OCS Study 

MMS 2003-041 

 

Coastal Marine Institute 

Changing Patterns of Ownership and 

Control in the Petroleum Industry: 

Implications for the Market for Oil and Gas 

Leases in the Gulf of Mexico OCS Region, 

1983-1999 

430

373 335

425

308

371

456 453 486

411

688

730 747 760

708 672 701 668 650 674

521 516 499 461 454 446 429 425

472

522

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Herfindahl Index Value

Leases Acquired OCS Production US Production Highly Concentrated Concentrated

Concentrated 

Highly Concentrated 

Cooperative Agreement 

Coastal Marine Institute 

Louisiana State University 

U.S. Department of the Interior 

Minerals Management Service 

Gulf of Mexico OCS Region 
U.S. Department of the Interior Cooperative Agreement 

Minerals Management Service Coastal Marine Institute 

Gulf of Mexico OCS Region Louisiana State University 

OCS Study 

 MMS 2003-041 

 

Coastal Marine Institute 

Changing Patterns of Ownership and 

Control in the Petroleum Industry: 

Implications for the Market for Oil and Gas 

Leases in the Gulf of Mexico OCS Region, 

1983-1999 

Authors 

Allan G. Pulsipher 

Omowumi O. Iledare 

Dmitry V. Mesyanzhinov 

October 2003 

Prepared under MMS Contract 

14-35-0001-30951-16806 

by 

The Center for Energy Studies 

Louisiana State University 

Baton Rouge, Louisiana 70803 

Published by 
iii

DISCLAIMER

This report was prepared under contract between the Minerals Management Service (MMS) and 

Louisiana State University’s Center for Energy Studies. This draft report has not been technically 

reviewed by MMS. Approval does not signify that the contents necessarily reflect the view and 

policies of the Service, nor does mention of trade names or commercial products constitute 

endorsement or recommendation for use. It is, however, exempt from review and compliance 

with MMS editorial standards. 

REPORT AVAILABILITY

Extra copies of the report may be obtained from the Public Information Office (Mail Stop 5034) 

at the following address: 

 

 U.S. Department of the Interior 

 Minerals Management Service 

 Gulf of Mexico OCS Region 

 Public Information Office (MS 5034) 

 1201 Elmwood Park Boulevard 

 New Orleans, Louisiana 70123-2394 

 Telephone Number: 1-800-200-GULF 

CITATION

Suggested Citation: 

Pulsipher, A.G., O.O. Iledare, and D.V. Mesyanzhinov. 2003. Changing Patterns of Ownership 

and Control in the Petroleum Industry: Implications for the Market for Oil and Gas 

Leases in the Gulf of Mexico OCS Region, 1983-1999. Prepared by the Center for 

Energy Studies, Louisiana State University, Baton Rouge, LA. U.S. Dept. of the Interior, 

Minerals Management Service, Gulf of Mexico OCS Region, New Orleans, LA. OCS 

Study MMS 2003-041. 86 pp. 

ACKNOWLEDGMENTS

This report is based primarily on OCS leasing records made available to the Center for Energy 

Studies by the Minerals Management Service, New Orleans. Barbara Kavanaugh, Versa Stickle, 

Ric Pincomb, Yan Zhang, and Williams Olatubi were very helpful in obtaining and processing 

these data. 
v

ABSTRACT 

 

Has consolidation of ownership and control in the petroleum industry reduced the extent of 

competition for or lowered the value of oil and gas leases on the Gulf of Mexico OCS? 

Neither aggregate measures used by economists to analyze concentrated market and industry 

structures, nor patterns of joint bidding and cooperation among firms active in the offshore Gulf 

of Mexico suggest either a decrease or a deficiency in the competitiveness of the lease sales held 

by the U.S. Minerals Management Service (MMS). Whether leases acquired at the sales, or 

production by firms bidding for leases, are used as the base of the concentration measures, they 

indicate a competitive industry bidding for leases in a competitive market. 

The effects of mergers are not reflected in the trends observable in these measures over time. 

