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Quantitative techniques in competition analysis Prepared for the Office of Fair Trading by LECG Ltd October 1999

Research paper

17 OFT 266

LECG Ltd provides sophisticated economic and financial analysis, public policy analysis, litigation support and strategic management consulting. It is a subsidiary of Navigant Consulting Inc, a US-based company with offices in the United States, Canada, New Zealand, Argentina and Europe. This report was prepared by Thomas Hoehn James Langenfeld Meloria Meschi Leonard Waverman based at LECG Ltd 40-43 Chancery Lane London WC2A 1SL England

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Please note any views expressed in this paper are those of the authors: they do not necessarily reflect the views of the Director General of Fair Trading.

QUANTITATIVE TECHNIQUES IN COMPETITION ANALYSIS PREFACE This paper is the 17th of a series of research papers (listed overleaf) to be published by the Office of Fair Trading. These papers report the findings of projects commissioned by the OFT as part of its ongoing programme of research into aspects of UK Competition and Consumer Policy. The intention is that research findings should be made available to a wider audience of practitioners, both for information and as a basis for discussion. Any views expressed in this paper are those of the authors: they do not necessarily reflect the views of the Director General of Fair Trading. This report is not and should not be treated as a guideline issued as a consequence of the Director General’s obligation to publish general advice and information under the Competition Act 1998. Comments on the paper should be sent to me, at the address shown below. Research proposals on other aspects of UK Competition and Consumer Policy would also be welcomed. Requests for additional copies of this paper (or copies of earlier papers in this series) should, however, be sent to the address shown on page 2. Peter Bamford Chief Economist Office of Fair Trading Chancery House 53 Chancery Lane London WC2A 1SP



Market Definition in UK Competition Policy, National Economic Research Association, February


Barriers to Entry and Exit in UK Competition Policy, London Economics, March 1994


Packaged Mortgages: Results of Consumer Surveys, Research Surveys of Great Britain, June 1994


Consumers’ Appreciation of Annual Percentage Rates - Taylor Nelson AGB survey results, June 1994


Predatory Behaviour in UK Competition Policy, Geoffrey Myers, November 1994


Underwriting of Rights Issues: a study of the returns earned by sub-underwriters from UK rights issues, Paul Marsh, November 1994


Transparency and Liquidity: a study of large trades on the London Stock Exchange under different publication rules, Gordon Gemmill, November 1994


Gambling, Competitions and Prize Draws - Taylor Nelson AGB survey results, September 1996


Consumer dissatisfaction - Taylor Nelson AGB survey results, December 1996


The Assessment of Profitability by Competition Authorities, Martin Graham and Anthony Steele, February 1997


Consumer detriment under conditions of imperfect information, London Economics, August 1997


Vertical Restraints and Competition Policy, Paul W Dobson and Michael Waterson, December 1996


Competition in retailing, London Economics, September 1997


The effectiveness of undertakings in the bus industry, National Economic Research Associates, December 1997


Vulnerable Consumer Groups: Quantification and Analysis, Ramil Burden, April 1998


The Welfare Consequences of the Exercise of Buyer Power, Paul Dobson, Michael Waterson and Alex Chu, September 1998

Copies of these papers are available, free of charge, from: Office of Fair Trading, PO Box 366, Hayes UB3 1XB Tel: 0870 60 60 321, Fax: 0870 60 70 321, e-mail:


CONTENTS Preface Office of Fair Trading Research Papers List of Abbreviations

1 2 4

PART I: INTRODUCTION AND OVERVIEW 1 Introduction 2 How quantitative techniques support antitrust analysis in practice

5 11

PART II: STATISTICAL TESTS OF PRICES AND PRICE TRENDS 3 Cross-sectional price tests 4 Hedonic price analysis 5 Price correlation 6 Speed of adjustment test 7 Causality tests 8 Dynamic price regressions and co-integration analysis

43 49 53 57 59 63

PART III: DEMAND ANALYSIS 9 Residual demand analysis 10 Critical loss analysis 11 Import penetration tests 12 Survey techniques

69 77 81 83

PART IV: MODELS OF COMPETITION 13 Price-concentration studies 14 Analysis of differentiated products: the diversion ratio 15 Analysis of differentiated products: estimation of demand systems 16 Bidding studies

87 93 97 103

PART V: OTHER TECHNIQUES AND CONCLUSIONS 17 Time series event studies of stock markets’ reactions to news 18 Conclusions

107 109

Appendix A Glossary of terms B Bibliography C List of case summaries

117 127 137


LIST OF ABBREVIATIONS Below are the full versions of those abbreviations which occur regularly in the text. For an explanation of some common words or phrases please refer to Appendix A.


Bureau Européen des Unions de Consommateurs - also known as the European Consumers’ Organisation


Director General of Fair Trading


United States’ Department of Justice


European Court of Justice


United States’ Federal Trade Commission


Herfindahl-Hirschman Index


Independence of Irrelevant Alternatives Assumption


Institute for Fiscal Studies


industrial organisation


Monopolies and Mergers Commission


Ordinary Least Squares (see also glossary)


a dedicated statistical package


a dedicated statistical package





Background to the study 1.1

LECG Ltd has been commissioned by the Office of Fair Trading (OFT) to undertake a study of the main quantitative techniques used in competition, that is, antitrust, analysis.


This project is one of a series of studies the OFT has commissioned on issues relevant to UK competition policy. The purpose of these studies is to stimulate debate among a wider audience of antitrust practitioners. These studies do not necessarily reflect the views of the Director General of Fair Trading (DGFT). In particular, this report should not be taken as providing general advice and information about the way the Director General expects competition policy to operate. Other related past studies include Market Definition in UK Competition Policy by National Economic Research Associates, 1993, and Barriers to Entry and Exit in UK Competition Policy by London Economics, 1994.


Over recent years, the use of quantitative analysis in antitrust has increased for a variety of reasons. These reasons include the development of modern and fairly reliable quantitative techniques, advancements in user-friendly software and cheap hardware, availability of more and better data and, not least, an increasing use of economists and economic evidence, by antitrust authorities and the companies concerned. None of the previous OFT studies have dealt specifically with the range of quantitative techniques used in competition policy cases. This project therefore fills an important gap in the series of research papers published by the OFT.

Antitrust legislation in the UK 1.4

Competition policy in the UK is conducted within the framework of certain pieces of legislation. The main areas of antitrust that fall under the jurisdiction of the OFT and/or the Competition Commission - formerly the Monopolies and Mergers Commission (MMC) - are:


monopoly and abuse of dominant position;

mergers; and

agreements between firms (vertical and horizontal).


Until 1998, UK policy in these areas of antitrust was covered by four main pieces of legislation. Mergers are governed by the Fair Trading Act 1973, which together with the Competition Act 1980 also covers monopolies and anti-competitive practices. Agreements between firms could be investigated as part of monopoly enquiries under the Fair Trading Act or under the Restrictive Trade Practices Act 1976 and the Resale Prices Act 1976.


Competition policy legislation in the UK is currently however, in the process of change with the introduction of the Competition Act 1998. The Competition Act 1998 replaces or amends much of the above legislation, notably the Restrictive Trade Practices Act, the Resale Prices Act, and the majority of the Competition Act 1980. Some parts of the previous legislation remain unchanged, such as the provisions for dealing with mergers under the Fair Trading Act.


The new legislation introduces two prohibitions: one of agreements (whether written or not) which prevent, restrict or distort competition and may affect trade within the UK; the other of conduct by dominant companies which amounts to an abuse of their position in a market in the UK. The two prohibitions come into force on 1 March 2000. The prohibitions in the Competition Act are based on Articles 85 and 861 of the EC Treaty. The Competition Act gives the DGFT powers to investigate undertakings believed to be involved in anti-competitive activities, and to impose financial penalties where appropriate.


The Competition Act is applied and enforced by the DGFT and, in relation to the regulated utility sectors, concurrently with the regulators for telecommunications, gas, electricity, water and sewerage and railway services. A new Competition Commission, incorporating the former MMC, has been established which hears appeals.


As of May 1 1999, Articles 85 and 86 have been renumbered as Articles 81 and 82 under the Treaty of Amsterdam. This report however generally refers to the prohibitions as Articles 85 and 86.



In March 1999, the DGFT published a series of guidelines 2 under the Competition Act setting out general advice and information about the application and enforcement of the prohibitions. As noted earlier this report is an independent piece of economic research and, as such, does not constitute a guideline under the Act.

Antitrust issues requiring quantitative techniques 1.10

The application of quantitative techniques to antitrust has arisen naturally from the need to answer the central questions of antitrust analysis, many of which may involve quantification, as these examples make clear: 3



Market definition: What products, geographic areas, and suppliers/buyers form part of the relevant market?

Market structure issues: How should one measure concentration, market shares, entry barriers and exit conditions?

Pricing issues: Are movements in market prices consistent with competition, with monopoly or with collusion? Do we observe prices in one geographic market that are significantly higher than in others? Are price-cost relationships consistent with predatory pricing?

Other behavioural issues: To what extent do leading firms’ non-price strategies, for example, on matters such as supply constraints, distribution agreements, investment, advertising or patent licensing, lessen competition or improve industry performance? Where both effects exist, which is more significant? Do variations in cost efficiency explain profit variations?

Vertical issues: To what extent does vertical integration or contracting (such as tied pubs or other exclusive arrangements) by leading firms reduce competition and/or yield efficiencies?

Special merger issues: How much might the merger concerned change pricing and other market behaviour, either by lessening competition or by promoting efficiency?

The Competition Act 1998: OFT 400, The Major Provisions; OFT 401, The Chapter I Prohibition; OFT 402,The Chapter II Prohibition; OFT 403, Market Definition; OFT 404, Powers of Investigation; OFT 405, Concurrent Application to Regulated Industries; OFT 406, Transitional Arrangements; OFT 407, Enforcement; OFT 408, Trade Associations, Professions and Self-Regulating Bodies. These approaches may not always be appropriate under the Competition Act 1998.



Potential entry and competitive expansion: How responsive are both potential entrants and competing fringe firms to increased prices or margins in the relevant market?

Each of these areas provides scope for some degree of quantitative analysis; however not all such analyses need to use complex formal mathematical or statistical techniques. It should be noted that quantitative analysis interacts with qualitative analysis in a complex way. Rarely will quantitative techniques and analysis alone decide matters, though they can provide very valuable evidence in cases. It should be stressed that the weighing and sifting of evidence will always involve expert judgement on the part of the competition authorities.

Classification of quantitative techniques 1.12

The techniques outlined in this review are those designed to test an economic hypothesis to the exclusion of exploratory data analysis. The menu of selected techniques below ranges from uncomplicated descriptive statistics (for example, average price levels, and sales and price trends) to advanced econometric estimation of demand and supply functions. Statistical tests of prices and price trends (Part II) 

Cross-sectional price test

Hedonic price analysis

Price correlation

Speed of adjustment test

Causality test

Dynamic price regressions and co-integration analysis

Demand analysis (Part III) 

Residual demand analysis

Critical loss analysis

Import penetration tests

Survey techniques


Models of competition (Part IV)


Price-concentration studies

Analysis of differentiated products

Bidding studies

Other techniques not covered by this report in detail are: 

analysis of profitability;

analysis of acquisition price;

time series event studies of stock market’s reactions to news;

econometric and Data Envelopment Analysis (DEA) of relative efficiency;

cost analysis; and

cluster analysis, discriminant analysis, factor analysis etc.

Aim and structure of this report 1.14

This study reviews the application of quantitative techniques from both a technical and practical perspective. Each technique, or major group of techniques, is summarised in terms of its main elements. At the same time, care is taken to put these techniques into context and provide an overview of their uses.


Each technique is discussed under three headings. First, each statistical test is described briefly. Secondly, comments on data requirements and ease of computation are added. Thirdly, the technique is discussed in terms of problems of interpretation. The latter heading is vital, as it is the interpretation of statistical relationships that is crucial in antitrust cases. Economic significance can be different from statistical significance.


Before dealing with the various tests we also present an overview in Chapter 2 of the general uses and applications of quantitative techniques in US, UK and EU competition policy. This overview places the techniques that are examined in later chapters into a broader context and offers an illustration of the competition issues that typically require quantification using statistical and econometric tests.


Throughout the report we include case studies that illustrate the applications of quantitative techniques. These case studies often deal with more than one technique and so should be read within the context of the entire report. However, they do illustrate how quantitative questions can be at the core of a case and its outcomes. 9


In the following six chapters, we describe statistical techniques that analyse price only: price tests, covering cross-sectional statistical comparisons (Chapter 3); hedonic price analysis (Chapter 4); and time series price comparisons (Chapters 5 to 8). Next, we examine quantitative techniques that are more closely connected to economic theory, and are used in antitrust analysis for market definition and for the analysis of demand: residual demand analysis and critical loss analysis (Chapters 9 and 10); import penetration tests (Chapter 11); and survey techniques (Chapter 12). In the final four chapters, we describe those techniques that are used to estimate or simulate models of competition in order to detect anti-competitive behaviour: price-concentration studies (Chapter 13); analysis of differentiated product markets using the diversion ratio (Chapter 14); analysis of differentiated product markets using an estimation of demand system (Chapter 15); bidding techniques (Chapter 16); and, time series event studies (Chapter 17).





This chapter reviews the use of quantitative techniques in the context of antitrust analysis and draws on the application of these techniques to real life cases in the UK, US and EC. While this cannot be comprehensive, the aim is to illustrate the central role that quantitative questions and techniques can play in casework. It is not the intention in this chapter to go into each and every detail of how a specific technique is employed. The reader who wants to have more detailed information about a specific technique can turn to later pages of the report where fuller explanations can be found.


The analysis of mergers, restrictive agreements and abuses of a dominant position have all followed a similar analytical approach. This approach has involved a number of steps: Step 1 Identification of the firms concerned; preliminary analysis of their activities; determination of relevant jurisdiction Step 2 Definition of the affected markets in their product and geographic dimension, leading to an assessment of the position of the firms in the affected markets Step 3 Assessment of any potential adverse effects on competition of the alleged restrictive or abusive behaviour, or the proposed merger Step 4 Assessment of possible efficiency defences and other relevant public interest justifications.


Each step may involve a degree of quantification. Step 1 requires the description of the activities of a firm principally through the use of financial indicators. Quantitative techniques that are essentially economic and of a minimum degree of technical sophistication have more often been used in Steps 2 and 3. Step 2 dominates merger proceedings, where the assessment of the competitive effect of an acquisition of a competitor is highly dependent on the assessment of the change in concentration in the affected markets. In monopoly situations Steps 2 and 3 have generally been given equal importance as both a monopoly and its abuse must be found, while restrictive agreements or practices focussed more on Steps 3 and 4. In general, the more contentious and complex the case, the more sophisticated the techniques that have been applied.



The use of quantitative techniques has differed from country to country and, in some instances, between different authorities within the same country. In the US, antitrust authorities and the courts have a longer tradition of relying on economic analysis and empirical verification. This is partly due to the increased influence of economists in the Department of Justice, which became noticeable during the 1970s, but is also due to the more litigious nature of US antitrust policy which is very demanding in terms of supporting economic and factual evidence. Expert testimony is more often required in a litigation setting where the adversarial process pitches expert against expert and where each party tries to expose the weakness of the other parties’ arguments and evidence. An investigative procedure poses different demands on the parties involved and does not allow them to influence the investigative process as much. On the contrary it is the investigating authority which drives the process and this is typically the case in Europe (MMC, the European Commission’s DGIV, etc). 4


European antitrust authorities have only more recently paid more explicit attention to empirical evidence provided by economists. The introduction of the European Merger Regulation 4064/89 has arguably provoked a major change in the use of economics and expert economic evidence, culminating in the recent Commission Notice on market definition,5 which explicitly advocates the use of quantitative techniques to provide evidence of demand substitution: ‘There are a number of quantitative tests that have specifically been designed for the purpose of delineating markets. These tests consist of various econometric and statistical approaches: estimates of elasticities and cross-price elasticities for the demand of a product, tests based on similarity of price movements over time, the analysis of causality between price series and similarity of price levels and/or their convergence. The Commission takes into account the available quantitative evidence capable of withstanding rigorous scrutiny for the purposes of establishing patterns of substitution in the past.’6


Four basic applications of quantitative analysis in antitrust are examined in the rest of this chapter:  the determination of relevant antitrust markets;  the analysis of market structure;

4 5


For an overview see Doern, G.B., 1996. Commission Notice on the definition of the relevant market for the purposes of Community competition law OJ 1997 C372/5. Supra footnote 5.



the analysis of competition, in particular the analysis of pricing behaviour; and the analysis of efficiency effects.

The delineation of markets 2.7

Because of the increased importance of quantitative analysis for defining antitrust markets it is worth spelling out in more detail the type of empirical evidence that is required to establish the extent of demand-side and supply-side substitution, these being the key criteria for defining a relevant antitrust market.


The most well known and largely accepted method used by competition authorities in many countries is the hypothetical monopolist, or cartel, test. This test seeks to identify the smallest set of products and producers (containing the product under investigation), where a hypothetical monopolist or cartel, controlling the supply of all the products in that set, could increase profits by instituting a small, but appreciable, permanent increase in price over the competitive level. This is also known as the SSNIP (Small but Significant, Non-transitory Increase in Price) test. The underlying approach can be applied to geographic market identification as well as to product market identification.


This approach was pioneered in the USA where the competition authorities - primarily the Department of Justice (DOJ) and the Federal Trade Commission (FTC) - first set out these principles in the 1984 Horizontal Merger Guidelines which have since been revised several times.7 This approach has also been set out by the European Commission in its


Department of Justice and Federal Trade Commission, 1992, Horizontal Merger Guidelines, Antitrust and Trade Regulation Report, 69(1559), Washington D.C. For a more thorough discussion on the application of the guidelines see Langenfeld, J., 1996, The Merger Guidelines as Applied, in Coate, M. and A. Kleit, eds., The Economics of the Antitrust Process. An interesting discussion on the evolution of US merger policy with respect to market definition can be found in Lande, R. and J. Langenfeld, 1997, From Surrogates to Stories: The Evolution of Federal Merger Policy, Antitrust Magazine, Spring: 5-9


Notice on market definition.8 In the UK, the OFT has recently referred to the use of this approach in the guideline Market Definition (reference: OFT403) issued under the Competition Act 1998. 2.10

In the past, MMC reports have not always set out a rigorous definition of the relevant market. There are of course exceptions such as the London Clubs International/Capital Corporation merger report (1997) where the SSNIP test is used to define the relevant market.


The correct definition of the relevant antitrust market is an important feature of an accurate competition analysis. A too narrowly defined market can lead to unnecessary competition concerns, and on the other hand, a too widely defined market may disguise real competition problems. This will certainly be the case if too much emphasis is placed on the market share arising from an ‘incorrect’ market definition.

Demand-side substitution 2.12

Analysis of demand-side substitution focuses on what substitutes exist for buyers and whether enough customers would switch, in the event of a price increase, without incurring a cost,9 to constrain the behaviour of suppliers of the products in question. This is an essentially empirical question.


Substitutes do not have to be identical products to be included in the same market. Indeed most products and services today are differentiated products. Nor do product prices have to be identical. For example, if two products serve the same purpose, but one is of a different specification, perhaps a higher quality, they might still be in the same market, as long as customers prefer it due to a higher price-quality ratio. For example, a Mercedes-Benz may last 400,000 kilometres, but a luxury Ford may last 200,000 kilometres. If the two cars were the same except for the life of the car, then one would not expect their prices to be the same. If the price of the Mercedes were to increase, its cost per kilometre driven would then be greater than the Ford’s and some consumers would switch to the Ford. In addition, products do not have to be direct substitutes to be


In its Notice on market definition the European Commission says: ‘The question to be answered is whether the parties’ customers would switch to readily available substitutes or to suppliers located elsewhere in response to a hypothetical small (in the region of 5-10%) permanent relative price increase in the products and areas being considered. If substitution would be enough to make the price increase unprofitable because of the resulting loss of sales, additional substitutes and areas are included in the relevant market. This would be done until the set of products and geographic areas is such that small permanent increases in relative prices would be profitable.’


This Notice is also referred to in paragraph 2.5. The cost here does not have to be in monetary terms, for example, if a consumer has to take a bus three hours earlier than his normal time to be able to switch to another operator, it is a cost to this customer.


included in the same market. There may be a chain of substitution between them. 10 2.14

Moreover, it is not necessary for all consumers, or even the majority, to switch actively to substitute products for the products still to be regarded as substitutes and in the same market. The important factor is whether the number of customers likely to switch is large enough to prevent a hypothetical monopolist maintaining prices above competitive levels. In fact if a 10% price increase were to lead to as little as 10-20% of customers switching to substitute products the benefit of the price increase would be lost and it would be unprofitable for the company to make the price increase.11 The behaviour of so-called ‘marginal consumers’ who are most likely to switch keeps prices competitive not only for themselves but also for other consumers who are not able to switch, assuming that suppliers cannot price discriminate among customer groups. Clearly the stronger the evidence that consumers would switch, the less likely it is that a particular product or group of products is in a market on its own. 12


The costs of switching can, however, be very important for customers. For example, changing from electric to gas heating, following a fall in the price of gas, may involve a substantial amount of investment in new equipment. Another example of a market where switching costs can be significant, is the market for video games. Here consumers are faced with video games developed around different hardware – the PC, or the console – giving rise to switching costs for consumers in the video games market. 13 In the presence of switching costs there may be a large gap between short and long run demand substitution.

Supply-side substitution 2.16

In the absence of demand-side substitution market power may still be constrained by supply-side substitution.14 Supply-side substitution occurs where suppliers are able to respond rapidly to small but permanent changes in relative prices by switching production to the relevant products, without incurring significant additional costs or risks.

10 11




See diversion analysis below, for a more detailed discussion of this concept. The profit change is not simply the result of demand side changes but depends also on the way lower output, arising as a result of the increased price, affects costs. The European Commission is known to take evidence of demand-side substitution in the range of 1020% very seriously. The MMC investigated the relevance of switching costs in their report on Video Games (1995) and undertook a similar analysis in the context of Telephone Number Portability (1995). Cfr. DOJ-FTC, 1992, Horizontal Merger Guidelines, op. cit., paragraph 2.9.


In these circumstances, the potential for supply-side substitution will have a similar disciplinary effect to demand-side substitution on the competitive behaviour of the companies involved. 2.17

As with demand-side substitution, supply-side substitution needs to be relatively quick, for without speed its effectiveness in constraining current market power is reduced. It is a matter of opinion about how quickly supply-side substitution should take place, to distinguish it from entry, but it is often set by competition authorities to within a year.


An example of this is the supply of paper used in publishing. 15 Paper is produced in various grades dependent on the coating used. From a customer’s point of view these types of paper are not viewed as substitutes. However, these grades are produced with the same plant and raw materials so it is relatively easy for manufacturers to switch production between different grades. If a hypothetical monopolist in one grade of paper tried to set prices above competitive levels, manufacturers currently producing other grades can start to supply this grade.


Analysing short run supply-side substitution raises similar issues to the consideration of barriers to entry. Both are concerned with establishing whether firms will be able to begin supplying a product in competition with another existing firm. The distinction is only one of timing, that is, the speed of set-up.


The European Commission now makes explicit reference to short run supply-side substitution as a factor that should be considered in defining markets. This reflects the European Court of Justice’s judgement in Continental Can,16 which was critical of the failure by the Commission to include supply-side substitutes within the market.


The type of evidence to be used in an assessment of supply-side substitution include the following:17   

15 16 17

systematic analysis of firms that have started or stopped producing the products in question; the time required to begin supplying the products in question; enquiries of potential suppliers to see if substitution is possible (even if the potential supplier currently has no plans to enter the market) and at what cost;

European Commission, 1992, Case IV/M.0291, 1992, Torras/Sarrio. European Commission,1973, Case 6/72, ECR 215, Europemballage Corpn and Continental Can Co Inc The same factors apply to the analysis of consumer switching costs for the assessment of demand-side substitutability.




enquiries of firms might be included to determine whether existing capacity is tied up, perhaps because of long term contracts; the views of customers - in particular, their views on whether they would switch to the new supply, and whether the costs of switching were prohibitive; and an evaluation of the ‘sunk costs’ of switching, to see if potential suppliers can begin producing the products in question without risking substantial investment.

It is probably fair to say that quantitative measurement techniques have so far been applied rather more to the demand side than to the supply side, in competition cases.

CASE STUDY 1: NESTLE-PERRIER MERGER The Nestle-Perrier merger case 18 of 1992 is interesting for a number of reasons. First, the Nestle-Perrier case is interesting because it provides an example of how markets can be defined for antitrust purposes. In this instance very little empirical analysis was undertaken to determine the correct antitrust market. Nestle notified the Commission of its intention to buy Perrier and cede Volvic, a Nestle mineral water brand, to BSN, the second biggest competitor. This was designed to pre-empt any involvement by the Commission on the basis of concerns about Nestle’s market position. Nestle and Perrier together had 75%, by volume, of the ‘market for mineral waters’. Nestle was claiming that the relevant market was the ‘non-alcoholic refreshing drinks’ market, which would include colas and all other soft drinks. To determine the relevant market for still and sparkling mineral waters the Commission used surveys and comparisons supplied by consumers, trade associations and supermarkets. There appears to have been some emphasis on price correlation, and graphs showing parallel price movements over time for all mineral water. Secondly, the Nestle-Perrier case provides a good example of barriers to entry and the conclusions of the European Commission as to their relevance in merger cases. In the NestlePerrier case the Commission pointed to the high degree of brand recognition in the mineral water industry. This brand recognition was due to intensive advertising campaigns that the three major firms (Nestle, Perrier and BSN) had undertaken over several years. New entrants to the market would have faced similar expenditure requirements to attract and retain customers. The Commission concluded that a new brand would require a long lead-time and heavy investment in advertising and promotion to compete in the market, and would have difficulty establishing a presence in the market due to the large number of brands already introduced by the top three firms. In its judgment, the Commission implicitly appealed to the theory that advertising is a barrier to entry because it is a sunk cost that cannot be recovered nor transferred to other uses.


