Lifetime Earnings, Race, and Wealth
Friday, Jan. 6, 2023 8:00 AM - 10:00 AM (CST)
- Chair: Jeffrey Thompson, Federal Reserve Bank of Boston
Incarceration, Employment, and Earnings: Dynamics and Differences
AbstractWe study the dynamics of incarceration, employment, and earnings. Our hidden Markov model distinguishes between first-time and repeat incarceration, between persistent and transitory nonemployment and earnings risks, and accounts for nonresponse bias. We estimate the model via maximum likelihood using the National Longitudinal Survey of Youth 1979, accounting for the large differences in incarceration rates by race, education level, and gender. First-time incarceration is associated with 32% (51%) lower expected lifetime earnings and 6 (10) fewer years of employment for Black (White) men with a high school degree. Among less-educated men, differences in incarceration and nonemployment can explain around half the Black-White lifetime earnings gap.
Racial Wealth Disparities: Reconsidering the Roles of Lifetime Earnings and Intergenerational Transfers
AbstractIn this paper we present updated measures of racial disparities in wealth using the most recent data from the Survey of Consumer Finances (SCF), augmented by household-level estimates of Defined Benefit (DB) pension wealth developed by Sabelhaus and Volz (2020). Including this important asset, we find that racial wealth disparities are considerably smaller than the numbers typically discussed in other research or in the media, but the disparities remain substantial. The paper proceeds by exploring two specific factors that have long been identified as playing potentially important roles in generating disparities in wealth by race, namely differences in earnings (education/human capital) and intergenerational transfers in the form of inheritances and inter vivos gifts. We contribute to the existing literature by introducing several data innovations in the exploration of these factors using the SCF. We augment the SCF data with individual-level lifetime earnings histories (developed by Jacobs et al, 2020, 2021) and enhanced measures of intergenerational transfers (developed by Feiveson and Sabelhaus, 2018, 2019). We also create an expanded set of variables that capture the range of pension coverage and generosity across workers. Using all three of these new data components, we use non-parametric decomposition techniques to estimate their contributions to racial wealth gaps between white and non-white families. Differences in lifetime earnings, pension generosity, and a handful of other human capital and work-related variables explain three-quarters of white/Black wealth gaps and between 80 to 90 percent of white/Hispanic gaps. Reweighting white family wealth to match the distribution of human capital traits of “other” race families (including Asian, Native American and other groups) raises counterfactual white wealth up to the level of “other” family wealth, nearly closing the white/”other” gap. Differences in intergenerational transfers are found to account for 14 to 16 percent of white/non-white private wealth gaps.
The Dynamics of the Racial Wealth Gap
AbstractWhat drives the dynamics of the racial wealth gap? We answer this question using a dynamic general equilibrium heterogeneous-agents model that matches racial differences in earnings, wealth, bequests, and returns to savings. Our calibrated model endogenously produces a racial wealth gap matching that observed in recent decades along with key features of the current cross-sectional distribution of wealth, earnings, and race. Our model predicts that equalizing earnings is the quickest way to close the racial wealth gap, with much smaller roles for bequests or returns to savings. One-time wealth transfers have only transitory effects unless they address the racial earnings gap.
- J3 - Wages, Compensation, and Labor Costs
- E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy