0.0098) (0.0105) (0.0115) (0.0150) (0.0225)

Venezuela 0.252∗∗∗ 0.251∗∗∗ 0.263∗∗∗ 0.272∗∗∗ 0.276∗∗∗ 0.286∗∗∗ 0.283∗∗∗ 0.300∗∗∗ 0.290∗∗∗

(0.0128) (0.0128) (0.0141) (0.0146) (0.0187) (0.0196) (0.0233) (0.0170) (0.0213)

Demographic controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Country fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes

City size Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 47240 46745 41955 38027 33470 29767 25766 21614 17457R2 0.361 0.362 0.372 0.379 0.388 0.395 0.401 0.401 0.398Outcome variables: Years of schooling.


17

Table 9: Estimations without normalization of own and parental education - Cohort 1988 - 1995

(1) (2) (3) (4) (5) (6) (7) (8) (9)no restrict. age>17 age>18 age>19 age>20 age>21 age>22 age>23 age>24

Argentina 0.245∗∗∗ 0.244∗∗∗ 0.215∗∗∗ 0.215∗∗∗ 0.209∗∗∗ 0.224∗∗∗ 0.230∗∗∗ 0.250∗∗∗ 0.235∗∗∗

(0.0209) (0.0211) (0.0185) (0.0198) (0.0167) (0.0222) (0.0348) (0.0118) (0.0110)

Bolivia 0.303∗∗∗ 0.303∗∗∗ 0.323∗∗∗ 0.331∗∗∗ 0.342∗∗∗ 0.365∗∗∗ 0.370∗∗∗ 0.392∗∗∗ 0.381∗∗∗

(0.0141) (0.0142) (0.0081) (0.0136) (0.0175) (0.0206) (0.0110) (0.0069) (0.0117)

Brazil 0.291∗∗∗ 0.305∗∗∗ 0.311∗∗∗ 0.299∗∗∗ 0.291∗∗∗ 0.254∗∗∗ 0.286∗∗∗ 0.290∗∗∗ 0.270∗∗∗

(0.0116) (0.0244) (0.0206) (0.0366) (0.0408) (0.0360) (0.0262) (0.0147) (0.0179)

Chile 0.226∗∗∗ 0.226∗∗∗ 0.248∗∗∗ 0.254∗∗∗ 0.289∗∗∗ 0.325∗∗∗ 0.369∗∗∗ 0.542∗∗∗ 0.331∗∗∗

(0.0293) (0.0293) (0.0197) (0.0262) (0.0291) (0.0576) (0.0966) (0.1369) (0.0577)

Colombia 0.296∗∗∗ 0.297∗∗∗ 0.293∗∗∗ 0.275∗∗∗ 0.219∗∗∗ 0.180∗∗∗ 0.203∗∗∗ 0.134∗∗ 0.187∗∗∗

(0.0161) (0.0160) (0.0280) (0.0368) (0.0598) (0.0517) (0.0511) (0.0505) (0.0286)

Costa Rica 0.241∗∗∗ 0.241∗∗∗ 0.248∗∗∗ 0.250∗∗∗ 0.270∗∗∗ 0.295∗∗∗ 0.359∗∗∗ 0.402∗∗∗ 0.393∗∗∗

(0.0140) (0.0140) (0.0132) (0.0189) (0.0423) (0.0360) (0.0219) (0.0201) (0.0220)

Dominican Rep. 0.274∗∗∗ 0.273∗∗∗ 0.255∗∗∗ 0.235∗∗∗ 0.241∗∗∗ 0.217∗∗∗ 0.264∗∗∗ 0.261∗∗∗ 0.202∗∗∗

(0.0258) (0.0258) (0.0328) (0.0314) (0.0477) (0.0491) (0.0152) (0.0392) (0.0244)

Ecuador 0.393∗∗∗ 0.394∗∗∗ 0.394∗∗∗ 0.374∗∗∗ 0.380∗∗∗ 0.367∗∗∗ 0.371∗∗∗ 0.263∗∗∗ 0.158∗∗∗

(0.0175) (0.0172) (0.0178) (0.0252) (0.0357) (0.0351) (0.0079) (0.0831) (0.0134)

