Competition in Retail Gasoline Markets

Mariano Tappata and Jing Yan∗

July 30, 2013

Abstract

We study the relationship between prices and market structure in geograph-ically isolated markets that are exposed to large demand shocks. The temporalvariation in market size allows us to overcome the classical endogeneity biasin standard concentration-performance regressions. We find evidence of localmarket power in gasoline markets due to product differentiation. Additionally,the high margins that characterize concentrated markets dissipates quickly withthe number of competitors. Ignoring market structure endogeneity leads to un-derestimating the effect of market concentration on prices between 55 and 70percent.

1 Introduction

Few industries attract the attention of consumers, antitrust authorities and academicsas much as gasoline retailing. In the last two decades, higher oil prices and increasedmerger activity have coincided with a rise in accusations of price gauging by laymen,investigations by governments and regulation proposals from legislators and consumergroups. On the academic side, the availability of high-frequency, station-level data hasspawned a vast literature, ranging from the existence of Edgeworth cycles (Eckert,2002, 2003; Noel, 2007a,b; Wang, 2009) and “rockets and feathers” (Bacon, 1991;Borenstein et al., 1997; Verlinda, 2005; Deltas, 2008; Balmaceda and Soruco, 2008) tothe effect of vertical contracts on retail prices (Shepard, 1993; Slade, 1998; Hastings,2004) and collusion (Slade, 1987, 1992; Borenstein and Shepard, 1996; Clark et al.,2011).1 However, and perhaps partially due to the well-known endogeneity problem inconcentration-performance studies, little is known about the effect of market structureon prices in this industry. This paper tries to fill this gap by investigating pricingby gasoline stations in geographically isolated markets. These markets range frommonopolies to dense oligopolies, and they share the special feature of being located

∗mariano.tappata@sauder.ubc.ca and jing.yan@sauder.ubc.ca. Strategy and Business Eco-nomics Division, Sauder School of Business, University of British Columbia. 2053 Main Mall, Van-couver, BC; Canada, V6T–1Z2. We thank Tom Ross, Chuck Weinberg and Ralph Winter for theirhelpful comments and discussions.

1This list is by no means comprehensive. See Eckert (2013) for a detailed survey of the empiricalliterature on gasoline retailing.

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near the entrance to national parks and therefore exposed to temporal and very largedemand shocks.

The study of market power in the gasoline industry is of special relevance to pol-icy makers and antitrust authorities that need to regulate and predict the effect ofhorizontal mergers. Unfortunately, our understanding of the effect of station con-centration on market prices is far from complete. Regardless of the methodologyused, the results from the existing literature have been mixed. Part of this literatureexamines approved mergers and measures the ex-post effect on retail prices usingdifference-in-difference estimations. Simpson and Taylor (2008) found no evidence ofhigher prices for consumers after the 1999 Marathon Ashland Petroleum acquisitionof Ultramar Diamond Shamrock assets. Hastings (2004) reports that the acquisitionof 265 Thrifty stations by ARCO in Southern California led to higher market prices.Taylor et al. (2010), on the other hand, found the price effect of the same event to bean order of magnitude smaller.

Our paper falls in a strand of the literature that uses reduced-form estimationsof the relationship between price (or markup) levels and covariates that include sta-tion attributes and market characteristics. The findings are also mixed within thisgroups. Barron et al. (2004) show that adding one gas station within a local market(i.e., 1.5mi ring) leads to a price reduction that varies across cities from seven to 20cents per gallon (cpg).2 However, Hosken et al. (2008) use a larger dataset and find norelationship between firm density and market price.3 In general, these studies treatthe number of firms in the market as an exogenous variable in the pricing or markupequation. Therefore, the effect of market structure, as determined from these estima-tions, is likely biased due to correlation between the number of firms in the marketand unobservables affecting prices.4 In other words, the equilibrium number of firmsis endogenous and is likely to be a function of unobserved demand and cost shifters(Bresnahan, 1989; Schmalensee, 1989). To estimate the true effect of competitionon prices, one needs to use instrumental variables. However, valid instruments forthe number of firms are not readily available, since they must affect firms’ entry–exitdecisions but be exogenous in the pricing equation.

A distinctive feature of the markets we study is that their size varies significantlyover time and is only partially explained by the town’s permanent population. Ourdata includes weekly prices for gas stations in 45 isolated markets and spans morethan three years. The number of visitors to a national park in a given month can

2Shepard (1993) and Eckert and West (2005) also find a negative relationship between price andfirm density in local markets.

3A third group of papers in the literature uses structural estimation of demand and supplyto simulate counterfactual changes to market structure. Houde (2012) and Manuszak (2010) findevidence of little market power. Nonetheless, Houde (2012) estimates that the small change in marketstructure associated with the Ultramar/Sunoco merger in Quebec City led to a 4-11% margin increase(0.57-1.7cpg). Despite the popularity of this method in other industries, the lack of station-levelvolume data imposes a major restriction to its application in gasoline markets.

4It must be emphasized that the main goal of these papers is not to find causality but to relatethe theoretical literature to the empirical correlations between market structure, price levels andprice dispersion.

