Big Data Approaches to Modeling the Labor Market

Anton Gerunov

MPRA Paper from University Library of Munich, Germany

Abstract: The research paper leverages a big dataset from the field of social sciences – the combined World Values Survey 1981-2014 data – to investigate what determines an individual’s employment status. We propose an approach to model this by first reducing data dimensionality at a small informational loss and then fitting a Random Forest algorithm. Variable importance is then investigated to glean insight into what determines employment status. Employment is explained through traditional demographic and work attitude variables but unemployment is not, meaning that the latter is likely driven by other factors. The main contribution of this paper is to outline a new approach for doing big data-driven research in labor economics and apply it to a dataset that was not previously investigated in its entirety.

Keywords: Labor market; Unemployment; Big data; WVS (search for similar items in EconPapers)
JEL-codes: C55 J21 (search for similar items in EconPapers)
Date: 2014
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Published in Proceedings of the International Conference on Big Data, Knowledge and Control Systems Engineering, 2014 (2014): pp. 47-56

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:68798

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