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

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.