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Intergenerational Social Mobility in Latin America:

A Review of Existing Evidence

by

Viviane Azevedo*

Csar P. Bouillon

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* Viviane Azevedo ([email protected] ) and Cesar Bouillon ( [email protected] ) are at

the Research Department, Inter-American Development Bank. The views and interpretations in

this document are those of the authors and should not be attributed to the Inter-American

Development Bank, or to any individual acting on its behalf.

Abstract

This paper article reviews evidence on intergenerational social mobility in LatinAmerica. Several studies have us ed data sets that collect intergenerational socioeconomic information. The data on intergenerational social mobility , thoughlimited, suggest that social mobility is low in the region, even when comparedwith low social mobility developed countries like the United States and UnitedKingdom . The evidence suggests , with high levels of immobility at the lower andupper tails of the income distribution. While Latin America has improvedintergenerational education mobility in recent decades, which may have translatedinto higher income mobility for younger cohorts, the region still presents, exceptfor Chile, lower intergenerational education mobility than in developed countries.According to a series of studies The paper also review sed in the article studies onthe main determinants of the regions low levels of intergenerational socialmobility , can be associated with including social exclusion, low access to higher education, and labor market discrimination.

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1. Introduction: Perceptions of Intergenerational Mobility in Latin Americaand the Role of Social Exclusion

Latin America and the Caribbean continue to have relatively high income inequality compared to

other regions (see Figure 1). Even though this per se is a grave concern for policymakers in the

region, it is important to note that cross-sectional data show only snapshots of income

distributions in a moment in time. But income distributions may change significantly over time

due to the differential effects of economic growth, changes in human capital of different

population groups, changes in returns to assets, including human capital, and changes in labor

market opportunities, among other factors. These changes are important, as they may

systematically benefit or harm certain groups of the population, thus preventing societies fromensuring equal opportunities for all. Two societies with similar snapshots of income distribution,

for instance, can have different welfare levels depending on the degree of social mobility. The

analysis of social mobility aims to track the evolution of income distributions over time, looking

at the income dynamics of specific agents and their position across the income distribution over

long periods of time, and even over generations.

Figure 1.Inequality (Gini Coefficient) in LAC and other World Regions

0

10

20

30

40

50

60

Latin Americaand the

Caribbean

Sub-Saharan Africa

East Asia andthe Pacific

Middle East andNorth Africa

OECD South Asia Europe andCentral Asia

Source: Word Development Indicators data (Circa 2006)




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Depending on the importance of inherited abilities, intergenerational social mobility is

closely related to the degree of equality of opportunities in a country. What separates the

winners and losers or the haves and the have-nots in a society has been pointed out not only

to hinder economic growth but also to be a major force of political instability and violence.

Many authors have argued that one of the positive outcomes of market reforms and market-based

industrial and post-industrial economic structures is a constant expansion of social mobility

opportunities for the population (Corts and Escobar, 2005; Featherman, Jones and Hauser,

1975).

The concepts of social exclusion, income inequality, inequality of opportunities, poverty,

social mobility and growth are intimately related. As noted by Ocampo ( 2004) social exclusion

manifests itself in Latin America and the Caribbean most clearly in persistent unequal income

distribution, which gives rise to poverty that is worse than the regions level of development

would suggest.

This paper article summarizes key concepts related to ssocial mobility, as well as its

measurement and determinants, focusing on intergenerational mobility and relating them it to the

concept of social exclusion and to changes in democratization and its effects on social spending,

globalization and technological change and its effects on labor markets, with a focus on Latin

America. The paper will try to address a series of key questions related to intergenerational

social mobility in the region under the constraints imposed by existing data and studies. These

questions include: Can we measure intergenerational social mobility in Latin America? Is socialmobility in Latin Americait lower than in other regions in the world, and if so, why? What are

the its determinants of social mobility in Latin America ? How has social mobilityit evolved in

recent decades in Latin America ? How has it been affected What have been the effects of the

recentby democratization, increases in social spending and expansion of access to education,

changes in labor markets due to globalization and technological change, urbanization on social

mobility?