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change from zero to 10 times or even 100 times the permanent population of thetown closest to the park. In this setting, we can use past visits as a valid instrument.Past visits are a proxy for the expected long-term market size and therefore affectentry and exit decisions. Moreover, gasoline prices in any given week are influencedby the contemporaneous market size, which, in our environment, is not correlated tothe average market size. Our IV estimates suggest that ignoring the endogeneity biasleads to underestimation of the effect of competition on prices by around 55 to 70%depending on the specification used.

The size and location of our markets allow us to circumvent market boundaryissues that arise in larger metropolitan areas. We measure competition using the countof gas stations in each town and find that, as predicted from oligopolistic models, thenumber of firms in the market affects equilibrium prices in a non-monotonic way.Entry by an additional firm leads to a large and negative price reduction in marketswith few incumbents. This effect diminishes drastically in markets with more than sixor seven firms. Like Sen (2005) and Hastings (2004), we find that a station’s brandand its competitors’ brand affiliations play a significant role in explaining price levels.Our findings are consistent with the predictions from product differentiation modelswith free-entry. We exploit the large cost and demand shocks in our data to study theprice pass-through conditioning by market structure. Again, our results reinforce thelocal market power hypothesis. We find that firms in markets with few competitorsface less elastic demands and therefore react to a demand shock by increasing pricesmore than firms in less concentrated markets. Similarly, the proportion of a costshock passed to prices increases with the number of firms in the market.

The rest of the paper is organized as follows. Section 2 discusses the empiricalspecification and the theoretical predictions. The data, instrument and markets aredescribed in Section 3. Section 4 presents the results, and concluding remarks areoffered in Section 5.

2 Empirical Specification

The goal in this paper is to identify the effect of competition on market prices. Theempirical specification is straightforward since, unlike other industries, gasoline re-tailing resembles regular textbook markets. The product under study is well defined,and firms use simple pricing policies.5 The prices observed in the data are the uni-form transacted prices paid by customers.6 The product is fairly homogeneous, anddifferentiation, if any, is easily captured by brand identity, fuel type, or station loca-tion.7 In addition, the production technology is very rigid and similar across stations.

5Gas stations are true brick and mortar stores, so we do not need to worry about the possibilityof an online substitute product when defining market boundaries.

6Promotions, discounts and third-degree price discrimination strategies are very rare in thisindustry.

7Hosken et al. (2008) show that other stations’ attributes are not statistical significant if stationbrands are included.

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Moreover, capacity constraints appear to be irrelevant in gasoline retailing.8

Consistent with the industry description provided above, our baseline specificationhas the following form:

pitm = β0 + β1wt + β2Fueli + β3Bdumi + β4Xmt + β5Nm + uitm (1)

where pitm is the price charged by station i at time t in city/market m. The variablesFueli and Bdumi measure the vertical differentiation attributes fuel type and stationbrand. Wholesale cost shocks are assumed to be symmetric and are captured by wt.

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Lastly, Nm includes different competition metrics (more on this below), and the vectorXmt has market-level demographic variables (income and population), including thenumber of visitors to the closest national park. We also include other covariatesrelated to market structure that can affect prices (station i’s distance to the parkentrance, distance to the closest competitor, and that competitor’s brand).

The estimation of the coefficient for Nm presents two problems. First, as men-tioned before, direct estimation of equation 1 can generate biased estimators due tothe endogeneity of the number of firms. The term uitm might contain market-specificunobservables that affect cost or consumers’ willingness to pay, and therefore thenumber of active firms in the market. For example, if unobservables are associatedwith positive demand shifters, the OLS estimates would predict a weaker or evenpositive relationship between price and firms. But those estimates would be cap-turing the fact that—other things being equal—markets that support higher pricesalso attract more firms. The bias could go the other way if the unobservables areassociated with lower costs. In such a case, the effect of competition on prices wouldbe overestimated: prices are lower when there are more firms, but in addition, thereare more firms in markets where costs are lower.

We address the endogeneity problem using instrumental variables that affect thenumber of firms in the market but are exogenous in the pricing equation. The in-struments we use are based on the average number of visitors to the park from 2002to 2005. Past average visits are a proxy for the expected long-run market size andtherefore affects the profit equation that underlines entry and exit decisions by firmsin a given market. At the same time, the large volatility in visitors makes the aver-age market size orthogonal (after controlling for other covariates) to everyday pricingdecisions by firms. It is important to note that this instrument would not be valid incontexts where the market size is stable.

The second issue relates to the fact that, in theory, the relationship between priceand the number of firms need not be linear. Assuming no cartel behavior, we ex-pect prices to be lower in markets that—other things being equal—have more firms.However, we are agnostic about the speed at which this process occurs or the num-ber of firms required to reach a competitive outcome. A Cournot model with linear

8This assumption is common in metropolitan areas, though it might not be true in isolatedmarkets like the ones used in this study.

9The assumption of constant marginal cost is shared in the gasoline literature.

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demand and constant marginal cost predicts a convex relationship between marketprices and the number of firms. At the same time, a Bertrand model with homo-geneous products only requires two firms to achieve the competitive outcome, andadditional firms have no impact on final prices. The latter extreme result is temperedonce we allow for product differentiation, and the price-competition relationship ap-proaches the Cournot non-linear prediction. For example, a logit model generatesfree-entry equilibrium prices that are a linear function of the marginal cost, con-sumers’ preference for diversity and the ratio between entry cost and number of firms(Anderson and De Palma, 1992). On the other hand, consumer search models predictnon-monotonic relationships between price and the number of firms. In these models,posted prices can even increase in more competitive markets (Janssen and Moraga-Gonzalez, 2004).10 As we show in Section 4, examination of our data supports anegative and non-linear relationship between prices and the number of firms.

