Useful Excess Capacity?
An Empirical Study of U.S. Oil & Gas Drilling
Jun Ishii ∗
Amherst College
January 2011
Abstract
Modern microeconomic theory offers several possible explanations for why firms may persistently carry costly excess capacity. Excess capacity may be precautionary or speculative,
an artifact of uncertain demand. It may also stem from efforts by the firm to avoid cost nonconvexities, such as from labor hiring and firing costs. Finally, excess capacity may have strategic
value, as a preemptive strike on rivals and/or enforcement mechanism for tacit collusion. We use
firm-level and market aggregate panel data for the major U.S. oil & gas (land) drilling markets,
from 1999 to 2008, to investigate empirically each of these motives for persistent excess capacity.
Our analysis suggests “labor hoarding” as the main motive underlying much of the persistent
excess capacity at the market level. But for larger firms, especially in markets where they have
leadership, the precautionary/speculative motive seems just as important. We also find some
evidence consistent with the strategic use of excess capacity by such firms. We consider the
welfare implications of our results and possible consequences of further consolidation in the U.S.
oil & gas (land) drilling industry.
∗
309 Converse Hall, Amherst, MA, 01002 E-mail: [email protected]. The author thanks Mingzi Shao, Tracy
Huang, and Rohan Mazumdar for excellent research assistance, RigData and Ryan Kellogg for data assistance, and
seminar participants at 2009 Camp Energy (UCEI) and UMass Resource Economics for valuable comments. Part of
the paper was written while the author was visiting the School of Management and the Institution for Social and
Policy Studies at Yale University; the author thanks both institutions for their hospitality
1
Introduction
The persistence of “excess” capacity, capacity exceeding current and recent levels of peak demand,
is a characteristic of many modern industries. In such industries, firms are often required to sink
their capacity decision well before the realization of demand. With uncertain demand, firms must
balance the incremental cost of additional capacity with their stock-out concerns, and arrive at their
expected profit maximizing capacity choice. Seemingly wasteful excess capacity can materialize if
stock-out concerns are critical and/or realized demand less than anticipated. Resources are sunk
toward increments of capacity that, ex post, do not expand economic trade. In this paper, we
use recent data from the U.S. oil and natural gas (land) drilling industry to explore motives for
persistent excess capacity that do not necessarily stem from demand uncertainty.
Capacity utilization rate is often used as a barometer of industry health; an industry with
low capacity utilization rate, and thus high excess capacity, is one where much invested capital
is not explicitly supported by increased economic trade. The low utilization rate may indicate
low returns to capital and eventual firm exit, as non-sunk capital is reallocated to more productive
industries. This argument reflects the view that excess capacity is simply an artifact of uncertainty.
If firms knew demand, they would choose capacity levels that closely followed demand. But modern
microeconomic theory provides reasons why firms may knowingly carry excess capacity. Depending
on the extent to which these reasons are applicable, capacity utilization rates and other measures
based on excess capacity may be misleading barometers.1
More specifically, excess capacity that facilitates no immediate additional trade may still
be useful to firms as instruments of cost saving and/or strategy. In the presence of significant
non-convexities in capacity cost, firms may choose to hold excess capacity to smooth out the
non-convexities and reduce overall cost. Firms may also use excess capacity strategically, as a
preemptive strike against rival firms or as an enforcement mechanism for tacit collusion that further
elicits surplus from consumers. While cost saving and strategic value may both explain why firms
deliberately hold excess capacity, the consequence on social welfare drastically differs between the
two. Excess capacity is welfare improving if the firm chooses it for cost savings that expand total
surplus. Strategic uses of excess capacity are socially wasteful as the additional resources are
expended only to effect a transfer.
The U.S. (land) drilling industry is well suited for a study of the roles demand uncertainty,
cost saving, and strategic motives play in spurring persistent excess capacity. First, we observe
firms making frequent capacity decisions, with the decision of study not the long-run choice of how
1
Berndt & Morrison (1981) also raises some of these issues, in the context of selecting utilization measures
1
many rigs to own but rather the medium-run decision of how many rigs to crew. Second, firm
perception of demand uncertainty is well understood in this industry, with much of it embodied
in the oil and natural gas futures markets. Third, there are significant a priori reasons to believe
that both cost saving and strategy may be major factors underlying observed excess capacity.
Using extensive data on the U.S. drilling industry, ranging from oil and gas futures/spot
prices to drilling project counts to firm-level fleet data, we investigate the degree to which proxies
for each of the major motives help predict observed levels of excess capacity, at the market aggregate
and firm levels. We find that the prevalent motive for observed excess capacity seems to be cost
smoothing, as firms seek to minimize hiring costs by carrying additional crews during lean times.
But for the leading firms we find some evidence suggestive of the strategic use of excess capacity.
This suggests that much of the excess capacity we observe in recent years in the U.S. (land) drilling
industry is healthy from a firm and social perspective, with the caveat that some may be used to
effect transfers from consumers to producers.
2
U.S. Land Drilling Industry
In the United States, the drilling of oil & gas (land) fields is largely contracted out to independent,
third party firms who own, crew, and operate a fleet of drilling rigs. These contractors often
maintain an operational (“marketed”) fleet well in excess of their current and recent project load.
This excess capacity is costly as the contractor must not only maintain the unused marketed rigs
(as opposed to “stacking” them in storage) but also pay the idled crews. Figures 1 and 2 illustrate
this excess capacity for the Permian Basin market – the U.S. drilling market serving West Texas
oil & gas fields – for the market as a whole and for leading contractor Patterson Drilling.2
For each figure, the top line (“Total”) depicts the total number of available rigs. This represents the medium-run capacity constraint, the capacity available without costly acquisition of
additional rigs. The line below it (“Marketed”) depicts the total number of available rigs that are
also crewed and prepped for immediate work. This represents the short-run capacity constraint. In
this paper, we focus on marketed rigs as the capacity of interest. The U.S. land drilling industry
is considered a declining industry, with many domestic land fields “tapped out” and most drilling
activity at offshore or foreign fields. Much of the changes in total rigs owned by a contractor stems
from acquisition of existing rig fleets or, less common, transfer of rigs from other markets. For
example, the large jump in rigs for Patterson in 2001 reflect its merger with rival UTI Energy.
While there are some new rig acquisitions, most of these rigs are specialized, built for particular
fields.3 Thus, we believe that marketed rigs is the capacity of interest for our study.
When a contractor chooses not to market a rig, it puts the rig in storage, an act known as
2
3
The figures are based on data from Land Rig Newsletter. See Data Appendix
The rigs are capable of certain types of “directional” drilling, often built with specific projects in mind
2
“stacking.” By stacking a rig, the contractor avoids regular maintenance and the labor bill for the
laid-off crew. This comes at a modest fixed cost associated with prepping the rig for storage (and
a similar cost when reactivating the rig). The difference in sum between “Total” and “Marketed”
rigs is our measure of the number of rigs stacked in the market. As the figures demonstrate, this
stacking decision occurs frequently, with rigs being stacked and un-stacked on a monthly, or possibly
biweekly, basis. But the figures also suggest some ramping constraints that bound the swings in
total marketed rigs, excepting acquisitions/mergers.
These figures contrast with the less volatile stacking pattern depicted in Corts (2008). Corts
(2008) studies the stacking decision of offshore drilling companies whose fixed costs for stacking and
reactivating a rig are sizable.4 Thus, the offshore stacking decision has a stronger “irreversibility”
factor, from the larger sunk costs, that discourages the drilling firm from stacking (reactivating)
a rig unless it believes the rig to be uneconomical (economical) for a long time. For land rigs, as
will be elaborated later, the main concern to stacking is not these fixed costs but rather the cost
associated with hiring a new crew (a concern also shared by offshore firms). This difference in the
sunk costs associated with stacking and reactivating rigs helps explain the difference in volatility.
A similar difference in the operating and regular maintenance costs, components of capacity cost,
suggests that deliberate excess capacity is more likely for land than offshore drilling; deliberate
excess capacity is profitable only under suitably low capacity cost.
The difference in sum between “Marketed” and “Active” rigs represent the rigs that are crewed
but idle and is our measure of excess capacity. For the Permian Basin market as a whole, excess
capacity was, on average, around 30% of the marketed total, from 1999 to 2008. Interestingly, this
excess capacity persists despite the study period coinciding with a long boom in oil and natural gas
exploration activity. Figure 3 illustrates both the monthly drilling starts (for natural gas targets)
in the six main U.S. land drilling markets and the monthly average spot price for natural gas.
Natural gas is chosen as it is the primary drilling target during the study period. Drilling starts
reflect the market equilibrium quantity demanded and spot prices demand shifters, as the ultimate
interest of drilling “consumers” is the value of the hydrocarbon obtained from drilling.
For either measure of demand, there is a decline in drilling demand in 2001 and 2002 but a
sustained increase thereafter. This suggests that land drilling contractors are actively maintaining
this excess capacity. If excess capacity was simply rigs waiting for projects then excess capacity
would fall as demand rose. But total marketed rigs rise with demand, leading to persistence of
excess capacity. Contractors un-stack rigs as demand rises. Figure 2 shows Patterson maintaining
an excess capacity of roughly 20 rigs in the Permian Basin market for much of the 2003 to 2008
boom, with the increase in drilling activity partly offset by the un-stacking of rigs.
4
Offshore rigs are much larger, more complicated machineries.