From a perspective of guaranteeing equal economic opportunities for all, intergenerational social

mobility should be the focus of social mobility analysis. Thus, while the paper cent ers on

intergenerational social mobility, it also analyzes existing evidence on intragenerational social

mobility, as recent developments in labor markets and social policies have been analyzed in the

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available literature through the lens of intragenerational mobility and the dynamics of labor

income.

The next section of this paper briefly summarizes the discussions on the definition of

intergenerational social mobilityis devoted to key concepts and definitions , and the third section

concentrates on earnings mobility elasticities a common measure used in empirical work in

Latin America.seeks to measure both intergenerational and intragenerational social mobility in

the region. Section 4 summarizes the methods used and the empirical evidence available for

measuring intergenerational social mobility in Latin America , comparing them with evidence for

OECD countries, while Section 54 focuses presents on subjective data and describes some

perceptions of mobility in the region and its relationship with inequality . , while Section 5

reviews perceptions of social mobility in Latin America. Section 6 5 collects evidence on

empirical work that shed light s on answering the following question: What hinders

intergenerational social mobility in the region? analyzes the determinants of social mobility in

Latin America, and Section 7 outlinespresents the final remarks. concludes.

2. What is Social Mobility ? : Some Basic Concepts

Social mobility is usually defined as the way individuals or groups move upwards or downwards

from one status or class position to another within the social hierarchy. 1 More specifically,

sociologists define social mobility as movement between different social classes or occupationalgroups and the related positive and negative returns. The latter are measured in terms of income,

employment security, and opportunities for advancement, among other considerations.

While the sociological literature generally defines social mobility in terms of movements

between social classes or occupational groups, the economics literature largely concentrates on

earnings or income and income mobility. While income has advantages, since it represents a

direct measure of resourcesat least at a least at a specific point in timesocial class may

represent a better measure of life opportunities.

From an economics point of view the concept of social mobility lacks a standard

(consensualized?) precise definition and varies from study to study. The general idea it conveys

is to break the dependence of individual outcomes on initial conditions. As pointed out by Fields

1 This definition is from a n online dictionary. http://www.allwords.com

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(2005), the concept of social mobility is multifaceted and can produce different empirical

answers to basic questions unless the mobility concept under examination is precisely defined.

Behrman ( 2000) states social mobility is used by scientists to refer to movements by

specific entities between periods in socioeconomic status indicators. This definition seems to be

representative of the economics literature on social mobility ; however we there is a need to

analyze the different mobility concepts that are embedded in it. 2 To shed light on such concepts,

we will follow the work of Behrman ( 2000), Fields ( 2000 and 2005 ) and Galiani ( 2006).

Moreover, in order to discuss social mobility it is necessary to have some measurement of social

inequality, in order to assess whether there is change or movement s in the so that we can argue

that there is change or movements along the distribution of some social outcome. Even though

the theoretical literature on social mobility usually focuses on broader measures of social

mobility, more specific indicators such as income, educational attainment or profession are used

to measure social mobility.

Timing is also an important dimension in measuring social mobility. In the

intragenerational mobility context, the time frame in consideration is individuals lives or

adulthoods. For example, individuals social status at a later date can be analyzed relative to their

social status at an earlier date. In the intergenerational mobility context, which is the focus of this

review, the recipient unit is usually the family, and the analysis is based on more than one

generation, focusing instead on dynasties by tracking social indicators of the parent and the child.

The choices of social indicators to track depend on what aspect of mobility is of interest. Some types of mobility are especially worrisome in the development literature. These

include (i) lack of total mobility in very unequal societies ;: (ii) asymmetrical changes in shares of

income among the poorest and richest tails of the income distribution, which is a concept

strongly linked to the literature of pro-poor growth ; and (iii) lack of mobility in the tails of the

income distribution. These movements may be caused by exclusion of the poorest groups from

basic assets and human capital accumulation or by exclusion of significant segments of the

population from high-earning assets, including higher education.

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3. Measuring Intergenerational Measuring Social Mobility: A simple

metricIn Latin America

As highlighted in the previous section, there is a range of intergenerational social mobility

measures and methodologies that have been used to measure social mobility in the region, in this

section we focus on a subset of them used by the empirical literature in the region.

