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

Letter

Background

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.

While Refinery Outages Can Have Large Price Effects on Rare Occasions, in Most Instances and on Average, Price Effects of Outages Are Relatively Small

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.

Extreme Outage Events Can Lead to Large Temporary Price Increases

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.

On Average, Price Effects Associated with Outages Are Relatively Small, and Depend on Key Factors

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.

Gaps in Federal Data Constrain Analyses of Outage Effects and Other Related Issues

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.

Conclusions

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.

Recommendations for Executive Action

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.

Agency Comments and Our Evaluation

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.

Appendix I: Scope and Methodology

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.

Appendix II: GAO’s Quantitative Methodology for Determining Impacts of Refinery Outages on Wholesale Prices

Introduction

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.

Econometric Model Specification and Methodology

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.

L Econometric Model and Data

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).

Appendix III: GAO Contact and Staff Acknowledgments

GAO Contact

Staff Acknowledgments

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.