The extent of local market power is related to the elasticity of the demand facedby each firm in the market. At one extreme of the competition spectrum, monopolistsand cartels serve the entire market demand. At the other extreme, firms in perfectlycompetitive markets cannot sell at prices different than the market price. The demandelasticity faced by oligopolistic firms lies somewhere between these two extreme casesand depends on the strength of product differentiation. At the same time, the latteris a function of consumer preferences and the number of firms populating the productspace. In other words, the way market demand translates into firm demands when wedepart from the monopoly outcome depends on competition conduct and consumers’preferences.

A way to assess the effect of additional firms on the demand elasticity perceived byfirms is to estimate the impact of cost and demand shocks on market prices. Again,the monopoly and perfectly competitive market structures provide us with the bench-marks to compare our estimates. The post-shock new equilibrium price is determinedfrom the marginal cost and marginal revenue equality in the monopoly case andfrom market demand and short-run supply curves in the competitive case. Therefore,competitive markets are associated with larger cost pass-through and smaller priceincreases after a demand shock than are monopolies.11 Our data allow us to study theeffects that cost and demand shocks have on prices under different market configura-tions. In that sense, we can see the extent to which we depart from the monopoly-likeresult as markets become more dense. Moreover, we can discard cooperative pricingif we observe that the shock transmission varies significantly with market structure.12

10Note, however, the the relevant variable is the average price paid by consumers, which differssignificantly from the average posted price in these models.

11The slope of the short-run supply curve, which is always lower than the slope of the monopolist’smarginal cost, is critical in determining whether demand shocks translates to equilibrium prices incompetitive markets.

12Evidence of explicit and tacit collusion in gasoline markets has been studied by Clark et al.(2011) and Slade (1992), respectively.

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3 Data Description

We combined data from four different sources. The first dataset contains informationon gasoline prices and stations’ characteristics. The data were originally collectedby the Oil Price Information Service (OPIS), from credit card and fleet card trans-actions.13 One advantage of using OPIS data is its extensive coverage of stations.Nonetheless, not all stations may be included.14 The data covers daily pricing by267 stations in 45 isolated markets from January 2006 to May 2009. We collectedinformation on the price charged, fuel type, date, station brand and station address.The data include all types of service stations: independently owned, jobber ownedand company owned. As in most studies in this industry, the data present two short-comings: quantities sold are not available, and the panel is unbalanced, since pricesare not reported for all stations and fuel types on a given week.

The markets we study are in towns located near the entrances to US national parks(see Tables 4 and 5 in the Appendix). The demand for gasoline in these markets istherefore determined by the stable population and by park visitors. More importantly,the seasonality of visits causes the market size to vary significantly from summer towinter. We obtained data on monthly recreational visits to the parks from the USNational Park Service. National parks commonly comprise large areas of land andare sometimes associated with many towns through their various entrances. Whenthis is the case, we allocate park visits amongst the associated towns using availableinformation by count location (see Table 5 in the Appendix).15 Data availability ongas station prices, and the fact that some parks are not accessible by car, led to ourfinal sample containing 31 parks (21 national parks and 10 national historical parks)associated with 45 towns. The average population of these towns is 5,634 inhabitants.Eleven of the towns have just one gasoline station, and only five towns have morethan 10 stations.

Table 4 in the Appendix lists the parks and summary statistics of monthly vis-its during the period analyzed. The table shows significant time and cross-sectiondispersion in park visits. The cross-section dispersion is mainly explained by thepopularity of a park and its distance from major urban centers. On the other hand,time dispersion is mainly driven by seasonality in tourism. Figure 1 shows represen-tative patterns in the data. For illustration purposes, we scaled visits by creating anindex that captures the deviation from the park mean in a given calendar year. Forexample, visits to Yellowstone National Park are highly seasonal and can easily triplethe average annual visits in the summer months; visits to Colonial National HistoricalPark are less volatile but still show seasonality.

13OPIS data was publicly available trough the American Automobile Association’s website.14OPIS coverage of stations has improved significantly over time. Chandra and Tappata (2011)

used similar data for the states of CA, FL, NJ and TX and found that OPIS coverage was morethan 90% of the stations reported by the Census Bureau for those states.

15Park visits are obtained from vehicle counts in different count locations across the park. Theconversion and aggregation process is described for each park at https://irma.nps.gov/Stats.We split visits to a park using town and vehicle count locations only when the park is associatedwith more than one town.

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Figure 1: Park visits and seasonality

We calculated the driving distance between all pairs of stations in each town. Wealso calculated the driving distance from each station to the park’s closest entrance.16

We consider market size to match the town boundaries. This is a natural definition,given the small size of the towns in our database. Alternatively, and consistentwith the literature and industry practice in larger metropolitan areas, we consider adefinition of local markets that includes all stations within two driving miles arounda given station. The latter definition implies that there are as many “markets” asstations in the sample, and that many of these markets overlap. Figure 2 showsthe distribution of stations per town and the average number of stations within thetwo-mile market definition.

