metadata: authors: 0: author: Van Der Weide,Roy
1: author: Lakner,Christoph
2: author: Mahler,Daniel Gerszon
3: author: Narayan,Ambar
4: author: Nichanametla Ramasubbaiah,Rakesh Gupta
count: World
docna: 0: docna: Intergenerational Mobility around the World
docty: Policy Research Working Paper
subtopic: Educational Sciences,Public Financial Management,Public Sector Economics,Public Finance Decentralization and
            Poverty Reduction,Inequality,Gender and Development,Early Child and Children's Health,Reproductive Health,Early Childhood Development,Nutrition
teratopic: Public Sector Development,Poverty Reduction,Gender,Education,Health, Nutrition and Population
abstracts: cdata!: Using individual data from over 400
            surveys, this paper compiles a global database of
            intergenerational mobility in education for 153 countries
            covering 97 percent of the world’s population. For 87
            percent of the world’s population, it provides trends in
            intergenerational mobility for individuals born between 1950
            to 1989. The findings show that absolute mobility in
            education—the share of respondents that obtains higher
            levels of education than their parents—is higher in the
            developed world despite the higher levels of parental
            educational attainment. Relative mobility—measuring the
            degree of independence between parent and child years of
            schooling—is also found to be greater in the developed
            world. Together, these findings point to severe challenges
            in intergenerational mobility in the poorest parts of the
            world. Beyond national income levels, the paper explores the
            correlation between intergenerational mobility and a variety
            of country characteristics. Countries with higher rates of
            mobility have (i) higher tax revenues and rates of
            government expenditures, especially on education; (ii)
            better child health indicators (less stunting and lower
            infant mortality); (iii) higher school quality (more
            teachers per pupil and fewer school dropouts); and (iv) less
            residential segregation.
repnme: repnme: Intergenerational Mobility around the World
display_title: Intergenerational Mobility around
            the World
listing_relative_url: /research/2021/06/33197072/intergenerational-mobility-around-world
topicv3: Public Finance Management m1326316 716,Poverty m1326687 695,Parent Education m1327024 1586,Education m1327048 470,Gender m1327621 615
publishtoextweb_dt: 2021-06-22T00:00:00Z
dois: 10.1596/1813-9450-9707
historic_topic: Public Sector Development,Poverty Reduction,Gender,Education,Health, Nutrition and Population
origu: DECRG: Poverty & Inequality (DECPI)
no_of_pages: 47
author_keys: 0: author_key: 677092
1: author_key: 1206398
2: author_key: 1539953
3: author_key: 167324
4: author_key: 1538071
model_extractions: Policy Research Working Paper 9707 Intergenerational Mobility around the World _Roy van der Weide Christoph Lakner Daniel Gerszon Mahler Ambar Narayan Rakesh Ramasubbaiah_ Development Economics Development Research Group June 2021
Policy Research Working Paper 9707

# **Abstract**

Using individual data from over 400 surveys, this paper compiles a global database of intergenerational mobility in education for 153 countries covering 97 percent of the world’s population. For 87 percent of the world’s population, it provides trends in intergenerational mobility for individuals born between 1950 to 1989. The findings show that absolute mobility in education—the share of respondents that obtains higher levels of education than their parents—is higher in the developed world despite the higher levels of parental educational attainment. Relative mobility—measuring the degree of independence between parent and child years of schooling—is also found to be greater in the developed world. Together, these findings point to severe challenges in intergenerational mobility in the poorest parts of the world. Beyond national income levels, the paper explores the correlation between intergenerational mobility and a variety of country characteristics. Countries with higher rates of mobility have (i) higher tax revenues and rates of government expenditures, especially on education; (ii) better child health indicators (less stunting and lower infant mortality); (iii) higher school quality (more teachers per pupil and fewer school dropouts); and (iv) less residential segregation.

This paper is a product of the Development Research Group, Development Economics. 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 authors may be contacted at rvanderweide@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
# **Intergenerational Mobility around the World**<sup>*****</sup>

Roy van der Weide<sup>†</sup> Christoph Lakner<sup>‡</sup> Daniel Gerszon Mahler<sup>§</sup> Ambar Narayan<sup>¶</sup> Rakesh Ramasubbaiah<sup>‖</sup> Originally published in the <u>Policy Research Working Paper Series on</u> _June 2021._ This version is updated on _August 2021._ To obtain the originally published version, please email prwp@worldbank.org.

Keywords: Intergenerational mobility, Inequality, Education, Poverty. JEL codes: D63, I24, J62, O15.

> * The authors would like to thank Alexandru Cojocaru, Miles Corak, Francisco H. G. Ferreira, Berk Özler, Silvia Redaelli, Bob Rijkers, Carolina Sánchez-Paramo, and Stefan Thewissen for their generous comments and inputs. The research presented in this paper featured in the report titled “Fair Progress? Economic Mobility Across Generations Around the World” (Narayan et al., 2018). The Global Database on Intergenerational Mobility compiled for this study can be accessed at <u>http://www.worldbank.org/en/topic/poverty/brief/what-is-the-global-database-onintergenerational-mobility-gdim. The estimates are also included in the Equal Chances database at http://www.equalchances.org/web/index.php. We gratefully acknowledge financial support from the UK government</u> through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme.

