WORKING 

PAPERS 

The Impact of Outages on Prices and Investment 

in the US Oil Refining Industry

Matthew Chesnes 

WORKING PAPER NO. 322 

June 2014 

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BUREAU OF ECONOMICS 

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WASHINGTON, DC 20580
The Impact of Outages on Prices and Investment in the US Oil Refining Industry

Matthew Chesnes*

June 12, 2014

Abstract

This paper considers the effects of refinery outages (due to planned turn-arounds or unplanned events) on current petroleum product prices and future refinery investment. Empirical

evidence on these relationships is mixed and highly dependent on the size and duration of the

outage, the geographic area considered, the level of inventories available at the time of the

outage, and the tightness of the market as measured by the capacity utilization rate. Using a detailed database of plant-level refinery outages for both upstream and downstream refining units,

I estimate the effects of outages on product prices controlling for the crude oil price and the

ability of operating plants to respond to the outage. I also consider the effect of current market

profitability on the likelihood of planned refinery outages and the effects of high utilization rates

and planned maintenance on the prospects for unplanned outages. I then use plant-level capacity data to analyze the effects of outages, profitability, and utilization rates on future investment

decisions of the refinery.

Results based on OLS and probit models show that planned outages tend to occur during the

spring and fall and during times of relatively low margins as measured by the crack spread. The

length of time since the last plant turn-around is positively associated with future unplanned

outages. Price regressions show that atmospheric distillation and catalytic cracking outages are

positively associated with gasoline prices and the association is stronger the higher is the utilization rate at the time of the outage. The relationship between both upstream and downstream

investment and outages is mixed though refiners tend to invest less when nearby plants have

made investments in the prior year. While causal relationships between outages, prices, and

investment are difficult to estimate due to simultaneity and unobserved variables, these descriptive results show that outages are an important factor affecting refined product prices and future

refinery investment.

*Email: mchesnes@ftc.gov. Web: http://www.chesnes.com. I thank John Rust, Ginger Jin and Peter Cramton for their support. I am also grateful to Louis Silvia, Chris Taylor, Nick Kreisle, David Meyer, David Schmidt, Daniel Hosken, Ted Rosenbaum

and seminar participants at the Federal Trade Commission, the University of Maryland, the Federal Reserve Board of Governors,

and La Pietra-Mondragone Workshop in Economics for their suggestions and comments. All remaining errors are my own. The

opinions expressed here are those of the author and not necessarily those of the Federal Trade Commission or any of its Commissioners.

1
1 Introduction

The United States is the largest consumer of crude oil in the world and this resource accounts for about 50%

of the country’s total energy needs.1 Although a majority of this consumed oil comes from foreign sources,

almost all is refined domestically. Refineries convert crude oil into a large number of products such as

gasoline, distillate fuel oils (diesel fuel and heating oil), and jet fuel. While much attention has been paid to

the upstream crude oil production industry (see Hamilton (1983) and Hubbard (1986)), and the downstream

retail sector (see Borenstein (1991 & 1997), Lewis (forthcoming), Noel (2007), and Chesnes (under review),

very little research has focused on the role of the refining industry.

In the short-run, refiners face a complicated linear programming problem of optimizing their operations

using multiple types of crude oil and various configurations of upstream and downstream refining units.

All this takes place in a world where both crude oil and refined products prices are constantly changing.2

This challenging problem is made even harder when considering the potential for full or partial plant outages

which frequently occur. Over the longer term, refiners are also optimizing over the size of their plant, making

investments in the capacities of upstream and downstream units to both process more crude oil and to have

the flexibility to use different types of crude oil and change their output slate in the face of relative price

changes. This paper considers the relationship between refinery outages, utilization rates, price spreads, and

investment.

The optimal choice of capacity accumulation, i.e., the increased ability to refine crude oil into higher

valued products, is a long-term decision. Capacity is expensive to build and takes time to come online

so forecasts of future market conditions are crucial. A shorter-term problem involves a refiner’s choice of

capacity utilization. This rate measures the intensity with which a firm uses its capital, which for a refinery

may include the use of boilers, distillation columns, and downstream cracking units.3In addition to planned

outages that involve a plant going offline for preventative maintenance, unplanned outages also occur that

can affect the entire plant or just individual units.4 Since a refiner is interested in maximizing profits, the

prices of both inputs (crude oil, oxygenates, etc.) and outputs (gasoline, diesel fuel, etc.) are crucial to the

short-run and long-run production and investment decisions. The crack spread, or the difference between

the prices of refined products and crude oil, is a proxy for the profitability of turning a barrel of oil into

higher-valued products.

I first consider planned outages (also called turn-arounds) at US refineries. There is strong seasonality

in the demand for refined products and refineries tend to choose to schedule planned turn-arounds when

demand is low and spare capacity or product inventories can fill in for the lost production. However, refiners

also face uncertain demand and may push back or move up planned outages when profitability is relatively

high or low respectively. Shocks, such as hurricanes, also can occur, sometimes during periods when there

1Source: 2011 Annual Energy Review, Energy Information Administration (EIA).

2Different types of crude oil are better adapted to producing certain refined products. Plants are heterogeneous in their complexity so some are able to process a wider variety of crude oils.

3More details on the refining process can be found in the next section.

4A refinery is generally composed of an upstream atmospheric distillation unit that first separates the crude oil and many

downstream units, such as cracking units, hydrotreaters and reformers that further process the crude into higher-valued products.

Though an upstream outage can have a domino-effect on downstream units, refineries can also buy feedstocks from other plants to

partially mitigate the outage.

2
is little excess capacity. Therefore, I next focus on unplanned outages. While weather events can occur

randomly and affect a large number of plants, other idiosyncratic outages that only affect one plant (such as

a refinery fire) may be related to the utilization rate at which the plant is running or the time since the plant

last performed a turn-around.5

Once I understand when and why outages occur, I then focus on the effect of the outages on product

prices. Some studies (EIA, (2011)) have found very little correlation between outages and product prices

with crude oil price fluctuations being the primary driver of the variation in product prices. One benefit of

the detailed outage data that I employ is that outages are reported by refining unit. Since some units (such as

the Fluid Catalytic Cracking or FCC unit) are more important for the production of certain products (such

as gasoline), I can determine how certain types of outages affect different product prices. Outages that occur

during periods of low demand or high inventories likely have less of a price effect compared to periods

where inventories are relatively low and/or utilization rates are high because plants are less able to respond

to nearby outages. Therefore, my analysis will control for the level of market tightness at the time of the

outage when assessing its impact.

Finally, I consider the effects of outages, price spreads, and utilization rates on investment decisions

of the refiners. I expect that unplanned outages during a given year might lead to future investment as

a refiner wants to update their plant and avoid future outages. In years following relatively wide crack

spreads, one might expect more investment if refiners expect that profitability will remain favorable in

the future. However, since the crack spread is highly variable, it may serve as a poor predictor of future

investment. Investments in capacity may also be larger if a plant found it optimal to run at a high utilization

rate in the prior year. If high utilization rates generally lead to more unplanned outages, then investing in

more capacity can help avoid future outages.6 My data allow me to study investments in both upstream

(atmospheric distillation) capacity and in downstream units. This is important because refiners may find it

optimal to increase the complexity of their plant by investing in downstream units such as hydrocrackers

and reformers that allow them more flexibility in their crude oil or production slates.

While a fully structural model of the refining industry may provide important insights into the industry

and how it responds to shocks, the complexity of the input and output choices, the heterogeneous technology,

and other factors make modeling this behavior intractable.7 The reduced-form approach in this paper allows

to me assess the relationships between key variables and gain insights into how the oil refining industry

responds to shocks, while averaging over some of the variation not captured by the model (such a refiner’s

choice of different types of crude oil). There are likely unobserved variables that affect product prices,

outages, and refiners’ investment decisions. In addition, it is possible that there are simultaneity issues

(for example, between planned outages and product prices) that make conclusions about causality difficult.

Therefore, while these results are descriptive, they show that outages are an important factor in determining

5Unfortunately, I do not observe plant-level utilization rates, but I do observe utilization rates at the refining district level. However, even these utilization rates only reflect the rate of atmospheric distillation (the first phase of refining) and not the production

intensity of downstream units.

6

I also consider how product inventories and investments by competing plants may affect future investment decisions.

7

In technical terms, the state space of crude and product prices, capacities, and inventories (to name a few) is very large.

Modeling only a subset of these state variables masks important variation that is important to the refiner as he optimizes production

each period.

3
refined product prices and future refinery investment.

Plants need to perform regular maintenance each year no matter how high their margins so some plants

still perform planned turn-arounds even during periods of high crack spreads. However, my results indicate

that planned outages tend to occur more often during the spring and fall when demand for gasoline is

generally lower and during times of relatively low margins as measured by the crack spread. The length

of time since the last plant turn-around is positively associated with future unplanned outages. Unplanned

outages are actually negatively associated with the utilization rate, though utilization is only available at

the PADD8level and is therefore a imprecise measure of the impact of plant-level production intensity on

unplanned outages.9

Price regressions show that atmospheric distillation and catalytic cracking outages have positive effects on gasoline prices and these effects are larger the higher the utilization rate at the time of the outage.

Distillate prices also respond positively to atmospheric distillation outages, but are unaffected by catalytic

cracking outages, a unit better-equipped for producing gasoline. Investment in certain refining units is positively associated with planned and unplanned outages of those units, but in general, the relationship between

investment and past outages is mixed. Investment in distillation capacity tends to be more likely in a year

following relatively high utilization rates and less likely when nearby plants have recently made investments of their own. Refiners may also be responding to unobserved longer-term trends in the operations and

profitability of their plants.

The remainder of this paper is organized as follows. In section 2, I provide an overview of the oil refining

industry to better understand the complicated problem facing the refiner. I describe my data in section 3 and

describe my empirical specifications and results in section 4. Section 5 concludes and provides a discussion

of potential extensions.

2 Background on the US Oil Refining Industry

The oil industry is broadly comprised of several vertically oriented segments. They include crude oil exploration and extraction, refineries which convert crude oil into other products, pipeline distribution networks,

terminals that store the finished product near major cities, and tanker trucks which transport products to

retail outlets.10 The largest refined product, gasoline, accounts for about 52% of total production, while

distillate and jet fuel make up another third.11 Of the petroleum consumed in the United States, about 70%

is used in the transportation sector.12 Figures 1 and 2 provide a description of the production process and

average product yields.13 The main distillation process produces very little final products like gasoline, but

8Petroleum Administration for Defense District.

9This is also suggestive that refiners are successful at minimizing unplanned outages during periods of high demand (and

utilization) by performing planned maintenance in low-demand periods. Further, this result is consistent with the explanation that

refiners perform planned maintenance in low-demand (low-utilization) periods and unplanned outages arise during the maintenance.

1075% of terminals in the US are owned by independent oil companies and wholesalers and not major oil companies. See US

Senate Report (2002), p. 40.

11See http://www.eia.gov/dnav/pet/pet_pnp_wprodrb_dcu_nus_4.htm.

12See http://www.eia.gov/totalenergy/data/monthly/.

13Note the motor gasoline blending components are shown here as a part of refinery production, even though EIA reports them

as a (negative) input into refining since they leave the refinery as an unfinished product, later to be mixed with other chemicals

4
it is complemented by other units that extract more of the highest valued products. Technical details of the

refining process and background on the types of crude oil available can be found in the appendix.

Figure 1: Production Process (Source: EIA)

process and average product yields.9 The main distillation process produces some final

products like gasoline, but it is complemented by other units that extract more of the

highest valued products. Technical details of the refining process and background on the

types of crude oil available can be found in the appendix.

Figure 1: Production Process (Source: EIA)

The market for refined oil products is large and growing, with the US consuming

388 million gallons of gasoline each day and one quarter of the world’s crude oil.10

Aside from refining crude oil into gasoline, refineries produce many products that are

important inputs into other industries. Retail gasoline prices have recently experienced

increased variability in the US and in summer 2008 hit an all time high of $4.11 per

gallon. Wholesale prices peaked around $3.40 a gallon in the same period.11 Many justify

the high prices as a result of the growing demand for gasoline and supply limitations,

including the scarcity of crude oil, Middle East uncertainty, hurricanes, and the OPEC

cartel. Others claim the high prices result from coordinated anticompetitive behavior by

big oil companies. Outages, investment and utilization choices by oil refineries may also

play a significant role in affecting downstream prices.

