metadata: authors: 0: author: Van Der Weide,Roy
1: author: Woolcock,Michael
count: World
docna: 0: docna: What Data-Rich Assessments of Socioeconomic
            Inequality and Mobility in Rich Countries Overlook in Poor Countries
docty: Policy Research Working Paper
abstracts: cdata!: Advances in the scale and sophistication
            of individual income data compiled across high-income
            countries have enabled researchers to measure inequality and
            intergenerational mobility at a highly granular level and
            over long periods of time. The edited volume Measuring
            Distribution and Mobility of Income and Wealth (Chetty et al
            (2022), comprising 23 empirically rich studies, provides a
            comprehensive stocktaking of the recent empirical evidence.
            This paper reviews the volume&#x2019;s key findings
            obtained for high-income countries and raises several
            questions that extend to low-income countries. What stands
            out is that income and wealth inequality are increasing,
            while intergenerational mobility is declining, across most
            high-income countries. This begs the following questions. Is
            the Kuznets curve due for a revision? How can countries
            achieve (and maintain) higher levels of socioeconomic
            mobility as they develop? To what extent do inequality and
            mobility trajectories unfold in different ways for countries
            at varying levels of economic development? How and why do
            certain subgroups within societies consistently remain at
            the bottom of the income distribution? Finally, what policy
            measures that redress inequality and enhance mobility are
            likely to be both more effective and politically supportable?
display_title: What Data-Rich Assessments of
            Socioeconomic Inequality and Mobility in Rich Countries
            Overlook in Poor Countries
listing_relative_url: /research/2024/08/34381605/data-rich-assessments-socioeconomic-inequality-mobility-rich-countries-overlook-poor-countries
topicv3: Economic and Social Mobility m1326259 1152,Income Inequality m1503891 2668
publishtoextweb_dt: 2024-08-26T00:00:00Z
dois: 10.1596/1813-9450-10880
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 14
author_keys: 0: author_key: 677092
1: author_key: 166689
model_extractions: Policy Research Working Paper 10880 What Data-Rich Assessments of Socioeconomic Inequality and Mobility in Rich Countries Overlook in Poor Countries _Roy Van der Weide Michael Woolcock_ Development Economics Development Research Group August 2024
Policy Research Working Paper 10880

# **Abstract**

Advances in the scale and sophistication of individual income data compiled across high-income countries have enabled researchers to measure inequality and intergenerational mobility at a highly granular level and over long periods of time. The edited volume _Measuring Distribution and Mobility of Income and Wealth_ (Chetty et al (2022), comprising 23 empirically rich studies, provides a comprehensive stocktaking of the recent empirical evidence. This paper reviews the volume’s key findings obtained for high-income countries and raises several questions that extend to low-income countries. What stands out is that income and wealth inequality are increasing, while intergenerational mobility is declining, across most high-income countries. This begs the following questions. Is the Kuznets curve due for a revision? How can countries achieve (and maintain) higher levels of socioeconomic mobility as they develop? To what extent do inequality and mobility trajectories unfold in different ways for countries at varying levels of economic development? How and why do certain subgroups within societies consistently remain at the bottom of the income distribution? Finally, what policy measures that redress inequality and enhance mobility are likely to be both more effective and politically supportable?

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 and mwoolcock@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
# **What Data-Rich Assessments of Socioeconomic Inequality and Mobility in Rich Countries Overlook in Poor Countries**

Roy Van der Weide (World Bank) and Michael Woolcock (World Bank)<sup>1</sup> _Key words:_ inequality, mobility, measurement _JEL codes:_ D31, D63, I24, J62, O15

> 1 The views expressed in this paper are those of the authors alone, and should not be attributed to the World Bank, its executive directors, or the countries they represent. We are grateful to Francisco Ferreira, Stephen P. Jenkins, and Vijayendra Rao for helpful comments on an earlier draft of this review; remaining errors of fact or interpretation are ours alone. This paper was commissioned by the _Journal of Economic Inequality_ . Email address for correspondence: <u>rvanderweide@worldbank.org.</u>
# Review of evidence for high-income countries