Earnings Mobility Elasticities

This section concentrates on eEarnings mobility elasticities are one of the most a common

method to measure intergenerational social mobility and aremeasure used in empirical work in

Latin America and in developed countries . As highlighted in the previous section, there is a

range of social mobility measures and methodologies that have been used to measure social in

the region will be discussed in the sections that follow.

The most suitable data to empirically characterize intergenerational social mobility is

long spans of panel data tracking some socio economic variable across different generations of a

family dynastyon some socio economic variable that can characterize social economic status

related to status . However, But such type of data is usually available only for developed

countries and in some cases for small local areas in developing countries (see, for example,

Fuwa, 2006, for a study based on data on a village in the Philippines)..

3.1 Earnings M obility Elasticities

A large number ofMost empirical earning elasticity studies on estimatessocial mobility are based

on regression analyses offocus on regressing log earnings levels . Most estimates of

intergenerational earnings mobility useing a simple reversion to the mean empirical model ,

regression to mean, which is described below :

lnY i,t lnY t mean = P + F( ln Y i,t-1 lnY t-1mean ) + I i,t

lnY i,t =( lnY t mean

+ P F lnY t-1mean

)+ F ln Y i,t-1 + I i,t lnY i,t = E + FlnY i,t-1 + I i,t (1)

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where Y represents permanent income, t is an index of generations and Y mean is the average

permanent income of the individuals generation. The parameter F measures intergenerational

income elasticity, i.e., the intergenerational income correlation. The parameter E in equation (1)

is a fraction (1- F ) of the log of average income of generation t -1 plus log average income growth.

I reflects external characteristics that are not directly linked to parental income. (1- F ) is a

measure of the degree of intergenerational mobility.

In this model F reflects the fraction of economic advantage that is on average transmitted

across generations. The coefficient usually falls between 0 and 1. A positive F implies an

intergenerational persistence of income advantages in which higher than average parental income

is associated with higher than average childrens income. For example, if F is 0.3 5 and fathersearnings exceeds the mean sample income of his cohort by 30 percent, the model predicts that

his sons income will exceed the mean of the sons cohort by 10. 5 percent (i.e., 0.3 5*30 percent).

In this specification more mobile societies would have values of F closer to 0. This simple

model captures most intergenerational mobility estimates by looking at the fraction of permanent

income differences between parents that on average is observed among their children in

adulthood. Most elasticities found in the empirical literature for developing countries are based

on ordinary least squares estimation.

3.2 Caveats

There are some caveats that are worth pointing out when estimating equation (1) to measure

intergenerational social mobility. First, the estimation of the degree of intergenerational mobility

using earnings or wages is subject to bias due to measurement errors. This occurs not only

because of misreporting, but also because of life cycle fluctuations in earnings. As pointed out by

Grawe ( 2003) , there is evidence that increases in the variance of earnings along the life cycle

lead to smaller estimates of earnings persistence when the fathers are observed later in life. On

the other hand, in the sons sample, if we consider their income is considered at the beginning of

their career we know that some of the young professionals in the sample are going to have much

more rapid income growth than others. This measurement error is mean-reverting and leads to anunderestimation of the slope coefficient (as it compresses the variation of the dependent

variable).


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For this reason, it is recommended that an average of fathers income and the last

available observation(s) of the sons income be used. The most common approach to correct for

life-cycle bias when these data are not available is to estimate a least-squares regression of sons

earnings on fathers earnings controlling for ages on both generations. A We can also construct a

measure of permanent earnings for both fathers and sons can also be constructed to decrease

measurement errors (Ferreira and Veloso, 2006). Differences in the variance of income across

generations can also bias the estimation of intergenerational elasticity. To control for this, F can

be corrected by the ratio of standard deviation of income across generations, to estimate the

intergenerational partial correlation r :

r = F )(

)(

,

1,

t i

t i

LnY SD

LnY SD

Early studies for the US indicated rapid mean regression in income. 3 However, recent

studies show that such values were downward biased due to measurement errors. Solon (199 2)

and Zimmerman (199 2) use data from the Panel Study of Income Dynamics (PSID) and the

National Longitudinal Survey (NLS) and argue that the corrections for measurement error would

increase the estimated degree of income persistence by between 33 to 66 percent.

