WORKING 

 PAPERS

Vertical Relationships and Competition 

in Retail Gasoline Markets: Comment

Christopher T. Taylor

Nicholas Kreisle

Paul R. Zimmerman

WORKING PAPER NO. 291

September 2007

___________________________________________________________________________

FTC Bureau of Economics working papers are preliminary materials circulated to stimulate discussion

and critical comment. The analyses and conclusions set forth are those of the authors and do not

necessarily reflect the views of other members of the Bureau of Economics, other Commission staff, or

the Commission itself. Upon request, single copies of the paper will be provided. References in

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author to protect the tentative character of these papers.

______________________________________________________________________________

BUREAU OF ECONOMICS

FEDERAL TRADE COMMISSION

WASHINGTON, DC 20580
Vertical Relationships and Competition in Retail Gasoline Markets: Comment 

Christopher T. Taylor*

Federal Trade Commission 

Nicholas Kreisle 

Federal Trade Commission 

Paul R. Zimmerman 

Federal Trade Commission 

September 27, 2007

Abstract: In a paper in the March 2004 AER, Justine Hastings concludes that the acquisition of an 

independent gasoline retailer, Thrifty, by a vertically integrated firm, ARCO, is associated with sizable 

price increases at competing stations. To better understand the novel mechanism to which she attributes 

this effect – which combines vertical integration and rebranding – we attempted but ultimately failed to 

reproduce the results using alternative data. In addition, we show that the welfare effects of the 

transaction are ambiguous in the theoretical model which she posits as underlying the empirical results. 

Keywords: Gasoline, Merger Retrospective, Retail, Replication 

JEL: L41, L42, L71

 

*

 Corresponding author, Christopher Taylor, Bureau of Economics, Federal Trade Commission, 600 Pennsylvania 

Ave, NW, Washington, DC 20580, ctaylor@ftc.gov, 202-326-3491. Views and opinions expressed in this paper are 

solely those of the authors and should not be interpreted as reflecting the views of the Federal Trade Commission, 

any of its individual Commissioners, or other members of the staff. Comments by Patrick DeGraba, Jeffery Fischer, 

Daniel Hosken, David Meyer, Louis Silvia, John Simpson, Michael Vita and excellent research assistance by 

Elisabeth Murphy and Brian Murphy are appreciated. 
In a paper in the March 2004 American Economic Review, Justine Hastings studies the 

acquisition of a sizable independent gasoline retailer, Thrifty Oil Company (Thrifty), by a vertically 

integrated refiner/retailer, ARCO. She employs a difference-in-differences approach on a panel of 

station-specific prices to examine the price effects at competing stations of this transaction. She finds that 

the loss of an independent marketer increased retail gasoline prices by five cents per gallon, but changes 

in horizontal concentration and differences in the degree of vertical control ARCO exerted over its newly 

acquired branded outlets did not affect prices. Further empirical results lend support to a particular model 

of consumer brand loyalty as the underlying mechanism for the post-acquisition price increase. 

These results have several implications. Previous research had generally shown that greater 

degrees of vertical integration are associated with lower retail gasoline prices.1

 Hastings’ results suggest 

that regulations aimed at restricting refiners’ vertical control may in fact be benign. Also, merger 

enforcement may be an appropriate instrument to protect against price effects from the decline in 

independents’ market share from their acquisition by vertically-integrated refiners. In addition, the 

merger policy implications may transcend gasoline retailing since brand loyalty drives consumption 

decisions in a variety of product markets. 

The sheer size of the estimated price effect – five cents per gallon amounts to a 50 percent 

increase in retail margins – along with a desire to better understand the novel mechanism behind it, 

motivated us to revisit Hastings’ analysis.2

 Being unable to acquire her data, we used an alternative 

source, which in aggregate is very similar to the original data set. While there are differences between the 

two data sets, the five-cent effect is large enough that we would expect to find an effect of a similar order 

of magnitude. Ultimately, however, we find an effect of approximately three-tenths of a cent per gallon, 

which is of little economic (and often statistical) significance. This finding is robust to using various sub-

 

1

 For examples of papers that find vertical integration pro-competitive or bans on vertical integration being 

anticompetitive see Barron and Umbeck (1984), Shepard (1990, 1993) and Vita (2000). 2

 The average price of regular grade gasoline, without tax, to end users in California in 1997 was 86 cents per gallon. 

The average wholesale price of regular gasoline in California was 76 cents per gallon in 1997. Department of 

Energy, Energy Information Service, gasoline prices by formulation, grade and sales type. Available at 

http://tonto.eia.doe.gov/dnav/pet/pet_pri_allmg_c_SCA_EPMR_cpgal_a.htm. 

1
samples, analysis of higher-frequency data, unavailable in Hastings’ data, and whether or not we use 

clustered standard errors at the station-level. 

 In addition, we examine the theory of brand loyalty as outlined by Hastings. She employs a 

modified Hotelling model in which both firms will raise prices after an independent converts to a branded 

station. We point out that the corresponding increase in gross consumer utility in the model implies that 

total welfare must also increase as a consequence of rebranding. Furthermore, we find parameter values 

such that total consumer surplus increases after rebranding, even in the face of an across-the-board price 

increase. 

