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 per-sistently 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 non-convexities, such as from labor hiring and firing costs. Finally, excess capacity may have strategicvalue, as a preemptive strike on rivals and/or enforcement mechanism for tacit collusion. We usefirm-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 persistentexcess capacity at the market level. But for larger firms, especially in markets where they haveleadership, the precautionary/speculative motive seems just as important. We also find someevidence consistent with the strategic use of excess capacity by such firms. We consider thewelfare 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: jishii@amherst.edu. The author thanks Mingzi Shao, TracyHuang, and Rohan Mazumdar for excellent research assistance, RigData and Ryan Kellogg for data assistance, andseminar participants at 2009 Camp Energy (UCEI) and UMass Resource Economics for valuable comments. Part ofthe paper was written while the author was visiting the School of Management and the Institution for Social andPolicy 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

1Berndt & Morrison (1981) also raises some of these issues, in the context of selecting utilization measures

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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 rep-

resents 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

2The figures are based on data from Land Rig Newsletter. See Data Appendix3The rigs are capable of certain types of “directional” drilling, often built with specific projects in mind

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“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.

4Offshore rigs are much larger, more complicated machineries. Trade press discussions suggest stack-ing/reactivation costs an order of magnitude (or more) larger for offshore rigs

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Table 1: Monthly Excess Capacity as % of Marketed across 1999-2008

Nabors/Patterson Grey Wolf/H&P Other Oligopolists Market AggregateMarket Obs Mean Obs Mean Obs Mean Obs MeanArkLaTex 240 0.2095 156 0.1702 167 0.1335 120 0.2422Gulf Coast 240 0.3015 240 0.2642 75 0.2502 120 0.3577Midcontinent 214 0.2164 61 0.1029 289 0.2547 120 0.2998Permian Basin 240 0.2330 48 0.1497 295 0.2391 120 0.3050Rockies 240 0.2275 70 0.1303 412 0.2474 120 0.2876South Texas 240 0.2294 240 0.1490 97 0.1640 120 0.2860Unbalanced samples reflect both firm entry/exit and tracking changes by data provider, Land Rig Newsletter

Table 2: Monthly Excess Capacity as % of Marketed across the Six Markets

Nabors/Patterson Grey Wolf/H&P Other Oligopolists Market AggregateYear Obs Mean Obs Mean Obs Mean Obs Mean1999 120 0.3458 60 0.2968 138 0.3993 72 0.48952000 120 0.1660 60 0.1388 133 0.2133 72 0.31872001 140 0.2403 60 0.1889 133 0.1713 72 0.26252002 144 0.3469 60 0.2240 135 0.2977 72 0.36322003 144 0.2028 61 0.2213 144 0.1905 72 0.27772004 144 0.1942 73 0.1823 144 0.1650 72 0.26652005 144 0.1837 96 0.1171 123 0.1542 72 0.22182006 144 0.2230 108 0.1517 132 0.1828 72 0.21782007 144 0.2507 117 0.1896 123 0.2489 72 0.27432008 144 0.2207 120 0.1731 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,

5Grey Wolf is acquired by Canadian public drilling firm, Precision Drilling Trust, in 2008

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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 realiza-

tion 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 specula-

tive/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 un-

6See DeVany & Saving (1977) and DeVany & Frey (1982)

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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 considera-

tions) 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 de-

mand, 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 over-

all 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

7Earlier Tables 1 and 2 reveal greater excess capacity for the market aggregate than the oligopolists, suggestinggreater fringe excess capacity. This is consistent with the proposed fringe stacking behavior and higher cost rigs

8The figures are similar when using shorter lags, e.g. 2 month ahead NYMEX futures prices, lagged 2 month9Demand 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 forecastsare used to predict wholesale electricity demand.

