WORKING 

PAPERS 

Merger Policy at the Margin: 

Western Refining’s Acquisition of Giant Industries 

Nicholas Kreisle 

WORKING PAPER NO. 319 

September 2013 

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 

publications to FTC Bureau of Economics working papers by FTC economists (other than 

acknowledgment by a writer that he has access to such unpublished materials) should be cleared with the 

author to protect the tentative character of these papers. 

BUREAU OF ECONOMICS 

FEDERAL TRADE COMMISSION 

WASHINGTON, DC 20580
Merger Policy at the Margin:

Western Refining’s Acquisition of Giant Industries

Nicholas Kreisle*

Federal Trade Commission

September 2013

ABSTRACT

In May 2007 the Federal Trade Commission failed to win a preliminary injunction in U.S. District

Court that would have blocked the merger of two refiners that served Albuquerque, NM and

surrounding areas.  This study compares estimates of the post‐merger price effect to the price

effects predicted by economic experts on both sides of the case.  I find little scope to interpret

the evidence as consistent with an anticompetitive post‐merger price effect. I also highlight

difficulties involved in econometrically identifying small effects even with an abundance of

pricing data.

 

* I would like to thank Lou Silvia, Chris Taylor, Matthew Chesnes, and Dan Hosken for their comments, and

Dan Greenfield for assistance in compiling Table 1.  The views expressed are those of the author and do

not necessarily represent those of the U.S. Federal Trade Commission or any individual Commissioner.
2

1. Introduction

As noted by Carlton (2009) and others, the value of retrospective merger analysis lies not only in

understanding the impact of a particular merger – or, more speculatively, the likely impact of

similar mergers – but also in evaluating the accuracy of prospective merger analysis by the

relevant antitrust authority.  However, this evaluation can only take place when the antitrust

authority has precisely stated its forecast of any post‐merger competitive effect.  In that vein,

this paper reflects upon Western Refining’s acquisition of Giant Industries in May 2007 after a

U.S. District Court denied the Federal Trade Commission’s request for a preliminary injunction to

stop the transaction.  During the trial, both the FTC and the merging parties employed economic

experts who made predictions on the public record as to the merger’s likely impact on the

wholesale price of gasoline in and around Albuquerque, New Mexico.  Therefore, in addition to

assessing the narrow question of whether this particular merger affected prices, I can also

address the broader issue of how any such impact aligned with the Commission’s (or the

parties’) predicted effect.

I also use this merger to compare empirical methods in retrospective merger estimation.  

Petroleum prices often exhibit nonstationarity, so I compare two versions of my difference‐in‐

differences approach, each of which theoretically produces consistent estimates but which in

practice often disagree.  First, I run OLS regressions using autocorrelation‐consistent standard

errors (Newey‐West).  Second, I explicitly estimate an autoregressive term in the error (Prais‐

Winsten).  I exploit an abundance of pre‐merger data to compare these approaches both for the

actual merger effect and for various placebo merger dummies in the pre‐merger period to

investigate the ability of either econometric procedure to identify small price changes reliably.   

This particular merger is also unusual because the FTC predicted that an ongoing output

expansion project at Giant would lower prices in the absence of the merger.  If that

characterization is correct, then the relevant question for this study is not just whether prices

increased relative to some control market.  Even if prices fell, anticompetitive harm could still be

inferred if they did not fall “enough” relative to the FTC’s predicted effect from the output

expansion.  Indeed, the econometric evidence generally suggests that gasoline and diesel prices

did, in fact, decline at both the wholesale and retail levels, although not quite to the most

extreme level the FTC predicted could arise in the but‐for world.  While this finding leaves some

room to infer an anticompetitive merger effect, it hinges crucially on whether the FTC’s theory

of the counterfactual seems most plausible.  

The following section describes the antitrust case and what has happened to the acquired assets

after the merger.  Ensuing sections describe the research design, and analyze the robustness of

the results across products, control cities, and estimation method.  In an attempt to understand

the mechanism by which prices may have declined, I also use firm‐specific pricing in

Albuquerque to look for changes in downstream behavior after the merger.  The final section

offers concluding thoughts on the extent to which the retrospective evidence conflicts with the

theories put forth by both the FTC and the merging parties.
3

2. The Competitive Overlap

The ability of the Western/Giant transaction to affect competition lies in Albuquerque’s relative

isolation with regard to petroleum product supply, as summarized in Figure 1.1

  Sitting at or near

the end of three pipelines, only five refiners had direct access to Albuquerque.  Giant delivered

product to Albuquerque by truck from its two Four Corners refineries, which had a total capacity

of 37,000 barrels per day.  Western supplied Albuquerque from its 124,000 barrel‐per‐day

refinery in El Paso, Texas via the common carrier Plains Pipeline.  ConocoPhillips, Valero, and

Holly also delivered products to Albuquerque on their own proprietary pipelines from refineries

similar in size to Western’s.  However, the FTC pointed to marketing and supply constraints that

limited the supply responsiveness of these three refiners. Likewise, while many large,

sophisticated refineries on the Gulf Coast near Houston could reach El Paso by pipeline, capacity

constraints on the Plains Pipeline restricted their ability to reach Albuquerque.   

In the eyes of the FTC, the merger raised competitive concerns because these supply constraints

gave Giant the ability and incentive to expand production and lower Albuquerque prices, which

would diminish with the merger with Western. Declining crude oil production in the Four

Corners area had caused Giant’s refinery utilization rate to fall from 72 percent in 2002 to 60

percent in 2006.2

  To combat this problem, Giant acquired a pipeline capable of delivering crude

oil from West Texas that it expected to place in service in 2007.  The FTC argued that, given

Albuquerque’s supply constraints, Giant’s output expansion was likely to substantially lower

prices in the absence of the merger.  Based on documentary evidence and analysis from its

economic expert, Hal White, the FTC argued that Albuquerque prices would decline between 6

and 10 cents per gallon (cpg) in the absence of the merger.3

  The upper end of that range would

be large by the standards of the existing literature on refinery mergers, which is summarized in

Table 1.  The FTC’s theory, then, was that the Western/Giant transaction would reduce the

incentive to increase product supplied to Albuquerque, and effectively eliminate the entirety of

the impending 6‐10 cpg price decline.

