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entitled 'Energy Markets: Estimates of the Effects of Mergers and
Market Concentration on Wholesale Gasoline Prices' which was released
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Report to Congressional Requesters:
United States Government Accountability Office:
GAO:
June 2009:
Energy Markets:
Estimates of the Effects of Mergers and Market Concentration on
Wholesale Gasoline Prices:
Energy Markets:
GAO-09-659:
GAO Highlights:
Highlights of GAO-09-659, a report to congressional requesters.
Why GAO Did This Study:
In 2008, GAO reported that 1,088 oil industry mergers occurred between
2000 and 2007. Given the potential for price effects, GAO recommended
that the Federal Trade Commission (FTC), the agency with the authority
to maintain petroleum industry competition, undertake more regular
retrospective reviews of past petroleum industry mergers, and FTC said
it would consider this recommendation. GAO was asked to conduct such a
review of its own to determine how mergers and market concentration—a
measure of the number and market shares of firms in a market—affected
wholesale gasoline prices since 2000.
GAO examined the effects of mergers and market concentration using an
economic model that ruled out the effects of many other factors. GAO
consulted with a number of experts and used both public and private
data in developing the model. GAO tested the model under a variety of
assumptions to address some of its limitations. GAO also interviewed
petroleum market participants.
What GAO Found:
GAO examined seven mergers that occurred since 2000—ranging in value
and geography and for which there was available gasoline pricing data
(see table)—and found three that were associated with statistically
significant increases or decreases in wholesale gasoline prices.
Specifically, GAO found that the mergers of Valero Energy with Ultramar
Diamond Shamrock and Valero Energy with Premcor, which both involved
the acquisition of refineries, were associated with estimated average
price increases of about 1 cent per gallon each. In addition, GAO found
that the merger of Phillips Petroleum with Conoco, which primarily
involved the acquisition of oil exploration and production assets, was
associated with an estimated average decrease in wholesale gasoline
prices across cities affected by the merger of nearly 2 cents per
gallon. This analysis provides an indicator of the impact that
petroleum industry mergers can have on wholesale gasoline prices.
Additional analysis would be needed to explain the price effects that
GAO estimated.
Table: Seven Mergers That GAO Studied, and the Estimated Wholesale
Gasoline Price Effects:
Merger: Chevron/Texaco;
Date: 10/16/2000;
Value (Dollars in millions): $44,838;
Cities affected: 37;
Estimated price effect: Not statistically significant.
Merger: Phillips/Tosco;
Date: 2/4/2001;
Value (Dollars in millions): $9,828;
Cities affected: 8;
Estimated price effect: Not statistically significant.
Merger: Valero/Ultramar Diamond Shamrock;
Date: 5/7/2001;
Value (Dollars in millions): $6,442;
Cities affected: 26;
Estimated price effect: +1.06 cents per gallon.
Merger: Shell/Texaco;
Date: 10/9/2001;
Value (Dollars in millions): $3,860;
Cities affected: 35;
Estimated price effect: Not statistically significant.
Merger: Phillips/Conoco;
Date: 11/19/2001;
Value (Dollars in millions): $31,282;
Cities affected: 47;
Estimated price effect: -1.64 cents per gallon.
Merger: Premcor/Williams;
Date: 11/26/2002;
Value (Dollars in millions): $367;
Cities affected: 2;
Estimated price effect: Not statistically significant.
Merger: Valero/Premcor;
Date: 4/25/2005;
Value (Dollars in millions): $7,588;
Cities affected: 20;
Estimated price effect: +1.13 cents per gallon.
Source: GAO analysis of information from IHS Herold and Oil Price
Information Service.
[End of table]
GAO used two separate measures of market concentration, one which
measured the number of sellers at wholesale gasoline terminals and
another which measured the market share of refiners supplying gasoline
to those sellers, and found that less concentrated markets were
statistically significantly associated with lower gasoline prices. For
example, for wholesale terminals with more sellers—i.e., terminals that
were less concentrated—GAO estimated that prices were about 8 cents per
gallon lower at terminals with 14 sellers than at terminals that had
only 9 sellers. This result is consistent with the idea that markets
with more sellers are likely to be more competitive, resulting in lower
prices. Using the second measure of concentration, GAO similarly found
a statistically significant association between prices and the level of
refinery concentration, with less concentrated groups of refineries
associated with lower prices.
What GAO Recommends:
This study reinforces the need to review past petroleum industry
mergers, and GAO continues to recommend that FTC conduct such reviews
more regularly and develop risk-based guidelines to determine when to
conduct them. FTC reviewed a draft of this report and supports GAO’s
recommendation to conduct more reviews of past petroleum industry
mergers.
View [hyperlink, http://www.gao.gov/products/GAO-09-659] or key
components. For more information, contact Mark Gaffigan at
gaffiganm@gao.gov, (202) 512-3841 or Tom McCool at mccoolt@gao.gov,
(202) 512-2700.
[End of section]
Contents:
Letter:
Background:
Some Petroleum Industry Mergers Were Associated with Small Increases
and Decreases in Wholesale Gasoline Prices:
Analysis Suggests Less Concentrated Markets Were Associated with Lower
Wholesale Gasoline Prices:
Concluding Observations:
Agency Comments and Our Evaluation:
Appendix I: Technical Discussion of Objectives, Scope, and Methodology:
Appendix II: Comments from the Federal Trade Commission:
Appendix III: Summary Information on the Seven Mergers Reviewed in
GAO's Econometric Model:
Appendix IV: Additional Market Concentration Information:
Appendix V: GAO Contacts and Staff Acknowledgments:
Tables:
Table 1: Summary Information for Mergers Reviewed in Model:
Table 2: Effects of the Number of Sellers on Unbranded Wholesale
Gasoline Prices at the Terminals in the 78 Cities We Studied:
Table 3: Effects of Market Concentration on Unbranded Wholesale
Gasoline Prices at Terminals Supplied by Seven Spot Markets:
Table 4: Data Used in Our Econometric Model:
Table 5: Regression Results for Mergers' Effect on Unbranded Gasoline
Prices--Dependent Variable Is the Logarithm of Unbranded Gasoline
Price:
Table 6: Regression Results for Mergers' Effect on Branded Gasoline
Prices--Dependent Variable Is the Logarithm of Branded Gasoline Price:
Table 7: Regression Results for Effect of Spot Market HHI on Unbranded
Gasoline Prices--Dependent Variable Is the Logarithm of Unbranded
Gasoline Price:
Table 8: Regression Results for Effect of Spot Market HHI on Branded
Gasoline Prices--Dependent Variable Is the Logarithm of Branded
Gasoline Price:
Table 9: Regression Results for Effect of the Number of Sellers at the
City Terminal on Unbranded Gasoline Prices--Dependent Variable Is the
Logarithm of Unbranded Gasoline Price:
Table 10: Regression Results for Effect of the Number of Sellers at the
City Terminal on Branded Gasoline Prices--Dependent Variable is the
Logarithm of Branded Gasoline Price:
Table 11: Effects of the Number of Sellers on Branded Wholesale
Gasoline Prices at the Terminals in the 82 Cities We Studied:
Table 12: Effects of Market Concentration on Branded Wholesale Gasoline
Prices at Terminals Supplied by Seven Spot Markets:
Table 13: Number of Sellers at Wholesale Terminals in 2008:
Figures:
Figure 1: Example of a Gasoline Supply Chain:
Figure 2: Number of Wholesale Gasoline Sellers at Terminals in 2008:
Figure 3: Cities Affected by Chevron/Texaco Merger:
Figure 4: Cities Affected by Phillips/Tosco Merger:
Figure 5: Cities Affected by Valero/UDS Merger:
Figure 6: Cities Affected by Shell/Texaco Merger:
Figure 7: Cities Affected by Phillips/Conoco Merger:
Figure 8: Cities Affected by Premcor/Williams Merger:
Figure 9: Cities Affected by Valero/Premcor Merger:
Figure 10: Yearly Concentration Levels in the Seven Spot Markets That
We Analyzed:
Abbreviations:
CARB: California Air Resources Board:
CBG: Cleaner Burning Gasoline:
DOJ: Department of Justice:
EIA: Energy Information Administration:
FTC: Federal Trade Commission:
HHI: Herfindahl-Hirschman Index:
MTBE: Methyl tertiary-butyl ether:
OPIS: Oil Price Information Service:
PADD: Petroleum Administration for Defense Districts:
RFG: reformulated gasoline:
RVP: Reid vapor pressure:
UDS: Ultramar Diamond Shamrock Corporation:
[End of section]
United States Government Accountability Office:
Washington, DC 20548:
June 12, 2009:
The Honorable Charles E. Schumer:
Vice Chairman:
Joint Economic Committee:
United States Congress:
The Honorable Herb Kohl:
Chairman:
Subcommittee on Antitrust, Competition Policy and Consumer Rights:
Committee on the Judiciary:
United States Senate:
The Honorable Henry A. Waxman:
Chairman:
Committee on Energy and Commerce:
House of Representatives:
The Honorable Dianne Feinstein:
United States Senate:
In 2008, GAO reported that more than 1,000 mergers occurred in the
petroleum industry between 2000 and 2007.[Footnote 1] These mergers
were mostly between firms involved in crude oil exploration and
production, and were generally driven by the challenges associated with
producing oil in extreme physical environments such as offshore in deep
water and increasing concerns about competition with large national oil
companies. Other mergers took place in the segment of the petroleum
industry that refines and sells petroleum products. These mergers were
generally driven by the desire for greater operational efficiencies and
cost savings. We reported that while mergers could help oil companies
overcome some of these challenges, they also have the potential to
increase firms' market power--allowing them to raise gasoline prices
without being undercut by other firms.[Footnote 2]
The Federal Trade Commission (FTC) has lead responsibility for federal
reviews of petroleum industry mergers. In evaluating mergers, FTC staff
try to predict the impact of a merger on gasoline prices by reviewing
factors that affect competition, including the market concentration.
Market areas with a number of small firms are considered to be
unconcentrated or moderately concentrated, while areas with fewer,
larger firms are highly concentrated. Mergers that lead to a more
concentrated market might also improve efficiency and reduce costs, and
firms may pass these savings on to consumers in the form of lower
prices. At the same time, mergers that cause a market area to become
highly concentrated potentially allow one firm, or a small group of
firms, to increase consumer prices above competitive levels. However,
our 2008 review was limited to FTC's efforts to maintain competition in
the petroleum industry; it did not address the impacts mergers or
subsequent changes in market concentration may have had on prices. In
this context, we were asked to study how (1) selected mergers, and (2)
market concentration, have affected wholesale gasoline prices since
2000.
To study the impacts of selected mergers and market concentration on
wholesale gasoline prices, we developed and extensively tested an
econometric model that examined the statistical relationship between
mergers, market concentration, and gasoline prices. We limited our
analysis to mergers (1) that occurred between 2000 and 2007, (2) that
had transaction values of $200 million or greater, and (3) for which we
had useful and complete gasoline price data where each merger occurred.
These criteria provided seven mergers for our analysis. To provide
context on petroleum industry mergers, we interviewed a number of
petroleum industry representatives and FTC staff. In developing our
model, we consulted with a number of economists in industry and
academia who had completed similar studies, as well as with economists
at FTC. We also varied the design of our model to ensure that our
results were not highly dependent on any single assumption. Our model
required data on mergers and wholesale gasoline prices, as well as
other factors that might have affected gasoline markets, so that we
could control for them and isolate the effects of mergers and
concentration.
We purchased data from IHS Herold on the nature and size of petroleum
industry mergers between 2000 and 2007.[Footnote 3] We also purchased
data from the Oil Price Information Service (OPIS) on historical
gasoline prices at wholesale gasoline terminals located across the
United States.[Footnote 4] The price data provided by OPIS reflect 60
percent of the gasoline sold at these wholesale terminals.[Footnote 5]
We looked at prices at one terminal in each of 78 cities. We also used
additional data from OPIS to control for the effects of special
gasoline types that varied across cities in our analysis. Further, we
used a number of data sets from the Energy Information Administration
(EIA), including historical data on crude oil prices, refinery
utilization rates, and gasoline sales. We assessed the reliability of
the data and found them sufficiently reliable for the purposes of this
report.
Despite our efforts to carefully design our analysis, there were
limitations. For example, we were not able to fully account for all the
conceivable factors that affect gasoline markets, including disruptions
to local gasoline supply markets from weather-related events,
interruptions in refinery or pipeline operations, or other changes in
local gasoline supply. As such, the price impacts we present from our
model are estimates. In addition, because some cities were affected by
multiple mergers, may have had changes in market concentration, and may
have been affected by factors for which we did not have data, we cannot
describe how wholesale prices may have changed overall in each
location. Therefore the strength of this analysis is to provide an
indicator of the potential impacts of mergers and market concentration
rather than to suggest that these factors were the sole source of
gasoline price changes in the cities we chose to study. See appendix I
for a more detailed description of our objectives, scope, and
methodology.
We conducted this performance audit from October 2008 to June 2009 in
accordance with generally accepted government auditing standards. Those
standards require that we plan and perform the audit to obtain
sufficient, appropriate evidence to provide a reasonable basis for our
findings and conclusions based on our audit objectives. We believe that
the evidence obtained provides a reasonable basis for our findings and
conclusions based on our audit objectives.
Background:
The U.S. petroleum industry consists of firms of varying sizes that
operate in one or more of three broad segments--the upstream, which
consists of the exploration for and production of crude oil; the
midstream, which consists of pipelines and other infrastructure used to
transport crude oil and refined products; and the downstream, which
consists of the refining and marketing of petroleum products such as
gasoline and heating oil. While some firms operate in only one or two
of these segments, fully vertically integrated oil companies
participate in all of them. Chevron is an example of a fully integrated
petroleum company, with operations in all three segments, while Wawa--
the convenience store chain--is an example of a firm operating in only
one market segment as a downstream independent fuel retailer.
Refiners produce gasoline and then arrange its delivery, usually via
pipeline, but also via barge, truck, or rail, from their refineries to
any of the nearly 400 wholesale terminals located throughout the
country. Terminals can be near refineries, pipelines, or water ports,
and can involve a wide-ranging number of wholesale gasoline sellers,
including refiners or importers.[Footnote 6] The number of sellers at a
wholesale terminal is not necessarily related to the number of
refineries near the terminal; in some markets, a single refinery can
produce gasoline for a number of sellers if they have supply
arrangements with that refinery.[Footnote 7] At wholesale terminals,
the majority of gasoline is purchased by marketers or distributors, for
subsequent resale at retail gasoline stations, while the rest is sold
directly to retailers (see figure 1). Market dynamics anywhere along
the supply chain can influence consumer prices, beginning with upstream
crude oil production, all the way through downstream refining and
retailing.
Figure 1: Example of a Gasoline Supply Chain:
[Refer to PDF for image: illustration]
Refiner (seller): to:
* Seller;
* Wholesale terminal.
Importer (seller): to:
* Wholesale terminal.
Seller: to:
* Wholesale terminal.
Wholesale terminal: to:
* Distributor (marketer);
* Retail gasoline stations.
Distributor (marketer): to:
* Retail gasoline stations.
Source: GAO.
[End of figure]
Gasoline from a wholesale terminal can also be either branded or
unbranded. Branded gasolines are those supplied from major refiners
selling under their trademarks, such as BP or Marathon, and often
contain special additives, while unbranded gasolines may be supplied by
major or independent refiners, but are not sold under a refiner's
trademark. Branded prices include a premium reflecting the recognized
brand name, fuel additives, and other costs, such as
advertising.[Footnote 8] Unbranded prices, which tend to be lower than
those for branded, are paid by distributors who deliver gasoline to
retail locations ranging from large supermarkets to small independent
retailers that are not affiliated with a major refiner.[Footnote 9]
FTC's merger review process is conducted by staff in various bureaus
and offices throughout the agency, but mainly by the Bureau of
Economics and the Bureau of Competition. In reviewing proposed mergers,
FTC follows guidelines that it developed jointly with the Department of
Justice (DOJ) for predicting the effects of mergers on competition. The
unifying theme in the guidelines is that mergers should not be
permitted to enhance a firm's market power or to make it easier for a
firm to exercise market power. In its review, FTC examines whether
market conditions, including market concentration, would be conducive
for firms to act unilaterally or to coordinate to raise prices.
Unilateral effects occur when the merged firm profitably reduces its
own supply and raises prices, even though other competitors may respond
by increasing their own output. Such behavior can be profitable if the
merged firm has a significant share of sales and the response of
competitors is limited. Coordinated behavior occurs when each firm
remaining in the market reduces its output, increasing prices. In their
reviews of petroleum industry mergers, FTC staff seek to avoid the
possibility of price increases even as small as 1 cent per gallon
because the petroleum industry sells large volumes of fuel at thin
margins, and price changes of this magnitude can affect industry
decisions regarding production or sales. In addition, in some markets,
even 1 cent per gallon price increases can lead to more than $100
million per year in additional costs for consumers, according to FTC
analysis.