 Thus, our empirical results cast doubt on whether ARCO’s acquisition of Thrifty led to higher 

prices. In addition, our theoretical analysis suggests that, even if the acquisition led to a price increase, 

the welfare effects are ambiguous in Hastings’ model of brand loyalty. 

I. The ARCO/Thrifty Transaction 

 Hastings (2004) examines the price effect of ARCO’s 1997 long term lease of 265 retail sites 

from Thrifty. The Thrifty stations were located primarily in San Diego and Los Angeles. This transaction 

marked the exit of the largest independent/unbranded chain of gasoline stations in Southern California at 

the time. We refer the reader to Hastings (2004) for a discussion of the details of the transaction. 

However, we add two pieces of context: this transaction was part of a trend in California and the firms 

had a prior vertical relationship. 

 In 1982, even though it was a vertically-integrated refiner with a branded retail presence, ARCO 

adopted a low-price strategy, making its retail prices similar to those of Thrifty and other unbranded 

outlets. In 1996 the introduction of CARB Phase II gasoline led to the exit of several small refineries – 

most of which supplied the unbranded wholesale market – and an output reduction from the California 

refineries that remained.3

 The tighter supply of gasoline in California resulted in periodic pricing 

 

3

 CARB is an acronym for the California Air Resources Board. California adopted standards for cleaner-burning 

gasoline in 1990-1992, Phase II began in 1996. According to data from the Energy Information Administration, in 

2
inversions, whereby unbranded prices rise above branded prices at either wholesale or retail.4

 

Consequently, during periods of 1996 ARCO was setting retail prices below those of some independent 

rivals. To avoid such undercutting, World Oil, a sizable independent retailer in Southern California, 

signed a branding agreement with Exxon around the time of the ARCO/Thrifty transaction. 

 Prior to the announcement of the ARCO/Thrifty transaction in March 1997, Thrifty was one of 

ARCO’s largest dealers. Specifically, 44 of the stations that ARCO acquired were already selling ARCObranded gasoline.5

 In January 1997 a press report stated that ARCO had signed an agreement to supply 

unbranded gasoline to 110 of Thrifty’s stations. Thus, over half of Thrifty’s 265 stations were already 

being supplied by ARCO before the transaction was consummated. 6

 In total, over 15 percent of the 

stations ARCO leased were already branded ARCO and half of the stations were being supplied by 

ARCO. Therefore, the change in branding and vertical market structure induced by the transaction 

affected a smaller number of stations than might appear. 

II. Analysis 

 A. Data Sets 

Hastings uses data from the Whitney Leigh Corporation (W-L), including a census of station 

locations in Southern California and pricing data on a 20 percent sample of stations in this census report. 

Ninety-nine of the 669 sampled stations are within a mile of a rebranded Thrifty station. The pricing data 

are reported to be “the prices posted at the end of the volume collection period in February, June, October 

 1991 there were 32 refineries with a capacity of 2,185,600 barrels per day (bpd) in California. By 1995, only 25 

refineries remained with a capacity of 1,910,300 bpd. Sources: <http://www.arb.ca.gov/html/brochure/history.htm> 

and <http://tonto.eia.doe.gov/dnav/pet/pet_pnp_cap1_dcu_SCA_a.htm>. 

4

During inversions unbranded stations may not simply purchase branded gasoline. For an example and discussion of 

this issue see Office of the Attorney General, State of Arizona, 2005 Gasoline Report, available at 

<http://www.azag.gov/consumer/gasoline/PublicGasReport2005.pdf>. Contrary to what is stated in footnote 7 of 

Hastings (2004), unbranded stations are not usually free to purchase the cheaper of branded or unbranded gasoline. 

5

 An issue we planned to examine was whether price increased at stations that surrounded the pre-transaction 

ARCO-branded Thrifty locations. This would have allowed us to estimate the effect of branding separately from the 

effect of vertical control. Since we could not find a sizable robust price effect, this became a moot point. 6

 This description of the gasoline market in California in 1996-1997 is drawn from Platt’s Oilgram News, November 

4 and 26, 1996, and January 13 and March 5, 1997 and 21st Century Fuels, April 1 and May 1, 1997. 

3
and December in 1997” (Hastings, p. 321). In other words, there are four data points for each station, two 

before and two after the transaction. 

 Being unable to acquire Hastings’ data, we examine a panel of station-specific retail prices for 

gasoline outlets in Los Angeles and San Diego from February 1996 through December 1998 from the Oil 

Price Information Service (OPIS).7

 Most of our results rely on the 1997 data. OPIS collects the data 

from fleet card transactions.8

 We use the average weekly price charged at a given outlet for a gallon of 

regular unleaded gasoline.9 We also obtained a 1997 census of gasoline station locations from the 

California Energy Commission (CEC). 

 It is important to gauge the extent to which the OPIS data differ from the W-L data. Table 1 

compares the distribution of brands in the OPIS and W-L data sets against the CEC census, for both Los 

Angeles and San Diego. Both the OPIS and W-L data under-sample minor brands and independents, as 

measured by the CEC census. 10 However, the overall distribution of brands in the W-L data better aligns 

with their actual brand shares as measured by the CEC census. Although the OPIS data set contains more 

stations, it omits some major brands (specifically, ARCO, Chevron, and Unocal) as well as a number of 

minor brands and independents.11 Neither the OPIS nor W-L data set captures price information at 

rebranded Thrifty stations either before or after the transaction. 