More recent mega-mergers are only reflected in the last two or three years of the data series 

analyzed, but their effects do not change the quantitative measures of the concentration of the 

industry or lease market. Comparing major and non-major companies as defined by EIA criteria 

does not indicate significant differences. Similarly, an analysis of patterns of bidding by those 

firms on MMS’ Restricted Bidders List does not suggest non-competitive behavior, although an 

analysis of the criteria stipulated for compiling the list raises questions about the list’s relevance 

and rationale. 

Effects of mergers were analyzed following two complementary approaches. In the first, leases 

acquired during the 1983 to 1999 period were divided into groups based upon merger and 

acquisition experience. Over the entire time period the comparisons indicated that leases 

acquired by firms without merger and acquisition experience elicited significantly higher bids 

than those acquired by firms with merger and acquisition experience. However, if the leases were 

subdivided into those acquired during the 1983 to 1989 period, and those acquired in the 1990 to 

1999 period, the differences became insignificant in the later period. 

Following the second approach entailed making comparisons between majors and non-majors 

and between those firms listed on MMS’ Restricted Bidders List and those not on the list. These 

comparisons showed the same pattern of significant differences in the 1983-89 period weakening 

in the 1990-99 period for the financial variables measuring the average value of bids. However, 

the structural variables measuring the number of bidders and the number of bids per lease 

showed consistent, significantly lower, values for restricted bidders in both time periods. 

Econometric analyses were also applied to data on lease sales in the U.S. Gulf of Mexico OCS 

region in order to incorporate other relevant factors into the analysis. The same two approaches 

were followed. In the first, regression equations were estimated for each of the groups of leases, 

referred to previously, to identify potential effects of mergers and acquisitions on the mean value 

of high bonus bids, along with other hypothesized determinants, such as: intensity of 

competition, extent of competition, type of bid, lease location/water depth, economic conditions, 

and structural changes in the E&P industry. A Wald coefficient restriction test was applied to the 

regression results to ascertain differences in coefficients in the equations. 

The parameters designated as fixed effects (intercepts) were significantly different in magnitude 

among the groups. The differences suggest that the relative change in the mean value of high 
vi

bonus bids for OCS leases, ceteris paribus, was relatively smaller for leases in which 

participating bidders include a firm or firms with M&A experience during the period 1983-1999, 

when other factors have been accounted for. 

This result could be interpreted either as suggesting these firms were able to exercise some 

degree of oligopsony power, or as suggesting that they possessed better information or had more 

leasing experience, which enabled them to bid more efficiently. Since measures of competition 

were not significantly different, the second option seems more plausible. 

In the second approach, a set of interactive dummy variables was employed to define eight 

combinations of: 1) type of bid (joint or solo), 2) existence and extent of competition (ex post) 1

in bidding, and 3) firm size, (using the major/non major or the restricted bidder/unrestricted 

bidder classifications as proxies for firm size). Merger and acquisition experience was included 

in the regression equation and had the same significant, negative relationship as in the first 

model. 

In general, both econometric approaches led to findings that are consistent with other studies and 

theoretical expectations. Both also show that as ex post competition increases, the average value 

of the high bonus bids increases and this is true regardless of the mergers and acquisition 

experience of the participants winning the lease. Similarly, the study shows that joint bidding for 

leases does not lead to anticompetitive bidding outcomes, again regardless of the M&A status of 

the bidders. However, taking account of the effects of the other intervening factors that could be 

expected to affect the value of high bids, the econometric analysis suggests that bidders with 

M&A experience paid less than bidders without M&A experience for OCS leases. 

