Price uniformity and competition in a retail gasoline market

Journal of Economic Behavior & Organization Vol. 56 (2005) 219–237

Price uniformity and competition in a retail gasoline market Andrew Eckert1 , Douglas S. West∗ Department of Economics, University of Alberta, Edmonton, Alta., Canada T6G 2H4 Received 17 June 2002; received in revised form 11 August 2003; accepted 5 September 2003 Available online 29 July 2004

Abstract The purpose of this paper is to examine the price uniformity prediction of the competitive market model of retail gasoline pricing using station specific data on gasoline prices from Vancouver, BC. The specified econometric model also generates results that describe the actual pricing pattern in the market and that permit an assessment of tacit collusion and imperfectly competitive non-collusive competition as possible alternative explanations for the results. Contrary to the competitive model, variables measuring brand effects, spatial and product characteristics, local market structure, and time series variation do affect the probability that a station matches the market mode price. © 2004 Elsevier B.V. All rights reserved. JEL classification: L11; L41; L81 Keywords: Tacit collusion; Retail gasoline pricing

1. Introduction The Canadian petroleum industry has been under scrutiny for the past 50 years. Antitrust petroleum industry inquiries have occurred regarding possible violations of the conspiracy, price discrimination, predatory pricing, resale price maintenance, merger, and abuse of dominance provisions of the Competition Act or its predecessor, the Combines Investigation ∗

1

Corresponding author. Tel.: +1 780 492 7646; fax: +1 780 492 3300. E-mail addresses: [email protected] (A. Eckert), [email protected] (D.S. West). Tel.: +1 780 492 3959; fax: +1 780 492 3300.

0167-2681/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.jebo.2003.09.006

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Act. However, with respect to gasoline retailing, a number of analysts have essentially agreed with the conclusion of the (Restrictive Trade Practices Commission, 1986, p. 406) inquiry into the Canadian petroleum industry that “Regional price differences and swings in price over time are due to variations in competitive conditions.” While gasoline prices in certain areas are acknowledged to be relatively stable and uniform, the explanation given is that retail gasoline markets are competitive.1 The competitive market theory of gasoline price uniformity in Canada is unfortunately never fully specified. While there are various references to it and summary statements of it, there is no single elaborated competitive market model of retail gasoline pricing. In addition, there is little in the way of hypothesis testing that has been done to confirm any predictions that the model might make. The purpose of this paper is to examine the price uniformity prediction of the competitive market model of retail gasoline pricing using station specific daily data on gasoline prices from Vancouver, BC, obtained from an internet data collection site. The econometric model used for this purpose will also generate results that describe the actual pricing pattern observed in the market. If the competitive market model can be rejected, one can then determine whether the pricing pattern is more consistent with the type of pattern that could result from alternative types of pricing behavior. Two alternative types of pricing in the retail gasoline market are considered: tacitly collusive pricing at the brand level and imperfectly competitive, non-collusive pricing in a spatial market. In order to study price uniformity in a market in which prices change frequently, stationspecific daily retail gasoline price data are preferred. One should also collect data from a geographic area large enough to contain at least one relevant geographic market for retail gasoline stations. Both of these requirements are satisfied by the data set used in this study. In some earlier studies, only a small number of stations in a limited geographic area are considered. (See, for example, Noel, 2001.) Other studies have large cross sections of stations, but either study prices for a short period (Shepard, 1991) or have access only to prices surveyed with a frequency of no more than once per week (Haining, 1983; Plummer et al., 1998; Barron et al., 2000). The econometric analysis of retail gasoline prices shows that major brand stations with supplier control over price are most likely to match the market mode price. In addition, retail gasoline prices do vary across geographic space, proximity to and concentration of competitors do affect whether price matching behavior occurs, and certain station and market characteristics affect the probability that a station will match the market mode price. These results are inconsistent with the competitive market model of retail gasoline pricing, and some results are inconsistent with imperfect non-collusive pricing (particularly those related to the effects of local competition on the probability of price matching). They could be consistent with a tacit collusion model of retail gasoline pricing. In the next section of this paper, the competitive market model of retail gasoline price uniformity, as it has appeared in the policy literature, will be discussed. Alternative theories of price uniformity will also be discussed. In Section 3, the data used to estimate the empirical model are described. Section 4 presents the econometric model specification, while Section 5 reports the results of the estimation. Section 6 contains a summary and some concluding remarks. 1

See the Conference Board of Canada (2001, p. iv).

