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Dissecting the Pandemic Retirement Surge

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Abstract

One of the most pressing questions facing policy makers is how quickly the labor force might return to its pre-pandemic strength. Answering this question requires understanding the employment and retirement trajectories of older workers in the pandemic. The labor force is roughly 3.8 million workers short of where it would be if we still lived in the pre-Covid economy, and nearly half of this shortfall is accounted for by those 55 and older. Meanwhile, the retired share of the population is roughly 2 percentage points above its pre-pandemic trend line (Davis, 2021). Dissecting the Pandemic Retirement Surge finds that: In explaining the sharp increase in the retired share since Covid-19 hit, the drop in “unretirement,†that is, the decrease in flows from retirement to employment, plays only a minor role. The most important driver of the recent increase in retirements consists of higher flows into retirement among workers who were employed in the year before the pandemic. The retirement of workers who became unemployed due to the pandemic also contributed significantly to the increased retired share.

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  • Owen Davis & Siavash Radpour, 2021. "Dissecting the Pandemic Retirement Surge," SCEPA publication series. 2021-05, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    • Handle: RePEc:epa:cepapb:2021-05
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    1. Maurice J.G. Bun & Frank Kleibergen, 2013. "Identification and inference in moments based analysis of linear dynamic panel data models," UvA-Econometrics Working Papers 13-07, Universiteit van Amsterdam, Dept. of Econometrics.
    2. Leah Platt Boustan & Matthew E. Kahn & Paul W. Rhode, 2012. "Moving to Higher Ground: Migration Response to Natural Disasters in the Early Twentieth Century," American Economic Review, American Economic Association, vol. 102(3), pages 238-244, May.
    3. Pesaran, M. Hashem, 2015. "Time Series and Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780198759980, Decembrie.
    4. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    5. Sayan Chakrabarty, 2015. "A Nexus Between Child Labour and Microfinance: An Empirical Investigation," Economic Papers, The Economic Society of Australia, vol. 34(1-2), pages 76-91, June.
    6. Nerlove,Marc, 2005. "Essays in Panel Data Econometrics," Cambridge Books, Cambridge University Press, number 9780521022460.
    7. James J. Heckman, 1976. "Introduction to "Annals of Economic and Social Measurement, Volume 5, number 4"," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, National Bureau of Economic Research, Inc.
    8. Ellen Webbink & Jeroen Smits & Eelke Jong, 2013. "Household and Context Determinants of Child Labor in 221 Districts of 18 Developing Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 110(2), pages 819-836, January.
    9. Barrett , Christopher B & Carter , Michael R & Ikegami , Munenobu, 2008. "Poverty traps and social protection," Social Protection Discussion Papers and Notes 42752, The World Bank.
    10. Daniel L. Millimet & Ian K. McDonough, 2017. "Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 725-743, June.
    11. Beegle, Kathleen & Dehejia, Rajeev H. & Gatti, Roberta, 2006. "Child labor and agricultural shocks," Journal of Development Economics, Elsevier, vol. 81(1), pages 80-96, October.
    12. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    13. Bretschger, Lucas & Vinogradova, Alexandra, 2019. "Best policy response to environmental shocks: Applying a stochastic framework," Journal of Environmental Economics and Management, Elsevier, vol. 97(C), pages 23-41.
    14. World Bank, 2013. "World Development Indicators 2013," World Bank Publications - Books, The World Bank Group, number 13191, December.
    15. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1, July-Dece.
    16. Chakrabarty, Sayan & Grote, Ulrike, 2009. "Child Labor in Carpet Weaving: Impact of Social Labeling in India and Nepal," World Development, Elsevier, vol. 37(10), pages 1683-1693, October.
    17. Alvi, Eskander & Dendir, Seife, 2011. "Weathering the Storms: Credit Receipt and Child Labor in the Aftermath of the Great Floods (1998) in Bangladesh," World Development, Elsevier, vol. 39(8), pages 1398-1409, August.
    18. Jihye Kim & Wendy Olsen & Arkadiusz Wiśniowski, 2020. "A Bayesian Estimation of Child Labour in India," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 13(6), pages 1975-2001, December.
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    More about this item

    Keywords

    older workers; retirement; Covid-19; labor force flows;
    All these keywords.

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • J62 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Job, Occupational and Intergenerational Mobility; Promotion
    • J38 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Public Policy
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • J83 - Labor and Demographic Economics - - Labor Standards - - - Workers' Rights
    • J32 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Nonwage Labor Costs and Benefits; Retirement Plans; Private Pensions

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