metadata: authors: 0: author: Munoz Saavedra,Ercio Andres
count: Latin America & Caribbean
docna: 0: docna: Does It Matter Where You Grow Up ? Childhood
            Exposure Effects in Latin America and the Caribbean
docty: Policy Research Working Paper
abstracts: cdata!: This paper studies whether the observed
            differences in intergenerational educational mobility across
            regions in Latin America and the Caribbean are due to the
            sorting of families or the effect of growing up in these
            different places. The analysis exploits differences in the
            ages of children at the time their families moved across
            locations, to isolate regional childhood exposure effects
            from sorting. The findings show a convergence rate of 3.5
            percent per year of exposure between age 1 to 11, implying
            that children who moved at age of 1 would pick up 35 percent
            of the observed differences in mobility between origin and
            destination. These results are robust to using a
            specification that identifies the effect of place within
            households, the use of only anomalously high migration
            outflows, instrumenting the choice of destination with
            historical migration, and a combination of both approaches.
display_title: Does It Matter Where You Grow Up
            Childhood Exposure Effects in Latin America and the Caribbean
listing_relative_url: /research/2022/05/33812084/matter-grow-up-childhood-exposure-effects-latin-america-caribbean
topicv3: Economic and Social Mobility m1326259 1152,Educational Equity m1326554 2168,Equality of Opportunities m1327180 1167,Spatial Inequality m1327181 1166,Early Child Development m1327727 479
publishtoextweb_dt: 2022-05-06T00:00:00Z
dois: 10.1596/1813-9450-10037
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1462299
model_extractions: Policy Research Working Paper 10037 Does It Matter Where You Grow Up?

Childhood Exposure Effects in Latin America and the Caribbean _Ercio Muñoz_ Poverty and Equity Global Practice May 2022
Policy Research Working Paper 10037

# **Abstract**

This paper studies whether the observed differences in intergenerational educational mobility across regions in Latin America and the Caribbean are due to the sorting of families or the effect of growing up in these different places. The analysis exploits differences in the ages of children at the time their families moved across locations, to isolate regional childhood exposure effects from sorting. The findings show a convergence rate of 3.5 percent per year of exposure between age 1 to 11, implying that children who moved at age of 1 would pick up 35 percent of the observed differences in mobility between origin and destination. These results are robust to using a specification that identifies the effect of place within households, the use of only anomalously high migration outflows, instrumenting the choice of destination with historical migration, and a combination of both approaches.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at emunozsaavedra@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Does It Matter Where You Grow Up? Childhood Exposure Effects in Latin America and the Caribbean**<sup>∗</sup>

Ercio Mu ̃noz<sup>†</sup>

**JEL-Codes** : D63, I24, J13, J62, R23, Z13.

**Keywords** : _Educational intergenerational mobility, Geography, Latin America, Neighborhood effects_ .

> ∗I thank Jonathan Conning, N ́uria Rodr ́ıguez-Planas, Wim Vijverberg, Roy van der Weide, Chico Ferreira, Lucila Berniell, Dolores de la Mata and several seminar/conference participants for helpful comments. Pablo Vargas and Marcelo Gomez provided outstanding research assistance. I greatly acknowledge the financial support of CAF -development Bank of Latin America, the Center for Latin American, Caribbean and Latino Studies at CUNY, the doctoral student research grant round 16 from CUNY, and the Mario Capelloni dissertation fellowship from CUNY. The usual disclaimers apply. First version: July 2021.

> †Email: emunozsaavedra@worldbank.org.
# **I Introduction**

There is substantial variation in intergenerational mobility (IGM) in education–measured as the likelihood of children completing at least primary education when parents did not complete primary school–across provinces and districts of 24 countries in the Latin America and the Caribbean (LAC) region (see Munoz, 2021a).<sup>1</sup> This variation in upward mobility can be the result of sorting of families (i.e., the fact that different families choose to live in different places) or, alternatively, can be the result of the existence of causal effects of regions (i.e., the place where people live affects the level of upward mobility independently of the characteristics of the family).

In this paper, I use 21 census samples that span 11 countries in LAC to investigate how much of the variation in upward mobility can be attributed to the effect of being exposed to certain places during childhood. In particular, I use families that moved across districts/provinces and exploit variation in children’s age at the time of the move to isolate sorting from regional childhood exposure effects. This empirical strategy that relies on observational data was first proposed in Chetty and Hendren (2018a) to study the effect of commuting zones on IGM in income in the US and adapted by Alesina, Hohmann, Michalopoulos, and Papaioannou (2021) to the context of educational IGM in developing countries.

