Crossing the Border:How Political Boundaries Affect Gas Price Competition

and State Motor Fuels TaxWorking Paper

David Coyne

November 20, 2017

Abstract

This paper analyzes how taxes are passed through to consumers around state borders in the con-text of state motor fuels taxes. Using high-frequency retail gas price data from Gasbuddy, a popularprice tracking platform with user-submitted gas prices, and precise location data, I compare howprices respond to changes in tax rates conditional on distance to an out-of-state competitor. I showthat stations within 10 miles of an out-of-state competitor pass through about 93 percent of a taxchange. This is 43 percentage points lower than stations on the interior of a state. Furthermore, Ishow that stations near a border pass-through tax changes from neighboring states, as well, albeit ata discounted rate of about 35 percent. I also test for asymmetry in pass-through by which side of atax gradient a station is located and by whether the station is responding to a tax hike or tax cut, butdo not find strong evidence for either. The results of this analysis suggest that interstate gas pricecompetition significantly adds to motor fuels elasticity of demand when measured at the state level,and suggest that the incidence of a motor fuels tax falls more heavily on residents towards the interiorof a state and more heavily on firms closer to state borders.

Keywords: tax incidence, motor fuels tax, tax competition

1 Introduction

One of the most important commodities for many American families is retail gasoline. Most auto-mobile owners rely on gasoline to power their vehicle to go to work, bring their children to school, orparticipate in any number of leisure activities. The commodity also plays an important role in federal, s-tate, and local revenues through motor fuels taxes. In FY 2014, the federal government raised $35.5 billionthrough the motor fuels tax ($25 of which came from gasoline taxes and the rest from diesel and other spe-cial fuels taxes), while state and local motor fuels tax revenue amounted to nearly $43 billion (Tax PolicyCenter, 2017; Federal Highway Administration, 2015).

In addition to providing a source of general revenue, gas taxes serve several other roles for govern-ments, as well. Many states earmark gas tax revenues for road maintenance and other highways projects.Many states collect an underground storage fee that is put into an insurance fund in case of undergroundstorage accidents. Gas taxes can be viewed as a green policy because they raise the price of gasoline, lim-iting congestion and emissions by encouraging carpools and discouraging unnecessary driving. As carsbecome increasingly fuel efficient, governments face declining highway funds, prompting many states toraise their taxes to pay for transportation projects and maintenance.

When taxes rise, so does the price of gasoline facing consumers. Depending on the level of competi-tion in retail gasoline markets, a higher or lower amount of a motor fuels tax may be passed through toconsumers. The retail gasoline market has the appearance of being a very competitive industry. Stationsoften find competitors as close as across the street. Prices are visibly posted outside the station on largesigns, limiting asymmetric information in prices across competing stations. Recent technology has fur-ther mitigated any asymmetric information, with a number of platforms through which both gas stationmanagers and consumers can search for the lowest prices in their area.

However, because retail gasoline markets are likely not separated by political boundaries that separatestates, tax rate differentials may play an important role in how stations set their prices. Despite the earlieracademic interest in gasoline markets and frequent media attention garnered by gas tax hikes and gasprices around the country, relatively little attention has been paid to the effects of geographic competitionbetween states, or the size and nature of geographic markets in relation to state boundaries.

Doyle and Samphantharak (2008) look at the geographic competition at state borders in the contextof a gas tax moratorium in Indiana and Illinois in 2000. They find that pass through rates are between 60to 80 percent in general, and that there is mixed, but generally consistent evidence that the effects of taxesbeing passed across borders. However, the moratorium studied in that paper was a temporary policy thatwas advertised as such, and may not be robust to more permanent changes to tax rates. The moratoriumalso only influenced the sales tax, which is just one component of those states’ gas tax and fee structure.

This analysis, on the other hand, studies all state-level gas tax changes over a two-year period (March

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2015 through March 2017). This paper attempts to answer three important questions stemming from thisproblem. First, do stations pass through a different proportion of a tax near state borders than fartherinland? Second, does a tax hike in a bordering state affect gas prices in neighboring states? That is, do taxhikes spillover state borders? Finally, if tax spillovers are present, how large are they, and how far do theypersist into neighboring states?

Using high frequency gas price data from GasBuddy, I show that gas stations do indeed pass througha different proportion of a tax hike depending on how close they are located to a state border and theirtax rate relative to the rates of neighboring states. I find that stations within 10 miles of an out-of-statecompetitor pass about 93 percent of a tax through to prices. This pass-through increases with distanceto an out-of-state competitor. The pass-through that I find for stations more than 10 miles away fromout-of-state competitors is greater than full pass-through, around 136 percent, suggesting that retail gasmarkets show signs of imperfect competition despite the industry frequently being described as beingvery competitive.

I also show that tax changes in a neighboring state are passed through to consumers, but at a dis-counted rate compared to their own tax changes. I find that stations within 10 miles of an out-of-statecompetitor pass through about 35 percent of their neighbor’s tax changes. This pass-through diminishesas stations become farther from an out-of-state competitor.

I also search for evidence of asymmetry in two other dimensions. I find little difference for stations onthe high-tax versus low-tax side of a tax gradient, except that the pass-through grows more rapidly withincreased distance to an out-of-state neighbor for those on the high-tax side of the gradient. There is alsosuggestive evidence that stations on the high-tax side pass-through slightly more of their own tax changebut slightly less of a neighboring state’s tax change, though these effects are not statistically significant.I find that pass-through for stations within 10 miles of an out-of-state competitor does not depend ontax hikes as opposed to tax cuts. I do find that the pass-through grows faster with distance after tax hikesfor a station’s own tax changes, and that it declines faster with distance after tax hikes for out-of-stateneighbors.

2 Background

The first state to charge a motor fuels tax was Oregon in 1919. Within a decade, each state and Wash-ington, D.C. had their own motor fuels tax. As of July 2013, all 50 states and Washington, D.C. charge agasoline excise tax. In addition to an excise tax on gasoline, some states also charge the state sales tax, grossreceipts taxes, oil inspection fees, underground storage tank fees, and other miscellaneous environmentaltaxes for gasoline sales. Furthermore, some county and local governments also charge taxes on gasoline

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sales.The variation in motor fuels tax rates between states is large. This includes both the level of tax rates

and the form those taxes take. Most excise taxes and other taxes are imposed on a per gallon basis, butsome (such as a traditional sales tax) are levied as a percent of either the retail or wholesale price. Somestates may choose to substitute one type of gas tax for another for political reasons, though theoreticallythe incidence of said taxes should ultimately not be impacted by the form.1 As of April 2017, the lowesttax rate in the nation is Alaska’s, which is 30.65¢ per gallon. The highest rate in the country belongs toPennsylvania at 77.7¢ per gallon (American Petroleum Institute, 2017). While there appears to be somecorrelation in gas tax rates and neighboring state’s rates, there are still state borders that have differencesof 30¢ per gallon or more (see Figure 1).

States regularly change their gas taxes for a number of reasons. Commonly cited reasons include rais-ing revenues to fund state road maintenance and/or highways and mass transit projects; controlling con-gestion, local air pollution, and CO2 emissions; and limiting the exposure of the state or local economyfrom disruptions stemming from world oil price shocks. Many states actually legislate variable tax ratesbased on a number of factors. Some of these rates are variable simply because sales tax rates apply to gas(e.g., Hawaii, Illinois, and Michigan), and thus vary when the price of gas changes. Others, such as Penn-sylvania and Virginia, calculate a motor fuels tax as a percentage of gas prices. A few states, includingFlorida, Maryland, and Rhode Island, index their gas tax rates to the consumer price index. The frequen-cy with which these rates are updated varies across states, but most tend to update either annually orsemiannually.

If the demand for gasoline is not perfectly elastic, then at least some portion of tax increases will bepassed onto consumers. Indeed, there is a strong correlation with tax rates and average gas prices acrossstates (see Figure 2). In some cases the price gradient between two states can be quite steep, even betweentwo residential areas that are not separated by any geographic barrier. For example, the border betweenNew York and New Jersey has historically had a steep price gradient despite having portions that arefairly densely populated (see Figure 3). On December 1, 2013, New Jersey had relatively low gas taxes(totaling 32.9¢ per gallon) while New York had the third highest gas taxes in the country (68.3¢ per gallon).Interestingly, it also seems in the New York and New Jersey case that gas stations sorted to the New Jerseyside of the border.

1From this point forward the motor fuels tax rate is defined as all taxes, charges, and fees charged on gasoline sales, includingthe 18.4¢ per gallon federal tax rate unless otherwise stated.

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2.1 Gasoline supply and demand

This paper assumes that consumers respond to differences in gasoline prices, that is, that demand forgasoline is not perfectly inelastic. Evidence on this elasticity has been mixed in the literature. Most earlierstudies have found that the elasticity of demand for gasoline is quite low. For example, Hughes, Knittel,and Sperling (2008) find that the short-run elasticity of demand in the United States was between -0.034and -0.077 from 2001–2006. Similarly, Coyle, DeBacker, and Prisinzano (2012) use IRS excise tax returnsto find a price elasticity of demand of -0.07 from 1990–2009. This is significantly more inelastic than theshort-run elasticity of decades previous. (For example, Hughes et al. (2008) estimate the elasticity from1975-1980 to be between -0.24 to -0.34).

