Energy Markets: Refinery Outages Can Have Varying Gasoline Price Impacts, but Gaps in Federal Data Limit Understanding of Impacts
Published: 2009-07-30T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: In 2008, GAO reported that, with the exception of the period following Hurricanes Katrina and Rita, refinery outages in the United States did not show discernible trends in reduced production capacity, frequency, and location from 2002 through 2007. Some outages are planned to perform routine maintenance or upgrades, while unplanned outages occur as a result of equipment failure or other unforeseen problems. GAO was asked to (1) evaluate the effect of refinery outages on wholesale gasoline prices and (2) identify gaps in federal data needed for this and similar analyses. GAO selected refinery outages from 2002 through September 2008 that were at least among the largest 60 percent in terms of lost production capacity in their market region and lasted at least 3 days. GAO developed an econometric model and tested a variety of assumptions using public and private data.
section_title: What GAO Found
paragraphs: While some unplanned refinery outages, such as those caused by accidents or weather, have had large price effects, GAO found that in general, refinery outages were associated with small increases in gasoline prices. Large price increases occurred when there were large outages; for example, in the aftermath of hurricanes Katrina and Rita. However, we found that such large price increases were rare, and on average, outages were associated with small price increases. For example, GAO found that planned outages generally did not influence prices significantly--likely reflecting refiners' build-up in inventories to meet demand needs prior to shutting down--while for unplanned outages, average price effects ranged from less than one cent to several cents-per-gallon. Key factors influenced the size of price increases associated with unplanned outages. One such factor was whether the gasoline was branded--gasoline sold at retail under a specific refiner's trademark--or unbranded--gasoline sold at retail by independent sellers. Our analysis showed that during an unplanned outage, branded wholesale gasoline prices had smaller price increases than unbranded, suggesting that refiners give preference to their own branded customers during outages, while unbranded dealers must seek out supplies in a more constrained market. Another factor that affected the size of price increases associated with outages was the type of gasoline being sold. Some special blends of gasoline developed to reduce emissions of air pollutants exhibited larger average price increases than more widely used and available conventional gasoline, suggesting that these special gasoline blends may have more constrained supply options in the event of an outage. Existing federal data contain gaps that have limited GAO's and Department of Transportation's (DOT) analyses of petroleum markets and related issues. For example: (1) Data linking refiners to the markets they serve were inadequate for GAO to fully evaluate the price effects of unplanned outages on individual cities, limiting the analysis to broader average effects. (2) Pipeline flow and petroleum product storage data were inadequate for DOT to fully address a January 2009 Congressionally mandated study to identify potential pipeline infrastructure constraints, and limited GAO's ability to identify re-supply options for cities experiencing outage disruptions. Federal agencies generally have continued to update their data collection surveys to meet their respective needs and emerging changes in the energy sector. However, in some cases the individual agency efforts have resulted in the collection of information that does not necessarily meet the data needs of other agencies or analysts who monitor petroleum product markets.

section_title: Letter
paragraphs: The 150 refineries in the United States play an important role in the  nation’s economy and energy security by supplying consumers and  industry with needed petroleum products. Unplanned refinery outages— such as those caused by hurricanes, fires, or refinery equipment failures— have raised questions about the stability and cost of U.S. gasoline and  other petroleum product supplies. In addition to unplanned outages,  refineries must periodically undergo planned outages, during which they  shut down major pieces of equipment to perform maintenance, overhaul,  and repair operations. In October 2008, we reported that, with the  exception of impacts in 2005 related to Hurricanes Katrina and Rita,  refinery outages across the United States generally did not show  discernible trends in reduced production capacity or in the frequency and  location of outages from 2002 through 2007. In addition, in March 2007, the  Department of Energy’s (DOE) Energy Information Administration (EIA)  reported that unplanned refinery outages can result in local supply  disruptions that result in temporary price increases; however, refinery  outages do not always affect prices. Moreover, analyses by EIA and the  California Energy Commission have described how an unplanned refinery  outage under certain conditions—for example, a tight market supply and  demand balance for refined products coupled with low inventories or  other sources of re-supply to meet demand in the event of an unplanned  outage—can trigger price increases. Still, the impacts on gasoline prices  due to refinery outages and other disruptions are not fully understood. In  particular, while it is well understood that extreme events that disrupt  crude oil or petroleum product supplies can have significant effects on the  prices of these commodities, the price effects of less dramatic disruptions,  such as routine refinery outages, are not well understood.
Further compounding the potential impact of refinery outages, in recent  years prior to the current economic recession, global demand for crude oil  and petroleum products such as gasoline, diesel fuel, and jet fuel had  grown more quickly than available capacity to produce them.  Furthermore, some refiners had been running near capacity, particularly  during the peak-demand summer months. During tight market conditions,  unexpected refinery outages could stress the petroleum product supply  system, affecting operations at refineries, pipelines, and storage terminals.  In addition, the proliferation of special fuel blends—gasoline that has  special characteristics designed to meet federal, state, and local air quality  requirements—as well as the increasing use of biofuels such as ethanol as  a component of gasoline, have complicated the manufacturing and  distribution processes for petroleum products. Once produced, the various  blends of petroleum products must be kept separate throughout shipping  and delivery. Other disruptions, such as a pipeline break, can hamper the  ability of the supply infrastructure to deliver the steady supply of gasoline  and other petroleum products that U.S. consumers and businesses depend  on. In the past, local supply disruptions could be addressed more quickly  because additional fuel of the same formulation could be purchased from  numerous sources, but with the proliferation of special fuel blends,  replacement supplies of a special blend might not be as readily available,  and refineries with the capability to produce them could be hundreds of  miles away.
A number of federal agencies—including EIA, the Environmental  Protection Agency (EPA), the Department of Transportation (DOT), and  the Federal Energy Regulatory Commission (FERC)—have a role in  monitoring the effects of outages and ensuring the safe, efficient, and  adequate supply of petroleum products during and after those outages. In  this context, you asked us to study and evaluate (1) how refinery outages  have affected U.S. wholesale gasoline prices since 2002 and (2) to what  extent available federal data allow for the evaluation of the impacts of  refinery outages on petroleum product prices and reflect emerging trends  in petroleum product markets that may be important to future analytical  needs.
To evaluate how refinery outages have affected U.S. wholesale gasoline  prices since 2002, we purchased data that included detailed information on  refinery outages from Industrial Information Resources, Inc. (IIR); data  estimating the quantity flows of gasoline and other petroleum products  produced at most U.S. refineries and then transported to those U.S. cities  that make up the main markets for those products from Baker & O’Brien;  and weekly wholesale price data for 75 U.S. cities from the Oil Price  Information Service (OPIS). We also obtained and analyzed data from  EIA’s monthly refinery production survey, form EIA-810, and other EIA  data collection surveys. We determined that these data were sufficiently  reliable for the purposes of this report. Specifically, we determined these  data were sufficient to complete our analyses of the immediate average  wholesale price impacts associated with refinery outages on various  gasoline types, but they were neither sufficient to determine the effects  experienced by individual cities nor the longer term or dynamic effects of  outages on prices. We developed, and extensively tested, an econometric  model that examined the statistical relationship between refinery outages  and gasoline prices. We analyzed commercial data for 20 gasoline types  and distinguished between branded and unbranded gasoline to determine  if those factors influenced the price effect of an outage. We limited our  analysis to outages that were determined to be 1) at least among the  largest 60 percent of outages in terms of lost production capacity within  their market region and 2) that lasted at least 3 days. In our model, we  limited the effect of an outage on prices to one week, after which time we  assumed that petroleum products were supplied from an alternate source.  As a result, our analysis evaluated the short-term effects of outages but did  not evaluate the length of time those effects occurred. Limitations on  available public data on the production and supply of petroleum products  restricted our analysis to those cities for which Baker & O’Brien had  collected and maintained data; these cities generally represented the  United States geographically, but might not have been representative of all  cities. To control for the effects on gasoline prices, our model  incorporated data on numerous factors—such as gasoline inventory levels,  refinery capacity utilization, and gasoline specifications. Our modeling  results reflect a particular city’s reliance on the refinery experiencing the  outage.