 

1

 “Ex post competition” is used since the lease sale is a sealed bid auction and bidders do not necessarily know if 

other operators are bidding on the lease. They may have such information, however, from other sources. 
vii

TABLE OF CONTENTS

Page

List of Figures................................................................................................................................ ix 

List of Tables ................................................................................................................................. xi 

Executive Summary ........................................................................................................................1 

Section 1 Introduction ...........................................................................................................17 

Section 2 OCS Lease Market Structure, Conduct and Performance ....................................19 

 2.1 Background on OCS Leasing ..........................................................................19 

 2.2 Competitive Structure of the Market for OCS Leases ....................................20 

 2.3 Bidding Arrangements for OCS Leases ..........................................................32 

 2.4 Characterizing the Market for OCS Leases ...................................................34 

Section 3 Mergers and Acquisitions, Firm Size and the Market for OCS Leases ................47 

 3.1 Effects of Mergers and Acquisitions. ..............................................................47 

 3.2 Comparing the Lease Records of Majors and Non-Majors ............................53 

 3.3 The Restricted Joint Bidders and OCS Lease Market ....................................58 

 3.4 Conclusions .....................................................................................................68 

Section 4 Econometric Analysis of the Market for OCS Leases ..........................................69 

 4.1 Model Specification ........................................................................................69 

 4.2 Model Estimation and Regression Results .....................................................72 

 4.3 Effects of Mergers and Acquisitions on the Value of OCS Leases ................75 

 4.4 Firms of Different Sizes and the Value of OCS Leases ..................................77 

Section 5 Conclusions and Implications ...............................................................................84 

Section 6 References .............................................................................................................85 
ix

LIST OF FIGURES

Figure Description Page

 

ES1 Herfindahl Indices of Industry Competitiveness ................................................................3 

ES2 Z-Scores for Average Bids (All Bids) ................................................................................5 

2.1 Annual Oil and Gas Production by Firms from the Gulf of Mexico OCS Measured 

in MMBarrels of Oil Equivalent, 1947-2001 ....................................................................21 

2.2 Cumulative Oil and Gas Production by Firms from the Gulf of Mexico OCS 

Measured in MMBarrels of Oil Equivalent, 1947-2001 ...................................................21 

2.3 Share of Production from the U.S. GOM, U.S. and the World for the Top 50 

 Firms Operating in the U.S. Gulf of Mexico OCS ...........................................................27 

2.4 Average Number of Bids Per Lease in Different Periods .................................................32 

2.5 Trend in the Value of the High Bonus Bids, 1983-1999 ..................................................35 

2.6 Trend in the Index of the Annual Average Value of High Bids .......................................36 

2.7 Z-Scores for Average High Bids (Shallow Water) ...........................................................41 

2.8 Z-Scores for Average High Bids (Deep Water) ................................................................42 

2.9 Z-Scores for Average High Bids (Competitive Bids) .......................................................43 

2.10 Z-Scores for Average High Bids (Single Bids) ................................................................44 

2.11 Z-Scores for Average High Bids (All Bids) .....................................................................45 

3.1 Percent Decline in Aggregate Lease Market Performance Attributes Between the 

1980s and 1990s ................................................................................................................57 

3.2 Percent Decline in Average High Bonus Bids by Lease Structure and Conduct 

Between the 1980s and 1990s ...........................................................................................57 

3.3 Share of GOM Production by MMS’ Restricted Joint Bidders, 1982-2000 .....................61 

3.4 Total Production for MMS’ Restricted Bidders in the GOM, 1983-2000 ........................61 

3.5 Percent of JV High Bids and JV Bonuses for MMS’ Restricted Bidders, 1983-2000 .....63 

3.6 Location of Texaco/Chevron’s Joint Bids, 1996-1999 .....................................................66 
xi

LIST OF TABLES 

Table Description Page

ES1 Measures of the Competitive Structure, Type of Bid and Value of Mean High Bids 

 for OCS Oil and Gas Leases by Lease Category Groups A and B, 1983-1989 and 

 1990-1999 ...........................................................................................................................7 

ES2 Comparison of OCS Lease Sales by Context, Outcome, and Period for Majors 

 and Non-Majors ..................................................................................................................8 

ES3 Bidding by Context, Outcome and Period for Leases Won by Restricted and NonRestricted Bidders .............................................................................................................11 

