Estimating the impact of refinery outages on petroleum product prices

Energy Economics 32 (2010) 1291–1298

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Energy Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e n e c o

Estimating the impact of refinery outages on petroleum product prices☆ Michael Kendix a, W.D. Walls b,⁎ a b

U.S. GAO, United States University of Calgary, Canada

a r t i c l e

i n f o

Article history: Received 19 October 2009 Received in revised form 20 May 2010 Accepted 31 May 2010 Available online 9 June 2010 JEL classification: L71 Q4 Keywords: Refinery outages Gasoline prices Boutique fuels

a b s t r a c t We quantify the impact of refinery outages on petroleum product prices. The empirical analysis focuses on wholesale gasoline prices in the US using weekly data collected from January 2002 through September 2008, a period including many refinery outages. We match refinery unit output to specific wholesale gasoline markets, and then estimate panel data regressions to quantify the impact of refinery unit outages on wholesale gasoline prices while controlling for time-specific effects, city-specific effects, fuel-specific effects, refinery concentration, and other factors that could impact the price of refined petroleum products. The estimation results show that refinery outages have a statistically significant positive impact on refined product prices, and that the magnitude of this effect is larger for certain special fuel blends. Policy implications are discussed. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Unplanned outages at petroleum refineries—the industrial plants that transform crude oil into liquid transportation fuels such as gasoline, diesel fuel, and jet fuel—have caused major disruptions to transport fuel supplies in North America, which in turn have been associated with volatility in fuel prices. An interesting empirical and policy-relevant question is the extent to which prices for refined petroleum products are affected by unplanned refinery outages. The causes of particular refinery outages are many. Extreme weather events, such as hurricane Katrina in 2005, can cause major disruptions to refining operations: that hurricane disabled some 25% of US refining capacity, causing prices of refined products to spike regionally as well as having market impacts on product prices and flows as far away as Canada and Europe. More geographically isolated weather events, as well as other refinery-specific idiosyncratic events such as fires and equipment failures, can also have impacts on refining operations causing palpable supply disruptions.1 Refinery outages

☆ The views expressed in this paper are those of the authors and are not to be attributed to their employers. ⁎ Corresponding author. Department of Economics, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4. E-mail address: [email protected] (W.D. Walls). 1 For example, a severe storm in July 2009 led to the shutdown of the Petro-Canada and Imperial Oil refineries in Alberta, Canada causing about 15% of Petro-Canada's retail outlets in that province to run out of gasoline and necessitating emergency shipments from as far away as Montreal. Other Alberta refinery outages led to shortages of gasoline in Western Canada in Summer 2008 and a shortage of diesel fuel in Fall 2008. 0140-9883/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.eneco.2010.05.016

can have important economy-wide impacts—especially in North America—because the transportation infrastructure in many parts of the world is fueled by the finished petroleum products refined in close proximity to demand centers. Interest in studying pricing in markets for finished petroleum products, especially gasoline, is not new. For example, a number of academic studies have examined in detail the market for gasoline (for example 3,4 and 20). Although these previous studies provide useful guidance on how to model markets for liquid transportation fuels, none of them consider the impact of refinery outages on the market for refined petroleum products. The 24 produced an analysis using aggregate data showing that while unplanned outages can disrupt local supplies and lead to elevated prices, they do not always have price impacts. The analysis in this paper builds upon the previous research by using more finely disaggregated data. The specific empirical work in this paper is focused on wholesale markets for gasoline in the US. This market provides a data set rich in the variation required to reliably estimate the parameters of a statistical model. We observe weekly fuel prices from 2002 to 2008 across seventy-five wholesale fuel terminals. During this time there were a large number of refinery outages during which fuel prices varied considerably both across terminals as well as through time. We find that refinery outages in the US have had a very small positive (and statistically significant) impact on wholesale gasoline prices. However, the impact of refinery outages is substantially larger for special ‘boutique’ fuels than it is for conventional clear gasoline. A more fungible set of fuels would increase the capacity of the supply infrastructure and this would serve to mitigate the price impacts of idiosyncratic refinery outages.

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Table 1 Major data sources and description of variables. Variable name

Narrative description of variable

Source of data

Terminal price WTI price Outage %

Wholesale gasoline price in cents per gallon Price of West Texas Intermediate crude oil in dollars per barrel Percentage of the usual amount of supply at wholesale terminal affected by refinery outage(s), varies across terminals and through time Herfindahl index of market concentration, measured by refinery capacity of companies in each spot market Set of dummy variables for the specific blend of gasoline. Disaggregated by fuel blend, specification, and Reid vapor pressure

OPIS EIA Calculated. IIR Inc. and Baker & O'Brien data Calculated

Spot market HHI Fuel-specific indicators

OPIS

OPIS—Oil Price Information Service. EIA—Energy Information Administration.

Data collection was a significant component of the research and is described in detail in Section 2. In Section 3 an econometric model of refined product prices is developed. The estimation results are presented and discussed in Section 4, and Section 5 concludes.

2. Building the data set Much available data on the US petroleum industry, such as the wealth of information collected and reported by the US Energy Information Administration, are aggregated geographically to the level of regions known as PADDs or Petroleum Administration for Defense Districts. The aggregation of the 50 US States and the District of Columbia into five PADD Districts, while useful in many respects, does not facilitate a systematic empirical analysis of how individual refinery outages affect specific wholesale fuel markets.2 In this study we build a more finely disaggregated data set to examine the linkages between petroleum refineries and wholesale fuel markets. Table 1 displays a brief description of the main variables used in the empirical analysis and their sources. We shall now discuss in some detail the collection and construction of the variables.