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2. Theories of price uniformity in a market 2.1. The competitive market model A competitive market model of retail gasoline pricing has been adopted in a variety of Canadian industry and government studies. This adoption is based on the belief that retail gasoline markets in Canada satisfy the following assumptions: (1) consumers are mobile and can, at low or zero cost, check gasoline prices charged at different stations in the same geographic market, (2) gasoline stations post their prices so that rival stations can check each other’s prices at low or zero cost, (3) individual gasoline stations set their own prices, and (4) gasoline stations act as though they are undifferentiated firms competing in a spaceless world.2 Because these assumptions eliminate all spatial product differentiation and costly consumer search, it has been predicted that retail gasoline prices (for the same type of gasoline) will be the same everywhere in the market, irrespective of location, proximity to competitors, or the characteristics of the retailers. In addition, it has been predicted that the retail price of gasoline established in a market is a competitively determined price. The empirical analysis carried out in this paper focuses on the first prediction of the competitive market model. The competitive market model as stated may appear to be easy to reject: if all prices in the market are not the same, then the model is rejected. However, rejection of the model on this basis would not be sufficient to persuade its proponents that alternative models better explain retail gasoline station pricing. What is required is the specification of an econometric model that contains variables that the competitive model suggests should not affect the probability that a station charges the market price. Such a model is set out in Section 4. In the absence of a clear rejection of the competitive market model as the explanation for retail gasoline pricing, antitrust policy decisions will continue to be guided by the belief that retail gasoline markets are competitive, and anti-competitive conduct in these markets cannot succeed. 2.2. Tacit collusion One alternative explanation for price uniformity that is consistent with both spatial and product differentiation is that certain firms use price uniformity to support tacit collusion and coordinate behavior. In general, economic theories are unable to predict the prices at which firms will tacitly collude. According to the folk theorem of repeated games, a wide range of payoffs can be sustained in a collusive equilibrium.3 Similarly, in models 2 An extreme statement of the competitive pricing model has appeared on page 21 of the “Consent Order Impact Statement” in Director of Investigation and Research v. Imperial Oil Limited (1989). This view of retail gasoline pricing has apparently had an impact on the Competition Bureau’s subsequent investigations of retail gasoline pricing complaints. See the “Backgrounder: Gasoline Prices in Kenora, Ontario”, February 4, 2000; “Report on the Saskatchewan Gasoline Industry”, November 30, 1999; and “Price-Fixing (Gasoline Inquiry #1)”, July 22, 1999, all posted on the Competition Bureau’s website at http://www.competition.ic.gc.ca. Other studies adopting the competitive model of retail gasoline pricing include those by the Canadian Petroleum Products Institute (1995), M.J. Irwin and Associates (1998, pp. 19–21), and Conference Board of Canada (2001). 3 See, for example, Fudenberg and Tirole (1991, Chapter 5).

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of staggered price setting that have been applied to gasoline pricing, a large number of tacitly collusive prices can be sustained, although only one at a time. (For examples of applications of alternating move models to retail gasoline, see Castanias and Johnson, 1993; Noel, 2001.) The selection of a collusive price is further complicated by product and spatial differentiation, so that collusive pricing could involve different retailers setting different prices. The lack of a unique collusive price poses co-ordination difficulties for firms that may be alleviated through price matching. Tacitly collusive retail gasoline pricing behavior in a market leads to different predictions regarding price than the competitive market model. 2.2.1. Prices above and below the tacitly collusive price For a station that is not with a firm in the tacitly collusive group, the optimal price could be either above or below the tacitly collusive price. On the one hand, an isolated station may attempt to set a monopoly price for that area that exceeds the tacitly collusive price. On the other hand, small deviations below the tacitly collusive price by non-colluding stations may be observed as these stations attempt to overcome disadvantages in location or product. 2.2.2. Major firms vs. fringe firms The largest firms serving a market have the greatest incentive to set the tacitly collusive price because the cost of not doing so is larger for these firms than it is for smaller firms. One would expect that the participation of the larger firms is necessary for tacit collusion to be successful. Smaller firms might undercut the tacitly collusive price to increase market share, believing that the cost to the larger firms of abandoning the tacitly collusive price exceeds the potential gain from responding to the lower price of the smaller firms. Larger firms might also have a greater ability to coordinate behavior given the possibility of multimarket contacts among these firms. 2.2.3. Spatial and product differentiation Unlike the competitive market model, tacit collusion theory does not assume that consumers view gasoline at different stations to be homogeneous products or that consumers care only about price. It does predict, however, that characteristics should not affect the probability that tacitly colluding firms match prices. Allowing station characteristics to affect pricing would make achieving and maintaining the tacitly collusive price more difficult. Whether a station sets the tacitly collusive price is expected to depend on its distance from rival stations and local gas station concentration. Regions that have higher station concentration or a smaller number of stations are expected to be more likely to sustain tacit collusion than regions with many firms and stations. 2.3. Imperfect non-collusive competition in space A third possible explanation of price uniformity over space is imperfect non-collusive competition (INC). One way for INC to produce price uniformity in a spatial market is to assume a high density of independently owned homogeneous outlets everywhere in the market. In the limit, this would effectively lead to spaceless Bertrand competition, since spatial differentiation is diminished with the higher outlet density. Alternatively, one

