Thomas Peeters

Thomas Peeters is an Associate Professor in the Department of Applied Economics at the Erasmus School of Economics, Erasmus University Rotterdam. He serves as the director of the Department of Applied Economics and coordinates the Erasmus Center for Applied Sports Economics (ECASE). He is a research fellow at the Tinbergen Institute and a member of the Erasmus Research Institute of Management (ERIM), reflecting his strong integration into leading research communities.

• PhD in Applied Economics, University of Antwerp
• PhD Fellow, Flanders Research Foundation
• Visiting Research Fellow, University of Michigan

His research spans sports economics, industrial organization, strategy economics, and innovation. He investigates topics such as labor markets in sports, managerial impact on performance, financial regulations in football, and open science practices. His work is published in top journals including Management Science, Economic Policy, and Journal of Law, Economics, and Organization.

His recent publications show a consistent focus on empirical economic analysis in sports and organizational settings, particularly examining team performance, labor dynamics, and institutional rules. The research combines rigorous econometric methods with real-world data, often using sports as a laboratory for broader economic principles.

He has received several honors:

• ERIM Open Science Award (2023)
• ERIM Top Article Award (2022)
• Societal Impact Award from ESE (2019)
• Young Economist Award (2011)

Peeters is actively involved in academic service, including editorial roles at Jahrbücher für Nationalökonomie und Statistik and Sports Economics Review, organizing the European Conference on Sport Economics, and contributing to public discourse on open science. He played a key role in creating the interdisciplinary minor 'Sports @ Society' across faculties. While there is no public list of advisees, he has supervised academic work. His research is supported by data transparency, as seen in his multiple replication datasets.