About one-half of US production occurs near the Gulf of Mexico in Texas and

9Note the motor gasoline blending components are shown here as a part of refinery production, even

though EIA reports them as a (negative) input into refining since they leave the refinery as an unfinished

product, later to be mixed with other chemicals (usually ethanol) by a blender.

10Annual world consumption of crude oil totals 30 billion barrels, of which 7.5 billion barrels comes

from the US. About 60% of crude oil used by refineries is imported and US consumption of refined

gasoline represents 40% of world consumption.

11US regular gasoline, source: EIA.

6

The demand for refined oil products in the US grew steadily during the 1980s and 1990s before peaking at 756 million gallons per day (gpd) 2005. The US consumed 390 million gpd of gasoline in 2007

though consumption fell to 365 million gpd in 2012.14 Aside from refining crude oil into gasoline, refineries

produce many products that are important inputs into other industries. Retail gasoline prices have recently

experienced increased variability in the US with prices ranging from $1.09 in 2001 to a high of $4.06 in

the summer 2008 before declining to about $3.60 in the summer of 2013. Wholesale prices peaked around

$3.40 a gallon in 2008.15 Many justify the high prices as a result of the growing demand for petroleum

products and supply limitations, including the scarcity of crude oil, Middle East uncertainty, hurricanes,

and the OPEC cartel. Others claim the high prices result from coordinated anticompetitive behavior by big

oil companies or speculation by the financial industry. Outages, investment and utilization choices by oil

refineries may also play a significant role in affecting downstream prices.

(usually ethanol) by a blender.

14Annual world consumption of crude oil totals 30 billion barrels, of which 7.5 billion barrels is produced in the US. About 60%

of crude oil used by refineries is imported and US consumption of refined gasoline represents 40% of world consumption. See

http://www.eia.gov/dnav/pet/pet_cons_psup_dc_nus_mbbl_a.htm.

15US regular gasoline, source: EIA.

5
Figure 2: Average Refinery Yields, 2010 (Including Gasoline Blending Components)

Finished Motor Gasoline

19%

Distillate Fuel Oil

32%

Kerosene + Jet Fuel

10%

Residual Fuel Oil

4%

Motor Gasoline Blending

Components

35%

Source: EIA

*Motor gasoline blending components are

reported as a (negative) input into a refinery

since they are not a finished product.

Figure 2: Average Refinery Yields, 2010 (Including Gasoline Blending Components)

Louisiana, though there are significant operations in the Northeast, the Midwest, and

California. During World War II, the country was divided into Petroleum Administration

for Defense Districts (PADDs) to aid in the allocation of petroleum products. Figure 3

displays a map of refinery locations along with delineations of the five PADDs and ten

refinery districts.

While retail markets for gasoline tend to be very small, markets for wholesale gasoline

are relatively large due to the extensive pipeline network use to transport most refined

products. While a PADD may have roughly approximated a market in 1945, these

delineations were made before the pipeline network had been fully developed, so they

are now just a convenient way to report statistics on the industry.12 A map of major crude

oil and production pipelines is shown in figure 4. With important pipelines connecting

the Gulf Coast production center to the population centers in the Northeast and the

Midwest, PADDs I, II, and III are closely linked and may constitute one large wholesale

gasoline market. The Rocky Mountain region (PADD IV) is fairly isolated from the rest

of the country and imports only limited refined product from other regions. Finally,

12For instance, the Colonial pipeline, which runs from the Gulf Coast up to the Northeast, was built

in 1968. Pipelines now carry 70% of all refined products shipped between PADDs.

7

About one-half of US production occurs near the Gulf of Mexico in Texas and Louisiana, though there

are significant operations in the Northeast, the Midwest, and California. During World War II, the country

was divided into PADDs to aid in the allocation of petroleum products. Figure 3 displays a map of refinery

locations along with delineations of the five PADDs.16

While some refined products are consumed in areas close to a refinery, an extensive pipeline network

transports most refined products to more distant consumption points.17 Figure 4 shows the major refined

products pipelines in the US. With important pipelines connecting the Gulf Coast production center to the

population centers in the Northeast and the Midwest, PADDs I, II, and III are closely linked economically.

The Rocky Mountain region (PADD IV) is fairly isolated from the rest of the country and imports only

limited refined product from other regions. Finally, refiners on the West Coast (PADD V) are also relatively

isolated from other refining regions. PADD V also includes California, a state that, due to strict environmental regulations, is limited in its ability to use products that are refined for consumption in other states.

16PADDs II and III are also subdivided into refining districts.

17For instance, the Colonial pipeline, which runs from the Gulf Coast up to the Northeast, was built in 1968. Pipelines now carry

70% of all refined products shipped between PADDs.

6
Figure 3: US Refineries, PADDs, and Refining Districts

PADD Refining District

I East Coast

Indiana-Illinois-Kentucky

Minn.-Wisc.-N. and S. Dakota

Oklahoma-Kansas-Missouri

Louisiana Gulf Coast

New Mexico

Texas Gulf Coast

Texas Inland

IV Rocky Mountain

V West Coast

II

III

Figure 3: Location and US Refineries, PADDs, and Refining Districts

refiners on the West Coast (PADD V), which includes California, a state that, due to

strict environmental regulations, are limited in their ability to use products that are

refined in other states.

Figure 4: Major Refined Product Pipelines (Source: Allegro)

Aside from the domestic refining industry, US refiners face limited competition from

8

Figure 4: Major Refined Product Pipelines

PADD Refining District

I East Coast

Indiana-Illinois-Kentucky

Minn.-Wisc.-N. and S. Dakota

Oklahoma-Kansas-Missouri

Louisiana Gulf Coast

New Mexico

Texas Gulf Coast

Texas Inland

IV Rocky Mountain

V West Coast

II

III

Figure 3: Location and US Refineries, PADDs, and Refining Districts

refiners on the West Coast (PADD V), which includes California, a state that, due to

strict environmental regulations, are limited in their ability to use products that are

refined in other states.

Figure 4: Major Refined Product Pipelines (Source: Allegro)

Aside from the domestic refining industry, US refiners face limited competition from

8

Source: Allegro Energy Group

Aside from the domestic refining industry, US refiners face limited competition from abroad. While the

US is very dependent on foreign oil, domestic refinery production accounts for about 90% of US gasoline

consumption, though the import share has grown since the mid-1990s. These imports come primarily into

the Northeast, which receives 45% of its supply from outside sources, such as the US Virgin Islands, the

United Kingdom, the Netherlands, and Canada.

7
2.1 Capacity and Utilization of US Oil Refineries

The refining industry consists of 142 refineries owned by 61 refining companies in January 2011. However,

no new complex refineries have been built in the US since 1976.18 In fact, many plants have closed and the

number of refineries has fallen from 223 in 1985. However, most of these closures were small and inefficient

plants, and those that remain have expanded, so total operable capacity has grown from 15.6 million barrels

per day (bpd) in 1985 to over 18 million bpd today (atmospheric distillation capacity). The overall number of

refineries along with their production capacity are displayed in figure 5. The average plant size has increased

from 74,000 bpd in 1985 to almost 128,000 bpd in 2011. The largest refiner (Exxon Mobil) controls about

10% of the total US refining capacity and the top five refiners account for 43% of total capacity.

Though the atmospheric distillation capacity of oil refineries is the most often cited figure when talking

about the size of plants, consideration of the capacity of downstream units is also important as refiners seek

maximum flexibility in their production slate. Figure 6 displays the average size of downstream refining

units as a proportion of total downstream capacity by PADD. While there are other downstream units, such

as hydrotreaters and vacuum distillation units, these four units make up a majority the typical refinery’s

downstream capacity. (Fluid) Catalytic Cracking units make up the largest percentage of downstream capacity for all five PADDs. These units break up heavy gas oils into smaller and more valuable molecules.

Catalytic reformers are the next largest group of units and these are generally used to increase the octane

level of petroleum products. Instead of breaking down molecules like a cracker, reformers reconfigure

molecules to make them more valuable. Thermal cracking and catalytic hydrocracking are the smallest of

the downstream units, though used relatively more in PADD V. These also break apart chains of hydrocarbons into smaller chains either using heat (thermal) or using a catalyst and hydrogen (hydrocracking). One

extreme form of thermal cracking is known as coking, which breaks apart heavy feedstocks into lighter oils.

Hydrocrackers are relatively more efficient at making distillate than making gasoline.19

Capacity utilization rates at US refineries had been rising throughout the 1990s, but have fallen throughout the 2000s to an average of about 85% in 2011 as shown in figure 7.20 From 2000 to 2008, the average

utilization rate in all US manufacturing industries was 77%, so even with the recent drop, refiners still operate their plants at high rates.21 Also shown in figure 7 is the average utilization rate by month (averaged

across years). It is clear that although annual averages have fallen, refiners still run their plants at a high rate

during the high-demand summer driving months with utilization rates averaging over 90%. Note these rates

are an average across plants, some of which may be shut down for a turnaround, while others are running

near their maximum rate. Unfortunately, I do not observe the utilization rate of individual plants.

18Several small refineries have been built, all with less than 100,000 barrels per day of distillation capacity. See http://www.

eia.gov/tools/faqs/faq.cfm?id=29&t=6.

19Total downstream capacity as a percentage of upstream distillation capacity (a measure of plant complexity) also varies significantly from 60% in PADD I to 84% in PADDs III and V.

20See http://www.eia.gov/dnav/pet/pet_pnp_unc_dcu_nus_m.htm.

21See http://www.federalreserve.gov/releases/G17/caputl.htm.

8
Figure 5: Number of Plants and Capacity over Time

14,000,000

14,500,000

15,000,000

15,500,000

16,000,000

16,500,000

17,000,000

17,500,000

18,000,000

18,500,000

120

130

140

150

160

170

180

190

1994 1995 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Operable Atmospheric Distillation Capacity (BPD)

Number of Plants

Year

Plants Capacity

Figure 5: Number of Plants and Refining Capacity over Time

77%, so even with the recent drop, refiners still operate their plants at high rates.13 Also

shown in the figure is the average utilization rate by month (averaged across years). It

is clear that although annual averages have fallen, refiners still run their plants at a high

rate during the high-demand summer driving months with utilization rates averaging

over 90%.

Building a new refinery is very expensive, and environmental requirements and permits create significant hurdles.14 Evidence from a 2002 US Senate hearing estimated the

cost of building a 250,000 bbl/day refinery at around 2.5 billion dollars, with a completion time of 5-7 years (Senate (2002)). This assumes the various environmental hurdles

and community objections are satisfied. No one wants a dirty refinery operating near

them.15 In May 2007, the chief economist at Tesoro, Bruce Smith, was quoted as saying

that the investment costs in building a new refinery are so high that “you’d need 10 to

15 years of today’s margins [at the time, around 20%] to pay it back.”16

13See http://www.federalreserve.gov/releases/G17/caputl.htm.

14One of the few new plants in development is in Yuma, Arizona. The builder is still acquiring all

the necessary permits to begin construction, but plans to be up and running in 2013. Another project

in Elk Point, South Dakota is also underway.

15Commonly referred to as “NIMBY,” an acronym for Not In My Back Yard.

16The National Petrochemical & Refiners Association estimates that the average return on investment in the refining industry between 1993-2002 was 5.5%. The S&P 500 averaged over

10

Building a new refinery is very expensive, and environmental requirements and permits create significant

hurdles.22 Evidence from a 2002 US Senate hearing estimated the cost of building a 250,000 bpd refinery

at around 2.5 billion dollars, with a completion time of 5-7 years (Senate (2002)). This assumes the various

environmental hurdles and community objections are satisfied. In May 2007, the chief economist at Tesoro,

Bruce Smith, was quoted as saying that the investment costs in building a new refinery are so high that

“you’d need 10 to 15 years of today’s margins [at the time, around 50%] to pay it back.”23

22One of the few new plants in development is in North Dakota. The 20,000 bpd plant is scheduled to be completed by late 2015.