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could generate price uniformity in a spatial market by assuming uniform population density and equally spaced, independently owned outlets. In the absence of high outlet density everywhere or equal spacing of independently owned outlets, one would expect different outlets to charge different prices if prices are set at the outlet level. These prices would depend on costs, outlet ownership, local demand, and the number, locations and types of local competitors.4 Price uniformity could also be produced if (i) all outlets are members of chains, (ii) members of a given chain set the same price5 , and (iii) members of different chains are clustered at the same locations. As will be shown below, these assumptions do not describe the locations and ownership distribution of gas stations in Vancouver (or likely in any other major urban area). INC models generally predict a lack of price uniformity across space, with the possible exception of price uniformity within the member outlets of a retail chain. Price uniformity can be imposed on chain members directly. However, different chains would have different uniform prices, and these could differ from the prices set by independents. In the discussion in the preceding section, certain variables were suggested as determinants of whether stations would be able to set or maintain uniform prices. In Section 4, the extent to which the predictions regarding the variables explaining price uniformity differ between tacitly collusive and INC models will be discussed. 2.4. Existing evidence on retail gasoline pricing Price uniformity in retail gasoline markets has received very little attention in the empirical literature.6 Most studies focus on price movements and use weekly or monthly prices averaged across stations or prices for a small number of stations. Representative studies include Borenstein et al. (1997), Borenstein and Shepard (1996), Slade (1987), and Sen (2003). In general, these studies conclude that retail price movements are consistent with imperfect competition or tacit collusion. However, given the data they use, they cannot examine the extent of price uniformity. A small number of studies examine price variations across stations, without analyzing price uniformity over geographic space. In general, these studies find evidence of price discrimination according to local income levels and through service levels (e.g. Shepard), as well as evidence that station characteristics, location, and local competition affect stationlevel pricing (e.g., Plummer et al., 1998; Barron et al., 2000). In contrast to Barron et al. (2000), Hastings (2002) found that station level after-tax margins decrease with the incomes of local consumers and that the conversion of independent stations in Los Angeles and San 4 Lindsey et al. (1991) have derived Bertrand Nash equilibrium prices for a set of video rental store locations in Edmonton by first ignoring product variety, chain stores, and integer pricing, and then by allowing for each of these characteristics in turn. Price uniformity is not a characteristic of equilibrium. 5 Assuming that chains do not engage in tacit collusion, members of the same chain might set the same price in a given market because price is advertised across the entire market or perhaps due to the transactions costs involved in setting and changing outlet specific prices that are responsive to local demand and competitive conditions. 6 An exception is Livingston and Levitt (1959), who consider explanations for price uniformity in six metropolitan areas in the Midwest United States. The authors find that stations might not charge the mode price for many reasons, including disadvantageous location, differences in service, aggressive pricing strategies, or ignorance regarding the mode price.

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Table 1 Numbers of service stations by brand Brand

Total number of stations

Vancouver

Surrey

Burnaby

Other municipalities

Major brands ARCO/Tempo Other brands All brands

342 54 30 426

79 7 8 94

62 15 4 81

32 7 1 40

169 25 17 211

Diego to the ARCO brand resulted in an increase in retail prices, relative to unaffected markets. In the remainder of this paper, station-specific price data from a large Canadian urban area are used to examine to what extent the pattern of price uniformity in the market is consistent with competitive, tacitly collusive, or imperfectly competitive non-collusive retail pricing.

3. The market and its price patterns 3.1. The market In this paper, the predictions of the competitive market model are examined using data on retail gasoline prices and station characteristics from the Vancouver, BC, metropolitan area. Gas station addresses and characteristics were obtained from Kent Marketing Limited year 2000 outlet facility reports. In the Vancouver metropolitan area, there were 426 gasoline stations. A breakdown of these stations by municipality and by brand is provided in Table 1. Approximately 80 percent of stations operate under the brand names of the five largest chains: Chevron, Esso, Shell, Petro-Canada, and Husky-Mohawk. These “major brands” are marketed by vertically integrated oil companies. Esso, Shell, and Petro-Canada are major national chains, whereas within Canada, Chevron operates exclusively on the west coast, and Husky-Mohawk operates primarily in the western provinces. Of the remaining 84 stations, 64 percent sell the gasoline of two other refiner brands, ARCO and Tempo. ARCO (which is owned by BP Amoco), operates in Canada only in BC, supplying its stations from a US refinery approximately an hour outside of Vancouver. Tempo outlets market Co-op refinery gasoline throughout the western provinces. The remaining 7 percent of retail gasoline outlets in the Vancouver metropolitan area consist of those operated by supermarkets and major retail stores, convenience store chains, independent gasoline retail chains with little presence in Vancouver, and unbranded independents. 3.2. Prices This study uses station-specific retail gasoline prices for the period from March 1 to August 31, 2000, for the Vancouver, BC, metropolitan area. The retail prices used were reported by consumers to the website http://www.gastips.com, which collects, for each price report, the price charged, the station location, the station brand, and the time and date of the report.

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48 46

cents / liter

44 42 40

Mode price

38

Rack price

36 34 32

8/30/00

8/16/00

8/2/00

7/19/00

7/5/00

6/21/00

6/7/00

5/24/00

5/10/00

4/26/00

4/12/00

3/29/00

3/15/00

3/1/00

30

Fig. 1. Vancouver mode price (before tax), and rack price.

