Policy Research Working Paper 10036 The Geography of Intergenerational Mobility in Latin America and the Caribbean Ercio Muñoz Poverty and Equity Global Practice May 2022 Policy Research Working Paper 10036 Abstract This paper estimates intergenerational mobility in educa- wide cross-country and within-country heterogeneity. The tion using data from 91 censuses that span 24 countries paper documents a declining trend in the mobility gap in Latin America and the Caribbean over half a century. It between urban and rural populations, and small differences measures upward mobility as the likelihood of obtaining by gender. Within countries, the level of mobility is highly at least a primary education for individuals whose parents correlated with the share of primary completion of the pre- did not finish primary school, whereas downward mobility vious generation, which suggests a high level of inertia. In is the likelihood of failing to complete primary education addition, upward (downward) mobility is negatively (pos- for individuals whose parents completed at least primary itively) correlated with distance to the capital and the share school. In addition, the paper explores the geography of of employment in agriculture, but positively (negatively) educational intergenerational mobility using nearly 400 correlated with the share of employment in industry. “provinces” and more than 6,000 “districts”. It documents 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 The Geography of Intergenerational Mobility in Latin America and the Caribbean ∗ noz† Ercio Mu˜ JEL-Codes: D63, I24, J62. Keywords: Socioeconomic mobility, Education, Latin America and the Caribbean. ∗ I thank Bennett Callaghan, Miles Corak, Christos Giannikos, Marco Ranaldi, Nuria Rodriguez-Planas, Roy van der Weide, and Wim Vijverberg, and several seminar/conference participants for helpful comments; Pablo Vargas and Joaquin Prieto provided outstanding research assistance. I greatly acknowledge the fi- nancial support of the Center for Latin American, Caribbean and Latino Studies at CUNY and the Mario Capelloni dissertation fellowship from CUNY. The usual caveats apply. First version: November 2020. † Email: emunozsaavedra@worldbank.org. I Introduction Intergenerational mobility (IGM) has gained interest in the economic literature among other things thanks to its importance for equity, social cohesion, and economic growth. Its ob- served correlation with income inequality, commonly named “the Great Gatsby Curve”, has contributed to the desire for understanding IGM given the documented rise of inequality over the last decades in rich countries (see Corak, 2013). In the case of the developing economies, the Latin America and the Caribbean (LAC) region is of particular interest because of its historically documented high levels of income inequality relative to other regions of the world.1 However, the scarcity of high-quality data (e.g. long panel data sets or tax records with linked generations) has limited the study of IGM in income. These constraints are also common in other regions (e.g., Africa), so efforts to document IGM on a global scale have taken an alternative path given by the measurement of mobility in education. These measures are of interest in and of themselves, but they are also a proxy for economic status given the close relationship between education and income. Recent studies have used household and public opinion surveys with retrospective infor- mation about parents’ education to document the levels of IGM in education in LAC at ofer, the country level (for an example, see Hertz et al., 2007; Narayan et al., 2018; Neidh¨ Serrano, & Gasparini, 2018). However, analyzing IGM at a more geographically disaggre- gated level, as argued in Narayan et al. (2018), is valuable because it can help researchers understand the importance of localized patterns and drivers of IGM, as shown for the case of developed countries. Along these lines, Chetty, Hendren, Kline, and Saez (2014) state that the United States can be better described as a collection of societies, some of which are “lands of opportunity” with high rates of mobility across generations, and others in which only a few children escape poverty. Thus far, this type of analysis has not been conducted in LAC countries as a whole due to the inadequacy of most survey data for this purpose. This paper fills that gap in the literature by generating estimates of IGM in education at smaller 1 See for example Messina and Silva (2019) for an analysis of wage inequality over the last two decades. 2 geographical levels. In this paper, I estimate intergenerational mobility in education for LAC countries at a disaggregated regional level using data from 91 censuses. The analysis covers 24 countries spanning more than half a century (between 1960 and 2012). I rely on samples of co-residents (i.e., children living with their parents or older relatives). To minimize the impact of co- residence, I investigate mobility in education at the bottom of the educational attainment distribution by focusing on primary education, which can be measured with a high degree of confidence between ages 14 and 18. Furthermore, an important share of the population does not attain more than primary education in the period analyzed and this focus allows me to create indicators that are directly comparable to the estimates recently generated for 27 countries in Africa (see Alesina, Hohmann, Michalopoulos, & Papaioannou, 2021), a continent that shares the feature of having high levels of income inequality despite its lower levels of income and higher poverty rates. The estimates of upward (and downward) mobility measured as the likelihood of finishing (or failing to finish) primary education, conditional on having parents who failed to finish (or who were able to finish) primary school, show wide cross-country and within-country heterogeneity. In LAC, the distance between the most and least upwardly mobile coun- tries is similar to what has been recently documented in Africa, although the least mobile countries in Africa are less mobile than the least mobile country in LAC. I find only small differences by gender, but I do document a declining trend in the mobility gap between urban and rural populations. At the sub-national level, there is heterogeneity in mobility across districts/provinces, and some countries show lower levels of mobility in the northern regions (e.g., Brazil), whereas the opposite is true for Mexico. However, the variability is much lower in countries with lower number of regions and less population. The level of mobility at the sub-national level is highly positively correlated to the share of primary completion of the previous generation, which suggests a high level of inertia. In addition, geographical corre- lates do not appear to be highly correlated to mobility except for distance to the capital. 3 Similarly, some proxies of economic development like the share of employment in industry and agriculture at the beginning of the sample period seem to be associated to the levels of mobility at the district level. This paper contributes to several strands of the economic literature. First, it adds to the literature on intergenerational mobility in general (see Black & Devereux, 2011, for a survey) but specifically to the literature focusing on the geography of socioeconomic mobility that recently received more attention in part because of the work of Chetty et al. (2014), which shows important variation across commuting zones in the United States. Second, it adds to the recent wave of research that looks at intergenerational mobility in education (see Emran & Shilpi, 2021; Torche, 2019, for recent surveys focused on developing countries). This set of papers includes on the one hand those that use household survey data or opinion surveys. For example, Hertz et al. (2007), Narayan et al. (2018), and Van der Weide, Lakner, Gerszon Mahler, Narayan, and Ramasubbaiah (2021) that document IGM for a very large set of countries across the world,2 and Neidh¨ ofer et al. (2018) that focus on 18 countries from Latin America. This paper expands over them in terms of country coverage of the region and cross-country comparability. In addition, these estimates use the same type of data of recent estimates available for Africa, allowing a cross-regional comparison that was not available.3 On the other hand and more closely related to this paper, it contributes to the pool of studies using administrative data or census data. For instance, Asher, Novosad, and Rafkin (2021) study mobility among different marginalized groups and analyze geographic differences in India; Card, Domnisoru, and Taylor (2022) use 1940 census data to study the role of school quality in mediating upward mobility in the US; Van der Weide, Ferreira de Souza, and Barbosa (2020) study mobility at the sub-national level in Brazil; and most closely related to this paper, Alesina et al. (2021) document patterns of IGM in Africa using 2 The former documents mobility for 42 countries (7 from LAC) and the latter 153 countries (16 from LAC). 3 Narayan et al. (2018) and Van der Weide et al. (2021) allow regional comparison but pooling together estimates generated with retrospective information and those with coresident samples, which may be prob- lematic (see Munoz & Siravegna, 2021). 4 census data and estimate regional childhood exposure effects using migrants. To the best of my knowledge, this is the first paper to document IGM at a very disaggregated regional level for almost the entire population in LAC. The paper is organized as follows. Section II describes data and methodology. Section III reports the main descriptive results at country level and the geography of mobility. Section IV looks at correlates of intergenerational mobility. Section V concludes with final remarks. II Data and Methodology Three sources of data have been typically used to estimate intergenerational mobility: (1) cross-sectional samples of adult populations with retrospective questions about parental education, for example, Narayan et al. (2018) use household survey data that covers the 96% of the world population; (2) panel data long enough in its time dimension to include the socioeconomic or educational attainment of two generations, for example, Celhay, Sanhueza, and Zubizarreta (2010) use the Chilean CASEN to estimate mobility in schooling and income; and (3) administrative/registry data with linked information for parents and adult children, for example, Chetty et al. (2014) use tax records in the U.S. to estimate income mobility. In the case of Latin America, most of the literature has used household survey data or ofer public opinion surveys (see for example, Hertz et al., 2007; Narayan et al., 2018; Neidh¨ et al., 2018) given that long panel data as well as administrative/registry data that allow the researcher to link generations are rare. In contrast, in this paper, 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. In particular, I use 91 samples of population and housing censuses from 24 countries, which are run to compute the total population and contain an educational attainment question in their questionnaire.4 The key advantage of this data set is that it contains the entire population 4 Because the individuals are not organized into households, I do not use Chile 1960, Colombia 1964, 5 (or at least a large share of it publicly available) at a point in time, allowing me to analyze mobility at a very disaggregated geographical level. However, the main disadvantage of this data set is that it does not link all the individuals with their parents because both (individuals and parents) need to be part of the same household. Below, I explain how this is addressed and I refer to recent evidence showing that the coresidence bias is likely to be very small for the indicators used in this paper. II.1 Countries and smaller administrative units The 24 countries under study are: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Trinidad and Tobago, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay, Saint Lucia, ublica Bolivariana de Venezuela (see Table A1 in the Appendix for Suriname, and the Rep´ the details about the fraction of the data available by census), and they represent 91 samples of these 24 countries drawn at various points from 1960 to 2012. In terms of geography, IPUMS reports residence at the time of the interview for at most two levels of administrative units in which the households were enumerated. These variables contain the geographies for every country harmonized spatio-temporally to provide spatially consistent boundaries across samples in each country. This allows me to assign individuals to “coarse” (roughly similar to states in the U.S.) and “fine” administrative units (roughly similar to counties in the U.S.). The sample spans 400 provinces (admin-1 units) and 6,684 districts (admin-2 units). The baseline estimates will make use of the former to avoid issues derived from having a reduced number of observations per administrative unit but estimates using the latter are also reported in the Appendix. Costa Rica 1963, Dominican Republic 1960 and 1970, Ecuador 1962, Honduras 1961 and Mexico 1960. I also omit the 1995, 2005 and 2015 inter decennial Census counts of Mexico. 