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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 + εit (1)

for i ∈ {ArkLaTex ... S. Texas} 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 infor-

mation.10 The predicted value based on the estimated coefficients from this residual regression is

our proxy for contractor perceived forecast uncertainty.(PSit − PSit

)2= e2it = Quarter F.E.it + γ0iProject Startit−4

+ γ1i NGas Spott−4 + γ2i NGas 4mo Aheadt−4

+ γ3i Oil Spott−4 + γ4i Oil 4mo Aheadt−4 + ηit (2)

for i ∈ {ArkLaTex ... S. Texas} 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

The empirical relationship between observed excess capacity and these proxies (PSit, e2it) can

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

10The intuition motivating this exercise follows that of Breusch & Pagan (1979)11The 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

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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 develop-ment,’ said Edward E. Thiele, chief financial officer at Rowan. ‘You train all these peopleand then you lay them off, and then you have to train new people all over again.’ Most compa-nies 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 thelack of hands is bad pay. The current squeeze is most acute for roughnecks–the rig workers whoearn an average of $10 to $11.50 per hour, according to industry analysts. That’s especially lowcompared with the higher wages available in areas such as construction, which is credited withhaving 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 em-

ployers 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

12We abstract from the strategic value of labor hoarding – raising rivals cost and increasing entry barriers13To 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

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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 examin-

ing 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

14See Wenders (1971), Spence 1977), and Dixit (1980)15See Spulber (1981) and Fudenberg & Tirole (1983)16See Bernheim (1984) and Gilbert & Vives (1986), although Waldman (1987) re-raises the concern when there is

market uncertainty17These studies do not explicitly control for other, non-strategic drivers of excess capacity

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

18There is a related literature discussing the role of capacity constraints in tacit collusion, with collusion sustainedby limiting deviation (“cheating”) profits. See Staiger & Wolak (1992), Reynolds & Wilson (2000), and Fabra (2006)

19See Bajari & Summers (2002) for a survey on collusion in procurement auctions20The strategic value stems from increasing market price by withholding supply. Firms with more inframarginal

units benefit more from the higher price.

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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 ShareNabors 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 0.06472004 - 2008 0.2488 0.1220 0.1722 0.0952

Midcontinent 1999 - 2003 0.1690 0.1252 — 0.02612004 - 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 0.14272004 - 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 equalsProject Starts (Nat. Gas) 92.06 project startsNat. Gas 4 Month Ahead Futures $2.69 per million BTUNat. Gas Spot $2.06 per thousand cubic ftOil 4 Month Ahead Futures $27.34 per barrelOil Spot $26.76 per barrel

Construction Wage $2.02 per hourRoughneck Wage $2.00 per hour

4 Month Ahead Predicted Demand 93.32 project starts4 Month Lagged Prediction Variance 1051.98 (project starts)2

4 Month Ahead Predicted Growth 14.44 project starts4 Month Lagged Prediction Error 22.87 project startsStandard 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

WithinMarket 4 weeks 6 weeks 8 weeksArkLaTex 62.70 83.12 91.65Gulf Coast 40.09 58.08 72.00Midcontinent 58.88 78.06 87.26Permian Basin 70.51 82.17 87.72Rockies 66.30 81.50 88.86South Texas 50.80 71.76 83.25Share 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 precau-

tionary value [2] cost saving through labor hoarding [3] strategic value. The first is proxied by

forecasted market growth (PSit+4 −PSit) and forecast uncertainty (e2it) estimated by our ancillary

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

+ β1 PSit + β2 Rig Shareitf × PSit︸ ︷︷ ︸Current Demand

+ β3 NG Spott + β4 Oil Spott︸ ︷︷ ︸Current Info

Interact withRig Shareitf

=⇒

+ β5 eit + β6 e

2it + β7

(PSit+4 − PSit

)︸ ︷︷ ︸

Uncertain Demand+ β8 Construction Wageit + β8 Roughneck Wageit︸ ︷︷ ︸

Cost Adjustment

+ νift (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-valueConstant -129.183 46.220 0.005 -300.806 88.687 0.001Project Starts 9.672 1.442 0.000 11.775 1.863 0.000Nat. Gas Spot -8.710 1.901 0.000 -8.683 2.146 0.000Oil Spot -2.087 3.220 0.517 -1.356 3.248 0.676Construction Wage 40.881 9.555 0.000 54.447 11.398 0.000Roughneck Wage 1.024 1.931 0.596 22.844 10.309 0.027Pred. Variance 5.545 1.390 0.000 6.648 1.649 0.000Pred. Growth 4.421 0.899 0.000 4.447 0.974 0.000Pred. Error -3.162 1.038 0.002 -4.080 1.246 0.001Mkt/Yr/Qtr F.E. ? Yes YesInstrument for Roughneck Wage ? No Yes, Housing StartsR2, # 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 precaution-

