25 years of European merger control |
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| Institution: | 1. Deutsches Institut für Wirtschaftsforschung (DIW Berlin) & Technische Universität (TU) Berlin;2. Deutsches Institut für Wirtschaftsforschung (DIW Berlin), Technische Universität (TU) Berlin, Centre for Economic and Policy Research (CEPR) & CESifo, Mohrenstr. 58, Berlin 10117, Germany;3. Wirtschaftsuniversität Wien, Austria;1. Bucknell University, United States;2. Texas Tech University, United States;1. University of Milano, Department of Economics, Management, and Quantitative Methods Via Conservatorio, 7, Milano 20122, Italy;2. Aix-Marseille Univ., CNRS, EHESS, Centrale Marseille AMSE, France;3. CEF.UP, University of Porto, Portugal;1. Toulouse School of Economics, France;2. Hanken School of Economics and Helsinki Graduate School of Economics, Finland;1. University of Bologna, Department of Economics, Piazza Scaravilli 2, Bologna 40126, Italy;2. Lear, Via di Monserrato 48, Rome 00186, Italy;3. Ofcom, Riverside House, 2a Southwark Bridge Road, London SE1 9HA, United Kingdom;4. Deutsches Institut für Wirtschaftsforschung (DIW Berlin), Technische Universität (TU) Berlin, Berlin Centre for Consumer Policies (BCCP), CEPR, and CESIfo. Mohrenstr. 58, Berlin 10117, Germany;5. Lear, Via di Monserrato 48, Rome 00186, Italy;1. School of Economics, University of Queensland, St Lucia QLD 4069, Australia;2. Department of Economics, Tulane University, New Orleans, USA;1. Universität Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany;2. WZB, Reichpietschufer 50, Berlin 10785, Germany;3. Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany;4. DIW Berlin, Mohrenstr. 58, 10117 Berlin, Germany |
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| Abstract: | We study the determinants of common European merger policy over its first 25 years, from 1990 to 2014. Using a novel dataset at the level of the relevant antitrust markets and containing all relevant merger cases notified to the European Commission, we evaluate how consistently arguments related to structural market parameters – dominance, rising concentration, barriers to entry, and foreclosure – were applied over time and across different geographic market definitions. On average, linear probability models overestimate the effects of structural indicators. Using non-parametric machine learning techniques, we find that dominance is positively correlated with competitive concerns, especially in markets with a substantial increase in post-merger concentration and in complex mergers. Yet, its importance decreased following the 2004 merger policy reform. Competitive concerns are also correlated with rising concentration, especially if entry barriers and foreclosure are of concern. The impact of these structural indicators in explaining competitive concerns is independent of the geographic market definition and does not change over time. |
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