Working Papers

PDF, BibTeX, OTE Blog Post, VoxEU Column, Media Coverage: Wall Street Journal  

Abstract: We document large differences in lifetime hours of work using data from the NLSY79 and argue that these differences are an important source of inequality in lifetime earnings. To establish this we develop and calibrate a rich heterogeneous agent model of labor supply and human capital accumulation that allows for heterogeneity in preferences for work, initial human capital and learning ability, as well as idiosyncratic shocks to human capital throughout the life-cycle. Our calibrated model implies that almost 20 percent of the variance in lifetime earnings is accounted for by differences in lifetime hours of work, with over 90 percent of this effect due to heterogeneity in preferences. Higher lifetime hours contribute to lifetime earnings via two channels: a direct channel (more hours spent in production at given productivity) and a human capital channel (more hours spent investing in human capital, which increases future productivity). Roughly one-half of the effect of lifetime hours on lifetime earnings is due to the human capital channel. Higher lifetime hours are also an important source of upward earnings mobility over the life-cycle for many workers.

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Abstract: We measure how workers use genAI for their jobs in a nationally representative survey linking genAI adoption to detailed occupations and tasks. Our data provide the first task-level genAI adoption indexes, which we show can inform analyses of genAI's labor market impact. Exposure scores explain some, but far from all, of the variation in adoption across occupations and tasks. We also distinguish our indexes from measures based on genAI platform chat logs, which differ conceptually and tend to over-classify chats into generic activities spanning many occupations. Finally, we highlight that current adoption is widespread but shallow: genAI is used across many occupations and tasks, yet within most of them, fewer than half of workers adopt. This indicates substantial variation among workers doing very similar work, suggesting that understanding who adopts may matter as much as understanding which tasks genAI assists.

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Abstract: While employer-to-employer (E2E) transitions are by now well-documented, these data alone cannot reveal what drives mobility: who searches, why, and how search translates into transitions. Using novel panel data from the ECB and NY Fed consumer expectations surveys, we provide the first systematic cross-country analysis of on-the-job search (OJS) and E2E transitions across 11 euro-area countries and the U.S. Our data uniquely include direct measures of OJS and its motives (pay satisfaction for job ladder, job-loss expectations for precautionary) for all workers, not just searchers. We find OJS is widespread, making employed workers the majority of searchers, and it strongly predicts E2E transitions. Motives differ dramatically: The job ladder motive dominates in the U.S., while precautionary search dominates in Europe. OJS is highly persistent, with 40\% continuing to search even after starting a new job.

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*A previous version was titled "Work from Home and Interstate Migration"

Abstract: We study how full-time work from home (WFH) affects migration and economic activity across cities. Using ACS and novel survey data, we show WFH workers migrate 40--50 percent more than comparable commuters, commuters who switch to WFH migrate more, plausibly exogenous WFH expansions raise migration, and WFH workers migrate to lower-cost cities than commuters. The post-Covid expansion in WFH coincided with a large increase in migration; WFH accounts for half of this increase and much of the cross-city variation in migration changes. Recently, WFH has stabilized at twice its pre-Covid rate. We study the long-run implications of this shift in a dynamic spatial equilibrium model of remote work, costly migration, and job search. Expanded WFH raises migration by shifting workers into more mobile remote jobs, reallocating people and tax revenue out of expensive cities and narrowing rent differentials. Welfare gains are sizable but concentrated among remote-capable workers in commuting jobs just before the shock, especially those in large cities. A higher WFH share also shifts more of the burden of local shocks onto commuters.

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Overview: Shortly after the COVID shock hit the US, we started running a Current Population Survey (CPS)-style online survey (initially twice a month, between October 2020 and June 2021 once a month). Between May 2020 and June 2021 the survey was run in collaboration with the Federal Reserve Bank of Dallas. The goal was twofold: first, to provide real-time data on the state of the labor market in the US; second, to gather extra information that the CPS and many other datasets do not have, or that will only become available to researchers with a significant time lag. With every new data release, we published an update on the state of the US labor market. The PDF linked above was the last of these reports, all other reports including media coverage are available on the project website. For the surveys covering the same reference week as the CPS, we released a forecast about three weeks before the release of the Employment Situation Report by the BLS. Therefore in analogy to the CPS, we called the survey the Real-Time Population Survey. We used the additional questions in our survey to address questions of interest, both for academic research and for public policies. We published two academic papers based on the RPS in the Review of Economic Dynamics and the American Economic Journal: Macroeconomics. We are also using the data in our recent working paper on interstate migration, and Laura Pilossoph and Jane Ryngaert added some questions to our survey for their research on job search, wages, and inflation.