> † Development Economics Research Group, World Bank (rwanderweide@worldbank.org) (corresponding author).

> ‡ Development Economics Data Group, World Bank.

> § Development Economics Data Group, World Bank.

> ¶ Poverty and Equity Global Practice, World Bank.

> ‖ Development Economics Research Group, World Bank.
# **1. Introduction**

A society with high intergenerational mobility (IGM) is one where an individual’s socioeconomic success is less dependent on the socioeconomic success of his or her parents. Low mobility can lead to unrealized human potential and a misallocation of resources as talented individuals from disadvantaged families are excluded from opportunities. Policies promoting higher intergenerational mobility are likely to promote growth that is more inclusive in nature. Theoretically, higher levels of IGM have been shown to enhance both efficiency and equity by strengthening the correlation between education and ability (e.g. Glomm and Ravikumar, 1992; Maoz and Moav, 1999; Lloyd-Ellis, 2000).

Historically, the United States is viewed as the land of opportunity. The American dream embodies the belief in opportunity for each according to ability and effort. Chetty et al. (2014) show that the United States is better described by a collection of smaller societies.<sup>1</sup> In some parts of the country, mobility is on par with some of the most mobile countries in Europe, while in other parts, children struggle to escape poverty when born into it. Chetty et al. (2014) also find that areas with relatively high rates of mobility tend to be ones that are less residentially segregated (i.e. the extent to which households from different socio-economic backgrounds and different races live in separate neighborhoods), have lower inequality, higher quality public school systems, stronger social networks, and stronger family structures. Corak (2021) and Connolly et al. (2021) show that similar patterns also prevail in Canada.

Looking beyond the United States and Canada, where are the lands of opportunity globally, i.e. what would a global map of social mobility look like? If the geographic patterns observed in the United States are also observed across countries, then the least mobile countries are more likely to be found in the developing world. If there is indeed a socio-economic mobility gap between the developing world and the developed world, is that gap closing over time?

There is considerable empirical evidence on intergenerational mobility in the developed world (see Black and Devereux (2011) and Corak (2013) for reviews of the literature). Most of the empirical studies focus on the United States, Canada and select countries from Europe, including the United Kingdom, France, Germany, and the Scandinavian countries. Evidence for the developing world is scarcer. It includes a study by Alesina et al. (2021) on Africa, a study by Asher et al. (2018) on India, and a cross-country study on Latin America by Neidhöfer et al. (2018). The largest cross-country study on intergenerational mobility prior to this paper is Hertz et al. (2007) who provide estimates for 42 countries.

> 1 Chetty et al. (2014) estimate intergenerational income mobility at the municipality level for the United States. Several studies have followed this approach and estimated intergenerational mobility at local administrative levels for large countries. Corak (2021) and Connolly et al. (2021) obtain similarly disaggregated estimates of income mobility for Canada. Asher et al. (2018) and Alesina et al. (2021) are the first to attempt this for the developing world by providing highly disaggregated estimates of intergenerational education mobility for India and Africa, respectively.

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The Global Database on Intergenerational Mobility (GDIM) compiled by this study significantly expands the evidence base on intergenerational mobility in education by providing estimates for 153 countries representing about 97 percent of the world’s population born in the 1980s.<sup>2</sup> For 114 countries, or 87 percent of the world’s population, estimates of mobility span four decades: from those born in the 1950s to those born in the 1980s. The 1980s cohort, those born between January 1, 1980 and December 31, of 1989, represent the youngest generation of adults who would have completed their education at the time of data collection (as most survey data used are from 2010 or later). For each country we identified the survey data best suited for the estimation of intergenerational mobility in education.

We consider two concepts of IGM. Absolute mobility measures the share of individuals who surpass the education of their parents. Relative mobility captures the degree to which individual socioeconomic success is independent of the socioeconomic success of one’s parents. We will focus on mobility in education for several reasons.<sup>3</sup> Firstly, human capital is a key aspect of economic well-being. Secondly, intergenerational data on education is more widely available than on income. Thirdly, the estimation of educational mobility involves fewer methodological challenges. Unlike income, the level of education, once acquired, does not vary across an individual’s lifecycle. Fourthly, individuals can report their parents’ education level with a high degree of precision, whereas the same is not true for income, making it possible to study mobility in education without panel data. A limitation of focusing on education mobility is that it does not capture distortions in the labor market, such as the extent to which access to good jobs is contingent on the socioeconomic background one is born into.

Our database makes it possible, for the first time, to paint a truly global picture of intergenerational mobility. This in turn allows us to test whether insights from the existing literature, which has mostly focused on developed countries, generalize to developing countries. We test two main hypotheses: First, whether absolute mobility is lower in the developed world where education levels are higher on average, making it harder for the next generation to surpass the education levels of their parents. Second, whether relative mobility increases with national income levels. Richer countries have the necessary resources to fund public interventions that compensate for private disadvantages (e.g. provide high quality public education for children born into disadvantaged backgrounds). This would be consistent with the findings of Chetty et al. (2014), who show that lower levels of intergenerational mobility are more likely to be found in the lagging areas of the United States.