Tracking socio-economic mobility and inequality across space and time, and building an understanding of where, how, and for whom observed differences have occurred, is both important and challenging. The extensive 2022 NBER conference volume by Raj Chetty and four-co-editors – featuring contributions by many of the world’s leading researchers on income and wealth distributions – is a collection of carefully executed empirical studies that do exactly that, drawing on a wealth of data across countries in the developed world (Chetty et al 2022). These efforts have yielded an overwhelming amount of empirical evidence – some new, others refining, expanding, and consolidating what others have previously reported – the most striking and consequential of which is that both income and wealth inequality are increasing, and intergenerational mobility is declining, at least across most high-income countries.

A wide array of detailed findings supporting and extending these key conclusions are presented across the Introduction and 23 substantive chapters that comprise _Measuring Distribution and Mobility of Income and Wealth_ (hereafter MDMIW); the chapters, in turn, are structured around five themes: Income Inequality, Wealth Inequality, Income and Wealth Mobility, Mitigating Inequality, and Distributional National Accounts. In the process of unpacking these themes, readers are taken on detailed descents into datasets across various high-income countries (mostly from the United States and Europe) on labor and housing markets, earnings and consumption, and inheritance, administrative, tax, and national accounts data. Apparent anomalies and necessary qualifiers are noted along the way, but by the end it’s abundantly clear that, no matter how one analyzes any of the comprehensive datasets, the early 21<sup>st</sup> century has only exacerbated the enduring “worlds of difference” (Alesina and Glaeser, 2004) experienced by the rich and poor in otherwise prosperous countries.

A risk in focusing so intensely on the scale and sophistication of measurement issues, however, is that it leaves vastly less room for broader but important considerations as to why we should care about rising inequality and falling mobility. There are obvious moral reasons for why one might care about inequality, socio-economic mobility, and the basis on which wealth is accumulated: because it denies people opportunities to find and develop their talents, because it reduces their happiness, because we have prior values (some enshrined in long-standing international declarations) affirming the equal moral worth of all human beings, because the gains to some may have been ill-gotten and the losses to others a result of overt discrimination or outright theft. But are there also sound economic reasons? The opening paragraph of the introduction makes a strong case that the answer is resoundingly ‘yes’. From the outset, the editors of MDMIW ask us to consider the question of whether individuals are locked in their respective initial place in this distribution or whether there is the broadly shared possibility for mobility. Research has focused not only on measuring inequality and mobility but also on understanding its historical, economic, and social determinants, and on how policies might affect these distributions. In addition, it is now 2
recognized with increased clarity that distributional differences may affect the transmission of macroeconomic shocks or responses to fiscal or monetary stimulus. (p. 1) Recent research strongly suggests that a society where many individuals are indeed ‘locked’ in their initial positions is at risk of underutilizing its human capital, which in turn begs the question of whether higher mobility is necessarily efficiency enhancing, i.e., good for economic growth and accompanying improvements in education, health, and general well-being. Indeed, there is a small but growing literature that examines the conditions under which improvements in socio-economic mobility can reasonably be expected to stimulate economic growth (in addition to addressing inequality; see for example Marrero and Rodriguez 2013).

While MDMIW touches on these questions to motivate the study of inequality and mobility, however, the constituent chapters themselves do not address them at any considerable length, which is – in our view – a missed opportunity in a collection over 700 pages long. It would be great if future NBER conference volumes could go beyond the measurement of inequality and also delve into the nature, extent, causes, and consequences of inequality, and what to do about it. An implication of prioritizing advances in measurement is that concerns about inequality have hitherto remained of secondary importance because comprehensive data has been lacking. Such concerns, unfortunately, are more assumed than demonstrated; while more and better data and cleaner methods are always desirable, explaining rising concerns about widening inequality within and (especially) beyond the research community, and marshalling broader political support for policies that might explain and redress the array of factors underpinning them, is ideally the canvas on which an edited book should paint. The world cares about rising inequality and shrinking mobility, in short, because moral and economic concerns are necessary complements; and the world has come to care with increasing intensity about both domains because of efforts that predate recent data expansions and methodological innovations, even as they have clearly been amplified by them (see below).

