Predicting Retirement and Social Security Claiming Decisions using Machine Learning

Alexander Kwon and Lilia Maliar

No 19198, CEPR Discussion Papers from Centre for Economic Policy Research

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.

Keywords: retirement; and; Social; Security (search for similar items in EconPapers)
JEL-codes: C53 H55 J14 J26 (search for similar items in EconPapers)
Date: 2024-07
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