23The National Petrochemical & Refiners Association estimates that the average return on investment in the refining industry

between 1993-2002 was 5:5%. The S&P 500 averaged over 12% for the same period. See “Lack of Capacity Fuels Oil Refining

Profits” available online at http://www.npr.org/templates/story/story.php?storyId=10554471 (downloaded: 09/13/2008).

Crude oil acquisition costs on the West Coast were about $1.30 per gallon in early 2007 compared with wholesales gasoline prices

of around $2.00 per gallon (source: EIA).

9
Figure 6: Downstream Capacity by PADD, 2010

64%

45% 43% 45%

33%

28%

31%

24%

28%

22%

6%

14%

19%

20%

23%

3%

9% 13%

7%

21%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

12345

Percent of Total Downstream Capacity

PADD

Average Firm Downstream Capacities, July 2010

By PADD

Catalytic Cracking Catalytic Reformers Thermal Cracking Catalytic Hydrocracking

These units account for most of a

refinery's downstream capacity,

though there are other smaller units.

Figure 6: Downstream Capacity by PADD, 2010

82.00

84.00

86.00

88.00

90.00

92.00

94.00

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Average Annual Utilization Rate

Year

82.00

84.00

86.00

88.00

90.00

92.00

94.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Average Monthly Utilization Rate

Month

Figure 7: Average Annual Utilization Rate and Average Monthly Utilization Rate

11

Figure 7: Average Utilization Rates: Annual (L) and Monthly (R)

64%

45% 43% 45%

33%

28%

31%

24%

28%

22%

6%

14%

19%

20%

23%

3%

9% 13%

7%

21%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

12345

Percent of Total Downstream Capacity

PADD

Average Firm Downstream Capacities, July 2010

By PADD

Catalytic Cracking Catalytic Reformers Thermal Cracking Catalytic Hydrocracking

These units account for most of a

refinery's downstream capacity,

though there are other smaller units.

Figure 6: Downstream Capacity by PADD, 2010

82.00

84.00

86.00

88.00

90.00

92.00

94.00

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Average Annual Utilization Rate

Year

82.00

84.00

86.00

88.00

90.00

92.00

94.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Average Monthly Utilization Rate

Month

Figure 7: Average Annual Utilization Rate and Average Monthly Utilization Rate

11

Even without new refineries, existing refineries have invested to expand capacity.24 The distribution of

historical investments in atmospheric distillation capacity is shown in figure 8. While the mean investment

has been 1.3% per year, the median is zero as plants tend to make very infrequent investments. Even restricting the sample to non-zero changes as shown in the graph, investments tend to be small, with almost 85% of

the non-zero changes less than 10%. Although almost 75% of plant-year observations in the sample show

24I observe change in the capacity of different units at a refinery over time, but not the cost to make those changes.

10
no change in atmospheric distillation capacity, there is some investment in either upstream or downstream

units in over 63% of the observations.

Figure 8: Distribution of Non-Zero Atmospheric Distillation Investments

−100 −50 0 50 100 150 200 250

0

20

40

60

80

100

120

Investment (Percent)

Frequency

Aside from the recession of 2008, while total refining capacity has risen in the past 10 years, it has not

kept up with demand growth. Capacity of oil refiners has increased by 10% in the past 10 years, while

demand for gasoline has increased about 17%. The gap has been filled by new requirements that gasoline

be blended with ethanol and to some degree, growing imports. However, new regulations requiring the shift

from MTBE25 oxygenates to ethanol poses a problem for this segment of supply because foreign refiners

have not invested in the facilities to produce ethanol-blended gasoline.26 Even with excess capacity, at

certain times of the year supply alternatives can be limited so even a minor supply disruption (or a major

one like Hurricane Katrina) can have a large price impact.27

25Methyl Tertiary Butyl Ether.

26See http://www.eia.gov/pub/oil_gas/petroleum/feature_articles/2006/mtbe2006/mtbe2006.pdf.

27Following Hurricane Katrina on 9/23/05, capacity fell by 5 million bpd. This represented a full one third of US refining

capacity. Inventories are also limited as there is only about 20-25 days worth of gasoline in storage at any time.

11
2.2 Profitability (Crack Spreads)

Although oil refining has historically been an industry plagued by thin profit margins, refiners typically

see larger profits when crude oil prices are low and/or product demand is relatively high.28 Taking into

account approximate relative importance of the two leading refined products, gasoline and distillate, that

hypothetically might be produced from a single barrel of crude oil, one simple measure of the profit margin

at any point in time at a refinery is the “crack spread.”29 The crack spread, expressed in dollars per barrel, is

calculated as:

Crack =

1 ∗Price(distillate) +2 ∗Price(gasoline)−3 ∗Price(crude oil)

3

. (1)

The crack spread for refineries in three states are shown in figure 9. Data are from EIA and are based

on the first purchase price of crude oil and wholesales prices of gasoline and distillate in each state.30 The

crack spread fluctuates quite a bit from month to month, generally peaking in the summer months of each

year. Refineries in each state may be using very different crude oils. While most refineries use multiple

types of crude oil, Brent is relatively more important on the East Coast, WTI in the Midwest, and Alaskan

North Slope on the West Coast. Though the crack spreads shown tend to move together, the levels vary and

refineries in one area of the country may have better price spreads than in another area and these relationships

change over time.

28See, for example, ftp://www.eia.doe.gov/pub/oil_gas/petroleum/analysis_publications/petroleum_issues_

trends_1996/CHAPTER7.PDF.

29The most popular crack spread is known as the “3-2-1 crack spread” referring to the approximate ratio of 3 barrels of crude oil

to 2 barrels of gasoline and 1 barrel of distillate. Less complex refineries may use other ratios reflecting their typical production

levels (e.g., 2-1-1).

30See: http://www.eia.gov/dnav/pet/pet_pri_dfp1_k_m.htm and http://www.eia.gov/dnav/pet/pet_pri_

refoth_a_epm0_pwg_dpgal_m.htm.

12
Figure 9: Crack Spreads for Three States

$0.00

$0.20

$0.40

$0.60

$0.80

$1.00

$1.20

1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Crack Spread ($/Gal)

Year‐Month

california pennsylvania texas

Figure 9: 3-2-1 Crack Spread for Three States.

have a major impact on production capability. The EIA divides refinery outages into

four classes, summarized in table 2.3.

Type Typical Length of Outage Frequency

Planned Shutdowns 1-2 Weeks Every year

Unplanned Shutdowns 2-4 Weeks -

Planned turn-arounds 3-9 Weeks Every 3-5 years

Emergency Shutdowns Varies -

Source: EIA.

Table 1: Refinery Downtime

Planned turn-arounds are major refinery overhauls, while planned shutdowns bridge

the gap between turn-arounds. Unplanned shutdowns involve unexpected issues that

may allow for some strategic planning of the downtime, but often may force a refinery

to reduce production sub-optimally. Finally, emergency shutdowns are those that cause

14

2.3 Refinery Maintenance and Outages

An oil refinery is a complex operation that requires frequent maintenance, ranging from small repairs to major overhauls.31 The regular maintenance episodes tend to be short and have minimal impact on production

as they are strategically scheduled for low demand periods. Unplanned outages, by definition, can take place

at any time and can have a major impact on production capability. The EIA divides refinery outages into

four classes, summarized in table 1.

Table 1: Refinery Downtime

$0.00

$0.20

$0.40

$0.60

$0.80

$1.00

$1.20

1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Crack Spread ($/Gal)

Year‐Month

california pennsylvania texas

Figure 9: 3-2-1 Crack Spread for Three States.

have a major impact on production capability. The EIA divides refinery outages into

four classes, summarized in table 2.3.

Type Typical Length of Outage Frequency

Planned Shutdowns 1-2 Weeks Every year

Unplanned Shutdowns 2-4 Weeks -

Planned turn-arounds 3-9 Weeks Every 3-5 years

Emergency Shutdowns Varies -

Source: EIA.

Table 1: Refinery Downtime

Planned turn-arounds are major refinery overhauls, while planned shutdowns bridge

the gap between turn-arounds. Unplanned shutdowns involve unexpected issues that

may allow for some strategic planning of the downtime, but often may force a refinery

to reduce production sub-optimally. Finally, emergency shutdowns are those that cause

31Refinery maintenance is crucial not only for production sustainability, but also for the safety of the plant. A 2005 fire at BP’s

Texas City refinery killed 15 workers and injured over 100 more.

13
Planned turn-arounds are major refinery overhauls, while planned shutdowns bridge the gap between

turn-arounds. Unplanned shutdowns involve unexpected issues that may allow for some strategic planning

of the downtime, but often may force a refinery to reduce production sub-optimally. Finally, emergency

shutdowns are those that cause an immediate plant breakdown like a refinery fire.

Figure 10: Planned and Unplanned Outages by Year

an immediate plant breakdown like a refinery fire.

 ‐

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Through

8/11

Barrels Offline (KBBLS)

Year

Planned and Unplanned Outages

All Units

All Planned Outages All Unplanned Outages

Figure 10: All Planned and Unplanned Outages by Year.

Organization for planned turn-arounds typically start years in advance, and cost millions of dollars to implement, in addition to the revenue lost from suspending production.

Due to the hiring of outside personnel, major refineries often have to plan these turnarounds at different times because of the shortage of skilled labor to implement them.

Given the typical seasonal variation in product demand, the ideal periods for maintenance are the first and third quarter of the year, though in some northern refineries, cold

winter weather forces shifts in planned downtimes. Figure 10 shows the planned and unplanned outages over time for all US plants. Clearly seen in the figure is the increase in

unplanned outages following the hurricanes in 2005 and the increase in planned outages

in 2009 as refiners went offline for maintenance as demand fell during the recession.

Even though refineries consist of several components, such as distillation columns,

reformers and cracking units, these components are dependent on one another so a breakdown of any one component can affect the production capability of the entire refinery.

Downstream units include hydrocrackers, reformers, fluid catalytic cracking (FCC) units,

alkylation units, and coking units. They are responsible for breaking down hydrocarbons

15

Organization for planned turn-arounds typically start years in advance, and cost millions of dollars

to implement, in addition to the revenue lost from suspending production. Due to the hiring of outside

personnel, major refineries often have to plan these turnarounds at different times because of the shortage of

skilled labor to implement them. Given the typical seasonal variation in product demand, the ideal periods

for maintenance are the first and third quarter of the year, though in some northern refineries, cold winter

weather forces shifts in planned downtimes. Figure 10 shows the planned and unplanned outages over time

for all US plants. Clearly seen in figure 10 is the increase in unplanned outages following the hurricanes in

2005 and the increase in planned outages in 2009 as refiners went offline for maintenance as demand fell

during the recession.

Even though refineries consist of several components, such as distillation columns, reformers and cracking units, these components are dependent on one another so a breakdown of any one component can affect

the production capability of the entire refinery. Downstream units include hydrocrackers, reformers, fluid

catalytic cracking (FCC) units, alkylation units, and coking units. They are responsible for breaking down

hydrocarbons into more valuable products and removing impurities such as sulfur. For example, in a typical

refinery, only 5% of gasoline is produced from the primary distillation process; the rest comes hydrocrackers

(5%), reformers (30%), FCC and alkylation units (50%), and coking units (10%). Not all refineries have

14
all of these components, so such refineries are even more affected when one component goes down (EIA

(2007)).

Figure 11 shows the percent of capacity offline by year and for various refining units. Though the

percentages tend to move roughly together, certain units are more affected in some years (e.g., most catalytic

hydrocracking capacity is located along the Gulf Coast so was particularly affected by the hurricanes in

2005). Since 2005, typically 5-8% of each unit’s capacity is offline in a given year for either planned

maintenance or unplanned outages.

Figure 11: Outages by Unit and Year

into more valuable products and removing impurities such as sulfur. For example, in a

typical refinery, only 5% of gasoline is produced from the primary distillation process;

the rest comes hydrocrackers (5%), reformers (30%), FCC and alkylation units (50%),

and coking units (10%). Not all refineries have all of these components, so such refineries

are even more affected when one component goes down (EIA (2007)).

Figure 11 shows the percent of capacity offline by year and for various refining units.

Though the percentages tend to move roughly together, certain units are more affected in

some years (e.g., most catalytic hydrocracking capacity is located along the Gulf Coast so

was particularly effected by the hurricanes in 2005). Since 2005, typically 5-8% of each

unit’s capacity is offline in a given year for either planned maintenance or unplanned

outages.