Trade press discussions suggest stacking/reactivation costs an order of magnitude (or more) larger for offshore rigs
3
Table 1: Monthly Excess
Nabors/Patterson
Market
Obs
Mean
ArkLaTex
240
0.2095
Gulf Coast
240
0.3015
Midcontinent
214
0.2164
Permian Basin
240
0.2330
Rockies
240
0.2275
South Texas
240
0.2294
Capacity as % of
Grey Wolf/H&P
Obs
Mean
156
0.1702
240
0.2642
61
0.1029
48
0.1497
70
0.1303
240
0.1490
Marketed across 1999-2008
Other Oligopolists
Market Aggregate
Obs
Mean
Obs
Mean
167
0.1335
120
0.2422
75
0.2502
120
0.3577
289
0.2547
120
0.2998
295
0.2391
120
0.3050
412
0.2474
120
0.2876
97
0.1640
120
0.2860
Unbalanced samples reflect both firm entry/exit and tracking changes by data provider, Land Rig Newsletter
Table 2: Monthly Excess
Nabors/Patterson
Year
Obs
Mean
1999
120
0.3458
2000
120
0.1660
2001
140
0.2403
2002
144
0.3469
2003
144
0.2028
2004
144
0.1942
2005
144
0.1837
2006
144
0.2230
2007
144
0.2507
2008
144
0.2207
Capacity as % of
Grey Wolf/H&P
Obs
Mean
60
0.2968
60
0.1388
60
0.1889
60
0.2240
61
0.2213
73
0.1823
96
0.1171
108
0.1517
117
0.1896
120
0.1731
Marketed across the Six Markets
Other Oligopolists
Market Aggregate
Obs
Mean
Obs
Mean
138
0.3993
72
0.4895
133
0.2133
72
0.3187
133
0.1713
72
0.2625
135
0.2977
72
0.3632
144
0.1905
72
0.2777
144
0.1650
72
0.2665
123
0.1542
72
0.2218
132
0.1828
72
0.2178
123
0.2489
72
0.2743
130
0.2439
72
0.2718
Unbalanced samples reflect both firm entry/exit and tracking changes by data provider, Land Rig Newsletter
Tables 1 and 2 provide the mean excess capacity, as % of marketed at the monthly level, for
leading drilling contractors. The first table provides the mean for the six markets and the second
for the ten study years 1999-2008. Nabors and Patterson are the two industry leaders. Grey Wolf
and Helmerich & Payne are the other two large public firms in the industry.5 “Other oligopolists”
refer to the remaining major contractors tracked by Land Rig Newsletter. The tables confirm the
general persistence of excess capacity but also reveal some panel variation. In the next section, we
consider theories on why contractors may choose to maintain a persistent level of excess capacity.
3
Theories of Excess Capacity
3.1
Uncertain Demand
The conventional theory for excess capacity consists of the view that such capacity is the artifact
of firm’s speculative or precautionary approach to uncertain demand. A firm may believe that the
market is booming, but it does not know the precise magnitude of the boom nor what share of the
boom the firm will successfully capture. If the cost of capacity is sufficiently low compared to firm
market expectations, the firm may maintain more rigs than necessary to satisfy expected demand,
5
Grey Wolf is acquired by Canadian public drilling firm, Precision Drilling Trust, in 2008
4
as a form of (ex ante) profitable speculation; the firm, with additional capacity, is buying an option
on the upside gain in the market. Alternatively, if stock-out creates costs additional to the foregone
trade, such as from loss of reliability reputation, firms may insure itself from stock-out losses by
maintaining more rigs than necessary for expected demand.6 Analogous arguments may be made
when the firm believes that the market is declining.
This suggests that the firm’s current capacity choice will rise not only with its near future
forecast of market demand but also the uncertainty (variance) surrounding such forecast. A realization of demand lower than expected can lead to some idiosyncratic excess capacity but meaningful
uncertainty surrounding the forecast can lead to excess capacity even in expectation. This speculative/precautionary excess capacity would persist with the forecast uncertainty. This theory might
be explored if both forecasts for market demand and the perceived variance for such forecasts were
observable. A positive relationship between prior market demand forecasts (and their variance)
and current level of excess capacity would support the theory. Fortunately, two aspects of the U.S.
land drilling industry allow us to pursue such an empirical investigation.
First, drilling demand is ultimately derived from hydrocarbon demand. Futures markets,
such as those run by the New York Mercantile Exchange (NYMEX), provide extensive demand
forecasts for oil & gas. Second, the market structure for U.S. land drilling markets suggest that
uncertainty surrounding oil & gas demand is the main source of uncertainty surrounding drilling
demand faced by major contractors. Combined, this suggests that drilling market forecasts and
the uncertainty surrounding such forecasts can be derived from available information on oil &
gas demand. Specifically, futures and spot prices for the relevant hydrocarbons summarize the
information available to contractors. Auxiliary regressions can be run, using observed drilling
demand and lagged spot/futures prices, to infer industry-wide contractor (rational) expectations
for market demand and the variance surrounding such expectations. We elaborate below.
The market structure for U.S. land drilling markets can be characterized as an oligopoly with
a competitive fringe and a market demand largely vertical with respect to drilling price. Figure 4
illustrates the residual demand faced by U.S. land drilling oligopolists. Contractors in these markets
consist of a few large (often publicly traded) firms that own a majority of the available rigs and
many small private firms that own a handful of (usually) older rigs. The latter are the competitive
fringe, mostly holdovers from before industry consolidation during the 1990s. Their small fleet of
costly rigs make it impractical for the fringe to withhold supply and affect price. Fringe contractors
do stack and un-stack their rigs, but in the “Last-In, First-Out” (LIFO) manner noted in Corts
(2008); when demand is anticipated to be low for an extended period, the most costly fringe rigs are
stacked and when anticipated to be high for an extended period, the least costly fringe rigs are un6
See DeVany & Saving (1977) and DeVany & Frey (1982)
5
stacked.7 Fringe supply, in this manner, is fairly stable. Uncertainty surrounding residual demand
for the large oligopolist contractors stem from uncertainty surrounding overall market demand.
The largely vertical demand stems from the process by which drilling projects are assigned:
an informal procurement auction where the firm bidding the lowest price (caveat quality considerations) wins the project, as long as the bid is below the leaseholder’s reservation price. The decision
of whether to bring a project to auction is driven largely by the value of the underlying oil & gas,
much less the going price of drilling. Figure 5 illustrates this point, plotting monthly (natural gas)
drilling project starts against 4 month ahead (natural gas) NYMEX futures prices, lagged 4 month,
across all six major markets for 1997 to 2008.8 Under the “futures market as prediction market”
view, the 4 month ahead futures price is arguably the best market-wide predictor of the spot price
of natural gas 4 month from now and a summary statistic for the 4 month ahead forecast of the
oil & gas market. The graph shows a strong linear relationship between lagged futures price and
drilling demand. The relationship is tight for months where (lagged) futures price fell within the
familiar $2 ∼ 6 per mm BTU range but looser for months associated with unusually high futures
prices. This suggests that contractors can predict drilling demand using futures price and that
uncertainty surrounding these predictions increase with anticipated demand (futures prices).9
Project starts, which is market equilibrium quantity, is used as our measure of drilling demand, as overall market demand is largely price inelastic and the trade-off exploited by oligopoly
contractors between higher price and losses of projects to the competitive fringe, not losses in overall trade. Figure 6 further supports this point; monthly project starts are plotted against monthly
average drilling price (dayrate – price per drilling day) for each month in years 1997 through 2008
for two similar sized markets, Permian Basin and Midcontinent. The plots show a strong upward
linear relationship, suggestive of shifts in vertical demand tracing an upward sloping (fringe) supply
curve. Plots of other markets are qualitatively similar.
Contractor forecasts for drilling demand are inferred by regressing current project starts
against lagged information available to the contractors: spot and futures prices for natural gas
and oil and contemporary drilling demand. The predicted value based on the estimated coefficients
is our proxy for contractor forecasts. The regression is run separately for each of our six major
7
Earlier Tables 1 and 2 reveal greater excess capacity for the market aggregate than the oligopolists, suggesting
greater fringe excess capacity. This is consistent with the proposed fringe stacking behavior and higher cost rigs
8
The figures are similar when using shorter lags, e.g. 2 month ahead NYMEX futures prices, lagged 2 month
9
Demand is similarly modeled/forecasted in wholesale electricity markets. Retail consumers face a fixed price,
leading to a price inelastic wholesale demand that responds primarily to non-price factor weather. Weather forecasts
are used to predict wholesale electricity demand.
6
markets, for the months in our study years plus two previous (January 1997 to December 2008).
Project Startit = PSit
=
Quarter F.E.it + α0i Project Startit−4
+ α1i NGas Spott−4 + α2i NGas 4mo Aheadt−4
+ α3i Oil Spott−4 + α4i Oil 4mo Aheadt−4 + it
for i ∈ {ArkLaTex ... S. Texas}
(1)
t ∈ {Jan 1997 ... Dec 2008}
Contractor perceived forecast uncertainty is inferred using an auxiliary regression, where the
squared residuals from the earlier regression are themselves regressed against the lagged information.10 The predicted value based on the estimated coefficients from this residual regression is
our proxy for contractor perceived forecast uncertainty.
2
ˆ it
PSit − PS
= e2it = Quarter F.E.it + γ0i Project Startit−4
+ γ1i NGas Spott−4 + γ2i NGas 4mo Aheadt−4
+ γ3i Oil Spott−4 + γ4i Oil 4mo Aheadt−4 + ηit
for i ∈ {ArkLaTex ... S. Texas}
(2)
t ∈ {Jan 1997 ... Dec 2008}
In each of the regressions above, a lag of 4 month is used. There is no obvious lag choice. 4 month
is chosen as it is the furthest ahead NYMEX future contract for which complete historical data is
publicly available.11
ˆ it , ê2 ) can
The empirical relationship between observed excess capacity and these proxies (PS
it
be used to explore the extent to which demand uncertainty explains persistent excess capacity
in U.S. land drilling markets. Idiosyncratic excess capacity, stemming from the realization of
lower-than-anticipated demand can similarly be explored using the empirical relationship between
observed excess capacity and forecast error (the residual eit ). In the next section, we consider
non-convexities in capacity cost as another motive driving persistent excess capacity.
3.2
Cost Saving
The main sunk cost associated with stacking is the loss of crew. Reactivating a stacked rig requires
the contractor to hire a new crew. While the most skilled crew members, such as drillers and
toolpushers, are often kept, reassigned to other rigs until reactivation, the lower level positions –
“roughnecks” and “roustabouts” – have to be replaced with new workers often with no previous
drilling experience. The work is physically demanding and requires long hours, every day of the
week, for weeks at a time, at isolated sites. The job is intermittent and comes with little benefits.