The third dataset includes a proxy for the wholesale cost of gasoline. Stationsacquire gasoline at different prices, depending on their degree of vertical integration.For example, open dealers and independents pay the branded and unbranded “rack”prices at the terminal.17 Company operated stations are fully integrated while leaseedealers obtain the gasoline directly from the refinery at the “Dealer Tank Wagon”(DTW) price, which is private and includes delivery to the station. We do not ob-serve these wholesale prices. Instead, we use weekday spot prices from the EnergyInformation Administration (EIA). These prices are transacted further upstream inthe vertical chain and behave very similarly to the DTW and rack prices. Basedon EIA surveys of station, the correlation between the rack and spot prices is above99% (Chandra and Tappata, 2011).18 Note that our goal is to use the spot price as

16We used Google’s driving directions application to record driving distances. Given the natureof these markets, using Euclidean distances can easily lead to underestimates.

17Independents arbitrage by always buying the cheapest rack price. This is usually the unbrandedprice although there are cases of inverted rack prices.

18The EIA collects DTW and rack prices through monthly surveys and makes these data availableonline.

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a) Town b) Two-mile boundary markets

Figure 2: Distribution of firms by market definition

a wholesale cost or markup shifter and not to estimate with precision the markuplevel. We complement our data with income and population controls from the 2010US Census. Income is measured at the county level, and population is based on thepopulation of the census tracts surrounding a gas station.19

The summary statistics for most of the variables are presented in Table 1. Eachobservation corresponds to a station-week pair. The price per gallon shows significantvariation even after subtracting our wholesale cost proxy.20 As mentioned above,visitors to a given park can increase market size by various orders of magnitudein a given month of the calendar year. We display the absolute value of monthlyvisits (Visitors) as well as the deviation from the average monthly visits in a givencalendar year (Visits). An average market has 13 gasoline stations, dropping to 5 ifwe consider two-mile driving distance rings around each station.21 The median gasstation is located just under four miles from the park entrance. This value drops to1.22mi if we only consider the gas stations closest to the entrance in each town. Last,we followed the literature and created a “brand” dummy (Bdum) to classify stationsinto high- and low-value brands such that we capture consumers’ perceived qualitydifferences. The classification identifies 55% of our stations as being branded (Table5 in the Appendix) and accounting for about 62% of the price data.

19Except for the larger cities, this is the same as the total town population displayed in Table ??nthe Appendix.

20Since the spot price is the same for different octane grades of unleaded gasoline, we only displaythe “markup” for regular unleaded.

21As expected, the two-mile ring markets have a lower number of firms per squared mile thanthose in metropolitan areas covered by Chandra and Tappata (2011) using the same data sourceand time period.

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Table 1: Summary Statistics

Mean Median SD Min Max Obs

Prices (cpg)

Regular 281.5 284.3 67.2 132.3 555.9 18,100Midgrade 288.7 291.9 66.5 141.9 509.9 6,450Premium 298.3 299.9 65.9 145.9 523.9 5,684Diesel 316.3 294.9 74.4 184.9 599.9 9,688

Markups (cpg)Regular 82.3 77.6 28.7 2.1 324.3 18,100Diesel 116.2 108.8 43.3 18.7 433.7 9,688

Market CharacteristicsVisitors 89,504 30,097 124,683 0 691,787 39,922Visits (dev) 5,386 -302 72,459 -208,653 471,543 39,922Population 5,634 5,403 3,582 28 14,102 39,922Income 43,746 42,395 14,801 22,264 78,234 39,922N (town) 13 12 9 1 25 39,922N (2mi) 5 5 3 1 13 39,922Dist closest rival (mi) 0.73 0.29 1.45 0.00 19 38,766Dist to park (mi) 4.97 3.88 3.89 0.03 31 38,766

4 Results

We first investigate the relationship between market prices and different measures ofcompetition. Table 7 in the Appendix displays estimations of equation 1 using town(N) and two-mile ring (N2mi) market definitions as well as alternative metrics for Nm.Specifically, we used polynomial, log and the inverse of the number of stations. Wereport the t–statistics using clustered standard errors.22 The relationship between thenumber of firms and the market price is negative and significant. The point estimatesfor the linear specifications (columns 1 and 5) indicate that adding one station reducesmarket prices between 1 and 1.6cpg when town and two-mile ring market definitionsare used. The predicted prices as a function of the number of stations are (locally)very similar across all specifications, suggesting that competition affects prices ina non-linear fashion. Unlike the expected outcome from collusive markets, pricesare significantly lower in markets that have more stations. Based on the discussionof oligopoly models in Section 2, our preferred competition metric is the inverse ofthe number of firms. Using the log of the number of firms predicts a similar prices-competition relationship for the “average market” (five firms in two-mile ring marketsand 13 firms in town markets), however, the predictions differ at the extremes (i.e.,markets with too few or too many firms). We therefore report results under bothspecifications in the analysis that follows. Additionally, we avoid market boundaryconcerns by using the entire town to define a market.