3%

3%

3%

3%

5%

6%

7%

4%

5%

4%

5%

4% 4%

5%

4%

7%

7%

5% 5%

8%

6%

6%

3%

4%

3%

5%

11% 11%

8%

6%

7%

7% 6%

3%

4%

4%

5%

7% 7%

5%

6%

5%

6%

6%

3% 3%

5%

5%

7%

5% 5%

5%

6%

2%

6%

0%

2%

4%

6%

8%

10%

12%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Through 8/11

Percent of Capacity Offline

Year

Percent of Capacity Offline

All US Refineries, By Refining Unit

Atmospheric Distillation Catalytic Cracking Catalytic Hydrocracking Catalytic Reforming Thermal Cracking

Figure 11: Outages by Unit and Year.

At the PADD level, EIA reports that in the 1999-2005 period, refineries experienced

reductions in monthly gasoline and distillate production of up to 35% due to outages. At

the monthly frequency, there is little effect of outages on product prices. This is primarily

because most (planned) outages occur during the low-demand months when markets are

not tight; most outages last less than a month; and the availability of imports, increased

production from other refineries, and inventories provide a cushion to supply. However,

unplanned outages, like those caused by a hurricane, still affect may have significant

effects on the downstream prices and profitability of all refineries.

16

At the PADD level, EIA reports that in the 1999-2005 period, refineries experienced reductions in

monthly gasoline and distillate production of up to 35% due to outages. At the monthly frequency, there is

little effect of outages on product prices. This is primarily because most (planned) outages occur during the

low-demand months when markets are not tight; most outages last less than a month; and the availability

of imports, increased production from other refineries, and inventories provide a cushion to supply. However, unplanned outages, like those caused by a hurricane, still affect may have significant effects on the

downstream prices and profitability of all refineries.

Overall, the oil refining industry features several economic puzzles, some of which I explore in this

paper. While the industry is relatively competitive, refiners at times can earn significant profits, as measured

by the crack-spread. However, entrants have yet to overcome the regulations and costs of setting up a new

plant and existing firms have been cautious in their expansion. As a result, plants may run at high rates

of utilization, which leads to instability in the face of unexpected capacity disruptions. These outages can

impact both product prices and the investment decisions of refiners.

15
3 Data

I collect outage data on all refineries from January 2001- August 2011 from Petrocast (Industrial Info Resources). Outage data are available by plant and unit type (atmospheric distillation, FCC, hydrocrackers,

reformer, and thermal crackers) and planned and unplanned outages are reported separately. I observe the

length of the outage (in days) and the number of barrels that were offline.32 Descriptive statistics on the

outages data are shown in table 2. Of the 13,696 plant-year-month observations in the data, 3,544 contain

some type of outage. The average monthly atmospheric distillation outage is around 203,000 barrels.

Table 2: Refinery Outages, 2001-August 2011

p, py g,

which leads to instability in the face of unexpected capacity disruptions. These outages

can impact both product prices and the investment decisions of refiners.

3 Data

I collect outage data on all refineries from 2001 2010 from Petrocast (Industrial Info

Resources). Outage data are available by plant and unit type (atmospheric distillation,

FCC, hydrocrackers, reformer, and thermal crackers) and planned and unplanned outages

are reported separately. I observe the length of the outage (in days) and the number of

barrels that were offline. 21 Descriptive statistics on the outages data are shown in table

2. Of the 13,696 plant-year-month observations in the data, 3,544 contain some type of

outage. The average monthly atmospheric distillation outage is around 203,000 barrels.

N (>0) Min Mean Max Std. Dev.

Outages (KBPM)

All 3,544 0 457 28,943 1,646

Atmospheric Distillation 1,564 0 203 14,203 869

Catalytic Cracking 1,441 0 91 5,425 397

Catalytic Hydro-Cracking 687 0 30 3,720 198

Catalytic Reforming 1,572 0 52 4,340 247

Thermal Cracking 814 0 30 4,340 182

Descriptive statistics reported at the plant-year-month level in thousands of barrels per 

month for each unit from 2001 - August 2011. There are a total of 13,696 observations 

(107 plants * 128 months).

Table 2: Descriptive Statistics: Refinery Outages, 2001 - August 2011

I match the outage data with investment data that is publicly available from the

US Energy Information Administration (EIA). The data reflect the current atmospheric

distillation capacity of the plant, but also the capacities of the downstream units mentioned above. Capacity data is available at an annual level and descriptive statics for

2010 are shown in table 3. Almost all plants in the database have atmospheric distillation and reforming capacity and most have catalytic cracking units.22 Investments in

physical capacity are infrequent given the high costs of taking units offline while the

21There are other potentially interesting dimensions to the data that I plan to exploit in future work.

I mention a few in section 5.

22Of the 107 plants in the database, 103 are active in 2010.

17

I match the outage data with investment data that is publicly available from the US Energy Information

Administration (EIA). The data reflect the current atmospheric distillation capacity of the plant, but also

the capacities of the downstream units mentioned above. Capacity data is available at an annual level

and descriptive statics for 2010 are shown in table 3. Almost all plants in the database have atmospheric

distillation and reforming capacity and most have catalytic cracking units.33 Investments in physical capacity

are infrequent given the high costs of taking units offline while the changes are made. Therefore annual data

is appropriate for measuring these changes, however smaller increases in capacity throughput (known as

“capacity creep”) may occur throughout the year. Since EIA and Petrocast do not share a common plant

identifier, I manually match plants between the two datasets based on their name and location, which results

in a database containing 107 plants and representing about 92% of total US refining capacity.

32There are other potentially interesting dimensions to the data that I plan to exploit in future work. I mention a few in section 5.

33Of the 107 plants in the database, 103 are active in 2010.

16
Table 3: Plant Capacities, 2010 

however smaller increases in capacity throughput (known as “capacity creep”) may occur

throughout the year. Since EIA and Petrocast do not share a common plant identifier,

I manually match plants between the two datasets based on their name and location,

which results in a database containing 107 plants and representing about 92% of total

US refining capacity.

N (>0) Min Mean Max Std. Dev.

Capacity (BPD)

Atmospheric Distillation 103 6,500 161,296 584,000 122,438

Catalytic Cracking 84 0 55,086 243,000 51,621

Catalytic Hydro-Cracking 43 0 16,570 130,000 25,140

Catalytic Reforming 91 0 33,120 146,000 30,811

Thermal Cracking 56 0 23,311 122,000 29,684

Descriptive statistics reported at the plant level in barrels per day for each unit in 2010. 

Table 3: Descriptive Statistics: Plant Capacities, 2010

Finally, I collect refiner wholesale prices of gasoline, distillate (diesel fuel), and firstpurchase prices of crude oil from which I create a simple 3-2-1 crack spread described in

section 2.23 Both the crude oil and product prices are usually available at the state-level

so all refineries in a given state are matched to the same set of prices. For states that

EIA does not report a crude oil price, I use the corresponding PADD price. In some

regressions below, I also use PADD-level gasoline and distillate prices that are averages

of the state-level prices. Gasoline and distillate stocks (available at the PADD level) and

utilization rates (available at the PADD-district level) are also matched to the data.24

Descriptive statistics on utilization rates, prices, and refinery stocks are shown in table

4. Utilization rates during my sample average about 89% of atmospheric distillation

capacity and crude prices average 58 dollars per barrel (though peak around $134 in

2008). The crack spread experiences considerable variability over the sample period,

ranging from 20 cents up to almost one dollar per gallon.

23EIA defines a first-purchase price as “An equity (not custody) transaction involving an arms-length

transfer of ownership of crude oil associated with the physical removal of the crude oil from a property

(lease) for the first time. A first purchase normally occurs at the time and place of ownership transfer

where the crude oil volume sold is measured and recorded on a run ticket or other similar physical

evidence of purchase.”

24Data are available here:

capacity: http://www.eia.gov/petroleum/refinerycapacity/

product prices: http://www.eia.gov/dnav/pet/pet pri refoth dcu nus m.htm

crude prices: http://www.eia.gov/dnav/pet/pet pri dfp1 k m.htm

utilization Rates: http://www.eia.gov/dnav/pet/pet pnp unc dcu nus m.htm

stocks:http://www.eia.gov/dnav/pet/pet stoc wstk a epm0 sae mbbl m.htm

18

Finally, I collect refiner wholesale prices of gasoline, distillate (diesel fuel), and first-purchase prices of

crude oil from which I create a simple 3-2-1 crack spread described in section 2.34 Both the crude oil and

product prices are usually available at the state-level so all refineries in a given state are matched to the same

set of prices. For states that EIA does not report a crude oil price, I use the corresponding PADD price. In

some regressions below, I also use PADD-level gasoline and distillate prices that are averages of the statelevel prices. Gasoline prices are a sales-weighted average price of all grades sold by refiners. Gasoline and

distillate stocks (available at the PADD level) and utilization rates (available at the refining district level) are

also matched to the data.35

Descriptive statistics on utilization rates, prices, and refinery stocks are shown in table 4. Utilization

rates during my sample average about 89% of atmospheric distillation capacity and crude prices average 58

dollars per barrel (though peak around $134 in 2008). The crack spread experiences considerable variability

over the sample period, ranging from 20 cents up to almost one dollar per gallon.

34EIA defines a first-purchase price as “An equity (not custody) transaction involving an arms-length transfer of ownership of

crude oil associated with the physical removal of the crude oil from a property (lease) for the first time. A first purchase normally

occurs at the time and place of ownership transfer where the crude oil volume sold is measured and recorded on a run ticket or other

similar physical evidence of purchase.”

35PADDs I, II, and III are further divided into several sub-districts. Data are available here:

capacity: http://www.eia.gov/petroleum/refinerycapacity/

product prices: http://www.eia.gov/dnav/pet/pet_pri_refoth_dcu_nus_m.htm

crude prices: http://www.eia.gov/dnav/pet/pet_pri_dfp1_k_m.htm

utilization Rates: http://www.eia.gov/dnav/pet/pet_pnp_unc_dcu_nus_m.htm

stocks:http://www.eia.gov/dnav/pet/pet_stoc_wstk_a_epm0_sae_mbbl_m.htm.

17
Table 4: Utilization Rates, Prices, and Stocks, 2001-August 2011

N Min Mean Max Std. Dev.

Utilization Rate (%)

Atmospheric Distillation 128 80.19 89.16 97.35 4.21

Prices ($/Gal)

WTI Crude 128 0.46 1.39 3.19 0.64

Brent Crude 126 0.45 1.37 3.16 0.68

Gulf Conventional Gas 126 0.50 1.57 3.28 0.70

Gulf Distillate (Heaing Oil) 126 0.52 1.61 3.80 0.77

Crack Spread 128 0.20 0.44 0.98 0.16

Stocks (KBPM)

Gasoline 128 190,020 210,605 235,157 9,595

Distillate 128 97,181 132,881 172,731 18,288

Descriptive statistics reported at the year-month level from 2001 - August 2011. Some 

prices through June 2011. Converting to other units, the average price of WTI is about 

58 dollars per barrel and the crack spread has averaged about 18.6 dollars per barrel over 

the sample.

Table 4: Descriptive Statistics: Utilization Rates, Prices, and Stocks, 2001 - August 2011

4 Empirical Specifications and Results

In the following section, I outline my empirical specifications and results regarding the

relationships between oil refinery outages, profitability, utilization and investment. I

first consider planned outages and how they are affected by profitability and time-ofyear effects. Then I move to unplanned outages and consider how the intensity at

which a plant is running and the time since the last maintenance episode affect the

likelihood of future outages. Once I understand the causes of outages, I then turn to

their effect on prices, specifically considering how the current tightness of the market as

measured by utilization rates and product stocks affect the impact of outages on prices.

The last empirical specification brings everything together to determine how planned

and unplanned outages, utilization rates, the crack spreads affect the future investment

decisions of refiners.