In any event, the empirical literature on wealth, inequality, and mobility is now unambiguously a fast-growing field that has arguably reached mainstream status. The "Opportunity Insights" project (formerly known as the “Equality of Opportunity project") alone has amassed over a dozen publications in top-5 journals since its inception in 2011. In the United States, the Center of Equitable Growth has emerged to liaise between academia and policy makers in an effort to stimulate evidence-based policy making and lobby for policies that would help create equality of opportunity; the UK has established the International Inequalities Institute (at the London School of Economics). This recent flurry of interest, however, stands in marked contrast to prevailing views across economics through most of the previous century. Though the drivers and consequences of class and stratification have long been central to analyses in political science and sociology (e.g., Davis and Moore 1944, Tilly 1998, Savage, 2021, <mark>Valentino and Vaisey 2022),</mark><sup>2</sup> <mark>t</mark> he importance of inequality and socio-economic mobility was not always clear to

> 2 A notable exception in MDMIW, by Fessler and Schurz (Chapter 8), assesses wealth inequality by taking a class-based approach, in which they assess the relative wealth trajectories in the US and Europe of three

3
economists, and “lack of data” was not the binding constraint. When and why did the discipline come to care vastly more about inequality only quite recently? As Ferreira (2022) stresses, disparaging views on inequality such as those expressed by Nobel Laureate Robert Lucas (2004) – that “[of] the tendencies that are harmful to sound economics, the most seductive, and in my opinion the most poisonous, is to focus on questions of distribution” – were quite mainstream into the 1980s and mid-1990s. Though major advances in the quality and scale of data are part of the reason for shifts in this view, they do not adequately account for the combination of antecedent events (including initiatives by the World Bank and the IMF<sup>3</sup> ) that rendered “questions of distribution” not merely less “poisonous” but, today, morally important, economically consequential, and politically salient.

Intergenerational mobility is closely related, both conceptually and empirically, to equality of opportunity (e.g., Jantti and Jenkins, 2015; Brunori et al., 2023).<sup>4</sup> Measures of inequality of opportunity (IOp) aim to quantify the extent to which individual income success is contingent on circumstances that are beyond the individual’s control – e.g., race, gender, place of birth, parental education and income, and family background more generally. Measures of intergenerational mobility focus on one specific circumstance, namely parental income (or education), and have risen in stature; the IOp literature too is growing, covering theory, philosophy, and empirical economics (see the seminal work by Roemer 1993, 1998; Van de Gaer, 1993; Roemer et al., 2003), but has had limited success convincing mainstream economists of its importance. Chetty et al, Mitnik, Helso, and Bryant (Chapter 11) offer several compelling reasons for why studies of IOp have thus far struggled to gain broader recognition. To highlight a few, their chapter argues that IOp scholars (a) are not focusing on long-run income (something the literature on intergenerational mobility does), (b) prioritize theoretical properties over interpretability, (c) often only have data on an incomplete set of circumstances (interestingly, the intergenerational mobility literature conveniently avoids this issue by focusing on one circumstance to begin with), and relatedly (d) have to work with data on a different set of circumstances for different countries which complicates cross-country comparisons (see e.g., Ferreira et al., 2018). Chapter 11 addresses these concerns, which by itself offers a methodological contribution to the IOp literature.<sup>5</sup> In their empirical application using data for the United States and Denmark, the authors restrict the circumstances to gender and parental income rank to facilitate cross-country comparability, in the process showing

> different interest groups pertaining to housing – ‘renters’, ‘owners’ and ‘capitalists’ – finding that (a) across all categories there is wide variation in wealth trajectories between countries (because of their respective policy histories) and yet (b) renters in general (except in the UK) are most likely to be at the bottom of the wealth distribution.

> 3 For example, _World Development Report 2006: Equity and Development_ (World Bank 2006) and a series of high-profile papers by senior IMF staff led by Andrew Berg (e.g., Berg et al 2018).