Because the competitive market model makes a prediction regarding the degree of price uniformity within a market, the focus in this study is on the extent to which stations each day charge the mode price of that day. The daily mode price (before taxes) and the wholesale price for the Vancouver area are plotted in Fig. 1.7 As demonstrated in Fig. 1, the mode price is characterized by periods of constant prices, interrupted by adjustments to new mode price levels, and occasional price reductions lasting one or two days. Interest in this study centers on the dispersion of prices once a “market price” has been established and before adjustment to a new market price has begun. Therefore, price observations are confined to days on which the mode price is the same as the mode price from the previous day and followed by days sharing the same mode price. The final sample consists of 6651 unique price reports over 80 days.8 Of the 426 stations in the market, 35 were not observed in this sample. Of the remaining 391 stations, each station was observed an average of 17 of the 80 days. The competitive market model assumes that commuting removes the effects of spatial differentiation from the market, intensifying competition and resulting in a single marketwide price. To document the degree of price uniformity in the Vancouver area, dit is defined as the price of station i on day t less the mode price on day t. The distribution of dit is 7 The wholesale price series was obtained from M.J. Ervin & Associates. This series reports the rack price averaged across suppliers on a weekly basis. For each day in our sample, the rack price used was the last one reported. 8 Versions of the models estimated in this paper were also estimated over the entire sample, with the exception that the models controlled for whether the mode for the previous day was higher than, less than, or equal to the current mode. The only appreciable difference is that prices above the mode occur most frequently when the mode has just fallen, and prices below the mode occur most frequently when the mode has just risen. These facts suggest that when the mode price adjusts, it does so within 1 or 2 days, and that on such days one would need to observe the stations at a higher frequency in order to measure price uniformity, providing additional justification for dropping days of adjustment.

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Table 2 Distribution of dit

dit dit dit dit dit

< −2 ∈ [−2, −1) ∈ [−1, 0) =0 >0

All brands (percent)

Major brands (percent)

ARCO–Tempo (percent)

Other brands (percent)

9.0 8.9 15.4 60.6 6.1

6.6 7.7 14.6 64.7 6.5

20.1 17.9 23.5 36.4 2.2

25.0 10.4 11.7 44.3 8.6

provided in the second column of Table 2; 60.6 percent of the observations on dit are zero, 33.3 percent are negative, and 6.1 percent are positive. These figures indicate that there is a high degree of matching and that prices above the mode price are rare. However, the competitive market model’s prediction of a single price within the market does not find support in the data. As well, most price deviations from the mode are below it, calling into question whether the mode price is in fact competitive. Table 2 also shows that the distribution of dit varies considerably between major brands (i.e. Chevron, Esso, Shell, Petro-Canada, and Husky-Mohawk), ARCO–Tempo, and other brands. The major brands match the mode price more frequently than brands in the other categories, who predominately undercut. ARCO and Tempo undercut with 61.5 percent of price reports, while other brands undercut the mode price with 47.1 percent of price reports. These differences in matching propensities could be the result of differences in station characteristics or market characteristics. This possibility is explored in the following sections. 4. The econometric model This section sets up an econometric model that will be used to describe the equilibrium pricing pattern and to test certain predictions about this pattern made by the competitive and tacitly collusive hypotheses. Because of the small number of price observations above the mode price, the sample is divided into two groups: those prices below the mode price and those at or above the mode price.9 Define Iit as an indicator variable that equals one if station i sets the mode price or above on day t, and zero otherwise. The econometric model is
1 if Xit β + εit > 0 Iit = 0 otherwise, where εit is distributed normal with mean zero and variance one, and where Cov(εit , εis ) = 0 for i = j or t = s or both. This model is therefore a simple probit model.10 9 Alternatively, a multinomial logit model could be developed in which a firm can choose one of three possibilities: pricing above the mode, below the mode, or equal to the mode. Estimating such a model yields similar results. As well, the model was estimated dropping prices above the mode, with little change in results. 10 Alternative specifications were estimated under different assumptions regarding the distribution of the random disturbances and yielded qualitatively similar results. See Eckert and West (2003) for a discussion.

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The variables in X were chosen to capture brand effects, spatial and product characteristics, market structure, and time series variation. These variables are described in turn. 4.1. Brands Brands are controlled for by using two dummy variables: Majorit equals one if the station sells a major brand of gasoline (i.e. Esso, Shell, Petro-Canada, Chevron, Husky-Mohawk) and equals zero otherwise. ArcTempit equals one if a station sells ARCO or Tempo brand gasoline and equals zero otherwise. Majors have a greater incentive to set the tacitly collusive price, so one would expect their stations to have a higher probability of matching the mode price than either other brands or ARCO–Tempo. Conditional on characteristics, INC would not predict brand specific differences in the probability of matching. 4.2. Contracts Whether or not a station sets the mode price could depend on whether it is the station dealer who sets the station’s price or the supplier, which will depend on contractual arrangements. At company operated stations and stations with commissioned dealers, the supplier owns the station and sets the price. At lessee operated stations and branded independents, the station operator or dealer sets the price.11 While we were unable to obtain the contract type used by each major brand station, sufficient information was available to construct a proxy; the variable contractit equals one if the station is a major brand station that is predicted to have pricing power at the supplier level and zero otherwise. This proxy was based on the economics literature and retail marketing surveys conducted by Octane Magazine. Further details are given in Appendix A, located on the Journal of Economic Behavior and Organization’s website for this article. While the competitive model would suggest that contract type does not affect the probability of setting the mode price, both tacit collusion and INC models would predict positive coefficients on contractit . 4.3. Distances The competitive model predicts that the distance of a station from its competitor should not affect whether the station matches the mode price. First, in the competitive model, commuting makes consumers aware of prices at many stations. Second, either the transportation costs faced by consumers in buying gasoline are zero or they do not vary by station choice 11 A station that has nominal price control may in fact have little real control over its price. If lessees and branded independents know that the supplier has a preference for uniform brand pricing, then a station’s persistent deviation from the supplier’s price could result in loss of a facility lease or of the supplier’s brand banner. It could also result in a loss of price support from the supplier. The British Columbia Inquiry into Gasoline Pricing (1996), Final Report, p. 37, found that major companies will protect retailing margins at branded stations by lowering wholesale prices to match decreases in retail prices during price wars, but only if the retailer did not start the price war.

