Choosing Among Tools for Assessing Merger Effects Luke Froeb Vanderbilt University Mason AG Education Program Advanced Policy Institute on Antitrust Economics 26 June 2011 George Mason Law School

Acknowledgements Gregory Werden, US Dept of Justice coauthor Michael Doane, Competition Economics, LLC Consulting partner Forbes Belk, Competition Economics, LLC Research Assistant

“Take-aways” Models help agencies figure out: – (i) What matters – (ii) Why it matters – (iii) How much it matters. Finding a model that can describe significant features of competition is the hard part – Once that is done, the rest is easy Do the best with the information you have, and with where you are in the investigation

Outline Do we need more than shares and concentration? Beware experts bearing models – Particularly those with “UPPI” Every merger is different – Coated Recycled Board – Super-premium ice cream – Parking, Cruise Lines, Paris Hotels Conclusions

Notes: Dashed line represents estimated values US data: # of Transactions

Notes: Dashed line represents estimated values US data: Investigation Rate

FTC Merger Challenges,96-03

FTC Merger Challenges,98-07

Can shares or concentration predict merger effects? Can shares or concentration predict merger effects? Empirical search for a “critical” concentration ratio was fruitless Price-concentration regressions For differentiated products mergers: – No clear line between “in” and “out” of market – Shares not necessarily good proxies for loss of competition following merger

How well does ∆HHI predict effects of Bertrand mergers? Small ∆HHI  small MERGER EFFECT BIG ∆HHI  BIG prediction error

Using Models in Enforcement Analysis of models provides a solid foundation for enforcement concerns about unilateral merger effects. Analysis of models clarifies the precise nature and determinants of unilateral effects in particular settings. Application of models to cases permits a fact-based, quantitative assessment of unilateral merger effects.  Models tell us 1 what matters, 2 why it matters, 3 how much it matters

Aren’t models built on unrealistic assumptions? Behind every competitive effects analysis is an (implicit) economic model. – Make the model explicit – Force economists to make analysis a transparent “map” from evidence to opinion Every model makes unrealistic assumptions – Key question: does model ignores significant features of competition that bias predictions? – “fit” criterion of Daubert

How do we assess model reliability? No methodology has been shown to predict effects of real mergers – No coordinated effects theory, – No unilateral effects theory, – No market concentration theory. Subject each significant modeling choice to: – If it matters, have a justification or do sensitivity analysis and “bound” your conclusions. – Werden, Gregory J., Luke M. Froeb, and David T. Scheffman, A Daubert Discipline for Merger Simulation, Antitrust, 18:3 (Summer, 2004) pp

Does modeling sway decision- makers at agencies? Merger simulation is a standard methodological tool – No tool is definitive. – Used to organize evidence, not to substitute for it. First used in 1994 in US v. IBC – Expert declaration published in Int’l J. Economics of Bus. with five other examples from real cases. Use in litigated cases – Lagardere; Oracle/Peoplesoft;

Doesn’t simulation always predict a price increase? Every anticompetitive theory predicts price increase – We have safe harbours for concentration Use simulation to organize evidence, focus investigation, benchmark efficiency claims, evaluate remedies. – Can compute cost reductions that offset price increase.

Price Competition (Bertrand models) Suppose that you discover 1. consumers choose among alternatives on basis of price & quality. 2. firms compete on the basis of price only 3. No entry, exit, repositioning, promotional or advertising, capacity constraints Then, Bertrand model may be appropriate What next?

Compensating Marginal Cost Reductions CMCRs are the reductions in marginal costs that exactly offset the unilateral price effects of a merger. CMCRs have been used since the mid 1990s. Calculating CMCRs does not require assuming functional forms: a function of own and cross elasticities ONLY. CMCRs can be used in a quantitative analysis or just to identify the key determinants of unilateral effects.

Cournot CMCR s 1 and s 2 are the quantity shares of the merging firms, and e is the market demand elasticity. Example: if e=1, s1=s2=20%, MC have to go down by 20% to offset incentive of merged firm to raise price Smaller CMCR’s with More elastic demand (e) Smaller ∆HHI

Bertrand CMCRs This expression applies to single product firms, but it generalizes to multiproduct firms. d ij and d ji are diversion ratios between merging products; m i and m j are their margins; and p i and p j their prices.

Approximate Bertrand CMCRs This expression is essentially a first-order approximation to the CMCRs and omits terms containing d ij d ji. If the diversion ratios are relatively low, this expression provides a fairly good approximation to the CMCRs. Example: {equal prices, margin=50%, diversion=10%}  5% reduction in price of each good necessary to offset incentive to increase price

Pricing Pressure Indicies Salop and O’Brien observed that the first-order impact of a Bertrand merger on prices is determined by m j d ij. Farrell and Shapiro proposed gross (GUPPI) and net (UPPI) upward pricing pressure indexes scaled in monetary units. GUPPI = m j d ij p j, is the profit recapture for one merged product as the price of another is increased. UPPI = GUPPI – 10% of marginal cost.