By contrast, the merging parties and their expert, Joseph Kalt, claimed that neither the output

expansion nor the merger would have any effect on price.  They pointed to the potential for Gulf

Coast supply, including trucking product from El Paso to avoid capacity constraints on the Plains

Pipeline, as limiting Albuquerque prices both before and after Giant’s output expansion.4

  

Notably, they estimated that the amount of gasoline trucked from El Paso to the Albuquerque

area exceeded the FTC’s predicted incremental product supply from Giant after its output

expansion.  If Giant’s post‐expansion production simply displaced some but not all of this

 

1 All facts described herein are publicly available. Key sources include the FTC’s First Amended Complaint

(hereinafter, “Complaint”) and the FTC’s Memorandum of Points and Authorities (hereinafter, “Points and

Authorities”), both available at http://www.ftc.gov/os/caselist/0610259/index.shtm; and the district

court’s Memorandum Opinion, Findings of Fact, Conclusions of Law, and Order (hereinafter, “Opinion”),

cited as FTC v. Foster, et al., 2007 WL 1793441 (D.N.M. Apr. 29, 2007), which can be found by entering

07cv00352 in the case number field at http://www.nmcourt.fed.us/Drs‐Web/input.   2 Opinion, ¶ 34. 3 See Opinion, ¶ 423 and 442, and p. 6 of the Court’s April 12, 2007 Memorandum Opinion and Order

granting the FTC’s application for a temporary restraining order.  The latter is available at

http://www.ftc.gov/os/caselist/0610259/order.pdf; it erroneously cites an effect of “six to eight percent”

which instead should be an effect in cents per gallon.   4 Opinion, ¶ 259‐262; 447‐453.
4

trucking from El Paso, then Giant’s supply is inframarginal and Albuquerque prices would

continue to be determined by the cost of trucking from El Paso.   

Consequently, the state of affairs was such that both the FTC and the merging parties claimed

that Albuquerque prices would not change if the merger were to proceed.  The FTC, however,

argued that the merger would prevent an imminent and substantial decline in Albuquerque

prices. For the purposes of this study, then, a finding of any post‐merger price increase would

contradict the predictions put forth by both sides. A sufficiently large price decrease might

suggest that the FTC was correct to predict that the increased output after Giant’s output

expansion would indeed affect Albuquerque prices, while the merging parties were correct to

predict that existing competition would maintain the merged firm’s incentive to send additional

production to Albuquerque.

Ultimately, while the Court agreed that the market was concentrated, it appeared to believe the

parties rebutted any presumption that the merger would have anticompetitive effects.5

  Thus,

on May 31, 2007, Western closed its acquisition of Giant.  Western followed through with the

output expansion at the Giant refineries, commencing pipeline deliveries in August 2007. But at

some point in 2008 it reversed course and shut the pipeline down.6

  In November 2009, Western

ceased production at one of the two Giant refineries altogether.7

  However, declining demand in

the wake of the 2008‐09 recession and increased use of renewable fuels, rather than changed

incentives resulting from the merger, might justifiably have led to the closures of these relatively

small and isolated refineries.

3. Data and Research Design

The data for this study come from the Oil Price Information Service (OPIS). The data consist of

weekly observations on rack (wholesale) and retail prices for both gasoline and diesel.  The trial

itself focused on gasoline, but the FTC initially alleged anticompetitive harm in the market for all

light petroleum products – comprising gasoline, diesel, and jet fuel – as well.8 The FTC’s

predicted price effects on the public record are for gasoline only. Nevertheless, since diesel

supply largely mirrors that for gasoline in the Albuquerque area, analysis of diesel prices

provides a useful comparison for understanding the merger’s competitive effects. Jet fuel

typically is sold under long‐term contracts directly into airports, so its price is not similarly

observable.

Another useful dimension for comparison is looking at both rack and retail price effects.  

Economic analysis in the case generally focused on rack prices at petroleum terminals, which are

the first observable prices downstream of the competition between Giant’s and Western’s

refineries.9

  Yet, the FTC’s predicted price effects were understood to apply to both rack and

 

5 Opinion, ¶ 219‐221. 6 See Western’s 2008 Form 10‐K, p. 7. In December 2011 Western sold the southernmost 80 miles of the

pipeline while the rest of it remains unused; see Western’s 2011 Form 10‐K, p. 4. 7 See Western’s 2009 Form 10‐K, p. 4. 8 Points and Authorities, pp. 10‐16.   9 While vertical contracting varies substantially across states, in New Mexico in 2007 over 78 percent of

refiner sales of gasoline occurred at the rack.  See

http://www.eia.gov/dnav/pet/pet_cons_refmg_c_SNM_EPM0_mgalpd_a.htm. By contrast, rack prices
5

retail, and retail prices reflect consumer impact more directly.10  While pass‐through from rack

to retail should be close to one in the long run (see Chesnes (2010) for evidence), Table 1 shows

that existing studies have found varying effects across these levels. For that reason I look at both

rack and retail prices, with rack as a baseline since that was the focus of the case.

As in many other merger retrospectives, I employ difference‐in‐differences to estimate the price

effect of the merger.  Ashenfelter, Hosken, and Weinberg (2009) provide an overview of this

literature, including a discussion of several refinery mergers.  As is well known, the validity of

the difference‐in‐differences approach hinges on the selection of a control market. The research

design requires that any control city be sufficiently different from the treatment city so that the

merger impact is not felt in the former, yet sufficiently similar that identical demand and cost

shocks can be netted out by differencing.  If Albuquerque is the treatment city ܶ, then for any

control city ܥ the estimating equation is:  

௧݌ (1)

் െ ݌௧

஼ ൌ ሺߙ் െ ߙ஼ሻ ൅ ෍ሺߜ௠

௠ߜ െ ்

஼ ሻܦ௠௧

ଵଵ

௠ୀଵ

௧ߝሺ ൅ ௧ݎ݃݁ݎ݁ܯݐݏ݋ܲ ∙ ்ߚ ൅

் െ ߝ௧

஼ሻ

This specification allows for seasonal shifts in demand, represented by monthly dummy

variables ܦ௠௧, to affect prices differently across cities, i.e. ߜ் ് ߜ஼.    

