Staggered Difference-in-Differences Estimation for Antitrust Analysis: A Review of New Literature and Recommendations for Practitioners

Hassan Faghani and Steven VanOmmeren discuss new literature surrounding difference-in-differences (DiD) methods for antitrust analysis with staggered treatment mechanisms. The authors explain how a popular antitrust analysis technique can be biased in common applications and how academics have provided new estimators and sensitivity tests to address its weaknesses. They perform a Monte Carlo analysis to examine how new estimators compare with existing methods when applied to data patterns often seen in antitrust analysis.

The authors discuss the implications of three important topics (parallel trends, the not-yet-treated group, and data with customer entry and exit); and argue that the new tools available for DiD analysis should bring about a higher standard for robust antitrust work.

Read the article, which was originally published on SSRN in April 2024. (subscription required)

In a previous article, the authors examined developments in DiD theory and provided guidance on what noneconomists need to know to make informed decisions with DiD in antitrust applications.