I find evidence of regional childhood exposure effects as well as significant sortingselection. I estimate a convergence rate of 3.5% per year of exposure between the ages 1 to 11, which implies that children who move at age 1 would pick up 10 _×_ 3 _._ 5 = 35% of the observed difference in permanent residents’ outcomes between their origin and destination regions. In addition, I find significant selection effects of approximately 42%, which implies that individuals who move to a region where permanent residents have 1 percentage point higher upward mobility have close to 0.42 higher mobility themselves purely due to selection. Given that exposure effects are identified under the strong assumption that the timing of

> 1See Figure A1 in the Appendix for a reproduction of the map of IGM at the district-level.

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the move is unrelated to other determinants of primary completion, I also estimate these effects within households, using plausibly exogenous migration outflows, and instrumenting destinations with past migration patterns. All these exercises produce results that are qualitatively similar. Moreover, I explore potential heterogeneity by estimating childhood exposure effects by sub-populations and I explore an alternative outcome that computes upward mobility with secondary education as the level of interest. I do not find much evidence of heterogeneity and the results of the estimates using secondary education show similar patterns to the baseline exercise but with larger convergence rates.

This paper is related to three strands of the economic literature. First, it is related to the recent literature that estimates intergenerational mobility (IGM) in education within countries and documents important variation across places in developing countries (see for example, Alesina, Hohmann, Michalopoulos, & Papaioannou, 2020; Alesina et al., 2021; Asher, Novosad, & Rafkin, 2021; Munoz, 2021a, 2021b; Van der Weide, Ferreira de Souza, & Barbosa, 2020). This is an extension of the literature on intergenerational income mobility within countries initiated by Chetty, Hendren, Kline, and Saez (2014) that is mostly focused on high-income economies, and the intergenerational mobility in education at the countrylevel (see Torche, 2019, for a survey focused on developing countries).<sup>2</sup> Second, it adds to the literature that studies how the place in which children grow up matters for medium or long-term socioeconomic outcomes (see Chyn & Katz, 2021, for a recent survey about place effects). This includes studies that use random or quasi-random variation to identify the effects (see for example Chetty, Hendren, & Katz, 2016; Chyn, 2018; Damm & Dustmann, 2014) and those using observational data exploiting samples of movers (Alesina et al., 2020, 2021; Chetty & Hendren, 2018a; Deutscher, 2020; Lalibert ́e, 2021; Ward, 2020). Third, this paper relates to the set of papers studying the different drivers of intergenerational mobility with the help of data at the sub-national level and the application of quasi-experimental methods (see for example, Card, Domnisoru, & Taylor, 2022; Derenoncourt, 2021; Sharkey

> 2Important recent contributions are Narayan et al. (2018); Van der Weide, Lakner, Gerszon Mahler, Narayan, and Ramasubbaiah (2021) documenting IGM in education at the country-level for 153 economies.

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& Torrats-Espinosa, 2017).

The paper is organized as follows. Section II describes the data set. Section III discusses the empirical methods. Section IV goes over the empirical results. Section V offers some robustness exercises and additional results. Finally, section VI concludes with final remarks.

# **II Data**

I use census data obtained from IPUMS International (Integrated Public Use Microdata Series, IPUMS, 2019), hosted at the University of Minnesota Population Center, which reports harmonized representative samples (typically 10%) of full census micro data sets for a large number of countries.

**Countries.** I use individual records, retrieved from 21 national censuses from 11 countries: Brazil, Colombia, Cuba, Ecuador, El Salvador, Guatemala, Jamaica, Mexico, Panama, Trinidad and Tobago, and Uruguay (see Table A1 in the Appendix for the details about years and the fraction of the data available by census sample). There is a much longer list of censuses available for Latin America and the Caribbean that can be used to estimate educational intergenerational mobility (see Munoz, 2021a), however, the main constraint that limits the number of samples being used in this study is the availability of the information necessary (which is detailed below) to analyze within-country migration as it is required in the empirical strategy.

**Education.** The data set contains a variable reporting educational attainment that is re-coded by IPUMS to capture educational attainment in terms of the level of schooling completed without necessarily reflecting any particular country’s definition of the various levels of schooling in terms of terminology or number of years of schooling. It contains four categories: 1) Less than primary completed, 2) primary completed, 3) secondary completed, and 4) university completed. This variable applies, to the extent possible, the United Nations standard of six years of primary schooling, three years of lower secondary schooling, and

4
three years of higher secondary schooling. In addition, there is a variable reporting years of schooling available for some samples (17 out of 21 samples).

**Linking generations.** The data collection is organized at the household level,<sup>3</sup> so it is possible to link individuals who live with their parents in the same household at the time of the interview using a variable that details the relationship between each individual and the head of the household. For simplicity, I use individuals linked to their probable father and probable mother according to the procedures used by IPUMS for family interrelationships.<sup>4</sup> This method of linking generations differs from the one used in Munoz (2021a) for the main analysis, which links individuals ages 14-25 to all the older relatives living in the same household, however, as mentioned in the robustness section of Munoz (2021a), both methods give indicators of mobility that are close to being perfectly correlated.