2.1. Wholesale fuel prices The Oil Price Information Service (OPIS) reports wholesale gasoline price data for more than 300 city terminals located in the US. Because many cities with wholesale terminals are in close proximity, they may not represent independent markets. For this reason, we use a subset of cities in the most relevant and important areas needed to model product flows from refineries to wholesale markets.3 At each of the fuel terminals listed in Table 2, we collected weekly average data on wholesale prices for both branded and unbranded gasoline. The OPIS data may list numerous fuel prices for a particular city's terminal, especially for terminals that are in close proximity to a major refining center or at a major pipeline hub. We constructed a single price series for each wholesale city terminal that represents the price of the fuel actually used in that city in that particular week. Because the fuel required in any given market varies across summer and winter seasons—and because the fuel use regulations vary across years in some cities—the price series for each city will typically represent multiple fuel types. The data contain variation in fuels within city markets as well as variation across city markets, and this

2 Energy statistics aggregated to the PADD level have been used in several empirical studies of US gasoline markets, such as those of 19 and 16, but even those excellent studies would have been much improved if more disaggregated data had been available. 3 The selected cities are from the Baker & O'Brien set of product flow data, required to link terminal markets to refineries. The cities selected by Baker & O'Brien are the key market cities in this industry. Indeed, Baker & O'Brien selected them explicitly for the purpose of estimating flows for refined products to wholesale markets.

richness will be exploited by the cross-sectional time-series statistical model discussed in Section 4. 2.2. Identifying refinery outages Data on specific outage occurrences were obtained from Industrial Information Resources, Inc. These data provide information about the outage, including the date of the outage, the length of the outage, the capacity of the affected unit(s), as well as the type of unit affected. Because our product price data are observed with a weekly frequency, our analysis excludes outages with a duration of less than three days. 2.3. Linking outages with wholesale terminals In order to analyze the impact of outages on gasoline prices, we must first determine which cities' wholesale racks are served by each refinery experiencing an outage. Any given city may be served by several refineries depending on the particular gasoline product type. Cities with standard gasoline requirements may be able to receive product from a larger number of refineries. In contrast, cities requiring special fuel blends to meet local fuel requirements may rely on only one or two refineries to supply their needs. Further, the situation is more complex in the sense that in theory, any refinery could hypothetically supply any city—the issue being one of time and cost— although in practice the number of potential suppliers would be more limited. We use data on estimated flows of refined products from Table 2 City-terminal markets included in empirical analysis. State/terminal city

State/terminal city

State/terminal city

AK/Anchorage AK/Fairbanks AL/Mobile AR/El Dorado AR/Ft Smith AZ/Phoenix AZ/Tucson CA/Los Angeles CA/Sacramento CA/San Diego CA/San Francisco CO/Denver FL/Miami FL/Tampa GA/Atlanta IA/Des Moines ID/Boise IL/Champaign IL/Chicago IL/Robinson IL/Rockford IN/Indianapolis KS/Kansas City KY/Louisville KY/Paducah

LA/Baton Rouge LA/Lake Charles LA/New Orleans MD/Baltimore MI/Bay City MI/Detroit MN/Minneapolis MO/Columbia MO/Springfield MO/St Louis NC/Greensboro ND/Fargo NE/Omaha NJ/Newark NM/Albuquerque NM/Bloomfield NV/Las Vegas NV/Reno NY/Albany NY/Syracuse OH/Cincinnati OH/Cleveland OH/Columbus OH/Lima OH/Toledo

OK/Oklahoma City OK/Tulsa OR/Portland PA/Harrisburg PA/Philadelphia PA/Pittsburgh SC/Spartanburg SD/Sioux Falls TN/Knoxville TN/Memphis TN/Nashville TX/Amarillo TX/Beaumont TX/Corpus Christi TX/Dallas TX/El Paso TX/Houston TX/Tyler UT/Salt Lake City WA/Seattle WA/Spokane WI/Green Bay WI/Madison WI/Milwaukee WY/Cheyenne

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Baker & O'Brien to match refinery suppliers with each of the cities in our analysis. The key outage variable we calculate measures the percent of a city's product supply affected by an outage Sir′ t = Outageir′ t ×

Qir′ t ∑Rr = 1 Qir′ t

ð1Þ

2.4. Controlling for market structure We constructed the Herfindahl–Hirschman Index (HHI) of the concentration of refining capacity. The HHI was calculated using shares of refinery company capacity in each spot market.4 Spot markets, in contrast to PADD regions, reflect the refining industry's own grouping of US refineries into seven refining centers: Los Angeles, San Francisco, the Gulf Coast, New York Harbor, Chicago, Tulsa (or Mid-continent), and the Pacific Northwest. Additionally, Alaska is defined as a separate spot market.5 Gasoline production and deliverability in these spot markets drive the pricing of gasoline that is bought and sold at wholesale terminals. Our analysis uses the allocation of individual refineries to refining industry spot markets reported in 9. The numerical calculation of the HHI is as follows: There are S spot markets, each containing Fs refining firms, s = 1, 2, …, S. A single refining firm may own more than one refinery within a spot market. Let cfst denote the refining capacity of firm f at time t in spot market s. The HHI for each spot market is calculated as Fs

2

HHI = ∑ cfst ; s = 1; 2; …; S and t = 1; 2; …; T f =1

Table 3 Description of fuel blends and attributes. Fuel or attribute

Description

CARB

California Air Resource Board gasoline, special low-emissions blend of gasoline refined for the California market Clean burning gasoline, alternative special low-emissions blend of gasoline Regular clear gasoline, baseline blend of gasoline in emissions attainment areas Reformulated gasoline, federal low-emissions blend of gasoline used in emissions non-attainment areas Ethyl alcohol, used as an oxygenate to promote more complete combustion Methyl tertiary butyl ether, used as an oxygenate during much of the sample period Reid vaporization pressure, a measure of the tendency for a liquid to evaporate

CBG Conventional

where Outageir′t is equal to 1 when an outage occurs at time t in the r′th refinery that serves the ith city, and the remaining term is the proportion of product provided by that refinery to that city. When there is no outage, Outageir′t is equal to zero. Thus, this variable measures a particular city's reduction in product due to a refinery outage (or outages). Observe that the outage variable accounts explicitly for the possibility of multiple refinery outages affecting any given city in any single week. The outage impact may also have varied according to the type of fuel. The variable Sir′t measures the percentage of supply of product that was interrupted, but 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 will also include in our regression analysis a set of interaction terms of our outage variable with each of the fuel specification dummy variables which is discussed below.

