This paper combines cross-sectional and longitudinal income data to present the evolution of absolute intergenerational income mobility in ten developed economies in the 20th century. Absolute mobility decreased during the second half of the 20th century in all these countries. Increasing income inequality and decreasing growth rates have contributed to the decrease. Yet, growth is the dominant contributor in most countries. We show that detailed panel data are unnecessary for estimating absolute mobility over the long run.
Homoploutia: Top Labor and Capital Incomes in the United States, 1950–2020 (with Branko Milanovic)
Homoploutia describes the situation in which the same people (homo) are wealthy (ploutia) in the space of capital and labor income in some country. It can be quantified by the share of capital-income rich who are also labor-income rich. In this paper we combine several datasets covering different time periods to document the evolution of homoploutia in the United States from 1950 to 2020. We find that homoploutia was low after World War II, has increased by the early 1960s, and then decreased until the mid-1980s. Since 1985 it has been sharply increasing: In 1985, about 17% of adults in the top decile of capital-income earners were also in the top decile of labor-income earners. In 2018 this indicator was about 30%. This makes the traditional division to capitalists and laborers less relevant today. It makes periods characterized by high interpersonal inequality, high capital-income ratio and high capital share of income in the past fundamentally different from the current situation. High homoploutia has far-reaching implications for social mobility and equality of opportunity. We also study how homoploutia is related to total income inequality. We find that rising homoploutia accounts for about 20% of the increase in total income inequality in the United States since 1986.
Mobility and Inequality in US Growth, 1968–2018 (with François Bourguignon)
This paper combines cross-sectional and longitudinal labor income data to present a comparison between anonymous and non-anonymous growth incidence curves in the United States during the past 50 years. If anonymous growth incidence tend to be upward sloping because of increasing inequality during that period, the same is not true of non-anonymous curves. The latter prove to be flat or non-significantly downward sloping, suggesting some neutrality of growth when initial income positions are accounted for. This is true when using either panel data or synthetic panels based on CPS data and one-parameter functional representations of income mobility. Flat non-anonymous curves are observed even in periods of increasing cross-sectional income inequality. Differences between anonymous and non-anonymous curves thus matter for the interpretation of inequality changes, social welfare and policy.
This paper combines historical cross-sectional and longitudinal data in the US to study patterns of economic growth within the income distribution. We quantify absolute mobility as the fraction of families with higher income over a period of several years. The rates of absolute mobility over periods of two to four years are procyclical and are largely confined within 45%–55%. We also find that absolute mobility decreases with income. Individuals and families occupying the lower ranks of the income distribution have a higher probability of increasing their income over short time periods than those occupying higher ranks. This also occurs during periods of increasing inequality. Our findings stem from the importance of the changes in the composition of income percentiles. These changes are over and above mechanical labor market dynamics and life cycle effects. We offer a simplified model to mathematically describe these findings.
Income inequality and income intergenerational mobility are negatively associated empirically across countries and across time. There is also a known mechanical relationship between measures of income inequality and intergenerational mobility. This paper tests whether the mechanical relationship explains the empirical association. We find that this relationship alone explains at least 64% of the variance in mobility across 36 countries. We also show that the mechanical relationship accords well with income inequality data across time for the United States. This suggests that policy aiming to achieve more equal outcomes will likely lead to more equal opportunities and vice versa. Yet, these findings also imply that validating empirically causal mechanisms for links between mobility and inequality require being over and above the mechanical relationship.
This paper uses Bureau of Labor Statistics employment and wage data to study the distributional impact of the COVID-19 crisis on wages in the United States by mid-April. It answers whether wages of lower-wage workers decreased more than others', and to what extent. We find that the COVID-19 outbreak exacerbates existing inequalities. Workers at the bottom quintile in mid-March were three times more likely to be laid off by mid-April compared to higher-wage workers. Weekly wages of workers at the bottom quintile decreased by 6% on average between mid-February and mid-March and by 26% between mid-March and mid-April. The average decrease for higher quintiles was less than 1% between mid-February and mid-March and about 10% between mid-March and mid-April. We also find that workers aged 16-24 were hit much harder than older workers. Hispanic workers were also hurt more than other racial groups. Their wages decreased by 2-3 percentage points more than other workers' between mid-March and mid-April.
Risk Preferences in Time Lotteries (with Mark Kirstein)
An important question in economics is how people choose when facing uncertainty in the timing of rewards. In this paper we study preferences over time lotteries, in which the payment amount is certain but the payment time is uncertain. In expected discounted utility (EDU) theory decision makers must be risk-seeking over time lotteries. Here we explore growth-optimality, a normative model consistent with standard axioms of choice, in which decision makers maximise the growth rate of their wealth. Growth-optimality is consistent with both risk-seeking and risk-neutral behaviour in time lotteries, depending on how growth rates are computed. We discuss two approaches to compute a growth rate: the ensemble approach and the time approach. Revisiting existing experimental evidence on risk preferences in time lotteries, we find that the time approach accords better with the evidence than the ensemble approach. Surprisingly, in contrast to the EDU prediction, the higher the ensemble-average growth rate of a time lottery is, the less attractive it becomes compared to a sure alternative. Decision makers thus may not consider the ensemble-average growth rate as a relevant criterion for their choices. Instead, the time-average growth rate may be a better criterion for decision-making.
WORK IN PROGRESS
A Simplified Mortality Multiplier Method: Historical Wealth Inequality Estimates (with Facundo Alvaredo and Salvatore Morelli)
Long Run Inequality and Two Types of Intergenerational (Im)mobility (with Ravi Kanbur)
PUBLISHED PAPERS OR IN PRESS
Inequality, Identity, and the Long-Run Evolution of Political Cleavages in Israel 1949-2019 (to be published as a chapter in Political Cleavages and Social Inequalities, eds. Amory Gethin, Clara Martínez-Toledano and Thomas Piketty, forthcoming in Harvard University Press)
On the Distribution of Estates and the Distribution of Wealth: Evidence from the Dead (with Salvatore Morelli, chapter forthcoming in NBER book Measuring and Understanding the Distribution and Intra/Inter-Generational Mobility of Income and Wealth, eds. Raj Chetty, John N. Friedman, Janet C. Gornick, Barry Johnson, and Arthur Kennickell)
Wealth Inequality and the Ergodic Hypothesis: Evidence from the United States (with Ole Peters and Alex Adamou, forthcoming in the Journal of Income Distribution)
Microfoundations of Discounting (with Alex Adamou, Dio Mavroyiannis and Ole Peters, forthcoming in Decision Analysis)