The main draw of the job is the wage, among the highest for “blue collar” jobs not requiring higher
10
The intuition motivating this exercise follows that of Breusch & Pagan (1979)
The data can be downloaded from the U.S. Department of Energy’s Energy Information Administration (EIA)
website. Note that NYMEX markets for longer futures have less trading volume and are less reliable predictors
11
7
education. The job primarily attracts transient, relatively young men who seek to earn sizable
income in a short time. This makes it difficult for drilling contractors to re-hire laid off roustabouts
and to advertise their job effectively to potential workers.
“ ‘Layoffs look good to shareholders at a particular time, but they harm long-term development,’ said Edward E. Thiele, chief financial officer at Rowan. ‘You train all these people
and then you lay them off, and then you have to train new people all over again.’ Most companies succumbed to the pressure to pare their work forces, but many layoff victims do not return.”
“A Second Oil Shortage: Experienced Workers,” New York Times, 07/01/2001
The presence of sizable hiring and firing costs can make labor a “quasi-fixed” input, as noted
in Oi (1962) and developed more recently in Bentolila & Bertola (1990) and Bertola (1992). The
hiring and firing costs act as sunk costs that create an option value to moderating workforce
fluctuations. Sizable firing costs provide a disincentive against hiring new workers and sizable
hiring costs a disincentive against firing existing workers. This latter disincentive, referred to as
“labor hoarding,” provides a motive for excess capacity in land drilling markets; contractors can
avoid the future cost of finding and training new roughnecks by maintaining excess crews today.
The contractors weighs the hiring cost of a new crew when the rig is reactivated with the labor bill
from maintaining the crew throughout the idle period.12
“The industry may have no one but itself to blame for its troubles. One major reason for the
lack of hands is bad pay. The current squeeze is most acute for roughnecks–the rig workers who
earn an average of $10 to $11.50 per hour, according to industry analysts. That’s especially low
compared with the higher wages available in areas such as construction, which is credited with
having lured a big share of defecting oil field workers.”
“Wanted: A Few Roughnecks,” Businessweek, 07/17/2000
The main determinant of hiring cost is the opportunity cost for the potential roughneck.
Drilling contractors not only compete amongst each other for these workers but also with employers from other industries. Specifically, contractors in the construction industry seek similarly
qualified workers. An increase in labor demand from these industries increases the hiring cost for
drilling contractors. This suggests that the wage for the (roughneck equivalent) entry-level job in
construction can proxy for the opportunity cost and the wage difference between the two entry-level
jobs the hiring difficulty.
Figure 7 graphs the difference in mean hourly wage between roughnecks (BLS occupation
code 47-5071) and construction laborers (BLS occupation code 47-2061) for each market and year.13
The wage differential ranges from $1 to 3 per hour for most markets and years, with Midcontinent
the notable exception. This persistent positive difference may be compensation for the difference
12
We abstract from the strategic value of labor hoarding – raising rivals cost and increasing entry barriers
To aggregate to the drilling market, we used the employment weighted average of the mean hourly wages for
metropolitan statistical areas (MSAs) within the drilling market
13
8
in work environment; construction work often occurs in population centers but drilling in remote
locations. The graph shows significant panel variation, with no market or year strictly dominating
another. This suggests that labor hoarding as a motive for excess capacity may be explored examining the empirical relationship between observed excess capacity and the wage (or wage differential)
for the outside job, construction labor. In the next section, we consider possible strategic uses of
excess capacity.
3.3
Strategy
Game theoretic models of capacity choice conjecture that firms may choose to carry excess capacity
as a credible commitment toward aggression against rival firms. The earliest of these models involve
the “Excess Capacity Hypothesis,” where incumbent firms use excess capacity to deter entry.14
Potential entrants are scared away by incumbents threatening to use the excess capacity to make
entry unprofitable. Similar arguments can be made for excess capacity as a preemptive strike
against rival incumbents, with the intuition along the lines of the familiar Stackelberg leadership.
Two criticisms of these early models are that [1] the proposed equilibrium may not be subgame
perfect as incumbents may not wish to carry out the threat when called and [2] freeridership
among incumbents (entry deterrence as a public good) may lead to under-investment in excess
capacity. Both concerns have been tackled in later work. Conditions, mainly on the nature of
post-entry competition, under which excess capacity may serve as a credible entry deterrent have
been derived.15 And, freeridership may not be a concern as excess capacity may also confer private
value to the actual investing firm, such as from preemption against rival incumbents.16
Existing empirical studies on the deterrence/preemption use of excess capacity is mixed. Hilke
(1984) and Masson & Shaanan (1986) find negative correlation between entry rates and measures
of excess capacity, for various U.S. manufacturing industries, supporting the deterrence hypothesis.
Reynolds (1986) finds some support for the preemptive use of excess capacity, through simulations
of the aluminum industry. However, Lieberman (1987) and Gilbert & Lieberman (1987) find little
evidence supporting the deterrence and (strong) preemption hypotheses, respectively, in their study
of chemical industries and conclude that such uses of excess capacity must not be widespread. More
recently, Conrad & Veall (1991) and Ma (2005) find some support for the preemptive use from their
estimated stylized model of West German brewery and Taiwanese flour supply, respectively.17 The
view of U.S. land drilling as a declining industry and the presence of other barriers to entry (sizable
physical and human capital requirements) indicate limited appeal of excess rig capacity as an entry
14
See Wenders (1971), Spence 1977), and Dixit (1980)
See Spulber (1981) and Fudenberg & Tirole (1983)
16
See Bernheim (1984) and Gilbert & Vives (1986), although Waldman (1987) re-raises the concern when there is
market uncertainty
17
These studies do not explicitly control for other, non-strategic drivers of excess capacity
15
9
deterrent. However, excess rig capacity may be attractive as a preemptive strike against existing
rivals, as contractors fight to survive the industry shake-out.
Deterrence and preemption are not the only conjectured strategic use of excess capacity.
Excess capacity may also be used as an enforcement device for tacit collusion. The threatened
Nash reversion punishment awaiting defecting firms is made harsher with the presence of excess
capacity; excess capacity enables the punishing firms to drive down market price even further.18
The idea is formally developed in Brock & Scheinkman (1985), Benoit & Krishna (1987), and
Davidson and Deneckere (1990). Knittel & Lepore (2006) extend the analysis to the case with
cyclical demand and find that, if marginal cost of capacity is sufficiently low, prices will be higher
during periods of anticipated demand growth, similar to Haltiwanger & Harrington (1991).
While, to the best of our knowledge, there is little empirical study of excess capacity as
collusion enforcement, there is some supporting anecdotal evidence, most notably the behavior of
Saudia Araba in the OPEC cartel and Archer Daniels Midland (ADM) in its infamous lysine cartel.
Connor (2001) provides details on the latter cartel. One of the few empirical studies is Rosenbaum
(1989), which regresses industry “price cost margin” against a measure of industry excess capacity,
for the U.S. aluminum ingot industry, and interprets the estimated positive coefficient as evidence
for the collusive use of excess capacity. Perhaps more relevant, evidence of collusion among firms
in other procurement markets suggest that the collusive use of excess capacity may be possible in
land drilling markets.19 The small number of (price-setting) contractors in each drilling market
and the presence of significant entry barriers further facilitate possible tacit collusion.
Without “smoking gun” evidence, distinguishing among strategic uses requires an explicitly
structural analysis that allows the researcher to infer the relevant firm primitives. In this paper, we
consider the strategic uses, jointly, based on the following insight: firms with a greater share of the
inframarginal supply stand to benefit more from the strategic use of excess capacity.20 Thus, the
presence of a strong strategic motive for excess capacity would lead firms with more inframarginal
rigs to carry more excess capacity, ceteris paribus. Ideally, the inframarginal rigs of a firm would
be identified by ordering all available rigs by their operational cost, forming a system marginal
cost curve. Rigs that lie on the system marginal cost curve below anticipated demand would be
deemed inframarginal. However, we have only limited rig information. Furthermore, Kellogg (2009)
suggests that rig information alone may be inadequate; he finds human capital acquired through
experience (learning by doing) to be an important driver of land drilling productivity.
We proxy for the share of inframarginal supply with the share of total available rigs (marketed
18
There is a related literature discussing the role of capacity constraints in tacit collusion, with collusion sustained
by limiting deviation (“cheating”) profits. See Staiger & Wolak (1992), Reynolds & Wilson (2000), and Fabra (2006)
19
See Bajari & Summers (2002) for a survey on collusion in procurement auctions
20
The strategic value stems from increasing market price by withholding supply. Firms with more inframarginal
units benefit more from the higher price.
10
or stacked) in the market. Industry press indicate that the few major contractors own not only a
majority of the available rig but also an even larger share of the newer, more capable rigs. This
suggests a positive relationship between overall scale and share of inframarginal units, with the
larger contractors owning larger shares of inframarginal rigs. We propose to explore the strategic
motive for excess capacity by investigating the empirical relationship between total rig share and
observed excess capacity. Table 3 provides the average rig share for four major public contractors,
by market and time period.
Table 3: Average Rig Share for Major Public Contractors
Market
Years
Average Rig Share
Nabors
Patterson Grey Wolf
H&P
ArkLaTex
1999 - 2003
0.2458
0.1336
0.1372
—
2004 - 2008
0.1943
0.1613
0.0961
0.0626
Gulf Coast
1999 - 2003
0.2863
0.0947
0.2042
0.0647
2004 - 2008
0.2488
0.1220
0.1722
0.0952
Midcontinent
1999 - 2003
0.1690
0.1252
—
0.0261
2004 - 2008
0.1421
0.1039
—
0.0533
Permian Basin 1999 - 2003
0.0811
0.2755
—
—
2004 - 2008
0.0743
0.4316
0.0912
—
Rockies
1999 - 2003
0.1955
0.0319
—
—
2004 - 2008
0.1960
0.0937
0.0406
0.0989
South Texas
1999 - 2003
0.1836
0.1583
0.1653
0.1427
2004 - 2008
0.1579
0.1861
0.1457
0.1701
Figures 8 and 9 are evocative of the strategic motive. The two graphs depict the difference in
excess capacity (as % of marketed) between similar-sized markets Permian Basin and Midcontinent,
for the market aggregate and leading contractor Patterson, respectively. For the market aggregate,
the difference fluctuates around zero. But for Patterson, the difference is positive during the
low (and anticipated to be low) demand months. In Permian Basin, Patterson is the leading
contractor, with a rig share some multiple of its closest rivals. But in Midcontinent, Patterson
is one of several similarly sized contractors. The difference in the two patterns is consistent with
Patterson using excess capacity to coordinate collusion in the market in which it has leadership.