ð2Þ

where each city rack i is assigned to the spot market s in which it is located. 2.5. Special “boutique” fuels In addition to the variables of primary interest, the model controls for other important factors related to the price of gasoline in a given

4 An alternative way of quantifying market structure is to simply count the number of competing suppliers at a wholesale terminal as is done by 20 and 6. We feel that it is more appropriate to have a measure of market structure based on a weighted average of supplier capacity instead of using an unweighted average based on quantity supplied. 5 Refineries in Alaska and Hawaii primarily supply their own regions. Hawaii was not included in our analysis due to lack of wholesale price data.

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RFG Ethanol MTBE RVP

location at a given time. Earlier work has shown the importance of controlling for specific fuel types in explaining the level and variation in wholesale gasoline prices at terminals (23; 16; 9; 6; 25). There are dozens of different types of gasoline sold across the US at any particular time, and the particular type of gasoline used in a given city can change over time. Table 3 lists the main fuel blends, oxygenates/ additives, and other specifications. Through various combinations of the fuel type, the concentration of additives, and other attributes, a large number of distinct fuels are created. The particular fuel types observed in our data set and the variables we use to identify each fuel are set out in Table 4. In all, there are some 20 different gasoline types observed in our sample; the empirical analysis will control for this variation by including a dummy variable for each unique fuel, taking conventional clear gasoline as the base category.

Table 4 Specific fuels controlled for in statistical analysis. Fuel label

Blend, specification and RVP

B0 Bg0 Ce0 Ce7 Cm0 Cm7 Cm8.2 Cn0 G0 G7 G7.2 G7.8 G8.2 G9 Ge0 Gf0 Gf9 Gg0 Gg7 Gg7.8 Gg9 L0 L7 L9 Rt0 Rg0 Rg8.2 Rm0 Rm7 Rm7.2 Rm8.2

CBG CBG with 10% ethanol CARB with 5.7% ethanol CARB with 5.7% ethanol 7.0 RVP CARB with MTBE CARB with MTBE 7.0 RVP CARB with MTBE 8.2 RVP CARB with no additive Conventional (base category) Conventional with 7.0 RVP Conventional with 7.2 RVP Conventional with 7.8 RVP Conventional with 8.2 RVP Conventional with 9.0 RVP Conventional with 5.7% ethanol Conventional with 7.7% ethanol Conventional with 7.7% ethanol & RVP 9.0 Conventional with 10% ethanol Conventional with 10% ethanol & RVP 7.0 Conventional with 10% ethanol & RVP 7.8 Conventional with 10% ethanol & RVP 9.0 Low sulfur Low sulfur 7.0 RVP Low sulfur 9.0 RVP RFG with 5.7% ethanol RFG with 10% ethanol RFG with 10% ethanol & 8.2 RVP RFG with MTBE RFG with MTBE & 7.0 RVP RFG with MTBE & 7.2 RVP RFG with MTBE & 8.2 RVP

Source: Oil Price Information Service.

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2.6. Inventories and refinery capacity utilization

Table 5 Descriptive statistics on planned outages by spot market.

Product inventories can affect the availability of gasoline at the wholesale level and affect contemporaneous prices. Refinery capacity utilization can also affect the wholesale price of gasoline through changes in the availability of supply. The precise relationships between inventories, capacity utilization, and finished product prices may be complex and dynamic with product prices possibly having a causal impact on these other variables. In the statistical analysis to follow, we allow for the possibility that both of these variables are jointly determined with finished product prices.

Spot market

Minimum

Median

Maximum

Mean

Std dev

Alaska Mid-continent Gulf Los Angeles NY Harbor Northwest San Francisco

33.080 0.430 0.148 0.868 2.670 2.543 7.394

62.817 17.813 12.409 14.031 65.349 21.638 24.784

100.000 89.189 100.000 66.166 70.513 46.889 100.000

64.678 22.274 18.284 17.439 50.545 23.095 27.781

33.758 16.059 18.201 13.739 26.277 11.475 13.030

Descriptive statistics on percent of unbranded terminal market supply affected by refinery outage, given that an outage affecting a terminal within the spot market occurred.

3. Statistical model of refined product prices The purpose of the statistical model is to quantify how finished product prices—in particular the price of gasoline at wholesale terminals—are affected by outages at petroleum refineries, controlling for exogenous and possibly endogenous factors that may also impact refined product prices. Because we will examine data from a crosssection of 75 wholesale fuel terminals located across US cities for 352 weeks, we employ a statistical model especially suited to exploit the variation in pooled cross-sectional time-series data: Pit −WTIt = β′ ðxit ; wit Þ + δi + μt + it

Percentage of terminal supply affected

ð3Þ

where Pit − WTIt is the wholesale price of gasoline less the price of the crude oil input—also known as the crack spread—in the ith city at time t; xit is a vector of pre-determined control variables that are independent of the random disturbance it; and wit is a vector of control variables that are possibly endogenous such as refinery capacity utilization, stocks of finished products, and industry concentration; and β is a vector of parameters to be estimated. The explanatory variables contained in x and w may not account for all conceivable factors affecting city-terminal prices. With this in mind, the model explicitly contains effects δi for each individual city terminal i, and μt for each distinct time period t. The former account for otherwise unobservable effects that are assumed to be constant over time and which affect individual city terminals—such as markets that are geographically isolated—and the latter for effects that are constant across cities but vary over time—such as weather-related or geopolitical supply disruptions. In our empirical application we will have a large number of cross-sectional units—75 city terminals—that we will observe at a weekly frequency over a period of nearly 7 years. Our dependent variable captures the variation in the crack spread at the level of city-specific terminals. Most empirical studies related to wholesale gasoline markets have used the city-level terminal as the unit of analysis; see, for example, the studies by 3,4 and 20. Because we have included a time dummy variable for every week of the sample, there is no need in our equation to deflate the wholesale price by a price index or measure the price relative to the price of crude oil since those variables vary only through time and not across city-terminal markets.6 In the fixed-effects formulation used in our statistical analysis, we will make inferences that are conditional on the terminal-specific and time-specific effects in the sample of data (13). In practice, the choice between fixed-effects and random-effects models can be a difficult issue because parameter estimates sometimes differ significantly when a large number of cross-sectional units are observed over a small number of time periods (11). In our application, we observe prices across 75 terminals for 352 weeks, and the usual effect of having a large number of time observations for the cross-sectional 6 Given the time-specific fixed effects in the regression specification, the crack spread model is essentially the same as a nominal price model because the time effects are perfectly correlated with WTI crude prices or any other explanatory variable that does not vary across city terminals. Thus, treating the dependent variable as the logarithm of the nominal city-terminal price yields an equivalent result and simplifies the interpretation of the estimation results.