For the main analysis, I use a sample of individuals co-residing with at least one “probable” parent, I focus on individuals with age in the range 14-25 in the main analysis where completion of primary education is the outcome of interest. However, when I explore completion of secondary education as the outcome of interest, I use individuals with age in the range 18-25. These age ranges are chosen to target individuals with an age sufficiently large so they can complete the educational level of interest if progressing in line with the official age for which the educational system is designed but young enough so they are likely to still live with their parents. The effort of maximizing the co-residence rate attempts to minimize potential truncation bias in the estimates of intergenerational mobility (see Emran, Greene, & Shilpi, 2018; Francesconi & Nicoletti, 2006). Nonetheless, Munoz and Siravegna (2021) show that the bias is small for estimates of absolute mobility that use census data. In addition, there is a low level of re-ranking when these estimates are used to rank economies across time and space by level of mobility relative to the ranking obtained with estimates that use retrospective information (i.e., surveys that ask all individuals for the level of education of their parents).

> 3I exclude several census samples for which this is not the case.

> 4More details can be found in the following link: https://usa.ipums.org/usa/chapter5/chapter5.shtml.

5
<!-- Start of picture text --> 0 faction: by parent’: : attainnvent, 1<br>'<br>E<br>By<br>=6 = Less than primary<br>5 4 = Atleast primary<br>3<br>8<br>>2<br>0<br>Less than primary- at teast primary-<br><!-- End of picture text --> <!-- Start of picture text --> 0 fraction2 by parentalA 6 attainment8 1<br>1<br>5<br>s<br>a® 5 = Less than secondary<br>5 4 = Atleast secondary<br>no]<br>fo)<br>= 2<br>0<br>Less than secondaYcleast secondary<br><!-- End of picture text -->
Provinces, which are administrative units roughly similar to states in the US; and 2) Districts, which are fine administrative units similar to counties in the US. These variables contain the geographies for every country harmonized spatio-temporally to provide spatially consistent boundaries across samples in each country.

**Migration.** IPUMS International reports, when available in the original source, the administrative unit in which the individual was born, the previous location of the previous residence, and the number of years living in the current location. For some censuses, this information is all at the level of province (e.g., Brazil 2010 has only state-level information) and for others, information at the district level is also available (e.g., Cuba 2012 has municipality-level information). I use these variables to identify a sample of movers, i.e. individuals who at the time of the interview live in a place different than the one in which they were born, as well as a sample of permanent residents (or non-movers) that correspond to individuals living in the same place in which they were born. Hence, I consider the place of origin as the place where they were born, the current place as the destination, and I construct a variable that records age at move using current age minus the time they have been living in the current place.

Table 1 provides a set of summary statistics comparing the characteristics of individuals (and their families) that are non-movers versus those that are movers.<sup>5</sup> In terms of family characteristics, movers show slightly higher income percentile (according to average income of parents) and urban status (at destination in the case of movers). However, permanent residents have a higher share of dwelling ownership and are more likely to be couple families. In the case of educational outcomes, individuals who are permanent residents complete primary education with exactly the same likelihood but exhibit lower years of schooling than movers. Overall, the table suggests that permanent residents are relatively similar to those individuals who move (or at least they are not remarkably different).<sup>6</sup>

> 5A caveat of this comparison is that some characteristics such as household income and years of schooling are not available in all the surveys, hence, the sample composition may vary across rows.

> 6Table A2 in the Appendix describes the sample size by census sample distinguishing between movers

7
**Table 1:** Summary statistics for permanent residents and movers

||Mean|Permane<br>Std. dev.|nt residen<br>Median|ts<br>N|Mean|Mo<br>Std. dev.|vers<br>Median|N|
|---|---|---|---|---|---|---|---|---|
|_Family characteristics_|||||||||
|Income percentile|49.72|28.89|49|8409346|52.52|28.50|54|928209|
|Ownership of dwelling|0.76|0.43|1|12843887|0.60|0.49|1|2242290|
|Urban status|0.72|0.45|1|12542856|0.81|0.39|1|2020376|
|Number of siblings|2.23|1.30|2|13257078|2.22|1.38|2|2341902|
|Couple family|0.56|0.50|1|13257078|0.40|0.49|0|2341902|
|_Educational outcomes_|||||||||
|Completed primary|0.63|0.48|1|13234115|0.63|0.48|1|2324712|
|Years of schooling|6.16|3.68|6|8993451|6.34|3.67|6|1763570|

Notes: The samples consider the characteristics of individuals age 14-25. Permanent residents are those individuals living in the same region in which they were born. Movers are those individuals living in a region different than the one where they were born. Average income percentil is computed using percentiles by census-year using the average total income of parents in the previous month or year. The sample size varies across variables due to different availability of data between censuses and also differences in the amount of missing data.

