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Granular Income Inequality and Mobility using IDDA: Exploring Patterns across Race and Ethnicity

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Listed:
  • Illenin O. Kondo
  • Kevin Rinz
  • Natalie Gubbay
  • Brandon Hawkins
  • John L. Voorheis
  • Abigail K. Wozniak

Abstract

We explore the evolution of income inequality and mobility in the U.S. for a large number of subnational groups defined by race and ethnicity, using granular statistics describing income distributions, income mobility, and conditional income growth derived from the universe of tax filers and W-2 recipients that we observe over a two-decade period (1998–2019). We find that income inequality and income growth patterns identified from administrative tax records differ in important ways from those that one might identify in public survey sources. The full set of statistics that we construct is available publicly alongside this paper as the Income Distributions and Dynamics in America, or IDDA, dataset. Using two applications, we illustrate IDDA’s relevance for understanding income inequality trends. First, we extend Bayer and Charles (2018) beyond earnings gaps between Black and White men and document that, unlike those for other groups, earnings for both Black men and Black women fell behind earnings for White men following the Great Recession. This trend lasted through 2019, the end of the data period. Second, we document a significant reversal in the convergence of earnings for Native earners in Native areas.

Suggested Citation

  • Illenin O. Kondo & Kevin Rinz & Natalie Gubbay & Brandon Hawkins & John L. Voorheis & Abigail K. Wozniak, 2024. "Granular Income Inequality and Mobility using IDDA: Exploring Patterns across Race and Ethnicity," NBER Working Papers 32709, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:32709
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    References listed on IDEAS

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    1. Brian Erard & Patrick Langetieg & Mark Payne & Alan Plumley, 2020. "Flying under the Radar: Ghosts and the Income Tax," CESifo Economic Studies, CESifo, vol. 66(3), pages 185-197.
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    5. Florian Hoffmann & David S. Lee & Thomas Lemieux, 2020. "Growing Income Inequality in the United States and Other Advanced Economies," Journal of Economic Perspectives, American Economic Association, vol. 34(4), pages 52-78, Fall.
    6. Mary Layne & Deborah Wagner & Cynthia Rothhaas, 2014. "Estimating Record Linkage False Match Rate for the Person Identification Validation System," CARRA Working Papers 2014-02, Center for Economic Studies, U.S. Census Bureau.
    7. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, Institute of Labor Economics (IZA).
    8. David H. Autor & Lawrence F. Katz & Melissa S. Kearney, 2008. "Trends in U.S. Wage Inequality: Revising the Revisionists," The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 300-323, May.
    9. Bruce D. Meyer & Nikolas Mittag, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," NBER Working Papers 25738, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Maggie R. Jones & Adam Bee & Amanda Eng & Kendall Houghton & Nikolas Pharris-Ciurej & Sonya R. Porter & Jonathan Rothbaum & John Voorheis, 2024. "Mobility, Opportunity, and Volatility Statistics (MOVS): Infrastructure Files and Public Use Data," Working Papers 24-23, Center for Economic Studies, U.S. Census Bureau.

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    More about this item

    JEL classification:

    • D1 - Microeconomics - - Household Behavior
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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