Introduction
Cartels
(Draft)
Chi Leng, Ph.D.
David C. Sharp, Ph.D.
Nathan Associates Inc.
October 2010
General Types of Antitrust Analyses
• Economic antitrust analysis can be broken
into two broad categories
• Exclusion - firms attempt to raise prices by
excluding rivals
• Exclusive dealing
• Tying contracts
• Collusion (broadly defined) – firms attempt
to raise prices through collaboration with
rivals
• Horizontal mergers
• Price fixing
1
Price Fixing
• Agreements between business rivals to sell
(or buy) the same product or service at the
same artificially elevated (or depressed)
price
• Considered by many to be the most central
element of competition economics
• Regarded with approval even by those generally
skeptical of government competition policy*
• Price fixing is perhaps the most common
form of collusion
* Whinston, M.D. (2006). Lectures on Antitrust Economics, p. 15.
2
Theory of Collusion - Competitive Market
P
CONSUMER
SURPLUS
PCOMP
MC = ATC
D
QCOMP
Q
3
Theory of Collusion - Collusive Market
P
CONSUMER
SURPLUS
PCOLL
CARTEL
PROFITS
PCOMP
MC = ATC
D
DEADWEIGHT LOSS
QCOLL
QCOMP
Q
4
Theory of Collusion
• The basic theory reveals a strong incentive
(i.e., profits) for firms to jointly reduce output
and increase price
• But basic theory also shows a strong
incentive for individual firms to cheat on the
cartel
• Cheater produces extra output or lowers its price
• Common “real world” defense in antitrust litigation that
actually reconciles with theory
• Incentive to cheat best illustrated with a “Prisoners’
Dilemma” (duopoly) example
5
Prisoners Dilemma: Airlines Example
• American Airlines and United Airlines*
• The two compete for customers on flights
between Chicago and Los Angeles
* From Perloff, J.M. (2004). Microeconomics, 3rd Edition, p, 427
6
Prisoners Dilemma: Airlines Example
Nash
Equilibrium
Cartel
Equilibrium
7
Classic Conditions for Cartel Success
•
•
•
•
•
Concentrated industry (few firms)
High barriers to entry
Homogeneous product
Inelastic demand
No monopsony power (many small
buyers)
• Mechanism to monitor cartel
• Industry organization
• Gives pretext for meetings
• Detects cheating
8
Duration of Cartels
• Many cartel scholars use duration to
measure cartel success, but recognize it is a
highly imperfect measure
• Results from the literature are mixed, due to
different samples (i.e., types of cartels and
time periods) observed
• But two relatively recent studies calculate
the average duration at 5.4 years
• Gallo, J.C., Craycraft, J.L., Dau-Schmidt, K. and Parker, C.A. (2000).
Department of Justice Antitrust Enforcement, 1955-1997: An Empirical Study.
Review of Industrial Organization, 17(1).
• Levenstein, M.C. and Suslow, V.Y. (2004). International Cartels: Then and Now.
Working Paper presented to the NBER Development of the American Economy
Summer Institute.
9
Duration of Cartels
Distribution of Duration for
Contemporary International Cartels
10
9
Number of Cartels
8
7
6
Mean = 5.4
5
4
3
2
1
0
1
2
3
4
5
6
7
8
9
11
13
17
20
23
Years Duration
From Levenstein, M.C. and Suslow, V.Y. (2004). International Cartels: Then and Now. Working Paper
presented to the NBER Development of the American Economy Summer Institute.
10
What Else Does the Literature Say?
• Cartels are more likely to form in industries
where prices have been falling
• Stigler (1964)* argued that cartels are
fundamentally unstable: firms agree to restrict
output, then engage in secret cheating that
erupts in price wars
• Modern studies indicate that cartels break up
occasionally because of cheating, but biggest
challenges are entry and responding to
changing economic conditions**
* Stigler, G. (1964). A Theory of Oligopoly. Journal of Political Economy, 72(1).
** Levenstein, M.C. and Suslow, V.Y. (2006). What Determines Cartel Success? Journal of Economic
Literature, 44.
11
How Do We Measure Compensatory Damages?