> 2 In parallel to our study, Brunori et al. (2021) developed the “Equal Chances” database that includes estimates of intergenerational income mobility for 27 countries as well as estimates of inequality of opportunity for 47 countries. Our estimates of intergenerational mobility in education for 153 countries are also included in this database.

> 3 Alesina et al. (2021), Neidhöfer et al. (2018), Asher et al. (2018), and Hertz et al. (2007) similarly focus on education, arguably for the same reasons.

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We obtain the following empirical findings. First, for the youngest cohorts in our sample, both absolute and relative mobility in education are estimated to be lower on average in the developing world than in the developed world. Second, we find evidence against both hypotheses in the relationship between mobility and national income across the wide range of countries. While our observations are consistent with the existing literature that focuses primarily on the high-income world, expanding the coverage of the low-income world reveals that the relationship between IGM and national income is non-monotonic.

Absolute mobility is found to be lowest in the world’s poorest countries and the world’s richest countries, but arguably for different reasons. In the poorest countries, the scope for surpassing parents is greatest but the capacity to educate children is lowest. By contrast, in the world’s richest countries, the capacity to educate children is greatest but the scope to surpass parents is lowest. Jointly, these findings produce an _inverse_ U-pattern between absolute mobility and national income.

Relative mobility on the other hand is found to be highest in the richest and the poorest nations, resulting in a _regular_ U-pattern between relative mobility and national income. We propose a candidate explanation for this stylized fact. In countries where a large share of the population lives in poverty and where most parents have no education, parental education will be a weak predictor of child education as there is little variation between parents. As countries develop, the gaps between poor, middle-class, and better-off parents become more pronounced. In the absence of public interventions, it will make a big difference whether one is born into a poor, middleclass, or upper-class family, such that relative mobility will start to decline. As countries further increase their national income and adopt public policies aimed at leveling the playing field, relative intergenerational mobility will increase. We observe a positive correlation between relative mobility and public spending, particularly public spending on education, while controlling for national income, which is consistent with this rationale.

Beyond government expenditures, we identify four types of country characteristics that are correlated with intergenerational mobility. First, child health: countries where children are in good health (i.e. low stunting rates, low infant mortality rates) on average report higher levels of mobility. Second, proxies for school quality: countries with a larger number of teachers per pupil and lower drop-out rates also have higher rates of mobility. Third, variables that are indicative of the reach of public interventions: countries with higher mobility are more likely to be small in terms of population and/or land area and tend to be more urbanized. Fourth, measures of residential segregation: lower rates of intergenerational mobility are more likely to be found in countries that are more residentially segregated.

When we disaggregate intergenerational mobility by gender, we find that boys in the highincome world and developing world have on average similar chances of surpassing their parents. By contrast, girls in the high-income world and developing world face starkly different odds of surpassing their parents. If current time-trends prevail however, girls are on track for surpassing 4
boys in terms of absolute mobility in the developing world in the coming decade(s). In the highincome world girls have already overtaken boys on average in terms of absolute mobility. We do not observe notable gender gaps in relative mobility.

The remainder of the paper is organized as follows. Section 2 describes our methodology and presents our measures of intergenerational mobility. Section 3 describes the Global Database of Intergenerational Mobility. Section 4 presents the trends and patterns in intergenerational mobility over time, across the world, and by gender. Section 5 analyzes correlates with intergenerational mobility. Section 6 presents robustness checks while Section 7 concludes.

# **2. Intergenerational mobility in education**

Intergenerational mobility is interpreted here in two distinct but related ways, which we will refer to as absolute mobility and relative mobility. What we term absolute versus relative mobility may differ slightly from how these terms are used in other studies, in part because this terminology has been used loosely (for a recent review of different measures see Jäntti and Jenkins (2015) and Fields and Ok (1996, 1999)). Table 1 summarizes the measures of absolute and relative mobility used in this paper, which will be explained in more detail in this section.

The estimation of intergenerational mobility between parents and children requires specifying which parents and children one is referring to. Unless otherwise specified, all children are considered in the estimation of mobility. Whenever parental education is referred to, the maximum education attained by the parents is used.<sup>4</sup> Our motivation for using maximum parental education is three-fold. Firstly, it provides a more accurate measure of the parental human capital that can be transferred to the parents’ children. Secondly, it arguably provides a more accurate proxy of the household resources that can be invested in the human capital growth of the children. Thirdly, maximum parental education is probably a more stable measure to the extent that it is more robust to the expansion of female educational attainment and the increasing correlation of educational attainments between parents (i.e. assortative mating). Using maximum parental education produces lower levels of absolute mobility such that our approach may yield conservative estimates.<sup>5</sup>

> 4 Estimates using fathers and mothers separately, as well as estimates based on parents’ average years of schooling are available in the online version of the Global Database of Intergenerational Mobility. For three countries, information on fathers’ years of schooling but not on mothers’ years of schooling is available (Benin, Democratic Republic of Congo, and Chad). In these cases, it is assumed that the maximum value of parental years of schooling is equivalent to the father’s years of schooling.