Another data problem that typically arises in this context is that the data set that contains

information on the sons income does not contain information on the fathers income ; this

problem is particularly very common in data sets from developing countries. If there are other

measures of social status, such as years of education, occupation or social class, a two-stage

estimation is recommended. 4 The first step consists of estimating the coefficients of empirical

earnings determinants for the fathers using another data set that is compatible with the fathers

generation. Then one can estimate the sons earnings based on the predicted income of the

father s. Note that the fathers social status is correlated with his earnings and is also a good

predictor of the sons income.

3 Earlier studies for the United States found a F around 0. 2 . See for example Sewell and Hauser (197 5), Biebly andHauser (1977) and Behrman and Taubman (19 85)4 Dunn ( 2004) refers to this technique as two-sample two-stage least squares.



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Under the two-sample instrumental variables estimation or two sample least squares

methodology (see Arellano and Meghir, 199 2 , and Angrist and Krueger, 199 2) equation (1)

would be estimated as:

LnY i,t = E + F(Z i,t-1 [ ) + R i,t (2)

Since LnY i,t-1 in equation (1) is not observed in data set I (i I ), in this regression [ is obtained

from the following regression:

LnY j,t-1 = Z j,t-1 [ + \ j,t-1 (3)

The error term in ( 2) includes determinants of sons income not correlated with fathers income,

biases in the estimation of [ and unobservables from (3):

R i,t = I i,t + F(Z i,t-1([ - [ )) + F\ i,t-1

The problem arises when in the second stage the fathers social status indicator is used to predict

the fathers earnings but is not added as an explanatory variable. This generates the omitted

variable bias that tends to overestimate intergenerational income elasticity, underscoring the

difficulty of comparing estimates of intergenerational mobility of earnings across countries.

4 . Inte rg ene r ational Social Ea r nin g s Mobility Estimates in Latin Ame r ica

This section serves two purposes: first it compiles the empirical evidence on social mobility for

Latin America aiming at comparing the region estimates with developed countries ; second, it

summarizes the methodologies used to estimate de degree of social mobility.

SeveralMost studies that estimate intergenerational social mobility for the region address the lack of long-run panel data by combining datasets that capture information on childrens income and

parents education and occupational variables with earlier labor market surveys to estimate



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parents wage regressions. In this two-stage approach the fathers social status is correlated with

his earnings and is also a good predictor of the sons income. Estimates for Chile, Brazil, and

Peru using this two-stage approach suggest that social mobility in Latin America is lower than in

developed countries, including those with the lowest levels of mobility (the United States and the

United Kingdom). These estimates, as well as estimates for selected developed countries are

presented in Figure 2.

Figure 2Intergenerational Income Elasticities for a sample of developed and

LAC countries

0.19 0.19

0.320.41

0.47 0.500.52

0.5 $ 0.60

0.000.100.200.300.400.500.600.70

N o r d i

c c o u n

t r i e s

C a n a d

a

G e r m

a n y F r a

n c e U S U K C h i l e

B r a z i

l P e

r u

Sour ce: For developed countries, Corak ( 2006) ; for Brazil, Ferreira and Veloso ( 200 64 ); for Chile,

Nez and Miranda ( 2006) ; for Peru, Grawe ( 2001).

The figure presents some estimates of the intergenerational elasticity of earnings or

wages that were presented in the literature. The dD eveloped country estimates are drawn from

Corak ( 2006) and are selected by the author as best comparators or adjusted to be comparable by

a meta-analysis procedure to the U.S. estimate of 0. 47 by Grawe ( 2004). The average U.S.

estimate is around 0. 40, while evidence for European countries and Canada shows that thesecountries have higher mobility (lower persistence estimates). For example, estimates for Finland

and Canada are 0.13 and 0. 23, respectively.



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For Latin America, the intergenerational elasticities reported in Figure 2 are not based on

father-child pairs, but rather combine information from two data sets to generate father-child

income pairs and estimate the intergenerational income elasticity using the two-sample

instrumental variables estimation or two-sample least squares methodology previously

discusseddescribed above .

In a recent study for Chile, Nez and Miranda ( 2006) use two-sample instrumental

variables estimation to calculate intergenerational income elasticity, finding estimates of 0. 52 -

0.58 for Greater Santiago and 0. 52 -0.67 for Chile as a whole. Their instrumental variable IV

estimate for all sons 23-65 in Greater Santiago including potential experience, occupation and

schooling to predict fathers income is the one included in Figure 2.