• Damage methodologies compare the
prices paid during the period of alleged
wrongdoing with the “but for” price
• The “but for” price is the price that would
have prevailed in the absence of (i.e., but
for) the cartel
• Two general approaches
• Utilizing benchmarks
• Econometrics
12
Benchmarks
• “Before-during-after” approach
• Examine product price before, during, and after cartel period;
difference between the cartel price and the competitive price
measures the damage
• May meet objection that changes in factors other than the cartel may
have produced price changes during cartel period
• “Yardstick” approach
• Examine price movements of a comparable product, unaffected by the
cartel, and compare to price of the cartelized product to determine
damages
• Variant examines production costs, and “cost plus” pricing determines
what the price would have been absent the cartel
• “Geographic area” approach
• Examine product price from a region or part of the world where the
cartel did not occur. Prices in the affected and unaffected areas are
compared to estimate damages
13
Benchmarks
• Benchmarks may be fine (or necessary
due to data constraints) in some cases
• But they may fail to account for other
systematic factors (other than the
cartel) that may have influenced price
during the cartel period
• We need an approach that allows us to
account for all relevant factors
14
Econometrics (Multiple Regression)
• Econometrics is the application of statistical
methods to economic data
• The basic idea underlying econometrics is to
build a model that accurately describes the
“real world,” in equation form
• It is a technique that allows us to account for
any factor (variable) thought to be potentially
relevant, and have its actual influence
examined
15
Econometrics (Multiple Regression)
• Each variable’s impact is disentangled from
all others, allowing us to measure the
isolated influence of each
• In price-fixing, the allegation is that cartel
members conspired to impact the price
• With econometrics, we can measure the
cartel’s impact on price, alone, net of all
other influences
16
Case Study: Graphite Products*
• Graphite is an intermediate product
used in diverse downstream industries
•
•
•
•
•
Chemicals
Glass
Aerospace
Metallurgy
Semiconductors
* While there were allegations of price fixing in various graphite product markets, the discussion below is
entirely hypothetical for the purposes of this case study.
17
Case Study: Graphite Products*
• Three U.S. suppliers of graphite
products:
• Company A
• Company B
• Company C
• Each company had 25% of the market
• Imports from China, India and other
countries represented the other 25%
* While there were allegations of price fixing in various graphite product markets, the discussion below is
entirely hypothetical for the purposes of this case study.
18
Case Study: Graphite Products*
• Inputs to graphite production
•
•
•
•
Petroleum coke
Natural gas
Electricity
Labor
* While there were allegations of price fixing in various graphite product markets, the discussion below is
entirely hypothetical for the purposes of this case study.
19
Graphite Case Study:
Price Fixing Data
$20
Competitive Period
Conspiracy Period
$18
$16
$14
$12
$10
$8
Cartel members claim
this price increase is
attributable to rising
costs
$6
$4
$2
Enforcement authority
alleges rising costs
were a pretext for
additional conspiratorial
price hikes
Jan-99
Oct-98
Jul-98
Apr-98
Jan-98
Oct-97
Jul-97
Apr-97
Jan-97
Oct-96
Jul-96
Apr-96
Jan-96
Oct-95
Jul-95
Apr-95
Jan-95
Oct-94
Jul-94
Apr-94
Jan-94
Oct-93
Jul-93
Apr-93
$0
Actual Price
Let’s use econometrics to disentangle supply, demand, and cartel effects
20
Graphite Case Study:
Binary Model Equation
• General form:
price = (supply, demand, conspiracy)
Coefficients:
• Linear form:
Measure the impact of the
explanatory variables on the
dependent variable
price = α + β1(supply) + β2(demand) + β3(cartel)
Dependent
Variable:
The variable to
be explained
Intercept:
Reveals the
value of the
dependent
variable when
all explanatory
variables take
on a 0 value
Explanatory Variables:
Aid in explaining the dependent variable
