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Three Types of Inefficiency in Strategic Offers: Empirical Identification from Merger Remedy

Settlements

Luke Garrod, Bruce Lyons and Andrei Medvedev

The institutions of EC remedy agreement provide a natural experiment to test theory of strategic offers

INTRODUCTION

If both parties are rational and have complete information, mutually beneficial agreement will…

• definitely be reached and• be reached immediately

Incomplete information can explain some delay…• screening for other’s type• signalling of own type

Evidence from…• experiments • labour bargaining

EC remedy agreements involve…• bargaining strategy from firms• more passive competition agency

The institutions of EC remedy agreement provide a natural experiment to test theory of strategic offers

INTRODUCTION

Two parties - Competition agency and merging firms

Discrete ‘rounds’ (2-phase investigation)• More information gathered in phase II• Allows early (phase I) or late agreement (phase II)… or no agreement

Legally specified… • Time limits to each phase (i.e. limited evidence gathering)• Order of who can make offers and who can accept/reject• Agency decision must be based on evidence

Three types of error…

• Type 1 – remedy too stringent• Type 2 – remedy insufficient to prevent market power• Type 3 – remedy agreed late

We present a theoretical model based on Lyons and Medvedev (2007) which allows us to predict which types of errors will occur and when

EMPIRICAL PREDICTIONS

Delay to Phase II more likely if:• Complex or imprecise merger appraisal (high σ1)• Delay is relatively less costly to the firms (K/π)

Model does not predict any effect of:• Obvious harm of the merger (αT)

Remedies in phase I:• Too stringent (Type 1 error) if issues are simple and/or delay is costly to firms

• Insufficient (Type 2 error) if issues are complex and/or delay is not costly

Remedies in phase II are:• Late (Type 3 error) if issues are simple and/or delay is costly to firms

We collected data from mergers with remedies agreed for horizontal aspects in phase I and phase II during the period 1999-2006

THE VARIABLES

variable exp sign description

ComplexityNumber of markets low ? with [x-35)% market share

uncertshare + with [35-45]% market sharehigh ? with 45+% market share

Coordinated effects collusion + =1 if coordinated effects considered; 0 otherwise

Transatlantic help EEAUS - =1 if 1 firm from US & 1 from EEA; 0 otherwiseUSonly ? =1 if firms from US; 0 otherwisenonUS base =1 if neither firm from US; 0 otherwise

ExperienceIndustrial indexp1990 - # mergers with 2-digit NACE classification beforeCoordinated effects coordexp1990 - # mergers with coordinated concern before

Cost of delayRemedy how much? concernprc + (# concern markets)/(# markets)

Does size matter?mean market shares s1s2 ? for concern markets

si ? increment for concern markets

phase I phase II

variable obs mean std dev min max obs mean std dev min max

markets 92 12.60 15.41 1 75.0 38 18.03 27.87 1 142.0high barriers % 92 30.80 35.20 0 100.0 38 57.40 39.10 0 100.0low barriers % 92 6.60 18.00 0 92.9 38 4.00 11.20 0 50.0

concern 92 5.44 7.83 1 38.0 38 10.97 20.08 1 113.0high barriers % 92 49.30 45.50 0 100.0 38 66.40 41.30 0 100.0low barriers % 92 0.00 0.00 0 0.0 38 0.00 0.00 0 0.0

Phase II documents tend to be more complete and detailed compared to those produced for phase I decisions. Without the correct attention, this could lead to bias.

POTENTIAL BIAS

The bias in the dataset can be highlighted by the entry barriers variable

Another bias: only cursory analysis for simple markets in phase II documents…

… filter used to eliminate markets not usually discussed in phase II documents

At low levels, the market share filter removes mostly markets that do not cause competition concerns

MARKETS REMOVED

If there is no bias we would expect the market share filter to removed a proportion of (1340/2084=) 0.643 phase I markets from the dataset

PROPORTION REMOVED

Sample size is n = 130… 30 other mergers are not included in the dataset due to lack of data or because they raised predominantly vertical issues

DESCRIPTIVE STATISTICS

Variable Obs Mean Std Dev Min Max

phase2 130 0.292 0.457 0 1 nonus 130 0.754 0.432 0 1 usonly 130 0.162 0.369 0 1 eeaus 130 0.085 0.279 0 1 combrev2006 84 31649 34009 2613 193941

markets 130 14.185 19.907 1 142 prodmark 130 6.485 8.098 1 54 geomark 130 2.419 2.617 1 17

concern 130 7.054 12.859 1 113 coord 130 0.300 1.111 0 9 noncoord 130 6.754 12.865 0 113

collusion 130 0.200 0.402 0 1 coord concern 130 0.115 0.321 0 1

Sample size is n = 130… 30 other mergers are not included in the dataset due to lack of data or because they raised predominantly vertical issues

DESCRIPTIVE STATISTICS

Variable Obs Mean Std Dev Min Max

s1s2 130 60.972 16.743 28.33 100 si 130 18.388 9.555 1 50 sr 122 18.623 11.537 0 60

ns1ns2 103 36.171 9.957 20 70 nsi 103 10.215 4.830 2.5 30 nsr 86 26.275 9.980 5 60

Probit Analysis shows what factors affect the likelihood that a merger will fail to be agreed in phase I

