Presentation on theme: "TOPIC 12:HORIZONTAL MERGERS AND THE ANALYSIS OF COMPETITIVE EFFECTS Topic 12| Part 222 October 2013 Date ANTITRUST ECONOMICS 2013 David S. Evans University."— Presentation transcript:
TOPIC 12:HORIZONTAL MERGERS AND THE ANALYSIS OF COMPETITIVE EFFECTS Topic 12| Part 222 October 2013 Date ANTITRUST ECONOMICS 2013 David S. Evans University of Chicago, Global Economics Group Elisa Mariscal CIDE, Global Economics Group
2 Overview Part 1 Legal and Economic Background of Mergers Merger Screening Unilateral Effects: Economic Theory Part 2 Quantitative Techniques for Estimating Price Effects Mergers in Two- Sided Markets Coordinated Effects: Economic Theory and Evidence
Quantitative Techniques for Estimating Price Effects 3
4 Methods for Estimating Price Effects of a Merger Critical loss and diversion analysis and other “light” merger simulation which requires estimates of marginal costs and demand substitution. Natural experiments that examine what happened to prices in similar circumstances such as in another geographic market or in another merger. “Heavy” merger simulation which uses econometric estimates of demand and supply to “simulate” market before and after the merger.
5 The LOVEFiLM Acquisition and Light Merger Simulation LOVEFiLM online DVD rental subscription service, which operated in the UK, wanted to acquire in 2008 Amazon’s online DVD rental subscription service in the UK. Overlap product is ODR service in which customers pay a fixed monthly fee for receiving by mail DVDs they have selected online. ODR is one of many channels for accessing film and TV video content such as brick and mortar, DVD retail, Pay TV, Video on Demand, Free TV, Internet, and Premium TV channels. OFT used a simple back-of-the envelope “light” merger simulation to analyze market definition.
6 “Light” Simulation of Unilateral Effects MeasureImplication for Price of Firm A Gross margin of firm BHigher margin indicates larger gain from sale diverted to firm B Diversion ratio for firm B when firm A raises its price Higher diversion ratio indicates more sales lost pre-merger by firm A could be kept post-merger by the combined firm
7 Gross Margin Estimates CompanyGross Margin Amazon[20-30%] LOVEFiLM[30-40%] OFT’s estimate of gross margins where variable costs include retention marketing, collection costs, costs of exchanges, customer service, and library expenses.
8 Diversion Ratio Analysis Diversion ratio is share of sales lost as a result of a small increase in price obtained by substitute products offered by other firms. Diversion ratios estimated from survey of consumers. Consumers were asked what video service they would switch to if the price of their existing ODR provider increased by 10%. Postpone discussion of potential issues with survey design.
9 Diversion Ratio Estimates Including Don’t KnowsApportioning Don’t Knows ToFrom LOVEFiLMFrom Amazon From LOVEFiLMFrom Amazon LOVEFiLM -[30-40]-[70-80] Amazon [0-10]-[30-40]- Blockbuster [0-10] [30-40][0-10] Other [0-10] Don’t Know [50-60][30-40]
10 Price Increase Estimates LOVEFiLM Price Increase Amazon Price Increase Including Don’t Knows From LOVEFiLM to Amazon [0-10] From Amazon to LOVEFiLM [0-10] Apportioning Don’t Knows From LOVEFiLM to Amazon [0-10] From Amazon to LOVEFiLM [40-50]
11 Comments on Use of Surveys Good source of data for diversion ratios are company won-loss reports. Often available for B2B businesses but not for B2C businesses. For B2C businesses need to do surveys of consumers (or rely on surveys conducted in the normal course of business) to determine diversion. Common survey method involves hypothetical questions (like survey OFT relied on). Consumer answer to hypothetical question not necessarily what they would do in the actual situation. Reliability also depends on how survey question is posed and what the consumer is asked to assume.
12 “Heavy” Merger Simulation Based on Econometric Models “Structural model” assumes shape of demand schedules (linear, logistic, etc.), consumer decision making, product differentiation, nature of competition (Bertrand, Cournot, etc.), costs, and other features; obtains estimates of demand and competitive interactions and marginal cost. Merger simulation uses the estimated structural model to simulate the effect of a merger of firms. Merger simulation can also be used to model synergies and other efficiencies of merger. Generally estimated from historical data using sophisticated econometric techniques. Results are highly assumption driven.
13 Elasticities for Ready to Eat Cereals (Nevo 2000 Study) Results suggest that individual price sensitivity is heterogeneous. Most of the heterogeneity is explained by demographics. Own-price elasticities are not linear in price. This is due to heterogeneity in price sensitivity. Consumers who purchase different products have different price sensitivities. In addition, substitution patterns across brands are driven by product characteristics.