Nestle Perrier, 92/553/Cee, OJ 5-12-1992 vol. L 356


It is also using the brand proliferation argument of Schmalensee. 19 Both of these factors have been recognised as potential barriers to entry in modern IO literature. However, both are difficult to apply and the brand proliferation argument, in particular, has been criticised. Subsequent analysis showed both the pre- and post-merger market shares of Nestle and BSN to be very high. So, although Perrier had chosen to divest Volvic to BSN, the market positions of the two were effectively made more symmetric without their combined market share falling at all. In other words, the divestiture would strengthen the duopolistic structure of the market. Despite this, the Commission would not block the merger outright, instead negotiating a remedy with Nestle and Perrier. Nestle was allowed to keep Perrier, but had to sell eight of its lesser brands of mineral water to another company, which was not in the market at that time, to create competition in the market. 20

The relevant geographic market 2.23

The relevant geographic market is the area over which demand-side and supply-side product substitution takes place. Of particular importance in defining the geographic market is the degree to which chains of substitution extend the market, and the role played by imports in conditioning the ability of local suppliers to raise prices.


The type of evidence that can be used to determine the extent of a geographic market includes surveys of consumer and competitor behaviour; estimates of price elasticities in different areas; and analysis of price changes across contiguous geographic areas. The latter can provide reasonable evidence that two areas are in the same market if the prices for the product under examination move together in the two areas for reasons unrelated to changes in the costs of production.

19 20

Schmalensee, R., 1978, Entry deterrence in the ready-to-eat breakfast cereal industry, Bell Journal of Economics, 9:305-27 in Office of Fair Trading Research Paper 2 1994, Barriers to entry and exit in UK competition. The other important feature of the case was that the Commission for the first time investigated the matter not as a single firm dominance case (monopoly) but multiple firm dominance case (oligopoly).


CASE STUDY 2: DEFINING THE GEOGRAPHICAL EXTENT OF US PETROL MARKETS During the 1980s a number of mergers occurred between petrol producers in the US, and the issue of the determination of the relevant geographic markets for wholesale fuel became a heavily contested issue. There is very little doubt that the area west of the Rocky Mountains constitutes an isolated geographic market, as no petrol is transported there from the rest of the country. All petrol consumed on the West Coast is either imported or refined west of the Rockies. It is more difficult to establish in which way the other areas of the country are connected. Refineries located in the Gulf Coast, which produces about half of the total US production, provide virtually all the petrol sold in the South-East. The petrol flows to the region via two pipelines, the Colonial and the Plantation. Terminals are clustered along these pipelines, usually close to main urban areas, and petrol is transported from these terminals to the final destination by lorry. North-eastern buyers are supplied with petrol by three main sources: local refineries located near the larger cities (the North-East produces about 15% of the total US output); the Gulf Coast refineries via the Colonial pipeline; and, foreign refineries. The existence of two different and one common source of supply (the Colonial pipeline) make it impossible to determine, without a detailed investigation, whether the North-East and the South-East of the US are in the same geographic market. In a leading article on the use of quantitative techniques in market definition, Sheffman and Spiller21 used residual demand analysis to determine whether the relevant antitrust market for gasoline refining in the eastern United States covers the whole area east of the Rocky Mountains, or whether the Gulf Coast and the North-East together, or the North-East alone, form an antitrust market. This analysis used monthly data for the period April 1981 to February 1985 in order to estimate residual demand functions for wholesale gasoline; the regressors include a vector of cost shift variables 22 for potential competitors, to account for supply-side substitution; and a vector of demand shift variables 23 to account for demand-side substitution. The results showed that there were two relevant antitrust markets east of the Rocky Mountains relevant for merger analysis: the Gulf Coast alone, for mergers that happened between refineries within this area, and the area that is comprised of the North-East plus the Gulf Coast for mergers that included the North-East. The results obtained with residual demands differed from those from correlation tests that show highly correlated prices across the whole area east of the Rockies 24 leading to a wide market definition. In other words, the economic market was larger than the antitrust market. To be more specific, while ‘historical’

21 22 23 24

Sheffman, D.T. and P.T. Spiller, 1987, Geographic market definition under the US Department of Justice Merger Guidelines, Journal of Law and Economics, 30: 123-47. The vector contains crude oil price, energy use, transport costs for crude oil, refining capacity, and a weather variable. See Chapter 9 for a description of residual demand analysis. The vector contains per capita income, industrial production, and seasonal factors. This result is confirmed by Stigler, G.J. and R.A. Sherwin, 1985, The Extent of the Market, Journal of Law and Economics, 28: 555-85. See paragraph 5.8 below for a more detailed discussion.


petrol prices tend to converge across the area west of the Rockies, refineries in the Gulf Coast have the potential power to promote a long-lasting price increase by cutting capacity in the Gulf area alone. Such a price increase cannot travel beyond the regional boundaries of the Gulf Coast, which makes that area an antitrust market.

Quantitative tests for market definition 2.25

There are a number of quantitative tests available to help market definition, and there is much debate on which is the most adequate. In Chapters 5 to 8 we review tests that are based on the analysis of price trends as well as the more sophisticated test of demand (Chapter 9) or a system of demand equations (Chapter 15). Generally, tests based on price trends alone should be treated with caution, as they do not allow an assessment of whether prices could be profitably raised by market participants. However, the paucity of the data available often prevents the analyst from estimating more appropriate demand models, so that antitrust markets are defined on the basis of price tests alone. The two examples in this chapter on the definition of the relevant market for radio advertising and the relevant market for petrol highlight some of the techniques used in the definition of the relevant product market and the geographic market respectively.

Analysis of market structure 2.26

The traditional analysis of antitrust is firmly rooted in the structure-conduct-performance paradigm developed by Bain. 25 According to this view, it is the structure of the market that determines its performance, via the conduct of its participants. Performance is measured by the ability to charge a price above the competitive level, thereby earning a positive mark-up. In line with this paradigm the degree of concentration in a market has long been considered one of its major structural characteristics and analysis of market structure then becomes a key indicator of actual or potential market power.


It is now recognised at both a theoretical and empirical level that the structure-conductperformance approach is overly simplistic and that matters are more complex. Nevertheless, considerable emphasis in antitrust cases does still seem to be put on structural data, perhaps partly because it is relatively easy to collect. Concentration indices 2.28

Here, market shares are calculated for all firms identified as participants in the market. They may be calculated on the basis of a firm’s sales or shipments or capacities, 25

Bain, J.S., 1956, Barriers to New Competition: Their Character and Consequences in Manufacturing Industries, Cambridge: Harvard University Press.; and Bain, J.S., 1951, Relation of Profit Rates to Industry Concentration: American Manufacturing, 1936-1940, Quarterly Journal of Economics, 65: 293-324.


depending upon the nature of the market. Note that total sales (or capacity) include those that are likely to arise in response to a small, non-transitory increase in price. So, even firms not currently producing for, or selling in, the market are assigned ‘hypothetical’ market shares. As already noted, the simple market share held by a firm, or a merged firm, may be used to trigger an investigation. 2.29

The Herfindahl-Hirschman Index (HHI) is also used, for example, by the US competition authorities to measure market concentration. 26 The HHI is simply the sum of squares of individual market shares (and so it gives proportionately greater weight to larger firms). According to the FTC/DOJ Merger Guidelines, an ‘unconcentrated market’ has an HHI less than 1000, a ‘moderately concentrated market’ has an HHI between 1000 and 1800, and a ‘concentrated market’ an HHI greater than 1800, while a pure monopoly would have an HHI of 10,000. 27 Any merger which would leave the HHI below 1000 is considered unlikely to raise concerns that it will significantly reduce competition. A merger leading to an increase in the HHI of less than 100 points, when HHI is between 1000 and 1800, will also not normally be investigated. When the HHI exceeds 1800 and a proposed merger leads to an increase of more than 50 points, serious competitive concerns are deemed to be raised.


Measures of market concentration are only as good as the implied definition of the market. Even when that is deemed to be unproblematic however, market concentration measures are economically difficult to interpret, and the connection between market share and market power is far from clear. Farrell and Shapiro28 have argued that the HHI is a poor reflector of the welfare consequences which flow from a merger, and that ‘increases in the HHI are not associated with a lessening of economic welfare’. In fact, in some simple Cournot models, ‘more concentration amongst the non-merging firms makes it more likely that the merger will be welfare enhancing.’


Further: ‘Implicitly the guidelines assume a reliable (inverse) relationship between market concentration and market performance. In particular the entire approach presumes that a structural change, such as a merger, that increases the equilibrium value of [the] HHI also symmetrically reduces equilibrium welfare defined as the sum of producer and consumer surplus. Is there in fact such a reliable relationship between changes in market concentration and changes in economic welfare? In some very special circumstances there is…, but 26



There are other indices that provide a measure of market concentration. The HHI is simply the one that is used most widely. HHI levels of 1000 and 1800 correspond to a four-firms concentration ratio of 50-70% respectively. See also paragraphs 3.2 to 3.6 for a further discussion of HHI. Farrell , J. and C Shapiro, 1990, Horizontal Mergers: An Equilibrium Analysis, American Economic Review, 80: 107-26


if the competing firms are not equally efficient, or there are economies of scale, there is no reason to expect that concentration and welfare will move in opposite directions.’ 2.32

Here the now familiar objections to an overly deterministic and structuralist approach to competition are being made. Concentration indices, like all indices, are at best rough measures of the quantities of interest – in this case market power – and must be used with care. Analyses relying exclusively on such measures are likely to be led into error.

Price-concentration analysis 2.33

A frequently-used test to assess the impact of concentration in an industry (market) is to compare a number of local markets in terms of their supply characteristics. The hypothesis is that higher degrees of concentration go hand-inhand with higher prices and price-cost margins. Such tests are sometimes used in retailing markets that are local. The SCI/Plantsbrook (1995) case outlined in paragraphs 13.14 to 13.18 and the case study below on the US merger of two office supply chains give examples of this analysis. Other examples are the petrol enquiry by the MMC in 1989 and the merger of betting shop chains, Grand Met and William Hill, also in 1989. The MMC found that the merger of Grand Met and William Hill bookmakers would create a number of local monopoly situations leading to reduced competition in off-course betting at a local level, and that Grand Met should therefore divest certain betting offices. Offices should be divested where former Mecca betting offices (belonging to Grand Met) and William Hill betting offices were within quarter of a mile of each other, and, where there were no other betting offices within a quarter of a mile of one of these offices. There was some debate however, over whether the divestiture went far enough. The existence of barriers to entry at the local level as well as at the national level, where the two chains of bookmakers compete in terms of advertising and promotion, would suggest that the merger needed to be examined more closely with regard to its negative effect on competition. 29

CASE STUDY 3: STAPLES AND OFFICE DEPOT 30 Following industry consolidation Staples and Office Depot are two of the three remaining office supply ‘superstore’ chains in operation in the US - the other being Office Max. In September 1996 Staples Inc agreed to acquire rival Office Depot in an acquisition valued



London Economics, 1997, Competition in Retailing, Office of Fair Trading Research Paper 13 discusses the analysis of competition in retail markets Frankel, A. And J. Langenfeld, 1997, Sea Change or Submarkets?, Global Competition Review, June/July: 29-30


at $3.4 billion. Prior to the emergence of superstores in the mid-1980s, businesses and consumers typically purchased office supplies through dealers that offered items listed in a catalogue published by one of several office supply wholesalers. The superstore chains followed a different strategy. They constructed large, efficient warehouse-style stores where a variety of items were offered. Although this fell far short of the variety offered by traditional wholesalers with their immense catalogues, the cost savings on popular items were passed on to consumers in the form of lower prices. Most consumable office supplies are still however sold through other channels, including traditional distributors and their dealers, contract stationers, mass merchandisers and others. In April 1997 the FTC rejected an offer by Staples to divest up to 63 stores to Office Max as a condition for permitting the acquisition to proceed. This was unusual for a US antitrust agency that often settles merger challenges with this type of consent agreement. The FTC argued that the companies’ documents and statistical evidence demonstrated that Staples and Office Depot are particularly close competitors. That is, in geographic markets (metropolitan areas) in which two firms compete with one another, office supply prices are significantly lower than in metropolitan areas in which only one or the other is present. The FTC further concluded that the relevant product market includes only ‘the sale of office supplies through office superstores’ and that Staples’ acquisition would lessen competition in violation of antitrust legislation. Staples and Office Depot rejected the FTC’s statistical study of the relationship between price and head-to-head superstore competition on two grounds. First, they claimed that the FTC’s results were unrepresentative because of the particular set of office supply products analysed. Secondly, they argued that the FTC’s results did not take proper account of the fact that higher prices were found in the cities which generally have higher costs for doing business. Furthermore, Staples and Office Depot together account for only 5% of total annual sales of office supply products in the US. Both the FTC and the parties seem to agree that the advent of superstores has brought systematically lower prices to office supply consumers. Staples and Office Depot argued that the merger would allow more of the same, while the FTC maintained that it is this very cost and price reduction caused by the formation of superstores that has effectively turned them into their own relevant market. This case was interesting in that it showed the FTC’s general tendency towards focusing on anti-competitive effects involving either narrow markets or small parts of larger markets. It also highlighted their increasing reliance on sophisticated economic theories and statistical techniques to define these narrow markets and estimate the likely competitive effects of mergers.

Barriers to entry 2.34

Increases in concentration do not necessarily result in higher prices. If market entry is easy, the threat of entry by potential competitors reduces the ability to exercise market 23

power. The prevalence of barriers to entry has been a long-standing issue of debate among economists. Barriers to entry can be detected and their magnitude assessed by examining the profitability of firms from their activities in the relevant market. This is often done by comparing the Accounting Rate of Return (ARR) with the risk adjusted cost of capital. Martin Graham and Anthony Steele have described a superior technique in which the Certainty Equivalent Accounting Rate of Return (CARR) is compared with the risk free rate.31 Recent, largely theoretical, work in industrial organisation has greatly clarified the approach that should be taken to the analysis of entry conditions and barriers to entry. The so-called ‘new industrial organisation’ economics (‘the new IO’) 32 has brought strategic issues to the fore and has contributed greatly to our understanding. 2.35

In particular the new IO allows us to isolate a small number of factors of crucial importance in assessing barriers to entry, and questions concerning market power, and so directs attention towards particular aspects of the firm’s technology, market structure and firm behaviour. To be more specific, the new IO has revealed the fundamental importance of sunk costs, the nature of post-entry competition and strategic interaction between incumbents and entrants as being crucial to any analysis or case study of entry conditions in particular markets.


Against this background it is surprising to find that so much analysis of mergers is based on the structure-conduct-performance paradigm. The MMC, DOJ as well as the European Commission’s DGIV, put a lot of emphasis on the analysis of market structure and concentration ratios.


For example, the existence of high economies of scale is the typical Bainian barrier – the incumbents can set pre-entry output at such high levels that new entrants would be forced to sell at below cost. In a number of cases the MMC or the European Court of Justice (ECJ) have concluded that economies of scale deter potential competitors. Although it is unusual to find significant economies of scale without associated sunk costs, it is nevertheless important to make the distinction between economies of scale that involve sunk costs and those which do not.33 The existence of sunk costs which are irrecoverable if entry is unsuccessful is clearly recognised in United Brands: 34 ‘The particular barriers to entry to competitors entering the market are the exceptionally large capital investments required for the creation and running

31 32



Martin Graham and Anthony Steele, 1997, The Assessment of Profitability by Competition Authorities, OFT Research Paper 10 Harbord, D. and T. Hoehn, 1994, Barriers to Entry and Exit in European Competition Policy, International Review of Law and Economics: 411-35. Baumol, W. J., J.C. Pauser and R.D. Willig, 1982, Contestable Markets and the Theory of Industrial Structure, NY: Hartcourt Brace Jovanovich. The other side of this problem is that sunk costs also constitute a barrier to exit for the incumbent firms. Paragraph 122 of the decision in the ECJ case 27/76


of a banana plantation, …and the actual cost of entry made up inter alia of all the general expenses incurred in penetrating the market such as the setting up of an adequate commercial network, the mounting of very large scale advertising campaigns, all those financial risks, the costs of which are irrecoverable if the attempt fails.’ 2.39

The view that sunk costs are a barrier to entry is also argued by the Commission in the de Havilland case 35. Aerospatiale and Alenia, who control the largest European and worldwide producer of regional aircraft, proposed to acquire the second largest producer, de Havilland. The aircraft industry is characterised by high sunk costs in both plant and equipment, and in the costs of changing designs, which deter post-design alterations. The Commission found that a time-lag of two to three years for market research was required to determine the type of plane a market needed, and that the total lag time was six to seven years from initial research to point of delivery. Potential entrants from around the world were identified, but the Commission concluded that the additional investment required in research and development, and in design changes, made entry unlikely.


In their research report for the OFT, London Economics recommend a seven-step procedure to assess the existence of barriers to entry.36 The US approach is similar in that the DOJ focuses on the history of entry as well as the cost conditions under which viable entry is expected to occur. The US approach outlines three ways to deal with barriers to entry: 

Assess actual experience with entry into the market under investigation. The turnover of firms in the industry can be taken as an indication of ease of entry and exit. The higher the turnover, the easier is entry.

Estimate the ‘minimum viable scale’ (MVS). The US DOJ Merger Guidelines provide an heuristic test for assessing entry into markets for homogenous goods: if a firm can profitably enter the market with a market share of less than 5%, then entry can be assumed to be likely. The reasoning for the 5% benchmark is as follows. Assuming a unit price elasticity of demand, if the market price is raised by 5% due to a merger or other anti-competitive act, market demand will decrease by 5%. This creates an opportunity for entry, because it frees 5% demand capacity. The test consists of determining whether there could be a firm that would enter the market, produce that extra 5% and still be in business when the price goes down by 5% and back to its original level, as a result of the increased supply due to the new entry. Historical data on entry patterns in the market under investigation can be used as evidence for this test; the analyst will look at the size

35 36

[1991] OJ L 334/42. London Economics, 1994, The Assessment of Barriers to Entry and Exit in UK Competition Policy, OFT Research Paper 2.


of entrants in terms of market share at the time of entry, and at the size they reached after one or two years. In the absence of historical data, a less preferred alternative would be to look at the size and profitability of current firms. This way of proceeding will give the analyst an idea of whether entry could be possible, but it will not shed light on whether some new firm could actually enter the market: that depends on sunk costs. However, in the absence of information on sunk costs, this methodology might be the only one available to the analyst. 


Undertake pro forma calculations in a business-type analysis. The analyst who has information on the current market price, variable cost and initial investment will calculate whether it would be profitable to enter the market. From these calculations one can assess how large the company would have to be in order to be profitable, and so estimate the minimum viable scale.

The London Economics approach is more wide-ranging and includes the analysis of strategic behaviour which could also be considered to be part of the assessment of competition and anti-competitive behaviour.

Analysis of competition and the scope for market power Analysis of prices and price trends 2.42

The analysis of prices, price trends and relative price levels can be an important part of a competition investigation. Price analyses are particularly useful in investigations of alleged price fixing and bid rigging during procurement auctions, and can contribute to market definition analysis (as indicated above). The simple analysis of prices already provides a significant amount of information and once prices of products are analysed together with the respective quantities sold in the market then additional information is generated.37 More generally, prices are the main element in competition (although nonprice factors can also be important) and so price levels directly affect consumer welfare. Problems in competition normally manifest themselves in non-competitive price levels.


Below are just a few examples of instances in the past ten years where competition issues were raised on price grounds. Typically the public or some public watchdog argued that price was too high and that competition was in danger of malfunctioning unless the authorities intervened.


Hayek’s dictum that all relevant economic information is contained in a product’s price dates from his classic article and remains, despite its oversimplification, a powerful statement today. Hayek, F., 1945, The Use of Knowledge in Society, American Economic Review, 35: 519-30.


CASE STUDY 4: THE SUPPLY OF RECORDED MUSIC The 1993 MMC inquiry38 into the supply of recorded music was prompted by the concern about the prices of compact discs (CDs), with particular emphasis on the difference in prices between the UK and the US. The Consumers’ Association presented the MMC with a body of evidence gathered during their on-going observation of CD prices. They highlighted that CD prices had remained high since their introduction into the UK market while the price of CD players had fallen substantially; that the production costs for CDs had fallen; and, that there was widespread consumer dissatisfaction with the level of CD prices in the UK compared to the US. For the purposes of investigation, the MMC commissioned a survey that compared the retail prices of pre-selected, full price album titles, for both CDs and cassettes, across the UK, USA, Germany, France and Denmark. The average prices for the pre-selected CDs, without tax and adjusted to pound sterling equivalents, were then compared across the five countries. The results of the survey are presented in the table below. The results show that the average CD was priced 8% lower in the US than the UK and that average prices in the other countries were higher than the UK. Cassette prices exhibited a similar pattern with the US being 12.9% lower than the UK. One of the record companies, Sony, carried out its own survey on the prices of a larger sample of titles in the UK and US. Looking at weighted average prices of full price titles, they found that prices in the US were 5.8% lower than in the UK, for CDs, and 11% lower than in the UK, for cassettes. These results are similar to the MMC’s results. However, because of the larger sample the statistical significance of the smaller difference in the Sony survey could be confirmed. Furthermore Sony found - from an analysis of the average retail prices (unweighted) of Sony CDs in different US cities - that the price range was actually greater within the US than between the US and the UK. Table 1: Cost in Pounds Sterling of Pre-selected CD Titles in Europe and the US

Pre-selected Titles






Diva – Annie Lennox Soul Dancing – Taylor Dayne Zooropa – U2 Keep the Faith – Bon Jovi River of Dreams – Billy Joel Timeless – Michael Bolton Tubular Bells II – Mike Oldfield What’s Love Got to Do With It? – Tina Turner 10.06 Column Average

11.78 11.25 10.22 10.56 10.33 11.26 11.71

10.21 9.83 9.85 10.53 9.45 10.45 10.21

13.03 12.87 11.88 12.81 11.74 12.41 12.71

11.23 11.19 10.72 10.89 10.75 11.00 10.92

11.38 11.58 11.33 11.50 11.28 11.28 11.25


9.67 13.12 11.15 11.44 10.90 10.03 12.57 10.98 11.38

Monopoly and Mergers Commission, 1994, The Supply of Recorded Music. A Report on the Supply in the UK of Pre-recorded Compact Discs, Vinyl discs and tapes containing music.


* F is France * G is Germany Source: BMRB International Survey of retail prices, September 1993 Given the consistent findings of lower prices for recorded music in the US than in the UK, the MMC asked a specialist retail consultancy to assess whether the differences in CD and cassette prices between the US and the UK were reflected by similar differences in prices of other products. A price audit on a carefully matched basket of manufactured leisure goods, sold at similar prices to recorded music, was undertaken in late 1993. The audit found that on average the US prices (using the same exchange rate as the original MMC survey and without tax) were 8% lower than the same goods in the UK. This result was in line with the results of the price surveys for CDs indicating that there was nothing unusual or atypical about this market. These findings contributed to the MMC’s conclusion that the complex monopoly in the supply of recorded music did not act against the public interest.


In the petrol enquiry the MMC report discussed at some length and summarised empirical evidence regarding the transmission of price changes for crude oil to prices at the petrol station. This involved the analysis of long-term price trends through dynamic regression analysis. The justification of this analysis was the apparent uniformity of prices at petrol stations in local areas on the one hand and the speed at which prices were adjusted upwards when the price for crude oil rises on the Rotterdam spot market. The MMC investigated the supply of petrol in the UK and in its report cleared the industry of any anti-competitive practices. 39


Monopoly and Mergers Commission, 1989, The Supply of Petrol.



The analysis of competition in antitrust investigations often takes the form of seeking to establish the effects of a particular set of actions or a change in behaviour. This is the case when the formation of a cartel is said to have led to higher prices than otherwise would have been the case. Similarly the merger of two companies active in the same market is an event that may lead to a change in output, quality or price, to the detriment of consumers. While in the case of a notification of a merger the event is in the future, in many cases there is a historical event that can be evaluated in an empirical fashion.


There is no single quantitative technique that is designed to capture the historical impact of an event. Rather, event analysis or impact analysis, is an element in most quantitative techniques. For example, the analysis of price trends mentioned above could entail the statistical test for a structural break of the time series. Did the break up of the international coffee cartel lead to change in green coffee bean prices traded on the international commodity markets? Did the ending of anti-dumping duties on soda ash lead to a decrease in soda ash prices in the European Union? Did the co-ordinated price announcement of the European carton board producers lead to higher prices than the free interaction of supply and demand would lead us to expect? More generally, events can be analysed in a number of ways:


Time series of prices can be evaluated in terms of structural breaks (for example, standard tests exist in most econometric packages; dummy variables that relate to the event can be included in regression equations).

Actual price trends can be compared with what a model of competition in the absence of an event would predict (bidding models, for example, or oligopoly models where the number and identity of players are changed).

The counterfactual can be empirically established in some instances (stock market reactions to news of a merger or price announcement can be compared to an index).

