David F. Larcker is the James Irvin Miller Professor of Accounting at Stanford Graduate School of Business; Charles C.Y. Wang is the Glenn and Mary Jane Creamer Associate Professor of Business Administration at Harvard Business School; and Andrew Baker is a J.D. candidate at Stanford Law School. This post is based on their recent paper. Related research from the Program on Corporate Governance includes Short-Termism and Capital Flows by Jesse Fried and Charles C. Y. Wang (discussed on the Forum here).
Difference-in-differences (DiD) has been the workhorse statistical methodology for analyzing regulatory or policy effects in applied finance, law, and accounting research. A generalized version of this estimation approach that relies on the staggered adoption of regulations or policies (e.g., across states or across countries) has become especially popular over the last two decades. For example, from 2000 to 2019, there were 751 papers published in (or accepted for publication by) top tier finance or accounting journals that use DiD designs. Among them, 366 (or 49%) employ a staggered DiD design. Many of the staggered DiD papers address significant questions in corporate governance and financial regulation.
The prevalent use of staggered DiD reflects a common belief among researchers that such designs are more robust and mitigate concerns that contemporaneous trends could confound the treatment effect of interest. However, recent advances in econometric theory suggest that staggered DiD designs often do not provide valid estimates of average treatment effects.
In a paper recently posted on SSRN, we find that staggered DiD designs often can, and have, resulted in misleading inferences in the literature. We also show that applying robust DiD alternatives can significantly alter inferences in important papers in corporate governance and financial regulation.