4.1 Planned Outages

In this subsection, I try to answer the question, “Do refiners generally take planned

downtime for maintenance when profit margins are low and do they delay taking their

plants offline when margins are high?” To answer this question, I estimate the following

regression:

P r(Planned Outagejm) = β0 + β1(Crack Spreadm) + μm + jm. (1)

Planned outages by refinery j in month m are regressed on the crack spread (avail

4 Empirical Specifications and Results

In the following section, I outline my empirical specifications and results regarding the relationships between

oil refinery outages, profitability, utilization and investment. I first consider planned outages and how they

are affected by profitability and time-of-year effects. Then I move to unplanned outages and consider how

the intensity at which a plant is running and the time since the last maintenance episode affect the likelihood

of future outages. Once I understand the causes of outages, I then turn to their effect on prices, specifically

considering how the current tightness of the market as measured by utilization rates and product stocks affect

the impact of outages on prices. The last empirical specification brings everything together to determine

how planned and unplanned outages, utilization rates, and crack spreads affect the investment decisions of

refiners.

4.1 Planned Outages

“Do refiners generally take planned downtime for maintenance when profit margins are low and do they

delay taking their plants offline when margins are high?” To answer these questions, I estimate the following

regression:

Pr(PlannedOutagejm) = β0 +β1(CrackSpreadm) + μm +εjm. (2)

18
Planned outages by refinery j in month m are regressed on the state-level crack spread and month fixed

effects.36 I estimate a simple probit regression predicting the probability of a planned outage, running

separate models for all outages, atmospheric distillation outages, and outages at major downstream refining

units. Controlling for month effects is crucial because it is well know that plants take annual maintenance

in the low-demand periods (usually early spring and again in the fall) and my goal is to estimate the effect

of the crack spread changing throughout the year.

Table 5: Probit Results: Pr(Planned Outage)

Variable: Coeff. t-stat Coeff. t-stat Coeff. t-stat

Constant -0.686*** 14.451 -1.307*** 21.858 -1.391*** 20.774

Crack ($/Gal) -0.456*** 7.151 -0.214*** 2.662 -0.449*** 4.622

Month FE

Observations

McFadden R2

Variable: Coeff. t-stat Coeff. t-stat Coeff. t-stat

Constant -1.990*** 22.806 -1.232*** 20.930 -1.910*** 22.406

Crack ($/Gal) 0.084 0.790 -0.336*** 4.195 -0.147 1.509

Month FE

Observations

McFadden R2

An observation is a plant-month for 107 plants from January 2001 - August 2011. 

3-2-1 crack spread based on state-level first-purchase prices of crude oil, gasoline, 

and distillate for each plant. The dependent variable is an indicator variable that 

equals one if a planned outage occured on the specified unit in a given month. 

Planned Cat. 

HydroCrack 

Yes

13,807

0.02

Dependent 

Variable: 

0.04

13,807

0.02

Planned Reformer 

Outages (1/0)

Planned Thermal 

Cracking Outages 

Yes Yes

13,807

13,807 13,807 13,807

0.04 0.03 0.05

Dependent 

Variable: 

Any Planned 

Outages (1/0)

Planned Atmos. 

Dist. Outages (1/0)

Planned Cat. Crack 

Outages (1/0)

Yes Yes Yes

Results of this regression are shown in table 5. The coefficient on the crack spread is negative and

significant when considering the probability of any planned outage meaning that relatively more profitable

periods are associated with fewer planned outages. The coefficient on the crack spread continues to be

negative and statistically significant in the specifications for atmospheric distillation, catalytic cracking, and

reformer outages, while it is insignificant for catalytic hydrocracking and thermal cracking units. The crack

spread is a rough measure of refinery profitability measuring only the relationship between the price of crude

oil and two refined products (gasoline and heating oil) so other prices and constraints may be important for

downstream units that primarily produce other products.

36The crack spread is one measure of the opportunity cost of shutting down the refinery. Since the outage itself may lead to

higher product prices and a larger crack spread, the contemporaneous crack spread may overstate this cost. However, including the

crack spread in the previous month produces similar results.

19
Figure 12: Probit Marginal Effects: Planned Outages by Month

0 2 4 6 8 10 12

0.05

0.1

0.15

0.2

0.25

0.3

Month

Probability of Any Planned Outage

10th Pctile Crack

Median Crack

90th Pctile Crack

Interpreting the magnitude of these coefficients is easier by considering the marginal effects. In figure

12, I plot the marginal effects by the month of the year. The graph shows the strong seasonality in predicted

planned outages, peaking in May and again in October. I evaluate these effects at three different levels of

the crack spread: the 10th, 50th and 90th percentiles. The higher crack spreads are associated with a lower

predicted probability of a planned outage. Figure 13 shows the marginal effects of varying the crack spread

for three different months of the year. The predicted probability of a planned outage ranges from over 0.3

when crack spread is low to less than 0.05 when the crack spread is relatively high.

20
Figure 13: Probit Marginal Effects: Planned Outages by Crack Spread

−0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Crack Spread ($/Gal)

Probability of Any Planned Outage

Feb

May

Jul

4.2 Unplanned Outages

Next, I move on to the question, “How are unplanned outages affected by utilization rates and the time

since the last plant turn-around?” Unfortunately, utilization rates are only measured at the refinery district

level so are an imperfect proxy for the production intensity of any given plant. They also measure only the

atmospheric distillation utilization, which is an important unit at a refinery, but only one of many that is

involved in the production process. In light of this limitation, I estimate the following regression:

Pr(UnplannedOutagejm) = β0 +β1(Utilizationm−1) +β2(T SLTAjm) (3)

+ β3(Hurricanepm) + μm +εjm.

Again, a probit model is estimated predicting the probability of an unplanned outage at refinery j in

month m. The variable TSLTA measures the time since the last turnaround in months. I calculate this time

using the last planned outage of the atmospheric distillation unit at a given plant. Turn-arounds generally

involve complete plant shut downs so planned atmospheric distillation outages are a good indicator of turnarounds.37 Hurricanes and other major weather events may cause many plants to experience an unplanned

outage at the same time. Therefore, I include and indicator that equals one if the PADD in which the plant

37For robustness, I have also used both the time since the last planned FCC and hydrocracker outage and the results are similar.

21
is located is impacted by a hurricane during the month of the reported outage.38 Month fixed effects are

included to control for seasonality of unplanned outages not accounted for by the hurricane indicator.

Table 6: Probit Results: Pr(Unplanned Outage)

Variable: Coeff. t-stat Coeff. t-stat Coeff. t-stat

Constant -0.074 0.383 0.198 0.818 -0.760*** 3.312

Utilization Rate (%) -0.014*** 6.607 -0.023*** 8.646 -0.010*** 4.215

Time Since Last T/A 0.003*** 3.063 0.003*** 2.951 0.003*** 3.295

Hurricane ID 0.791*** 10.526 0.992*** 11.884 0.911*** 11.105

Month FE

Observations

McFadden R2

Variable: Coeff. t-stat Coeff. t-stat Coeff. t-stat

Constant -0.160 0.546 0.037 0.151 -0.659** 2.166

Utilization Rate (%) -0.023*** 7.444 -0.021*** 7.766 -0.017*** 5.089

Time Since Last T/A 0.005*** 3.909 0.002 1.466 0.005*** 3.532

Hurricane ID 0.911*** 9.489 0.964*** 11.513 0.910*** 9.745

Month FE

Observations

McFadden R2

Dependent Variable: 

An observation is a plant-month for 107 plants from January 2001 - August 2011. "Time Since Last T/A" 

measures the number of months since the plant last experienced a planned outage (or Turn-Around). 

Utilization rate measured at the refining district level. "Hurricane ID" equals one if a hurricane made landfall 

in the PADD in which the plant is located in a particular month. The dependent variable is an indicator 

variable that equals one if a unplanned outage occured on the specified unit in a given month. 

Dependent Variable: Any Unplanned 

Outages (1/0)

Unplanned Atmos. Dist. 

Outages (1/0)

Unplanned Cat. Crack 

Outages (1/0)

0.05

13,721

Yes Yes Yes

Yes

0.05

13,721

0.05

Unplanned Reformer 

Outages (1/0)

Unplanned Thermal 

Cracking Outages (1/0)

Yes Yes

13,721

13,721 13,721 13,721

Unplanned Cat. 

HydroCrack Outages 

(1/0)

0.02 0.05 0.03

Results of this regression are presented in table 6. The TSLTA and hurricane variables are consistently

positive and significant as expected. However, the utilization rate is estimated to be negative and significant

in all specifications. This is likely because the rate is measured at the PADD-district level and a large

outage may affect all plants in a district over multiple months. While the hurricane variable will control

for contemporaneous weather-related effects, a widespread outage in a prior month may result in lower

district-level utilization rates in the current month as plants take time to come back online.

38Data on past hurricanes is collected from the National Oceanic and Atmospheric Administration:http://www.aoml.noaa.

gov/hrd/hurdat/All_U.S._Hurricanes.html.

22
Figure 14: Probit Marginal Effects: Unplanned Outages by Time Since Last T/A

0 20 40 60 80 100 120

0.1

0.15

0.2

0.25

TSLTA (Months)

Probability of Any Unplanned Outage

Utilization = 75%

85%

95%

Again, the magnitude of the effects are better seen with a graph of the marginal effects. Figure 14 shows

the predicted probability of an unplanned outage as a function of the time since the last planned plant turnaround.39 This effect is increasing though lower for higher utilization rates, ranging from about 0.10 for

plants that have recently performed maintenance to 0.25 for plants that have not experienced a planned turnaround in 8-9 years.40 Unfortunately, most of the data are not rich enough to separate unplanned outages

due to weather from those due to mechanical problems.

4.3 Product Prices

The last two subsections showed that planned outages tend to occur during the spring and fall and during

times of relatively low margins as measured by the crack spread. The amount of time since the last plant

turn-around is positively associated with future unplanned outages. But the question then becomes, “Do

these outages have an effect on prices?” An outage that occurs during a time when inventories are relatively

high and/or nearby utilization rates are low, should have less of an effect on output prices than when the

39Figure 18 in the appendix shows the marginal effects as they vary by the district-level utilization rate.

40The data are not rich enough to show planned turn-arounds that are spurred on by unplanned outages, but for robustness I

calculate the TSLTA variable based on the time since the last outage (of any type) and the results are similar. Note, the mean and

median time since the last planned atmospheric distillation outage are 17.5 and 13 months respectively. 8-9 years is the longest

spell in the data without a planned turn-around. The 75th percentile of TSLTA is just over two years.

23
market is relatively tight. Therefore, I estimate the following regression equation using OLS:

GasolinePricepm = β0 +β1(CrudePricepm) +β2(Hurricanep,m−1) +β3(Outagespm) (4)

+ β4(GasolineStockspm) + μm + μy + μp +εpm.

Since wholesale prices are generally determined by supply and demand forces beyond those in individual

states (due to pipelines, imports, etc.), I run this regression on gasoline prices at the PADD-month level.

The independent variables include the crude oil price, aggregate outages in the PADD, an indicator for a

hurricane impacting the PADD in the previous month, and gasoline stocks. Month and PADD fixed effects

are also included to account for seasonality and any geographic variation in the level of prices unrelated

to outages respectively. Year fixed effects are included to account for exogenous changes in gasoline price

levels over time. I also run the model on distillate prices.

Table 7: OLS Results: Effects of Outages on Prices - Planned versus Unplanned

Variable: Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat

Constant 0.510*** 5.141 0.495*** 4.913 0.180*** 3.579 0.190*** 3.757

Crude Price ($/Gal) 0.927*** 50.204 0.928*** 50.207 0.992*** 56.999 0.993*** 56.627

Hurricane ID (m-1) 0.025 0.780 0.029 0.912 0.015 0.496 0.016 0.513

Planned Atmos Capacity Offline (%) 0.003* 1.765 0.004*** 2.921

Unplanned Atmos Capacity Offline (%) 0.005*** 2.604 0.002 1.080

Planned FCC Capacity Offline (%) 0.003 0.986 0.002 0.597

Unplanned FCC Capacity Offline (%) 0.010** 2.529 0.004 1.211

Gas Stocks (Mbbls) -0.005*** 3.253 -0.005*** 3.081

Distillate Stocks (Mbbls) -0.002*** 2.580 -0.002** 2.548

Month FE

Year FE

PADD FE

Observations

R2

Dependent Variable: Gasoline Price 

($/Gal)

Gasoline Price 

($/Gal)

An observation is a padd-year-month covering January 2001 - August 2011. Gasoline, distillate, and crude oil prices are statelevel first-purchase prices matched to each plant. Plant outages are aggregated to the padd-month level.