“Cartel” is a binary variable. It takes on a value of 1
during the cartel period and a value of 0 during the
competitive period
21
Graphite Case Study:
Ordinary Least Squares (OLS) Estimation
• Ordinary Least Squares (OLS) estimates
values of α and the βs
• Not a new tool, going back in its origins to Carl
Friedrich Gauss (1777-1855)
• OLS also provides tests of statistical
significance (T-stats, F-stat) and
goodness of fit measures (Adjusted R2)
• OLS estimation can be done with a
variety of software, such as SAS® and
Stata® (and even Excel®)
22
Graphite Case Study
OLS Estimation of the Binary Model
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.9755
R Square
0.9515
Adjusted R Square
0.9494
Standard Error
0.6368
Observations
72
Adj R2: Approximately
95% of the variation in
price is explained by the
three explanatory variables
ANOVA
df
Regression
Residual
Total
Intercept
Supply
Demand
Cartel
SS
541.3121
27.5769
568.8890
MS
180.4374
0.4055
F
Significance F
444.9284
0.0000
Coefficients Standard Error
19.6984
α -56.0090
1.7421
0.0884
β1
β2
4.3440
1.5627
β3
2.0354
0.3271
t Stat
-2.8433
19.6994
2.7799
6.2232
P-value
0.0059
0.0000
0.0070
0.0000
3
68
71
T stats: Easy rule of
thumb… if > |2|, it is
statistically significant
Lower 95%
Upper 95% Lower 95.0% Upper 95.0%
-95.3166
-16.7014
-95.3166
-16.7014
1.5656
1.9185
1.5656
1.9185
1.2258
7.4622
1.2258
7.4622
1.3828
2.6881
1.3828
2.6881
price = -56.01 + 1.74(supply) + 4.34(demand) + 2.04(cartel)
23
Graphite Case Study
OLS Predictions & Inference with the Binary Model
• Let’s see how well it predicts prices for, say, April 1993 (i.e., the
beginning)
• In April 1993, supply (costs) = $5.53, demand (index) = 12.51, & cartel=
0
price = -56.01 + 1.74(supply) + 4.34(demand) + 2.04(cartel)
price = -56.01 + 1.74(5.53) + 4.34(12.51) + 2.04(0)
price = -56.01 + 9.62 + 54.29
price = $7.90
• Actual April 1993 price = $7.94
• Error = predicted – actual = -$0.04
• Direct interpretation of the cartel’s impact, in isolation
• Average overcharge during the cartel period = $2.04 per unit
• Damages = the quantity sold (Q) multiplied by the average
overcharge
24
A Variant: Forecast Model
• With the forecast method we estimate the
coefficients using data during the
competitive period only; there is no cartel
binary variable
• Use the estimated coefficients above with
values for the explanatory variables during
the cartel period to predict what prices
would have been, but for the cartel
25
Forecast Data
$20
Estimation Period
Forecast Period
$18
$16
$14
$12
$10
$8
Estimate the equation
using prices and
explanatory variables
during this period only
$6
$4
$2
Use that equation to
forecast price during this
period (i.e., the cartel
period)
Jan-99
Oct-98
Jul-98
Apr-98
Jan-98
Oct-97
Jul-97
Apr-97
Jan-97
Oct-96
Jul-96
Apr-96
Jan-96
Oct-95
Jul-95
Apr-95
Jan-95
Oct-94
Jul-94
Apr-94
Jan-94
Oct-93
Jul-93
Apr-93
$0
Actual Price
26
Forecast: Predicted and Actual
$20
Estimation Period
Forecast Period
$18
$16
$14
$12
$10
$8
The difference
between actual
and predicted is
damages per
unit
$6
$4
$2
Actual Price
Jan-99
Oct-98
Jul-98
Apr-98
Jan-98
Oct-97
Jul-97
Apr-97
Jan-97
Oct-96
Jul-96
Apr-96
Jan-96
Oct-95
Jul-95
Apr-95
Jan-95
Oct-94
Jul-94
Apr-94
Jan-94
Oct-93
Jul-93
Apr-93
$0
Predicted Price
27
Binary Model
• Advantage:
• Direct estimation of the average
overcharge across the cartel period
• Disadvantage:
• No pretty picture; it does not provide a
clear graphical comparison of the actual
and “but for” price
28
Forecast Model
• Advantage:
• Provides a clear graphical comparison of the
actual and “but for” price
• Disadvantages:
• Damages calculated month-by-month (no big
deal, really)
• Literature suggests that forecast models tend
to produce large confidence intervals*
* Rubinfeld, D.L. (1985). Econometrics in the Courtroom. Columbia Law Review, 85(5), pp.
1048-1097.
29
Conclusion
• Economic analysis is an integral part of
competition enforcement
• Econometric analysis has also become
prevalent, particularly in price-fixing
cases
30
Conclusion
Cartels
(Draft)
Chi Leng, Ph.D.
David C. Sharp, Ph.D.
Nathan Associates Inc.
October 2010