6 II.2 Linking generations and coresidence The data collection is organized at the household level, so it is possible to link only those individuals who live in the same household at the time of the interview. The data set includes a variable that by means of 62 different values details the relationship between the individual and the head of the household. Based on this variable, I classify individuals into five different generations where the head corresponds to generation zero (see Table 1), and based on the generation number I use individuals who live with at least one member of the immediately previous generation, where these old generation members are considered as “pseudo-parents”.5 Table 1 provides the details of the assignment. Table 1: Relationship to household head and identification of different generations Relationship to the head Generation Relationship to the head Generation Grandparent -2 Sibling of sibling-in-law 0 Great grandparent -2 Ex-spouse 0 Parent/parent-in-law -1 Child 1 Parent -1 Biological child 1 Stepparent -1 Adopted child 1 Parent-in-law -1 Stepchild 1 Aunt/uncle -1 Child-in-law 1 Head 0 Spouse/partner of child 1 Spouse/partner 0 Unmarried partner of child 1 Spouse 0 Nephew/niece 1 Unmarried partner 0 Foster child 1 Same-sex spouse/partner 0 Tutored/foster child 1 Sibling/sibling-in-law 0 Tutored child 1 Sibling 0 Grandchild 2 Stepsibling 0 Grandchild or great grandchild 2 Sibling-in-law 0 Great grandchild 2 Cousin 0 Great-great grandchild 2 Notes: Categories not classified are: Other relative, not elsewhere classified; other relative with different family name; non-relative; friend; housemate/roommate; visitor; godparent; godchild; do- mestic employee; relative of employee; spouse of servant; child of servant; other relative of servant; roomer/boarder/lodger/foster child; boarder; boarder or guest; lodger; employee, boarder or guest; other specified non-relative; agregado; temporary resident, guest; group quarters; group quarters, non-inmates; institutional inmates; non-relative, n.e.c.; other relative or non-relative; unknown. Figure 1 shows the unweighted average rate of coresidence by age in the sample pooling all 5 A similar approach is followed by Alesina et al. (2021) with Census data from Africa. 7 the countries and years. There are rates above 90% for individuals before reaching 18 years old that then start decreasing more rapidly getting close to 40% for people who are 25 years old. When the coresidence rate is computed with samples that distinguish urban/rural or gender, I find negligible differences in the former and a steeper fall in the rate of coresidence by age for women relative to men (see Figure A1 in the Appendix). Figure 1: Coresidence rate by age Coresidence rate by age 100 97.6 96.5 94.7 91.6 87.1 81.4 80 74.4 68.3 61.4 60 54.2 48.6 42.7 40 20 0 14 15 16 17 18 19 20 21 22 23 24 25 Notes: Coresidence is defined as living with at least one relative of the immediately previous generation. The data in the graph is unweighted. Figure 2 disaggregates the coresidence rate by country, displaying some variability in the magnitude of the fall in coresidence with age. This figure also suggests that the magnitude of the fall in coresidence around age 25 is driven by Brazil, which is the most populated country in Latin America. Table A3 in the Appendix provides coresidence rates by country for different age groups. A concern associated with the use of coresidents is that it may generate bias in the es- 8 Figure 2: Coresidence rate by age and country Brazil Bolivia Honduras Guatemala Jamaica Dominican Republic Saint Lucia Paraguay Ecuador Panama Costa Rica Uruguay El Salvador Mexico Venezuela Nicaragua Haiti Colombia Chile Argentina Peru Trinidad and Tobago Cuba Suriname 0 20 40 60 80 100 Coresidence rate by age from 14 to 25, younger to older Notes: Coresidence is defined as living with at least one relative of the immediately previous generation. The data in the graph is unweighted. timates of IGM as individuals who reside with their parents may systematically differ from those not residing with them (see for example, Emran, Greene, & Shilpi, 2018; Emran & Shilpi, 2021; Francesconi & Nicoletti, 2006). However, Munoz and Siravegna (2021) show that the average coresidence bias when computing upward mobility (measured as the likeli- hood of completing primary for those whose parents did not complete primary) for individ- uals aged 21-25 years (with coresidence rates of less than 50% on average) is approximately 2%. In addition, the ranking obtained using these coresident samples closely follows the one obtained with a sample that includes all children (the Spearman rank correlation between the estimates with the full sample and those with coresident samples is 0.91). Given these findings, the potential for coresidence bias in my estimates is small as they are computed using individuals aged 14-18 (or 14-25) years, a group with much higher rate of coresidence.6 6 Figure A2 in the Appendix display visually how the estimates computed in Munoz and Siravegna (2021) with all children compare to the estimates with coresidents. 9 II.3 Education Why is education a suitable variable by which to measure IGM? Education as a measure of socioeconomic status relative to income in the context of developing countries has at least three advantages: (1) it contains less measurement error, reducing potential attenuation bias (see Solon, 1992); (2) it is fixed early in the life cycle, which avoids the life-cycle bias found in studies that use income (see Haider & Solon, 2006), in addition, education is closely linked to income and it is important by itself in terms of human development; and (3) it can be completely attributed to a specific individual, while income sometimes is hard to assign within a household (e.g., a household with multiple persons and home production, which may be specially relevant in the case of rural populations in poor countries). There are two questions about educational attainment in the data set. The first one re- ports the total years of schooling completed by each individual (formal schooling regardless of the track or kind of study), and the second one is re-coded by IPUMS to capture educa- tional attainment in terms of the level of schooling completed7 and contains four categories: (1) less than primary completed, (2) primary completed, (3) secondary completed, and (4) university completed. In the main analysis of the paper, I use the latter variable, which has a lower number of missing values and it is available for more countries than the former.8 This variable applies, to the extent possible, the United Nations standard of six years of pri- mary schooling, three years of lower secondary schooling, and three years of higher secondary schooling. In the sample, a majority of individuals report levels of education that correspond to less than completed secondary and near 50% less than primary (see Figure 3a), which supports the focus on primary completion that I will detail later as most of the action happens at lower levels of completion.9 In addition, although the level of education in Latin America 7 It does not necessarily reflect any particular country’s definition of the various levels of schooling in terms of terminology or number of years of schooling. 8 Years of schooling is not available for Brazil 2010, Cuba 2002, Saint Lucia 1991, Suriname 2012, Trinidad and Tobago 1970, and Uruguay 2011. 9 An additional reason to focus on primary completion is that these estimates are directly comparable to 10 Figure 3: Educational Attainment .5 .4 .3 Density .2 .1 0 0 1 2 3 4 Educational attainment (a) Histogram of attainment (b) CDF of years of schooling Notes: The graphs use different samples as years of schooling is not available or is top-coded in six country-year samples (Brazil 2010, Colombia 1993 and 2005, Peru 1993 and 2007, and Uruguay 2011). The graph includes only individuals older than 25 from decade cohorts 1900 to 1980. The plot on the right shows the CDF by birth decade (e.g., 1980 considers those born between years 1980 and 1989). and the Caribbean has been increasing across cohorts (see Figure 3b), the continent still shows a share of around 60 percent with at most nine years of schooling in the most recent cohort (those born in the 1980s), which roughly corresponds to the completion of lower secondary education. Nonetheless, I also provide an Appendix using estimates that focus on the completion of secondary level. Figure 4 shows the transition matrix for individuals older than 25 to get a rough idea of the patterns of intergenerational education mobility present in the data set.10 This plot highlights that the action is terms of mobility happens in the lower two levels of educational attainment, qualitatively similar to what can be seen in Alesina et al. (2021) for the African continent. The same mosaic plot can be found by country in the Appendix. Two countries that stand out in terms of low and high levels of parental attainment of primary education the ones recently documented in Alesina et al. (2021) for Africa. 10 I use individuals older than 25 years as younger ones are unlikely to have completed their education. The main analysis uses younger individuals as the focus is on primary completion. Figure A3 in the Appendix reproduces this mosaic with individuals aged 14-25 years. 11 are Jamaica and Guatemala11 (see Figure 5).12 Figure 4: Educational Attainment Transition Matrix fraction by parental attainment 0 .2 .4 .6 .8 1 1 likelihood of child attainment .8 .6 Less than primary Primary completed Secondary completed .4 University completed .2 0 ry ted ted ted ma ple ple ple n pri om omcom tha c c ry ary sity ss ma nd er Le Pri ecoUniv S Notes: The sample is constructed with individuals older than 25 that coreside with at least one in- dividual of the generation above. The figure displays the transition matrix between the educational attainment of individuals in the sample and their parents. The horizontal axis is divided according to the share of parents with each level of educational attainment. The height of each rectangle within the figure is the likelihood of child educational attainment conditional on the attainment of their parents. II.4 Methodology For each individual in the sample, I analyze the relationship between their own educational attainment against the average attainment of individuals one generation older living in the same household, rounded to the nearest integer. For this I consider a measure of absolute 11 Saint Lucia shows similar patterns but contains a much smaller population. 12 The same plot using individuals aged 14-25 years can be found in the Appendix (see Figure A4). 12 Figure 5: Transition matrix for selected countries fraction by parental attainment fraction by parental attainment 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 1 1 likelihood of child attainment likelihood of child attainment .8 .8 .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary let ed ed ed let let ary ed tetd let ple le ed p rim p mp mp rim mp om mp an om co co np co y y c co th aryc ary sity tha ary d r it ss nd iver ss ea rs Le Prim o ec Un Le PrimeUo cn n iv S S (a) Jamaica (b) Guatemala Notes: The sample is constructed with individuals older than 25 that coreside with at least one in- dividual of the generation above. The figures display the transition matrix between the educational attainment of individuals in the sample and their parents. The horizontal axis is divided according to the share of parents with each level of educational attainment. The height of each rectangle within each figure is the likelihood of child educational attainment conditional on the attainment of their parents. intergenerational mobility that reflects the likelihood that a child completes a strictly higher or lower education level than the members of the immediately previous generation in the household (parents and/or extended family members, such as aunts and uncles). Upward mobility at the country level. To estimate upward IGM, I estimate the following econometric specification, pooling observations from all the censuses and countries: up up b b yicoyt = αc + γo + γy + θt + icoyt (1) up where yicoyt is a dummy variable that takes a value equal to one when individual i completes b b at least primary education and zero otherwise. The parameters γo , γy , θt refer respectively to fixed effects by decade-cohort of the individual i, decade-cohort of the generation above that co-resides with individual i, and census year. This regression uses a sample of individuals with ages between 14 and 18 (or 14 to 25), for whom the generation above (parents or up older relatives) have on average less than primary education. Hence, αc is the parameter 13 of interest and measures the likelihood of completing primary for children whose “parents” did not complete primary net of cohort and census year effects. This empirical approach has been used in Alesina et al. (2021) with data from Africa and delivers a measure of mobility comparable between countries that captures some long-term patterns over half a decade by netting out common (across countries) birth cohorts and census year effects. Downward mobility at the country level. To estimate downward IGM, I use a similar econometric specification, pooling observations from all the censuses and countries: down down b b yicoyt = αc + γo + γy + θt + icoyt (2) down where yicoyt is a dummy variable that takes a value equal to one when individual i does b b not complete primary education and zero otherwise. The parameters γo , γy , θt again refer respectively to fixed effects by decade-cohort of the generation above that co-resides with individual i, decade-cohort of the individual i, and census year. This regression uses a sample of individuals with ages between 14 and 18 (or 14 to 25), for whom the generation above (parents or older relatives) have on average completed at least primary education.13 Hence, down αc is the parameter of interest and measures the likelihood of failing to complete primary for children whose “parents” completed at least primary school net of cohort and census year effects. Upward and downward mobility at finer geographical level. To estimate IGM at a more disaggregated level (i.e., provinces or districts), I use the following econometric specifications run country by country: up up b b yicroyt = αcr + γo + γy + θt + icroyt (3) down down b b yicroyt = αcr + γo + γy + θt + icroyt 13 I use the average attainment of the generation above instead of the maximum to make these estimates directly comparable to those in Alesina et al. (2021). However, this decision makes little difference as I explain in the robustness section later. 