Table 2 displays the main results of the paper. Columns 1–4 show the results fromthe naive (OLS) and 2SLS instrumental variable regressions. Columns (2) and (4)

22We cluster by station and time. That is, the standard errors used are robust to arbitraryheteroskedasticity, within-panel autocorrelation, and contemporaneous cross-panel correlation.

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a) ∆PIV

∆Nb) Bias=∆PIV

∆N− ∆POLS

∆N

Figure 3: Price reductions after entry by one firm

indicate that ignoring the market structure endogeneity leads us to underestimateof the effect of competition on prices. We used the average annual visits to eachpark during the 2002–2005 period (in levels and squared) as instruments. Table 8in the Appendix displays the first-stage regression results. We cannot reject the nullhypothesis of the price equation being identified (Kleibergen-Paap LM statistic) orthat the instruments are valid (Hansen J statistic). Figure 3a shows the predictedprice reductions when a station is added to the market, using the point estimates fromTable 2. The marginal effect of entry drops sharply in markets with few stations, butthe predictions are not symmetric across specifications. For example, the results whenNm = 1/N (column 2) imply that moving from a monopoly to a duopoly would reduceprices by 70cpg. Results using Nm = ln(1 + N) (column 4) suggest a drop of only11cpg. The predictions are almost identical for markets that move from six to sevenstations (3.5cpg and 3.3cpg reductions, respectively) and diverge in markets withmore stations, although these differences are very small (less than 1cpg). Overall,the message from these numbers is that, similar to the findings of Bresnahan andReiss (1991), the competitive conduct changes quickly as the number of incumbentsincreases.

Figure 3b shows how the magnitude of the naive estimation bias changes withmarket structure. The non-linear relationship between market structure and pricesmeans a larger bias in absolute values for the OLS estimates in markets with fewerfirms. Note, however, that the relative bias is constant. The point estimates from thetable reveal a relative bias of -55 percent if ln(1 + N) is used and -70 percent when1/N is used instead.

The estimators on the other covariates do not change significantly once the endo-geneity is corrected (columns 1–4).23 As expected, midgrade and premium gasoline

23This is not the case for the Population and Income variables. However, the standard deviation

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Table 2: OLS and IV RegressionsDependent Variable: Price (cpg)

(1) OLS (2) 2SLS (3) OLS (4) 2SLS (5) 2SLS* (6) 2SLS*Spot 1.011 1.006 1.010 1.007 1.008 1.009

(45.802) (46.447) (45.436) (45.332) (46.382) (45.829)Fueltype==MU 7.119 9.288 6.344 6.552 8.589 6.969

(5.689) (5.304) (4.905) (4.093) (5.559) (4.531)Fueltype==PU 16.155 17.598 15.852 16.247 17.254 16.235

(13.456) (12.346) (12.614) (10.915) (11.257) (10.847)Fueltype==DI 8.900 8.310 9.329 9.535 9.409 8.843

(3.642) (3.128) (3.840) (3.798) (3.643) (3.525)Visits 0.583 0.630 0.584 0.610 0.567 0.558

(6.013) (6.906) (6.088) (6.684) (6.286) (6.018)Bdum==1 8.236 9.429 8.893 10.315 12.037 11.275

(3.460) (2.552) (3.913) (3.713) (4.157) (4.306)Population (x10,000) -9.379 -1.290 -5.676 3.068 2.569 6.305

(-2.999) (-0.322) (-1.883) (0.752) (0.621) (1.606)Income (x10,000) 2.683 0.974 1.968 0.203 0.747 -0.195

(2.739) (1.033) (2.262) (0.247) (0.989) (-0.252)1/N 42.458 139.673 171.323

(3.492) (5.945) (6.498)ln(1 +N) -11.977 -26.451 -27.232

(-6.433) (-7.716) (-8.115)RBdum==1 6.222 7.450

(1.985) (2.621)Distance2park (mi) 1.338 1.379

(3.680) (3.894)Dist closest rival (mi) -1.897 -1.521

(-1.457) (-1.359)Constant 61.448 48.368 98.095 135.481 34.453 126.920

(10.248) (6.395) (12.299) (12.752) (4.301) (11.836)Adj R2 0.841 0.773 0.844 0.821 0.819 0.837Number of obs (station–week) 39922 39922 39922 39922 38766 38766

* Oligopolistic markets only. Omitted fuel type==RU. Standard errors clustered in two ways: by station and bydate. T-statistics in parentheses.

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increase the price of a gallon by around nine and 17cents, respectively (column 2).Markets with larger populations and lower incomes tend to have lower prices, al-though these effects are not significantly different from zero in specifications (2) and(4). Interestingly, shocks to the spot price have a one-to-one relationship with prices.Note, however, that in order to relate cost pass-through to the elasticity of the firm’sdemand, one must take into account that gasoline prices include the federal, stateand local taxes. That is, letting specific taxes be T and the ad valorem tax rate be t,the price of a gallon of gasoline can be represented by p = (w+c+T )(1+t)θ, where wand c describe the wholesale and other marketing costs, respectively. Departures fromthe long-run perfect competition benchmark would imply θ 6= 1. An upper-boundestimate of the tax rate corresponding to the towns in our sample is approximately25.5%, providing us with a lower-bound for θ of around 0.8.24 That is, at least 80%of a cost shock is passed to the before-tax retail price.