> 4 Other dimensions on inequality – stemming from Sen’s (1980) important question ‘Equality of what?’ – include Rao and Walton’s (2004) argument for considering inequality of agency, given the large differences between the rich and poor in forms and levels of social and cultural capital.

> 5 Another recent contribution to this literature is the study by Brunori et al. (2023).

4
that Denmark is more effective in equalizing opportunities than the United States. These kinds of findings can presumably be expanded in future work.

The book makes its most important contributions on its central terrain of measurement. Even if no researcher disputes that studying different concepts of inequality clearly requires reliable data on inequality, the measurement of inequality and socio-economic mobility is inherently challenging – so much so that the ideal data is rarely available, which makes it hard to piece together reliable trends and patterns in inequality and mobility across time, space, and groups. MDMIW documents impressive improvements in the measurement of income inequality, wealth inequality (which is even harder to measure) and socio-economic mobility that make creative use of new (and big) data sources. Even so, it is clear that tracking changes over time remains an enormous challenge, especially when trying to discern trends across the full spectrum of country experience. In the World Bank’s most recent Poverty and Shared Prosperity Report (World Bank 2022), for example, data across two time periods on ‘shared prosperity’ – the rate of growth of the bottom 40% of a country’s income distribution – is only available for a total of 91 economies,<sup>6</sup> and of these, only four are low-income and 21 are lower-middle-income; put most starkly, though approximately 650 million people live in the world’s poorest economies, there is change-in-inequality data (itself of variable quality) on only 13 million of them (see World Bank 2020: 64-65). In short, despite ‘promoting shared prosperity’ being one of the World Bank’s “twin goals” (the other being the eradication of extreme poverty), at the global level progress has been slow in terms of collecting and curating widespread data on changes in inequality (and for the moment is likely to remain so, since at least two datasets, separated by a time span of 5-10 years, are required to determine any such change). In this sense, the rising global interest in and policy stature of inequality, especially as it pertains to the world’s poorest people, _precedes_ the recent academic data revolution that has transpired for inequality research in high-income economies.

The world’s richest countries offer the best possible data for the study of income and wealth distributions and the intergenerational transmission of income and wealth. Making good use of these data, MDMIW offers a multitude of empirical insights. Allow us to highlight some (not being featured here is by no means a judgement of the study). First, returning to the observation highlighted in our opening paragraph, “there appears to be a broad trend across many countries toward an increase in wealth inequality” (Chetty et al., 2022; page 5). More specifically, the collective empirical findings point to recent increases in income inequality and a decline in intergenerational mobility in the high-income world, consistent with the Great Gatsby Curve (Corak, 2013). Chapter 3 (by McKinney, Abowd, and Sabelhaus) observes a recent increase in inequality and decline in income mobility in large metropolitan areas of the United States (over the period 1998-2017). Fisher and Johnson (in chapter 14) similarly observe an increase in inequality in the United States. Using Canadian census and tax returns data linked across

> 6 When discussing matters pertaining to economic life and comparing global trends, the World Bank uses the designation of ‘economies’ (rather than the more familiar term ‘countries’) in deference to those national territories whose formal status as a ‘country’ is contested or unwarranted.

5
generations, Conolly, Haeck, and Laliberte (Chapter 10) find that income mobility is also declining in Canada. More evidence of a recent decline in intergenerational mobility is documented in chapter 13 (by Garbinti and Savignac), who observe a decline in wealth mobility (proxied by home ownership mobility) in France.

The book also includes several chapters that study the distribution (and transmission) of wealth. In addition to chapter 13, chapter 5 (by Gale, Gelfond, Fichtner, and Harris) confirms that age is an important factor when it comes to wealth accumulation; in an empirical application to the United States, the authors observe an upward shift in wealth among older individuals but a decline in wealth among younger individuals. Switzerland is shown to stand out with exceptionally low levels of real-estate wealth among the bottom 50 percent (i.e., exceptionally high concentration of real-estate wealth) when compared to other high-income nations; see chapter 4 (by Martinez). In an empirical application to Italy, chapter 6 (by Acciari and Morelli) observes that the value of inheritance as a share of national income has nearly doubled from around 8% in 1995 to 15% in 2016, while tax revenues corresponding to inheritance transfers have declined – hinting at the significance of tax evasion.

