Working Paper: NBER ID: w14719
Authors: Dennis W. Carlton
Abstract: In this article, I explain the inadequacy of our current state of knowledge regarding the effectiveness of antitrust policy towards mergers. I then discuss the types of data that one must collect in order to be able to perform an analysis of the effectiveness of antitrust policy. There are two types of data one requires in order to perform such an analysis. One is data on the relevant market pre and post merger. The second is data on the specific predictions of the government agencies about the market post-merger. A key point of this article is to stress how weak an analysis of only the first type of data is. The frequent call for retrospective studies typically envisions relying on just this type of data, but the limitations on the analysis are not well understood. As I explain below, retrospective studies that ask whether prices went up post merger are surprisingly poor guides for analyzing merger policy. It is only when the second type of data is combined with the first type that a reliable analysis of antitrust policy can be carried out. There is a need both to collect the necessary data and to analyze it correctly.
Keywords: No keywords provided
JEL Codes: C01; K21; L10; L41
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| retrospective studies relying solely on post-merger price data (G34) | inadequate assessment of merger policy effectiveness (L49) |
| systematic biases in government predictions (D72) | misrepresentation of true impact of merger policy (L49) |
| combining pre and post-merger data with government predictions (C80) | more accurate estimation of bias in merger policy (L41) |
| systematic biases (D91) | misleading conclusions about antitrust policy being too lax or too stringent (L49) |