14 Median and Cross-Price Elasticities of Ready to Eat Cereals K Rice Krispies GM Cheerios GM Lucky Charms P Grape Nuts Q LifeR ChexN Shredded Wheat K Rice Krispies 1.3200.0690.0410.0500.0480.0810.049 GM Cheerios 0.1061.7090.0490.0890.080.1060.099 GM Lucky Charms 0.0250.021.9450.0250.0720.0240.099 P Grape Nuts 0.030.0370.0262.0960.0280.0270.115 Q Life 0.0330.0280.1490.0320.1030.0310.02 R Chex 0.0240.0210.0110.0130.0141.7490.014 N Shredded Wheat 0.0180.0240.0090.070.0150.0172.268
15 Predicted percent change in price as a result of a merger Post and Nabisco GM and Nabisco GM and ChexKellogg and Quaker Oats GM and Quaker Oats PQPQPQPQPQ K Rice Krispies 0.00.1 0.20.10.45.1-18.104.22.168 GM Cheerios 0.00.20.7-0.91.1-22.214.171.124.1-3.5 GM Lucky Charms 0.00.10.3-0.40.7-0.80.83.39.3-10.6 P Grape Nuts 1.5-126.96.36.199.00.40.12.30.13.0 Q Life 0.00.10.00.30.10.515.5-16.723.8-25.3 R Chex 0.00.20.00.312.2-19.00.02.10.13.4 N Shredded Wheat 3.1-8.67.5-18.80.00.40.01.90.02.5
16 Percent Reduction in Marginal Cost Required for No Change in Predicted Post-Merger Price Post and Nabisco GM and Nabisco GM and Chex Kellogg and Quaker Oats GM and Quaker Oats K Rice Krispies 00016.50 GM Cheerios 02.13.4012.1 GM Lucky Charms 00.91.6019.2 P Grape Nuts 2.60000 Q Life 00016.820.1 R Chex 0022.100 N Shredded Wheat 5.110.4000
17 “Natural Experiments” Basic idea is to find real-world analogies to the world with the merger and then compare prices and other competitive conditions to actual data on prices and competitive conditions pre-merger. Was there a similar change in concentration in the part from which one can infer competitive effects? Is it possible to compare geographic areas that look like result “post merger” and compare to situation “pre-merger”. May need to use statistical methods to control for other differences between situations so that the comparison is “all else equal” except whether of not there is a difference in market structure.
18 Staples Office Depot Merger Background Office superstores provide one-stop shopping for small businesses and home-office customers. By mid 1990s in US Staples, Office Depot, and OfficeMax were leading office superstore competitors. Staples and Office Depot competed directly in 40 metropolitan areas. September 1996 Staples and Office Depot announced plan to merge.
20 FTC’s Econometric Estimate of Price Changes TABLE 1 PX-400: SIMULATED IMPACT ON STAPLES OFFICE PRODUCTS PRICES OF ELIMINATING OFFICE DEPOT: Staples Stores with Some Office Depot Competition Model 1Model 2Model 3*Model 4*Model 5Model 6Model 7 Simulated Price Change1.10%0.80%2.90%3.70%4.00%3.70%8.60% t-Statistic11.194.798.889.1610.339.1214.99 Observations in Simulation6,8961,6851,8171,3151,4651,3953,038 Sample is: Parties SampleYes Complete SampleYes Unit of Observation: Weekly/StoresYes Monthly/StoresYes Dependent variable is: Parties Price IndexYes Recalculated Price IndexYes Competitor Variables: Circle-based**Yes MSA-based***Yes *Models 3 and 4 are based on the same regression model. Model 3 reports the simulated impact of eliminating Office Depot in markets where either the MSA-based competition data or the Circle-based competition data indicate that a Staples store faces Office Depot competition. Model 4 reports the simulated impact of eliminating Office Depot in markets where both the MSA-based competition data and the Circle-based competition data indicate that a Staples Store faces Office Depot Completion. **Variables which control for the number of Office Depot, OfficeMax, computer superstores and warehouse clubs within 5 miles, 5-10 miles, and 10-20 miles of the Staples store. ***Variables which control for the number of Staples, Office Depot, OfficeMax, Wal-mart, Sam’s Club, Computer City, BestBuy, Office1Superstore, Costco, BJ’s, CompUSA, Kmart and Target stores in the MSA.