Distillate Price 

($/Gal)

Yes Yes Yes

Distillate Price 

($/Gal)

Yes

Yes

635

0.98

Yes Yes Yes Yes

Yes Yes Yes

635 635 635

0.98 0.98 0.98

Table 7 presents results of four specifications run on gasoline and distillate prices using either atmospheric distillation outages or FCC (fluid catalytic cracking) outages. In each regression, I include planned

and unplanned outages separately to determine if product prices are less sensitive to planned outages. The

results clearly show that the variation in the crude oil price is the primary driver of the level of gasoline and

distillate prices with coefficients very close to one. The gasoline price regressions also show that unplanned

atmospheric distillation outages have a positive and significant effect on prices, and the effect is almost twice

as large as the effect of planned outages. The results are similar for FCC outages with the coefficient on

unplanned outages over three times the size the coefficient on planned outages (the latter coefficients are not

statistically significant). Gasoline stocks have an expected negative and significant association with prices

and the hurricane indicator is not significant, though crude oil prices and the month fixed effects likely pick

24
up much of the variation in prices caused by weather-related shocks.

Table 8: OLS Results: Effects of Outages on Prices - By Refining Unit

Variable: Coeff. t-stat Coeff. t-stat

Constant 0.519*** 5.188 0.195*** 3.891

Crude ($/Gal) 0.925*** 50.018 0.990*** 57.060

Hurricane ID (m-1) 0.022 0.690 0.008 0.259

Atmos Cap Offline (%) 0.005*** 2.837 0.005*** 3.361

CatCrack Cap Offline (%) 0.004 1.416 0.000 0.111

CatHydroCrack Cap Offline (%) 0.003 0.375 -0.009 1.166

Reformer Cap Offline (%) -0.017** 2.525 -0.017*** 2.695

Thermal Cracking Cap Offline (%) 0.005 0.783 0.016** 2.534

Gas Stocks (Mbbls) -0.006*** 3.397

Distillate Stocks (Mbbls) -0.002*** 2.834

Month FE

Year FE

PADD FE

Observations

R2

635 635

Yes Yes

Dependent Variable: Gasoline Price 

($/Gal)

Distillate Price 

($/Gal)

An observation is a padd-year-month covering January 2001 - August 2011. 

Gasoline, distillate, and crude oil prices are state-level first-purchase prices matched 

to each plant. Plant outages are aggregated to the padd-month level.

Yes Yes

Yes Yes

0.980 0.98

The distillate price regressions show that planned atmospheric distillation outages are positively associated with distillate prices and FCC outages show no significant effect. The FCC unit is relatively more

important for gasoline production so this result is not unexpected though it is surprising that unplanned

atmospheric outages show no significant effect. To better account for the fact that a refinery is composed

of many refining units and each is more or less important for producing gasoline and distillate, I run two

regressions of prices on the outages of individual refining units. These results are presented in table 8. The

results show that atmospheric distillation and catalytic cracking have the largest positive effect on gasoline

prices and thermal cracking is also important for distillate prices. In both specifications, the coefficient on

reformer outages is negative and significant. Recall the gasoline price is a sales-weighted average of all

grades (octane levels) of gasoline sold by refiners and reformers are used to increase the octane level of

gasoline. Therefore, if the reformer goes down, plants may end up producing more (lower octane) regular

grade gasoline, depressing the weighted-average price.

25
Figure 15: The Effects of Outages on Gas Prices: By Utilization Rate

‐10

‐8

‐6

‐4

‐2

0

2

4

6

8

10

< 80% 80%‐85% 85%‐90% 90%‐95% > 95%

Change in Gasoline Price (CPG)

Utilization Rate

Predicted Gasoline Price Change (CPG) Lower 95% Upper 95%

Predicted change in the gasoline price from an average outage of atmospheric distillation capacity at different

utilization rates.

In figure 15, I show the estimated price effect of atmospheric distillation outages and how the effect

varies with the prevailing utilization rate.41 Confidence intervals on the estimated coefficient are also shown

on the graph. The estimates are generally increasing in the utilization rate indicating that outages that occur

during periods of high utilization rates have more of an effect on prices than when utilization rates are

low. The economic importance of these results can be seen by estimating the predicted effect on prices

for typical outages seen in the real world. For example, at a utilization rate between 90% and 95%, if an

average atmospheric distillation outage occurred (about 4.15% of a PADD’s capacity), the model predicts

that gasoline prices would rise by 1.5 cents per gallon. When utilization rates are between 80% and 85%

(the 2012 average was 87.5%), the same outage would have no statistically significant effect on gasoline

prices.42

41I estimate equation 4 interacting outages with indicators for months when the utilization rate in the PADD-district was less than

80%, between 80% and 85%, between 85% and 90%, etc. Of course outages cause the utilization rate to fall so I select the months

in each regression based on the utilization rate just prior to an outage.

42The first estimate is found by multiplying the predicted effect (0.35 cpg) by the size of an average outage measured as the

percent of a PADD’s capacity (4.15).

26
Table 9: Gasoline Price Effects of an Average Outage

A Atmospheric Distillation Unit 3,395,525                        141,058 4.15% 0.352 1.46 (0.48, 2.45)

Planned 83,064                2.45% 0.268 1.11 (‐0.13, 2.35)

Unplanned 58,006                1.71% 0.480 1.99 (0.49, 3.49)

C Catalytic Cracking Unit 1,151,442                          62,782 5.45% 0.580 3.16 (0.53, 5.80)

Planned 34,515                3.00% 0.311 1.70 (‐1.68, 5.08)

Unplanned 28,267                2.45% 1.003 5.47 (1.22, 9.72)

By Unit (Planned+Unplanned)

Atmospheric Distillation 3,395,525                        141,058 4.15% 0.465 1.93 (0.59, 3.27)

Catalytic Cracking 1,151,442                           62,782 5.45% 0.410 2.24 (‐0.87, 5.34)

Catalytic Hydro Cracking 344,020                              20,851 6.06% 0.313 1.89 (‐8.02, 11.80)

Reformer 684,763                              35,992 5.26% ‐1.698 ‐8.92 (‐15.86, ‐1.98)

Thermal Cracking 486,775                              21,120 4.34% 0.534 2.32 (‐3.50, 8.13)

E

Regressions are run on data at the PADD‐month level from January 2001 ‐ December 2011.  Prices are regressed on the crude oil price, the percent of

the PADD's capacity that is offline, a hurricane indicator, gasoline stocks, and month, year, and PADD fixed effects.  Separate regressions are run on

each specification above (indicated in column 1).  The predicted price effect in specification A is found as follows: the model predicts that a 1%

atmospheric distillation outage in a PADD‐month is associated with a 0.352 CPG increase in the gasoline price.  An average outage in a PADD‐month is

4.154% of the total PADD capacity, so an average outage is predicted to raise gasoline prices by 0.352*4.15 = 1.46 CPG.

95% C.I. Specification Outage Type

B

D

Average Outage

(BPD)

Average PADD

Capacity (BPD)

Average

Outage (%)

Predicted Price

Effect (CPG)

Coeff.

Estimate

In table 9, I calculate the predicted price effects of an average outage of each refining unit.43 I also

consider planned and unplanned outages separately for the main distillation unit and the catalytic cracking

unit. As expected, planned outages of the distillation and catalytic cracking units are not associated with

a significant price effect because they tend to occur during periods of relatively low gasoline demand in

the spring and fall (see figure 12). In contrast, unplanned outages of these two units have a positive and

significant effect on prices with an average unplanned outage to a refinery’s catalytic cracking unit associated

with a 5.47 cpg gasoline price increase. In specification (E), planned and unplanned outages of five important

refining units are all included in the same regression. While coefficients on all units except the reformer are

positive, only the coefficient on atmospheric outages is positive and significant. The coefficient on reformer

outages is negative and significant as in table 8, possibly due to more lower octane gasoline being produced

as a result of the outage.

One way to interpret these predicted price effects is to compare them to what one would expect given

estimates of the price elasticity of demand for gasoline. Following an average PADD outage of 4.15%,

my predicted price effect in specification (A) is 1.46 cpg, or 0.93% of the average gasoline price observed

during the sample period. If there were no other supply responses, these price and quantity changes would

imply a price elasticity of demand estimate of -4.46. This estimate is far more elastic than estimates in the

literature, which are generally around -0.2, and suggests that overall output does not fall by nearly as much

due to offsetting output reactions by operating plants and inventory management. In fact, the reduction in

output that is consistent with an elasticity of -0.2 and a 0.93% price increase is 0.186%, which implies that

over 95% of the average PADD outage was offset.

43Specifications (B) and (D) of Table 9 correspond to the first two specifications of Table 7 and specification (E) corresponds to

the first specification in Table 8. For brevity, Table 9 shows only the coefficients corresponding to different types of outages. All

regressions control for the crude oil price, hurricanes, inventories and include month, year and PADD fixed effects. Specifications

(A) and (C) aggregate planned and unplanned outages.

27
4.4 Investment

The last set of regressions consider how planned and unplanned outages, utilization rates, profitability and

recent investments by competing plants affect the future investment behavior of refiners. Investment is

measured as the change in capacity (in barrels) at a plant from one year to the next. I expect that refinery

investment should be positively related to the prior year utilization rate and crack spread while negatively

related to recent increases in refined products stocks and investment by competing plants.44 Outages may

lead to more investment in the future if they make it more likely for a plant to perform investments because

the plant is already shut down.

I estimate the investment relationship in two stages. I first investigate the extensive margin: a refiner’s

decision whether to make any positive investment in capacity. I create an indicator variable that equals one

if a plant’s capacity rises from one year to the next and estimate the following probit model:45

Pr(Investmentjy > 0) = Φ



β0 +β1(CrackSpreads,y−1) (5)

+ β2(Utilizationd,y−1) +β3(Utilizationd,y−1 ∗Hurricanep,y−1)

+ β4(PlannedAtmosOutagesj,y−1) +β5(PlannedAtmosOutagesj,y−2)

+ β6(UnplannedOutagesj,y−1) +β7(UnplannedOutagesj,y−1 ∗Hurricanep,y−1)

+ β8(∆GasolineStocksp,y−1) +β9(CompetitorInvestmentp,y−1)

+ μy +εjy.

Investment in capacity by plant j in year y is regressed on last year’s average crack spread (at the state

level), the PADD subdistrict utilization rate, planned and unplanned outages, inventories, and investment

by other plants in the same PADD. I interact the utilization rate and unplanned outages with a hurricane

indicator that equals one if the PADD in which the plant is located experienced a hurricane in the prior year.

I also include the change in planned outages in the prior two years because investments may take multiple

years to complete and prior planned outages may contribute to the investment process. Year fixed effects are

included to control for the interest rate that may be changing over time and affecting a refiner’s investment

decision.46

In stage two, I investigate the intensive margin: a refiner’s decision over how large of an investment in

capacity to make, conditional on making any positive investment. This stage is operationalized by running a

regression of the change in capacity on the same set of explanatory variables as above, restricting the sample

to only those plant-year observations where investment is positive. I estimate the following equation by

OLS:

44I calculate competitor investment as the change in capacity by other plants located in the same PADD. A binary indicator that

equals one when competitor investment is positive is used in the empirical specifications.

45I focus only on positive investments in capacity because less than 5% of observations show a reduction in capacity.

46While it may be tempting to use refined product and crude oil futures prices as an independent variable in this regression,

several papers have shown that futures prices provide no additional predictive power over current prices. See, for example, Wu and

McCallum (2005) and Alquist and Kilian (2008).

28
(Investmentjy|Investmentjy > 0) = β0 +β1(CrackSpreads,y−1) (6)

+ β2(Utilizationd,y−1) +β3(Utilizationd,y−1 ∗Hurricanep,y−1)

+ β4(PlannedAtmosOutagesj,y−1) +β5(PlannedAtmosOutagesj,y−2)

+ β6(UnplannedOutagesj,y−1) +β7(UnplannedOutagesj,y−1 ∗Hurricanep,y−1)

+ β8(∆GasolineStocksp,y−1) +β9(CompetitorInvestmentp,y−1)

+ μy +εjy.

