Research

Interest

Causal Machine Learning, Economics of Education, Labor Economics, 'Using Data from Sports to Answer Economic Questions', Microeconometrics, Econometrics, Statistical Learning,

Working / Discussion Papers

Goller, Daniel & Gschwendt Christian & Wolter, Stefan C., 2023 "'This time it's different' Generativ artificial intelligence and occupational choice" working paper; Media: SRF Tagesschau 02.12.2023 (Hauptausgabe)  Swissinfo

Späth, Maximilian & Goller, Daniel, 2023. "Gender differences in investment reactions to irrelevant information" working paper; pre-analysis plan

Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning" working paper

Peer Reviewed Journals

"Tournaments, Contestant Heterogeneity and Performance", with Brox, Enzo, just accepted, Journal of Political Economy: Microeconomics, https://doi.org/10.1086/735786 (working paper)

"Reaching for Gold! The impact of a positive reputation shock on career choice", with Wolter, Stefan C., 2025, European Economic Review, 175, 105017, (working paper); Media: Der Bund (german)  IZA World of Labor (english)

"Virtual vs. in-person fairs: The impact on search activity and diversity", with Graf, Chiara & Wolter, Stefan C., 2025, Applied Economics Letters, 1-4, (working paper) 

"'Good Job!' The impact of positive and negative feedback on performance", with Späth, Maximilian, 2024, Sports Economics Review, 8, 100045, (working paper)

"A general framework to quantify the event importance in multi-event contests", with Heiniger, Sandro, 2024, Annals of Operations Research, 341: 71-93. https://doi.org/10.1007/s10479-023-05540-x (working paper)  Replication Code & Event Importance Values

 "Sitting next to a dropout: Academic success of students with more educated peers", with Diem, Andrea, & Wolter, Stefan C., 2023, Economics of Education Review, 93, 102372. (working paper)

"Analysing a built-in advantage in asymmetric darts contests using causal machine learning", 2023, Annals of Operations Research, 325(1): 649-679. (working-paper)

"'Too shocked to search' The COVID-19 shutdowns' impact on the search for apprenticeships", with Wolter, Stefan C., 2021, Swiss Journal of Economics and Statistics, 157 (6): 1-15. (working paper); Media: Aargauer Zeitung (german)

"Let's meet as usual: Do games played on non-frequent days differ? Evidence from top European soccer leagues", with Krumer, Alex, 2020, European Journal of Operational Research, 286 (2): 740-754 (working paper)

"Does the Estimation of the Propensity Score by Machine Learning Improve Matching Estimation? The Case of Germany's Programmes for Long Term Unemployed", with Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020, Labour Economics, 65 (working paper)

Book chapters

"Predicting Match Outcomes in Football by an Ordered Forest Estimator", with Knaus, Michael C. & Lechner, Michael & Okasa, Gabriel, 2021 Chapter 22 in A Modern Guide to Sports Economics, ed. Ruud H. Koning & Stefan Kesenne, https://doi.org/10.4337/9781789906530.00026 (working paper)

Or here:

Google Scholar, REPEC, Research Gate, ORCID