14 where the variables and subscripts in common have similar interpretation as in Equations 1 and 2, and the additional subscript r refers to the district or province according to the level of geographical dis-aggregation used in the analysis (provinces as the baseline estimates and districts as an additional exercise reported in the Appendix). Why is primary education a suitable variable by which to measure IGM? The focus on primary education is based on the fact that a non-negligible share of the population in Latin America and the Caribbean has an educational attainment of less than primary as shown in the previous subsection. Furthermore, this focus makes the analysis directly comparable to the recent work of Alesina et al. (2021) in Africa and allows me to minimize the potential bias that comes from using samples of co-residents. Nonetheless, the focus on the lowest level of education can also be justified from a conceptual point of view. Development policy discussions often claim that the poorest should not be left behind and this focus is related to the school of moral philosophy exemplified by the principle of justice proposed by Rawls (1971).14 Robustness. As a robustness check, I compute upward and downward mobility using some alternative options in terms of data construction. First, I use the maximum attainment of the generation above instead of average. This change produces estimates with differences that are negligible (for example, Pearson correlation coefficient between the measures using average versus maximum at the country, province, and district level are approximately 1). Second, I estimate mobility using a sample of individuals linked to (probable) parents as done by IPUMS (2019). This change produces estimates that are also highly correlated (for example, Pearson correlation coefficient between the measures using average versus maximum at the country, province, and district level are 0.98, 0.97 and 0.93, respectively). Alternative measures of IGM. I estimate a set of additional measures of intergener- ational mobility, which are less focused on the bottom of the educational attainment distri- bution. In contrast to the estimates that focus on primary education, these measures are 14 See Ravallion (2016) as an example of the focus on the poorest in the context of poverty measurement. 15 computed using individuals between ages 19 and 25. First, I estimate upward and downward mobility considering secondary education instead of primary. Second, I estimate upward mo- bility as the likelihood of finishing at least secondary education for those whose generation above were not able to complete primary school. These indicators are more prone to suffer from coresidence bias but they still provide valuable information. For example, Munoz and Siravegna (2021) show that the rank correlation between indicators of upward mobility using secondary level computed with all children versus coresidents is approximately 0.86.15 III Intergenerational Mobility in LAC III.1 Country-level estimates Table 2 summarizes the estimates of mobility at the country level. On average, close to 50 percent of children with parents that did not finish primary education (from now on, illiterate parents) are able to complete primary. On the other hand, downward mobility is close to 10 percent, as one out of 10 children with parents that finished primary education (from now on, literate parents) do not complete primary. There is substantial heterogeneity within LAC countries. The probability of completing primary for children of illiterate parents ranges from 18% in Guatemala to 87% in Jamaica. In the case of downward mobility, the estimated probability of not completing primary for children of literate parents ranges from being null in Jamaica to 23% in Haiti. The heterogeneity found in upward mobility in Latin America (e.g., the 69 percentage points gap between Jamaica and Guatemala) is relatively similar to the one documented for African countries (e.g., the 75 percentage points gap between South Africa and South Sudan) by Alesina et al. (2021), although with higher minimum and maximum values. Furthermore, the level of upward mobility among countries in LAC shows substantial overlap with that of Africa. Countries such as Haiti, Guatemala, and Nicaragua with the lowest levels of upward 15 Using 72 country and 5-year birth cohorts that span 18 countries in Latin America. 16 mobility in LAC are more upwardly mobile than the five lowest (Malawi, Ethiopia, Sudan, Mozambique, and South Sudan) out of the 27 countries for which Alesina et al. (2021) provide estimates. Table 2: Country-Level Estimates of Educational Intergenerational Mobility (1) (2) (3) (4) (5) (6) mobility / N census years upward upward downward downward N N age range 14-18 14-25 14-18 14-25 14-18 14-25 Jamaica 1982,1991,2001 .868 .864 -.004 .003 43,404 77,227 Trinidad and Tobago 1970,1980,1990,2000,2011 .839 .833 .023 .023 41,253 81,100 Argentina 1970,1980,1991,2001,2010 .762 .789 .035 .034 1,068,471 2,017,618 Chile 1970,1982,1992,2002 .682 .709 .05 .044 344,149 651,737 Uruguay 1963,1975,1985,1996,2006,2011 .668 .685 .064 .052 108,528 199,653 Cuba 2002,2012 .662 .688 .027 .024 101,268 214,486 Panama 1960,1970,1980,1990,2000,2010 .635 .665 .049 .04 86,527 157,906 Costa Rica 1973,1984,2000,2011 .634 .643 .086 .068 107,088 197,018 Bolivia 1976,1992,2001,2012 .609 .634 .068 .057 206,745 358,013 Mexico 1970,1990,2000,2010 .602 .622 .048 .042 2,811,581 4,961,471 Ecuador 1974,1982,1990,2001,2010 .543 .572 .089 .074 373,130 667,055 Suriname 2012 .535 .563 .042 .031 2,999 6,141 Venezuela 1971,1981,1990,2001 .533 .587 .096 .08 517,834 940,766 Saint Lucia 1980,1991 .523 .492 .126 .142 2,089 3,679 Peru 1993,2007 .48 .524 .115 .088 357,472 668,806 Paraguay 1962,1972,1982,1992,2002 .432 .463 .116 .096 118,082 207,766 Colombia 1973,1985,1993,2005 .402 .437 .142 .114 886,765 1,605,718 Honduras 1974,1988,2001 .398 .433 .151 .133 109,458 182,786 Dominican Republic 1981,2002,2010 .376 .442 .15 .124 173,340 312,654 Brazil 1960,1970,1980,1991,2000,2010 .367 .422 .171 .128 10,755,296 18,713,402 El Salvador 1992,2007 .342 .374 .164 .138 85,402 150,582 Haiti 1971,1982,2003 .212 .266 .226 .178 104,465 183,588 Nicaragua 1971,1995,2005 .194 .238 .223 .18 93,635 167,740 Guatemala 1964,1973,1981,1994,2002 .181 .212 .159 .129 238,047 402,133 mean / total .52 .548 .101 .084 18,737,028 33,129,045 Notes: Columns (1) and (2) give upward-IGM estimates. They reflect the likelihood that children, aged 14-18 and 14-25, whose parents have not completed primary schooling will manage to complete at least primary education. Columns (3) and (4) give downward-IGM estimates. They reflect the likelihood that children, aged 14-18 and 14-25, whose parents have completed primary schooling or higher will not manage to complete primary education. Columns (5) and (6) give the number of observations used to estimate the country-specific IGM statistics (children whose parental education is reported in the censuses). Countries are sorted from the highest to the lowest level of upward IGM in the 14-18 sample (column (1)). “mean” gives the unweighted average of the 24 country-estimates. Figure 6 maps the country-level estimates of upward and downward mobility in education. It highlights the heterogeneity found across the continent, showing that the patterns of upward mobility are inversely related to downward mobility and that there are combinations of low and high mobility countries in South America, as well as in Central America and the Caribbean. The estimates of upward and downward mobility at the level of country are 17 Figure 6: Intergenerational Educational Mobility in LAC Mexico Cuba Mexico Cuba Dominican Republic Dominican Republic Jamaica Jamaica Honduras Haiti Honduras Haiti Guatemala St. Lucia Guatemala St. Lucia El Salvador Nicaragua Trinidad and Tobago El Salvador Nicaragua Trinidad and Tobago Costa Rica Costa Rica Venezuela Venezuela Panama Panama Colombia Suriname Colombia Suriname Ecuador Ecuador Peru Peru Brazil Brazil Bolivia Bolivia Upward Mobility Paraguay Downward Mobility Paraguay 0.66 to 0.87 −0.004 to 0.046 0.59 to 0.66 Uruguay 0.046 to 0.072 Uruguay 0.49 to 0.59 Argentina 0.072 to 0.115 Argentina 0.37 to 0.49 Chile 0.115 to 0.154 Chile 0.18 to 0.37 0.154 to 0.226 (a) Upward Mobility (b) Downward mobility Notes: Upward mobility reflects the likelihood that children, aged 14-18, whose parents have not completed primary schooling will manage to complete at least primary education. Downward mobility reflects the likelihood that children, aged 14-18, whose parents have completed primary schooling or higher will not manage to complete primary education. Both estimates are net of cohort and census year effects. significantly negatively correlated (see Figure A13 in the Appendix). Country-level estimates of intergenerational mobility focused on secondary education can be found in Table A7 of the Appendix. The level of upward (downward) mobility is considerably lower (higher) and the samples smaller. Similar to the estimates using primary education, there is significant variation across countries. In the case of upward mobility measured as the likelihood that children complete at least secondary education when their parents were not able to complete primary, we see lower levels of mobility at the country level as one may have expected (see Table A10 in the Appendix). III.1.a Urban-rural Given that an important feature of most developing countries is the gap in living stan- dards between rural and urban residents (see Lagakos, 2020), I explore the heterogeneity 18 Figure 7: Intergenerational Educational Mobility in LAC - Urban/rural urban downward IGM - rural downward IGM BOL LCA JAM 0 CHL CRI gap = 10.692 + -.0053 cohort .5 BOL MEX ECU urban upward IGM - rural upward IGM ECU PAN BOL (3.1274) (.0016) CRI URY CRI ARG ECU ARG PER PRY MEX PAN DOM PER CHL CRI COL BRA COL ARG SLV DOM SLV URY PAN BOL MEX -.1 PAN CRI PAN HND PRY .4 PAN VEN NIC COL BOL URY CHL GTM VEN HND NIC PAN BRA BRA GTM COL PER BOL PRY VEN PER GTM COL PRY MEX VEN HTI BOL -.2 URY CHL GTM GTM COL HND BRA .3 NIC MEX URY GTM HND HND NIC PRY BOL HTI PRY HTI PRY BRA COL HND GTM CHL BRA PAN BRA BRA GTM VEN BRA BOL GTM ARG -.3 BRA PAN HND .2 MEX HTI COL CRI CHL PRY PAN URY ARG VEN COL URY PER COL DOM ARG PER SLV HND -.4 PRY BRA SLV CRI CRI .1 CRI CHL LCA PRY MEX BRA ECU ECU DOM BOL URY CHL CRI ECU gap = -9.263 + .0046 cohort MEX -.5 (2.2662) (.0012) JAM GTM 0 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 Birth decade Birth decade countries with data for 1950s, 1960s, 1970s, 1980s countries with data for 1950s, 1960s, 1970s, 1980s other countries other countries mean among countries with data 1950s-1980s mean among countries with data 1950s-1980s (a) Upward mobility (b) Downward mobility Notes: These estimates correspond to the probability of completing at least primary education for those whose parents did not finish primary school in the case of upward mobility and probability of not completing primary education for those whose parents completed primary school in the case of downward mobility. They are estimated for individuals aged 14-18 years by country, birth decade of the “children” and urban/rural status of the household residence. in IGM between these populations and document how they have evolved across birth co- horts. I do so by estimating upward and downward mobility16 by country, birth decade of the “children” and urban/rural status of their residence. Figure 7 reports the gap between the upward/downward mobility in urban-rural areas over birth cohort. I find a positive gap that has been declining from 36 percentage points (i.e., upward mobility in urban areas is on average 36 percentage points higher than in rural areas for the cohort born in years 1950- 1959) to 20 percentage points as one moves towards older birth cohorts. Similarly, the gap in downward mobility is closing from below moving from 29 percentage points for 1950 birth decade to 15 percentage points for 1980 birth decade. Figure A14 and Figure A15 in the Appendix show estimates by sub-population rather than the gap between them for countries with data for at least 4 decades, suggesting that the gap has been decreasing because of an increase (decrease) in upward (downward) mobility. 16 The probability of completing at least primary education for those whose parents did not and the probability of not completing primary for those whose parents complete primary school, respectively. 19 III.1.b Gender As discussed in a recent survey on IGM in developing countries (see Torche, 2019), gender gaps in education have been disappearing or even moving in favor of women. I examine whether these patterns hold in this novel data set by estimating IGM for males and females separately and documenting how the gap between these populations has evolved across birth cohorts. I estimate upward and downward mobility17 by country, birth decade of the “children” and gender. I do not find systematic differences by gender for older birth cohorts but it appears that there is a trend towards higher upward mobility for women as they have 3 percentage points higher upward mobility in the 1980s birth cohort (see Figure 8) while the gap in downward mobility move around similar values (the gap in favor of women is approximately 3 percentage points for 1980s birth cohort) with a flatter trend. Figure A16 and Figure A17 in the Appendix show estimates by sub-population rather than the gap between them for countries with data for at least 4 decades, suggesting that the gap has been increasing in favor of women because of an increase (decrease) more than proportional for them in upward (downward) mobility. III.1.c Evolution over time As mentioned in the data section, the coverage over time is unbalanced with some countries spanning more years than others. This limits the analysis of trends over time and the conclusions that can be derived from comparisons between them at given points in time or for a given cohort. Nevertheless, I document estimates of mobility by country for the different birth cohorts that are available. Figure 9 reports these estimates. It is clear how the level of upward mobility has been increasing at the same time that downward mobility has been falling. This is not surprising given the fact that educational attainment has increased in 20 Figure 8: Intergenerational Educational Mobility in LAC - Gender male downward IGM - female downward IGM BOL LCA .2 male upward IGM - female upward IGM gap = 3.535 + -.0018 cohort gap = -.8180 + .0004 cohort .2 BOL (1.854) (.0009) (.6631) (.0003) .15 BOL .1 GTM PER GTM BOL .1 GTM PER BRA ECU PER GTM MEX DOM 780 PRY ECU PRY PAN 192 BOL DOM BRA BRA GTM HTI ECU JAM NIC NIC 0 MEX JAM SLV MEX NIC HND BRA DOM PRY PAN BRA PAN CHL PRY PAN 780 ECU PRY PAN ECU BRA BRA SLV VEN HTI .05 URY COL HND HTI MEX PAN NIC VEN HND BRA VEN CRI NIC HND CRI ARG 192 CHL 780 GTM HTI PRY COL HND COL URY CRI VEN 780 PAN BRA JAM CHL ARG HND URY SLV COL PAN VEN PAN 780 CRI PRY PRY COL SLV URY CHL URY ARG 780 CRI 740 CRI HND URY CRI CHL CHL URY PAN URY HTI PAN URY ARG ARG URY COL COL ARG URY ARG ECU ARG CHL COL ARG PAN CRI DOM CRI NIC BRA HND COL URY PRY MEX GTM CRI JAM ARG MEX SLV 780 MEX CHL ECU JAM ECU MEX GTM JAM ECU 192 -.1 VEN NIC 780 ECU PRY 780 PER PER BOL 740 780 192 VEN COL NIC PER BOL GTM 0 LCA BRA DOM BRA LCA VEN GTM SLV DOM BOL HTI BOL LCA BOL -.05 -.2 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 Birth decade Birth decade countries with data for 1950s, 1960s, 1970s, 1980s countries with data for 1950s, 1960s, 1970s, 1980s other countries other countries mean among countries with data 1950s-1980s mean among countries with data 1950s-1980s (a) Upward mobility (b) Downward mobility Notes: These estimates correspond to the probability of completing at least primary education for those whose parents did not finish primary school in the case of upward mobility and probability of not completing primary education for those whose parents completed primary school in the case of downward mobility. They are estimated by country, birth decade of the “children” and gender. Figure 9: Intergenerational Educational Mobility in LAC across cohorts Jamaica Cuba Bolivia Suriname Trinidad and Tobago Jamaica Cuba Mexico Mexico Bolivia Uruguay Panama Chile Uruguay Suriname Chile Costa Rica Costa Rica Peru Trinidad and Tobago Ecuador Colombia Argentina Peru Panama Argentina Colombia Ecuador Dominican Republic Dominican Republic Paraguay Venezuela Saint Lucia Paraguay Brazil El Salvador Venezuela Saint Lucia El Salvador Brazil Honduras Honduras Nicaragua Guatemala Guatemala Nicaragua Haiti Haiti 0 .2 .4 .6 .8 1 0 .1 .2 .3 .4 IGM UP IGM Down 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 (a) Upward mobility (b) Downward mobility Notes: The estimates are done by birth decade cohort of the children. 