El Salvador 0.345∗∗∗ 0.344∗∗∗ 0.371∗∗∗ 0.390∗∗∗ 0.412∗∗∗ 0.494∗∗∗ 0.561∗∗∗ 0.549∗∗∗ 0.434∗∗∗

(0.0206) (0.0209) (0.0351) (0.0520) (0.0639) (0.0358) (0.0416) (0.0576) (0.0155)

Guatemala 0.391∗∗∗ 0.391∗∗∗ 0.399∗∗∗ 0.369∗∗∗ 0.407∗∗∗ 0.360∗∗∗ 0.411∗∗∗ 0.398∗∗∗ 0.506∗∗∗

(0.0234) (0.0234) (0.0374) (0.0438) (0.0237) (0.0138) (0.0852) (0.1142) (0.0188)

Honduras 0.485∗∗∗ 0.485∗∗∗ 0.542∗∗∗ 0.545∗∗∗ 0.520∗∗∗ 0.512∗∗∗ 0.517∗∗∗ 0.631∗∗∗ 0.569∗∗∗

(0.0107) (0.0108) (0.0182) (0.0278) (0.0284) (0.0466) (0.0673) (0.0530) (0.0365)

Mexico 0.249∗∗∗ 0.249∗∗∗ 0.253∗∗∗ 0.245∗∗∗ 0.258∗∗∗ 0.246∗∗∗ 0.259∗∗∗ 0.142∗∗∗ 0.142∗∗∗

(0.0221) (0.0223) (0.0185) (0.0216) (0.0377) (0.0494) (0.0348) (0.0256) (0.0296)

Nicaragua 0.378∗∗∗ 0.420∗∗∗ 0.430∗∗∗ 0.434∗∗∗ 0.455∗∗∗ 0.492∗∗∗ 0.532∗∗∗ 0.661∗∗∗ 0.547∗∗∗

(0.0181) (0.0276) (0.0327) (0.0330) (0.0398) (0.0499) (0.0548) (0.0896) (0.0285)

Panama 0.383∗∗∗ 0.383∗∗∗ 0.379∗∗∗ 0.389∗∗∗ 0.388∗∗∗ 0.322∗∗∗ 0.332∗∗∗ 0.520∗∗∗ 0.499∗∗∗

(0.0422) (0.0424) (0.0458) (0.0620) (0.0506) (0.0811) (0.0445) (0.0222) (0.0533)

Paraguay 0.178∗∗∗ 0.178∗∗∗ 0.154∗∗∗ 0.146∗∗∗ 0.096∗∗ 0.076∗∗∗ 0.016 -0.064 -0.212∗∗∗

(0.0305) (0.0308) (0.0339) (0.0369) (0.0471) (0.0245) (0.0158) (0.1023) (0.0211)

Peru 0.320∗∗∗ 0.320∗∗∗ 0.300∗∗∗ 0.290∗∗∗ 0.270∗∗∗ 0.271∗∗∗ 0.334∗∗∗ 0.350∗∗∗ 0.327∗∗∗

(0.0239) (0.0239) (0.0259) (0.0165) (0.0274) (0.0115) (0.0186) (0.0174) (0.0197)

Uruguay 0.331∗∗∗ 0.331∗∗∗ 0.371∗∗∗ 0.376∗∗∗ 0.383∗∗∗ 0.375∗∗∗ 0.453∗∗∗ 0.637∗∗∗ 0.639∗∗∗

(0.0201) (0.0204) (0.0137) (0.0302) (0.0207) (0.0409) (0.0580) (0.0263) (0.0320)

Venezuela 0.199∗∗∗ 0.198∗∗∗ 0.195∗∗∗ 0.194∗∗∗ 0.190∗∗∗ 0.223∗∗∗ 0.322∗∗∗ 0.304∗∗∗ 0.215∗∗∗

(0.0186) (0.0182) (0.0216) (0.0177) (0.0185) (0.0181) (0.0254) (0.0799) (0.0292)

Demographic controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Country fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes

City size Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 16918 15880 11114 7984 5295 3517 2071 1093 595R2 0.397 0.389 0.391 0.382 0.397 0.418 0.452 0.491 0.492Outcome variables: Years of schooling.


18

(a)

(b)

Figure .119

(a)

(b)

Figure .220

Figure .3

21

Figure .4

22

(a)

(b)

Figure .523

Figure .6

24

intergenerational mobility and the rise and fall of

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