> 5 The existing literature of earnings mobility tends to focus on father-son mobility in large part because of the low levels of female labor force participation observed during the periods when this literature originated, and to avoid the associated selection bias. Since then female employment has increased substantially, which has weakened the

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**Table 1: Measures of absolute and relative mobility used in this paper**

|**Absolut**|**e mobility**|||
|---|---|---|---|
|_Name_|_Description_|_Formula_<br>|_Treatment_|
|CAT|Share of respondents with a higher educational<br>category than both parents conditional on neither<br>parent having tertiary education.|P�cchii> cppp� cppp< tttt)<br> <br>|Main<br>measure|
|YOS|Share of respondents with more years of schooling<br>than both of their parent’s conditional on parents<br>not having the maximum years of schooling<br>observed in the sample.|P�tchii> tppp � tppp<br>< max ti stsst)<br>|Robustness<br>section|
|DIF|Mean difference in years of schooling conditional<br>on parents not having tertiary.|E�tchii−tppp� cppp< tttt)<br> <br>|Appendix|
|MIX|Share<br>of<br>respondents<br>with<br>strictly<br>higher<br>educational category than parents if parents do not<br>have tertiary, or with tertiary education if either<br>parent has tertiary.|<br>P�cchii> cppp ot cchii<br>= tttt�|Appendix|
|**Relative**<br>|**mobility**<br>|<br>||
|_Name_|_Description_|_Formula_<br> <br>|_Treatment_|
|1-COR|1 minus the correlation coefficient between<br>respondent and parents’ years of schooling.|1 −ρ,<br>ρ= cot(tchii, tppp)<br> <br>|Main<br>measure|
|1-BETA|1<br>minus<br>the<br>coefficient<br>from<br>regressing<br>respondents’ years of schooling on parents’ years<br>of schooling.|<br> <br>1 −β, ftos<br>tchii= α+ β∗tppp+ ε<br>|Robustness<br>section|
|MU050|The expected educational rank of respondents<br>born to parents in the bottom half.|<br>E(tirchii|tirppp< 50)<br>|Appendix|
|BHQ4<br>|The probability that respondents born in the<br>bottom half reaches the top quarter.<br>|P(tirchii> 75|tirppp< 50)<br>|Appendix<br>|

_Note: c_ refers to the highest educational category completed of the following five categories, (1) less than primary, (2) primary, (3) lower secondary, (4) upper secondary, (5) tertiary. _y_ refers to years of schooling completed.

# **2.1. Absolute mobility**

_Absolute mobility_ will measure the extent to which living standards of a given generation are higher than that of their parents. We measure absolute mobility (denoted _CAT_ ) by the share of respondents that have attained a higher educational category than their parents, conditional on the parents not having obtained tertiary education, such that all included individuals have a chance of surpassing their parents. For this measure, we categorize individuals and parents according to their highest educational attainment in the following categories (see Section 3 for more details): (i) less than primary, (ii) primary, (iii) lower-secondary, (iv) upper-secondary, or (v) tertiary. The motivation for using these educational categories is two-fold. First, it improves cross-country comparability as the education data in several countries do not disaggregate beyond these five categories. This avoids a situation where an individual surpassing her parents by one year of education is treated as mobile in one country (with detailed categories) but not in another country (with coarse categories). Second, completing a full additional category signifies argument of focusing on father-son pairings and strengthened the argument for including mothers and daughters in the measurement of intergenerational mobility.

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a more meaningful increment in human capital; one additional year of schooling is unlikely to be rewarded in the labor market unless it yields the completion of a full category.

The fact that there is a maximum level of education, with very few individuals obtaining more than 21 years of schooling, introduces a ceiling effect. As countries develop and average years of schooling increase, it becomes increasingly more difficult for individuals to outperform their parents. When parents have tertiary education as well as their children, one may not want to classify these children as failing to be mobile as this may introduce a mechanical decline in absolute mobility at higher levels of development. For this reason, we will consider the subset of respondents whose parents are not in the top category.

For robustness, we also construct a measure of absolute mobility that uses education data in its most disaggregated form. The results obtained with this measure are presented in Section 6. In Appendix A, we consider two alternative measures of absolute mobility, one which considers everyone with tertiary education as mobile (denoted by _MIX_ ) and one which accounts for the magnitude of which children surpass or fall short of their parents’ outcomes (denoted by _DIF_ ).

# **2.2. Relative mobility**

_Relative mobility_ is the extent to which an individual’s position in the distribution of educational attainment is independent of the position of his/her parents. We consider the correlation coefficient between respondents’ years of schooling and the years of schooling of their most educated parent, denoted by _COR_ , as our primary measure of intergenerational persistence, and 1- _COR_ as our primary measure of relative mobility. The regression coefficient from the intergenerational regression of respondents’ years of schooling on parents’ years of schooling (denoted by _BETA_ ) denotes an alternative measure of intergenerational persistence and will serve as our robustness check (see Section 6).