Event analysis – or impact analysis – played an important part in the Carton board case40. Here the European Commission imposed heavy fines on companies who had formed a cartel, which among others co-ordinated regular price announcements over a period of five years. The decision was recently confirmed in an appeal at the Court of First Instance (the decision of May 14 1998). As part of their defence to argue mitigating circumstances a study of actual price trends was commissioned by a group of companies and submitted to the Commission. This study analysed the actual behaviour of prices as against the trend of prices implied by the series of price announcements and showed a major divergence of these two price series. The study showed how the prices achieved 40

Carton board, 1994, Case IV/33833, OJ L243.


in the market place only followed the price announcements initially and even then only to a small degree. The Court noted the lack of any verification by the Commission of the actual effect of the cartelistic practice but this did not affect the decision to uphold the Commission’s findings. Models of competition 2.48

A number of empirical approaches and simulation models have been developed with the aim to assess directly the scope for market power following a merger between producers of differentiated products. Similarly, existing rather than potential market power may manifest itself through anti-competitive behaviour which limits the extent to which a leading firm will lose sales to close competitors. One technique that seeks to quantify these effects is the so-called ‘diversion ratio’ which measures the degree to which a firm is subject to loss of sales to competitors who provide many customers’ first and second preferred choices (with an homogenous, or identical, product market any price differences should, all other factors being equal, lead to full and immediate substitution to a rival).


With the same objective (to establish the scope for independent behaviour), the Department of Justice has employed statistical estimation of the demand for the differentiated products of competing firms, and then simulated potential price increases based on models in which consumers’ rankings of goods are independent of prices—in technical terms, logit demand models.41 Such or related methods have been used in cases concerning fragrances, desk-top publishing software, wholesale bread bakers, and many others.


Other empirical and simulation approaches follow a similar approach to the logit model, but are less restrictive in their assumptions. They also use data on prices, quantity of sales, margins, and costs in attempts to estimate the full system of demand equations for competing differentiated products.42 Then, the effects of a merger (or other practice) on price is simulated from the elasticities and other relations from these estimated equations. Such methods have been used in presentations to the US enforcement agencies, most often for consumer products for which scanner-based price data is available. In Europe such techniques have been little used. One notable exception is the case of Kimberley Clark/Scott where such techniques were employed. Another case where the nature of competition was modelled explicitly is the Boeing/McDonnell Douglas merger. There an interested third party provided empirical evidence of the bidding process in the sale of civilian aircraft.



Sources include Werden, G.J. and L.M. Froeb, 1994, The Effects of Mergers in Differentiated Products Industries: Logit Demand and Merger Policy, Journal of Law, Economics, and Organization; 10(2): 407-26. A leading example is Hausman, J.J., G. Leonard, and J. D. Zona, 1994, Competitive Analysis with Differentiated Products, Annales d'Economie et de Statistique,, 34: 159-80.


CASE STUDY 5: THE BOEING/MCDONNELL DOUGLAS MERGER This merger 43 between two of the three main suppliers of civil aircraft, has been the cause of much debate. In normal circumstances a merger that reduced the number of firms in an industry from three to two, and saw a large increase in the HHI would have provoked an antitrust suit by the FTC and have faced stiff resistance from the European Commission. Instead the merger was allowed to proceed with the European Commission imposing the relatively weak remedy of accounting separation for the military and civil side of the combined Boeing/McDonnell Douglas Corporation. One of the arguments used by Airbus, the main competitor to Boeing and the McDonnell Douglas Corporation (MDC), to influence this decision was a bidding study. This study revealed that in 54 ‘campaigns’ or bidding procedures, the presence of the MDC as a bidder played a vital role in reducing the prices paid by airlines. On average, prices were 7.6% higher when the MDC did not bid. The result was the same, regardless of whether the size of the sale and other factors, were statistically controlled. These results would suggest that the merger might be anticompetitive as competition in the civil aircraft market was likely to be reduced, so increasing the prices paid by airlines and the fares subsequently paid by consumers. The obvious conclusions that were drawn from the bidding study assumed that the future would be similar to the past in the absence of the merger. Boeing, however, argued that the MDC was a failing firm (an argument often used in antitrust cases) and that no-one would buy MDC products again. The argument succeeded even though there was evidence to suggest that the MDC was not a failing firm in the classical sense. Indeed, for its newest aircraft there is an orders backlog for years of production work, and even as a spares business the MDC would be profitable for several years. It is interesting that Airbus was the only party to oppose the merger. Even the US customers of Boeing, did not question the amalgamation of the two suppliers. This lack of involvement by US airlines was used to support the decision to allow the merger. Airbus on the other hand,


Bishop, B., 1997 The Boeing/McDonnell Douglas Merger, European Competition Law Review, 18(7): 417-19.


which,based on the results of the bidding study was set to benefit from increased prices, opposed the merger. It feared that Boeing would use its new market power in a predatory manner, using offset deals on military aircraft produced by the military unit of MDC.

Predatory pricing 2.51

In contrast to concerns that prices are too high, prices that are too low may also be troublesome. As an empirical matter it is very hard to determine when pricing is predatory. The offence – low pricing – is also a prime virtue of the competitive process. Distinguishing predatory from normal competitive behaviour is therefore a subtle task. Among others, London Economics (1994) proposed a two-part test for predatory pricing.44 The first step is an analysis of market structure to determine whether predatory behaviour is potentially a rational strategy. The crucial question is whether the alleged predator, if successful in deterring entry or inducing exit, could recover the short-term losses incurred. The second step is an examination of conduct or market behaviour using a price-cost test, such as the one suggested by Areeda and Turner 45 which seeks to establish whether prices are below variable costs. In addition it is useful to investigate the history of entry-deterring behaviour in the market and evidence of intent. Modern theory also suggests that capital market imperfections, for example, information asymmetries and financial constraints, can be important in supporting predatory behaviour.


Areeda and Turner’s46 price-cost test excludes only the following from variable costs: (i) capital costs, (ii) property and other taxes, and (iii) depreciation. It is important is to determine which costs were truly ‘avoidable’ in the sense that they would not have been incurred otherwise, that is, if prices had not been lowered and output or sales thereby increased. As stated by Areeda and Hovenkamp: 47 ‘Which costs are to be considered variable and fixed is a function of the time period and how large a range of output is being considered. All costs are variable in the long run...A predatory pricing rule should focus on those costs which are



46 47

Similar tests have been proposed, and used, by the UK’s Office of Fair Trading, see G. Myers, 1994, Predatory Behaviour in UK competition policy, OFT Research Paper 5 which adds to the analysis of predation with an assessment of intent as well as a discussion of the cost tests that can be applied. See also Judge Easterbrook in A.A. Poultry Farms Inc. v. Rose Acre Farms Inc. F.2d 1396 (7th Cir. 1989), and Klevorick (1993). Areeda, P. and D. Turner, 1975, Predatory Pricing and Related Practices Under Section 2 of the Sherman Act, Harvard Law Review, 88: 697-33. Areeda, P. and D. Turner, 1975, op. cit. Areeda, P. and H. Hovenkamp, 1992, Antitrust Law: An Analysis of Antitrust Principles and Their Application, 1992 Supplement. Boston: Littlebrown.


variable in the relevant time period. ...The cost-based rules must focus on costs which the defendant should have considered when setting the allegedly predatory price.’ So, the distinction between fixed and variable costs will depend upon the range of output and the time period involved, the nature of the firm’s contracts with its input suppliers, whether or not it has excess capacity, etc. The important point is the identification of the avoidable costs upon which economic decisions are based.

CASE STUDY 6: AKZO The classic finding of predation in EU competition law is the AKZO case.48 AKZO Chemie was the major European producer of organic peroxides, one of which, benzoyl-peroxide, was used in flour additives in the UK and Ireland. Most sales of organic peroxide however were in the European plastics market, where AKZO was a dominant supplier. 49 ECS, a UK producer of flour additives, began to produce benzoyl-peroxide for its own use in 1977 after a series of price rises by its main supplier, AKZO. 50 When ECS started to expand into the more lucrative European plastics market in 1979, AKZO responded with direct threats of overall price reductions in the UK flour additives market and price cuts targeted at ECS’s main customers, if ECS did not withdraw from the plastics sector. In December 1979, ECS was granted a High Court injunction in the UK to prevent AKZO from implementing its threats. An out-of-court settlement was subsequently reached in which AKZO undertook not to reduce its selling prices in the UK or elsewhere ‘with the intention of eliminating ECS as a competitor’. Prior to the dispute AKZO regularly increased its prices to its UK customers by increments of 10%. ECS tended to follow AKZO’s UK price increases whilst maintaining its own prices approximately 10% below AKZO’s. In March 1980, following the out-of-court settlement, AKZO again increased its UK prices by 10%, but on this occasion ECS did not follow, increasing the normal price gap between the two companies. Some of AKZO’s large customers subsequently approached ECS for price quotations. AKZO responded by matching or bettering ECS price offers, and undercutting ECS’s prices to its own customers. 51 This resulted in AKZO gaining market share at the expense of ECS. The price history as described by the Commission would appear to be consistent with vigorous price competition following a breakdown of previously co-ordinated pricing strategies, or with

48 49 50


Phlips, L. And I.M. Moras, 1993, The AKZO Decision: A Case of Predatory Pricing?, Journal of Industrial Economics, 41: 315-21. AKZO’s market share in the EC market for organic peroxides in 1981 was 50%. In 1982 AKZO’s market share in the UK flour additives market was 52%, followed by ECS with a market share of 35%, and Diaflex with 13%. Diaflex purchased its raw materials from AKZO. There were three large buyers of roughly comparable size with a combined market share of 85%, plus a number of smaller independent flour mills. Diaflex followed suit and offered prices similar to those quoted by AKZO to two large independent customers of ECS. Price cutting continued until 1983 when ECS was grated interim measures by the Commission.


predation. 52 The Commission concluded in favour of predation on the basis of internal AKZO documents which indicated that eliminating ECS was its strategy, and internal management documents apparently demonstrating the AKZO prices for selected customers were less than average variable, or marginal, costs. The Commission argued that AKZO’s predatory behaviour was creating a barrier to entry, and pointed to evidence of other predatory episodes as well as evidence of financial difficulties at ECS which limited its ability to sustain a prolonged price war. This case contains practically all of the ingredients required for successful predation. AKZO had significant market power in each of the markets in question (that is, large market shares in both the UK flour additives market and the EU plastics market), evidence of predatory intent was given, as well as evidence of previous predatory episodes. Prices were found to be below average variable cost in targeted market segments and ECS was found to be financially constrained. In addition, AKZO was apparently targeting a market of minor importance to protect its more lucrative European plastics market, so minimising the costs of predation, while inflicting maximum damage on its competitor. The case has received widespread attention. On appeal the ECJ supported the main findings of the Commission, despite a dissenting opinion by the Advocate-General, 53 and AKZO was fined ECU 7,500,000.

Evaluation of efficiency defences 2.53

A major area of economic antitrust analysis that requires quantification is the so-called ‘efficiency defence’. This relates to claims of efficiency gains from certain restrictive practices between firms and proposals for establishing joint ventures or full-blown mergers. Economics distinguishes three types of efficiency and each presents a number of problems when it comes to empirical verification: 



allocative efficiency, which means that prices reflect costs such that firms produce relatively more of what people want and are willing to pay for. As a result, resources are allocated within the economy in such a way that the output most valued by consumers is produced;

See Phlips, L. And I.M. Moras, 1993, op. Cit., who interpret the price history as evidence of ‘the reaction of a dominant firm that lost its price leadership and tries to discipline a deviant’, the result being a shift from ‘a price leadership situation towards a more competitive one’. We are not unsympathetic to this interpretation, although their argument that the market was characterised by ‘ complete information’, making predation a non-credible strategy, strikes us as far-fetched. The Advocate-General disagreed with the Commission’s approach to market definition, and argued that, in any case, it was not sufficiently proved that AKZO held a dominant position in the relevant market. He also found insufficient evidence of abuse of dominant position.



productive (internal) or technical efficiency, which means that, given output, production takes place in practice using the most effective combination of inputs: so productive efficiency implies that internal slack is absent; and

dynamic efficiency, which means that there is an optimal trade-off between current consumption and investment in innovation and technological progress.

The role of antitrust legislation is to improve allocative efficiency without restricting the productive and dynamic efficiency of firms to the extent that there is no gain, or a net loss, in consumer welfare.54

Allocative efficiency 2.55

Traditionally, the economic literature has put particular emphasis on how competition might promote allocative efficiency. This mechanism is easier to understand and study empirically than other notions of efficiency. Quite simply, competitive pressures tend to push prices towards marginal costs by eroding market power. Given a certain number of firms in a market, the alignment of prices with marginal costs generates allocative efficiency. In this perspective, collaboration between independent firms or mergers that reduce the number of firms are unlikely to promote allocative efficiency. The verification or falsification of this hypothesis is the subject of the price-cost margin and concentration ratio analysis discussed above in paragraphs 2.42 to 2.52. Such a test is designed to support or reject the hypothesis that allocative efficiency is being impaired by a merger proposal.


However in some circumstances marginal cost pricing may conflict with other objectives. In particular in the presence of increasing returns to scale, increasing the competitive pressures on prices may not give the optimal incentive for market entry. More competition (in the sense of more firms) causes prices to fall towards marginal costs, but at the same time less advantage is taken of scale economies related to fixed entry costs, and so average cost rises. Under fairly general conditions, the negative externality that an additional entrant imposes on existing firms, by taking business from them, may outweigh the positive externality to consumers in terms of lower price.


In this perspective the assessment of economies of scale and, in particular, their empirical verification becomes central to an antitrust assessment. The measurement of economies


Bork, R., 1978, The Antitrust Paradox, Maxwell Macmillan: Oxford


of scale is, however, not a trivial task.55 While it is in general possible to use accounting data to derive point estimates of costs at different levels of output it is only through a proper econometric estimation of cost functions that more reliable estimates can be obtained. Numerous empirical studies of economies of scale have been undertaken at the industry level. 56 In the European context the study by Davies and Lyons 57 is a good example. Another excellent study that goes beyond cross-sectional analysis of industries and tries to explore the way unique cost structures govern the performance of selected industries is Sutton. 58 In his path-breaking study he deals with, among others, the salt, sugar, soft drinks and beer industries and shows how the existence of exogenous sunk cost (technological) and endogenous sunk costs (advertising) interact with economies of scale and determine minimum boundaries of concentration of a specific industry. 2.58

In the context of antitrust proceedings the estimation of economies of scale is very timeconsuming and cannot usually be undertaken in merger investigations that are subject to tight deadlines. However, major industry investigations allow room for more in-depth research. In the UK the beer industry, 59 and the salt industry, 60 for example, have been extensively investigated. In Europe, the sugar industry has been investigated by the European Commission on a number of occasions. 61


Another area of antitrust where cost structures and, in particular economies of scale, are absolutely central to the considerations of an investigating authority is the failing company defence. Only if economies of scale exist and are very large with respect to the size of a market, can this argument for a merger be made. The acquisition of British Caledonian by British Airways in 1987 was such a case, albeit controversially so. The failing company defence was also used in the Boeing/McDonnell Douglas merger, a summary of which can be found on page 30. In the European Union the issue of unviable cost structures and the need to achieve efficient levels of production is the key element





59 60 61

Not least because many production processes are multi-product and not simple single production processes. See Baumol W. J., J. C. Panzar and R. D. Willig (1982), Contestable markets and the theory of industry structure. Harcourt Brace Jovanovich: New York. See, for example, the overview by Schmalensee, R., (1989) Inter-Industry Studies of Structure and Performance, in R. Schmalensee and R.D. Willig (eds.), The Handbook of Industrial Organisation Volume II, North Holland: New York. Davies S. and B. Lyons, 1996, Industrial organisation in the European Union: Structure, strategy and competitive mechanism, Clarendon press: Oxford. Sutton, J., 1991, Sunk Costs and Market Structure: price competition, advertising and the evolution of concentration, MIT Press: Cambridge Monopoly and Mergers Commission, 1989, The Supply of Beer Monopoly and Mergers Commission, 1986, White Salt. The European Commission has undertaken several antitrust investigations including Irish Sugar case 97/624, Sugar Beet, case 90/45, and Napier Brown/British Sugar case 88/518.


in State Aid proceedings under Article 92 of the Treaty of Rome. Typically the Commission undertakes an industry and cost analysis before deciding whether or not to allow state support to an individual company. 62 Productive efficiency 2.60

The causality between competition and productive efficiency is deeply rooted in economic folklore: starting from Hicks’ notion that ‘the best of all monopoly profits is a quiet life’, economists have always had a ‘vague suspicion that competition is the enemy of sloth’.63 The theoretical literature is not in agreement on the exact nature of this relationship. However the empirical literature provides a relative wealth of evidence to support the notion that competition enhances productive efficiency. To mention but a few, Caves and Barton, 64 and Caves 65 use frontier production function techniques to estimate technical efficiency indices in a number of industries, and relate these to concentration (as a proxy for competition). They find that increases in concentration beyond a certain threshold tend to reduce technical efficiency. Nickell 66 finds that market concentration has an adverse effect on the level of total factor productivity. This means that, all other factors being equal, an increase in market concentration should be followed by a fall in productivity.


The MMC has not relied on such techniques very often. The major exception is the assessment of mergers in the utility sector where the justification depended on claims of significant productive efficiency gains. Two parallel merger references to the MMC in 1996 are good examples: Severn Trent/South West Water and Wessex Water/South West Water. Both relied on extensive empirical analysis of expected efficiency gains with several consultant studies being submitted to the MMC.



64 65


For a review of the state aid system see Hancher, L.T., T. Ottervanger and P.J.Slot, 1994, Chancery Law publishing and Chapter 12 in London Economics, 1997, Competition Issues, Volume 3, Subseries V, Single Market Review 96. Office for Official Publications of the European Communities. Caves, R. , 1990, Industrial Organisation, corporate strategy and structure, Journal of Economic Literature, 64-92 Caves,R. and D.E. Barton, 1990, Efficiency in US Manufacturing Industries, MIT Press: Cambridge. Caves, 1992, Productivity dynamics in manufacturing plants, Brookings papers on Economic Activity, 187-267 Nickell, 1992, Productivity Growth in UK Companies, 1975-86, European Economic Review, Vol 36, 1055-91.


CASE STUDY 7: SOUTH WEST WATER SERVICES LTD In the South West Water Services Ltd (SWWS) 67 case of 1995, the MMC was required to determine the adjustment factor (K), and the standard amounts charged for infrastructure as calculated for SWWS from 1995 to 2005. The adjustment factor is the percentage by which the weighted average charges for the supply of water and sewerage services is allowed to change relative to the retail price index. Infrastructure charges are a way of recovering the costs of making new water and sewerage connections. SWWS’s adjustment factors and infrastructure charges were significantly greater than those calculated by the Director of Water Services. The Office of Water Services (OFWAT) commissioned an analysis of the efficiency of the various companies providing water and sewerage services throughout England and Wales. Several regressions were then undertaken for water and sewerage services. These regressions were to explain some of the variation between different companies in the costs of carrying out certain activities, in terms of physical or demographic variables not directly under managerial control. Once the appropriate variables were accounted for, any remaining variation was then attributed to errors in the data, the fit of the model or the greater/lesser efficiency of the company. The results of these regressions were then used to rank the companies into ten efficiency bands in order of the difference between their actual operating expenditure and the operating expenditure predicted by the regression equation. OFWAT also commissioned Data Envelopment Analysis (DEA). Separate DEA runs were carried out for water distribution and treatment. The sum of the expected costs for each company from these two runs was then added to overall average water business activities costs and the result divided by actual distribution, treatment and business activities costs to give an overall efficiency ratio. For most of the companies the results of the DEA runs were similar to those of the regressions. Where they were significantly better, that particular company was raised an efficiency band. The same methods were then used to analyse sewerage service. The MMC mentioned that the use of DEA in this context was relatively novel and that it requires more development. Presently it is used to test and confirm the results produced by other, less formal, analysis. On the basis of this and other evidence, the MMC’s findings broadly followed those of OFWAT although the MMC did allow a slightly larger adjustment factor to account for the substantial investment program being undertaken by SWWS to meet environmental standards. Generally however, the MMC felt that SWWS’s rate of return is well in excess of the cost of this capital and that there is scope for reducing rates of return to the benefit of customers without hindering the company’s ability to finance its investment program.

Dynamic efficiency


Monopoly and Mergers Commission, 1995, South West Water Services Ltd: A report on the determination of adjustment factors and infrastructure charges for South West Water Services Ltd.



Dynamic efficiency is defined as the optimal trade-off between current consumption and investment in technological progress. The intensity of competition may be expected to affect the incentive to undertake research and development, since it will condition the firm’s rewards from innovation.68 Early discussions of the relationship between productmarket competition and innovation (for example, that of Schumpeter) held that the driving force in the process were firms with ex-ante market power, rather than just the prospect of market power. In this Schumpeterian view, as the product market becomes more competitive, the payoff to innovation would become lower, and the incentive for research and development would be blunted.


However this conjecture has been extensively challenged. More product market competition could lead to stronger incentives to innovate, since a potential benefit of innovation is escape from tough competition, by earning a monopoly right to an invention protected by a patent.69 The more recent theoretical literature on the subject has treated innovation as a patent race between firms, where the ‘prize’ for being first (and so being able to appropriate the profits from the innovation) is the incentive spurring firms along. These micro models of research and development investments actually suggest that competitive pressures typically boost, rather than dampen, innovation. The optimal innovation pace will accelerate under the threat that actual (or potential) competitors may register the patent first.


These models are predicated on the existence of patents and other intellectual property rights (IPRs) to provide the necessary reward function for research and development. If these are missing, too narrowly defined or, are costly to enforce, then achieving the correct trade-off between allocative and dynamic efficiency becomes an issue for antitrust authorities. Similarly, there may be cases where IPRs are too widely defined or are used to leverage legally protected market power into other markets where these do not have any justification.


In the US the recognition of the importance of dynamic efficiency has led to a debate over the best ways of incorporating these considerations into competition policy practice. Various proposals have been put forward. The two most relevant approaches are (i) to extend the time scale for considering potential entry within the traditional analytical framework from the usual two years to four years and (ii) to separately identify so-called innovation markets and analyse the nature of competition within them.



It is worth pointing out that there is, in principle, a trade-off between allocative and dynamic efficiency: costs might fall as a result of competition in technological innovation, yet this may actually lead to increased market concentration. Prices in excess of marginal costs might also be necessary in order to give firms a suitable return on their R & D effort, see von Weizsäcker, C.C., 1980, A Welfare analysis of Barriers to Entry, Bell Journal of Economics, Vol. 11: 399-420. Arrow, K. J., 1962, Economic Welfare and the Allocation of Resources for Invention, in NBER, The Rate and Direction of Inventive Activity: Economic and Social Factors, Princeton University Press: 609-25



An innovation market is a term capturing the research and development activity that occurs, normally within a company, which provides the springboard for future generation products and processes.70 ‘Future generation products’ are products that do not presently exist but will result from current, or proposed, research and development. While it is relatively easy to talk about these different markets conceptually, as one moves from the first to the third market the uncertainties of analysis rapidly increase. These uncertainties are associated both with data availability and one’s ability to analyse the interaction of technology, competitive behaviour and market structure. This makes the application of the innovation market concept difficult in practice and in the US, the debate about its applicability is still ongoing.


In Europe, innovation markets have not been explicitly recognised. However, according to John Temple Lang, 71 in a number of cases involving high tech industries the Commission has arrived ‘at much the same results by using the more traditional concept of competition by two companies in research and development directed towards the same goal.’ Cases mentioned by Temple Lang include: Upjohn-Pharmacia, Glaxo-Welcome, Elf Atochem/Union Carbide, and Enichem Union Carbide. 72 Temple Lang observes that these decisions of the Commission show a willingness to let mergers, joint ventures or other restrictive agreements go through.


In the UK, such issues have also played a role in recent cases involving video games and telephone number portability73 where an incumbent was found to inhibit innovation by imposing switching costs on entrants and their customers. The GEC/VSEL report 74 also canvassed these ideas with regard to the high tech issues in the defence industries.



72 73


Gilbert, R.J., 1995, The 1995 Antitrust Guidelines for the Licensing of Intellectual Property: New Signposts for the Intersection of Intellectual Property and Antitrust Laws, Paper given at the ABA section of antitrust law spring meeting, Washington DC. Temple Lang, J., 1996, Innovation markets and high technology industries, Paper presented at the Fordham Corporate Law Institute. Twenty-fifth Report on Competition Policy, European Commission, 1995 Monopolies and Mergers Commission, 1995, Video Games, and Monopolies and Mergers Commission, 1995, Telephone Number Portability. Monopolies and Mergers Commission, 1995, The General Electricity Company plc and VSEL plc


Conclusions 2.72

In this chapter we have provided an overview of some of the key issues in antitrust with the aim of showing how empirical economic analysis supports the application of competition law in practice. Four main areas of antitrust have been covered:  market definition;  market structure analysis;  models of competition; and  efficiency defences.


This overview has demonstrated that in many cases, it is only by the use of appropriate statistical and econometric techniques that the case be sensibly progressed and analysed. This is especially important under a rule of reason approach. Quantification of economic relationships in antitrust is not all about measuring demand-side substitutability for market definition purposes. There are many tests that have been applied by antitrust economists to determine, for example, the effect of a merger on prices, or to understand the cost savings of a merger in the utility industries. What is equally clear from this review is that the range of techniques is very wide, both in terms of technical and economic sophistication. For example, the analysis of price trends ranges from simple price comparisons across countries to the analysis of structural breaks or co-integration analysis of time series. Similarly the analysis of demand can become very sophisticated once the interdependence of a system of demand equations is analysed simultaneously.


In the following chapters, we describe those statistical and econometric techniques that are most commonly used in antitrust analysis. The techniques are described in ascending order of difficulty, from the simplest comparisons of prices, to the estimation of fullyfledged econometric models stemming directly from theoretical economic models.






Cross-sectional price tests use hypothesis testing to establish whether two sets of prices are uniform, taking into account differences in costs or other external forces that could affect prices. The two sets can pertain to either two geographic areas, or to two products, or to two periods of time, and support the assessment of market power or the effect of cartelisation. These tests are based on comparisons of cross-sectional data, and make use of purely statistical tests,75 that is, no economic theory or behaviour is explicitly analysed.