# **III Methods**

As discussed in a recent survey by Chyn and Katz (2021), the potential influence of neighborhoods on individual outcomes can operate through contemporaneous (or situational effects) and past neighborhoods through exposure (or developmental) effects that accumulate during childhood. The econometric approach followed here is focused solely on the latter, which has the key prediction that the gains from moving to places with beneficial characteristics is larger for children who are younger at the time of the move and thus exposed for a longer period.

Given the focus on exposure, my goal is to determine how much a child’s potential outcomes would improve on average if he were to grow up in a region where the permanent residents’ outcomes are 1 percentage point higher. Following Chetty and Hendren (2018b), I define exposure effect at age _m_ as the impact of spending year _m_ of one’s childhood in a region where the outcomes of permanent residents are 1 percentage point higher.

and non-movers.

8
To understand better the meaning of exposure effects consider the following hypothetical scenario. If I were able to randomly assign individuals to new regions _d_ starting at age _m_ for the rest of their childhood, a simple econometric approach to estimate the mean impact of spending year _m_ of childhood and onward in a region where permanent residents have 1 percentage point better outcomes (here defined as _ξm_ ) would be: where _αm_ are age at the time of move fixed effects, _y_ ̄ _db_ is the average outcomes of permanent residents in region _d_ of the same cohort _b_ as the individual _i_ , and _εi_ captures all other determinants of the outcomes studied. The fact that individuals were randomly assigned would ensure that the OLS estimate of _ξm_ in equation 1 is consistent. These coefficients then could be used to compute the exposure effect as _ωm_ = _ξm − ξm−_ 1.

However, the estimation of equation 1 in observational data will almost surely give an inconsistent estimate because there may be unobservables correlated to the outcome of interest. Hence, the empirical approach to obtain an estimate of exposure effects requires a strong assumption, which is discussed after presenting the baseline semi-parametric specification.

I use the research design proposed by Chetty and Hendren (2018a) to estimate the role of commuting zones in shaping intergenerational income mobility in the US, which was adapted to educational mobility by Alesina et al. (2020, 2021). This empirical strategy uses observational data to isolate regional childhood exposure effects from sorting and consists of two steps. First, I estimate the observed outcomes for individuals who are permanent residents, by region and birth decade. Second, I use individuals who move during childhood between regions to estimate how much of the previously estimated differences in intergenerational mobility between the place of origin and destination (for individuals in their same birth decade) is reflected in their chances of finishing primary school and how this changes with the age at the time of the move. If regions affect individual mobility, the impact should be 9
stronger, the longer the exposure to the new region (i.e., the difference between origin and destination is reflected in their chances in a larger magnitude when the move happens in earlier ages). These two steps are explained in detail in the next subsections.

# **III.1 Predicted upward mobility using permanent residents**

As a first step, I estimate the level of upward mobility in education (by region and birth decade) defined as the likelihood of obtaining at least a primary education for individuals whose parents did not finish primary school using a sample of permanent residents with age between 14 and 25. I define permanent residents as individuals who live at the time of the census interview in the same region (i.e., province or district) in which they were born. Hence, the indicator of upward intergenerational mobility ( _γrb_ ) used corresponds to the estimation of the following conditional probability by region _r_ and birth cohort _b_ : where _Dirb_<sup>_children_</sup> is an indicator variable equal to one if individual _i_ , living in region _r_ , and born in decade _b_ obtains at least primary education. Similarly, _Dirb_<sup>_parent_</sup> = 0 is an indicator variable equal to one if the parents of the same individual were able to complete at least primary school.

The sample estimates of _γrb_ are then used to construct the main variable of interest for the next step, which is defined as: This variable captures the difference in upward intergenerational mobility of permanent residents of a given birth decade _b_ living in region _i_ and those living in region _j_ .

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# **III.2 Estimating regional childhood exposure effects using movers**

In the second step, I consider only individuals with age between 14 and 25 who moved between regions at age 1-20 and whose parents did not complete primary school. The idea is to estimate how the chances of finishing primary school are affected by the difference between the chances of permanent residents in their new region of residence relative to the region of origin depending on the age at which they move. This is estimated using two econometric approaches, a semi-parametric and a parametric specification.

