Transcript The Role of Econometric Analysis in Antitrust

Regression Analysis
& Market Delineation
Luke M. Froeb
Vanderbilt University &
ERSGroup.com
26 March, 2008 8:45-11:45am
Antitrust Economics & Econometrics
ABA Spring Meetings
Acknowledgements
• Henry McFarland, Economists, Inc.
• David Scheffman, Vanderbilt & LECG
• Gregory Werden, US Dept of Justice
Vanderbilt University
2
Take-away: economists can help,
but only if you understand what
they are doing
• Regression creates “experiments” from nonexperimental data
– What else could have accounted for estimated
effect?
– How well does “experiment” mimic effect we are
trying to isolate?
• Quantitative market delineation requires
careful thought about how to apply monopoly
model
3
Click & Learn Regression
<<pull up program>>
• “But for” regression model.
• Which functional Form?
– How well does it fit?
4
Bid Rigging: Frozen Fish Conspiracy
5
1976 Folding Cartons Conspiracy
• DOJ investigation resulted in indictment of 23
firms
• Difficult to prove “conspiracy” or “meeting of
the minds”
– But ring leader was compulsive note taker
– Testified in exchange for no jail time
• But judge thought outcome was “unfair.”
6
Follow-on Damage Estimation
• Forecast showed big damages
– Shift of intercept AND slope
• Backcast showed negative damages
• What to do?
• <<Click&Learn backcast vs. forecast>>
7
Merger Analysis: Staples-Office Depot
• Prices in two-office-superstore cities estimated to be
7% lower than in one-office-superstore city.
• 15% estimated pass-through (from cost to price)
– 85% reduction in costs to offset merger effect
• Critique:
– Could unobserved costs account for relationship?
– How well does experiment mimic merger effect?
• Did experts “cancel” each other out?
• <<Click&Learn dummy variable regression>>
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Consummated Mergers
• Control Group: Pre-merger period
• Experimental Group: Post-merger period
• Did price increase?
• BIG question:
“Compared to what?”
– “Control” cities hit by same demand and cost shocks
• “Differences-in-Differences” Estimation
– First difference: pre- vs. post-merger
– Second difference: target vs. control cities
(Marathon/Ashland Joint Venture)
• Combination of marketing and refining assets
of two major refiners in Midwest
• First of recent wave of petroleum mergers
– January 1998
• Not Challenged by Antitrust Agencies
• Change in concentration from combination of
assets less than subsequent mergers that were
modified by FTC
Merger Retrospective (cont.):
Marathon/Ashland Joint Venture
• Examine pricing in a region with a large change in
concentration
– Change in HHI of about 800, to 2260
• Isolated region
– uses Reformulated Gas
– Difficulty of arbitrage makes price effect possible
• Prices did NOT increase relative to other regions
using similar type of gasoline
Difference Between Louisville's Retail Price and Control Cities' Retail Price
25.00
Merger Date
20.00
15.00
10.00
-10.00
-15.00
-20.00
-25.00
Week
Chicago
Houston
Virginia
11/1/1999
9/1/1999
7/1/1999
5/1/1999
3/1/1999
1/1/1999
11/1/1998
9/1/1998
7/1/1998
5/1/1998
3/1/1998
1/1/1998
11/1/1997
9/1/1997
7/1/1997
5/1/1997
-5.00
3/1/1997
0.00
1/1/1997
Cents
5.00
BIG Policy Question
• What are ex-ante incentives created by expost enforcement?
– Enforcement vs. regulation?
• Type I error (over-deterrence): don’t raise
price, even if costs increase
• Type II error (under-deterrence): wait 2 years
and then raise price
Will your merger be challenged?
• Rule of thumb
– Is there a benign or pro-competitive reason for
merger?
– Are customers complaining?
– Will merger lead to price increase?
FTC Merger Challenges,96-03
90
80
Number of Markets
70
60
50
40
30
20
10
0
2 to 1
3 to 2
4 to 3
5 to 4
6 to 5
7 to 6
Significant Com petitors
Enforced
Closed
8+ to 7+
What’s Wrong w/Structural
Presumptions?
• 1. Market delineation draws bright lines even
when there may be none
– No bright line between “in” vs. “out”
• 2. Market Shares may be poor proxies for
competitive positions of firms
– Market shares and concentration may be poor
predictors of merger effects
• HOWEVER:
a market
you still have to delineate
– Rookie mistake to bring a case without one
The Hypothetical Monopolist Test in the U.S.