In addition to establishing trends in inequality (and intergenerational mobility) and disaggregating these by selected sub-groups of the population, MDMIW also provides several comparative studies that highlight differences observed within the high-income world. Chapter 1 (by Gornick, Milanovic, and Johnson) conducts a comprehensive study of inequality among OECD countries, considering different choices of income and between-group decompositions. The United States is found to come out on top with high levels of inequality relative to its OECD peers. This observation holds up across the different sub-groups and measures of income considered. The results obtained in chapter 11 (by Mitnik, Helso, and Bryant) show that the United States also ranks unfavorably in terms of inequality of opportunity (in addition to inequality of outcomes).

The fifth and last section of MDMIW consists of five chapters that combine macro data (national accounts) with micro data (household surveys). Household survey data are well known to undercover households from the very top of the income distribution for a variety of reasons, while they are the ideal source of data for incomes below the 95<sup>th</sup> percentile. Adequate coverage of top incomes however is crucial for the estimation of income inequality. National accounts on the other hand are an ideal source of data for capturing top incomes but may be a less reliable source of data below the top-income level. That highlights the potential for combining these two sources of data. The concluding chapter (chapter 23 by Dominic Webber, Richard Tonkin, and Martin Shine), for example, develops a novel methodology employed by the UK’s Office for National Statistics (ONS) that utilizes that potential to produce a new top income adjusted series. The UK's statistical agency is the first among its peers to do this. (See also the recent study by Jenkins 2022.) Chapters not highlighted here are no less impressive and we encourage the interested reader to explore these chapters. One chapter that is missing in the book, however, is a concluding 6
remarks section (which might be a missed opportunity, since clearly there are all manner of empirical, theoretical and policy issues that these chapters open up). While MDMIW offers an incredibly rich and wide-ranging set of empirical observations, it stops short of providing a metaanalysis that extracts broad insights and connects these observations. There may also be value in connecting with the findings from the different chapters, and acknowledging issues such as how it is that persistent inequality endures for certain groups over long periods of time – think of African Americans in the United States, disadvantaged castes in India, Rohingya in Myanmar etc. (see also Mookherjee and Ray 2003, Elbers et al 2004).<sup>7</sup> While the book does examine between-group inequalities, its focus is largely on shifts across broad demographic categories (e.g., age, occupation, and gender) rather than the micro-social and political dynamics shaping the plight of those particular groups (by race, ethnicity, religion) whose prospects for mobility are either ignored or overtly and persistently thwarted.<sup>8</sup> (The authors of Chapter 5, Gale et al, in their discussion of Millennials in the United States, explore these issues but to a limited extent, in the sense that little recognition is given to the wide variations within these categories – which could be more fruitfully explored using qualitative methods<sup>9</sup> – and the pathways by which certain members within these varied sub-groups rise, stagnate, or fall.)

# Less evidence, more questions for low-income countries

Conspicuously missing are chapters investigating countries from the developing world. The book focuses exclusively on high-income countries. No doubt a major reason for this is because highincome countries simply offer vastly more and better data for the measurement of inequality and socio-economic mobility. Even in those countries, the available data is often not perfect. Having said that, the greatest scope for improvements in socio-economic mobility is arguably to be found in the developing world, and influential researchers concerned about global inequality should surely be calling loudly and frequently for vastly more resources to be allocated to efforts to better understand the circumstances of those at the lowest end of the spectrum. Inferior data is possibly not the only reason for the under-representation of low-income countries, however. An empirical study that hypothetically managed to compile top-notch data for, say, Cameroon or Cambodia will find it harder to secure a spot in a top economics journal. The field of economics in general appears to have an implicit bias towards high-income countries, if not a bias towards the United States (see Das et al, 2013). It is somewhat ironic that a field that claims to be concerned with income

> 7 World Bank (2005) introduced the concept of ‘inequality traps’ – where economic mobility is thwarted by legal, social, and political structures – to help explain why certain groups endure persistent inequalities. 8 Recent research has shown that group-based inequality can be just as bad (or worse) as group characteristics are broken down into smaller units, suggesting that inequality can be “fractal” (Joshi et al 2022).