For this study, the most compelling control city is El Paso, the largest nearby city with a pipeline

connection to Albuquerque.  The ability of refiners from the highly competitive Gulf Coast region

to reach El Paso by pipeline should ensure that El Paso prices reflect competitive levels before

and after the merger.  Because Giant did not supply El Paso from its Four Corners refineries,

neither the merger nor Giant’s output expansion should affect prices there.  Thus, the

interpretation of ߚ் depends on which theory of the counterfactual one believes. Under the

FTC’s theory, in which the merger prevents an imminent decline in Albuquerque prices, the

merger would be anticompetitive unless  ߚ் is sufficiently large and negative. Under the

merging parties’ theory, ߚ் should be zero since the marginal supply to both El Paso and

Albuquerque would not change with either the merger or the output expansion.

For robustness, I also consider Amarillo and Flagstaff as potential control cities.  Pipelines

connect Amarillo to both Albuquerque and El Paso (Figure 1), and neither Giant nor Western

delivered product to Amarillo so the merger should have no impact there. However, Amarillo is

less similar in population to Albuquerque and El Paso and served by fewer refineries (see Table

2).  Flagstaff suffers similar problems, but its appeal as a control city lies in its status as one of

Giant’s primary destination markets, along with Albuquerque and the local Four Corners area.  

Figure 2 plots price levels in Albuquerque against each of the three potential control cities. The

most striking feature is the rapid rise and descent of prices in 2008. As Figure 3 shows, however,

price differentials between Albuquerque and each control city are noticeably better behaved.

Not only is the amount of variation smaller, but the variation is relatively similar in the pre‐ and

 

may be less meaningful in places like California, where the equivalent figure is less than 20 percent at the

time of the mergers considered in Hosken, Silvia and Taylor (2011).   10 Opinion, ¶ 250.
6

post‐merger periods, as documented in Table 3. These features of the data support estimating

equation (1) in differences rather than levels.  Additionally, differences across control cities

support estimating equation (1) pairwise rather than pooling all four cities into a single

equation.11  

Estimating equation (1) in differences also ameliorates nonstationarity problems often apparent

in petroleum price data.  Dickey‐Fuller tests cannot reject the null hypothesis that each weekly

gasoline rack price series (pABQ, pELP, pAMR, pFLG) has a unit root over the June‐2005 to May‐

2009 period.  However, Dickey‐Fuller tests for each gasoline rack price differential (dELP=pABQ‐

pELP, etc.) reject the null of a unit root at the 95 percent confidence level.  To address the

degree of autocorrelation that remains, Figure 4 plots the first 52 (i.e. up to one year)

autocorrelation coefficients and partial autocorrelation coefficients for each price differential.  

These plots exhibit characteristics of an AR(1) process: autocorrelation coefficients that decay to

zero, whether monotonically or in a sinusoidal pattern, and partial autocorrelation coefficients

that, for all lags beyond the first period, abruptly drop to zero and essentially remain there; see

Hamilton (1994).  Similar patterns emerge in the analysis of the residuals from OLS estimation of

equation (1).  Examination of price differentials for retail gasoline prices, and rack and retail

diesel prices also suggests they follow an AR(1) process.   

There are two standard approaches for autocorrelation in the merger retrospective literature.  

One approach, as in Hosken, Silvia, and Taylor (2011), recognizes that OLS remains unbiased and

consistent in the presence of autocorrelation and simply employs the Newey‐West standard

error correction up to some lag. A second approach, as in Taylor and Hosken (2007), is to model

the autocorrelation explicitly using a generalized least squares method such as Prais‐Winsten,

which should be more efficient.  However, in estimating the coefficient on a merger dummy

variable using Prais‐Winsten, a concern could be that the merger dummy in Prais‐Winsten

becomes translated from something like {0, 0, 0, 1, 1, 1} to {0, 0, 0, 1, 1െߩ, 1െߩ{.  As the

autocorrelation coefficient ߩ approaches one, identification of the merger effect can hinge on a

single observation.  However, a Monte Carlo study suggests Prais‐Winsten performs reasonably

well under these conditions.  Figure 5 compares average Newey‐West (OLS) and Prais‐Winsten

coefficient estimates from 10,000 simulations in which the dependent variable is equal to a

constant plus a dummy variable (with an actual coefficient of 5) and an AR(1) error term. The

dummy variable turns on in the middle of 200 observations, approximating the conditions of the

current study. While neither approach is unambiguously better at identifying the true coefficient

across all levels of autocorrelation, Prais‐Winsten actually has a notable edge at  ߩ ൌ 0.99.

4. Results

Table 4 reports results of Newey‐West and Prais‐Winsten estimation of equation (1) for rack and

retail prices of gasoline and diesel relative to El Paso.  The estimated merger coefficients are all  

 

11 As an alternative estimation method, I have employed a simple linear version of the White (2006)

dynamic estimator, which attempts to avoid issues in control city selection.  That approach searches for

the combination of lagged dependent variables, input prices, and demand/cost shifters, such as

employment and weather, that best predicts various subsets of pre‐merger data.  It uses the optimal

regression to simulate a hypothetical post‐merger counterfactual price series, but tends to predict poorly

unless control cities are included, in which case the results do not materially differ from those presented

here using the standard difference‐in‐differences approach.
7

negative.  However, recall that under the FTC’s theory, gasoline prices should have fallen 6‐10

cpg in the absence of the merger due to the output expansion at Giant’s refineries.  While the

Newey‐West estimates are generally larger than those using Prais‐Winsten, they are in fact

generally in the range the FTC’s theory would predict in the absence of the merger.  This

suggests the merger itself would not be associated with an anticompetitive effect.  That said,

the results display some notable inconsistencies, apart from just differences in the coefficient

estimates for each econometric method.  The retail effects tend to be larger than the rack

effects, with the exception of the diesel results using Prais‐Winsten.  It is difficult to envision a

mechanism by which, for instance, a 4cpg decrease in the rack price of gasoline leads to an 8cpg

price decrease at retail.   