**Baseline semiparametric specification.** Following Alesina et al. (2021), I estimate regional childhood exposure effects with the following specification<sup>7</sup> :

where the subscripts refer to individual _i_ , member of household _h_ , born in decade _b_ , and who moves at age _m_ from region of origin _o_ to region of destination _d_ . Parameters _αob_ are fixed effects by region of origin and birth cohort. Parameters _αm_ are fixed effects by age at which the individual moved to capture disruption effects. The set of interactions allow the effect of ∆ _odb_ , which is the difference in upward mobility between the region of origin _o_ and region of destination _d_ for cohort _b_ as defined in the previous section (see Equation 3), on the chances of obtaining at least primary school to differ by age at which individuals move.

As discussed in Chetty and Hendren (2018b), the set of _βm_ coefficients captures both a standard selection effect that measures how parental inputs and other determinants of children’s outcomes for movers co-vary with permanent resident outcomes, and the impact of regions ( _ξm_ ) as defined in equation 1. The key identification assumption necessary for causal interpretation is that the timing of the move is unrelated to other unobserved determinants of primary completion. In other words, families that moved may be different from those who did not, but migrant families may differ only in their timing (e.g., two families moving from

> 7A formal and detailed description of this research design can be found in Chetty and Hendren (2018a).

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region _i_ to _j_ when the child is 2 years old and 3 years old are not systematically different). This assumption allows me to use the estimates of _βm_ for different ages to get the exposure effect _ωm_ as _βm_ = _ξm_ + _φ_ , where _φ_ denotes the age invariant selection effect that gets canceled out when taking the difference.

Intuitively, this specification tries to mimic in a quasi-experimental way the ideal (but infeasible) experiment where I randomly force families to move from one region (with a given IGM) to another region (with different level of IGM) at different ages to study how IGM varies by the age of move.

**Parametric specification.** I also estimate regional childhood exposure effects using the following more parsimonious specification:

where now the equation imposes a piecewise linear structure, allowing the regional exposure effects to differ for pre-school years (ages 1-4), the ages (arguably more) relevant for primary school (ages 5-11), and post-primary education years (ages 12-20).

The only difference between the specifications discussed in this section (see equations 4 and 5) and the ones used in Alesina et al. (2021) is that I do not include a set of interactions between indicator variables by birth cohort and the difference in upward mobility between destination and origin. These interactions are motivated in Chetty and Hendren (2018b) to alleviate concerns about differences in measurement error across cohorts due to changes in the ability to measure parents’ locations. These concerns are not present in my set up and adding them makes little difference in practice.<sup>8</sup>

> 8The results including this set of interactions are available in the Appendix.

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**Within-household identification.** In addition, I estimate similar specifications adding household fixed effects such that equation 4 in the baseline semi-parametric approach becomes:

and similarly, equation 5 of the parametric approach becomes: In both cases, the identification now comes from comparing siblings in households with more than one individual with age between 14 and 25. This tackles concerns of endogeneity as a result of time-invariant unobservables at the household-level.

# **IV childhood in LAC Regional exposure effects**

In this section, I present the main empirical results. First, I display the variation in upward mobility between origins and destinations faced by those who move across regions. Second, I report the results of the baseline semi-parametric estimation. Third, I report the results of the parametric estimation.

## **IV.1 Observed differences between origin and destination**

As discussed in the previous section, the empirical strategy uses the intergenerational upward mobility of permanent residents to describe regions. In practice, this implies that the difference in this indicator between region of destination and region of origin becomes the independent variable of interest in the regressions. Figure 2 displays the distribution of this 13
<!-- Start of picture text --> ~~ Mean: .069<br>°<br>a<br>So ~ ~ -<br>“| -5 0 5 1<br>Observed difference in upward mobility between origin and destination<br><!-- End of picture text -->

<!-- Start of picture text --> + Mean: .079<br>oo)<br>a<br>oO == -<br>“| -5 0 5 1<br>Observed difference in upward mobility between origin and destination<br><!-- End of picture text -->
negligible selection effects that are captured by the average level (0.418) of the coefficients _βm_ associated to ages at move greater than 11, the age at which most individuals finish primary education if they follow the typical schedule of the educational system. This implies that individuals who move to a region where permanent residents have 1 percentage point higher upward mobility have 0.418 higher mobility themselves purely due to selection effects. Second, a flatter segment can be appreciated for very young ages that corresponds to the period before starting formal schooling and a much stepper decline is observed between ages 5 and 11. The average exposure effect is obtained as the average _βm − βm−_ 1 between ages 1-11 is 3.5%. This implies that a child with parents that did not complete primary school that moves at age 1 to a region with higher chances of finishing primary will pick up on average 35% of the difference between origin and destination.

These results are remarkably similar to the findings in previous studies for different contexts and measures of intergenerational mobility (see for example, Alesina et al., 2021; Chetty & Hendren, 2018a; Deutscher, 2020).