Following Haltiwanger & Harrington (1991) and Knittel & Lepore (2006), firms must invest more
in punishment (more excess capacity) during periods when demand is anticipated to stay low.
4
Regression Analysis
We use regression analysis to consider the varying theories of excess capacity, jointly. The data
used in the regressions and earlier figures are explained in the Data Appendix. The purpose of the
regressions is to investigate the amount of excess capacity predicted, within sample, by changes in
each of the theory proxies. To expedite this purpose, the key explanatory variables in the regression,
except rig share, are normalized by their sample standard deviation. Thus, the estimated coefficients
11
reflect the predicted change in excess capacity, in terms of number of idle rigs, for a one standard
deviation increase from the mean in the explanatory variable.
Table 4: Standard Deviation for Explanatory Variables
Variable
1 Standard Deviation equals
Project Starts (Nat. Gas)
92.06 project starts
Nat. Gas 4 Month Ahead Futures
$2.69 per million BTU
Nat. Gas Spot
$2.06 per thousand cubic ft
Oil 4 Month Ahead Futures
$27.34 per barrel
Oil Spot
$26.76 per barrel
Construction Wage
Roughneck Wage
$2.02 per hour
$2.00 per hour
4
4
4
4
93.32 project starts
1051.98 (project starts)2
14.44 project starts
22.87 project starts
Month
Month
Month
Month
Ahead Predicted Demand
Lagged Prediction Variance
Ahead Predicted Growth
Lagged Prediction Error
Standard deviations for the balanced sample of 10 years × 12 months × 6 markets = 720
except for wage data which is annual and undisclosed for 4 market/years
A month is chosen as the unit of time for our analysis. The drilling time for a project can
vary, depending on proposed well depth and geologic formation of the target field. Thus, rigs finish
projects and become available on a staggered basis. The Smith Bits weekly rig count data provides
an indication of the number of weeks a rig was active in a particular project. Table 5 provides the
cumulative share of projects started during 1999-2008 for each market. The majority of projects
finish within a month (4 weeks) and a vast majority within two.
Table 5: Cumulative Share (%) of Projects by Duration
Within
Market
4 weeks 6 weeks 8 weeks
ArkLaTex
62.70
83.12
91.65
Gulf Coast
40.09
58.08
72.00
Midcontinent
58.88
78.06
87.26
Permian Basin
70.51
82.17
87.72
Rockies
66.30
81.50
88.86
South Texas
50.80
71.76
83.25
Share is of dayrate projects started sometime during 1999-2008
Based on author’s calculation using Smith Bits rig counts
This suggests that a month may be an appropriate time unit of analysis. A month is long enough
such that most engaged rigs will finish a job and become available sometime during the period
but not so long that they may finish multiple projects – allowing the contractor to make one
marketing/stacking decision for each rig during the period. Daily futures/spot prices and biweekly
firm rig data were aggregated up to the month by simple averaging. The available annual wage
value was assigned to each of the months in the year.
12
We consider three motives for carrying deliberate excess capacity: [1] speculative or precautionary value [2] cost saving through labor hoarding [3] strategic value. The first is proxied by
ˆ it+4 − PSit ) and forecast uncertainty (ê2 ) estimated by our ancillary
forecasted market growth (PS
it
regressions; the second by our roughneck and construction wages; the third by interaction of firm
total rig share in the market with select explanatory variables, but especially market forecast. To
mitigate spurious correlation from omitted factors, we include four sets of fixed effects: market, year,
quarter, and firm. The firm fixed effects are for each of the four major public contractors: Nabors,
Patterson, Grey Wolf (now Precision), and Helmerich & Payne. To account for idiosyncratic excess
capacity stemming from lower than anticipated demand, the difference between realized demand for
a given month and the forecast of that demand – the residual eit from the first ancillary regression
– is included. The full regression specification is provided below.
Excess Capacityitf
=
Market Fixed Effectsi + Year Fixed Effectst + Quarter Fixed Effectst
+ Firm Fixed Effectsf + (Rig Share × Market)itf
Interact with
Rig Shareitf
+ β1 PSit + β2 Rig Shareitf × PSit + β3 NG Spott + β4 Oil Spott
|
{z
}
{z
}
|
Current Info
Current Demand

ˆ it+4 − PSit

+ β5 eit + β6 ê2it + β7 PS



{z
}
|

Uncertain Demand
=⇒

+ β8 Construction Wageit + β8 Roughneck Wageit


|
{z
}


Cost Adjustment
+ νif t
(3)
for
i ∈ {ArkLaTex ... S. Texas} t ∈ {Jan 1997 ... Dec 2008}
f ∈ {Contractors Tracked by Land Rig Newsletter }
The specification for the market aggregate regression is the same as above, except firm fixed
effects and Rig Share interactions, none of which are applicable, are excluded. We do not include
the full set of current information (i.e. natural gas and oil futures prices) for parsimony. The futures
prices are highly correlated with the analogous spot prices – 0.9532 (natural gas) and 0.9975 (oil)
over the study period – and their inclusion does not qualitatively alter the results.
4.1
Regression Results
Table 6 provides the estimation results for the market aggregate regression.
13
Table 6: Regression Results – Market Aggregate
est
s.e.∗ p-value
est
s.e.∗ p-value
Constant
-129.183 46.220
0.005 -300.806 88.687
0.001
Project Starts
9.672
1.442
0.000
11.775
1.863
0.000
-8.683
2.146
0.000
Nat. Gas Spot
-8.710
1.901
0.000
Oil Spot
-2.087
3.220
0.517
-1.356
3.248
0.676
Construction Wage
40.881
9.555
0.000
54.447 11.398
0.000
Roughneck Wage
1.024
1.931
0.596
22.844 10.309
0.027
Pred. Variance
5.545
1.390
0.000
6.648
1.649
0.000
Pred. Growth
4.421
0.899
0.000
4.447
0.974
0.000
-4.080
1.246
0.001
Pred. Error
-3.162
1.038
0.002
Mkt/Yr/Qtr F.E. ?
Yes
Yes
Instrument for Roughneck Wage ?
No
Yes, Housing Starts
R2 , # of Obs.
0.610, 672
0.544, 672
∗
White robust standard errors are reported
White robust standard errors are reported. OLS, cluster by year, and cluster by market/year
standard errors are qualitatively similar.
The estimates are consistent with the earlier discussion. The speculative and/or precautionary motives for excess capacity are supported by the large positive estimated coefficients for the
two market forecast variables: (4 month lagged) predicted variance and (4 month ahead) predicted
growth. A one standard deviation increase in (4 month lagged) market forecast uncertainty predicts, within sample, an increase of 5.5 rigs in current (monthly average) market excess capacity.
Similarly, a one standard deviation increase in (4 month ahead) predicted growth predicts an increase of 4.4 rigs in current market excess capacity. The estimated positive coefficients are also
consistent with the stacking pattern observed in Corts (2008): contractors stack less when they
anticipate market growth, due to sunk costs. Idiosyncratic excess capacity stemming from lessthan-anticipated realized demand is captured by the negative estimated coefficient for (4 month
lagged) prediction error; one standard deviation increase in the difference between realized and
predicted demands predicts, within sample, 3.2 fewer excess capacity rigs.
The substantial estimated coefficient for construction wage, vis-á-vis other slope coefficients,
suggest that cost saving (“labor hoarding”) may be the major motive underlying much of the
market-wide persistent excess capacity. Construction wage is our measure of the opportunity cost
facing a potential roughneck and, thus, of contractor hiring cost. A one standard deviation increase
in this hiring cost proxy predicts, within sample, 41 additional rigs in current excess capacity. The
estimated positive coefficient for roughneck wage is puzzling, as the labor bill is the primary cost of
holding capacity; an increase in capacity cost would presumably make excess capacity less attractive.
Estimation of the roughneck wage coefficient is problematic as roughneck wage may be endogenous with respect to omitted factors driving excess capacity at the market aggregate level. The
other included variables are arguably exogenous, as they are primarily determined by the (global)
14
market for oil and gas or the larger regional economy (construction wage), much less any omitted
factors driving excess capacity. Additionally, the troublesome omitted factors would have to be
market and time varying, as market and time fixed effects are included. Wage endogeneity is much
less of a concern for the firm level regressions, as omitted factors driving firm level excess capacity
are less likely to drive roughneck wages; the nature of the roughneck labor market makes it difficult
for individual contractors to exert monopsony power.
We explore possible roughneck wage endogeneity in the market aggregate regression by also
estimating the model using instrumental variables (IV). We use the total number of housing construction starts in the market as our instrument for roughneck wage. Housing starts may serve as an
appropriate instrument as it is correlated with roughneck wage through construction labor demand
but unlikely to be correlated with any omitted factor driving market aggregate excess capacity (not
already factored by the included fixed effects). Both the magnitude and statistical significance of
the IV estimates are similar to those of the OLS estimates, except for roughneck wage which has
grown in both magnitude and statistical significance. The OLS estimates, including the puzzling
coefficient for roughneck wage, seem qualitatively robust to possible roughneck wage endogeneity.