Table 5 and Table 6 present results when using Amarillo and Flagstaff as control cities.  The

results are generally either negative or statistically indistinguishable from zero, with the results

for gasoline prices relative to Flagstaff as the notable exception.  One possible explanation is

that if Giant constitutes the marginal supply to Flagstaff, then Flagstaff prices might arguably

reflect the impact of Giant’s output expansion as well. In that case the estimate of ߚ் could

reflect only the merger effect, since Flagstaff prices would already incorporate the effect of the

output expansion. Unfortunately, there were no statements on the public record as to the

output expansion’s effect on Flagstaff prices. In addition, the differences between gasoline (all

coefficient estimates positive) and diesel (all negative) suggest that, whether or not it controls

for the effects of the output expansion, Flagstaff is not a particularly good control for

Albuquerque. The Amarillo results tell a story similar to those from El Paso, suggesting that

Albuquerque prices declined by several cents per gallon at rack, and by somewhat larger (in

absolute value) amounts at retail.  However, the El Paso results in Table 4 tend to tell the most

consistent story across estimation method (Newey‐West and Prais‐Winsten), product (gasoline

and diesel), and distribution level (rack and retail) relative to both Amarillo and Flagstaff.

Table 7 reports some basic robustness checks of the baseline Newey‐West results for

Albuquerque rack gasoline prices relative to El Paso.  The baseline results, as in Table 4, use four

years of weekly data surrounding the merger.  Table 7 demonstrates that the result does not

change substantially when estimated on four alternative sets of data. First, I drop the first year

of post‐merger data, to ignore the intermediate period when Western was incorporating all of

Giant’s assets under its control. Second, I include dummy variables for various events that could

potentially have altered the El Paso/Albuquerque pricing dynamic.12 Third, I use all weekly data

from January 2000 through May 2009. Finally, I use the original four‐year period aggregated up

to the monthly level, where serial correlation is less prevalent (e.g. a Prais‐Winsten regression

on monthly data generates an estimate of ߩ at 0.46).  The results are similarly robust to these

changes when using Prais‐Winsten estimation.  Price effects at retail (instead of rack) exhibit

similar robustness as well.

 

12 These events include expansion of the Kinder Morgan pipeline from El Paso to Phoenix in June 2006, as

well as a separate event for a delay in the opening of that pipeline between February and June 2006; Gulf

Coast refinery closures after Hurricanes Katrina and Rita from late August to mid‐November 2005; a fire at

Valero’s Amarillo refinery that shut production from mid‐February to mid‐April 2007; Western’s use of the

crude pipeline to expand output at Giant’s former refineries between August 2007 and December 2008;

Gulf Coast refinery closures due to Hurricanes Gustav and Ike between September and early October

2009; and Holly’s 15,000 barrel per day expansion at its Artesia, NM refinery after April 2009. The

Petrocast database from Industrial Info Resources identifies no other significant (more than 30‐day)

unplanned outages at the five major refineries supplying Albuquerque over this four year period.
8

As another robustness check I also ran a series of regressions using various four‐year

subsamples of the data and testing for placebo merger effects in each week of the data prior to

the actual merger.  Figure 6 plots the coefficient estimate and confidence interval for each

placebo merger dummy, estimating equation (1) by OLS with Newey‐West standard errors for

Albuquerque gasoline rack prices relative to El Paso.  For example, the first point on the chart

represents the coefficient estimate (and 95 percent confidence interval) for a placebo merger

dummy that occurs on January 1, 2002, using data from January 1, 2000 to January 1, 2004.  The

final point on the chart uses data from May 31, 2003 to May 31, 2007 with a placebo merger

occurring on May 31, 2005.   

The results graphed in Figure 6 show that Albuquerque prices often shifted relative to El Paso in

the pre‐merger period, and by an order of magnitude (as high as 4cpg) similar to the estimated

merger effect in Table 4 and the FTC’s predicted 6‐10 cpg effect.  Using Newey‐West standard

errors, the placebo results were statistically significant a large majority of the time as well.  The

frequency of such false positives casts doubt on the validity of this particular research design.  

One potential explanation might be that there are numerous events in either Albuquerque or El

Paso that shift the competitive dynamic in ways similar to the Western/Giant transaction but

which are not accounted for in the placebo regressions.  However, including controls for such

events does not substantially reduce the rate of false positives, at least when using the Newey‐

West approach.13  Table 8 shows that including controls for known events in the pre‐merger

period increases the frequency with which the hypothesis of a null effect cannot be rejected

from 16 percent to only 34 percent for gasoline rack prices.  The remaining rows of Table 8 show

similar patterns for retail and diesel prices under Newey‐West.  

By contrast, the right side of Table 8 shows that employing Prais‐Winsten estimation, especially

in conjunction with the inclusion of control variables, increases the rate at which the placebo

merger dummies are statistically insignificant to relatively acceptable levels.  This finding

restores some faith in the validity of El Paso as a control market.  Figure 7 plots the Prais‐

Winsten estimates and confidence intervals corresponding to those in Figure 6 for gasoline rack

prices.  While Prais‐Winsten does appear to do better in terms of fewer false positives, Figure 7

shows how it also appears particularly sensitive to shifting the time domain one week at a time.   

