New:   How to Detect and Measure Labor Market Collusion?  

(Awarded  the CPI Young Researcher Prize  at CRESSE, 2023)

Abstract: With the aim of expanding the set of tools available to antitrust practitioners, this paper develops two new econometric methods to detect and measure the effects of labor market cartels. The first method is reduced form and aims to estimate wage loss. It exploits the inter-percentile difference between high earners and low earners within a difference-in-differences framework. This approach is simple to implement,  can easily be explained to non-economists, measures heterogeneous effects, and requires no additional data compared to that necessary for a before-after analysis. The method is illustrated by revisiting the 1986-8 case of collusion in Major League Baseball, measuring an average yearly income loss of 26%. Second, this paper  develops a structural model of labor market competition for the purpose of detecting collusive behavior.  Applied to the data, it reveals that at the beginning of the cartel, there were heightened barriers to mobility across firms, rising profits, and a decreasing labor share of income. Surprisingly, these patterns sustain past the end date of the cartel, suggesting important and underestimated long-run effects. Finally, the structural model is used to simulate counter-factual wages, revealing that the yearly average wage should have been at least 30% higher.