The market aggregate regression suffers from a difficulty in investigating the strategic motive
for excess capacity. The earlier discussion concerning identification of the strategic motive focused
on across firm differences, differences that are not available at the market aggregate. One idea may
be to use market concentration measures, under the guise that more concentrated markets are the
ones where tacit collusion may be easier to coordinate and, thus, excess capacity as an enforcement
device more attractive. But there are other compelling reasons for a positive relationship between
excess capacity and market concentration. For example, a rapidly declining market (e.g. empty
fields) may induce greater firm exit (market concentration) and more excess capacity held by the
survivors. To explore strategic motives, we examine the firm level regressions.
Table 7 provides the estimates for three firm level regressions. The first uses all available
firm data; the second just for the four major public; the last just for the two industry leaders.
15
Table 7: Regression Results – Firm Level
All Oligopolists
Major Public
est
s.e.∗ p-value
est
s.e.∗ p-value
Constant
-12.574 4.922
0.011 -20.793
7.719
0.007
Project Starts
0.776 0.271
0.004
1.528
0.458
0.001
× Rig Share
2.788 2.680
0.298
-1.340
3.599
0.710
-0.338
0.227
0.137
Nat. Gas Spot
-0.458 0.173
0.008
Oil Spot
-0.888 0.246
0.000
-1.029
0.301
0.001
Construction Wage
2.753 1.142
0.016
4.981
1.789
0.005
× Rig Share
-3.019 8.885
0.734 -14.874 11.223
0.185
-1.226
0.585
0.036
Roughneck Wage
-0.382 0.377
0.311
× Rig Share
-3.440 3.572
0.336
0.956
4.388
0.827
Pred. Variance
-0.949 0.170
0.000
-1.468
0.252
0.000
× Rig Share
15.575 2.144
0.000
18.463
2.489
0.000
Pred. Growth
0.881 0.174
0.000
1.164
0.273
0.000
-9.392
2.144
0.000
× Rig Share
-7.766 1.718
0.000
Pred. Error
0.425 0.160
0.008
0.367
0.236
0.120
× Rig share
-7.936 1.770
0.000
-7.230
2.079
0.001
Mkt/Yr/Qtr F.E. ?
Yes
Yes
Firm F.E. ?
Yes
Yes
Rig Share × Mkt ?
Yes
Yes
R2 , # of Obs.
0.5710, 3304
0.5688, 2143
∗
White robust standard errors are reported
Nabors & Patterson
est
s.e.∗ p-value
-36.330 11.641
0.002
0.140
1.195
0.906
6.030
7.471
0.420
-0.374
0.336
0.266
-1.694
0.459
0.000
8.220
2.844
0.004
-23.192 15.522
0.135
-1.043
0.840
0.215
-0.274
5.681
0.962
-2.033
0.624
0.001
21.918
3.914
0.000
1.507
0.413
0.000
-10.779
2.644
0.000
0.829
0.432
0.055
-9.221
2.853
0.001
Yes
Yes
Yes
0.5702, 1328
The firm level results are largely consistent with the market aggregate results; we find evidence
supporting speculation/precaution and labor hoarding as important motives underlying persistent
firm level excess capacity. But the rig share interactions and comparison of estimates across the
firm level regressions provide further nuance.
The estimated coefficient before construction wage is increasing across the regressions, from
all tracked firms (“All Oligopolists”) to just the industry leaders, hinting that larger contractors respond to changes in hiring cost more. This is consistent with the greater crewing needs of the larger
contractors. The interaction of construction wage with rig share, though imprecisely estimated, is
negative, which seems to indicate some scale economies in hiring. The largest contractors respond
less to changes in hiring cost in markets where they have larger rig share, perhaps because, in such
markets, they are able to hire more effectively; prospective roughnecks may approach the largest
contractors in a market first. These comments are caveat the large standard errors associated with
the estimated interaction terms.
The estimated coefficients before the uncertain demand proxies – (4 month lagged) prediction
variance, (4 month ahead) predicted growth, (4 month lagged) prediction error – also point to
larger contractors responding more to changes, and differently according to their rig shares. Market
leaders carry a rig share around 0.2 and 0.3, with Patterson’s more recent 0.4 in Permian Basin
the outlier. This implies, for market leaders, a net effect of a one standard deviation increase in
prediction error of −1.2 to −2.0, −1.1 to −1.8, and −1.0 to −1.9 rigs for “All Oligopolist,” major
16
public, and the two leaders regressions, respectively.21 The market aggregate regression associates
a one standard deviation increase in prediction error with 3.2 fewer idle rigs. Combined with the
above net effect calculations, this seems to indicate that much of the idiosyncratic excess capacity,
from lower than anticipated realized demand, is borne by the market leaders.
The rig share interactions suggest, for market leaders, a net effect of a one standard deviation
increase in prediction variance of +2.2 to +3.7, +2.2 to +4.1, and +2.4 to +4.5 rigs, respectively,
for the three regressions. The speculative/precautionary motives for excess capacity seem more
important for larger contractors. This fits the view of such contractors having a more operationally
efficient rig fleet; the larger contractors are the ones most able to benefit from speculation – most
likely to win procurement auction for the marginal project. But the estimates may also reflect
greater reputation concerns for larger contractors.
The rig share interactions with prediction growth implies, for market leaders, a net effect
of −0.7 to −1.4, −0.7 to −1.7, and −0.6 to −1.7 rigs, respectively, for the three regressions.
The negative net effect for predicted growth (for market leaders) seems at odds with conventional
theories of excess capacity; both the speculative/precautionary and option value motives support
greater excess capacity with anticipated growth. But the strategic motive, especially tacit collusion
enforcement, supports lower excess capacity with anticipated growth; Nash reversion punishment
grows stronger with demand growth, requiring less amplification with excess capacity.22 This
suggests that, for smaller contractors, predicted growth elicits the conventional response but for
larger contractors, especially in markets where they have leadership, predicted growth may reduce
the excess capacity maintained for strategic purposes.
Table 8: Regression Results – Firm Level, Rig Share × Market Dummies
All Oligopolists
Major Public
Nabors & Patterson
est
s.e.∗ p-value
est
s.e.∗ p-value
est
s.e.∗ p-value
ArkLaTex
81.468 29.607
0.006 115.708 37.167
0.002 156.169 51.259
0.002
Gulf Coast
61.963 30.934
0.045
90.551 38.957
0.020 142.663 54.454
0.009
Midcontinent
97.247 46.709
0.037 168.425 59.544
0.005 284.467 82.635
0.001
Permian Basin 78.487 27.894
0.005 110.856 35.594
0.002 143.680 49.022
0.003
Rockies
99.529 36.412
0.006 145.182 46.317
0.002 195.637 62.845
0.002
0.000 160.619 46.924
0.001
South Texas
76.452 26.986
0.005 126.475 33.591
∗
White robust standard errors are reported
Lastly, Table 8 presents the estimated rig share interactions with market dummies. The
interaction allows rig share to have a different estimated effect in each market. For all six markets,
observed excess capacity seems to increase with rig share. When rig share is viewed as a proxy for a
contractor’s share of the inframarginal rigs in a market, the estimated interactions further support
the strategic motive; contractors who gain the most from the exercise of market power carry the
21
22
The term “effect” is used in lieu of “within sample prediction” for simplicity
The intuition follows Haltiwanger & Harrington (1991), and Knittel & Lepore (2006) with low capacity costs
17
most excess capacity, ceteris paribus. But the estimates are not conclusive for the strategic motive
as there are other possible explanations.23 They do, however, point toward some possible residual
scale effect on excess capacity.
Overall, the regression analysis appears to paint the following picture: for smaller contractors,
the main motive underlying persistent excess capacity seems to be labor hoarding, with changes
in the construction wage having the largest within sample predicted effect. For larger contractors,
labor hoarding remains an important concern but the speculative/precautionary motives, embodied
in the coefficients for prediction variance, are similarly if not more important. Furthermore, there is
some evidence pointing to the strategic use of excess capacity by these larger contractors in markets
in which they have leadership – most notably the negative (net) predicted effect of predicted growth
for such contractors and markets. The rig share interactions with market dummies suggest some
residual scale effects, which may also (but not necessarily) be signs of strategic excess capacity.
The regression analysis suggests that much of the observed persistent excess capacity may be
attributable to efforts by contractors to facilitate future economic trades: speculation, precaution,
and labor hoarding. These deliberate “excess” units are (at least ex ante) useful from a firm
perspective; speculation and precaution allow for possible additional high value trades, even if
improbable, and labor hoarding reduces the overall cost of trade by favorably exchanging additional
wages for lower hiring costs. From a social perspective, these deliberate excess units are also useful,
given the market structure; the contractors would not find these units profitable, in expectation,
unless the expected additional surplus generated from these units was less than the capacity cost.
But further improvements to social welfare may be possible through industry consolidation.
Though statistically weak, there is some evidence that hints at scale economies in hiring. If so,
the acquisition of smaller contractors by larger ones may reduce the excess capacity maintained
for labor hoarding. But the labor cost saving from such acquisitions would have to be measured
against the consequences of possible market power exacerbation, as the smaller firms provide much
of the residual demand elasticity faced by major contractors. Moreover, if consolidation leads to
the emergence of market leaders, total excess capacity may not even decline; the decrease in excess
capacity from reduced labor hoarding could be offset by an increase from strategic use by the new
market leaders. We discuss these issues further in the conclusion.
4.2
Robustness Checks
We consider two robustness checks of our regression analysis. In the first, we consider possible complications stemming from our unbalanced sample. In the second, we examine regression estimation
using shorter prediction horizons.
23
For example, if the distribution of rig type were similar across the studied contractors, with fleet size (scale) being
the main firm difference, the coefficient before rig share would also be positive, even without any strategic motive.
18
4.2.1
Unbalanced Sample
Not all studied contractors are observed in every market and every month in the study period. Some
of the omission is due to firm entry and exit. Some is due to the tracking decision of our rig fleet
data provider. These omissions expose our analysis to complications associated with selection on
unobservables; unobserved factors that affect sample selection (tracking decision) may also affect
firm excess capacity decision, introducing bias into our regression analysis. To investigate, we
estimate two regressions using balanced samples: (1) the four major public firms for the Gulf Coast
market (2) Nabors and Patterson for all six markets from 2002 onward. Each has its drawback:
(1) eliminates market heterogeneity and (2) most firm heterogeneity.24 While (2) uses the full
regression specification, all year fixed effects were excluded from (1) as the observed wage data is
annual, making the year dummies perfectly multicollinear with the wage variables.