21 the region over the last decades. III.2 Spatial variation of intergenerational mobility in LAC Table 3 summarizes the estimates of mobility at the province level. These results show that there are countries with substantial variance in mobility levels across provinces. This is for example the case of Paraguay, Mexico, Guatemala, Bolivia, and Peru, where the difference in upward mobility between the most upwardly mobile to the least upwardly mobile is more than half the range found in the case of countries in Latin America. However, there are also particular cases with either high or low upward mobility at the country level and a very small variation within country, such as Jamaica and Haiti, although somewhat expected as they correspond to countries with small number of administrative units and population. In the case of downward mobility, the variability is much smaller. However, Paraguay stands out as a case where the range between the provinces with the minimum and the maximum level of downward mobility is relatively wide. Figure 10 and 11 maps the same estimates that are summarized in Table 3. We can see some interesting patterns in some countries. For example, Mexico shows a somewhat lower level of upward mobility in the south and you can identify a lighter spot in the middle of the country that corresponds to the region of the capital. In contrast, Brazil shows much lower level of mobility in the northern regions and higher mobility in the East coast near ao Paulo and Rio de Janeiro. Overall, the continent shows higher levels in the the states of S˜ south, especially in the Pacific coast and some heterogeneous level in the case of Islands of the Caribbean region with important contrasts between Cuba and Haiti. In the Appendix, I report similar estimates (see Table A6) and maps (see Figure A5 and A6) at the district level, which corresponds to the finest administrative unit available in the data set. The patterns are qualitatively similar, however, given the level of dis-aggregation there are some districts with just few observations used for the estimation that produces 17 The probability of completing at least primary for those whose parents did not and the probability of not completing primary for those whose parents complete primary school, respectively. 22 estimates that end up outside the [0,1] range. Similarly, summary statistics of alternative estimates of intergenerational mobility that consider secondary education at the province and district levels can be found in the Appendix (see Table A8, A9, A11, and A12). They are consistent with the country-level estimates, in the sense that relative to my baseline estimates using primary education, they show lower levels of upward mobility, higher levels of downward mobility, smaller samples, and significant within-country variation. Table 3: Summary Statistics: Province-Level Estimates of Educational IGM upward downward country provinces mean median stdev min max Nmin Nmean mean median stdev min max Nmin Nmean Cuba 14 .917 .932 .056 .757 .972 63 146 .011 .011 .003 .006 .017 889 7104 Suriname 7 .897 .897 .095 .83 .965 56 73 .012 .013 .005 .005 .021 72 395 Jamaica 14 .888 .893 .029 .84 .936 106 322 .029 .028 .006 .018 .042 1193 2779 Trinidad and Tobago 4 .872 .871 .043 .822 .923 66 1763 .033 .034 .005 .027 .037 1272 8550 Chile 44 .773 .767 .066 .655 .915 93 1523 .064 .065 .019 .027 .113 256 4804 Peru 25 .749 .702 .115 .555 .93 298 5728 .07 .072 .028 .03 .139 699 8571 Argentina 24 .702 .691 .087 .545 .874 204 9763 .061 .058 .02 .021 .099 2329 34757 Costa Rica 7 .693 .693 .054 .623 .753 2261 4929 .083 .071 .023 .058 .112 5091 10369 Uruguay 19 .679 .677 .048 .598 .781 281 1418 .064 .065 .012 .04 .086 734 4294 Mexico 32 .674 .67 .079 .498 .899 2265 38282 .053 .052 .016 .015 .1 6269 49580 Bolivia 9 .651 .641 .097 .504 .814 534 9900 .071 .062 .025 .04 .125 968 13072 Ecuador 14 .622 .602 .057 .561 .718 1371 10618 .091 .082 .031 .06 .179 1322 16034 Panama 7 .596 .629 .108 .401 .744 802 3829 .084 .068 .051 .046 .197 481 8532 Venezuela 22 .545 .526 .079 .402 .708 801 10079 .131 .133 .025 .097 .193 707 13459 El Salvador 14 .538 .541 .062 .436 .669 1740 3346 .16 .158 .033 .098 .218 479 2754 Colombia 22 .519 .526 .094 .373 .724 164 19078 .118 .118 .033 .052 .179 897 21230 Saint Lucia 4 .474 .475 .049 .429 .516 325 446 .155 .155 .01 .148 .162 79 111 Paraguay 14 .458 .412 .118 .33 .777 1740 5381 .147 .138 .046 .04 .207 953 3701 Dominican Republic 23 .451 .469 .071 .302 .584 688 2176 .149 .149 .023 .109 .206 340 2693 Honduras 18 .381 .377 .094 .22 .575 211 4291 .219 .217 .066 .12 .397 255 1790 Nicaragua 12 .349 .366 .109 .205 .529 1211 5000 .211 .198 .063 .137 .35 246 2803 Brazil 25 .285 .249 .103 .144 .493 7290 332632 .21 .23 .052 .123 .299 5407 97580 Guatemala 22 .256 .256 .085 .099 .479 2399 8340 .229 .239 .037 .12 .282 548 2480 Haiti 4 .223 .218 .032 .191 .266 5399 20467 .341 .363 .052 .262 .375 832 5649 total 400 .587 .604 .203 .099 .972 56 29432 .112 .087 .076 .005 .397 72 17814 Notes: This table shows summary statistics for province level estimates of IGM. Upward reflects the likelihood that children, aged 14-18, whose parents have not completed primary schooling will manage to complete at least primary education. Downward reflects the likelihood that children, aged 14-18, whose parents have completed primary schooling or higher will not manage to complete primary education. “Total” shows the unweighted summary statistics across all provinces. The columns “Nmin” and “Nmean” report respectively the smallest and average sample size across provinces. Countries are sorted from the highest to the lowest average level of upward IGM across provinces (column “mean”). Provinces with less than 50 observations are omitted. 23 Figure 10: Upward Mobility in LAC Notes: Upward mobility reflects the likelihood that children, aged 14-18, whose parents have not completed primary schooling will manage to complete at least primary education. 24 Figure 11: Downward Mobility in LAC Notes: Downward mobility reflects the likelihood that children, aged 14-18, whose parents completed primary schooling will not manage to complete at least primary education. 25 IV Correlates of Intergenerational Mobility In this section, I explore a set of correlates of regional IGM with the aim of uncovering a set of stylized facts that help characterize its geography. A necessary caveat is that the set is relatively small given the difficulty of collecting data that is comparable for all the administrative units.18 An additional and perhaps more important caveat is that the analysis does not provide any causal interpretation and is solely descriptive. I run univariate regressions pooling all the countries linking IGM to geographical and initial conditions that have been discussed in previous studies on intergenerational mobility outside the continent (for example, see Alesina et al., 2021). This is done by estimating the following econometric specification: d d αcr = ηc + β d Zcr + d cr (4) where d = [up, down], the dependent variable corresponds to the measure of upward or downward intergenerational mobility previously estimated for province/district r in country d c, ηc denote country fixed effects, Zcr and β d are respectively the covariate and the coefficient of interest. The latter summarizing the linear association between intergenerational mobility and the covariate. IV.1 Education of the old generation First, I analyze the share of the old generation that was able to complete primary education. Alesina et al. (2021) finds this measure to be strongly associated with mobility in Africa. This correlate in part reflects the initial outcomes at the province/district-level for parents. I compute this variable using an econometric specification similar to the one used to compute 18 For example, Munoz (2021) and Van der Weide et al. (2020) use full-count census data for individual countries focusing on a much larger set of correlates. 26 Figure 12: Intergenerational Mobility and Literacy of the Old Generation 1 Brazil Uruguay IGM Down = .242 -.255 lit_par .4 Jamaica Bolivia Uruguay Jamaica Trinidad and Tobago (.0127) (.0215) Cuba Haiti Trinidad Trinidad andand Tobago Tobago and Tobago Peru Trinidad Mexico Suriname Cuba Chile Uruguay .8 Peru Trinidad and Tobago Argentina Costa Rica Ecuador Uruguay Chile Mexico JamaicaUruguay Argentina Argentina Brazil .3 Costa Rica Haiti Argentina Panama Mexico Bolivia Saint Lucia Dominican Republic Colombia Dominican Republic Brazil Ecuador Panama Panama Paraguay Haiti Bolivia Colombia Colombia Nicaragua Saint Lucia El Salvador Brazil Honduras CostaElRica Panama Venezuela Costa Rica Salvador Downward IGM Upward IGM ParaguayEcuador Costa Rica Argentina Chile .6 Venezuela Ecuador Honduras Colombia Peru Venezuela .2 Guatemala Venezuela Nicaragua Dominican Republic Guatemala Mexico Nicaragua Paraguay Colombia Guatemala Dominican Republic Panama Brazil Chile Honduras Ecuador Brazil Panama Brazil Paraguay Paraguay Honduras El Salvador ElUruguay Salvador Honduras Brazil Chile Nicaragua Paraguay Saint Lucia Uruguay Colombia Bolivia Bolivia Venezuela Venezuela Ecuador Guatemala Venezuela Panama Costa El Salvador .4 Honduras Dominican Republic Costa Rica Ecuador Nicaragua Peru Rica Paraguay Costa Rica .1 Chile Paraguay Honduras Venezuela Panama Nicaragua El Bolivia Salvador Dominican Republic Saint Lucia Bolivia Ecuador Argentina Panama Bolivia Brazil Mexico Paraguay Guatemala Haiti Colombia Honduras JamaicaEcuador Argentina Bolivia Guatemala Costa Rica Panama Brazil Mexico PeruColombia Panama Trinidad and Tobago Argentina Colombia Mexico Colombia Chile Uruguay Chile and Tobago Trinidad Peru Argentina Panama Argentina Uruguay andand Trinidad Trinidad Tobago Tobago Bolivia Costa Rica Mexico Guatemala Haiti Nicaragua Uruguay Trinidad Jamaica Cuba and Jamaica Tobago Suriname Guatemala Cuba .2 Paraguay 0 Guatemala Nicaragua Ecuador Uruguay Guatemala Brazil Uruguay Brazil Haiti IGM UP = .165 + .7996 lit_par (.0211) (.0358) -.1 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 Share literate old Share literate old 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 (a) Upward mobility (b) Downward mobility Notes: These estimates are computed by birth decade cohort of the children. mobility at regional level (see Equation 3) run country by country: b b eicroyt = δcr + γo + γy + θt + icroyt (5) where eicroyt is a dummy variable equal to 1 if the completed educational level of the old generation observed for individual i from country c region r is at least primary. Similar to b b before, γo and γy are birth-decade fixed effects for parents and children, and θt a census year fixed effect. In other words, δcr estimates the share of “parents” who complete primary by region netting out cohort and census year effects. I find a strong positive (negative) correlation between upward (downward) mobility and literacy of the old generation (see Figure A18). This suggests the existence of a high level of inertia, confirming the findings of Alesina et al. (2021). Similar patterns are found at the country-birth cohort (see Figure 12). 