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since a commuter passes many stations along her commute. Under the tacit collusion hypothesis, distances may have an effect on the probability of matching. A greater distance of a station from one that is not setting the tacitly collusive price should have a positive impact on the probability of matching since the closer a station is to a firm that is undercutting, the more likely the station will need to match the undercut to maintain market share. On the other hand, the closer a tacitly colluding firm’s station is to a tacitly colluding station owned by a different firm, the greater the likelihood of setting the market mode price in order to prevent the breakdown of tacitly collusive pricing. An INC model would predict that the greater the distance a station is located from its competitors, the greater the likelihood of charging a higher price. In this context, a higher price would be the mode price or a price above. The effects of distance will be controlled through the variables majdistit ,ArcTempdistit , and odistit . Distances are measured in kilometers with majdistit measuring the distance of a station from the nearest major brand station of a different brand. Similarly, ArcTempdistit measures the distance to the nearest competing ARCO or Tempo station, and odistit measures the distance to the nearest other brand station. The coefficients on these distance variables are allowed to differ according to whether the station from which distance is being measured is either a major brand, ARCO/Tempo, or other brand, for a total of nine distance coefficients. 4.4. Traffic flows The competitive model would predict that stations that are not on roads that are a part of the commuter network would charge the same prices as stations on the major roads. If firms are behaving in a tacitly collusive fashion, whether or not the station is on a major road may determine the likelihood that the station matches the mode price. For tacit collusion to be sustainable, mode pricing is most important at stations whose prices are easily observed and for whom undercutting would attract the largest market share from rival firms. An INC model would also predict whether a station being on a major road should affect the probability that the station matches the mode price; stations on major roads would be less likely to match because station locations on major roads should result in more intensive competition between stations. To control for this effect, the variable roadit is defined to equal one if the station is located on a major road and zero otherwise. A station is classified as being on a major road if the Vancouver Street Atlas, published by MapArt, indicates that the road is either a major artery or a highway. As with the distance variables, separate road variables are defined for whether the station is a major brand, ARCO or Tempo, or other brand. 4.5. Degree of local market concentration and competition The competitive market model predicts that neither the number of nearby competing stations nor the extent to which local competition is dominated by the majors will have any impact on the probability of matching. Under the tacit collusion hypothesis, one would expect the tacitly collusive price to be set more frequently in areas dominated by the majors and where there is a smaller number of local competitors. To determine whether the intensity of local competition or dominance by the majors matters, the variable stationsit is defined to

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measure the number of other stations within a 2 km radius of station i. This variable could have either a positive or negative coefficient under the tacit collusion hypothesis depending on whether localized competition is dominated by major brand stations or not. An INC model would suggest a negative sign for this coefficient. The variable majshareit is defined to measure the fraction of stations in station i’s municipality that bears the brand name of one of the five major brands.12 An INC model yields the same positive sign prediction as the tacit collusion model for the coefficient of this variable. 4.6. Income The competitive market model would predict that a demographic characteristic such as income will have no impact on a store’s probability of matching the mode price. If, however, firms are engaged in tacit collusion or INC, the level of income of consumers located close to a particular station could have an impact on the price set by the station. Higher income households may face higher search costs or may have a higher willingness to pay. Therefore, one might expect that the mode price is more frequently set in neighborhoods with high incomes. To control for income, the average household income of the census tract in which each station is located is obtained from 1996 census data. In cases in which a station is located on the border of two census tracts, the average of the two census tract incomes is used. The variable incdumit equals one if the station is in a tract with income higher than the average across all tracts in the sample and zero otherwise. This variable allows for a potential threshold effect as opposed to simply including income. 4.7. Station capacity In the competitive model, a station’s capacity should not affect whether it sets the market price. In the tacitly collusive model, a non-colluding station’s price below the mode could be sustained in equilibrium if the station’s capacity is sufficiently small so as not to draw a significant amount of business from tacitly colluding rivals. However, in both INC and tacitly collusive models, small capacity stations have limited incentives to undercut their rivals in general. To allow for a possible impact of capacity on the probability of setting the mode price, a capacity variable is defined as the number of unleaded pumps at the station, denoted pumpsit . 4.8. Weekend effects A weekend dummy variable is defined to control for weekend effects: the variable weekendit equals one on Saturdays and Sundays and zero otherwise. Weekend effects should not be significant in the competitive market model. However, in both INC and tacitly collusive models, weekends can have opposing effects. First, on weekends, consumers may 12 In some cases, small municipalities were combined to form larger regions. A list of the regions used in constructing this variable, and a breakdown of the stations in each, is given in Appendix B, which appears on the Journal of Economic Behavior and Organization’s website for this article.