ary 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 pre-

dicts, 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 in-

crease 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 less-

than-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-a-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 en-

dogenous 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 con-

struction 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 Nabors & Pattersonest s.e.∗ p-value est s.e.∗ p-value est s.e.∗ p-value

Constant -12.574 4.922 0.011 -20.793 7.719 0.007 -36.330 11.641 0.002Project Starts 0.776 0.271 0.004 1.528 0.458 0.001 0.140 1.195 0.906× Rig Share 2.788 2.680 0.298 -1.340 3.599 0.710 6.030 7.471 0.420

Nat. Gas Spot -0.458 0.173 0.008 -0.338 0.227 0.137 -0.374 0.336 0.266Oil Spot -0.888 0.246 0.000 -1.029 0.301 0.001 -1.694 0.459 0.000Construction Wage 2.753 1.142 0.016 4.981 1.789 0.005 8.220 2.844 0.004× Rig Share -3.019 8.885 0.734 -14.874 11.223 0.185 -23.192 15.522 0.135

Roughneck Wage -0.382 0.377 0.311 -1.226 0.585 0.036 -1.043 0.840 0.215× Rig Share -3.440 3.572 0.336 0.956 4.388 0.827 -0.274 5.681 0.962

Pred. Variance -0.949 0.170 0.000 -1.468 0.252 0.000 -2.033 0.624 0.001× Rig Share 15.575 2.144 0.000 18.463 2.489 0.000 21.918 3.914 0.000

Pred. Growth 0.881 0.174 0.000 1.164 0.273 0.000 1.507 0.413 0.000× Rig Share -7.766 1.718 0.000 -9.392 2.144 0.000 -10.779 2.644 0.000

Pred. Error 0.425 0.160 0.008 0.367 0.236 0.120 0.829 0.432 0.055× Rig share -7.936 1.770 0.000 -7.230 2.079 0.001 -9.221 2.853 0.001

Mkt/Yr/Qtr F.E. ? Yes Yes YesFirm F.E. ? Yes Yes YesRig Share × Mkt ? Yes Yes YesR2, # of Obs. 0.5710, 3304 0.5688, 2143 0.5702, 1328∗White robust standard errors are reported

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 re-

spond 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 & Pattersonest 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 0.002Gulf Coast 61.963 30.934 0.045 90.551 38.957 0.020 142.663 54.454 0.009Midcontinent 97.247 46.709 0.037 168.425 59.544 0.005 284.467 82.635 0.001Permian Basin 78.487 27.894 0.005 110.856 35.594 0.002 143.680 49.022 0.003Rockies 99.529 36.412 0.006 145.182 46.317 0.002 195.637 62.845 0.002South Texas 76.452 26.986 0.005 126.475 33.591 0.000 160.619 46.924 0.001∗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

21The term “effect” is used in lieu of “within sample prediction” for simplicity22The 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 com-

plications stemming from our unbalanced sample. In the second, we examine regression estimation

using shorter prediction horizons.

23For example, if the distribution of rig type were similar across the studied contractors, with fleet size (scale) beingthe 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 Oligopolistsest s.e.∗ p-value est s.e.∗ p-value

Constant -16.789 10.465 0.109 -13.623 10.445 0.193Rig Share 87.133 81.202 0.284 100.748 72.830 0.167Project 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 0.327Oil Spot -2.079 0.551 0.000 -1.870 0.457 0.000Construction 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 QtrFirm F.E. ? Yes YesRig Share × Mkt ? Just Rig Share Just Rig ShareR2, # 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 esti-

mates 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

24Gulf Coast and South Texas are the only markets for which all four major public contractors are present through-out the study period and 2002 to 2008 the years for which the two industry leaders are present in all six markets. Wedo not use the South Texas balanced sample as there is little rig share variation across the major public contractorsin 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 Oligopolistsest s.e.∗ p-value est s.e.∗ p-value