27 IV.2 Other covariates Given the high level of inertia, the correlation analysis of the remaining correlates is per- formed one by one and also partialing out the effect of the educational attainment of the old generation. The idea is to test whether any potential relationship with the covariate of interest remains after removing the effect of the covariate on “initial conditions”. This is done estimating the following specification (in addition to equation 4): up/down αcr = ηc + βZcr + γWcr + cr (6) up/down where mcr corresponds to the measure of upward or downward IGM for province/district r in country c, ηc denote country fixed effects, Wcr is the share of literacy of the parents in region r, and Zcr and β are respectively the covariate and the coefficient of interest. Alesina et al. (2021) has shown that some geographical characteristics are also correlated to the level of intergenerational mobility. In this paper I consider distance to the capital, distance to the border, and distance to the coast. I also consider other characteristics of the districts that are proxies of the level of development at the beginning of the period of study. These are the urban share of the population, the share of employment in agriculture, the share of employment in industry, and the share of employment in service. These last four covariates are computed restricting the sample to only individuals born before 1960. The results are reported in Figure 13 for upward and downward mobility respectively. Although upward mobility seems to be correlated with most of the proxies of development, the correlations become insignificant at the 5% when controlling by education of the old generation in all the cases. Only the share of employment in industry, which is positively associated with upward mobility, is statistically significant at the 10%. In the case of down- ward mobility, I find a significant correlation at the standard level, even conditioning on education of the old generation, with the share of employment in industry and agriculture, although with opposite signs. Higher share of employment in agriculture is associated with 28 Figure 13: IGM and Correlates urban share (born < 1960) urban share (born < 1960) agr. empl. share (born < 1960) agr. empl. share (born < 1960) ind. empl. share (born < 1960) ind. empl. share (born < 1960) ser. empl. share (born < 1960) ser. empl. share (born < 1960) ln(distance to capital) ln(distance to capital) ln(distance to coast) ln(distance to coast) ln(distance to border) ln(distance to border) -0.6 -0.4 -0.2 0 0.2 0.4 0.6 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 unconditional unconditional conditional on literacy share of old population conditional on literacy share of old population (a) Upward mobility (b) Downward mobility Notes: The graph plots the estimated coefficients and 95% confidence intervals computed clustering standard errors by country. The analysis is done at district-level running regressions by covariate as in equation 4 and 6. The coefficients are standarized. higher downward mobility while higher share of employment in industry is associated with lower downward mobility. In the case of geographical correlates, distance to the border and coast are not significantly correlated to either measure of mobility. This is in line with Alesina et al. (2021) in the case of the border but differ relative to their results for the coast. However, distance to the capital is negatively (positively) correlated to upward (downward) mobility although weakly (statistically significant at the 5% for upward and at 10% for downward mobility). V Final Remarks This paper examines intergenerational educational mobility for countries in Latin America and the Caribbean at a disaggregated regional level using census data spanning more than half a century. I investigate mobility in education at the bottom of the educational attain- ment distribution by focusing on the likelihood of completion of primary education for those whose parents did not complete the level, which can be measured with a high degree of con- 29 fidence between ages 14 and 18. Similarly, I measure downward mobility as the likelihood of not completing primary for those whose parents were able to complete at least primary school. I find wide cross-country and within-country heterogeneity. In LAC, the distance between the most and least upwardly mobile countries is relatively close to what has been recently documented in Africa, although the least mobile countries in Africa are less mobile than any country in LAC. Similarly, the median country in LAC shows higher upward mobility than the median country in Africa. I do not find significant differences by gender, but I do document a declining trend in the mobility gap between urban and rural populations. Within-country mobility shows a variety of patterns. For example, there are countries with higher mobility in the northern regions (e.g., Mexico), whereas others show higher mobility in the southern regions (e.g., Brazil). The level of heterogeneity within country also varies country by country with the lowest levels found in the smallest and less populated ones. In terms of correlates within countries, the level of mobility is highly correlated to the share of primary completion of the previous generation, which suggests a high level of inertia. In addition, upward mobility appears weakly positively correlated to the share of employment in industry and distance to the capital, whereas downward mobility is significantly correlated to the shares of employment in industry and agriculture, and only weakly correlated to distance to the capital. Given the unbalanced nature of the data set in terms of coverage over time and across countries, further research could shed more light on potential determinants of mobility in Latin America by focusing on the analysis of particular countries with relatively high data coverage, such as Chile, Mexico, or Brazil, which makes the collection of correlates by ad- ministrative unit easier. This paper contributes to this goal by creating the estimates of mobility at a disaggregated geographical level and making them available in an online data appendix for future research. 30 References Alesina, A., Hohmann, S., Michalopoulos, S., & Papaioannou, E. (2021). Intergenerational Mobility in Africa. Econometrica , 89 (1), 1–35. Asher, S., Novosad, P., & Rafkin, C. (2021). Intergenerational Mobility in India: New Methods and Estimates Across Time, Space, and Communities. Working Paper. Available at: http://paulnovosad.com/pdf/anr-india-mobility.pdf (accessed November 2021)) . Black, S. E., & Devereux, P. J. (2011). Recent Developments in Intergenerational Mobility (Vol. 4b). Card, D., Domnisoru, C., & Taylor, L. (2022). The Intergenerational Transmission of Human Capital: Evidence from the Golden Age of Upward Mobility. Journal of Labor Economics , 40 (S1), S39–S95. Celhay, P., Sanhueza, C., & Zubizarreta, J. (2010). Intergenerational Mobility of Income and Schooling: Chile 1996-2006. Revista de Analisis Economico , 25 (2), 43–63. Chetty, R., Hendren, N., Kline, P., & Saez, E. (2014). Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States. The Quarterly Journal of Economics , 129 (4), 1553–1623. Corak, M. (2013). Income Inequality, Equality of Opportunity, and Intergenerational Mo- bility. Journal of Economic Perspectives , 27 (3), 79–102. Emran, M. S., Greene, W., & Shilpi, F. (2018). When Measure Matters: Coresidency, Truncation Bias, and Intergenerational Mobility in Developing Countries. Journal of Human Resources , 53 (3), 579–607. Emran, M. S., & Shilpi, F. (2021). Economic Approach to Intergenerational Mobility: Mea- sures, Methods, and Challenges in Developing Countries. In V. Iversen, A. Krishna, & K. Sen (Eds.), Social mobility in developing countries: Concepts, methods, and determi- nants. Oxford University Press. Francesconi, M., & Nicoletti, C. (2006). Intergenerational Mobility and Sample Election in Short Panels. Journal of Applied Econometrics , 21 , 1265–1293. Haider, S., & Solon, G. (2006). Life-Cycle Variation in the Association between Current and Lifetime Earnings. American Economic Review , 96 (4), 1308–1320. Hertz, T., Jayasundera, T., Piraino, P., Selcuk, S., Smith, N., & Verashchagina, A. (2007). The Inheritance of Educational Inequality: International Comparisons and Fifty-Year Trends. The B.E. Journal of Economic Analysis Policy , 7 (2). 31 IPUMS. (2019). Integrated Public Use Microdata Series, International: Version 7.2 [dataset]. Minneapolis, MN: IPUMS: Minnesota Population Center. Lagakos, D. (2020). Urban-Rural Gaps in the Developing World: Does Internal Migration Offer Opportunities? Journal of Economic Perspectives , 34 (3), 174–192. Messina, J., & Silva, J. (2019). Twenty Years of Wage Inequality in Latin America. Policy Research Working Paper (8995). Munoz, E. (2021). Intergenerational Educational Mobility within Chile. Available at SSRN: https://ssrn.com/abstract=3969270 or http://dx.doi.org/10.2139/ssrn.3969270 . Munoz, E., & Siravegna, M. (2021). When Measure Matters: Coresidence Bias and Inter- generational Mobility Revisited. Available at SSRN: https://ssrn.com/abstract=3969270 or http://dx.doi.org/10.2139/ssrn.3969270 . Narayan, A., Van der Weide, R., Cojocaru, A., Lakner, C., Redaelli, S., Gerszon Mahler, D., . . . Thewissen, S. (2018). Fair Progress?: Economic Mobility Across Generations Around the World. The World Bank. ofer, G., Serrano, J., & Gasparini, L. (2018). Educational Inequality and Intergenera- Neidh¨ tional Mobility in Latin America: A New Database. Journal of Development Economics , 134 , 329–349. Ravallion, M. (2016). Are the World’s Poorest being Left Behind? Journal of Economic Growth , 21 , 139–164. Rawls, J. (1971). A Theory of Justice. Cambridge, MA: Harvard University Press. Solon, G. (1992). Intergenerational Income Mobility in the United States. The American Economic Review , 82 (3), 393–408. Torche, F. (2019). Educational Mobility in Developing Countries. WIDER Working Pa- per (88), 1–31. Van der Weide, R., Ferreira de Souza, P., & Barbosa, R. (2020). Intergenerational Mobility in Education in Brazil. mimeo . Van der Weide, R., Lakner, C., Gerszon Mahler, D., Narayan, A., & Ramasubbaiah, R. (2021). Intergenerational Mobility Around the World. Policy Research Working Paper , 9707 . 32 Appendices In this Appendix I provide details on the sample construction and some additional tables and graphs. Table A1 list the Census’ samples obtained from IPUMS-International and the size of the extract. Table A2 reports sample size from raw data to samples restricted by age and by avail- ability of information on education. Table A3 reports the rates of co-residency by country for different ages. Table A4 reports the rates of co-residency by country-sample for different ages. Figure A1 displays rates of coresidence by urban/rural population and by gender. Figure A2 displays a comparison of estimates of upward mobility for the same country- cohort with all children versus coresident children. The source of these estimates is Munoz and Siravegna (2021). Figure A3 displays the educational attainment transition matrix for individuals aged 14-25 years. Figure A4 displays the educational attainment transition matrix for individuals aged 14-25 years in selected countries. Table A5 summarizes the education level by cohort using data on individuals at least 25 years old. Table A6 reports district-level estimates of intergenerational mobility. Figure A5 and A6 displays maps of mobility at the district-level for LAC. Figure A7 and A8 displays maps of mobility at the district-level for LAC using secondary education. Table A7, A8, and A9 report estimates of IGM that consider secondary education. Table A10, A11, and A12 report estimates of IGM that consider the likelihood of completing secondary education when parents completed less than primary. 33 Figure A13 displays the negative relationship between upward and downward mobility. Figure A14 displays estimates of upward mobility by urban/rural status for selected countries. Figure A15 displays the estimates of downward mobility by urban/rural status for selected countries. Figure A16 displays estimates of upward mobility by gender for selected countries. Figure A17 displays the estimates of downward mobility by gender for selected countries. Figure A18 shows scatter plots between IGM and share of the old generation that com- pletes at least primary education by district. 34 A Sample coverage and construction Table A1: Census’ samples N Country Year Fraction Households Persons N Country Year Fraction Households Persons (%) (%) 1 Argentina 1970 2 129,728 466,892 47 Haiti 2003 10 219,633 838,045 2 Argentina 1980 10 672,062 2,667,714 48 Honduras 1974 10 49,064 278,348 3 Argentina 1991 10 1,199,651 4,286,447 49 Honduras 1988 10 77,406 423,971 4 Argentina 2001 10 1,040,852 3,626,103 50 Honduras 2001 10 123,584 608,620 5 Argentina 2010 10 1,217,166 3,966,245 51 Jamaica 1982 10 54,526 223,667 6 Bolivia 1976 10 121,378 461,699 52 Jamaica 1991 10 62,291 232,625 7 Bolivia 1992 10 177,926 642,368 53 Jamaica 2001 10 64,317 205,179 8 Bolivia 2001 10 239,475 827,692 54 Mexico 1970 1 82,856 483,405 9 Bolivia 2012 10 292,117 1,003,516 55 Mexico 1990 10 1,648,280 8,118,242 10 Brazil 1960 20 3,066,365 14,983,769 56 Mexico 2000 10.6 2,312,035 10,099,182 11 Brazil 1970 25 5,111,039 24,789,716 57 Mexico 2010 10 2,903,640 11,938,402 12 Brazil 1980 25 6,716,885 29,378,753 58 Nicaragua 1971 10 36,063 189,469 13 Brazil 1991 10 4,024,553 17,045,712 59 Nicaragua 1995 10 82,815 435,728 14 Brazil 2000 10 5,304,711 20,274,412 60 Nicaragua 2005 10 119,339 515,485 15 Brazil 2010 10 6,192,502 20,635,472 61 Panama 1960 5 11,869 53,553 16 Chile 1970 10 199,041 890,481 62 Panama 1970 10 31,755 150,473 17 Chile 1982 10 282,356 1,133,062 63 Panama 1980 10 47,726 195,577 18 Chile 1992 10 373,964 1,335,055 64 Panama 1990 10 61,458 232,737 19 Chile 2002 10 486,115 1,513,914 65 Panama 2000 10 84,346 284,081 20 Colombia 1973 10 349,853 1,988,831 66 Panama 2010 10 95,579 341,118 21 Colombia 1985 10 571,046 2,643,125 67 Paraguay 1962 5 18,307 90,236 22 Colombia 1993 10 774,321 3,213,657 68 Paraguay 1972 10 43,883 233,669 23 Colombia 2005 10 1,054,812 4,006,168 69 Paraguay 1982 10 60,465 301,582 24 Costa Rica 1973 10 36,323 186,762 70 Paraguay 1992 10 100,704 415,401 25 Costa Rica 1984 10 56,186 241,220 71 Paraguay 2002 10 113,039 516,083 26 Costa Rica 2000 10 106,973 381,500 72 Peru 1993 10 564,765 2,206,424 27 Costa Rica 2011 10 124,693 430,082 73 Peru 2007 10 821,675 2,745,895 28 Cuba 2002 10 371,878 1,118,767 74 Saint Lucia 1980 10 2,674 11,451 29 Cuba 2012 10 416,577 1,115,643 75 Saint Lucia 1991 10 3,394 13,382 30 Dominican Rep 1981 8.5 103,904 475,829 76 Suriname 2012 10 14,037 53,636 31 Dominican Rep 2002 10 247,375 857,606 77 Trinidad and Tobago 1970 10 15,871 69,349 32 Dominican Rep 2010 10 309,624 943,784 78 Trinidad and Tobago 1980 10 23,870 105,464 33 Ecuador 1974 10 145,902 648,678 79 Trinidad and Tobago 1990 10 27,561 113,104 34 Ecuador 1982 10 195,401 806,834 80 Trinidad and Tobago 2000 10 35,715 111,833 35 Ecuador 1990 10 243,898 966,234 81 Trinidad and Tobago 2011 8.8 41,606 116,917 36 Ecuador 2001 10 354,222 1,213,725 82 Uruguay 1963 10 79,403 256,171 37 Ecuador 2010 10 386,944 1,448,233 83 Uruguay 1975 10 95,935 279,994 38 El Salvador 1992 10 125,695 510,760 84 Uruguay 1985 10 105,761 295,915 39 El Salvador 2007 10 172,012 574,364 85 Uruguay 1996 10 118,067 315,920 40 Guatemala 1964 5 40,220 210,411 86 Uruguay 2006 8.4 85,316 256,866 41 Guatemala 1973 5.5 59,622 289,458 87 Uruguay 2011 10 118,498 328,425 42 Guatemala 1981 5 65,555 302,106 88 Venezuela 1971 2 284,336 1,158,527 43 Guatemala 1994 10 