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Predicting Retirement and Social Security Claiming Decisions using Machine Learning

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  • Kwon, Alexander
  • Maliar, Lilia

Abstract

We demonstrate that machine learning substantially improves predictions of individual decisions about retirement and Social Security (SS) claims. When predicting the number of people receiving SS, we achieve an error of less than 1%, while the benchmark model employed by the Social Security Administration (SSA) results in a greater than 4% error, and in forecasting SS claiming decisions, we attain an error of 0.2%, while the benchmark exceeding 2%. Based on averages, we show that a 3% difference in prediction amounts to 39.6 billion dollars annually. The set of important variables selected by our model significantly differs from that of the SSA model. We use Shapley values to evaluate the non-linear contributions of the selected variables to predictive outcomes.

Suggested Citation

  • Kwon, Alexander & Maliar, Lilia, 2024. "Predicting Retirement and Social Security Claiming Decisions using Machine Learning," CEPR Discussion Papers 19198, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:19198
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    More about this item

    Keywords

    retirement and Social Security;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • H55 - Public Economics - - National Government Expenditures and Related Policies - - - Social Security and Public Pensions
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
    • J26 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Retirement; Retirement Policies

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