 To summarize, both the W-L and OPIS data sets provide retail price information at the stationspecific level. While the W-L data set is a more representative sample of the true distribution of brands, 

the OPIS data are available at a greater frequency (i.e., daily or weekly), for a larger number of stations 

 

7

 Professor Hastings denied requests for access to her data set since it is copyrighted. We were not able to purchase 

the data since the Whitney Leigh Corporation is now defunct. Professor Hastings provided us with a list of the 91 

stations found in both the W-L and OPIS data. Our results are unchanged if we restrict analysis to the overlap 

stations. 

8

A fleet card is type of credit card a company issues to its employees (salesmen, insurance claims adjusters, etc.) to 

pay for gasoline and possibly repairs. Fleet cards are used to monitor what items employees charge to the firm. 

9

 The OPIS data are collected on a daily basis. However, we choose to rely upon station-specific weekly averages 

for the price series since a given station does not necessarily have a price observation for each day of the week. 10 In addition to the Unbranded category, we considered the following brands in the CEC census to be either “minor 

brands” or “independents”: Alliance, American Gas, Circle K, Citgo, Fastrip, Olympic, Thrifty, Ultramar, United 

Oil, USA, and World Oil. 

11 This is due to the fact that some brands (ordinarily) disallow OPIS from reporting their fleet card purchases, or 

because some brands do not accept fleet cards (e.g., ARCO). The decision to accept a fleet card is by brand. The 

decision to accept fleet cards is not made by each station. 

4
and for a longer time period; possibly allowing for a more precise estimate of the extent to which the 

ARCO/Thrifty transaction affected retail gasoline prices.12 

 The ultimate question is to what extent the distribution of brands in the OPIS data could bias our 

results. We can make a conjecture based on Hastings’ Table 3, which documents how the ARCO/Thrifty 

transaction differentially affected “high-share”, “mid-share”, and “low-share” brands.13 (The effect on 

ARCO’s pre-merger stations is estimated separately.) Column (7) of our Table 1 displays Hastings’ 

classification for each brand. Hastings posits that market share is positively correlated with the degree of 

brand loyalty. Consequently, independents (e.g., Thrifty) compete most closely with low-share brands 

(e.g., Citgo), so that the ARCO/Thrifty transaction should increase prices the most at low-share branded 

stations. Indeed, using the W-L data Hastings finds that prices increased at low-share, mid-share, and 

high-share brands by approximately $0.07, $0.05, and $0.03 per gallon, respectively. Consistent with its 

low-price strategy, the estimated effect on ARCO is similar to the low-share group. 

Based on the OPIS data over-sampling high- and mid-share brands at the expense of low-share 

brands and ARCO, we expect our estimate of the transition’s effect on all stations to be lower than that 

estimated by Hastings. However, this difference should be small, since the effect on high- and mid-share 

brands was $0.03 and $0.05. Additionally, the OPIS data over-samples a low-share brand, Citgo, in San 

Diego, especially relative to the W-L data. So the effect of the distribution of brands in the San Diego 

OPIS data is ambiguous. For the OPIS pooled sample (consisting of both Los Angeles and San Diego), 

we expect that the difference between the OPIS and W-L data sets may still lead to a slightly smaller 

estimated effect. We would expect our estimate to be at least $0.03 per gallon, possibly higher. 

B. Graphical Analysis 

 

12 The higher frequency of the OPIS data also allows for checks on the accuracy of the data. For approximately ten 

percent of the stations there were days when premium or mid-grade prices were reported as regular grade. Due to the 

higher frequency of the data we were able to identify these misclassified observations and perform additional 

sensitivity analyses. 

13 There was one exception to the relationship between market share and assigning firms to a brand category as 

described in footnote 25 of Hastings (2004). Mobil had a large market share in California but was assigned to the 

middle category based on similarity of the price response of Mobil stations and Texaco stations to the ARCOThrifty transaction. 

5
Following Hastings, we begin our replication attempt by considering simple before-and-after 

changes in the average price of gasoline, measures across all stations in the OPIS samples, around the 

time of the ARCO/Thrifty transaction. Figure 1 presents three graphs corresponding to the pooled, Los 

Angeles, and San Diego OPIS price data. The graphs reflect the average weekly retail price in each area 

as measured during the last week of February, June, October, and December of 1997. 

Despite the use of differing price series, it is striking how closely the graphs presented in Figure 1 

match the general shape and levels of the corresponding graphs, Figure 1, presented in Hastings’ study. 

In each city the average gasoline price level for each set of stations peaks in October 1997, with the rise 

and fall more pronounced in Los Angeles than in San Diego. Hastings’ graphs exhibit the same features. 

However, the June price levels in our OPIS data exceed those in the W-L data by a few cents per gallon, 

within each city and for both sets of firms. 