An advantage of the correlation coefficient is that it is invariant to changes in the marginal distributions of child and parents’ education. The values of _COR_ , however, do not have a natural economic meaning. By contrast, the regression coefficient tells us how much one year of additional schooling of parents is transmitted to their children on average. While _BETA_ has a natural interpretation, it is sensitive to the marginal distributions. This means that changes in _BETA_ may in part reflect changes in inequality levels rather changes in the intergenerational dependence structure.<sup>6</sup> Further robustness checks are included in Appendix A where we consider: (a) the share of individuals who make it to the top quarter in terms of years of schooling of their generation out of those who were born to parents who ranked in the bottom half of their generation (denoted by

> 6 The two measures are related as follows: = C SS (( <u>chtsi))</u> ~~,~~ where S (. ) denotes the standard tt deviation. ttsst ttiitt BBEEB CC∗

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_BHQ4_ ), and (b) the expected rank of an individual in the education distribution whose parents rank in the bottom half of the parent education distribution (denoted by _MU050_ ).

# **2.3. Mechanisms and related literature**

Alesina et al. (2018) compare people’s perceptions of mobility to recent data on actual intergenerational mobility. They find that “Americans are more optimistic than Europeans about intergenerational mobility, and they are over-optimistic relative to actual mobility in the United States, especially about the probability of a child from a family in the bottom quintile making it to the top quintile: the ‘American dream.’ ... [P]aradoxically, optimism is particularly high in US states where actual mobility is particularly low.” What makes a society intergenerationally mobile in actuality?

Public interventions that aim to equalize opportunities are a plausible channel through which mobility increases as countries become richer. Arenas and Hindriks (2020), for example, find that unequal school quality and unequal access to good schools reduce intergenerational mobility, and that mobility can be increased by means of school equalization interventions and desegregation policies (see also Hassler et al. 2007; Lee and Seshadri, 2019). In the absence of public interventions, models predict that mobility will decline as incomes increase (Becker and Tomes, 1979, 1986; Becker et al., 2018). Under the assumption that parents seek to optimize the incomes of their children (along with their own consumption), private investments in child human capital will generally increase both with parental income and parental human capital (Becker and Tomes, 1979, 1986; Loury, 1981; and Becker et al., 2018). It follows that children born to highly educated parents are twice fortunate; they benefit from the exposure to their parents’ higher human capital but also from the higher (monetary) investments their parents make in their human capital (Guryan et al., 2008; Ramey and Ramey, 2010; Duncan and Murnane, 2011).

Children born to educated parents are more fortunate still when parent human capital and investments in their children act as complements, i.e. the efficiency of investments in child human capital increases with income. This seems a reasonable assumption (e.g. Lareau, 2011; Heckman and Mosso, 2014; Becker et al., 2018). High human capital parents may: (a) be better placed to navigate school systems (which can be key, particularly when there is a large heterogeneity in school quality) or more generally may be better at utilizing public education for their children, (b) reside in neighborhoods with better schools where their kids interact with similarly advantaged kids, (c) help them with homework, expose them to books, read their kids bedtime stories, and play educational games, (d) arrange additional tutoring etc. This would predict that the intergenerational persistence of human capital will grow stronger as incomes rise.

Credit market imperfections can further strengthen the intergenerational transmission of human capital, i.e. lower intergenerational mobility, most notably for families toward the bottom of the income distribution. If parents with low incomes have limited access to credit to invest in their children, then it can be expected that low levels of human capital will be transmitted from one 8
generation to the next. Restuccia and Urrutia (2004) find that approximately 50 percent of the intergenerational persistence in earnings can be accounted for by parental investments in their children’s education.

Can public spending alter the relationship between socio-economic mobility and national income? Naturally, this will depend on the magnitude of public spending relative to private spending and on how governments will choose to allocate their public investments. Assume that the government sees its role as an equalizer of opportunities, by raising investments in the human capital of poor children toward levels that are more comparable to the investments received by children from richer families. Public investments could include policies to promote public school quality, education subsidies, and campaigns to improve aspirations of disadvantaged youth. This would reduce the importance of parental background in determining an individual's human capital (Kearney and Levine, 2016; Lee and Seshadri, 2019). Public interventions are found to be most effective when they focus on early childhood (Restuccia and Urrutia, 2004; Herrington, 2015; Blankenou and Youderian, 2015; Lee and Seshadri, 2019). The more resources the government dedicates to leveling the playing field, the larger the positive effect on intergenerational mobility will be.<sup>7</sup> This brings us to our first hypothesis: To the extent that governments in more developed countries have more resources available than governments in less developed countries, intergenerational mobility can be expected to increase as countries get richer. This would be consistent with the empirical observation that national income growth is good for the poor (Dollar and Kraay, 2002; Dollar et al., 2016.). Growth can fund public spending on mobility enhancing interventions that will benefit the poor. Equity in turn is found to be good for growth (Marrero and Rodriguez, 2013), which suggests the possibility of a positive feedback loop.