To assess the extent to which available federal data allowed for the  evaluation of refinery outage impacts, we reviewed federal government  data collection surveys from federal agencies including EIA, EPA, and  FERC, as well as private companies. We reviewed the surveys for  comprehensiveness, utility, potential gaps or limitations, and to  understand the extent to which the surveys collect useful data to analyze  the impacts of disruptions. We reviewed past GAO reports and other  federal agency or intergovernmental agency studies on petroleum product  markets to identify data gaps, limitations, or inconsistencies. Furthermore,  we interviewed key industry and expert institution representatives  regarding data utility and limitations in their own work. Our work was not  a comprehensive evaluation of all federal energy data, but rather an  assessment of key data GAO used in this and past reports, and other select  data that we determined during the course of our review to have posed  limitations for GAO’s or other agencies’ evaluations of important policy  questions related to petroleum markets. See appendix I for a more detailed  description of our objectives, scope, and methodology.
We conducted our work from October 2008 through July 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.
subsections: section_title: Background
paragraphs: Refineries process crude oil into petroleum products through a  combination of distillation and other processes. A single barrel of crude oil  produces a varying amount of gasoline, diesel, jet fuel, and other  petroleum products depending on the configuration—or complexity—of  the refinery and the type of crude oil being refined.
This report focuses on the production of finished gasoline. Finished  gasoline is primarily defined by three characteristics: blendstock, vapor  pressure, and oxygenate content. Blendstock is the designation for the  base gasoline produced so that other materials can be blended in to meet  various air quality or other local specifications. Vapor pressure, also  known as Reid Vapor Pressure (RVP), measures the gasoline’s evaporation  characteristics or volatility. Oxygenates are fuel additives, particularly  alcohols and ethers, which increase gasoline octane levels and reduce  carbon monoxide pollution associated with automobile emissions. The  most widely used oxygenate in the United States is ethanol, which may be  added to gasoline in varying percentages. Federal regulations specify that  no more than 10 percent ethanol can be blended into gasoline. Ethanol is  generally blended with gasoline at the terminal or wholesale “rack”—the  distribution center between refineries and retail fueling stations. For the  purposes of this report, conventional gasoline does not contain special  federal, state, or local blendstock, RVP, or oxygenate requirements unless  otherwise noted, while “special fuel blends” refer to blends of gasoline  that are designed to be cleaner burning and generally contain either a  certain blendstock, RVP, or oxygenate requirement to meet federal, state,  or local fuel specifications. An example of a gasoline used to meet a state  fuel specification is California Air Resources Board (CARB) gasoline,  which is designed to reduce harmful exhaust emissions that cause smog  and is used exclusively in California.
Petroleum product markets are evolving in part as a result of the  increasing use of biofuels—fuels derived from plant or animal products— throughout the country. The Energy Policy Act of 2005 generally required  that at least 7.5 billion gallons of biofuels be blended into motor vehicle  fuels in the United States by 2012. These targets were later amended under  the Energy Independence and Security Act of 2007, which increased the  volume of biofuels to be blended with gasoline from 9 billion gallons in  2008 to 36 billion gallons in 2022. EPA was charged with implementing the  Renewable Fuel Standard (RFS) program and issuing regulations to ensure  that the annual volumes of biofuels specified by the legislation are being  blended into motor vehicle fuels. In addition, some states require the use  of biofuels. For example, in Minnesota all fuel must contain 10 percent  ethanol, while a number of other states offer consumers incentives—such  as tax credits and rebates—for purchasing ethanol or other biofuels. The  steadily increasing use of biofuels in the United States has complicated the  production and distribution of gasoline. Biofuels such as ethanol are  produced at dedicated biofuel production facilities—not at refineries—and  currently cannot be transported by most petroleum product pipelines.
Therefore in order for ethanol to be blended with gasoline, it must be  shipped to the terminal by truck or rail, where it is then mechanically  mixed with gasoline as it is delivered into trucks for shipping to retail.
Gasoline with or without biofuels is typically sold as either branded or  unbranded. Branded gasoline is that supplied from major refiners and sold  at retail stations under these refiner’s trademarks, and often contains  special additives. Contracts for branded gasoline tend to be less flexible  than contracts for unbranded gasoline but guarantee a more secure  supply. Conversely, unbranded gasoline may be supplied by major or  independent refiners, but is not sold under a refining company’s  trademark. Buyers of unbranded gasoline may or may not have a binding  contractual arrangement with a refiner.
The supply infrastructure—which includes pipelines and terminals that  hold supply inventories—is a critical component of the nation’s petroleum  product market in that it facilitates the flow of crude oil and petroleum  products from one geographic region to another. Crude oil pipelines  connect several large refining centers to crude oil sources, and petroleum  product pipelines connect these refineries to population centers all over  the country. Thus, a disruption in one geographic region can affect the  supply and prices in another geographic region. To help mitigate the  effects of potential supply disruptions caused by refinery outages or  sudden increases in demand and to facilitate smooth supply operations,  refiners, distributors, and marketers of petroleum products maintain  inventories of crude oil and petroleum products. Inventories represent the  most accessible and readily available source of supply in the event of a  production shortfall, such as one caused by a refinery outage, or increase  in demand.
In October 2008, we reported that unplanned and planned refinery  outages across the United States did not show discernible trends in the  frequency or location of outages from 2002 through 2007, with the  exception of impacts beginning in 2005 related to Hurricanes Katrina and  Rita. During that study, however, we found that EIA does not collect  information on refinery outages directly and thus the information it  collects on its monthly refinery survey and uses to indirectly estimate  outages has a number of limitations. Specifically, EIA’s method of using  EIA-810 data to estimate outages cannot distinguish between planned and  unplanned outages, which could have different impacts on petroleum  product prices for consumers. Also, as we reported, because the monthly  refinery survey data are monthly aggregate data, major outages that  straddle the end of one month and the beginning of the next may be  difficult to identify and the observable effects of those outages could be  diluted. We further reported that the exact date and length of an outage  may be difficult to determine from EIA’s monthly refinery survey data,  making it difficult to use the data to determine whether a specific outage  had a significant effect on the production capacity for some petroleum  products as well as market prices.
Several U.S. agencies have jurisdiction over and monitor the U.S. refining  and supply infrastructure industries and petroleum product markets.
