Big Data Applications in Labor Economics

In the digital age, Big Data offers an unparalleled lens into the intricacies of human behavior. Data sourced from job boards, social media platforms, or news websites allows researchers to answer questions that could not be answered with conventional data sources. Labor markets are no exception here: every day, millions of workers and firms interact, and big data allows us to better understand the complex dynamics arising from worker-firm interactions.

This volume showcases new, original research using Big Data to gain fresh insights into how labor markets work. The volume is compiled by Solomon Polachek, a pioneer in gender-related labor market research, and Benjamin Elsner, an expert on causal inference and the economics of migration. Topics include labor force transition dynamics, the labor demand side of involuntary part-time employment, the insights gained from wages in online job postings regarding wage growth, the role of online vacancies in labor market performance, the demand for personality traits, and an analysis of job descriptions from university job boards. All chapters use a combination of innovative data sources and machine learning methods to enhance our understanding of how labor markets work.

Benjamin Elsner, an Assistant Professor of Economics at University College Dublin since 2018, specializes as an applied microeconomist, focusing on fields such as education, migration, and microeconometrics.

Solomon W. Polachek, a Distinguished Professor at the State University of New York at Binghamton, has significantly impacted labor economics and international relations through his extensive research and academic work.