The Determinants of Retail Tyre Price Dispersion in the Uk*

THE DETERMINANTS OF RETAIL TYRE PRICE DISPERSION IN THE UK*

JUAN DELGADO (UNIVERSIDAD CARLOS III DE MADRID) AND MICHAEL WATERSON (UNIVERSITY OF WARWICK)

Abstract We investigate price dispersion in a retail market (tyres) characterised by outlets each selling a range of products. Consumers face substantial price dispersion across outlets even for very tightly defined products. Our modelling incorporates search behaviour and some market power at the retail level. Empirically, we employ both matched pair and regression approaches. Significant components of price dispersion are accounted for by our finding that retail chains as a group are more expensive for tyres than standalone outlets. One specific result is that chains owned by manufacturers sell other manufacturers’ tyres over 17% more expensively than do independent stores.

Keywords: price dispersion, vertical restraints, search behaviour JEL nos: L13, L81, D83

This version: May 1999 *We would like to thank the Consumers' Association for providing the tyre price data samples. We also thank seminar audiences at Carlos III, Copenhagen, Edinburgh, Fundación Empresa Pública, IHS Vienna, XII Jornadas de Economía Industrial, LSE, Queen Mary Westfield, Warwick and York. In particular we thank Steve Dowrick, Chris Gilbert, Morten Hviid, Francine Lafontaine, Dennis Leech, Pedro Marín, Peter Mollgard, Andrew Oswald, Consuelo Pazó, Margaret Slade, Dylan Supina and John Sutton for comments at various stages, Jeremy Smith for his patience and tyre industry representatives at retail and manufacturer level for general discussions and specific help. Juan Delgado would like to thank The British Council and the “Fundación Cultural Caja de Ahorros del Mediterráneo” for financial support at the initial stages of this research. The usual disclaimer applies.

Corresponding Author: Professor Michael Waterson Department of Economics University of Warwick Coventry CV4 7AL Phone: 01203 523427 Fax 01203 523032 email: [email protected]

2 1. Introduction This paper investigates the determinants of price dispersion in a retail market in which the product may be closely defined and is widely available. Pricing dispersion within a market for tightly defined products may exist for two reasons. First, because suppliers can sort on observable types or because consumers self-select amongst packages offered them. This dispersion can persist in the presence of extensive consumer search. Airline travel between city pairs provides a classic example which has been the subject of several studies (e.g. Borenstein and Rose, 1994). Second, it exists because consumers engage in only limited search. Thus, even in the absence of different customer types observable by suppliers, price dispersion can persist. Salop and Stiglitz (1977) were amongst the first to demonstrate this theoretically. Our focus is on this second type of market, which has received much less attention. We look for specific patterns in the pricing structure beyond the basic dispersion identified in search theoretic analyses and resulting from consumer behaviour. Within this context, we believe ours is the first paper to carry out such an empirical examination on retail markets. Several studies (e.g. Pratt, Wise and Zeckhauser, 1979) have found considerable price dispersion, but they have not gone on to investigate its character by reference to firm behaviour1. We treat consumer behaviour as given and examine firm behaviour. In our empirical analysis, we investigate a specific product (retail tyres). We have several reasons for the choice of product: the good may be very tightly defined (a particular brand and class), consumers have considerable choice amongst stores but do little search, all stores sell a range of types, and the retail structure provides a rich variety of arrangements. This allows for examination of differing firm behaviours across local markets. Modelling suggests a pattern in which the lowest prices are set by small independent stores. Chains will charge more on average, with those chains which are vertically integrated with tyre manufacturers setting higher prices for other manufacturers’ tyres than their own, all else equal. Thus the central argument, which receives substantial empirical support from two quite different approaches, is that it is necessary to consider the vertical linkages in the tyre industry in order to understand the pattern of retail tyre prices across outlets2.

1 We should also note the important papers by Shepard (1991), on petrol stations, which examines price dispersions of the first type identified above, and by Roberts and Supina (1997) on firm effects on dispersion for essentially homogenous basic manufactures. 2 It is no part of our purpose to test the contention that there is a degree of monopoly power enjoyed by the manufacturers of popular branded tyres in respect of replacement purchases. Our data are not particularly well designed to examine this issue, but in any case it is generally accepted in the industry

3 In examining the nature of competition in tyre retailing, we first wish to investigate whether, for a given tyre brand and type, prices are the same across outlets in the same market. What is somewhat surprising is the degree of price variation for a homogeneous product, despite the urban nature of the samples and the consequent proximity of the retailers. To illustrate, consider the following snapshot from our data. A consumer living in Gloucester (a city of around 100,000 people) wishing to buy a specific leading brand, size and rating of tyre, namely a Michelin Classic 175/70R13 T could, amongst other stores, visit at least five outlets in or around the same street where it is on sale at prices between £40 and nearly £62! The important new point we establish in this paper is that the price variation is decidedly non-random. Specifically, to preview results, we find that manufacturer-owned outlets sell rivals' tyre brands over 17% more expensively than do standalone stores, whilst also being expensive for their own tyres. This finding is systematically true across the sample. Some independent tyre chains pitch their prices up to 25% above standalone store prices, and large chains are more expensive in general. Thus our own investigations suggest that to a significant extent we can explain such price dispersion by means of industry structural features. We choose to develop initial hypotheses using the lens of search theory, which allows price divergences even in a market which is competitive in the sense that firms break even. We then add to the model the possibility that an element of market power at an upstream level, transmitted to the downstream, bears responsibility along with downstream market power for price differences. Candidate models are developed in Section 3 below, following a brief section describing the market. Our data are built round two sets of observations of prices collected by agents of the UK Consumers' Association in February 1994 and February 1996. We have copies of the original data sheets, which turn out to provide a remarkably useful sample. From their sample, we select only observations from outlets in narrowly specified urban markets. Unusually, we then adopt two quite different approaches. One is very exclusive, involving only comparisons between matched observations. The other is a more standard econometric approach. Remarkably and reassuringly, both yield very similar conclusions. In Section 4 we describe the data more fully, then in Section 5

that tyre manufacturers supply product to car assemblers at relatively low prices safe in the knowledge that a considerable proportion of consumers wanting replacement tyres buy the brand which matches their original equipment. This is of course consistent with normal profits overall for tyre manufacturers, or supernormal profits.

4 we describe the estimations using these two approaches. Section 6 contains a few concluding remarks.

2. The Market for Retail Car Tyres in the UK3 We consider here only the replacement or "aftermarket", the market for tyres other than original equipment (and only that part of it relating to private motorists).4 Replacement tyres are certainly the most important spare part market for cars in the UK, by value worth over £800m in 1995, making it over 20% of the total replacement car parts market (Euromonitor 1996). It is the only significant such market in which manufacturer brands play a part. The value share of the top five manufacturers is approximately 79% (Euromonitor 1996). For many customers (possibly up to 60%, MMC, 1990) matching brand with original equipment is important. Thus there is not perfect substitutability between brands, and some may command a price premium over others, particularly if they are widely specified as original equipment,5 ie there is clearly some manufacturer market power. In industry parlance, tyres are said to be a "distress" purchase. According to industry sources almost all consumers buy tyres only when prompted by failure of the tyre by incident or at the annual roadworthiness test, or as a result of their inspection that a tyre is worn out. In this case, economists' models of consumers either buying one unit of a good, or not, make good sense. Of course, tyres are necessary in order to travel by car but, at current prices, only a small fraction of the value of the typical car. Hence by Marshall's rules we would expect the consumer’s derived demand to be very inelastic (Layard and Walters, 1978, ch. 9).6 There are many outlets from which one can purchase tyres, but in the UK over 90% of purchases (MMC, 1990) are from a specialist tyre dealer who supplies and fits for a fixed price (normally providing the same service for exhausts but only a very limited range of other goods). The

3 Useful descriptions of the industry are available in Monopolies and Mergers Commission (MMC 1990 and 1992), also Euromonitor (1996). 4 Many new cars are purchased for fleet use in the UK. However, because these are retained only for relatively short periods, and despite high relative mileage, around 2/3 of tyre purchases (MMC, 1990) are by private buyers. Privately owned cars amounted to over 80% of the motor vehicle “parc” in 1994 (Euromonitor, 1996). 5 Explicitly disentangling such original equipment effects empirically would be too problematic to be worthwhile. Car manufacturers generally use different brands and specifications of tyres across models (or even within them) and over time, commonly authorising three brands per model. Also it is the stock of cars which is important in determining tyre demand. 6 Not everyone who needs tyres buys new ones. By volume, one industry estimate is that remoulds account for 12.3% of the market and part-worn tyres 2.8% in 1995. In value terms, of course, they are significantly less important.