After reviewing a merger, FTC has three options: (1) to not challenge
the merger; (2) to challenge the merger in court; or (3) to not
challenge the merger as long as certain agreed upon remedial actions
are met, such as firms selling off, or divesting, overlapping assets
that have the greatest potential to harm competition.[Footnote 10] FTC
also performs other activities to monitor petroleum markets, including
monitoring fuel prices and conducting special investigations. For
example, FTC's price-monitoring program tracks retail gasoline and
diesel prices in 360 cities across the nation and wholesale prices in
20 major urban areas. In addition, on April 16, 2009, FTC issued a
Revised Notice of Proposed Rulemaking seeking public comment on a
revised proposed rule that would prohibit market manipulation in the
petroleum industry. The revised proposed rule would prohibit fraudulent
and deceptive conduct that could harm wholesale petroleum markets, but
it is not yet clear how this new rule will affect FTC's monitoring of
petroleum industry markets. However, FTC staff indicated that because
FTC is an enforcement agency, they focus on merger and antitrust
enforcement, rather than ongoing monitoring of the petroleum industry,
as a regulatory agency would likely undertake. According to FTC, during
the latter part of 2008, approximately 125 FTC staff members--
attorneys, economists, paralegals, research analysts, and others--
worked to some extent on matters involving antitrust and pricing issues
in the oil and natural gas sectors, and about 6 or 7 staff economists
from the Bureau of Economics were involved in ongoing monitoring of the
petroleum industry, although these economists also devoted a portion of
their time to other industries. These staff economists also
occasionally perform analysis of past mergers, and FTC has indicated
retrospective merger reviews are a valuable part of antitrust decision
making. If FTC finds anticompetitive behavior in retrospective reviews,
it has the ability to conduct further in-depth investigations into the
merger and collect substantial company-specific data in order to pursue
corrective action to reintroduce competition into the market such as
forced divestitures or conduct-based remedies.
However, as we reported in 2008, FTC does not regularly look back at
past mergers in the petroleum industry to assess their actual effects
on prices--there had been only three such retrospective reviews,
between 2000 and 2007.[Footnote 11] We recommended that FTC undertake
more regular retrospective reviews of past petroleum industry mergers
and develop risk-based guidelines to determine when to conduct them. In
commenting on this, FTC noted that our recommendation was consistent
with a recent self-evaluation initiative and would consider it in that
regard. Although these reviews can be resource intensive, experts,
industry participants, and FTC agreed that regular retrospective
reviews would allow the agency to better inform future merger reviews
and better measure its success in maintaining competition. In this
regard, the National Bureau of Economic Research published a study in
March 2009 entitled Generating Evidence to Guide Merger Enforcement,
which noted the importance of conducting retrospective merger reviews.
[Footnote 12] The study found that retrospective merger reviews can
help to evaluate the impacts of past merger enforcement decisions and
can allow antitrust agencies to develop better techniques to predict
the effects of future mergers on competition. The study also suggested
that it made sense to focus retrospective reviews on completed mergers
with the greatest likelihood of anticompetitive effects, such as
mergers in highly concentrated markets.[Footnote 13] FTC is currently
working on a fourth retrospective review of a past petroleum industry
merger, which is expected to be released later this year.
Some Petroleum Industry Mergers Were Associated with Small Increases
and Decreases in Wholesale Gasoline Prices:
We studied the effects of seven petroleum industry mergers that
occurred since 2000 on wholesale gasoline prices and found three that
were associated with small changes in wholesale gasoline prices.
Specifically, we developed an econometric model to isolate the effects
on wholesale gasoline prices of seven mergers--(1) Chevron Corporation/
Texaco, (2) Phillips Petroleum Company/Tosco Corporation, (3) Valero
Energy Corporation/Ultramar Diamond Shamrock Corporation (UDS), (4)
Royal Dutch Shell Group/Texaco, (5) Phillips Petroleum Company/Conoco,
(6) Premcor/Williams Companies, and (7) Valero Energy Corporation/
Premcor. These mergers ranged widely in the size of transaction, from
the Chevron/Texaco merger, valued at about $45 billion, to the Premcor/
Williams merger, valued at $367 million. Five of the seven mergers were
focused primarily on the downstream sector, with refining, marketing,
or retail operations as the key assets that changed ownership, while
the other two mergers were concentrated in the upstream exploration and
production sector, with oil reserves as the key asset that changed
ownership. The rationale for some of these mergers, according to
industry officials, was generally to increase operational efficiencies
and reduce costs through economies of scale.[Footnote 14] Summary
information about the mergers is provided in table 1.
Table 1: Summary Information for Mergers Reviewed in Model:
Merger[A]: Chevron Corp./Texaco;
Announced date: Oct. 16, 2000;
Transaction value (U.S. dollars in millions) and key assets: $44,838;
oil and gas reserves;
Number of cities affected[B]: 37;
FTC response to merger: Challenged: divestitures required in refining
and marketing;
GAO's estimated effect on wholesale gasoline prices (cents/gallon)[C]:
Results not statistically significant.
Merger[A]: Phillips Petroleum Company/Tosco Corp.;
Announced date: Feb. 4, 2001;
Transaction value (U.S. dollars in millions) and key assets: $9,828; 8
refineries and approximately 6,400 retail stations;
Number of cities affected[B]: 8;
FTC response to merger: Not challenged;
GAO's estimated effect on wholesale gasoline prices (cents/gallon)[C]:
Results not statistically significant.
Merger[A]: Valero Energy Corp./Ultramar Diamond Shamrock (UDS) Corp.;
Announced date: May 7, 2001;
Transaction value (U.S. dollars in millions) and key assets: $6,442; 7
refineries and approximately 5,000 retail stations;
Number of cities affected[B]: 26;
FTC response to merger: Challenged: divestitures required in refining
and retailing;
GAO's estimated effect on wholesale gasoline prices (cents/gallon)[C]:
+1.06 (branded); Unbranded results not statistically significant.
Merger[A]: Royal Dutch Shell Group/Texaco;
Announced date: Oct. 9, 2001;
Transaction value (U.S. dollars in millions) and key assets: $3,860;
Texaco's share of Motiva and Equilon downstream joint ventures[D];
Number of cities affected[B]: 35;
FTC response to merger: Not challenged;
GAO's estimated effect on wholesale gasoline prices (cents/gallon)[C]:
Results not statistically significant.
Merger[A]: Phillips Petroleum Company/Conoco;
Announced date: Nov. 19, 2001;
Transaction value (U.S. dollars in millions) and key assets: $31,282;
oil and gas reserves, refining and marketing assets;
Number of cities affected[B]: 47;
FTC response to merger: Challenged: divestitures required in refining
and marketing;
GAO's estimated effect on wholesale gasoline prices (cents/gallon)[C]:
-1.64 (branded); -1.14 (unbranded).
Merger[A]: Premcor/Williams Companies;
Announced date: Nov. 26, 2002;
Transaction value (U.S. dollars in millions) and key assets: $367; 1
refinery;
Number of cities affected[B]: 2;
FTC response to merger: Not challenged;
GAO's estimated effect on wholesale gasoline prices (cents/gallon)[C]:
Results not statistically significant.
Merger[A]: Valero Energy Corp./Premcor;
Announced date: Apr. 25, 2005;
Transaction value (U.S. dollars in millions) and key assets: $7,588; 4
refineries;
Number of cities affected[B]: 20;
FTC response to merger: Not challenged;
GAO's estimated effect on wholesale gasoline prices (cents/gallon)[C]:
Branded results not statistically significant; +1.13 (unbranded).
Source: GAO analysis of information from IHS Herold,, FTC, and OPIS.
[A] GAO criteria for selection of mergers included (1) mergers that
occurred between 2000 and 2007, (2) a minimum merger transaction value
of $200 million, and (3) the availability of useful and complete
gasoline price data.
[B] The cities affected include those, out of the 78 examined in GAO's
model, with wholesale terminals where both companies operated before
the merger.
[C] The price effects we report were statistically significant, meaning
that we were able to reasonably rule out the effects of chance on the
estimated impacts on wholesale gasoline prices.
[D] The Equilon Enterprises joint venture included approximately 4,500
Shell-branded and 4,500 Texaco-branded gasoline service stations, four
refineries, and 65 product terminals and ports. The Motiva Enterprises
joint venture included approximately 4,800 Shell-branded and 8,200
Texaco-branded stations, four refineries, seven lubricants facilities,
and 50 product terminals.
[End of table]
As shown in table 1, the seven mergers we analyzed ranged widely in the
number of cities with wholesale terminals that were affected by the
merger. We analyzed the effects of the seven mergers at terminals in 78
cities across the United States. The three mergers affecting terminals
in 35 or more cities each--Chevron/Texaco, Shell/Texaco, and Phillips/
Conoco--reflect a wide geographic area, as each merger affected cities
across a number of regions of the country. The Valero/Premcor and
Valero/UDS mergers, each of which affected terminals in 20 or more
cities, were more concentrated geographically, primarily affecting
cities in the eastern and western United States, respectively. The two
mergers affecting terminals in fewer than 10 cities each--Phillips/
Tosco and Premcor/Williams--reflect narrower geographic areas, with the
former affecting a few cities in the Southeast and Southwest and the
latter affecting 2 cities in the Southeast. See appendix III for more
information on the geographic regions affected by each merger.
Antitrust enforcement actions taken in response to the mergers varied,
depending on the characteristics of the firms, the geographic areas
affected, and the specifics of the transaction. As shown in table 1,
the FTC challenged three of the mergers, as originally proposed, on the
basis of potential threats to competition in one or more sectors of the
industry.[Footnote 15] In response to these potential anticompetitive
threats, FTC required the merging firms to divest key assets in the
sectors of identified concern. In the case of the Chevron/Texaco
merger, FTC identified potential threats to gasoline marketing in 23
states across the western and southern United States, as well as
potential threats to refining in California and the Pacific Northwest,
among others. As a result, it ordered the divestiture of Texaco's
downstream assets in marketing and refining, as well as in pipelines.
[Footnote 16] In the case of the Valero/UDS merger, FTC identified
potential threats to the refining and supply sectors in California and
subsequently required the divestiture of a UDS refinery in Avon,
California, as well as the divestiture of numerous supply contracts and
70 retail outlets across the West. In the case of the Phillips/Conoco
merger, FTC identified a number of potential concerns, including
threats to gasoline refining and supply in various western and
midwestern states. In response, FTC required divestitures in key areas
of concern, including the sale of a Phillips refinery near Salt Lake
City and marketing assets in northern Utah, as well as the sale of
Conoco's Denver-area refinery and Phillips's marketing assets in
eastern Colorado. In the case of the remaining four mergers, FTC did
not identify competitive concerns and consequently did not require
divestitures or other remedial actions.
As highlighted in table 1, the results of our analysis suggest that two
of the seven mergers were associated with small increases in wholesale
gasoline prices, while one was associated with a small decrease in
wholesale gasoline prices. In the case of these three mergers, the
model results were statistically significant, meaning that we were able
to reasonably rule out the effects of chance on the estimated impacts
on wholesale gasoline prices. In addition, our model held constant the
effects of a number of other key variables, including changes in
gasoline inventory, refinery capacity utilization, and the type of
gasoline sold, although data were unavailable on additional factors
that may have affected prices. According to these results, the 2005
acquisition by Valero of four refineries owned by Premcor was
associated with an increase of 1.13 cents per gallon for unbranded
gasoline. Similarly, the model suggests that the 2001 acquisition by
Valero of seven refineries and approximately 5,000 retail stations
owned by UDS was associated with an increase in branded wholesale
gasoline prices of approximately 1.06 cents per gallon[Footnote 17]. By
contrast, the model suggests that the 2001 merger of Phillips and
Conoco, including oil reserves, as well as refining and marketing, was
associated with a decrease in branded wholesale gasoline prices of
approximately 1.64 cents per gallon and a decrease of 1.14 cents per
gallon for unbranded gasoline. The price effects observed in these
three cases reflect an average increase or decrease in wholesale
gasoline prices at terminals across the cities affected by the merger
for the period of time following the merger through September 200
[Footnote 18]8. In the case of the remaining four mergers--Chevron/
Texaco, Phillips/Tosco, Shell/Texaco, and Premcor/Williams--the results
of our model were not statistically significant.
Given the complexities of the petroleum industry's supply chain, we
could not provide an explanation as to why certain mergers were
associated with changes in wholesale gasoline prices. Gasoline moves
through an often complicated supply network, and the efficiency gains
associated with mergers, or likewise the opportunities for market
participants at any level of the network to exercise market power,
could play out in any number of ways. For example, some marketers we
spoke with indicated that mergers sometimes spurred refiners to
renegotiate the terms of their supply agreements, making them less
favorable and potentially indicating the exercise of market power by an
individual refiner. On the other hand, mergers can create operational
efficiencies and economies of scale that can allow refiners and
marketers to pass on savings, in the form of lower prices, to
consumers. At the terminal level, there is limited information on
gasoline's refinery of origin, including whether it was even refined
domestically, further adding to the difficultly in pinpointing how and
where the impacts from a merger are felt. For example, marketers we
spoke with indicated that they could not be sure where gasoline shipped
via pipeline came from, since similar products are intermingled in the
system. In addition, refiners we spoke with indicated that they were
able to exchange gasoline with each other, enabling them to have a
marketing presence in a city that was not very close to one of their
refineries. These "exchange agreements" add to the efficiency of the
supply network, because refiners can trade fuel across locations rather
than ship it, although these agreements can also greatly add to its
complexity. As such, our model does not provide further explanation as
to the underlying forces that contributed to any correlation between
the three mergers and changes in wholesale gasoline prices, nor does it
provide conclusive evidence of unilateral or coordinated behavior to
influence gasoline prices. To do this we would have had to conduct in-
depth investigations into each merger and collect substantial company-
specific data. Nonetheless, our model provides an indicator of the
impact that petroleum industry mergers can have on wholesale gasoline
prices. And given the substantial size of the gasoline market, even
small increases or decreases in wholesale prices can have a significant
impact on consumer spending.[Footnote 19]
Analysis Suggests Less Concentrated Markets Were Associated with Lower
Wholesale Gasoline Prices:
We also used our model to analyze market concentration and found that
less concentrated wholesale gasoline markets--i.e., wholesale terminals
with more sellers--were significantly associated with lower gasoline
prices at terminals located in 78 cities across the United States.
[Footnote 20] For example, we estimated that prices were about 8 cents
per gallon lower at terminals with, for example, 14 sellers compared
with prices at terminals that had only 9 sellers. We also measured the
concentration of groups of refineries that supplied gasoline to sellers
at wholesale terminals in these cities and similarly found that prices
were lower if a terminal was supplied by a less concentrated group of
refineries.
Measures of market concentration often take into account both the
number of firms in a market and the market share of each firm, and one
such measure, the Herfindahl-Hirschman Index, or HHI, gives
proportionally greater weight to firms with larger market shares.
[Footnote 21] According to FTC and DOJ guidelines, an unconcentrated
market has an HHI of less than 1,000; a moderately concentrated market
has an HHI between 1,000 and 1,800; and a highly concentrated market,
with the greater likelihood that a firm could exercise market power,
has an HHI over 1,800. We measured market concentration affecting
wholesale terminals in two ways: (1) by counting the number of sellers
at each wholesale terminal, and (2) by calculating the HHI of refinery
groups that supplied gasoline to sellers at wholesale terminals.
In our first approach, the number of sellers at wholesale terminals was
inversely related to the level of concentration, with terminals with
few sellers having high levels of concentration. Although this measure
was not technically a measure of market concentration, it closely
reflected supply conditions at wholesale terminals in the 78 cities we
studied.[Footnote 22] In our second approach, we moved up the supply
chain and measured the number and size of the refineries that were the
original source for the gasoline delivered to the sellers at each
terminal. We determined the production capacity of refineries in the
seven historical U.S. refinery groups known as spot markets and then
determined which spot market groups supplied gasoline to sellers at
individual wholesale terminals, allowing us to estimate a refinery HHI
for individual wholesale terminals in the 78 cities we studied.
[Footnote 23]
Both of our measures indicated that less concentrated markets were
significantly associated with lower wholesale gasoline prices, as shown
in tables 2 and 3. Although we did not observe large changes in market
concentration over time, there was variation in market concentration
across the wholesale terminals in our analysis. In order to demonstrate
the size of the effect that market concentration had on wholesale
gasoline prices, we chose to look at the expected changes in wholesale
prices across two ranges of market concentration--one range was between
the 25th and 75th percentiles of market concentration values in our
analysis, and the other was between the 10th and 90th percentiles. We
calculated the expected price differences if a terminal were to have
moved from the higher end of either of these concentration ranges to
the lower end.
We found that the terminals with more sellers and therefore lower
levels of concentration would be expected to have lower wholesale
gasoline prices (see table 2). We estimated that if a terminal were to
have gained 5 wholesale gasoline sellers, we would expect prices to be
8 cents per gallon lower at that terminal. In addition, if a terminal
were to have gained 11 sellers, we estimated that prices would be 18
cents per gallon lower. We present the number of sellers at each of the
terminals in the 78 cities we examined, which ranged from 3 to 21 in
2008, with a median of 11, in appendix IV.
