Estimation and Identification of Merger Effects: An Application to Hospital Mergers (WP-06-01)
Advances in structural demand estimation have substantially improved economists’ ability to forecast the impact of mergers. However, these models rely on extensive assumptions about consumer choice and firm objectives, and ultimately observational methods are needed to test their validity. Observational studies, in turn, suffer from selection problems arising from the fact that merging entities differ from nonmerging entities in unobserved ways. To obtain an accurate estimate of the ex-post effect of consummated mergers, the author proposes a combination of rival analysis and instrumental variables. By focusing on the effect of merger on the behavior of rival firms and instrumenting for these mergers, unbiased estimates of the effect of a merger on market outcomes can be obtained. Using this methodology, she evaluates the impact of all independent hospital mergers between 1989 and 1996 on rivals’ prices. She finds sharp increases in rival prices following merger, with the greatest effect on the closest rivals. Results for the hospital industry are more consistent with predictions from structural models than with prior observational estimates.
Leemore Dafny,Assistant Professor of Management and Strategy, Kellogg School of Management and Institute for Policy Research, Northwestern University; and NBER