5 UK has around 4000 such dealers (Financial Times, 1990). Each dealer carries a range of brands in each size. Essentially, the dealers belong to three broad categories: standalone outlets, so-called “equity chains” owned by tyre manufacturers and “independent chains” of tyre retailers non-aligned with particular brands of tyre (though as a recent development some of these now belong to franchised car dealers7). Each of the top five tyre manufacturers by sales owns an equity chain in the UK and these chains are an important element of the UK scene, constituting 41% of tyre sales in 19888 (MMC, 1990). This feature of the retail market, together with the high density of tyre retailers, makes the UK a particularly useful case to examine. In no case do equity chains, in presenting quotes or in general marketing insignia, fascias etc, give any substantive indication of which manufacturer owns them. The independent chains are probably growing in importance at the expense of standalone stores, but the latter sector is still large.9 Chains tend not to have nationwide pricing policies. The cost to retailers of purchasing tyres from manufacturers varies by volume. Data on the extent to which this is true is unfortunately incomplete (through deliberate excision) in the published version of the MMC report, but one "ballpark" figure, which proves very useful in later interpretation, is that they differ by 6% of final selling price from the smallest to the largest purchaser (MMC, 1990). In any case, in recent years the role of independent wholesalers has become more important (MMC, 1990, p. 79). Most cities would have one or more. They buy in bulk and distribute within hours to retailers in the locality in response to orders. Hence the scope for cost differentials to retailers is strictly limited and we might consider this 6% an upper estimate. This is very small by comparison with the price differences we observe.

3. Modelling Retail Tyre Price Distributions One possible null hypothesis would be that, for a given brand, size, and speed rating of tyre (tyres are not mileage rated in the UK), prices including fitting onto the car, within a densely populated urban area where there are many retailers, should scarcely differ at all as a result of

7 Apart from this, franchises are an unimportant element in tyre retailing. 8 This is quite different from the situation in the US, where equity chains took only about 13% of the market in 1989 (French, 1989). 9 Unfortunately, there is no industry-wide association of retailers, rather different types may choose to belong to different associations (eg The Independent Tyre Dealers Association). There is therefore no authoritative unbiased industry source of data on the split between types.

6 competition amongst themselves10. But as our vignette from Gloucester has shown, this is not the case. So far as consumer behaviour is concerned, the reason is that UK consumers in fact engage in very little such search, despite the ease of obtaining telephone price quotations (without the market segmentation or qualification which exists, for example, in telephone quotations for insurance products). Consider the following empirical finding, derived from a sizeable market research survey carried out in the UK for a major tyre manufacturer in 1995: A remarkable 71% of tyre buyers questioned contacted only one outlet (i.e. the outlet where they purchased) before buying. Less than 18% contacted three or more outlets. Surprisingly little search is taking place11. We model the situation as follows. Assume first that, for whatever reason, search costs are important. Prices may well differ, and we need to tease predictions out of the available models, a task tackled in section 3.1. In addition there may be an important role played by the vertical linkages existing in the equity chains. This is examined in section 3.2 below.

3.1 Search-Theoretic Models Once we move away from the hypothesis that perfect information characterises the tyre market, a number of considerations of imperfect information become relevant. First we might say that there are distinct groups of consumers facing different ex ante search costs. Some, those who face an urgent need for tyres (as a result, for example, of sudden tyre failure), do not have the luxury of search available to consumers whose tyres simply wear down gradually to unacceptable levels. This would suggest a “tourists and natives” model of the Salop/Stiglitz (1977) type, in which some tyre outlets aim deliberately at charging relatively high prices and attracting only consumers who have not engaged in search, whilst others charge relatively low prices and attract some lucky consumers who happen upon them by accident, plus some consumers who have engaged in search.12 There is

10 In their extensive analysis of the determination of concentration (or as they prefer it, entry) in retailing in the US, Bresnahan and Reiss (1991) include a final section in which they carry out some analysis of tyre prices. It should however be noted that their purpose was very different from ours. They were concerned with small isolated markets, and the influence of horizontal competition on price. In monopoly and to a lesser extent duopoly markets, prices were significantly higher than in markets with several players, with prices in the San Francisco Bay area being lower that the latter and the authors took these to be competitive. It is markets of the latter type which form the focus of our study. 11 Private written and oral communications with a retail tyre industry executive are the source of these points- the survey is proprietary to his employer. Other aspects of the survey, for example that business customers did less search than private customers, give credence to its findings. 12 Their model is somewhat generalisable whilst remaining robust with respect to predictions - see Stiglitz (1989) for an exposition and Guimaraes (1996) for a recent extension. Other generalisations, for

7 price discrimination between consumer types. But the theory is silent about which outlets are high priced. An alternative but essentially complementary model is developed by Butters (1977). In it there is ex ante homogeneity but ex post heterogeneity in consumer information as a result of a stochastic advertising mechanism by which some receive messages and others do not. Again, there is a practical motivation for this; some retailers engage much more in advertising their outlets and prices than others (Meal, 1996).13 Thus it would be quite feasible that consumers engaged in telephone shopping (but not, of course, carrying out an exhaustive search) contacting one, two or three tyre dealers, might commonly select such national or regional names and not come into contact, in particular, with small independents. Presuming that complete search is not economic, it is sufficient that search is biased in favour of certain outlets, for those outlets to have some market power. In Butters’ model, sellers charging high prices advertise more than low price sellers because they must advertise more to get the same number of customers. Moreover the high prices generate revenue sufficient to pay for the extra advertising. At the level of producing cross-sectional predictions regarding price this would seem to be observationally equivalent to a framework in which advertising confers an element of monopoly power which enables an increase in price-cost margins for heavy advertisers above less heavy advertisers (eg a Sutton-type 1991, mechanism). Under either scenario, higher advertising is associated with higher prices. Of course the latter allows some supernormal profit, whilst the former does not. Png and Reitman (1995) talk generally of a “consumer information theory”. Brands provide the mechanism “by which sellers can [through advertising expenditure] commit to product attributes that are difficult for third parties....to verify.....When consumers pay a premium for a branded product, they are paying for an implicit guarantee of superior quality” (pp 207-8). Again this is rather similar to Butters, with the embellishment that the advertising is directed towards embedding a national or local brand in consumers’ minds, perhaps so that search is biased. In their empirical tests however, they choose to focus not upon the outcomes (higher prices etc) but rather on the

example to limited search, would lead to more noise in the prices - ie a price distribution rather than to two prices. But the predictions would be preserved with respect to mean values. 13 Much of this advertising expenditure in the UK is television advertising which is explicitly not about price. However, price information, when provided in press advertisements for tyre dealers in the UK, is typically both selective and noisy; the price commonly does not truly represent the final bill and does not lend itself to straightforward comparison between dealers for a consumer seeking a specific product.