The effects of demand shocks are reflected in the coefficient estimated for thevariable Visits. A 10,000 increase in park visits leads to more than half a cent priceincrease.25 This, together with the cost pass-through result, is consistent with the ideathat gasoline stations face downward-sloping demands, hence enjoy market power.Evidence of vertical and spatial product differentiation also supports this view of localmarket power. The variable Bdum indicates that prices at stations that consumersregard as high-quality are around 3.5% (10cpg) higher than at stations perceived aslower quality.26

We add covariates associated with local competition in specifications (5) and (6)of Table 2. These results are valid for markets with two or more firms. The pointestimate for RBdum indicates that prices are higher by about 6.22 and 7.45cpg if theclosest competitor is affiliated with a high-quality brand station. This is consistentwith results from the literature supporting vertical product differentiation and brandloyalty. Hastings (2004) shows that stations charge 5cpg less when facing competitionfrom an independent or unbranded competitor.27 Distance to the park entrance isassociated with higher prices, reflecting the fact that park entrances are on the out-skirts and the town center is the preferred location for consumers. On the other hand,the distance to the closest competitor does not seem to affect prices.28 The coefficient

values of those point estimates are much larger than for the rest of the explanatory variables, andoverlap.

24Gasoline is taxed at the federal, state and local government levels. The federal tax is 18.4cpg forgasoline and 24.4cpg for diesel. State and local taxes include specific and ad valorem taxes (excise tax,environmental fees, storage tank taxes, general sales tax, and other fees or taxes). These taxes varyat the county level, and the American Petroleum Institute reports the average implied tax “rate” foreach state (http://taxfoundation.org/article/state-gasoline-tax-rates-2009-2013). Weused the January 2010 values reported for the states in our sample. This is an upper bound, sincemany local taxes are not ad valorem.

25To put this number in perspective, the standard deviation of Visits in our sample is larger than70,000.

26Again, this coefficient is not necessarily associated with customers’ perception of gasoline quality.Station characteristics are highly correlated with brand.

27Similarly, Sen (2005) finds lower prices in markets with a higher proportion of small firms (i.e.,independent retailers)

28Hosken et al. (2008) also find no significant effect of distance to rival on prices.

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is negative and, together with the estimated effect of the distance to park entrance,supports the idea that location might not be a relevant dimension of differentiationwithin a small markets. That is, a station faces a lower demand than its competitorsonly if located outside a certain distance threshold.29

We now explore in more detail the link between our results in Table 2 and localmarket power. As discussed in Section 2, we are interested in measuring the extentto which a firm’s market power decreases as we depart from a monopoly marketstructure. We therefore look at the cost and demand shocks separately for differentmarket configurations. Table 3 adds to equation (1) various interactions between thespot price and monthly park visits, with dummies indicating whether the market isa monopoly or an oligopoly with only a few stations (between two and six). Theomitted market category includes competitive oligopolies: markets with more thansix stations.30 The results are similar across specifications, although the monopolypoint estimates are larger when we use Nm = 1/N . All coefficients have the signs andmagnitudes expected in markets in which local market power decreases as the numberof competitors increases. Markets with more firms translate a higher proportion ofcost shocks to prices. The results are all significant at the 1% and 5% levels. Usingthe estimates from column (1) and our approximated tax rate of 25.5% we find a costpass-through of 17, 71 and 87% for monopolies, oligopolies with few firms and denseoligopolies, respectively.31

Similarly, the coefficient for the Visits level and the interaction terms reveal thatthe same demand shock increases prices by more in less competitive markets. Fromcolumn 1 (2), a monopolist reacts to a 10,000 increase in park visits by raising prices6.5cpg (2.63cpg), while oligopolies with few firms increase prices by only 1.03cpg(1.1cpg). Last, more competitive oligopolies increase prices by 0.33cpg (0.36cpg).The strong and robust result for demand shocks can also be associated with changesin the composition of buyers, not just a proportional outward shift in demand. It isreasonable to assume that park visitors are less informed about gas station locationsand prices and therefore have higher reservation values than permanent residents. Ifthis is the case, the increase in park visits means a larger and less elastic firm-leveldemand. To summarize, Table 3 provides further support that local market powerdrops abruptly as the number of firms in the market rises.

5 Conclusions

In this paper we revisit an old question in the field of industrial organization: What isthe effect of competition on gasoline market prices? We answer it by studying isolatedmarkets that face significant demand shocks, allowing us to use valid instruments

29The median value for distance to closest rival is 0.29mi, and the third quartile is still less thana mile (0.69mi).

30To highlight the large impact that clustering has on the standard errors, we report t-statsassociated with both, cluster-robust, and robust-only standard errors.