On the other hand, Levin, Lewis, and Wolak (2016) use daily expenditure data to show that the own-price elasticity is on the range of -0.29 to -0.61, much more elastic than previous studies. They also find thatconsumers’ demand is more elastic in the first couple days following a price change, because consumerswill immediately drive to the pump and fill their tanks when prices fall, but will wait to fill up whenprices rise. This effect dissipates after four to five days. Levin et al. (2016) also show that demographiccharacteristics and commuting patterns affect price elasticity across metro areas.

On the supply side, there have been relatively fewer studies. Coyle et al. (2012) find a price elasticityof supply of 0.29, suggesting that supply is relatively inelastic in the short-run. Previous papers makeassumptions on supply elasticity without any econometric analysis. For example, Congressional BudgetOffice (2003) assumes an elasticity of supply of 2 based on a Energy Information Administration (EIA)forecast. On the other hand, Davis and Kilian (2011) assume a perfectly elastic supply curve in the long-run, but perfectly inelastic supply curve in the short-run.

2.2 Behavioral responses to tax and price increases

It is also possible that a distaste for taxes may play a role in consumer demand for gasoline. Li, Linn,and Muehlegger (2014) find that a gas tax increase of 5¢ reduces gas consumption by 1.3 percent, whichis much larger than the effect from a 5¢ rise in the tax-exclusive price of gasoline. This suggests that con-sumers not only respond to higher prices, but also the source of that higher price. Stations then, mightbe less likely to pass through a large portion of a tax hike if the tax is particularly salient. Krishna andSlemrod (2003) suggest that posting a tax-inclusive price, as retail gasoline stations do, may help to masktax changes, making them less salient to consumers. Chetty, Looney, and Kroft (2009) show experimentalevidence that consumers underreact to tax changes that are not salient.

Given that consumers may be both price and tax averse, it is reasonable to assume that if a residentlives relatively close to a state border, he or she may travel across the border to buy cheaper gas. This

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notion of tax exporting would not be unique to the retail gasoline market. Mikesell (1970) was amongthe earlier papers to empirically note sales losses to bordering cities with lower municipal sales tax rates.Similarly, Chiou and Muehlegger (2014) find evidence that smokers travel to lower-tax jurisdictions tobuy cigarettes in response to tax increases. Chiou and Muehlegger find that a 10¢ tax increase for a store27.5 miles away from low-tax jurisdiction corresponds with a 7 percent decline in sales. A 10¢ tax increasefor a store 5 miles away from low-tax jurisdiction corresponds with a 32 percent decline in sales.

An interesting point to note is that Chiou and Muehlegger’s results were mostly observed on con-sumers buying cigarettes by the carton, which they could stockpile and use over several months. Export-ing of gasoline sales would not likely stretch 30 miles into a neighboring state, due the logistical difficultyin stockpiling gasoline. Because of this, the geographic size of gasoline markets may be quite differentfrom cigarette markets.

2.3 Competition in gas station prices

The final piece of the story is how gas stations set prices to compete with each other. Marvel (1978)discusses the emergence of the “gas wars” in the early 1970s, under which collusive pricing became lesscommon and increasing competition lowered gas prices in major U.S. cities. Hastings (2004) shows thatthe acquisition of an independent retailer by a vertically integrated refiner/retailer raises the price pergallon by about 5¢, suggesting that large brand name companies charge a higher price for similar products.

Hosken, McMillan, and Taylor (2008) find that stations selling at very high or low prices tend tomaintain their relative pricing position when price shocks occur, while firms closer to the mean frequentlychange their relative position. They do, however, find that gas stations do not follow any simple, staticmodel of price setting. Rather, they find that there is considerable heterogeneity in price setting behavior.

A number of papers have found evidence of price cycling, in which stations in a market experiencea sharp price increase followed by more gradual price reductions (e.g., Eckert & West, 2004; Noel, 2007;Zimmerman, Yun, & Taylor, 2013). The prevalence and frequency of these cycles appears to increase inmarkets with more small firms (Noel, 2007). Zimmerman et al. (2013) find evidence that average pricestend to be lower in MSAs that exhibit price cycling relative to MSA’s that do not cycle.

Chouinard and Perloff (2004) show that an increase in crude oil prices is the major factor driving ageneral rise in prices over several decades. However, they also show that tax variation and mergers con-tribute more to geographic variation in prices than price discrimination, cost factors, or pollution control-s. Cooper and Jones (2007) find that the strongest sources of competition are sellers of different brandsand from nearest neighbors.2

2Note also that some states and localities have specific requirements for gasoline that may influence prices. For a morein-depth look at these requirements, please refer to Appendix A.

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The nature of competition in retail gasoline markets will determine the extent to which a tax is passedthrough to prices. (Fullerton & Metcalf, 2002) provides a concise summary of expected pass-throughunder standard economic theory and several forms of competition. Under perfect competition, the long-run price is tied to the long-run average cost (a flat supply curve), and thus the entire burden of a tax willbe passed through to consumers. With an upward sloping supply curve, as we may well expect in theshort-run, less-than-full pass-through will occur. On the other hand, under imperfect competition, wemay actually see more-than-full pass-through of taxes (see also, Katz & Rosen, 1985).3 Several studies havefound empirical evidence of this more-than-full pass-through (e.g., Poterba, 1996; Besley & Rosen, 1999;Kenkel, 2005).

Empirical evidence on pass-through for gas taxes has generally suggested high rates of pass-through.For example, Alm, Sennoga, and Skidmore (2009) show that excises taxes are completely passed throughto prices within the first month of a tax change, though find that the pass-through is less than full in morerural states. Chouinard and Perloff (2004) find that 50 percent of the federal excise tax is passed through,75 percent of ad valorum taxes are passed through, and about 100 percent of state excise taxes are passedthrough to prices. Marion and Muehlegger (2011) suggest that the pass-through rate declines when thesupply chain is constrained. Doyle and Samphantharak (2008) find that about 70 percent of savings fromtax decreases are passed through, while 80–100 percent of tax hikes are passed through.

Standard incidence theory predicts that the statutory and economic incidence of a tax are independentfrom one another. However, Kopczuk, Marion, Muehlegger, and Slemrod (2016) find that diesel taxesare passed through to a greater extent when the point of collection for a tax is further up the supply chain,one in which the wholesale price for diesel would be affected. This means that the various types of stategasoline taxes may be passed through at different levels depending on the point of collection.

Finally, there is a question of how proximity to a border influences prices. If retail gasoline marketssimply ended at state borders, then we would expect the incidence of a tax to be completely containedwithin a state. We might also expect that proximity to a border alone would have little impact on inci-dence.

However, simply looking at how prices change when a tax change occurs suggests this is not true. Forexample, on January 1, 2017, Michigan raised their state excise tax by 7.3 cents per gallon (cpg). Figure4 shows how prices evolved over the following weeks for stations in Michigan and one of its neighbors,Ohio. Stations are grouped based on being within 10 miles from a competitor in the other state (“borderstations”), or farther from the border (“interior stations”). Prior to the tax change, all four groups weretrending similarly. However, in the first week of the tax hike, both Michigan groups increased their price,

3While this may run counter to conventional wisdom, firms, as profit maximizers, may raise prices higher than an increasein tax to compensate for the expected fall in demand.

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while both Ohio groups maintained a similar price to the previous week. The interior stations in Michi-gan increased their price by a couple percent more than the border stations did. The following week, allbut one group saw a similar reduction in prices (likely due to shifting cost factors). However, the borderstations in Ohio actually increased their prices, matching the total price change of the Michigan borderstations. Over the course of the next few weeks, the relationship between the four groups’ prices appearsto have stabilized, with Michigan’s interior stations showing the highest price growth (or rather, smallestreduction), followed by the stations on the Michigan side of the border, then the stations on the Ohioside of the border, and then Ohio’s interior stations.

Overall, the anecdotal evidence from the Michigan tax hike suggests that markets do, in fact, cross statelines, and that the incidence of a tax not only differs within the state, but also may spillover to residentsof neighboring states, as well. This serves as motivation for the analysis in this paper.

3 Conceptual framework

In the classic Bertrand model of competition, firms compete on price, with the Nash equilibriumsuggesting that firms price a homogeneous good at marginal cost. Assuming the cost of production isrelatively similar for viable gas stations in close proximity, we should then expect the price of gasoline atthese stations to be highly correlated.

However, there is also a spatial component of this competition. In the Hotelling location game, firmsselling homogeneous goods at the same price ultimately choose to locate in the same place. If firms havethe ability to change their price, then they may choose to locate elsewhere. For example, a gas stationmay choose to locate near a residential neighborhood, thus potentially lowering the cost of gas in termsof driving farther to the pump. This would allow them to potentially charge a higher price than station-s farther away. Through this choice of location, gas stations are able to provide some level of productdifferentiation.

In a long-run equilibrium, we should expect that stations sort in terms of location and price such thatall firms make zero economic profits. However, in the short-run, there is not frictionless movement offirms. Opening a gas station in a particular location requires several nontrivial preparations, includingdigging underground storage tanks and meeting all necessary safety regulations. As such, changes to theeconomic environment may have a differential impact in the short-run.

Consider a local market surrounding an arbitrary political boundary which is costless to traverse. As-sume that residents are uniformly distributed along the road, and will purchase gasoline at the cheapestprice available to them, including the costs associated with travel to a gas station. At a long-run equilib-rium, Station 1 chooses to locate in State A, while Station 2 chooses to locate in State B. There may or

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may not be a price gradient between the two stations, but assume that they have reached an equilibriumwhere stations are maximizing profits given residents’ location and possibly heterogeneous preferences(see Figure 5a).

