Individual earnings and family income: Dynamics and distribution

Altonji, Joseph G.; Hynsjö, Disa; Vidangos, Ivan

doi:10.1016/j.red.2022.08.005

Cited by 7 publications

(4 citation statements)

References 101 publications

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“…A large body of work focused on individual life-cycle earnings dynamics prompted the development of the Global Repository of Income Dynamics project. Guvenen, Karahan, Okan, and Song (2021) and Altonji, Hynsjo, and Vidangos (2022) are two of the more recent papers summarizing the existing literature. 1 Quantifying earnings disparities by race and sex characterizes our research.…”

Section: Introductionmentioning

confidence: 99%

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U.S. long‐term earnings outcomes by sex, race, ethnicity, and place of birth

McKinney

Abowd

Janicki

2022

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This paper is part of the Global Repository of Income Dynamics (GRID) project cross‐country comparison of earnings inequality, volatility, and mobility. Using data from the U.S. Census Bureau's Longitudinal Employer‐Household Dynamics (LEHD) infrastructure files, we produce a uniform set of earnings statistics for the U.S. From 1998 to 2019, we find U.S. earnings inequality has increased and volatility has decreased. The combination of increased inequality and reduced volatility suggest earnings growth differs substantially across different demographic groups. We explore this further by estimating 12‐year average earnings for a single cohort of age 25–54 eligible workers. Overall, differences in labor supply (hours paid and quarters worked) are found to explain almost 90% of the variation in worker earnings at the mean, although substantial earnings differences across and within groups remain unexplained. Using a quantile regression approach, we estimate counterfactual earnings distributions for each demographic group. We find that at the bottom of the earnings distribution differences in characteristics such as hours paid, geographic division, industry, and education explain almost the entire earnings gap; however, above the median the contribution of the differences in the returns to characteristics is the dominant component.

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Section: Introductionmentioning

confidence: 99%

“…McKinney, Green, Vilhuber, and Abowd (2020) show that the missing at random assumption holds for education and that this method of imputation is reliable.13 There is a large existing literature on measurement of earnings inequality. SeeGuvenen et al (2021) andAltonji, Hynsjo, and Vidangos (2022) for examples and further references.…”

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confidence: 99%

U.S. long‐term earnings outcomes by sex, race, ethnicity, and place of birth

McKinney

Abowd

Janicki

2022

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“…We expand the analysis to all individuals, and we illustrate the importance of participation and unemployment in driving secular and cyclical dynamics of inequality at the bottom of the earnings distribution. 5 Our analysis at the household level connects to a large quantitative literature emphasizing the key roles of female labor supply (Juhn and Murphy, 1997;Hyslop, 2001;Blundell et al, 2016), sorting (Mare, 1991;Greenwood et al, 2014), income pooling among spouses (Altonji et al, 2022;Santos and Tertilt, 2023), and government redistribution (Heathcote et al, 2014;Borella et al, 2022) as determinants of inequality dynamics.…”

Section: More Unequal We Stand? Inequality Dynamics In the United Sta...mentioning

confidence: 99%

More unequal we stand? Inequality dynamics in the United States, 1967–2021

Heathcote

Perri

Violante

et al. 2023

Review of Economic Dynamics

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“…This limited space precludes our doing justice to a thorough review of the work in this area. See Katz and Autor (1999) and Acemoglu and Autor (2011) for surveys of the income inequality literature, and Meghir and Pistaferri (2011) and Altonji, Hynsjö, and Vidangos (2022) for reviews of the income dynamics literature that goes back to the 1970s. The approach to income dynamics in GRID is most closely related to a recent strand of literature that emphasizes nonparametric approaches in measurement and modeling that allow for nonlinearities and nonnormalities in income dynamics.…”

Section: Introductionmentioning

confidence: 99%

Global trends in income inequality and income dynamics: New insights from GRID

Guvenen

Pistaferri

Violante

2022

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The Global Repository of Income Dynamics (GRID) is a new open‐access, cross‐country database that contains a wide range of micro statistics on income inequality, dynamics, and mobility. It has four key characteristics: it is built on micro panel data drawn from administrative records; it fully exploits the longitudinal dimension of the underlying data sets; it offers granular descriptions of income inequality and income dynamics for finely defined subpopulations; and it is designed from the ground up with the goals of harmonization and cross‐country comparability. This paper introduces the database and presents a set of global trends in income inequality and income dynamics across the 13 countries that are currently in GRID. Our results are based on the statistics created for GRID by the 13 country teams who also contributed to this special issue with individual articles.

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Individual earnings and family income: Dynamics and distribution

Cited by 7 publications

References 101 publications

U.S. long‐term earnings outcomes by sex, race, ethnicity, and place of birth

U.S. long‐term earnings outcomes by sex, race, ethnicity, and place of birth

More unequal we stand? Inequality dynamics in the United States, 1967–2021

Global trends in income inequality and income dynamics: New insights from GRID

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