Experimental Gasoline Markets *

Cary A. Deck 

Department of Economics 

Walton College of Business 

The University of Arkansas 

Fayetteville, AR 72701 

Phone: (479) 575-6226 

Fax: (479) 575-3241 

E-mail: cdeck@walton.uark.edu

Bart J. Wilson**

Interdisciplinary Center for Economic Science 

George Mason University 

4400 University Drive, MSN 1B2 

Fairfax, VA 22030-4444 

Phone: (703) 993-4845 

Fax: (703) 993-4851 

E-mail: bwilson3@gmu.edu 

August 2003 

 

Abstract: Zone pricing in wholesale gasoline markets is a contentious topic in the public policy 

debate. Refiners contend that they use zone pricing to be competitive with local rivals. Critics 

claim that zone pricing benefits the oil industry and harms consumers. With a controlled 

experiment, we investigate the competitive effects of zone pricing on consumers, retail stations, 

and refiners vis-à-vis the proposed policy prescription of uniform wholesale pricing to retailers. 

We also examine the issue of divorcement and the “rockets and feathers” phenomenon. The 

former is the legal restriction that refiners and retailers cannot be vertically integrated, and the 

latter is the perception that retail gasoline prices rise faster than they fall in response to random 

walk movements in the world price for oil. 

JEL Classifications: C9, D4, L2, L4, L9 

Keywords: gasoline industry, zone pricing, vertical integration, asymmetric price adjustment, 

 experimental economics 

 

 

*

 This paper has benefited from conversations with Charissa Wellford, Jeffrey Fischer, David Meyer, Mary 

Coleman, David Scheffman, Christopher Taylor, Michelle Burtis, and the anonymous reviewers for the FTC 

Working Paper Series. We thank Jeffrey Kirchner for writing the software for the experiments. The authors 

gratefully acknowledge financial support from the Federal Trade Commission and the National Science Foundation. 

This report reflects the opinions of the authors and does not necessarily reflect the position of the Federal Trade 

Commission or any individual Commissioner. 

** Corresponding author. 
1. Introduction 

 Few industries evoke such strong sentiments by consumers, retailers, wholesalers, and 

policy makers as gasoline. The structure of the gasoline industry is extremely complex, making 

the impact of common practices unclear and thus the focus of much public scrutiny. The 

practice of zone pricing has been a particularly contentious topic in the public policy debate for 

the past several years.1

 Zone pricing is the industry term to describe the practice of refiners 

setting different wholesale prices for retail gasoline stations that operate in different geographic 

areas or zones. Refiners contend, as Chevron does on its website, that they employ zone pricing 

to “price our wholesale gasoline to our dealers at prices that will allow them to be competitive in 

relation to their nearby competition.”2

 However, state legislators and attorneys general propose 

legislation to ban zone pricing claiming that it “only benefits the oil industry, to the detriment of 

consumers.”3

Another controversial issue that is debated in the gasoline industry is divorcement, the 

legal restriction that refiners and retailers cannot be vertically integrated. Maryland was the first 

state to pass such legislation in 1974 with a handful of other states following suit. A 2000 report 

from Bill Lockyer, the Attorney General for the State of California, states that “members of the 

Task Force believe the key to enhancing competition at the retail level is to eliminate vertical 

integration by petroleum companies (p. 33).” However, in a prepared statement made before a 

subcommittee of the U.S. Senate, Hastings (2002a) testified that “Divorcement will not lead to 

lower prices, and may increase inefficiency (p. 7).” 

The objective of our study is to design an environment that captures the essential 

components of wholesale and retail gasoline markets and to use a controlled experiment to test 

these opposing viewpoints. Within the confines of the laboratory, the impact of zone pricing and 

divorcement can be evaluated while holding the geographic markets, production and delivery 

costs, and the associated consumer demographics constant.4

 Additionally, we can also preclude 

 

1

 For example, Maryland set up The Governor’s Task Force on Gasoline Zone pricing in 2000. The task force’s 

report in September of 2001 promotes a prohibition on zone pricing. See Isaac, Oaxaca, and Reynolds (1998) for a 

detailed discussion of retail price differentials in Arizona. 

2 http://www.chevron.com/about/currentissues/gasoline/pricing_qanda/gasoline_pricing_qa.shtm#7. This view is 

also supported by Comanor and Riddle (2003) who examine the California market in the late 1990s. 

3

 See “Testimony of Connecticut Attorney General Richard Blumenthal Before the House Judiciary Committee,” 

http://www.house.gov/judiciary/blum0407.htm, April 7, 2000. The California State Assembly recently considered 

banning zone pricing (see Douglas (2003)). 

4

 Smith (1994) lists evaluation of policy proposals as one reason to conduct experiments. 
2

entry at the retail level, thus setting a condition most unfavorable to zone pricing in terms of its 

potential harm to consumers. Experimental economics is an ideal tool for addressing zone 

pricing. Currently, gasoline wholesalers are free, as Shepard (1993, p. 63) notes, to charge 

“station-specific” wholesale prices. In an experiment, we can control the extent, if any, to which 

zone pricing can be employed in situations where explicit collusion among the wholesalers is not 

possible. Allowing zone pricing in one treatment serves as a benchmark for evaluating the 

complementary treatment, banning zone pricing by mandating uniform wholesale prices. Such a 

comparison affords a direct examination of the welfare effects of the proposed legislation on 

consumers, retailers, and wholesalers. Similarly, we can vary the degree of vertical integration 

to assess the impact of divorcement. A chief advantage of a controlled laboratory study is that we 

can precisely measure buyer welfare, which cannot be done in naturally occurring gasoline 

markets because consumer preferences are not observable. With an experiment we also have 

precise data on the actual transaction prices paid by consumers as opposed to just posted prices at 

retail stations. 

With our data set, we also explore the phenomenon that retail gasoline prices adjust 

asymmetrically to cost shocks, yet another topic that has led to much public outcry. Several 

papers have found that gasoline prices rise more rapidly than they fall (see Johnson, 2002; Reilly 

and Witt, 1998; Borenstein et al., 1997; Castanias and Johnson, 1993; and Bacon, 1991).5

 Even 

though the data suggest that the asymmetry is relatively short lived (see Johnson, 2002), when 

prices climb quickly, the popular press is filled with stories about price gouging, exploitation of 

market power and the possibility of collusion. Of course, there are less pejorative explanations 

as well. One such gasoline spike occurred in the Midwest during the spring of 2000. Bulow, 

Fischer, Creswell, and Taylor (2003) study this period and determine that the price response was 

due to supply interruptions and argue against the possibility of collusive explanations. Further, 

they conclude that such episodes are likely to occur with increasing frequency as more and more 

 

5

 As Peltzman (2000) reports, this phenomenon is not unique to gasoline. It thus remains unclear why consumers 

respond so strongly to changes in gasoline prices. One potential explanation is that unlike other products, 

consumers process a large volume of gasoline price information daily even when they are not considering a purchase 

due to the prominence of posted pricing in this market. Conspicuous price information for an extensively used 

product may make it easier for people to detect price changes when retailer cost changes are less obvious. 
3

localities place constraints on gasoline blends.6 Chouinard and Perloff (2002) find that the price 

of crude oil is the only major factor explaining the recent movement of gasoline prices. 

Other explanations for asymmetric responses include inventory costs, menu costs, trigger 

pricing strategies, and consumer search costs (see Johnson 2002, Castanias and Johnson 1993 

and references therein). Using weekly gasoline prices from Los Angeles between 1968 and 

1971, Castanias and Johnson (1993) argue that the data closely match the predictions of Maskin 

and Tirole’s (1988) adjustment model. Johnson (2002) argues that empirical data from the mid 

to late 1990s are consistent with a search cost model. This search cost hypothesis asserts that 

when prices are falling consumers have less incentive to search out new retailers, making it less 

attractive for rivals to lower prices. To explore the impact of search costs, Johnson compares 

unleaded price responses to the responses of diesel prices with the underlying assumption that 

consumers in diesel markets such as trucking companies engage in more search. Our laboratory 

investigation allows direct empirical evidence to be collected on asymmetric price responses and 

their indicatives of anticompetitive behavior, while explicitly controlling factors such as buyer 

search and menu costs. 

2. Industry Background 

The first step in the production and delivery of gasoline is the extraction of crude oil. 

Oil is then traded in a global market and transported to refineries. At the refinery, oil is 

converted into gasoline and other products including diesel fuel, asphalt and jet fuel. Gasoline is 

then piped to various distribution terminals. In the U.S., these terminals are located throughout 

the country near most major metropolitan areas. At this point in the process, gasoline is a pure 

commodity in that the supplier is indistinguishable and, in fact, pipe lines carry gasoline from 

multiple suppliers. At the terminal, gasoline is collected in large holding tanks by the various 

wholesalers operating in the proximity. At this point in the process brand specific additives are 

inserted.7

 The price of gasoline from these holding facilities is referred to as the rack price. The 

gasoline is then shipped by tanker truck to individual retail locations. 