8 determinants - the factors which make particular sites choose branding. This is more relevant in the petrol case, where a variety of contractual forms are observed, than in tyres, where the development of chains is generally planned centrally. The retail tyre industry is also more complex because different brands attach to the product and to the outlet. This leads to a further complication. Models of imperfect information generally focus on the one-product firm (and indeed, on possibility results rather than predictions about price distributions). Each consumer indeed purchases only one product, a particular tyre (or set of tyres), but not all consumers purchase the same product; some are keener on one brand, some on another. Hence we might say that the representative consumer purchases a bundle of goods from a multiproduct retailer or retailers. This case is explored by McAfee (1995). His model generally exhibits multiple equilibria. However “One trivial testable prediction of the model is that, in any equilibrium, no firm will offer the highest price on more than one good. In most of the equilibria, prices across goods will tend to be negatively correlated.” (p.95). To summarise, received theory suggests that in the presence of imperfect information, rather than tyre prices being the same everywhere, there may well be both be dispersion across customer types, with consumers shopping at branded retail outlets paying higher prices, and dispersion across brands, whereby some stores charge relatively high prices for some brands and low prices for others, and vice versa. There is no necessary prediction about the relative profitability of outlets. We do not broach the question of whether on average demand for some brands is relatively elastic compared to others, so leading to differential markups.14 None of the foregoing takes any real account of a significant facet of the UK tyre trade, namely the presence of “equity chains” and independent retail chains. Therefore it ignores the interaction between potential market power at the retail level, created by retail branding (being the only convenient representative of “chain X”) and market power at manufacturer level, due to brand image creation but additionally use as original fitment and, possibly, superior characteristics. The purpose of the next subsection is to analyse, in a rather crude and simplistic yet suggestive way, the implications of retail market types.

14 As well as selling different brands of the same tyre, retail outlets also sell different sizes. Again there may be discrimination between sizes in terms of price in relation to cost. Consumers are unlikely to be induced to move between sizes by price differences, so discrimination is quite feasible. It may be that

9 3.2 A Model with Some Retail Market Power Let us consider a market in which, because consumers engage in little search and/or because they advertise relatively heavily, the retailer (potentially) has an element of market power. Initially, we model it as a (local) monopolist. Our model is very simple in order to preserve tractability and is meant only to be suggestive. However, it captures the key features of reality that various vertical arrangements are in place in the market and that retailers sell a range of tyres- the latter makes it somewhat different from existing modelling incorporating vertical linkages (e.g. Rey and Stiglitz, 1995) 15. Later, we investigate its robustness. The retailer sells two brands which are imperfect substitutes, with demand functions:

q1 = a -bp1 + g p 2

q 2 = a -bp2 + g p1 g being the measure of the degree of substitutability (g Î(0,b )) . Marginal cost of producing the tyre and supplying to a retailer is set at ci, which we can vary across retail types, i. For simplicity, the marginal cost of fitting a tyre onto a car is set at zero, rather than a positive constant. We develop three alternative regimes and for the moment we model each separately. In the first regime the retailer is integrated with the manufacturer of product 1 in an "equity chain" and so

16 transfers input at marginal cost, but also purchases from the other manufacturer, at a cost of r2 per unit. Equilibrium in the final stage is in prices, yielding reaction functions whose level depends upon r2. This gives a derived demand curve to manufacturer 2, who then optimises r2. Hence we solve for p1 and p2. In regime 2, corresponding by assumption to an independent chain with a well-known brand identity, there is a two stage maximisation procedure. At the final stage, input prices r1 and r2 are given and the firm maximises profit subject to these. Derived demand curves for both inputs are

those who buy higher specification tyres are less price sensitive. But in addition prices of such tyres are advertised less frequently, so fewer advertising messages will be received by their typical consumer. 15 This paper has also been used as a basis for empirical modelling (see e.g. Slade 1998). The Rey and Stiglitz model is in some ways more general (except that their company owned outlets by assumption do not sell rival products). However from the point of view of practical implementation, simplification to constant elasticity or linear demand is required. As Slade points out, with poor cost data, there are advantages in a linear demand framework, and the elasticities she calculates come from such a framework (Slade 1986). 16 In a passage in MMC (1990) the Commission report (without accepting) the claim that ATS deals with Michelin on an arms-length basis. If this is really true, it is difficult to see why Michelin, and other manufacturers with dominant brands, have found it so vital to cultivate equity chains, or why the chains specialise in their own manufacturer’s tyres.

10 obtained as a result, from which the manufacturers optimise their transfer prices. Thus there is a double marginalization. Regime 3 has price set equal to marginal cost of supply for both products at the retail level, representing an independent retailer with no market power of its own. Therefore there is simply a one-stage maximisation by each manufacturer. The model is developed specifically for this paper, but the algebra of these three regimes is very standard and therefore we do not set it out here. Some important magnitudes are given in Table 1. Based upon these, we make the key set of predictions:

(p1, p2, regime 2) >(p2 , regime 1) >(p1, regime 1) >(p1, p2, regime 3) (1)

There are five important assumptions underlying these predictions of our model. The first is that of linear pricing between vertical levels. However, we can incorporate nonlinear pricing without difficulty, since we know the players cannot do better jointly than choose a price which maximises joint system profits. Therefore, they are only likely to deviate from linear to nonlinear pricing if an agreement enabling system profits to be increased (and shared amicably) can be reached. In our model, system profits are maximised at a price for each tyre equal to ( p1, regime 1). Thus we know that the higher prices will not be reduced below this level. Thus the effect of relaxing this assumption would be to make the first two inequalities in (1) non-strict. The second assumption is that the manufacturers' marginal costs of serving different dealer types do not differ. It might be the case that the costs of supplying small independent dealers are higher than the costs of supplying the other types. If so, small retailers might not have the lowest prices- again something we can test. The third assumption is that in the standalone dealer case (regime 3), final price is set at marginal cost, so that the dealer has no funds with which to cover its fixed costs. This might be viewed as objectionable. A reasonable alternative approach to take comes from the Bliss (1988) model of retailing, in which the retailer aims to choose margins so as to reduce consumer utility as little as possible consistent with covering its overheads. Applying his framework in the particular context of our demand functions results in a markup above marginal cost in regime 3 which is constant across the products. Hence it barely affects the conclusions.

11 A fourth implicit assumption, with rather more far-reaching implications, is that market power in regimes 1 and 2 is at the same level, and at the same level across players of a particular type. But this raises the question, for example, of the point at which an independent retailer becomes a retail chain- there will not in practice be a clear break. We will need to tackle this problem in our estimation strategy. Related to this is the final point: Our model develops each regime separately. In practice, matters are more complex. First, the asymmetric regime with one “equity chain” coexists in practice with other equity chains. Using a similar model to that developed here, Waterson (1996) has shown that an outcome in which each chain features one relatively cheap product and one more expensive one is quite feasible. The second form of coexistence is that we should consider relaxing the three separate scenarios and assume they merge into one. What are the implications? With fierce competition between the formats (perhaps because of extensive search) we would expect prices to fall into line across outlets. If competition is important, but not so much as to have this effect, we might find different players seeking out different niches of the market, with search being biased by advertising, so that some consumers search only over retailers they have received messages about. We might also find differences between prices within equity chains over tyre types persisting to some extent. However, the important point is that such factors will weaken our basic predictions, so that if we find no systematic effects, the precise reasons will be unclear. If we find our predictions maintained, then we know the reasons are not spurious - the extensions suggested here do not overturn but merely weaken the predictions. To summarise, our model leads to the prediction that an equity chain retailer will discriminate in favour of a lower price on its own brand, against the rival brand. 17 Standalone outlets are predicted to set the lowest prices, so long as the input prices they face are not appreciably above other types. Powerful independent chains will set the highest prices. Finally, note that the predictions of the model assuming a degree of retailer market power nest the predictions of the search theoretic approach described earlier in which more than one price may coexist, and there may be discrimination between brands within dealers. But search theory by itself provides no specific prediction about the nature of the discrimination. These specific predictions arise within the market power model as a result of the presence or absence of vertical

17 This is of course consistent with the empirical fact (MMC, 1990) that equity chains sell relatively more of their own brand on average.

12 links and retailer power. In a competitive retail framework, as represented by the analysis of section 3.1, all the inequalities in (1) above, save the last, would likely be nonstrict18.