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be less price aware than when they are commuting, tending to enhance price dispersion. Therefore, one is less likely to see price uniformity on the weekends. Second, demand could be higher on the weekends, and if it is, that could either increase the incentive of a station to undercut rivals, reducing the likelihood of matching behavior, or increase the incentive to charge higher prices because of higher demand. 4.9. Margins The variable marginit measures the distance between the before-tax mode price and the wholesale price. Knittel and Stango (2001) argue that the greater the distance between a tacitly collusive focal price and marginal cost, the greater the temptation to deviate. However, given that the focus in this paper is on periods when the mode price is being sustained, it is unclear which sign should be expected according to the collusive model. Under the competitive model, this variable should not have an effect on the probability of matching. Imperfectly non-collusive competition does not provide a clear prediction regarding the sign of this variable. 4.10. Past behavior Time series variation is controlled for in two ways. A variable lagmatchit is defined as the fraction of stations within the station’s region observed the previous day who set the mode price.13 A second variable lengthit is defined as the number of days that a particular mode price has been in effect. It could be the case that undercutting increases over time because stations respond to rival undercuts with a lag. 4.11. Station characteristics The competitive market model suggests that gasoline is a commodity and that a consumer’s purchase is based on price, so that station and product characteristics should not affect whether a station matches price. Alternatively, if firms are pricing in a tacitly collusive or imperfectly competitive non-collusive fashion, but stations are not homogeneous, then pricing may depend on station characteristics. The following variables are defined to control for station characteristics: carwashit , which takes a value of one if the station has a carwash and zero otherwise, storeit , which takes a value of one if the station has a convenience store and zero otherwise, fullserveit , which takes a value of one if the station sells full service gasoline and zero otherwise, serviceit , which takes a value of one if the station has automobile repair service facilities and zero otherwise. The tacitly collusive model predicts that characteristics should not affect whether a station charges the mode price because deviations from the mode price on account of differences in station characteristics can lead to a breakdown of tacit collusion. Station characteristics could affect pricing behavior under imperfect non-collusive competition, 13

Because of data limitations, this variable was constructed based on five regions. These regions are discussed in Appendix B, located on the Journal of Economic Behavior and Organization’s website for this article.

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but the predicted signs of coefficients on station characteristic variables depend on the nature of that competition. Summary statistics for the variables defined above appear in Web Table 1, which appears on the Journal’s website for this article. For dummy variables, the mean represents the proportion of the sample for which the variable equals one. In our sample, 84 percent of price reports correspond to major brand stations, emphasizing the dominance of the major brands. Eighty-two percent of major brand price reports are for stations that are believed to have pricing power at the supplier level. Because of the predominance of major brands, the average distance of a station to a major brand station of a different brand is smaller than the distance to the nearest ARCO, Tempo, or other brand station. On average, 81 percent of the stations in a region are selling a major brand. This percentage ranges from 63 percent in the Langley area to 93 percent in the Delta region. The sample is dominated by price reports from stations on major roads (86 percent of price reports). Note that of the 426 stations in the sample, 87 percent are on major roads, so it is not apparent that major road stations are sampled more frequently than others.14

5. Estimation results 5.1. The results Estimates of the coefficients in the probit equation, along with standard errors, are given in Table 3.15 Most coefficients are significantly different from zero at a 10 percent significance level. The estimated model is highly significant, with a likelihood ratio test of the hypothesis that the coefficients are zero based on a Wald value of 1250 with 27 degrees of freedom. One important question is whether the function determining the probability of matching is the same for majors as for other firms. Wald tests were used to test the hypothesis that the coefficients applying to major brands are the same as those applying to ARCO–Tempo. This hypothesis can be rejected at the 1 percent level. Similarly, at the 1 percent level, the hypothesis that the coefficients for major brands are equal to the coefficients for other brands and the hypothesis that the coefficients for ARCO–Tempo are equal to the coefficients for other brands can be rejected. We next consider whether, controlling for other characteristics, major brands are more or less likely to match than ARCO–Tempo or other brands. Because probit coefficients are not easily interpreted, one must compute the effects of variables on the probability that a station will match the mode price on a particular day. All continuous variables are evaluated at their means, and the weekday pricing of a station selling self serve in a high income neighborhood, located on a major road and without a store, carwash or automobile service facilities is considered. Table 4 reports the probability that this average station matches the mode price on the average day, given that the station is either a major brand station with supplier control over price, a major brand station with dealer control over price, an 14 An examination of correlation coefficients revealed none sufficiently large to give rise to a concern over possible multicollinearity. 15 Standard errors reported are robust to heteroskedasticity.

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Table 3 Coefficient estimates for the probit equation Variable

Coefficient (S.E.)

