Tacit collusion, firm asymmetries and numbers: evidence from EC merger cases M. Olczak & S. Davies with H. Coles Royal Economic Society PhD Presentation Meeting, UCL, January 2009

Prelude: our wider research programme Empirical exploration of nexus between tacit collusion & cartels – especially the role of asymmetries Does one model fit all? Are they substitutes? Problem: little known empirically about prevalence of tacit collusion, and no prospect of useable database becoming available, cf. cartels One policy area where competition authorities (CAs) are required to assess likelihood of tacit collusion = coordinated effects mergers. Objective of this paper: To identify EC’s implicit model of tacit collusion & market structure from analysis of those mergers which involved/might have involved collective dominance (coordinated effects)

Previous literature (i)tacit collusion and symmetry of firms: Conventional wisdom: tacit collusion less likely, the less symmetric are firms, eg Compte et al (2002) and Kühn (2002). (ii)predicting CAs’ decisions: empirical literature attempting to explain decisions of CAs in a number policy areas: e.g. Bergman et al (2005) for EC, Coate & Ulrick (2006) for US In the case of mergers, typically use large samples of merger decisions to estimate the probability of intervention (P): S vector of concentration/market share variables; X vector of other market characteristics, such as entry barriers, buyer power; Z vector of sundry political/institutional factors

Limitations Rarely discriminate between unilateral and coordinated effects Treatment of the X variables (e.g. entry barriers): these variables are important factors in the CA’s decision BUT very difficult to measure objectively Missed opportunity: analysis usually conducted at merger level, taking no account of assessments of the merger’s impact in individual markets - discards considerable information on intra-merger variation

Decisions All Cases (2425) Significant discussion of CD? YES (62 mergers) Intervention in 1 or more market? YES (25 mergers) INTMERGER CD (44 mkts) SD (74 mkts) NI (104 mkts) NO (37 mergers) NONINTMERGER NI (234 mkts) NO (2363 mergers)

A model of EC’s decision making For a given merger, the Commission makes a choice, for each market, between NI, SD & CD. Two routine parts to the assessment: 1)Checklist of market characteristics (X): high entry barriers, price transparency, no buyer power, etc? 2)Structural indicators (S) – market shares, concentration – indicate potential dominance? And, if so, which theory of harm? The checklist amounts to necessary conditions for dominance but are difficult to measure or proxy Our way forward is to control for these X factors by careful sample selection coupled with a key assumption about the way the EC treats multiple markets within the same merger: Mergers are X-homogeneous: all markets covered by a given merger share the same X market characteristics

The Structural Model Only if the X conditions are satisfied: EC simultaneously chooses between the 3 alternatives depending upon the post merger market shares: concentration (SUM=S1+S2) & asymmetry (RATIO = S2/S1) Empirical strategy: 1) estimate structural model, but only for INTMERGER sample. 2) identify INTMERGER markets where incorrectly predict CD 3) Use model in 1) to ‘predict’ decisions for mkts in NONINTMERGER In (2) and (3) when CD is incorrectly predicted, note EC’s stated reasons for non intervention. Expect the reasons to: i)Not be structural for either INTMERGER or NONINTMERGER ii)Not invoke X factors for INTMERGER but often will for NONINTMERGER

Structural Model INTMERGERS (N=222) multinomial logit results: ** indicates significance at 99% level, Std errors in parentheses, Pseudo R² = AllSDCDNI Correct Predictions (%):

Graphical implications of estimated structural model SD CD NI

Incorrect CD predictions INTMERGERNONINTMERGER No. of markets for which CD predicted4342 Of which CA did not intervene7 (16%)52 (100%) Number for which CA cites X factors amongst reasons for non-intervention 3 (43%)43 (83%) Structural reasons 02 (4%) Asymmetric market shares01 4 large rivals post merger01 Other 4 (57%)6 (11%) No reason given 01(2%)

Conclusions High predictive power Commission applies different structural models to SD and CD – size asymmetries have a key role What have we learnt about tacit collusion? Arguably nothing, unless we have some faith in the Commission! Tacit collusion rare with N>2 (cf experimental research) Symmetry has a crucial role, and we now have some practical idea of what constitutes symmetry (in terms of market shares) Contrast with cartels, on both firm numbers and asymmetries