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

Shifting earnings inequality among U.S. workers over the last five decades has been widely stud ied, but understanding how these shifts evolve across smaller groups has been difficult. Publicly available data sources typically only ensure representative data at high levels of aggregation, so they obscure many details of earnings distributions for smaller populations. We define and construct a set of granular statistics describing income distributions, income mobility and con ditional income growth for a large number of subnational groups in the U.S. for a two-decade period (1998-2019). In this paper, we use the resulting data to explore the evolution of income inequality and mobility for detailed groups defined by race and ethnicity. We find that patterns identified from the universe of tax filers and W-2 recipients that we observe differ in important ways from those that one might identify in public sources. The full set of statistics that we construct is available publicly as the Income Distributions and Dynamics in America, or IDDA, data set.