Table 9: Robustness Checks – Just Gulf Coast
Major Public (Balanced)
All Oligopolists
est
s.e.∗
p-value
est
s.e.∗
p-value
Constant
-16.789
10.465
0.109
-13.623
10.445
0.193
Rig Share
87.133
81.202
0.284
100.748
72.830
0.167
Project Starts
-13.230
9.833
0.179
-14.702
7.996
0.067
× Rig Share
60.994
60.802
0.316
63.643
55.136
0.249
Nat. Gas Spot
0.445
0.612
0.468
0.502
0.512
0.327
Oil Spot
-2.079
0.551
0.000
-1.870
0.457
0.000
Construction Wage
2.838
3.391
0.403
3.189
2.874
0.268
× Rig Share
-13.166
24.465
0.591
-18.433
21.995
0.402
Roughneck Wage
1.367
1.195
0.254
1.027
0.986
0.298
× Rig Share
-3.089
8.714
0.723
-1.346
7.944
0.866
Pred. Variance
-28.608
12.199
0.019
-26.105
9.724
0.007
× Rig Share
194.516
77.778
0.013
193.443
69.513
0.006
Pred. Growth
-2.780
1.708
0.104
-2.754
1.363
0.044
× Rig Share
9.557
10.471
0.362
9.273
9.481
0.329
Pred. Error
-0.939
2.164
0.665
-0.389
1.721
0.821
× Rig Share
1.525
14.797
0.918
-0.581
13.123
0.965
Mkt/Yr/Qtr F.E. ?
Only Qtr
Only Qtr
Firm F.E. ?
Yes
Yes
Rig Share × Mkt ?
Just Rig Share
Just Rig Share
R2 , # of Obs.
0.2596, 480
0.3678, 555
∗
White robust standard errors are reported
Table 9 provides the estimates using data just for the Gulf Coast market. We provide estimates from both the balanced sample, using data only from the four major public contractors, and
the unbalanced sample involving all tracked contractors. The two sets of estimates are qualitatively
similar with each other. This implies that the addition of the 555 − 480 = 75 selected observations
24
Gulf Coast and South Texas are the only markets for which all four major public contractors are present throughout the study period and 2002 to 2008 the years for which the two industry leaders are present in all six markets. We
do not use the South Texas balanced sample as there is little rig share variation across the major public contractors
in South Texas during the study period
19
does not seem to distort the results too much. The differences we do see are for coefficients that
are estimated highly imprecisely.
Table 10: Robustness Checks – Just 2002 to 2008
Nabors & Patterson (Balanced)
All Oligopolists
est
s.e.∗
p-value
est
s.e.∗
p-value
Constant
-60.851
14.319
0.000
-21.533
6.164
0.000
Project Starts
0.034
1.426
0.981
0.780
0.303
0.010
× Rig Share
15.614
8.953
0.081
6.926
3.101
0.026
Nat. Gas Spot
0.380
0.369
0.303
-0.043
0.193
0.825
-1.128
0.259
0.000
Oil Spot
-2.165
0.458
0.000
Construction Wage
11.451
3.115
0.000
3.990
1.262
0.002
7.848
9.811
0.424
× Rig Share
-24.122
17.170
0.160
Roughneck Wage
-1.081
0.932
0.246
0.229
0.438
0.601
-9.309
4.127
0.024
× Rig Share
-2.847
6.332
0.653
Pred. Variance
-1.318
0.729
0.071
-0.739
0.168
0.000
× Rig Share
17.097
4.476
0.000
12.863
2.115
0.000
Pred. Growth
1.699
0.397
0.000
0.873
0.164
0.000
× Rig Share
-10.783
2.431
0.000
-6.916
1.642
0.000
Pred. Error
0.539
0.499
0.281
0.143
0.164
0.384
× Rig Share
-6.356
3.125
0.042
-4.009
1.785
0.025
Mkt/Yr/Qtr F.E. ?
Yes
Yes
Firm F.E. ?
Yes
Yes
Rig Share × Mkt ?
Yes
Yes
R2 , # of Obs.
0.6381, 1008
0.6167, 2574
∗
White robust standard errors are reported
Table 10 presents the estimates from the regression using data just from year 2002 onward, for
Nabors and Patterson (balanced) and all tracked contractors (unbalanced). This balanced sample
has greater market and rig share variation but involves fewer contractors. Comparison of the two
regression results above and their analogues using the all years, we find that the main impact of
including the unbalanced observations from 1999 to 2001 is on the apparent precision with which
the prediction variance and error coefficients are estimated. The main qualitative conclusions drawn
from the regressions remain the same.
4.2.2
Prediction Horizon
In our regression analysis, we assumed that the relevant prediction horizon was 4 months. We
consider the sensitivity of our results to the use of shorter prediction horizons by conducting the
regressions using a 2 month horizon. Table 11 provides those results
20
Table 11: Robustness Check – Prediction Horizon of 2 Months
All Oligopolists
Major Public
Nabors & Patterson
est
s.e.∗ p-value
est
s.e.∗ p-value
est
s.e.∗ p-value
Constant
-8.044 4.946
0.104 -12.200
7.781
0.117 -23.991 11.674
0.040
Project Starts
0.766 0.332
0.021
1.363
0.581
0.019
2.082
1.355
0.125
× Rig Share
7.151 3.381
0.035
3.813
4.678
0.415
0.202
8.853
0.982
-0.733
0.235
0.002
-1.032
0.354
0.004
Nat. Gas Spot
-0.782 0.176
0.000
Oil Spot
-1.149 0.256
0.000
-1.259
0.312
0.000
-2.063
0.482
0.000
Construction Wage
1.754 1.155
0.129
2.984
1.802
0.098
5.280
2.865
0.066
× Rig Share
8.397 8.921
0.347
0.160 11.089
0.989
-3.120 15.390
0.839
-1.044
0.599
0.082
-0.964
0.861
0.263
Roughneck Wage
-0.294 0.389
0.450
× Rig Share
-5.503 3.717
0.139
-1.635
4.544
0.719
-1.747
5.881
0.766
Pred. Variance
-0.004 0.179
0.984
0.147
0.323
0.649
-0.077
0.926
0.934
× Rig Share
0.844 2.221
0.704
-0.273
2.982
0.927
-0.206
6.099
0.973
Pred. Growth
0.913 0.174
0.000
1.166
0.273
0.000
1.892
0.452
0.000
-6.963
2.103
0.001
-9.327
2.734
0.001
× Rig Share
-5.635 1.641
0.001
Pred. Error
0.215 0.151
0.156
0.135
0.238
0.569
-0.077
0.406
0.850
× Rig Share
-5.099 1.661
0.002
-4.046
2.046
0.048
-2.443
2.684
0.363
Mkt/Yr/Qtr F.E. ?
Yes
Yes
Yes
Firm F.E. ?
Yes
Yes
Yes
Rig Share × Mkt ?
Yes
Yes
Yes
R2 , # of Obs.
0.5482, 3304
0.5410, 2143
0.5401, 1328
∗
White robust standard errors are reported
The net effects for the three forecast variables are similar to our main estimates, but with diminished
magnitudes. The estimates above suggest little net effect of (2 month lagged) prediction variance
and about half as much of the net effect for the 4 month analogues for (2 month ahead) predicted
growth and (2 month lagged) prediction error for market leaders. The results are consistent with
the view that there is substantially less uncertainty 2 months ahead vis-á-vis 4 months ahead.
To explore the issue further, we estimate our firm level regressions including both sets of
forecast variables: two and four month horizons. The estimates are presented in the appendix.
The combined estimated net effect, across two and four month horizon forecast variables, are
similar to those of our main model (utilizing only the four month horizon) for prediction variance
and prediction error. This is expected as the two versions of those forecast variables are highly
correlated with each other:
Table 12: Two and Four Month Horizon Forecast Variables
Mean∗
Correlation∗
2 Month 4 Month between 2 and 4 Month
Prediction Variance
0.396
0.487
0.498
Predicted Growth
0.080
0.233
0.712
Prediction Error
0.031
0.030
0.786
Mean and correlation after normalizing standard error to 1
over all six markets and months in 1999-2008
The estimated coefficients for the 4 month prediction variance are quantitatively close to those
21
from our main regressions (only 4 month horizon). And, the estimated coefficients for the 2 month
prediction variance jointly statistically insignificant. The 2 month prediction variance estimates,
across all three regressions, fail to reject the F-test of joint significance at conventional significance
levels.25 We find that, while longer forecast horizons may still alter our conclusions, the inference
we draw from our regressions is robust to the inclusion of shorter horizon forecast variables.
5
Conclusion
The credit crunch and economic slowdown resulting from the recent global financial crisis have led
to a dramatic decline in drilling activity. While contractors are rapidly stacking rigs and shedding
labor, some are considering maintaining some idle rigs and crews to facilitate the (hopeful) industry
resurgence following the recovery of the global economy
“ ‘Although headcount reductions will be made, our goal is to minimize the number of employees
affected, to avoid the high recruitment and training costs we incur when industry fundamentals
improve,’ Halliburton CEO David Lesar told investors on Monday.”
“Rough for Roughnecks: Oil Industry Layoffs Spread,” Wall Street Journal, 01/27/2009
But the drilling slowdown may also hasten industry consolidation. Smaller contractors may opt
to exit – sell their rigs to larger contractors – rather than stack rigs for later costly reactivation.
The last major industry decline, during the 1990s, led to substantial industry consolidation.26 Our
study here suggests that the welfare consequence of further industry consolidation is unclear.