Ferreira and Veloso ( 20064) estimate the degree of intergenerational mobility of wages

for Brazil. They estimate equation (1) by a two-sample instrumental variable method and find

that the F coefficient for Brazil ranges from 0. 58 to 0.66 depending on the controls. In another

study for Brazil, Dunn ( 2004) calculates the intergenerational persistence of earnings and finds a

similar value of 0.69. 5

The estimates for Latin American and Caribbean countries presented in Figure 2 are

subject to present two sources of bias when compared with the U.S. estimates in Grawe ( 2004).

First, estimates in studies with data only from urban areas or capital cities are likely biased

downward, as they exclude less mobile rural and isolated areas that typically show lower long-

term incomes than urban areas. Second, an upward bias may arise from the fact that sons

cohorts cover longer spans than in developed countries. The evidence from Chile shows that,

either due to increased mobility or to life-cycle effects on earnings, mobility seems to be higher

for younger cohorts (see Table 1).

Table 1.

Chile Estimates for Intergenerational Mobility Elasticities

for Different Son Cohorts in Chile

Son Cohort Parent-son labor income elasticity

5 Dunn ( 2004) uses fathers education as an instrument. As pointed out before, this procedure causes an upward biasin the estimates



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23-34 0.46

35-44 0.52

45 -54 0.65

55 -65 0.58

Full Sample 0. 54

Sour ce: Nez and Miranda 2006.

4 .1. Nonlinear Earnings Mobility Estimates Rank Mobility

Intergenerational earnings elasticities assume that the income advantage that parents transmit to

their offspring is linear across the distribution of parents income. This assumption, however, can

be restrictive. High levels of social immobility at the lower tails of the parents childrens income

distributions (i.e., high intergenerational transmission of the income disadvantage of the poorest

parents) can be associated with exclusion from basic services and markets (due to geographical

isolation or segregation) or with labor market discrimination. Likewise, low mobility at the upper

tail may reflect exclusion of the majority of the population from high income earning

opportunities (higher education). Credit constraints tend to decrease mobility, since investment

in children usually depends on family resources. This may explain why persistence is higher at

the upper end of the conditional wage distribution.

In order to capture nonlinear patterns of intergenerational mobility, researchers use

regressions techniques that include a quadratic or cubic term as well as transitional matrices such

as rank mobility, which estimates the probability that the offspring will belong to a particular

category given the fathers category.

It is a common practice to estimate nonlinear regressions of sons earnings on fathers

earnings. For example, Behrman and Taubman (1990), Solon (199 2), and Grawe ( 2001) include

a quadratic term in their mobility regressions, and they implicitly assume that the regressionwould be linear in the absence of borrowing constraints. As pointed out by Grawe ( 2001),







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nonlinearities may occur even in the absence of borrowing constraints depending, for example,

on how ability affects wages.

4 .1.1 Transition Matrices: Rank Mobility

The degree of rank mobility analyzed through transition matrices is recognized in the

literature as the first methodological way of estimating mobility, even before mean regression.

When data are represented in a transition matrix, much information is compressed into

bracketsthe principal shortcoming of this approach, since much information is thereby lost.

For example, consider a transition matrix that analyzes income levels. Income is a cardinal

measure, but in order to be displayed as a transition matrix it becomes an ordinal measure

(income ranks), reducing the information into income groups while the data have many income

levels.

The reading of a transition matrix, however, is straightforward, as the matrix shows the

extent to which the distribution of childrens status depends on their parents status. Table 2

below shows transition matrices for several developed countries and two Latin America

countries: Brazil and Chile.

Table 2 . Comparative Evidence on Income Persistence

in Bottom and Top Quintiles and Quartiles(transition matrices between father and son position in income distribution)

Country Study

Bottom

Quartile

Bottom

Quintile

Top

Quartile

Top

Quintile

Developed Count r ies

Canada Fortin and Lefebvre (199 8) n.d. n.d. 0.3 2 -0.33 n.d.

Sweden Osterberg ( 2000) n.d. n.d. 0. 25 n.d.

UK Blanden, Gregg and Machin ( 2005) 0.37 n.d. 0. 40 n.d.

