How did the US become a land of opportunity? Previous historical research on intergenerational mobility has focused on father-son income correlations, masking the role of mothers. We introduce a new mobility measure that incorporates both parents' human capital, develop a latent variable method leveraging literacy as a proxy, and construct a representative linked panel that includes women. We find that intergenerational mobility—in both human capital and income—rose sharply from the 19th to the 20th century. Initially, maternal human capital was most predictive of children's outcomes. However, as schooling expanded, this reliance declined and intergenerational mobility rose. America's investment in mass education has therefore been central to its rise as a mobile society.
This article studies the long-run effects of slavery and restrictive Jim Crow institutions on Black Americans' economic outcomes. We track individual-level census records of each Black family from 1850 to 1940 and extend our analysis to neighborhood-level outcomes in 2000 and surname-based outcomes in 2023. We show that Black families whose ancestors were enslaved until the Civil War have considerably lower education, income, and wealth than Black families whose ancestors were free before the Civil War. The disparities between the two groups have persisted substantially because most families enslaved until the Civil War lived in states with strict Jim Crow regimes after slavery ended. In a regression discontinuity design based on ancestors' enslavement locations, we show that Jim Crow institutions sharply reduced Black families' economic progress in the long run.
High-income business service workers dominate the economies of major US cities, and their spending supports many local consumer service jobs. As a result, business services' high remote work potential poses a risk to consumer service workers who could lose an essential source of revenue if business service workers left big cities to work from elsewhere. We use the COVID-19-induced increase in remote work to provide empirical evidence for this mechanism and its role in shaping the pandemic's economic impact. Our findings have broader implications for the distributional consequences of the transition to more remote work.
Artificial intelligence is transforming the task content of work. Predicting the labor market consequences requires understanding how workers' skills determine productivity across tasks, how workers adapt by changing occupations and acquiring new skills, and how wages adjust in general equilibrium. We introduce a dynamic task-based model in which workers accumulate multidimensional skills that shape their comparative advantage across tasks and, in turn, their occupational choices. We then develop an estimation strategy that recovers (i) the mapping from skills to task-level productivity, (ii) the law of motion for skill accumulation, and (iii) the determinants of occupational choice. We use the quantified model to study generative AI's impact through task augmentation, automation, and simplification. We predict long-run average wage gains of 24 percent and a substantial reduction in wage inequality. The distributional effects arise almost entirely due to task simplification—the degree to which AI reduces the skill level required to perform tasks. We show that AI's labor market effects critically hinge on its technological scope by contrasting generative AI with physically-capable AI robots.
The World War II GI Bill was the largest education subsidy in US history and a cornerstone of the postwar US transition to a knowledge economy. The GI Bill's language was race-blind, but Southern lawmakers secured decentralized administration that gave local officials wide discretion, opening the possibility of unequal benefits by race. This paper quantifies the GI Bill's impact on Black and white Americans' economic outcomes across two generations, using a regression discontinuity around WWII service eligibility cutoffs and a new data linkage from veterans in the 1940 and 1950 censuses to their sons' outcomes in the 2000s. The GI Bill widened racial inequality, doubling white veterans' college completion while steering Black veterans into often-fraudulent vocational programs with no earnings returns. The disparities propagated across generations, widening the white-Black gap in sons' adult-neighborhood outcomes, including a 5 percentage point (47 percent) widening of the racial college gap. Veterans' race itself drove these disparities; decompositions show that racial differences in prewar economic status and residential location played a negligible role.
Two Steps Forward, One Step Back: Racial Income Gaps among Women since 1950
This paper studies the evolution of Black-white income gaps among women since 1950. I document that the gap in incomes of Black and white women narrowed substantially in the 1960s, around the end of Jim Crow. While the South was the epicenter of racial inequality during Jim Crow, its Black-white gaps have since converged with other regions. The improvements in the Black-white gap were shared among Black women across the income distribution. However, there were two distinct drivers. At the bottom and middle of the income distribution, the pre-1980 compression of the distribution's lower tail narrowed the Black-white gap. In contrast, Black women at the top experienced a substantial improvement in the rank they occupied in the white distribution, narrowing the Black-white gap despite rising inequality at the distribution's higher tail.
Despite large estimated productivity gains from AI across a wide range of tasks, the labor market effects are not well understood. This paper develops a methodology to estimate workers' comparative advantage across tasks and the labor market effects of task-biased technical change. We propose and estimate a dynamic general equilibrium model in which workers build multi-dimensional skills and can switch occupations based on their evolving comparative advantage. The model is informed by existing detailed data on tasks in the US labor market and new data on their skill requirements.
This dataset comprises crosswalks to link census records for men and women in the United States from 1850 to 1950. It covers 42 million Americans who are linked across 186 million census records. The dataset tracks individuals across multiple censuses despite name changes (e.g., due to marriage) by combining historical census records with Social Security Number application data. The resulting panels are representative, making them particularly valuable for including women in the study of intergenerational mobility.