At time T , State A chooses to raise its motor fuels excise tax rate by ∆τ , while State B’s rate remainsconstant. Under full pass-through, the price at Station 1 rises by ∆τ . If the price at Station 2 were toremain constant, then residents would shift towards buying gas at Station 2 (Figure 5b). This includesresidents of State A crossing the border to buy cheaper gas at Station 2. But notice that this new pricingscheme likely does not maximize profits for either firm. In fact, in a differentiated goods Bertrand model,a shock to cost for one firm translates into higher prices for both firms at equilibrium.

Thus the tax change alters the optimal pricing decision for Station 1 and Station 2, since they nowneed to consider the additional cost of the tax hike. We should expect that the price of gas at Station 1 will(weakly) rise in response to the tax. However, Station 2 faces no such tax hike in State B. In a perfectlycompetitive market, we might expect the price at Station 2 to remain unchanged. However, in the limitedcompetition provided in this small market, Station 2 could raise its price in response to the rise in priceat Station 1 and make greater profits. In this sense, the tax hike in State A has spilled over into State B(Figure 5c).

The degree to which a tax hike is actually passed through into higher prices depends on the relativeelasticities of supply and demand determined by the market. Notice that equilibrium in a competitivemarket in the presence of a tax (borne on firms) requires that quantity demanded, QD(P ), must equalthe quantity supplied,QS(P, τ). Totally differentiating this equilibrium condition yields

∂QD

∂P∗ ∂P∂τ

=∂QS

∂P∗ ∂P∂τ

+∂QS

∂τ,

which rearranges to the pass-through tax condition4

∂P

∂τ=

εS

εS − εD. (1)

If supply is perfectly inelastic (εS = 0), then there is no change in price, and Station 1 bears theentire burden of the tax. If demand is perfectly inelastic (εD = 0), then the entire tax hike, ∆τ , is passedthrough to consumers in the form of higher prices.

Analyzing ∂P∂τ

can thus reveal some information about the relative elasticities of demand and supply.It can also help to describe the competition underlying retail gasoline markets. Finally, it can help toinform policy decisions. For example, it would help explain who benefits from a gas tax holiday, or who

4This rearrangement assumes that firms treat price changes the same as tax changes for making production decisions, i.e.∂QS/∂P = −∂QS/∂τ .

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bears the burden of raising rates at certain times of year. Furthermore, seeing how ∂P∂τ

varies near stateboundaries can tell us how the incidence of a tax is distributed geographically.

4 Empirical model

The analysis uses a panel approach to analyze the degree to which taxes are passed through to pricesaround state borders. A naı̈ve panel model would regress the price at station i at time t on its relevant taxrate, τit:

Priceit = βτit + αi + λt + εit, (2)

where εit is a mean zero error term, αi are station fixed effects, and λt are time fixed effects.It could be the case that stations with high tax rates always price higher than other stations that have

lower tax rates. This does not necessarily reflect the true pass-through of tax, but rather just an inherentdifference between stations. I therefore include the station fixed effects to capture any time-invariant vari-ation in pricing between stations.5 Second, economic changes may happen simultaneously with tax ratechanges, driving price changes. I include time fixed effects to capture all national shocks to prices, such asthose stemming from oil price shocks or seasonality. These additions build a more accurate counterfac-tual, allowing β to describe how a one cpg tax rate hike at station i impacts the price relative to if stationi did not face the tax hike at a particular point in time.

Next, since the goal of the empirical model is to isolate the variation in tax rates at gas stations nearstate borders and see how that variation influences variation in prices, I include an interaction betweentax rate and distance to an out-of-state competitor:

Priceit = βτit + δτit ×Distanceit + εit. (3)

The coefficient δ shows how the pass-through changes when stations are closer or farther from out-of-state competitors. Because this paper focuses on what happens near state borders, I take an additionalstep and look at what happens specifically at stations within 10 miles from an out-of-state competitor byincluding a dummy variable for stations being within 10 miles and interact it with the tax variables. I alsoinclude the tax rate of stations more than 10 miles from an out-of-state competitor to be able to check fora difference in pricing decisions between border and interior stations.6

5Note that including station fixed effects means that the distance variable captures how changing a station’s distance to anout-of-state competitor impacts the pass through. This is identified off of stations opening or closing in the station’s geographicmarket (since the station itself is not mobile). While this is not overly common in the relatively short time frame covered bymy sample, it does eliminate the possibility that inherent differences in stations that locate closer to borders are driving thedistance result.

6I do not calculate distance to an out-of-state competitor for stations more than 10 miles away, and so do not include

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There may be some lingering concerns that national economic trends do not accurately describe thecosts facing different regions. For example, a new pipeline may cut costs in the Southeast but have noeffect on costs on the West Coast. Thus, I interact the week fixed effects with a set of dummy variables for7 Petroleum Administration for Defense Districts (PADDs).7 These regions were originally designated tohelp organize the allocation of fuels. They are still used for data collection purposes, and stations withinthe same PADD often face very similar supply chain shocks. As a result, these fixed effects provide a moredetailed description of regional cost factors than the national week fixed effects.

Then the main estimating equation becomes:

Priceit = γ · 1(< 10 miles) + β ·Own Tax Rateit × 1(< 10 miles)+δ ·Own Tax Rateit ×Distanceit × 1(< 10 miles)+η ·Own Tax Rateit × 1(> 10 miles) + αi + λpt + εit,

(4)

where λpt refers to the PADD-week fixed effects and 1(< 10 miles) is a dummy variable equal to one ifthe station is within 10 miles of an out-of-state competitor, while 1(> 10 miles) is equal to one if thestation is farther from out-of-state stations. I cluster all standard errors by state-month to allow for thepossibility that residuals are correlated within states over time.

Exploiting only changes in the tax rate may bias the results, since neighboring states may simultane-ously change their tax rate. Notice that if both states on either side of the border raise their tax by thesame amount, there is no change to the relative price between the two stations. Therefore, I measure thetax differential between the station’s home state and its competitors in nearby states,

τ -diffit = τit −∑j∈J

τjt · wjt, (5)

where J is the set of out-of-state stations within 10 driving miles of station i and the weight vector wjtis given by the inverse driving distance between station i and j over the sum of inverse distances for allout-of-state stations within 10 driving miles of station i. For a station near a single state border, the taxdifferential is just the difference between the two state’s tax rates. This helps to eliminate the possibilitythat neighboring policy changes are biasing the estimated pass-through.

I also further augment the models to test for various forms of asymmetry in the data. I do this byincluding measures of out-of-state competitors’ tax rates, interactions with which side of the tax gradienta station resides, and interactions with dummies for whether or not the most recent tax change was a tax

distance as an explanatory variable for interior stations.7There are 5 PADDs—East Coast, Midwest, Gulf Coast, Rocky Mountain, and West Coast. However, the East Coast

PADD is further divided into New England, Central Atlantic, and Lower Atlantic.

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hike or a tax cut.

5 Data and GIS analysis

Daily gas price data is collected from GasBuddy, a popular gas price-tracking platform. The platformis built around a mobile app available for both Android and iOS devices. Users of the app can both entercurrent prices for a station they are at and search for local gas prices based on entries from other users.In addition to users entering prices, some station or regional managers directly upload their prices toGasBuddy. The platform boasts a community of over 60 million users and lists over 140,000 retail gasstations in the United States, Canada, and Australia.

The data from GasBuddy also include latitude and longitude coordinates. This allows me to plotthe precise location of each gas station in my sample. Using ArcGIS, I generate a 10-mile buffer aroundeach station and build a list of potential competitors for each station. This likely provides a conservativeestimate of which stations actually compete with one another, as 10 miles as the crow flies serves as a lowerbound for the true driving distance between two stations.

Distances between stations is calculated using the Open Source Routing Machine (OSRM) as drivingdistance between the reported longitude and latitude of the origin and destination stations (see Luxen& Vetter, 2011).8 Using the driving distance instead of the Euclidean distance more accurately describesthe distance drivers would consider when deciding which station to buy gasoline from. Notice that twostations could be located directly across a major highway from one another, but inaccessible from theother without driving down the highway to the next exit and backtracking. I consider all stations that fallwithin a 10-driving-mile buffer to be competitors for a station. I measure both the number of competitorsand the number of out-of-state competitors within those 10 driving miles. I also measure the distance tothe closest competitor and closest out-of-state competitor. Of all stations in my sample, just under 10percent have an out-of-state competitor within 10 driving miles. A histogram of competitors separatedby in-state and out-of-state is shown in Figure 7.

Motor fuels tax rates are collected from the American Petroleum Institute (API). The API reportsquarterly on state excise tax rates, as well as other taxes and fees levied on a per gallon basis for each ofthe 50 states and Washington, DC. When there is a change in rates across quarters, more precise data onwhen the policy change took effect is found via state department of revenue websites. Similarly, for statesthat observe local variation in tax rates, more detailed information is found from those states’ departmentof revenue websites. For states that levy a sales tax on gas based on a percent of the retail price, the cpg

8OSRM is essentially a free alternative to the Google Maps API, which allows for web or mobile apps to search for direc-tions, distances, and estimates of arrival times, with very similar results.

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equivalent is calculated using the retail price and other available information on rates. I calculate thetax differential for a particular station as its own tax rate minus the tax rate of out of state neighbors asoutlined above.