Table 2: Effects of the Number of Sellers on Unbranded Wholesale
Gasoline Prices at the Terminals in the 78 Cities We Studied:
Change in number of sellers at the wholesale terminal: Change in
unbranded wholesale gasoline price in cents per gallon[A];
Gain of 5 sellers (9 sellers to 14 sellers): -8;
Gain of 11 sellers (6 sellers to 17 sellers): -18.
Source: GAO analysis of OPIS data.
Note: We present the results for branded gasoline in appendix IV. These
results were similar and also statistically significant.
[A] These results were statistically significant at the 1 percent
level. The 9 to 14 seller range represents the 25th to the 75th
percentile of values that we observed at terminals in our analysis. The
6 to 17 seller range represents the 10th to the 90th percentile.
[End of table]
We also found that terminals supplied by the refinery spot markets with
the lower HHIs would be expected to have lower wholesale gasoline
prices (see table 3). We estimated that if a spot market supplying
gasoline to a terminal were to have become less concentrated by moving
from an HHI of 930 to 790, we would expect prices to be about 2 cents
per gallon lower at that terminal. In addition, if a spot market
supplying gasoline to a terminal were to have become less concentrated
by moving from an HHI of 1470 to 700, we estimated that prices would be
about 13 cents per gallon lower at that terminal. In general, our
findings were consistent with the idea that markets with more sellers
or more refiners supplying those sellers are likely to be more
competitive, resulting in lower prices. We present trends in spot
market concentration in appendix IV that ranged from 666 to 3,729. The
median HHI across all markets was 906.
Table 3: Effects of Market Concentration on Unbranded Wholesale
Gasoline Prices at Terminals Supplied by Seven Spot Markets:
Refinery spot market HHI: Change in unbranded wholesale gasoline price
in cents per gallon[A];
Decrease in HHI from 930 to 790: -2;
Decrease in HHI from 1,470 to 700: -13.
Source: GAO analysis of OPIS data.
Note: We present the results for branded gasoline in appendix IV. These
results were similar and also statistically significant.
[A] These results were statistically significant at the 1 percent
level. The 790 to 930 range represents the 25th to the 75th percentile
of values that we observed at terminals in our branded analysis. The
700 to 1,470 range represents the 10th to the 90th percentile.
[End of table]
In estimating these results, we treated market concentration as
endogenous--meaning that changes in wholesale gasoline prices could
affect market concentration in addition to changes in concentration
affecting prices. For example, this could occur if high prices at one
terminal spur new sellers to enter the market, thus decreasing
concentration. This assumption was supported by statistical tests that
we conducted, although because this assumption was likely to have a
noticeable impact on our results, we also analyzed our data without it
and found that the impact on prices of our concentration measures was
statistically significant but smaller. For example, for unbranded
prices, in the case of the refinery spot market HHI, the impact on
wholesale prices was about half the size without this assumption. For
the number of sellers at the terminal, the impact was about one-sixth
of the size without this assumption.
As noted above, we did not observe a trend of increasing market
concentration nationwide between 2000 and 2008, either in the number of
sellers at wholesale terminals or in our HHI numbers calculated for
refinery spot market groups. For example, the average number of sellers
at terminals across the country remained almost the same since 2000,
with terminals averaging 11 sellers by 2008 and most having between 7
and 11 sellers during that year (see figure 2). However, of the
terminals located in the 78 cities we studied, we did find that 8
terminals lost 5 or more sellers and 39 lost between 1 and 4--the
remainder had no change or actually gained sellers since 2000.
Figure 2: Number of Wholesale Gasoline Sellers at Terminals in 2008:
[Refer to PDF for image: vertical bar graph]
Number of sellers: 2-6;
Number of terminals: 11.
Number of sellers: 7-11;
Number of terminals: 36.
Number of sellers: 12-16;
Number of terminals: 22.
Number of sellers: 17-21;
Number of terminals: 9.
Source: GAO analysis of OPIS data.
[End of figure]
Most of our refinery spot market HHI numbers remained moderately
concentrated or unconcentrated during the span of our analysis, and
this was consistent with the findings in our 2008 report, where we
indicated that concentration was generally moderate and changed little
in spot markets throughout the United States since 2000, except in the
case of the New York Harbor spot market, which became more
concentrated. However, as we reported, the New York Harbor trend may
not be completely reflective of actual market conditions because
foreign refineries ship a significant amount of gasoline into the East
Coast (around 60 percent of consumption). Because we were unable to
account for this fuel, the high measure of concentration probably
overstates the actual concentration for the market.[Footnote 24]
However, in this current analysis we also found that refinery market
concentration in Alaska was very high because of the isolated nature of
that state.
Concluding Observations:
Because of the complexity of the U.S. petroleum industry, it can be
difficult to predict the impact of mergers before they are completed.
Refined products move through a complicated supply network, where it
can be difficult to identify the origin of fuel supplied to wholesale
markets, making it challenging to anticipate the actual impacts of
petroleum industry mergers on gasoline prices before the deals are
completed. In light of these difficulties, reviewing the effects of
past mergers on fuel prices could allow FTC to determine whether the
actual effects of a merger reflect the anticipated effects. Although
there are some limitations to the analytical approaches used in
isolating the effects of past mergers and market concentration on
prices, we believe the approach we used in our analysis provides a
starting point for potential further studies of these impacts.
Conducting retrospective reviews of past mergers could also allow FTC
to better understand the impacts of assumptions it makes during merger
reviews and to identify the types of mergers that are potentially
problematic, allowing it to improve its approach to future merger
reviews.
As the authors of the recent study published by the National Bureau of
Economic Research noted, it makes sense for an antitrust agency to
focus retrospective reviews on completed mergers with the greatest
likelihood of having reduced competition, such as mergers in highly
concentrated markets, and in doing so the agency can focus its limited
resources on the mergers with the greatest risk of having adversely
affected prices.[Footnote 25] Given the significant relationship
between wholesale gasoline prices and market concentration that we
found, we also conclude that it may be useful to focus retrospective
merger reviews on highly concentrated market regions. Such
retrospective reviews would provide FTC greater assurance that its
efforts result in consumer prices that are determined in a fair and
competitive marketplace. This study reinforces the need to review past
petroleum industry mergers, and we continue to recommend that FTC
conduct such reviews more regularly and develop risk-based guidelines
to determine when to conduct them.
Agency Comments and Our Evaluation:
We provided a copy of our draft report to FTC for its review and
comment. FTC's Chairman provided written comments, which are reproduced
in appendix II, along with our responses. In general, the Chairman
agreed with our recommendation that FTC conduct more reviews of past
petroleum industry mergers and that FTC focus those retrospective
efforts on mergers that present the greatest likelihood of
anticompetitive effects. The Chairman also noted some of the
limitations and an apparent inaccuracy in our presentation of the
effects of market concentration on wholesale gasoline prices, which we
addressed in appendix II. Nonetheless, the Chairman said that FTC will
continue to use risk-based criteria for identifying past mergers for
review and will direct its staff to evaluate more fully GAO's
contributions as it moves forward with its merger retrospectives and
enforcement programs.
As agreed with your offices, unless you publicly announce the contents
of this report earlier, we plan no further distribution until 14 days
from the report date. At that time, we will send copies to the
Chairman, Federal Trade Commission; appropriate congressional
committees; and other interested parties. In addition, the report will
be available at no charge on the GAO Web site at [hyperlink,
http://www.gao.gov].
If you or your staffs have any questions about this report, please
contact us at (202) 512-3841, gaffiganm@gao.gov, or (202) 512-2700,
mccoolt@gao.gov. Contact points for our Offices of Congressional
Relations and Public Affairs may be found on the last page of this
report. GAO staff who made major contributions to this report are
listed in appendix V.
Signed by:
Mark E. Gaffigan:
Director, Natural Resources and Environment:
Signed by:
Thomas McCool:
Director, Center for Economics Applied Research and Methods:
[End of section]
Appendix I: Technical Discussion of Objectives, Scope, and Methodology:
Introduction:
The objectives of this study were to examine the impacts of selected
mergers and market concentration on wholesale gasoline prices between
2000 and 2008.
We developed an econometric model to explain the impact of mergers and
market concentration, while controlling for other important factors
that may also affect gasoline prices. Our model examined how wholesale
gasoline city terminal (rack) prices were affected by mergers and
variation in market competition.
Econometric Model Specifications and Methodology:
Our model examined how wholesale gasoline city terminal prices were
affected by mergers and two measures of market competition. We used
data from 78 (and in some cases 82) wholesale city terminals from
January 2000 through September 2008.[Footnote 26] We used monthly
average data on wholesale city terminal gasoline prices. We believe
that the increased information from higher-frequency data, for example,
from using weekly data, would be outweighed by the extra noise
generated by such relatively high-frequency data. Further, in general,
the control variables are available only at monthly intervals, and some
only at quarterly intervals. In developing our model, we consulted with
a number of economists in industry and academia who had completed
similar studies, as well as with economists at the Federal Trade
Commission (FTC). We incorporated their suggestions when possible and
where we thought appropriate. In addition, a number of economists also
provided us with feedback on our preliminary results.
The Dependent Variable: Wholesale Gasoline Price:
* Our dependent variable was the logarithm of the wholesale terminal
price of gasoline. We used an Augmented-Dickey-Fuller test designed for
panel data to test for stationarity in levels of our dependent
variables, in the case of both unbranded and branded prices.[Footnote
27] Our tests showed that our unbranded and branded dependent variable
was stationary in levels.
* We estimated separate models for unbranded and branded products to
test for the consistency of our results.
* There may be multiple gasoline prices reported for a given city
terminal on a given date; in general, we used the wholesale terminal
price of gasoline that is required in that specific locale. We believe
that such a focus allows us to address the issue of what is happening
in the market for gasoline in that city.
* Our model specification controls for the effects of changes in the
average price level and changes in the price of crude oil over time. We
controlled for this and other time-varying effects in our regressions
by including a complete set of time dummy variables--one for each
month's observation in the data.
Explanatory Variables That Measure the Impact of Mergers and Market
Concentration on Gasoline Prices:
Our primary interest was to identify the impact of (1) oil company
mergers, and (2) market concentration on gasoline prices.
* We limited our analysis to mergers (1) that occurred between 2000 and
2007, (2) that had transaction values of $200 million or greater, and
(3) for which we had useful and complete gasoline price data where each
merger occurred. There were seven mergers that met these criteria. We
used data from IHS Herold and the Oil Price Information Service (OPIS)
to identify these mergers and then had FTC review the list.
* Our analysis used two measures of market concentration:
1. The number of sellers that sold products during that month--we used
OPIS data to acquire the list of sellers. Our hypothesis is that a
larger number of sellers is likely to result in a more competitive
market environment, in contrast to a situation where a small or a
single seller might be able to engage in price setting, and hence
charge higher prices. We recognize that this measure has drawbacks; in
particular, it does not, in general, measure market share but rather
weights each seller equally. However, it has the advantage that it is
measured at the city level, namely, at the same level as our price
data. Further, this measure has been used by other investigators to
capture variation in local market structure.[Footnote 28]
2. Spot market Herfindahl-Hirschman Index (HHI) was measured for groups
of refineries that supply wholesale terminals. We used spot markets as
the basis for defining these refinery groups geographically, which
reflect the historical grouping of U.S. refineries into seven refining
centers. Energy traders consider gasoline available for delivery at
these refining spot markets in order to price gasoline that is bought
and sold at wholesale terminals, and gasoline production in these
refining groups drives prices on the spot markets. The seven spot
markets in the United States are in Los Angeles, San Francisco, the
Gulf Coast, New York Harbor, Chicago, Tulsa (or Midcontinent), and the
Pacific Northwest. In addition, we defined Alaska as a separate market.
To define these, we collaborated with staff from OPIS, Energy
Information Administration (EIA), and FTC who had expertise on
petroleum product markets and who helped us to assign individual
refineries to spot markets based on the regions in which they sold most
of their fuel. In some cases, a refinery operated in more than one
region, so its capacity was included in both regions' HHI calculation.
Experts from EIA, FTC, and OPIS mentioned that refineries in states
like Alaska and Hawaii primarily supply their local regions. Our study
does not include any cities from Hawaii, and in the case of Alaska, as
mentioned above, we treated these refineries as a separate group and
created its own HHI.[Footnote 29] We then used EIA-810 data on refinery
operable capacity in order to make the HHI calculations.[Footnote 30]
Finally, we used OPIS data to match each of our cities to the spot
market in which it was located.
Other Explanatory Variables:
In addition to the impact of mergers and market concentration, our
model includes other important variables that may influence the price
of gasoline.
* Volume of inventory of gasoline relative to the volume of sales of
gasoline. This could affect the availability of gasoline at the
wholesale level and hence affect prices. All other things equal,
gasoline prices should decrease when inventories are high relative to
sales and conversely when inventories are low relative to sales.
Further, inventories may themselves respond to changes in wholesale
gasoline prices, so this variable may be endogenous.
* Refinery capacity utilization rate--this could affect the wholesale
price of gasoline through changes in the availability of gasoline
product. One possibility is that when utilization rates are high,
supply would be increased, resulting in lower prices, and conversely if
utilization rates are low. However, it is possible that as utilization
rates approach very high levels, there would be significant increases
in the cost of production, which could then result in higher prices. As
with the inventory-sales ratio, the capacity utilization rate may
itself be affected by gasoline prices; for example, if gasoline prices
are high, refineries may operate at higher capacity, so this variable
may be endogenous.
* Lagged dependent variable--lagged values of the left-hand side
variable. Gasoline price data are sometimes autocorrelated, and it is
reasonable to include the effect of past gasoline prices on current
gasoline prices.[Footnote 31]
* Time fixed effects (dummy variable for each time period in the
analysis)--January 2000 through September 2008 is 105 months of data.
City fixed effects (dummy variables for each city in the analysis)--
our analysis uses between 78 and 82 cities' data (we included a fixed
effect for each city). These city fixed effects assist in controlling
for unobserved heterogeneity.
* Product specification dummy variables for the different types of
gasoline used for price. There are over 30 different gasoline types
used in our analysis, and to control for this variation, we include a
dummy variable for each type.
* Selection of cities to include in the model--the OPIS data contain
393 city wholesale terminals. Some of the cities with wholesale
terminals may be close geographically so they may not represent
independent markets. As a result, we used a subset of either 78 or 82
of these cities that were in the most relevant and important
metropolitan areas needed to model refinery product flows and product
costs. Most cities only had one terminal and we chose to examine only
one terminal in the few cases where there was more than one. We
determined which cities each merger affected by identifying cities
where each firm had posted either branded or unbranded wholesale prices
for 26 of the 52 weeks before the merger's announced date.
We assessed the reliability of these data and found them sufficiently
reliable for the purposes of this report. This included conducting
tests for missing and out-of-range values and checking for completeness
and accuracy of the data.
Data Sources:
Table 4: Data Used in Our Econometric Model:
Variable: Prices;
Description: Wholesale gasoline price in cents per gallon. Branded and
unbranded. Monthly data;
Source: OPIS.
Variable: West Texas Intermediate crude oil price;
Description: Price per gallon of West Texas Intermediate. Monthly data;
Source: EIA.
Variable: Spot Market HHI;
Description: Market concentration, measured by refinery capacity of
corporations in each spot market. Monthly data;
Source: EIA, GAO analysis.
Variable: Number of sellers at the city terminal;
Description: Number of sellers that quoted prices at the city terminal
during a given month. Monthly data;
Source: OPIS.
Variable: Merger dummy variables;
Description: Dummy variable equal to 1 from the effective date of the
merger to the end of the study in September 2008.[A] Equal to 0 before
the effective date of the merger. We also included dummy variables for
the period of time between the announced date and the effective date of
the merger;
Source: OPIS, IHS Herold.
Variable: Inventory-sales ratio;
Description: Ratio of gasoline inventories to gasoline sales. Monthly
data;
Source: EIA.
Variable: Refinery capacity utilization rate;
Description: Capacity utilization rate. Monthly data;
Source: EIA.
Variable: Fuel type dummy variables;
Description: Set of dummy variables for the gasoline fuel type. Details
the main fuel type, presence of additives, and Reid vapor pressure
(RVP);
Source: OPIS.
Variable: Producer Price Index;
Description: Producer Price Index. Monthly data;
Source: Department of Labor.
Variable: Employment growth;
Description: Percent growth in employment at the state level. Monthly
data;
Source: Department of Labor.
Variable: Unemployment rate;
Description: Percent unemployment rate at the state level. Monthly
data;
Source: Department of Labor.
Variable: Real personal income growth;
Description: Percent growth in personal income at the state level
deflated by the consumer price index. Quarterly data;
Source: Bureau of Economic Analysis.
Variable: Consumer price index;
Description: Consumer price index. Monthly data;
Source: Department of Labor.
Source: GAO.
