Regression Analysis & Market Delineation Luke M. Froeb Vanderbilt University & ERSGroup.com 26...
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![Page 1: Regression Analysis & Market Delineation Luke M. Froeb Vanderbilt University & ERSGroup.com 26 March, 2008 8:45-11:45am Antitrust Economics & Econometrics.](https://reader035.fdocuments.in/reader035/viewer/2022062407/56649d235503460f949f97c6/html5/thumbnails/1.jpg)
Regression Analysis& Market Delineation
Luke M. FroebVanderbilt University &
ERSGroup.com
26 March, 2008 8:45-11:45amAntitrust Economics & Econometrics
ABA Spring Meetings
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Acknowledgements
• Henry McFarland, Economists, Inc.• David Scheffman, Vanderbilt & LECG• Gregory Werden, US Dept of Justice
Vanderbilt University 2
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Take-away: economists can help, but only if you understand what
they are doing• Regression creates “experiments” from non-
experimental 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
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Click & Learn Regression<<pull up program>>
• “But for” regression model.• Which functional Form?
– How well does it fit?
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Bid Rigging: Frozen Fish Conspiracy
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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.”
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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>>
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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
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(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
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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
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Difference Between Louisville's Retail Price and Control Cities' Retail Price
-25.00
-20.00
-15.00
-10.00
-5.00
0.00
5.00
10.00
15.00
20.00
25.001/
1/19
97
3/1/
1997
5/1/
1997
7/1/
1997
9/1/
1997
11/1
/199
7
1/1/
1998
3/1/
1998
5/1/
1998
7/1/
1998
9/1/
1998
11/1
/199
8
1/1/
1999
3/1/
1999
5/1/
1999
7/1/
1999
9/1/
1999
11/1
/199
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Week
Cen
ts
Chicago Houston Virginia
Merger Date
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BIG Policy Question
• What are ex-ante incentives created by ex-post 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
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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?
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FTC Merger Challenges,96-03FTC Merger Challenges,96-03
0
10
20
30
40
50
60
70
80
90
2 to 1 3 to 2 4 to 3 5 to 4 6 to 5 7 to 6 8+ to 7+
Significant Competitors
Nu
mb
er o
f M
arke
ts
Enforced Closed
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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: you still have to delineate a market– Rookie mistake to bring a case without one
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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”)
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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%
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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)]
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Pre-merger margin
10% 30% 50% 70% 90%
Critical sales loss
33% 14.2% 9.1% 6.7% 5.2%
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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.”
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US v. Mercy Health Services902 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%.
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FTC v. Swedish Match131 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.”
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• 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.”
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U.S. v. SunGard Data Sys., Inc.172 F. Supp. 2d. 172 (D.D.C. 2001)
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• 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
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FTC v. Whole Foods Appeal from the United States District Court
for the District of Columbia, Civ. No. 07-cv-Ol021-PLF
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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
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• 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
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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%
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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
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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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