4. The Price Data We have two sets of pricing data, one for 1994 and one for 1996. The first set of data relates to 637 observations of prices of different brands of tyres, all the same specification (155 R13, S or T rating, one of the commonest and most basic types of tyre as fitted to many small family cars in the UK). They were gathered by ten agents of the Consumers Association spread around the UK who were each asked to visit ten tyre outlets in a particular week in February 1994 and, posing as a customer, enquire about availability and price of 15 brands of tyre, covering 9 manufacturers, and the price of balancing. In all, responses were obtained from 96 out of 101 depots visited19. We have copies of the original data sheets, from which we may ascertain names and locations of outlets. It is also evident from these sheets that where agents did not follow instructions to obtain the valve, fitting, and VAT inclusive price but not including balancing, ex post adjustments were made to bring the data into line20. We use the data including balancing cost since this is the basis for the 1996 sample and since it is normal to have tyres balanced at the same time as fitting. Results for 1994 without balancing are very similar, and can be seen in our earlier working paper on the 1994 sample (Waterson and Delgado, 1995). Table 2 provides information about the distribution of observations in our sample among brands and manufacturers, ranked by coefficient of variation in observed prices. Despite the standardised nature of the products, and the construction of the experiment (which chose agents in relatively populous areas in the UK, and in effect gave them incentives to shop locally) it is evident that large price dispersions exist across the sample. The gap between minimum and maximum prices

18 Our predictions are all in terms of prices. We do not see them as carrying measurable implications for profits. We cannot measure the typical small independent's profits. Equity chain profits are normally consolidated in the accounts of their parent, and cannot easily be separated out. Thus, we have no basis for comparison with those independent chains whose profits we can ascertain. 19 One agent made a replacement. We are very grateful to the Consumers’ Association for providing both sets of pricing data. The data were collected to form elements of reports on tyres in the Association’s publication “Which?” for April 1994 and April 1996. 20 VAT is a fixed percentage across the UK.

13 of each brand exceeds £20 ie 50% of the mean in most cases, for a completely defined product21. Of course we cannot rule out recording errors entyrely, but we are not interested in whether there is price dispersion per se, rather in systematic differences.22 The most popular brands judged by availability are Michelin, Dunlop and Goodyear and this matches with market shares. These brands command a substantial premium over the "second brand" Fulda and Gislaved for example. Table 3 shows the distribution of observations in our 1994 sample between regions and outlet type. Thirty nine percent of the observations correspond to individual outlets (or very small chains), thirty percent to manufacturer-owned outlets and the remaining thirty one percent to independent chains. Within the last group, chains which provide service facilities (oil changes, etc.) may be somewhat over-represented by comparison with the local position because the instructions asked agents to ensure they included one such in their set of ten outlets.23 Otherwise the data have some very attractive features. All equity chains are represented and all but one by at least six dealer observations. Several of the independent chains are represented, with the most important ones cropping up several times. All the important brands are well-represented in the data. Thus substantial testing of robustness can be pursued. An attractive feature of the data is that a similar survey was carried out approximately two years later. This time, the survey (or, rather, two contemporaneous surveys) covered five tyre sizes. Eleven agents (grouped as ‘A’) were asked each to make eight shop visits covering three sizes of tyre, including the fairly standard 175/70 R13T (sample A1) and the more up-market 185/60 R14 T and H rated tyres (samples A2 and A3).24 A second group of twelve agents (‘B’) in mostly different areas were asked each to make eight shop visits covering two types of tyre, the 185/65 R14T and 185/65 R14H (samples B1 and B2, respectively). These are also relatively upmarket tyres. Coverage of manufacturers varied between samples and we note that sample A3 is rather small

21 It is worth noting that Pratt et al. (1979) found evidence of a rather similar degree of price dispersion for homogeneous product outlets based upon their survey of prices in the Boston area. They did not investigate tyre prices but did gather information inter alia from 19 exhaust (muffler) sellers and 15 auto tune-up outlets for a specific product in each case. Their coefficients of variation in prices for these products were 0.174 and 0.184 respectively, very much in line with our data in Table 2. 22 There is a slight ambiguity concerning the valve, which is almost universally changed on fitting a new tyre but which may not have been included in a small number of cases. However it is a small component of the final price, typically less than 5%, and there is not evidence of any systematic divergence, so that any effect will be to reduce the overall degree of explanation. 23 It is also possible that agents would have under-represented the least-known independents. 24 These tyres are slightly up-market from the 155 R13 S or T of the 1994 sample. S and T are standard mph ratings. H is a higher rating. The figure after the slash indicates a lower aspect (more expensive) tyre than standard.

14 (well under 100 observations) and problematic in this sense- it will not be reported on further. Again we have raw data sheets, this time plus formatted files. Tables 4 and 5 illustrate the spread of brands and prices for samples A1 and A2 respectively (space precludes showing each table, but a data appendix is available on request from the authors). We observe that the 1994 “snapshot” of widely varying prices is not an isolated artefact; price dispersion is maintained in 1996. Also certain conjectures based upon the first sample may be re-examined with the aid of the second. For example, it has been suggested to us that there is something significant in the observation from table 3 that independent outlets and equity chains have a little over 7 price observations per outlet whilst independent chains have only around 5.7. In fact these figures are repeated almost exactly for sample B2. However for sample A2, independent outlets and independent chains have almost exactly the same ratio of price observations (3.5 and 3.6 respectively), so any impact should not bear on purchasers of that size, allowing some control of the effect. We can also see whether “meet the competition” clauses, which have become more popular in the period between the two samples, have had any impact on the results.25

5. Empirical Implementation 5.1 Matched Pair Analysis The first of our two approaches develops a comparison using pairs (or triples) of retail outlets. In order to be considered for inclusion in an individual comparison, the outlet needs to be in the same street within the same town as another outlet (or two) - i.e. very close and easy to travel between. There are 27 such cases in our entire sample set. However, there is no point in including cases where there is no possible test based on our predictions from inequality (1). Thus for example, two adjacent standalone outlets do not provide anything interesting. Also, cases where one chain is an equity chain but the other outlet does not stock any of that equity’s brand of tyre are uninteresting. All comparisons take place for the set of tyres which are in common between the outlets, averaging across relevant sets. This brings us to 21 comparisons in total; two involve three outlets. There are four relevant types of comparison based on inequality (1): (a) C/S - chain v. standalone: the prediction is that the chain will be more expensive on average. There are five such cases.

25 However, the theoretical predictions are not clear-cut. See e.g. Hviid and Shaffer (1996).

15 (b) E/S- equity v standalone: the prediction is that the equity chain will be more expensive on average and relatively more expensive for other manufacturers' tyres. There are six such cases. (c) E/C- equity v independent chain: the prediction is that the independent chain will be more expensive on average and particularly so for the equity's brand. There are eight cases in this group. (d) E/E- equity v equity: this provides us with the most distinctive prediction, namely that each equity will be relatively cheap for its own tyres. Thus in terms of difference between prices, the biggest price differences are for own brands, with lesser differences for neutral brands. There are only two such cases in our sample but note that each involves three predictions, relating to each equities’ brand(s) plus neutral tyre brands, which should occupy a middle position. Generally, the results of these comparisons are strongly supportive of the predictions in (1). In (a), 4/5 cases meet the prediction. For (b), all six cases have the standalone cheaper on average. Five meet the prediction that the equity is relatively cheap for its own tyres. From case (c) however, independent chains are not more expensive than equities on average. Nevertheless, in 6/8 cases the equity is relatively cheap for its own tyres. Finally, in (d), both cases meet all three predictions. Despite the small numbers, the results provide powerful tests against the null hypothesis that the probability of one price being higher than another is 0.5. Thus they provide very strong evidence for our view that price dispersion is non-random. Using a binomial framework, the probability of the null hypothesis, that standalones are no cheaper than chains, being true is 0.005. The probability of the null, that equities are not relatively cheap for their own tyres compared with standalones and independents, is 0.022. The probability of the null, that equities are no cheaper for their own tyres and not relatively expensive for other brand equities, is 0.028. Finally we should point out that the magnitudes of the price differences are definitely non-trivial. On average one can save over 10% and, dependent upon the tyre, up to 30%, as between two adjacent outlets.