Constant -60.851 14.319 0.000 -21.533 6.164 0.000Project 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 0.825Oil Spot -2.165 0.458 0.000 -1.128 0.259 0.000Construction Wage 11.451 3.115 0.000 3.990 1.262 0.002× Rig Share -24.122 17.170 0.160 7.848 9.811 0.424

Roughneck Wage -1.081 0.932 0.246 0.229 0.438 0.601× Rig Share -2.847 6.332 0.653 -9.309 4.127 0.024

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 YesFirm F.E. ? Yes YesRig Share × Mkt ? Yes YesR2, # 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 & Pattersonest 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 0.040Project 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

Nat. Gas Spot -0.782 0.176 0.000 -0.733 0.235 0.002 -1.032 0.354 0.004Oil Spot -1.149 0.256 0.000 -1.259 0.312 0.000 -2.063 0.482 0.000Construction 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

Roughneck Wage -0.294 0.389 0.450 -1.044 0.599 0.082 -0.964 0.861 0.263× 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× Rig Share -5.635 1.641 0.001 -6.963 2.103 0.001 -9.327 2.734 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 YesFirm F.E. ? Yes Yes YesRig Share × Mkt ? Yes Yes YesR2, # 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-a-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 MonthPrediction Variance 0.396 0.487 0.498Predicted Growth 0.080 0.233 0.712Prediction Error 0.031 0.030 0.786Mean and correlation after normalizing standard error to 1over 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 employeesaffected, to avoid the high recruitment and training costs we incur when industry fundamentalsimprove,’ 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

25The F-test statistics were 1.61, 1.92, and 1.52, respectively, with degrees of freedom of 3256, 2096, and 128326Further 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

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

References

[1] P. Bajari and G. Summers. Detecting collusion in procurement auctions. Antitrust Law

Journal, 70(1):143–170, 2002.

[2] J.-P. Benoit and V. Krishna. Dynamic duopoly: Prices and quantities. Review of Economic

Studies, 54(1):23–35, January 1987.

[3] S. Bentolila and G. Bertola. Firing costs and labour demand: How bad is eurosclerosis? Review

of Economic Studies, 57(3):381–402, July 1990.

[4] E. R. Berndt and C. J. Morrison. Capacity utilization measures: Underlying economic theory

and an alternative approach. American Economic Review, 71(2):48–52, May 1981.

[5] B. D. Bernheim. Strategic deterrence of sequential entry into an industry. RAND Journal of

Economics, 15(1):1–11, Spring 1984.

[6] G. Bertola. Labor turnover costs and average labor demand. Journal of Labor Economics,

10(4):389–411, October 1992.

[7] D. Besanko and U. Doraszelski. Capacity dynamics and endogenous asymmetries in firm size.

RAND Journal of Economics, 35(1):23–49, Spring 2004.

[8] D. Besanko, U. Doraszelski, L. Lu, and M. Satterthwaite. On the role of demand and strategic

uncertainty in capacity investment and disinvestment dynamics. International Journal of

Industrial Organization, forthcoming.

[9] T. Breusch and A. Pagan. A simple test for heteroscedasticity and random coefficient variation.

Econometrica, 47(5):1287–1294, September 1979.

[10] W. A. Brock and J. A. Scheinkman. Price setting supergames with capacity constraints. Review

of Economic Studies, 52(3):371–382, July 1985.

[11] J. Connor. Our customers are our friends: The lysine cartel of 1992-1995. Review of Industrial

Organization, 18(1):5–21, 2001.

[12] K. Conrad and M. Veall. A test for strategic excess capacity. Empirical Economics, 16:433–445,

1991.

[13] K. S. Corts. Stacking the deck: Idling and reactivation of capacity in offshore drilling. Journal

of Economics and Management Strategy, 17(2):271–294, Summer 2008.