Within the Department of Energy (DOE), the Energy Information  Administration (EIA) collects and analyzes data, including supply,  consumption, and prices of crude oil and petroleum products; inventory  levels; refining capacity and utilization rates; and some petroleum product  movements into and within the United States. Much of the data that the  agency collects is obtained by surveys under EIA’s Petroleum Supply  Reporting System (PSRS). The PSRS is comprised of 16 data collection  surveys and includes, among others, weekly and monthly surveys of  refiners, terminals, and pipelines. The purpose of the PSRS is to collect  and disseminate basic and detailed data to meet EIA’s responsibilities and  energy data users’ needs for credible, reliable, and timely information on  U.S. petroleum product supply. EIA generally updates its PSRS surveys  every 3 years and has issued such updates in 2003, 2006, and 2009. EIA  also conducts analyses in support of DOE’s mission and in response to  Congressional inquiries. For example, EIA recently conducted its  semiannual forecast of planned refinery outage effects. EIA evaluates a  wide range of trends and issues that could have implications for U.S.  petroleum product trends and markets, and each year issues a publication  known as the Annual Energy Outlook.
The Environmental Protection Agency (EPA), among other things,  develops and enforces regulations that implement environmental laws that  aim to control the discharge of pollutants into the environment by refiners  and other industries. The EPA, with the concurrence of DOE, can grant  waivers on fuel requirements that allow petroleum product markets to be  more easily re-supplied should an “extreme and unusual” situation—such  as a problem with distribution of supply to a particular region, a natural  disaster, or refinery equipment failure—occur. In addition, EPA oversees  the Reformulated Gasoline (RFG) program. This program was developed  in response to a requirement in the Clean Air Act that cities with the most  severe smog pollution use reformulated gasoline—gasoline blended to  burn cleaner and reduce smog-forming and toxic pollutants in the air—to  reduce emissions. EPA is also responsible for implementing and issuing  regulations to ensure that gasoline sold in the United States contains a  minimum volume of biofuels, such as ethanol or biodiesel, and its reports,  according to EPA officials, are geared toward collecting data on fuel  quality which is enforced at the refinery. Under EPA’s Renewable Fuel  Standard (RFS) program, refiners, importers, and blenders are required to  use a minimum volume of biofuels each year, determined as a percentage  of the total volume of fuel the company produces, blends, or imports.  Entities that are unwilling or unable to meet this percentage standard may  purchase biofuel credits from other obligated parties in order to satisfy the  requirement. EPA monitors RFS program compliance and has the  authority to waive the standard if it determines that specified biofuel  volumes would cause severe harm to the economy or the environment in a  particular region, state, or the country or that there is an inadequate  domestic supply.
The Department of Transportation’s (DOT) Pipeline and Hazardous  Materials Safety Administration focuses on pipeline safety and establishes  standards for transmission and distribution systems for crude oil and  petroleum product pipeline. Among other things, it oversees pipelines’  design, maintenance, and operating procedures to maintain the safe,  efficient, and reliable delivery of petroleum products.
The Federal Energy Regulatory Commission (FERC) monitors energy  markets and regulates rates and practices of oil pipeline companies  engaged in interstate transportation of natural gas, oil and electricity. It  establishes and enforces the rates, known as “tariffs,” for transporting  petroleum and petroleum products by pipeline.
section_title: While Refinery Outages Can Have Large Price Effects on Rare Occasions, in Most Instances and on Average, Price Effects of Outages Are Relatively Small
paragraphs: While it can be expected that some refinery outages have quite large price  effects, the results of our analysis found that on average refinery outages  were associated with small increases in gasoline prices. Based on our  analysis of wholesale prices across 75 U.S. cities from 2002 through  September 2008, planned outages generally did not influence prices, while  unplanned refinery outages had generally small wholesale gasoline price  effects in the cities they serve. Price increases varied depending on  whether the gasoline was branded or unbranded and according to the  gasoline type affected by the outage.
subsections: section_title: Extreme Outage Events Can Lead to Large Temporary Price Increases
paragraphs: On rare occasions, refinery outages can have large temporary effects on  gasoline prices. For example, as we recently testified, petroleum product  prices increased dramatically following Hurricanes Katrina and Rita. This  occurred in part because many refineries are located in the Gulf Coast  region and power outages shut down pipelines that refineries depend on  for crude oil supplies and to transport refined petroleum products,  including gasoline to wholesale markets. DOE reported that 21 refineries  in affected states were either shut down or operating at reduced capacity  in the aftermath of the hurricanes. In total, nearly 30 percent of the  refining capacity in the United States was shut down, disrupting supplies  of gasoline and other products. Two pipelines that send petroleum  products from the Gulf Coast to the East Coast and the Midwest were also  shut down as a result of Hurricane Katrina. For example, Colonial  Pipeline, which transports petroleum products to the Southeast and much  of the East Coast, was not fully operational for a week after Hurricane  Katrina due to large-scale power outages and flooding. Consequently,  according to the Federal Trade Commission, average gasoline prices for  the nation increased 45 cents-per-gallon between August 29 and  September 5, 2005; short-term gasoline shortages occurred in some places;  and the media reported gasoline prices greater than $5 per gallon in  Georgia. The hurricane came on the heels of a period of high crude oil  prices and a tight balance worldwide between petroleum demand and  supply, and illustrated the volatility of gasoline prices given the  vulnerability of the gasoline infrastructure to natural or other disruptions.
While extreme outages can cause large temporary price increases, such  events were relatively uncommon during the period of our analysis. For  example, for unbranded prices, of the approximately 1100 unplanned  outages we evaluated, 99 percent of the time they were associated with  wholesale price increases of no more than about 32 cents-per-gallon, and  75 percent of the time they were associated with price increases of less  than 6 cents-per-gallon in the cities affected.
section_title: On Average, Price Effects Associated with Outages Are Relatively Small, and Depend on Key Factors
paragraphs: Overall, our analysis indicated that planned outages—where refineries  temporarily shut down to perform routine maintenance or equipment  upgrades—generally did not have a significant effect on wholesale  gasoline prices. As we reported in October 2008, planned outages are  typically scheduled during periods of less demand and interspersed among  refiners and refineries. In addition, the equipment and labor are generally  booked months—or even years—in advance, and can be arranged with  those customers with whom the refiners have long-term contracts at a cost  less than would be required in an emergency or unplanned situation.  Industry representatives told us that because a refinery must draw on a  limited number of equipment makers and skilled laborers, the refinery’s  plans for maintenance eventually become public knowledge. In this case,  the market “expects” the outage to occur, therefore planned outages do  not generally trigger significant price responses, unless something  unexpected occurs or the market is disrupted elsewhere. Furthermore,  refineries stockpile petroleum products in preparation for planned outages  and therefore do not experience the same shortage of production  materials experienced during unplanned outages.
Unplanned outages, on the other hand, were associated with gasoline  price increases but these increases were generally small and depended on  key factors, including whether or not the gasoline was branded or  unbranded and the type of gasoline being sold. With respect to the  distinction between branded and unbranded gasoline, our analysis showed  that in the event of an unplanned refinery outage, unbranded gasoline was  generally associated with greater wholesale price increases than branded  gasoline. Specifically, we found that for conventional gasoline—the most  common and widely available gasoline blend—unbranded gasoline had an  average 0.5-cents-per-gallon increase in price associated with unplanned  refinery outages, while branded gasoline had a smaller—about 0.2-cents- per-gallon—increase. The price effects observed in these cases reflect an  average increase in prices at the wholesale terminals in the 75 cities over  the study period. These results suggest that—as some traders and other  market participants have told us—during disruptions, refiners generally  choose to give priority in supplying those customers with whom they have  long-term supply contracts, which typically are for branded gasoline.  Therefore, in such conditions independent marketers—which typically sell  unbranded gasoline—may be forced to pay higher prices to obtain product  to sell. On the other hand, industry experts told us that unbranded sellers  may be able to buy wholesale gasoline at lower prices than branded sellers  during normal market conditions.