31The predicted cost pass-through values, using results from column 2, are 66, 71 and 85%,respectively

13

Table 3: Market Structure, Demand and Cost Shocks

(1) (2)Spot 1.097 1.071

(328.544) (363.335)[38.285] [41.309]

Visits 0.332 0.358(17.287) (18.803)[3.344] [3.618]

Bdum==1 9.012 8.660(25.950) (28.576)[3.092] [3.616]

1N=1× Spot -0.880 -0.243(-44.358) (-26.389)[-5.461] [-2.942]

1N∈[2,6]× Spot -0.200 -0.179(-44.088) (-45.298)[-5.290] [-5.630]

1N=1× Visits 6.137 2.272(10.688) (4.231)[4.169] [3.560]

1N∈[2,6]× Visits 0.694 0.740(14.787) (18.466)[3.888] [5.367]

1/N 243.823(58.173)[6.923]

ln(1 +N) -38.419(-62.483)[-7.923]

Constant 33.439 169.727(31.577) (99.479)[3.811] [12.596]

Adj R2 0.796 0.835Number of obs (station–week) 39,922 39,922

2SLS estimations. Coefficients for fuel type, Income andPopulation not reported. T-statistics using robust SE reported inparentheses. T-statistics using clustered (station and time) SE insquare brackets.

14

in a concentration-performance, reduced-form estimation. We show that ignoringendogeneity in standard regressions leads to significant underestimation of the effectof the number of firms on market prices. We reject the idea of competition conductin these markets being either collusive or perfectly competitive. Stations’ brandaffiliations and market configuration have a major influence on prices and marginlevels. Prices and margins drop quickly as the number of competitors in the marketincreases. In addition, we find the price pass-through of cost and demand shocks tobe consistent with the predictions from models of product differentiation.

15

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Appendix

Table 4: Park Visits (Jan 2006–May 2009)

Recreational visits per monthPark Mean Max Min SD CV

Abraham Lincoln Birthplace NHP 16,544 37,586 4,087 9,297 0.56Badlands NP 65,869 217,962 7,008 73,762 1.12Black Canyon of the Gunnison NP 14,048 33,598 2,070 10,283 0.73Capitol Reef NP 45,386 92,630 5,075 30,992 0.68Colonial NHP 275,045 391,445 130,641 94,040 0.34Cumberland Gap NHP 75,183 135,909 40,281 24,519 0.33Cuyahoga Valley NP 209,907 427,995 95,854 90,125 0.43Everglades NP 81,173 241,397 27,457 33,373 0.41Glacier NP 147,963 621,046 8,017 194,498 1.31Great Basin NP 6,039 15,267 925 4,736 0.78Haleakala NP 106,902 148,140 66,799 17,726 0.17Joshua Tree NP 112,627 260,407 46,833 43,288 0.38Kenai Fjords NP 20,266 89,462 0 31,120 1.54Mammoth Cave NP 83,001 256,832 14,034 57,909 0.70Marsh-Billings-Rockefeller NHP 2,425 7,996 227 2,275 0.94Mount Rainier NP 84,567 267,285 663 88,629 1.05Natchez NHP 39,686 77,958 17,836 16,669 0.42Nez Perce NHP 15,541 42,201 2,524 9,871 0.64Olympic NP 98,424 310,024 34,294 71,000 0.72Pecos NHP 2,731 4,920 492 1,321 0.48Redwood NP 31,959 82,714 10,852 18,948 0.59Rocky Mountain NP 217,383 653,885 50,974 204,247 0.94Saratoga NHP 7,830 15,330 1,049 4,764 0.61Sequoia NP 76,401 176,568 25,630 47,757 0.63Sitka NHP 22,835 68,065 5,050 20,249 0.89Theodore Roosevelt NP 36,703 108,614 351 38,060 1.04Voyageurs NP 17,257 49,304 210 18,289 1.06Women’s Rights NHP 1,530 4,939 183 1,159 0.76Yellowstone NP 230,800 822,773 12,382 277,826 1.20Yosemite NP 271,548 550,172 78,795 161,230 0.59Zion NP 214,961 368,739 56,137 110,222 0.51

Total 84,920 822,773 0 121,862 1.44

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Table 5: Parks and Nearby Towns

Park/Town Visitors/month Population

Abraham Lincoln Birthplace NHPHodgenville (KY) 16,544 3,212

Badlands NPInterior (SD) 31,775 95Wall (SD) 34,093 770

Black Canyon of the Gunnison NPCrawford (CO) 14,048 431

Capitol Reef NPTorrey (UT) 45,386 181

Colonial NHPYorktown (VA) 275,045 65,459

Cumberland Gap NHPCumberland Gap (TN) 37,220 494Middlesboro (KY) 37,963 10,321

Cuyahoga Valley NPIndependence (OH) 209,907 7,113

Everglades NPEverglades City (FL) 81,173 402

Glacier NPBabb (MT) 43,408 195Columbia Falls (MT) 49,867 4,685West Glacier (MT) 54,689 367