The effect of public spending on intergenerational mobility may only “kick in” from middle income levels onwards, when government spending becomes sufficiently large. The positive relationship between relative mobility and national income may then only be observed for countries with national incomes above a certain threshold. For countries below this national income threshold, where public interventions that could equalize opportunities are comparatively small, relative mobility will be shaped by private resources. More specifically, by parent’s optimizing behavior, in which case relative mobility may decline as income levels go up (see e.g. Becker et al., 2018). This suggests the possibility of a non-monotonic relationship between relative mobility and national income.

> 7 What may matter more than overall spending is the extent to which investments produce key inputs into improving access to and quality of education. The existing literature observes that public interventions are more likely to increase mobility when: (a) public investments are sufficiently large (Iyigun, 1999), (b) are targeted to benefit disadvantaged families/neighborhoods (Mayer and Lopoo, 2008; Herrington, 2015; Blankenou and Youderian, 2015), (c) focus on early childhood (Herrington, 2015; Blankenou and Youderian, 2015), and (d) when political power is not captured by the rich unless the rich have the interests of the poor at heart (Uchida, 2017).

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The relationship between absolute mobility and national income may similarly be non-monotonic as opposing forces are at play. On the one hand, we hypothesize that absolute mobility decreases with national income on the grounds that richer countries have more educated populations, making it harder for children to surpass the educational attainment of their parents. On the other hand, however, the capacity to educate children increases as countries become richer, which suggests a positive effect of national income on absolute mobility. The relationship between absolute mobility and national income will thus be determined by which of these two effects is stronger at different stages of national development.

# **3. Data**

## **3.1. Identifying relevant surveys**

To construct our Global Database on Intergenerational Mobility (GDIM), a comprehensive review of surveys that ask respondents about their parents’ educational attainment was conducted. Eighty percent of the surveys selected were conducted after 2011 such that respondents born in the 1980s had a chance to complete their education by the time the survey data was collected. Only individuals aged 21 or more are included in the sample to minimize the fraction of individuals still enrolled in school while not removing too many individuals from the sample. If more than one relevant survey was found for a country, the survey was selected based on sample size and the quality of the parental education information.

Broadly speaking, three different types of surveys are used. For most developing countries, crosssectional household income or expenditure surveys are used. For most countries in Europe and Latin America, social surveys such as the European Social Survey (ESS), the Latinobarometro, and the Life in Transition Survey (LITS) are used.<sup>8</sup> For a selected number of high-income countries, annual panels such as the Panel Study of Income Dynamics in the United States and the Labor & Income Panel Study in the Republic of Korea are used. A table with the list of surveys and years used can be found in Table B.1 in Appendix B.

The social surveys tend to have smaller sample sizes, so when multiple waves of the same survey contain relevant information on educational attainment, these waves are pooled. This includes three waves of the ESS (from 2010-2014), six waves of the Latinobarometro (from 2008-2015), and two waves of the LITS (2006 and 2011).

For several countries, including some of the developing world’s largest countries such as Bangladesh and a notable number of countries in Sub-Saharan Africa, there are no surveys with retrospective data on parental education. In these cases, high-quality household surveys without

> 8 Like Neidhöfer et al. (2018), Latinobarometro is used instead of Lapop, which is another social survey covering the Americas. Lapop only asks for mother’s education. Conversely, Latinobarometro records the highest education level of father and mother, which is the variable used in this paper.

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retrospective data are used instead. Information on parental education is obtained for respondents who reside in the same household as their parents – so-called co-residents.<sup>9</sup> Since coresiding adults need not be representative of the target population, estimates derived from this type of data may be subject to co-residence bias (Emran et al., 2017). The magnitude of this bias depends on the share of adults who co-reside with their parents and the extent to which coresiding adults differ from adults who live away from their parents.

To minimize co-residence bias, samples are restricted to co-residents aged 21-25 at the time of the survey. These respondents, who are part of the 1980s cohort, are often old enough to have had a chance to complete their education yet young enough such that a large majority still co-resides with their parents. Since co-resident surveys only allow us to estimate mobility for this cohort, we are unable to track mobility over time (i.e. across cohorts) for these countries. When time trends are analyzed, the same set of countries is used throughout, so these countries are dropped.

In Figure C.1 of Appendix C we use surveys with retrospective data on parental education to test the bias of estimating mobility with and without only relying on co-residents. We find that the size of the bias does not seem to be large enough to generate notable re-rankings.

# **3.2. Global coverage**

In total, the GDIM provides estimates of intergenerational mobility – absolute and relative – for 153 countries representing about 97 percent of the world’s population born in the 1980s (see Table 2). For 114 countries, or about 87 percent of the world’s population, estimates of mobility span four decades: from those born in the 1950s to those born in the 1980s. In all regions but the Middle East and North Africa, the population coverage is greater than 90%. For the Middle East and North Africa, 83% of the population is covered (51% with retrospective questions).<sup>10</sup>

> 9 For a handful of countries, recent surveys with co-resident data and older surveys with retrospective questions were combined; the co-resident data is used for the 1980s cohort and the older survey for the 1950s through 1970s cohorts. This applies to Pakistan, Mauritania, Philippines, Rwanda, and Guinea. For two countries, neither recent co-resident surveys nor retrospective surveys were identified, while older surveys with retrospective questions exist. This concerns New Zealand (2000 survey) and Bhutan (2003 survey). To maximize coverage, these surveys are utilized as well. Tabulations show that in Bhutan most respondents born in the 1980s had completed their education by the time of the survey. For New Zealand, it is assumed that the estimate for the 1980s cohort equals the estimate for the 1970s cohort.