units is that the fixed-effects and random-effects estimates will be nearly identical. Given the context of our statistical analysis, making statistical inferences conditional on the city-terminal-specific effects in the sample seems appropriate, as the city effects are likely to remain fixed for the wholesale terminals instead of being randomly reassigned in repeated samples. The parameters of the model can be estimated using least squares, an instrumental variable estimator (such as two-stage least squares), or a general method of moments (GMM) estimator. The choice of estimator depends on the presence of endogenous regressors and arbitrary heteroskedasticity. Our regression equation contains an independent variable that measures market concentration, the Herfindahl–Hirschman Index (HHI). It is well-known that in regressions of price on market structure, estimates maybe biased if market structure is treated as exogenous (8). Also, some of the other explanatory variables—namely gasoline inventories and refinery capacity utilization—may be determined simultaneously with gasoline prices. In the following section, we test for the endogeneity of the suspect explanatory variables. We also perform other diagnostic tests relating to the appropriateness of the instrumental variables chosen and the presence of heteroskedasticity.7 4. Estimation procedure and discussion of results Prior to estimating the basic instrumental variable fixed-effects model set out in Eq. (3) we investigated the time-series properties of our dependent variable, the city-terminal ‘crack spread’ Pit − WTIt. Many empirical studies that use high-frequency time-series price data find evidence of a unit root in the price series and this requires econometric methods that explicitly account for the time-series properties of the data. We tested for the presence of a unit root in the weekly crack spread data series for branded and unbranded fuels by using 14 implementation of the augmented-Dickey–Fuller test designed for panel data. The test results indicated that the crack spread series were stationary.8 We quantify the magnitude of planned and unplanned refinery outages in Table 5 and Table 6, respectively. The median planned outage varies from about 63% for Alaska, where there are few refinery outages, to about 12% for the Gulf coast, which has the highest concentration of refineries in the US. Unplanned outages varied from a median of about 10% for the Gulf Coast to nearly 57% for the NY Harbor market. With the exception of the Mid-continent market area, the median unplanned outage is always of smaller magnitude than the 7 See 12 for a formal treatment of the estimation techniques used in this research. A very applications-oriented introduction to the generalized method of moments and its use is contained in 2. 8 The 14 t-bar statistics were − 2.96 and − 2.88, respectively, for unbranded and branded fuel prices. Because the augmented-Dickey–Fuller statistics have a value exceeding (in absolute value) the 5% critical value of − 2.36 we reject the null hypothesis of a unit root; the test statistics also exceed (in absolute value) the 1% critical value of − 2.43 (14, pp. 61–62).

M. Kendix, W.D. Walls / Energy Economics 32 (2010) 1291–1298 Table 6 Descriptive statistics on unplanned outages by spot market.

Table 7 Estimation results: planned outages.

Percentage of terminal supply affected

Variable

Spot market

Minimum

Median

Maximum

Mean

Std dev

Mid-continent Gulf Los Angeles NY Harbor Northwest San Francisco

0.319 0.141 0.001 6.418 0.693 6.692

19.832 9.992 12.356 56.544 25.718 22.740

87.210 100.000 66.166 73.281 32.074 54.153

21.168 16.360 14.930 43.205 19.358 24.803

15.129 17.919 11.839 29.237 12.336 10.938

Descriptive statistics on percent of unbranded terminal market supply affected by refinery outage, given that an outage affecting a terminal within the spot market occurred; no unplanned outages occurred in Alaska.

median planned outage. Mean unplanned outages are lower than mean planned outages in each spot market. There is substantial variation in outages across refinery spot markets, and there is also substantial variation over time due to the extreme weather events experienced in the US Gulf Coast over the sample period. We initially estimated the model set out in Eq. (3) using two-stage least squares, with inventory-sales ratio, capacity utilization rate, and the spot market HHI as endogenous.9 As instruments for the inventorysales ratio and capacity utilization we used—within the state of the wholesale terminal—the state-level percent growth in employment, percent unemployment rate, and percent growth in personal income; for the spot market HHI we used merger dummy variables for several mergers that occurred during the period as instruments. For each model, we tested for instrument relevance by examining the first-stage Fstatistic as suggested by 22, and in each case we found that the instruments were highly correlated with the potentially endogenous variables. We also calculated Sargan–Hansen J-test statistics to examine the validity of our instruments being uncorrelated with the model's random disturbance μit in the instrumental-variables regressions.10 We also tested for the endogeneity of our endogenous regressors using a Ctest or a difference-in-Sargan/Hansen test.11 Models were estimated separately for branded fuel prices and for unbranded fuel prices for the base model and for versions augmented with interaction effects between the outages variable and fuel types and monthly effects.12 In each of the estimations, we found evidence that the instrumental variables chosen were relevant—highly correlated with the endogenous variables for which they served as instruments as evidenced by the first-stage regression F-statistic— and also not correlated with the model's random disturbance as evidenced by the Sargan–Hansen J-test statistic. We also found strong evidence from the C-test to support treating HHI as endogenous, but we could not reject the null hypothesis that inventories and capacity utilization were in fact exogenous in our regression equation. Finally, we test for the presence of heteroskedasticity in each of the models estimated by two-stage least squares using a variety of tests.13 In each of the estimations, the null hypothesis of homoskedasticity was