Crude oil is an essential ingredient in all forms of retail gasoline. Therefore, the price of crude oilreflects the national cost of production of retail gasoline. Spot prices for crude oil are reported on a dailybasis by the EIA based on the price of West Texas Intermediate (WTI), the underlying commodity of oilfutures traded on the New York Mercantile Exchange, in Cushing, Oklahoma, a major trading hub forcrude oil. This particular price of crude is closely related to other measures of crude prices, including thatof Brent crude (extracted from the North Sea) and the OPEC basket. I average these spot prices acrossweeks to correspond to the weekly retail gas prices.9

The sample used for this paper contains weekly observations from over 137,000 stations in the 48contiguous states and the District of Columbia.10 The sample runs from March 2015 through March2017, with week 1 of my sample corresponding to the week of March 22, 2015, and the final week (week103) corresponding to the week of March 5, 2017. Summary statistics of relevant variables can be seen inTable 1.

A histogram of prices is shown in Figure 6.11 The average price in the continental United States duringthe sample was about $2.25. Prices started around $2.40 in March 2015, and rose to a peak near $2.78 inJune 2015. Then prices experienced a sharp decline to a low price of $1.70 in February 2016. Since thattime, prices have generally risen again, though have not yet caught up to their previous highs. Thesemovements in price closely track the WTI crude oil spot price (see Figure 8).

Over the same period, average tax rates followed a similar pattern, as well. Because state tax ratesgenerally only change at the start of a fiscal quarter, most of the variation in tax rates happens at severaldiscrete moments in time. Nonetheless, it is clear that there were some tax hikes in the first half of 2015,followed by some reductions in tax rates in the later half of the year. Tax rates in 2016 were relatively stablewith only some small hikes in some states. The first quarter of 2017 saw some large hikes (see Figure 9).While the magnitude of changes to the average tax price are small, this is largely because only a limitednumber of states make tax rate changes at any particular point in time. Some periods also see tax hikes

9Note that in models that use week fixed effects, the spot price for crude oil adds no additional information, since it isconstant for all stations within a week.

10I use week observations to mitigate potential data issues from station prices not being uploaded for a particular day duringthe sample. While many stations do receive daily updates, some only update a few times a week. Averaging across a week allowsme to avoid oversampling stations that are more frequently updated.

11Note that there are some outliers at both ends of the distribution, namely 14 observations with a price under $1 and 208observations with prices above $5.50. These are likely the result of user error uploading prices to GasBuddy. Dropping theseobservations have a negligible effect on the results of the analysis. Some of the quoted prices, especially those of stations thatreceive fewer updates per week may similarly be susceptible to data error, though the prices fall within a seemingly reasonablerange.

13

in some states, but cuts in others. The reasons for these tax hikes and cuts are outside the scope of thisanalysis, but are likely to differ by state, as well.

6 Results

6.1 Tax pass-through near state borders

The first four columns of Table 2 show how the variation in tax rates affect prices within 10 miles ofa state border. Without any fixed effects, I find a pass-through of over 130 percent, though controllingfor station effects reduces that pass-through to 81 percent. Once controlling for station and week fixedeffects, I find a pass-through of about 94 percent, close to full pass-through. There is also suggestive,though insignificant evidence of pass-through rising with distance to the border.

When I more accurately account for regional cost factors with week-PADD fixed effects in column(4), I find that the pass-through is lower, at 71 percent. This is significantly less than full pass-through,with a 95 percent confidence interval of 58 to 84 percent. Again, the pass-through rises as distance to anout-of-state neighbor increases, but economically this effect is negligible. Using the pass-through rate ruledescribed in equation 1, and plugging in the estimate of the elasticity of supply from Coyle et al. (2012),I estimate a price elasticity of demand of about -0.12. This is roughly 2–4 times as high as some of themore recent estimates. Note, however, that this elasticity refers to the elasticity of demand for a particularstation, not necessarily the elasticity of demand for gasoline more generally.12

In column (5) I test how this pass-through compares to the pass-through of stations closer to theinterior of the state. For stations not within 10 miles of an out-of-state competitor, I find a pass-throughof almost 135 percent, with a 95 percent confidence interval spanning from 109 to 161 percent. Becausethis is greater than full pass-through, the data suggest that retail gasoline markets (at least at the interiorof states) may be more accurately described by imperfect competition as opposed to perfect competition.13

For those stations that are within 10 miles from an out-of-state competitor, the pass-through is about 30percentage points lower, suggesting that stations closer to state borders are not able to pass as much ofa tax increase through to prices.14 The pass-through rises as distance to an out-of-state competitor rises,as we might expect. Based on the estimated coefficient, a station 10 miles from a border is able to passthrough over 8 percent more of a tax than a station at the border. This evidence, taken together, suggeststhat stations near state borders face a different competitive environment than those located in the interiorof a state.

12Furthermore, because the pass-through rule is based on the assumption of a competitive market, it is important to notethat this estimate is likely only a noisy approximation.

13I explore possible models of imperfect competition and their predictions for tax pass-through in Appendix B.14The effect for stations within 10 miles is not statistically significantly different from full pass-through in this model.

14

In both of the previous two models, stations closer to a state border appear to observe more elasticdemand than the overall population of stations. The difference in elasticity could feasibly be due to res-idents crossing state borders to purchase cheaper gas. This would provide stations near state borders anadditional competitive element to their market that those in the interior of a state do not need to worryabout.

When I use the tax differential instead of the tax rate in column (6), I find qualitatively similar results,though a much smaller pass-through for border stations. I find that stations pass through just 43 percentof an increase in their relative tax price (compared to that of their out-of-state competitors). I find nosignificant effect of distance to the border in this particular model.

There are two possibilities for why changes in the tax differential show a weaker correlation thanchanges in the actual tax rate. First, it is possible that simultaneities in tax rate changes for adjacent stateshelp raise the price on both sides of a border, mitigating the pressure of border stations to keep theirprices low. Thus a change in the tax differential more accurately shows how a shift in relative tax ratesaffects prices.

The second possibility is that the tax differential captures two possible changes that may yield differentresponses. As noted above, a change in the tax differential can originate in a change to the home state’stax rate, or a change in the tax rate of neighboring state competitors.

6.2 Interstate tax competition

I explicitly test for the possibility of asymmetry in response to tax differential by origin of the changein column (7). I include the tax rates of out-of-state competitors and the interaction between competi-tors’ tax rates and distance to an out-of-state competitor. Under this specification, I find a pass-throughfor own taxes of 93 percent for border stations. This is about 43 percentage points lower than the pass-through of stations in the interior of a state.15

I also find a much stronger distance effect in this specification. It suggests that stations 10 miles froman out-of-state competitor pass through about 23 percentage points more of a tax than those directly nextto their out-of-state competitors on the border. This means that over half the difference in pass-throughbetween border and interior stations can be accounted for by the first 10 miles away from out-of-statestations.

The coefficient on the out-of-state competitor’s rate suggests that about 35 percent of a neighbor’stax change is passed through to prices. This estimate is significantly lower than pass-through of a sta-tion’s own tax. This makes sense for two reasons—first, stations have no obligation to comply with an

15This estimate is not significantly different from full pass-through, but it is significantly lower than the estimate of pass-through for interior stations.

15

out-of-state tax change and only change their price to maximize profits given the shift in competitivepricing, and second, most stations still compete with more in-state competitors than out-of-state com-petitors. Nonetheless, this provides compelling evidence of tax spillovers. The estimate also suggests thatif a neighboring state were to simultaneously raise their rate by the same amount as the home state, thenmost (over 80 percent) of the difference in pass-through between border and interior stations is eliminat-ed. This is reassuring, since the market around a border would face no differential tax treatment in thatcase.

The coefficient on the interaction between out-of-state taxes and distance suggest that this pass-throughof neighbor’s tax changes decreases with distance to an out-of-state neighbor, which we should expect giv-en that stations are passing through neighbor’s tax changes. Nearly half of the out-of-state pass-throughis wiped out for stations 10 miles from an out-of-state competitor.

Column (8) actually shows the same model, just with a reorganization of the variables to confirm thatcontrolling for neighbors’ tax rates reconciles the τ -diff model with the tax rate model.

6.3 Other sources of asymmetry

I first test whether there is asymmetry that depends on which side of a tax gradient a station is located.For example, we might expect that a station on the low-tax side of a border has a competitive advantageover competitors on the high-tax side, and thus can pass a higher amount of the tax through. On theother hand, a station on the high-tax side of the border may face less pressure to keep prices low.

Table 3 shows the results from regressions testing for this form of asymmetry. Column (1) shows asimplified version of the model in which others’ rates are not considered. In this model, stations on thehigh-tax side of a gradient pass through slightly less of a tax than stations on the low-tax side, but theeffect is economically small (about 4 percentage points). I do find that stations on the high-tax side of theborder have a stronger distance effect, suggesting that pass-through rises over twice as fast with distanceto an out-of-state competitors than on the low-tax side.

Column (2) shows the full model including out-of-state competitors’ rates. In this model, the high-tax side actually passes through more of a tax (about 12 percentage points), but the effect is not statisticallysignificant. The distance effect is again over twice as strong on the high-tax side of the border relative tothe low-tax side. In terms of passing through neighbors’ tax changes, I find no asymmetry between thelow- and high-tax side of a border, though the estimated coefficients suggest that stations on the high-taxside of a border may pass through a lower amount of their neighbors’ tax changes.