[A] The effective dates correspond to the completion of the deal after
announcement and are as follows: Chevron/Texaco, Oct. 9, 2001;
Phillips/Tosco, Sept. 17, 2001; Valero/UDS, Dec. 31, 2001;
Shell/Texaco, Dec. 31, 2001; Phillips/Conoco, Aug. 30, 2002;
Premcor/Williams, Mar. 31, 2003; Valero/Premcor, Sept. 1, 2005.
[End of table]
Econometric model:
Our fixed effects model can be written as follows:
yit = (Xit, Wit)B+ Ci + ft + uit, i = 1,2,...N; t = 1,2,...T (1),
where:
yit is the logarithm of wholesale terminal gasoline price at city i in
month t.
Xit is a vector of predetermined variables for city i in month t that
are assumed to be independent of the error term uit. This vector
includes a lagged value of our dependent variable.
Wit is a vector of possibly endogenous variables, at city i in month t.
ci is the fixed effect or dummy variable for city i.
ft is the fixed effect or dummy variable for month t.
B is a vector of parameters to be estimated.
* We used xtivreg2 in the Stata statistical software package. Our
parameter estimates are consistent given the assumptions of our model.
Our standard error estimates are robust to heteroskedasticity and
autocorrelation.
* We estimated the model using the logarithm of price as the dependent
variable. Note that because we have time dummies, we do not need to
control for variables that vary over time but not cities, such as the
price of crude oil because these variables would be collinear with the
time dummies.
* Measures of market concentration, such as the HHI, have been shown to
be endogenous, so we tested for endogeneity and used two-stage least
squares when appropriate, using merger events and other measures of
economic activity as instruments.[Footnote 32] It is also possible that
the merger events themselves were endogenous, but in our work, we
treated them as exogenous or predetermined, primarily because we had
insufficient data to provide instruments for the seven separate
mergers.
* We estimated the model with inventory-sales ratio and the capacity
utilization rate as endogenous. In general, our results for the effect
of market concentration and mergers were not substantively affected by
whether these were treated as exogenous or endogenous.
* Some of our results for the inventory-sales ratio showed a
significant positive relationship with respect to price, an outcome
that was contrary to our expectations. It is possible that either the
inventory-sales ratio is misspecified in our model or there may be a
complex dynamic relationship that describes how inventories affect
prices and vice versa, conditions that could negate the direction of
this relationship.
* We estimated separate models for unbranded prices and branded prices.
Results:
Table 5: Regression Results for Mergers' Effect on Unbranded Gasoline
Prices--Dependent Variable Is the Logarithm of Unbranded Gasoline
Price:
Variable name: Inventory-sales ratio;
Coefficient: 0.13805;
Standard error: 0.07596;
Significance: significant at the 10 percent level.
Variable category: Capacity utilization rate;
Coefficient: -0.00054;
Standard error: 0.00114;
Significance: [Empty].
Variable name: Log of price lagged 1 period;
Coefficient: 0.46971;
Standard error: 0.03118;
Significance: significant at the 1 percent level.
Variable category: Merger dummies;
Variable name: Chevron-Texaco merger dummy;
Coefficient: -0.00906;
Standard error: 0.00862;
Significance: [Empty].
Variable category: Merger dummies;
Variable name: Phillips-Conoco merger dummy;
Coefficient: -0.00767;
Standard error: 0.00403;
Significance: significant at the 10 percent level.
Variable category: Merger dummies;
Variable name: Phillips-Tosco merger dummy;
Coefficient: 0.00311;
Standard error: 0.00646;
Significance: [Empty].
Variable category: Merger dummies;
Variable name: Premcor-Williams merger dummy;
Coefficient: 0.00648;
Standard error: 0.00747;
Significance: [Empty].
Variable category: Merger dummies;
Variable name: Shell-Texaco merger dummy;
Coefficient: 0.00483;
Standard error: 0.00465;
Significance: [Empty].
Variable category: Merger dummies;
Variable name: Valero-Premcor merger dummy;
Coefficient: 0.00752;
Standard error: 0.00244;
Significance: significant at the 1 percent level.
Variable category: Merger dummies;
Variable name: Valero-UDS merger dummy;
Coefficient: 0.00296;
Standard error: 0.00384;
Significance: [Empty].
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Chevron-Texaco "mid" dummy;
Coefficient: -0.03155;
Standard error: 0.01420;
Significance: significant at the 5 percent level.
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Phillips-Conoco "mid" dummy;
Coefficient: -0.01902;
Standard error: 0.00543;
Significance: significant at the 1 percent level.
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Phillips-Tosco "mid" dummy;
Coefficient: -0.01355;
Standard error: 0.01116;
Significance: [Empty].
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Premcor-Williams "mid" dummy;
Coefficient: 0.01574;
Standard error: 0.01017;
Significance: [Empty].
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Shell-Texaco "mid" dummy;
Coefficient: 0.00276;
Standard error: 0.01616;
Significance: [Empty].
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Valero-Premcor "mid" dummy;
Coefficient: -0.00554;
Standard error: 0.00442;
Significance: [Empty].
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Valero-UDS "mid" dummy;
Coefficient: 0.00089;
Standard error: 0.00825;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CBG fuel dummy;
Coefficient: 0.00104;
Standard error: 0.01516;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CBG with 10% ethanol fuel dummy;
Coefficient: 0.00043;
Standard error: 0.00810;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with 5.7% ethanol fuel dummy;
Coefficient: -0.01857;
Standard error: 0.02322;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE fuel dummy;
Coefficient: -0.01386;
Standard error: 0.02530;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE 7.0 RVP fuel dummy;
Coefficient: -0.01623;
Standard error: 0.02853;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE 8.2 RVP fuel dummy;
Coefficient: 0.06125;
Standard error: 0.03080;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with no additive fuel dummy;
Coefficient: -0.02359;
Standard error: 0.02343;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.0 RVP fuel dummy;
Coefficient: 0.00642;
Standard error: 0.01385;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.2 RVP fuel dummy;
Coefficient: 0.00057;
Standard error: 0.01363;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.8 RVP fuel dummy;
Coefficient: -0.00394;
Standard error: 0.00831;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 8.2 RVP fuel dummy;
Coefficient: -0.02133;
Standard error: 0.01345;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 9.0 RVP fuel dummy;
Coefficient: 0.00000;
Standard error: 0.00651;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 5.7% ethanol fuel dummy;
Coefficient:
-0.00389;
Standard error: 0.02424;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.7% ethanol fuel dummy;
Coefficient:
-0.00367;
Standard error: 0.01138;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.7% ethanol & RVP 9.0 fuel dummy;
Coefficient: 0.02101;
Standard error: 0.01357;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol fuel dummy;
Coefficient: 0.00121;
Standard error: 0.00886;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 7.0 fuel dummy;
Coefficient: 0.01349;
Standard error: 0.01523;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 7.8 fuel dummy;
Coefficient: 0.00709;
Standard error: 0.01108;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 9.0 fuel dummy;
Coefficient: 0.00250;
Standard error: 0.01175;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Low sulfur fuel dummy;
Coefficient: 0.02275;
Standard error: 0.00584;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: Low sulfur 7.0 RVP fuel dummy;
Coefficient: 0.01268;
Standard error: 0.01446;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with 10% ethanol fuel dummy;
Coefficient: 0.03312;
Standard error: 0.01146;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with 10% ethanol & 8.2 RVP fuel dummy;
Coefficient: 0.05801;
Standard error: 0.01609;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE fuel dummy;
Coefficient: 0.03958;
Standard error: 0.01015;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 7.0 RVP fuel dummy;
Coefficient: 0.03050;
Standard error: 0.01445;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 7.2 RVP fuel dummy;
Coefficient: 0.03062;
Standard error: 0.01317;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 8.2 RVP fuel dummy;
Coefficient: 0.03315;
Standard error: 0.01539;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with 5.7% ethanol fuel dummy;
Coefficient: -0.00795;
Standard error: 0.01252;
Significance: [Empty].
R-squared: 0.99;
J-statistic P value: 0.77;
Observations: 8112;
Number of cities: 78.
Source: GAO analysis of various data sources (see table 4 for a list of
data sources).
Abbreviations used to describe various gasoline types are as follows:
CBG-Cleaner Burning Gasoline; CARB-California Air Resources Board; MTBE-
Methyl tertiary-butyl ether; RFG-reformulated gasoline; RVP-Reid vapor
pressure.
Note: the standard error estimates are robust to heteroskedasticity and
autocorrelation. The regression model included fixed effects for the
cities and time dummies for each month of data. The model is estimated
using two-stage least squares, treating the inventory-sales ratio and
the capacity utilization rate as endogenous.
[End of table]
Table 6: Regression Results for Mergers' Effect on Branded Gasoline
Prices--Dependent Variable Is the Logarithm of Branded Gasoline Price:
Variable name: Inventory-sales ratio;
Coefficient: 0.08322;
Standard error: 0.06111;
Significance: [Empty].
Variable name: Capacity utilization rate;
Coefficient: 0.00127;
Standard error: 0.00079;
Significance: [Empty].
Variable name: Log of price lagged 1 period;
Coefficient: 0.53191;
Standard error: 0.02778;
Significance: significant at the 1 percent level.
Variable category: Merger dummies;
Variable name: Chevron-Texaco merger dummy;
Coefficient: 0.00509;
Standard error: 0.00637;
Significance: [Empty].
Variable category: Merger dummies;
Variable name: Phillips-Conoco merger dummy;
Coefficient: -0.01098;
Standard error: 0.00397;
Significance: significant at the 1 percent level.
Variable category: Merger dummies;
Variable name: Phillips-Tosco merger dummy;
Coefficient: 0.00372;
Standard error: 0.00669;
Significance: [Empty].
Variable category: Merger dummies;
Variable name: Premcor-Williams merger dummy;
Coefficient: 0.00898;
Standard error: 0.00804;
Significance: [Empty].
Variable category: Merger dummies;
Variable name: Shell-Texaco merger dummy;
Coefficient: 0.00309;
Standard error: 0.00406;
Significance: [Empty].
Variable category: Merger dummies;
Variable name: Valero-Premcor merger dummy;
Coefficient: 0.00424;
Standard error: 0.00269;
Significance: [Empty].
Variable category: Merger dummies;
Variable name: Valero-UDS merger dummy;
Coefficient: 0.00705;
Standard error: 0.00327;
Significance: significant at the 5 percent level.
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Chevron-Texaco "mid" dummy;
Coefficient: -0.01474;
Standard error: 0.00968;
Significance: [Empty].
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Phillips-Conoco "mid" dummy;
Coefficient: -0.01494;
Standard error: 0.00439;
Significance: significant at the 1 percent level.
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Phillips-Tosco "mid" dummy;
Coefficient: -0.01190;
Standard error: 0.00713;
Significance: significant at the 10 percent level.
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Premcor-Williams "mid" dummy;
Coefficient: 0.01418;
Standard error: 0.00946;
Significance: [Empty].
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Shell-Texaco "mid" dummy;
Coefficient: 0.01059;
Standard error: 0.01404;
Significance: [Empty].
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Valero-Premcor "mid" dummy;
Coefficient: -0.01126;
Standard error: 0.00391;
Significance: significant at the 1 percent level.
Variable category: Dummies for period between announced and effective
merger dates;
Variable name: Valero-UDS "mid" dummy;
Coefficient: -0.00225;
Standard error: 0.00599;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CBG fuel dummy;
Coefficient: -0.00951;
Standard error: 0.01344;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CBG with 10% ethanol fuel dummy;
Coefficient: -0.02969;
Standard error: 0.01326;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with 5.7% ethanol fuel dummy;
Coefficient: -0.06737;
Standard error: 0.02192;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with 5.7% ethanol 7.0 RVP fuel dummy;
Coefficient: -0.05816;
Standard error: 0.02734;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE fuel dummy;
Coefficient: -0.03905;
Standard error: 0.02751;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE 7.0 RVP fuel dummy;
Coefficient: -0.04107;
Standard error: 0.02400;
Significance: significant at the 10 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE 8.2 RVP fuel dummy;
Coefficient: -0.00534;
Standard error: 0.01776;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with no additive fuel dummy;
Coefficient: -0.04876;
Standard error: 0.02465;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.0 RVP fuel dummy;
Coefficient: 0.00547;
Standard error: 0.01046;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.2 RVP fuel dummy;
Coefficient: 0.01217;
Standard error: 0.01081;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.8 RVP fuel dummy;
Coefficient: -0.00309;
Standard error: 0.00592;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 8.2 RVP fuel dummy;
Coefficient: 0.00877;
Standard error: 0.01011;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 9.0 RVP fuel dummy;
Coefficient: 0.00156;
Standard error: 0.00549;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 5.7% ethanol fuel dummy;
Coefficient: 0.01944;
Standard error: 0.02297;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.7% ethanol fuel dummy;
Coefficient: -0.00723;
Standard error: 0.00855;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.7% ethanol & RVP 9.0 fuel dummy;
Coefficient: 0.01901;
Standard error: 0.01180;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol fuel dummy;
Coefficient: 0.00362;
Standard error: 0.00804;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 7.0 fuel dummy;
Coefficient: 0.01143;
Standard error: 0.01666;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 7.8 fuel dummy;
Coefficient: 0.00462;
Standard error: 0.01162;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 9.0 fuel dummy;
Coefficient: 0.01221;
Standard error: 0.00907;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Low sulfur fuel dummy;
Coefficient: 0.02723;
Standard error: 0.00516;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: Low sulfur 7.0 RVP fuel dummy;
Coefficient: 0.01702;
Standard error: 0.01180;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Low sulfur 9.0 RVP fuel dummy;
Coefficient: 0.03884;
Standard error: 0.01154;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with 10% ethanol fuel dummy;
Coefficient: 0.05681;
Standard error: 0.01580;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with 10% ethanol & 8.2 RVP fuel dummy;
Coefficient: 0.08463;
Standard error: 0.01839;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE fuel dummy;
Coefficient: 0.05851;
Standard error: 0.01404;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 7.0 RVP fuel dummy;
Coefficient: 0.03412;
Standard error: 0.01602;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 7.2 RVP fuel dummy;
Coefficient: 0.06048;
Standard error: 0.01625;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 8.2 RVP fuel dummy;
Coefficient: 0.06052;
Standard error: 0.01712;
Significance: significant at the 1 percent level.
R-squared: 0.99;
J-statistic P value: 0.10;
Observations: 8528;
Number of cities: 82.
Source: GAO analysis of various data sources (see table 4 for a list of
data sources).
Abbreviations used to describe various gasoline types are as follows:
CBG-Cleaner Burning Gasoline; CARB-California Air Resources Board; MTBE-
Methyl tertiary-butyl ether; RFG-reformulated gasoline; RVP-Reid vapor
pressure.
Note: the standard error estimates are robust to heteroskedasticity and
autocorrelation. The regression model included fixed effects for the
cities and time dummies for each month of data. The model is estimated
using two-stage least squares, treating the inventory-sales ratio and
the capacity utilization rate as endogenous.
[End of table]
Table 7: Regression Results for Effect of Spot Market HHI on Unbranded
Gasoline Prices--Dependent Variable Is the Logarithm of Unbranded
Gasoline Price:
Variable name: Inventory-sales ratio;
Coefficient: 0.28754;
Standard error: 0.12988;
Significance: significant at the 5 percent level.
Variable name: Capacity utilization rate;
Coefficient: -0.00050;
Standard error: 0.00158;
Significance: [Empty].
Variable name: Log of price lagged 1 period;
Coefficient: 0.41312;
Standard error: 0.04668;
Significance: significant at the 1 percent level.
Variable name: Spot market HHI;
Coefficient: 1.08939;
Standard error: 0.38344;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: CBG fuel dummy;
Coefficient: 0.03871;
Standard error: 0.03917;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CBG with 10% ethanol fuel dummy;
Coefficient: 0.01963;
Standard error: 0.02343;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with 5.7% ethanol fuel dummy;
Coefficient: -0.01767;
Standard error: 0.05553;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE fuel dummy;
Coefficient: -0.04559;
Standard error: 0.05912;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE 7.0 RVP fuel dummy;
Coefficient: -0.03326;
Standard error: 0.06649;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE 8.2 RVP fuel dummy;
Coefficient: 0.09736;
Standard error: 0.07046;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with no additive fuel dummy;
Coefficient: -0.05538;
Standard error: 0.05364;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.0 RVP fuel dummy;
Coefficient: -0.01360;
Standard error: 0.02037;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.2 RVP fuel dummy;
Coefficient: -0.03513;
Standard error: 0.02531;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.8 RVP fuel dummy;
Coefficient: -0.01720;
Standard error: 0.01315;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 8.2 RVP fuel dummy;
Coefficient: -0.05693;
Standard error: 0.02413;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: Conventional with 9.0 RVP fuel dummy;
Coefficient: -0.01234;
Standard error: 0.01116;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 5.7% ethanol fuel dummy;
Coefficient: -0.05425;
Standard error: 0.04087;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.7% ethanol fuel dummy;
Coefficient: -0.01808;
Standard error: 0.01680;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.7% ethanol & RVP 9.0 fuel dummy;
Coefficient: 0.00361;
Standard error: 0.02321;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol fuel dummy;
Coefficient: -0.01459;
Standard error: 0.01438;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 7.0 fuel dummy;
Coefficient: -0.01429;
Standard error: 0.02394;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 7.8 fuel dummy;
Coefficient: -0.01371;
Standard error: 0.01834;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 9.0 fuel dummy;
Coefficient: -0.01909;
Standard error: 0.01976;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Low sulfur fuel dummy;
Coefficient: 0.02051;
Standard error: 0.00835;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: Low sulfur 7.0 RVP fuel dummy;
Coefficient: -0.01002;
Standard error: 0.02154;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with 10% ethanol fuel dummy;
Coefficient: 0.01726;
Standard error: 0.03048;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with 10% ethanol & 8.2 RVP fuel dummy;
Coefficient: 0.03990;
Standard error: 0.03678;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE fuel dummy;
Coefficient: 0.04091;
Standard error: 0.02955;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 7.0 RVP fuel dummy;
Coefficient: 0.02401;
Standard error: 0.04173;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 7.2 RVP fuel dummy;
Coefficient: 0.01629;
Standard error: 0.03185;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 8.2 RVP fuel dummy;
Coefficient: 0.02247;
Standard error: 0.03317;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with 5.7% ethanol fuel dummy;
Coefficient: -0.03001;
Standard error: 0.03247;
Significance: [Empty].