2.1 Measures of Competitiveness Based on Leases Acquired, 1983 to 1999 .........................23 

2.2 Measures of Competitiveness Based on Production in the Gulf of Mexico OCS ............25 

2.3 Gulf of Mexico (GOM) Production and Leases, U.S. Production and Global 

 Production for the Top 50 Firms in Either GOM Production or Leases, 

 1990 to 1999 .....................................................................................................................26 

2.4 Concentration Ratios, HHI Indices and Total Production for U.S. & GOM ....................29 

2.5 Share of Leases Acquired by Top 4, 8, and 20 Producing Firms .....................................29 

2.6 Top 20 Firms in Total Oil and Gas Production or Total Leases Acquired in the 

 Gulf of Mexico, 1990-1999 ..............................................................................................30 

2.7 Measures of the Intensity of Competition for OCS Leases ..............................................31 

2.8 Percentage Frequency Distribution of Leases by the Number of Bids .............................33 

2.9 Percent Share of Bids that Were Joint Venture Bids ........................................................34 

2.10 Average Value of High Bonus Bids, 1983 to 1999 ($000) ...............................................36 

2.11 Top Twenty Firms in Total Oil and Gas Production or Total Leases Acquired, 

 Average High Bid/Lease for Total, Shallow, Deep, Competitive and Non- 

 Competitive Leases, 1990-1999 ........................................................................................38 

2.12 Top Fifty Firms in Either Production or Leases Acquired from 1990 to 1999 

 Ranked by Z-Scores for Average High Bids, with Z-Scores for Total, Shallow, 

 Deep, Competitive, and Non-Competitive Average High Bids ........................................39 

3.1 Aggregate Values of OCS Lease Attributes .....................................................................48 

3.2 Measures of the Competitive Structure, Type of Bid and Value of Mean High 

 Bids for OCS Oil and Gas Leases by Lease Category Groups A, B and C, 

 1983-1999 .........................................................................................................................50 

3.3 Measures of the Competitive Structure, Type of Bid and Value of Mean High 

Bids for OCS Oil and Gas Leases by Lease Category Groups A and B, 

1983-1989 and 1990-1999 ................................................................................................51 

3.4 Frequency Distribution of Bidding by Joint Venturing ....................................................52 

3.5 Mean Value of High Bids by Structure, Conduct and Lease Category ($ Million) ..........53 

3.6 Comparison of OCS Lease Attributes by Structure and Conduct, 1983-1999 .................54 

3.7 Comparison of OCS Lease Attributes by Structure, Conduct, and Period .......................56 

3.8 List of Restricted Joint Bidders 1982-2001: Category and Share of Total Gulf of 

 Mexico Production ............................................................................................................59 

3.9 Number of High Joint Bids by Firms on the Restricted Bidders List and Other 

Firms: 1983 to 1999 ..........................................................................................................63 

3.10 Lease Attributes for Restricted and Unrestricted Bidders ................................................67 
xii

LIST OF TABLES (continued) 

Table Description Page

4.1 Variable Definitions and Hypothesized Signs of the Parameters .....................................73 

4.2 Estimated Model of the Value of High Bonus Bids on the Gulf OCS, 1983-99 ..............73 

4.3 Estimated Model of the Value of High Bonus Bids with Coefficient Restrictions ..........76 

4.4 Estimated CAT Model of the Relative Change in the Value of High Bonus Bids 

 with NMSN as the Base Case ...........................................................................................79 

4.5 Estimated Model of the Relative Change in the Value of High Bonus Bids with 

 Varying Base Case ............................................................................................................79 

4.6 Estimated CAT Model of the Relative Change in the Value of High Bonus Bids 

 with USN as the Base Case ...............................................................................................82 

4.7 Estimated Model of the Relative Change in the Value of High Bonus Bids with 

 Varying Base Case ............................................................................................................83 
EXECUTIVE SUMMARY 

Mergers, acquisitions and other arrangements to achieve control, cooperation, or coordination 

have characterized the petroleum industry almost from the time that kerosene replaced whale oil 

as the primary fuel for illumination. During the past decade, most of the largest petroleum 

companies operating in the United States have joined in “mega-mergers.” Large independents 

active in the Gulf of Mexico as well as many smaller independents also merged. 