Columns (3) and (4) reiterate the previous two models using the τ -diff specifications. Notice thatcolumn (3) is different from column (1) in that it implicitly combines own-state tax changes with out-of-state changes of the opposite sign. Column (4)’s results mirror those in column (2). A simple summary

16

of these effects based on the estimated coefficients can be seen in Table 4.Next, I look to see whether there is asymmetry in pass-through depending on whether the state has

experienced a tax hike or a tax cut. Recent research has noted that prices tend to respond more to taxincreases than decreases (e.g., Benzarti, Carloni, Harju, & Kosonen, 2017). The models presented forthese tests rely on information about the most recent in-sample tax change. As a result, the number ofobservations for these tests is less than half of the sample used for the previous tests. Because of this, someof the point estimates are different from earlier estimates. I prefer the previous estimates in terms of theirlevels, since they use a more complete dataset. Nonetheless, this sample is useful for comparing recenttax hikes and recent tax cuts. That is, the relative estimates for hikes and cuts are useful in testing for anasymmetric response. A station is said to be responding to a tax hike (cut) if the most recent tax changewas an increase (decrease) in tax rate.

In the restricted model in column (1) of Table 5, I find no asymmetry between tax hikes and cuts.Similarly, in the full model of column (2), I find little difference in pass-through for hikes versus cuts forall stations within 10 miles of an out-of-state competitor. In fact, the estimated coefficients suggest thatpass-through is only about 5 percentage points lower under a tax hike to a station’s home state than undera tax cut, a difference that is not statistically significant. However, I do find that the pass-through risesnearly 60 percent faster with increased distance to an out-of-state competitor for tax hikes than tax cuts.

Similarly, for changes to neighboring states’ tax rates, I find that tax hikes are passed through onlyabout 5 percentage points more than tax cuts, again an insignificant estimate. I also find that the pass-through of the neighboring state’s tax change decreases over 70 percent faster with increased distance toan out-of-state competitor for tax hikes as opposed to tax cuts.

Interestingly, I find that the pass-through for stations more than 10 miles from an out-of-state com-petitor is higher for tax hikes than tax cuts, though the effect is economically quite small. Columns (3)and (4) again reiterate the previous two models in the τ -diff specifications.

7 Discussion and Long-run Implications

The motor fuels tax is often referred to as a “benefit tax,” since drivers essentially pay to maintain roadsand fund other transportation-related expenses. However, the results of the analysis suggest that taxes arenot uniformly passed through to consumers. This means that a tax hike potentially disproportionatelyaffects different geographic areas of a state.

Furthermore, the results suggest that gas tax hikes can spill over state borders, raising prices for res-idents in neighboring states with no added benefit to their state of residence. This means that stationsnear the border in neighboring states receive a windfall, while residents of those areas are forced to pay

17

higher prices. Even if those residents can travel to a different station farther towards the interior of thestate and still purchase gas at the same price, they incur the additional travel cost.

One possible mitigating factor for this spillover is creating a more flexible state motor fuels tax. Thiswould allow a state to raise or lower the motor fuels excise tax closer to state borders in an effort to main-tain an more equal rise in price across the state. However, this is unlikely to be politically popular, sinceit would have the appearance of treating different areas within a state unequally due to unequal tax rates.

Allowing counties to manage their own motor fuels taxes may also help alleviate the spillover, butthose revenues would be collected by the county, which may not be able to achieve the same transporta-tion projects as the state could with the same level of funding. Transportation projects spanning multipledistricts may also become more difficult, as they will require coordination and agreement across multiplegovernments.

Some states actually do allow city or county local option motor fuels taxes. For example, in Oregon,two counties impose an optional county tax rate of 1 and 3 cents per gallon. Both of these counties arelocated along the border with Washington, which has a relatively higher motor fuels tax rate. Portland,which is located right next to the state border, voted to impose a temporary (4-year) city rate of 10 cents pergallon that took effect on January 1, 2017. However, the reason for enacting that tax appears to be raisingrevenue to fund a number of road projects, included much needed repairs. Nonetheless, opponents ofthe bill cited incentives of drivers to buy gas elsewhere as one of their main arguments against the tax.

Note, however, that simply allowing local option taxes will do little to avoid the short-run pass-through described by the analysis in this paper. Rather, a county would need to be able to quickly reactto a neighboring or home state tax change such that the two rates could roll out simultaneously to mit-igate the shifting incentives.16 This does, of course, create other tax gradients at county borders within astate, but perhaps keeping the gradients smaller by spreading the tax change out geographically helps tomaintain greater price stability.

Note also that localities would also need to have enough policy room to maneuver the necessarychanges to balance their tax rate. That is, they would need to be able to raise or lower their rate enoughto match an appropriate portion of their neighbor’s change. It would be impossible to lower a local op-tion rate past the zero bound without the state allowing the county to reduce the applicable state rateor actually subsidizing gas consumption. Appropriate subsidies may not be financially feasible for mostlocalities.

There are also potential long-run implications of the results. First, if station owners are risk averse andfear that future tax changes will give them a competitive disadvantage, they may choose to locate farther

16For comparison, Washington County in Oregon enacted their 1 cpg county gas tax in 1977, and it has not been used toreact to statewide tax changes in Oregon or Washington.

18

towards the interior of a state. Furthermore, given that stations on the low-tax side of a border are ableto pass a higher proportion of a tax hike through to consumers, we might expect that stations will sort tothe low-tax side of the border in the long-run.17 Anecdotally there is some evidence that this may happen.As noted earlier, many more stations have decided to locate on the New Jersey side of the New York/NewJersey border, at least partially due to the historical gap in tax rates between the two states.

Similarly, if we look at the distribution of stations around the Michigan/Ohio border mentioned inthe motivating example (see Figure 10a), it is clear that there are many more stations on the Ohio side ofthe border than the Michigan side. Part of this is surely due to Toledo, OH being a large city just overthe border, but Michigan has also historically had higher tax rates than Ohio. We can also look at theGeorgia/Florida line, where Florida historically has had higher gas tax rates (see Figure 10b). Along thisborder there are no major cities, though the outskirts of Jacksonville, FL are close to the state line. Still,we see more stations on the Georgia side of the border, where tax rates have historically been lower.

These instances are anecdotal, and likely rely on historical factors other than just the tax rate (suchas the population center of Toledo on the Ohio side of the border). A more appropriate test would beto see if stations are more likely to shut down closer to the border when taxes rise. While the time framein my sample is likely too short to capture historical tax gradients and test for this long-run sorting ina rigorous manner, I can show some suggestive evidence running a couple simple models. First, I run aspecification similar to that of the main analysis, but change the dependent variable to the number ofin-state competitors. I find that for interior stations, a tax hike of 10 cpg leads to stations having about0.39 fewer in-state competitors within 10 miles. This effect is slightly stronger at state borders (about0.48 stations fewer), though the difference is not statistically significant. Furthermore, if I regress theprobability of shutting down on both home and competitors’ rates and distance interactions for stationswithin 10 miles of the border, I find no effect of own tax rates, but I do find that there is a small negativeeffect of out-of-state competitors’ rates on shutting down.18 This effect dissipates with distance from anout-of-state competitor. These results should be taken with a grain of salt, since the data is not designed tomeasure long-run effects. Nonetheless, they do suggest that a more in-depth analysis of long-run impactof tax changes on border areas could be helpful for policymakers.

8 Conclusion

This analysis shows strong evidence that geographic markets span state borders. While this shouldcome as no surprise, the magnitudes of tax pass-through measured suggest that own and neighbors’ tax

17We should not expect that all stations will sort to that side of the border, since locating away from other stations may bea useful way of differentiating.

18This model includes state and week-PADD fixed effects.

19

rates are an important factor in pricing decisions for firms located near borders. I find that pass-throughat the interior of a state is about 136 percent, while stations near the border have a lower pass-through ofabout 93 percent. This difference highlights the competitive pressure from stations across the border thatdo not experience a tax change, but serve the same market as border stations facing the tax change.

The results of the analysis suggest that retail gasoline markets, while surely exhibiting some signs ofbeing particularly competitive (such as historically low profit margins), may in fact be more monopolisticin nature, where firms differentiate at least in part via location. This imperfect competition allows stationsto pass through different amounts of a tax depending on what price maximizes their profits.

Out-of-state neighbors facing no policy change of their own can benefit from these tax changes, rais-ing their own prices in response to the price rise of their out-of-state competitors in their market. I mea-sure this pass-through to be roughly 35 percent. The pass-through is lower for out-of-state stations fartherfrom a border.

The results suggest that state policymakers must take care when choosing their tax rates, carefullyconsidering neighboring tax rates and how a policy shift will affect residents and stations in different cor-ners of the state. The results also suggest that neighboring states may be able to mitigate tax competitionaround their borders by coordinating policy shifts to reduce the impact at state borders.

In the future, incorporating information on where people live and work and fitting those commondestinations into the model may provide an even more accurate estimate of tax pass-through. More de-tailed tax rate data at the local level may help provide additional variation. There is no economic reasonwhy local differences in tax rates, such as a county or city motor fuels excise tax would not generate similarresponses along their respective borders. With a longer time horizon, it would be more feasible to studythe long-run impact of a tax hike. Finally, using station-level daily revenue data would allow for a muchmore in-depth analysis of retail gasoline elasticities that could be useful in characterizing the market andinforming policy.

