Working Papers

PDF, BibTeX

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.

PDF, BibTeX, OTE Blog Post, RPS Website

*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.

PDF, BibTeX, OTE Blog Post (I,II,III), VoxEU Column, Brookings Podcast

Abstract: This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI.

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Abstract: We measure how workers use genAI in their jobs using a nationally representative survey that links genAI use to detailed tasks. GenAI currently assists a broad range of work, with at least one in five workers using genAI in 80% of occupations and 40% of job tasks. Yet in most of these cases adoption rates remain below 50%, with some individuals systematically adopting genAI for more tasks than others who perform similar work. As a result, although genAI ``exposure” measures correlate positively with adoption, they explain only about half of the variation across workers. The nature of genAI use also varies across occupations, with workers in some occupations using it mainly for high-expertise work and others for low-expertise work. Survey-based task shares differ systematically from estimates from genAI platform chat-log data, primarily because chat data overclassify basic, generic tasks relative to the ONET framework. We organize our findings into occupation- and task-level adoption indexes, providing an input for research on the labor market effects of genAI.

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.

PDF, BibTeX, RPS Website

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.