With regard to the type of gasoline fuel blend being sold, our analysis  shows that the price increases associated with an unplanned refinery  outage were significantly greater for 8 of the 19 “non-base-case” gasoline  types we identified than our “base case” conventional clear gasoline, while  the price increases for other gasoline types were generally about the same  as those of conventional gasoline. In our analysis, we selected  conventional gasoline as our base case and used our model to determine  whether there were significant differences between this base case and  other fuel types with respect to the relationship between unplanned  refinery outages and price changes. We looked at 19 other non-base case  fuel types that were in use in the 75 cities we reviewed. We compared the  results of these 19 other fuel types to our conventional gasoline base case  and measured the price differences. The price increases associated with  unplanned refinery outages for various branded and unbranded gasoline  types that were higher than our conventional gasoline base case are shown  in table 1.
The results suggest that some special fuel blends that include such  characteristics as unusual oxygenate requirements, lower RVP  requirements, or unusual oxygenate/RVP combinations may be more  sensitive to unplanned outages than other special fuel blends. For  example, for unbranded gasoline, the prices of some special fuel blends— such as CARB, conventional gasoline with oxygenate formulations such as  5.7 percent ethanol, or uncommon oxygenate/RVP formulations such as  conventional gasoline with 10 percent ethanol and a 7.0 RVP—were more  sensitive to unplanned refinery outages than conventional gasoline  without such specifications. Specifically, the largest price differences  between our conventional gasoline base case and special gasoline blends,  were for CARB without oxygenate and conventional gasoline blended with  10 percent ethanol and a 7.0 RVP. In these instances, prices were about 10- cents and 8-cents-per-gallon higher than our base case. The results show  that the prices of unusual oxygenate/RVP combinations that are not  commonly produced at most refineries may be more sensitive to  unplanned outages than conventional gasoline, which can be more readily  re-supplied to a city experiencing an outage.
Our analysis also shows that a number of other special fuel blends did not  experience significant price increases associated with unplanned refinery  outages above that of conventional gasoline, although the fuel types  affected depended partly on whether the gasoline was branded or  unbranded. These fuel types and the locations that require them are shown  in table 2.
Finally, it should be noted that individual outages may have different  effects on prices depending on a variety of factors beyond those discussed  above. As discussed previously in this report, and in work by EIA and the  California Energy Commission, under certain conditions—such as low  inventories, high seasonal demand, certain special fuel requirements, and  geographic conditions that may hinder easy re-supply to the market—an  unplanned refinery outage could be expected to result in a price surge in  some cases. However, in some cases, unobserved factors can mitigate the  effects of outages, or even cause prices to fall, making it appear as if the  outage caused prices to fall. For example, a large shipment of a particular  special fuel blend located just offshore or beyond the Canadian border  could be a significant source of re-supply in the event of a disruption. In  addition, while our analysis examined the effect of about 1,100 unplanned  outages and 1,000 planned outages, our model did not differentiate  between the types of refinery equipment that went out of service, which  could have varying effects on wholesale gasoline prices. For example, an  unplanned outage of a fluid catalytic cracker—a type of processing  equipment used to maximize the production of gasoline—could be  expected to have a more significant effect on wholesale gasoline prices  than an unplanned outage on a piece of equipment—such as a certain type  of hydrotreater—that is designed to maximize production of distillates  such as diesel fuel or heating oil. Because our model does not distinguish  between the type of unit experiencing an outage, our results show average  impacts across different types of refining units, which means we tend to  underestimate the effect of an outage at a unit such as a fluid catalytic  cracker, and overestimate that of a non-gasoline producing unit.
section_title: Gaps in Federal Data Constrain Analyses of Outage Effects and Other Related Issues
paragraphs: Existing federal data contain gaps that limit analyses of refinery outages  on petroleum product prices and in some cases do not reflect emerging  trends—although agencies continue to take steps to improve their data  collection. These data gaps created challenges to our, and another federal  agency’s, analyses and ability to respond to Congressional inquiries.  Specifically, we were limited in this report in our ability to fully evaluate 1)  the price effects of unplanned outages at individual cities and 2) a city’s  gasoline re-supply options in the event of an outage.
Our ability to fully evaluate the price effects of unplanned outages at  individual cities—for example, price effects in Atlanta, Georgia associated  with outages related to Hurricanes Ike and Gustav—was limited because  federal data do not link refiners to the cities they serve. Although federal  data exist regarding most refinery activities, the refiner-to-market link  contains key gaps.
While EPA’s annual reformulated gasoline area report requires each  refinery to identify the cities the refinery believes it supplies with  reformulated gasoline, this reporting is limited to reformulated gasoline.  As such, the reports do not capture the estimated refiner-to-city link for a  majority of gasoline types—including conventional gasoline and special  fuel blends—sold in the United States. Further, the reports are not  intended to identify the quantities of gasoline distributed.
EIA’s monthly refinery survey, the EIA-810, collects data regarding the  volume of certain petroleum products being produced at refineries,  including gasoline and unfinished gasoline blending components, but does  not distinguish among all types of gasoline, such as premium versus  regular or summer versus winter RVP, or identify which cities refineries  serve.
Our ability to identify a city’s gasoline re-supply options in the event of an  outage was also limited because of gaps in federal pipeline flow data.  Although we identified flow data collected at three agencies, the data were  of limited use because they did not show the volumetric entry, flow, and  exit of specific petroleum products through the pipeline. These specific  data are important to understanding which refiners can and cannot supply  various cities in the event of an outage and thus can be used to help  determine potential price impacts.
FERC’s quarterly reports by pipeline operators specify the number of  barrels of petroleum products pipeline companies transport, but these  data do not identify the entry and exit points of petroleum products along  the pipeline infrastructure system, or the specific type of fuels transported.
EIA’s monthly pipeline survey collects data on pipeline shipments between  Petroleum Administration for Defense Districts (PADD)—a geographic  aggregation of the 50 states and the District of Columbia split into five  districts—as well as pipeline inventories by PADD. However, data at the  PADD level do not correspond to particular cities and therefore the data  cannot be used to identify the states and/or cities in which petroleum  product flows originate and terminate.
DOT’s annual report on hazardous liquids collects pipeline flow data, but  DOT officials told us, and we also found, that these data are highly  aggregated and the annual collection of information is too infrequent to be  informative in many cases. Further, these data are not designed to show  the discrete movement of petroleum products through the pipeline  infrastructure.
To help address these gaps in federal data, we purchased commercial data  for our analysis from the energy consulting company Baker & O’Brien (see  app. I). These data estimate the quantity flows of gasoline and other  petroleum products produced at most U.S. refineries and transported to  those U.S. cities that make up the main markets for these products. While  we found the Baker & O’Brien data to be sufficiently reliable for the  purposes of our analysis, these data are estimates only. Although we  determined the commercial data that we purchased to perform our  analyses were sufficient to describe the wholesale price impacts  associated with refinery outages on various gasoline types, the data were  not sufficient to accurately estimate the effects experienced by individual  cities. Further, the comprehensiveness of the data we purchased was  limited in part because private companies do not have the same ability as  the federal government to require refiners to provide comprehensive and  accurate information.