21 Economic Evidence Not the End of the Story The FTC sought to enjoin the merger and the parties decide to fight it out in court. The parties presented econometric evidence that rebutted the FTC’s econometric evidence on the grounds that it failed to control for differences in stores and markets and ignored efficiencies. The judge ignored econometric evidence. Instead he relied on company documents that group metropolitan areas into price zones based on superstore competitors and on simple comparisons. Judge agreed to block the merger. Postscript: Office Depot and Office Max announced merger in early 2013 which the FTC is reported to be likely to clear
23 What’s Different When Markets Are Multi-Sided? Unilateral effects analysis needs to consider total price effect of merger recognizing that one side could down and another up. Consumer impact of merger depends on total price. Simple merger simulation formulas for price effects are wrong (e.g. the LOVEFiLM framework cannot be applied as is). Can be modified but simple formulas replaced by complex and hard to estimate ones. Structural econometric models work so long as they are modified to account for interdependent demand. There is no presumption that prices will increase post merger even ignoring usual cost efficiencies since increased demand-side network effects can counter increased market power effects.
24 Non-Econometric Approaches Suppose one side is free and unlikely to change post-merger. Then analyze impact of price change on paid side and assess whether cross-demand effects will alter conclusion. Eg. if profitable to raise 3% without considering other side then less than 3% once considered. Eg. if profitable to raise 10% without considering other side then question is whether considering other side would reduce estimate enough to allay concerns. Traditional unilateral effects analysis on each side and assess biases. Simple natural experiments looking at different platform market structure configurations in past or in other markets.
25 Two-Sided Econometric Analysis of Dutch Newspapers “In our case, the effects of the hypothetical merger on subscription prices and readers’ welfare are found to be small. Concerns mainly arise with respect to the advertising side. Importantly to this regard, with the exception of market concentration analysis, there does not seem to be a significant difference between the different methods used to assess the unilateral effects of the hypothetical merger we analyzed. This is because we used SSNIP and UPP formulas adjusted for two-sided platforms, so that only the HHI-based analysis did not take the two-sided nature of the market into account. So, for the example studied here, we find that commonly used methods to assess mergers work well in two- sided markets as long as one properly adjusts them—in the way we have described above—for the two-sided nature of the market. ” Filistrucchi, Klein, and Michielsen, “Assessing Unilaterial Merger Effects in a Two-Sided Market: An Application to the Dutch Daily Newspaper Industry.
26 Comparison of One Sided and Two-Sided Analysis of HHIs shows concern on reader side. However, in addition to market definition issues use of HHI for market power likely to overstate market power. Single-sided UPP finds no pressure on advertising side but two-sided does as a result of accounting for cross demand effects. Single and two-sided similar for reader side. SSNIP shows higher increase in prices when firm can adjust prices on both sides. Concern primarily on the advertising side. Full econometric analysis shows no change in reader welfare but higher per-reader prices per advertiser. Consistent with two-sided SSNIP and UPP but not with HHI analysis.
Coordinated Effects: Economic Theory and Evidence 27
28 Coordinated effects Co-ordination over prices. Market sharing. Merger can increase the potential for coordination/tacit collusion. Is co-ordination feasible or likely in the relevant market?. Ability to coordinate. Ability to monitor competitors. Ability to discipline deviations. Lack of external constraints. Does the merger increase the risk of co-ordination?. Issues to consider.
29 Ability to coordinate Coordination more difficult when there is a large number of firms. Small number of competitors. Coordination more difficult with differentiated products or wide product ranges. Unless there exist focal products or prices. Homogeneous products. Similar sizes and costs structures. More difficult to (tacitly) agree on the profit maximising price if firms have different cost structures. Symmetric firms.
30 Ability to monitor competitors High incentive to cheat if demand is rising. More difficult to monitor competitors’ behavior if demand is increasing and output changing. Stable demand. Makes it easier to monitor competitors behavior. Can be facilitated by trade associations, or third-party agencies. (Price transparency is often also good for consumers and can increase competition!). Transparent pricing.
31 Ability to Discipline Deviations Cut prices to punish cheat, but everyone suffers (in the short run). There is usually a short-term cost to punishing cheaters. High margins increase the incentives to cheat and increase the cost of punishing deviations. Profit margins. Cheating in one market can be punished in a different market. Less incentive to cheat in one market if it leads to retaliation in many others. Multi-market contacts.
32 Lack of External Constraints Lack of substitute products/Low elasticity of demand. Barriers to entry. Lack of buyer power.
33 End of Part 2, Next Class Topic 13 Part 1 Price Discrimination and Other Complex Pricing Limit Pricing Part 2 Predatory Pricing Loyalty Rebates