UPPI Rescaled This expression is the Farrell and Shapiro UPPI after dividing by p i to convert monetary units into a pure number. The first term, the GUPPI, is the approximate CMCR. The second term is the arbitrary 10% efficiency credit. BOTTOM LINE: ONCE YOU KNOW BERTRAND IS APPROPRIATE, CHOICE OF TOOL DOES NOT MATTER

US v. Altivity and Graphic (2008) US DOJ challenges CRB Merger – Altivity (35%) + Graphic (17%) of North American capacity Remedy – Divest 2 plants representing 11% of capacity

Nick Hill, “Mergers w/capacity closure,” DOJ working paper Model: Once built, mills produce at capacity; and merger would create incentive to close one or more mills – Mill shutdown  supply decrease  higher price for remaining production – Merger changes the usual “shut down” calculus to make it more profitable to shut down Model of Harm

Model tells you: – 1. What matters Elasticity of demand for CRB Elasticity of foreign supply –FX, transport cost, other commitments Facility & closing costs – 2. Why it matters Increases profitability of shut down – 3. How much it matters Which divestitures are sufficient? How modeling can help an enforcement agency

Does the model capture significant competition? Product Market: CRB Geographic market: North America Is CRB market operating at near capacity? Can model predict what we can observe? – Pre-merger: NOT profitable to shut down – Post-merger: profitable to shut down mill

Super-premium ice cream in North America – Nestlé 36.5% + Dreyer 19.5% revenue share Remedy: divest 3 brands to new entrant FTC v. Nestlé and Dreyer (2002)

Models help delineate markets Question: Is super-premium a relevant product market? Answer: Simulate merger-to-monopoly of four super-premium ice cream producers If price goes up by 5% then it is a relevant product market

29 FTC Inputs to unilateral effects analysis: Own- and Cross-Elasticity Estimates Tenn et al., “Mergers when firms compete using price and promotion,” Int’l. J. Ind. Org.

Models help interpret data Question: how did new entrant Dreyer obtain a 20% share without affecting incumbent price? – Does this mean that super-premium is not a relevant antitrust market? Answer: Build a model of post-merger world, simulate exit (by raising Dreyer’s MC), and see what happens to price – Does incumbent pricing change?

Models help interpret data (continued) Question: How does promotional activity affect merger analysis and tools that economists use? – What happens if we ignore promotional activity? Answer: Build a model of promotion + price. – If promotion affects elasticity, then it matters; if not then it doesn’t

Demand, prices, and promotion level 1.None, 2.display, 3.feature, 4.both

33 FTC Table 4: Elasticity Varies with Promotion Own-price

Answer: promotion matters in this case Price-only merger models under-predict (5% instead of 12%) the price effects of mergers in industries where firms compete using price and promotion – Estimation bias: demand is too elastic – Extrapolation bias: promotion decreases 31% in post-merger equilibrium 34 Vanderbilt

Estimation Bias vs. Extrapolation Bias 35 Vanderbilt B,C merge Merger to monopoly

Parking lot merger 1999 Central Parking $585 million acquisition of Allright. Remedy: divestitures if merged share >35% in 4X4 block area is – Divestitures in 17 cities

Models help economists understand competition in different settings Froeb et al. (2002) criticize DOJ by arguing that the merger would not have raised price because there is very little uncertainty about parking demand. Price to fill capacity, pre- and post-merger – Pricing practice: “is the lot full by 9am?” Capacity constrained  no merger effect – How many of the lots are capacity constrained?

Model of downtown 16 blocks 3 lots Building height represents demand

Cruise line merger: What about uncertainty? 2003, the European Commission (EC) gave their approval to Carnival's $5.5 billion takeover of rival cruise operator P&O Princess – Followed UK and US approvals

Deterministic profit function w/ tightly binding capacity constraint

Expected profit function (solid) w/tightly binding constraint

Paris hotels: Trying to reduce uncertainty? 2005, six luxury hotels in Paris exchanged information about occupancy, average room prices, and revenue – French competition agency: "Although the six hotels did not explicitly fix prices, …, they operated as a cartel that exchanged confidential information which had the result of keeping prices artificially high" (Gecker, 2005) – industry executives insisted that their information sharing was to "to bring more people to the area and to maximize hotel utilization"

Testing for merger effects US Price and occupancy data from Smith Travel Research (STR). – 32,314 U.S. hotels reported to STR the average room-night price actually received each day, as well as the total number of rooms available and the number of rooms sold. – 97 monthly observations from 2001 –2009 for each hotel for occupancy and price. – These 32,314 hotels represent about 95% of chain- affiliated properties in the United States and about 20% of independent hotels and motels.

Results Relative to non-merging hotels, mergers increase occupancy – Gain $1700-$3300 per month for a 100-room hotel. But only in capacity-constrained and uncertain markets – Mergers allow hotels to better forecast demand. No evidence hotel mergers decrease occupancy or raise price. – “traditional” models would not predict this

Conclusions Models help. But finding the right model is hard. Do the best you can with what you have where you are. An agency should make the best possible use of the information it has at each stage of a merger assessment. One size does not fit all. An agency should determine which of the many specialized tools to apply by first understanding of how competition works and thus which model, if any, fits the industry.