The Broad Impact of Competition Policy: How to measure the Deterrent Effects?

Transcription

1 The Broad Impact of Competition Policy: How to measure the Deterrent Effects? Fabienne Ilzkovitz and Adriaan Dierx European Commission, DG Competition* CRESSE Special Policy Session 2: Identifying the Impact of Competition Policy Rhodes, 1 July 2016 *This presentation has been prepared under the sole responsibility of its author. It does not represent the views of the European Commission 1

2 Outline Presentation The Broad Impact of Competition Policy: How to measure the Deterrent Effects? I. Definition and motivation II. III. IV. Measurement of deterrent effects Integration of deterrent effects in model simulations Areas for future research 2

3 I. Definition and motivation I. Definition of deterrent effects The interventions of effects: competition authorities have two main Direct effects = To stop and punish anti-competitive behaviour Deterrent effects = To discourage future anti-competitive behaviour Deterrent effects are affected by: The perceived probability of detection of the anticompetitive behaviour The expected punishment following detection The reputation of the competition authority 3

4 I. Definition and motivation I. Motivation Deterrent effects are important because: Leverage effect Change companies' behaviour in the longer term Important for competition authorities to be able to measure the deterrent effects of their interventions: To have a clearer picture of the total (direct + deterrent) effects of their interventions To better illustrate the benefits of competition policy To better target interventions 4

5 II. II. Measurement Measurement of deterrent effects Deterrent effects can be measured as: The number of deterred mergers/cartels per detected merger/cartel The deterred merger/cartel harm in comparison with the detected merger/cartel harm Benefits from deterrence cannot be easily derived from the direct effects of competition authorities' interventions: Direct and deterrent effects can be negatively correlated Sample selection bias 5

6 II. Measurement 1. Direct and deterrent effects can be negatively correlated: Strong merger enforcement discourages anticompetitive merger formation, reducing the number of decisions involving remedies and limiting the measured direct effects of merger control Similarly, an effective cartel regime may lower the number of cartels detected since more cartels are deterred But : Higher detection rates may lead to greater deterrence in the future 6

7 2. Sample selection bias The observed sample of cartels detected and of mergers notified is unlikely to be representative of the total population of cartels and mergers: II. Measurement Cartel control seems to be more effective in deterring cartels with very low and very high overcharges Mergers with clear anti-competitive effects are more likely to be discouraged by merger control 7

8 III. Measurement Undetected (1-σ) 2. Sample selection bias Population of cases Undeterred (1-ω) Deterred (ω ) Detected (σ) ω = probability that a case is deterred σ = probability that a case is detected If N = total population of cases (mergers/cartels), the number of detected cases equals: N D = (1-ω)σN. If the sample of detected cases is representative of the total population of cases, the sample harm can be calculated as follows: H D = (1- ω)σh, with H being the total potential harm created by cartels (H). The deterred harm can then be calculated as: H DR =ωh =ωh D /(1- ω)σ. If the sample is not representative of the total population, this simple multiplier approach cannot be used and we need assumptions regarding the distribution of harm in the total population of cases. 8

9 II. Measurement II. Measurement of deterrent effects More literature on the deterrent effects of cartel policy in comparison with merger control But the deterrent effects of merger control could be less a concern than those of cartel policy Why? Undeterred and undetected cartels can cause a lot of damage Non-deterred anti-competitive mergers need to be notified anyway 9

10 II. Measurement II. Measurement of deterrent effects 4 main methods: 1. Surveys 2. Comparison across countries and over time 3. Statistical models 4. Theoretical models 10

11 III. Measurement 1. Surveys Multiplicative factor based on surveys and interviews For 1 merger blocked or remedied 2-8 mergers abandoned or modified For 1 cartel detected 5-28 cartels deterred Limitations: Surveys and interviews can be unreliable and imprecise Sample selection bias 11

12 III. Measurement 2. Comparison across countries and over time Clarke and Evenett (2003): International vitamin cartels increased their prices more in jurisdictions without active anti-cartel enforcement Baker (2003) : Many US industries exercise market power during period of lax antitrust enforcement 12

13 III. Measurement 3. Statistical models Davies and Ormosi (2014): Use available information on detected cartels or notified mergers to make inferences about the total population of cartels and mergers and the distribution of potential harm caused by these cartels and mergers Calibrate the distribution of cartel harm on the basis of a comparison of overcharges associated with legal and illegal cartels 13

