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)

González Garibay, Montserrat (author), Srakar, Andrej (author), Bartolj, Tjaša (author), Sambt, Jože (author)

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.

• Language: English
• Keywords: statistics, machine learning, random forests, survival analysis, retirement, time-dependent covariates
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: EF - School of Economics and Business
• Publication status: Published
• Publication version: Version of Record
• Year: 2022
• Number of pages: 25 str.
• Numbering: Vol. 10, iss. 1, art. 152
• PID: 20.500.12556/RUL-134971
• UDC: 004.8:311
• ISSN on article: 2227-7390
• DOI: 10.3390/math10010152
• COBISS.SI-ID: 92089347
• Publication date in RUL: 15.02.2022

Record is a part of a journal

• Title: Mathematics
• Shortened title: Mathematics
• Publisher: MDPI AG
• ISSN: 2227-7390
• COBISS.SI-ID: 523267865

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 04.01.2022

Secondary language

• Language: Slovenian
• Keywords: statistika, strojno učenje, naključni gozdovi, analiza preživetja, upokojitev

Projects

• Funder: EC - European Commission
• Funding programme: European Social Fund
• Name: Upgrade of Analytical Models Concerning the Pension Scheme

• Funder: Other - Other funder or multiple funders
• Funding programme: Slovenia, Ministry of Labour, Family, Social Affairs, and Equal Opportunities
• Name: Upgrade of Analytical Models Concerning the Pension Scheme