PROBIT ANALYSIS

phase2=1 exp sign

constant -1.2955 *low -0.0282uncertshare + 0.0694 *high 0.0189collusion + 0.8170 *coordexp1990 - -0.0805 *indexp1990 - -0.0568 ***concernprc + 0.0190 ***s1s2 0.0109si -0.0312 *usonly 0.9013eeaus - -1.0439 **

Pseudo R-squared 0.2764

coefficient

Probit Analysis shows what factors affect the likelihood that a merger will fail to be agreed in phase I

FURTHER SPECIFICATION

phase2=1 exp sign

constant -1.6622 **low -0.0395uncertshare + 0.1376 ***high -0.0007collusion + 0.7647coordexp1990 - -0.4770 **indexp1990 - -0.0866 ***coordconprc + 0.1241 ***noncoordprc + 0.0212 ***s1s2 0.0193lowsi 0.0111bigsi -0.0491 ***sdbigsi 0.0608 *usonly 1.2751 *eeaus - -1.5107 ***

Pseudo R-squared 0.3956

coefficient

Probit Analysis shows what factors affect the likelihood that a merger will fail to be agreed in phase I

MARGINAL EFFECTS

phase2=1 exp sign marginal effects

constant -1.3952 *low -0.0268uncertshare + 0.0801 ** 0.0235high 0.0030collusion† + 0.8438 * 0.2865coordexp1990 - -0.0803 * -0.0236indexp1990 - -0.0621 *** -0.0182concernprc + 0.0205 *** 0.0060s1s2 0.0103lowsi -0.0035bigsi -0.0384 ** -0.0113sdbigsi 0.0555 * 0.0163usonly 0.9022eeaus† - -1.1205 ** -0.2313

Pseudo R-squared 0.2960

coefficient

Our results are not dependent upon the level of the market share filter, as the results are relatively robust for a number of different filters (including 0%)

SENSITIVITY ANALYSIS

phase2=1 exp sign 15% 20% 25%

constant -1.1147 -1.2459 * -1.3952 * -1.3953 *low -0.0099 -0.0226 -0.0268 -0.0491uncertshare + 0.0655 * 0.0804 * 0.0801 ** 0.0860 **high 0.0061 0.0031 0.0030 0.0037collusion + 0.8441 * 0.9087 ** 0.8438 * 0.8761 **coordexp1990 - -0.0845 * -0.0888 * -0.0803 * -0.0772 *indexp1990 - -0.0630 *** -0.0636 *** -0.0621 *** -0.0611 ***concernprc + 0.0215 *** 0.0220 *** 0.0205 *** 0.0186 ***s1s2 0.0086 0.0086 0.0103 0.0109lowsi -0.0132 -0.0139 -0.0035 -0.0004bigsi -0.0412 ** -0.0396 ** -0.0384 ** -0.0389 **sdbigsi 0.0532 0.0553 * 0.0555 * 0.0584 *usonly 0.8196 0.8839 0.9022 0.8914eeaus - -1.1252 ** -1.1490 ** -1.1205 ** -1.0457 **

Pseudo R-squared 0.3093 0.3159 0.2960 0.2868

0%

The theory suggests Type 1 errors are likely occur when probability of phase II is low and Type 2 are likely to occur when probability of phase II is high

TYPE 1 AND 2 ERRORS

All phase II mergers are Type 3 errors but those with a low probability of phase II are likely to be explained by poor strategic play as opposed to complexity and delay cost

TYPE 3 ERRORS

We present a theoretical model based on Lyons and Medvedev (2007) which allows us to predict which types of errors will occur and when

CONCLUSIONS

Delay in reaching agreement arises when: • competition issues are complex and • delay is costly to the firms

Theory suggests:Remedies in phase I:

• Too stringent (Type 1 error) if issues are simple and/or delay is costly to firms• Insufficient (Type 2 error) if issues are complex and/or delay is not costly

Remedies in phase II are:• Late (Type 3 error) if issues are simple and/or delay is costly to firms

Empirics back up the theory and suggest:• Type 1 errors are more common than Type 2 errors• Type 3 errors also occur from poor strategic actions from players

Here are the 'top five' mergers for each error type that, according to our analysis, are most likely to be errors

TOP FIVES

Merger Predicted probability of phase 2

TYPE 1 M.3558 CYTEC / UCB - SURFACE SPECIALTIES 6.14E-07M.3593 APOLLO / BAKELITE 0.0000576M.3544 BAYER HEALTHCARE / ROCHE (OTC BUSINESS) 0.0002739M.2854 RAG / DEGUSSA 0.0016818M.1932 BASF / AMERICAN CYANAMID (AHP) 0.0031749

TYPE 2 M.1795 VODAFONE AIRTOUCH / MANNESMANN 0.7607729M.3770 LUFTHANSA / SWISS 0.6541686M.1571 NEW HOLLAND / CASE 0.6272017M.3235 TEIJIN / ZEON / JV 0.6139716M.3225 ALCAN / PECHINEY (II) 0.5641928

TYPE 3 M.3916 T-MOBILE AUSTRIA / TELE.RING 0.0301744M.2060 BOSCH / REXROTH 0.1079204M.1673 VEBA / VIAG 0.1168239M.2972 DSM / ROCHE VITAMINS 0.1559581M.1845 AOL / TIME WARNER 0.1822359