R-squared: 0.98;
J-statistic P value: 0.93;
Observations: 8112;
Number of cities: 78.
Source: GAO analysis of various data sources (see table 4 for a list of
data sources).
Abbreviations used to describe various gasoline types are as follows:
CBG-Cleaner Burning Gasoline; CARB-California Air Resources Board; MTBE-
Methyl tertiary-butyl ether; RFG-reformulated gasoline; RVP-Reid vapor
pressure.
Note: the standard error estimates are robust to heteroskedasticity and
autocorrelation. The regression model included fixed effects for cities
and time dummies for each month of data. The model is estimated using
two-stage least squares, treating the inventory-sales ratio, the
capacity utilization rate, and the spot market HHI as endogenous.
[End of table]
Table 8: Regression Results for Effect of Spot Market HHI on Branded
Gasoline Prices--Dependent Variable Is the Logarithm of Branded
Gasoline Price:
Variable name: Inventory-sales ratio;
Coefficient: 0.22304;
Standard error: 0.08163;
Significance: significant at the 1 percent level.
Variable name: Capacity utilization rate;
Coefficient: 0.00137;
Standard error: 0.00098;
Significance: [Empty].
Variable name: Log of price lagged 1 period;
Coefficient: 0.47259;
Standard error: 0.03589;
Significance: significant at the 1 percent level.
Variable name: Spot market HHI;
Coefficient: 0.67462;
Standard error: 0.35754;
Significance: significant at the 10 percent level.
Variable category: Gasoline specification dummies;
Variable name: CBG fuel dummy;
Coefficient: 0.00546;
Standard error: 0.01316;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CBG with 10% ethanol fuel dummy;
Coefficient: -0.03869;
Standard error: 0.01656;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with 5.7% ethanol fuel dummy;
Coefficient: -0.10739;
Standard error: 0.02891;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with 5.7% ethanol 7.0 RVP fuel dummy;
Coefficient: -0.12379;
Standard error: 0.04177;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE fuel dummy;
Coefficient: -0.10515;
Standard error: 0.03823;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE 7.0 RVP fuel dummy;
Coefficient: -0.08938;
Standard error: 0.03365;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE 8.2 RVP fuel dummy;
Coefficient: -0.00762;
Standard error: 0.01859;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with no additive fuel dummy;
Coefficient: -0.09747;
Standard error: 0.03220;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.0 RVP fuel dummy;
Coefficient: -0.01341;
Standard error: 0.01455;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.2 RVP fuel dummy;
Coefficient: -0.01588;
Standard error: 0.01524;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.8 RVP fuel dummy;
Coefficient: -0.01426;
Standard error: 0.00863;
Significance: significant at the 10 percent level.
Variable category: Gasoline specification dummies;
Variable name: Conventional with 8.2 RVP fuel dummy;
Coefficient: -0.01686;
Standard error: 0.01398;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 9.0 RVP fuel dummy;
Coefficient: -0.01031;
Standard error: 0.00800;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 5.7% ethanol fuel dummy;
Coefficient: -0.03183;
Standard error: 0.02743;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.7% ethanol fuel dummy;
Coefficient: -0.01807;
Standard error: 0.01396;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.7% ethanol & RVP 9.0 fuel dummy;
Coefficient: 0.00432;
Standard error: 0.01580;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol fuel dummy;
Coefficient: -.01362;
Standard error: 0.01032;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 7.0 fuel dummy;
Coefficient: -0.02321;
Standard error: 0.02243;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 7.8 fuel dummy;
Coefficient: -0.02101;
Standard error: 0.01558;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 9.0 fuel dummy;
Coefficient: -0.00775;
Standard error: 0.01230;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Low sulfur fuel dummy;
Coefficient: 0.02327;
Standard error: 0.00506;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: Low sulfur 7.0 RVP fuel dummy;
Coefficient: -0.00468;
Standard error: 0.01276;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Low sulfur 9.0 RVP fuel dummy;
Coefficient: 0.01447;
Standard error: 0.01231;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with 10% ethanol fuel dummy;
Coefficient: 0.03085;
Standard error: 0.01638;
Significance: significant at the 10 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with 10% ethanol & 8.2 RVP fuel dummy;
Coefficient: 0.05823;
Standard error: 0.02164;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE fuel dummy;
Coefficient: 0.04701;
Standard error: 0.01341;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 7.0 RVP fuel dummy;
Coefficient: 0.01448;
Standard error: 0.01472;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 7.2 RVP fuel dummy;
Coefficient: 0.03414;
Standard error: 0.01730;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 8.2 RVP fuel dummy;
Coefficient: 0.04025;
Standard error: 0.01824;
Significance: significant at the 5 percent level.
R-squared: 0.99;
J-statistic P value: 0.37;
Observations: 8112;
Number of cities: 78.
Source: GAO analysis of various data sources (see table 4 for a list of
data sources).
Abbreviations used to describe various gasoline types are as follows:
CBG-Cleaner Burning Gasoline; CARB-California Air Resources Board; MTBE-
Methyl tertiary-butyl ether; RFG-reformulated gasoline; RVP-Reid vapor
pressure.
Note: the standard error estimates are robust to heteroskedasticity and
autocorrelation. The regression model included fixed effects for cities
and time dummies for each month of data. The model is estimated using
two-stage least squares, treating the inventory-sales ratio, the
capacity utilization rate, and the spot market HHI as endogenous.
[End of table]
Table 9: Regression Results for Effect of the Number of Sellers at the
City Terminal on Unbranded Gasoline Prices--Dependent Variable Is the
Logarithm of Unbranded Gasoline Price:
Variable name: Inventory-sales ratio;
Coefficient: 0.15181;
Standard error: 0.08918;
Significance: significant at the 10 percent level.
Variable name: Capacity utilization rate;
Coefficient: -0.00100;
Standard error: 0.00118;
Significance: [Empty].
Variable name: Log of price lagged 1 period;
Coefficient: 0.43843;
Standard error: 0.03796;
Significance: significant at the 1 percent level.
Variable name: Number of sellers at the city terminal;
Coefficient: -0.01165;
Standard error: 0.00340;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: CBG fuel dummy;
Coefficient: -0.03264;
Standard error: 0.01827;
Significance: significant at the 10 percent level.
Variable category: Gasoline specification dummies;
Variable name: CBG with 10% ethanol fuel dummy;
Coefficient: -0.03719;
Standard error: 0.01386;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with 5.7% ethanol fuel dummy;
Coefficient: -0.01623;
Standard error: 0.02612;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE fuel dummy;
Coefficient: -0.03248;
Standard error: 0.03018;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE 7.0 RVP fuel dummy;
Coefficient: -0.04034;
Standard error: 0.03383;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE 8.2 RVP fuel dummy;
Coefficient: 0.04463;
Standard error: 0.03589;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with no additive fuel dummy;
Coefficient: -0.02932;
Standard error: 0.02699;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.0 RVP fuel dummy;
Coefficient: 0.00715;
Standard error: 0.01527;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.2 RVP fuel dummy;
Coefficient: -0.03138;
Standard error: 0.02314;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.8 RVP fuel dummy;
Coefficient: -0.00442;
Standard error: 0.00929;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 8.2 RVP fuel dummy;
Coefficient: -0.01883;
Standard error: 0.01782;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 9.0 RVP fuel dummy;
Coefficient: -0.00072;
Standard error: 0.00792;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 5.7% ethanol fuel dummy;
Coefficient: -0.00177;
Standard error: 0.02786;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.7% ethanol fuel dummy;
Coefficient: -0.00148;
Standard error: 0.01368;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.7% ethanol & RVP 9.0 fuel dummy;
Coefficient: 0.01260;
Standard error: 0.01689;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol fuel dummy;
Coefficient: 0.00155;
Standard error: 0.01081;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 7.0 fuel dummy;
Coefficient: 0.01456;
Standard error: 0.01590;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 7.8 fuel dummy;
Coefficient: 0.00020;
Standard error: 0.01415;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 9.0 fuel dummy;
Coefficient: 0.00641;
Standard error: 0.01412;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Low sulfur fuel dummy;
Coefficient: 0.00574;
Standard error: 0.01174;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Low sulfur 7.0 RVP fuel dummy;
Coefficient: -0.00515;
Standard error: 0.01756;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with 10% ethanol fuel dummy;
Coefficient: 0.00387;
Standard error: 0.01598;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with 10% ethanol & 8.2 RVP fuel dummy;
Coefficient: 0.04506;
Standard error: 0.02021;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE fuel dummy;
Coefficient: 0.01437;
Standard error: 0.01251;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 7.0 RVP fuel dummy;
Coefficient: -0.01360;
Standard error: 0.01697;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 7.2 RVP fuel dummy;
Coefficient: 0.00870;
Standard error: 0.01573;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 8.2 RVP fuel dummy;
Coefficient: -0.00514;
Standard error: 0.02362;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with 5.7% ethanol fuel dummy;
Coefficient: -0.02068;
Standard error: 0.01752;
Significance: [Empty].
R-squared: 0.99;
J-statistic P value: 0.82;
Observations: 8112;
Number of cities: 78.
Source: GAO analysis of various data sources (see table 4 for a list of
data sources).
Abbreviations used to describe various gasoline types are as follows:
CBG-Cleaner Burning Gasoline; CARB-California Air Resources Board; MTBE-
Methyl tertiary-butyl ether; RFG-reformulated gasoline; RVP-Reid vapor
pressure.
Note: the standard error estimates are robust to heteroskedasticity and
autocorrelation. The regression model included fixed effects for cities
and time dummies for each month of data. The model is estimated using
two-stage least squares, treating the inventory-sales ratio, the
capacity utilization rate, and the number of sellers at the city
terminal as endogenous.
[End of table]
Table 10: Regression Results for Effect of the Number of Sellers at the
City Terminal on Branded Gasoline Prices--Dependent Variable is the
Logarithm of Branded Gasoline Price:
Variable name:
Inventory-sales ratio;
Coefficient: 0.08564;
Standard error: 0.06035;
Significance: [Empty].
Variable name:
Capacity utilization rate;
Coefficient: 0.00086;
Standard error: 0.00083;
Significance: [Empty].
Variable name: Log of price lagged 1 period;
Coefficient: 0.51420;
Standard error: 0.02868;
Significance: significant at the 1 percent level.
Variable name: Number of sellers at the city terminal;
Coefficient: -0.00869;
Standard error: 0.00240;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: CBG fuel dummy;
Coefficient: -0.03247;
Standard error: 0.01646;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: CBG with 10% ethanol fuel dummy;
Coefficient: -0.05299;
Standard error: 0.01589;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with 5.7% ethanol fuel dummy;
Coefficient: -0.06950;
Standard error: 0.02312;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with 5.7% ethanol 7.0 RVP fuel dummy;
Coefficient: -0.07087;
Standard error: 0.03008;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE fuel dummy;
Coefficient: -0.05644;
Standard error: 0.03010;
Significance: significant at the 10 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE 7.0 RVP fuel dummy;
Coefficient: -0.06272;
Standard error: 0.02727;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: CARB with MTBE 8.2 RVP fuel dummy;
Coefficient: -0.02308;
Standard error: 0.01900;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: CARB with no additive fuel dummy;
Coefficient: -0.05001;
Standard error: 0.02466;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.0 RVP fuel dummy;
Coefficient: 0.00439;
Standard error: 0.01015;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.2 RVP fuel dummy;
Coefficient: -0.00966;
Standard error: 0.01553;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.8 RVP fuel dummy;
Coefficient: -0.00378;
Standard error: 0.00579;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 8.2 RVP fuel dummy;
Coefficient: 0.01249;
Standard error: 0.01174;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 9.0 RVP fuel dummy;
Coefficient: 0.00134;
Standard error: 0.00578;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 5.7% ethanol fuel dummy;
Coefficient: 0.02627;
Standard error: 0.02227;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.7% ethanol fuel dummy;
Coefficient: -0.00632;
Standard error: 0.00913;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 7.7% ethanol & RVP 9.0 fuel dummy;
Coefficient: 0.01576;
Standard error: 0.01102;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol fuel dummy;
Coefficient: 0.00211;
Standard error: 0.00819;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 7.0 fuel dummy;
Coefficient: 0.01572;
Standard error: 0.01698;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 7.8 fuel dummy;
Coefficient: -0.00135;
Standard error: 0.01164;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Conventional with 10% ethanol & RVP 9.0 fuel dummy;
Coefficient: 0.01250;
Standard error: 0.00928;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Low sulfur fuel dummy;
Coefficient: 0.01225;
Standard error: 0.00825;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Low sulfur 7.0 RVP fuel dummy;
Coefficient: 0.00267;
Standard error: 0.01151;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: Low sulfur 9.0 RVP fuel dummy;
Coefficient: 0.01309;
Standard error: 0.01183;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with 10% ethanol fuel dummy;
Coefficient: 0.03960;
Standard error: 0.01807;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with 10% ethanol & 8.2 RVP fuel dummy;
Coefficient: 0.08056;
Standard error: 0.02043;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE fuel dummy;
Coefficient: 0.04405;
Standard error: 0.01613;
Significance: significant at the 1 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 7.0 RVP fuel dummy;
Coefficient: -0.00111;
Standard error: 0.01658;
Significance: [Empty].
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 7.2 RVP fuel dummy;
Coefficient: 0.04595;
Standard error: 0.01796;
Significance: significant at the 5 percent level.
Variable category: Gasoline specification dummies;
Variable name: RFG with MTBE & 8.2 RVP fuel dummy;
Coefficient: 0.03782;
Standard error: 0.02071;
Significance: significant at the 10 percent level.
R-squared: 0.99;
J-statistic P value: 0.18;
Observations: 8528;
Number of cities: 82.
Source: GAO analysis of various data sources (see table 4 for a list of
data sources).
Abbreviations used to describe various gasoline types are as follows:
CBG-Cleaner Burning Gasoline; CARB-California Air Resources Board; MTBE-
Methyl tertiary-butyl ether; RFG-reformulated gasoline; RVP-Reid vapor
pressure.
Note: the standard error estimates are robust to heteroskedasticity and
autocorrelation. The regression model included fixed effects for cities
and time dummies for each month of data. The model is estimated using
two-stage least squares, treating the inventory-sales ratio, the
capacity utilization rate, and the number of sellers at the city
terminal as endogenous.
[End of table]
* We found that some mergers correspond with price effects, but these
effects vary in direction and significance.
* We tested for the endogeneity of our measures of market
concentration: the spot market HHI and the number of sellers. With the
exception of the spot market HHI in the branded price model, in both
our unbranded price and branded price models our C-statistic test
rejected the null hypothesis of exogeneity of our measures of market
concentration. We treated these variables as endogenous in all our
models, but we also estimated our model treating these variables as
exogenous so we could compare the two sets of results.
* We tested for whether the inventory-sales ratio and the capacity
utilization rate were endogenous. We used a C-statistic to test for the
joint exogeneity of these variables. In some cases, the null hypothesis
of exogeneity was accepted and in other cases not. In order to be
conservative in the sense of presenting estimates that are consistent,
we modeled these variables as endogenous, although we recognize that
this may not be the statistically efficient estimator in some cases.
* We used Hansen's J-statistic to test for over identification of our
instruments: namely, that they should be correlated with the regressors
but uncorrelated with the regression errors. In every case, the J-
statistic accepted the null hypothesis that our instruments were valid.
* In general, the results for both measures of market concentration--
the number of sellers in the city and the HHI for the spot market--
showed a significant correspondence between higher prices and a less
competitive market environment.
* We used the model's results to calculate the dollar value impact on
gasoline prices of the significant merger effects and changes in market
concentration.