5.2 Regression Results The results above are necessarily small scale. An obvious alternative is to work on a larger sample using a more conventional regression approach. Again based on equation (1), a model along the following lines is proposed for implementation:

i n T pijk= Si aidi + b1zij + b2zij + Smcmicm +eAj + g r + uijk (2) where pijk is (log) price of brand i at outlet j in region k,

16 di = {1 if price corresponds to a brand i product {0 else

i z ij = {1 if the sale of brand i is by an outlet j belonging to a manufacturer of brand i (own brand) {0 else

n z ij = {1 if the sale of brand i is by an outlet j belonging to a manufacturer of brand n (n ¹ i) {0 else

icm = {1 if outlet j belongs to independent chain m {0 else r = a vector of variables measuring district dummies or demand and cost effects relating to location of the outlet.

Aj = a function of advertising expenditure by the chain owning outlet j (actual or log expenditure, depending on model). We accept this is potentially an endogenous variable. The values a-g are sets of coefficients. Thus, for example, consider the equity chain ATS. An Avon or Goodyear tyre has zn=1, zi = 0, but a Michelin tyre has zi = 1, zn = 0, since Michelin owns ATS. Any of the tyres sold at

i n Kwikfit, an independent chain, attract icm = 1, but z = z = 0. At Bill’s tyres (a fictitious independent) all the dummies are zero. This is in addition, in each case, to the tyre name dummy. Note that these dummies capture both quality differences and manufacturer monopoly power. Inequality (1) predicts that

cm [+ai] > b2 [+ai] > b1[+ai] > 0[ai]. (3)

However the model nests the less forthright predictions of the framework set out in section 3.1.26 Given our discussion in 3.2, in this section we experiment a little around formulation (2), examining whether all equity chains operate similar policies (for clarity, this is not represented above), whether

26 Though the regression model is inspired directly by the analysis of section 3.2, it can be thought of more broadly as fitting firmly within the genre of “New empirical I-O” modelling (Bresnahan, 1989); for an example with some parallels see Graddy (1997).

17 independent chains are essentially similar in their actions or not (which is illustrated above), and concerning appropriate methods for dealing with regional effects on equation (1), Regarding the other data, the tyre and retailer dummy variables are straightforwardly calculated given knowledge of chains and brands. A range variable was calculated as a measure of how many brands are carried - more brands mean more likelihood of satisfying a customer drawn at random, but also higher costs of carrying a greater range.27 Given that virtually all outlets carry the "top 3", the latter effect might perhaps be expected to dominate. Advertising data, relating to the past year’s figures, were obtained from MEAL (1994,1996). We wish to ensure so far as possible that our observations used in the statistical analysis below relate to defined markets (albeit necessarily more broadly defined than being in the same street as in section 5.1) and accordingly we employ two criteria for a particular outlet's observations to be included. First, it should be located within a contiguous and natural area (defined by postcode) which can be traversed by car in 15 minutes and which is based on a definite population centre. Second, there should be at least three qualifying outlets within the area. The impact of these criteria is that some individual agents' observations fall into a single area, others into two or even three areas, with some observations being dropped. Cost and demand effects naturally differ across these areas. This is captured in one of two alternative ways. First, a series of local area dummies were used, one for each area. Second, economic measures of cost and demand differences between outlets were employed. For the latter alternative, we collected data for each area on unemployment, male wages, incomes, the price of housing, rental levels and the density of population using the lowest available level of aggregation. In collecting these data, we aimed for a “district” level of aggregation where possible, since this is most appropriate for the tyre market. For each market, we also obtained the number of tyre dealers in that area in order to examine competitive effects directly. Details of the data sources including descriptions of the districts used are listed in Appendix 1. The distribution of raw prices is generally skewed, but the skewness is more often than not much reduced when log. prices are used; details are in Appendix 2. Hence for consistency, all the estimates below aim at explaining log. prices. The approach we take is to discuss in some detail our

27 Note that the objective of the original sampling was not to take a census of the total number of brands offered but rather gather price information about specific brands. Hence this variable is subject to measurement error.

18 results on the 1994 sample, restricted as described above, so containing 491 of the original 637 prices, then to examine the 1996 results in a more summative way, bringing out contrasts. A basic regression estimating model (2) is illustrated as column 1 of Table 6. This contains dummy variables for each brand of tyre bar one (Avon), not reported, area dummies (not reported), plus the variables zi, zn and ic (as a single dummy variable). It may be observed that the prediction b2 > b1 > 0 is upheld as is cm > 0, in line with the model developed earlier. These are all statistically significant at 5% or better. The prediction that cm > b2 is not upheld although this conclusion is modified in later experimentation in the table. The brand/type dummies take on sensible values. Michelin tyres are the most expensive, and their coefficient is extremely significant (t=12.14). Other brands with values significantly above Avon include Dunlop, Goodyear, Uniroyal, both Continental tyre types, and to a lesser extent Pirelli and Bridgestone. Nokia, a brand not well known in the UK until recently, has a value significantly below. Our data are not well designed to evaluate the extent to which these high prices for well- known brands are a result of manufacturers' monopoly power since we have no good measures of quality. Note however that, assuming the model is estimated correctly, the high prices for some brands are independent of any effects due to distribution outlet type. Guarantees for tyres are homogeneous across outlets. Also, the highest premia are associated with brands widely used as original equipment, and with heavily-advertised brands, but not uniformly or particularly with long life. This would suggest the evidence is at least consistent with an element of manufacturers' market power. The area dummies (also not reported for brevity) seem generally in line with expectations. An F test (F (14,457) = 5.1531) establishes that the set of area dummies as a group is significant. The negative effect of retailer advertising is confusing. The independent chain with the highest expenditure on advertising is the one we call K, whose prices then were slightly lower than the independent outlets’ prices. But leading on from this, how much variation within types of observation exists? We may think of the matrix of prices as having two dimensions.28 One is brand. Does the relationship vary much between brands? The other is outlet type. Fortuitously, each of the equity chains and all the main independent chains are well-represented in the data, leading to a generally healthy number of degrees of freedom in these tests.

28 We are grateful to Chris Gilbert for expressing this point so clearly at an early presentation of the paper.

19 Let us first consider the independent chains, where there is an important ambiguity which needs clearing up. We have defined all retailers with more than 10 outlets as chains29. Specifically, when might a chain have sufficient market presence to behave differently from a completely independent dealer (what we have called a standalone)? Our approach to this question is to take the subsample of the standalone dealers and estimate the relationship (log. price) on brand and area dummies, then to add independent chains to the sample and perform a Chow test, the null hypothesis being that the chain in question behaves in exactly the same way as do standalones as a group in setting prices. On doing this, we find that chains coded K, B, and E could not be considered as behaving identically to standalones. We therefore included a separate dummy for each. The result is illustrated in column (2) of table 6. Second, there is the group of equity chains to investigate. Clearly, they behave differently from standalones, but differently from each other? Again, this may be examined chain by chain and as a group using area, brand and, where relevant, z dummies. On performing this experiment it is apparent that there is some (statistically significant) difference between the chains in policies, though this is difficult to categorise (partly because the group of acceptable dummies differs rather markedly between cases).30 Nevertheless, these slope dummies lose their significance, both individually and together, within the sample as a whole where the dummies can both be included. Hence they are not included within the whole sample regressions. Third, there is the question of whether the brands are well enough represented simply by intercept dummies, or whether brand effects need to be more sophisticated. To examine this we compared the drop in explanation between the 15 individual equations, one for each brand/type, with the whole sample.31 The indications are that pooling the equations is legitimate. Based upon this, we are happy to retain the formulation where each brand merely has an intercept dummy to represent it.