 

6

 The Midwest gas spike of 2000 centered on the implementation of a new standard for cleaner burning reformulated 

gasoline. 

7

 Unbranded gasoline is gasoline with no branded additives. Special gasoline blends such as the CARB gasoline 

required for California are produced at the refinery. 
4

 Recently the industry has experienced considerable consolidation with the merging of 

large integrated companies such as BP and Amoco and Exxon and Mobil. Using simulations 

Manuszak (2001) investigates refiner mergers.8

 His model predicts that the merged wholesaler 

will increase its price but its rivals will not alter behavior significantly. As stations are not 

expected to change their mark-ups, retail prices would remain unchanged except at stations 

affiliated with the merged refiner, which simply pass the higher prices on to the final consumer. 

Not only can consolidation change the number of competitors, it can also change the 

degree of vertical integration present in a given geographic market as there are several different 

contractual arrangements between retail outlets and refiners. Branded stations must sell the 

specified brand of gasoline. Some branded stations are company operated, meaning that the 

refiner owns the retail outlet and sets retail prices. Alternatively, a branded station can be either 

a lessee dealer or a dealer owned station in which case the retail outlet sets the retail price but it 

is obligated to buy the refiner’s brand of gas. The difference between a lessee dealer and a dealer 

owned station pertains to the ownership of the retail facility. The price of gasoline delivered to a 

station is referred to as a Dealer Tank Wagon (DTW) price. In practice, refiners can engage in 

zone pricing by setting the DTW price to reflect market conditions in very specific geographic 

locations, possibly as small as a single station. Under these types of arrangements, the stations 

can either be supplied directly by the refiner or can purchase gas from a branded jobber. The 

term jobber refers to an intermediary that delivers gas from the rack to the retail location.9

 A 

fourth category of retailer is the independent station. Independent stations typically sell 

unbranded gasoline. They are also free to set their own retail prices and can acquire gas directly 

from the terminal or via a jobber with the constraint that only a branded jobber can deliver 

branded gasoline.10 While a small percentage of independent stations are dealer supplied, most 

are supplied by jobbers. 

Hastings (2002b) uses ARCO’s acquisition of the independent chain Thrifty in southern 

California to examine the impact of a reduction in the number of non-integrated independent 

 

8

 Manuszak (2001) uses data from two Hawaiian islands where vertical integration is limited. 9

 Like refiners, jobbers can also set station-specific prices. 10 During rack inversions, a situation in which branded rack prices are below unbranded rack prices, wholesalers of 

branded gasoline can place limits on the quantity jobbers acquire at the terminal effectively preventing independent 

stations from acquiring the cheaper branded gasoline. In addition, in times of shortages, unbranded stations are 

typically supplied last. 
5

retailers in the market.11 Comparing prices before and after ARCO’s branding of Thrifty outlets, 

Hastings finds that retailers who had competed against the independent chain raised prices 

relative to retailers who had not been competing directly with Thrifty. One should be careful to 

note that the welfare effects of such a merger are unclear. While prices rose in some locations, 

consumers in these locations now have an additional brand of gasoline. If consumers have a 

preference for the branded gasoline, then this situation actually could generate additional utility. 

Hastings also observes no retail price differential due to the new ARCO store being either 

a company operated or dealer operated station. This finding argues against the divorcement 

proposals issued by several communities that would force wholesalers and retailers of branded 

gasoline to be separate. In the event that the retailers have market power,12 forcing retailers to be 

independent of the wholesaler could lead to a double marginalization problem, resulting in 

higher retail prices for non-integrated outlets. Barron and Umbeck (1984) and Vita (2000) find 

support for this hypothesis. 

While vertical separation may have unfavorable price implications for consumers, the 

situations in which such separation will be observed are less clear. In a study comparing outlet 

ownership structure and the non-gasoline aspects of the outlet, Shepard (1993) finds that stations 

where monitoring is relatively more straightforward tend to be company operated and vice versa. 

An alternative explanation for vertical integration is risk sharing between the retailer and the 

wholesaler. However, Lafontaine and Slade (1997) do not find support for this explanation in 

their data. Slade (1998) examines a principle agent problem explanation as well as strategic 

motives for separation.13 She argues that separation is a dominant strategy and points to agency 

costs as the reason some stations remain integrated. Using 1983 gasoline data from Vancouver, 

Slade does find that in situations where the gains from separation are predicted to be the largest, 

the ownership is more likely to be separated.14 

Given the complexity of the gasoline industry and the incompleteness of naturally 

occurring field data, it is not surprising that there is ambiguity about the effects of certain 

actions. For example, the estimation results of Slade (1998) are based upon a linear demand 

 

11 Hastings (2002b) also investigates the impact of a reduction in the overall number of competitors but concludes 

that the elimination of a competitor is not responsible for the observed 5 cents per gallon price increase. 

12 Hastings (2002b, p. 6) notes that “Borenstein, Cameron, and Gilbert (1997), Borenstein and Shepard (1996), and 

Slade (1992) provide empirical evidence consistent with local retail market power.” 13 The basis of the strategic motives for vertical separation is a theoretical paper by Rey and Stiglitz (1995). 

14 The predicted profitability of the ownership structure is based upon estimates of own price and cross price 

elasticities. 
6

curve which “facilitates testing in the absence of reliable data on marginal cost (p. 92).” Further, 

many factors change simultaneously thus making the process of disentangling effects difficult. 

Hastings (2002b) compares prices pre- and post-merger. The underlying assumption is that 

changes in local market characteristics are the same across the local markets. Thus changes preand post-merger are attributable to either city wide trends or ownership structure. Additionally, 

Hastings faces a potential endogeneity problem in identifying the effects of (a) acquiring the 

chain and (b) changing the ownership structure of specific stations. Both were not random 

choices, but were part of the profit-maximizing decision by ARCO to acquire Thrifty in the first 

place. Also, there is a lack of information about consumer behavior including preferences and 

search patterns. Many of the empirical studies discussed above are based upon assumptions 

about buyer behavior. For example, Johnson (2002) hypothesizes that gasoline consumers 

search relatively infrequently. Hastings (2002b) proposes two alternative structures of consumer 

preferences to guide her analysis. In contrast, the laboratory provides an environment in which 

factors such as buyer preferences and search patterns can be held constant. By exogenously 

manipulating the strategic capabilities of wholesalers, policies such as zone pricing and 

divorcement can be directly evaluated. Further, in the laboratory direct evidence can be gathered 

about responsiveness of the retail prices to supply shocks including changes in crude oil prices 

and the impact of changes in the number or composition of competitors. Beyond profits and 

retail prices, laboratory studies can also evaluate social welfare which is not possible in the field 

as data are not available as to the value consumers receive from purchasing gasoline. 

3. A Vertical Model of Gasoline Markets 

Our model of the gasoline industry consists of N refiners who compete to sell branded15

gasoline to R gasoline retailers,16 who in turn compete to sell gasoline to final consumers. Only 

refiner i can sell its branded gasoline bi. The cost per unit to refiner i for supplying gasoline of 

type bi is ci. Based upon the types of contracts that exist in the retail gasoline market, each retail 

station is constrained to sell a specific brand of gasoline at an exogenously determined location 

on a grid. The location of outlet ρ is given by an integer pair, (s, a) where s ∈ {1, 2,..., S} and a

 

15 As the primary focus of this paper is on zone pricing, divorcement, and asymmetric price responses, the 

environment is simplified by only considering branded gasoline. The impact of branded versus unbranded gasoline 

is beyond the scope of this paper. 

16 R and r are used to denote retail level players and the Greek versions, Ρ and ρ of these letters denote the retail 

outlets. These groups are not isomorphic as a retailer can operate multiple outlets. 
7

∈ {1, 2,..., A}. Thus each station r

s a bi ρ( , ), is indexed by location, brand, and retailer identity r 

∈ R. Note that identifying the location and the retailer is not redundant as multiple outlets can 

operate at the same location. It is quite common for two stations to be located on opposite sides 

of the street at the same intersection. Further, the retailer identity and the brand type are not 

redundant as there could be multiple retailers selling the same brand, as would occur in a market 

with both company operated and lessee dealer stations. The per unit price charged to a consumer 

by a retail outlet is r

i s a b

pρ( , ),. 

Each buyer has a value v for one unit of gasoline. Buyers in the market are characterized 

by brand preference and location. A fraction bi ω of the buyers have a preference for brand bi, 

meaning that these buyers gain additional utility > 0 bi β if they consume brand bi. The fraction 

of consumers who do not have a brand preference is defined as b0 ω with ∑=

= −

N

n

b bn

1

1 0 ω ω . For 

customers with no brand preference we define 0 0 β b ≡ . To distinguish the location of a 

consumer from the location of a retailer, the buyer’s location is denoted by the pair (σ, α) where 

σ ∈ {1, 2,..., S} and α ∈ {1, 2,..., A}. The percentage of buyers at a particular location is 

determined by the density function f (⋅) defined over the S × A possible locations 

withΣΣ f (s, a) = 1 s a . Buyers incur a travel cost ) d(⋅ which is increasing and convex in the 

distance traveled. In this model, distance is defined by the norm 

(s,a),(σ ,α) = s −σ + a −α which gives the number intersections a buyer located at (σ, α) 

must travel to reach the retail station r

s a bi ρ ( , ), .