Smaller contractors are socially useful in that they help discipline larger contractors and
restrict the exercise of market power. The nature of the market and our suggestive evidence for the
strategic use of excess capacity point to sizable market power potential in this industry. But this
benefit comes at a cost, namely the capacity cost of the idle rigs. The standard argument advancing
consolidation is that consolidation substantially reduces the idle capacity stemming from business
stealing: redundant rigs maintained by contractors competing for the same projects. However,
our study indicates that much of the persistent excess capacity carried by smaller contractors may
not be for business stealing; smaller contractors, in order to reduce the hiring cost associated with
supplying during high demand, may carry excess capacity during the interim low demand periods.
Industry consolidation would not substantially reduce excess capacity stemming from labor
hoarding, unless there are scale economies in hiring. Our regression analysis provides some weak
statistical evidence supporting such scale economies; the rig share interaction with construction
wage is negative (with the magnitude growing from the “All Oligopolists” to the Industry Leaders
25
The F-test statistics were 1.61, 1.92, and 1.52, respectively, with degrees of freedom of 3256, 2096, and 1283
Further consolidation has already begun in the related drilling and well services industry. In 2009, Baker Hughes
acquired BJ Services. In February 2010, Schlumberger announced merger plans with Smith International
26
22
regression), but imprecisely so. There are conceptual arguments suggesting the presence of hiring
scale economies. For example, industry prominence may help attract prospective workers to their
firm. Also, larger contractors may reduce hiring costs through diversification afforded by a larger
fleet of active rigs; crew “shortages” in some rigs and markets may be fulfilled with “surplus”
workers in other rigs and markets. The welfare benefits from consolidation hinges on the unknown
magnitude of these scale economies.
Additionally, reductions in excess capacity driven by labor hoarding may, partially, be offset
by excess capacity spurred by greater strategic opportunities generated by further consolidation. If,
for example, consolidation leads to the emergence of market leaders, the new market leaders may
attempt to effect tacit collusion using greater excess capacity. Our empirical study hints at such
practice; the rig share interaction with predicted growth is negative, with the net effect of predicted
growth on excess capacity negative for contractors with substantial market presence. The estimate
is consistent with, but not conclusive of, excess capacity used as an enforcement mechanism for
tacit collusion by market leaders.
Evaluation of the welfare consequences of further drilling industry consolidation requires direct
inference on capacity cost, capacity adjustment cost (e.g. labor hiring cost), and the impact of such
costs on the ability of firms to exercise market power. The descriptive/predictive statistical analysis
adopted in this study does not allow for such inference. But recent developments in the empirical
modeling of dynamic competition among strategic firms with endogenous capacity may. Specifically,
the Markov Perfect Equilibrium (MPE) framework of Ericson & Pakes (1995), extended recently
by Besanko & Dorazelski (2004) and Besanko, Dorazelski, Lu, & Satterthwaite (2009), may be
adapted to estimate the relevant firm primitives.
Some of the same features of the industry that aid our regression analysis, such as the nature
of demand uncertainty, may help the estimation of an empirical MPE model; the relevant Markov
state variables defining a contractor’s information set are largely known and observed and each
market has only a handful of truly strategic players, simplifying the solving of market equilibrium.
The estimated firm primitives could then be used to evaluate counterfactuals, based on further
consolidation, that compare the labor cost saving with the increase in market power deadweight
loss. We consider this future research.
The focus of this study has been on the U.S. land drilling industry but we believe that the
analysis has general lessons applicable to other industries with excess capacity. The study provides
empirical evidence of deliberate (“useful”) excess capacity and cautions against blind condemnation
of observed “excess” capacity as hazardous to industry health. Also, we should not necessarily
expect excess capacity to fall significantly with industry consolidation. Depending on the motives
underlying the excess capacity, consolidation may not lead to substantial reductions.
23
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26
Data Appendix
Five categories of data were used in this paper
1. Drilling Market Data
2. Drilling Project Data
3. Oil & Gas Spot and Futures Prices Data
4. Occupational Wage Data
5. Housing Starts Data
Drilling Market Data
Drilling market data for each of the six main land drilling markets (ArkLaTex, Gulf Coast, Midcontinent, Permian Basin, Rockies, South Texas) from 1999 through 2008 were obtained from
the publishers of Land Rig Newsletter via personal correspondence. Land Rig Newsletter is the
major trade press for the U.S. land drilling industry. More information about Land Rig Newsletter and other data products offered by the parent company, RigData, can be found online at
http://www.rigdata.com
Land Rig Newsletter provided us with two sets of spreadsheets. One set provided the total, marketed, and active rigs for tracked drilling contractors and market aggregate on a biweekly basis.
Each rig variable was aggregated to the monthly level by averaging the corresponding biweekly
values. Much of the data in the first set of spreadsheets is sound. But there are some missing and
suspicious values. The raw data was graphed and observations that corresponded to unrealistic
spikes were marked suspicious.
Many of these suspicious values are obvious data entry errors, e.g. transposed digits, and corrected
accordingly. The remaining suspicious values and missing values were replaced by their imputed
values, based on simple linear imputation (to nearest integer) using nearby observations. There are
some inconsistency between the “marketed” rig count at the market aggregate and at the firm levels
in some markets in 2007 and 2008. We were, unfortunately, unable to resolve this inconsistency.
The data collection method used by Land Rig Newsletter and comparison with other data suggest
that the possible error is in the market aggregate values. Accordingly, we de-emphasize the market
aggregate and focus on the firm level data.
27
The second set provided the quarterly average dayrate, the per diem rate charged by drilling
contractors, by market and drilling depth category. The drilling depth categories are: [1] less
than 6000 feet [2] 6000 ∼ 9999 feet [3] 10000 ∼ 12999 feet [4] 13000 ∼ 15999 feet [5] “Big Rigs”
(16000+ feet). Dayrates are the traditional prices negotiated by drilling contractors and operators.
The monthly average dayrate for a market, aggregated across depth categories, was calculated by
taking the weighted average of the appropriate quarterly average dayrate, with monthly project
start (by drilling depth category) as the weights.
P̄month,year,market =
X PSmonth,year,market,depth × P̄quarter,year,market,depth
P
depth PSmonth,year,market,depth
depth
Drilling Project Data
Data on actual drilling projects were obtained from the “rig count” database maintained by
Smith Bits. The raw data can be downloaded from the Smith Technologies S.T.A.T.S. website,
http://stats.smith.com/new/. The rig counts maintained by Smith Bits and rival firm Baker Hughes
are considered the main measures of U.S. drilling activity. Smith Bits, a major global supplier of
drilling parts, uses its sales force to collect weekly data on North American drilling activity.
The data reports, for each Friday of the week, drilling location and type, start date, and identities
of the operator and contractor involved. Weekly rig activity observations were assigned project IDs
by drilling location and (operator, contractor) pair. To further distinguish multiple projects with
the same (operator, contractor) and location, strings of consecutive drilling activities in the same
location by the same (operator, contractor) pair were considered different projects if the strings
were assigned start dates that were at least 4 weeks apart. Start date was missing for some strings.
For those strings, the first Friday date of the string was used as the start date. Only observations
corresponding to land drilling projects contracted on a dayrate basis were used.
Drilling activity was assigned to a market using the reported drilling location (county). Maps of
the six markets, posted on the RigData website, were used to match counties to markets. Weekly
drilling activity data is aggregated into monthly “project starts” by counting all the projects with
a start date in the month. The project starts were calculated by (market, month, and year) as well
as proposed drilling depth and well target (oil, natural gas, others). The “project starts” variable
used in the regressions is the sum of the natural gas project starts, across the five depth categories,
for a given (market, month, year). Natural gas was chosen as it was the dominant well target
during the study period – over 80% of the dayrate land drilling projects across the six markets.
28
Oil & Gas Spot and Futures Prices Data
Futures prices are from the New York Mercantile Exchange (NYMEX). Specifically, the oil futures
prices are for the (Crude Oil, Light-Sweet, Cushing, Oklahoma) contracts and the natural gas
futures prices for the (Natural Gas, Henry Hub) contracts. Historical price data for these contracts,
from one to four month ahead, are available online from the U.S. Department of Energy, Energy
Information Administration (EIA)
• Natural Gas Futures: http://tonto.eia.doe.gov/dnav/ng/ng pri fut s1 d.htm
• Oil Futures: http://tonto.eia.doe.gov/dnav/pet/pet pri fut s1 d.htm
The EIA reported monthly averages were used.
Historical spot prices for oil and natural gas were also obtained from the EIA website. The Henry
Hub spot price was used for natural gas and the “West Texas Intermediate (WTI) - Cushing,
Oklahoma” spot price for oil.
• Natural Gas Spot: http://tonto.eia.doe.gov/oog/info/ngw/historical/ngwu historical.html
• Oil Spot: http://tonto.eia.doe.gov/dnav/pet/pet pri spt s1 d.htm
Monthly average spot prices were calculated by averaging the corresponding daily spot prices.
Occupational Wage Data
Occupational wage data was obtained from the U.S. Department of Labor, Bureau of Labor Statistics (BLS). Specifically, data from the annual Occupational Employment Statistics (OES) program
was used. The program offers annual wage/income data by occupation and location (state, MSA),
beginning with 1999. Data for past years are archived online at http://www.bls.gov/oes/oes arch.htm.
Wages for the following occupation classes were used:
• Construction Wage: OES Occupation Code 47-2061 “Construction Laborers”
• Roughneck Wage: OES Occupation Code 47-5071 “Roustabout, Oil & Gas”
The reported annual mean hourly wage was used. The wages were aggregated to the drilling
market level by taking the employment weighted average of the relevant disclosed wage across the
metropolitan statistical areas (MSAs) within the drilling market.
¯
Wage
job,year,market =
¯
EMPjob,year,MSA × Wage
job,year,MSA
P
MSA∈Market EMPjob,year,MSA
MSA∈Market
X
Disclosure/confidentiality rules led to unavailable data for Roughneck Wage for four market/years:
Midcontinent 1999, Permian Basin 1999, and Rockies 1999-2000.