When considering the trends in the time series of those stations that competed against a Thrifty 

versus those that did not in the OPIS data, there is substantially less agreement with the W-L data. For 

Los Angeles the two series track each other very closely, with the prices charged by Thrifty-competing 

stations always (i.e., both before and after the ARCO/Thrifty transaction was consummated) lying 

beneath those charged by those stations not located close to a Thrifty. This trend differs markedly from 

that in the W-L data for Los Angeles, which show that relative to the control group, Thrifty competitors 

have lower prices before and higher average prices after the transaction. 

 The comparison of average prices for control and treatment groups in the San Diego OPIS data 

more closely resembles the W-L data as depicted by Hastings. Both her graph and ours demonstrate that 

Thrifty competitors have lower average prices before the transaction and higher prices afterwards. 

However, the size of the effect is smaller in our data. The W-L San Diego data indicate that Thrifty 

competitors had a $0.02-$0.03 per gallon lower average price than other stations before the transaction 

and $0.02-$0.03 per gallon higher average price afterwards. This trend suggests that the result of the 

ARCO/Thrifty transaction may have been to raise the price at competing stations proximate to a Thrifty 

6
by $0.04-$0.06 per gallon. By contrast, the San Diego OPIS data show that Thrifty competitors were 

only $0.01 below and then $0.01 above other stations, suggesting an effect of only $0.02 per gallon. 

 The graph for the pooled sample for the OPIS data presented in Figure 1 again shows no effect on 

the relative positioning of the control or treatment group price series over the period. This result is not 

surprising given that most of the observations in the pooled sample come from Los Angeles. 

C. Econometric Analysis 

 In this section we consider several empirical specifications based upon the econometric research 

design employed by Hastings. Specifically, we adopt Hastings’ fixed-effects or difference-in-differences 

approach to identifying the impact of Thrifty conversions on market prices, both in the aggregate and by 

individual brand, and for both the pooled and individual OPIS city samples. 

 Table 2 presents the results of estimating the following regression: 

 (1) 

1

, ,

1 1

N T

jk k j

i t i i t i t it

j k

p μα β C onversion δ γτ ε

−

= =

=+ + + ∑ ∑ + ,

where denotes the average price charged by the i-th station during the last week of the t-th time 

period. The dummy variable takes a value of one if station i is located within a mile of a 

Thrifty station during period t (i.e., “competed” with an independent Thrifty outlet prior to its conversion, 

February and June). Thus, a negative estimate of the coefficient implies that the transaction (the loss 

of an independent competitor) is correlated with an increase in the average price at these competing 

stations. The city-time fixed effects are captured by the interaction of city dummies and time 

dummies, . For the individual city regressions the city-time effects are simply time effects. The 

variables , , and represent the constant term, the station-specific fixed effect and the error term, 

respectively. The results shown in Table 2 are estimated using the four monthly time periods {February, 

June, October, December} in 1997 used by Hastings. 

pi t,

Conversioni t,

β

j

i γ

k τt

μ αi εi t,

7
 The above model is identical to Hastings’ except in two regards.14 First, in classifying Thrifty 

competitors she uses actual driving distance of one mile, whereas we employ the simpler one-mile radius 

“as the crow flies.” Our methodology likely counts more stations as Thrifty competitors than Hastings’ 

would. If the transaction’s effect diminishes with driving distance, our results would be biased 

downward. Since Hastings reported, in footnote 15, that half mile changes in the definition of which 

stations competed with a Thrifty did not significantly change the results, this small difference in should 

not explain the gap between our estimates and Hastings’. Second, Hastings includes a dummy variable 

indicating whether ARCO operated the station after the rebranding. We lack this information. But 

Hastings finds that the point estimate of the coefficient on this variable is less than one cent per gallon 

and is statistically insignificant. 

 Table 2 presents our results separately for the pooled, Los Angeles, and San Diego OPIS samples 

as well as Hastings’ results for the pooled sample. The estimated standard errors used in constructing the 

reported t-statistics are obtained using the Huber/White heteroskedasticity-consistent covariance matrix 

estimator. The models appear to fit the data relatively well, with the R-squared ranging from 0.90 to 0.92. 

The coefficient estimates for the individual city-month interactions are economically relevant, ranging 

from $0.02 to $0.13 per gallon. These estimates closely resemble Hastings’ findings using the W-L data, 

which is not surprising given the similarity of the time trends in the graphical analyses. 

By contrast, the coefficient estimates pertaining to the conversion variable in our Table 2 differ 

substantially from Hastings’. Hastings’ regression results for the pooled W-L sample indicate that the 

loss of Thrifty as a competitor is associated with a price increase of $0.05 per gallon. For the OPIS 

pooled sample, the coefficient estimate on the conversion variable is likewise negative – however, it is an 

order of magnitude smaller at just two-tenths of a cent per gallon and not significantly different from zero 

at conventional levels of statistical significance. For the Los Angeles OPIS sub-sample, the point estimate 

 

14 A third, technical, difference is that Hastings considers whether each station competes with any independent, not 

just Thrifty. However, she notes in her footnote 18 (p. 324) that the only source of variation in this variable comes 

from the Thrifty conversion. Thus, the station-specific fixed effect should capture any effect of competition from 

non-Thrifty independents, with no effect on our results. 

