Why we need to measure the effect of merger policy and how to do it

Competition Policy International

Volumn 5, Issue 1, 2009, Pages 77-90

  Carlton, Dennis W ^a  

^a UNIVERSITY OF CHICAGO   (United States)

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EID: 70349224184     PISSN: 15540189     EISSN: 15546853     Source Type: Journal    
DOI: None     Document Type: Article

References (11)

1. Antitrust Modernization Report, 2007, available at http://govinfo. library.unt.edu/amc/report recommendation/toc.htm.
2. I discuss in Section V how to use data on challenged mergers. For simplicity, I use the DOJ as the government agency responsible for mergers. What I say obviously applies also to the FTC.
3. With fixed cost of litigation, one might want to require a positive δP, but this is a detail for the point being made in the text. Indeed, the government can challenge a merger only if it "substantially" lessens competition. I am, for simplicity, assuming that the DOJ is using a consumer (not total) surplus standard.
4. If the government is lax (e.g., S = -5), then it will allow a merger where δP = $5. Hence, the boundary in Figure 1 between "challenge" and "allow" moves to the right to δP = $5.
5. Orley Ashenfelter & Daniel Hosken, The Effect of Mergers on Consumer Prices: Evidence from Five Selected Case Studies, NBER WORKING PAPER 13859, 2008, is a recent exception.
6. In the absence of data on DOJ predictions, it might still be possible to estimate S. If one can observe δP for each unchallenged merger, then one can draw the distribution of δP. Under the assumptions in the text, the largest observed value of δP will approximate -S. To see this, notice in Figure 1 that the line δP = -S is the dividing line between the area labeled "challenge" and "allow" when S ‡0. Because δP is an expectation, not an actual value, the method just described needs to be adjusted slightly. I discuss this adjustment in Section V. Estimating S as described in the text is likely to produce more accurate estimates of S since it utilizes more data.
7. The addition of a stochastic component, E, means that the procedure to estimate S described in endnote 6 needs to be modified. The actual distribution of δP* for unchallenged mergers is a mixture of the distribution of δP (truncated at δP = -S) and the distribution of E. Under certain assumptions on the distributions, one can estimate the (truncated) distribution of δP and then estimate S as max -δP.
8. 2) are jointly normal and X is a vector of characteristics (e.g., HHI, industry profitability) that predict whether the DOJ fails to challenge the merger. One could, for example, estimate a probit model to predict a decision not to challenge and perform a "Heckman" correction. (Id., Ch. 22).
9. The group of unchallenged mergers that have been "fixed" might be an interesting one to study separately.
10. A more complicated model for dataset 1 could analyze the decision of the merging parties to settle (fix the case or abandon it) based on what their estimates of winning in court are. This would provide additional information to estimate S.
11. A specific issue that I do not address is that the underlying distribution of δP may depend on S. For example, as merger policy becomes lax, more mergers with high δP may be attempted. This means that the distribution of δP from merger activity and S should be modeled together. This is a topic for future research.