Regulatory authorities have analyzed the possible effects of each merger and ordered divestitures 

or other arrangements where the new organization threatened competition or other desirable 

economic attributes or ends. The regulators’ attention has been focused on retail or wholesale 

markets for petroleum products, although on the west coast impacts on refinery inputs have also 

been a source of concern. 

The focus of this study is different. Our interest is the possible effects of mergers and 

acquisitions on the U.S. Treasury as the recipient of royalties and bonuses paid by companies to 

explore for and produce oil and gas from reserves on the Outer Continental Shelf (OCS). 

Data and Methods: Descriptive and econometric analyses were applied to data on lease sales in 

the U.S. Gulf of Mexico OCS region during the period 1983-1999. The analyses were limited to 

1983-1999 to correspond to the period since the area-wide leasing policy began. The study’s unit 

of analysis is primarily the sale of each lease with the participants in, or bidders for, the lease 

divided into different groups. For some analyses, leases are divided into three groups depending 

on whether the firm or group of firms submitting the winning bid included: 

 

1) Firms with merger and acquisition experience at the time of the bid, 

2) Firms who would be involved in mergers or acquisitions in the future, or 

3) Firms who were without such experience during the study period. 

Comparisons were made to see if there were significant differences among the three groups, and 

this grouping was also used in econometric analyses. 

A supplementary set of analyses focusing on firm-size was conducted with leases classified by: 

1) whether the winning bid included firms classified as “majors/non-majors” by the Energy 

Information Agency (EIA), and 2) whether the winning bid included firms named on the 

Restricted Bidders List that MMS is required to publish twice a year. 

Leases also were classified into eight categories based on firm size, bidding strategy and lease 

structure. The second and third chapters of the study are a descriptive analysis of the data that 

included tests for the significance of differences in the mean value of some lease attributes. The 

fourth chapter is an econometric analysis of high or winning bonus bids that incorporates other 

relevant factors in the analysis. 
2

Industry Characteristics and Background: Annual and cumulative production since offshore 

development began in the late 1940s reveal important elements of both stability and change. 

• Chevron, Shell, Exxon, Mobil, and Texaco who are leading oil and gas producers in the 

Gulf of Mexico today, and are responsible for a correspondingly substantial share of 

cumulative production, provide the stability. 

• But comparing annual and cumulative production also reveals significant changes in the 

offshore industry. For example: 

o BP is a top-five producer today, but as a relative newcomer to the Gulf of Mexico 

still ranks only 23rd in cumulative production. 

o Conoco, an offshore pioneer, ranks 26th in annual production but still is among the 

top-five in cumulative production. 

o Eight of today’s top-30 annual producers have not produced enough to be ranked 

amongst the top-30 cumulative producers. 

o In 1980 the combined production by all non-top-30 producers ranked 16th in terms 

of annual production. Today the combined group ranks second. 

Mergers, acquisitions, divestitures, changing corporate strategies, good and bad investment 

decisions, technological innovation, international politics, global economics and luck are 

reflected in these changes, but the data indicate a history of substantial change in internal 

organizational as well as external circumstances. 

Measuring Concentration and Competition: An after-the-fact-indicator of competition is the 

extent to which leases are concentrated. If leases are won or held by only a few firms, the 

implication is that competition in lease sales has been less effective or intense than if leases were 

distributed more broadly. An issue raised in our study is whether concentration should be 

measured directly by the distribution of leases acquired, or indirectly by the share of production 

accounted for by the firms bidding for leases. And, if production is used, should it be production 

in the Gulf of Mexico, in the U.S., or global production. 

The most comprehensive and commonly used measure of concentration is the Herfindahl Index 

(HHI) that measures both the absolute and the relative concentration of the market shares. The 

HHI is defined as the sum of the squares of the market shares of all participating firms in the 

lease auction market in a given period or year—considering the size of all participants, not just 

the largest four or largest eight. 