14 III. Measurement 3. Statistical models Share of total cartel harm removed (in %) Mean Less effective competition authority More effective competition authority Total harm Detected Deterred Undetected Source: Davies and Ormosi (2014) 14

15 II. Measurement 4. Theoretical models Define models of cartel behaviour and assess the impact of cartel policy enforcement Katsoulacos, Motchenkova and Ulph (2016): Develop a model of birth and death of cartels in the presence of three types of interventions by competition authorities: detection and prosecution elimination of cartels in the short term prevention of re-emergence of prosecuted cartels and use the model to compare the effectiveness of different interventions 15

16 II. Measurement 4. Results Total and deterred harm in comparison with detected harm Cartels Davies and Ormosi Katsoulacos et al. Surveys (Number) Detected harm Deterred harm Total harm na Fraction of cartel harm removed by the interventions of competition authorities ranges between: 55% and 87% for Davies and Ormosi 16% and 84% for Katsoulacos et al. obtained for a wider range of parameter values 16

17 II. Measurement 4. Results There are still large uncertainties regarding the magnitude of the deterrent effects Some converging conclusions: The harm detected by competition authorities accounts for only a small proportion of total potential harm The deterrent effects of competition authorities' interventions are larger than their direct effects It would be misleading to judge the success of a competition authority by its detection rate 17

18 III. Macro-modelling III. Macro-modelling of the impact of competition policy 1. Analytical framework 18

19 III. Macro-modelling The model 2 region open economy DSGE model 2 types of households low skilled, liquidity constrained high-skilled, non-constrained Product market: monopolistic competition with firms charging a mark-up over marginal costs Monetary and fiscal authority Forward-looking utility and profit maximization 19

20 III. Macro-modelling Novelties Measure the strength of EU competition policy by the number and importance of key merger and cartel decisions taken by the European Commission Reinforce the micro-foundations of a macroeconomic assessment of EU competition policy by making a link between the strength of competition policy and mark-up shocks applied to a DSGE model Propose a method to integrate the deterrent effects of competition policy 20

21 2. Integration of the deterrent effects of competition policy decisions Inter-temporal deterrence: the competition authority is supposed to exercise its authority forever Sectoral deterrence: Deterrent effects extend from affected market to subsector defined at the NACE 4-digit level: - Mergers are more likely to be abandoned or modified following a recent inquiry in the sector - Cartel detection in a market decreases the rate of cartel formation in related markets Literature review used to define upper threshold on the ratio between deterred harm and detected harm (H DR /H D ): - 15 for merger decisions - 30 for cartel decisions III. Macro-modelling 21

22 III. Macro-modelling 3. Mark-up shocks applied to the macro model Database of EU merger and cartel decisions provides information on overcharges, their duration and the size of the affected market in 2012, 2013 and 2014 allows calculation of direct mark-up shock in 2014 Information on size of direct and deterrent effects is used to calculate a total mark-up decline of 0.8 pp, i.e. a reduction of the mark-up from 12.7% to 11.9% Size of shock is similar to that used in studies aimed at assessing the impact of competition friendly structural reforms in the EU (1.5 pp) 22

23 III. Macro-modelling 4. Results Percentage point change after five years Percentage point change after fifty years GDP deflator Consumption Investment Labour productivity Employment GDP

24 III. Macro-modelling Sensitivity of GDP impact to the deterrent effects Scenario Sector spill-over Merger deterrence threshold Cartel deterrence threshold GDP effect after five years Baseline Yes 15x 30x +0.40% Lower bound deterrence threshold Upper bound deterrence threshold Yes 5x 10x +0.13% Yes 30x 30x +0.45% 24

25 IV. Future research IV. Open questions Areas for future research Total harm avoided by competition authorities and its decomposition into detected, deterred and undetected harm 1. How can observable information about detected and investigated cartels (such as their frequency, duration and estimated overcharges) be used to make inferences about the population of potential cartels and the total harm created by cartels? 2. How can observable information about merger proposals submitted to competition authorities (number of submissions, market shares of parties involved, etc.) be used to make inferences about the number deterred mergers and the total harm avoided as a result of merger control? 3. How to reduce the range of estimates obtained for the deterrence effects? 25

26 IV. Future research IV. Open questions (continued) Areas for future research Integration of the deterrent effects in macro-modelling: 4. How to give empirical validity to the main assumptions used to describe deterrence in the model (intertemporal deterrence and sectoral spill-overs)? 5. How can responses to the above questions be used to assess the macro-impact of competition policy? 26