Horizontal Merger Guidelines
• …group of products and a geographic area
such that a hypothetical profit-maximizing
firm likely would impose at least a “small but
significant and nontransitory” increase in price
– Depends only on demand
– Tests whether merger creates market power
– Not designed to test whether a firm is already
exercising significant market power
(“Dominance”)
17
Quantitative Market Delineation
• Critical Elasticity of Demand Analysis
– Profit-Maximization Calculation
– Breakeven Calculation*
• Critical Sales Loss Analysis
– Profit-Maximization Calculation
– Breakeven Calculation*
-------*covered today
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Critical Elasticity of Demand Analysis
• Breakeven Calculation: The maximum
elasticity of demand a monopolist could face
at pre-merger prices and still not experience a
net reduction in profits from a given price
increase, e.g., 5%
• Depends on demand functional form
– Linear:
1/(m+t)
– Constant elasticity: [log(m+t)-log(m)]/log(1+t)
where m=margin, t=5%
19
Critical Sales Loss Analysis
• Breakeven Calculation: The maximum
reduction a monopolist could experience in its
quantity sold and still not experience a net
reduction in its profits from a given price
increase, e.g., 5% [critical loss=t/(m+t)]
Pre10%
merger
margin
Critical
33%
sales loss
30%
50%
70%
90%
14.2% 9.1%
6.7%
5.2%
20
FTC v. Tenet Health Care Corp.
17 F. Supp. 2d 937 (E.D. Mo. 1998),
rev’d, 186 F.2d 1045 (8th Cir. 1999)
• District court accepted FTC’s contention that
the geographic scope of relevant market was a
50-mile radius around Poplar Bluff, Missouri.
• On appeal, the defendant argued that its
critical loss analysis demonstrated that the
FTC’s market was too narrow.
• Eighth Circuit held that the FTC failed to show
that hospitals outside its alleged market were
not “practical alternatives for many Poplar
Bluff consumers.”
21
US v. Mercy Health Services
902 F. Supp. 968 (N.D. Iowa 1995),
vacated as moot, 107 F.3d 632 (8th Cir. 1997)
• Relying on defendant’s breakeven critical loss
of 8%, the court found sufficient switching
would occur “in the event of a 5% price rise”
“to make the price rise unprofitable.”
• Govt. predicted the total elimination of
managed care discounts—a far larger price
increase, so the court also considered a larger
(albeit not large enough) price increase.
• Court reckoned the critical loss at 20–35%,
although it was actually about 46%.
22
FTC v. Swedish Match
131 F. Supp. 2d 151 (D.D.C. 2001)
• Both experts relied on critical elasticity
analyses, which differed
• Court discussed these analyses in detail, but
found neither expert’s evidence “persuasive.”
• Court applied its own critical loss analysis,
finding that “it cannot be unprofitable for the
hypothetical monopolist to raise price . . .
because the hypothetical monopolist would
lose only a small amount of business.”
23
U.S. v. SunGard Data Sys., Inc.
172 F. Supp. 2d. 172 (D.D.C. 2001)
• Court noted defendants’ contention that
margins > 90% so critical loss was very low.
– Government said nothing about this analysis.
• Court held that the government had failed to
show that the customers who would not
switch in the face of a price increase were
“substantial enough that a hypothetical
monopolist would find it profitable to impose
such an increase in price.”
24
FTC v. Whole Foods
Appeal from the United States District Court
for the District of Columbia, Civ. No. 07-cv-Ol021-PLF
• XX% retail margins XX% critical loss
– Defense expert inferred actual loss from
marketing studies
– FTC expert inferred actual loss from store closing
“experiments”
• If we [close the Wild Oats Store right across the street],
we believe approximately 50% of the volume their
store does will transfer to our store, with the other 50%
migrating to our other competitors (these estimates are
based on our past experience with similar situations).
– Whole Foods website
25
Assessing Price-Cost Margins
• Never simply use whatever the parties call
their margins; rather, get data from which
margins can be computed.
– Get disaggregated revenue and cost data.
– Find out exactly how the data were complied.
• Treat the determination of margins as a
central task of the investigation and anticipate
the parties’ arguments.
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Paradox of High Margins
• A high pre-merger margin implies a low critical
elasticity and critical sales loss
– Does this suggest a broad market?
• In oligopoly models, a high margin implies low
actual demand elasticity and actual sales loss.
– And large merger effects
• Small differences in demand elasticities are
important
– but may be difficult to measure precisely
27
Can Modify Monopoly Model
to Fit Industry Features
• Adjust model to account for:
– Different “types” of consumers;
– monopolist may price discriminate;
– prices may increase non proportionally on
different goods
• Standard formulae presume constant marginal
cost and no avoidable fixed costs, but actual
cost functions may be quite different.
• Profit maximizing monopoly price increase
may be much larger than 5%
28
Oligopoly
Models
• “Mergers Among
Parking Lots,” J.
Econometrics
• Capacity
constraints on
merging lots
attenuate price
effects by more
than constraints
on non-merging
lots amplify them
Bottom Line:
Advantage of Quantitative Analysis
• More persuasive: “Some number beats no
number”
– Models, natural experiments are complements,
not substitutes
• Use models to interpret experiments; and
• Use experiments to inform models
• Clearer mapping from evidence to opinion
– Sharpen focus: tells you what matters and how
much it matters
– Calculation replaces intuition
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