**Within-household results.** The previous results are based on the strong assumption that selection does not vary with age at move. An obvious concern is that this assumption may not hold in practice and for that reason Chetty and Hendren (2018a) offer a set of validation exercises to mitigate those concerns and validate the proposed research design. In line with that and with the aim of adding evidence in support of the empirical strategy, I also estimate these effects exploiting variation within households. In other words, I run a similar regression as before but including household fixed effects (see equation 6) to base the estimation on the comparison of “siblings” of different ages that had different lengths of exposure to the same places.

Figure 4 plots the estimated _βm_ coefficients by age at move using a restricted sample of households with more than one individual aged 14-25. Figure 4a plots the estimated coefficients obtained when the model does not include household fixed effects and Figure 4b plots the same estimated when the model includes household and there15
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specification), and similarly a flatter segment after age 11.<sup>10</sup> The choice of age kinks is mainly motivated by how the educational system is structured. However, a data driven approach would point out to the same kinks (see Figures A3 in the Appendix). Table A4 in the Appendix reports coefficient estimates and the respective measures of goodness of fit for different options in terms of the age kinks used in the parametric specification. Metrics such as the R-squared and the information criteria AIC or BIC indicate that the model provides practically the same fit if the kinks are moved from 4 to 5 (i.e., first segment being ages 1-5) and 11 to 10 (i.e., last segment being ages 10-20).

**Table 2:** Parametric estimates of regional childhood exposure effects

|||(1)<br>IGM|(2)<br>IGM|(3)<br>IGM|
|---|---|---|---|---|
|_β_:|1-4|0.000524|-0.0140*|-0.0247**|
|||(0.007)|(0.008)|(0.012)|
|_γ_:|5-11|0.0494***|0.0512***|0.0391***|
|||(0.004)|(0.005)|(0.006)|
|_δ_:|12-20|0.0155***|0.0201***|0.0125***|
|||(0.003)|(0.004)|(0.004)|
|R-|squared|0.095|0.092|0.685|
|N||436792|271984|271984|
|Ho|usehold FE|No|No, hhfe sample|Yes|

Notes: The dependent variable in all specifications is an indicator that takes the value of one for children of parents without completed primary school who have completed at least primary education and zero otherwise (IGM). The independent variables comprise a linear in origin-average-IGM (calculated for the birthcohort relevant to the individual among nonmovers) term, ageat-move indicator variables, birth-decade _×_ destination indicators interacted with destination-minus-origin differences in upward IGM, all of which are not reported, and three linear terms for destination-minus-origin differences in the relevant-birthcohort-nonmover average IGM for moves taking place when the child moves, ages 1-4, 5-11, and 12-18. Double clustered at the origin and at the destination region standard errors are reported in parenthesis.

> 10Table A3 in the Appendix reports results from a parametric specification that includes an interaction between the destination-origin differences in mobility and indicators by cohort.

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# **V Robustness and additional results**

In this section I run three additional exercises. First, with the aim of addressing concerns of endogeneity, I investigate whether the baseline results are robust to the use of a subset of moves that are plausibly more exogenous, to instrumenting destinations with historical migration, and to the blending of both approaches. Second, I explore potential heterogeneity by estimating regional childhood exposure effects for sub-populations. Third, I explore whether the evidence of regional childhood exposure effects obtained in the baseline specification holds if I focus on intergenerational upward mobility computed with secondary education as an alternative outcome. The motivation is that primary education as the threshold of interest may seem less policy relevant for current cohorts, although as I have shown, the shares of parents with less than primary school, as well as the children with less than primary school, in the sample under analysis are significant.

## **V.1 Addressing concerns of endogeneity**

### **V.1.a Anomalous migration outflows**

To alleviate concerns that time-varying factors may jointly drive household moves and children’s educational investments in proportion to exposure to the region with higher mobility, I re-estimate the model using a subset of moves that are more likely to reflect plausibly exogenous moves.

For this purpose, I construct a panel data set of outflows by region-of-origin-and-year-ofmove by counting the number of movers by region of origin and year of move (i.e., census year minus age at move) regardless of their destinations. Next, I use this panel data set to regress outflows on a constant and a linear trend by region of origin, compute the residuals from these regressions, and use them to sort (in ascending order) observations within each region of origin (assigning them percentile ranks). In this way, years that are assigned large percentile ranks (i.e., years with large residuals) can be considered as periods with anomalously large 19
migration outflows.

I use the percentile ranks to identify a subset of years for each region that can be considered to be a period with large anomalous outflows (or at least larger than usual) and run the baseline parametric regression on them. Figure 5 displays the estimates of regional exposure effects for the three age segments used in the parametric specification (see equation 5).<sup>11</sup> I find estimates that are very stable until the percentile 50 and then become more volatile. However, despite the volatility, the estimates remain qualitatively similar with slopes that are non-statistically significant in early ages, a significant childhood exposure effect in the segment of primary school age, and smaller significant effect post primary school age. This suggests that the regional childhood exposure effects found in the baseline results are not driven by families choosing the timing of the move.