> 10 High-income countries are defined using the World Bank’s income classification as of July 1, 2019. High-income countries are also referred to as “the developed world” in this paper. The remaining countries, referred to as the “developing world”, are split further into geographic regions. More information on the income classifications and geographic regions is available here: <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bankcountry-and-lending-groups.</u>

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**Table 2: Coverage**

||**Number of coun**<br>With<br>|**tries covered**<br>|**Percent ofpopul**<br>With<br>|**ation covered**<br>|
|---|---|---|---|---|
|**Income group/region**|retrospective<br>data|Total|retrospective<br>data|Total|
|**High-income countries**|38|38|93%|93%|
|**Developing countries**|76|115|86%|98%|
|East Asia and Pacific|8|18|92%|99%|
|Europe and Central Asia|20|20|99%|99%|
|Latin America and the Caribbean|15|16|95%|97%|
|Middle East and North Africa|6|10|51%|83%|
|South Asia|5|8|89%|100%|
|Sub-Saharan Africa|22|43|72%|97%|
|**World**|114|153|87%|97%|

_Note:_ The table shows the number of countries our database covers, and the population share that these countries account for.

# **3.3. Coding of education data**

To improve comparability of estimates between surveys, a globally harmonized categorical measure of educational attainment is constructed. This measure represents the lowest common denominator across surveys, thus invariably reducing the amount of detail exploited in some countries. With minor exceptions, all surveys contain the following five categories, which are based on UNESCO’s International Standard Classification of Education (ISCED): (i) less than primary (ISCED 0), (ii) primary (ISCED 1), (iii) lower secondary (ISCED 2), (iv) upper secondary or postsecondary nontertiary (ISCED 3–4), and (v) tertiary (ISCED 5–8).

The categories refer to the highest education level completed by the respondent. A few highincome countries do not have data on the first of these five categories. Since these countries have mandatory schooling at the primary level, it is assumed that all individuals have completed at least primary in these cases.<sup>11</sup> Respondents who are younger than 21 years old or are still enrolled in school are excluded. The only exception to this rule is respondents who are enrolled in school, have completed upper secondary, and are 21 years or older. It is assumed that these individuals complete the lowest tertiary degree (ISCED 5).<sup>12</sup>

> 11 This concerns some economies where LITS or ESS is used, as well as the United States; Taiwan, China; the Republic of Korea, Japan, Canada, and Australia. In these economies, no category below primary exists. For a few other countries, it was unclear how to code respondents who have completed Koranic or other religious schools. In general, we assume that these respondents completed education corresponding to the primary level.

> 12 This assumption may matter for the estimates. If most individuals who are enrolled in tertiary and 20 years or older end up completing a master’s degree (ISCED 7), they are assigned too little education. If these individuals, simultaneously, tend to have highly educated parents, mobility is underestimated. If these individuals instead were dropped from the analysis, representativeness would likely decrease.

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Many of the surveys contain a direct question eliciting years of schooling completed. For the surveys where no such variable is available, UNESCO sources containing country- and yearspecific mappings on the duration of educational programs are relied upon to construct a measure of years of education based on the answers to the categorical question.<sup>13</sup> The length of the various ISCED categories varies across countries, and changes over time within countries. Individuals are assigned to an educational regime based on their birth year and the school entry age. When reforms occur at an education level, it is assumed they apply to individuals who have not yet entered that education level. For example, it is assumed that an extension of lower secondary from 3 to 4 years does not affect children who are already enrolled in lower secondary.<sup>14</sup> Years of schooling do not account for differences in the quality of education. A year of schooling in, say, Singapore may be of very different quality than a year of schooling in Malawi. Due to a lack of global data on educational quality, especially for the cohorts considered in this paper, years of schooling is relied upon here. To the extent that quality of schooling is relatively stable over time (i.e. varies primarily between countries and comparatively less within country and over time), comparisons of individual educational attainment relative to that of their parents would be approximately independent of school quality. In that case, the implication of not observing educational quality for the estimation of intergenerational mobility is conceivably modest.

# **4. Intergenerational mobility around the world**

## **4.1. Absolute mobility is highest in the developed world**

Absolute mobility in education is found to vary considerably across the 153 economies (for those born in the 1980s), with a clear geographic pattern (Figure 1). Sub-Saharan Africa stands out as the region with some of the lowest rates of absolute mobility. In some of the poorest or most fragile countries in the region, the share of respondents that have more education than their parents is less than 20 percent, compared to over 80 percent in parts of East Asia. Of the 15

> 13 Two sources of information are used. The first source (http://uis.unesco.org/en/isced-mappings) is not available for all countries and for the most part only conveys the duration of ISCED categories in 1997 and 2011. This source is supplemented with information from UNESCO’s online database <u>(http://data.uis.unesco.org/), which outlines the</u> length of durations of ISCED categories by year from 1970.