8
of the conversion indicator is actually positive – indicating a price decrease after the acquisition – but 

quite small, less than one-tenth of a cent per gallon. For the San Diego OPIS sub-sample, the point 

estimate is found to be negative and statistically significant at the 1 percent level. The magnitude of the 

estimated conversion effect is $0.01 per gallon. This estimate comports with our graphical analysis 

Figure 1(c) but is smaller than the effect in Hastings’ San Diego graph, $0.04 to $0.06 per gallon. This 

estimate is also less than our expected effect given that the San Diego OPIS data over-sample a low-share 

brand, Citgo. 

 As mentioned earlier, the OPIS data possess the benefit of being available at a higher frequency 

and also allows for the effects of the ARCO/Thrifty transaction to be considered over a longer time period 

than Hastings’ W-L data. Table 3 presents the results of estimating the equation (1) where t spans all 

individual weeks. This “two-way” fixed-effects specification controls for both station-level (group) and 

week (period) fixed-effects within each sample. The week fixed effects control for any week-specific 

unobserved factors that influence retail gasoline prices symmetrically across all stations in a city. This 

equation was estimated separately for Los Angeles, San Diego and the pooled samples. In Table 3, the 

first column for each sample is estimated using the 52 weeks of data in 1997. The second column for each 

sample is estimated using three years of weekly data, 1996-1998. 

 One concern with the specification in equation (1) when we go from quarterly to weekly data is 

the average price at a given station for a given week is likely to be highly correlated to the average price 

at the station during the previous week (or weeks). Bertrand et al. (2004) show that difference-indifferences models relying on panel data that are highly autocorrelated can result in severely downwardbiased estimated standard errors, thereby leading to misleading findings of statistical precision. To 

control for the effects of autocorrelation, the bottom row of Table 3 presents t-statistics that reflect 

clustering of the estimated standard errors at the individual-station level. 

In these specifications, the estimated coefficients on the conversion indicators have the expected 

sign (negative, indicating a price increase) but again are small, less than $0.01 per gallon. To the extent 

that the ARCO/Thrifty transaction had any upward impact on market-level retail gasoline prices, these 

9
results strongly suggest that it was much smaller than that suggested by Hastings’ study using the W-L 

data. The bottom row of Table 3 shows that statistical significance diminishes considerably when we 

cluster standard errors to control for within-station autocorrelation. The conversion effect’s statistical 

significance tends to diminish as well when estimating the model using all three years of data.15

 As discussed above, Hastings concludes her analysis by disaggregating the effect of the 

transaction by high-, mid-, and low-share brands to see if the pricing patterns support her theory of 

product differentiation with brand loyalty. While these results are generally consistent with the theory – 

the magnitude of the effect increases as we move from high- to mid- and then low-share brands – the 

evidence suggesting significant across group differences is rather weak. The coefficient estimate on the 

mid-share variable is not statistically different from that on the low-share variable, while the coefficient 

estimate on the high-share variable is statistically different from that on the mid-share variable at the 10 

percent level. It seems that these results would have been even weaker had Mobil been placed in the high 

brand group, which would have been more consistent with its market share as reported in footnote 25 of 

Hastings (2004). 

 We also tested this theory with the OPIS data. Table 4 presents the results of estimating equation 

(1) by individual brand (i.e., where the dependent variable is the average weekly price charged by each 

station) for each of the brands for which sufficient price observations are available. Like the previous 

regressions, these models compare the prices in a control group, the branded stations not near a rebranded 

Thrifty, to a treatment group. These regressions use data for all weeks in 1997 controlling for both station 

and week fixed effects. We present t-statistics using Huber/White robust standard errors as well as 

station-level clustering. In Table 4, we order the brands according to Hastings’ classification as high- 

(Shell), mid- (Mobil and Texaco), or low-share (Citgo) brands. 

 

15 Additional results showing the robustness with respect to the treatment of stations with mis-classified premium 

and mid-grade gasoline prices are available, currently, Table A-1. The results did not change by more than onetenth of a cent per gallon when we either dropped those stations or when we used the minimum daily price instead of 

the average of the daily prices when aggregating the daily prices to weekly prices. 

10
 The rightmost column of Table 4 affords the most direct comparison with Hastings’ Table 3. 

That the level of the estimated effect is much smaller in our table is unsurprising. However, the pattern 

across brands apparent in Hastings’ table is not present in ours. Based on her results using the W-L data, 

and consistent with her theory of brand loyalty, the magnitude of the coefficient estimates should increase 

as we move down the table from a high-share to mid- and then low-share brands. However, this pattern 

does not emerge in our table, for the pooled sample or either of the city sub-samples. In fact, the estimate 

pertaining to the low-share brand, Citgo, is positive at $0.01 per gallon. This latter result is directly 

opposite to that predicted by Hastings’ theory as it suggests that the ARCO/Thrifty transaction lowered

prices by $0.01 per gallon at a competitor that was “close” to Thrifty in product space. Hastings’ 

theoretical hypothesis and empirical results using the W-L data suggest that this type of firm should 

experience the largest price increase upon the re-branding of an independent competitor. 

 

III. Theoretical Model of Brand Preferences

Hastings posits that a demand structure consistent with her empirical results using the W-L data 

involves heterogeneous preferences over brands of gasoline. In this section we employ such a model to 

demonstrate that even when rebranding leads to price increases, its effect on welfare remains ambiguous. 