29 This is consistent with Png and Reitman (1995) . But some are much larger than others; the biggest have over 500 outlets. 30 For example, whether a chain has a second brand becomes important in determining the form of estimating equation. However there is some evidence that the chains owning the strongest brands price those brands relatively more highly than otherwise would be expected, and slope dummies used on the group of equity chains confirm this. 31 It is a little difficult to do this, because the equations have different parameters and are not easily comparable. However an earlier test on the complete sample gave an F value of 1.25 which allows us to accept the null hypothesis of equality of coefficients at 1%.

20 Finally we replace local area dummies by the more direct indicators of differential cost and demand effects. In Column (3), male earnings have a clear positive influence, as may be expected (either on cost or on demand grounds). Unemployment has an unexpected positive sign and is significant. The other cost/demand variables are individually insignificant, and the group as a whole is significant. One noteworthy finding is that the “dealer numbers” variable is negative and very significant, though small in size. It indicates that, all else equal, more competition lowers prices, as we would expect from general models of market power. Let us turn now to the 1996 datasets. In table 7 we illustrate the outcome of estimating an equation like column (3) of table 6 for each of the four subsamples A1, A2, B1 and B2. Apart from one additional variable (log alignment price), the formulation is identical as between 1994 and 1996.32 Basic results on this group of samples may be summarised as follows in terms of statistical significance:

1994 Sample: cm > b2 > b1 = 0

Sample A1: cm = b2 > b1 = 0

Sample A2: cm > b2 = b1 > 0

Sample B1: cm > b2 > b1 > 0

Sample B2: cm > b2 > b1 > 0 where > stands for “greater at 95% significance”, and = for “statistically indistinguishable”. In each case, cm refers to the highest coefficient on an independent chain- others may be equal to or below b2 in magnitude.

32 This variable is the (logged) price of front wheel alignment. One query which has been raised regarding our 1994 data is whether quality of service received really is the same across outlet types. For example, are chains big, clean and friendly whilst standalones are small, oily and unwelcoming? To some extent, this query can be answered a priori from the construction of the sample. Rational individuals asked to pick their own sample of outlets will not willingly visit unfriendly establishments. It might also be considered irrelevant, since it is unlikely to affect the marginal cost of serving a customer. But we also have a potentially objective measure of quality. Of the tasks carried out at tyre dealers, one of the most sophisticated is alignment testing and rectification. Consumer surveys have shown this almost as often leads to a deterioration rather a than correcting of alignment problems. Charging a high price for alignment signals that you consider the service you offer relatively sophisticated. Plausibly then, it is an indicator of outlet quality, and is included on this basis. However it is not significant.

21 These results, together with some significance of the advertising variable,33 are very consistent with the model underlying equation (1) and the idea that advertising among other things gives firms power in their pricing behaviour. The most expensive independent chains price significantly higher than the equity chains. For example, Chain B is expensive, both in 1994 and 1996. Chain K appears to have changed its policy. This is consistent with the fact that it was rapidly growing and had ambitious plans to expand, plans which had been essentially fulfilled by the time of the second survey.34 Clearly one potential way to expand is to advertise heavily (which it still does) and to charge low prices. More generally, the results are consistent with a view in the tyre industry that standalone outlets are particularly cheap on the higher specification tyres (ie those apart from A1 and the 1994 sample). The results for the equity chains remain very robust across the sample. There is some significance for density, unemployment and dealer numbers. However, since several observations share the same control variables, it is possible that our sample could suffer from the problem identified by Moulton (1986), rendering the standard errors artificially small. Applying the suggested test for this shows that the problem is not present in the equations with economic control variables (although it could be a problem in the regional dummy equations.)35 Having satisfied ourselves regarding the robustness of the estimates, we turn to the main coefficient magnitudes. These are surprisingly high. For example: the coefficient b2 on equity chains’ “other brand” prices averages around 0.18 in the 1994 sample and around 0.16 in the 1996 sample

33 In unlogged form, this is more often positive and significant. There is no particular theoretical reason to prefer logged advertising expenditure. We do it purely for consistency with other money values. Note that price advertising is mainly directed on mass-market tyres. 34 In November 1993 (recall the first survey was in February 1994) chain K announced a change in its pricing policy, cutting prices sharply with the aim of increasing its sales by 10% above previously predicted levels (Financial Times 1993). It had 12% of the replacement tyre market in 1988 (Gallager and Scott, 1995) and aimed for 18% in early 1994. This was the major competitive move over the period in question. 35

c12 (Prob>chi2) - 1% critical value 6.6 Sample Economic control vars. Regional dummies 1994 3.71 (0.0540) 6.23 (0.0125) 1996 A1 1.22 (0.2692) 4.79 (0.0286) 1996 A2 4.16 (0.0414) 4.54 (0.0331) 1996 B1 4.38 (0.0364) 6.95 (0.0084) 1996 B2 0.73 (0.3939) 6.70 (0.0097)

22 and is highly significant. Recall that we are explaining (natural) log prices and adopt the lower of these two figures. Then the effect on actual prices of this variable can be evaluated as at least (e0.16 - e0) x 100% =17.4% higher than at independents. This is a very substantial effect indeed. These results are consistent with relatively low proportional sales of other manufacturers’ brands at equity chains. The coefficient b1 is less well determined, but is on average around 0.05 in 1994 and 0.065 in 1996. Taking the latter figure and performing the calculation shows prices of own-manufactured tyres around 6.7% higher than at individual independent outlets. This is again fairly substantial. The independent chains present a more mixed picture, but a ballpark estimate is that the largest of them (not the highest priced) prices on average 13.6% above independents’ prices in 1996, all other things equal. Notice that all these percentage price differences outweigh likely differences in purchase costs between suppliers. Our results are consistent with a secular decline in the equity chains’ share of the replacement tyre business. But the strength and growth of the independent chains over independent outlets must be sought elsewhere that in cheap prices, presumably in brand recognition, nonprice factors, or a mistaken impression that big firms are cheap.

6. Concluding Remarks So, what have we shown? In our chosen retail market (or set of markets) which might be thought reasonably competitive, search behaviour appears too limited to bring prices into line, for whatever reason. We think it novel to have demonstrated that in markets with many sellers, pricing dispersion displays a very strong and consistent pattern between cases, a pattern which is consistent with elements of market power. But more significantly, observed behaviour is very much in line with what would be expected from a straightforward model in which vertical linkages through “equity chains” to manufacturers are important. This result comes equally from a small-scale “matched pair” analysis or a larger-scale regression approach. Omitted variables cannot be the explanation for this finding. Although the equity chains stock competitors' brands, they sell them at very high prices, over 17% above independent prices36. Their own brands are not cheap. In addition, large independent chains, who may also have some buying power, commonly appear to choose to exploit

36 Note that this most striking finding relates to price patterns within equity outlets where the service and guarantee does not vary across brands, hence is unaffected by any possible reporting errors or outlet differences.

23 their name, often to a considerable extent, by raising prices. The degree to which they can do this is very significant.

24 Table 1 : Final good and Transfer Prices in the Three Regimes

r1 p1 r2 p2

Regime 1 c1 a c1 a c æ aö r2 a + + 1 ç1+ ÷ + 2(b - g ) 2 2b 2 è bø 2 2(b - g ) Regime 2 a + bc r a a + bc r a 2 1 + 2 2 + 2b - g 2 2(b - g) 2b - g 2 2(b - g)

Regime 3 a + bc3 r1 a + bc3 r2 2b - g 2b - g

The r1 are transfer prices from manufacturer to retailer, p1 are final prices for products 1 and 2 at the retailers. Regime 1 has manufacturer 1 integrated with the retailer. Regime 2 has an independent two stage maximisation with monopoly power at both ends, whilst in Regime 3 the retailer has no independent power.