# **V.1.b Expected destination of moving households**

To alleviate concerns that time-varying factors may jointly drive household choice of destination and children’s educational investments in proportion to exposure to the region with higher mobility, I use past migration destinations from each origin to predict where moving household will settle with a “shift-share” design.

For each year of move _y_ and origin _o_ , I compute the share that moves to destination- _d_ as _σody_ = _D_ <u>�</u> _xy−_ =10 _Ty_ <u>0</u> _−_<sup>_movers_</sup> 10<sup>_odxD_isthetotalnumberofregionsinagivencountryand</sup> <u>�</u> _d_ =1 <u>�</u> _x_ = _T_ 0<sup>_moversodx_where</sup> _T_ 0 is the first year in which I observe a mover from this origin.

For individuals who move in year _y_ from region _o_ to region _d_ , I compute the predicted difference in mobility for permanent residents ∆<sup>ˆ</sup> _odby_ as the historic share-weighted analog, ˆ∆ =<sup>�</sup><sup>_D_Figure 6 plots the predicted difference against the actual difference</sup> _oby d_ =1<sup>∆</sup><sup>_odb×σody_.</sup> for the sample of movers.

I use the predicted difference in upward mobility between origin and destination to in-

> 11Table A5 in the Appendix reports these estimates.

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is a variation of equation 5: where the parameters carry similar meaning as in equation 5 but now the specification includes a set of interactions with an indicator variable 1( _Di_ = 1), which takes the value of 1 when the individual have a given characteristic or belong to a given sub-population. I consider male/female, rural/urban, dwelling non-owner/owner, and movers facing a negative/positive difference in upward mobility between destination and origin.

Table 4 reports the results of this exercise. I find only little evidence of heterogeneity in the dimensions under analysis. In particular, childhood exposure effects in the age segment 5-11 appears to be slightly higher for those moving to urban places and slightly lower for those households that are dwelling owners. The slopes past school age appear flatter in the case of women and steeper for households that are dwelling owners.

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**Table 4:** Parametric estimates of regional childhood exposure effects

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||IGM|IGM|IGM|IGM|IGM|IGM|IGM|IGM|IGM|IGM|IGM|IGM|
|_β_: 1-4, base|-0.000785|-0.0267**|-0.0144|-0.0319*|0.0114|-0.0429*|0.00791|-0.00404|0.0106|0.0322***|0.0236|0.0465*|
||(0.008)|(0.010)|(0.009)|(0.017)|(0.016)|(0.023)|(0.009)|(0.033)|(0.017)|(0.011)|(0.029)|(0.025)|
|_γ_: 5-11, base|0.0491***|0.0409***|0.0517***|0.0404***|0.0333***|0.0462***|0.0597***|0.0424***|0.0636***|0.0501***|0.0474***|0.0583***|
||(0.004)|(0.006)|(0.004)|(0.007)|(0.011)|(0.009)|(0.004)|(0.007)|(0.006)|(0.007)|(0.011)|(0.007)|
|_δ_: 12-20, base|0.0288***|0.0188***|0.0350***|0.0282***|0.0253**|0.0311***|0.00301|0.00610|0.00707|0.0244***|0.0261**|0.0378***|
||(0.004)|(0.004)|(0.004)|(0.010)|(0.011)|(0.009)|(0.006)|(0.006)|(0.007)|(0.009)|(0.010)|(0.013)|
|_β_: 1-4, dif|0.00580|0.0103|0.00804|0.0383**|-0.0430*|0.0284|-0.00961|-0.0260|-0.0313*|-0.0414**|-0.0730|-0.0866**|
||(0.010)|(0.018)|(0.011)|(0.018)|(0.025)|(0.025)|(0.010)|(0.029)|(0.018)|(0.019)|(0.053)|(0.037)|
|_γ_: 5-11, dif|0.000200|-0.00371|-0.00182|0.0112**|0.00726|0.00708|-0.0155***|-0.00562|-0.0183***|0.00139|-0.0125|-0.00720|
||(0.003)|(0.003)|(0.004)|(0.005)|(0.012)|(0.008)|(0.006)|(0.008)|(0.006)|(0.010)|(0.013)|(0.012)|
|_δ_: 12-20, dif|-0.0361***|-0.0195***|-0.0402***|-0.0144|-0.0138|-0.0125|0.0264**|0.0142|0.0286***|-0.0116|-0.0186|-0.0229*|
||(0.006)|(0.004)|(0.008)|(0.010)|(0.010)|(0.009)|(0.013)|(0.011)|(0.010)|(0.011)|(0.012)|(0.013)|
|R-squared|0.105|0.689|0.103|0.126|0.684|0.125|0.099|0.685|0.096|0.097|0.685|0.095|
|N|436792|271984|271984|422994|263373|263373|433793|270266|270266|436792|271984|271984|
|Household FE|No|Yes|No, hhfe sample|No|Yes|No, hhfe sample|No|Yes|No, hhfe sample|No|Yes|No, hhfe sample|
|Base category|Male|Male|Male|Rural|Rural|Rural|Nonowner|Nonowner|Nonowner|Negative|Negative|Negative|