 Consider a Hotelling model of product differentiation, with two firms A and B at the endpoints of 

a line with length l.

16 Three consumer types are uniformly distributed along the line with unit demands. 

Proportions α and β are brand-loyal to A and B respectively, while a proportion γ views gasoline as an 

homogenous product. With transport costs of t per unit, if both firms find it profitable to sell to brandloyal as well as non-brand-loyal customers then equilibrium prices will be 

( ) ( ) 2 2 , 2 3 3 A B

t t

p ct α β p ct α β

γ γ

= + + 2+ = + + + l l

 

16 In an earlier working paper, Hastings (2002, p. 30) specifies precisely this model. 

11
where by assumption each price lies below some reservation price r which represents the value to brandloyal consumers of purchasing their preferred brand in an adjacent market. Note in particular that each 

price is increasing in the proportion of brand-loyal consumers of both types. 

Assume for the moment that firm A is branded (say, Chevron) while B is an independent. Thus, 

all the β-type consumers would purchase their preferred brand of gasoline (say, ARCO) in the adjacent 

market at the reservation price r. Suppose further that A finds it profit-maximizing to compete with B

over the γ-type (non-brand loyal) consumers. Then firm A sells to both α- and γ-types, B sells only to γtypes and equilibrium prices are as above but with β = 0. 

 If firm B is acquired by and rebranded as ARCO, then in an interior equilibrium we have β > 0 

and prices rise for firms A and B. Although rebranding harms α- and γ-type consumers through higher 

prices, β-type consumers are better off since they no longer must travel to the adjacent market to obtain 

their preferred brand of gasoline. Whereas they previously paid r, after rebranding they pay pB < r. 

 Indeed, with identical costs for all firms and unit demands, rebranding must lead to an increase in 

total welfare: total output and total production costs remain the same, while β-type consumers avoid the 

costs of traveling to the adjacent market for their preferred brand of gasoline. In addition, having 

identical (branded) firm types at each end of the Hotelling line leads to a more efficient matching of nonbrand-loyal consumers with firms in terms of minimizing transportation costs (at least when the α- and βtypes exist in roughly similar proportion). 

 Furthermore, we found parameter values such that rebranding increases total consumer surplus, 

even while both prices rise. This occurs when the gains that β-type consumers reap from rebranding 

outweigh the harm to α- and γ-type consumers. In most such examples we found, the proportion of nonbrand loyal consumers (γ) is relatively large.17 Intuitively, the firms compete fiercely over this segment, 

 

17 Specifically, here are the parameter values for one example: α = 9/32, β = 1/32, γ = 11/16, c = 1, t = 1, l = 1, r = 

15/4, v1 = 5, v0 = 3. The parameter v1 represents the gross utility to α-type and β-type consumers from purchasing 

their preferred brand of gasoline, while v0 represents the gross utility to γ-type (non-brand-loyal) consumers from 

purchasing any brand of gasoline. 

12
both before and after rebranding. Consequently, the post-rebranding price increases are small. So long as 

the reservation price r is high – but not so high to induce firms to ignore the non-brand loyal segment – 

the gain to β-type consumers outweighs the effect of the price increase on α- and γ-type consumers. 

 Thus, while Hastings’ theoretical model does show that rebranding leads to across-the-board 

price increases, this result arises only because some consumers receive higher gross utility from 

purchasing branded product. Consequently, total welfare must increase as well, and in some situations 

consumer welfare will simultaneously rise. 

IV. Conclusions

 According to Hastings’ analysis of the W-L data, the ARCO/Thrifty acquisition increased prices 

at nearby competing stations by $0.05 per gallon, on average. Our analysis of the OPIS data provides a 

very different estimated price effect of this transaction, suggesting that if there was any price effect, it was 

an order of magnitude smaller. In a number of instances we cannot reject the hypothesis of no effect at 

standard levels of significance. While we used a different data set, we have found no reason that would 

explain this discrepancy. Regardless of which data set more accurately depicts the transaction’s actual 

impact on prices, our theoretical analysis reveals some difficulties in inferring welfare effects from price 

changes when consumers attach value to particular brands of gasoline. 

 Merger retrospectives are inherently important. They not only inform antitrust policy but also 

increase our understanding of consumer behavior and competitive forces. Hastings’ research design 

provides an excellent example of the methodology to use and the type of event that needs to be studied. 

Unfortunately, demand for merger retrospectives continues to exceed supply.18 

 In addition, this paper reinforces the point, made by Hamermesh (2007), that additional attempts 

to replicate and reproduce empirical results in published papers would be beneficial. More scientific 

replication will increase the credibility of empirical economic research and may increase its use by 

policymakers. 

 

18 For a literature review of merger retrospectives see, Pautler (2003). The bulk of the published merger 

retrospectives are in the banking and airline industries. Merger retrospectives in the petroleum industry include 

Hastings and Gilbert (2005), Taylor and Hosken, (2007) and Simpson and Taylor (forthcoming). 

13
 Works Cited 

Barron, J. M. & J. R. Umbeck, (1984), “The Effect of Different Contractual Arrangements: The Case of 

Retail Gasoline Markets,” Journal of Law and Economics, 27(2), pp. 313-328. 