25 Table 2 - Data Statistics by Brand, 1994 sample

Manufacturer Brand and Model/Type Obs. Mean S.D. Min. Max. CV Price£ Bridgestone Bridgestone SF 228 26 £41.41 10.58 25.51 64.2 0.25549 Avon Avon CR 22 45 £39.20 6.92 28.35 52.38 0.17653 Nokia Nokia NRT 7 £27.78 4.8 23.5 37.84 0.17279 Vredestein Vredestein Sprint + 19 £36.86 5.82 28.35 47.82 0.15789 Goodyear* Goodyear GT 86 £39.94 6.02 30.73 58.41 0.15073 Sumimoto* Dunlop SP6 89 £40.76 6.11 29.55 56.18 0.1499 Goodyear* Fulda Diadem 2 17 £34.53 4.99 24.1 47.19 0.14451 Continental* Continental Ecocontact 13 £46.09 6.62 35.85 54.31 0.14363 Continental* Gislaved Speed 316 12 £35.59 5.06 27.62 42.7 0.14217 Continental* Semperit Top Life 25 £39.13 5.44 27.02 47.82 0.13902 Pirelli* Pirelli P1000 64 £39.39 5.47 29.55 56.18 0.13887 Continental* Continental CT 22 40 £43.19 5.99 32.31 55 0.13869 Bridgestone Firestone F 570 39 £34.21 3.92 27.79 46.47 0.11459 Continental* Uniroyal R 380 ** 63 £38.78 4.23 30.55 47.82 0.10908 Michelin* Michelin MXT 92 £49.67 5.15 39.55 66.13 0.10368

TOTAL 637 £40.79 7.27 23.5 66.13

All tyres are 155R13 S or T rating.

Manufacturers with an equity chain are marked with an asterisk.

** Note that in Europe, Continental retains the right to exploit the Uniroyal brand name into the new millennium despite Michelin’s acquisition of Uniroyal Goodrich Tyre in 1989. (See Economist, 6 March 1995)

26 Table 3: 1994 sample: 155 R13 tyres; distribution by response and outlet type

Area Price Outlets Equity Chains Independent Independent (city) obs Chains Outlets Code Obs. Outlets Obs. Outlets Obs. Outlets 6 73 9 10 2 12 2 51 5 7 33 10 13 4 9 3 11 3 8 60 9 17 2 25 4 18 3 9 65 10 16 2 28 5 21 3 10 79 10 26 3 45 6 8 1 11 55 10 8 2 29 5 18 3 12 85 10 27 3 23 4 35 3 13 65 10 20 3 11 2 34 5 14 69 8 39 4 0 0 30 4 15 53 10 14 2 18 4 21 4 Total 637 96 190 27 200 35 247 34

27 Table 4: 1996 sample: Size 175/70 R13T: Brands and Manufacturers

Manufacturer Brand and Model Obs. Mean S.D. Min. Max. CV Price Toyo Toyo Vario +S 4 43.19 13.19 33.44 62.39 0.30539 Sumimoto Dunlop All Season M2 18 60.51 14.92 35.51 79.47 0.24657 Michelin Ricken Road Arrow 7 42 9.19 29.99 52.39 0.21881 Sumimoto Pneumant P72 17 35.87 7.33 28.21 55.14 0.20435 Continental Continental Eco Contact 32 52.55 10.5 37.48 85.48 0.19981 Goodyear Goodyear GT 2E 53 50.49 9.04 38 74.89 0.17905 Continental Uniroyal Rallye 580 54 48.7 7.88 38.98 71.44 0.16181 Sumimoto Dunlop SP9 35 48.2 6.81 35.51 61.54 0.14129 Bridgestone Bridgestone B 320 30 48.64 6.76 39.99 71.44 0.13898 Michelin Michelin Classic 80 53.1 5.95 39.99 69.25 0.11205

Total 330 50.14 9.48 28.21 85.48

Table 5: 1996 sample: Size 185/65 R14 H: Brands and Manufacturers Manufacturer Brand and Model Obs. Mean S.D. Min. Max. CV Price Continental Continental Eco Contact 24 64.25 14.58 50.1 107.81 0.22693 Goodyear Goodyear Vector 2 18 68.77 14.78 42.3 96.06 0.21492 Continental Semperit Top Life 22 57.6 10.68 45.54 78.15 0.18542 Bridgestone Firestone F570 47 53.02 9.17 37.99 86.35 0.17295 Goodyear Fulda Diadem 21 47.52 8.14 35.99 64.62 0.1713 Bridgestone Bridgestone B 320 21 63.69 10.65 47.45 89 0.16722 Goodyear Goodyear GT2 76 61.76 9.8 47 90.64 0.15868 Sumimoto Dunlop SP 65 78 62.41 9.46 45.41 89.26 0.15158 Continental Uniroyal R380 56 60.25 8.52 47 79.99 0.14141 Continental Continental CT 22 49 61.61 8.44 46.94 79.99 0.13699 Continental Viking VSS 365 9 52.37 7.15 43.9 66.1 0.13653 Vredestein Vredestein Sprint + 19 49.79 6.74 39.94 59.38 0.13537 Sumimoto Dunlop AS M2 8 68.14 7.75 58.48 79.99 0.11374 Pirelli Pirelli P2000 83 60.09 6.19 49.35 78.25 0.10301 Michelin Kleber C 651 T 12 59.07 6.05 47.53 70.5 0.10242 Continental Gislaved Speed 316 8 50.77 4.84 43.9 58.48 0.09533 Nokia Nokia NRT 6 44.11 4.17 38.91 49.93 0.09454 Michelin Michelin Energy MXT 85 72.07 6.53 57.1 91.84 0.09061

Total 642 61.19 10.77 35.99 107.81

28 Table 6: Regression results on the 1994 sample Dependent variable: log price of 155R13 tyre (see text for details)

Variables (1) (2) (3) Constant 3.0328 3.5660 1.3453 9.609 130.893 2.935 Own-brand equity 0.1107 0.0445 0.01978 chain 4.928 2.255 0.997 “Other” brand equity 0.2326 0.1621 0.1423 chain 11.790 10.532 8.881 Independent chain 0.68879E-01 3.859 Chain K -0.1096 -0.1377 -6.019 -7.690 Chain B 0.23437 0.2077 8.677 6.721 Chain E 0.1913 0.2059 7.692 8.009 Range -0.1010E-02 -0.218E-02 0.11116E-02 stocked -0.416 -1.007 0.514 Log. advertising -0.2331E-02 0.781E-02 0.1113E-01 -1.057 4.419 5.799 Log. male earnings 0.3027 2.538 Log. density -0.1102E-01 -1.326 Log. unemployment 0.9078E-01 2.328 Log. housing price 0.2483E-01 0.490 Log. rentals 0.4784E-01 1.137 Dealer numbers -0.5997E-01 -5.134 14 brand & 14 14 brand & 14 14 brand area dummies area dummies dummies Adjusted R2 0.6120 0.6185 0.6449 Observations 491 491 491

Bold letters indicate 95% significance. * 90% significance. Heteroskedasticity corrected t-statistics are displayed in brackets