17 

In naturally occurring economies, gasoline retailers must first acquire inventory from the 

refiner before selling the gasoline to the consumer. This sequencing puts the refiners that are not 

vertically integrated in a theoretically advantageous position relative to their stations, but 

generates the typical double marginalization problem. In the downstream market, retailers 

maximize their profits conditioned on upstream prices. The upstream refiners are able to 

incorporate retailer reaction when setting DTW prices.18 

 

17 As the retailers and consumers are located on a grid, this taxi cab metric provides a more natural notion of 

distance than does the standard Euclidian metric. 

18 In what follows we use the terms wholesale price and DTW price interchangeably. 
8

A buyer with a preference for brand j attempts to 

max ( (s,a ,( , ) )

( , ), ( , ),

( , ),

ς β σ α

ρ ρ

ρ

+ − − ) ∈Ρv p r d i s a b j r

i r s a b

a bi s

j b

subject to ( (s,a ,( , ) ) 0

( , ), ( , ),

+ ς β − − ) σ α ≥

ρ ρ

v p r d i s a b j r

i s a b j b

where ρ ς j is an indicator function that takes on the value 1 if station r

s a bi ρ ( , ), sells brand j and is 0 

otherwise.19 The problem is similar for buyers with no brand preference. If the consumer cannot 

achieve a nonnegative utility from buying from any retailer, then the consumer does not purchase 

gasoline and receives a utility of 0. In the event that two stations offer the same total utility to a 

buyer, a buyer with a preference for bi selects an outlet selling this brand over an outlet selling 

any other brand. Otherwise such ties are broken randomly. 

A retailer’s problem is to set prices at the retail outlets it operates so as to maximize its 

expected profit. Let r

i s a b

w

ρ ( , ),

denote the per unit price charged by refiner i to a retail outlet r

s a bi ρ ( , ),

for gasoline of type i. In addition to the cost of gasoline, a retailer incurs an additional cost of 

r

i s a b

e

ρ( , ),

.

20 This cost is interpretable as an effort cost or as the cost associated with operating 

service bays or convenience stores. Refiner i’s problem is to set r

i s a b

w

ρ ( , ),

for all r

s a bi ρ ( , ), ∈ Ρ to 

maximize profits. The equilibrium solution is a set of optimal prices for the refiners and the 

retailers.21 

 In the gasoline industry, gasoline is delivered to retailers by tanker trucks. The retailer 

then holds this gasoline in its inventory and sells the product to consumers over some period of 

time. To incorporate this into the model, we suppose that when a retailer sells out of gasoline, a 

tanker delivers K units of gasoline at a per unit price determined as described above. Each 

 

19 To include search costs, the buyer’s problem becomes one of maximizing expected benefits net of search costs 

based upon the buyer’s beliefs about the distribution of prices. Again, as the primary focus of this paper is on zone 

pricing, divorcement, and asymmetric price responses, we simplify the environment by not incorporating search 

costs. 

20 For simplicity, the role of the jobber has been suppressed in this model. The price charged by each retailer can be 

considered to include delivery or this additional cost could be thought of as a separate jobber fee. 

21 The specified model is a generalization of the spatial model originally due to Hotelling (1929) and subsequently 

extended by numerous researchers, see e.g., Salop (1979), Scholer (1993), Vandenbosch and Weinberg (1995), 

Veendorp and Anjum (1995), Irmen and Thisse (1998), and Heywood, Monaco and Rothschild (2001). For 

equilibrium solution techniques the reader is referred to these works and papers cited therein. In these models, 

retailers only compete against their nearest rivals. Not only does a spatial model intuitively capture the gasoline 

market quite well, use of such a model is further justified by the results of Pinkse, Slade and Brett (2002) who 

conclude that retail gasoline competition is in fact very localized. 
9

period, one buyer enters the market at a randomly chosen location and considers purchasing a 

single unit of gasoline. A retailer only refills its tanks once it sells K units. 

 Before discussing our experimental design we briefly comment on the objective of this 

section. This section constructs a model of wholesale and retail gasoline markets that captures 

the essential features of naturally occurring markets. In particular, we chose those elements 

(agents, variables, and actions) that are necessary to generate data to address the questions posed 

in Sections 1 and 2. Our methodological objective for the experiment on which the model is 

based is heuristic or exploratory in nature (Smith, 1982), and hence we are more interested in the 

richness of the strategic interactions in these markets than in sharply defined predictions of a 

model. If out of this complexity emerges a distinct order of outcomes in the experiment, theory 

would play a critical role in summarizing the statement of the observed behavioral regularities. 

But that is beyond the scope of this paper. 

4. Experimental Design and Procedures 

 Our laboratory gasoline markets consist of N = 4 refiners that each produce branded 

gasoline and R = 4 retailers who each operate 2 locations on a 7 × 7 grid. To the subjects, each 

brand is distinguished by its color: b1 = blue, b2 = pink, b3 = green, and b4 = red. Figure 1 depicts 

the location and brand for each retail station.22 

Buyers are assumed to be uniformly distributed over the grid, 49

1 f (s,a) = . Further, the 

likelihood that a buyer prefers brand i is 20 = 0. bi ω and the benefit a buyer who prefers brand i

received from purchasing it is 25 = bi β . As each buyer has an inelastic demand for 1 unit valued 

at v = 240 and has an insignificant impact upon the market, truthfully revealing robots serve as 

the final consumers. With the market shown in Figure 1, there is a “center” area at (s, a) = (4, 4) 

where each of the four branded retailers are “clustered”. Each retailer also operates an outlet in a 

relatively “isolated” area in the “corner” of the grid. These two types of areas are meant to 

address the claim of refiners that they use zone pricing to be competitive with their local rivals.23 

The cost of traveling is 2 d(x) = 4x for x = 0, 1, ..., 12. Given that 265 + v = bi β , no 

consumer is willing to travel further than 8 blocks to purchase gasoline and would only be 

 

22 The information was presented graphically to the subjects. 23 See footnote 2. 
10

willing to travel 7 intersections to a non-preferred brand outlet. The buyers have complete 

information about current retail prices.24 Each laboratory session lasts 1200 periods. In each 

period, which is every 1.7 seconds, a robot buyer from a randomly drawn location enters the 

market, observes retail prices and makes a purchase decision. 

 We consider three experimental treatments to identify the impact of banning zone pricing 

and limiting vertical integration in retail gasoline markets. In the zone pricing (or baseline) 

treatment, refiners have the ability to set r

i s a b

w

ρ ( , ),

 for each r

s a bi ρ ( , ), ∈ Ρ, as described in the 

preceding section. In this treatment, each retailer observes two location specific wholesale prices 

but could not shift inventory between locations.25 Our uniform pricing treatment represents the 

environment after legislation banning zone pricing is enacted. In terms of the model described 

above, the uniform pricing treatment imposes the restriction that w wi r

i s a b

= ρ ( , ),

for every station 

selling bi. It is important to note that uniform pricing at the wholesale level does not imply 

uniform retail prices. In both the zone pricing and uniform pricing treatments, refiners are able 

to change wholesale prices throughout the 1200 periods. We measure the effects of divorcement 

by comparing the baseline treatment with a company operated (company-op) treatment. In the 

company-op treatment all of the retail stations are vertically integrated, which essentially 

removes the intermediary and eliminates double marginalization. This is operationalized by 

automatically setting r

i s a b

w

ρ ( , ),

= ci.

26 In all three treatments, the non-gasoline expenses of each 

retailer are 10

( , ), r =

i s a b

e

ρ . 

For the first 600 periods, c i bi = 100 ∀ . In the remaining 600 periods, the refiners’ costs 

follow a random walk to simulate changes in price for crude oil on the world market. The 

stochastic shocks are distributed as N(0, 15). The number of periods until the next permanent 

shock are distributed as U[20, 35]. This means that the refiners’ costs change every 34 to 60 

seconds. The subjects are not given this information on the nature of the cost shocks. To reduce 

intersession variation, we hold the set of cost realizations constant across all sessions. 

 

24 This assumption could be relaxed; however, a priori one would expect that search costs could have the same price 

increasing effect in both the zone pricing and the uniform pricing treatments. 25 Generally, stations are contractually prohibited from shifting inventory in natural contexts. 

26 The price setting role of the refiner is eliminated in the company-op treatment and hence no subjects are placed in 

the role of refiner in these sessions. 
11

 Retailer r sets r

i s a b

pρ( , ), and could adjust this price at any time during the 1200 periods. 

Retailers and refiners observe all current retail prices including those set by rival outlets. 

However, the current DTW prices are known only by the refiner and the associated retailer. At 

the beginning of sessions in the zone and uniform treatments, refiners set initial wholesale 

prices, which the branded retailers are forced to accept for the initial inventory of K = 10 units. 