Similar gaps in federal data also limited a recent effort by another federal  agency to fully address Congressional concerns regarding potential  pipeline constraints and agency concerns regarding refinery outages.
In a January 2009 Congressionally mandated study to identify potential  pipeline infrastructure constraints, DOT was unable to fully address the  study’s objectives due to the lack of appropriate federal pipeline flow and  petroleum product storage data. In its report, DOT noted that “a need  exists to develop more robust metrics for such (pipeline flow)  measurements.” The report also stated that “reliable data on storage  facilities is sparse” and emphasized the need for additional data on oil and  petroleum product storage terminals, including the location, size, and  volumetric capacity of existing facilities to assess whether stored  petroleum products are sufficient to mitigate supply disruptions. In  addition, the study noted that additional data regarding the changing  location and arrangement of petroleum product pipelines would be  necessary to evaluate volumes of petroleum products transported. DOT  concluded that an analysis sufficient to address Congress’s directives in  the 2006 law would require further quantitative and analytical modeling. In  particular, DOT officials told us the federal interagency effort to collect  data would need to result in more comprehensive data—including  volumetric pipeline entry, flow, and exit information—as well as more  reliable storage terminal and inventory data in order to more fully assess  the current and future reliability of the nation’s pipeline infrastructure and  ability to respond to market disruptions.
The absence of key data also limits the ability of federal agencies to  monitor the effect of emerging trends such as the use of biofuels—for  example, ethanol—in petroleum product markets. Specifically, we found  that gaps in federal data do not allow agencies to track where gasoline  blended with ethanol ultimately winds up in the fuel stream. Not having  this information may be at odds with consumer’s interests. Since,  according to EPA, a gallon of ethanol contains two-thirds the energy of a  gallon of gasoline, when gasoline blended with ethanol is sold in areas  with no ethanol or oxygenate requirement, consumers may be purchasing  fuel that provides fewer miles-per-gallon without being aware of it. Our  analysis of gasoline sales data shows that from 2002 through 2008,  conventional gasoline blended with ethanol had been sold in areas with no  ethanol or other oxygenate mandates in at least 32 states. Agency and  industry officials told us that as the volume of biofuels to be blended with  gasoline continues to grow to 36 billion gallons in 2022, ethanol will  increasingly be distributed in locations that do not have requirements for  oxygenate content.
Despite these gaps in federal data, individual agencies have generally  continued to take steps to update their data collection surveys to meet  their respective agency objectives or needs, and have often coordinated to  more efficiently obtain petroleum product data needed for a variety of  purposes at multiple agencies.
In 2009, EIA began collecting data regarding the production, stocks at  production facilities, sales for resale, and end-use sales of biodiesel fuel.  Also, three existing EIA forms were expanded to collect biodiesel imports  and biodiesel blending and stocks at terminals and refineries. Our work  indicates this new survey will help analysts identify how and where  biodiesel is being used, a key emerging trend in the petroleum industry. In  addition, these data will be used by EPA to help monitor the volumes of  biofuel use specified in the RFS.
Effective January 2009, EIA consolidated reporting of inventory  information at refineries, pipelines and terminals from two surveys to one.  This action will permit a more detailed and reliable analysis of petroleum  product terminal operations and provide a baseline for the volume of  petroleum products at various terminal locations that can potentially re- supply a city in the event of a major disruption. While this partially  addresses our need to have federal data that shows the re-supply options  in the event of a disruption, it neither shows the refiner-to-market link nor  does it provide detailed batch information on petroleum product flows  that would facilitate future analyses. Comprehensive inventory  information may be particularly useful to DOT should it be tasked with  completing another study to identify potential petroleum product  infrastructure constraints.
EPA officials told us they have worked with the Department of Agriculture  and DOE in recent years regarding the recently issued 2007 Renewable  Fuels Standard program guidance. The aim of such guidance is to monitor  biofuel use—a key emerging market trend—and monitor compliance with  biofuels specified in the RFS.
Nonetheless, in some cases the individual agency efforts have resulted in  the collection of information that does not necessarily meet the data needs  of other agencies or analysts who monitor petroleum product markets. For  example, federal reporting efforts have evolved such that EIA maintains  primary responsibility for collecting information on total gasoline supply,  including gasoline blendstocks, while EPA maintains primary  responsibility for capturing another key characteristic—RVP—of certain  gasoline blendstocks. Specifically, EIA’s surveys are structured to collect  data on total gasoline supply, including blendstocks, on a monthly basis,  whereas EPA collects RVP information on each batch of reformulated  gasoline on a quarterly basis, and for all conventional gasoline supplied by  a particular refiner on an annual basis. This means that companies report  key information regarding gasoline components to two different federal  entities, and analysts who need information regarding the blendstock and  RVP of gasoline must go to two federal entities to obtain what is available;  in addition, the data are not comparable in terms of periodicity. Finally, as  described earlier, three different agencies collect a limited amount of  pipeline flow data to meet their specific agency’s objectives, but  collectively these data do not allow analysts to fully monitor the flow of  petroleum product markets. This limited not only our ability to identify a  city’s gasoline re-supply options in the event of an outage in this analysis,  but also DOT’s efforts to fully address a Congressional mandate. In sum,  these separate pieces of data do not come together to form a complete  picture of current petroleum product markets.
section_title: Conclusions
paragraphs: To the extent reasonable, the collection of petroleum product data by  federal agencies should allow these and other agencies and analysts to  form a clear picture of U.S. petroleum product markets while minimizing  the government’s costs of collecting and maintaining, as well as the costs  to industry of providing, these data. In our work we identified gaps in  public data, some of which we could address by purchasing privately  collected data, and some of which led to limitations to what our analysis  could address. Specifically, we were unable, with publicly available data,  to identify which refiners serve various cities across the country, and by  extension, which refineries produce special fuel blends designed to meet  federal, state, and local requirements. While the available public data,  along with the commercial data we purchased, allowed us to analyze the  broad impacts of refinery outages on various gasoline types on average;  during the initial week of the outage, the data were not sufficient to  determine the effects at individual cities. We also found an absence of  some data on emerging market trends in biofuels that is troubling, given  the rapid expansion of biofuel production and use in recent years. Some  data gaps we identified may exist because data collection efforts generally  reflect individual agency needs and, thus, may not necessarily consider the  broader needs of other federal agencies and analysts. We recognize that  agencies have a primary responsibility to perform their individual missions  and that these agencies face their own specific budgetary constraints.  However, we note the importance of each agency acknowledging that the  collection of individual pieces of federal data contributes to a larger data  universe and taking reasonable steps to ensure that the totality of these  data allow for meaningful understanding and oversight of petroleum  markets. In addition, agencies must be conscious of efficiency by  considering the costs associated with gathering and maintaining data.