160,603 833,139 89 Venezuela 1981 10 323,321 1,441,266 44 Guatemala 2002 10 222,770 1,121,946 90 Venezuela 1990 10 468,808 1,803,953 45 Haiti 1971 10 95,145 434,869 91 Venezuela 2001 10 646,080 2,306,489 46 Haiti 1982 2.5 28,698 128,770 35 Table A2: Sample sizes All observations Obs. with education All observations Obs. with education Country Year age: All age: 14-18 age: 14-25 age: 14-18 age: 14-25 Country Year age: All age: 14-18 age: 14-25 age: 14-18 age: 14-25 Argentina 1970 466,892 42,317 96,744 31,411 59,124 Haiti 2003 838,045 103,088 218,016 72,705 130,436 Argentina 1980 2,700,000 241,353 532,289 193,448 348,232 Honduras 1974 278,348 32,262 64,660 24,018 37,966 Argentina 1991 4,300,000 392,977 844,871 347,074 611,881 Honduras 1988 423,971 47,258 95,944 37,642 62,769 Argentina 2001 3,600,000 321,380 764,630 295,621 596,468 Honduras 2001 608,620 73,272 154,339 62,008 105,745 Argentina 2010 4,000,000 354,910 813,073 323,256 621,385 Jamaica 1982 223,668 27,612 58,456 17,270 28,729 Bolivia 1976 461,699 51,674 109,380 35,230 57,307 Jamaica 1991 232,625 25,145 56,810 17,326 32,498 Bolivia 1992 642,368 69,992 147,085 46,235 75,965 Jamaica 2001 205,179 21,357 47,770 14,349 25,241 Bolivia 2001 827,692 90,786 199,275 63,080 111,001 Mexico 1970 483,405 54,069 111,210 41,915 64,605 Brazil 1960 15,000,000 1,600,000 3,500,000 1,300,000 2,200,000 Mexico 1990 8,100,000 1,000,000 2,100,000 900,739 1,500,000 Brazil 1970 25,000,000 2,800,000 6,000,000 2,300,000 3,700,000 Mexico 2000 10,000,000 1,100,000 2,400,000 963,638 1,700,000 Brazil 1980 29,000,000 3,300,000 7,400,000 2,700,000 4,600,000 Mexico 2010 12,000,000 1,300,000 2,700,000 1,200,000 2,200,000 Brazil 1991 17,000,000 1,800,000 4,000,000 1,600,000 2,800,000 Nicaragua 1971 189,469 22,601 44,957 16,771 26,368 Brazil 2000 20,000,000 2,200,000 4,800,000 1,900,000 3,400,000 Nicaragua 1995 435,728 51,956 107,402 42,619 74,447 Brazil 2010 21,000,000 1,900,000 4,500,000 1,700,000 3,200,000 Nicaragua 2005 515,485 60,691 136,084 50,811 95,961 Chile 1970 890,481 96,432 203,625 73,392 123,911 Panama 1960 53,553 5,481 11,869 3,368 5,498 Chile 1982 1,100,000 130,958 293,439 106,794 197,946 Panama 1970 150,473 15,817 34,219 11,310 18,797 Chile 1992 1,300,000 121,069 290,349 100,838 199,734 Panama 1980 195,577 22,673 47,420 17,725 30,333 Chile 2002 1,500,000 130,506 297,907 110,343 214,019 Panama 1990 232,737 25,536 57,471 19,537 36,604 Colombia 1973 2,000,000 245,355 493,144 172,222 281,047 Panama 2000 284,081 27,438 62,585 21,924 41,171 Colombia 1985 2,600,000 312,063 705,404 245,920 466,142 Panama 2010 341,118 30,266 70,017 26,170 49,837 Colombia 1993 3,200,000 336,233 758,037 263,014 485,909 Paraguay 1962 90,236 10,003 20,431 6,011 10,224 Colombia 2005 4,000,000 399,870 860,151 325,438 579,432 Paraguay 1972 233,669 27,630 54,005 18,806 31,105 Costa Rica 1973 186,762 23,539 46,832 18,809 30,070 Paraguay 1982 301,582 34,248 74,515 25,177 45,971 Costa Rica 1984 241,220 28,005 64,067 23,982 44,198 Paraguay 1992 415,401 41,705 89,839 30,061 52,473 Costa Rica 2000 381,500 40,582 88,091 36,085 63,624 Paraguay 2002 516,083 59,365 125,811 48,042 85,609 Costa Rica 2011 430,082 40,703 98,328 36,805 74,880 Peru 1993 2,200,000 245,196 539,320 183,244 335,766 Cuba 2002 1,100,000 82,556 180,787 69,378 132,152 Peru 2007 2,700,000 280,035 636,955 222,254 419,885 Dominican Republic 1981 475,829 62,387 126,838 49,358 84,310 Saint Lucia 1980 11,451 1,516 2,985 1,076 1,754 Dominican Republic 2002 857,606 85,616 194,479 69,843 128,140 Saint Lucia 1991 13,382 1,455 3,406 1,138 2,154 Dominican Republic 2010 943,784 98,661 221,932 78,426 142,857 Trinidad and Tobago 1970 69,349 8,259 16,684 6,398 10,873 Ecuador 1974 648,678 72,812 162,826 49,142 82,561 Trinidad and Tobago 1980 105,464 13,096 28,713 11,078 20,578 Ecuador 1982 806,834 89,627 194,868 64,889 112,394 Trinidad and Tobago 1990 113,104 10,646 24,520 9,232 18,279 Ecuador 1990 966,234 108,806 237,150 83,171 146,856 Trinidad and Tobago 2000 111,833 12,444 26,458 10,890 20,515 Ecuador 2001 1,200,000 126,354 287,034 100,955 186,327 Trinidad and Tobago 2011 116,917 8,325 22,630 7,288 17,595 Ecuador 2010 1,400,000 145,454 326,549 117,218 212,597 Uruguay 1963 256,171 20,618 47,079 15,749 28,722 El Salvador 1992 510,760 62,794 129,373 44,508 74,325 Uruguay 1975 279,994 24,213 53,152 18,704 33,222 El Salvador 2007 574,364 62,912 131,762 55,338 100,318 Uruguay 1985 295,915 23,728 55,355 18,881 35,368 Guatemala 1964 210,079 22,674 46,804 17,177 27,249 Uruguay 1996 315,920 26,188 60,440 21,870 41,399 Guatemala 1973 289,446 33,148 71,814 24,569 39,263 Uruguay 2006 256,866 21,943 45,451 20,277 36,604 Guatemala 1981 302,106 33,771 72,879 26,958 45,277 Uruguay 2011 328,425 26,825 60,496 23,925 43,382 Guatemala 1994 833,137 97,480 196,310 82,505 135,877 Venezuela 1971 1,200,000 133,044 282,119 87,971 144,465 Guatemala 2002 1,100,000 127,311 269,696 114,181 200,981 Venezuela 1981 1,400,000 166,729 367,032 133,566 238,340 Haiti 1971 434,869 51,096 101,984 35,014 58,427 Venezuela 1990 1,800,000 199,055 445,482 149,752 269,185 Haiti 1982 128,770 15,471 36,494 8,349 15,840 Venezuela 2001 2,300,000 234,403 534,204 204,784 394,511 Notes: This table reports the total sample size by country-year Census, and for restricted population by age and keeping only observations with information of education for children and parents. 36 B Rates of co-residence This table shows the co-residency rate by country for different ages. The co-residence rate is the total number of individuals that co-reside with at least one member of an immediately older generation in the household divided by the total number of individuals in the age group. The sample considers only individuals for whom their own educational attainment and the relationship to household head are observed. Table A3: Co-residence rates Rate Observations (thousands) 14-18 18-25 21-25 20-23 14-18 18-25 21-25 20-23 Argentina 95.7 72.1 63.1 72.2 1246 1746 1067 870 Bolivia 86.7 57.6 48.8 56.4 263 358 218 180 Brazil 93.7 63.0 51.7 62.0 12292 16695 10015 8312 Chile 95.4 72.7 63.8 73.3 410 570 351 285 Colombia 93.4 68.4 59.6 68.2 1086 1451 888 717 Costa Rica 94.5 68.3 58.8 68.0 122 173 105 87 Cuba 91.6 74.6 68.7 74.8 141 217 136 107 Dominican Republic 89.0 63.4 54.1 62.7 222 307 182 153 Ecuador 92.8 64.8 55.2 64.2 451 621 378 311 El Salvador 90.8 66.8 57.8 66.1 110 138 82 68 Guatemala 92.8 63.4 52.8 62.6 286 363 214 180 Haiti 94.4 71.6 60.3 71.1 123 158 88 76 Honduras 91.1 62.3 52.1 60.9 136 168 98 83 Jamaica 90.5 65.2 55.2 64.7 58 76 45 37 Mexico 93.8 69.1 59.4 68.5 3363 4318 2536 2112 Nicaragua 92.4 67.7 59.1 67.2 120 156 93 78 Panama 92.5 66.8 57.7 66.3 108 150 91 74 Paraguay 94.7 67.4 57.4 67.2 136 177 107 89 Peru 93.3 69.8 61.8 69.4 436 604 371 301 Saint Lucia 94.7 66.3 55.7 65.2 2 3 2 2 Suriname 95.7 81.2 75.6 82.2 4 5 3 3 Trinidad and Tobago 96.1 78.1 70.4 78.8 47 66 40 32 Uruguay 95.4 68.9 59.2 68.6 125 175 107 87 Venezuela 92.6 67.7 59.0 67.1 630 858 518 428 37 Figure A1: Coresidence rate by age for subgroups Age Age 14 96.1 14 96.8 15 94.6 15 96.1 16 92.8 16 95.7 17 89.5 17 94.3 18 84.8 18 91.9 Rural 19 20 72.3 78.7 Male 19 20 83.8 88.6 21 66.7 21 78.8 22 60.5 22 72.9 23 53.5 23 65.7 24 48.4 24 60.3 25 43.2 25 53.9 14 96.7 14 96.4 15 95.5 15 94.9 16 94.3 16 92.2 17 91.5 17 88.3 18 87.4 18 82.4 Urban 19 20 76.5 82.6 Female 19 20 69.2 76.2 21 71.3 21 63.8 22 64.8 22 57.4 23 58.5 23 51.8 24 53.4 24 46.8 25 48.1 25 41.9 0 20 40 60 80 100 0 20 40 60 80 100 Coresidence rate Coresidence rate (a) Coresidence rates by urban-rural status (b) Coresidence rate by gender Notes: Coresidence is defined as living with at least one individual of the immediately previous generation. The data in the graph is unweighted. 38 Figure A2: Comparison of IGM with all versus coresident children Notes: The source of these estimates is Munoz and Siravegna (2021). It shows the relationship be- tween estimates of the conditional probability of completing at least primary school for individuals whose parents did not complete primary using two data sources. One set of estimates. computed with census data, use individuals aged 21-25 that coreside with at least one parent. The second set of estimates use the equivalent five birth-cohorts of each census sample with data from Latino- barometro where individuals are asked about the educational attainment of their parents. These 72 estimates span 18 countries in Latin America. 39 Figure A3: Educational Attainment Transition Matrix fraction by parental attainment 0 .2 .4 .6 .8 1 1 .8 likelihood of child attainment .6 Less than primary Primary completed Secondary completed .4 University completed .2 0 ary ted ted ted prim ple ple ple an com c om com s th ary y dar rsit y Les Prim con Unive Se Notes: The sample is constructed with individuals aged 14-25 years that coreside with at least one individual of the generation above. The figure displays the transition matrix between the educational attainment of individuals in the sample and their parents. The horizontal axis is divided according to the share of parents with each level of educational attainment. The height of each rectangle within the figure is the likelihood of child educational attainment conditional on the attainment of their parents. 40 Figure A4: Transition matrix for selected countries fraction by parental attainment fraction by parental attainment 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 1 1 .8 .8 likelihood of child attainment likelihood of child attainment .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary ted ted ted ary ted lete ted d prim ple ple ple prim ple mp ple n c om om om an com ry cyoc om tha ary yc yc s th ary nda Les s dar iversit Les e rsit Prim c o n Un Prim o c iv Se Se Un (a) Jamaica (b) Guatemala Notes: The sample is constructed with individuals aged 14-25 years that coreside with at least one individual of the generation above. The figures display the transition matrix between the educational attainment of individuals in the sample and their parents. The horizontal axis is divided according to the share of parents with each level of educational attainment. The height of each rectangle within each figure is the likelihood of child educational attainment conditional on the attainment of their parents. 41 Table A4: Co-residence rate by sample Rate Observations (thousands) Year 14-18 18-25 21-25 20-23 14-18 18-25 21-25 20-23 Argentina 1970 95.0 69.7 59.3 69.8 33 48 29 24 Argentina 1980 94.6 68.2 58.8 68.6 204 276 174 138 Argentina 1991 95.6 69.4 59.1 69.3 364 468 281 231 Argentina 2001 96.7 76.4 68.7 76.8 306 464 288 238 Argentina 2010 95.5 73.1 64.5 72.9 338 490 295 240 Bolivia 1976 90.8 54.8 43.0 52.8 39 52 31 26 Bolivia 1992 93.1 58.1 46.7 56.9 52 68 42 34 Bolivia 2001 85.9 57.5 49.3 56.6 74 102 62 52 Bolivia 2012 82.3 58.4 51.6 57.4 98 137 83 68 Brazil 1960 94.9 61.3 48.5 60.1 1386 1824 1069 905 Brazil 1970 95.7 62.9 49.4 61.4 2383 2963 1714 1474 Brazil 1980 94.4 59.6 47.1 58.2 2907 3972 2407 1987 Brazil 1991 92.3 63.5 53.3 62.8 1710 2347 1433 1166 Brazil 2000 92.6 65.5 54.8 64.6 2064 2837 1665 1404 Brazil 2010 91.6 66.2 58.0 66.1 1842 2753 1727 1377 Chile 1970 95.8 68.8 58.0 69.1 77 91 55 45 Chile 1982 96.2 74.0 64.5 74.5 111 150 91 75 Chile 1992 94.5 71.2 62.5 72.2 107 166 105 82 Chile 2002 95.1 75.1 67.8 75.6 116 163 101 83 Colombia 1973 93.8 66.2 54.8 64.8 185 212 122 103 Colombia 1985 95.4 73.1 64.5 73.8 260 370 225 183 Colombia 1993 94.3 68.7 60.1 68.6 282 398 251 197 Colombia 2005 91.1 65.5 57.4 64.9 360 472 290 233 Costa Rica 1973 95.5 64.4 50.9 62.4 20 23 13 11 Costa Rica 1984 95.0 66.2 55.3 65.8 25 38 23 19 Costa Rica 2000 94.5 67.5 57.5 66.7 38 50 30 25 Costa Rica 2011 93.7 71.7 64.4 72.3 39 63 40 32 Cuba 2002 91.3 73.5 66.7 73.3 76 103 62 48 Cuba 2012 92.0 75.5 70.4 75.9 65 113 74 59 Dominican Republic 1981 91.7 67.1 56.9 65.7 54 67 38 33 Dominican Republic 2002 90.0 63.8 54.9 63.6 78 111 68 56 Dominican Republic 2010 86.6 61.2 52.0 60.4 91 129 76 64 Ecuador 1974 92.8 62.0 51.2 60.7 53 68 40 33 Ecuador 1982 93.8 64.4 54.1 63.6 71 94 57 48 Ecuador 1990 93.1 65.1 54.8 64.8 90 122 74 60 Ecuador 2001 92.3 65.3 56.2 64.8 110 159 98 82 Ecuador 2010 92.4 65.5 56.6 64.8 128 178 109 88 El Salvador 1992 91.3 61.8 51.0 61.0 49 61 37 30 El Salvador 2007 90.5 70.7 63.2 70.1 61 77 46 38 Guatemala 1964 91.6 56.1 44.8 55.6 19 23 14 11 Guatemala 1973 88.4 50.4 39.8 48.9 28 38 22 19 Guatemala 1981 92.7 59.4 47.9 58.3 29 40 23 20 Continued on next page 42 Table A4 – continued from previous page Year 14-18 18-25 21-25 20-23 14-18 18-25 21-25 20-23 Guatemala 1994 93.5 64.5 53.5 63.5 88 104 61 51 Guatemala 2002 93.5 67.8 57.8 67.2 122 159 93 80 Haiti 1971 94.9 66.9 52.8 66.8 37 45 25 21 Haiti 1982 93.8 67.7 56.3 67.9 9 14 8 7 Haiti 2003 94.3 74.3 64.3 73.6 77 99 55 47 Honduras 1974 92.0 59.4 48.2 58.2 26 31 18 15 Honduras 1988 92.6 64.6 54.7 63.5 41 48 29 24 Honduras 2001 89.9 62.0 52.0 60.5 69 89 51 45 Jamaica 1982 90.9 65.0 53.7 64.2 20 25 14 12 Jamaica 1991 91.7 67.5 57.8 67.6 20 28 17 14 Jamaica 2001 88.5 62.5 53.6 61.6 17 23 14 11 Mexico 1970 94.7 58.2 44.0 56.8 44 51 29 24 Mexico 1990 94.0 66.5 55.3 65.8 958 1191 689 579 Mexico 2000 93.0 66.8 57.4 66.4 1079 1442 869 708 Mexico 2010 94.2 73.3 64.7 72.8 1282 1634 949 801 Nicaragua 1971 93.1 61.8 49.6 60.8 18 20 12 10 Nicaragua 1995 93.5 69.4 60.4 68.8 46 56 33 28 Nicaragua 2005 91.2 68.1 60.5 67.6 56 80 49 40 Panama 1960 91.3 52.8 40.6 52.3 4 5 3 3 Panama 1970 91.7 57.8 46.4 56.5 12 16 10 8 Panama 1980 92.7 65.9 55.1 65.1 19 24 14 12 Panama 1990 93.2 69.9 61.0 69.6 21 30 18 15 Panama 2000 93.3 68.8 60.4 68.4 24 33 21 16 Panama 2010 91.8 68.6 61.0 68.5 29 41 25 20 Paraguay 1962 95.7 63.1 51.9 64.3 6 8 5 4 Paraguay 1972 96.0 66.5 55.3 67.1 20 23 14 11 Paraguay 1982 94.7 67.6 57.9 67.8 27 37 23 19 Paraguay 1992 93.2 61.8 52.0 60.9 32 44 27 22 Paraguay 2002 95.1 72.0 62.5 71.5 51 64 37 32 Peru 1993 94.0 69.5 60.9 69.1 196 267 165 135 Peru 2007 92.7 70.0 62.5 69.6 240 337 206 166 Saint Lucia 1980 95.3 64.5 51.5 63.2 1 1 1 1 Saint Lucia 1991 94.0 67.7 58.6 66.9 1 2 1 1 Suriname 2012 95.7 81.2 75.6 82.2 4 5 3 3 Trinidad and Tobago 1970 97.0 72.1 59.5 72.0 7 8 4 4 Trinidad and Tobago 1980 95.2 73.2 63.2 73.6 12 16 9 8 Trinidad and Tobago 1990 95.8 76.9 69.4 78.0 10 14 9 7 Trinidad and Tobago 2000 96.4 81.4 74.2 81.2 12 15 8 7 Trinidad and Tobago 2011 96.5 84.4 80.1 86.2 8 14 9 7 Uruguay 1963 97.1 70.6 60.0 70.3 16 