The two sets of prices to be compared can be considered as random sampling from two populations.76 The test for price uniformity then is a test of the null hypothesis that the distributions of the two price populations are identical. The testing procedures vary according to the sampling methodology adopted. We will first consider the case where the two price samples are independent; then we will consider the case of paired samples. To give an example, if we want to establish whether there is evidence that prices are higher in one area than in another, due, for instance, to price fixing, then we collect price samples from the two areas; these samples can be considered independent. If we suspect that producers have fixed the price of a certain range of products at some point we can compare two sets of prices before and after the alleged fixing has taken place; these samples cannot be considered as being independent of each other and will be paired.

Description of the technique: case of independent samples 3.3

Consider first the case of two independent samples. Two sets of prices, P 1 and P 2, are drawn from two normal distributions with means Âľ 1 and Âľ 2 and identical 77 variance . The average prices in the two samples are unbiased estimates of the population means (P and P ) . The pooled sample variance S2 is an unbiased estimate of the population 1





Technically, a statistical test is a statistic calculated from a sample in order to test a hypothesis about the population from which the sample is drawn. When testing hypotheses concerning more than one population, the test statistic is computed from more than one sample. There are cases when the analyst needs to compare several samples of data. There are several tests available for this task. An excellent exposition can be found in Chapter 12 of Rice, J.A., 1995, Mathematical Statistics and Data Analysis, 2 nd Edition. Duxbury Press. Statistical packages such as SPSS will test the validity of this assumption, and if rejected, will present an alternative, more robust test.


variance. A test statistic of the hypothesis µ 1=µ 2 when the sample sizes are n and m is given by: t  [(P  P ) / S (1/n1/m)] 1




which is distributed as a Student’s t-statistic with degrees of freedom equal to the total number of observations minus 2. If the means of the two populations are the same, the estimated t has to be smaller than the tabulated critical values for those degrees of freedom and a significance level of 10% or less. As a rule of thumb, estimated t values of less than or equal to two supports the hypothesis that prices are uniform across two populations.

Description of the technique: case of matched samples 3.4

We now consider the case of so-called paired samples. These samples are not independent because they consist of matched observations, often ‘before’ and ‘after’ measurements on the same set of prices. The set of prices, P1 and P2 , are drawn from two normal distributions with means µ 1 and µ 2 and variances  12 and  22. The test of the hypothesis that the two population means are the same is equivalent to a test of µ 1-µ 2 =0. The average prices in the two samples (P and P ) are unbiased estimates of the 1


population means, and the pooled sample variances (S12 and S22 ) are unbiased estimates of the population variances. Then the unbiased sample estimates of (µ 1-µ 2) and of its variance are D = (P  P ) and SD2 = (S12 + S22 - 2S12 ). A test statistic of the hypothesis 1


µ 1-µ 2 =0 is given by: [EQUATION 2]

t = [(D)/SD(n)]

which is distributed as a Student’s t-statistic with degrees of freedom equal to the total number of pairs minus one. Once the test statistic has been computed, the testing procedure proceeds as above. 3.5

Where possible, paired samples should be used. Since SD < S the paired-sample test is potentially a more powerful discriminator. So, for example, when analysing the supply of recorded music, the MMC compared prices of paired samples of CDs across various countries.78


See case study, page 30, for a more detailed exposition of the MMC enquiry.


Data and computational requirements 3.6

The implementation of this test is quite straightforward and does not require sophisticated computer packages or computational skills, although a dedicated statistical package such as SPSS or SAS will often be convenient. It should be noted, however, that at least 20 observations (as a rough guide) are needed in order to apply this test. A problem can arise if the distribution of the price data is not normal but shows some degree of skewness.79 In such a case, the theory of normal distributions cannot be applied. Then it is advisable to transform the data before performing the t-test. The most popular transformations to solve the problem of skewness are taking the logs or the squared roots.


With SPSS the user is prompted as to whether the test is used on paired or matched samples. If using a spreadsheet the implementation of the relevant hypothesis test involves creating a new variable equal to the difference between each pair of matched prices. Then, the mean and standard deviation of the new variable are computed: the test statistic is simply the ratio between these two values.

Interpretation 3.8

As with all techniques, the data must be able to inform the analysis and be capable of rejecting a given hypothesis. In international price comparisons in particular, there are complications arising from the different treatment of taxes (in the US, for example, local sales taxes are added to the indicated price where European prices might include VAT) and the use of an appropriate exchange rate. The actual rate of exchange between two currencies does not make allowances for the different purchasing power that a unit of each of the currencies possesses. This suggests the use of an exchange rate that reflects the purchasing power parity (PPP) between the two currencies. While the use of PPP adjusted exchange rates may in theory improve the usefulness of international price comparisons they introduce a degree of uncertainty into the analysis as the calculation of PPPs is not straightforward. Many a formula has been developed to calculate PPP rates, and it is a matter of wide debate which one is the best, if any. Selection problems also arise when using actual exchange rates; the choice between spot rates and averages over the relevant period has to be informed by a deep understanding by the analyst of the data.


Provided the data is appropriate the testing procedures we have just described lead to unequivocal conclusions about whether the means of the two distributions are statistically the same. However, if the two means turn out to be different, but their difference is very small, the analyst will have to use her/his judgement to decide how large a mean difference is needed to, say, unequivocally establish that differences in product prices across the two sets are economically significant. Also, the fact that the average prices 79

When data is not normal, alternative non-parametric testing procedures (like the Wilkoxon test statistic) are available. See Rice, J.A. (1995), op. cit., for a discussion.


turn out to be different in two countries can be ascribed to many different causes, as the MMC has found for instance in its investigations.

Application: international comparison of prices 3.10

In 1993 there was a press campaign about prices of CDs in the UK being up to 50% higher than in the US. This case is more thoroughly discussed in the boxed case study on page 30. The following is a summary of the events that took place.


After a highly charged public debate and a parliamentary investigation by the Trade and Industry Committee of the House of Commons the competition authorities were asked to investigate whether the record companies were preventing the parallel imports of CDs to the disadvantage of UK consumers.


In the subsequent enquiry the MMC commissioned market research into relative prices of a range of consumer goods in the US and the UK and received other evidence of the distribution of prices of recorded music across different categories, types of outlets and geographical areas. 80 Note that in this case establishing a price difference was not sufficient. The overall difference in prices across countries was also deemed relevant.


The UK tended to be cheaper than France, Germany, or Denmark but dearer than the USA (after taking account of taxes). As may be expected, this was found to be sensitive to the exchange rate used. The report noted the difficulties of interpretation when using international price comparisons (see paragraph 7.105 of the report). However, this may be because no clear conclusions of excessive pricing were demonstrated.


Another frequent complaint of discriminatory pricing which adversely affects UK consumers concerns the pricing of motor cars in Europe. Large price differentials have been observed between prices of cars sold in Belgium and the UK. 81 The European Commission regularly publishes a report on price differentials across the EU.82 Both the MMC and the European Commissionâ&#x20AC;&#x2122;s DGIV have undertaken major investigations into car pricing. The MMC reported its findings in a 1992 report 83 and the European

80 81



Supra, footnote 38. Ashworth, M.H., J.A. Kay and T.A.E. Sharpe, 1982, Differentials Between Car Prices in the United Kingdom and Belgium, IFS Report Series No 2, Institute for Fiscal Studies. European Commission, 1995, Car Price Differentials in the European Union on 1 May 1995, IP/95/768. Monopoly and Mergers Commission, 1992, New Motor Cars. A Report on the Supply of New Motor Cars Within the United Kingdom.


Commission recently fined the Volkswagen Group for distribution agreements and practices that were held to be anti-competitive because they stopped consumers from exploiting international price differentials. 3.15

In the motor cars enquiries of the MMC and the European Commissionâ&#x20AC;&#x2122;s DGIV it was necessary to undertake price surveys across different countries. These surveys had to deal with the problem of price comparisons of models that are not sold to the same specification and therefore differ in terms of their characteristics. To deal with these problems of comparability, hedonic price indices had to be employed.






Hedonic price analysis is used to compare the price of products whose quality changes over time or over product space, due to either technological or subjective factors, or other services and optional equipment. Typical examples of products whose quality differs at one point in time, or whose quality varies dramatically over time, are cars and computers. In such circumstances, price analysis has to be adjusted to account properly for quality differences or quality changes. Hedonic price analysis is a particular kind of regression analysis that has been developed to purge prices of the effect of quality differences, so that the pure price difference between â&#x20AC;&#x2DC;standardisedâ&#x20AC;&#x2122; products can be isolated. Purged prices can then be used to carry out other price tests.

Description of the technique84 4.2

We consider a product W with, say, three distinctive characteristics, (X,Y,Z); for example, if W were cars, the characteristics could be horsepower, weight, luxury or basic, etc. There are many brands of W, and each brand supplies more than one version of W. How do we compare the prices of the different versions of W for sale, and how do we compare the price of W over time, let us say over three years, if (X,Y,Z) tend to change quickly. The price of W can be expressed as: log P(Wi)= 1 + 2D2 + 3D3 + b1Xi + b2Yi + b3Zi + ui


where the subscript i denotes one of the many brands or versions of W available for sale. D2 is a dummy variable equal to one in period 2 and to zero in the other two periods. Likewise, D 3 is equal to one in period 3 and to zero in the other two periods. Ui is a random error term with mean zero and constant variance. After data on price, and on characteristics X, Y, and Z are gathered for many brands and versions of W for the three periods, Equation 3 is estimated by OLS. Quality-adjusted price indices for the years 1, 2, and 3 can be obtained very simply by taking anti-logarithms of the estimated coefficients of the dummy variables, a2 and a3 . Normalising the base year value to unity in year 1, the hedonic price index will be 1 in period 1, exp(2 ) in period 2 and exp(3 ) in period 3. So, if the hedonic price index is 1 in period 1 and 0.87 in period 2 the quality-adjusted average price of W has gone down by 13% between periods 1 and 2. If the non-adjusted price of W had actually gone up substantially, prompting allegations of price fixing or monopolistic abuse, hedonic price analysis would show that all the price increase was due to changes in the quality mix of the product, not necessarily to anticompetitive behaviour by the producers or retailers.


For an excellent exposition of this technique, see Chapter 4 in Berndt, E.R., 1991, The Practice of Econometrics: Classic and Contemporary. Addison Wesley.


Data and computational requirements 4.3

The implementation of this technique, for the above example, requires a combination of time series and cross-sectional data on the product price and its characteristics. Data will be needed on a range of prices over a number of years. It is advisable to have a large enough number of observations for the results to be meaningful; the analysis should be carried out with at least 20 to 30 observations plus as many observations as the number of regressors in the estimated equation. The estimation of hedonic price regression can be carried out using all econometric packages and those spreadsheets that have a built-in routine to run multivariate regressions.

Interpretation 4.4

The computation of hedonic price indices requires a detailed knowledge of the demand and supply of the product for which the index is calculated, as it is of crucial importance to the unbiasedness of the results that no significant quality variable is omitted from the regression. The quality of the results, as with all empirical analyses, depends crucially on how well the model explains the variability of the dependent variable (here, prices). This is measured by the R2 of the regression.85 Another problem that is often encountered when dealing with cross-sectional data is that of heteroscedasticity, that is, non-constancy of the error term variance. Although heteroscedasticity does not affect the unbiasedness of the results, it invalidates the significance tests. Heteroscedasticity can be dealt with using weighted rather than Ordinary Least Squares regression: the procedure is available in most econometric packages.

Application: car price differentials 4.5

One of the most common tests of progress towards European Union market integration is the trend towards price convergence. The question is whether the prices charged in national markets for similar products differ significantly, and if so, whether prices are converging. In the case of cars, models differ widely across countries and their characteristics change substantially over time. Hence this particular nature of the market makes the hedonic price analysis the best methodology to carry out comparisons of prices between national markets.


For the UK, the first available study is a 1982 IFS report86 analysing car prices in



The R2 measure the percentage of the variability in the dependent variable which is ‘explained’ by the regression. An R2 of 0 means that the regression does not have any explanatory power, while with an R2 of 1, 100% of the variability is explained by the regression (‘perfect fit’). It should be noted that the R2 tends to be quite low when the estimation is carried out with cross-sectional data, and this should be taken into account when interpreting the results. Ashworth, M.H., J.A. Kay and T.A.E. Sharpe, 1982, op. cit.


the UK and Belgium. The report showed that the average price differential, at 39%, underestimated the quality-adjusted differential, which was 44%. Ten years later the MMC compared car prices in the UK, Germany, France, the Netherlands and Belgium.87 The study sought to control for differences in characteristics, but not all specification differences were eliminated. Also, comparisons referred to the year 1990, when the UK and the other countries were at very different phases of the economic cycle. Surprisingly, the MMC report found no significant differentials in general, and only significant differences in the prices of smaller models. The conclusions reached by the MMC report were not substantiated by further studies: a report by LAL88 found quality-adjusted differentials between the UK and other countries ranging from 13.8 to 35% for four models. All of the UK studies have used the hedonic price analysis. The variation in their results may be due to the incorrect filtering of the effect of the varying characteristics, which would lead to the results being somewhat biased. 4.7

At the European level, a report by the BEUC 89 on behalf of the European Commission’s DGXI estimated differentials of between 12% to 50% for UK cars compared to Belgium, Germany, Greece, Spain, France, Ireland and Luxembourg and the Netherlands. The results of the study are unreliable, as they were not quality-adjusted. Flam and Nordstrom90 found price differences as high as 50%, averaging at around 12%. Model comparisons carried out in 1995 by the European Commission 91 showed differentials in excess of 20%. Although cars were compared by model, the specifications of each model tend to vary across countries and we expect the estimated differentials to be biased. Table 2 Study IFS BEUC MMC LAL

87 88

89 90


Estimated Differentials UK – Other Countries Reference Year 1981 1989 1990 1991

Countries Used in the Comparison

Estimated Difference

B B, DK, G, GP, E, F, Irel, Lux, N G, F, B, NL n.a.

44% 12-50% None Significant 13.8-35%

Monopoly and Merger Commission, 1992, op. cit. Euromotor, 1991, Year 2000 and Beyond – The Car Marketing Challenge in Europe, Euromotor Reports. BEUC, 1989, EEC Study on Car Prices and Progress Towards 1992, BEUC/10/89. Flam, H. and H. Nordstrom, 1995, Why Do Pre-Tax Car Prices Differ So Much Across European Countries?, CEPR Discussion Paper No 1181. European Commission, 1995, op. cit.



Hedonic price adjustment is therefore an important tool to deal with the prices of differentiated products, but results are dependent on model specification of the quality attributes.





Price correlation is frequently used to determine whether two products or two geographic areas are in the same economic market. It is also often used to measure the degree of interdependence between prices and market shares or the concentration of sellers. Correlation analysis does not allow the analyst to make an inference on the causation of the relationship between the two variables being examined, but only on their degree of association. As with other techniques, this provides one piece of evidence in a case.

Description of the technique 5.2

Correlation analysis is a statistical technique used to measure the degree of interdependence between two variables. Two variables are said to be correlated if a change in one variable is associated with a change in the other. This need not imply a causal relationship between the two since the movement in both variables can be influenced by other variables not included in the analysis. Correlation is positive when the changes in the two variables have the same sign (that is, they both become larger or smaller), and negative otherwise (that is, one becomes larger while the other becomes smaller). Variables that are independent do not depend upon each other and will only be correlated by chance (â&#x20AC;&#x2DC;spurious correlationâ&#x20AC;&#x2122;).


The degree of association between two variables is sometimes measured by a statistical parameter called covariance, which is dependent on the unit of measurement used. The correlation coefficient between two variables, x 1 and x 2, is, however, a standardised measure of association between two variables:  = 12 / 12 where 12 is the covariance between x1 and x2, and 1 and 2 are the square roots of the variances of x1 and x2 respectively. The correlation coefficient is a number ranging between -1 and 1. A coefficient of -1 implies perfect negative correlation, a coefficient of 1 implies perfect positive correlation, and a coefficient of zero implies no correlation (although it does not necessarily imply that they are independent).


Data and computational requirements 5.4

The implementation of price correlation tests requires time series of data which have at least 20 observations.92 It is customary to compute the correlation coefficient using the natural logarithm (log) of the price series, both due to efficiency reasons and because the first log difference is an approximation of the growth rate. Equal changes in the log represent equal percentage changes in price. Correlations should always be computed both between levels and differences in the log prices. The computational requirements to carry out the test are minimal; all statistical and econometric packages and most spreadsheets have in-built routines to compute correlation coefficients. Packages such as SPSS also provide results from significance tests on the estimated correlation coefficients.

Interpretation 5.5

Stigler and Sherwin93 argued that given time series price data for two products or areas, the correlation coefficients between their levels and first differences can be used to determine whether these products or areas are in the same market. 94 Prices can differ because of transport and transaction costs or because of temporary demand or supply shocks, so that the correlation coefficients will be less than 1 even in a perfect market. It is however impossible to determine how big the correlation coefficient needs to be in order for the analysis to conclude that two areas or products are in the same market. Stigler and Sherwin 95 â&#x20AC;&#x2DC;... believe that no unique criterion exists, quite aside from the fact that the degree of correspondence of two price series will vary with the unit and duration of time, the kind of price reported, and other factorsâ&#x20AC;&#x2122;. In other words, even if the estimated correlation coefficient is statistically different from zero, the economic interpretation of the test is not straightforward. This is due to the lack of an obvious cutoff point where it can be decided whether the estimated degree of interdependence between the prices can be taken as an indication of price uniformity.


A further problem with the use of the correlation coefficient is that if there are common factors influencing prices this statistic can lead to erroneous conclusions. 96 To see why, consider the case of two producers using the same input, so that the prices of their products are highly correlated with the input price. The analyst will find a high


93 94

95 96

Computing correlation coefficients with less than 15 observations is meaningless from a statistical point of view. Stigler, G.J. and Sherwin, R.A., 1985, op. cit. See Waverman, L., 1991, Econometric Modeling of Energy Demand: When are Substitutes Good Substitutes, in D. Hawdon (ed.), Energy Demand: Evidence and Expectations. Academic Press. ibid, p 562. Stigler G.J. and R.A. Sherwin, 1985, op. cit., are aware of this problem, and discuss the possible solutions.


correlation coefficient between the prices of the two products: that is because both their prices are influenced by the input price, not because they are interdependent. Unless the influence of common factors is purged, the use of the correlation coefficient as a test of price interdependence leads to wrong conclusions, regardless of the size or the statistical significance of the estimate. This is especially the case when the series cover periods of high inflation and are therefore trended, or when the data is seasonal. The influence of common factors can be purged by de-trending all the variables first or by using regression analysis: the price is regressed on the influencing factor (input price, or a time trend, or seasonal dummies, etc), and the residuals from that regression are taken to represent the purged series. 5.7

A further problem with using correlation analysis lies in the fact that price responses for some products, and in some areas, might be delayed. This would be the case when, for instance, prices are negotiated at discrete time intervals which are not synchronised: the analyst could find a very low correlation when in fact the series are highly correlated in the long run. A visual inspection of the plotted price series can be of help in such cases. Another instance when prices of products that are closely related have low correlation is when the products are good substitutes and their supply is elastic.

Application: wholesale petrol markets in the US 5.8

Stifler and Sherwin97 have used correlation analysis to test whether the cities of Chicago, Detroit and New Orleans are in the same market for wholesale petrol. They correlate monthly fuel prices in the three cities during the period 1980-82 inclusive. Stigler and Sherwin98 eliminate the effect of serial correlation by taking the first difference on every third price. They also remove the effect of common factors, which is a very important step in the analysis of petrol prices, as fluctuations in the price of crude oil tend to influence the price of refined petrol quite heavily. The results are taken by the authors as indicating that the correlation coefficients are very high: the coefficient between New Orleans and Chicago is 0.792; that between New Orleans and Detroit is 0.967; and that between Chicago and Detroit is 0.77. These results indicated to the authors, that the three cities are in the same economic market. However, correlation analysis, as the sole means of reducing market breadth, is no longer considered a sufficiently robust approach.

97 98

Ibid. Ibid.






In this and the following two chapters we review quantitative techniques based on dynamic models. Economic theories tell us what happens in equilibrium; for example, economic theory predicts that if two homogeneous products are in the same market their prices have to be the same, making due allowances for transportation costs. Reality is however different. When shocks happen, there are adjustment lags before the system returns to equilibrium. Dynamic models account for this. The speed of adjustment test is a market definition test which is based on the idea that if two products are in the same market the difference in their prices are stable over time,99 so that relative prices tend to return to their equilibrium value after a shock. The variable of interest here is the price difference, and the underlying dynamic assumption is that the current difference between two prices is a fraction of the past difference observed in the last period. This technique has been hardly ever used, however, as it is fundamentally flawed.

Description of the technique 6.2

The test is carried out by estimating a linear relationship between current and past price differences:100 (log P1 - log P2) =  +  (log P1 - log P2)t-1 + ut


where 1 and 2 are the two products or regions;  is a parameter measuring the speed of adjustment to the equilibrium; and u t is a random error term with mean 0 and constant variance. Equation 4 represents a first order auto-regressive process as the current value of the price difference is a function of its past value plus a random element with mean zero and constant variance.  is the long-run price difference. In order for this process to be stationary101 the absolute value of  has to be less than one. If  is equal to zero in Equation 4 adjustment is instantaneous. The larger the estimate of , the slower the adjustment process.




The test was developed in Horowitz, I., 1981, Market Definition in Antitrust Analysis: A Regression Approach, Southern Economic Journal, 48: 1-16. Note that it is common practice to use the log form when running regressions involving rates of growth, or when estimating demand functions. This has desirable properties for interpretation. However, it is an empirical matter and whether the functional relationship is better estimated by levels or logs, can be tested. A time series is said to be stationary if the properties (that is, mean and variance) of its elements do not depend on time. This requires the mean and variance to be constant over time. If the absolute value of the auto-regressive parameter, , is equal or bigger than one, the series is non-stationary because its variance becomes infinite over time.


Data and computational requirements 6.3

To carry out this test, time series of price data is needed. The series are first transformed into logs and the price difference variable is created. Using linear regression analysis (OLS) this variable is regressed on a constant and on its lagged value. All statistical and econometric packages and most spreadsheets have in-built routines to estimate OLS regressions.

Interpretation 6.4

A simple t-statistic is used to test the hypothesis that  is equal to zero; the t-statistic will be automatically supplied with the regression output. If  turns out to be bigger than zero, the analyst is faced with the same problem mentioned in relation to the correlation test, that is, to determine the critical value of  in economic terms. Again, there is no predetermined rule.


The speed of adjustment test has serious drawbacks. First of all, it is sensitive to the frequency of observation: 102 a slow adjustment with daily data might appear as instantaneous with quarterly or annual data, for example, a 5% adjustment on a daily basis implies adjustment within a month.103 Secondly, the above model assumes that the ut are serially independent. This would be violated if each price series itself follows a first order auto-regressive process, the estimated  measures the degree of dependence of each price on its past value, not whether the price difference is constant or not. So, if the price series are highly auto-correlated the estimated  will be overestimated and the analyst will draw the erroneous conclusion that the speed of adjustment is slow. 104 Similar problems arise if the price series are trended or follow a seasonal pattern. As it is quite likely for price data to exhibit these characteristics, the use of this technique is not advisable. Finally, this technique is overly restrictive because it imposes a particular pattern to the dynamic adjustment process.

102 103


See Stifler and Sherwin, op.cit., p. 583 for a discussion. Although we should expect this result, it sometimes turns out that  is similar with different time frames. This makes interpretation difficult, and suggests that the estimate is not robust. For further discussion, see Werden, G.J. and L.M. Froeb, 1993, Correlation, Causality and All that Jazz: The Inherent Shortcomings of Price Tests for Antitrust Markets, Review of Industrial Organization, 8: 329-53, p. 341.





Rather than looking at the degree of interdependence between two prices, causality tests seek to determine if there is causation from one series to the other, or if they mutually determine each other. Although the definition of causality is not a straightforward matter, one econometric testing procedure has gained considerable success in recent years: Granger causality testing. 105

Description of the technique 7.2

The idea behind Granger causality is simple: consider two time series of price data, P 1 and P2. P2 is said to Granger-cause P1 if prediction of the current value of P1 is enhanced by using past values of P2. This can be shown using Equation 5: T


s =1

s =1

P1 = ∑ β s P1t − s + ∑ γ s P2t − s + u t


where u t is a random error term with mean 0 and constant variance. The empirical implementation of the test proceeds as follows. P1 in Equation 5 is regressed on its past values and on past values of P2. Although the choice of lags is arbitrary, in order to avoid omitted variable bias it is customary to start with a high number of lags, choosing the same number of lags for both price series, and then reduce the number of lags by dropping those that are not significant. The analyst will have to keep in mind, however, that the lagged price variables typically tend to be highly correlated. This creates multicollinearity, resulting in very high standard errors, and therefore low t-ratios. The choice of the number of lags has to be made bearing this problem in mind. If past levels of P2 have no influence in determining the current value of P1 , then all the coefficients on the lagged values of P2 in Equation 5 have to be equal to zero. This hypothesis is tested by means of an F-test. The same method is followed to test whether P2 Grangercauses P1.

Data and computational requirements 7.3

The computational requirements to perform causality testing are pretty much the same as those for the speed of adjustment test. It is, however, advisable to have a substantially longer time series of data. This is because for each additional lag, two extra right-hand 105

Granger causality is a specific econometric concept which does not necessarily imply causality in the normal sense of the word.


side variables are introduced into the equation and one additional observation is lost. For instance, if there are five lags in the regression, five observations are lost and there are ten regressors in the equation. A sample of about 50 observations would be the minimum requirement. To perform the estimation and testing it is preferable to use econometric packages rather than spreadsheets, because packages such as Microfit and PC-Give have built-in routines to perform the F-test for causality.

