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Big Data Approaches to Modeling the Labor Market

Author

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  • Gerunov, Anton

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.

Suggested Citation

  • Gerunov, Anton, 2014. "Big Data Approaches to Modeling the Labor Market," MPRA Paper 68798, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:68798
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    File URL: https://mpra.ub.uni-muenchen.de/68798/1/MPRA_paper_68798.pdf
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    More about this item

    Keywords

    Labor market; Unemployment; Big data; WVS;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

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