Point estimates (and confidence intervals) can shift considerably from week‐to‐week; the largest

jumps appear correlated with jumps in the Prais‐Winsten estimate of the AR(1) coefficient in the

error term, which is also plotted in Figure 7.  Thus, while Prais‐Winsten may be preferable in

terms of minimizing false positives, Newey‐West appears less sensitive to small changes in the

data sample.  While the comparison is not exactly analogous, Prais‐Winsten was far more

effective than Newey‐West at including the true effect within the 95 percent confidence interval

of the coefficient estimate in the Monte Carlo experiments conducted for Figure 5.  With

ߩ ൌ 0.9, Prais‐Winsten correctly failed to reject the null hypothesis in 91 percent of the

experiments, while Newey‐West did so only 46 percent of the time.  Prais‐Winsten’s success

rate only improved over Newey‐West’s as the AR(1) coefficient grew larger.

 

13 In addition to the pre‐merger events described in footnote 12, I have included controls for the

expansion of the Amarillo‐to‐Albuquerque pipeline in January 2002 and the opening of the Longhorn

pipeline from Houston to El Paso in January 2005. The pattern of false positives when using Amarillo or

Flagstaff as the control market is roughly the same as in Table 8.
9

Ultimately, El Paso remains the best available control city.  While the placebo tests using Newey‐

West standard errors raise some concern about its overall validity as a control, the results using

Prais‐Winsten (especially while controlling for known events in the pre‐merger period) are more

reassuring.  Since the actual merger effect estimates in Table 4 do not differ substantially across

estimation method, at least for gasoline and diesel rack prices (while retail prices point in the

same direction), El Paso appears to be a viable control market.  In addition, since the result

persists across the various subsamples of the data in Table 7, it seems fair to conclude that the

El Paso results provide a reasonable estimate of the change in Albuquerque prices after the

merger – including, potentially, effects from the output expansion at Giant.   

The El Paso results suggest that the Western/Giant merger was associated with a decline in

prices of about 3‐4cpg at rack and 4‐8cpg at retail.  These estimated price declines fall short of

the FTC expert’s predicted 10cpg decline that would have occurred but for the merger, but are

more consistent with the documentary evidence that suggested a 6‐8cpg decline.  The larger

point, however, is that at trial both sides predicted prices would not change at all in the wake of

the merger.  Because prices did not fall to the levels envisioned by the FTC but for the merger,

there remains some scope for inferring an anticompetitive price effect, but only narrowly.

Moreover, because the 10 cpg effect is large by the standards of previous retrospective merger

analysis in the refinery industry (see Table 1), it becomes even more difficult to see these results

as consistent with an anticompetitive merger effect.

Lastly, in an attempt to understand the mechanism by which Albuquerque prices may have

declined, I also compared firm‐specific pricing at the Albuquerque rack pre‐ and post‐merger.

Table 9 lists the frequency at which each seller at the rack posted the lowest gasoline price in a

given week (technically, the Thursday of that week) during each of the four years surrounding

the merger.  Table 10 does the same for diesel prices.  I chose this specific comparison since, in

some of its pre‐trial filings, the FTC claimed that Giant was a “maverick” competitor.14  However,

analysis of the median difference between each firm’s price and the lowest weekly price reveals

similar trends.  An important caveat is that this analysis excludes Holly’s terminal, from which

sellers tend to offer a discount to compensate for its location 40 miles east of Albuquerque.   

Interestingly, Western was the lowest‐priced rack seller of gasoline more often than was Giant

in the two years before the merger (note that Giant’s branded rack sales were under the

Mustang brand, which Western subsequently acquired).  Moreover, the combined number of

weeks that Giant or Western was the low‐priced rack seller of gasoline in the year preceding the

merger (23) is substantially exceeded by the number of weeks that Western (including the

Mustang brand) was the lowest‐priced seller in the year afterward (30).  In the second year after

the merger, the frequency with which Western was the lowest‐price seller drops considerably.

As for diesel, while Western was not a rack seller of diesel prior to the merger, after taking over

Giant’s business it appears to be the lowest‐priced rack seller about as (in)frequently as Giant

was prior to the merger.  Thus, this simple exercise does not appear to suggest any particular

harm from the elimination of potentially disruptive behavior by Giant.  Western appears to have

priced about as aggressively as Giant did prior to the merger.

 

14 See Opinion ¶ 458, and the FTC’s Points and Authorities, p. 13. Recall that this connotation has a

specific meaning in the context of section 2.1.5 of the DOJ/FTC Horizontal Merger Guidelines, in which a

maverick is defined as “a firm that plays a disruptive role in the market to the benefit of customers.”
10

5. Conclusions

During the same month as the Western/Giant trial, the Director of the FTC’s Bureau of

Economics testified in Congress regarding the Commission’s “particularly vigilant” enforcement

in the petroleum industry: “Unlike in other industries, the Commission has brought enforcement

actions (and obtained merger relief in many cases) in petroleum markets that are only

moderately concentrated.”15  Thus, the Western/Giant merger presents an interesting case for

retrospective analysis due not only to the Commission’s unsuccessful challenge, thereby

creating a data point for a consummated merger that exceeded the agency’s threshold for likely

consumer harm, but also because that threshold is admittedly low for the petroleum industry.  

Such a policy seems worthy of scrutiny, even if this merger retrospective provides only a single

observation regarding its effectiveness.

Moreover, this retrospective is also informative about the tools of prospective merger analysis.  

The evidence suggests that, if anything, Albuquerque prices declined in the wake of the merger,

at least relative to El Paso.  Both the Commission and the merging parties predicted that prices

would remain unchanged after the merger, although the Commission’s analysis did admit the

possibility for prices to fall.  The parties’ assertion that pre‐merger supply was highly (if not

perfectly) elastic – that any additional production at Giant’s refineries would simply be backed

out by other suppliers (Opinion ¶ 442) – appears inconsistent with the observed decline in

prices by several cents per gallon.  For its part, the Commission may have underestimated the

willingness of other suppliers to increase shipments to Albuquerque, which could be the source

of the price decrease.  Unfortunately, the firm‐specific quantity data that might identify the

source of any such increased production is proprietary.  As was expected at the time of the

merger (Opinion ¶ 121), Holly completed a 15,000 bpd expansion of its Navajo refinery in early

2009 – roughly the size same as Giant’s output expansion – although that appears to be too late

to explain the entirety of estimated price effect.