[14] C. Davidson and R. Deneckere. Excess capacity and collusion. International Economic Review,

31(3):521–541, August 1990.

24

[15] A. DeVany and F. Frey. Backlogs and the value of excess capacity in the steel industry.

American Economic Review, 72(3):441–451, June 1982.

[16] A. DeVany and T. Saving. Product quality, uncertainty, and regulation: The trucking industry.

American Economic Review, 67(4):583–594, September 1977.

[17] A. Dixit. The role of investment in entry-deterrence. Economic Journal, 90(357):95–106,

March 1980.

[18] R. Ericson and A. Pakes. Markov-perfect industry dynamics: A framework for empirical work.

Review of Economic Studies, 62(1):53–82, January 1995.

[19] N. Fabra. Collusion with capacity constraints over the business cycle. International Journal

of Industrial Organization, 24:69–81, 2006.

[20] D. Fudenberg and J. Tirole. Capacity as a commitment: Strategic investment to deter mobility.

Journal of Economic Theory, 31(2):227–250, December 1983.

[21] R. Gilbert and X. Vives. Entry deterrence and the free rider problem. Review of Economic

Studies, 53(1):71–83, January 1986.

[22] R. J. Gilbert and M. Lieberman. Investment and coordination in oligopolistic industries.

RAND Journal of Economics, 18(1):17–33, Spring 1987.

[23] J. Haltiwanger and J. E. Harrington. The impact of cyclical demand movements on collusive

behavior. RAND Journal of Economics, 22(1):89–106, Spring 1991.

[24] J. C. Hilke. Excess capacity and entry: Some empirical evidence. Journal of Industrial

Economics, 33(2):233–240, December 1984.

[25] R. Kellogg. Learning by drilling: Inter-firm learning and relationship persistence in the texas

oilpatch. NBER Working Paper 10506, June 2009.

[26] C. R. Knittel and J. J. Lepore. Tacit collusion in the presence of cyclical demand and endoge-

nous capacity levels. NBER Working Paper 12635, October 2006.

[27] M. B. Lieberman. Excess capacity as a barrier to entry: An empirical appraisal. Journal of

Industrial Economics, 35(4):607–627, June 1987.

[28] T.-C. Ma. Strategic investment and excess capacity: A study of the taiwanese flour industry.

Journal of Applied Economics, 8(1):153–170, May 2005.

[29] R. T. Masson and J. Shaanan. Excess capacity and limit pricing: An empirical test. Economica,

53(211):365–378, August 1986.

25

[30] S. Reynolds and B. J. Wilson. Bertrand-edgeworth competition, demand uncertainty, and

asymmetric outcomes. Journal of Economic Theory, 92(1):122–141, May 2000.

[31] S. S. Reynolds. Strategic capital investment in the american aluminum industry. Journal of

Industrial Economics, 34(3):225–245, March 1986.

[32] D. I. Rosenbaum. An empirical test of the effect of excess capacity in price setting, capacity-

constrained supergames. International Journal of Industrial Organization, 7:231–241, 1989.

[33] A. M. Spence. Entry, capacity, investment and oligopolistic pricing. Bell Journal of Economics,

8(2):534–544, Autumn 1977.

[34] D. F. Spulber. Capacity, output, and sequential entry. American Economic Review, 71(3):504–

514, June 1981.

[35] R. W. Staiger and F. A. Wolak. Collusive pricing with capacity constraints in the presence of

demand uncertainty. RAND Journal of Economics, 23(2):203–220, Summer 1992.

[36] M. Waldman. Noncooperative entry deterrence, uncertainty, and the free rider problem. Review

of Economic Studies, 54(2):301–310, April 1987.

[37] J. T. Wenders. Excess capacity as a barrier to entry. Journal of Industrial Economics, 20(1):14–

19, November 1971.

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, Mid-

continent, 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 Newslet-

ter 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, mar-

keted, 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.

Pmonth,year,market =∑

depth

PSmonth,year,market,depth × Pquarter,year,market,depth∑depth PSmonth,year,market,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 Statis-

tics (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.

