4.2 The Effects of Mergers and Regulations
Other types of events have also been analysed. Erutku and Hildenbrand (2010) examine a price fixing cartel in the province of Quebec, and use a difference in difference approach to consider whether the announcement of the criminal investigation caused a price reduction. Data used are weekly city-average prices for Sherbrooke, one of the cities involved in the conspiracy. Montreal and Quebec City are used as controls. The authors conclude that the announcement caused a price reduction of 1.75 cents per litre.23
Several studies examine the impact of regulations in specific markets, again typically using difference in difference approaches. Carranza et al. (2011) consider the impact of a price floor introduced in Quebec in 1997, using data on cities in Ontario as controls, and find that the long run effect of the regulation was to lower prices and productivity. Sales below costs laws are also studied by Anderson and Johnson (1999) using a panel data set of weekly city level prices for the March 1992 to December 1993 period. They conclude that sales below cost laws result in higher retail margins. In contrast, Skidmore et al. (2005) find that sales below cost laws in the USA were associated with a 1 cent reduction in price after 5 years, along with an increase in outlets. A discussion of sales-below cost laws in Wisconsin can be found in Brannon (2003). Doyle and Samphantharak (2008) used a temporary gasoline tax moratorium in Illinois and Indiana to examine the passthrough of gasoline taxes to consumers, and find that 70% of the initial tax reduction and up to 100% of the tax reinstatement were passed on.24Johnson and Romeo (2000) examine the price and station characteristic impact of self-service bans in New Jersery and Oregon, concluding that these bans have a positive but small impact on retail margins.
Regulations regarding vertical integration and restraints have received considerable attention, as have regulations regarding ‘zone pricing’ at the wholesale level, whereby refiners offer different wholesale prices to retailers in different zones but uniform prices within zones. Vita (2000) studies the impact of divorcement regulations that restrict vertical integration of refining and retailing, often with the motivation of preventing predatory behaviour by refiners. The author applies reduced form methods to a panel data set of monthly retail prices, by state, for the 1995–1997 period. Controlling for the presence of local divorcement and sales below cost regulations using dummy variables, the author finds that divorcement regulations increase retail prices. Barron and Umbeck (1984) examine the impact of a 1974 divorcement law in Maryland, using data on 99 stations that were franchised by refiners after the regulation was imposed, and the stations identified as their competitors. The authors find that prices at the newly franchised stations increased, and service hours decreased, relative to competitors. Weak evidence suggests that competitor prices increased.
A number of other papers discuss the potential impacts of proposed regulations concerning vertical restrictions and integration, without analysing the impact of such restrictions were already in place. Blass and Carlton (2001), in an analysis of non-price data on newly constructed stations from 10 integrated refiners for the year 1988, conclude that national divorcement regulations could have costs greater than $1 billion, and that integration decisions are efficiency driven, rather than motivated by predation. Discussions of the possible implications of branded open supply and the removal of zone pricing can be found in Comanor and Riddle (2003), Keely and Elzinga (2003), Barron et al. (2004b) and Langenfeld et al. (2003). The general consensus is that such regulation would lead to higher retail prices.
4.3 Station Level Prices, Price Dispersion and Uniformity
Much attention has been paid to cross sectional price variation at the station level. Empirical studies of station level price variation have attempted to explain why observed prices are diverse or uniform, and to identify the main variables associated with price differentials across stations and products, and price variation within markets.
Several empirical studies involve regression analysis in which the price or margin at a particular station is a function of station and market characteristics. Although price control variables vary by study, variables can typically be divided into different broad categories: (1) local demographics and station location; (2) physical station characteristics; (3) brand and contractual arrangement and (4) station density and local concentration. As a general overview of this literature, Table 2 provides a list of recent papers presenting regression analysis on station or local retail prices or margins (Clemenz and Gugler (2006) use price data for Austrian districts), indicating which categories of explanatory variables are controlled for and found to be associated with price levels. Findings regarding specific variables in each category, and the signs of the associations, will be discussed below. Note that Table 2 focuses on regressions that relate price levels or margins to the variables discussed; related studies not reported include Barron et al. (2000) and Shepard (1991), which examine price differentials by grade or service, and Eckert and West (2005a), which examines whether individual stations match the market mode price. As noted in the literature, interpretation of the findings of these studies can be difficult because local market structure may be jointly determined. See, for example, Hosken et al. (2008) and Barron et al. (2004a) for discussions on this point.
A sizeable literature has arisen considering the role of geographic space on retail gasoline prices, and the degree to which market power is localized; the distinct (and much smaller) literature on the determinants of location will be discussed in Section 6. The focus of this literature is on station level prices and the relationship between pricing and local market structure. As well, studies using station level data that are focused on non-spatial questions will frequently control for or examine the role of local market power.
Reduced form studies of individual station prices frequently ask whether station level price is associated with local concentration or station density. Typically, regressions will be estimated in which station level price or station level margin over wholesale cost is a function of station level characteristics, local market characteristics and region or time period fixed effects. Often, local station density is defined according to a fixed and arbitrary radius; Hosken et al. (2008) and Barron et al. (2004a), for example, consider the number of stations within a 1.5 mile radius, whereas Eckert and West (2005a) employ a 2 km radius. In other cases, ‘local’ is determined according to political boundaries (Van Meerbeeck, 2003, looks at station density within a municipalty, whereas Clemenz and Gugler, 2006, employ Austrian administrative districts), or other considerations. Cooper and Jones, 2007 account for the number of competing stations along the same commuter route; further evidence that interactions between stations are associated with routeways is presented in Haining (1983). Alternatively or in addition, some authors control for distance to the nearest competitor or some measure of local concentration within a certain radius.