Bertrand, M. E. Duflo, & S. Mullainathan, (2004) “How Much Should We Trust Difference-inDifferences Estimates?” Quarterly Journal of Economics, 119(1), pp. 249-275. 

Hamermesh, D., (2007) “Replication in Economics,” National Bureau of Economic Research, NBER 

Working Papers, 13026. 

Hastings, J., (2002), “Vertical Relationships and Competition in Retail Gasoline Markets: Empirical 

Evidence from Contract Changes in Southern California,” Program on Workable Energy Regulation, 

University of California Energy Institute Working Paper, PWP-075. 

Hastings, J., (2004), “Vertical Relationships and Competition in Retail Gasoline Markets: Empirical 

Evidence from Contract Changes in Southern California,” American Economic Review, 94(1), pp. 317-

328. 

Hastings, J. & R. Gilbert, (2005), “Market Power, Vertical Integration and the Wholesale Price of 

Gasoline,” Journal of Industrial Economics, 53(4), pp. 469-492. 

Pautler, P., (2003), “Evidence on Mergers and Acquisitions,” Antitrust Bulletin, 48(1), pp. 119-221. 

Simpson J. & Taylor, C., (forthcoming), “Do Gasoline Mergers Affect Consumers' Prices? The Marathon 

Ashland and UDS Transaction,” Journal of Law and Economics. 

Shepard, A., (1990), “Pricing Behavior and Vertical Contracts in Retail Markets,” American Economic 

Review, 80(2), pp. 427-431. 

Shepard, A., (1993), “Contractual Form, Retail Price, and Asset Characterization in Gasoline Retailing,” 

Rand Journal of Economics, 24(1), pp. 58-77. 

Taylor, C. & Hosken, D., (2007), “The Economic Effects of the Marathon-Ashland Joint Venture: The 

Importance of Industry Supply Shocks and Vertical Market Structure,” Journal of Industrial Economics, 

55(3), pp. 421- 453. 

Vita, M., (2000), “Regulatory Restrictions on Vertical Integration and Control: The Competitive Impact 

of Gasoline Divorcement Policies,” Journal of Regulatory Economics, 18(3), pp. 217 -233. 

14
FIGURE 1. TREATMENT AND CONTROL GRAPHS FOR POOLED, LOS ANGELES, AND SAN DIEGO SAMPLES

(a) POOLED SAMPLE

(b) LOS ANGELES SAMPLE

(C) SAN DIEGO SAMPLE

Notes: Data are derived from OPIS and correspond to the average price of the last week in the listed 

month. All data pertain to the year 1997. 

1.25

1.3

1.35

1.4

1.45

1.5

February June October December

Within 1 mile of a Thrifty

All Other Stations

1.2

1.25

1.3

1.35

1.4

1.45

February June October December

Within 1 mile of a Thrifty

All Other Stations

1.3

1.35

1.4

1.45

1.5

1.55

February June October December

Within 1 mile of a Thrifty

All Other Stations
Table 1 

Comparison of Brand Shares Across Alternative Samples, 1997 

 (1) (2) (3) (4) (5) (6) 

CA Energy Comm. CA Energy Comm. Whitney Leigh Whitney Leigh OPIS OPIS Hastin

Brand (LA) (SD) (LA) (SD) (LA) (SD) BranAlliance 0.04% American Gas 0.35% ARCO 11.41% 11.16% 19.41% 13.21% Chevron 12.23% 10.34% 17.84% 17.61% 0.35% 0.48% HCircle K 0.13% 3.13% Citgo 0.30% 8.70% 0.32% 18.69% Exxon 0.13% 2.42% Fastrip 0.04% Mobil 13.75% 8.44% 15.88% 13.21% 51.19% 23.60% Olympic 0.04% Shell 14.05% 12.38% 14.12% 17.61% 37.79% 29.81% HSinclair 0.49% Texaco 5.03% 10.34% 8.43% 12.58% 9.88% 24.99% Thrifty 4.34% 4.76% Ultramar 0.30% 1.50% Unbranded 19.43% 19.73% United Oil 1.65% Unocal 15.13% 8.71% 12.55% 11.95% HUSA 0.78% 0.54% World Oil 0.87% 0.27% Minor Brands 5.25% 8.18% Independents 6.52% 5.66% Total Stations 2354 806 510 159 582 209 Notes: Brand shares reported Columns (3) - (4) are from Hastings (2004) Table 1. Brand shares may not sum to 100% due to roundin
Table 2 

Estimated Effect of Thrifty Station Conversions on the Market Price for Retail Regular-Grade Gasoline, 1997 

 Variable 

Los Angeles 

Sample 

San Diego 

Sample Pooled Sample 

Hastings (2004) 

Pooled Sample 

Conversion .000334 -0.011** -0.002 -0.050**  (0.16) (2.99) (1.39) (4.95) LA*February -0.051** -0.050** 0.018**  (31.44) (31.55) (2.77) 

LA*June 0.022** 0.023** .0243**  (14.75) (15.64) (3.74) LA*October 0.131** 0.131** 0.139**  (95.65) (95.61) (21.72) 