Once a location stocks out, the retailer completely replenishes its inventory of K = 10 units at the 

current wholesale price. In the event that wholesale prices fall, it is possible that a retail outlet 

has gas in its inventory that cost more than current rival retail prices. To avoid the retailer 

having to fully absorb losses, the refiners can offer rebates to the retailers for unsold units in 

inventory. 

 We conducted a total of twelve laboratory sessions, four in each treatment. Each session 

lasted no longer than 90 minutes and consisted of 8 subjects in the zone and uniform treatments 

and 4 subjects in the company-op treatment, who were recruited from undergraduate classes in 

economics, management, and engineering at George Mason University. In each session subjects 

were randomly assigned a role.27 Prior to beginning the actual experiment, subjects were given 

ample opportunity to ask questions. Each subject only participated in one session and received 

US$1 for every 800 of experimental profit. The average payoff across all subjects was $18.25, 

including $5 for showing up on time. Subjects received their payments in private at the 

conclusion of the session. 

5. Experiment Findings 

In what follows we present the results of our experiment as a series of nine findings. We 

break down the discussion of the results into two subsections. The first subsection covers the 

results from the first 600 periods with stable wholesale costs. We control for learning effects by 

focusing our attention on the last half these periods (301-600). For this set of periods we first 

estimate the comparative static effects of the zone and uniform pricing treatments and the 

 

27 To avoid the potentially loaded terms associated with gasoline markets, the refiners and station owners were 

referred to as suppliers and store owners, respectively, who were buying and selling a fictitious product. A copy of 

the instructions is available from the authors upon request. In an attempt to aid comprehension of the environment, 

prior to beginning the experiment, each subject experienced the opposite role for 300 periods (except in the 

company-op treatment for which there is only one role). That is, a refiner (retailer) first read the instructions and 

participated as a retailer (refiner) for 300 periods, before reading the instructions as a refiner (retailer) and 

participating for 1200 periods as a refiner (retailer). 
12

location effect of corner and center stations. Then we consider the effects of divorcement by 

estimating the comparative static effects of lessee dealers with zone pricing versus vertically 

integrated, company-owned stations. The second subsection analyzes the dynamic adjustment of 

station prices when the wholesale costs are nonstationary in periods 601-1200. 

5.1 Comparative Static Effects with Constant Wholesale Costs 

5.1.1 Zone versus Uniform Pricing 

As an introduction, consider the qualitative results reported in Figures 2 and 3. Figure 2 

displays average wholesale prices for the four sessions in each of the two treatments. It is 

evident that the refiners have endogenously adopted zone pricing to the retail stations when 

permitted. The average wholesale price to the corner stations is 174 versus 147 to the center 

stations. When the refiners are forced to charge uniform prices, the average wholesale price is 

151, but it is unclear how meaningful this average is considering that there is a large variation in 

refiner prices across and within sessions. Wholesale prices in two of the uniform pricing 

sessions are as high as the average corner station wholesale prices under zone pricing. In another 

uniform pricing session, wholesale prices are approximately equal to average center wholesale 

prices under zone pricing. Average wholesale prices in the remaining uniform pricing session 

are below the average center wholesale prices with zone pricing. 

Figure 3, which contains histograms of all of the posted retail prices, reveals that 

consumers do not see lower retail prices with uniform wholesale pricing. The mode for the 

corner stations is 200 in both the zone and uniform treatments. The posted prices are slightly 

higher in the uniform treatment, as there is considerably more mass distributed across the 210-

230 bins in the uniform treatment. The effect of a uniform pricing policy on retail prices is 

considerably more striking at the central stations. The entire distribution of posted prices shifts 

to the right under uniform wholesale pricing. The mode in the uniform treatment is 190, whereas 

the mode is only 150 with zone pricing. 

We first assess the effect of zone and uniform pricing on the transaction prices in each 

market. Unlike field studies which rely on posted prices, our dataset contains the actual prices 

paid by each buyer. The quantitative results are derived by analyzing the data with a linear 

mixed-effects model for repeated measures.28 The treatment effect (Zone vs. Uniform wholesale 

 

28 See Longford (1993) for a description of this technique commonly employed in experimental sciences. 
13

pricing) and location effect (Center vs. Corner station) and an interaction effect are modeled as 

zero-one fixed effects, while the 8 independent sessions and the 4 subjects within each session 

are modeled as random effects, ei and ζ ir , respectively. Specifically, we estimate the model 

ir t i ir Uniformi Corner Uniformi Corner ir t Price e l l l l = μ + + ζ + β + β + β × + ε 1 2 3 , 

where ~ (0, )

2 N σ 1 ei , ~ (0, )2 ζ ir N σ 2 , and ~ (0, ) 2

ir t N σ 3,i ε l . The sessions are indexed by i; the 

subjects acting as retailers within each session are indexed by r = 1, 2, 3, 4; and the repeated 

periods are indexed by t (e.g., t = 301, 302, ..., 600). Cornerl = 0 if l = (s, a) = (4, 4) and 1 

otherwise. The dependent variable ir t Price l is the transaction price received by subject r in 

session i at station location l in period t. We also accommodate heteroskedastic errors by 

session when estimating the model via maximum likelihood. 

Estimates of the treatment and location effects are easy to compute with this 

specification. The intercept μ is the expected price in the zone pricing treatment at the center 

location, μ + β1 is the expected price in the uniform pricing treatment at a center location, μ + β2

is the expected price in the zone pricing treatment at the corner location, and μ + β1 + β2 + β3 is 

the expected price in the uniform pricing treatment at a corner location. 

Finding 1: Retail transaction prices are statistically higher in the isolated areas than in the 

clustered area. Uniform pricing in the wholesale market increases retail transaction prices in 

the clustered area, but has no significant effect on transaction prices in the isolated areas. 

Evidence: The mixed effects estimation results presented in Table 1 provide the quantitative 

support for this finding. The average retail transaction price with zone pricing at the wholesale 

level is μˆ = 149.98 at a station in the clustered area and 2

ˆ μˆ + β = 191.58 in an isolated area, a 

27.7% increase. This effect of location is highly significant ( 2

ˆ

β = 41.60, p-value = 0.0000). 

With uniform pricing in the wholesale market, the average retail transaction price is 1

ˆ μˆ + β = 

166.38 at a station in the center and 1 2 3 ˆ ˆ ˆ μˆ + β + β + β =195.87 in the corner. Again the effect of 

location is highly significant ( 2 3 β

ˆ βˆ + = 29.49, p-value=0.0000). The uniform pricing treatment 

effect of 1 β

ˆ = 16.40 at center stations is statistically significant (p-value = 0.0311) and nontrivial 

in economic terms—uniform pricing at the wholesale level increases retail transaction prices in 
14

the clustered area by 10.9%. Transaction prices in the isolated stations are slightly higher with 

uniform pricing than with zone pricing ( 1 3 ˆ ˆ β + β = 4.29), but this effect is statistically 

insignificant (p-value = 0.4926).

Given the data generated by our experiment we are able to determine that high retail 

prices in the isolated areas are not the result of high wholesale prices with zone pricing, but 

rather the cause of high wholesale prices. Figure 4 plots average wholesale prices and average 

posted retail prices by location for the first 300 periods when subjects are learning about the 

competitive pressures or lack thereof. Notice that, unlike Figure 2, wholesale prices to corner 

stations have a noticeable upward trend in the zone pricing treatment. Over the first 100 periods, 

corner station retail prices are very high. As the refiners recognize that these isolated stations are 

very profitable at those prices, the refiners use zone pricing to capture some of the rents from the 

corner stations. The clustered area stands in rather marked contrast. As station prices tumble due 

to the competition, wholesale prices also fall as the refiners use zone pricing to be more 

competitive. Only after station prices stabilize around period 250 do wholesale prices start to 

rise as refiners attempt to capture the retailer profits in the clustered area. Ultimately, the 

refiners capture more of the profits with zone pricing, but not to the detriment of consumers. 

 Also, we are able to gain insight as to why uniform pricing in the wholesale market 

actually increases transaction prices for consumers in the clustered area. Fundamentally, the 

reason is that uniform wholesale pricing forces the refiner to forgo profits in the corner to be 

competitive in the center. Thus refiners forced to sell at a uniform price have an incentive to 

keep wholesale prices elevated relative to the central wholesale prices with zone pricing. This is 

demonstrated in Figure 5, which plots the red refiner’s wholesale price and red retailer’s posted 

price for the center red branded station and the average decision by their counterparts for one 

session. At the beginning of the session, the red refiner’s price quickly plummets from 231 to 

120 and the red center station (s, a) = (4, 4) eventually follows suit. The refiner observes that 

there are substantial profits accruing at the red corner station (s, a) = (6, 6) and in period 176 

raises the wholesale price to 200. When in period 190 the red center station restocks at the new 

wholesale price of 200, the station owner is forced to raise the center station’s retail price. The 

red retailer maintains a high price at the center location because it cannot compete with the other 

central stations which have average retail prices below the red retailer’s wholesale price. As the 
15

red station will not sell these higher-cost units, competition in the center is weakened and 

gradually the prices of the other refiners and stations drift upwards. The end result is that 

uniform pricing at the wholesale level stymies retail competition in the clustered center. 