Improving the usefulness and completeness of publicly held data—as well  as reducing the associated costs—will require that each agency be aware  of the part of the overall data picture it is responsible for, as well as the  usefulness of these data beyond the immediate agency mission. Continued  and improved coordination between such agencies, including EIA, EPA,  DOT, and FERC, could improve the collective understanding and oversight  of the refining industry and petroleum product markets.
section_title: Recommendations for Executive Action
paragraphs: To evaluate existing, publicly held petroleum products market data and to  determine if they are sufficient to meet the current and expected future  missions and needs of the Congress, federal agencies, and other public and  private stakeholders, we recommend that the Administrator of the EIA  convene a panel comprised of agency officials from EIA, EPA, DOT,  FERC, and other relevant agencies, industry representatives, public  stakeholders, and other analysts and data users, to collect these data and  develop a coordinated interagency data collection strategy. The panel  should:    assess the costs and benefits of collecting    more systematic information about which refiners serve which cities  and    more discrete reporting of the volumetric entry, flow, and exit of  petroleum products through the pipeline infrastructure system; identify additional data that would be useful to track and evaluate  emerging market trends—such as the proliferation of biofuels and special  blends—and assess the costs and benefits of collecting such data; identify opportunities to coordinate federal data collection efforts so that  agencies can respond fully to Congressional requests and meet  governmentwide data needs to monitor the impact of petroleum product  market disruptions; and identify areas where data collection is fragmented—such as multiple  survey instruments collecting similar information—to determine if those  efforts can be consolidated and modified to enhance the overall usefulness  and improve the efficiency of collecting and reporting these data.
section_title: Agency Comments and Our Evaluation
paragraphs: We provided a draft of this report to the Department of Energy (DOE) and  its Energy Information Administration (EIA), the Environmental  Protection Agency (EPA), and the Department of Transportation (DOT)  for review and comment. DOE’s EIA agreed with our recommendations  and provided additional comments regarding the recommendations and  the report’s discussion of data gaps, which are summarized below. EIA  also provided technical and clarifying comments, which we incorporated  as appropriate into the report. EPA and DOT provided only technical  comments, which we also incorporated as appropriate.
Regarding our recommendations, EIA stated that it supports the  recommendations, including our specific suggestions to review data  adequacy, strengthen interagency coordination of data collection and use,  and fully engage government, industry and public stakeholders. EIA stated  that it believes it has a strong program to address all of these suggested  actions, and is working closely with other federal entities through  established joint programs, as well as informally to coordinate data  collection. For example, the agency noted it has been working with an  interagency group comprised of 40 federal agencies to facilitate the  development of a trade processing system for U.S. Customs and Border  Patrol.
In commenting on the report’s discussion of data gaps, EIA stated it agrees  that a review of possible data gaps is necessary and noted that it is  currently—as of July 2009—reviewing the adequacy and quality of  currently collected and commercially available refinery outage  information. The agency believes, and we agree, that the adequacy of  refinery outage data for analysis is one that EIA has taken seriously. To  this end, EIA noted it published Federal Register notices on December 9,  2008, and February 28, 2009, informing the public of the agency’s intended  review of refinery outage data. EIA plans to complete its review and  provide its recommendation regarding additional government data  collection this fall in its mandated semiannual refinery outage study. EIA  stated it then plans to publish its analysts’ assessment and  recommendations to solicit the broadest possible comment. At that time  EIA will consider the use of a panel of government, industry, and public  stakeholders—as we suggested—to determine its future steps. We support  EIA’s efforts to address data issues and believe that its current plans are a  step in the right direction toward ensuring that the best data are available  to help achieve its mission of producing independent and unbiased  research to help the Congress, public, and international community better  understand energy markets and promote sound policy-making.
We are sending copies of this report to interested Congressional  committees; the Administrator of the Energy Information Administration,  the Administrator of the Environmental Protection Agency; the Secretary  of the Department of Transportation; and other interested parties. This  report also will be available at no charge on the GAO Web site at  http://www.gao.gov.
If you or your staffs have any questions concerning this report, please  contact me at (202) 512-3841 or ruscof@gao.gov. Contact points for our  Offices of Congressional Relations and Public Affairs may be found on the  last page of this report. Major contributors to this report are  acknowledged in appendix III.
section_title: Appendix I: Scope and Methodology
paragraphs: We addressed the following questions during our review: (1) How have  refinery outages affected U.S. wholesale gasoline prices since 2002? (2) To  what extent do available federal data allow for the evaluation of the  impacts of refinery outages on petroleum product prices, and do these  data reflect emerging trends in petroleum product markets that may be  important to future analytical needs? For the purposes of this report, we  define the various types of outages as follows:  Planned outages are p processing units or possibly the entire refinery to perform maintenance,  inspection, and repair of equipment or to replace process materials and  equipment that have worn out or broken, in order to ensure safe and  efficient operations. eriodic shutdowns of one or more refinery  Unplanned outages are events where an entire unit or refinery must be  brought down immediately and without advance notice and are caused by  unplanned circumstances such as a fire or a power outage.
To determine trends in refinery outages over the time period from 2002  through September 2008, we purchased data from Industrial Information  Resources, Inc. (IIR) that contained detailed information on refinery  outages, including the estimated dates of the outages, whether the outages  were planned or unplanned, and the amount of reduced production  capacity due to each outage. We evaluated the data and found they provide  reliable estimates of outages from 2002 onward. In our analysis, we  counted an outage event as the halting of production capacity on any piece  of equipment at the refinery; where multiple units such as a crude  distillation and one or more secondary processing units were  simultaneously down, we counted this as a single outage event in our  model.
To evaluate how refinery outages have affected U.S. wholesale gasoline  prices we obtained and analyzed data from Energy Information  Administration (EIA)’s monthly refinery production survey form, EIA-810,  from 2002 through 2006, and other EIA surveys. We also purchased (1)  data that included detailed information on refinery outages between 2002  and 2008 from Industrial Information Resources, Inc. (IIR), a private  company that provides research and forecasts for various large industries;  (2) data estimating the quantity flows of gasoline and other petroleum  products produced at most U.S. refineries’ and transported to those U.S.  cities that make up the main markets for these products from Baker & O’Brien, an energy consultancy company whose software is licensed to  of the 10 top U.S. refining companies; and (3) weekly wholesale price da for 75 U.S. cities gasoline markets from Oil Price Information Service, a  private company that provides pricing and othe “rack” level. We determined that these data were sufficiently reliable for  the purposes of this report. We used the Baker & O’Brien quantity estimates to measure the proportion of each city’s product that is  generally supplied by a particular refinery. We developed, and extensivel tested, an econometric model that examined the statistical relationshi between refinery outa o their market region and that lasted at least 3 days, (2) had a correspondi market city in the Baker & O’Brien data, and (3) for which we had useful  and complete gasoline price data at the wholesale terminal l m which time we assumed that petroleum products were supplied from an  alternate source. As a result, our analysis evaluated the short-term effects ed.   of outages but did not evaluate the length of time those effects occurr In our model, we incorporated data on numerous factors that could affect  gasoline prices—such as gasoline inventory levels and gasoline  specifications—in order to rule out, or “control” for their effects on prices.  e  Because we were able to control for these other factors, we believe w were able to isolate the impacts of outages on prices given the inherent  issues with the various datasets. There were some factors that pote affected gasoline prices over time and city-specific information could not include, although we were able to use econometric techniqu to contro factors that affected gasoline prices, we were able to estimate the average  impact of outages on wholesale gasoline prices. The statistical significan of our findings are noted throughout the report. Although we focused our  study on wholesale prices, we cannot be certain that the price effects at  the retail level would be the same, although some research has shown th wholesale price changes are generally passed on to the retail level. In  r data at the wholesale or  ges and gasoline prices. We limited our analysis to  utages that (1) were determined to be of the largest 60 percent within  odel, we limited the effect of an outage on prices to one week, after  l for some of these factors. After controlling for the additional  at  developing our model, we consulted with a number of economists and  incorporated their suggestions wherever possible. Finally, we performed  an analysis to test the robustness of our model, including changing vari assumptions regarding the model in order to ensure that our results were  not highly dependent on any single specification of the model.
