Session 4: Case study session Analysis of competition

Measuring unobservable change:Quantitative methods in merger control

Dr Cristina CaffarraACE Meeting, Rome 15 October 2005

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Outline

Assessing merger effects means comparing two states of the world although in practice we only observe one.

How do we draw inferences about the unobserved state of the world? Two ways: inferences from theory, and natural experiments.

Range of techniques high-tech and low-tech Calibrated economic models:

- Merger simulation models- Coordinated effects models- Vertical arithmetic models

Lower-tech:- Bidding studies- Price (margin)-concentration studies- Switching studies/diversion ratios

Choice of technique depends on what data are available, time, budget, and economic receptiveness of the authority

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Merger simulation models in unilateral effects analysis

A merger brings together assets (brands, capacities etc.) that were previously competing. Competition between them is internalised: a portion of switchers are now captured by the merged entity.

Without true synergies, incentive is for merged firm to raise prices especially for closely-competing differentiated products. Given this, it may be optimal for other firms also to raise prices. But by how much?

These are the most immediate and measurable effects of a merger. Intuitions are simple, and lend themselves to empirical investigation.

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Merger simulations (2/2)

Front-end assume a demand system and obtain estimates of own and cross-price elasticities of the competing brands/products.

Back-end number of stages: Choose tractable model that best captures type of competition (differentiated or

homogeneous goods? Bertrand/Cournot).

Calibrate demand system to market in question (fitting assumed demand system with estimated elasticities, so that it predicts pre-merger prices and quantities).

Recover estimates of marginal costs.

Estimate post-merger prices and quantities: assume merged entity chooses prices to maximise its profits from all the products it controls.

Compare pre-merger and post-merger equilibrium.

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Recent cases

Used in the US both by regulators and merging parties e.g. Clairol/Wella, Heinz/Beechnut, P&G/Gillette, many others

Sparingly used in European merger control (still very much the exception rather than the rule)

Volvo/Scania, Philip Morris/Papastratos, Lagardere/Natexis/VUP, Oracle/PeopleSoft

Occasionally used by the UK authorities but typically only when put forward by the parties (Centrica/Dynegy, few other CC cases)

Other competition authority use them very occasionally.

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Limitations: apparent precision driven by strong assumptions

What are customer preferences? Linear demand, constant elasticity or other? Are preferences nested?

How do firms compete? Price or quantity competition? Bargaining model? Auction model?

What do firms costs look like? Constant, rising or falling costs? Capacity constraints?

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Results hugely sensitive to elasticity estimates

Need to make assumptions about how consumers structure their purchase decisions, and on the shape of demand

Analyses show predicted post-merger price rises can vary greatly depending on functional form Log-linear, linear, logit and AIDS

Relatively small inaccuracies in elasticity estimates can have large effects on predicted post-merger price rises

Data intensive, usual data/estimation issues Need price/quantity data for all main competing products over time, plus

additional data to control for other factors that affect demand; rubbish in, rubbish out, endogeneity, heteroscedasticity, serial

correlation

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Choice of model hugely important

Is the Bertrand model appropriate for the industry at hand?

Is it consistent with pre-merger competition? Is it a realistic representation of how firms compete? Sometimes a different model of competition may be more appropriate (e.g. Cournot, an auction model or a bargaining model).

Bargaining models: in many industries prices are negotiated between the buyer and the seller, so an oligopoly model that assumes that sellers set take-it-or-leave-it prices is inadequate.

But we still need a better understanding of these models and howpredictions differ from standard oligopoly theories.

Auction models: in other cases, competition takes the form of a series of discrete events in each of which suppliers compete to win the customers business.

Again results very different from conventional, Bertrand, seller-sets-uniform-price model.

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Example: Oracle /PeopleSoft Oracle and PeopleSoft both active in the sale of large and complex software,

especially Human Relations Management (HRM) software and Financial Management Systems (FMS).

US DOJ identified this as a market with 1-3 year tender process to procure software. This entails an entirely separate competition in each case. Following the merger, for high-end customers the credible choices would go from 3 to 2.

DOJs economic expert McAfee simulated the merger using oral auction model. Simulations predicted avg price increases of 511% for FMS, and 1330% for HRM.

Commissions economists also estimated merger simulation model based on auction theory. This predicted that reduction from 3 to 2 would lead to a significant price increases and reduce product variety. But this was heavily criticised, e.g.

assumed there was a high end market for these applications; assumed the only suppliers were Oracle, PeopleSoft and SAP etc.

(Note: in the US, Judge Walker did not much care for the auction model and applied the more conventional framework used for differentiated consumer goods where all customers face the same price (big debate). Concluded other players had to be included, Microsoft likely entrant, scope for repositioning. Also in the EU, the Commission decision accepted a significant fringe (i.e. not 3-to-2)).

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A lot is typically left out of merger simulations.

Models are greatly simplified Complete models would be intractable Additional complexity requires more assumptions

Incompleteness explains certain predictions e.g. the prices always rise prediction

Merger simulations omit important factors Merger simulation models usually omit non-price issues, but often

carried out in branded goods industries, where non-price issues are important

Models typically do not reflect dynamic changes: no analysis of entry, barriers to entry and expansion, no product repositioning

No account for buyer power Do not deal with potential for post-merger co-ordination

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What good are these models?

Can sharpen intuitions and analytical thinking Directional effects Provide explicitly coherent economic framework for overall

merger assessment Allows possible trade-off effects of efficiencies to be incorporated by changing

estimate of marginal cost used to calculate post-merger equilibrium. Allows the effects of possible divestments to be analysed, assuming some of the

parties brands acquired by a rival, or are independent.

However NOT true that merger simulations allow one to dispense with market definition and competitive effects analysis and instead go straight to the answer

Still need to carry out the competitive effects analysis, so these models currently do not have the role it is claimed they can have.

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Example that worked: Centrica/Dynegy CC Inquiry

Centrica acquired Rough (Dynegy), a depleted gas storage facility in the UK. Unilateral concern: Will Centrica have an incentive to restrict output - i.e.

withhold part of its other flexible capacity, so as to manipulate the price of Rough storage?

Step 1: Estimate Centricas residual demand curve. We could then calculate how much winter gas Centrica would have to withhold to raise the value of Rough by 1p per storage unit

Step 2: Used spreadsheet model to assess incentive/ability to manipulateflexibility

Detailed quantification of costs and benefits to Centrica of driving up price of winter gas (and thereby price of gas storage) by a given amount over a given period (e.g. 1p/therm in Q1, other possibilities)

Benefits: higher revenues from increased price of storage to customers

Results (accepted by the CC): Centrica had no profit incentive to withhold gas following the transaction, as the cost to Centrica of withholding gas would exceed the benefit it would receive from higher Rough prices.

Sensitivity analyses to test robustness of conclusions: different withholdingstrategies (constantly over Q1, only in peak demand days, etc.), different costs, different elasticities, different pass-through assumptions.

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Simpler techniques

In the vast majority of cases, issues of timing, data availability, client budget constraints or judgments on the receptiveness of the competition authority mean that we use simpler empirical analyses - Switching studies - Shock analysis/natural experiments- Bidding studies- Price (or margin)-concentration studies- Surveys

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Switching studies

Switching da