Interpretation 7.4

Causality testing is an atheoretical method and does not require assumptions on the dynamic properties of the price adjustment mechanism. As noted above, Granger causality is not causality in the normal sense of the word. At best it may provide circumstantial evidence. As with many statistical tests a negative result may be easier to interpret then a positive one. Because of the nature of the possible spurious correlations, it is important to purge the price data from the effect of common factors. This can be done in two ways. First, the series can be purged independently by regressing each of them on the vector of common factors and using the residuals from those regressions as the purged variables. Secondly, the common factors can be added to the estimated Equation 5. If the influence of common factors is not eliminated, the results will be misinterpreted.


One major drawback associated with the use of this methodology is that the presence of auto-correlation in the error term attached to Equation 5 invalidates the F-test. In time series data, random shocks have effects that often persist for more than one time period; also, owing to inertia, past actions often influence current actions. In these cases it is said that the disturbances are auto-correlated, and their covariance is different from zero. The presence of serial correlation in the error term does not affect the unbiasedness of the estimated parameters, but invalidates the F-test. In order to solve this problem it is customary, before running the regression, to transform the data series, so as to eliminate the auto-correlation in the errors. There is, however, great debate among econometricians as to how to transform the data and, more worryingly, the extent to which the test results change as a consequence of the transformation used. 106


Finally, the results obtained from this exogeneity test are not always clear-cut, and there is one problem that analysts often have to face. Suppose that the F-test results suggest that the coefficients on past values of P2 in Equation 5 are simultaneously equal to zero and that the t-tests on the individual coefficients show that one (or more) of them is


See Kennedy, P., 1993, A Guide to Econometrics, 3rd Edition. Oxford:Basil Blackwell, page 68 for a discussion of this issue, and of this technique in general.


significantly different from zero. How to interpret this result? It is up to the analystâ&#x20AC;&#x2122;s experience and wisdom to decide whether the size of the impact of such variables is economically significant.

Application: US petrol markets 7.7

Margaret Slade107 has used causality tests to determine whether the north-eastern, south-eastern and western regions of the US are in the same market. Using weekly data on wholesale prices for the year from March 1981 to February 1982 (that is, 52 observations), Slade chose two cities for each region, namely Greensboro and Spartanburg for the South-East; Baltimore and Boston for the North-East; and Los Angeles and San Francisco for the West Coast. Causality tests were performed between each pair of cities, both within and across regions. 108 The tests for exogeneity between each pair of cities lead to the following conclusions. The South-East represents one geographic market. There is some evidence of interrelation between city pairs in the North-East and SouthEast, but it is weak and therefore inconclusive. The West Coast and the SouthEast form quite distinct markets, as might be expected.



Slade, M.E., 1986, Exogeneity Tests of Market Boundaries Applied to Petroleum Products, The Journal of Industrial Economics, 34: 291-302 Slade added a vector of common factors to each regression in order to eliminate the effect of common factors. Such vector contained quadratic functions of time. The length of the lags to be added to each equation was set at five, as the addition of five lags eliminated all traces of autocorrelation.






Dynamic price regressions and co-integration analysis techniques109 are used to determine the extent of the market and to analyse the mechanisms by which price changes are transmitted across products or geographic areas. Price adjustments across markets may take place over a period of time rather than instantaneously, so that assessing whether markets are integrated can depend critically on the length of the price adjustment. The reactive adjustment process to changes in one price through a set of products or geographic areas can be represented by a class of econometric models called error correction models (ECM). ECM can be used to test whether two or more series of price data exhibit stable long-term relationships and to estimate the time required for such relationships to be re-established when a shock causes them to depart from equilibrium. Although the analysis of prices alone is not sufficient to establish whether a market is not an antitrust market, it is often the case that no other data but time series of prices are available to the analyst. In that case, the techniques developed in this chapter should be used, as they are the most correct ones from a statistical point of view.

Description of the technique 8.2

Consider the following general-lag model, where capital letters indicate natural logarithms: P1t = 0 + 0P2t + 1P2t-1 + P1t-1 + ut


Subtracting P1t from both sides of the equation, and adding and subtracting 1 P2t-1 from the right-hand side, after simple manipulations we obtain: P1t = 0 + 0P2t - (1-) {P1t-1 - [(0 + 1) / (1-)] P2t-1} + ut


where P1t = (P1t - P1t-1); P2t = (P2t - P2t-1) and ut is a random error term with mean 0 and constant variance. 0 is the long term difference between the two prices. Equation 7 is called the Error Correction representation of Equation 6. The last term, (1-) {P1t-1 - [(0 + 1) / (1-)] P2t-1}, is called the error-correction term because it reflects the current â&#x20AC;&#x2DC;errorâ&#x20AC;&#x2122; in attaining long-run equilibrium: it measures the extent to which the two prices have diverged. The parameter  has to be less than one for the system to be stable, that is, to ensure convergence towards the equilibrium. Then -(1-) is negative, which implies that the deviation from the long-run equilibrium is corrected during the next periods. If  were equal to zero, the adjustment would be instantaneous. Another 109

The interested reader will find an excellent exposition of time series techniques in Charemza, W.W., and D.F. Deadman, 1997, New Directions in Econometric Practice, 2 nd edition. Edward Elgar.


advantage of using Equation 7 rather than 6 is that by regressing P 1t on P 2t and the levels we have less multicollinearity, and therefore more precise estimates. 8.3

Error correction models are a powerful tool in econometrics, as they allow the estimation of equilibrium relationships using time series of data that are non-stationary. Generally speaking, a stationary series has a mean to which it tends to return, while non-stationary series tend to wander widely; also, a stationary series always has a finite variance (that is, shocks only have transitory effects) and its autocorrelations tend to die out as the interval over which they are measured widens. These differences suggest that when plotting the data series against time, a stationary series will cut the horizontal axis many times, while a non-stationary series will not. Econometricians have discovered that many time series of economic data are non-stationary, more precisely that they are integrated of order 1. A series is said to be integrated of order 1 if it can be made stationary by taking its first difference. Two non-stationary time series are said to be co-integrated if they have a linear combination that is stationary. Engle and Granger 110 have supplied many examples of non-stationary series that might have stationary linear combinations. Among them, they cited the prices of ‘close substitutes in the same market’.111 Engle and Granger112 also showed that integrated series whose relationship can be expressed in the form of an ECM are co-integrated. So, rather than having to estimate statistical models using differenced data, and losing valuable economic information in the process, the problem of non-stationarity can be solved by estimating an ECM, from which we can estimate directly, the speed of adjustment of price movements to their equilibrium relationship after a shock has taken place. That is, we can use price levels that contain more information than price differences to measure the reactive adjustment of prices between regions.

Data and computational requirements 8.4

Although the empirical implementation of the convergence test in Equation 7 does not appear difficult as it involves running an OLS regression and using a t-test for the null hypothesis that  is equal to zero, the situation can become complicated. This happens if the estimated  is positive or greater than one in absolute value. Then there is evidence of non-stationarity and new solutions need to be found. The correct way of proceeding is as follows. First, the analyst has to test for stationarity in the two price series via a unit root test.113 If, and only if, the test results show that the data is non-stationary, then the 110

111 112 113

Engle, R.F. and C.W.J. Granger, 1987, “Co-integration and Error Correction: Representation, Estimation and Testing”, Econometrica, 55: 251-76. Op. cit. page 251 Ibid. Consider the simplest example of a I(1) series, a random walk. Let xt =x t-1 +ut where ut is a stationary error term. We can see that x is I(1) as xt=ut which is I(0). Now let us consider a more general form xt=axt-1+ut. If the absolute value of a is equal to 1 then xt is I(1), that is, non-stationary. If the absolute value of a is less than 1 then x t is I(0), that is, stationary. Formal tests for stationarity are tests of the null hypothesis that a=1, and so the name unit root test. There is a wide variety of unit root tests


analysis requires testing whether they are co-integrated. Testing for co-integration implies testing whether there exists a linear combination of the two series that is stationary. 114 If evidence of co-integration is found, then the conclusion can be drawn that the relationship between price movements tends to equilibrium in the long run. So, if a simple ECM representation cannot be found, co-integration analysis becomes more sophisticated and needs to be carried out by experienced analysts. Moreover, if the analysis involves more than two prices, it can only be performed using specialist software. This technique requires the availability of long time series of data, with at least 50 observations.

Interpretation 8.5

There are some caveats that need to be carefully considered before drawing any conclusions from Equation 7, even when the estimated  has the correct value and sign. First, it is advisable to account for the influence of common factors in much the same way as when using causality tests or correlation analysis. Secondly, the lag structure of the model may be more complex than in Equation 7. It is advisable to introduce more lags and test for their significance before discarding them. Thirdly, if the error term is autocorrelated this invalidates the significance test. To test for auto-correlation, the residuals from Equation 7 are regressed on their lagged values and on the regressors of Equation 7. An F-test is then used to test the joint significance of the coefficients of the lagged residuals.


The estimated speed of adjustment in Equation 7 might be too slow to make any economic sense. This problem is even more severe when co-integration tests are used to determine convergence. This is because co-integration is a long-run concept, and a cointegrating relationship will be found even when one price changes and it takes several years for the other price(s) to adjust.


All techniques based on the analysis of prices alone, including co-integration techniques, are very useful to define economic markets, but they should be used with care when establishing relevant antitrust markets. The fact that prices in one area are found to affect prices in another area is not sufficient proof of the existence of a wider antitrust market. What needs to be determined in defining an antitrust market is whether two areas that are in the same market at historical prices would still be in the same market if the producers in one area would increase their price by some significant and non-transitory amount. This question cannot be answered by looking at price movements alone.


available. The critical value for establishing whether the results of the test imply a unit root differ however according to what kind of integrated process is assumed, that is, a random walk, or a random walk with drift, or a random walk with drift and added time trend, etc. The setting up and interpretation of these tests require an experienced practitioner. If two I(1) variables are co-integrated, then the regression of one on the other produces I(0) residuals. Most tests for co-integration are unit root tests applied to the residuals.


Application: the supply of soluble coffee 8.8

In 1990 the MMC115 was asked to investigate and report on the sale of soluble coffee in the UK retail market. The Director General of Fair Trading was concerned that The Nestle Company Ltd (Nestle) was in a dominant position, given that it supplied 48% of the volume and 56% of the value of soluble coffee for retail sale in the UK. Nestleâ&#x20AC;&#x2122;s profitability was also found to be higher than the majority of other firms in the industry. The MMC confirmed that a scale monopoly existed with respect to Nestle (whose main brand is Nescafe) but did not find any behaviour that operated against the public interest.


Of particular interest to the Commission was the slow adjustment of soluble coffee prices to changes in the prices of coffee beans which, just prior to the time of the enquiry, had experienced major fluctuations. This can be seen in the graph overleaf. Nescafe was particularly slow to adjust to these price changes. Nestle claimed that several factors influenced their decision whether or not to transmit the frequent changes in green coffee bean prices to consumers. First, the level of price volatility would be confusing to the customer and be difficult for the trade as a whole to manage. Secondly, in order to maintain consumer confidence Nestle avoided sharp price changes by smoothing out price increases. The MMC considered the following two studies of the price transmission mechanism that applied quantitative techniques. 116



Monopolies and Mergers Commission, 1991, Soluble Coffee, A Report on the Supply of Soluble coffee for Retail Sale in the UK. Similar studies were undertaken in the course of the MMCâ&#x20AC;&#x2122;s investigation into the supply of petrol in 1989 (Monopoly and Mergers Commission, 1989, The Supply of Petrol). One study was undertaken, by the Department of Energy and the other by economic consultants on behalf of the Petrol Retailersâ&#x20AC;&#x2122; Association (PRA). The Department of Energy study examined the relationship between UK retail petrol prices and spot petrol prices in the Rotterdam market. It found that pump prices adjusted to spot prices, over the long run, in similar ways across the six markets investigated (Belgium, France, West Germany, Italy, Netherlands and the United Kingdom) . There was also evidence that the UK prices were more responsive than elsewhere. The study also found that pump prices in all six countries followed movements in spot prices with a short lag. The study on behalf of the RPA looked at the relationship between crude oil prices and retail petrol prices in the UK. This study found that retail prices rose following a rise in crude prices in the period 1977 to 1984 but did not experience an equivalent fall when crude prices dropped during 1985 to 1988. The consultants also looked at the relationship between retail prices and the Rotterdam price for the same two periods. They concluded that margins between crude, Rotterdam petrol and UK retail prices had widened since 1985.


Figure 1: The Nescafe Wholesale List Price and the Green Bean Price Lagged


An internal study by the Ministry of Agriculture, Fisheries and Food (MAFF) comparing the prices of instant and ground coffee with changes in green coffee bean prices between 1979 and 1989 was based on quarterly data from the National Food Survey. MAFF analysed the correlation of retail prices of ground and instant coffee with the level of the raw bean equivalent price based on the sub-group indices of the Producer Price Index. The results from this analysis showed that a closer relationship between ground coffee prices and raw bean prices than between soluble coffee prices and raw bean prices. The correlation became stronger if the price of ground coffee was lagged two quarters, while the instant coffee price was best correlated after one quarter. Furthermore the study found that, on average, both instant and ground coffee prices respond more to raw bean price rises than price falls.


GFL, the second largest supplier of soluble coffee in the UK market, submitted a study undertaken by economic consultants which analysed the relationship between input and output prices of soluble coffee. The analysis focused particularly on how the price changes in the green coffee bean market fed through to retail prices and the extent to which this transmission explained changes in retail coffee prices. The econometric estimation showed that an increase in the cost of beans for delivery led to an almost exact 67

increase in retail selling prices. Furthermore, estimations of the relationship between green coffee bean prices and wholesale realisations (1981-90) found that these prices also moved closely together. Over 50% of the change in the purchase cost of beans fed through to wholesale realisations within three months, and 75% of any change in wholesale realisations were transmitted to retail prices in the same quarter. When testing for asymmetry in the relationship between green bean and output prices for periods of green bean price rises against price falls, the consultants found no asymmetry â&#x20AC;&#x201C; both increases and decreases were reflected in output prices to a similar extent and within similar time periods. The price of Maxwell House (GFLâ&#x20AC;&#x2122;s leading brand) was found largely to follow movements in the price of Nescafe.

Application: petrol markets in Colorado 8.12

Langenfield and Watkins 117 use co-integration analysis to test whether petrol prices in Denver are linked with those in the neighbouring towns of Tulsa, Kansas City, Cheyenne and Billings. They use weekly price series for the period from January 1992 to July 1997 inclusive, purged of the common effect of the price of crude oil. They find that Denver petrol prices have an equilibrium relationship with those in Tulsa, Kansas City, Cheyenne and Billings, although the relationship with Billing is somewhat weaker. The evidence therefore shows that these five towns are part of the same market for wholesale petrol.


Langenfeld, J.L. and G.C. Watkins, 1998, Geographic Oil Product Market Test: An Application Using Pricing Data, Mimeo: LECG Inc.






The residual demand facing a firm or a group of firms is the demand function specifying the level of sales made by the firm or group as a function of the price they charge.118 The estimation of the residual demand allows the analyst to understand the competitive behaviour of a firm or group of firms, by accounting for supply substitution effects. In the discussion below, the term ‘firm’ is used, but it can be substituted with ‘a group of firms’ without loss of generality.


A firm operating in a competitive environment does not have the power to raise the price above the competitive level. Let us define own-price elasticity as the percentage decrease in any product’s demand due to a percentage increase in its price given that the prices of other products remain unchanged. Accordingly, the own-price elasticity will always be negative, although it is often discussed in terms of the absolute value. Any segment of a demand curve is said to be elastic if, when the price rises by x% the quantity demanded decreases by more than x%. This corresponds to an absolute value of the elasticity larger than one. A firm operating in a perfectly competitive market faces an infinitely elastic residual demand curve. This is because if the firm raises the price of its product even slightly, it will lose all its customers to the competition. The fewer the competitive constraints from other products or firms, the less elastic the residual demand curve faced by the firm. What this implies is that, by reducing the quantity it supplies, the firm could cause a long-lasting price increase. So, the elasticity of the residual demand curve conveys invaluable information on the competitive situation of a firm. In general, the higher the elasticity, the lower is the potential power of the firm to force a significant and non-transitory price increase in the market for its product.


Formally, the residual demand faced by any firm is that part of the total demand which is not met by the other firms in the industry:

Dr(p) = D(p) - So(p)


where Dr(p) is the residual demand; D(p) is total demand and So(p) is the supply of the 118

See Carlton, D.W. and J.M. Perloff, 1994, Modern Industrial Organization, New York: Harper Collins, for an excellent exposition of the theory of residual demand.


other firms in the industry; all three are a function of own and competitors’ prices, p. Equation 8 shows that the residual demand also depends on the supply response of the other firms. As with demand, supply is said to be elastic when a price increase of x% causes an increase in supply of more than x%. If there are n identical firms in the market, the demand elasticity for any of the n firms is given by: r = n-o(n-1)

r<0 ; <0; >0


where r is the price elasticity of residual demand,  is the elasticity of total demand for the (homogeneous) product, and 0 is the supply elasticity of the other firms in the industry. So, if n=1 the economic market is a monopoly, and the residual and total demand elasticities coincide. The larger n, the larger in absolute value  r will be even if  and o are small,119 in simple economic models. 9.4

As pointed out by Werden and Froeb,120 in homogeneous product markets the elasticity of the residual demand conveys all the information that is needed to define an antitrust market, as defined through the hypothetical monopolist test. From an estimate of such elasticity, the analyst can infer whether a firm or a group of firms could cause a significant and long-lasting price increase. If that is the case, then the product sold by the firms, or the geographic area in which they operate, constitutes an antitrust market. Residual demand analysis is so powerful because it can be applied to any kind of market, and with due modifications it can be widened to allow for the analysis of markets with differentiated products.121 In what follows we discuss how residual demand analysis can be implemented empirically.

Description of the technique


120 121

Carlton and Perloff (1994 p102) calculate the elasticity of residual demand facing a firm operating in a market with increasing numbers of firms. Assuming that the elasticity of supply of firms is zero, they present three scenarios. In the first, the total demand is inelastic (elasticity = 0.5) the elasticity of residual demand varies between –5 (with no firms in the market) and –500 (with 1,000 firms). When the elasticity of total demand is unitary, that of the residual demand rests between –10 (n=10) and –1000 (n=1000). Finally, with a moderately elastic total elasticity of –5, the effect of residual demand varies from –50 (n=10) to –5000 (n=1000). To show that the elasticity of residual demand depends crucially on the number of competitors, Carlton and Perloff, 1994, p.103, compute it for US agricultural markets where the elasticity of supply is 0. The demand for many crops is fairly inelastic but farmer numbers are high. For instance, the elasticity of demand for apples is –0.21 but with 41187 farmers in the market each farm faces an elasticity of –8,649. The sweet corn market discussed has unitary elasticity (-1.06), but with 29,260 producers each farmer has an elasticity of –31,353! What this implies is that if a single farmer increases its price by one thousandth of one percent, his demand would fall by 31%. This is evidence enough to show that farmers are price-takers. Werden, G.J. and L.M. Froeb, 1993, op. cit. A model to analyse residual demand in markets with differentiated products was developed in Baker, J.B. and T.F. Bresnahan, 1985, The Gains from Mergers or Collusion in Product Differentiated Industries, Journal of Industrial Economics, 35: 427-44.



The objective of residual demand analysis, applied to antitrust problems, is to obtain an unbiased estimate of the own-price elasticity of the residual demand curve. In general, this can be done by using Instrumental Variables (IV) or other simultaneous equations techniques or simply a ‘reduced form’ simple regression. The empirical test revolves around finding an answer to the following question. Assume a group of firms have the following residual demand function: Gi = f(Pi, X, Y)


where the subscript i identifies the group of firms, P i is the price they charge, which subsumes their cost structure, X is a vector of cost shift variable affecting the groups’ rivals, and Y is a demand shift variable affecting the behaviour of consumers (such as income, for instance). The problem with the formulation above is that the quantity sold and the price charged by group i are simultaneously determined. In order to obtain an unbiased estimate of the residual demand the analyst needs to reformulate the problem in the following way. The price charged by group i depends on the costs its members face. Assume that there is a cost shift variable, Z, that only affects the costs of the firms in the group, not their rivals’ costs. If the members of group i were able to transfer an increase in the price of Z directly onto the customers, then they would form an antitrust market. As Gi and Pi are simultaneously determined, and they are both a function of the cost shift variable Z, we can express the so-called reduced-form price and quantity equations as: Gi=G(Z,X,Y) Pi = P(Z, X, Y)


where ‘reduced-form equation’ means an equation in which all the right-hand side variables are not influenced by the left-hand side variable. Technically speaking, a reduced-form equation is one with no endogenous (simultaneous) variables on its righthand side and its estimation yields unbiased parameter estimates. It is customary in the literature to estimate the reduced-form price equation, rather than the quantity equation. There are two main reasons for this. First, it can be shown that a test of the null hypothesis that Pi / Z =0 is all that is needed to test for market power. If Pi / Z = 0 the group of firms under investigation do not form an antitrust market because they cannot pass on price increases unique to the group to their customers, due to competition from other firms. Secondly, data on prices is often more precise than data on quantities, and this affects the precision of the estimates.



To implement the test, the analyst must proceed as follows. First, data is gathered on the price, quantity sold and cost shift variable for the group of firms under analysis; on cost shift variables for rival firms in the industry; and on the demand shift variable. Secondly, the price equation is estimated by instrumental variable techniques. This requires regressing the quantity sold by the group of firms on (Z, X, Y): Gi = a0 + a1Z + a2Y + a´X+ ui


Then using the estimated parameters, the fitted values for G i are calculated as: i = â0+â1Z +â2Y + â´X


Finally, the price equation is estimated as the last step of the instrumental variable (IV) regression procedure: Pi = b0 + b1i + b2Y + b´X + ei


This procedure provides the estimated price elasticity of the residual demand equation, which is given by 1/b1. The estimate can be used to test for the null hypothesis that such an elasticity be equal to any pre-specified value;122 the test statistic used will be a simple t-test. If the analyst is not interested in the estimated price elasticity, but only in whether this is perfectly elastic, that is, if costs limited to the group cannot be passed on because of competition from others, a simpler procedure can be followed. This involves estimating the reduced-form price equation: Pi = c0 + c1 Z + c2Y + c´X + vi


and testing, by means of a t-test, the null hypothesis that c 1=0.

Data and computational requirements 9.7

The implementation of this technique requires the use of a time series of data on prices, quantities, cost and demand-shift variables. The analyst should make sure that there are enough observations for the results to be meaningful: a minimum of about 50 observations could suffice, but it is strongly recommended to use longer series, especially lagged values. The dependent variables also need to be added to the regression to model price dynamics. The estimation of residual demand regressions can be carried out using any econometric package. The most user-friendly packages, such as Microfit, contain inbuilt routines to automatically perform IV estimation.


A good example of this test can be found in Scheffman, D.T. and P.T. Spiller, 1987, op. cit., Table 5, p. 144.


Interpretation 9.8

The estimation of the elasticity of residual demand requires an in-depth knowledge of the production process for the product or service under study. It is of crucial importance that the cost shift variable for the group of firms under analysis be chosen correctly: if the instrumental variable is poorly chosen, the estimated elasticity will be misleading. Also, it is crucial for the unbiasedness of the result that no important cost or demand shift variable be omitted from the analysis. One problem that is often encountered by analysts wanting to use this technique is the lack of data on cost shifters; more precisely, it is often very difficult to identify a variable that is a cost shifter only to the firm(s) of interest. In the particular case when a merger is investigated between a home producer and a producer abroad, the exchange rate can be used as a cost-shift variable. However, if there are many competitors in the home and abroad markets the costs are different and the estimation becomes extremely difficult.


Moreover, as pointed out by Baker,123 there are some technical problems associated with the use of this technique that have to be carefully addressed and solved before conclusions can be drawn from the results. These problems relate to aggregation across the firms in the group under analysis; and to the dynamic specification of the price regression (especially error auto-correlation).


In contrast to the empirical analysis of price movements, the analysis of residual demand stems from theoretical economic models: the estimated own-price elasticity is directly derived from an equilibrium model of supply, demand, and behavioural assumptions. Based on behavioural assumptions, residual demand analysis allows the analyst to draw conclusions on whether a group of firms could impose a non-competitive price for its product. If the answer is positive, then the analyst concludes that those firms represent an antitrust market. So, while price analysis is a useful tool in the definition of an economic market, that is, a market within which the law of one price holds, and can offer significant insights into antitrust markets, residual demand analysis is the technique that has to be used to define an antitrust market directly.


The definition of an antitrust market is a fundamental issue in merger analysis, where the ability of two merging firms to generate a significant and non-transitory price increase is the first and foremost indication of a likely anti-competitive outcome from a merger. However, it is important to notice that if the analysis reveals that the firm or group of


Baker, J.B., 1987, Why Price Correlations Do Not Define Antitrust Markets: On Econometric Algorithms for Market Definition, Working Paper No 149, Washington DC: Bureau of Economics Federal Trade Commission.


firms under scrutiny have the ability to exercise some degree of market power, it is the analyst who has to determine whether such market power is large enough to generate concern.

Application: National Express Group plc and Midland Main Line Ltd 9.12

In 1996 the MMC investigated the National Express Group plc (NEL) and Midland Main Line Ltd (MML) case. In this instance the MMC was asked to examine the extent to which competition between NELâ&#x20AC;&#x2122;s coach services and MMLâ&#x20AC;&#x2122;s rail services were affected by the merger of these two companies. The quantitative evidence in the case centred around cross-price and own-price elasticities.


One study looked at the cross-price elasticities between coach and rail. The results of the study showed that there were positive and significant cross-price elasticities for rail travel with respect to coach prices. Another study examining the determinants of rail demand found a smaller relationship between the extent of coach competition and demand for rail services. These results may, however, be explained by a more aggressive pricing policy by rail companies in response to coach deregulation.