To assess the extent to which available federal data allow for the  evaluation of the impacts of refinery outages and determine whether the  data reflect emerging trends in petroleum product markets, we revieweddata collection instruments from federal agencies—including EIA,  Environmental Protection Agency (EPA), the Federal Energy Regulatory  Commission (FERC), and the Department of Transportation (DOT)—andreviewed them for comprehensiveness, utility, accessibility, and potentialgaps or limitations. In addition, we reviewed past GAO and other federa agency or intergovernmental agency studies on refined product markets to  identify data gaps, limitations, or inconsistencies. Finally, we interviewed key industry, expert institution, and academic representatives regarding  data limitations and utility in their own work and what other data  concerns or needs they might have for future analyses. Our work was not comprehensive evaluation of all federal energy data, but rather, an  assessment of key data GAO used in this and past reports, and select othe data that were determined during the course of our review to have posed  limitations for GAO’s or other agencies’ evaluations of important questions.
We conducted this performance audit from October 2008 through July  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.
section_title: Appendix II: GAO’s Quantitative Methodology for Determining Impacts of Refinery Outages on Wholesale Prices
subsections: section_title: Introduction
paragraphs: We developed an econometric model to explain the impact of refinery  outages on gasoline prices. Our model controlled for as many contributing  factors as possible, however, there were not always sufficient data  available to control for all possible factors affecting wholesale gasoline  prices. Our model examined how wholesale gasoline city rack prices were  affected in the week during which a large unplanned refinery outage  occurred.
section_title: Econometric Model Specification and Methodology
paragraphs: We examined weekly average data on wholesale city rack gasoline prices.  We used data from 75 wholesale city racks from January 2002 through  September 2008. We believe that the increased information from higher  frequency data—for example, by using daily data—would be outweighed  by the extra noise generated by such relatively high frequency data.  Further, using lower frequency data, such as monthly data, runs the risk of  obscuring some of the less extended but important effects of unplanned  outages on gasoline prices. Another limitation of our analysis is that, in  some cases, our data series for the control variables, described below, are  generally available only on a monthly basis, in which case these values  are assigned to the corresponding weekly observations. We consulted with  government and academic experts to help develop our econometric  model.
Our variable of interest was the price of gasoline, specifically the  wholesale rack price of gasoline.
Our dependent variable was the logarithm of the wholesale city rack price of gasoline. Note that we include a time dummy variable for every time  period so we do not have to deflate the wholesale price by a price index  such as the producer price index or the price of crude. 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. Our tests showed that our unbranded and  branded dependent variable was stationary in levels.
We examined 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 rack on a  iven date. In general, we used the wholesale rack price of gasoline that is  g required in that specific city because we were interested in determining  whether areas with non-standard gasoline specifications experienced  larger gasoline price increases when a refinery that supplied their  particular specification had an outage.
By including a complete set of time dummy variables–one for each week’s  observation in the data–our model controlled for factors that vary only  over time (and are invariant across cities), such as the national average  price level, the price of crude oil, and seasonal effects.
Explanatory Variables—Measuring the Impact of an Outage on Gasoli Prices  Our primary interest was to examine the impact of refinery outages on  gasoline prices. There are two key issues:  1.  Identifying an outage. We acquired data on outage occurrenc IIR. These data provide inform whether the outage was planned or unplanned, the date of the outage,  the duration of the outage, and the capacity of the unit that was offline  due to the outage.  ation about the outage, including  2.  Measuring the impact of a given outage on a particular city. Fo each city, we estimated the proportion of its product that it generally  received from each refinery; a city may be served by one or more  refineries.
Our measure of an outage’s impact that was generally supplied by the refinery (or refineries) experiencing an  outage. If a city was generally estimated to receive no product from the  refinery experiencing the outage, then the effect was zero, the explanatory  variable was zero, and the refinery outage had no impact on that city’s  gasoline price. Alternatively, if, for example, a city received 20 percent of  its product from said refinery, the explanatory variable had a value of 0.20  for that time period. It is also possible that a single city may have been  impacted by more than one refinery outage at the same time, so in that  case we would sum these effects. For instance, if in addition to the 20  percent impact example above, there was an outage at a refinery supply 10 percent of the city’s product, the explanatory variable would take a  combined value of 0.30.   was the proportion of a city’s product  In addition to the impact of outages, 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. Prices should decrease when inventories are  high relative to sales and should rise when inventories are low relative to  sales. However, inventories and sales may themselves respond to changes  in wholesale gasoline prices, so this variable may be endogenous.
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, there would be more  gasoline available, which would tend to lower prices; conversely if  utilization rates are low, less gasoline would be available, wh ich would  tend to raise prices. However, it is possible that as utilization rates  approach very high levels, there are significant increases in cost of  production, which could then result in higher prices. Further, capacity utilization may react to changes in gasoline prices, so it is possible that  this variable is endogenous.
Market concentration. Markets with fewer sellers of product or that are  more highly concentrated, may be associated with higher gasoline prices However, the direction of effect may run the other way too, such that  markets with higher prices may attract entrants, which may reduce the  level of market concentration. We treat market concentration as an  endogenous variable. .
Lagged dependent variable. Our model includes lagged values of the lef hand side variable; namely, the logarithm of the wholesale price of  gasoline. Gasoline price data may be serially correlated and it is  reasonable to include the effect of past gasoline prices on current gasoline  prices.
Time fixed effects. We included a dummy variable for each time period in  the analysis.
City fixed effects. We included a dummy variable for each city in the  analysis. These city fixed effects may assist in controlling for unobserved  heterogeneity.
Product specification. We included a dummy variable for each of the  different types of gasoline used in our model.
Interaction between the product specification dummy variables and the  outage impact variable. We included a set of interaction terms to test  whether cities that with special fuel requirements experience higher price  increases due to outages. y it is the logarithm of wholesale rack gasoline price at city i in week t.  xit is a vector of predetermined variables for city i in week t that are assumed to be independent of our error term, u it, including a lagged of our dependent variable.  wit is a vector of possibly end ogenous variables at city i in week t. c  is the fixed effect or dummy variable for city i.  f is the fixed effect of dummy variable for week t.
B is a vector of parameters to be estimated.
Our key outage effect variable measures the percent of a city’s product  supply affected by an outage; that is:  where Outage r efinery that serves the i-th city, and the remaining term is the proportion  of product provided by that refinery to that city. When there is no outa ge,   Outageir’t is equal to zero. Thus, this variable measures a city’s reduction inan outage (or outages). In the extreme case, there may be a  product due to  s the impact on product of an outage at that refinery on that city would b large, with a concomitant effect on that city’s gasoline prices.  ir’t is equal to 1 when an outage occurs at time t in the r’-th  ingle refinery that supplies 100 percent of a city’s product, in which case  The outage impact may also have varied according to the type of fuel. The  variable, sirt measures the percentage of supply of product that was  interrupted; it may not account completely for the difficulty in finding a  replacement for that product. If a city used a fuel that is commonly  produced, such as conventional clear gasoline, it would likely be more  straightforward to find an alternative source of supply. However, if the city  uses a special fuel, it may be more difficult to find an alternative refinery  to supply that product. Therefore, in addition to a set of dummy variables  for each fuel specification, we included a set of interaction terms of our  outage supply affect variable with each of the fuel specificati n dummy o variable s.
