Big Data Applications In Labor Economic

This volume contains an Open Access chapter.

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 recent trends in the digitalization of job postings, the use of online vacancy and job applicants’ data to study skill dynamics, the insights gained from linked vacancy data regarding skill demand and wages, the tracking of gender norms over time, the utilization of domain-specific word embeddings to examine the demand for skills, the latest evidence on employee agreements in the franchise sector, and the impact of vertical restraints on labor markets in franchised industries. 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.