20

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23

Table 1: Summary statistics

Panel A: All stations

Variable Mean Std. Dev. Min. Max. N

Price ($) 2.248 0.377 0.01 9.99 11,295,458Total Tax Rate ($/gallon) 0.480 0.093 0.329 0.766 11,295,458< 10 miles from out-of state competitor 0.094 0.292 0 1 11,295,458Distance to nearest competitor (miles) 0.773 1.233 0 10.000 11,228,334Competitors< 10 miles 87.263 92.243 0 494 11,295,458WTI Crude Spot Price ($/barrel) 46.267 7.425 28.144 60.072 11,295,458

Panel B: Stations within 10 miles of an out-of-state neighbor

Variable Mean Std. Dev. Min. Max. N

Price ($) 2.226 0.332 0.01 5.99 1,059,401Total Tax Rate 0.477 0.098 0.329 0.766 1,059,401Distance to nearest competitor 0.664 0.984 0 9.946 1,059,401Dist. to nearest out-of-state competitor 5.874 2.506 0.001 10.000 1,059,401Competitors< 10 miles 87.735 92.268 0 494 1,059,401Out-of-state Competitors< 10 miles 22.685 34.596 1 271 1,059,401Notes: Distance to nearest competitor is only calculated for those with a competitor within 10 driving miles.

24

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25

Table 3: Does side of the tax gradient matter?

Dependent Variable - Station weekly average gas price

(1) (2) (3) (4)

1(< 10 miles) 0.105*** -0.0408 0.633*** -0.0408(0.0349) (0.0387) (0.0592) (0.0387)

×Own Tax Rate 1.077*** 0.923***(0.0954) (0.0739)

×High-tax Side -0.0438* 0.124(0.0230) (0.110)

×Distance 0.00524* 0.0131***(0.00269) (0.00460)

×High-tax Side×Distance 0.0066*** 0.0159**(0.0026) (0.0080)

×OoS Competitors’ Tax Rate 0.460*** 1.383***(0.097) (0.115)

×High-tax Side -0.155 -0.031(0.111) (0.023)

×Distance -0.0113** 0.0018(0.0047) (0.0027)

×High-tax Side×Distance -0.0099 0.0059**(0.0082) (0.0026)

×τ -diff 0.773*** 0.923***(0.129) (0.074)

×High-tax Side -0.793*** 0.124(0.187) (0.110)

×Distance -0.0119** 0.0131***(0.0052) (0.0046)

×High-tax Side×Distance 0.0260* 0.0159**(0.0134) (0.0080)

1(> 10 miles)×Own Tax Rate 1.350*** 1.361*** 1.294*** 1.361***(0.133) (0.133) (0.133) (0.133)

Station FE X X X XWeek× PADD FE X X X X

Observations 11,295,458 11,295,458 11,295,458 11,295,458R2 0.954 0.953 0.954 0.954Notes: “< 10 miles” means the station is less than 10 miles away from the nearest out-of-state gas station. “Distance” is measured inmiles from nearest out-of-state station. “OoS Competitors’ Tax Rate” is a weighted average of out-of-state competitors’ tax rates basedon distance between the two stations. “High-tax side” refers to being on the higher side of a tax gradient. Standard errors are clusteredby state-month and are shown in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

26

Table 4: Summary of estimated effects around a state border by side of tax gradient

Low-tax side of border High-tax side of border

1 cpg tax hike on low-tax side of border ∆P = 0.92 ∆P = 0.31δ = 0.013 δ = −0.021

1 cpg tax hike on high-tax side of border ∆P = 0.46 ∆P = 1.05δ = −0.011 δ = 0.029

Notes: Estimates are in cents per gallon (cpg). δ refers to the change in price (in cpg) for each mile the station is farther froman out-of-state competitor.

27

Table 5: Do stations respond to tax hikes and cuts differently?

Dependent Variable - Station weekly average gas price

(1) (2) (3) (4)

1(< 10 miles) -0.049 -0.0890 0.852*** -0.0890(0.069) (0.076) (0.138) (0.076)

×Own Tax Rate 1.974*** 1.712***(0.246) (0.260)

×Hike -0.0059 -0.0497(0.0128) (0.0354)

×Distance 0.0008 0.0171(0.0056) (0.0106)

×Hike×Distance 0.0024** 0.0103***(0.0010) (0.0032)

×OoS Competitors’ Tax Rate 0.340*** 2.052***(0.093) (0.259)

×Hike 0.0555 0.0058(0.0408) (0.0145)

×Distance -0.0149* 0.0023(0.0079) (0.0056)

×Hike×Distance -0.0107** -0.0004(0.0042) (0.0016)

×τ -diff -0.266* 1.712***(0.146) (0.260)

×Hike 0.107*** -0.0497(0.0382) (0.0354)

×Distance 0.0121 0.0171(0.0124) (0.0106)

×Hike×Distance 0.0061* 0.0103***(0.0032) (0.0032)

1(> 10 miles)×Own Tax Rate 1.904*** 1.913*** 1.679*** 1.913***(0.289) (0.289) (0.276) (0.289)

×Hike 0.0179 0.0181* 0.0165 0.0181*(0.0109) (0.0109) (0.0108) (0.0109)

Station FE X X X XWeek× PADD FE X X X X

Observations 4,593,947 4,593,947 4,593,947 4,593,947R2 0.961 0.962 0.961 0.962Notes: “< 10 miles” means the station is less than 10 miles away from the nearest out-of-state gas station. “Distance” ismeasured in miles from nearest out-of-state station. “OoS Competitors’ Tax Rate” is a weighted average of out-of-statecompetitors’ tax rates based on distance between the two stations. “Hike” refers to the most recent tax change in the statebeing a tax hike. Standard errors are clustered by state-month and are shown in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

28

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29

Figure 2: Gas price gradients in the Eastern United States, December 1, 2013

Source: GasBuddy

30

Figure 3: Gas prices along border between New York and New Jersey, December 1, 2013

Source: GasBuddy

31

85

90

95

100

105

Pric

e in

dex

(Wee

k 93

=10

0)

90 92 94 96 98 100Week

MI Interior MI BorderOH Border OH Interior

Figure 4: Changes in prices stemming from a 7.3 cpg tax hike in Michigan (Week 94 = Jan. 1, 2017)

32

(a) Pre-tax change

(b) Post-tax change, full pass-through, no spillover

(c) Post-tax change, re-optimized prices

Figure 5: A theoretic tax change near a state border

33

0

.5

1

1.5

Den

sity

0 2 4 6 8 10Station Weekly Average Price ($)

Figure 6: Distribution of retail gas prices in U.S., March 2015–March 2017

Notes: Each observation in the sample describes the weekly average price for a given station in dollars per gallon.

34

0

.005

.01

.015

Den

sity

0 100 200 300 400 500Number of competitors within 10 miles

0

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Den

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0

.02

.04

.06

.08

Den

sity

0 100 200 300Number of out−of−state competitors within 10 miles (>0 only)

Figure 7: Distribution of U.S. gas station competitors, March 2015–March 2017

Notes: The top two panels and bottom left panel show all stations in the sample. Because so many stations have no out-of-statecompetitors, the bottom right panel shows the number of out-of-state competitors conditional on having at least 1 out-of-statecompetitor (about 10 percent of observations in the sample).

35

30

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50

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Average Gas Price WTI Crude Oil Spot Price

Figure 8: Average gas prices and crude oil spot prices in U.S., March 2015–March 2017

36

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Average Gas Price ($ per gallon) Average State Plus Federal Tax Rate ($ per gallon)

Figure 9: Average gas prices and state plus federal tax rate in U.S., March 2015–March 2017

37

2.1

2.2

2.3

2.4

2.5

Pric

e

−10 −5 0 5 10Distance to border

(a) Stations near the Michigan (-10,0) and Ohio (0,10) border, Jan. 1, 2017

2.4

2.6

2.8

3

Pric

e

−10 −5 0 5 10Distance to border

(b) Stations near the Georgia (-10,0) and Florida (0,10) border, Jul. 1, 2015

Figure 10: Distribution of stations around state borders

38

A Appendix - Differences in Gasoline

The analysis in Chapter 3 assumes that consumers treat gasoline from different stations as close sub-stitutes. It is true that different brands can use slightly different formulas for their gasoline, but the differ-ences are unclear. For example, Chevron stations use a patented additive called Techron, a detergent thatsupposedly helps keep engines clean.19 While this type of advertising may help differentiate the productfor some consumers, most consumers do not know the effect of Techron or other additives on their car’shealth or ability to function. In fact, gasoline companies themselves may not have reliable measures ofthe impact of their additives. If they did, we might expect those results to be more heavily advertised anddemand to shift more towards those brands.

There are, however, some differences in gasoline that are more well-documented. Gasoline is made bymixing a blending component from the refinery with ethanol at terminal racks just before it is deliveredto retail stations. The ethanol serves as an oxygenate, which is used to reduce carbon monoxide andother contaminants produced in the process of burning gasoline. Currently the EPA mandates that acertain stock of ethanol be blended into the U.S. fuel supply each year (about 19.3 billion gallons for2017). However, several states actually mandate a 10 percent blend of ethanol (dubbed E10).20

Aside from the concentration of ethanol, retail gas can differ within markets based on requirementsplaced on the blending component portion of gasoline. Most areas require what is known as convention-al gasoline blending components (CBOB). Other states and localities require a different blend knownas reformulated blendstock for oxygenate blending (RBOB).21 RBOB is a blend of components that re-moves more of the hydrocarbons that cause pollution. Because this blend reduces the pollution from carexhaust, it is required in many major metropolitan areas. The refining process to do this is more costlythan that for CBOB, as it requires more energy and is chemically more challenging. California has its ownblend requirement, dubbed CARBOB, which is even more expensive to produce, driving the state’s highgas prices. A map depicting these blend requirements is shown in Figure 11.

