John M. ConnorPurdue University, West Lafayette, Indiana jconnor@purdue.edu Douglas J. MillerUniversity of Missouri, Columbia, Missouri millerdou@missouri.edu

ObjectivePrimary: To estimate the determinants of fines imposed on companies by the EC for hard core global price-fixing.

To test whether the optimal-deterrence theory of crime has predictive validity.

To gather other evidence of the effectiveness of the EU’s anti-cartel enforcement.

MotivationsDG-COMP is an exemplar for many newer

antitrust authoritiesAfter 40 years, time for a retrospective

economic analysis of EC enforcementCritics of the EC’s cartel fining practices:

1. Assert that sentencing is idiosyncratic 2. Question predictability, transparency, and

proportionality.

Rates of Discovery by the European Commission Rising

04/10/23 4J M Connor, Purdue U.

Rates of Discovery of Global Cartels Peaked in 2000-2004

1.6

“Global” cartels affect prices in two or more continents

04/10/23 5J M Connor, Purdue U.

Optimal Deterrence ModelThe first order conditions for an optimally

deterring EC fine, ECF* is:

ECF* = (HARM/p) – OTHPEN, where

HARM is the antitrust injuries caused,p is the probability of detection & conviction,OTHPEN is all other penalties known or

expected at the time of the EC decision.

Previous StudiesCohen (1996) first to examine econometrically the

size of U.S. corporate criminal penalties (median $10K); sample of 961 companies during 1984 -1990, but only 8% antitrust; a subsample of 285 observations has an estimate of harm, but none were antitrust decisions.

Connor and Miller (2009) analyze 108 U.S. DOJ corporate fines for global price fixing for the years 1995 to 2008. This study is the immediate antecedent of the present paper: same data source, similar methods, and tests the same hypotheses suggested by optimal deterrence theory.  Interesting comparative findings.

Data Source: the PICs SetComprises 192 companies fined for cartel

violations 1/1990 to 1/2009. Mean affected sales US$3.2 billion in EUAll global cartels, mean global sales US$7.9

billionMean EC fine US$33 million, but highly skewed8% were big riggers41% in chemicals, 26% in ocean shipping

conferences6 received extra penalties for EU recidivism, but

73% became recidivists somewhere by 12/2008

Augmented Behavioral ModelLn(ECF) = α + β∙Ln(HARM) + γ∙(1/p) +

δ1∙OTHPEN + δ2∙OTHPEN2 + λ∙CONTROLS + ε.

Proxy for HARM is affected sales in EU. ASEU is positively correlated with HARMEU.

1/p is a vector of 8 proxies for detection & conviction.

OTHPEN is non-EC fines and settlements.A vector of control variables for time trends, new

EC fining policies, firm’s HQ,, and 7 industries.

HypothesesOptimal deterrence principles imply:

1. HARM should be positively related to penalties

2. Factors that raise p are negative (e.g., AMNESTY, LENIENCY 1 & 2 ), and vice-versa (N, BIDRIG, etc.)

3. OTHPEN ought to be negative

Controls include trend (+), Monti (+/-), Kroes

(+/-), GUIDELINES 1 & 2 (+), DURATION (+), CAP (-), NO AM and ASIA (+/-), and 7 industry dummies.

Methods and AdjustmentsWe had missing data (recoded as zero) for a few

variables. After initially estimating the model, Ramsey's RESET procedure found evidence of specification errors.

Because 7% of ECF censored, ran OLS and ML TOBIT

All monetary values were highly skewed, so we transformed ECF, HARM to natural logs.

Dropped 13 variables due to very low significance

White’s Test did not reject homoskedasticity

Estimation Results 1

OLS and ML Tobit have similar fit, signs, and significance of coefficients.

OLS R2 = 68.1% is very satisfactory.Fit is three times better than Cohen study

and about the same as our US fine regressions.

Estimation Results 2

Elasticity of ECF wrt HARM is ε = +0.27

Optimal deterrence ex post requires ε = 1.0By comparison:

1. Cohen (1996) estimates ε = +0.43 2. Connor and Miller (2009) estimate ε = +0.37.

Estimation Results 3All five detection-related variables have

correct signs:1. BIDRIG raises ECF by 87%, ceteris paribus

2. EU RECID typically raises fines 135% per prior instance

3. Non-EU recidivism typically raises fines 18%

(Above 3 highly significant, 2 below not quite)

1. AMNESTY2 lowered fines for non-amnestied firms 20%

2. No. of cartelists N has a negative sign

OTHPEN describes an upward-bending parabola, negative only if above $600 million (like US)

Estimation Results 4TIME adds 8.4% per year to expected ECF.Highly significant, GUIDELINES2 adds 107%.MONTI regime was 110% below trend.KROES regime (with TIME > 15 years) is on trend.Van Miert and earlier Commissioners is reference group.North American cartelists pay 62% more than EU firms.The 5 remaining industry dummies are strongly positive,

with METALS, ORGCHEM, GRAPHITE especially large.Reference group is shipping and misc. manufacturing .Chow test shows shipping fines same as rest of sample.

Policy DiscussionOptimal deterrence model predicts well for global

cartel fines by US DOJ and Eur. Commission.Emerging convergence: ε = +0.3 to +0.4

(suboptimal)EC ignores non-EU penalties, unless VERY largeRecidivism anywhere increases severity of ECFsTime controls show EC becoming more aggressive*Firms in cartel-prone industries will be hit hard*Bid rigging is an aggravating factor* Does NO AM result challenge proportionality?* * Unmentioned in EC Fining Guidelines

Sources Connor, John M. Cartels and Antitrust Portrayed: Detection: SSRN

Working Paper (March 2009). [http://ssrn.com/abstract=1372866 ] Connor, John M. and Douglas J. Miller. Determinants of EC

Antitrust Fines for Members of Global Cartels, paper at the 3rd Conference on “The Economics of Competition Law,” sponsored by LEAR, Rome, June 25-26, 2009.

Connor, John M. and Douglas J. Miller. Determinants of U.S. Antitrust Fines of Corporate Participants of Global Cartels, paper presented at the 7th International Industrial Organization Conference, Boston, April 3-5, 2009.

Cohen, Mark A. Theories of Punishment and Empirical Trends in Corporate Criminal Sanctions. Managerial and Decision Economics 17 (1996): 399-411.