Variable

Coefficient (S.E.)

major ArcTemp contract majdist: majors majdist: ARCO–Tempo majdist: other ArcTempdist: majors ArcTempdist: ARCO–Tempo ArcTempdist: other odist: majors odist: ARCO–Tempo odist: other length constant

0.920∗ (0.267) 0.757∗∗ (0.310) 0.192∗ (0.048) −0.221∗ (0.033) −0.215∗∗ (0.103) −0.101 (0.122) 0.062∗ (0.011) −0.016 (0.027) 0.169∗ (0.045) −0.052∗ (0.011) −0.035 (0.032) −0.110∗∗∗ (0.058) −0.143∗ (0.010) −4.175∗ (0.368)

road: majors road: ARCO–Tempo road: other stations majshare incdum pumps weekend margin lagmatch store carwash fullserve service

0.090 (0.063) −0.250∗∗∗ (0.138) 0.924∗ (0.185) −0.029∗ (0.007) 4.374∗ (0.347) 0.159∗ (0.048) −0.003 (0.007) −0.190∗ (0.044) 0.059∗ (0.014) 1.033∗ (0.069) 0.064 (0.061) −0.189∗ (4.090) −0.076∗∗∗ (0.046) 0.064 (0.046)

Pseudo R2 = 0.18 ∗ ∗∗ ∗∗∗

Indicates significance at 1 percent level. Indicates significance at 5 percent level. Indicates significance at 10 percent level.

ARCO or Tempo station, or finally an other brand station. Table 4 indicates that major brand stations with supplier price control are most likely to match the mode price, while ARCO and Tempo are most likely to undercut the mode. Other brands are as likely to match the mode price as the major brands, and supplier control over price increases the probability of a major brand station matching by 0.06. The effects of the other variables on the probability of matching are indicated in Table 5. For dummy variables, the effect of changing the value from zero to one is reported. For other variables, the derivative of the probability of matching is reported. In all cases, the effects are evaluated at the point described in the previous paragraph. As well, major brand stations are assumed to have price controlled by the supplier. The effect of a station’s distance to a rival brand depends on whether that brand is a major brand, an ARCO–Tempo, or an other brand, and on the brand with which the station is affiliated. For majors, a 1 km increase in the distance to the nearest rival major decreases the probability of matching the market mode by 0.07. This is consistent with the idea that focal pricing is most important for sustaining collusion when firms sell closer substitutes. A 1 km increase in the distance to the nearest ARCO or Tempo increases the probability Table 4 Probability of matching, by brand type Brand classification

Probability of matching

Major brands: price controlled by supplier Major brands: price controlled by station ARCO–Tempo Other brands

0.78 0.72 0.47 0.77

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Table 5 Effects on the probability of matching Variable

Major brands

ARCO–Tempo

Other brands

majdist ArcTempdist odist road stations majshare incdum pumps weekend margin lagmatch length carwash store fullserve service

−0.07 0.02 −0.02 0.03 −0.01 1.29 0.05 0.00 −0.06 0.02 0.30 −0.04 −0.06 0.02 −0.02 0.02

−0.09 −0.01 −0.01 −0.10 −0.01 1.74 0.07 0.00 −0.08 0.02 0.41 −0.06 −0.07 0.03 −0.03 0.03

−0.03 0.05 −0.03 0.35 −0.01 1.33 0.05 0.00 −0.06 0.02 0.31 −0.04 −0.06 0.02 −0.02 0.02

of matching by 0.02, suggesting that the negative effect of lower ARCO or Tempo prices on a major brand station’s ability to sustain the mode price is greater the nearer the ARCO or Tempo station. The greater the distance to other brands, the lower the probability that a major brand’s station matches the mode, suggesting that brands other than ARCO and Tempo behave more like major brands than like ARCO or Tempo. This is also supported by the fact that the signs on the distance variables are the same for other brands as for major brands. The effects on an ARCO or Tempo station of the distances to the nearest rival ARCO or Tempo and to the nearest other brand station are not significantly different from zero at the 5 percent level. The effect of the distance to the nearest major is negative. This is consistent with ARCO and Tempo stations located near majors also using focal pricing, while stations located far from the majors’ stations undercut to attract business. Local market structure appears to be important, contradicting the competitive model. The fraction of stations in an area that are major brands has a large effect, with a 0.1 increase in this fraction increasing the probability of matching by 0.13 for major brands, 0.17 for ARCO or Tempo, and 0.13 for other brands. The addition of one more station within a 2 km radius decreases the probability of matching the mode by 0.01. Stations in rich neighborhoods are approximately 6 percent more likely to match than stations in poor neighborhoods. Being on a major road appears unimportant for major brands and ARCO–Tempo. The large effect for other brands is possibly picking up the effect of Costco, which has a single station located off of a major road. Costco appears to undercut price aggressively.16 The coefficients on fullserveit , serviceit and storeit are not significantly different from zero at the 5 percent significance level. The coefficient on carwashit is significantly different 16

When Costco is dropped from the sample, the coefficient of being on a major road drops for independents and is no longer significantly different from zero.