While these differences are legislated by state and local law, it is unclear to what extent consumers areaware and care about the differences. Certainly some consumers have “green” preferences in which, allelse equal, they would prefer to use the more environmentally friendly blend over the conventional blend.However, this requires knowing exactly which stations are required to sell different kinds of gas and whattheir impact on the environment is. The former can be simple when requirements follow state borders,

19Lead, while dangerous for environmental and public health reasons, helped to keep deposits from building up in car en-gines prior to regulations from the Environmental Protection Agency (EPA) limiting and eventually banning leaded gasoline.

20Most modern cars can run on E10, but would require modification of internal components to run on higher blends. Somenewer cars can use mixtures that contain up to 15 percent ethanol (E15). So-called “flexible-fuel” cars, on the other hand, canoften run on blends that are up to 85 percent ethanol (E85).

21Schneider (2012) provides a more detailed summary of the differences between CBOB and RBOB.

39

but more convoluted once local regulations are considered. The latter is more difficult to ascertain, notto mention the marginal effect of a tank of gasoline is negligible to overall emissions.

What is more likely to sway consumer choice of gas station is the prices they face as a result of dif-ferent fuel requirements. Because the cost of producing RBOB blends is higher, prices are likely higherwhere RBOB is required. In some cases this may overlap with tax differences at state borders, leading to agrowth or reduction in the gap between equilibrium prices across borders. However, many of these fuelrequirements have been in place for a number of years and do not change every time tax rates change. Asa result, stations within a market should still achieve a long-run equilibrium with possibly different pricesdepending on demand for the particular blend they sell and their location. Furthermore, the existence ofthese requirements should have little bearing on the pass-throughs measured in the analysis unless majorchanges to fuel requirements happen simultaneously with tax rate changes, which does not appear to bethe case in my sample.

40

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41

B Appendix - Models of Competition

The analysis of Chapter 3 predicts a high tax pass-through of 135 percent for stations at the interiorof a state. This appendix presents a few simple models in which such a pass-through would be possible.The models are based off models presented in Fullerton and Metcalf (2002).

First assume a simple case where two identical gas stations compete on prices. For this classical Bertrandmodel, assume the two firms are directly next to each other, thereby eliminating any product differenti-ation in location. The firms lower their prices until they reach a shared marginal cost, at which pointboth firms price the same and earn zero economic profits, encouraging no long-run entry or exit of firms.As such, when a tax hike occurs, firms must pass through 100 percent of the tax, or the producer pricewould fall below marginal cost, leading to negative economic profits and firm exit. If one firm tries topass-through more than 100 percent, then the other simply undercuts, gaining the entire market’s busi-ness. Thus, a simple Bertrand model with homogeneous firms and products can onlypredict a 100 percentpass-through.

On the other hand, if the two firms compete on quantities and gasoline is perceived by consumersas homogeneous across competitors, with inverse demand described by p(Q) = p(qi + qj). Then firmsface a profit function given by

πi = p(qi + qj)qi − ci(qi)− τqi, (6)

where τ represents a specific tax per gallon of gasoline.22 The first order condition for profit maximizationis then given by

p′(qi + qj)qi + p(qi + qj)− c′i(qi)− τ = 0. (7)

If we further assume firms are identical, then we can solve for a symmetric equilibrium, where qi =

qj = q and ci(q) = cj(q) = c(q):

p′(2q)q + p(2q)− c′(q)− τ = 0. (8)

We can further differentiate this condition with respect to τs to show that

∂q

∂τ=

1

p′(Q)(

2 +Qp′′(Q)p′(Q)

+(

1− c′′(Q)p′(Q)

)) , (9)

22Note that most states charge an excise tax on gasoline, which would correspond to a specific tax, while some also charge asales tax, an ad valorem tax. In the main analysis, all taxes are converted to a per gallon equivalent. The model can be extendedto include ad valorem taxes (see Fullerton & Metcalf, 2002).

42

and thus∂Q

∂τ=

2

p′(Q)(

2 +Qp′′(Q)p′(Q)

+(

1− c′′(Q)p′(Q)

)) . (10)

This implies that a change in the excise tax leads to a corresponding change in price given by

∂P

∂τ=∂P

∂Q

∂Q

∂τ=

2

2 +Qp′′(Q)p′(Q)

+(

1− c′′(Q)p′(Q)

) . (11)

We can unpack the terms of this equation to gain an interpretation of the suggested pass-through.Notice that the numerator and first term of the denominator correspond to the number of firms in themarket (two in this case). The middle term of the denominator is the elasticity of the slope of the inversedemand curve. The final term of the denominator refers to 1 minus the ratio of the slope of marginal costto the slope of inverse demand.

Note that the second order condition for profit maximization ensures that the denominator is greaterthan 0, since the law of demand necessitates p′(Q) < 0. With this in mind, the model predicts that∂P/∂τ > 0, meaning that at least some of the tax will be passed through to consumers. If ∂P/∂τ > 1,then we would say the model predicts greater than full pass-through. Note that this is possible as long as

Qp′′(Q)

p′(Q)+

(1− c′′(Q)

p′(Q)

)< 0. (12)

A number of potential assumptions on demand and costs could satisfy this condition, leading togreater than full pass-through. For example, if c(q) is linear, then c′′ = 0, and we simply needQp′′(Q)

p′(Q)<

−1.23 Fullerton and Metcalf (2002) show that constant elasticity demand functions with elasticity ε < 0

satisfy this condition, since

Qp′′(Q)

p′(Q)=

1− εε

< −1 ∀ε < 0. (13)

We could then predict a 135 percent pass-through with two identical firms, by assuming linear costs anda constant elasticity demand function with elasticity ε = −1.9. Increasing the number of firms to 20changes the needed elasticity to ε = −0.2.

In my sample the average number of firms within 10 driving miles is 83. Given the estimated pass-through of 135 percent, this Cournot model under linear costs and a constant elasticity demand functionpredicts an elasticity of demand of about -0.05, which does fall roughly in line with previous estimates ofshort-run elasticity.

23Note that this model assumes no entry or exit. Fullerton and Metcalf (2002) show that the restrictions on demand topredict greater than full pass-through are actually weaker with free entry and exit.

43

While this model predicts greater than full pass-through under the specified conditions, it also re-quires that all firms share the same price in the market. While prices are very highly correlated within amarket, it is obviously not the case that all prices are identical. As such, I look to a third model with differ-entiated products. Note that even if consumers view gasoline from different stations as identical in termsof product quality and substance, stations still differentiate based on their location. As such, consumerswill likely be willing to pay slightly more for gas at a station closer to their home or place of work.

Consider a Dixit-Stiglitz model in which consumers maximize utility over differentiated productsq1, q2, ..., qN (see Dixit & Stiglitz, 1977). Utility is given by

U =

(N∑i=1

qρi

) 1ρ

, 0 < ρ < 1. (14)

The degree of substitutability between competing products is dictated by the parameter ρ, with ρ = 1

corresponding to perfect substitutes. Consumers face a budget constraint given by

I ≥N∑i=1

piqi. (15)

Demand functions for this problem are given in the form:

qi = (λpi)1

ρ−1 , (16)

where λ is the marginal utility of income. Notice that this reverts to the previous demand function out-lined in the Cournot case, where 1/(ρ− 1) = −ε. Firms maximize profits by solving

πi = piqi − ci(qi)− τqi. (17)

If we again assume a linear cost function with constant marginal cost ci, then the profit-maximizing priceis given by

pi =

(ε

ε− 1

)(ci + τ). (18)

This implies that∂pi∂τ

=ε

ε− 1. (19)

Note that this, by construction, is greater than full pass-through, since ε = 1/(1 − ρ) > 1. While thismodel has the advantage of allowing firms to set different prices in equilibrium, firm pricing decisions

44

are made independent of other firms’ pricing, which is likely unrealistic in this setting. Furthermore,the model assumes that consumers prefer some mixture of different brands of gasoline. While it may sohappen that gas consumption over time exhibits variety, there is little reason to assume that consumersprefer to mix across all local brands.24

24A final model to consider would be the Salop circle model (Salop, 1979). This model allows for differentiated productsin the context of location choice, but the symmetric equilibrium of the classical model predicts only full pass-through for aspecific tax, and so a more in-depth description of the model is omitted.

45

C Appendix - Sensitivity Analyses

I run a number of sensitivity checks to further explore the main analysis. First, I check the resultswhile dropping outliers on price that are likely to be data errors. Specifically, I drop 222 observations withweekly average prices less than $1 or more than $5.50. When I do this and run the preferred specification,I find that the differences to the results are negligible 6. This is expected given how few observations weredropped.

Next, I drop all stations with no out-of-state competitors within 10 miles. This leaves only borderstations in the analysis. When I do this, I find qualitatively similar results, though some of the magnitudeschange (Table 7). For example, the own-tax pass-through for border stations is 83 percent, down from 93percent, and the pass-through for neighboring tax rates is 23 percent instead of 35 percent. The differencein magnitudes is due to using only the border stations to estimate the week-PADD fixed effects as opposedto all stations.