Several studies were also conducted on the own-price elasticities of rail. One study on second class journeys between Nottingham and London found an own-price elasticity of â&#x20AC;&#x201C;1.48, implying that a 10 % reduction in rail fares would increase demand by 14.8 %. The MMC commissioned its own study to analyse elasticities for leisure passengers on this same route. Own-price elasticities were closer to zero than the above studies, suggesting more inelastic demand for rail journeys into London. The demand for coach travel was found to be more sensitive to price changes. The cross-price elasticities found in the MMC-commissioned study indicate that there is some degree of substitution between rail and coach travel, and so demand for rail travel is to some extent responsive to coach fares.


The various parties to the merger also commissioned studies based on cross-price and own-price elasticities. The results of these studies were different to those reached by the MMC-commissioned study. Many criticisms centred on the routes that the MMCcommissioned study analysed, in particular the use of data from regional railways to draw conclusions about routes that included London as their origin or destination. There was also dispute about elasticities that included all travel, and not just leisure or business travel. These are just some of the factors that will affect the results of elasticity studies.



The MMC, in deciding on the case, accepted that rail services provided stronger competition to coach then coach services to rail. However, the MMC felt that the evidence also suggested that coach services do provide a degree of competition to rail services, and that joint ownership of rail and coach services dilutes this competition.






Critical loss analysis is a necessary complement to residual demand analysis and survey techniques,124 as it supplies the critical value of the elasticity of residual demand to be used in antitrust analysis.


Firms operating in markets where there is some degree of market power will experience a loss in sales if they unilaterally raise the price for their product. This technique estimates the ‘critical’ loss in sales that would render unprofitable a unilateral price increase on behalf of a firm or group of firms. We can identify two effects on profits resulting from a unilateral price increase. On the one hand, as the price goes up some consumers switch to competitive or substitute products, and sales decline. On the other hand, profits on those sales that are retained increase. Any price increase therefore is only profitable if the second effect outweighs the first. Once the antitrust authorities have determined the size of the price increase, the investigator proceeds to establish whether such a price increase would, in fact, be profitable.


Critical loss analysis was developed by Harris and Simons125 who define the critical loss ‘for any given price increase, (as) the percentage loss in sales necessary to make the specified price increase unprofitable.’ 126 After having assessed the critical loss, it is possible to determine whether it is likely to occur as a result of a merger or other potentially anti-competitive agreement. If the results show that the actual loss in sales caused by the price increase will be less than the critical loss, then that price increase would be profitable and the investigation will proceed further.

Description of the technique 10.4

This technique relies on the use of simulation rather than econometric methods to assess the critical loss. The empirical test is aimed at finding the answer to the following two questions. (i) Assume that a firm raises its price by Y%, and loses some customers as a result. What would be the loss in sales that would leave profits unchanged and so cause the firm (or group of firms) to be indifferent between raising the price or not? Such loss in sales represents a critical value because for any larger loss, it will be unprofitable to increase the price; and for any smaller loss increasing the price will be profitable. (ii) What would be the actual loss in sales that resulted from the price increase?

124 125


For residual demand analysis and survey techniques, see Chapter 9. Harris, B.C. and J.J. Simons, 1989, Focusing Market Definition: How Much Substitution is Necessary?, Research in Law and Economics, 12: 207-226. Harris B.C. and J.J. Simons, 1989, op. cit., page 211.



Under the assumptions that before the price increase the market is competitive, and therefore the price equals marginal cost; and that fixed and average variables costs remain unchanged after the price increase, the critical loss in percentage terms is equal to: Critical Loss = [Y/(Y + PCM)]*100 127


where Y is the proportional hypothesised price increase 128 and PCM is the priceâ&#x20AC;&#x201C;cost margin, equal to [(Initial Price - Average Variable Cost)/Initial Price]. For example, assuming that the initial price for the product of interest is 100 and that the average variable cost is 60, the PCM is 0.4; if the hypothesised price increase is 5% (Y=0.05), then the critical loss is 11.1%. 10.6

The second step in the analysis is the assessment of the actual loss in sales due to the price increase. How many sales will be lost depends on the residual demand elasticity facing the firm or group under investigation. To each critical loss corresponds a critical elasticity, computed by dividing the critical loss by the assumed percentage increase in price. For the above example, the critical elasticity is 2.2. If the estimated residual elasticity is larger than this critical value, the actual loss will be larger than the critical loss, and the price increase will be unprofitable. We have discussed the estimation of residual demand elasticity in Chapter 9. This estimation is often difficult due to data limitations. In such a case, proxies for residual elasticity can be obtained by using the results from consumer surveys, described in Chapter 12, although this can be expensive and time-consuming.

Data and computational requirements 10.7

The estimation of the critical loss requires amazingly little information. The only two data points needed are the initial price and average variable cost, and these are very easily obtainable. As for computational requirements, all that is needed is pen and paper, or a pocket calculator. The estimation of the actual loss is much more difficult requiring estimation of the residual demand elasticity as in Chapter 9, or survey data as in Chapter 12.

Interpretation 10.8

Critical loss analysis provides a reliable rule upon which the profitability of price increases can be assessed. It provides a much needed benchmark for decision making in the definition of an antitrust market. This technique cannot be used on its own because,



This is Equation 13 in Harris, B.C. and J.J. Simons, 1989, op. cit. The reader is referred to the original article for the derivation of the formula. An antitrust market is defined as that set of product or geographical area where a hypothetical monopolist could impose a profitable â&#x20AC;&#x2DC;small but significant and non-transitory price increaseâ&#x20AC;&#x2122; (US DOJ Merger Guidelines). Such price increase is often taken to be 5% (so Y=0.05).


while it provides a yardstick, it does not tell us anything about the actual loss in sales which is likely to occur as a result of the hypothesised price increase. 10.9

The estimated critical loss is a function of the price increase and price-cost margin. As shown in the table below, for a given price increase, the higher the price-cost margin, the lower the critical loss. For a given price-cost margin, the higher the price increase, the higher the critical loss. This sensitivity of the critical loss to the assumed price increase makes it very important that the hypothesised price increase is correctly chosen.

Table 3

Critical Losses in Sales Necessary to Make a Price Increase Unprofitable under Various Assumptions

Price-Cost Margin Assumptions

20% 30% 40% 50% 60% 70%

Price Increase Assumptions 5%



20% 14% 11% 9% 8% 7%

33% 25% 20% 17% 14% 13%

43% 33% 27% 23% 20% 18%

10.10 For instance, say that a newspaper has 100 customers to whom it sells one newspaper each at $1, earns advertising revenues of $3 per customer and is earning a variable margin (over variable costs) of $3 per customer (that is, its variable costs are $1 per subscriber). If it raises price by $0.05 and loses only one customer, its profit gain is $0.05 * 99 (=$4.95) and its loss is $3 * 1 (=$3) and so this increase would be profitable. However, if it loses two customers, its profit gain is now $0.05*98 (=$4.90) and its losses are now $3*2 (=$6) and the price change is unprofitable. So, in this simple model, if the variable margin were 75%, and the price increase were 5%, a loss of more than 1% of customers would make the price increase non-profitable (assuming no impact on marginal costs from the customer loss). By contrast, if the only revenue the newspaper received per reader were $1 for the price of the newspaper and the variables margin still equalled 75%, a 5% increase in price would require a 6% decline in subscribers to be unprofitable. Table 3 shows the minimum customer loss required for a price increase to be unprofitable for various assumptions on percentage price increases and variable margins (assuming no impact on marginal cost). Table 3 considers two situations: (i) the impact on profits without considering the impact on advertising revenues; and (ii) the impact on profits


assuming that advertising revenue per subscriber is three times the original subscription price and advertising revenue per subscriber does not change as a result of the price increase.129 10.11 There are two circumstances in which critical loss analysis requires adjustments. First, the technique has to be modified if the firm(s) produce(s) more than one product and the reduction in the sales of one product allows the firm(s) to produce and sell more of another product. Then the computations have to take into account the increase in profits generated by these extra sales. Secondly, adjustments are required if the firm(s) sell(s) products that are production complements, so that the reduction in the sales of one product will force the firm(s) to produce, and sell, less of the other product as well. Then the computations have to take into account the decrease in profits stemming from the additional loss in sales.


So, each lost subscriber results in lost advertising revenues equal to the original advertising revenue per subscriber. For example, if subscription prices are $1, we assume that advertising revenues per subscriber equal $3 (consistent with the ratio of advertising to subscriber revenues). If subscription prices increase to $1.05, we assume that advertising revenues per subscriber remain at $3. Each customer who stops subscribing due to the price increase results in lost revenues of $1 for the subscription and $3 for the advertising revenue and lower costs in the amount of the marginal cost of producing a paper for that subscriber.





How foreign competition is treated is an important issue in antitrust analysis. The main question is whether foreign producers could thwart any attempt by domestic producers to raise prices. If that were the case, domestic producers could not effectively exercise market power. Import penetration tests may be used to assess whether imports are highly sensitive to variations in domestic prices, or better, to relative domestic and foreign prices. If imports are sensitive to domestic prices, and if there are no quotas or other restrictive trade practices, supply substitution is high. Consequently, the likelihood that a domestic firm or group of firms exercises market power is quite low.

Description of the technique 11.2

Formally, the sensitivity of imports to domestic prices is measured by the price elasticity of imports. Much the same as with domestic supply, import supply is said to be elastic if, when the domestic price rises by x% the quantity imported rises by more than x%. Of course, a rise in domestic, relative to foreign, prices does not necessarily have to be caused by domestic producers; domestic prices rise relative to foreign prices due to appreciation in the exchange rate of the domestic currency. Import elasticities estimated in the context of exchange rate fluctuations can then be used as proxies to analyse the effect of a potential price increase by a set of domestic producers on foreign suppliers.


The responsiveness of imports to price changes is estimated by regression methods. The logarithm of the quantity imported (Xt ) is regressed upon a constant term (), the log of the price of the product - or better, of the price relative to foreign prices, that is, the price expressed in a common foreign currency (Pt), and the log of the importing countryâ&#x20AC;&#x2122;s percapita income (Yt). Often a variable to proxy world demand conditions (such as the growth rate of consumption in OECD countries) 130 is added to the regression (d t), as well as a time trend (t) to pick up the effects of capacity and productivity growth: Xt =  + 1 Pt + 2 Yt + 3dt + 4t + ut


Where ut is an error term with usual properties. The estimated coefficient on the price variable measures the price elasticity of imports; if the estimated elasticity is higher than one, then import demand is elastic and there is limited room to exercise market power by the local producers.


For the use of this variable see, for example, Jacquemin, A. and A. Sapir, 1988, International Trade and Integration of the European Community: An Econometric Analysis, European Economic Review, 31: 1439-49.


Data and computational requirements 11.4

The estimation of the imports demand equation requires the use of a time series of data on all the relevant variables; there should be at least 35 to data points to estimate the simple equations described above, but longer series are strongly recommended. Any econometric package can be used to run the regression. Data on commodities imports, as on the other variables needed in the estimation, is readily available from official statistical sources.

Interpretation 11.5

The problems arising from the estimation of imports equations are mainly econometric in nature. The equation described above has no dynamic component. However, autocorrelation in the error term is a likely problem. Also, it would be advisable to test for co-integration, 131 and if evidence is found for the existence of a co-integrating relationship, the equation should be modified accordingly.


One main problem with estimating import elasticity using single equation methods is that of simultaneity bias: are we estimating the demand or the supply of imports? Can we really distinguish between the two? This problem can be serious and can invalidate the results. For this reason, it is preferable to estimate import elasticities within a welldefined model of supply and demand.


See Chapter 8 for a discussion of co-integration.





There are many situations in which raw price, quantity or other data, both on a crosssectional and time series basis, is not available. In this case techniques of the type outlined so far cannot be applied. One solution to this predicament is to create an ad hoc data set, from which the likely behaviour of consumers and market participants could be inferred. As data on the whole population of consumers and/or producers is generally not available, this is usually done through a survey, which can then be used to carry out national or international price comparisons, using cross-sectional price tests and hedonic price analysis;132 to estimate consumer demand functions;133 or to infer market definition. In this chapter we discuss the use of surveys for direct inference on market definition.

Description of the technique 12.2

To carry out a survey, it is first necessary to consider what information is required for the investigation being carried out, including some indication on the accuracy of the results. This information will guide the analyst in the selection of the sample, which has to be representative of the population the analyst wishes to investigate. Such a population could either be that of the actual consumers of a product, or that of the actual rivals in the production of such a product. The definition of the population under investigation is crucial, especially in situations were products are differentiated.


Secondly, a questionnaire has to be devised in such a way as to minimise the likelihood of obtaining biased responses. This should be piloted on a small sub-sample to identify ambiguities and other problems with the draft questionnaire. Thirdly, the questionnaires have to be mailed to the individuals in the samples, or interviews have to be conducted. Fourthly, the data has to be transferred into files and cleaned. If individuals or companies with certain characteristics have been over-sampled (for instance, consumers with higher incomes, or larger firms), weights have to be calculated at this stage.


Finally, the answers must be counted, and frequency counts and other statistical methods applied to make inference from the data. For instance, the proportion of customers that will switch product for a given price increase can be computed, and the relative demand elasticity calculated.

132 133

These techniques are discussed in Chapters 3 and 4 respectively. The estimation of demand equations using survey techniques is discussed in Chapter 14.


Data and computational requirements 12.5

Carrying out a survey can be a very expensive undertaking, and in general it should only be done if no other appropriate technique is available. The sampling, questionnairedevising and analysis phases all require the use of professional statisticians and it is best to consult them as soon as the possibility of a survey becomes apparent. Biased results are worse than useless since they can be seriously misleading. Generally speaking, the sample size has to be large, if it is to be representative of the population. For example, in the investigation of a merger between Virginia National Bank and the First State Bank of Wise County, Virginia, 134 a questionnaire was mailed to 750 households and over 1,400 companies in the County.


Surveys can also be informal in nature. In the US, expert economists will often interview customers, for example, to determine their likelihood of switching in the face of a price increase limited to products being considered for a hypothetical market definition. If such data is accurate, then they would help an economist answer the key questions in defining an antitrust market. However, one should be very careful in considering the views of a small number of people in a sector as representative: informal surveys are not statistically valid.


A representative survey usually results in a large dataset, which requires the use of a good statistical package, such as SAS or SPSS for the handling phases.

Interpretation 12.8

Using survey data, of the kind described above, to define markets can be misleading, as it elicits only information on what people say they did or would do, rather than what they would actually do in response to economic incentives or competitive pressures. In other words, the results depend heavily on the way the questions are phrased, and they are at best hypothetical. Given the nature of the analysis, the questions asked are necessarily of the kind ‘what would you do if...’. The answer to that question does not actually shed light on what the individual will necessarily do when faced with the situation.


It is also very important to ask the right question. In the Wise County case, they asked the question, ‘what would you have done if a bank in a specified town, or a bank in the next town, would have offered a 1% lower interest rate on a loan?’ This question poses two problems. First, the right question would have been about a response to a 1% price increase by the local bank. This way, the respondent would have defined his geographic


Shull, B., 1989, Provisional Markets Relevant Markets and Banking Markets: The Justice Department’s Merger Guidelines in Wise County, Virginia, Antitrust Bulletin, 34: 411-428.


market, rather than the questionnaire suggesting it. Secondly, one would expect that the answer would be yes to a question about whether the person would go to the next town to shop. So, the question allows the creation of a chain of substitution that would set too large a geographic market.135

Application: the Engelhard/Floridin merger136 12.10 In the Engelhard case, the District Court for the Middle District of Georgia, US disagreed with the US Department of Justice, who sought to block the acquisition of Floridin by Engelhard. Such a merger would have reduced the number of competitors in the market for the US gel clay (GQA) from three to two. The issue of whether the market for GQA was the relevant antitrust market was hotly debated. 12.11 One of the DOJ’s expert witnesses interviewed many existing customers of Engelhard and Floridin, asking whether they would switch for a 5 or 10% price increase in GQA. In addition, a number of customers were questioned directly. In general, the customers said that they would not switch to other products for a 5 to 10% price increase in GQA. 12.12 The Court explicitly disagreed with the DOJ and its’ experts use of the 5 to 10% price increase test for determining whether customers would substitute other products in sufficient numbers to make such a price increase unprofitable and, so, excluding other products from the relevant product market definition. 12.13 The Court noted that GQA accounted for less than 10% of the total cost of the final production, so that a 5 to 10% price increase would raise the final production cost by very little. Moreover, changing GQA supplier is costly, as it requires product testing and potential reformulation. So it is not surprising that some customers stated that they would not switch from Engelhard GQA to Floridin’s. Therefore, a higher price increase test would be necessary to establish the product market. 12.14 After considering all the evidence produced, the Court concluded that the evidence did not support the claim that GQA was the relevant product market. The Court concluded that there was insufficient evidence to define the market, and that the DOJ had therefore failed to carry its burden of persuasion for an essential element of the case.

135 136

See Shull, B., 1989, op. cit. Langenfeld, J., 1998, The Triumph and Failure of the US Merger Guidelines in Litigation, The Global Competition Review, December 1997 January 1998: 36-37.







Frequently applied in merger cases in the UK, price-concentration studies are based on the structure-conduct-performance paradigm developed by Bain. 137 According to this well known theory, market structure influences the performance of market participants via their conduct. Market concentration is the most commonly used proxy for market structure; and the so-called ‘market power hypothesis’ 138 implies that concentration affects market performance (that is, profit margins) via its effect on pricing. In merger cases, the market power hypothesis translates into the following testable proposition. If higher concentration is associated with higher prices/profits in the relevant market, then a merger that has a significant impact on concentration in a particular market raises anticompetitive concerns.

Description of the technique 13.2

The theoretical underpinning of the structure-market-performance paradigm can be traced to the Lerner139 index of monopoly power, that for any firm i can be written as: [(P – MCi) / P] = [i + (1 – i)si] / dp


Where P represents the market price; MC the marginal cost; s the share of firm i in total industry output; dp the (absolute) industry price elasticity of demand; and  the conjectural variation term. The conjectural variation term is a measure of the percentage change in output that firm i expects other firms in the industry to undertake in response to a 1% change in its own output. If  = 0, the firm expects no reaction on behalf of other firms to an increase in its own output: the firm operates in a Cournot oligopoly. If  = 1, the firm expects its rivals to, say, decrease their output by 1% in response to a 1% decrease in its own output; this implies that the other firms behave so as to make it

137 138


Bain, J.S., 1951, op. cit. See Fairburn, J. and P. Geroski, 1993, The Empirical Analysis of Market Structure and Performance, in Bishop, M. and J. Kay, eds., European Mergers and Merger Policy. Oxford: Oxford University Press, pp. 217-238 for a discussion of the market power hypothesis. Lerner, A., 1934, The Concept of Monopoly and the Measurement of Monopoly Power, Review of Economic Studies, 1: 157-75.


possible to restrict market output (thereby causing a price increase). Finally, if  = -1, the firm expects its rivals to offset its, say, 1% output reduction by a 1% increase in their own output. 13.3

If we assume that  i/ dp and (1 –  i)/ dp in Equation 18 are parameters to be estimated, the empirical test of the structure-conduct-performance paradigm at the industry level can be carried out by testing whether the price-cost margin increases with the degree of concentration in the industry. At the firm level, price-cost margins depend on both industry concentration and the firm’s market share. The degree of concentration is the variable used to proxy market structure. The most common measures of concentration are the k-firm concentration ratio, CRk, and the HHI. The CRk index measures the proportion of the market sales accounted for by the largest k firms, where k is normally taken as 4, 5, or 8, CR4, CR5, CR8. The HHI is the sum of the squared individual market shares for all the firms in the industry.


Price-cost margins are defined in terms of marginal costs. Unfortunately, marginal costs are not often observable, and various proxies have been used in the empirical literature for the degree of profitability. Accounting measures of profits (the rate of return on sales), the Tobin’s q (the ratio of the stock market value of a company to the replacement cost of its assets), and the rate of return on equity holdings have all been used as proxies for the price-cost margins. 140 Martin 141 shows ‘there is a one-to-one mapping between price models and price-cost margin models …In principle, if one has a model of profitability, one also has a model of price’. So the market power hypothesis, that is, the ability to impose prices that are above the competitive level, can be tested using either prices or one of the above profitability measures as the dependent variable in a regression model.


Price-concentration studies are carried out at either industry or firm level by running a regression with price levels as the dependent variable, and a concentration measure and other variables as regressors, usually all in levels. 142 As market power in concentrated industries cannot be exercised unless there are barriers to entry,143 such barriers have to be accounted for in the price regressions. Product differentiation, economies of scale and absolute cost advantages all represent barriers to entry. Product differentiation can be


141 142


See Martin, S., 1993, Advanced Industrial Economics, Oxford: Blackwells, Chapters 16 and 17 for an in-depth discussion of the issue. Martin, S., 1993, op. cit. page 547 Usually the concentration measure enters the regression linearly. However, if the degree of collusion is greater, the higher the market concentration, then the latter could influence prices in a non linear fashion. This could result in insignificant estimates for the coefficient of the concentration measure, and to the wrong inference being drawn. This problem can easily be solved by entering both the level and the square of the concentration variable as regressors; or by the use of dummy variables for different levels of concentration. Bain, J.S., 1956, op. cit.


proxied by using the advertising-to-sales ratio; while economies of scale can be proxied by using an estimate of the minimum efficient scale of production. The level of imports, or the imports-to-sales ratio, is also added as a regressor to account for the effect of foreign competition on market power. 13.6

From the regression results the analyst learns the degree to which prices, or profits, are dependent upon industry concentration. If the coefficient is high and significant, this can be taken as an indication that any further increase in market concentration, due to a merger for instance, would significantly increase market power and therefore the price that consumers of the product have to pay.

Data and computational requirements 13.7

The data requirements for this technique depend on whether the study is carried out at industry or firm level. Industry-level analyses require time series data, while firm-level analyses utilise either cross-sectional or - preferably - panel data. For time series analysis, a sufficiently long time series of data is necessary, and this can create problems for some of the variables. For cross-sectional analysis, data on single firms within the market of interest is required; a sample of around 40 firms should be sufficient - using less is not advisable. Smaller samples of firms can be used if observations on each firm are available for more than one time period, that is, if the data is in the form of a panel.


Time series and cross-sectional regressions can be carried out using most available econometrics packages, while panel data analysis requires specialised software packages, such as Limdep.


In the absence of information on the other variables, the correlation coefficient between price and concentration can be considered, but such practice should be avoided whenever possible, as omitting the effects of entry, foreign competition and/or product differentiation would lead to biased estimates.

Interpretation 13.10 The empirical analysis of the relationship between price and concentration relies on the assumption that marginal costs are constant: according to economic theory, in the presence of market power the price will be raised relative to marginal costs; therefore the analyst should look at the relationship between price and concentration, while holding marginal costs constant.144 The regression of concentration on prices yields biased results


Bain, J.S., 1956, op. cit.


if marginal costs are not constant and correlated with concentration. This is a major problem with the use of this technique, especially when industry rather than firm level data is used. 13.11 The implementation of this technique relies on the assumption that prices are influenced by concentration and by the other right hand side variables without influencing them in turn. Technically speaking, the regressors are assumed to be exogenous. However, a high price could result in new entry and more imports into the market, and ultimately lead to lower concentration. Therefore, concentration, imports and entry could themselves be influenced by price. In a detailed empirical investigation, however, Geroski 145 found supporting evidence only for the endogeneity of the trade variable. Imports or imports-tosales ratio then should be instrumented; their lagged values can be used to get around the problem. 13.12 The market power hypothesis has been criticised by the economists of the Chicago school. Demsetz146 argued that if large firms are more efficient, then a positive relationship between concentration and profitability will be due to higher efficiency, not to the presence of market power. This argument is, however, only valid if the low-cost firms operate at full capacity. In such a case, the high-cost firms will supply the market with the difference between the market demand and the low-cost firmsâ&#x20AC;&#x2122; supply, and the market price will be set at the high-cost firmsâ&#x20AC;&#x2122; level. Then the low-cost firms will earn an efficiency rent. If the low-cost firms, however, have spare capacity and restrict their output to keep prices artificially high so that high-cost firms will not be forced to exit the market, then low-cost firms will earn an economic profit that reflects the exercise of market power. This would be the typical case if there is collusion in the market. 13.13 Finally, this methodology has been criticised as it does not take into account the fact that in a market with differentiated products, the existence of substitute products, that are immediately outside the relevant market, could influence the pricing behaviour of the firms in the market, again no matter how concentrated the market. 147




Geroski, P., 1982, Simultaneous Equation Models of the Structure-Performance Paradigm, European Economic Review, 19: 145-58. Demsetz, H., 1973, Industry Structure, Market Rivalry and Public Policy, Journal of Law and Economics, 11: 55-65; and Demsetz, H., 1974, Two Systems of Belief about Monopoly, in Goldschmid, H.J., H.M. Mann, and J.F. Weston, eds, Industrial Concentration: The New Learning, Boston: Little Brown. See Baker, J.B. and T.F. Bresnahan, 1992, Empirical Methods of Identifying and Measuring Market Power, Antitrust Law Journal, 61: 3-16, for a discussion.


