Regression Analysis & Market Delineation

1. Regression Analysis& Market Delineation Luke M. Froeb Vanderbilt University & ERSGroup.com 26 March, 2008 8:45-11:45am Antitrust Economics & Econometrics ABA Spring Meetings

2. Acknowledgements • Henry McFarland, Economists, Inc. • David Scheffman, Vanderbilt & LECG • Gregory Werden, US Dept of Justice

3. 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

4. Click & Learn Regression<<pull up program>> • “But for” regression model. • Which functional Form? • How well does it fit?

5. Bid Rigging: Frozen Fish Conspiracy

6. 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.”

7. 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>>

8. 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>>

9. 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

10. (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

11. 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 NOTincrease relative to other regions using similar type of gasoline

13. 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

14. 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?

15. FTC Merger Challenges,96-03

16. 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

17. 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”)

18. Quantitative Market Delineation • Critical Elasticity of Demand Analysis • Profit-Maximization Calculation • Breakeven Calculation* • Critical Sales Loss Analysis • Profit-Maximization Calculation • Breakeven Calculation* -------- *covered today

19. 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%

20. 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)]

21. 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.”

22. 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%.

23. 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.”

24. 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.”

25. FTC v. Whole Foods Appeal from the United States District Courtfor 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

26. 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.

27. 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

28. 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%

29. 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

30. 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