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from zero with a negative sign. Table 5 indicates that having a carwash lowers the probability that a station will match the mode price by approximately 6 percent. This result suggests that stations could be using lower gasoline prices to attract customers to the carwash. Finally, the history of the market seems to be an important determinant of whether or not stations match. For each day that a particular mode price is in effect, the probability of matching falls by 0.04 for major brands and other brands, and by 0.06 for ARCO or Tempo. A 0.1 increase in the fraction of stations within the area matching yesterday increases the probability of matching today by approximately 0.03. Together, these numbers suggest a gradual erosion of the mode price. The mode price is most likely to be matched when it has been recently established. As time passes and more stations undercut, the ability of stations profitably to maintain the focal price decreases, and the mode price eventually erodes. Other variables have small effects. A one cent increase in the margin increases the probability of matching by 0.02. This is inconsistent with the suggestion that the incentive to undercut the collusive price is greatest when the margin is highest. Finally, undercutting is approximately 0.06 more likely on weekends than weekdays. This is consistent with the idea that consumers are more price aware on weekdays so that differentials are less sustainable. In general, the findings described above are inconsistent with the competitive model. Contrary to the competitive model’s predictions, certain firms’ stations are more likely to match the mode price than others, prices do vary across geographic space, proximity to and concentration of competitors do affect whether matching occurs, and certain other characteristics of gasoline retailers and the market affect the probability of matching the mode price. Several results are consistent with both an INC model and a model of tacit collusion. These include the effects of income levels, contracts, the number of nearby stations, weekends, and the regional market share of major brands. However, some results are inconsistent with an INC model. For example, non-cooperative imperfect competition does not predict that proximity to a competing major brand station increases the probability of matching the market mode price for a major brand station. This finding is, however, expected under a model of tacit collusion. Likewise, INC predicts that prices will vary according to station characteristics, while for three of the four characteristics considered, the hypothesis that the coefficient on the characteristic is zero cannot be rejected. Again, a model of tacit collusion would lead us to expect that characteristics would not affect the probability of matching. Therefore, the data are more strongly supportive of the tacitly collusive model as an alternative to the competitive model.

6. Conclusion The competitive market model of retail gasoline pricing has been frequently used by government, consultants and industry groups in Canada to explain why some jurisdictions have uniform gasoline prices. However, none of the studies or reports presenting conclusions regarding price uniformity in retail gasoline markets have actually presented a formal analysis of station-specific, daily retail gasoline price data for stations located over an entire metropolitan area. The purpose of this paper is to examine what can be derived as

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the price uniformity predictions of the competitive market model of retail gasoline pricing using station-specific data on retail gasoline prices from Vancouver, BC. If the competitive market model can be rejected, it is then appropriate to determine whether the observed pricing pattern is more consistent with the type of pattern that could result from tacitly collusive pricing behavior in the market or from imperfectly competitive but non-cooperative behavior. The main predictions of the competitive market model examined in this paper are that retail gasoline prices will be the same everywhere in the market and that the probability that a station sets the market mode price does not depend on location, proximity to competitors, or the characteristics of the retailer. To examine these predictions and to explore the determinants of price matching behavior, a probit model is specified whereby the probability that a particular station sets the mode price is hypothesized to depend on variables capturing brand effects, spatial and product characteristics, local market structure, and time series variation. To estimate the model, Vancouver metropolitan area station-specific price observations reported to a particular internet data collection site over the period March 1 to October 25, 2002 are used. Overall, the empirical results lead us to reject the competitive market model as the explanation for gasoline station pricing in Vancouver. The results are more consistent with tacitly collusive pricing behavior in the retail gasoline market in Vancouver. There are a number of reasons why one would have expected the uniform pricing prediction of the competitive market model to be rejected by our tests. First, gasoline retailing occurs in a spatial setting, and retailers can charge different prices in equilibrium in a full information spatial model. Second, not all individual stations set their retail prices; many stations’ prices are set by the supplier, and there is a small number of suppliers serving the market. Third, because of the small number of retail chains competing in the market, firms may behave strategically in competing with their rivals or attempt to engage in some form of coordinated behavior. In the latter case, tacit collusion can be consistent with major firms charging a different price than fringe firms, and whether a station charges the tacitly collusive price can be affected by spatial differentiation and measures of local competition. Given our results, a competition authority would be well advised to carry out a thorough competitive analysis before approving any merger among gasoline retailers serving the Vancouver market (or other retail gasoline markets characterized by a high degree of price stability and uniformity). Such a merger could further facilitate achieving tacitly collusive outcomes, as well as facilitate the exploitation of joint dominance in Vancouver’s retail gasoline market. While there are strong theoretical grounds for rejecting the competitive market model of gasoline retailing, it stubbornly remains a prominent explanation for retail gasoline pricing in Canada. Its acceptance can also predetermine the outcome of antitrust inquiries into alleged anti-competitive conduct in retail gasoline markets. This, in part, is why it is important to have strong empirical evidence that both leads one to reject the competitive model and supports an alternative. Competition authorities should not assume that markets displaying a high degree of price uniformity are highly competitive. The extent of price uniformity may not be as great as casual empiricism suggests, and the deviations from uniformity may in fact be explainable with variables that are inconsistent with a competitive market interpretation of the data.

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This paper has focused on price uniformity and has not addressed predictions of the competitive market model regarding the response of retail prices to changes in wholesale prices. The way in which prices adjust in retail gasoline markets should be of interest in its own right and will be the subject of our future research.

Acknowledgements The authors thank In-Touch Software Corporation for providing free of charge data collected through http://www.gastips.com. The authors also wish to thank the editor and two anonymous referees for helpful comments, the Social Sciences and Humanities Research Council and the University of Alberta for financial support of this research, and Patrick van Laake, Ryan Reichl and Steven Yong for research assistance.

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