regression analysis & market delineation luke m. froeb vanderbilt university & ersgroup.com...

Regression Analysis& Market Delineation

Luke M. FroebVanderbilt University &

ERSGroup.com

26 March, 2008 8:45-11:45amAntitrust 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 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

(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

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-5.00

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25.001/

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/199

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1/1/

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1/1/

1999

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Week

Cen

ts

Chicago Houston Virginia

Merger Date

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

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,96-03FTC Merger Challenges,96-03

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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

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Enforced Closed

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

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%

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)

• 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

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

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

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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