> 9 That said, we hasten to add that both long-standing and more recent (thus sometimes less formally articulated) understandings of inequality can and should be addressed using the full array of social science methods.

7
inequality ignores its manifestation in low-income countries, i.e., is biased towards high-income countries at the expense of low-income countries.

While empirical studies on inequality and socio-economic mobility focusing on the developing world make up a smaller share of the literature, important progress is being made in this domain nonetheless (the challenges notwithstanding). Studies on inequality for selected developing countries include Banerjee and Piketty (2005), Piketty et al. (2006), Piketty and Qian (2009), and Novokmet et al. (2018); see also Ravallion (2014) and the pioneering study of the Latin America region (De Ferranti et al 2004). Articles that study inequality across nations from both the developing and the developed world (in some cases to study the global distribution of income) include Bourguignon and Morrisson (2002), Lakner and Milanovic (2016), Milanovic (2015, 2023), Alvaredo et al. (2017), Ravallion (2018), Brunori et al. (2021), and Chancel and Piketty (2021). The literature on intergenerational mobility focusing on the developing world is also growing, see e.g., Hnatkovska et al. (2013), Alesina et al. (2021), Asher et al. (2022), Neidhöfer et al. (2018), and Van der Weide et al. (2024). New methodological developments may help expand the country coverage of the developing world where the available data is more limited, see for example the recent study by Ray and Genicot (2023).

Recent evidence indicates that socio-economic mobility is lower in the average low-income country (see e.g., Van der Weide et al., 2024). A plausible pathway towards convergence between the developing and developed world is one where lower and middle-income countries lift socioeconomic mobility (and lower inequality) as they grow richer, which would sit well with Kuznets (1955). The empirical evidence presented in this book suggests that socio-economic mobility may simultaneously be declining (and inequality increasing) in the high-income world, which too would facilitate convergence but would modify the Kuznets curve. This was also observed by Milanovic (2016) who termed it the “Kuznets wave”. Such conclusions suggest more work is needed to better understand how nations modulate socio-economic mobility and inequality as they develop. In a recent study on China for example, a country that has shown a rapid development over the past 40 years, Ravallion (2022) questions whether “economic growth through structural transformation in poor countries is necessarily inequality increasing, or that a turning point will eventually be reached after which that growth will be inequality decreasing”.

Historical data available for selected developed nations may offer another resource to this end. The book offers several chapters that examine data going back half a century in time or more. Building an understanding of the earlier stages of the high-income world’s development cycle may provide valuable insights that could (in part) contribute to the study of low-income world today. See for example Piketty et al. (2006) and Bourguignon and Morrisson (2002). France too was once a developing nation. This reality suggests that another fruitful line of inquiry for future research is revisiting economic history to better identify why and how certain now-prosperous countries implemented – intentionally or otherwise – more inclusive (inequality-mediating) policies in their early stages of development than others. Slack’s (2015) important research on England’s rise from relative backwardness in 1600 to a global power over the course of the 17<sup>th</sup> century, for example, emphasizes not only material progress in technology and information but novel ideas (individual 8
and societal ‘improvement’) and distinctive social policies (the Poor Law, which sought to both promote labor mobility and provide a ‘portable’ minimum income for the lower classes).

Circumstances are obviously different for today’s poorest countries and peoples and, to be sure, a host of key measurement questions remain to be answered; but articulating and implementing effective policy agendas for promoting economic opportunity and mobility – about which, in stark contrast to its empirical sophistication, MDMIW is exceedingly cautious – is surely the priority issue for the coming decades. A research agenda focused on securing clearer understandings of how we got here, and why – within and between countries – certain sub-groups have fared so much better (or worse) than others, offers a possible pathway for being both rigorous and relevant.

# **References**

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