“Big Data and Unemployment Analysis” published in the Journal of Renmin University of China

Internet or “big” data are increasingly measuring the relevant activities of individuals, households, firms and public agents in a timely way. The information set involves large numbers of observations and embraces flexible conceptual forms and experimental settings. Therefore, internet data are extremely useful to study a wide variety of human resource issues including forecasting, nowcasting, detecting health issues and well-being, capturing the matching process in various parts of individual life, and measuring complex processes where traditional data have known deficits.

A seminal article by Nikos Askitas and Klaus F. Zimmermann (2009) had demonstrated for the first time, how Google activity data measuring activity on the labor market can inform about official unemployment. This has opened the perspective to analyze real world phenomenon using internet data. This article has generated a strong and rising literature and caused a large number of cites in particular with reference to the unemployment issue (Google cites: March 9, 2018: 457).

Askitas, N., & Zimmermann, K. F. (2009). Google Econometrics and Unemployment Forecasting. Applied Economics Quarterly, 55(2), 107-120.

In a recent article on “Big Data and Unemployment Analysis” GLO Fellows Mihaela Simionescu (Romanian Academy, Bucharest) and Klaus F. Zimmermann (UNU-MERIT)  have revisited his topic and research strategy and surveyed the relevant literature so far. A pre-publication version of the paper is available as

Simionescu, Mihaela; Zimmermann, Klaus F. (2017) : Big Data and Unemployment Analysis, GLO Discussion Paper, No. 81

Free download in English

The GLO paper has been published in Chinese as the lead article in the Journal of Renmin University of China, 2017, Volume 31, No.6, 2 – 11.

 Simionescu (Bratu) Mihaela at Romanian Academy
 Mihaela Simionescu
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