Results of the extensive margin regression are shown in table 10. The five specifications presented

correspond to the atmospheric distillation unit and the four downstream refining units. Coefficients on

planned outages in the previous two years are positive and significant as expected in most specifications,

while unplanned outages show no significant effect except for the thermal cracking specification. Recent

investment by competing plants is negative and significant for the atmospheric distillation unit suggesting

that plants may be reacting to the increase in PADD capacity by holding off on expanding their own plant.47

The utilization rate is positive and strongly significant in the first two specifications, which implies that all

else equal, plants are more likely to expand capacity if plants in their refining district are running at a high

rate. It is surprising that the coefficients on the crack spread are negative and significant for three of the

units. One potential explanation is mean reversion: refiners expect profitability to fall to more typical levels

in years following large crack spreads.

47Further robustness checks show this effect is strongest in PADD I. Aside from PADD V, refiners in PADD I ship the smallest

quantity of refined products to other PADDs so plants may be less likely to expand if nearby plants also recently made investments.

29
Table 10: Probit Results: Investment, Extensive Margin

Variable: Coeff. t-stat Coeff. t-stat

Constant -3.937** 2.522 -5.455*** 3.133

Crack Spread ($/Gal,y-1) -0.849* 1.925 -1.025** 2.051

Utilization Rate (%,y-1) 0.040** 2.322 0.054*** 2.855

Utilization Rate(%,y-1)*Hurricane 0.000 0.213 0.000 0.123

Planned Unit Outages (%,y-1) 0.007 1.056 0.033*** 3.322

Planned Unit Outages (%,y-2) -0.005 0.628 0.022** 2.023

Unplanned Unit Outages (%,y-1) -0.010 0.643 -0.013 0.671

Unplanned Unit Outages(%,y-1)*Hurricane 0.013 0.607 0.032 1.369

Gasoline Stocks(%Chg,y-1) -0.001 0.067 -0.007 0.364

Competitor Investment(Indicator,y-1) -0.316* 1.808 -0.220 1.434

Year FE

Observations

R2

Variable: Coeff. t-stat Coeff. t-stat Coeff. t-stat

Constant -3.196 1.477 -3.820** 2.138 -1.485 0.791

Crack Spread ($/Gal,y-1) 0.811 1.459 -0.562 1.115 -1.744*** 2.906

Utilization Rate (%,y-1) 0.014 0.578 0.030 1.541 0.008 0.376

Utilization Rate(%,y-1)*Hurricane 0.004* 1.709 0.000 0.003 0.002 0.936

Planned Unit Outages (%,y-1) 0.034** 2.114 0.019* 1.702 0.068*** 5.497

Planned Unit Outages (%,y-2) 0.068*** 4.295 0.008 0.629 0.070*** 5.369

Unplanned Unit Outages (%,y-1) -0.001 0.083 -0.030 0.771 0.029** 2.185

Unplanned Unit Outages(%,y-1)*Hurricane 0.030 1.564 0.028 0.681 -0.034 1.137

Gasoline Stocks(%Chg,y-1) 0.020 0.786 -0.031 1.551 0.030 1.324

Competitor Investment(Indicator,y-1) -0.028 0.184 0.264* 1.828 -0.110 0.694

Year FE

Observations

R2

The dependent variable equals one if a plant increased its capacity in year y. An observation is a plant-year for 107 plants from 

2002 - 2011. Some plants are not observed in all years. 3-2-1 crack spread is the prior year average based on state-level firstpurchase prices of crude oil, gasoline, and distillate for each plant. Utilization rate is the prior year average for the refining district 

in which the plant is located. Gasoline stocks measure the PADD-level change in stocks from y-2 to y-1. Competitor Investment

is an indicator that equals one if capacity by other refiners in the plant's PADD increased in the previous year. Hurricane is an 

indicator that equals one if the plant is located in a PADD that experienced a hurricane the previous year.

0.05 0.11 0.03

Yes Yes

909 892 886

904 900

Dependent Variable: I(Invest > 0), 

Atmospheric Dist. Unit

I(Invest > 0), 

Catalytic Cracking

Yes Yes

0.05 0.07

Dependent Variable: I(Invest > 0), 

Cat. Hydro Cracking

I(Invest > 0), 

Thermal Cracking

I(Invest > 0), 

Cat. Reformer

Yes

Results of the intensive margin regression are shown in table 11. The estimated coefficients on the crack

spread and competitor investment for the atmospheric distillation unit remain negative and significant, while

planned outages are positively associated with investment for four of the five units. However, other types of

outages on the various refining units do not show a consistent or significant effect. Because investment is

measured as the change in capacity at each unit, it is possible that refiners are investing in improvements to

their refining units that do not effect capacity, such as, increasing their ability to process different types of

crude oil or improving their flexibility in adjusting their production slate.

30
Table 11: OLS Results: Investment, Intensive Margin

Variable: Coeff. t-stat Coeff. t-stat

Constant -2.724 0.172 2.311 0.531

Crack Spread ($/Gal,y-1) -10.983** 2.203 -0.100 0.068

Utilization Rate (%,y-1) 0.109 0.639 -0.010 0.202

Utilization Rate(%,y-1)*Hurricane -0.004 0.235 -0.001 0.344

Planned Unit Outages (%,y-1) 0.127* 1.800 0.045* 1.855

Planned Unit Outages (%,y-2) 0.025 0.356 0.102*** 3.742

Unplanned Unit Outages (%,y-1) -0.049 0.186 -0.071 0.960

Unplanned Unit Outages(%,y-1)*Hurricane 0.253 0.802 0.165** 2.105

Gasoline Stocks(%Chg,y-1) 0.121 0.582 0.059 1.178

Competitor Investment(Indicator,y-1) -4.005** 2.417 -0.302 0.902

Year FE

Observations

R2

Variable: Coeff. t-stat Coeff. t-stat Coeff. t-stat

Constant -10.048 0.662 2.763 0.323 -6.544 0.847

Crack Spread ($/Gal,y-1) 0.094 0.024 -1.906 0.701 2.028 0.824

Utilization Rate (%,y-1) 0.091 0.551 -0.009 0.103 0.071 0.866

Utilization Rate(%,y-1)*Hurricane -0.003 0.242 -0.003 0.353 0.010 1.445

Planned Unit Outages (%,y-1) -0.064 0.683 0.089* 1.654 0.079* 1.845

Planned Unit Outages (%,y-2) -0.091 1.141 -0.014 0.211 0.053 1.214

Unplanned Unit Outages (%,y-1) -0.003 0.020 -0.031 0.096 -0.018 0.455

Unplanned Unit Outages(%,y-1)*Hurricane 0.057 0.337 0.030 0.092 0.149 1.251

Gasoline Stocks(%Chg,y-1) 0.493*** 2.756 0.179** 1.971 0.011 0.119

Competitor Investment(Indicator,y-1) 1.521 1.218 -0.764 1.100 -0.043 0.074

Year FE

Observations

R2 0.38 0.18 0.14

77 123 117

Yes Yes Yes

Dependent Variable: (Invest|Invest > 0), 

Cat. Hydro Cracking

Dependent Variable: (Invest|Invest > 0), 

Atmospheric Dist. Unit

(Invest|Invest > 0), 

Catalytic Cracking

Yes Yes

194 153

0.16 0.26

(Invest|Invest > 0), 

Cat. Reformer

(Invest|Invest > 0), 

Thermal Cracking

The dependent variable is the change in capacity (in barrels) from year y-1 to year y. An observation is a plant-year for 107 plants 

from 2002 - 2011. Some plants are not observed in all years. 3-2-1 crack spread is the prior year average based on state-level firstpurchase prices of crude oil, gasoline, and distillate for each plant. Utilization rate is the prior year average for the refining district 

in which the plant is located. Gasoline stocks measure the PADD-level change in stocks from y-2 to y-1. Competitor Investment

is an indicator that equals one if capacity by other refiners in the plant's PADD increased in the previous year. Hurricane is an 

indicator that equals one if the plant is located in a PADD that experienced a hurricane the previous year. 5 Conclusion

The focus of this paper was measuring the effect of refinery outages on product prices and investment. It

is well known that crude oil prices are the primary driver of gasoline and other petroleum product prices.

However, I have shown that outages at refineries, both planned and unplanned, are associated with changes in

prices and the future investment decisions of refiners. Refineries are extremely complicated operations and

understanding how their operations and outages affect the price we pay for gasoline is difficult to determine.

However, with detailed data on both the capacities and outages of individual refining units, I find that

depending on the current market conditions (prevailing utilization rates and crack spread), refinery outages

31
can have an economically significant effect on product prices.

As expected, planned outages tend to occur during the low-demand periods and when crack spreads

are less favorable for production, while unplanned outages are more likely to occur after prolonged spells

between planned plant maintenance. Product prices are positively associated with outages, though the effect

varies with the type of outage and the level of tightness in the market as measured by the utilization rate

and product stocks. Finally, I showed that investment in certain refining units is positively associated with

recent outages to those units, but the effect is insignificant for other units suggesting that there are other

considerations affecting a refiner’s decision to invest in capacity. These likely include long-term forecasts

of product demand, crude oil supply and prices, and a regulatory environment that is constantly changing

and affecting a plant’s profitability.

There are several directions for future work exploiting a few of the unique features of the dataset. The

outage data includes information about planned outages that have been rescheduled or postponed. This may

allow me to better estimate how refiners respond to economic conditions as they determine when to perform

their planned maintenance. For more recent data (2009-2011), I also observe the reason for the outage

(planned turn-around, economic conditions, etc), which could help further identify outages that are likely

to have a more significant impact on product prices. Finally, understanding how outages affect refinery

investment, both in production capacity and the complexity of refining units, is an important area for future

research, particularly given the importance of gasoline and other refined petroleum products to the U.S.

economy.

32
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35
A The Refining Process

Since the various components of crude oil have different boiling points, a refinery’s essential task is to boil

the crude oil and separate it into the more valuable components. Figure 16 displays a simplified diagram of

a typical refinery’s operations.

Figure 16: Refinery Operations

Refinery Background 

Figure 10 is a simplified refinery diagram that illustrates the different units in a refinery 

that produce gasoline streams: straight run gasoline from the crude distillation unit; 

reformate; fluid catalytic cracking (FCC) gasoline; and alkylate (using olefin feedstock 

from the FCC unit). Each stream has different properties, which when combined, 

produce either a blendstock for ethanol blending or a finished gasoline that meets both 

emission and performance requirements. 

Figure 10. Simplified Refinery Diagram Distillation Tower

Reformer

Hydrotreating

Fluid

Catalytic

Cracking

Alkylation

Hydrotreating

Coker

Product Blending

Diesel, Heating Oil, 

Kerosene

Jet/Kerosene

Light Straight Run Gasoline

Reformate to Gasoline

Alkylate to Gasoline

FCC Gasoline

The FCC unit and the alkylation unit, which the FCC feeds, together affect about half of 

the gasoline volume produced at a refinery.14 As such, refinery production of gasoline is 

 

14 Energy Information Administration, “Refinery Outages: Description and Potential Impact on Petroleum 

Product Prices,” March 2007, SR/OOG2007-01, Chapter 2, 

http://www.eia.doe.gov/oiaf/servicerpt/refinery_outages/SROOG200701.pdf. 

Light Cycle 

Oil to 

Distillate

Coke

Crude 

Oil

Bottoms

Light 

Ends Vacuum Unit

Hydrotreating

Hydrocracker Hydrocrackate Gasoline,

Diesel/Heating Oil/Jet

Heavy 

Ends

Source: EIA 

Energy Information Administration 

Motor Gasoline Market Spring 2007 and Implications for Spring 2008 / April 2008

13

The first and most important step in the refining process is called fractional distillation. The steps of

fractional distillation are as follows:

1. Heat the crude oil with high pressure steam to 1,112◦fahrenheit.

2. As the mixture boils, vapor forms which rises through the fractional distillation column passing

through trays which have holes that allow the vapor to pass through.

3. As the vapor rises, it cools and eventually reaches its boiling point at which time it condenses on one

of the trays.

4. The substances with the lowest boiling point (such as gasoline) will condense near the top of the

distillation column.

