Big Data and Unemployment Analysis

Arbeitspapier

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. We focus here on the analysis of unemployment by means of internet activity data, a literature starting with the seminal article of Askitas and Zimmermann (2009a). The article provides insights and a brief overview of the current state of research.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: GLO Discussion Paper ; No. 81

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access ; General Aggregative Models: Forecasting and Simulation: Models and Applications ; Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
• Subject: big data ; unemployment ; internet ; Google ; internet penetration rate

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Simionescu, Mihaela ; Zimmermann, Klaus F.
• Event: Veröffentlichung
• (who): Global Labor Organization (GLO)
• (where): Maastricht
• (when): 2017

Further information

• Handle: http://hdl.handle.net/10419/162198
• Last update: 10.03.2025, 11:45 AM CET