Finally, I run several specifications to further explore the role of competition in tax pass-throughs.First, I generate bins of the number of stations within 10 driving miles of a station. The first bin corre-sponds to having no competitors within 10 miles. The other bins correspond to having 1–4 competitors,5–9 competitors, 10–24 competitors, 25–49 competitors, 50–99 competitors, and 100 or more competi-tors within 10 driving miles.25

I find that the number of stations within 10 driving miles actually has a fairly small impact on pricesfor most bins, with the first 6 bins all yielding prices 8–12 cents higher on average than those in the binwith the most nearby competitors (Table 8). It is somewhat surprising that stations with no competitorswithin 10 miles do not markup prices even more. However, it is unclear whether these stations actuallyhave no competition, or if their competitors just do not appear in the data.

I find that the number of competitors actually has very little impact on the pass-through for bothborder stations and interior stations. For border stations, I find a pass-through between 79–85 percentfor the second through sixth bins.26 The final bin has a pass-through of 96 percent, though this is notquite statistically significantly different from the pass-throughs of other bins.

On the interior of a state, I find pass-throughs between 125–132 for the first six bins. The final bin hasa pass-through of 148 percent, though the standard error is larger, making it statistically the same as theother bins.

25I use bins instead of a linear variable, since it is possible that the competitiveness of a market is not linearly related to thenumber of nearby stations. For example, a congested area like New York City has many gas stations that are relatively closeto each other. However, the travel time per mile in New York City is likely much higher than that in a more rural area due tocongestion. In this sense, the geographic size of markets may be smaller in very congested areas, meaning that some stations inurban areas may actually be in less competitive markets than the number of competitors would otherwise indicate.

26By construction there are no border stations in the first bin, since a border station is defined as a station with at least oneout-of-state competitor within 10 driving miles.

46

Next, I also interact the bins of competitors with neighboring tax rates. When I do this, I find asimilar pattern, but with noisier results. The own-tax pass-through for border stations ranges between80–94 percent. For the pass-through of out-of-state taxes, I find rates of 21–38 percent. A couple of thesebins are actually significantly different from each other, though there is no real pattern. Again, the highestpass-through is for what we would expect would be the most competitive bin (stations with 100 or morecompetitors). The own-tax pass-through for interior stations is very similar to the previous results.

Overall, based on the previous results, it appears that pass-through is not heavily influenced by thismetric of competition.

Finally, instead of modeling competition through the lens of the number of nearby stations, I in-stead model competition through the distance to the nearest competitor (regardless of being in-state orout-of-state). Here, I find that prices rise about 1 cent for each additional mile the nearest competitor islocated away from a station (Table 9). Because I only measure distances within 10 driving miles, I includea dummy for the nearest station being greater than or equal to 10 driving miles away. The coefficient onthis variable is about negative 7 cents, though it is important to note that this is an average effect for allstations with no nearby competitors, and distance to the nearest station can get quite large in some ruralareas.

I find that the own-tax pass-through for border stations is just over 1 cent lower for every mile fartherthe nearest competitor is. I find a slightly larger effect, about 1.7 cents per mile, for interior stations withcompetitors within 10 miles, though the average effect for all stations without a competitor within 10miles is only about 4 percentage points lower. Finding that pass-through is higher when competitors arecloser seems to make sense for border stations, where pass-through is less than full. When competitors arecloser, they pass-through closer to 100 percent of a tax, meaning that they more closely follow the classicalBertrand model. However, for interior stations, where pass-through is greater than 100 percent, having acloser competitor actually pushes stations away from full pass-through.

Nonetheless, while these effects are statistically significant, they are relatively small in magnitude. Thissuggests that this metric of competition also only has a limited effect on tax pass-through.

47

Table 6: Regression results dropping observations with average weekly prices less than $1 and more than$5.50

Dependent Variable - Station weekly average gas price

(1)

1(< 10 miles) 0.0092(0.0359)

×Own Tax Rate 0.927***(0.0782)

×Distance 0.0226***(0.0046)

×OoS Competitors’ Tax Rate 0.352***(0.0568)

×Distance -0.0171***(0.0038)

1(> 10 miles)×Own Tax Rate 1.361***(0.133)

Station FE XWeek× PADD FE X

Observations 11,295,235R2 0.954Notes: “< 10 miles” means the station is less than 10 miles away from the nearest out-of-state gas station. “Distance” ismeasured in miles from nearest out-of-state station. “OoS Competitors’ Tax Rate” is a weighted average of out-of-statecompetitors’ tax rates based on distance between the two stations. Standard errors are clustered by state-month and areshown in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

48

Table 7: Regression results dropping observations with no out-of-state competitors within 10 drivingmiles

Dependent Variable - Station weekly average gas price

(1)

Own Tax Rate 0.830***(0.0722)

×Distance 0.0175***(0.0042)

OoS Competitors’ Tax Rate 0.230***(0.0541)

×Distance -0.0112***(0.0039)

Station FE XWeek× PADD FE X

Observations 1,059,384R2 0.946Notes: “Distance” is measured in miles from nearest out-of-state station. “OoS Competitors’ Tax Rate” is a weightedaverage of out-of-state competitors’ tax rates based on distance between the two stations. Standard errors are clustered bystate-month and are shown in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

49

Table 8: Regression results with competition modeled through the number of competitors within 10driving miles

Dependent Variable - Station weekly average gas price

(1) (2)

1(< 10 miles) 0.0207 0.0276(0.0355) (0.0370)

×Own Tax Rate

×Distance 0.0232*** 0.0233***(0.0049) (0.0049)

×1–4 competitors 0.846*** 0.921***(0.091) (0.107)

×5–9 competitors 0.794*** 0.805***(0.088) (0.100)

×10–24 competitors 0.816*** 0.878***(0.0822) (0.0934)

×25–49 competitors 0.812*** 0.923***(0.0765) (0.0895)

×50–99 competitors 0.812*** 0.871***(0.0770) (0.0862)

×100+ competitors 0.963*** 0.939***(0.0878) (0.0817)

×OoS Competitors’ Tax Rate 0.350***(0.0571)

×Distance -0.0165*** -0.0168***(0.0040) (0.0040)

×1–4 competitors 0.263***(0.0595)

×5–9 competitors 0.327***(0.0601)

×10–24 competitors 0.270***(0.0555)

×25–49 competitors 0.218***(0.0562)

×50–99 competitors 0.280***(0.0555)

×100+ competitors 0.380***(0.0661)

1(> 10 miles)×Own Tax Rate

×0 competitors 1.315*** 1.315***(0.140) (0.140)

×1–4 competitors 1.311*** 1.310***(0.131) (0.130)

×5–9 competitors 1.257*** 1.257***(0.128) (0.128)

×10–24 competitors 1.256*** 1.252***(0.125) (0.125)

×25–49 competitors 1.277*** 1.271***(0.126) (0.125)

×50–99 competitors 1.315*** 1.312***(0.133) (0.133)

×100+ competitors 1.476*** 1.483***(0.154) (0.156)

0 competitors 0.0931* 0.0963*(0.0538) (0.0540)

1–4 competitors 0.0927** 0.0966**(0.0450) (0.0454)

5–9 competitors 0.117*** 0.120***(0.0440) (0.0445)

10–24 competitors 0.114*** 0.119***(0.0392) (0.0399)

25–49 competitors 0.102*** 0.109***(0.0330) (0.0340)

50–99 competitors 0.0813*** 0.0863***(0.0207) (0.0218)

Station FE X XWeek× PADD FE X X

Observations 11,295,458 11,295,458R2 0.954 0.954Notes: “< 10 miles” means the station is less than 10 miles away from the nearest out-of-state gas station. “Distance” ismeasured in miles from nearest out-of-state station. “OoS Competitors’ Tax Rate” is a weighted average of out-of-statecompetitors’ tax rates based on distance between the two stations. Standard errors are clustered by state-month and areshown in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

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Table 9: Regression results with competition modeled through distance to the nearest competitor

Dependent Variable - Station weekly average gas price

(1)

1(< 10 miles) 0.0106(0.0360)

×Own Tax Rate 0.934***(0.0792)

×Distance 0.0223***(0.0046)

×Dist. Nearest -0.0110*(0.0064)

×OoS Competitors’ Tax Rate 0.355***(0.0574)

×Distance -0.0177***(0.0039)

1(> 10 miles)

×Own Tax Rate 1.374***(0.136)

×Dist. Nearest×Nearest< 10 -0.0174***(0.0066)

×Nearest>= 10 -0.0433***(0.0623)

Dist. Nearest 0.0106***(0.0031)

Nearest>= 10 -0.0749***(0.0214)

Station FE XWeek× PADD FE X

Observations 11,295,235R2 0.954Notes: “< 10 miles” means the station is less than 10 miles away from the nearest out-of-state gas station. “Distance” ismeasured in miles from nearest out-of-state station. “Dist. Nearest” is the distance to the nearest station (regardless ofstate). “OoS Competitors’ Tax Rate” is a weighted average of out-of-state competitors’ tax rates based on distance betweenthe two stations. “Nearest< 10” and “Nearest>= 10” are dummies equal to one if the nearest station is within and outsideof 10 miles, respectively. Standard errors are clustered by state-month and are shown in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

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