Application: the SCI/Plantsbrook merger 13.14 In this case the MMC 148 analysed the degree of competition in the funeral service markets. The investigation was prompted by the proposed merger of Service Corporation International (SCI) and Plantsbrook Group PLC (Plantsbrook). The Commission concluded that the market for funeral directing services was local and relatively stable, and that although there was price competition, there was no consensus as to the strength of this competition. Several econometric studies were used to influence this conclusion. 13.15 An SCI-commissioned study undertook multiple regression analysis to explain differences in funeral prices by differences in six chosen variables. Two of these variables were designed to pick up the effects of local concentration – the level of concentration and the number of different firms within the areas chosen. Major differences in the quality of funerals were represented by dummy variables for coffin type, and for other ‘extras’ bought for the funeral. The two remaining variables were funeral costs and average wages in the locality. 13.16 These regressions concluded that the only significant variable was the one relating to ‘extras’, and therefore, price was not sensitive to the number of competitors. These results were essentially the same for funeral outlets outside London, and for those of the London study. The conclusion of the SCI-commissioned study was that there was no relationship between the price charged and the level of concentration regardless of how the market was defined (from half a mile to three miles). The study also analysed the relationship between the number of competitors and price increases, and again found no relationship. 13.17 An MMC-commissioned study reviewed the dataset used in the regression analysis. This study found several problems with the dataset and the model used. First, the measure of concentration used did not provide a good proxy for competitive pressure as differences between outlets were not considered (for example, larger outlets belonging to major competitors as opposed to small, independent outlets). Secondly, nearly all the explanatory power of the model came from the ‘extras’ and the variable for coffin types. However, these could also be consequences of concentration rather than independent determinants for price, that is, they could be endogenous to the model. Finally, standard econometric testing of the model showed it was not robust in that it could not generate reliable outcomes in terms of specified relationships. The MMC-commissioned study could not, however, generate an alternative model claiming that the dataset available made the development of a robust model impossible. 13.18 It seems that the MMC may have concluded that the gathered evidence meant either that


Monopoly and Mergers Commission, 1995, Service Corporation International and Plantsbrook Group Plc.


funeral markets were extremely localised or that there was no effective price competition even where there were several competitors. This then led to the recommendation that SCI divest itself of some funeral parlours where it appeared that the merger had created a local monopoly. It is interesting to note that FTC economists did the same type of statistical analysis in the US funeral homes mergers, and found that prices were higher in markets with few competitors and in rural markets.





Many markets are characterised by differentiated products, which are not perfect substitutes for one another. In markets with differentiated products, prices are set to balance the added revenue from marginal sales with the loss that would be incurred by losing such sales due to a higher price. When producers of two close substitutes merge, there is a strong incentive to raise unilaterally the price of at least one product above the pre-merger level.149 This is because those sales of one product that would be lost due to an increase in its price would be partly or totally recouped with increased sales of the substitute product. Whether such a price increase is profitable depends crucially on whether there are enough consumers for whom the two products represent their first and second consumption choice: the closer substitutes the two products are, the higher is the likelihood that a unilateral post-merger price increase would be profitable.


Antitrust authorities dealing with mergers in differentiated product markets need information about the substitutability of the two products, both between each other and with other products, in order to assess the likelihood of a price increase. This, and the following, chapter review the quantitative techniques available to assess the price effect of a merger in markets with differentiated products. The technique discussed in this chapter, diversion analysis, allows the analyst to make inferences about sales diversion between two competing products by using market share data only. This technique relies on very restrictive assumptions, but it is easy to implement and uses readily available data.

Description of the technique 14.3

Diversion analysis relies heavily upon the Independence of Irrelevant Alternatives Assumption (IIAA), which implies that the cross-elasticities of demand between product 1 and all the other products are identical. 150 If the IIAA holds, then the demand for a given product can be analysed using the multinomial logit model, which is an econometric model that allows the estimation of discrete demand models where individuals choose one in a set of differentiated, alternative goods. 151 The first step in investigating the effects of a merger between sellers of differentiated goods, say 1 and 2,




See Willig, R.D., 1991, Merger Analysis, Industrial Organisation Theory, and Merger Guidelines, Brookings Papers: Microeconomics, pp. 281-332 for a discussion. The cross-elasticity of demand between two goods, 1 and 2, is defined as the percentage change in the demand for good 1 when the price of good 2 is raised by x%. The logit model was developed in McFadden, D., 1973, Conditional Logit Analysis of Qualitative Choice Behavior, in Zarembka, P. ,ed., Frontiers in Econometrics. New York: Academic Press. For applications to antitrust issues, see the following. Froeb, L.M., G.J. Werden and T.J. Tardiff, 1993, The Demsetz Postulate and the Effects of Mergers in Differentiated Products Industries, Economic Analysis Group Discussion Paper 93-5 (August 24, 1993). Werden, G.J. and M.L. Froeb, 1994, op. cit. Willig R.D., 1991, op. cit.


is to assess the proportion of the sales of Good 1 that would be lost to Good 2 following a given price increase for Good 1. This can be done very simply by dividing the crosselasticity of demand between Products 1 and 2 by the own-price elasticity of demand for product 1. This ratio, defined the diversion ratio 152, can be immediately calculated if estimates of own- and cross-elasticities are available. However, this is not always the case, due to data and time limitations. A very useful property of the logit model is that it allows one to express the diversion ratio in terms of market shares. If all the products in the market are either ‘close’ or ‘distant’ substitutes for each other, then the diversion ratio between Products 1 and 2 (DR 12) is given by: DR12 = (Market Share of Product 2) / (1- Market Share of Product 1) [EQUATION 19] It is evident from the above formula that, all other factors being equal, the lower the market share of Product 2, the lower the diversion ratio. Also, all other factors being equal, the higher the share of Product 1, the higher the diversion ratio. These imply that the diversion ratio will be higher when one of the merging partners is a dominant firm. For example, if the market share of Product 2 is 20%, the diversion ratio will be 0.25 if the market share of Product 1 is 20%, and 0.33 if its market share is 33%. If on the other hand the market share of Product 2 is lower, say 10%, and that of Product 1 is again 20%, then the diversion ratio is 0.12. 14.5

Having computed the diversion ratio, the analyst proceeds to estimate the likely price increase resulting from the merger. Under the assumption that the elasticity of demand is constant over the price range that includes the pre- and post-merger prices, the formula to compute the post-merger profit-maximising price increase for Product 1 is: 153

Post Merger Price Increase = (mark-up × DR) / (1 – mark-up – DR)


Where, under the assumption that the merger has no effects on costs, the variable markup is the pre-merger mark-up for Product 1: (price – incremental cost) / price. Continuing with the example above, and assuming a mark-up of 40%, the resulting price increases will be: 29% for a diversion ratio of 0.25; 49% for a diversion ratio of 0.33; and 10% for a diversion ratio of 0.12.



Shapiro, C., 1995, Mergers with Differentiated Products, Address before the American Bar Association and International Bar Association program, The Merger Review Process in the US and Abroad, .Washington DC, November 9, 1995. Ibid.


Data and computational requirements 14.6

Diversion analysis is a simulation technique that requires the availability of only a few data points; the market shares of the merging partners, and the price mark-up. All that is required to compute the diversion ratio and the subsequent price increase is a pocket calculator.

Interpretation 14.7

Because of its simplicity, diversion analysis provides a very straightforward way to estimate the likely effects from mergers between producers of substitute products. It has, however, been fiercely criticised. 154 The problem with using this technique is that it is based on the IIAA assumption which is often not realistic. The IIAA imposes too strong a set of constraints on the cross-elasticities of demand. For example, if the price of a certain model of luxury cars went up, we would expect the demand for the other luxury models to go up, not the demand for, say, small hatchbacks. But the IIAA implies that the demand for all other cars will go up, with the models that sell most, no matter what their size, being the ones that receive the largest share of the diverted demand. This is obviously an untenable assumption.


Another very strong assumption is the constancy of the own-price elasticity of demand. If the own price elasticity for Product 1 rises as its price goes up, 155 then the use of the diversion ratio leads to an overestimate of the price increase. This problem is more serious, the higher the values of the Mark-up and DR. If the demand for the product is linear, the elasticity rises as the price rises, making the optimal post-merger price increase smaller. With linear demand, an alternative formula should be used to compute the postmerger price increase: [(Mark-upĂ&#x2014;DR)/2(1-DR)].


Due to the problems that arise with this technique, and reliance on restrictive assumptions that cannot be tested when data is not available, we advise caution in the use of diversion ratios. Diamond and Hausman156 make the very convincing argument that when data is not available, it is better to make no inference than the wrong one. Whenever data is available, it would be advisable to carry out a full fledged estimation of a demand model and estimate the elasticities directly.




See Hausman, J.J. and G.K. Leonard, 1997, Economic Analysis of Differentiated Products Mergers Using Real World Data, George Mason Law Review, 5: 321-46. The assumption that the price elasticity would go up with the price is quite a reasonable one. Let q be the quantity demanded and p the price; the own price elasticity is then ( q/ p)*(p/q). With a linear demand, the slope coefficient, q/ p, is constant, and if p increases more than q decreases so does the elasticity. Diamond, P. and J.J. Hausman, 1994, Contingent Valuation: Is Some Number Better Than No Number?, Journal of Economic Perspectives, 45.






The diversion analysis described in the last chapter is often used when it is unfeasible to estimate the matrix of cross-elasticities for a given product market, or when the IIAA hypothesis is thought to hold. Two recent articles by Hausman, Leonard and Zona 157 and by Hausman and Leonard158 develop a methodology to compute the likely price increase resulting from a proposed merger using econometric estimates of the matrix of market elasticities. The econometric estimation is based on supermarket scanner data. As the analysis is very technical, it should only be carried out by an expert practitioner. Once the elasticities have been estimated, however, it is very simple to compute the diversion ratio and then evaluate the price increase. This is a field of research that is developing rapidly.

Description of the technique 15.2

The econometric analysis of demand for substitute products is based on Gorman’s multistage budgeting theory. According to this approach, there are three levels of demand for a differentiated product, such as bread: (i) the ‘top’ level, at which the overall demand for the product (that is, bread) is determined; (ii) the ‘middle’ level, at which the demand for different types of the product (that is, white bread, wholemeal bread etc.) is determined; and (iii) the ‘bottom’ level, at which the demand for each brand within a type is determined. The estimation procedure starts at the bottom level, using an econometrics model known as AIDS: 159 Sint = in + ilog(ynt / Pnt) + j=1…J ijlogpjnt + int


Where the subscript i denotes the brand; t denotes the time period; and n denotes the city or area representing the sampling unit. S is the share of brand i in the total expenditure in the type analysed; y is the overall expenditure for that type; P is a price index; and p is the price for each brand in the segment. The intercept parameters, , measure the effect of the brand and area; the  parameters are brand-specific and measure the extent to which the expenditure share of brand i in total expenditure for that type depends on the total expenditure itself. Finally, the  parameters measure own and cross price effects (not elasticities as the dependent variable is not in logs). This system of demand equations has the very useful characteristic of being quite unrestrictive as to price effects. It allows one to estimate the whole matrix of price effects without making a priori assumptions. The econometric estimates of the ‘bottom’ level, which have to be carried 157

158 159

Hausman J.J., G.K. Leonard and J.D. Zona, 1994, Competitive Analysis with Differentiated Products, Annales D’Economie et de Statistique, 34: 159-180. Hausman J.J. and G.K. Leonard, 1997, op. cit. The model was developed in Deacon, A. and J. Muellbauer, 1980, An Almost Ideal Demand System, American Economic Review, 70: 313-326.


out as usual using instrumental variables due to the likely endogeneity of the regressors,160 can then be used to construct price indexes for each of the m product types, for each area n and time t, mnt. Such indices are used as instruments for the different types of the product in the estimation of the ‘medium’ level: logqmnt = mn + mlogYnt + m=1…M mlog mnt + mnt


where q is the quantity demanded in the market, the subscript m identifying the different types of product, and n and t are as defined above. Y denotes total expenditure on the product under analysis. The intercept terms, , measure the effect of the type and area; the parameters  measure the effect of changes in total product expenditure on the quantity demanded of each type of the product. Finally, the parameters measure the own and cross price elasticities of product types, and can be used to estimate a price index for the product as a whole, . Once the index has been constructed and suitably deflated, the ‘top’ level equation can be estimated: logGt = 0 + logYDt + log t + Zt + t


where G is total demand for the product, YD is real disposable income, and Z is a vector of variables affecting the demand for the product (these could be for instance demographic variables). 15.3

Once the model has been fully estimated, the full set of unconditional elasticities of demand for each brand in the market can be calculated. These figures will then be used to estimate the likely unilateral price increases from the merger under investigation. Assuming that marginal costs are identical pre- and post-merger, the percentage change in price for the product produced by the merging firms is equal to: Price Change = (P1-P0)/P0 = (1 – w0) / (1-w1) –1


where the superscripts 1 and 0 refer to post- and pre-merger respectively, P is the price and w is the vector of price-cost mark-ups. The mark-ups have to be computed for the pre- and post-merger periods, and w can be calculated using the following formula: w = -(E)-1R


where E is the matrix of own- and cross-price elasticities for the product of interest, multiplied by the revenue share for the product (that is, average retail price times the quantity sold) and R is the vector of revenues.


See Chapter 9 on residual demand analysis for a discussion of endogeneity in demand analysis.


Data and computational requirements 15.4

The data requirements for the analysis described above are demanding. Information is needed on prices and quantities sold at three levels of aggregation. The potential endogeneity of prices calls for the availability of appropriate instrumental variables. Supermarket scanner data can be used to perform the estimation procedures. However, computational requirements are such that estimation should only be carried out by expert practitioners.

Interpretation 15.5

Unlike most techniques reviewed in this paper, the interpretation of the coefficients obtained from the estimation of AIDS models is not straightforward. The coefficients of the ‘bottom’ level demand equation do not represent elasticities but have to be manipulated in order to obtain the elasticity matrix. Moreover, manipulations have to be carried out in order to obtain unconditional elasticities, as the estimated ones are conditional on the expenditure for a given product type, y nt.


Difficulties notwithstanding, this technique is very robust and powerful, and the ready availability of supermarket scanner data and excellent software should ensure that the assessment of likely unilateral effects from mergers is carried out using the estimation of demand system, rather than more mechanic - and often biased - techniques. However, care needs to be taken in using retail prices to assess markets at stages before retailing, say, manufacturing. If retail markets are not fully competitive, then retail prices may be biased and invalid.

Application: the Kimberley-Clark/Scott merger case 15.7

In 1995, Kimberley-Clark (KC) announced the merger of their world-wide operations with the Scott paper company. The two companies are among the largest world producers of hygienic paper products, and a merger between them would have resulted in the creation of the largest company in the world. Both the US DOJ and the European Commission investigated the merger.


Jerry Hausman and Gregory Leonard consulted for Kimberley-Clark, and presented very detailed evidence to the DOJ on the likely price-effects from the merger in the market for bath tissues in the US. At the time of the merger, KC had just introduced a new product, Kleenex Bath Tissue, in the ‘premium’ segment on the toilet tissue market. Scott produced two different brands: Cottonelle, a premium brand, and ScotTissue, an ‘economy’ brand. The issue at hand in the merger case was whether KS and Scott could 99

profitably raise the price of their three products. Using supermarket scanner data from five cities for the period from January 1992 to May 1995, Hausman and Leonard 161 presented evidence to the contrary. 15.9

The market for premium toilet paper was dominated by Charmin, a Procter & Gamble brand, with a 31% share of the whole tissue market; the share for the second and third brands, Northern and Angel Soft, were 12.4% and 8.8% respectively; Kleenex had a share of 7.5% and Cottonelle of 6.7%. The economy brands were dominated by ScotTissue, with a 16.7% share of the total market, followed by other brands, with 9.4%, and private labels with 7.6%.

15.10 Hausman and Leonard162 estimated a demand system for toilet tissue. They found that the own-price elasticity for Kleenex, Cottonelle and ScotTissue were â&#x20AC;&#x201C;3.4, -4.5 and -2.9 respectively, implying sales reduction of 34%, 45% and 29% respectively in response to a 10% increase in the own-price. The estimated cross-price elasticities were very low. The largest one was that between Kleenex and Charmin: at 0.68, it implies that sales of Kleenex would go up by 6.8% in response to a 10% increase in the price of Charmin. All the other cross-elasticities were lower than that. 15.11 Examining the elasticity matrix for toilet tissue, the following conclusions could be drawn. First, there was evidence of two separated market segments, the premium market and the economy market. Secondly, the cross-elasticities were all different from each other and asymmetric; that is, the elasticity between Kleenex and Charmin was different than that between Kleenex and Cottonelle, and from that between Charmin and Kleenex. This is hard evidence for the untenability of the IIAA assumption. 15.12 Using the estimated elasticities of demand, Hausman and Leonard163 then predicted the price effects from a merger between Kimberley-Clark and Scott. Assuming no cost efficiencies, the prices of Kleenex, Cottonelle, and ScotTissue would be raised by 2.4%, 1.4% and 1.2% respectively. These are very low values, and the merger was approved. It is interesting to note that diversion analysis based on share data would predict much larger price increases; for example, the simulated price increase for Kleenex would be 12.7%, more than five times as large.

161 162 163

Hausman J.J. and G.K. Leonard, 1997, op. cit. Hausman J.J. and G.K. Leonard, 1997, op. cit. Hausman J.J. and G.K. Leonard, 1997, op. cit.


15.13 As this example indicates, the use of diversion analysis to simulate price increases resulting from mergers can lead to very biased results when the products in the market are not perceived by consumers as equally substitutable. The margin of error is quite large, and this can lead the authorities to reach the wrong conclusions. 15.14 For the econometric estimation of full demand systems, the data requirements are quite large. Hausman and Leonard 164 have used retailer scanner data in the form of a panel, from which the responsiveness of brand sales to changes in own- and substitute prices can be inferred. The estimation of demand systems using econometric analysis requires specialised software, and specialised training in order to program the software and interpret the results.


Hausman J.J. and G.K. Leonard, 1997, op. cit.






Custom-made or otherwise unusual products, and large orders of a product, are often bought and sold by formal or informal bidding procedures. The range of products sold in this manner is extensive and includes contracts for major infrastructure and capital investment projects (for example, refineries/power plants) including their plant-specific equipment (for example, gas turbines, pumps). These auctions are characterised by informational asymmetries, as each bidder does not know what the competitors are bidding.165


This has serious implications for antitrust analysis. In circumstances where there are repeated bids there is a strong incentive for some of the bidders to collude and form a cartel. Among bidding studies, of particular importance are those aimed at detecting bid rigging in order to stop or prevent the anti-competitive behaviour of a cartel of bidders.

Description of the technique when sufficient data is available 16.3

In order to detect bid rigging, the first step is to distinguish between bidders who are suspected of forming a cartel and bidders who are not. If there is no bid rigging, the determinants of the bidding price for the two groups will not be very different. The empirical analysis then seeks to ascertain whether discrepancies can be identified. We will discuss a methodology introduced by Porter and Zona. 166


The econometric analysis of bid offers is carried out by running regressions of the bid price for the auctions to which at least two members of the alleged cartel have taken part: [EQUATION 26]

Log (bij) = aj + bXij + uij

Where bij is the price bid by bidder i in auction j; aj is an auction-specific variable equal to 1 for auction j and to zero for any other auction; X is a vector of variables affecting firm iâ&#x20AC;&#x2122;s winning chances (like geographic proximity) or its costs; and u ij is a variable conveying the information that the firm has at each auction. u ij is a stochastic term with



For an excellent survey of the economic literature on the subject, see McAfee, R.P. and J. McMillan, 1987, Auctions and Bidding, Journal of Economic Literature, 25: 699-738. There has been a host of empirical academic papers on auctions; for a comprehensive survey, see Hendricks, J. and P. Paarsh, 1995, A Survey of Recent Empirical Work Concerning Auctions, Canadian Journal of Economics, 28: 403-26. Porter R.H. and J.D. Zona, 1993, Detection of Bid Rigging in Procurement Auctions, Journal of Political Economy, 101: 518-38.


non-constant variance, as the variance varies with each auction. Due to this property of the error variance, the model has to be estimated using Generalised Least Squares (GLS) techniques. Estimation proceeds as follows: Step 1

Equation 1 is estimated by Ordinary Least Squares, and the residuals calculated.

Step 2

The mean residual among the bidders participating at each auction is computed and each data point is divided by the appropriate mean residual.

Step 3

The model is re-estimated.

Equation 1 has to be estimated over three samples: the whole sample; the sample including the potential cartel bidders; and the sample of non-colluding bidders. If there has been bid rigging, the price equation for the cartel will be significantly different from that of the non-colluding bidders. A simple Chow test is performed at this point to assess whether the two sets of parameters are the same. Define the whole sample as whole; the cartel sample as 1; the non-cartel sample as 2; let n be the number of bidders in 1; m the number of bidders in 2; k the number of regressors; and RSS the residual sum of squares from the regression. Then: [RSSwhole – RSS1 – RSS2) / k] / [(RSS1 + RSS2) / (n – m – k)]


is distributed as a Fisher’s F with k and (n – m – k) degrees of freedom. If the computed value is higher than the tabulated value, then there is evidence of bid rigging.

Data and computational requirements 16.5

The estimation of this technique requires data on successive bids for a number of bidders. The GLS method described above can be performed by using a good econometric package. The estimates can then be carried out quite easily, and the parameters, t-ratios and other statistics will all be correct.


Data on costs, however, might be difficult to come by, and less sophisticated, but still valid methods can be used to determine whether bid rigging has occurred. They will be discussed below.


Description of the technique when sufficient data is not available 16.7

Using data on market shares for the alleged cartel bidders, the analyst will investigate whether the distribution of market shares (as represented, say, by the numbers of bids submitted) has remained constant over the period when the cartel was supposedly in force. If the distribution has remained stable, this is an indication that the bidders might have been colluding, because by keeping shares stable the cartel removes the incentive for cheating.


An alternative methodology to test for the presence of a bidding cartel is by assessing whether the distribution of cartel bids is more concentrated than that of non-cartel bids. By using a simple test for the equality of means such as those described in Chapter 3, the analyst will discover whether the ratio between the lowest and second lowest bid for the cartel and non-cartel bidders is the same. If it is not, there is some evidence of bid rigging.

Interpretation 16.9

Bid rigging is a serious problem. Porter and Zona167 report that â&#x20AC;&#x2DC;between 1982 and 1988 more than half of the criminal cases filed by the Antitrust Division of the Department of Justice involved bid rigging or price fixing in auction marketsâ&#x20AC;Śâ&#x20AC;&#x2122;. The techniques we have discussed provide one formal and two informal tests to detect such behaviour.

16.10 One of the main problems associated with these techniques is that they rely heavily on the distinction between cartel members and other bidders. It is therefore very important that the samples are separated correctly. This requires that the analyst has an in-depth knowledge of the industry under investigation, especially its history. 16.11 A further problem is that bids may be different because the members of the alleged cartel are larger, more efficient firms, and therefore have lower costs. This allows them to bid lower than the competition, but this is due to their efficiency, not their anti-competitive behaviour. This suggests that costs should be looked at whenever possible when investigating alleged anti-competitive behaviour.


Porter, R.H.and J.D. Zona, 1993, op. cit., page 518.


Application: paving tenders in Suffolk and Nassau Counties, New York 16.12 This case relates to procurement contracts, namely paving contracts awarded by the Department of Transportation in Suffolk and Nassau counties, New York, between 1979 and 1985. Five firms were suspected of colluding, by designating a serious bidder for the contract and how much it would bid. In 1984, one of these five firms was convicted of bid ridding, and the other four listed as conspirators on a bid which resulted in a contract to build a motorway on Long Island. The five firms were sued repeatedly in various other instances. Using data on 75 auctions for paving contracts, in which there were 319 noncartel bids and 157 cartel bids, and applying the GLS methodology, Porter and Zona found convincing evidence that the bidding behaviour of cartel firms was different from that of non-cartel firms, and that therefore there was evidence of bid rigging.






The use of financial analysis in competition policy is outside the terms of reference of this project. For this reason, we provide here only a brief summary of the hypotheses underlying the time series analysis of stock markets’ reactions to news.


Stock markets’ reactions to news can be a particularly valuable source of information that may lead to inferences about the nature of a merger or a take-over. The rationale behind the analysis of stock markets’ reactions is quite simple, and stems from financial theory. The stock market is assumed to be efficient, so that asset prices reflect the true underlying value of a company. When a merger between two companies takes place, there are two possible outcomes in the product market.168 First, if market power increases substantially after a merger, the product price will increase and so will profits for both the merger partners and the other firms operating in the market, at least in the short run (that is, prior to entry by new players). This implies that, on the assumption that the stock market is efficient, both the merging firms and horizontal rival firms in the industry should earn positive abnormal returns when the anti-competitive merger is announced. Also, when steps are taken by the authorities to investigate the merger, negative abnormal returns should be observed.


Secondly, if the merger generates cost efficiencies, then the merged firm will be more profitable, than the sum of the pre-merger entities, all other factors being equal; such higher profitability, however, will not extend to other firms in the industry. This implies that the merging partners should gain positive abnormal returns around the date of the merger announcement, and negative abnormal returns around the date when antitrust investigations are announced. The situation for rival firms is, however, more complex. If the market expects the cost efficiencies to be easily passed along to other players in the industry, then rival firms should also earn positive abnormal results when the merger is


For a more exhaustive analysis of the subject, see Stillman, R., 1983, Examining Antitrust Policy Towards Horizontal Mergers, Journal of Financial Economics, 11: 225-40; and Eckbo, B.E., 1983, Horizontal Mergers and Collusion, Journal of Financial Economics, 11: 241-73.


announced, and negative returns when legal proceedings are announced by the authorities. Otherwise, negative abnormal returns can be expected for the rivals at the time the merger is announced and positive returns when the investigation is announced. 17.4

Summarising, observing positive abnormal returns for the merging partners at the time of the merger announcement does not allow the analyst to distinguish between a merger which is expected to raise prices and one which is expected to lower costs. Likewise, observing positive abnormal returns for the horizontal rivals does not make it possible to discriminate between the two hypotheses of a price-increasing versus cost-reducing merger. However, observing insignificant or negative abnormal returns for the rivals around the announcement date is a sufficient condition to conclude that the market expects the merger to be cost-reducing, not price-increasing.