* In many cases, our results showed the effects of gasoline
specification dummies to be either not statistically significant or
positive, a result we would expect given that our base-case is regular
clear gasoline. In some of our results, the coefficient was negative,
in particular for the CARB and CBG gasoline in the branded regressions.
CARB is generally sold only in California, and it is possible that in
some of our regressions, the California cities' fixed-effects are
picking up the effect of what we would expect to be higher-priced CARB
fuel. The presence of CBG in our data was also limited to one or two
cities, and a similar issue may have affected our results for this
gasoline specification.
Limitations of Our Econometric Model and Data:
* Our gasoline data were selected so as to generally reflect the type
of gasoline that would be sold in a city, given the local fuel
regulations. In most cases we were able to assign prices accordingly,
but in some cases other types of fuel were used in the data. In our
regression models, we control for whatever fuel type we did use.
* We used monthly data for prices and most of our control variables.
State-level personal income data were available only quarterly and were
applied to the appropriate months for that quarter.
* The inventory-sales ratio and capacity utilization rate were at the
PADD level, so we assigned the data observation according to which PADD
the city was located in.[Footnote 33] Similarly, we used state-level
data for personal income growth, employment growth, and the
unemployment rate, and we assigned the data observations according to
the state in which the city was located.
* Our analysis was performed at the city level, but some of our data
were available only at more aggregated geographic levels. The capacity
utilization rate and the inventory-sales ratio were available at the
PADD level only. Employment growth, personal income growth rate, and
the unemployment rate were available at the state level only. One of
our measures of market concentration was at the spot market level. It
is possible that in some cases these measures are too highly aggregated
and these control variables were less precise than would be ideal.
* We used merger events as instruments for our market concentration
measures, which, in general, were found to be endogenous.[Footnote 34]
It is possible that the merger events themselves are endogenous, but we
have no further data that we could have used to instrument the merger
variables.
* We also estimated our model treating the concentration measures, the
inventory-sales ratio, and the capacity utilization rate as exogenous.
In these results, we found that the impact on prices of our
concentration measures was statistically significant but smaller. For
example, in the case of the spot market HHI, the price effects were
about half the size in the case of the unbranded regressions, and for
the number of city sellers, about one-sixth of the size in the
unbranded regressions. While our tests for exogeneity of the
concentration measures generally rejected their being exogenous, we
wanted to display a range of possible results.
* We are aware of the limitations of using a fixed effects model to
study events such as mergers and to use dummy variables for mergers in
such a model.[Footnote 35] Further, we are aware that our model, or any
model, is unlikely to account for all conceivable factors affecting
prices. With this in mind, we used fixed effects for cities and time
dummy variables for every time period. The former accounts for special
(possibly unobservable) effects that are constant over time, affecting
an individual city, and the latter for effects that are constant across
cities but vary over time, such as national supply disruptions.
Nevertheless, we are aware that these cannot account for every factor
that, say, may affect a group of cities for a given period of time; for
example, a localized supply disruption, except insofar as this is
reflected in the level of inventories or capacity utilization rate in
the PADD.
* The concentration measures that we used are imperfect. On the one
hand, we used the number of sellers at the city terminal, a measure of
concentration at the city level that does not measure market share,
only market participation. Our other measure, the spot market HHI, is
broader geographically than is ideal, and it is measured at the
refinery level. The latter means that we are approximating market
shares of the sellers at the city with shares of refineries in the spot
market region.
* We used a number of methods to test our model but we recognize that
our results should be viewed carefully. In particular, we are concerned
that a difference in the effect of mergers may depend on whether we
used the announced or the effective merger dates. In order to address
this issue, our model of mergers included dummy variables for the
period of time between the announced date and the effective date of the
merger, as well as a dummy variable for the time following the
effective date of each merger.
* We understand that our methodology is not a substitute for an event
study. However, our methods could be used in conjunction with such--in
particular, as a broad means to address issues of whether an industry
is overly concentrated, since we recognize that it is resource-
intensive to conduct an event study for every merger. We are aware that
a difference-in-differences model provides an alternative methodology
but that method has its own limitations, in particular, the matching of
cities for treatment and controls.
* Our analysis did not account for all gasoline that is sold at
wholesale terminals. Our gasoline wholesale price data captured about
60 percent of gasoline sold in the United States, according to EIA
analysis. The remaining gasoline is sold directly to retailers, or
through other arrangements, and price data for these sales are not
always available. These transactions likely also affect the general
wholesale market for a particular city.
* Our model focused on wholesale gasoline prices, so we are unable to
determine the extent to which the price effects that we found would be
passed on to the retail level.
[End of section]
Appendix II: Comments from the Federal Trade Commission:
Note: GAO comments supplementing those in the report text appear at the
end of this appendix.
The Chairman:
Federal Trade Commission:
Washington, D.C. 20580:
June 3, 2009:
Mr. Mark E. Gaffigan:
Director, Natural Resources and Environment:
United States Government Accountability Office:
Washington, D.C. 20548:
Mr. Thomas McCool:
Director, Center for Economics, Applied Research and Methods:
United States Government Accountability Office:
Washington, D.C. 20548:
Dear Messrs. Gaffigan and McCool:
The Federal Trade Commission ("FTC" or "Commission") appreciates the
opportunity to comment on the draft report on Energy Markets: Estimates
of the Effects of Mergers and Market Concentration on Wholesale
Gasoline Prices (GAO-09-659) ("Report") that the Government
Accountability Office ("GAO") submitted to the Commission on May 18,
2009. The Report discusses GAO's examination of (1) the effects on
wholesale gasoline prices of a select set of past oil industry mergers,
and (2) the effects of market concentration on wholesale gasoline
prices. This comment addresses each of these examinations in turn.
Commission staff have been pleased to work with GAO staff during the
present inquiry, providing information and comments on the petroleum
industry, the Commission's merger enforcement activities, and GAO's
econometric methodology. GAO's draft Report provides important
information on mergers in the petroleum industry and on the
Commission's role in reviewing those mergers.[Footnote 39] GAO
recommends that the FTC undertake more regular retrospective reviews of
petroleum mergers and develop risk-based guidelines to determine when
to conduct such retrospectives. Citing a recent National Bureau of
Economic Research working paper co-authored by two FTC economists,
[Footnote 40] the Report observes that the Commission might
appropriately focus its retrospective analyses on completed mergers
with the greatest likelihood of anticompetitive effects.[Footnote 41]
We support GAO's recommendation that the FTC continue its regular
reviews of past petroleum industry mergers.[Footnote 42] We also agree
that the Commission should focus those retrospective efforts on mergers
that present the greatest likelihood of anticompetitive effects and, in
that regard, should pay attention (although not exclusively) to markets
that are concentrated. Further, we believe that the criteria that the
Commission has used to select mergers for its previous retrospective
studies satisfy GAO's recommendation to apply "riskbased criteria."
The Commission provides below some specific comments on GAO's merger
and concentration analyses.
The Report's Discussion of Past Mergers:
GAO studied seven large petroleum mergers between 2000 and 2007 to
determine how those transactions might have affected branded and
unbranded wholesale gasoline prices. GAO concluded that one merger was
associated with a price increase for branded gasoline of approximately
one cent per gallon, while a second merger was associated with about a
one-cent-per-gallon increase for unbranded gasoline. A third merger was
found to be associated with similarly small decreases in the prices of
both unbranded and branded gasoline. GAO found no statistically
significant change in either branded or unbranded wholesale prices for
the other four mergers. Taking these findings as a whole -and in view
of the large swings in gasoline prices over the period that GAO
studied - the GAO analysis suggests that recent large petroleum mergers
have had at most a minor impact on gasoline prices.
The Commission believes that GAO's merger analysis represents an
interesting approach to identifying the possible effects of consummated
mergers. Indeed, although it differs in some important respects - such
as how controls are constructed - GAO's methodology is broadly similar
to that used by FTC economists in their own merger retrospectives. As
the FTC moves forward with new retrospectives, our efforts will be
informed by GAO's econometric work and, in particular, will devote
resources to a more complete evaluation of the strengths and
limitations of GAO's methodology.[Footnote 43]
The Report's Price-Concentration Analyses:
GAO reports that it did not observe any trend toward increasing market
concentration nationwide between 2000 and 2008, either in the number of
sellers at wholesale gasoline terminals or in refinery capacity
concentration measures for certain "spot market groups" delineated by
GAO.[Footnote 44] Nonetheless, there was some significant variation in
the number of sellers at certain wholesale terminals, as well as in the
concentration of refiners supplying one spot market group (New York
Harbor). Based on its analysis of these variations in the number of
wholesale sellers and in refinery capacity concentration, GAO finds
that higher wholesale prices are associated with higher concentration
levels. GAO's results imply that even small concentration changes in
relatively unconcentrated markets are associated with large price
effects.
Inasmuch as market concentration is obviously relevant to merger
enforcement,[Footnote 45] the Commission takes great interest in GAO's
findings. Based on its experience in petroleum markets,[Footnote 46]
however, the FTC finds the strength of GAO's price-concentration
results - particularly at relatively low levels of concentration - to
be surprising. Indeed, some results set forth in the Report appear
inaccurate.[Footnote 47] More generally, a reliable estimate of
economic relationships between price and concentration must be premised
on, among other things, accurate measures of market concentration. It
appears that GAO's analyses suffer from significant measurement errors
in their concentration variables.[Footnote 48] For example, GAO finds
that approximately 60 percent of the gasoline consumed on the East
Coast comes from foreign countries. If that finding is correct, then
concentration based on a purely domestic measure of "New York Harbor"
refining capacity is highly unlikely to reflect the true level of
market concentration. In turn, such measurement errors would cast doubt
on the relationship that GAO's analysis suggests exists between price
and concentration.
Nonetheless, despite these and other possible shortcomings, which may
render GAO's results unreliable for purposes of formulating antitrust
policy, the Commission will carefully consider GAO's price-
concentration results and will direct its economists to evaluate
further the usefulness of GAO's findings.
Conclusion:
The Commission appreciates the opportunity to comment on the Report.
The Commission agrees with GAO on the need for regular merger
retrospectives in the petroleum industry. We plan to continue our
ongoing program with appropriately targeted retrospectives, and we will
continue to use risk-based criteria to identify past mergers for
review. The Commission will seriously consider GAO's findings and will
direct its staff to evaluate more fully GAO's contributions as the
Commission moves forward with its merger retrospectives and
merger enforcement programs.
By direction of the Commission.
Signed by:
Jon Leibowitz:
The following are GAO's comments on the Federal Trade Commission's
letter dated June 3, 2009.
GAO Comments:
1. Measures of market concentration are inherently difficult to develop
because information on relevant market boundaries and sales volumes by
gasoline sellers is not readily available. Although we made no changes
to the report based on the Chairman's comment, we wish to emphasize
that to improve the robustness of our concentration analysis, we used
two measures of market concentration--first, we used the number of
sellers at wholesale terminals, and second, we calculated the market
concentration of refineries in seven U.S. spot markets. We acknowledge
the limitations of our spot market measure that the Chairman noted,
especially in the case of the New York Harbor market, and stated these
limitations in the draft report. However, as stated above, we did not
rely solely on this one measure of market concentration, and we found a
qualitatively similar and statistically significant effect when we
estimated the impact of the number of sellers at wholesale gasoline
terminals on prices. In addition, for both of these measures, we
estimated the price effects under another set of statistical
assumptions, and found and reported in the draft these similar, though
smaller, price effects.[Footnote 36] Although our two measures of
market concentration may not be appropriate for formulating antitrust
policy, as the Chairman noted, our findings indicate that the effects
of market concentration on prices may occur at lower levels of
concentration than previously anticipated, and FTC's access to more
detailed petroleum industry data might allow them to make a more
precise estimate of these potential impacts.
2. Although our draft concentration results were correct, our
presentation showed that the median value of market concentration was
the midpoint in the range of price effects, when in fact the price
effects were not evenly spread around the median. We changed the tables
and text in our discussion of the market concentration results to
better reflect these price effects.
[End of section]
Appendix III: Summary Information on the Seven Mergers Reviewed in
GAO's Econometric Model:
Additional information on the specific mergers selected for review in
GAO's analysis, including the rationale for the merger, statements or
remedial actions identified by the FTC in addressing potential
anticompetitive concerns, and other relevant context surrounding the
merger, is outlined below.
Chevron Corporation and Texaco:
On October 16, 2000, Chevron Corporation and Texaco announced plans to
merge, in a transaction ultimately valued at about $45 billion. Both
firms were large, fully integrated firms, with operations in oil
exploration, pipeline transportation, and refining and marketing of
gasoline products, and were considered among the largest integrated oil
firms in the world. Chevron's stated goal in pursuing the merger was to
become the industry leader in total stockholder returns. Following the
merger, the newly merged firm was projected to become the world's
fourth largest firm in oil exploration and production. FTC's review of
the merger identified a number of antitrust concerns, including
coordinated threats in the refining and marketing sectors in a number
of regions across the United States, as well as threats in the
refining, pipeline, and marketing sectors, primarily across the West.
As a result of these threats, FTC required the divestiture of a number
of Texaco's downstream assets, most notably its share of a joint
venture with Royal Dutch Shell Group in the refining sector, as well as
its share of two major pipelines. See figure 3 for the cities in our
analysis where we identified competitive overlaps between these firms
before they merged.
Figure 3: Cities Affected by Chevron/Texaco Merger:
[Refer to PDF for image: U.S. map]
Cities affected by the Chevron/Texaco merger (unbranded gasoline):
Albuquerque, N.Mexico;
Amarillo, Texas;
Anchorage, Alaska;
Atlanta, Georgia;
Baltimore, Maryland;
Baton Rouge, Louisiana;
Beaumont, Texas;
Bloomfield, New Mexico;
Boise, Idaho;
Corpus Christi, Texas;
Dallas/Metro Texas;
El Paso, Texas;
Fairbanks, Alaska
Greensboro, North Carolina;
Houston, Texas;
Knoxville, Tennessee;
Lake Charles, Louisiana;
Las Vegas, Nevada;
Los Angeles, California;
Memphis, Tennessee;
Miami, Florida;
Mobile, Alabama;
Nashville, Tennessee;
New Orleans, Louisiana;
Phoenix, Arizona;
Pittsburgh, Pennsylvania;
Portland, Oregon;
Richmond, Virginia;
Sacramento, California;
Salt Lake City, Utah;
San Diego, California;
San Francisco, California;
Seattle, Washington;
Sparks/Reno, Nevada;
Spokane, Washington;
Tampa, Florida;
Tucson, Arizona.
Source: Copyright © Corel Corp. All rights reserved (map); GAO analysis
of OPIS data.
[End of figure]
Phillips Petroleum Corporation and Tosco Corporation:
On February 4, 2001, Phillips Petroleum Corporation and Tosco
Corporation announced plans to merge, in a transaction valued at $9.8
billion dollars. Prior to the merger, Phillips was a large firm with
refining and retail operations in the United States, and crude oil
production operations worldwide, while Tosco operated in the downstream
sector, with refining and marketing operations. In the transaction,
Phillips gained eight U.S. refineries and 6,400 retail stations in 32
states. According to IHS Herold information, Phillips' goal was to
increase the profitability of its downstream operations and realize
$250 million dollars in pretax cost savings. According to the Oil and
Gas Journal and FTC, there was actually little overlap between the
companies' refining and marketing systems, reducing the potential for
competitive concerns. In fact, FTC indicated that the two merging
companies substantially operated in different parts of the country, and
the combined sales of the two firms would not exceed 10 percent of the
oil-refining or gasoline-marketing sales across the country. In the few
cities where the firms' gasoline-marketing businesses would overlap
significantly, FTC indicated that the new firm would have a relatively
low market share, making it unlikely that the new firm would pose a
competitive threat to those markets. See figure 4 for the cities in our
analysis where we identified competitive overlaps between these firms
before they merged.
Figure 4: Cities Affected by Phillips/Tosco Merger:
[Refer to PDF for image: U.S. map]
Cities affected by the Phillips/Tosco merger (unbranded gasoline):
Atlanta, Georgia;
Greensboro, North Carolina;
Knoxville, Tennessee;
Las Vegas, Nevada;
Nashville, Tennessee;
Phoenix, Arizona;
Spartanburg, South Carolina;
Tucson, Arizona.
Source: Copyright © Corel Corp. All rights reserved (map); GAO analysis
of OPIS data.
[End of figure]
Valero Energy Corporation and Ultramar Diamond Shamrock Corporation:
On May 7, 2001, Valero Energy Corporation announced plans to acquire
Ultramar Diamond Shamrock Corporation (UDS) in a transaction valued at
$6.4 billion. Prior to the merger, both firms were focused on
downstream refining and retail operations, each owning seven
refineries. In the transaction, Valero acquired seven UDS refineries,
approximately 2,500 company-owned retail sites, and 2,500 branded
gasoline stations in the United States and Canada. In a press release,
Valero indicated that the merger would help create synergies and
strategic benefits. However, before allowing the transaction, FTC
required the divestiture of UDS's Golden Eagle refinery, located in
Avon, California, so as to remedy alleged anticompetitive concerns in
the gasoline-refining and supply markets in California. Without this
divestiture, competition would have been reduced by giving Valero
between a 40 and 45 percent market share of gasoline refining in
Northern California, thus enhancing its ability to unilaterally raise
prices or to coordinate with other California refiners to raise prices.