Notes: The dependent variable in all specifications is an indicator that takes the value of one for children of parents without completed primary school who have completed at least primary education and zero otherwise (IGM). The independent variables comprise a linear in origin-average-IGM (calculated for the birth-cohort relevant to the individual among nonmovers) term, age-at-move indicator variables, birth-decade _×_ destination indicators interacted with destination-minus-origin differences in upward IGM, all of which are not reported, and three linear terms for destination-minus-origin differences in the relevant-birth-cohort-nonmover average IGM for moves taking place when the child moves, ages 1-4, 5-11, and 12-18. Double clustered at the origin and at the destination region standard errors are reported in parenthesis.
## **V.3 Other outcomes**

All the previous analysis concerns the likelihood of individuals completing at least primary education conditional on having parents that did not complete that level. This margin is of interest historically, as an important share of the population had not completed it in the cohorts used in the study. However, secondary education would arguably be of more interest if one were thinking about more recent cohorts. In this subsection, I estimate regional childhood exposure effects defining upward intergenerational mobility as the likelihood of completing at least secondary education conditional on having parents that did not complete such level (or conditional on parents that did not complete primary education). The main limitation of this analysis is that I need to focus on older individuals because the educational system is designed so that students finish secondary around the age of 17 and given that co-residence rates drop with age, the sample size drops down. Hence, I estimate regional childhood exposure effects using individuals with age 18-25 co-residing with at least one parent.

Figure 8 reports the estimated slopes using the semi-parametric approach (see equation 4). The exposure effects computed with individuals who moved before being 18 years old is approximately 5.5%, and the selection effect (the average of coefficients after age 17) is approximately 37%. These results imply higher convergence rates than the baseline results but also support the main finding that there is evidence of regional exposure effects in Latin America and the Caribbean.

# **VI Final Remarks**

In this paper, I show in a new setting that every additional year that a child spends in a region with higher/lower upward mobility than her birthplace influences her chances of moving up the educational ladder. I replicate the approach of Alesina et al. (2021) for Africa to provide estimates of regional childhood exposure effects in Latin America and the Caribbean, a more 26
<!-- Start of picture text --> 12 Slope: -0.066 I! Slope: -0.052 |! Slope: -0.009<br>(0.031) (0.003) (0.021)<br>eve \ \ Selection: 0.331<br>2 | o | |<br>5<br>g ei@ I<br>> 8 | |<br>><br>3 6 @ :<br>sS I| e | |<br>4 I e le<br>& I ° | ee e<br>we 2 | |<br>|<br>| |<br>0 | |<br>12 3 4 5 6 7 8 9 10 141 12 13 14 15 16 17 18 19 20 21 22 23<br>age at move<br><!-- End of picture text -->

<!-- Start of picture text --> 12 Slope: -0.067 I! Slope: -0.041 |! Slope: 0.006<br>(0.023) (0.004) (0.013)<br>\ \ Selection: 0.370<br>» 17@e \ \<br>5 on<br>g I I<br>> 8 ee |<br>F | 8<br>>3 6 i ° @<br>sS I| e | |<br>o 4 I e~? ——————<br>& I e a<br>we 2 | |<br>| |<br>| |<br>0 I |<br>12 3 4 5 6 7 8 9 10 141 12 13 14 15 16 17 18 19 20 21 22 23<br>age at move<br><!-- End of picture text -->
of different measures such as intergenerational mobility in income computed with rank-rank regressions. These results suggest that place-based policies to increase the human capital accumulation of disadvantaged children that target regions left behind may be fruitful to increase the aggregate level of absolute intergenerational mobility in education in Latin America and the Caribbean. However, it is essential to note that the estimates presented in this paper speak about the levels of intergenerational mobility for birth cohorts that are already adults. Therefore, policy actions would require updated estimates that consider mobility indicators for later birth cohorts and focus on higher levels of completion such as secondary or college attendance, given the widespread adoption of compulsory schooling in the continent.

Finally, a fundamental limitation of this study is that it quantifies the average effect of exposure to regions during childhood without shedding light on the different potential mechanisms driving these effects. Hence, it leaves many open questions about the role of these mechanisms for further research.

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