In general, the findings have been mixed. Barron et al. (2004a), van Meerbeek (2003), Eckert and West (2004), Clemenz and Gugler (2006) and Shepard (1993) all report that station density is negatively associated with price. Cooper and Jones (2007) find an asymmetric effect; although increased density on a commuter route decreases prices, the count of competitors between a station and the central business district is more strongly related to price than the density of competitors further from the central business district. In many cases, however, the effects of station density are small; Barron et al. (2004a) report, for example, that a 50% increase in the station count within 1.5 miles is associated with price decreases of approximately 0.5%. Hosken et al. (2008) find no association between local station density and price when all brands are included in the regression; distance to the closest station becomes significant with a positive coefficient when one brand that systematically undercuts rivals is dropped.
Studies employing local concentration measures [such as the fraction of outlets within a certain radius that are associated with major refiners, or an Herfindahl-Hirschman Index (HHI)] as opposed to simple station density variables or competitor counts also find mixed results. Hosken et al. (2008) find no impact of local concentration measures, whereas Clemenz and Gugler (2006) find evidence that concentration within a station's ZIP code is associated with higher margins. Pennerstorfer (2009), applying a spatial lag model to station level price data for 400 Austrian stations, concludes that the competition-increasing effect of independent retailers is muted by a ‘composition effect’: as the local presence of unbranded independents increases, there will be fewer stations offering branded products of perceived higher quality, which can result in higher prices at major branded stations.
One important question in modelling the role of space in retail gasoline markets is how to measure proximity. As mentioned above, many studies have used distance bands for this purpose, whereas in spatial studies in other industries nearest point sets have been employed (see Siem and Waldfogel, 2010, for a recent application to liquor retailing). However, Houde (2009) demonstrates the importance of commuting routes (see also Cooper and Jones, 2007, above), by developing a structural spatial model of gasoline retailing that is applied to station level data for Quebec City. The author develops a spatial model of demand in which, instead of being located at a single point as in a standard Hotelling model, a consumer's ‘home’ is a commuting path, so that transportation costs are incurred when a consumer must leave her commuting path to make purchases. Houde (2009) finds that such a model better fits store level sales data than the standard Hotelling model. Different ways of incorporating geographic space into gasoline price competition are considered by Ning and Haining (2003) in a study of pricing in Sheffield, England. They find that for most time periods considered there is a statistically significant positive association between a station's price and prices of stations in the same local cluster.
A small number of spatial studies of gasoline retailing have attempted to estimate the willingness of consumers to travel in order to obtain lower prices, a question that has been of particular policy relevance in jurisdictions where it has been alleged that consumers will travel large distances to save tenths of a cent per litre, resulting in price uniformity across wide areas. An overview of these claims in a Canadian context can be found in Atkinson et al. (2009). Manuszak and Moul (2009), using data on tax regions near Chicago, estimate that the average consumer is willing to incur an additional mile round trip for savings exceeding approximately 7 cents per gallon.
Studies of price levels at individual stations have also concluded that associations exist between price levels and station characteristics, including whether the station has a car wash or service bay, whether it offers full service and the size of the station; see, for example, Eckert and West (2004, 2005a), Ning and Haining (2003), Hosken et al. (2008) and Barron et al. (2004a).25 In some cases, however, the impact of characteristics on price is quite small; for example, in Hosken et al. (2008), the only significant characteristic, whether the station provides repair service, raises price by less than 1 cent. Ning and Haining (2003) also report associations between retail price and certain locational characteristics, such as proximity to a supermarket. A firm's decision of whether to charge for the use of credit (through a ‘discount for cash’) is analysed by Barron et al. (1992). Some evidence has also been presented that station level prices vary by contractual form (see, e.g. Shepard, 1993; Hosken et al., 2008). Finally, brand specific effects are found to important in a range of studies.
Some attention has been paid to the determinants of the degree of equilibrium price dispersion or uniformity. Data from different studies suggest that the degree to which individual stations set uniform or dispersed prices can vary dramatically across markets. Eckert and West (2005a) use data reported to a consumer website for the Vancouver market, and find that on average 61% of stations with prices reported on a given day report the same price. Atkinson et al. (2009), using a panel of self-collected prices for Guelph, Ontario, find that on average 30% of the 27 stations exhibit the mode price at a given point in time, and that averaged across stations, a station exactly matches the price of its closest rival in 41% of observations. In contrast, in other markets, considerable price dispersion has been observed (see, e.g. Lewis, 2008).
Different theoretical frameworks are offered to explain the degree of price variation or uniformity observed in a market. Iyer and Seetharaman (2008) model the price and service quality decisions of competing gasoline retailers, and find that whether the observed pricing equilibrium exhibits identical or dispersed prices and station characteristics depends on the relative importance of geographic product differentiation versus dispersion in consumer quality valuation; when horizontal differentiation is relatively high, price uniformity is observed, whereas when a local area serves a population with a highly diverse valuation of service quality, price and quality dispersion is expected. In an empirical examination of price and quality dispersion within individual census tracts in the St. Louis Metropolitan area, the authors find support for these basic predictions.
Png and Reitman (1994) consider whether price dispersion across nearby stations may result from stations differentiating according to waiting times, based on theoretical models developed by Luski (1976) and Reitman (1991). Using station level data for four counties in Massachusetts, the authors find, as predicted by the theory, that price dispersion across stations increases among stations facing more direct competition.
Barron et al. (2004a) find that, controlling for station characteristics, price dispersion decreases in station density. Lewis (2008), in an examination of prices in San Diego, finds as well that price dispersion is associated with the number of local competitors, but also with the types of those competitors. He concludes that consumer search may play an important role in retail prices.