Moreover, we note that it only takes one refiner’s unilateral action to initiate this process of 

mitigating competition. 

Our second finding considers the effect of mandating uniform wholesale pricing on buyer 

utility. The ability to collect direct measures of consumer welfare and conduct this analysis is 

another major benefit of a laboratory study over a field study where such measures cannot be 

collected. Again, we use a linear mixed-effects model to estimate the quantitative effects of the 

treatment (Zone vs. Uniform Pricing) on buyer utility. We classify each buyer as one of three 

types: interior, equidistant, and periphery. Interior buyers are those closest to the center stations. 

These buyers originate at one of the following intersections: (4, 3), (4, 4), (4, 5), (3, 4), or (5, 4). 

A buyer is equidistant from the center stations and at least one corner station if it originates at (4, 

1), (4, 2), (4, 6), (4, 7), (1, 4), (2, 4), (6, 4), (7, 4), (3, 3), (3, 5), (5, 3) or (5, 5). All other buyers 

are relatively isolated, being located closer to a corner station, and are categorized as periphery 

buyers. The treatment effect, buyer types (Interior, Equidistant, and Periphery) and interaction 

effects are modeled as zero-one fixed effects. The sessions are again treated as random effects, 

ei, so that the model we estimate is 

i t i t it

it i i t t

 Uniform Equidistant Uniform Periphery

BuyerUtility e Uniform Equidistant Periphery

β β ε

μ β β β

× + × +

= + + + + +

4 5

1 2 3

where ) ~ (0, 2 N σ 1 ei and ~ (0, ) 2

it N σ 2,i ε . The sessions are indexed by i and the repeated periods 

by t (e.g., t = 301, 302, ..., 600). BuyerUtilityit is the utility that the buyer achieved in the 

market. We also include heteroskedastic errors by session in the maximum likelihood estimation 

of the model. 

Finding 2: Uniform pricing in the wholesale market decreases buyer welfare for the interior 

and equidistant buyers, but has no significant effect on periphery buyers. 

Evidence: Table 2 reports the quantitative support for this finding. The average buyer utility for 

interior buyers with zone pricing is μˆ = 101.58 but with uniform wholesale pricing, buyer 

welfare drops by 1

ˆ − β = 17.15, a 16.9% decrease that is statistically significant (p-value = 

0.0491). With zone pricing, equidistant buyers are worse off than interior buyers because they 
16

have further to travel to the lower-price center stations ( 2

ˆ

β = –17.45, p-value = 0.0000). 

However, with uniform wholesale pricing, the welfare for these buyers is even lower than 

equidistant buyers in the zone pricing treatment ( 1 4 β

ˆ βˆ + = –15.01, p-value = 0.0549). The point 

estimates indicate that periphery buyers are harmed by uniform pricing ( 1 5 β

ˆ βˆ + = –10.05), but 

this effect is statistically insignificant (p-value = 0.1555).

 These first two findings directly counter the claims that zone pricing harms consumers 

and that uniform pricing would benefit consumers. Uniform pricing in the wholesale market 

raises the actual prices that consumers pay and reduces the welfare to all buyers except those on 

the periphery. Our next finding reports the impact of uniform wholesale pricing on station and 

refiner profits. 

Finding 3: Uniform pricing significantly increases station profits, but has no effect on refiner 

profits. 

Evidence: Figure 6 provides the qualitative support for this finding. Each marker represents the 

profits for one station owner from both the center and corner stations plotted against the 

associated refiner’s profits. It is clear from the figure that station owner profits increase 

substantially with uniform wholesale pricing. The average station owner earns profits of 801 

with zone pricing and 2304 with uniform pricing. For the quantitative support for this finding, 

we use a Wilcoxon rank sum test to compare the total station owner profits of the four zone 

pricing sessions with the total station owner profits of the four uniform sessions. We reject the 

null hypothesis of equal station owner profits with a two-sided test (W = 26, n = 4, m = 4, p-value 

= 0.0286). Average refiner profits are slightly lower with uniform pricing, 2616 versus 3006 

with zone pricing, but a Wilcoxon rank sum test indicates that this difference is not statistically 

significant (W = 17, n = 4, m = 4, p-value = 0.8857).

 In sum, we find that uniform wholesale pricing (a) reduces consumer welfare by 

increasing the prices that buyers pay at the clustered stations, relative to retail prices under zone 

pricing, (b) has no statistical effect on consumer welfare and prices for buyers that are in isolated 

areas, (c) increases station owner profits, and (d) has no statistical effect on refiner profits. 
17

5.1.2 Zone Pricing (Lessee Dealers) versus Company-owned Stations 

The effects of vertical integration are also rather striking. Figure 7 displays histograms of 

the posted retail prices. The mode for the corner stations is 200 in both the zone and companyowned treatments; however, there is considerably more mass in the left tail of the companyowned treatments. The effect of vertical integration on retail prices is considerably more 

conspicuous at the center stations. The entire distribution of posted prices shifts to the left with 

company-owned stations. The mode with lessee dealers is only 120, whereas the mode is 150 

under zone pricing with lessee dealers. 

For our quantitative analysis, we estimate a linear mixed effects for transaction prices. 

The treatment effect (Lessee Dealers with Zone Pricing vs. Company-Owned Stations) and 

location effect (Center vs. Corner station) and an interaction effect are modeled as zero-one 

fixed effects, while the 8 independent sessions and the 4 retailers within each session are 

modeled as random effects. 

Finding 4: Retail transaction prices with company-owned stations are statistically lower in both 

the clustered area and the isolated areas than in the zone pricing treatment. 

Evidence: The mixed effects estimation results presented in Table 3 provide the quantitative 

support for this finding. With company-owned stations, the average retail transaction price is 

1

ˆ μˆ + β = 130.22 at a station in the center, which is 13.2% lower than transaction prices with 

lessee dealers ( μˆ = 149.97). This effect is statistically significant ( 1

ˆ

β = -19.75, p-value = 

0.0157). In isolated areas, transaction prices with company-owned stations are -( 1 3 β

ˆ βˆ + ) = 

31.53 less than transaction prices with lessee dealers. This effect is also statistically significant 

(p-value = 0.0022), reducing transaction prices by 16.5% from a level of 1 2

ˆ μˆ + β = 191.58 with 

lessee dealers to 1 1 2 3 ˆ ˆ ˆ μˆ + β + β + β = 160.04 with company-owned stations.

Finding 4 reports the extent to which a double markup by refiners and stations raises the 

prices that consumers pay vis-à-vis a single markup by company-owned stations. This finding 

complements the field studies of Barron and Umbeck (1984) and Vita (2000) and a laboratory 

study by Durham (2000), which also find that prices are lower with vertical integration than 
18

without. Our next finding quantifies the additional utility buyers receive from eliminating the 

double markup with company-owned stations. 

Finding 5: Relative to the zone pricing treatment, pricing with company-owned stations 

increases buyer welfare for all types of buyers: interior, equidistant, and periphery. 

Evidence: Table 4 reports that with company-owned stations the utility of interior and 

equidistant buyers increases by 1

ˆ

β = 20.45 (p-value = 0.0241). (The point estimate for 

equidistant buyers, 4

ˆ

β , is small and highly insignificant.) The utility of periphery buyers 

increases by 1 5 β

ˆ βˆ + = 25.94 (p-value = 0.0084). These absolute increases in buyer welfare 

translate into percentage increases of 20.1%, 24.4%, and 50.6% in buyer welfare for the interior, 

equidistant, and periphery buyers, respectively.

5.2 Dynamic Adjustments with Nonstationary Wholesale Costs 

We now turn our attention to how prices dynamically adjust to nonstationary costs. 

Figure 8 depicts wholesale costs, average retail prices, and average retailer costs (net of 

10

( , ), r =

i s a b

e

ρ ) for the selling a unit from inventory in period t. It is clear that retail prices more 

closely follow the station’s cost of gasoline than the wholesale costs. The noticeable lagged 

response of retail prices to changes in wholesale costs is presumably due to the inventories of the 

stations. In this subsection we investigate how retail prices adjust to changes in station costs. In 

particular, we investigate whether station prices respond symmetrically or adjust faster to cost 

increases than to decreases. 