29
Housing Starts Data
Housing starts data was obtained from the U.S. Census Bureau. The data is available online at
• http://www.census.gov/const/www/C40/table3.html
Housing starts refers to the total number of new privately owned housing units authorized by
building permits. We use the annual data available by metropolitan statistical areas (MSAs).
Housing starts for a drilling market is the sum of housing starts across MSAs within the drilling
market.
Appendix: Miscellaneous Tables
Table A1: Marketed and Idle Rigs by Major Public Contractor and Market
Nabors
Patterson
Grey Wolf
Helm. & Payne
Mean Std Dev Mean Std Dev Mean Std Dev Mean Std Dev
ArkLaTex
Marketed
52.1
21.1
43.9
18.2
25.8
9.0
28.5
7.0
Idle
9.1
6.5
9.5
6.0
4.1
3.3
4.8
2.5
Gulf Coast
Marketed
24.9
8.2
18.5
4.8
27.3
6.6
12.8
3.4
Idle
6.9
4.6
6.1
3.1
7.2
4.2
3.4
2.0
Midcontinent
Marketed
40.3
10.2
36.5
7.6
—
—
25.0
7.7
Idle
9.9
7.7
7.0
4.8
—
—
2.7
2.1
Permian Basin Marketed
19.7
4.9
82.8
24.4
11.4
2.0
—
—
Idle
3.9
2.7
21.0
10.8
1.8
1.6
—
—
Rockies
Marketed
56.7
23.8
27.3
16.3
15.8
1.5
45.9
7.2
12.1
8.3
6.3
5.2
2.6
1.9
2.7
1.5
Idle
South Texas
Marketed
22.4
5.8
32.9
11.0
26.0
3.5
27.8
6.1
Idle
5.3
3.5
8.0
5.2
4.4
2.8
3.8
2.5
Summary statistics are for tracked months in 1999-2008 (see Table 1); units are rigs
Table A2: Rig Share Summary Statistics by Firm Type and Market
All Oligopolist
Major Public
Nabors / Patt.
Market
Obs. Mean Std Dev. Obs. Mean Std Dev. Obs. Mean Std Dev.
ArkLaTex
563 0.1243
0.0656
396 0.1524
0.0576
240 0.1838
0.0480
Gulf Coast
555 0.1502
0.0759
480 0.1610
0.0762
240 0.1879
0.0827
Midcontinent
564 0.0996
0.0481
275 0.1178
0.0436
214 0.1362
0.0291
Permian Basin 583 0.1367
0.1242
288 0.1949
0.1500
240 0.2156
0.1562
Rockies
696 0.0906
0.0567
284 0.1206
0.0699
214 0.1411
0.0673
South Texas
577 0.1470
0.0421
480 0.1637
0.0214
240 0.1715
0.0239
Summary statistics are for tracked months in 1999-2008 (see Table 1)
30
Table A3: Robustness Check – Both 2 and 4 Month Horizon Forecast Variables
All Oligopolists
Major Public
Nabors & Patterson
est
s.e.∗ p-value
est
s.e.∗ p-value
est
s.e.∗ p-value
Constant
-11.948 4.901
0.015 -19.552
7.689
0.011 -32.957 11.514
0.004
Project Starts
0.909 0.314
0.004
1.506
0.538
0.005
0.448
1.281
0.727
× Rig Share
2.599 3.211
0.418
-0.733
4.312
0.865
5.467
8.129
0.501
-0.452
0.229
0.049
-0.603
0.341
0.078
Nat. Gas Spot
-0.596 0.175
0.001
Oil Spot
-0.970 0.250
0.000
-1.079
0.304
0.000
-1.758
0.464
0.000
Construction Wage
2.643 1.129
0.019
4.771
1.768
0.007
7.527
2.797
0.007
× Rig Share
-1.571 8.721
0.857 -13.113 10.982
0.233 -18.477 15.113
0.222
-1.268
0.574
0.027
-1.031
0.817
0.208
Roughneck Wage
-0.420 0.371
0.258
× Rig Share
-3.251 3.504
0.354
1.136
4.291
0.791
-0.260
5.518
0.962
Pred. Var. (4 mo)
-1.073 0.181
0.000
-1.624
0.277
0.000
-2.364
0.644
0.000
× Rig Share
16.499 2.240
0.000
19.703
2.656
0.000
23.975
4.131
0.000
Pred. Var. (2 mo)
0.287 0.173
0.098
0.606
0.316
0.055
1.433
0.872
0.100
-4.495
2.836
0.113
-9.697
5.583
0.083
× Rig Share
-2.479 2.181
0.256
Pred. Growth (4 mo)
0.487 0.212
0.021
0.567
0.350
0.105
0.239
0.648
0.712
× Rig Share
-5.300 2.340
0.024
-5.652
2.924
0.053
-4.103
4.095
0.317
Pred. Growth (2 mo)
0.590 0.202
0.004
0.820
0.338
0.015
1.777
0.678
0.009
× Rig Share
-3.080 2.277
0.176
-4.613
2.835
0.104
-8.603
4.201
0.041
Pred. Error (4 mo)
0.973 0.244
0.000
1.042
0.364
0.004
1.691
0.611
0.006
× Rig Share
-11.582 2.656
0.000 -11.735
3.111
0.000 -14.151
3.987
0.000
Pred. Error (2 mo)
-0.572 0.232
0.014
-0.659
0.354
0.062
-0.834
0.559
0.136
× Rig Share
4.217 2.573
0.101
4.888
3.033
0.107
5.210
3.612
0.149
Mkt/Yr/Qtr F.E. ?
Yes
Yes
Yes
Firm F.E. ?
Yes
Yes
Yes
Rig Share × Mkt ?
Yes
Yes
Yes
R2 , # of Obs.
0.5741, 3304
0.5723, 2143
0.5762, 1328
∗
White robust standard errors are reported
31
Figure 1: Permian Basin (1999-2008)
500
450
400
350
300
250
200
150
100
50
0
1/15/1999
1/15/2000
1/15/2001
1/15/2002
Rig Total
1/15/2003
1/15/2004
Marketed
Active
1/15/2005
1/15/2006
1/15/2007
"Excess" Capacity
Figure 2: Patterson Fleet / Permian Basin
180
160
140
120
100
80
60
40
20
0
1/15/1999
1/15/2001
Total
1/15/2003
Marketed
Active
1/15/2005
1/15/2007
"Excess" Capacity
1/15/2008
600
12
500
10
400
8
300
6
200
4
100
2
0
0
Month/Year
ArkLaTex
Gulf Coast
Midcontinent
Permian Basin
Rockies
South Texas
NG Spot Price
Figure
gu e 4: Residual
es dua Demand
e a d faced
aced by O
Oligopolists
gopo sts
Uncertainty in spot/future prices creates
uncertainty in Qtot and in residual demand
Day Rate
Q (PsM,PfM)
Day Rate
Q (PsH,PfH)
Q (PsL,PfL)
Fringe Supply
R id l D
Residual Demand d
QtotL
QtotM
QtotH
Projects
QtotL
QtotM
QtotH
Projects
Natural Gas Spot Price
oject Starts
Natural Gas Pro
Figure 3: Project Starts, Natural Gas, January 1997 to December 2008
16
14
12
10
8
6
4
2
0
0
200
400
600
800
1000
1200
Natural Gas Project Starts
Midcontinent 1997-2008
18000
16000
14000
12000
10000
8000
6000
4000
2000
000
0
0
200 400 600
Project Starts
Avg. Day Rate
e
Figure
g
6: Market Price and Quantity
Q
y
Avg. Day Rate
e
4 month A
Ahead Naturral Gas, Lagg
ged 4 Month
h ($/mm BTU
U)
Figure 5: Monthly, Across All Six Markets, 1999-2008
Permian Basin 1997-2008
18000
16000
14000
12000
10000
8000
6000
4000
2000
000
0
0
200 400 600
Project Starts
1400
Figure 7: Difference in Mean Hourly Wage by Market/Year
(Roustabout - Construction)
5
4
3
2
$ / hr
1
0
-1
1
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
-2
-3
-4
-5
ArkLaTex
Permian Basin
Gulf Coast
Rockies
MidContinent
South Texas
Aggregate "Excess" Capacity
160
70
60
140
50
120
40
20
80
10
60
0
40
-10
20
-20
0
-30
4 Mo Ahead NYMEX
Permian Basin - Midcontinent
% Marketed
30
Ju
n
S e -0 0
pDe 00
c
M -0 0
ar
J u - 01
n
S e -0 1
pDe 01
c
M -0 1
ar
J u 02
n
S e -0 2
p
De - 02
c
M -0 2
ar
J u - 03
n
S e -0 3
pDe 03
c
M -0 3
ar
J u - 04
n
S e -0 4
pDe 04
c
M -0 4
ar
J u - 05
n
S e -0 5
pDe 05
c
M -0 5
ar
J u - 06
n
S e -0 6
pDe 06
c
M -0 6
ar
J u - 07
n
S e -0 7
pDe 07
c
M -0 7
ar
J u - 08
n
S e -0 8
pDe 08
c08
$ / bbl
100
Ju
n0
Se 0
p0
De 0
c00
M
ar
-0
Ju 1
n0
Se 1
p0
D 1
ec
-0
M 1
ar
-0
Ju 2
n0
S 2
ep
-0
De 2
c0
M 2
ar
-0
Ju 3
n0
Se 3
p0
D 3
ec
-0
M 3
ar
-0
Ju 4
n0
S 4
ep
-0
De 4
c0
M 4
ar
-0
Ju 5
n0
S 5
ep
-0
De 5
c0
M 5
ar
-0
Ju 6
n0
Se 6
p0
D 6
ec
-0
M 6
ar
-0
Ju 7
n0
S 7
ep
-0
De 7
c0
M 7
ar
-0
Ju 8
n0
S 8
ep
-0
D 8
ec
-0
8
$ / bbl
160
80
20
60
10
40
0
-10
20
-20
0
-30
4 Mo Ahead NYMEX
Permian Basin - Midcontinent
% Marketed
Patterson "Excess" Capacity
70
140
60
120
50
40
100
30