Big Data Approaches to Modeling the Labor Market

Gerunov, Anton (2014): Big Data Approaches to Modeling the Labor Market. Published in: Proceedings of the International Conference on Big Data, Knowledge and Control Systems Engineering, 2014 (2014): pp. 47-56.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Labor market, Unemployment, Big data, WVS
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis; J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J21 - Labor Force and Employment, Size, and Structure

References

Greene, W. (2011). Econometric Analysis, 7th Edition. US: Prentice Hall.

Hastie, T., Tibshirani, R., & Friedman, J. (2011). The Elements of Statistical Learning. NY: Springer.

Romer, D. (2012). Advanced Macroeconomics. US: McGraw-Hill.

Kalil, A., Schweingruber, H. & Seefeldt, K. (2001).Correlates of Employment among Welfare Recipients: Do Psychological Characteristics and Attitudes Matter? American Journal of Community Psychology, Volume 29, Issue 5, 701-723.

Kessler, R. C., Turner, J. B., & House, J. S. (1987). Intervening processes in the relationship between unemployment and health. Psychological Medicine, 17, 949–961.

World Values Survey, Wave 1-6 1981-2014. (2014). World Values Survey Association (www.worldvaluessurvey.org).

Breiman, L. (2001). Random Forests. Machine Learning 45(1), 5-32.

Liaw, A. & Wiener, M. (2002). Classification and Regression by randomForest. R News, 2-3, 18-22.