9

We estimated the model using the 21 xtivreg2 module in Stata for Linux. Because we are estimating our model with robust standard errors, we report Hansen's J-test. This is a test of overidentifying restrictions with the joint null hypothesis that the instruments are uncorrelated with the error term and that the excluded instruments are correctly excluded from the estimated equation. See 12 for a discussion of the Sargan–Hansen tests of overidentifying restrictions. 11 The endogeneity test is that implemented by the ivreg2 routine in Stata using the endog option. It is defined as the difference of two Sargan–Hansen statistics: one for the equation with the smaller set of instruments, where the suspect regressors are treated as endogenous, and one for the equation with the larger set of instruments, where the suspect regressors are treated as exogenous. The test statistic is robust to violations of conditional homoskedasticity. For further details, see 12 and 1. 12 Branded and unbranded prices could differ for a variety of reasons, such as branded fuels having a premium that reflects the brand name, advertising costs, and other services or attributes associated with the brand. Branded fuel supplies might also be less susceptible to supply disruptions relative to unbranded supplies. 13 These tests included those of 18, 26/15, and 5/10/7. 10

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Branded fuels Coeff

Refinery HHI 0.1792387 Lagged log price 0.9013251 Inventory-sales ratio − 0.0005001 Capacity utilization − 0.0001981 rate Planned Outages % 3.4e−06 Fuel-Specific Indicators B0 0.0070360 Bg0 0.0032323 Ce0 − 0.0063668 Cm0 – G7 0.0054074 G7.8 0.0011614 G9 0.0024467 Ge0 0.0136527 Gf0 0.0021422 Gf9 0.0093674 Gg0 0.0035675 Gg7 0.0087140 Gg7.8 0.0034336 Gg9 0.0062393 L0 0.0050200 L7 0.0059564 L9 0.0006202 Rg0 0.0214113 Rm0 0.0207413 Terminal-specific Yes effects Week-specific effects Yes Sargan–Hansen J-test 0.3830 p-value R2 0.9976 Observations 26,325

Unbranded fuels Std err

Coeff

Std err

(0.0701700) (0.0030878) (0.0008879) (0.0000369)

0.2637632 0.8589791 − 0.0021160 − 0.0003404

(0.0903462) (0.0033407) (0.0011271) (0.0000470)

(0.0000128)

0.0000308

(0.0000191)

(0.0032889) (0.0022044) (0.0016709) – (0.0013688) (0.0006554) (0.0005764) (0.0026300) (0.0012285) (0.0022217) (0.0006172) (0.0030712) (0.0014731) (0.0008969) (0.0025553) (0.0027194) (0.0101813) (0.0030966) (0.0030734)

0.0061731 0.0094581 0.0025131 0.0027549 0.0100063 0.0029844 0.0032537 0.0101304 0.0047369 0.0165044 0.0054099 0.0114005 0.0079020 0.0067922 0.0061205 0.0085742 – 0.0158694 0.0140651 Yes

(0.0041097) (0.0027147) (0.0031707) (0.0031584) (0.0017732) (0.0008344) (0.0007145) (0.0029949) (0.0015750) (0.0038092) (0.0007619) (0.0032789) (0.0018374) (0.0011966) (0.0032242) (0.0034160) – (0.0037195) (0.0036931)

Yes 0.4477 0.9964 26,325

Estimates efficient for arbitrary heteroskedasticity and autocorrelation. Statistics robust to heteroskedasticity and autocorrelation.

rejected at a marginal significance level approaching zero.14 Given the statistical properties of our model, the preferred estimation technique is GMM. The GMM estimator of the parameters accounts properly for the endogenous regressor (HHI) and it is the efficient estimator in the presence of heteroskedasticity. Also, because the random disturbance in our model is possibly heteroskedastic, we report standard error estimates that are robust to heteroskedasticity and autocorrelation.15 In summary, the parameter estimates for our model are consistent and efficient, and the statistics that we report are robust to heteroskedasticity and serial correlation. Having thoroughly discussed the specification of the model and related diagnostic tests performed, the remaining discussion will focus on the coefficient estimates. To place the estimated effects of unplanned outages into context, we first estimated the basic model using planned outages as an independent variable. Planned outages occur due to scheduled maintenance as well as refinery configuration changes associated with the semi-annual seasonal switchover between the production of winter and summer fuels. While planned outages do have a seasonal component of predictability, they are planned far in advance of the weekly fluctuations in wholesale market prices.16 Our purpose in estimating the price impact of planned outages is to provide a basis of comparison for the price effects of unplanned outages to be reported later in this section. Table 7 reports the estimates of the model for 14 Homoskedasticity was rejected using the levels of the instrumental variables only and also when using the fitted values and their square values in conducting the test. 15 We use 17 bandwidth selection in the calculation of the robust standard estimates reported. 16 Since planned outages are chosen and not random one might consider selection bias to be an issue. However, the long planning horizon for refinery runs may allow planned outages to be exogenous with respect to weekly wholesale price fluctuations.

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Table 8 Estimation results: unbranded fuel prices. Variable

Refinery HHI Lagged log price Inventory-sales ratio Capacity utilization rate Unplanned Outages % Fuel-specific indicators B0 Bg0 Ce0 Cm0 G7 G7.8 G9 Ge0 Gf0 Gf9 Gg0 Gg7 Gg7.8 Gg9 L0 L7 Rg0 Rm0 Terminal-specific effects Week-specific effects Sargan–Hansen J-test p-value R2 Observations

Full sample

Omit EPA waiver periods

Coeff

Std err

Coeff

Std err

0.2677594 0.8587418 − 0.0020580 − 0.0003330

(0.0902227) (0.0033360) (0.0011251) (0.0000469)

0.2757415 0.8659424 − 0.0023440 − 0.0003434

(0.0876661) (0.0032783) (0.0010978) (0.0000539)

0.0001714

(0.0000186) 0.0001504

(0.0000187)