We used xtiverg2 in STATA. The xtivreg2 estimation procedure allow us to estimate standard errors that are robust to heteroskedasticity and  autocorrelation. ent  We estimated the model using the logarithm of price as the depend variable. Note that because we have time dummies, we do not need to  control for crude oil prices or price inflation beca invariant across cities for a given time period and so are collinear with the  time dummies. Our specification necessarily subsu variables that only vary over time and not vary across cities.
We used a C-statistic test to ascertain whether the and the capacity utilization rate should be treated as endogenous or  exogenous. In the case of both the unbranded gasoline prices and the  branded gasoline price models, our test could n hypothesis that these variables were exogenous.
Measures of market concentration, such as the Hirschman Herfindahl  Index (HHI), have been shown to be endogenous, so we tested for  whether it was exogenous and use two-stage least squares when  appropriate, using merger events as instruments. We used a C-statistic to  test for the exogeneity of the spot market HHI. In the case of the  unbranded gasoline price model, the test rejected the null hypothesis of  exogeneity. In the case of the branded price model, the test could not  reject the null hypothesis of exogeneity. We estimated both models  treating the spot market HHI as endogenous, which we recognize might be a less efficient estimator but is nevertheless a consistent estimator.
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-sta accepted the null hypothesis that our instruments were valid. tistic    We estimated separate models for unplanned and planned outages. While unplanned outages can be reasonably viewed as exogenous—random— events, planned outages need to be scheduled more than a year in advance  a nd may be scheduled to coincide with time periods of typically lower  seasonal demand. Therefore, we believe it was appropriate to model th two types of outages separately.
We estimated separate models for unbranded prices and branded prices.
We estimated the model (1) except that we dropped those ob here waivers were in effect.
We found increase in unbranded gasoline prices. We found this impact is generally  positive with respect to the price of all fuels. We further found this impact  is significantly greater than the c relative to the effect on conventional clear) for  unplanned outages were significantly associated with an  omparative or base e severa ffect (me l special  ed  .
In addition, we found unplanned outages were significantly associated  with an increase in branded gasoline prices but the effect was  for unbranded prices. This impact is generally positive with respect to th price of all fuels. There is also evidence that th special fuels although in fewer cases compared to the unbranded price  results. e impact is greater for som   Our results using planned outages to explain prices statistically significant impact on gasoline product prices, either branded  or unbranded.  found no general    We found no substantive difference in our results for outage effects when  we estimated the model (1) without those observations where waivers  were in effect.
section_title: L Econometric Model and Data
paragraphs: Cities included in model. Our selection of cities  availability from the Baker & O’Brien data. T wholesale racks in the U.S. however:  aker & O’Brien data contain data on only 89 cities, and only 75 of  those had complete series dat a that we could use for our model.   & O’Brien cities comprise the most important city racks.
Treating each of the 35 0 city racks as independent rack markets may  not be appropriate. Rather, we can obtain a national picture by  selecting the most important cities as per the list of cities in the Baker  & O’Brien data.
Time period of analysis. We se 2008 because we deemed 2002 onward. lected January 2002 through September   the IIR data to provide reliable information from  asoline type. The gasoline data from OPIS were selected so as to  G generally reflect the type of gasoline that would be sold in a city, giv 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.  e  However, in the regression model, we control for whatever fuel type w did use.
Outages data. We believe the outages data from IIR are fairly  comprehensive but there are no federal requirements for refineries to  report outages or an effort by the federal government to collect these data  on a national basis. Consequently, some outages may not appear in the  data, though it is unlikely that any major outages were missed during our  study period. Further, we limited inclusion of outages to those that were at  least 3 days in duration and ranked in the top 60 percent in terms of  recorded capacity offline for a refinery’s market region (as defined by IIR) Thus, we do not include every single outage but we have a broad  geographic range of the largest outages in the US. . inkage data between refineries and cities they serve. The Baker &  ’Brien data has the following limitations:    They are quarterly estimates of product flows and costs. These data  are intended to be reasonably reflective of actual product dispersion across the United States. However, in the course of our analysis we  had to interpolate some missing data and to extrapolate our data  beyond the end-points of the available data.
The Baker & O’Brien data did not always contain complete data for the particular fuel that regulations req cases, seasonal variations in fuel requirements, such as RVP or  oxygenate blending specifications, meant a precise match was not  uired be used in that city. In some  possible. However, in general, we were able to match the Baker & O’Brien fuel with these regulations.
Frequency o our other data were either monthly or quarterly, so we had to parse out th lower frequency observations accordingly.  f data. Except for our weekly wholesale gasoline price data,  Geographic level of a but some of the data we used were at a more aggregated geographic leve We used capacity utilization and inventory-sales ratio data at the PADD  level. We did not have a measure of city-level sales data to determine the  e to a local market, nor is there a relevant  size of inventories relativ measure of capacity utilization at city-level, therefore, PADD-level d were used.,  nalysis. Our analysis was performed at the city level,  l.
Economic indicators. Employment growth, personal income growth rate and the unemployment rate were available at the state-level only.
Market con measured using corporate refinery capacity at the spot market possible in some cases these measures were too highly aggregated and control variables were less precise than would be ideal.  centration. Our measure of market concentration was an HHI  Number of outages. We did not take account of multiple outages at the  same refinery on the same day–we simply established whether an outage  occurred in a particular week, at a particular refinery. Although the size o the outage determined whether it was included in our analysis, the impac is treated the same regardless of how large an impact on the refinery theoutage had.
Effects of an outage over time. effects of outages on prices in our model. We assigned an effect of outage  in the same time period (week), after which time our model implicitly  assumed that the product was supplied from an alternate source.
We did not attempt to include dynamic  Planned outages. We did not model planned outages in any detail. Thes planned events by definition, did not gen reductions in product supply. Hence, vendors had the opportunity to plan  ahead and make arrangements to receive alternative sources of product.  del to equation (1) and found  However, we did estimate an analogous mo no significant impact on prices.  erally give rise to surprise  Inventories. Inventories included those domestic and customs-cleared  foreign stocks held at, or in transit to, refineries and bulk terminals, and  stocks in pipelines.
Gasoline sold outside the city rack: Our analysis does not account for  gasoline that is not sold at the city rack. It is possible that significant  transactions occur elsewhere that may affect the general wholesale  market for a particular city.
Examining wholesale prices, not retail prices. Our analysis is at the  wholesale price level and the ramifications for retail prices are unclear.  The effect on retail prices would depend upon the extent to which  wholesale price changes are passed onto the retail sector.
Seasonal effects. Our model included of a set of time dummy variables,  which account for variation in prices due to seasonal effects. A more  complete model might have contained specific seasonal effects such as a  set of monthly dummy variables, interacted with the outage effect, and  also with each special fuel type. This would have allowed us to determine  whether outages had a differential impact on prices, according to the time of year and the fuel type. However, data limitations precluded a  comprehensive evaluation of such effects; specifically, this would have  required us to include more than 200 additional explanatory variables  (number of seasonal dummies times the number of special fuel types).
section_title: Appendix III: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
section_title: Staff Acknowledgments
paragraphs: In addition to the individual named above, Shea Bader, Divya Bali,  Benjamin Bolitzer, Dan Haas, Michael Kendix, Rob Marek, Michelle Munn,  Alison O’Neill, Rebecca Sandulli, Benjamin Shouse, and Barbara  Timmerman made key contributions to this report.