Finally, this study also provides some perspective on econometric methods in retrospective

merger analysis.  Estimating equation (1) by OLS with autocorrelation‐consistent (Newey‐West)

standard errors may be overly precise, in terms of the frequency with which many of the

placebo merger dummies were statistically significant, but appears less sensitive to small

changes in the underlying data than does the explicit inclusion of an AR(1) error term estimated

by Prais‐Winsten.  In this particular instance, Newey‐West and Prais‐Winsten produced

sufficiently similar estimates that permit at least some tentative inference about the path of

post‐merger prices.  However, the difficulties inherent in reaching even this simple conclusion

highlight the challenges facing antitrust authorities in petroleum markets, in which relatively

small yet hard to identify price effects of a few cents per gallon can lead to millions of dollars of

consumer harm on an annual basis.

 

15 See “Prepared Statement of the Federal Trade Commission: Petroleum Industry Consolidation” by

Michael A. Salinger, May 23, 2007, available at

http://www.ftc.gov/os/testimony/070523PetroleumIndustryConsolidation.pdf.  
11

References

Ashenfelter, Orley, Daniel Hosken, and Matthew Weinberg (2009). “Generating Evidence to

Guide Merger Enforcement,” Competition Policy International 5(1), Spring.

Carlton, Dennis (2009). “Why We Need to Measure the Effect of Merger Policy and How to Do

It,” Competition Policy International, Spring.

Chesnes, Matthew (2010). “Asymmetric Pass‐Through in U.S. Gasoline Prices.” FTC Working

Paper #302.

Federal Trade Commission Bureau of Economics (2011). Gasoline Price Changes and the

Petroleum Industry: An Update.  Washington, DC.

Government Accountability Office (2004). Effects of Mergers and Market Concentration in the

U.S. Petroleum Industry. Washington, D.C.

Government Accountability Office (2009). Estimates of the Effects of Mergers and Market

Concentration on Wholesale Gasoline Prices. Washington, D.C.

Hamilton, James (1994). Time Series Analysis, Princeton, NJ: Princeton University Press.

Hastings, Justine and Richard Gilbert (2005). “Market Power, Vertical Integration and the

Wholesale Price of Gasoline,” The Journal of Industrial Economics, 53: 469–492

Hosken, Daniel, Louis Silvia, and Christopher Taylor (2011). "Does Concentration Matter?

Measurement of Petroleum Merger Price Effects." American Economic Review, 101(3): 45‐50.

Silvia, Louis and Christopher Taylor (2013). “Petroleum Mergers and Competition in the

Northeast United States,” International Journal of the Economics of Business, 20(1): 97‐124.

Simpson, John and Christopher Taylor (2008). “Do Gasoline Mergers Affect Consumer Prices?

The Marathon Ashland Petroleum and Ultramar Diamond Shamrock Transaction,” Journal of

Law and Economics, 51(1):135‐152  

Taylor, Christopher, and Daniel Hosken (2007). “The Economic Effects of the Marathon‐Ashland

Joint Venture: The Importance of Industry Supply Shocks and Vertical Market Structure,” Journal

of Industrial Economics, 55:419–51.

White, Hal (2006). “Time Series Estimation of the Effects of Natural Experiments,” Journal of

Econometrics, Vol. 135, pp. 527‐566.
12

Figure 1: Refined Products Supply to Albuquerque.

El Paso 

Houston 

Dallas 

Amarillo 

Albuquerque 

Tucson 

Phoenix 

Flagstaff 

Plains 

Pipeline 

WESTERN 

GIANT 

VALERO 

CONOCOPHILLIPS 

HOLLY 

ALON 

Pipelines 

Refineries 

Bloomfield 

Gallup 
13

Figure 2: Weekly Gasoline Rack Price Levels in Albuquerque and Control Cities

Figure 3: Weekly Gasoline Rack Price Differentials between Albuquerque and Control Cities. Merger 100 150 200 250 300 350 Cents per Gallon

Jun-05 Jun-06 Jun-07 Jul-08 Jun-09

Albuquerque El Paso

Merger

100 150 200 250 300 350

Cents per Gallon

Jun-05 Jun-06 Jun-07 Jul-08 Jun-09

Albuquerque Amarillo

Merger

100 150 200 250 300 350

Cents per Gallon

Jun-05 Jun-06 Jun-07 Jul-08 Jun-09

Albuquerque Flagstaff

Source: OPIS

Merger

-10 0 10 20

Cents per Gallon

Jun-05 Jun-06 Jun-07 Jul-08 Jun-09

Albuquerque/El Paso Difference

Merger

-10 0 10 20 30

Cents per Gallon

Jun-05 Jun-06 Jun-07 Jul-08 Jun-09

Albuquerque/Amarillo Difference

Merger

-40 -20 0 20 40

Cents per Gallon

Jun-05 Jun-06 Jun-07 Jul-08 Jun-09

Albuquerque/Flagstaff Difference

Source: OPIS
14

Figure 4: Autocorrelations and Partial Autocorrelations of Weekly Gasoline Rack Price

Differentials. -0.50 0.00 0.50 1.00 Autocorrelations of dELP

0 10 20 30 40 50

Lag

Bartlett's formula for MA(q) 95% confidence bands

-0.50 0.00 0.50 1.00

Autocorrelations of dAMR

0 10 20 30 40 50

Lag

Bartlett's formula for MA(q) 95% confidence bands

-0.50 0.00 0.50 1.00

Autocorrelations of dFLG

0 10 20 30 40 50

Lag

Bartlett's formula for MA(q) 95% confidence bands

-0.200.000.200.400.600.80

Partial autocorrelations of dELP

0 10 20 30 40 50

Lag

95% Confidence bands [se = 1/sqrt(n)]

-0.50 0.00 0.50 1.00

Partial autocorrelations of dAMR

0 10 20 30 40 50

Lag

95% Confidence bands [se = 1/sqrt(n)]

-0.200.00 0.20 0.40 0.60 0.80

Partial autocorrelations of dFLG

0 10 20 30 40 50

Lag

95% Confidence bands [se = 1/sqrt(n)]
15

Figure 5: Monte Carlo Comparison of Newey‐West and Prais‐Winsten, N=200.