0.0061835 0.0092522 0.0025239 0.0026520 0.0099463 0.0029657 0.0032560 0.0101782 0.0047859 0.0164378 0.0053598 0.0113630 0.0076641 0.0066992 0.0063039 0.0085925 0.0159242 0.0141404 Yes

(0.0041035) (0.0027106) (0.0031661) (0.0031536) (0.0017706) (0.0008331) (0.0007134) (0.0029904) (0.0015726) (0.0038034) (0.0007607) (0.0032740) (0.0018348) (0.0011948) (0.0032194) (0.0034108) (0.0037139) (0.0036875)

(0.0040118) (0.0026373) (0.0030732) (0.0030715) (0.0017370) (0.0008206) (0.0007013) (0.0029021) (0.0015359) (0.0036999) (0.0007428) (0.0031813) (0.0018443) (0.0011729) (0.0031433) (0.0033294) (0.0036070) (0.0035817)

0.0063040 0.0088962 0.0028734 0.0026832 0.0101408 0.0025476 0.0031737 0.0101481 0.0043133 0.0160364 0.0050445 0.0106811 0.0065428 0.0066398 0.0061146 0.0077307 0.0152211 0.0139460 Yes

Yes 0.4158

Yes 0.4634

0.9964 26,325

0.9966 25,859

results. To explore this possibility, we re-estimated the model using a sample in which EPA waiver periods are omitted; these results are presented in the right-hand columns of Table 8 for unbranded fuels and in Table 9 for branded fuels. By eliminating the EPA waiver periods, the results now focus explicitly on idiosyncratic refinery outages. The price impact of refinery outages does not differ statistically between the full sample and the sample in which EPA waivers are in effect. In the basic outages model estimated above, outages are restricted to have a homogeneous impact on all types of gasoline. It is possible that the prices of certain special fuel blends could be differentially impacted by refinery outages because these “boutique” fuels are less fungible than the more widely used fuels. To directly address this possibility, we augmented the model by adding interaction terms between the set of fuel indicator variables and the refinery outages variable. In this specification, the price impact of outages can vary across the different types of gasoline. The estimates for this specification of the model are displayed in Table 10 for unbranded fuels and in Table 11 for branded fuels. For each of the augmented regression specifications, we tested the null hypothesis that the coefficients on the set of outage × fuel type were jointly equal to zero. In each case, we could reject this null hypothesis at marginal significance levels below 1% indicating that this set of variables belongs in the model. For unbranded fuels, the regression coefficient on the outages variable is significantly smaller when controlling for possible interactions between outages and fuel types compared to the model without the interaction terms. Also, the individual outage × fuel type coefficients are positive and statistically significant for six of the special fuel types, indicating that the price impacts of refinery outages differ by fuel type. For branded fuels, the regression coefficient on the

Estimates efficient for arbitrary heteroskedasticity and autocorrelation. Statistics robust to heteroskedasticity and autocorrelation. Table 9 Estimation results: branded fuel prices.

planned outages for branded and unbranded fuels. The estimates in both columns of the table reveal that planned outages have positive regression coefficients, but each does not statistically differ from zero at the 5% marginal significance level. The table also shows that most special fuels are associated with higher fuel prices, relative to the base category of conventional clear gasoline.17 The estimates of the basic regression model for unplanned outages are reported in Table 8 for unbranded fuels and in Table 9 for branded fuels. The GMM regression results for the full sample of observations are reported in the first columns of each table, where it can be seen that unplanned outages are associated with a statistically significant (at the 5% marginal significance level) increase in wholesale fuel prices for both branded and unbranded gasoline. We note that refinery concentration is highly significant and has a strong positive relationship with unbranded and branded gasoline prices. We also observe that many special fuels are associated with higher wholesale prices relative to conventional clear gasoline, a result consistent with earlier findings by a number of researchers (23; 16; 9; 6; 25). During the sample period, several extreme weather-related events caused a large proportion of US refining capacity to become inoperable. During these periods, the US Environmental Protection Agency (EPA) granted blanket waivers of regulations that require the use of special fuels in areas not meeting US Federal air quality standards. Because the EPA waiver periods represent extreme refinery outages that are highly correlated across market locations, they may represent influential observations which may be driving the empirical 17 While one should be cautious in making too much out of the results presented in this paragraph, they do seem to indicate that any increases in wholesale fuel prices associated with seasonal switchovers are mainly explained by the changing fuelspecific attributes and not by the planned outage events.

Variable

Refinery HHI Lagged log price Inventory-sales ratio Capacity utilization rate Unplanned Outages % Fuel-specific indicators B0 Bg0 Ce0 G7 G7.8 G9 Ge0 Gf0 Gf9 Gg0 Gg7 Gg7.8 Gg9 L0 L7 L9 Rg0 Rm0 Terminal-specific effects Week-specific effects Sargan–Hansen J-test p-value R2 Observations

Full sample

Omit EPA waiver periods

Coeff

Std err

Coeff

Std err

0.1802650 0.9013072 − 0.0004955 − 0.0001948

(0.0701598) (0.0030871) (0.0008874) (0.0000369)

0.2012530 0.9034133 − 0.0011780 − 0.0003092

(0.0697084) (0.0030666) (0.0008832) (0.0000433)

0.0000540

(0.0000148) 0.0000567

(0.0000152)

0.0069732 0.0031568 − 0.0063525 0.0053949 0.0011617 0.0024413 0.0137076 0.0021617 0.0093450 0.0035628 0.0087407 0.0033442 0.0062298 0.0050770 0.0059631 0.0007750 0.0213888 0.0207236 Yes

(0.0032881) (0.0022039) (0.0016704) (0.0013685) (0.0006552) (0.0005762) (0.0026294) (0.0012282) (0.0022211) (0.0006170) (0.0030704) (0.0014729) (0.0008966) (0.0025547) (0.0027187) (0.0101789) (0.0030959) (0.0030726)

(0.0032732) (0.0021854) (0.0016531) (0.0013676) (0.0006580) (0.0005775) (0.0026017) (0.0012251) (0.0022032) (0.0006146) (0.0030409) (0.0015128) (0.0008965) (0.0025427) (0.0027058) (0.0100665) (0.0030647) (0.0030410)

0.0068526 0.0036857 − 0.0060272 0.0062510 0.0013458 0.0027404 0.0139773 0.0022310 0.0097271 0.0037346 0.0090542 0.0039112 0.0068764 0.0049562 0.0061495 0.0007871 0.0211571 0.0208930 Yes

Yes 0.3711

Yes 0.4815

0.9976 26,325

0.9977 25,859

Estimates efficient for arbitrary heteroskedasticity and autocorrelation. Statistics robust to heteroskedasticity and autocorrelation.

