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SeaTE: Subjective ex ante Treatment Effect of Health on Retirement

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  • Pamela Giustinelli

    (Bocconi University, IGIER, and LEAP)

  • Matthew D. Shapiro

    (University of Michigan and NBER)

Abstract

This paper develops an innovative approach to measuring the effect of health on retirement. The approach elicits subjective probabilities of working at specified time horizons fixing health level. Using a treatment-effect framework, within-individual differences in elicited probabilities of working given health yield individual-level estimates of the causal effect of health (the treatment) on working (the outcome). We call this effect the Subjective ex ante Treatment Effect (SeaTE). The paper then develops a dynamic programming framework for the SeaTE. This framework allows measurement of individual-level value functions that map directly into the dynamic programming model commonly used in structural microeconometric analysis of retirement. The paper analyzes conditional probabilities elicited in the Vanguard Research Initiative (VRI)—a survey of older Americans with positive assets. Among workers 58 and older, a shift from high to low health would on average reduce the odds of working by 28.5 percentage points at a two-year horizon and 25.7 percentage points at a four-year horizon. There is substantial variability across individuals around these average SeaTEs, so there is substantial heterogeneity in taste for work or returns to work. This heterogeneity would be normally unobservable and hard to disentangle from other determinants of retirement in data on realized labor supply decisions and health states. The paper’s approach can overcome the problem that estimates of the effect of health on labor supply based on behavioral (realizations) data can easily overstate the effect of health on retirement whenever less healthy workers tend to retire earlier for reasons other than health.

Suggested Citation

  • Pamela Giustinelli & Matthew D. Shapiro, 2018. "SeaTE: Subjective ex ante Treatment Effect of Health on Retirement," Working Papers wp382, University of Michigan, Michigan Retirement Research Center.
  • Handle: RePEc:mrr:papers:wp382
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    Cited by:

    1. Gong, Yifan & Stinebrickner, Ralph & Stinebrickner, Todd, 2022. "Marriage, children, and labor supply: Beliefs and outcomes," Journal of Econometrics, Elsevier, vol. 231(1), pages 148-164.
    2. Gizem Koşar & Cormac O'Dea, 2022. "Expectations Data in Structural Microeconomic Models," NBER Working Papers 30094, National Bureau of Economic Research, Inc.
    3. Ye, Xiaoyang & Zhai, Muxin & Feng, Li & Xie, A’na & Wang, Weimin & Wu, Hongbin, 2022. "Still want to be a doctor? Medical student dropout in the era of COVID-19," Journal of Economic Behavior & Organization, Elsevier, vol. 195(C), pages 122-139.
    4. Stephane Bonhomme & Angela Denis, 2023. "Estimating Individual Responses when Tomorrow Matters," Papers 2310.09105, arXiv.org, revised May 2024.
    5. Pamela Giustinelli, 2022. "Expectations in Education: Framework, Elicitation, and Evidence," Working Papers 2022-026, Human Capital and Economic Opportunity Working Group.
    6. Aucejo, Esteban M. & French, Jacob & Ugalde Araya, Maria Paola & Zafar, Basit, 2020. "The impact of COVID-19 on student experiences and expectations: Evidence from a survey," Journal of Public Economics, Elsevier, vol. 191(C).
    7. Yifan Gong & Todd Stinebrickner & Ralph Stinebrickner, 2020. "Perceived and actual option values of college enrollment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 940-959, November.
    8. Favara, Marta & Freund, Richard & Perez-Alvarez, Marcello, 2023. "What If It Never Happened? Subjective Treatment Effects of a Negative Shock on Youth Labour Market Outcomes in Developing Countries," IZA Discussion Papers 16417, Institute of Labor Economics (IZA).

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • J26 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Retirement; Retirement Policies

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