23 14 11 Uruguay 1975 96.5 67.6 56.0 66.6 19 27 16 13 Uruguay 1985 96.9 67.1 57.5 67.6 19 29 19 15 Uruguay 1996 94.0 69.3 60.5 69.3 23 34 21 17 Uruguay 2006 95.0 74.9 65.7 74.6 21 27 16 13 Uruguay 2011 94.0 65.6 56.5 64.8 25 36 22 18 Continued on next page 43 Table A4 – continued from previous page Year 14-18 18-25 21-25 20-23 14-18 18-25 21-25 20-23 Venezuela 1971 93.7 60.5 48.4 58.8 97 121 71 60 Venezuela 1981 92.8 66.7 57.3 66.1 144 192 115 96 Venezuela 1990 91.7 66.6 57.9 65.8 168 227 137 112 Venezuela 2001 92.7 71.7 64.6 71.7 221 318 195 160 44 C Schooling by cohort In this section, I summarize the education level by country and cohort using data on indi- viduals at least 25 years old. Table A5: Education by cohort cohort mean years less primary primary secondary tertiary Argentina 1950 9.1 17.8 50.6 24.1 7.5 Argentina 1960 10.0 11.3 48.9 31.4 8.4 Argentina 1970 10.8 8.1 45.8 36.1 9.9 Argentina 1980 11.4 7.4 46.3 35.9 10.4 Bolivia 1950 6.3 46.1 30.7 17.4 5.9 Bolivia 1960 7.8 31.4 38.5 22.8 7.3 Bolivia 1970 9.2 22.7 37.7 28.7 10.9 Bolivia 1980 10.7 13.7 34.7 35.6 16.1 Brazil 1950 5.7 58.2 18.0 15.7 8.2 Brazil 1960 6.7 44.6 25.4 21.5 8.5 Brazil 1970 7.2 33.8 28.1 27.9 10.2 Brazil 1980 18.9 28.9 39.0 13.2 Chile 1950 9.2 19.0 47.2 28.7 5.1 Chile 1960 10.1 12.0 45.9 37.3 4.8 Chile 1970 11.3 6.8 39.4 46.2 7.6 Chile 1980 Colombia 1950 6.5 34.9 39.7 18.9 6.5 Colombia 1960 7.5 24.5 42.2 26.6 6.7 Colombia 1970 8.8 18.2 34.6 34.6 12.6 Colombia 1980 9.4 14.1 30.0 43.0 12.9 Costa Rica 1950 7.9 23.1 46.9 18.3 11.7 Costa Rica 1960 8.6 14.7 51.8 19.2 14.3 Costa Rica 1970 8.7 15.1 50.3 17.2 17.4 Costa Rica 1980 9.7 11.4 44.1 20.1 24.3 Cuba 1950 10.4 7.6 46.1 32.6 13.7 Cuba 1960 11.4 2.8 39.7 43.4 14.2 Cuba 1970 11.7 1.9 37.6 46.9 13.6 Cuba 1980 12.3 1.5 24.3 52.1 22.1 Dominican Republic 1950 6.3 50.5 29.0 12.3 8.1 Dominican Republic 1960 8.0 33.5 37.1 17.8 11.7 Dominican Republic 1970 8.6 27.4 39.4 22.3 10.9 Dominican Republic 1980 9.7 19.8 33.6 34.0 12.6 Ecuador 1950 7.4 34.3 39.8 17.7 8.2 Ecuador 1960 8.8 22.4 41.7 26.1 9.8 Continued on next page 45 Table A5 – continued from previous page cohort mean years less primary primary secondary tertiary Ecuador 1970 9.4 16.4 42.7 30.9 10.0 Ecuador 1980 10.2 11.1 39.5 37.2 12.1 El Salvador 1950 5.2 55.6 27.3 12.9 4.2 El Salvador 1960 6.5 45.0 31.7 18.3 5.0 El Salvador 1970 7.5 37.3 33.3 23.0 6.4 El Salvador 1980 8.1 31.5 37.2 25.7 5.6 Guatemala 1950 3.5 71.9 18.5 6.4 3.3 Guatemala 1960 4.5 62.3 24.6 9.1 3.9 Guatemala 1970 5.2 55.2 29.0 11.3 4.5 Guatemala 1980 Haiti 1950 3.0 71.9 21.3 6.0 0.8 Haiti 1960 3.4 67.7 18.5 12.6 1.2 Haiti 1970 5.2 52.6 28.1 18.2 1.2 Haiti 1980 Honduras 1950 4.5 61.8 25.8 9.7 2.7 Honduras 1960 5.4 50.7 33.2 13.0 3.0 Honduras 1970 6.0 41.5 42.2 13.9 2.5 Honduras 1980 Jamaica 1950 9.7 7.4 60.5 29.4 2.7 Jamaica 1960 11.2 2.7 44.7 50.2 2.4 Jamaica 1970 12.4 2.1 20.9 74.5 2.6 Jamaica 1980 Mexico 1950 6.8 37.4 41.8 11.4 9.5 Mexico 1960 8.3 23.6 47.8 17.3 11.4 Mexico 1970 9.2 13.8 54.0 19.9 12.3 Mexico 1980 10.1 9.8 50.3 24.1 15.8 Nicaragua 1950 4.9 59.7 24.4 9.3 6.5 Nicaragua 1960 6.0 48.1 31.6 13.9 6.5 Nicaragua 1970 6.4 42.9 33.2 16.5 7.4 Nicaragua 1980 6.8 39.3 32.7 20.3 7.7 Panama 1950 8.6 21.2 45.5 21.4 11.8 Panama 1960 9.7 12.4 45.0 29.0 13.6 Panama 1970 10.2 11.0 40.8 31.1 17.2 Panama 1980 10.7 8.8 36.7 36.3 18.2 Paraguay 1950 6.2 46.8 39.2 9.9 4.2 Paraguay 1960 7.3 34.1 43.7 16.9 5.3 Paraguay 1970 8.1 26.3 46.0 21.3 6.5 Paraguay 1980 Peru 1950 7.5 38.8 16.8 32.9 11.6 Peru 1960 8.4 28.2 19.2 41.4 11.2 Peru 1970 9.3 16.9 20.8 48.3 14.0 Peru 1980 9.7 11.9 21.2 55.2 11.6 Continued on next page 46 Table A5 – continued from previous page cohort mean years less primary primary secondary tertiary Saint Lucia 1950 9.4 72.3 3.9 20.8 3.0 Saint Lucia 1960 52.7 8.8 35.6 2.8 Saint Lucia 1970 Saint Lucia 1980 Suriname 1950 11.3 69.8 16.1 2.9 Suriname 1960 7.1 70.9 18.5 3.5 Suriname 1970 6.4 66.3 22.7 4.6 Suriname 1980 4.9 57.9 30.8 6.4 Trinidad and Tobago 1950 9.0 15.8 44.6 36.4 3.1 Trinidad and Tobago 1960 10.1 12.1 31.6 52.9 3.4 Trinidad and Tobago 1970 11.5 6.7 20.6 67.8 4.9 Trinidad and Tobago 1980 12.1 5.4 15.9 72.0 6.8 Uruguay 1950 8.9 17.7 53.0 23.3 5.9 Uruguay 1960 9.2 12.1 57.5 22.6 7.8 Uruguay 1970 9.7 11.8 53.0 26.9 8.3 Uruguay 1980 10.2 6.6 54.0 31.9 7.4 Venezuela 1950 7.4 26.0 46.2 25.5 2.2 Venezuela 1960 8.1 18.7 46.3 34.0 1.1 Venezuela 1970 8.6 14.6 43.0 42.1 0.2 Venezuela 1980 47 D District-level estimates Table A6: Summary Statistics: District-Level Estimates of Educational IGM upward downward country districts mean median stdev min max Nmin Nmean mean median stdev min max Nmin Nmean Cuba 137 .845 .872 .112 .722 .94 50 58 .012 .01 .007 0 .043 178 726 Uruguay 67 .798 .793 .056 .684 .94 50 151 .046 .043 .022 .003 .098 238 737 Chile 179 .758 .752 .079 .534 .969 68 378 .069 .065 .026 .014 .157 140 1181 Costa Rica 55 .714 .719 .07 .498 .878 110 627 .075 .072 .027 .033 .156 313 1320 Argentina 312 .713 .732 .123 .407 .986 56 756 .066 .054 .035 .013 .194 276 2674 Peru 168 .702 .688 .127 .339 .935 111 857 .097 .081 .053 .016 .342 64 1275 Bolivia 80 .627 .642 .13 .345 .948 179 1114 .111 .104 .059 .027 .317 80 1471 Mexico 2,331 .615 .612 .132 .192 1.133 50 551 .083 .071 .055 -.052 .504 50 702 Ecuador 78 .591 .599 .115 .306 .847 180 1930 .109 .095 .047 .054 .291 244 2915 Panama 35 .588 .593 .153 .253 .803 184 766 .095 .08 .052 .031 .241 152 1706 El Salvador 103 .553 .549 .091 .327 .754 92 459 .177 .168 .068 .043 .383 50 381 Venezuela 157 .52 .513 .103 .255 .746 194 1412 .158 .151 .05 .068 .334 135 1886 Colombia 434 .509 .498 .127 -.043 .88 123 967 .151 .145 .065 .037 .371 133 1076 Paraguay 63 .474 .477 .119 .116 .781 208 1146 .152 .143 .051 .039 .259 96 788 Dominican Republic 66 .462 .463 .082 .301 .667 73 770 .154 .147 .036 .082 .273 94 953 Brazil 2,040 .386 .387 .15 .019 .827 366 2514 .203 .184 .087 .046 .602 65 1089 Nicaragua 68 .361 .373 .11 .138 .582 264 882 .214 .2 .069 .103 .423 51 501 Honduras 96 .355 .346 .109 .112 .576 211 805 .24 .224 .08 .109 .44 52 359 Guatemala 191 .243 .237 .11 .03 .613 286 961 .268 .252 .095 .088 .649 50 329 Haiti 23 .196 .191 .063 .087 .373 845 3559 .412 .426 .087 .221 .569 91 982 total 6,683 .523 .539 .187 -.043 1.133 50 1296 .136 .115 .093 -.052 .649 50 1027 Notes: This table shows summary statistics for district-level estimates of IGM. Upward reflects the likelihood that children, aged 14-18, whose parents have not completed primary schooling will manage to complete at least primary education. Downward reflects the likelihood that children, aged 14-18, whose parents have completed primary schooling or higher will not manage to complete primary education. “Total” shows the unweighted summary statistics across all districts. The columns “Nmin” and “Nmean” report respectively the smallest and average sample size across districts. Countries are sorted from the highest to the lowest average level of upward IGM across districts (column “mean”). Districts with less than 50 observations are omitted. 48 E District-level maps of mobility Figure A5: Upward Mobility in LAC Notes: Upward mobility reflects the likelihood that children, aged 14-18, whose parents have not completed primary schooling will manage to complete at least primary education. This graph uses provinces for St. Lucia, Jamaica, Trinidad and Tobago and Suriname that do not have a finer administrative units in the data set. 49 Figure A6: Downward Mobility in LAC Notes: Downward mobility reflects the likelihood that children, aged 14-18, whose parents com- pleted at least primary schooling will not manage to complete primary education. This graph uses provinces for St. Lucia, Jamaica, Trinidad and Tobago and Suriname that do not have a finer administrative units in the data set. 50 F District-level maps of mobility in secondary Figure A7: Upward Mobility in LAC Notes: Upward mobility reflects the likelihood that children, aged 14-18, whose parents have not completed secondary schooling will manage to complete at least secondary education. This graph uses provinces for St. Lucia, Jamaica, Trinidad and Tobago and Suriname that do not have a finer administrative units in the data set. 51 Figure A8: Downward Mobility in LAC Notes: Downward mobility reflects the likelihood that children, aged 14-18, whose parents com- pleted at least secondary schooling will not manage to complete secondary education. This graph uses provinces for St. Lucia, Jamaica, Trinidad and Tobago and Suriname that do not have a finer administrative units in the data set. 52 G Estimates of IGM using secondary education Table A7: Country-Level Estimates of Educational Intergenerational Mobility (1) (2) (3) (4) (5) (6) mobility / N census years upward upward downward downward N N age range 19-25 20-25 19-25 20-25 19-25 20-25 Trinidad and Tobago 1970,1980,1990,2000,2011 .58 .579 .077 .072 51,140 21,370 Peru 1993,2007 .493 .504 .059 .049 348,429 220,485 Jamaica 1982,1991,2001 .458 .455 .138 .149 49,411 11,400 Saint Lucia 1980,1991 .42 .408 .084 .139 2,694 168 Cuba 2002,2012 .362 .381 .221 .205 84,252 123,515 Chile 1970,1982,1992,2002 .358 .369 .164 .148 431,534 129,491 Argentina 1970,1980,1991,2001,2010 .344 .358 .213 .198 1,276,838 447,347 Panama 1960,1970,1980,1990,2000,2010 .338 .351 .186 .174 97,802 31,532 Bolivia 1976,1992,2001,2012 .326 .338 .21 .2 211,870 62,971 Venezuela 1971,1981,1990,2001 .292 .297 .236 .233 636,479 130,603 Ecuador 1974,1982,1990,2001,2010 .289 .3 .194 .176 429,008 105,824 Costa Rica 1973,1984,2000,2011 .287 .296 .205 .19 122,731 40,554 Dominican Republic 1981,2002,2010 .257 .278 .243 .216 197,241 54,451 Brazil 1960,1970,1980,1991,2000,2010 .249 .268 .231 .203 12,610,650 1,718,702 Colombia 1973,1985,1993,2005 .242 .255 .2 .171 1,152,288 160,657 Mexico 1970,1990,2000,2010 .24 .252 .226 .202 3,392,481 506,282 Uruguay 1963,1975,1985,1996,2006,2011 .237 .24 .334 .31 130,248 34,931 Paraguay 1962,1972,1982,1992,2002 .206 .218 .143 .12 146,601 15,782 El Salvador 1992,2007 .185 .201 .224 .193 100,588 17,559 Haiti 1971,1982,2003 .157 .162 .425 .398 133,746 10,397 Guatemala 1964,1973,1981,1994,2002 .122 .131 .195 .169 278,412 19,341 Honduras 1974,1988,2001 .093 .096 .383 .371 121,155 11,281 Suriname 2012 .077 .114 .367 .334 4,413 1,318 Nicaragua 1971,1995,2005 .033 .043 .338 .309 118,929 15,611 mean / total .277 .287 .221 .205 22,128,940 3,891,572 Notes: Columns (1) and (2) give upward-IGM estimates. They reflect the likelihood that chil- dren, aged 19-25 and 20-25, whose parents have not completed secondary schooling will manage to complete at least secondary education. Columns (3) and (4) give downward-IGM estimates. They reflect the likelihood that children, aged 19-25 and 20-25, whose parents have completed secondary schooling or higher will not manage to complete secondary education. Columns (5) and (6) give the number of observations used to estimate the country-specific IGM statistics (children whose parental education is reported in the censuses). Countries are sorted from the highest to the lowest level of upward IGM in the 19-25 sample (column (1)). “mean” gives the unweighted average of the 24 country-estimates. 53 Table A8: Summary Statistics: Province-Level Estimates of Educational IGM upward downward country provinces mean median stdev min max Nmin Nmean mean median stdev min max Nmin Nmean Trinidad and Tobago 4 .62 .619 .121 .494 .746 835 7021 .1 .102 .015 .082 .117 327 2941 Peru 25 .534 .508 .157 .288 .787 441 7603 .114 .1 .049 .043 .249 291 4850 Cuba 14 .504 .501 .026 .472 .574 443 3264 .2 .199 .021 .171 .247 611 4823 Jamaica 14 .394 .398 .055 .322 .505 741 1957 .173 .168 .038 .117 .248 123 459 Bolivia 9 .364 .366 .081 .256 .485 707 12967 .201 .183 .061 .137 .306 154 3841 Saint Lucia 4 .325 .312 .065 .268 .406 262 373 .181 .181 .181 .181 62 62 Chile 44 .321 .314 .079 .154 .477 234 4438 .283 .28 .059 .175 .42 68 1627 Argentina 24 .317 .314 .064 .222 .56 1136 29270 .242 .241 .057 .136 .377 504 10278 Costa Rica 7 .297 .296 .061 .222 .391 4477 9661 .263 .227 .073 .193 .371 864 3186 Dominican Republic 23 .281 .29 .057 .136 .394 733 2938 .264 .234 .077 .176 .518 54 864 Mexico 32 .279 .274 .049 .193 .394 5016 58421 .217 .214 .025 .155 .263 1130 8763 Panama 7 .277 .297 .101 .087 .409 673 7697 .193 .19 .018 .173 .226 300 2912 Suriname 7 .273 .305 .124 .04 .392 62 344 .282 .282 .019 .269 .296 170 316 Ecuador 14 .266 .28 .052 .182 .342 1339 16814 .219 .191 .08 .139 .427 188 4181 Colombia 22 .257 .236 .075 .148 .435 645 28660 .228 .216 .067 .122 .42 132 4020 Venezuela 22 .253 .251 .049 .161 .36 894 15945 .291 .281 .07 .183 .487 153 3279 El Salvador 14 .208 .216 .066 .124 .385 1430 3963 .308 .298 .067 .207 .497 69 742 Uruguay 19 .189 .192 .029 .142 .272 647 3783 .492 .505 .053 .357 .574 109 1013 Brazil 25 .185 .168 .057 .11 .305 6098 280107 .264 .271 .049 .184 .352 1744 38217 Nicaragua 12 .164 .174 .075 .05 .286 918 5457 .298 .3 .048 .219 .391 70 782 Paraguay 14 .148 .13 .082 .08 .373 1844 6227 .275 .273 .05 .151 .345 60 723 Guatemala 22 .083 .076 .04 .03 .214 1967 6973 .305 .284 .064 .214 .426 51 486 Honduras 18 .074 .064 .04 .02 .176 297 3726 .482 .486 .079 .302 .573 65 440 Haiti 4 .059 .051 .021 .044 .09 4577 18354 .698 .707 .112 .553 .827 115 1426 total 400 .274 .267 .14 .02 .787 62 30464 .268 .247 .112 .043 .827 51 5629 Notes: This table shows summary statistics for province-level estimates of upward and downward IGM. Upward reflects the likelihood that children, aged 19-25, whose parents have not completed secondary schooling will manage to complete at least secondary education. Downward reflects the likelihood that children, aged 19-25, whose parents have completed secondary schooling or higher will not manage to complete secondary education. “Total” shows the unweighted summary statistics across all provinces. The columns “Nmin” and “Nmean” report respectively the smallest and average sample size across provinces. Provinces with less than 50 observations are omitted. 