Figure 6: Placebo Merger Dummies, Rack Gasoline Prices Relative to El Paso, Newey‐West

Estimation.

4.4

4.6

4.8

5.0

5.2

5.4

5.6

5.8

0.8 0.9 0.95 0.99

Estimate

AR(1) Coefficient

Newey‐West

Prais‐Winsten

Actual

‐6

‐4

‐2

0

2

4

6

1‐Jan‐02

1‐Mar‐02

1‐May‐02

1‐Jul‐02

1‐Sep‐02

1‐Nov‐02

1‐Jan‐03

1‐Mar‐03

1‐May‐03

1‐Jul‐03

1‐Sep‐03

1‐Nov‐03

1‐Jan‐04

1‐Mar‐04

1‐May‐04

1‐Jul‐04

1‐Sep‐04

1‐Nov‐04

1‐Jan‐05

1‐Mar‐05

1‐May‐05

Cents Per Gallon

95% Confidence Interval PostMerger Coefficient
16

Figure 7: Placebo Merger Dummies, Rack Gasoline Prices Relative to El Paso, Prais‐Winsten

Estimation.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

‐8

‐6

‐4

‐2

0

2

4

6

8

1‐Jan‐02

1‐Mar‐02

1‐May‐02

1‐Jul‐02

1‐Sep‐02

1‐Nov‐02

1‐Jan‐03

1‐Mar‐03

1‐May‐03

1‐Jul‐03

1‐Sep‐03

1‐Nov‐03

1‐Jan‐04

1‐Mar‐04

1‐May‐04

1‐Jul‐04

1‐Sep‐04

1‐Nov‐04

1‐Jan‐05

1‐Mar‐05

1‐May‐05

AR(1) Coefficient

Cents Per Gallon

95% Confidence Interval PostMerger Coefficient AR(1) Coefficient
17

Table 1: Summary of Retrospective Studies of Refinery Mergers.

Transaction Year Retrospective Study Estimated Price Effects (cents per gallon)

Tosco‐Unocal 1997

GAO (2004) CARB rack: branded +6.9, unbranded insignificant.

Hastings & Gilbert (2005) CARB rack: Tosco unbranded +0.4 where greater independent reHosken, Silvia & Taylor

(2011) CARB rack: branded ‐3.6 to ‐1.3, unbranded insignificant to +1.4. 

UDS‐Total 1997 GAO (2004) Conventional rack: branded ‐0.9, unbranded ‐1.3.Marathon‐Ashland 1998 GAO (2004) Branded rack: conventional +0.7, RFG +0.7. Unbranded rack: con

Taylor & Hosken (2007) Rack: +3.1 to +6.8 in two of four cities, others insignificant. Retai

Shell‐Texaco I 1998 GAO (2004) Branded rack: conv. +1.0, CARB ‐0.7. Unbranded rack: conv. +1.1Shell‐Texaco II 1998 GAO (2004) Branded rack: conv. ‐1.8, RFG ‐0.4. Unbranded rack: conv. ‐1.2, RBP‐Amoco 1998 GAO (2004) Conventional rack: branded +0.4, unbranded +1.0. RFG rack: insMAP‐UDS 1999

GAO (2004) Conventional rack: branded +1.4, unbranded +2.6.

Simpson & Taylor (2007) Retail: insignificant.

Exxon‐Mobil 1999 GAO (2004) Branded rack: conventional +3.7, RFG +1.6. Unbranded rack: conUDS‐Tosco 2000 Hosken, Silvia, & Taylor (2011) CARB rack: branded ‐2.1 to ‐5.5; unbranded ‐2.4 to ‐5.7. Retail: in

merger decreased market concentration.

Chevron‐Texaco 2001 GAO (2009) Rack: branded and unbranded insignificant.Phillips‐Tosco 2001 GAO (2009) Rack: branded and unbranded insignificant.Valero‐UDS 2001 GAO (2009) Rack: branded +1.0, unbranded insignificant.Shell‐Texaco 2002 GAO (2009) Rack: branded and unbranded insignificant.Phillips‐Conoco 2002 GAO (2009) Rack: branded ‐1.6, unbranded ‐1.1.Premcor‐Williams 2003 GAO (2009) Rack: branded and unbranded insignificant.Valero‐Premcor 2005

GAO (2009) Rack: branded insignificant, unbranded +1.1.

Silvia & Taylor (2013) Rack and retail: generally, no significant change.

Sunoco‐El Paso 2004 Silvia & Taylor (2013) Rack and retail: generally, no significant change.
18

Table 2: Comparison of Treatment and Control Cities, May‐2005 to May‐2009.

Albuquerque El Paso Amarillo Flagstaff

Avg rack price, pre‐merger 203.8 198.6 195.7 208.0

Avg rack price, post‐merger 236.6 235.8 231.7 236.9

# firms posting at rack 12 11 10 3

Population 515,107 602,672 185,743 59,280

Miles from Albuquerque  ‐‐ 266 289 323

Primary refinery supply       

Giant refineries X     X

Western refinery X X   

Conoco/Valero refineries X X X   

Holly refinery X X    X

Pipelines from Gulf * X   

Sources: OPIS, U.S. Census Bureau.

*Note: In practice, the FTC argued that pro‐rationing of the Plains pipeline limited the ability of

Gulf Coast bulk suppliers to deliver product to Albuquerque.