¯Wagejob,year,market =∑

MSA∈Market

EMPjob,year,MSA × ¯Wagejob,year,MSA∑MSA∈Market EMPjob,year,MSA

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. & PayneMean 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 7.0Idle 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 3.4Idle 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 7.7Idle 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 7.2Idle 12.1 8.3 6.3 5.2 2.6 1.9 2.7 1.5

South Texas Marketed 22.4 5.8 32.9 11.0 26.0 3.5 27.8 6.1Idle 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 0.0480Gulf Coast 555 0.1502 0.0759 480 0.1610 0.0762 240 0.1879 0.0827Midcontinent 564 0.0996 0.0481 275 0.1178 0.0436 214 0.1362 0.0291Permian Basin 583 0.1367 0.1242 288 0.1949 0.1500 240 0.2156 0.1562Rockies 696 0.0906 0.0567 284 0.1206 0.0699 214 0.1411 0.0673South Texas 577 0.1470 0.0421 480 0.1637 0.0214 240 0.1715 0.0239Summary 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 & Pattersonest 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 0.004Project 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

Nat. Gas Spot -0.596 0.175 0.001 -0.452 0.229 0.049 -0.603 0.341 0.078Oil Spot -0.970 0.250 0.000 -1.079 0.304 0.000 -1.758 0.464 0.000Construction 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

Roughneck Wage -0.420 0.371 0.258 -1.268 0.574 0.027 -1.031 0.817 0.208× 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× Rig Share -2.479 2.181 0.256 -4.495 2.836 0.113 -9.697 5.583 0.083

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 YesFirm F.E. ? Yes Yes YesRig Share × Mkt ? Yes Yes YesR2, # of Obs. 0.5741, 3304 0.5723, 2143 0.5762, 1328∗White robust standard errors are reported

31

500

Figure 1: Permian Basin (1999-2008)

400

450

300

350

200

250

150

200

50

100

0

1/15/1999 1/15/2000 1/15/2001 1/15/2002 1/15/2003 1/15/2004 1/15/2005 1/15/2006 1/15/2007 1/15/2008

Rig Total Marketed Active "Excess" Capacity

180

Figure 2: Patterson Fleet / Permian Basin

140

160

100

120

80

100

40

60

0

20

0

1/15/1999 1/15/2001 1/15/2003 1/15/2005 1/15/2007

Total Marketed Active "Excess" Capacity

12600

Figure 3: Project Starts, Natural Gas, January 1997 to December 2008

10500

8400

po

t P

rice

oje

ct S

tart

s

6300

Nat

ura

l Gas

S

Nat

ura

l Gas

Pro

2

4

100

200

00

Month/Year

ArkLaTex Gulf Coast Midcontinent Permian Basin Rockies South Texas NG Spot Price

Figure 4: Residual Demand faced by Oligopolistsgu e es dua e a d aced by O gopo sts

Uncertainty in spot/future prices creates uncertainty in Qtot and in residual demand

Q (PsM,PfM)

Day Rate Day Rate

Q (PsH,PfH)

Fringe Supply

R id l D d

Q (PsL,PfL)

Residual Demand 

QtotM Projects ProjectsQtotL QtotH QtotL QtotM QtotH

16

U)

Figure 5: Monthly, Across All Six Markets, 1999-2008

12

14h

($/

mm

BT

U

10

12

ged

4 M

on

th

6

8

ral

Gas

, L

agg

4

6

Ah

ead

Nat

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0 200 400 600 800 1000 1200 1400Natural Gas Project Starts

Figure 6: Market Price and Quantityg Q y

Midcontinent 1997-2008 Permian Basin 1997-2008

14000

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e 14000

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e

8000

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Day

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e

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.

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.

0

000

0 200 400 600

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0

000

0 200 400 600

Project StartsProject Starts Project Starts

Figure 7: Difference in Mean Hourly Wage by Market/Year(Roustabout - Construction)

3

4

5

1

2

3

-1

0

1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

$ / h

r

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ArkLaTex Gulf Coast MidContinent

Permian Basin Rockies South Texas

Aggregate "Excess" Capacity

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4 Mo Ahead NYMEX Permian Basin - Midcontinent