Great Basin NPBaker (NV) 6,039 68

Haleakala NPHana (HI) 106,902 1,235

Joshua Tree NPJoshua Tree (CA) 76,594 7,414Twentynine Palms (CA) 36,033 25,123

Kenai Fjords NPSeward (AK) 20,266 2,698

Mammoth Cave NPBrownsville (KY) 41,501 836Park City (KY) 41,501 537

Marsh-Billings-Rockefeller NHPWoodstock (VT) 2,425 3,048

Mount Rainier NPAshford (WA) 51,983 217Packwood (WA) 32,584 1,330

Park/Town Visitors/month Population

Natchez NHPNatchez Me (MS) 19,843 15,774Natchez Wj (MS) 19,843 15,774

Nez Perce NHPGrangeville (ID) 15,541 3,146

Olympic NPPort Angeles (WA) 51,493 19,068Sekiu (WA) 46,930 27

Pecos NHPPecos (NM) 2,731 1,392

Redwood NPKlamath (CA) 31,959 779

Rocky Mountain NPEstes Park (CO) 163,756 5,876Grand Lake (CO) 53,627 469

Saratoga NHPStillwater (NY) 7,830 1,740

Sequoia NPThree Rivers (CA) 76,401 2,182

Sitka NHPSitka (AK) 22,835 8,192

Theodore Roosevelt NPMedora (ND) 32,940 111Watford City (ND) 3,763 1,759

Voyageurs NPKabetogama (MN) 17,257 70

Women’s Rights NHPSeneca Falls (NY) 1,530 6,669

Yellowstone NPCooke City (MT) 8,823 140Gardiner (MT) 27,920 851West Yellowstone (MT) 194,056 1,272

Yosemite NPEl Portal (CA) 271,548 474

Zion NPOrderville (UT) 51,324 580Springdale (UT) 163,636 531

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Table 6: Brand DistributionBrand Bdum Avg Price N76 1 295.80 8ARCO 0 257.90 2BP 1 262.10 21CENEX 0 278.13 6CHEVRON 1 296.71 21CIRCLE K 0 290.25 5CITGO 0 256.49 23CONOCO 1 303.93 14CROWN 0 244.12 4CUMBERLAND 0 259.37 1DIAMOND SHAMROCK 0 299.62 2EXXON 1 297.79 18FINA 0 293.70 1GULF 0 339.01 2KROGER 0 259.74 1KUM & GO 0 283.66 1KWIK FILL 0 272.01 3MAPCO 0 256.27 1MARATHON ASHLAND 0 241.94 3MOBIL 1 281.81 14MURPHY USA 0 258.14 2OPTIMA 0 252.93 2PHILLIPS 66 1 285.27 8PILOT 0 283.78 2RACETRAC 0 253.04 2ROYAL FARMS 0 255.80 1SAFEWAY 0 297.58 1SHELL 1 286.63 28SINCLAIR 0 310.69 16STEWARTS 0 287.43 1SUNOCO 0 277.40 4TESORO 0 286.51 2TEXACO 1 291.37 11UNBRANDED 0 291.12 23UNKNOWN 0 282.15 4WAWA 0 256.30 1

The dummy brand (Bdum) aims to capture higher than averagequality perceived by customers.

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Table 7: Non-linear Effects of Competition. Dependent Variable: Price (cpg)

(1) (2) (3) (4) (5) (6) (7) (8)N -1.021 -2.610

(-6.877) (-3.146)N2 0.058

(2.051)1/N 42.458

(3.492)ln(1 +N) -11.977

(-6.433)N2mi -1.672 -2.651

(-3.703) (-1.642)N2

2mi 0.080(0.659)

1/N2mi 19.792(3.144)

ln(1 +N2mi) -10.168(-3.685)

Constant 83.986 87.858 61.448 98.095 78.105 80.120 62.468 86.452(12.583) (12.643) (10.248) (12.299) (10.640) (9.624) (9.790) (10.025)

R2 0.842 0.844 0.841 0.844 0.834 0.834 0.834 0.834Number of obs (station–week) 39922 39922 39922 39922 39922 39922 39922 39922

OLS regressions. Covariates included and not reported: Spot, Fueltype, Visits, Income, Population, Bdum and Dist2Park. Standarderrors clustered by station and time. T-statistics in parentheses. N = number of stations in town. N2mi = number of stations within atwo-mile driving distance ring.

22

Table 8: IV Regressions: First- and Second-Stage ResultsDependent Variable for Second Stage: Price (cpg)

1st (1/N) 2SLS 1st (ln(1 +N)) 2SLSSpot 0.000 1.006 -0.000 1.007

(2.054) (46.447) (-2.507) (45.332)Fueltype==MU -0.013 9.288 -0.036 6.552

(-1.380) (5.304) (-0.909) (4.093)Fueltype==PU 0.003 17.598 -0.071 16.247

(0.413) (12.346) (-1.874) (10.915)Fueltype==DI 0.007 8.310 0.007 9.535

(0.720) (3.128) (0.183) (3.798)Visits -0.001 0.630 0.003 0.610

(-1.809) (6.906) (2.320) (6.684)Bdum==1 -0.046 9.429 0.274 10.315

(-1.763) (2.552) (2.536) (3.713)Population (x10,000) -0.047 -1.290 0.412 3.068

(-1.688) (-0.322) (3.138) (0.752)Income (x10,000) 0.041 0.974 -0.251 0.203

(2.854) (1.033) (-5.190) (0.247)IV (x100,000) 0.022 -0.113

(2.565) (-4.517)IV2 -0.001 0.004

(-3.552) (6.106)1/N 139.673

(5.945)ln(1 +N) -26.451

(-7.716)Constant -0.004 48.368 3.327 135.481

(-0.070) (6.395) (15.554) (12.752)Adj R2 0.169 0.773 0.341 0.821Number of obs (station–week) 39922 39922 39922 39922

Omitted Fueltype==RU. Standard errors clustered in two ways: by station and by date.T-statistics in parentheses.

23