M. Kendix, W.D. Walls / Energy Economics 32 (2010) 1291–1298 Table 10 Estimation results: unbranded fuel prices. Variable

Table 11 Estimation results: branded fuel prices.

Full sample Coeff

Refinery HHI 0.2675072 Lagged log price 0.8591930 Inventory-sales ratio − 0.0017688 Capacity utilization − 0.0003329 rate Unplanned Outages % 0.0001390 Fuel-specific indicators B0 0.0063216 Bg0 0.0095625 Ce0 0.0087077 Cm0 0.0068114 G7 0.0104757 G7.8 0.0031415 G9 0.0034308 Ge0 0.0090297 Gf0 0.0046520 Gf9 0.0172709 Gg0 0.0053244 Gg7 0.0089392 Gg7.8 0.0081827 Gg9 0.0068013 L0 0.0037418 L7 0.0082232 Rg0 0.0157201 Rm0 0.0143326 Outages % × fuel indicators B0 0.0000716 Bg0 − 0.0000950 Ce0 0.0000900 Cm0 0.0012250 Cn0 0.0043038 G7 − 0.0002587 G7.8 4.58e−6 G9 0.0000134 Ge0 0.0013536 Gf0 0.0000939 Gf9 − 0.0003329 Gg0 − 6.79e−6 Gg7 0.0010770 Gg7.8 − 0.0000907 Gg9 0.0000351 L0 0.0008390 L7 0.0001150 Rg0 0.0001459 Rm0 − 0.0000292 Terminal-specific Yes effects Week-specific effects Yes Sargan–Hansen J-test 0.4064 p-value R2 0.9964 Observations 26,325

1297

Omit EPA waiver periods Std err

Coeff

Std err

(0.0900323) (0.0033270) (0.0011231) (0.0000468)

0.2740443 0.8663362 − 0.0020526 − 0.0003417

(0.0874341) (0.0032686) (0.0010954) (0.0000538)

(0.0000295) 0.0001031

(0.0000296)

(0.0041322) (0.0027352) (0.0032344) (0.0032348) (0.0018058) (0.0008418) (0.0007154) (0.0030323) (0.0015818) (0.0039110) (0.0007692) (0.0034887) (0.0018832) (0.0012123) (0.0033334) (0.0035713) (0.0037050) (0.0036801)

0.0063665 0.0091066 0.0090654 0.0068488 0.0106781 0.0027591 0.0033129 0.0089743 0.0041338 0.0168267 0.0049628 0.0081592 0.0069084 0.0066706 0.0058319 0.0066688 0.0149405 0.0142206

(0.0040415) (0.0026603) (0.0031386) (0.0031491) (0.0017721) (0.0008288) (0.0007030) (0.0029419) (0.0015444) (0.0038030) (0.0007510) (0.0033884) (0.0018893) (0.0011887) (0.0033113) (0.0034917) (0.0035974) (0.0035737)

(0.0003430) (0.0001608) (0.0001183) (0.0002298) (0.0004466) (0.0002657) (0.0000572) (0.0000538) (0.0006857) (0.0002305) (0.0004705) (0.0001041) (0.0005208) (0.0001270) (0.0000971) (0.0003021) (0.0002691) (0.0000678) (0.0000749)

0.0001199 − 0.0000462 0.0001199 0.0012606 0.0043507 − 0.0002502 − 9.69e−6 0.0000460 0.0014061 0.0001383 − 0.0002992 0.0000317 0.0011278 − 0.0000453 0.0000768 0.0000701 0.0003090 0.0002028 − 0.0000639 Yes

(0.0003332) (0.0001561) (0.0001155) (0.0002230) (0.0004332) (0.0002580) (0.0000598) (0.0000528) (0.0006651) (0.0002237) (0.0004564) (0.0001012) (0.0005056) (0.0001280) (0.0000959) (0.0003938) (0.0002842) (0.0000666) (0.0000869)

Yes 0.4558 0.9966 25,859

Estimates efficient for arbitrary heteroskedasticity and autocorrelation. Statistics robust to heteroskedasticity and autocorrelation.

outages variable is about the same as it was in the regression model not controlling for differential effects of outages across fuel types. However, several of the outage × fuel coefficients are positive and statistically significant, indicating that outages differentially affect fuel prices. For both branded and unbranded fuels, the effect of omitting the EPA waiver periods from the estimations was to lower the estimated impact of unplanned outages on fuel prices and this is consistent with our expectations: Idiosyncratic outages should have a smaller impact on prices as compared to multiple refinery outages affecting multiple wholesale terminals because widespread outages increase the cost of obtaining fuel from alternative sources of supply. The estimation results also speak to the immediate market impact of outages as compared to outages persisting for multiple weeks. The regression coefficient on the outages variable quantified the immediate-period market impact of outages. Because the econometric