FTC also indicated that the claimed efficiency gains of the merger
would have been small compared with the magnitude of the potential harm
to consumers in California had it not required the divestiture, which
with even a 1 cent per gallon increase, would have cost consumers an
extra $150 million per year. See figure 5 for the cities in our
analysis where we identified competitive overlaps between these firms
before they merged.
Figure 5: Cities Affected by Valero/UDS Merger:
[Refer to PDF for image: U.S. map]
Cities affected by the Valero/UDS merger (unbranded gasoline):
Albuquerque, New Mexico;
Amarillo, Texas;
Fort Smith, Arkansas;
Baton Rouge, Louisiana;
Beaumont, Texas;
Columbia, Missouri;
Corpus Christi, Texas;
Dallas/Metro, Texas;
Denver, Colorado;
Des Moines, Iowa;
El Paso, Texas;
Houston, Texas;
Kansas City, Kansas;
Lake Charles, Louisiana;
Las Vegas, Nevada;
Minneapolis, Minnesota;
Oklahoma City, Oklahoma;
Omaha, Nebraska;
Phoenix, Arizona;
Sacramento, California;
San Francisco, California;
Sioux Falls, South Dakota;
Springfield, Missouri;
Tucson, Arizona;
Tulsa, Oklahoma;
Tyler, Texas.
Source: Copyright © Corel Corp. All rights reserved (map); GAO analysis
of OPIS data.
[End of figure]
Royal Dutch Shell Group:
On October 9, 2001, Texaco signed a memorandum of understanding with
Royal Dutch Shell Group and Saudi Refining to sell Texaco's shares of
the Equilon Enterprises and Motiva Enterprises joint ventures with
Shell and Saudi Refining.[Footnote 37] The joint ventures included the
refining, transportation, and marketing activities of Shell and Texaco
in the United States, as operated by Equilon Enterprises in the West
and Midwest and Motiva Enterprises in the East. The memorandum of
understanding came about in response to FTC's review of the proposed
merger between Chevron Corporation and Texaco and subsequent concern
about unilateral and coordinated threats posed by the merger in the
refining and marketing sectors. Specifically, FTC found that, absent
any divestitures, the Chevron/Texaco merger would violate antitrust law
by reducing competition in markets such as the following: gasoline
marketing in the West; refining, marketing, and bulk supply of CARB
(California Air Resources Board) gasoline in California; and the
terminaling and bulk supply of gasoline in a number of states in the
West and Southwest. In response, FTC issued a decision and order
requiring Texaco to divest all of its interests in the joint ventures,
which included gasoline marketing in numerous western states, including
CARB gasoline, as well as refining and bulk supply of gasoline in
California and the Pacific Northwest, among others. Under the terms of
the memorandum of understanding, Shell received 100 percent interest in
Equilon, including approximately 9,000 retail stations and four
refineries, and Shell and Saudi Refining each 50 percent interest in
Motiva, including approximately 13,000 stations and four refineries.
FTC approved the divestiture as proposed in the memorandum of
understanding, subsequently allowing for the approval of the Chevron/
Texaco merger. See figure 6 for the cities in our analysis where we
identified competitive overlaps between these firms before they merged.
Figure 6: Cities Affected by Shell/Texaco Merger:
[Refer to PDF for image: U.S. map]
Cities affected by the Shell/Texaco merger (unbranded gasoline):
Albany, New York;
Albuquerque, New Mexico;
Amarillo, Texas;
Atlanta, Georgia;
Baltimore, Maryland;
Baton Rouge, Louisiana;
Beaumont, Texas;
Bloomfield, New Mexico;
Boise, Idaho;
Dallas/Metro, Texas;
El Paso, Texas;
Fort Smith, Arkansas;
Greensboro, North Carolina;
Harrisburg, Pennsylvania;
Houston, Texas;
Knoxville, Tennessee;
Las Vegas, Nevada;
Memphis, Tennessee;
Miami, Florida;
Mobile, Alabama;
Nashville, Tennessee;
New Orleans, Louisiana;
Phoenix, Arizona;
Portland, Oregon;
Richmond, Virginia;
Sacramento, California;
Salt Lake City, Utah;
San Francisco, California;
Seattle, Washington;
Sparks/Reno, Nevada;
Spartanburg, South Carolina;
Spokane, Washington;
St. Louis, Missouri;
Tampa, Florida;
Tucson, Arizona.
Source: Copyright © Corel Corp. All rights reserved (map); GAO analysis
of OPIS data.
[End of figure]
Phillips Petroleum Corporation and Conoco:
On November 19, 2001, Conoco and Phillips Petroleum Corporation
announced plans to merge in a deal worth $31 billion. Prior to the
merger, Philips was the third largest refiner in the United States,
with approximately 10 percent of U.S. capacity, and Conoco was
approximately the 11th largest refiner, with 3 percent of U.S. refining
capacity. Following the merger, the new company became the third
largest integrated energy company in the United States. Through the
merger, Conoco and Phillips stated that they hoped to realize major
synergies, more capital for upstream investment, and operational
efficiencies in the downstream sector. Prior to the completion of the
transaction, FTC analyzed the markets and assets involved in the merger
and identified a few areas of competitive concern. More specifically,
FTC determined that the new firm would have had sufficient market share
to be able to coordinate or to act unilaterally to raise gasoline
prices in eastern Colorado; northern Utah; Spokane, Washington; and
Wichita, Kansas. As a result, FTC required divestitures in the areas of
concern, namely the sale of Phillips's Woods Cross refinery near Salt
Lake City and marketing assets in northern Utah, as well as the sale of
Conoco's Denver-area refinery and eastern Colorado marketing assets.
FTC also required the sale of Phillips's gasoline terminal in Spokane
and required an agreement related to the use of Phillip's gasoline
terminal in Wichita. See figure 7 for the cities in our analysis where
we identified competitive overlaps between these firms before they
merged.
Figure 7: Cities Affected by Phillips/Conoco Merger:
[Refer to PDF for image: U.S. map]
Cities affected by the Phillips/Conoco merger (unbranded gasoline):
Albuquerque, New Mexico;
Amarillo, Texas;
Atlanta, Georgia;
Baton Rouge, Louisiana;
Beaumont, Texas;
Bloomfield, New Mexico;
Boise, Idaho;
Cheyenne, Wyoming;
Corpus Christi, Texas;
Dallas/Metro, Texas;
Denver, Colorado;
Des Moines, Iowa;
El Dorado, Arkansas;
El Paso, Texas;
Evansville, Indiana;
Fort Smith, Arkansas;
Greensboro, North Carolina;
Houston, Texas;
Kansas City, Kansas;
Knoxville, Tennessee;
Lake Charles, Louisiana;
Las Vegas, Nevada;
Los Angeles, California;
Madison, Wisconsin;
Memphis, Tennessee;
Minneapolis, Minnesota;
Mobile, Alabama;
Nashville, Tennessee;
New Orleans, Louisiana;
Oklahoma City, Oklahoma;
Omaha, Nebraska;
Phoenix, Arizona;
Portland, Oregon;
Sacramento, California;
Salt Lake City, Utah;
San Diego, California;
San Francisco, California;
Seattle, Washington;
Sioux Falls, South Dakota;
Sparks/Reno, Nevada;
Spartanburg, South Carolina;
Spokane, Washington;
Springfield, Missouri;
Superior, Wisconsin;
Tucson, Arizona;
Tulsa, Oklahoma;
Tyler, Texas.
Source: Copyright © Corel Corp. All rights reserved (map); GAO analysis
of OPIS data.
[End of figure]
Premcor and Williams Companies:
On November 26, 2002, Premcor announced its intention to acquire a
refinery located in Memphis, Tennessee owned by Williams Companies, in
a transaction valued at $367 million. Prior to the merger, both
companies were relatively small, with Premcor operating a few
refineries around the country and Williams a refinery in Alaska, in
addition to the Memphis facility. As part of the transaction, Premcor
acquired the refinery, as well as the related supply and distribution
assets in and around Memphis owned by Williams. In an initial press
release, Premcor noted that the Memphis refinery would help Premcor
grow its presence in the Southeast, in addition to providing the firm
with a strong, competitively positioned refinery, because of extensive
upgrades and improvements to the facility in previous years by
Williams. Furthermore, Premcor noted that, because of the refinery's
location, it expected to benefit from synergies with Premcor's Lima,
Ohio, refinery, as well as its midcontinent distribution system. In its
review of the merger, FTC did not identify any potential threats to
competition, either unilateral or coordinated. As such, the acquisition
proceeded as planned, without any challenge from FTC. See figure 8 for
the cities in our analysis where we identified competitive overlaps
between these firms before they merged.
Figure 8: Cities Affected by Premcor/Williams Merger:
[Refer to PDF for image: U.S. map]
Cities affected by the Premcor/Williams merger (unbranded gasoline):
Richmond, Virginia;
Spartanburg, South Carolina.
Source: Copyright © Corel Corp. All rights reserved (map); GAO analysis
of OPIS data.
[End of figure]
Valero Energy Corporation and Premcor:
On April 25, 2005, Valero Energy Corporation and Premcor announced
plans to merge in a deal worth $7.6 billion. At the time, Valero was
the fourth largest U.S. refiner, while Premcor was a smaller refiner
that owned only four U.S. refineries, which were located in Port
Arthur, Texas; Memphis, Tennessee; Lima, Ohio; and Delaware City,
Delaware. After this merger, Valero became one of the largest refiners
in the United States. Valero noted in a press release that the
acquisition would allow for synergies in the two companies' refining
operations. As we noted in our 2008 report, operational efficiencies at
refineries were reported as the rationale for some mergers, because
refinery operators can achieve cost savings by purchasing crude in
bulk, among other things.[Footnote 38] According to EIA, the
acquisition significantly increased Valero's refining presence on the
East Coast and in the Midwest. FTC conducted a nonpublic investigation
of this merger, which FTC staff indicated was closed with no action to
challenge the merger. See figure 9 for the cities in our analysis where
we identified competitive overlaps between these firms before they
merged.
Figure 9: Cities Affected by Valero/Premcor Merger:
[Refer to PDF for image: U.S. map]
Cities affected by the Valero/Premcor merger (unbranded gasoline):
Baltimore, Maryland;
Beaumont, Texas;
Chicago, Illinois;
Cleveland, Ohio;
Columbus, Ohio;
Detroit, Michigan;
Evansville, Indiana;
Harrisburg, Pennsylvania;
Indianapolis, Indiana;
Knoxville, Tennessee;
Lake Charles, Louisiana;
Lima, Ohio;
Memphis, Tennessee;
Nashville, Tennessee;
Newark, New Jersey;
New Orleans, Louisiana;
Rockford, Illinois;
Spartanburg, South Carolina;
St. Louis, Missouri;
Toledo, Ohio.
Source: Copyright © Corel Corp. All rights reserved (map); GAO analysis
of OPIS data.
[End of figure]
[End of section]
Appendix IV: Additional Market Concentration Information:
The estimated effects of the measures of market concentration on
branded wholesale gasoline prices are shown below.
Table 11: Effects of the Number of Sellers on Branded Wholesale
Gasoline Prices at the Terminals in the 82 Cities We Studied:
Change in number of sellers at the wholesale terminal: Change in
branded wholesale gasoline price in cents per gallon[A];
Gain of 6 sellers (8 sellers to 14 sellers): -8;
Gain of 12 sellers (5 sellers to 17 sellers): -15.
Source: GAO analysis of OPIS data.
[A] These results were statistically significant at the 1 percent
level. The 8 to 14 seller range represents the 25th to the 75th
percentile of values that we observed at terminals in our branded
analysis. The 5 to 17 seller range represents the 10th to the 90th
percentile.
[End of table]
Table 12: Effects of Market Concentration on Branded Wholesale Gasoline
Prices at Terminals Supplied by Seven Spot Markets:
Refinery spot market HHI: Change in branded wholesale gasoline price in
cents per gallon[A];
Decrease in HHI from 930 to 790: -1;
Decrease in HHI from 1,470 to 700: -8.
Source: GAO analysis of OPIS data.
[A] These results were statistically significant at the 10 percent
level. The 790 to 930 range represents the 25th to the 75th percentile
of values that we observed at terminals in our branded analysis. The
700 to 1,470 range represents the 10th to the 90th percentile.
[End of table]
Table 13: Number of Sellers at Wholesale Terminals in 2008:
City name: Anchorage;
State: Alaska;
Number of sellers: 4.
City name: Fairbanks;
State: Alaska;
Number of sellers: 3.
City name: Mobile;
State: Ala.;
Number of sellers: 6.
City name: El Dorado;
State: Ark.;
Number of sellers: 8.
City name: Fort Smith;
State: Ark.;
Number of sellers: 17.
City name: Phoenix;
State: Ariz.;
Number of sellers: 8.
City name: Tucson;
State: Ariz.;
Number of sellers: 10.
City name: Los Angeles;
State: Calif.;
Number of sellers: 7.
City name: Sacramento;
State: Calif.;
Number of sellers: 10.
City name: San Diego;
State: Calif.;
Number of sellers: 6.
City name: San Francisco;
State: Calif.;
Number of sellers: 8.
City name: Denver;
State: Colo.;
Number of sellers: 11.
City name: Miami;
State: Fla.;
Number of sellers: 14.
City name: Tampa;
State: Fla.;
Number of sellers: 14.
City name: Atlanta;
State: Ga.;
Number of sellers: 14.
City name: Des Moines;
State: Iowa;
Number of sellers: 21.
City name: Boise;
State: Idaho;
Number of sellers: 12.
City name: Champaign;
State: Ill.;
Number of sellers: 5.
City name: Chicago;
State: Ill.;
Number of sellers: 8.
City name: Robinson;
State: Ill.;
Number of sellers: 9.
City name: Rockford;
State: Ill.;
Number of sellers: 10.
City name: Evansville;
State: Ind.;
Number of sellers: 8.
City name: Indianapolis;
State: Ind.;
Number of sellers: 13.
City name: Kansas City;
State: Kans.;
Number of sellers: 19.
City name: Louisville;
State: Ky.;
Number of sellers: 6.
City name: Paducah;
State: Ky.;
Number of sellers: 4.
City name: Baton Rouge;
State: La.;
Number of sellers: 9.
City name: Lake Charles;
State: La.;
Number of sellers: 9.
City name: New Orleans;
State: La.;
Number of sellers: 10.
City name: Baltimore;
State: Md.;
Number of sellers: 17.
City name: Bay City;
State: Mich.;
Number of sellers: 7.
City name: Detroit;
State: Mich.;
Number of sellers: 11.
City name: Minneapolis;
State: Minn.;
Number of sellers: 16.
City name: Columbia;
State: Mo.;
Number of sellers: 16.
City name: Springfield;
State: Mo.;
Number of sellers: 18.
City name: St. Louis;
State: Mo.;
Number of sellers: 8.
City name: Greensboro;
State: N.C.;
Number of sellers: 13.
City name: Fargo;
State: N.Dak.;
Number of sellers: 14.
City name: Omaha;
State: Nebr.;
Number of sellers: 21.
City name: Newark;
State: N.J.;
Number of sellers: 20.
City name: Albuquerque;
State: N. Mex.;
Number of sellers: 11.
City name: Bloomfield;
State: N. Mex.;
Number of sellers: 11.
City name: Las Vegas;
State: Nev.;
Number of sellers: 3.
City name: Sparks/Reno;
State: Nev.;
Number of sellers: 8.
City name: Albany;
State: N.Y.;
Number of sellers: 15.
City name: Syracuse;
State: N.Y.;
Number of sellers: 15.
City name: Cincinnati;
State: Ohio;
Number of sellers: 4.
City name: Cleveland;
State: Ohio;
Number of sellers: 9.
City name: Columbus;
State: Ohio;
Number of sellers: 9.
City name: Lima;
State: Ohio;
Number of sellers: 5.
City name: Toledo; State:
Ohio;
Number of sellers: 9.
City name: Oklahoma City;
State: Okla.;
Number of sellers: 17.
City name: Tulsa;
State: Okla.;
Number of sellers: 13.
City name: Portland;
State: Ore.;
Number of sellers: 12.
City name: Harrisburg;
State: Pa.;
Number of sellers: 15.
City name: Philadelphia;
State: Pa.;
Number of sellers: 14.
City name: Pittsburgh;
State: Pa.;
Number of sellers: 12.
City name: Spartanburg;
State: S.C.;
Number of sellers: 14.
City name: Sioux Falls;
State: S.D.;
Number of sellers: 17.
City name: Knoxville;
State: Tenn.;
Number of sellers: 14.
City name: Memphis;
State: Tenn.;
Number of sellers: 11.
City name: Nashville;
State: Tenn.;
Number of sellers: 13.
City name: Amarillo;
State: Tex.;
Number of sellers: 8.