Industry or market concentration often is broadly characterized using the magnitude of the HHI 

index. A market that has HHI of less than 1,000 is less concentrated and can be described as 

being competitive. Dougher (1987) argues that a market is moderately concentrated if its HHI 

lies between 1,000 and 1,800, or highly concentrated when its HHI is greater than 1,800. 

Figure ES1 shows the HHI for the decade of the 1990s calculated with three different bases: 

leases acquired, production in the Gulf of Mexico OCS, and total U.S. production. All three 

indices are well below the 1000 minimal value for a “concentrated” designation, throughout the 

period. 
3

430

373 335

425

308

371

456 453 486

411

688

730 747 760

708 672 701 668 650 674

521 516 499 461 454 446 429 425

472

522

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Herfindahl Index Value

Leases Acquired OCS Production US Production Highly Concentrated Concentrated

Concentrated 

Highly Concentrated 

 Figure ES1: Herfindahl Indices of Industry Competitiveness. 

Leases are held for varying terms. Some are turned back after their initial 5-year term; others are 

held as long as there is production or related activity on the lease, such as pumping. Thus the 

stock or inventory of active leases that a firm controls will differ from the number of leases it 

acquires. The HHI for active leases held is 406, which is reasonably close to the average HHI 

based on leases acquired during the 1990 to 1999 period of 307, and well below the 1,000 mark 

of a concentrated industry. 

Competition measured in terms of bids per lease on the OCS, on average, appears to be 

significantly less intense during the period 1983-1999 compared to earlier history. A similar 

decline has occurred in the proportion of bids submitted as joint ventures of two or more firms, 

and as the proportion of bids by joint ventures declined through time, the proportion of high 

bonus bids awarded to joint venture bidders also declined. 

Although traditional measures, regardless of the base used, indicate the industry is well below 

levels conventionally used to define a concentrated industry, direct measures of participation in 

bidding indicate less intensity in the competition for leases than in early periods. 

The Value of High Bonus Bids: The value of high bonus bids provides a measure of the initial 

cash payment to the government for granting firms the right to explore in the OCS. As such it is 

an important variable to monitor and understand. Consistently, the data show higher bonus bids 

for competitive leases than for noncompetitive leases, irrespective of the type of bid. 

• When leases were won through competitive joint venture bidding, the average value of 
4

the high bonus bid per lease was $3.29 million, more than twice the value of 

noncompetitive, joint venture, high bonus bids, which was $1.18 million. 

• The average high bonus bid for competitive solo venture bids for the period 1983-1999 

was $1.75 million in comparison to an average high bid value of $0.614 million for 

noncompetitive solo ventures. 

There is also considerable variation among average bids by firms, especially at the extremes of 

the distributions. For all bids, the average ranges from Marathon’s high of $1,418,000 to a low of 

$186,000 for Zilkha—a ratio of 7.6:1. Neither of these firms were small or marginal participants 

in the lease market. Marathon ranked 10th in production and 18th in leases acquired while Zilkha 

was the sixth highest in terms of leases and 37th in production. It might be expected that the 

variation would narrow if leases were subdivided into shallow/deep and 

competitive/noncompetitive categories, but the high/low and standard deviation/mean ratios 

actually increase. 

Corporate strategy, experience, development strategy and objectives, and a host of other factors 

are related to the differences in average high bids among the firms listed in the table. Identifying 

and incorporating such factors into our analysis in a comprehensive way exceeds the scope of 

this project. However, we try to facilitate the comparison of patterns in bidding results among 

firms by showing the normalized standard deviation or “Z-score” for each of the average high 

bid series, as are illustrated in Figure ES2. 

The Z-score was calculated for the top 50 firms in either leases or production, resulting in a 

sample of 63 firms. A Z-score of zero means the average high bid for the firm lies on the average 

of all the firms in the distribution, a Z-score of + 1.00 means the firm’s average was one standard 

deviation above the mean of the distribution, a –1.87 means the firm’s average was 1.87 standard 

deviations below the mean of the distribution. 