Variable

Full sample Coeff

Refinery HHI 0.1780326 Lagged log price 0.9010581 Inventory-sales ratio − 0.0005669 Capacity utilization − 0.0002013 rate Unplanned Outages % 0.0000615 Fuel-specific indicators B0 0.0078414 Bg0 0.0042934 Ce0 − 0.0068131 G7 0.0055619 G7.8 0.0014233 G9 0.0025615 Ge0 0.0136184 Gf0 0.0021941 Gf9 0.0094540 Gg0 0.0034859 Gg7 0.0079754 Gg7.8 0.0035035 Gg9 0.0060153 L0 0.0038882 L7 0.0058231 L9 0.0008320 Rg0 0.0214670 Rm0 0.0214237 Outages % × fuel indicators B0 − 0.0002412 Bg0 0.0004399 Ce0 0.0002180 Cm0 − 0.0000659 G7 − 0.0000770 G7.8 0.0000982 G9 − 0.0000449 Ge0 0.0014336 Gf0 − 0.0000239 Gf9 − 0.0000372 Gg0 0.0000605 Gg7 0.0004484 Gg7.8 − 0.0000143 Gg9 0.0001430 L0 0.0004019 L7 0.0000442 Rg0 0.0001452 Rm0 − 0.0000137 Terminal-specific Yes effects Week-specific effects Yes Sargan–Hansen J-test 0.3721 p-value R2 0.9976 Observations 26,325

Omit EPA waiver periods Std err

Coeff

Std err

(0.0701278) (0.0030848) (0.0008874) (0.0000369)

0.1978783 0.9031754 − 0.0012350 − 0.0003113

(0.0696733) (0.0030645) (0.0008833) (0.0000432)

(0.0000237) 0.0000546

(0.0000242)

(0.0033163) (0.0022382) (0.0017094) (0.0013943) (0.0006626) (0.0005791) (0.0027047) (0.0012364) (0.0022666) (0.0006234) (0.0032669) (0.0015337) (0.0009069) (0.0026506) (0.0028586) (0.0101701) (0.0030961) (0.0030734)

0.0076595 0.0048034 − 0.0064820 0.0064306 0.0015563 0.0028383 0.0138586 0.0022378 0.0098068 0.0036555 0.0082817 0.0040264 0.0066491 0.0052008 0.0057944 0.0007959 0.0212725 0.0214793

(0.0033036) (0.0022196) (0.0016916) (0.0013941) (0.0006654) (0.0005804) (0.0026765) (0.0012334) (0.0022482) (0.0006211) (0.0032358) (0.0015749) (0.0009062) (0.0026851) (0.0028513) (0.0100591) (0.0030653) (0.0030424)

(0.0001997) (0.0001535) (0.0000883) (0.0002235) (0.0002055) (0.0000455) (0.0000431) (0.0055717) (0.0001806) (0.0003074) (0.0000755) (0.0005373) (0.0001020) (0.0000752) (0.0002405) (0.0002151) (0.0000543) (0.0000594)

− 0.0002123 0.0003030 0.0002124 − 0.0000556 − 0.0000972 0.0000875 − 0.0000415 0.0016147 − 0.0000101 − 0.0000349 0.0000628 0.0004488 − 4.00e−6 0.0001479 − 0.0000971 0.0000968 0.0001558 − 0.0000725 Yes

(0.0001978) (0.0001592) (0.0000879) (0.0002211) (0.0002035) (0.0000485) (0.0000432) (0.0055106) (0.0001788) (0.0003041) (0.0000750) (0.0005324) (0.0001048) (0.0000757) (0.0003197) (0.0002317) (0.0000544) (0.0000701)

Yes 0.4796 0.9977 25,859

Estimates efficient for arbitrary heteroskedasticity and autocorrelation. Statistics robust to heteroskedasticity and autocorrelation.

model also has a lagged price variable as a regressor, the price effects of a persistent outage accumulate over time. With a coefficient on the lagged price of about 0.86, as reported in Table 10, the price impact of 1 a persistent refinery outage would be or over 7 times the price 1−0:86 impact of a transitory outage. While the empirical model does not explicitly control for the expected duration of a given refinery outage, it does provide a crude way of estimating the price impact of prolonged outages relative to those occurring within an interstice. It is notable that refinery concentration, as measured by the HHI of refinery capacity within spot markets, has a positive and statistically significant coefficient. The estimated values of the refinery HHI vary from about 0.18 to 0.28 across the estimates reported in Tables 7–11; however, with estimated standard errors of about 0.06 to 0.09, there is no evidence that the estimated impact of refinery HHI differs statistically across the various estimations. We note that changes in refinery concentration appear to have much larger price impacts than

1298

M. Kendix, W.D. Walls / Energy Economics 32 (2010) 1291–1298

unplanned outages, with a one percentage point increase in HHI having more than an order of magnitude larger impact. This might indicate that structural factors within the US refining industry play a much more important role in determining the level of wholesale fuel prices than do unplanned refinery outages. The overall empirical results indicate that refinery outages in the US have had a very small positive (and statistically significant) impact on the price of gasoline at the wholesale terminal level. The impact of refinery outages is substantially larger for some special boutique fuels than it is for conventional clear gasoline, with the price impact being more than double in some cases. An implication of this research is that the proliferation of fuel types in the US increases not only the wholesale price level but also the magnitude of price increases due to refinery outages. Having a smaller number of more fungible fuels would increase the capacity of the supply infrastructure and this would largely mitigate the price impacts of idiosyncratic refinery outages in much the same way as the EPA currently does when it waives special fuel requirements in response to very large prolonged supply disruptions. It appears sensible to include the cost, frequency, and magnitude of supply disruptions for special fuels into the benefit– cost calculus. 5. Conclusion The research reported in this paper represents the first systematic disaggregated quantification of the impact of refinery outages on petroleum product prices. The analysis has focused specifically on the refining sector in the US, the largest in the world accounting for about a quarter of worldwide refining capacity and one which has experienced a wide range of unplanned outages in the past decade. Using a panel data set on wholesale gasoline prices across seventyfive wholesale terminals spanning 2002–2008, we estimated a statistical model of refined product prices that controlled for timespecific effects, city-specific effects, fuel-specific effects, refinery concentration, and other factors that could impact the price of refined petroleum products. The empirical results showed that refinery outages have had a statistically significant positive impact on refined product prices, and that the magnitude of this effect is larger for certain special fuel blends. Acknowledgments We acknowledge with gratitude the helpful comments and assistance of Ben Bolitzer, Dan Haas, Tom McCool, Michelle Munn, and Frank Rusco. Two anonymous referees provided numerous comments and suggestions that improved the exposition.

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