29 Table 7: Results from the 1996 samples Dependent variable: log price of tyres shown: see text for details. Sample A1 A2 B1 B2 Variables (1) (2) (3) (4) Constant -0.7923 1.8213 4.2431 3.7434 -0.220 0.512 12.728 10.78 Own brand -1.74E-01 0.0646 0.1482 0.0476 equity chain -0.517 2.305 7.331 2.681 “Other” brand 0.1569 0.0954 0.2068 0.1709 equity chain 6.634 3.96 13.359 11.009 Chain H -0.1155 -1.336 Chain J 0.1082* 0.1986 0.2094 0.1199 1.963 4.891 7.200 3.140 Chain K 0.1266 0.0931 0.1744 0.1162 3.554 2.180 7.969 5.761 Chain R -0.00682 -0.133 Chain CH 4.90E-01 1.352 Chain B 0.2736 0.2883 7.637 5.426 Chain E 0.2211 0.1805 7.341 5.084 Log alignment 2.49E-01 0.01316 -5.67E-02 7.61E-02 price 0.707 0.395 -0.723 1.111 Range Stocked -1.10E-02 0.00164 -2.47E-02* 9.59E-03 -0.487 0.861 -1.665 0.751 Log 1.15E-01 6.069E-03 7.99E-03 -1.62E-02 advertising 3.459 1.597 0.411 -0.861 Log Male 0.8572 0.2219 5.23E-01 6.98E-01 Earnings 1.389 0.349 0.629 0.876 Log Density -3.38E-01 -0.01106 7.51E-02 -7.69E-02 -3.264 -1.163 1.435 -1.518 Log 0.3008 0.1364 -8.49E-01 -0.1262 Unemployment 2.050 0.945 -1.586 -2.590 Log Housing -6.27E-02 0.035932 -1.05E-01 1.41E-01 Price -0.100 0.824 -0.805 1.135 Log. Rentals -0.19214* 0.1034 8.39E-03 6.96E-02 -1.738 0.843 0.109 0.862 Dealers -7.90E-02 -0.00372* -1.19E-02 1.76E-02 -3.617 -1.828 -0.830 1.262 Adjusted R2 0.5317 0.5678 0.7308 0.5380 9 brand dummies 8 brand dummies 10 brand 17 brand dummies dummies

30 Observations 255 232 408 544 Bold letters indicate 95% significance. * 90% significance. Heteroskedasticity corrected t-statistics are displayed in brackets.

APPENDIX 1 Data Sources

Tyre prices, dealer names, dealer locations, range etc - Consumer's Association: as described in text

Advertising: MEAL - quarterly digest of advertising expenditure (in the UK) For 1994: Advertising from February 1993 to January 1994 (inclusive) For 1996 samples: Advertising by chain from February 1995 to January 1996 (inclusive)

Number of dealers: count of dealers listed for the same outgoing postcodes as those sampled in an area, from Electronic Yellow Pages 1997. log housing: log of Average Semi-detached House Prices (First Quarter 1994), (at county level). Source Halifax Building Society.

Other data: Regional Trends (Central Statistics Office): log unemployment: log of Unemployment Claimants as a proportion of Economically Active Population (at district level). Source Regional Trends 29, Table 15.3 log density: log of population density (at district level) 1993. Source: Regional Trends 29, Table 15.1 log rentals: log of Local Authority Tenants Average Unrebated Rent per Dwelling (£) 1993 (at district level). Source: Regional Trends 30. Table 15.2 log male earnings: log of Average Gross Weekly Full-time Earning (£) April 1994, (at county level). Source Regional Trends 30, Table 14.4.

Certain of the pieces of data used in the text (not the estimates) came from proprietary information supplied on a confidential basis to us by industry participants.

31 Division of sample observations into areas 1994 Region Subarea Postcodes Observations in Dealers Names area 6 Abingdon OX14 61, 62, 69 7 Oxford City OX1-4 64, 65, 68, 60 9 7 Milton Keynes MK1-10 71,72,74,75,76,7 12 City 7,79 8 Solihull B90-93 81-86 6 9 North NE26-30 91-,96 7 Tyneside Newcastle NE1-4,6 97,98,99,90 11 Centre 10 Gloucester GL1,2 101-100 (all) 16 City 11 Long Eaton/ NG9,10 111-116 8 Stapleford 12 Bradford City BD1-5, 7,8 121-125, 128,129 26 13 Portsmouth PO1-5 133, 134, 135 7 Havant/ PO7-9 131, 132, 136, 9 Waterlooville 137 Chichester PO19,20 138, 139, 130 9 14 Harlow CM17-20 141, 142, 144 10 Hertford SG12-14 146-140 6 15 Edinburgh City EH1-8 152, 157, 158, 6 150

32 Division of sample observations into areas

1996

A. Region Subarea Postcodes Retailers in Area Total Names dealers London SE None Poole/Bournemouth Poole BH Chessington, Central, 7 12/14/15/17 Halfords, MrT Bournemouth BH 1-9 Kwikfit, A5, HiQ, Just 9 Tyre Exeter Exeter Central EX 1,2 part 4 All 8 16 Cambridgeshire Cambridge City Parts CB 1-5 All 8 14 St Albans/ Herts St Albans City AL 1-3 A5, ATS, College, 8 Central, Kwikfit Birmingham Birmingham B6, B19 Tyrecare, National, 8 NW Bannings Gloucester Gloucester City GL 1,2 All 8 16 Cardiff and District Cardiff City CF 1,2 All 8 22 West Yorkshire Bradford BD Alba, ETC, Halfords, 26 Central 1,2,3,5,7,8,9 National, Rapid Leicester Leicester SE LE 2,5,18 Formula1, Wigston, 12 Walkers, Kwikfit, Jocks, BigCity, Chessington Stoke/ Crewe Crewe CW 1,2 Smiley, Motorway, 8 National, Crewe, ATS, Kwikfit, Budget

33 1996: B Region Subarea Names Postcodes Retailers in Area Total Dealers Stoke on Trent Stoke on Trent City ST 1-5 All 8 18 West Midlands Oldbury/ West B 69,70 ATS, CharlieBrown, TyreCity, 15 Bromwich Guest, Kwikfit, JR, Halfords Leicester Central Leicester LE 1-5 Walkers, JustTyre, Central, ATS, 27 ETC, DriveIn, Kwikfit Cardiff Cardiff City CF 1,2 All 8 22 West Sussex Chichester PO 19, 20 ATS, Chessington, Rapid, 9 Kwikfit, CEDO, Master, Halfords Kent/ SE London Tonbridge/ TN 1,2,4,9 Freeway, Central, Halfords, TW, 8 Tunbridge Wells ATS, Watling, Pipers Cumbria Carlisle City CA 1-3 National, Smiley, Kwikfit, 9 Halfords, Tyreservices, CharlieBrown London NE Potters Bar/ Enfield EN 2,6 JustTyre, PottersBar, Speedyfit 8 Hertford SG 13 Hertford, CharlieBrown, Kwikfit 4 Dorset Poole BH Kwikfit, ATS, National, 7 12,14,15,17 CharlieBrown, Halfords Clwyd/ Cheshire Deeside CH 5-7 ATS, Buckley, Motorway, 13 Chestercar, Action Chester City CH 1,2 Central, Hoole, Rapid 8 Reading Wallingford OX 10 WallingfordAuto, 3 WallingfordTyre, Crowmarsh Reading Centre RG 1,2,6 Marshalls, Halfords, JustTyre, 12 part 4 SMC, Kwikfit Edinburgh/ Edinburgh City EH 1-8 Smiley, Motorway, National, 6 Borders Budget, Kwikfit

34 APPENDIX 2

Variable Mean Std. Dev. Skew. Minimum Maximum Cases Sample Price 40.935 7.1966 0.489 23.5 66.13 491 1994 Log Price 3.6968 0.17427 0.041 3.157 4.192 491 Sample Price 50.427 9.9325 0.656 28.56 85.48 255 A1 Log Price 3.9017 0.19404 0.049 3.352 4.448 255 Sample Price 65.846 11.229 0.2 19.95 109.8 232 A2 Log Price 4.1719 0.1816 -1.255 2.993 4.698 232 Sample Price 69.12 10.235 0.336 46.53 105 76 A3 Log Price 4.225 0.14937 -0.231 3.84 4.654 76 Sample Price 80.518 14.753 -0.079 37.54 117.5 408 B1 Log Price 4.3706 0.19321 -0.696 3.625 4.766 408 Sample Price 61.303 10.542 0.48 35.99 106.2 544 B2 Log Price 4.1012 0.17137 -0.055 3.583 4.665 544 Note: Samples A2 and B1 indicate that raw rather than log. prices are an appropriate explanand. However since for 1994, sample A1, and sample B2, log prices are clearly better we decided for consistency to use log. prices.

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