36
While some gasoline is produced from pure distillation, refineries normally employ several downstream

processes to increase the yield of high valued products by removing impurities such as sulfur. Cracking

is the process of breaking down large hydrocarbons into smaller molecules through heating and/or adding

a catalyst. Cracking was first used in 1913 and thus changed the problem of the refiner from choosing

how much crude oil to distill into choosing an appropriate mix of products (within some range). Refineries

practice two main types of cracking:

• Catalytic cracking: a medium conversion process which increases the gasoline yield to 45% (and the

total yield to 104%).

• Coking/residual construction - a high conversion process which increases the gasoline yield to 55%

(and the total yield 108%).

The challenge of choosing the right input and output mix given the available technology creates a massive linear programming problem.

B Crude Oil Quality

Crude oil is a flammable black liquid comprised primarily of hydrocarbons and other organic compounds.

The three largest oil producing countries are Saudi Arabia, Russia and the United States.48 Crude oil is the

most important input into refineries and this raw material can vary in its ability to produce refined products

like gasoline. The two main characteristics of crude that determine its quality are American Petroleum

Institute (API) gravity and sulfur content. The former is a measure (on an arbitrary scale) of the density of a

petroleum liquid relative to water.49 Table 12 summarizes these characteristics and includes some common

crude types and their gasoline yield from the initial distillation process.

Worldwide, light/sweet crude is the most expensive and accounts for 35% of consumption. Medium/sour

is less expensive and accounts for 50% of consumption while heavy/sour is the least costly and accounts for

15%. Figure 17 show how the average crude oil used by US refiners is becoming heavier and more sour over

time though leveling off toward the latter part of the 2000s. This means that the production costs of a gallon

of gasoline are changing as refineries must invest in more sophisticated technology in order to process lower

quality crude oil.

Since crude oil by itself has very little value to any industry, the price of a barrel of oil reflects the

net value of the downstream products that can be created from it. The two major sources of movements

in the crude oil price are upstream supply shocks (e.g., due to OPEC’s production quotas, international

tensions, and hurricanes affecting oil rigs in the Gulf of Mexico) and downstream demand shocks (mainly

due to consumer’s demand for refined products). The other source often sited by industry experts are refinery

inventories of crude oil. Maintaining stocks of crude oil allow the refinery to respond quickly to downstream

shocks like an unexpectedly cold winter increasing the demand for heating oil.

48Production in this sense refers to the quantity extracted from a country’s endowment.

49Technically, API gravity = (141.5 / specific gravity of crude at 60◦ F) -131.5. Water has an API gravity of 10◦

.

37
Table B.1: Crude Qualities

Table 12: Crude Qualities

API Sulfur Content

Gravity < 0.7% > 0.7%

< 22◦ Heavy Sweet Heavy Sour - 14% yield

(Maya, Western Canadian)

22◦ − 38◦ Medium Sweet Medium Sour - 21% yield

(Mars, Arab light)

> 38◦ Light Sweet - 30% yield Light Sour

(WTI, Brent)

Source: EIA.

rigs in the Gulf of Mexico) and downstream demand shocks (mainly due to consumer’s

demand for refined products). The other source often sited by industry experts are

refinery inventories of crude oil. Maintaining stocks of crude oil allow the refinery to

respond quickly to downstream shocks like an unexpectedly cold winter increasing the

demand for heating oil.

29.00

29.50

30.00

30.50

31.00

31.50

32.00

32.50

33.00

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

API Gravity (Degrees)

Sulfur Content (%)

Year

Sulfur API Gravity

Sour

Heavy

Light

Sweet

Figure B.1: Average Crude Oil Quality: Heavier and More Sour

Within the various types of crude oil, the prices of each quality respond differently

to shocks. The “light/heavy” differential is one measure that indicates the benefit a

refiner can achieve by investing in sophisticated equipment to process heavier crude oil

Figure 17: Average Crude Oil Quality: Heavier and Sour

API Sulfur Content

Gravity < 0.7% > 0.7%

< 22◦ Heavy Sweet Heavy Sour - 14% yield

(Maya, Western Canadian)

22◦ − 38◦ Medium Sweet Medium Sour - 21% yield

(Mars, Arab light)

> 38◦ Light Sweet - 30% yield Light Sour

(WTI, Brent)

Source: EIA.

rigs in the Gulf of Mexico) and downstream demand shocks (mainly due to consumer’s

demand for refined products). The other source often sited by industry experts are

refinery inventories of crude oil. Maintaining stocks of crude oil allow the refinery to

respond quickly to downstream shocks like an unexpectedly cold winter increasing the

demand for heating oil.

29.00

29.50

30.00

30.50

31.00

31.50

32.00

32.50

33.00

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

API Gravity (Degrees)

Sulfur Content (%)

Year

Sulfur API Gravity

Sour

Heavy

Light

Sweet

Figure B.1: Average Crude Oil Quality: Heavier and More Sour

Within the various types of crude oil, the prices of each quality respond differently

to shocks. The “light/heavy” differential is one measure that indicates the benefit a

refiner can achieve by investing in sophisticated equipment to process heavier crude oil

36

Within the various types of crude oil, the prices of each quality respond differently to shocks. The

“light/heavy” differential is one measure that indicates the benefit a refiner can achieve by investing in

sophisticated equipment to process heavier crude oil into highly-valued refined products. The differential

has varied significantly over the last 10 years from 3 dollars per barrel to almost 20 dollars per barrel. An

oil refinery faces a unique decision when making its production choice, one that provides for both flexibility

and complexity. One one hand, consumers do not care about the type of crude oil, oxygenates, or distillation

process used to make, for example, the gasoline they put in their cars. They just want their car to run

38
well. While this would appear to make a refiner’s problem easier, choosing their heterogeneous inputs, such

as crude oil, satisfying federal, state and city environmental regulations, and all while maximizing profits,

makes for an enormously complex optimization.

C Other Tables and Figures

Table 13: OLS Results: Planned Outages

Variable: Coeff. t-stat Coeff. t-stat Coeff. t-stat

Constant 0.04 15.99 0.06 12.41 0.02 1.81

Crack ($/Gal) -0.03 -4.83 -0.02 -3.24 0.01 1.07

Month FE

Plant FE

Observations

R2

No

Yes

No

Yes

Yes

Dependent 

Variable: 

An observation is a plant-month for 107 plants from January 2001 - August 2011. Refining 

units included are: atmospheric distillation, catalytic cracking, catalytic hydrocracking, catalytic 

reformers, and thermal cracking. 3-2-1 crack spread based on state-level first-purchase prices of 

crude oil, gasoline, and distillate for each plant.

0.001 0.01 0.09

13,807 13,807 13,807

All Planned 

Outages/Capacity (%)

All Planned 

Outages/Capacity (%)

All Planned 

Outages/Capacity (%)

No

Table 14: OLS Results: Planned Outages by Unit

Variable: Coeff. t-stat Coeff. t-stat Coeff. t-stat

Constant 0.02 1.42 0.00 0.23 0.05 2.82

Crack ($/Gal) 0.02 2.53 0.01 1.72 0.01 0.55

Month FE

Plant FE

Observations

R2

Dependent 

Variable: 

Planned Atmos. Dist. 

Outages/Capacity (%)

Planned Cat. Crack 

Outages/Capacity (%)

Planned Cat. 

HydroCrack 

Outages/Capacity (%)

Yes Yes Yes

Yes Yes Yes

An observation is a plant-month for 107 plants from January 2001 - August 2011. 3-2-1 crack 

spread based on state-level first-purchase prices of crude oil, gasoline, and distillate for each 

plant. The dependent variable is the percentage of an individual unit's capacity (e.g., catalytic 

cracking unit) that experiences planned outage in a given month.

13,492 10,967 5,217

0.08 0.02 0.04

39
Table 15: OLS Results: Unplanned Outages

Variable: Coeff. t-stat Coeff. t-stat Coeff. t-stat

Constant 0.14 13.45 0.16 14.04 0.16 11.15

Utilization Rate (%) -0.001 -12.34 -0.002 -13.49 -0.002 -13.86

Time Since Last T/A 0.000 3.32 0.000 3.04 0.000 3.01

Month FE

Plant FE

Observations

R2

11,461

0.02 0.02 0.11

No Yes

Dependent Variable: All Unplanned 

Outages/Capacity (%)

All Unplanned 

Outages/Capacity (%)

All Unplanned 

Outages/Capacity (%)

No Yes Yes

No

An observation is a plant-month for 107 plants from January 2001 - August 2011. Refining units included 

are: atmospheric distillation, catalytic cracking, catalytic hydrocracking, catalytic reformers, and thermal 

cracking. "Time Since Last T/A" measures the number of months since the plant last experienced a planned 

outage (or Turn-Around). Utilization rate measured at the refining district level.

11,461 11,461

Table 16: OLS Results: Unplanned Outages by Unit

Variable: Coeff. t-stat Coeff. t-stat Coeff. t-stat

Constant 0.142 10.018 0.137 7.146 0.291 10.074

Utilization Rate (%) -0.002 -12.441 -0.002 -8.796 -0.003 -11.062

Time Since Last T/A 0.000 3.453 0.000 3.195 0.000 2.933

Month FE

Plant FE

Observations

R2

11,448 9,299 4,402

0.09 0.08 0.16

An observation is a plant-month for 107 plants from January 2001 - August 2011. "Time Since Last T/A" 

measures the number of months since the plant last experienced a planned outage (or Turn-Around). 

Utilization rate measured at the refining district level.

Yes Yes Yes

Yes Yes Yes

Unplanned Atmos. Dist. 

Outages/Capacity (%)

Unplanned Cat. Crack 

Outages/Capacity (%)

Unplanned Cat. 

HydroCrack 

Outages/Capacity (%)

Dependent Variable: 

40
Table 17: Distillate Price Effects of an Average Outage

A Atmospheric Distillation Unit 3,395,525                         141,058 4.15% 0.327 1.36 (0.43, 2.28)

Planned 83,064                2.45% 0.419 1.74 (0.57, 2.91)

Unplanned 58,006                1.71% 0.188 0.78 (‐0.64, 2.20)

C Catalytic Cracking Unit 1,151,442                           62,782 5.45% 0.286 1.56 (‐0.91, 4.03)

Planned 34,515                3.00% 0.178 0.97 (‐2.23, 4.18)

Unplanned 28,267                2.45% 0.449 2.45 (‐1.52, 6.42)

By Unit (Planned+Unplanned)

Atmospheric Distillation 3,395,525                         141,058 4.15% 0.517 2.15 (0.89, 3.40)

Catalytic Cracking 1,151,442                            62,782 5.45% ‐0.030 ‐0.16 (‐3.06, 2.74)

Catalytic Hydro Cracking 344,020                               20,851 6.06% ‐0.914 ‐5.54 (‐14.88, 3.79)

Reformer 684,763                               35,992 5.26% ‐1.706 ‐8.97 (‐15.50, ‐2.43)

Thermal Cracking 486,775                               21,120 4.34% 1.623 7.04 (1.59, 12.50)

95% C.I.

B

D

E

Regressions are run on data at the PADD‐month level from January 2001 ‐ December 2011.  Prices are regressed on the crude oil price, the percent of

the PADD's capacity that is offline, a hurricane indicator, gasoline stocks, and month, year, and PADD fixed effects.  Separate regressions are run on

each specification above (indicated in column 1).  

Specification Outage Type Average PADD

Capacity (BPD)

Average Outage

(BPD)

Average

Outage (%)

Predicted Price

Effect (CPG)

Coeff.

Estimate

Figure 18: Probit Marginal Effects: Unplanned Outages by Utilization Rate

40 50 60 70 80 90 100 110 120

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Utilization Rate (%)

Probability of Any Unplanned Outage

0 Years since last T/A

1 Year

2 Years

41
Figure 19: Effects of Outages on Distillate Prices by Utilization Rate

‐10

‐8

‐6

‐4

‐2

0

2

4

6

8

10

< 80% 80%‐85% 85%‐90% 90%‐95% > 95%

Change in Distillate Price (CPG)

Utilization Rate

Predicted Distillate Price Change (CPG) Lower 95% Upper 95%

Predicted change in the distillate price from an average outage of atmospheric distillation capacity at different utilization rates.

42