Table 3: Standard Deviations of Gasoline Rack Prices Before and After 2008.

sd(pABQ‐pELP) sd(pABQ‐pAMR) sd(pABQ‐pFLG)

May‐05 to Dec‐07 3.7 6.4 12.1

Jan‐08 to May‐09 4.5 6.4 12.1

Table 4: Impact of Merger on Albuquerque Prices Relative to El Paso.

 Product Gasoline Diesel 

 Level Rack Retail Rack Retail 

OLS Merger Effect -4.370*** -8.265*** -6.695*** -11.907*** 

Newey-West s.e. (0.751) (1.239) (0.830) (1.425) 

PraisWinsten 

Merger Effect -2.779* -3.879 -4.989*** -0.246 

s.e. (1.546) (2.816) (1.421) (3.452) 

AR(1) 0.804 0.894 0.763 0.949 

Notes: N = 209.  Each regression includes month fixed effects.  Diesel regressions include

dummy variables controlling for differences in the Albuquerque specification of diesel between

March 2005 and December 2006.  Asterisks indicate significance at the 90, 95, and 99 percent

levels.
19

Table 5: Impact of Merger on Albuquerque Retail Prices Relative to Amarillo.

 Product Gasoline Diesel 

 Level Rack Retail Rack Retail 

OLS Merger Effect -3.082*** -6.409*** 0.478 -12.003*** 

Newey-West s.e. (1.161) (1.522) (1.876) (1.193) 

PraisWinsten 

Merger Effect 2.661 -3.267 -3.843 -8.767*** 

s.e. (2.466) (3.403) (2.603) (2.462) 

AR(1) 0.941 0.861 0.868 0.811 

Notes: N = 209.  Each regression includes month fixed effects.  Diesel regressions include

dummy variables controlling for differences in the Albuquerque specification of diesel between

March 2005 and December 2006. Asterisks indicate significance at the 90, 95, and 99 percent

levels.

Table 6: Impact of Merger on Albuquerque Retail Prices Relative to Flagstaff.

 Product Gasoline Diesel 

 Level Rack Retail Rack Retail 

OLS Merger Effect 3.869* 0.327 -8.518*** -3.102*

Newey-West s.e. (2.023) (2.025) (3.057) (1.615) 

PraisWinsten 

Merger Effect 3.574 0.800 -2.093 -2.290 

s.e. (3.950) (4.516) (4.740) (3.366) 

AR(1) 0.794 0.908 0.813 0.863 

Notes: N = 209.  Each regression includes month fixed effects.  Diesel regressions include

dummy variables controlling for differences in the Albuquerque specification of diesel between

March 2005 and December 2006. Asterisks indicate significance at the 90, 95, and 99 percent

levels.

Table 7: Robustness of Merger Impact on Albuquerque/El Paso Gasoline Rack Price

Differential across Various Subsamples.

  

Baseline,

2005‐2009

Drop 1st  

Year Post‐

merger  

Event

Dummies

All Data,

2000‐2009

Monthly

Data, 2005‐

2009

Merger Effect  ‐4.370***  ‐5.738*** ‐4.693*** ‐2.139***  ‐4.266***

Newey‐West s.e. (0.751) (0.745) (1.732) (0.610) (1.317)

N 209 157 209 490 49

Notes: Each regression includes month fixed effects. Asterisks indicate significance at the 90, 95,

and 99 percent levels.  
20

Table 8: Frequency with which Confidence Interval on Placebo Merger Dummies Includes Zero.

Estimation Method Newey-West Prais-Winsten 

Include controls? No Yes No Yes 

Gasoline Rack 16% 34% 53% 81%

Gasoline Retail 23% 45% 59% 94%

Diesel Rack 36% 20% 97% 97%

Diesel Retail 31% 44% 84% 95%

Notes: Using El Paso as the control city. Each entry refers to the percent of regressions over 178

four‐year subsamples of the weekly data from January 2000 to May 2005.

Table 9: Frequency of Having Lowest (Weekly) Rack Gasoline Price.

Firm

Brand

Indicator

Jun‐05 to

May‐06

Jun‐06 to

May‐07

Jun‐07 to

May‐08

Jun‐08 to

May‐09

COP   Branded 0 4 2 2

COP   Unbranded 8 3 6 1

Chevron Branded 4 5 1 8

DiamondShamrock Branded 0

Exxon Branded 3 3 1 1

Giant Unbranded 6 5

Mustang Branded 0 2 8

Shamrock Branded 5 3 2 2

Shell Branded 4 2 0 0

Shell Unbranded 10

Sinclair Branded 0 4 1 3

Texaco Branded 3 0 1 8

ValeroDS Branded 0 0 0 0

Valero Unbranded 19 7 13 22

Western Unbranded 3 16 22 3

Notes: Excludes Fina (Alon) and Navajo (Holly).  Mustang is a retail brand owned by Giant and

acquired by Western. Totals may sum to more than 52 weeks due to ties.
21

Table 10: Frequency of Having Lowest (Weekly) Rack Diesel Price.

Firm

Brand

Indicator

Jun‐05 to

May‐06

Jun‐06 to

May‐07

Jun‐07 to

May‐08

Jun‐08 to

May‐09

COP   Branded 1 15 1 2

COP   Unbranded 18 9 10 16

Chevron Branded 8 12 6 3

Citgo Branded 0 0

DiamondShamrock Branded 0

Exxon Branded 0 0 0 1

Giant Unbranded 2 3

Mustang Branded 2 1 2

Shamrock Branded 0 0 0 0

Shell Branded 0 1 0 1

Shell Unbranded 1 13 13

Sinclair Branded 0 2 3 0

Texaco Branded 10 1 2 3

ValeroDS Branded 0 0 0 0

Valero Unbranded 14 8 17 14

Western Unbranded 2 2

Notes: Excludes Fina (Alon) and Navajo (Holly). Mustang is a retail brand owned by Giant and

acquired by Western. Totals may sum to more than 52 weeks due to ties.