US Peters (199 2) n.d. n.d. 0.36-0. 40 n.d.

Grawe ( 2001) 0 40 n.d. 0. 41 n.d.

Latin Ame r ica

Brazil Ferreira and Veloso ( 20064) n.d. 0.3 5 n.d. 0. 43


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Chile Nuez and Miranda ( 2006) 0.39*-0. 50 0.30*-0.37 0. 54 -0.55 * 0. 47-0. 57*

n.d. = no data.

* Estimate comes from predicted income distribution.

Transition matrices for Brazil suggest a strong intergenerational persistence of wages at

both ends of the sons conditional wage distribution. This implies that wage mobility is low at

both tails of the distribution. In the case of Brazil, the probability that the sons of the fathers in

the lowest quintiles will remain there is 3 5 percent while the probability that the sons of the

fathers in the richest quintile will remain in the richest quintile is 43 percent (Ferreira and

Veloso, 20064). The lack of mobility at the tails of the income distribution may reflect two

sources of exclusion: the lack of opportunity for the children of the poor to acquire better skills

and improve their employment prospects and the reproduction of socioeconomic privileges

among the children on the well-off.

Additionally, the evidence shows that there is more upward mobility from the bottom of

the earnings distribution than downward mobility from the top. This means that there are more

chances for the poor to became rich than for the rich to become poor. In the case of Brazil, the

estimates of Ferreira and Veloso ( 200 64 ) show that the probability that an individual will move

from the lowest wage category to the highest is 6 5 percent while the probability of falling from

the highest to the lowest wage category is around 57 percent. The same pattern also holds for the

United States (Zimmerman, 199 2) and the United Kingdom ( Dearden, Machin and Reed, 1997)

Transition matrices also provide evidence on different sources of immobility along the

income distribution by population groups. Evidence from Brazil (Table 3) shows that, while

lower-tail immobility is particularly high among excluded groups such as Afro-descendants,

upper tail immobility is more prevalent across non-excluded groups such as whites.

T able 3. Income Persistence in Bottom and Top Quintiles by Race for Brazil

Population

Group

Bottom

Quintile

T op

Quintile

All 3 5 43










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Blacks 47 23

Whites 25 50

Sour ce: Ferreira and Veloso ( 20064).

Upper-tail immobility is usually linked to low access to high education opportunities , or

to segmentation in labor markets. Institutions such as credit markets, government loan guarantee

programs, and public schooling are important in determining the degree of income mobility.

Ferreira and Veloso ( 20064) present nonlinear estimates on the persistence of wages. The degree

of persistence is 0.6 2 for the sons of fathers with below-median wages, but much lower, 0. 53, for

fathers with above-median wages. This difference is consistent with the borrowing constraints

theory, since rich families are less likely to be constrained. 6 Andrade et al. ( 20043), also

considering for Brazil, test whether the presence of borrowing constraints affects the degree of

intergenerational persistence, and the evidence suggests that borrowing constraints may be an

important determinant of intergenerational mobility in Brazil.

4 .1.2 Rank Regressions

An alternative methodology for analyzing rank mobility is the rank regression . A rank

regression analyzes the relationship between earnings ranks instead of earnings levels . T he

equation below represents the rank alternative to equation ( 1 ).

r i,t = E r + Fr r i,t -1 + I r i,t (4)

where r i,t is the sons rank in the earnings distribution and is a function of the fathers rank

in his earnings regression r i,t -1. Fr is the rank degree of persistence, and it is equal to the

rank correlation coefficient, by definition it must lie between 0 and 1 if we assume a

positive correlation .

It is important to point out that the rank regression equation and the level -

regression equation are two different measures and that one does not necessarily imply the

6 Grawe ( 2001) points out that additional tests are needed to confirm the hypothesis that the degree of persistencedeclines with fathers wages are due to credit constraints. For the Brazilian case see Andrade et al.( 2003).



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other . For example, if F < 1 in the level regression, then the income expectations of future

generations will be mean reverting as the time horizon increases, but there are no

implications for the rank mobility, meaning that across generations the incomes will get

closer to the mean and the variance of the income distribution will diminish . However, the

poor sons in future generations may descend from todays poor generation since the F

azevedo and bouillon_sep 22

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