54 Table A9: Summary Statistics: District-Level Estimates of Educational IGM upward downward country districts mean median stdev min max Nmin Nmean mean median stdev min max Nmin Nmean Cuba 137 .496 .493 .052 .373 .632 90 333 .212 .209 .038 .107 .325 66 493 Peru 168 .437 .416 .176 .104 .84 156 1138 .144 .127 .075 .034 .444 50 1024 Chile 179 .318 .311 .123 .088 .82 140 1091 .273 .27 .087 .045 .533 50 513 Costa Rica 55 .316 .317 .071 .155 .488 288 1230 .244 .232 .082 .104 .474 59 412 Argentina 312 .287 .294 .08 .059 .562 192 2252 .25 .243 .067 .106 .532 50 868 Bolivia 80 .281 .267 .114 .102 .592 219 1459 .258 .25 .08 .124 .479 52 784 Dominican Republic 66 .277 .282 .058 .136 .466 111 1040 .26 .241 .073 .158 .518 51 427 Panama 35 .248 .233 .124 .019 .444 356 1539 .214 .198 .063 .075 .344 52 689 Ecuador 78 .237 .217 .081 .096 .423 331 3057 .238 .228 .076 .127 .465 53 952 Uruguay 67 .219 .193 .093 .051 .499 169 572 .471 .485 .121 .173 .71 50 233 Venezuela 157 .216 .211 .07 .067 .404 289 2234 .327 .317 .09 .125 .599 50 634 Brazil 2,040 .213 .208 .09 -.007 .528 365 2373 .273 .261 .096 .055 .659 50 484 El Salvador 103 .212 .188 .106 .03 .516 160 539 .279 .277 .073 .117 .464 51 293 Colombia 434 .21 .189 .098 -.09 .493 185 1453 .261 .249 .092 .077 .629 50 348 Mexico 2,331 .19 .181 .097 -.046 .663 50 813 .244 .234 .076 .059 .54 50 362 Nicaragua 68 .161 .159 .072 .034 .312 228 963 .287 .277 .081 .179 .476 50 323 Paraguay 64 .15 .132 .084 -.049 .374 187 1326 .264 .259 .064 .151 .406 53 377 Guatemala 191 .069 .059 .051 -.006 .268 199 803 .302 .282 .075 .162 .441 55 324 Honduras 96 .059 .052 .042 -.004 .219 195 699 .454 .461 .081 .298 .579 51 376 Haiti 23 .042 .035 .031 .001 .137 753 3192 .708 .717 .09 .52 .83 59 451 total 6,684 .217 .203 .117 -.09 .84 50 1490 .264 .249 .098 .034 .83 50 506 Notes: This table shows summary statistics for district-level estimates of upward and downward IGM. Up- ward reflects the likelihood that children, aged 19-25, whose parents have not completed secondary schooling will manage to complete at least secondary education. Downward reflects the likelihood that children, aged 19-25, whose parents have completed secondary schooling or higher will not manage to complete secondary education. “Total” shows the unweighted summary statistics across all districts. The columns “Nmin” and “Nmean” report respectively the smallest and average sample size across districts. Districts with less than 50 observations are omitted. 55 56 H Estimates of upward IGM using primary-to-secondary education Table A10: Country-Level Estimates of Upward IGM using primary-to-secondary education (1) (2) mobility / N census years upward N age range 19-25 19-25 Trinidad and Tobago 1970,1980,1990,2000,2011 .466 8,506 Peru 1993,2007 .416 131,085 Saint Lucia 1980,1991 .388 1,452 Jamaica 1982,1991,2001 .315 4,304 Bolivia 1976,1992,2001,2012 .237 66,410 Chile 1970,1982,1992,2002 .19 97,017 Brazil 1960,1970,1980,1991,2000,2010 .187 6,142,101 Cuba 2002,2012 .187 4,037 Uruguay 1963,1975,1985,1996,2006,2011 .178 25,192 Argentina 1970,1980,1991,2001,2010 .177 226,100 Dominican Republic 1981,2002,2010 .161 64,387 Panama 1960,1970,1980,1990,2000,2010 .161 23,221 Venezuela 1971,1981,1990,2001 .148 185,993 Costa Rica 1973,1984,2000,2011 .133 28,829 Ecuador 1974,1982,1990,2001,2010 .128 121,410 Colombia 1973,1985,1993,2005 .121 354,007 Mexico 1970,1990,2000,2010 .107 1,008,707 El Salvador 1992,2007 .092 37,462 Paraguay 1962,1972,1982,1992,2002 .085 54,934 Haiti 1971,1982,2003 .073 62,660 Guatemala 1964,1973,1981,1994,2002 .042 125,087 Honduras 1974,1988,2001 .036 52,754 Nicaragua 1971,1995,2005 -.004 47,560 Suriname 2012 -.094 200 mean / total .164 8,873,415 Notes: Column (1) gives upward-IGM estimates. It reflects the likelihood that children, aged 19-25, whose parents have not completed primary schooling will manage to complete at least secondary education. Column (2) gives the number of observations used to estimate the country-specific IGM statistics (children whose parental education is reported in the censuses). Countries are sorted from the highest to the lowest level of upward IGM (column (1)). “mean” gives the unweighted average of the 24 country-estimates. 57 Table A11: Province-Level estimates of upward IGM using primary-to-secondary education upward country provinces mean median stdev min max Nmin Nmean Peru 25 .481 .442 .165 .246 .748 250 5243 Cuba 14 .323 .342 .049 .231 .384 155 309 Bolivia 9 .251 .254 .08 .154 .384 348 7379 Chile 44 .203 .205 .062 .093 .331 114 1694 Dominican Republic 23 .194 .201 .046 .067 .275 588 1795 Costa Rica 7 .166 .167 .042 .12 .244 2051 4118 Argentina 24 .155 .15 .048 .089 .322 219 9421 Mexico 32 .155 .149 .039 .096 .244 2143 31522 Colombia 22 .149 .136 .047 .092 .254 141 16091 El Salvador 14 .148 .145 .044 .097 .27 1209 2676 Venezuela 22 .147 .147 .029 .082 .214 643 8454 Uruguay 19 .144 .139 .027 .094 .191 264 1326 Brazil 25 .14 .128 .05 .074 .249 4716 245684 Ecuador 14 .136 .134 .031 .098 .204 840 8672 Panama 7 .131 .127 .054 .055 .224 457 3317 Nicaragua 12 .107 .114 .051 .04 .19 807 3963 Paraguay 14 .076 .067 .048 .032 .211 1312 4225 Guatemala 22 .049 .047 .021 .012 .106 1614 5686 Haiti 4 .046 .041 .011 .039 .063 4211 15665 Honduras 18 .045 .036 .024 .01 .105 152 2931 total 371 .173 .149 .118 .01 .748 114 23884 Notes: This table shows summary statistics for province-level estimates of upward IGM. Upward re- flects the likelihood that children, aged 19-25, whose parents have not completed primary schooling will manage to complete at least secondary education. “Total” shows the unweighted summary statistics across all provinces. The columns “Nmin” and “Nmean” report respectively the smallest and average sample size across provinces. Provinces with less than 50 observations are omitted. 58 Table A12: District-Level Estimates of Upward IGM using primary-to-secondary education upward country districts mean median stdev min max Nmin Nmean Peru 168 .395 .365 .175 .084 .807 105 785 Cuba 137 .338 .354 .091 .149 .479 52 81 Bolivia 80 .202 .194 .095 .064 .473 150 830 Chile 179 .2 .192 .082 .04 .478 81 421 Dominican Republic 66 .193 .187 .056 .067 .433 65 635 Costa Rica 55 .179 .181 .06 .057 .304 117 524 Brazil 2,040 .174 .168 .079 -.018 .471 278 1990 El Salvador 103 .155 .139 .076 .023 .381 87 364 Uruguay 67 .15 .144 .065 .024 .355 51 156 Argentina 312 .139 .14 .053 .012 .325 54 727 Colombia 434 .131 .115 .066 -.097 .321 82 816 Venezuela 157 .128 .123 .046 .028 .257 190 1185 Ecuador 78 .126 .115 .049 .026 .241 142 1577 Panama 35 .123 .098 .075 .011 .276 176 663 Nicaragua 68 .11 .101 .054 .017 .232 192 699 Mexico 2,331 .109 .098 .068 -.041 .635 50 452 Paraguay 63 .077 .07 .051 -.067 .211 153 900 Guatemala 191 .043 .039 .03 -.005 .156 172 655 Honduras 96 .037 .032 .028 -.006 .141 152 550 Haiti 23 .034 .033 .024 -.003 .103 664 2724 total 6,683 .144 .127 .093 -.097 .807 50 1042 Notes: This table shows summary statistics for district-level estimates of upward IGM. Upward reflects the likelihood that children, aged 19-25, whose parents have not completed primary schooling will manage to complete at least secondary education. “Total” shows the unweighted summary statistics across all districts. The columns “Nmin” and “Nmean” report respectively the smallest and average sample size across districts. Districts with less than 50 observations are omitted. 59 I Transition matrix by country Figure A9: Transition matrix by country fraction by parental attainment fraction by parental attainment 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 1 1 likelihood of child attainment likelihood of child attainment .8 .8 .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary let ed ed ed let let ary ted let ed ted prim p mp mp rim ple mp mp le n c om co co np co m co y co tha ary ry y tha ary ry ss da ersit ss da ersit Le Prim n co niv Le Prim n co Univ Se U Se (a) Argentina (b) Bolivia fraction by parental attainment fraction by parental attainment 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 1 1 likelihood of child attainment likelihood of child attainment .8 .8 .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary let ed ted ted le ple ary ted ed ed let let prim mp mp rim ple mp mp n co y co co m np co m co co tha ary dar ity tha ary aryersity ss e r s ss d Le Prim eco U n iv n Le Prim on iv ec Un S S (c) Brazil (d) Chile fraction by parental attainment 0 .2 .4 .6 .8 1 fraction by parental attainment 0 .2 .4 .6 .8 1 1 1 likelihood of child attainment likelihood of child attainment .8 .8 .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary ted ted ted ary ted ed ed let let p rim ple plemple prim mp le mp mp an com com co tha n yc o co co s th ary ary ers ity ss ar daery r sity Les Prim c ondn iv Le Prim n co niv Se U Se U (e) Colombia (f ) Costa Rica 60 Figure A10: Transition matrix by country fraction by parental attainment 0 .2 .4 .6 .8 1 fraction by parental attainment 0 .2 .4 .6 .8 1 1 1 likelihood of child attainment likelihood of child attainment .8 .8 .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary ted te d d lete ary let ed let ed ted rim ple lep pm rim ple np com ry co mo np mp mp om tha ary nda yc ha co co c s rsit st ary da ry ity Les Prim co iv e s ers Se Un Le Prim co n Un iv Se (a) Cuba (b) Dominican Republic fraction by parental attainment 0 .2 .4 .6 .8 1 fraction by parental attainment 0 .2 .4 .6 .8 1 1 1 likelihood of child attainment likelihood of child attainment .8 .8 .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary ed ted letleted ary ted ed ed let let rim ple mp p rim le np om coc om np co mp mp mp co y co s tha a ry c dar r ysity tha ary ary sit Les Prim ec oniv e ss im d e r S Un Le Pr n co niv Se U (c) Ecuador (d) El Salvador fraction by parental attainment fraction by parental attainment 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 1 1 likelihood of child attainment likelihood of child attainment .8 .8 .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary ed tetd let ple le ed ary ed ted let ple let ed prim mp om mp np rim mp om mp tha n co y y c co tha co y c co s ary d a r r it s ss ary de arrsity L es PrimeU ne Le Prim ec n coniv o niv S S U (e) Guatemala (f ) Haiti 61 Figure A11: Transition matrix by country fraction by parental attainment fraction by parental attainment 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 1 1 likelihood of child attainment likelihood of child attainment .8 .8 .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary ed tet let ple le d ed ary let ed ed ed let let rim mp om mp rim mp mp mp np co y c co np co co co ha ary de arrsity ha ary ry y s st s st da ersit Le r im o n Le Prim n P ec niv co niv S U Se U (a) Honduras (b) Jamaica fraction by parental attainment fraction by parental attainment 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 1 1 likelihood of child attainment likelihood of child attainment .8 .8 .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary let ed ed ed let let ary let ed ted ted rim p mp mp prim mp ple ple np com co co n co omom tha ary ry y st ha ary rsyitc yc s s da ersit s dear Le Prim n co niv Le Pr im n coniv Se U Se U (c) Mexico (d) Nicaragua fraction by parental attainment fraction by parental attainment 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 1 1 likelihood of child attainment likelihood of child attainment .8 .8 .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary ted ed ed let let ary let ed eded letlet prim ple mp mp rim mp mpmp n om co co np co coco s tha aryc ary ersity s tha ary ar ys ity es d es d nive r L Prim n co niv L Prim ec on Se U S U (e) Panama (f ) Paraguay 62 Figure A12: Transition matrix by country fraction by parental attainment 0 .2 .4 .6 .8 1 fraction by parental attainment 0 .2 .4 .6 .8 1 1 1 likelihood of child attainment likelihood of child attainment .8 .8 .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary le te le td lee dd te ary t ed let ed ted rim p pp rim ple mp ple np cycm o oo c mm np com co om s tha a r a y yrit rs st ha ary ry yc Les r imn oiv d e s da rsit P cn Le Prim on iv e Se U S ec Un (a) Peru (b) Saint Lucia fraction by parental attainment fraction by parental attainment 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 1 1 likelihood of child attainment likelihood of child attainment .8 .8 .6 Less than primary .6 Less than primary Primary completed Primary completed Secondary completed Secondary completed .4 University completed .4 University completed .2 .2 0 0 ary ted ted ted rim ple ple ple ary ted ed ed let let np com om om rim le tha ry ry crsity c np co mp mp mp co co Les s Pr ima d a con Univ e st ha ary ry y Se s da ersit Le Prim n co niv Se U (c) Trinidad and Tobago (d) Uruguay fraction by parental attainment 0 .2 .4 .6 .8 1 1 likelihood of child attainment .8 .6 Less than primary Primary completed Secondary completed .4 University completed .2 0 ary let ed ed ed let let rim mp mp mp np co coy co ha ary ys st are r it L es Prim n d co niv Se U (e) Panama 63 Figure A13: Upward and downward mobility are highly negatively correlated 1 IGM UP = .8199 + -2.8106 IGM down (.0261) (.2211) Jamaica Trinidad and Tobago .8 Argentina Chile Uruguay Upward IGM Cuba Panama Costa Rica Mexico Bolivia .6 Suriname Ecuador Venezuela Saint Lucia Peru Paraguay Colombia Honduras .4 Republic Dominican Brazil El Salvador Haiti Nicaragua .2 Guatemala 0 .05 .1 .15 .2 .25 Downward IGM Figure A14: Upward mobility by urban/rural status BOL BRA CHL COL 1 .5 0 CRI GTM HND MEX 1 Upward mobility .5 0 1940 1950 1960 1970 1980 1990 PAN PRY URY 1 .5 0 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 Birth decade Urban Rural Graphs by Country 64 Figure A15: Downward mobility by urban/rural status BOL BRA CHL COL .8 .6 .4 .2 0 CRI GTM HND MEX Downward mobility .8 .6 .4 .2 0 1940 1950 1960 1970 1980 1990 PAN PRY URY .8 .6 .4 .2 0 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 Birth decade Urban Rural Graphs by Country Figure A16: Upward mobility by gender ARG BOL BRA CHL 1 .5 0 COL CRI ECU GTM 1 .5 Upward mobility 0 HND MEX NIC PAN 1 .5 0 PRY 780 URY VEN 1 .5 0 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 Birth decade Male Female Graphs by Country 65 Figure A17: Downward mobility by gender ARG BOL BRA CHL .5 0 COL CRI ECU GTM .5 Downward mobility 0 HND MEX NIC PAN .5 0 PRY 780 URY VEN .5 0 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 Birth decade Male Female Graphs by Country Figure A18: Intergenerational Mobility and Literacy of the Old Generation 1.5 .8 IGM UP = .262 + -.3085 lit_par (.0019) (.0042) .6 downward IGM residual Upward IGM residual 1 .4 .5 .2 0 IGM UP = .217 + .7873 lit_par (.003) (.0065) 0 0 .5 1 0 .5 1 Share literate old residual Share literate old residual (a) Upward mobility (b) Downward mobility Notes: This graph uses data at the district-level netting out country fixed effects. 66