As a first step, we must determine whether a long run relationship exists between station 

prices pt and costs ct (wholesale prices).29 If both pt and ct are nonstationary with a unit root, i.e., 

integrated of order 1, I(1), then we can operationalize the hypothesis of a long run (equilibrium) 

relationship between pt and ct using the concept of cointegration developed by Engle and 

Granger (1987). Station prices and costs are said to be cointegrated if a linear combination of the 

two series is stationary, I(0). If pt and ct have a long run equilibrium relationship, then the short 

run dynamics of the cointegrated system also have an error-correction representation. An error-

 

29 For ease of exposition we are dropping the location, retailer identity, and brand subscripts from per period prices 

and costs. 
19

correction model of the first differences (∆pt and ∆ct) includes a term that reflects the current 

“error” in the levels of pt and ct in achieving long-run equilibrium. To test whether prices adjust 

asymmetrically or symmetrically to changes in cost, we follow Granger and Lee (1989) in 

estimating a non-symmetric error correction model, namely, 

t t t t t t ∆p = α ∆c +α ∆p + φ z +φ z +ξ +

1 −1 2 −1 1 −1 2 −1 , 

where ~ (0, )

2 ξ t N σ , zt-1 is the error-correction term, and max( , 0) 1 −1

+

t− = t z z . If prices adjust 

symmetrically, the speed of the adjustment to the long run equilibrium is captured 

by 1 0 − < φ1 < , with 0. φ 2 = If prices respond faster to cost increases than decreases, then 

1 0 − < φ1 < and 0 φ2 > . 

 We begin this analysis by considering station prices and costs averaged across all 

sessions and subjects for each station location (corner and center) and treatment (zone, uniform, 

and company-owned), as depicted in Figure 8. Augmented Dickey-Fuller tests fail to reject the 

null hypothesis for all series. Given that the each of series are found to be I(1), we now consider 

whether a long run equilibrium exists between prices and costs for each location in each 

treatment. This is our sixth finding. 

Finding 6: With zone wholesale pricing and company-owned retail pricing, a long run 

relationship exists between station prices and station costs for both center and corner stations; 

however no such relationships exist with uniform pricing. 

Evidence: Table 5 reports the results of Johansen cointegration tests, which serve as the 

qualitative support for this finding.30 Likelihood ratio (LR) tests of the number of cointegrating 

equations indicate that there is 1 cointegrating equation at the 1% level of significance for both 

corner and center stations with zone wholesale pricing and that there is 1 cointegrating equation 

at the 5% level of significance for both corner and center stations with company-owned retail 

pricing. However, the LR tests reject any cointegration with uniform pricing at the 5% level of 

significance for either locale.

Finding 6 indicates that a long run equilibrium between station prices and costs with zone 

wholesale pricing and with company-owned retail pricing. Shocks to costs, both positive and 

 

30 Schwarz criteria indicate that a one period lag is superior to any other lag specification from two to thirty. The 

test assumption also assumes no deterministic trend in the data since none was included in the induced wholesale 

costs. 
20

negative, are passed-through to customers via changes in station prices according to a stable long 

run relationship between the two series. In contrast, we find that uniform wholesale pricing 

breaks down the long run relationship between costs and prices at both center and corner 

stations. This means that any relationship implied by a regression of prices on costs in levels is 

spurious. Changes in costs still may lead to changes in prices, as Figure 8 faintly shows, but 

there is no short run adjustment of prices toward a long run relationship with costs when costs 

experience a shock. Uniform pricing purges the responsiveness of retail prices to cost changes, 

the negative implication being that when wholesale costs fall, retail prices do not follow. This 

also means that retail prices are insulated to increases in wholesale costs, but we also observe in 

Finding 2 that uniform wholesale pricing generates high retail prices in the clustered area. 

Our seventh finding addresses the “rockets and feathers” phenomenon in the retail 

gasoline industry with zone wholesale pricing (7a) and company-owned retail pricing (7b), 

where a long run equilibrium relationship exists. 

Finding 7a: Station prices in the clustered area adjust quickly and asymmetrically to changes in 

costs with zone pricing. Station prices in isolated areas adjust more slowly, but symmetrically to 

changes in costs. 

Evidence: Table 6 and Figure 9 provide the support for this finding. Table 6 reports the 

estimates of the error-correction model for the average station prices and costs across all sessions 

and subjects by treatment. The error-correction term is highly significant and is largely 

responsible for explaining the adjustment of prices (p-value < 0.0001 for both center and corner 

stations). When the error-correction term is positive (i.e., costs are lower relative to what is 

specified in the long run equilibrium given prices), the speed of adjustment for center stations is 

considerably slower ( 1 2 ˆ ˆ φ + φ = -0.176 + 0.128 = -.048) than when error-correction term is 

negative ( 1

ˆ

φ = -0.176). Figure 9 plots the adjustment of prices to positive and negative cost 

shocks of 10 experimental dollars. For the center stations, over 90% of an increase in costs is 

reflected in the price in just 11 periods (or just 18.7 seconds of experiment time), but in the same 

amount of time only 40% of a decrease in costs is passed-through. In fact, it takes 45 periods (or 

76.5 seconds) for 90% of a cost decrease to be reflected in the price at center stations. The speed 

of adjustment for corner stations is slower than for center stations ( 1

ˆφ = -0.047) and is symmetric 

( 2

ˆφ is statistically insignificant with a p-value = 0.1650).
21

Finding 7b: With company-owned retail pricing, station prices adjust symmetrically and much 

more slowly to changes in station costs.

 

Evidence: As reported in Table 6, the error-correction term 1

ˆ

φ is significant for both center and 

corner stations (p-value = 0.0001 and 0.0353, respectively), but 2

ˆφ is statistically insignificant 

(p-values = 0.9522 and 0.7719). Figure 9 indicates that the adjustment of prices to positive and 

negative cost shocks is rather slow. For the center stations, 90% of an increase in costs is 

reflected in the price in 123 periods, and at corner stations it takes 152 periods for 90% of a cost 

increase to be reflected in the price.

Having found at an aggregate level that prices adjust asymmetrically at the center stations 

with zone wholesale pricing, we continue to exploit our dataset to investigate asymmetric price 

responses to costs. First, we estimate error-correction models for the center stations at the 

session level and then for each individual station owner in each of the four zone pricing 

sessions.31 In the interest of brevity, we focus on the estimates at the session level but only 

classify the individual station owners by whether 2

ˆφ is (a) statistically positive, (b) statistically 

not different than zero, (c) statistically negative, or by (d) not having cointegrated prices and 

costs. 

Finding 8: At the session level, station prices in the clustered area adjust asymmetrically to 

changes in costs with zone pricing. Individual station owners are predominantly and equally 

classified as one of two behavioral types: asymmetric and symmetric adjusters. 

Evidence: Table 8 reports that prices rise faster than they fall in response to cost changes in three 

of the four sessions ( 2

ˆ

φ > 0 with p-values = 0.0051, 0.0740, and 0.0000). In the fourth session, 

prices actually fall faster than they rise ( 2

ˆφ < 0 with p-value = 0.0107). The sessions that adjust 

faster to cost increases contain at least one station owner who is classified as an asymmetric 

adjuster with 2

ˆφ statistically greater than 0, as reported in Table 8. The fourth session contains 

three symmetric adjusters and one station owner who decreases prices faster than he increases 

 

31 Augmented Dickey-Fuller tests for all series fail to reject the null hypothesis of a unit root, and unless otherwise 

noted, Johansen cointegration tests indicate that all pairs of price and cost series are cointegrated. The Schwarz 

criteria also continue to indicate that 1 lag is superior to any other lag specification. 
22

them in response to cost changes. Of the 16 station owners, 6 respond faster to cost increases 

than to decreases and 6 respond symmetrically.

Finding 8 begs the question as to whether there are any reasons why some station owners 

adjust asymmetrically while others adjust symmetrically. Using a meta-analysis of several 

industries, Peltzman (2000) uncovers a stylized fact that more volatile input prices are correlated 

with less price asymmetries. In our laboratory experiment we can directly test whether the 

volatility of wholesale prices set by refiners affects the price adjustment behavior of station 

owners. This comprises our final finding. 

Finding 9: The volatility of wholesale prices is uncorrelated with whether or not a station 

owner adjusts prices asymmetrically or symmetrically. However, during regimes of increasing 

wholesale costs, more volatile wholesale prices are correlated with station owners who respond 

more quickly to wholesale price increases than to wholesale price decreases. 

Evidence: Using a Wilcoxon rank sum statistic we test whether the variance of the change in 

wholesale prices is correlated with a station owner being classified as an asymmetric ( 2

ˆφ > 0) or 

symmetric ( 2

ˆφ = 0) price adjuster. Specifically, we compare the variance of wholesale price 

changes for periods 601-1200 for the 6 asymmetric price adjusters to the 6 symmetric price 

adjusters and find that there is no statistical difference (W = 43, n = 6, m = 6, p-value = 0.5887). 

However, if we separately measure the variance of price changes when wholesale costs are rising 

(periods 778-853 and 1053-1200), volatility of wholesale prices is larger for the asymmetric 

price adjusters than for symmetric price adjusters (W = 27, n = 6, m = 6, p-value = 0.0649). 

When wholesale costs are falling in periods 601-777 and 854-1052, there is no statistical 

difference in the volatility of wholesale prices for the different retail types (W = 43, n = 6, m = 6, 

p-value = 0.5887). 

 The implication of Finding 9 is that refiners who have greater volatility in wholesale 

prices during periods of rising crude oil costs cause their station owners to increases their prices 

more quickly than these same individuals decrease their retail prices when wholesale prices and 

oil prices are falling. 
23

6. Conclusion 

 The gasoline industry is an intricate system, making the implications of policies such as 

prohibiting zone pricing and vertical integration unclear from anecdotal evidence alone. 

