美联储-社会保障与高频劳动力供给：来自优步司机的证据（英）

Finance and Economics Discussion SeriesFederal Reserve Board, Washington, D.C.ISSN 1936-2854 (Print)ISSN 2767-3898 (Online)Social Security and High-Frequency Labor Supply: Evidence fromUber DriversTimothy K. M. Beatty, Joakim A. Weill2024-079Please cite this paper as:Beatty, Timothy K. M., and Joakim A. Weill (2024).“Social Security and High-Frequency Labor Supply: Evidence from Uber Drivers,” Finance and Economics Discus-sion Series 2024-079.Washington: Board of Governors of the Federal Reserve System,https://doi.org/10.17016/FEDS.2024.079.NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminarymaterials circulated to stimulate discussion and critical comment. The analysis and conclusions set forthare those of the authors and do not indicate concurrence by other members of the research staff or theBoard of Governors. References in publications to the Finance and Economics Discussion Series (other thanacknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.SOCIAL SECURITY ANDHIGH-FREQUENCY LABOR SUPPLY:EVIDENCE FROM UBER DRIVERSTimothy K. M. Beatty∗Joakim A. Weill†‡September 2024AbstractWe estimate the impact of anticipated transfers on labor supply using confidentialdriver-level data from Uber. Leveraging the staggered timing of Social Securityretirement benefits within each month and a novel identification strategy, wefind that the labor supply of older drivers declines by 2%, on average, duringthe week of benefit receipt—a precisely estimated but economically small effect.Individual-level analyses reveal that the average effect obscures heterogeneousmicro-behavior: while the majority of drivers do not meaningfully adjust laborsupply in response to social security benefits, a small group reduces labor supplyby more than 40%. The results suggest that departures from standard models oflabor supply can be substantial, but only for a small number of individuals.∗Agricultural and Resource Economics, University of California, Davis, USA.†Federal Reserve Board of Governors, Washington, DC, USA. Email: joakim.a.weill@frb.gov‡We thank Uber for generously providing data access, Libby Mishkin for invaluable insights andsupport with this project, Mariya Shappo for constructive feedback, and Felipe Vial and TianxiaZhou for expert technical assistance. We also thank Marianne Bitler, Eirik Brandsaas, David Cho,Edmund Crawley, Thomas Crossley, John Driscoll, Hank Farber, Peter Ganong, Joaquin Garcia-CaboHerrero, Hilary Hoynes, Paul Lengermann, Jim Poterba, Brendan Price and Takuya Ura for thoughtfulsuggestions. We thank numerous seminar participants for their helpful comments. Uber data wereobtained by Joakim Weill prior to his employment at the Federal Reserve Board, while he was a PhDcandidate at UC Davis. The views expressed in this paper are solely the responsibility of the authorsand should not be interpreted as reflecting the opinions of the Federal Reserve Board or of any othe