> 14 The UNESCO information is not available before 1970, so for individuals who went to school before 1970 the durations from 1970 are applied. When information is missing in the UNESCO sources, additional country-specific information is used or the following rule for converting ISCED categories to completed years of schooling is applied: (i) ISCED 1: 6 years, (ii) ISCED 2: 9 years, (iii) ISCED 3: 12 years, (iv) ISCED 4: 13 years, (v) ISCED 5: 15 years, (vi) ISCED 6: 16 years, (vii) ISCED 7: 18 years, and (viii) ISCED 8: 21 years. In a handful of countries, a years of schooling variable is present but a categorical variable is not. In these cases, the reverse conversion using the country- and year-specific mappings is used.

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Economy Government expenditure (% of GDP) Tax revenue (% of GDP) Education Government expenditure on education (% of GDP) Children out of school (% of primary school age) Pupil-teacher ratio, primary Pupil-teacher ratio, secondary Gender parity for primary & secondary school enrollment Share of inequality in years of schooling due to location Health Mortality rate, under-5 (per 1,000 live births) Immunization, DPT (% of children ages 12-23 months) Prevalence of anemia (% of children under 5) Prevalence of underweight (% of children under 5) Prevalence of stunting (%o of children under 5) Prevalence of wasting (% of children under 5) Labor market Labor force participation rate (% of population ages 15-64) Labor force, female (% of total labor force) Returns to education total primary Returns to education total secondary Returns to education total tertiary Return to another year of schooling Demography Population ages 0-14 (% of total population) Age dependency ratio (% of working-age population) Log of total population size Rural population (% of total population) Ethnic polarization Ethnic fractionalization Language fractionalization Log of land area in km?

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Beyond a country’s fiscal space and public expenditure levels, we identify four different types of factors that are significantly correlated with intergenerational mobility. First, child health indicators are seen to be positively correlated with intergenerational mobility. We observe a negative coefficient for stunting, a positive coefficient for immunization rates, and negative coefficients for the prevalence of anemia and mortality rates. When children from disadvantaged backgrounds do not have access to adequate nutrition and health care, this may have long-term negative effects on their cognitive development. The resulting gaps in cognitive development means that disadvantaged and advantaged children could be learning at different rates, even when they would have access to schools of comparable quality.

Second, proxies for school quality are found to be positive factors: Intergenerational mobility tends to be higher in countries where there are more teachers per student (i.e. lower pupil-teacher ratios) and where the share of children dropping out of school early is low.

Third, proxies for segregation are negatively correlated with mobility: countries where the share of total inequality in years of schooling that is due to between-district variation in schooling (versus within-district variation) tend to report higher levels of intergenerational mobility in education. Ethno-linguistic fractionalization is similarly negatively correlated with mobility, albeit weakly.

Fourth, factors that may be indicative of the reach of public interventions are positively correlated with relative mobility: countries with higher mobility levels are more likely to be countries that are relatively small in terms of population and/or land area and tend to be more urbanized (possibly because it is easier to organize high-quality public interventions with a broad reach when countries are small with the population living in closer proximity to each other and to public services).

Labor market variables are found to exhibit little to no correlation with intergenerational mobility in education. These variables will conceivably report stronger correlations with measures of income mobility which, unlike education mobility, will be more sensitive to the functioning of the labor market.

# **6. Robustness**

In this section we re-evaluate the trends and patterns in intergenerational mobility using alternative measures for both absolute and relative mobility.

## **6.1. Intergenerational mobility across space and time**

### _Absolute mobility_

As a robustness check we also evaluate absolute mobility (denoted by _YOS_ ) using education data in its most disaggregated form, which is years of schooling completed. _YOS_ measures the share 24
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Our estimates suggest that for the majority of the world’s population, individual education success is highly contingent on the educational success of one’s parents. This intergenerational persistence in human capital, or lack of intergenerational mobility is particularly pronounced in the developing world. Our estimates indicate however that the relationship between intergenerational mobility and national income is not a monotonic one. Relative mobility is on average highest in the poorest and the richest countries, which may be explained as follows. In the world’s poorest countries, a large majority of parents have no education. When parents are equally deprived, it matters less what household one is born into, implying a high level of relative mobility. As countries increase their education and income levels, the gaps between poor, middleclass and better-off parents become more pronounced. Without public interventions, children’s education trajectories will eventually start to diverge depending on whether they are born into a poor, middle-class or upper-class family, which is when relative intergenerational mobility will decline. Further increases in national income can reverse this trend and increase intergenerational mobility levels when countries invest a share of the higher national incomes into public interventions that will partly compensate for inequalities in private investments.

Beyond national income levels, we identify a number of country characteristics that are correlated with intergenerational mobility. Countries with higher rates of mobility on average report (i) higher tax revenues and rates of government expenditures, especially on education, (ii) better child health indicators (less stunting and lower infant mortality), (iii) higher school quality (more teachers per pupil and lower school drop-outs), and (iv) less residential segregation.

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