However, such topics are regularly debated in the political arena. Consumers and media 

routinely scrutinize retail gasoline prices looking for evidence of anticompetitive behavior. The 

sheer magnitude and social interest in this market has led numerous research studies of the 

industry. Unfortunately, this field research must rely on incomplete information. In this study 

we detail a laboratory investigation of the gasoline industry, focusing specifically on uniform 

pricing at the wholesale level, divorcement, and asymmetric retail price responses to cost shocks. 

 Our study provides a series of insights into the effect of zone pricing. In many situations 

our results provide support for hypotheses formalized by previous researchers. First, prices in 

relatively isolated areas are higher than prices in areas with a clustering of stations. Contrary to 

the claims expressed by proponents of uniform pricing legislation, uniform wholesale pricing 

actually increases prices in the more competitive area and simultaneously does not alter prices in 

more isolated geographic areas that are less competitive. Due to this behavior, uniform pricing 

actually reduces the welfare of buyers who are closest to the center area, as well as those who are 

on the border of the center and isolated areas. The buyer losses are not the refiners’ gains. In 

fact we find that refiners’ profits are unaffected by the uniform pricing. Instead, it is the retailers 

that are extracting surplus from the consumers. The data offer a simple explanation for this 

result. Under uniform pricing, the refiners offer a price that is above the center area zone 

wholesale price and below the isolated area zone wholesale price. These refiners are balancing 

extracting economic rents from the isolated stations and remaining viable in the competitive, 

center area. Thus, a refiner’s gains in the center area, due to higher wholesale prices, are offset 

by reduced earnings in the isolated markets where wholesale prices have decreased and profits 

are unchanged. With uniform pricing the retailers do not gain a profit margin in the center area 

but do receive a larger margin in the isolated regions where retail prices are unchanged but 

wholesale prices have declined. 

 On the issue of divorcement we find that company-owned stations eliminate the double 

markup of prices. From this we can conclude that divorcement legislation harms consumers. All 

buyers, in clustered or isolated areas, pay lower prices and have substantially higher utility when 

stations are company-owned. This price finding in the laboratory (buyer utility cannot be 
24

directly measured in field studies) affirms the results from field studies, lending credence to our 

other findings. 

 Numerous studies have demonstrated an asymmetry in gasoline price responses. In the 

laboratory we are able to investigate this pattern while controlling for collusion, menu costs, and 

buyer search. With zone pricing, the practice in place when previous studies evaluated 

asymmetric price responses, we find that retail prices and retail costs are cointegrated, i.e., a long 

term equilibrium relationship exists between the two series. Our data indicate that prices in the 

center area adjust more quickly to costs increases than to cost decreases, but we find that in 

isolated areas price responses are symmetric. At the market (session) level we find that 

responses are influenced by the idiosyncrasies of individual retailers, some of whom respond 

symmetrically and some of whom respond asymmetrically. Further, a retailer’s type is not 

correlated with the volatility of its wholesaler’s prices. However, retailers who observe more 

volatile wholesale prices during periods of increasing wholesale costs (or world oil prices) are 

more likely to respond asymmetrically. With company-owned stations, prices adjust 

symmetrically to cost shocks. Further, this response is substantially slower in the companyowned treatment than with zone pricing. 

 In addition to benefiting retailers at the expense of consumers, another effect of uniform 

pricing is the destruction of the long term relationship between retail prices and costs. Formally, 

we find that with uniform wholesale pricing, retail costs and prices are not cointegrated and 

therefore any reduction in wholesale costs, say due to changes in the world price for oil, would 

not necessarily be reflected in retail prices. 

 As with any empirical study, the inherent specificity of a data set is determined by such 

details as the environment and institution, and raises the typical issues of inferring broader 

implications from the results. Further research would be useful in examining how the exogenous 

location and number of stations, as well as endogenous and costly entry and exit, affect our 

findings. Along the lines of Shepard (1993), it would also be useful to explore the endogenous 

formation of vertical relationships in laboratory markets.
25

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28

Table 2. Estimates of the Linear Mixed-Effects Model for Buyer Utility: 

Zone vs. Uniform Pricing

 

Estimate 

Std. 

Error 

Degrees of 

Freedom* t-statistic p-value

μ 101.58 4.68 2388 21.72 0.0000 

Uniform (β1) -17.15 6.57 6 -2.61 0.0401 

Equidistant (β2) -17.45 2.11 2388 -8.25 0.0000†

Periphery (β3) -50.31 1.90 2388 -26.45 0.0000†

Uniform × Equidistant (β4) 2.14 2.86 2388 0.75 0.4543 

Uniform × Periphery (β5) 7.10 2.57 2388 2.76 0.0058 

 2400 Obs. 

 Ha: β1 + β4 ≠ 0 0.0549 

 Ha: β1 + β5 ≠ 0 0.1555 

 †One-sided test. 

Table 1. Estimates of the Linear Mixed-Effects Model for Transaction Prices: 

Zone vs. Uniform Pricing

 

Estimate 

Std. 

Error 

Degrees of 

Freedom* t-statistic p-value

μ 149.98 4.13 2305 36.30 0.0000 

Uniform (β1) 16.40 5.85 6 2.80 0.0311 

Corner (β2) 41.60 0.75 2305 55.40 0.0000†

Uniform × Corner (β3) -12.11 1.05 2305 -11.56 0.0000 

 2339 Obs. 

 Ha: β1 + β3 ≠ 0 0.4926 

 Ha: β2 + β3 > 0 0.0000†

*N.B. The linear mixed effects model for repeated measures treats each session as one degree 

of freedom with respect to the treatments. Hence, with two treatment parameters, there are 6 

degrees of freedom for the estimates of the treatment fixed effect (8 sessions – 2 parameters). 

†

One-sided test. 

Note: The linear mixed-effects model is estimated by maximum likelihood. For brevity, the 

session and subject random effects are not included in this table or any others. 

Table 3. Estimates of the Linear Mixed-Effects Model for Transaction Prices: 

Zone Pricing with Lessee Dealers vs. Company-Owned Stations

 

Estimate 

Std. 

Error 

Degrees of 

Freedom t-statistic p-value

μ 149.97 4.99 2340 30.08 0.0000 

Company-owned (β1) -19.75 7.06 6 -2.80 0.0157†

Corner (β2) 41.60 0.75 2340 55.40 0.0000†

Company-owned × Corner (β3) -11.78 1.20 2340 -9.81 0.0000 

 2374 Obs. 

 Ha: β1 + β3 < 0 0.0022†

 Ha: β2 + β3 > 0 0.0000†

†

One-sided test. 
29

Table 5. Johansen Cointegration Tests of the Equation: pt = β0 + β1ct

 

Treatment and Location Eigenvalue 

LR 

statistic 

5% 

Critical 

Value 

1% 

Critical 

Value 

No. of 

Cointegrating 

Equations 

Zone Pricing, Center Stations 0.087171 55.27 19.96 24.60 0** 

0.001219 0.73 9.24 12.97 1 

 

Zone Pricing, Corner Stations 0.054853 35.43 19.96 24.60 0** 

0.002834 1.70 9.24 12.97 1 

 

Uniform Pricing, Center Stations 0.027930 18.03 19.96 24.60 0 

0.001816 1.09 9.24 12.97 1 

 

Uniform Pricing, Corner Stations 0.016531 10.91 19.96 24.60 0 

0.001572 0.94 9.24 12.97 1 

 

Company-Owned, Center Stations 0.042158 26.86 19.96 24.60 0* 

0.001847 1.11 9.24 12.97 1 

 

Company-Owned, Corner Stations 0.036239 22.91 19.96 24.60 0* 

0.001403 0.84 9.24 12.97 1 

 * Denotes rejection of the hypothesis at 5% significance level. 

 ** Denotes rejection of the hypothesis at 1% significance level. 

Table 4. Estimates of the Linear Mixed-Effects Model for Buyer Utility: 

Zone Pricing with Lessee Dealers vs. Company-Owned Stations

 

Estimate 

Std. 

Error 

Degrees of 

Freedom* t-statistic p-value

μ 101.59 5.82 2388 17.44 0.0000 

Company-owned (β1) 20.45 8.26 6 2.47 0.0241†

Equidistant (β2) -17.45 2.11 2388 -8.25 0.0000†

Periphery (β3) -50.32 1.90 2388 -26.44 0.0000†

Company-owned × Equidistant (β4) 0.05 3.11 2388 0.02 0.4940†

Company-owned × Periphery (β5) 5.49 2.81 2388 1.96 0.0253†

 2400 Obs. 

 Ha: β1 + β4 > 0 0.0216†

 Ha: β1 + β5 > 0 0.0084†

 †One-sided test. 
30

Table 6. Estimates of Error-Correction Model for ∆pt

 

Estimate 

Std. 

