Does Machine Learning Offer Added Value Vis-à-Vis Traditional Statistics? An Exploratory Study on Retirement Decisions Using Data from the Survey of Health, Ageing, and Retirement in Europe (SHARE)

Montserrat González Garibay, Andrej Srakar, Tjaša Bartolj and Jože Sambt
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Montserrat González Garibay: Institute for Economic Research (IER), SI-1000 Ljubljana, Slovenia
Andrej Srakar: Institute for Economic Research (IER), SI-1000 Ljubljana, Slovenia
Tjaša Bartolj: Institute for Economic Research (IER), SI-1000 Ljubljana, Slovenia
Jože Sambt: School of Economics and Business, University of Ljubljana, SI-1000 Ljubljana, Slovenia

Mathematics, 2022, vol. 10, issue 1, 1-25

Abstract: Do machine learning algorithms perform better than statistical survival analysis when predicting retirement decisions? This exploratory article addresses the question by constructing a pseudo-panel with retirement data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). The analysis consists of two methodological steps prompted by the nature of the data. First, a discrete Cox survival model of transitions to retirement with time-dependent covariates is compared to a Cox model without time-dependent covariates and a survival random forest. Second, the best performing model (Cox with time-dependent covariates) is compared to random forests adapted to time-dependent covariates by means of simulations. The results from the analysis do not clearly favor a single method; whereas machine learning algorithms have a stronger predictive power, the variables they use in their predictions do not necessarily display causal relationships with the outcome variable. Therefore, the two methods should be seen as complements rather than substitutes. In addition, simulations shed a new light on the role of some variables—such as education and health—in retirement decisions. This amounts to both substantive and methodological contributions to the literature on the modeling of retirement.

Keywords: machine learning; random forests; retirement; survival analysis; time-dependent covariates (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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