Early research into deterrent effects of competition policy focused on simple be-fore/after comparisons of notified mergers. Stigler [1966], for example, by looking at changes in the composition of U.S. mergers before vs. after the 1950 anti-merger
amendment to the Clayton Act, found that the amendment must have had a de-terring effect on horizontal mergers, since the share of horizontal mergers shrunk considerably. Stigler did not run any regressions at that time, which makes it difficult to draw any valid conclusions from his statistical exercise. Nevertheless, the merit of his analysis lies in the fact that he was one of the first scholars to form a connection between merging behaviour and government action so as to grasp potential deterrent effects of legal enforcement. Eckbo and Wier [1985] followed Stigler’s approach and compared mergers before and after the U.S. Hart-Scott-Rodino Act to gain insights about deterrent effects. Measuring the outside (rival) firms’ stock market premiums before and after the reform, their results suggest that less anticompetitive mergers were filed after the reform. The results still put into doubt the fact that those mergers which were picked up by the authority for investigation after the reform tended to be more anticompetitive than those not investigated; Eckbo and Wier did not find conclusive evidence that the reform had led to a more efficient identification and selection process of anticompetitive mergers.
The deterrence concept of public enforcement originally stems from the eco-nomics of crime literature and is based on the idea that the punishment of crim-inals is observed and probabilities of being captured and punished are being updated by potential offenders [Becker, 1968]. The deterrence of criminals thus depends on whether the expected profit of a crime exceeds the expected punish-ment. According to this strand of literature, criminals posit a detection proba-bility for their decision with such probaproba-bility being a function of the authority’s past performance which they have observed. Effective deterrence then means that the severity of punishment and the high detection probability renders the crime unprofitable.
The concept can be applied to the merger context analogously: Firms contem-plating a merger observe the policy decisions taken by the European Commission to decide whether or not to file for an intended merger [Seldeslachts et al., 2009].
They will update their expectations on the Commission’s actions and adjust their decisions accordingly. It is not enough to deter any merger – with the appropri-ate merger policy, firms should be encouraged to notify procompetitive mergers and deterred to notify anticompetitive mergers. Seldeslachts et al. [2009], instead of using conditional probabilities, used absolute numbers of actions and looked at their impact on the frequency of mergers over 28 antitrust jurisdictions be-tween 1992–2005. Their unit of observation is a jurisdiction. Their main result is that prohibitions have a deterrent effect on future merger frequencies, while other merger policy tools have no impact. As they outline, firms make inferences as to antitrust stances, i.e., the parameters eliciting a specific kind of action, given the past decision history and the imperfect information on the parameters of pre-viously proposed mergers. They react to and update their expectations in the face of a jurisdictional change, as it means that jurisdictional decision thresholds might have shifted and probabilities for specific decisions altered.
Clougherty and Seldeslachts [2013] made further advances into this topic by applying the conditional probabilities methodology employed by the economics of crime literature. They regressed conditional probabilities as explanatory vari-ables11 constructed from the U.S. Department of Justice and the Federal Trade Commission data against the number of horizontal mergers and composition of mergers between 1986–1999. The unit of observation is the 2-digit Standard In-dustry Classification (SIC) inIn-dustry. Their result was that merger challenges12 had a strong deterring effect on horizontal mergers.
Clougherty et al. [2013b] take a frequency-based and industry-level approach towards deterrence: Using the entire merger data from the European Commission and matching the data at an industry level with aggregated firm data, they run regressions of merger notifications against the Commission’s merger decisions differentiated in kind. They find Phase 1 Remedies sending strong signals into
11 E.g., the investigation rate, i.e., the number of investigations divided by the number of prohibitions.
12 E.g., policy actions divided by the number of investigations.
the market, whereas merger policy actions as a whole do not seem to have a significant deterrent effect. With respect to Phase 1 Remedies, the result is that they work best in low-competition industries. They use the Herfindahl-Hirschman Index and Boone’s Beta to measure the degree of competition in the respective industries. Clougherty et al. [2013b] control for merger waves, industry-specific determinants of mergers, and industry and time fixed-effects.
Duso et al. [2013] depart from the past deterrence approach by identifying an-ticompetitive mergers in their sample. As part of an overall economic assessment of the 2004 European merger reform look at how past decisions affect the prob-ability of a merger to be anticompetitive by estimating a probit equation. They use a subsample of the EU merger dataset of 326 mergers and add information on competitors. Their identification strategy for anticompetitiveness is to look at stock market reactions to merger announcements.13 As a novelty in the me-thodical approach, the authors undertake to measure not only the quantitative (frequency) side, but also the qualitative effects of deterrence, that is, whether merger control has been able to deter bad mergers and encourage good mergers (good deterrence). They find a negative and significant effect of Phase 1 Reme-dies and prohibitions in the period before the reform on anticompetitive mergers.
There seems to have been a policy shift after 2004 where the number of prohibi-tions went down radically; after the reform, withdrawals and aborprohibi-tions seems to have partially substituted for prohibitions in its deterrent effect.
This paper is similar in approach as Duso et al. [2013], but then combines a more comprehensive range of merger cases with the corporate financial data of Thomson Reuters Worldscope and the Thomson Reuters SDC M&A data. Iden-tifying past mergers as competitive or anticompetitive according to the profit differentials method discussed below, it measures the effect past merger decisions have had on the probability of filing an anticompetitive merger. Duso et al.
[2013], while not only using a small subsample of the merger database for their
13Cumulative aggregate abnormal returns, CAARs.
analysis, identified anticompetitive mergers with stock market reactions; I em-ploy a different measure to tag anticompetitive mergers by achieving a one-to-one match of the merging firms with external commercial data.14 The match with external data has furthermore the advantage that it mitigates potential endo-geneity issues arising out of regressing with data derived from information which the merger authority uses for its decision [Bergman et al., 2005].
As mentioned above, this paper signifies a first step which needs to be taken towards the measurement of deterrence beyond the tip of the iceberg. This would mean that we ultimately estimate the probability for filing a merger given merger and market characteristics and the firm’s expectations about how the Commission will decide. As suggested by [Seldeslachts et al., 2009], to model deterrence as the probability to merge given specific expectations of the merging parties shall bring deterrence estimation considerably closer towards describing the iceberg and thus going around the empirical self-selection problem.