Error t-statistic p-value

Zone Pricing 

All Center Stations 

∆ct-1 -0.098 0.052 -1.87 0.0620 

∆pt-1 0.009 0.039 0.23 0.8182 

zt-1 -0.176 0.026 -6.82 0.0000 +

t−1 z 0.128 0.035 3.67 0.0003 

 2 R = 0.10 

Zone Pricing 

All Corner Stations 

∆ct-1 0.022 0.044 0.49 0.6243 

∆pt-1 -0.001 0.040 -0.23 0.8182 

zt-1 -0.047 0.010 -4.60 0.0000 +

t−1 z 0.020 0.014 1.39 0.1650 

 2 R = 0.06 

Company-Owned 

All Center Stations 

 

∆ct-1 0.052 0.066 0.79 0.4298 

∆pt-1 0.092 0.040 2.28 0.0230 

zt-1 -0.065 0.016 -3.98 0.0001 +

t−1 z -0.002 0.026 -0.06 0.9522 

 2 R = 0.05 

Company-Owned 

All Corner Stations 

 

∆ct-1 -0.006 0.066 -0.09 0.9283 

∆pt-1 0.038 0.041 0.92 0.3579 

zt-1 -0.039 0.018 -2.11 0.0353 +

t−1 z 0.007 0.025 0.29 0.7719 

 2 R = 0.01 
31

Table 7. Estimates of Error-Correction Model for ∆pt 

by Session for Center Stations

 

Estimate 

Std. 

Error t-statistic p-value

Session 1 

∆ct-1 -0.048 0.047 -1.01 0.3129 

∆pt-1 0.008 0.040 0.20 0.8415 

zt-1 -0.152 0.027 -5.65 0.0000 +

t−1 z 0.101 0.036 2.81 0.0051 

 2 R = 0.07 

Session 2 

∆ct-1 -0.177 0.069 -2.55 0.0110 

∆pt-1 -0.042 0.040 -1.05 0.2941 

zt-1 -0.086 0.026 -3.32 0.0010 +

t−1 z 0.061 0.034 1.79 0.0740 

 2 R = 0.04 

Session 3 

∆ct-1 -0.174 0.051 -3.41 0.0007 

∆pt-1 0.015 0.039 0.38 0.7041 

zt-1 -0.244 0.029 -8.49 0.0000 +

t−1 z 0.178 0.041 4.37 0.0000 

 2 R = 0.13 

Session 4 

∆ct-1 -0.127 0.047 -2.68 0.0076 

∆pt-1 0.002 0.038 0.04 0.9681 

zt-1 -0.086 0.014 -6.19 0.0000 +

t−1 z -0.062 0.024 -2.56 0.0107 

 2 R = 0.10 
32

Table 8. Types of Station Owners

 Asymmetric 

2

ˆφ > 0 

Symmetric 

2

ˆφ = 0 

Asymmetric 

2

ˆφ < 0 

pt and ct are 

not cointegrated

Session 1 

X* 

No. Subjects 3 1 

Session 2 X 

No. Subjects 1 1 2** 

Session 3 X 

No. Subjects 2 1 1 

Session 4 X 

No. Subjects 3 1 

Totals 6 6 2 2 

 *See Table 7 for estimates. 

**The refiners for these station owners maintained somewhat high prices that did not 

 vary much over time. 

1st St.

2nd St.

3rd St.

4th St.

5th St.

1st Ave. 2nd Ave. 3rd Ave. 4th Ave. 5th Ave. 6th Ave. 7th Ave.

6th St.

7th St.

1

(4,4), b1 ρ

1

(2,2), b1 ρ

3

(4,4), b3 ρ

4

(4,4), b4 ρ

3

(2,6), b3 ρ

4

(6,6), b4 ρ 2(6,2), b2 ρ

2

(4,4), b2 ρ

1st St.

2nd St.

3rd St.

4th St.

5th St.

1st Ave. 2nd Ave. 3rd Ave. 4th Ave. 5th Ave. 6th Ave. 7th Ave.

6th St.

7th St.

1

(4,4), b1 ρ

1

(2,2), b1 ρ

3

(4,4), b3 ρ

4

(4,4), b4 ρ

3

(2,6), b3 ρ

4

(6,6), b4 ρ 2(6,2), b2 ρ

2

(4,4), b2 ρ

Figure 1. Geographic Depiction of the Laboratory Retail Gasoline Market 
33

Zone Pricing to Corner Stations

90

110

130

150

170

190

210

301 326 351 376 401 426 451 476 501 526 551 576

Period

Price

Wholesale Cost

Uniform Pricing to Center and Corner Stations

90

110

130

150

170

190

210

301 326 351 376 401 426 451 476 501 526 551 576

Period

Price

Wholesale Cost

Zone Pricing to Center Stations

90

110

130

150

170

190

210

301 326 351 376 401 426 451 476 501 526 551 576

Period

Price

Wholesale Cost

Figure 2. Average Wholesale Prices by Session 

Corner Stations with Uniform Pricing

0

200

400

600

800

1000

1200

1400

1600

1800

110 130 150 170 190 210 230 250 More

Price Bins

Frequency

Corner Stations with Zone Pricing

0

200

400

600

800

1000

1200

1400

1600

1800

110 130 150 170 190 210 230 250 More

Price Bins

Frequency

Center Stations with Zone Pricing

0

200

400

600

800

1000

1200

1400

1600

1800

110 130 150 170 190 210 230 250 More

Price Bins

Frequency

Center Stations with Uniform Pricing

0

200

400

600

800

1000

1200

1400

1600

1800

110 130 150 170 190 210 230 250 More

Price Bins

Frequency

Figure 3. Histograms of Station Posted Prices: Zone vs. Uniform Pricing 
34

100

120

140

160

180

200

220

1 26 51 76 101 126 151 176 201 226 251 276

Period

Price

Corner Station Price

Wholesale Price to Corner Stations

Center Station Price

Wholesale Price to Center Stations

Figure 4. Average Wholesale and Station Prices with Zone Pricing in Periods 1-300 

100

120

140

160

180

200

220

240

260

1 26 51 76 101 126 151 176 201 226 251 276 301 326 351 376 401

Period

Price

Red Station Price

Red Wholesale Price

Average Other Station Prices

Average Other Wholesale Prices

Figure 5. Example of How High Prices Arise in the Center with Uniform Wholesale Prices
35

0

1000

2000

3000

4000

5000

6000

7000

0 1000 2000 3000 4000 5000 6000 7000

Station Profits

Refiner Profits

Zone, Session 1

Zone, Session 2

Zone, Session 3

Zone, Session 4

Uniform, Session 1

Uniform, Session 2

Uniform, Session 3

Uniform, Session 4

Figure 6. Station vs. Refiner Profits for Periods 301-600 

Corner Stations with Zone Pricing

0

200

400

600

800

1000

1200

1400

1600

1800

110 130 150 170 190 210 230 250 More

Price Bins

Frequency

Center Stations with Zone Pricing

0

200

400

600

800

1000

1200

1400

1600

1800

110 130 150 170 190 210 230 250 More

Price Bins

Frequency

Company-Owned Center Stations

0

200

400

600

800

1000

1200

1400

1600

1800

110 130 150 170 190 210 230 250 More

Price Bins

Frequency

Company-Owned Corner Stations

0

200

400

600

800

1000

1200

1400

1600

1800

110 130 150 170 190 210 230 250 More

Price Bins

Frequency

Figure 7. Histograms of Station Posted Prices: Zone Pricing (Lessee Dealer) vs. Company-Owned Stations
36

Zone Pricing to Center Stations

50

100

150

200

250

601 651 701 751 801 851 901 951 1001 1051 1101 1151

Period

Price

Prices

Costs

Wholesale Cost

Zone Pricing to Corner Stations

50

100

150

200

250

601 651 701 751 801 851 901 951 1001 1051 1101 1151

Period

Price

Prices

Costs

Wholesale Cost

Uniform Pricing to Corner Stations

50

100

150

200

250

601 651 701 751 801 851 901 951 1001 1051 1101 1151

Period

Price

Prices

Costs

Wholesale Cost

Uniform Pricing to Center Stations

50

100

150

200

250

601 651 701 751 801 851 901 951 1001 1051 1101 1151

Period

Price

Prices

Costs

Wholesale Cost

Company-Owned Pricing to Corner Stations

50

100

150

200

250

601 651 701 751 801 851 901 951 1001 1051 1101 1151

Period

Price

Prices

Costs

Wholesale Cost

Company-Owned Pricing to Center Stations

50

100

150

200

250

601 651 701 751 801 851 901 951 1001 1051 1101 1151

Period

Price

Prices

Costs

Wholesale Cost

Figure 8. Station Prices and Costs in Periods 601-1200
37

-10

-8

-6

-4

-2

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Periods

Adjustment of Prices to 

Cost Decreases Cost Increases

Center Stations

Corner Stations

Zone Pricing Treatment 

-10

-8

-6

-4

-2

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Periods

Adjustment of Prices to 

Cost Decreases Cost Increases

Center Stations

Corner Stations

Company-Owned Treatment 

Figure 9. Adjustment of Station Prices to a 10 Experimental Dollar Innovation in Cost