The above-mentioned hypothesis is tested by using time series stock-price data for rival firms, and comparing their actual stock price returns around the announcement date with a counterfactual measure of what the return would have been had the merger not taken place, and summing over to obtain the cumulative abnormal returns. The counterfactual return for an asset can be calculated based either on the mean-adjusted return model (MARM); or on the market model; or the capital asset pricing model (CAPM); or the market index model. According to the MARM, the counterfactual return from an asset is simply the average return over a specified period. The market model return is the predicted value from a regression of actual returns on an intercept and the returns on a market index. The CAPM requires estimating the predicted values from a regression of actual returns on the returns on a risk-free asset and on the difference between the return on a market index and on the risk free asset. Finally, the counterfactual return for the market index model is simply the market index itself.


The implementation of this technique is simple, and it requires readily available data. However, it has to be borne in mind that unless the abnormal returns for the rival firms turn out to be negative or insignificantly different from zero, this technique does not lead to unambiguous conclusions. Similarly, the assumption of (strong form) capital market efficiency is not justified. Most studies show only weak form, or semi-strong, form efficiency. In these circumstances stock market data may not fully capture the truth about market power or cost reduction possibilities.





In this report we have investigated the use of quantitative techniques in antitrust analysis. This includes a review of the role of collecting and analysing empirical evidence in cases dealt with by antitrust authorities in the UK, US and the EC. The largest part of the report is devoted to a systematic survey of 15 techniques. Each technique is described in terms of the main characteristics it uses and its data requirements. A critical evaluation is provided through illustrative case summaries and in a discussion of the problems of interpreting the statistical and economic significance of each technique.

Role of quantitative techniques in antitrust analysis 18.2

Empirical analysis of economic issues is increasingly becoming an essential feature of any major antitrust investigation, whether it be a merger which gives rise to complaints of monopolisation, the abuse of a dominant position, a review of the compatibility of a joint venture, or other co-operative agreements between firms.


This trend is almost universal and particularly pronounced regarding the definition of the relevant markets in merger enquiries. Here the recent European Commission Notice on market definition from December 1997 (see paragraph 2.5) has marked a watershed. Like the US Merger Guidelines (see paragraph 2.9), which over the last 15 years have led to the development of a more consistent and empirically based approach to market definition, the Commission Notice can be expected to lead to similar changes in antitrust analysis in Europe.


Market definition is, however, not the only area of antitrust analysis that relies on quantification and the use of quantitative techniques. The analysis of market structure has regularly been subject to empirical analysis in a number of cases in the UK and the US. Usually it is the link between profit margins and levels of concentration across different geographic (local) markets that are studied through econometric analysis. Another area of antitrust analysis that calls for increased use of empirical analysis is the competitive behaviour of firms. With the advances in industrial economics, more and more models of competition are being applied empirically to study among other things, the effect of a merger between firms supplying similar but not identical goods (key techniques used in this context are diversion rations and demand analysis for differentiated products).


Finally, quantitative techniques can be used for the analysis of costs. While outside the terms of reference of this study, economic analysis of cost functions is nevertheless


useful for the assessment of economies of scale and the assessment of possible efficiency gains in a merger. It is here that economics and accounting come together to inform antitrust analysis. 18.6

While very data intensive and requiring sophisticated econometric techniques, the above models meet the main criteria for the appropriate use of quantitative techniques in antitrust analysis, namely that they are capable of providing statistical results that have an economic meaning. The same is true for bidding models, residual demand analysis, time series studies of stock market reactions to news and, to a lesser extent, co-integration analysis.


Quantitative techniques are only tools which are of assistance in assessing the structure and conduct of an industry. These tools can, and should be, used to give an answer to well-defined economic questions; these answers will be more accurate if the questions are properly framed; if the data is reliable; and if the statistical tests are strong.


In order to frame the question correctly, the analyst has to become familiar with the industry under investigation, and the behaviour of the main players within it. Only with an in-depth knowledge of the industry will the analyst be able to ask the right questions. Therefore one cannot begin with the use of these tools. They can only be employed after the analyst already has a good understanding of the background to the case, but is seeking specific answers to crucial questions - what is the exact market, what role does price formation play, what is likely to occur if the firms X and Y merge? Public sources, and interviews with businessmen and other parties involved in the investigation, will take the analyst a long way in understanding both the functioning and the focal points that characterise the case under investigation.


The process of translating this knowledge into an analytical framework allows the main issues to be targeted by the relevant empirical tests. The key issues in antitrust analysis are clearly market definition, market structure, and the nature of competition and efficiency effects. Each one of these issues should be analysed within a well-defined theoretical framework, from which hypotheses can be derived and be tested empirically.

18.10 The actual implementation of the empirical test should always be carried out using the best statistical techniques given the available data and the question to which an answer is sought. Therefore, the analyst must assess the reliability and suitability of the available data before implementing any empirical test. Statistical data is subject to sampling errors, biases, and changing definitions which have to be understood. The importance of this rather pedantic part of the investigation cannot be stressed enough. By simply plotting the different data series and computing basic statistics such as means, standard deviations, measures of skewness, or frequency counts, it is possible to gain a good 110

feeling for the quality of the data and the basic underlying facts. It is very important not to fall into the obvious temptation of trying to infer the hypothesis to be tested from what appears to be the answer in the data. The hypothesis has to come from economic theory. 18.11 In carrying out the empirical tests, the analyst should follow the golden rule - stick to the formulated hypothesis. Data mining especially, should be scrupulously avoided. By data mining we mean that procedure where, for example, dozens of different regressions are run with as many variables as possible expressed in as many forms, in the hope of obtaining statistically significant results. The truth is that if the data is â&#x20AC;&#x2DC;torturedâ&#x20AC;&#x2122; hard enough, we are quite likely to obtain at least one significant result. For instance, if we run ten regressions with the same dependent variable but different regressors we have a 40% probability of obtaining at least one regression with a significant coefficient, even though none of the regressors are related to the dependent variable. Significance obtained by mining the data is meaningless, and would not stand up to a thorough investigation.

18.12 When quantitative techniques are used correctly and rigorously, in the fashion described above, they can be helpful tools. They are not, however, magic bullets. All empirical analysis performed during an investigation has to be weighted before it is used to derive public policy. Obviously, the weaker the data, or the more primitive or potentially misleading the statistical technique used, the less weight the empirical analysis should receive. 18.13 With these preliminary observations about the preparation for, and the undertaking of, quantitative tests, we draw the following conclusions from our survey of 16 quantitative techniques.

Statistical tests of prices and price trends (Part II) 18.14 Chapters 3 to 8 above describe quantitative techniques used in antitrust analysis to analyse single variables, namely prices. Apart from hedonic analysis, these techniques are all geared to test whether there are systematic differences between the prices of two or more products or services, or between a productâ&#x20AC;&#x2122;s price across different areas or time periods. 18.15 Cross-sectional price tests are very simple statistical tests that are not based on any economic hypothesis. For this reason, they should be given little weight in the context of an antitrust investigation. More specifically, although these tests allow the analyst to establish whether two products or services have the same mean price across regions, or before and after an alleged anti-competitive event has taken place, they do not provide 111

any explanation as to why it is so. These tests are often used as a first step in an analysis or as the only step when no other data but price data is available. It is important to emphasise that price data has to be carefully examined before it is used. Questions to be considered here include: is the data actually in the form of transaction prices and not list prices; are all characteristics of prices known (for example, warranty terms); are the goods for which price data exists homogeneous? 18.6

The common price tests when time series data is available are listed below.


Hedonic price analysis is a good tool to be used as an intermediate step during an investigation into markets where product characteristics vary frequently. This technique allows the analyst to â&#x20AC;&#x2DC;deflateâ&#x20AC;&#x2122; the prices due to the effect of qualitatively different characteristics, thereby obtaining price series that are comparable. However, caution is important. The results obtained with hedonic price analysis are very sensitive to the correct specification of the productâ&#x20AC;&#x2122;s characteristics, so that an in-depth knowledge of the product qualities is required to obtain meaningful results.


Price correlation is a weak test, which should be used as a starting point in an investigation, but never as the sole piece of evidence. In too many analyses price correlations appear to be the end rather than the beginning of statistical analysis. Spurious correlation is a serious problem that can lead the analyst to conclude that two series of prices are associated to one another while in reality they are related to some other variable that is influencing them both.


Speed of adjustment tests and Granger causality are techniques that were used in antitrust analysis for a short time. They have since been subsumed by co-integration analysis. We have included these techniques because they represent important steps towards the production of a statistically correct methodology that is aimed at testing whether price series tend to move together.

18.10 Co-integration analysis is a very robust methodology. When used properly with adequate data, this test does provide statistically meaningful results, unbiased by problems such as spurious correlation. It is therefore one of our preferred tests. It does, however, require a highly skilled analyst for this test to carried out properly. It should therefore, only be undertaken by expert statisticians or econometricians trained in time series analysis.


18.11 All statistical tests of price homogeneity, no matter how sophisticated, supply weak evidence in antitrust analysis as they are only capable of providing information on whether price series are related to each other. The fact that the price of a certain product or area is found to affect prices of other products or areas is not sufficient proof that those products or areas are in the same antitrust market. What needs to be determined is whether areas or products that are in the same market at historical prices will still be in the same market if prices in one area, or for one set of products, were increased by a significant and non-transitory amount. 18.12 We conclude that quantitative techniques based on the analysis of prices alone are, aside from co-integration analysis, relatively weak tests and should only be used if no other data is available. When these techniques are used, they should carry a lighter weight in the context of an antitrust investigation as compared to techniques based on tests of welldefined economic hypotheses.

Analysis of demand (Part III) 18.13 The techniques discussed in the report dealing with demand analysis have been developed as quantitative tests of well-defined economic models. Since these tests derive from a theoretical structure they do not have some of the statistical and interpretation problems which exist with simpler price analyses described above. 18.14 Residual demand analysis is theoretically ideal, as it allows the direct estimation of supply substitution effects in the market for a product or service. It is a preferred technique. In a perfectly competitive market, a single firm does not have the power to raise its price beyond the market price, because it will lose its customers to the competition: hence the demand elasticity facing the firm is infinite. Residual demand analysis provides a test for this hypothesis of perfect competition. Unfortunately there is rarely enough information available to estimate a residual demand model correctly, as data is needed on cost shifting variables of the firms operating in the industry - both those under investigation and their competitors. 18.15 One interesting exception is when the market under investigation includes foreign competitors. In that case exchange rates can be used as cost shifters and the model estimated. 18.16 Residual demand analysis supplies the value of the elasticity of residual demand for the firm under investigation. The antitrust analyst is, however, concerned about whether â&#x20AC;&#x201C;


given such an elasticity - the firm can profitably raise the price by a significant amount. Economic theory provides no guide as to when the residual elasticity is big enough to infer effective competition. 169 18.17 Critical loss analysis provides an answer to whether competitive constraints are strong enough to consider a range of products in the same market. In this respect, critical loss analysis is a second-tier technique, used to calculate the change in the quantity sold that for a given level of price-cost margin and price increase, makes that price increase unprofitable. This technique is useful but cannot be used on its own; it has to be used as a complement to residual demand estimation. This is because critical loss analysis provides an estimate, for a given price-cost margin and a given price increase, of the critical value of the elasticity of demand. If the actual value - obtained with econometric analysis or other means - is larger than this critical value, then that price increase will be unprofitable. 18.18 Import penetration tests are a useful tool to measure the constraint imposed on local producers by foreign competition. How to treat foreign competition in antitrust analysis is a question that should be answered within a theoretical model of demand. The first step in the analysis of foreign competition should be a careful examination of what the imports are, and what proportion of sales they represent. Then, if it is found that the market has a strong foreign presence, and if sufficient data is available, the constraining impact of foreign competition has to be assessed. This can be done either by a direct estimate of the import elasticity with respect to domestic prices, or by incorporating the effect into a residual demand model. 18.19 Survey techniques should be used when sufficient data is not available to carry out fully-fledged demand estimation, provided the survey is carried out correctly. So, although surveys can provide a valuable insight into the questions being investigated, they should be designed and used carefully.

Models of competition (Part IV) 18.20 Within the structure-conduct-performance paradigm, price-concentration studies can be helpful in assessing the impact of market structure (that is, concentration) on the pricing behaviour of market participants. The appropriate test of the underlying economic theory requires the use of price-cost margins as a dependent variable in the econometric analysis. The hypothesis to be tested is that higher concentration leads to higher price-cost margins. The use of price data alone instead of margins relies on the rather restrictive assumption that marginal costs are constant. Price-concentration analysis can be carried out by calculating the correlation coefficient between the two variables.


One paper is Waverman, L., 1991, Econometric Modelling of Energy Demand: When are Substitutes Good Substitutes, Energy Demand: Evidence and Expectations, in D. Hawdon (ed.), Academic Press.


That is, however, not an advisable practice. Information about why the two variables are related is required and that cannot be achieved by using correlation analysis. Note that the underlying theory is the relationship at the individual firm level. 18.21 Often industry-wide rather than firm-specific tests are used. We would not recommend this approach. Industry-level analysis of price-concentration relationships using time series data should only be performed if firm-level data is not available. And if industry data is used, great care should be taken with the ensuing interpretation. One of the main problems with industry-level data is that market shares tend to be stable over time, so that the concentration measure varies very little. Variability is higher in firm-level data with a cross-sectional element. Also, the use of industry-level data does not allow for a distinction to be made between the market power and the efficiency hypotheses, which represents a serious drawback. 18.22 In recent years, the scope for abusing market power in differentiated product markets by merging competitors, has come to the forefront in antitrust analysis. Techniques have therefore been developed to test whether the merger between producers of competing products is likely to lead to an increase in the price of their product(s). Diversion analysis is a simulation technique that can be used for this purpose; it is very easy to carry out and requires very little data, but it has been strongly criticised, as the underlying economic assumptions are too restrictive. It is common to assume that the lost sales are diverted equally across all competitors, an assumption which is simply incorrect for any differentiated good industry. As supermarket scanner data on prices and quantities of products sold becomes increasingly available, we believe that the estimation of demand systems should become common practice. This technique is based on sound economic theory, and it is also econometrically sound. Demand system estimation is, however, a sophisticated analysis requiring the use of simultaneous equation techniques and may take the competition policy analyst far afield. 18.23 In Chapter 16 we have reviewed techniques aimed at detecting bid-rigging and cartel behaviour in procurement auctions. As investigations into these kinds of actions represent a good proportion of the activity of antitrust authorities, these techniques are important. They are also quite reliable, and easy to apply. 18.24 Finally, the anti-competitive effects of a merger can also be investigated using time series event data of stock marketsâ&#x20AC;&#x2122; reactions to news. We have discussed this technique very briefly, as the OFT has developed in-house literature on the subject. This type of analysis is quite useful as data is readily available and the technique is valuable. The main problem with this technique is that it is not easy to define when the new information was generally available and so the technique does not always provide unambiguous conclusions. 115

Concluding remarks 18.25 From this brief review of the strong points and pitfalls of the various techniques covered in this report, we arrive at the following conclusions. First and foremost, it is very important to correctly specify the hypothesis being tested and to ensure that it is in line with the underlying economic assumptions, that is, in line with a well-defined economic model. Secondly, there is no substitute for a good understanding of firm activities in the industry under investigation. Thirdly, care must be taken to obtain sufficient data, capable of being subjected to statistical tests. These aspects of any empirical investigation are interdependent. The empirical results obtained have to be analysed in light of the strength of the data and techniques used. Results derived using first class data and powerful empirical techniques should be given a heavier weight in the context of an investigation than results obtained using poor data and/or weak tests.





The glossary of terms starting overleaf explains some of the main statistical terms used in the text. The following references provide a more complete explanation of the underlying theory and concepts.

Fisher, F., 1980, Multiple regression in legal proceedings, in J. Monz (ed.), Industrial Organisation, Economics and the Law, The MIT Press: Cambridge, Mass. Kaye, D. and D. Freedman, 1994, Reference Guide on Statistics, Reference Manual on Scientific Evidence, Federal Judicial Centre. Wonnacott, T. and R. Wonnacott, 1990, Introductory Statistics for Business and Economics, 4th edition, John Wiley & Sons: New York


Autocorrelation (serial correlation)



In regression analysis, there is autocorrelation when the covariance of the error term is not constant, that is, each observation is statistically dependent on the previous ones. The vast majority of cases arise in the context of time series data. Correlation between the time series residuals at different points in time is called autocorrelation. Correlation between neighbouring residuals (at times t and t+1) is called first-order autocorrelation. In general, correlation between residuals at times t and t+d is called dth-order autocorrelation. When Least Squares techniques are being used to estimate the parameters of the regression, the estimates will be unbiased but inefficient in the presence of autocorrelation. Autocorrelation casts doubt on results of Least Squares and any inferences drawn from them. However, techniques are available to improve the fit of the model and the reliability of inferences and forecasts, for example, autoregressive models. A systematic tendency for an estimate to be too high or too low. 117


Coefficient of Determination (R2)


In regression analysis, the coefficient of determination (R2) is a measure of the proportion of the total variation in the dependent variable that can be explained by the regression equation. R2 varies between 0 and 1. An R2 with a value of zero implies that movements in the independent variable (X) do not explain any of the movement in the dependent variable (Y). The higher the R 2, the greater the association between movements in the dependent and independent variables. Obviously, an R2 of unity implies that the entire variation of the dependent variable can be explained by the model. R 2 is sometimes used as a measure of the strength of a relationship that has been fitted by Least Squares. R2 tends to be larger when the regression involves time series data, and lower where cross-sectional data is used. This is because in cross-sectional data, individual effects are more important. Where two regression models have the same dependent variables their explanatory power can be compared by using the adjusted coefficient of determination, which corrects for differences in the number of the regressors. When the dependent variables are different (for example, one regression uses price levels as the dependent variable and another uses log prices) then the explanatory power of the two regressions cannot be compared by using R 2.



Confidence Interval


An estimate, expressed as a range, for a quantity in the population. For example, a 95% Confidence Interval is the interval between which one can be 95% confident that the true population value lies. If an estimate from a large sample is unbiased, a 95% confidence interval is the range from two standard errors below to two standard errors above the estimate. Intervals obtained in this way cover the true value about 95% of the time. Put another way the Confidence Interval can be considered as simply the set of acceptable hypotheses. It reflects ones confidence in the estimation process of the population value. Therefore any hypothesis that lies outside the confidence interval may be judged implausible.


Used to measure how two variables, say X and Y, vary together, that is, the extent of joint variability or association. The covariance will be positive when both X and Y are large, or both are small. When the covariance is negative one will be large, while the other tends to be small.

Dummy variable

These are variables usually assigned a value of 1 or 0. They can be used to assign values to categories, such as male or female, thereby transforming them into numerical terms amenable to statistical tools. Dummy variables can also be used in regression analysis.

Efficient Estimator

Among all the possible unbiased estimators, the efficient estimator is the one with the minimum variance or equivalently, standard error. If the estimator is not efficient, inferences based on the t and F tests will be incorrect.




Fisherâ&#x20AC;&#x2122;s F-Test

When more than two population means have to be compared, the two-sample t-test can no longer be used. An extension is then provided by the F-test, which compares the variance explained by the difference between the sample means, with the unexplained variance within the sample means. An ANOVA (analysis of variance) table provides an orderly way to calculate F, to test whether there is a statistically significant difference between the populations.


One of the assumptions of the standard Least Squares model is that the variance of the errors is constant and does not depend on the variation of X (the independent variable). Errors with this property are said to be homoscedastic. If the variance of the errors is not constant, the errors are said to be heteroscedastic. There are many statistical tests to detect heteroscedasticity. When Least Squares techniques are being used to estimate the parameters of the regression, the estimates will be unbiased but inefficient in the presence of heteroscedasticity. Heteroscedasticity tends to be a problem when cross-sectional data is estimated. Most computer packages contain a routine that enables the analyst to correct the covariance matrix thereby solving the heteroscedasticity problem.


See Statistical Test.


Two variables, say X and Y, are independent if the value of X is not in any way affected by the value of Y.



Least Squares


Consider the simple case where there are only two variables, such that Y depends on the value of X, and that the relationship between X and Y is a straight line. In this case the equation which best fits the data is of the form  = a + bX. A formula to calculate a (the intercept) and b (the slope) then have to be found. The least square method calculates a and b so as to minimise the sum of the squares of the differences between the estimates () and the actual observations (Y). The differences are squared because some of the deviations (Yâ&#x20AC;&#x201C;) will be positive and some will be negative, that is, some estimates will lie above the fitted line, others below.

Null Hypothesis

See Statistical test.


An observation that is far removed from the majority of the data in a sample. Outliers may indicate an incorrect measurement (for example, the information was incorrectly entered into a spreadsheet), and may exert undue influence on a summary statistics such as the mean, and on statistical tests that incorporate these statistics such as regressions. As a general rule any observation that is more than three standard deviations from the mean needs to be treated with suspicion.


The total collection of objects or people to be studied, from which a sample is drawn. The population can be of any size.




Random sample

A sample drawn from a population where each observation is equally likely to be chosen each time an observation is drawn; by extension each of the possible samples of a given size have an equal probability of being selected. Random samples do not ensure that survey results are accurate but they do enable an assessment of the degree of accuracy of the results to be made using statistical techniques.


In its simplest terms a regression is designed to show how one variable effects another. If there is more than one explanatory variable, then the regression is called ‘multiple’. This is the most common form of regression in economic applications. There are two main uses of multiple regression. In the first use, ‘testing hypotheses’, the aim is to state whether or not a particular relationship is true. In the second use, ‘parameter estimation’, the interest is to establish the precise magnitude of the effects involved. There is a third use, which is not so widespread, and that is forecasting the values of some variable.


The difference between an actual and a predicted value typically drawn from a regression equation.



Standard Error


A statistic is simply a function of the variables in the sample. Through repeated sampling it would be possible to calculate all the possible values of the statistic that one could observe. These possible values constitute the sampling distribution of the statistic. The standard deviation of the sampling distribution for a statistic is often called the standard error of the statistic. Associated with the estimated value of each regression coefficient (a and b as mentioned above) is a figure known as the standard error of the coefficient. The standard error is a measure of the coefficientâ&#x20AC;&#x2122;s reliability. Generally, the larger the standard error, the less reliable or accurate is the estimated value of the coefficient. In large samples, it is generally the case that the true population mean is within approximately two standard errors of the estimated mean 95% of the time.

Stationary Process

A process whose statistical properties do not vary over time, that is, a series which has a constant mean and variance over time.




Statistical Test

Using sample data, a statistical test is a procedure performed to estimate the probability that a hypothesis about the population from which the sample was drawn is true. This involves testing a null hypothesis against an alternative hypothesis. The null hypothesis is often the hypothesis that there is no difference between the population parameters, or that the population parameter equals zero. The alternative hypothesis is the converse of the null hypothesis. An appropriate test statistic is chosen to evaluate the null hypothesis and calculated for the sample of data. The probability (significant level) is judged small enough, the null hypothesis is rejected. There is a wide range of statistical tests available. Among the most commonly used test statistics are the Student’s t, the 2, and the Fisher’s F.

Student’s t-statistic

A statistical test used to determine the probability that a statistic obtained from sample data is merely a reflection of a chance variation in the sample(s) rather than a measure of a true population parameter. In regression analysis the standard error of an estimated coefficient is used to make a statistical test of the hypothesis that the true coefficient is actually zero, that is, that the variable to which it corresponds has no impact on the dependent variable. The ratio of the estimated coefficient to its standard error is the t-statistic. In large samples, a t-statistic of approximately 2 means that there is less than a 1 in 20 chance that the true coefficient is actually 0 and that the larger coefficient is observed by chance. In this case the coefficient is said to be ‘significant at the 5% level’. A t-statistic of approximately 2 ½ means that there is only a 1 in 100 chance that the true coefficient is 0, that is, the coefficient is significant at the 1% level. In smaller samples the t-statistics need to be larger for any given significance level.



Unbiased Estimator


An estimate of a parameter is said to be unbiased when there is no systematic error in the estimation procedure used. If an estimate is unbiased, the expected value of the parameter estimate is equal to the expected value of the (unknown) population parameter. Put more simply, U is an unbiased estimator of G if the expected value of U is equal to G. For example, if  is, on average, equal to Âľ then it is an unbiased estimate of Âľ. Note that a systematic error is not a random error.




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Case study 1: Nestle-Perrier merger

following paragraph 2.22

Case study 2: defining the geographical extent of US petrol markets

following paragraph 2.24

Case study 3: Staples and Office Depot

following paragraph 2.33

Case study 4: the supply of recorded music

following paragraph 2.43

Case study 5: the Boeing/McDonnell Douglas merger

following paragraph 2.50

Case study 6: AKZO

following paragraph 2.52

Case study 7: South West Water Services Ltd

following paragraph 2.61

Application of cross-sectional price tests: international comparison of prices CDs and motor cars

paragraphs 3.10 to 3.15

Application of hedonic price analysis: car price differentials

paragraphs 4.5 to 4.8

Application of price correlation: wholesale petrol markets in the US

paragraph 5.8

Application of causality tests: US petrol markets

paragraph 7.7

Application of dynamic price regressions: the supply of soluble coffee

paragraphs 8.8 to 8.11

Application of co-integration analysis: petrol markets in Colorado

paragraph 8.12

Application of residual demand analysis: National Express Group plc and Midland Main Line Ltd

paragraphs 9.12 to 9.16

Application of survey techniques: the Engelhard/Floridin merger

paragraphs 12.10 to 12.14

Application of price-concentration studies: the SCI/Plantsbrook merger

paragraphs 13.14 to 13.18


Application of analysis of differentiated products - estimation of demand systems: the Kimberley-Scott/Clark merger case

paragraphs 15.7 to 15.14

Application of bidding studies: paving tenders in Suffolk and Nassau Counties, New York

paragraph 16.12



Prepared for the Office of Fair Trading by LECG Ltd Research paper October 1999 OFT 266 Please note any views expressed in this paper are th...