1. University of Missouri at Columbia
Determinants of U.S. Antitrust Fines of Corporate Participants of Global Cartels
John M. Connor
Purdue University
and 
Douglas J. Miller
University of Missouri at Columbia

2. Motivation
DOJ sentencing of antitrust violators is not transparent (esp. full & partial Leniency)
How well fines conform to optimal deterrence theory of crime is not known
DOJ officials say that fines are idiosyncratic, i.e., not predictable
Only one empirical study of variation in U.S. criminal fines, but excludes antitrust violators
J Connor & D MIller IIOC7

3. Importance of Topic
Becker’s theory of crime has received little empirical confirmation
The DOJ’s anti-cartel program is widely admired and imitated, so quantitative assessments have policy import
If prediction of fines is impossible, then deterrence impossible because would-be criminals need to make fine conjectures
J Connor & D MIller IIOC7

4. Optimal Deterrence Theory
In a nutshell, the optimal criminal fine is:
USF* =HARM/p - Other Penalties,
where HARM is the monetary injuries imposed on victims (or expected gain from the crime), p is the probability of detection and prosecution, and other penalties include expected future fines or civil settlements and the monetized penalties on individuals.
J Connor & D MIller IIOC7

5. (Subjective) p Difficult to Measure
Proxies we developed include:
Cartels facing many buyers implies p is high
Large cartel membership (N) increases p
Asymmetry among members may decrease p
BID RIGGING harder to detect? (decreases p)
GOVT. main buyer may decrease p
Long lasting PROBE a sign that defendants covered up (decreases p)
J Connor & D MIller IIOC7

6. Other Penalties
Optimal Deterrence theory predicts that other penalties are substitutes for U.S. fines (USF), to the extent that DOJ knows/expects them
OTHPEN= NonUSF + PVT settlements
EXECS = number of executives of this defendant that were penalized
J Connor & D MIller IIOC7

7. CONTROLS: Other Determinants from Laws, Guidelines, or DOJ Practices
Two industry dummies to represent demand elasticities, entry barriers, or collusion history
Time T because data covers 2 administrations
Cartel DURATION to correct for lower fines due to curtailment of collusive period
“Nationality” of firms to check proportionality
J Connor & D MIller IIOC7

8. Data Sample
118 companies penalized by the DOJ for participation in 30 global cartels from 1990 to Dec. 2008
All 118 convicted by guilty plea agreements
Half during Clinton, half during Bush II administration
Excludes about 30 Corporate Amnesty Program recipients
Excludes 206 apparently indictable cartelists penalized elsewhere (worthy of study in itself)
J Connor & D MIller IIOC7

9. General Behavioral Model
USF = α + β∙(HARM) + γ∙(1/p) δ∙OTHPEN + λ∙CONTROLS + ε.
J Connor & D MIller IIOC7

10. Model specification LEADER (asymmetry) insignificant, dropped
BIDRIG, GOVTBUYS, and SERVICE industry were nearly coincident, we kept BIDRIG
Controls ASIA and EUR were insignificant, so geographic origin had no explanatory power
Ramsey’s RESET procedure indicated missing nonlinearities; we substituted LN(USF) for USF and OTHPER + OTHPEN2 for OTHPEN
J Connor & D MIller IIOC7

11. Specific Regression Model
After noting large skewness in USF and HARM, dropping very weak explanatory variable, and finding the source of nonlinearities, our final specification is:
LN(USF) = α + β∙LN(HARM) + γ∙(1/p) δ1∙OTHPEN + d2∙OTHPEN ζ∙EXECS + λ∙CONTROLS + ε.
J Connor & D MIller IIOC7

12. General Estimation Results
The OLS model explains 71% of var. in LN(USF)
Because USF=0 for 6 obs., we also ran a Maximum Likelihood (ML) Tobit model, but OLS and ML Tobit were nearly identical.
Significant degree of collinearity indicated
White’s test for heteroskedasticity did not reject the H0 of homoskedasticity at 10%.
Ramsey’s RESET test procedure showed no misspecification/nonlinearities present
J Connor & D MIller IIOC7

13. OLS Estimation Results for LN(USF)
J Connor & D MIller IIOC7

14. Discussion ∂USF/∂HARM = 0.36, an elasticity <1 =>
suboptimal deterrence (unless OTHPEN high)
Deterrence proxies results are mixed
OTHPEN > $6.3 million => substitution effect for USF for 62% of observations
In a model not shown, with T and BUSH=1, BUSH fines are sig. and -4.9% lower
J Connor & D MIller IIOC7

15. Conclusions Sizes of global cartel fines are predictable
DOJ follows optimal deterrence principles wrt HARM, but adjusting fines to reflect the difficulty of detection is spotty.
When other anticipated corporate penalties are large, then U.S. fines are substitutes
EXECS is surprisingly complementary to USF
Bush DOJ was slightly lax in cartel fines
J Connor & D MIller IIOC7