SD*February -0.111** -0.114** -0.085** 

(40.64) (43.08) (23.61) 

SD*June -0.049** -0.051** -0.030** 

(20.06) (22.54) (8.44) 

SD*October 0.048** 0.048** 0.055** 

(22.97) (22.86) (15.14) 

Constant 1.292** 1.445** 1.333** 1.362**  (1204.10) (878.21) (1473.33) (47.45) 

N 2298 832 3130 2676 R2 0.91 0.92 0.90 0.72 

F-statistic (HO : All 

slopes = 0) 5330.49** 1390.09** 3845.68** Notes: The dependent variable is the average weekly retail price for regular unleaded gasoline by station for the last week in February, June, October, and December of 1997. Absolute values of robust t-statistics appear in parentheses. All models include full sets of station-specific dummies. The symbols * and ** indicate statistical significance at the 5% and 1% levels, respectively. 
Table 3 

Estimated Effect of Independent Retail Gasoline Station Up-Branding: All Weeks in 1997 and 1996-1998 

Los Angeles Sample San Diego Sample 

(1) 

1997 

(2) 

1996-1998 

(1) 

1997 

(2) 

1996-1998 

Conversion -0.003** 0.0006 -0.006** -0.010** -0

 (4.87) (1.58) (5.94) (13.36) (

Constant 1.172** 1.185** 1.289** 1.240** 1

 (1041.63) (873.46) (488.01) (380.26) (11

N 29,840 85,767 10,733 30,685 4

R2 0.93 0.91 0.89 0.84 

F(HO : All slopes = 0) 8,260.55** 13,212** 1,522.29** 3,249.84** 4,9

Absolute value of t-statistic of Conversion with station-level clustering 1.63 0.32 1.62 2.59* 2

Notes: The dependent variable is the OPIS average weekly retail price (measured in dollars per gallon) for regular unleaded gasoline by station. Absolute appear in parentheses. Estimated coefficients and t-statistics for station dummies and week dummies are not reported. The symbols * and ** indicate staand 1% levels, respectively. 
Table 4 

Estimated Coefficient on ARCO/Thrifty Conversion Indicator by Individual Brand, 1997 

 

Los Angeles Sample San Diego Sample Pooled Sample 

Brand Regression: 

 

Shell -0.004 -0.011 -0.006 

Robust t-statistic (4.49)** (8.87)** (7.49)** 

Clustered t-statistic [1.60] [3.13]** [2.65]** 

N 11276 3200 14476 

 

Mobil -0.001 -0.011 -0.002 

Robust t-statistic (1.42) (5.62)** (3.35)** 

Clustered t-statistic [0.38] [1.33] [0.88] 

N 15274 2533 17807 

 

Texaco -0.004 -0.015 -0.01 

Robust t-statistic -1.61 (6.02)** (5.17)** 

Clustered t-statistic [0.93] [2.55]** [2.51]** 

N 2947 2682 5629 

 

Citgo  0.012 0.012 

Robust t-statistic () (6.83)** (6.74)** 

Clustered t-statistic [] [2.32]* [] 

N  2006 2101 

 

Notes: Each row/brand corresponds to a separate regression. The estimates control for 

station- and week-specific fixed effects and a constant term. Robust t-statistics appear 

in parentheses, while t-statistics reflecting clustering at the station-level appear in 

brackets. The symbols * and ** indicate statistical significance at the 5% and 1% levels, 

respectively. The symbol "" indicates that the model could not be estimated. 
Table A-1 

Robustness of Estimated Effect of Independent Retail Gasoline Station Up-Branding – Minimum Price and Excluding Some Stat

Los Angeles Sample San Diego Sample P

(1) 

1997 

(2) 

Minimum 

(3) 

Excluding 

(1) 

1997 

(2) 

Minimum 

(3) 

Excluding 

(1) 

1997 

Conversion -0.003** -0.002** -0.003** -0.006** -0.005** -0.006** -0.004** 

(4.87) (3.72) (5.13) (5.94) (5.08) (4.44) (7.30) 

Constant 1.172** 1.168** 1.411** 1.289** 1.264** 1.313** 1.19** 

(1041.63) (959.62) (1390.27) (488.01) (413.41) (1383.80) (1139.26) 

N 29,840 29,840 27,687 10,733 10,733 8,133 40,573 R2 0.93 0.82 0.81 0.89 0.68 .64 0.65 

F(HO : All slopes = 0) 8,260.55** 7,547.57** 7,542.85** 1,522.29** 1,615.80** 570.06** 4,937.89** 

Absolute value of t-statistic of Conversion with station-level clustering 1.63 1.29 1.69 1.62 1.36 1.12 2.28* 

Notes: The dependent variable is the OPIS average weekly retail price (measured in dollars per gallon) for regular unleaded gasoline by station. Absolute appear in parentheses. Estimated coefficients and t-statistics for station dummies, week dummies, and the common week time trend not reported. The sstatistical significance at the 5% and 1% levels, respectively. The regression results reported in columns labeled with (1) are the results from Table 3. The the minimum price each week rather than the mean price for the week. The regression results reported in columns labeled with (3) exclude stations where 

grade gasoline was reported for regular gasoline.