City name: Beaumont;
State: Tex.;
Number of sellers: 11.
City name: Corpus Christi;
State: Tex.;
Number of sellers: 10.
City name: Dallas;
State: Tex.;
Number of sellers: 9.
City name: El Paso;
State: Tex.;
Number of sellers: 10.
City name: Houston;
State: Tex.;
Number of sellers: 12.
City name: Tyler;
State: Tex.;
Number of sellers: 7.
City name: Salt Lake City;
State: Utah;
Number of sellers: 9.
City name: Richmond;
State: Va.;
Number of sellers: 14.
City name: Seattle;
State: Wash.;
Number of sellers: 10.
City name: Spokane;
State: Wash.;
Number of sellers: 7.
City name: Green Bay;
State: Wis.;
Number of sellers: 11.
City name: Madison;
State: Wis.;
Number of sellers: 12.
City name: Milwaukee;
State: Wis.;
Number of sellers: 9.
City name: Superior;
State: Wis.;
Number of sellers: 6.
City name: Cheyenne;
State: Wyo.;
Number of sellers: 9.
Source: GAO analysis of OPIS data.
Note: We studied the price relationship to the number of sellers at
wholesale terminals in 78 cities throughout the United States. These
cities reflect a broad geographic range of locations where gasoline is
sold out of our data's nearly 400 wholesale terminal locations in the
United States. Most cities had only 1 terminal, and we chose to examine
only 1 terminal in the few cases where there was more than 1. The 82
cities we used in our analysis of branded gasoline were the same as for
unbranded, but included Great Falls, Mont.; Bismarck/Mandan N.Dak.;
Casper Wyo.; and Sinclair, Wyo.
[End of table]
As shown in figure 10, trends in refinery spot market concentration
were fairly stable over time, and most markets remained either
unconcentrated (below 1,000) or moderately concentrated (below 1,800),
with the exception of New York Harbor and Alaska, which were both
highly concentrated.
Figure 10: Yearly Concentration Levels in the Seven Spot Markets That
We Analyzed:
[Refer to PDF for image: multiple line graph]
Year: 2000;
Alaska HHI: 3729;
Tulsa (midcontinent) HHI: 911;
Gulf Coast HHI: 688;
Los Angeles HHI: 1472;
New York Harbor HHI: 2224;
Pacific Northwest HHI: 1401;
San Francisco HHI: 1657.
Year: 2001;
Alaska HHI: 3675;
Tulsa (midcontinent) HHI: 853;
Gulf Coast HHI: 666;
Los Angeles HHI: 1471;
New York Harbor HHI: 2218;
Pacific Northwest HHI: 1414;
San Francisco HHI: 1536.
Year: 2002;
Alaska HHI: 3639;
Tulsa (midcontinent) HHI: 940;
Gulf Coast HHI: 743;
Los Angeles HHI: 1472;
New York Harbor HHI: 2183;
Pacific Northwest HHI: 1407;
San Francisco HHI: 1817.
Year: 2003;
Alaska HHI: 3643;
Tulsa (midcontinent) HHI: 959;
Gulf Coast HHI: 797;
Los Angeles HHI: 1459;
New York Harbor HHI: 2193;
Pacific Northwest HHI: 1446;
San Francisco HHI: 1795.
Year: 2004;
Alaska HHI: 3651;
Tulsa (midcontinent) HHI: 942;
Gulf Coast HHI: 802;
Los Angeles HHI: 1439;
New York Harbor HHI: 2756;
Pacific Northwest HHI: 1416;
San Francisco HHI: 1798.
Year: 2005;
Alaska HHI: 3651;
Tulsa (midcontinent) HHI: 935;
Gulf Coast HHI: 834;
Los Angeles HHI: 1361;
New York Harbor HHI: 2889;
Pacific Northwest HHI: 1419;
San Francisco HHI: 1683.
Year: 2006;
Alaska HHI: 3651;
Tulsa (midcontinent) HHI: 926;
Gulf Coast HHI: 907;
Los Angeles HHI: 1345;
New York Harbor HHI: 3036;
Pacific Northwest HHI: 1417;
San Francisco HHI: 1657.
Year: 2007;
Alaska HHI: 3651;
Tulsa (midcontinent) HHI: 926;
Gulf Coast HHI: 906;
Los Angeles HHI: 1345;
New York Harbor HHI: 3036;
Pacific Northwest HHI: 1417;
San Francisco HHI: 1657.
Year: 2008;
Alaska HHI: 3651;
Tulsa (midcontinent) HHI: 926;
Gulf Coast HHI: 906;
Los Angeles HHI: 1345;
New York Harbor HHI: 3036;
Pacific Northwest HHI: 1417;
San Francisco HHI: 1657.
Source: GAO analysis of EIA data.
Note: We had data on market concentration up to 2006 and we
extrapolated them to 2008. In addition, none of the terminals we
studied were primarily served by the Chicago spot market, and we did
not calculate concentration for this spot market.
[End of figure]
[End of section]
Appendix V:GAO Contacts and Staff Acknowledgments:
GAO Contacts:
Mark Gaffigan, (202) 512-3841 or gaffiganm@gao.gov Thomas McCool, (202)
512-2700 or mccoolt@gao.gov:
Staff Acknowledgments:
In addition to the individuals named above, Daniel Haas (Assistant
Director), Shea Bader, Divya Bali, Frank Cook, Michael Kendix, Robert
Marek, Michelle Munn, Alison O'Neill, Susan Offutt, Frank Rusco,
Rebecca Sandulli, and Barbara Timmerman made important contributions to
this report.
[End of section]
Footnotes:
[1] GAO, Energy Markets: Analysis of More Past Mergers Could Enhance
Federal Trade Commission's Efforts to Maintain Competition in the
Petroleum Industry, [hyperlink,
http://www.gao.gov/products/GAO-08-1082] (Washington, D.C.: Sept. 25,
2008.)
[2] A merger, as defined in this analysis, involves the sale of either
all or part of the stock or assets of a company to another.
[3] IHS Herold is an independent research firm specializing in the
energy sector that provides financial and operational data for, as well
as analyses of, more than 400 oil and gas companies.
[4] OPIS is a private company that is a leading provider of gasoline
price information.
[5] The remaining gasoline is sold directly to retailers, or through
other arrangements, and price data for these sales are not always
available.
[6] Major oil companies own most of the terminals, although according
to OPIS, some are owned by pipeline operators or dedicated terminal
companies.
[7] These additional sellers include oil companies wishing to sell
gasoline in areas where they do not have refineries.
[8] In addition, when refiners sell branded gasoline to distributors
and retailers, the contracts tend to be less flexible than contracts
for unbranded gasoline but guarantee a more secure supply. Thus,
branded prices may also include a premium for this additional security.
[9] Buyers of unbranded gasoline may or may not have a binding
contractual arrangement with a refiner. Therefore, a buyer of unbranded
gasoline may not be guaranteed a secure supply or lower prices,
particularly during market shocks that reduce the gasoline supply.
Thus, when there is a disruption in the supply system, such as those
caused by pipeline or refinery breakdowns, unbranded prices at
wholesale terminals can be higher than those of branded.
[10] FTC can also challenge completed mergers if they violate antitrust
laws.
[11] None of the studies found that the mergers had any adverse effects
on gasoline prices.
[12] The National Bureau of Economic Research is a private, nonprofit,
nonpartisan research organization that disseminates unbiased economic
research among public policymakers, business professionals, and the
academic community.
[13] Orley C. Ashenfelter, Daniel Hosken, Matthew Weinberg, National
Bureau of Economic Research, Generating Evidence to Guide Merger
Enforcement; NBER Working Paper 14798, (Cambridge Mass., March 2009).
[14] See appendix III for more detailed information on each merger
transaction.
[15] Potential threats identified by the FTC can include both
unilateral and coordinated threats to competition.
[16] These assets included shares of two refining and marketing joint
ventures with Royal Dutch/Shell Group and Saudi Refining, as managed by
Motiva Enterprises and Equilon Enterprises. Subsequent to this order,
Shell became the sole owner of Equilon, and Shell and Saudi Refining
became the owners of Motiva.
[17] As noted earlier, these estimates may have been affected by the
effects of localized disruptions or changes to gasoline supply. In the
case of the Valero/Premcor merger, this could include potential
disruptions due to events surrounding Hurricane Katrina in 2005. In the
case of the Valero/UDS merger, this could include potential disruptions
due to new specifications for California gasoline beginning in December
2003. To address these issues, we would have to had made judgments
about the timing and regional impacts of these events without adequate
data.
[18] Our model included price data that we purchased from the OPIS for
gasoline sold at wholesale terminals, or racks, located in cities
throughout the United States. The remaining gasoline is sold directly
to retailers, or through other arrangements, and price data for these
sales are not always available.
[19] In its ruling on the Valero/UDS merger, FTC indicated that even a
1 cent per gallon increase in gasoline prices would cost California
consumers an extra $150 million per year.
[20] We examined prices at 1 terminal per city.
[21] For example, if there are two firms that sell products in a market
with market shares of 60 percent and 40 percent, respectively, the
calculation of HHI would be 602+ 402 = 5,200
[22] This approach did not allow us to capture whether there was one
large seller and a number of smaller sellers or whether all the sellers
sold relatively similar volumes of gasoline.
[23] Most of the nation's gasoline supply comes from one of seven
groups of refineries throughout the United States, which experts refer
to as spot markets. Energy traders use spot markets to price gasoline
that is bought and sold at the wholesale level. These spot markets are
defined by the refineries in and around San Francisco, Los Angeles, the
Pacific Northwest, the Gulf Coast, Tulsa (Midcontinent), Chicago, and
New York Harbor. None of the terminals in our analysis were served
primarily by the Chicago market, although we considered the refineries
in Alaska as a separate market. We used industry data to link these
spot markets to individual wholesale gasoline terminals in the 78
cities we studied. However, we were not able to account for gasoline
imported into the United States because we only had data on U.S
refinery production capacity. See appendix I for more information.
[24] GAO, Energy Markets: Analysis of More Past Mergers Could Enhance
Federal Trade Commission's Efforts to Maintain Competition in the
Petroleum Industry, [hyperlink,
http://www.gao.gov/products/GAO-08-1082] (Washington D.C.: Sept. 25,
2008).
[25] Orley C. Ashenfelter, Daniel Hosken, Matthew Weinberg, National
Bureau of Economic Research, Generating Evidence to Guide Merger
Enforcement.
[26] See appendix IV for a list of the cities in our analysis.
[27] See Kyong So Im, M. Hashem Pesaran, and Yongcheol Shin. "Testing
for Unit Roots in Heterogeneous Panels," Journal of Econometrics, 115,
53-74 (2003).
[28] Joris Pinkse, Margaret E. Slade, and Craig Brett. "Spatial Price
Competition: A Semiparametric Approach," Econometrica, Vol. 70, No. 3.
May 2002, 1111-1153.
[29] Our OPIS data did not contain gasoline prices from Hawaii. In
addition, none of the terminals in our analysis were served primarily
by the Chicago spot market.
[30] We dropped the non-gasoline-producing refineries (i.e., producers
of asphalt etc.) from these calculations by identifying refineries that
lacked gasoline-producing equipment. These data included only U.S.
refiners until 2006. We extrapolated the data to 2008.
[31] In commenting on GAO's prior work on oil companies, Energy
Markets: Effects of Mergers and Market Concentration in the U.S.
Petroleum Industry, [hyperlink, http://www.gao.gov/products/GAO-04-96]
(Washington, D.C.: May 17, 2004), Professor Halbert White of the
University of California at San Diego suggested that rather than impose
a specific error formulation such as an AR(1), it would be preferable
to explicitly include lags of various variables in the model directly.
We included a lagged dependent variable as a regressor but did not go
beyond that in including lags of other variables in the model.
[32] See, for example, W. N. Evans et al. "Endogeneity in the
Concentration-Price Relationship: Causes, Consequences, and Cures." The
Journal of Industrial Economics, vol. XLI, no. 4, Dec. 1993, 431- 438.
[33] There are five Petroleum Administration for Defense Districts
(PADD) in the United States. EIA collects much of its data according to
these regions.
[34] The tests rejected exogeneity in all cases except for the spot
market HHI in the branded gasoline prices model.
[35] For example, see Halbert White, "Time-Series Estimation of the
Effects of Natural Experiments," Journal of Econometrics, 135, 2006,
527-566.
[36] As noted on page 16, we treated market concentration as
endogenous--meaning that changes in wholesale gasoline prices could
affect market concentration in addition to changes in concentration
affecting prices. For example, this could occur if high prices at one
terminal spur new sellers to enter the market, thus decreasing
concentration. This assumption was supported by statistical tests that
we conducted, although because this assumption was likely to have a
noticeable impact on our results, we also analyzed our data without it
and found that the impact on prices of our concentration measures was
statistically significant but smaller. For example, for unbranded
prices, in the case of the refinery spot market HHI, the impact on
wholesale prices was about half the size without this assumption. For
the number of sellers at the terminal, the impact was about one-sixth
of the size without this assumption.
[37] Saudi Refining was only a partner, and subsequent buyer, in the
joint venture with Motiva Enterprises.
[38] GAO, Energy Markets: Analysis of More Past Mergers Could Enhance
Federal Trade Commission's Efforts to Maintain Competition in the
Petroleum Industry, [hyperlink,
http://www.gao.gov/products/GAO-08-1082] (Washington D.C.: Sept. 25,
2008).
[39] The Commission commented extensively on GAO's merger review
analysis in response to your 2008 report on Energy Markets: Analysis of
More Past Mergers Could Enhance Federal Trade Commission's Efforts to
Maintain Competition in the Petroleum Industry (GAO08-1082) (Sept.
2008), at 54-60 ("GAO 2008 Report"). The instant comments should be
read in combination with the FTC comments appended to the GAO 2008
Report.
[40] Orley C. Ashenfelter, Daniel Hosken & Matthew Weinberg, Generating
Evidence to Guide Merger Enforcement, NBER Working Paper No. 14798
(Mar. 2009), available at [hyperlink,
http://www.nber.org/papers/w14798.pdf?new_window=1].
[41] Report at 8.
[42] The Commission staff, which conducts merger retrospectives across
a number of industries, is working on a fourth retrospective review of
a petroleum industry merger. That retrospective is expected to be
released later this year.
[43] GAO appropriately notes the limitations of its analyses. For
example, the Report states that GAO did not control for all factors
affecting gasoline markets, including "disruptions to local gasoline
supply markets from weather-related events, interruptions in refinery
or pipeline operations, or other changes in local gasoline supply."
Report at 3. The Report also states that "because some cities were
affected by multiple mergers, may have had changes in market
concentration, and may have been affected by factors for which [GAO]
did not have data," the study cannot be certain whether, or how,
wholesale prices in each location were affected by
particular mergers. Id.
[44] Id. at 17.
[45] See, e.g., Federal Trade Commission And U.S. Department Of
Justice, Commentary On The Horizontal Merger Guidelines 20 (2006)
("Market shares and concentration nevertheless are important in the
Agencies' evaluation of the likely competitive effects of a merger.
Investigations are almost always closed when concentration levels are
below the thresholds set forth in section 1.51 of the Guidelines. In
addition, the larger the market shares of the merging firms, and the
higher the market concentration after the merger, the
more disposed are the Agencies to concluding that significant
anticompetitive effects are likely").
[46] The central element of the Commission's role in the petroleum
sector is an ongoing and vigorous law enforcement presence. The
Commission maintains a program of investigating and, where appropriate,
taking enforcement action against potentially anticompetitive mergers
and acquisitions, as well as non-merger conduct violations, in this
industry. For example, in 2007 the Commission applied for a preliminary
injunction in the United States District Court in New Mexico, seeking
to block Western Refining's acquisition of Giant Industries - a
transaction that the Commission alleged threatened to harm competition
in the bulk supply of light petroleum products in northern New Mexico.
(The District Court disagreed and declined to grant the injunction.) In
addition, the Commission currently is engaged in a proceeding, pursuant
to Section 811 of the Energy Independence and Security Act of 2007, to
determine whether to promulgate a rule prohibiting market manipulation
in wholesale petroleum markets. See [hyperlink,
http://www.ftc.gov/opa/2009/04/rnprm.shtm].
[47] For example, GAO's findings (Report at 16, Table 3) suggest that
an increase in concentration of 90 points in an unconcentrated market
(a Herfindahl-Hirschman Index increase from 700 to 790) is associated
with a 5.4-cent-per-gallon increase in wholesale prices. But the
analysis also suggests that an identical increase of 5.4 cents per
gallon is associated with a concentration increase of 540 points,
beginning from a more concentrated level (from 930 to 1470). These
findings would suggest, for example, that mergers with smaller
structural impact (as measured by concentration changes) - and
occurring at lower levels of premerger market concentration - could
have adverse effects on prices equal to the effects produced by mergers
yielding larger concentration changes and occurring at higher levels of
premerger concentration. We find this result difficult to reconcile
with economic theory and with our own antitrust
experience.
[48] Report at 15, 23, 41.
[End of section]
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