The number of firms that appear to bid either higher or lower than the norm is relatively small. 

• Keeping in mind that all the observations are winning bids, “high-winning bids,” i.e. bids 

that are significantly higher than the average winning bid, might suggest worse than 

average bidding (“worse” from the perspective of the shareholders of the bidders but 

“better” for the government’s revenues) and are referred to here as “too-high-winning 

bids.” 

• Six firms, Marathon, Anadarko, Statoil, Elf, Oxy, and Kerr-McGee, might be termed 

“too-high-winning” bidders and one firm, Zilkha, a “too-low-winning” bidder for all 

leases considered together. 

• The firms listed on MMS’ Restricted Bidders List, all fall in the lower end of each of the 

five bid categories calculated, but only Mobil was (barely) more than one standard 

deviation below the mean in any series. 
5

 Figure ES2: Z-Scores for Average Bids (All Bids). 
6

Effects of Mergers and Acquisitions: To explore the effects of mergers and acquisitions on the 

value of bids, all OCS leases from 1989-1999 were classified into three categories. 

 

Group A: Leases with high bids (solo or joint) by firms that were not involved in mergers 

and acquisitions from 1983-1999. This represented a control group in the analysis. 

Group B: Leases won by firms involved in mergers and acquisitions prior to the time of 

lease sales during the period 1983-1999. Joint ventures leases, which involved other firms 

without such experience, were also included in this category. 

Group C: Leases with winning bids submitted solely or jointly by firms that were 

candidates for mergers and acquisitions (“M&A”) between 1983-1999, but M&A had 

not occurred prior to the time of lease sales. As with Group B, joint ventures leases, 

which involved firms from Group A, were included. 

Differences in the values of variables that describe the context and outcome of the bidding 

among Groups A and B are described in Table ES1, with statistically significant differences 

indicated in bold type. 

The pattern apparent in Table ES1 is that bids per lease and bidders per lease variables show 

little consistent, significant difference in either period between Group A and Group B leases. 

However the financial variables measuring the average value of the high bid and the average 

amount of “money left on the table,” are consistently and significantly larger for the control 

group (Group A) than for the merger and acquisition group (Group B) in the earlier 1983 to1989 

period. But, the differences between Group A and Group B shrink to statistical insignificance in 

the later 1990 to 1999 period. 

Size of Firms and Lease Values: Implicit in the concern about possible effects of mergers and 

acquisitions on the leasing of oil and gas tracts in the Gulf of Mexico is the apprehension that 

firms participating in the process, thereby, may become large enough to exercise market power 

for their own advantage. Two groupings based on firm size are used to evaluate this concern—

the major/non-major classification used by EIA and the Restricted Bidders List published by 

MMS. 

Majors/Non-Majors: Table ES2 follows the same format and conventions used in Table ES1, 

but it compares the bidding context and outcome for majors and non-majors as defined by the 

EIA. 

Contextual variables are significantly lower for majors than non-majors in both periods. 

Differences in the average value of the high bid are less prevalent and are mixed in direction. 

The previous generalization that little if any differences between the groups exist in outcomes in 

the 1990 to 1999 period appears to apply more strongly. 
7

Table ES1 

Measures of the Competitive Structure, Type of Bid and Value of Mean High Bids 

for OCS Oil and Gas Leases by Lease Category Groups A and B, 1983-1989 and 1990-1999 

 1983-1989 1990-1999 

Variable Structure Type of Bid Group Aa Group B Group A Group B 

Competitive Joint 2.78 3.00 2.78 2.86 

 Solo 2.54 2.82 2.71 2.56 

 All 2.65 2.84 2.73 2.71 

Aggregate Joint 1.60 1.47 1.66 1.56 

 Solo 1.34 1.47 1.44(+) 1.27 

Bids/Lease 

 All 1.43 1.47 1.49 1.38 

Noncompetitive Joint 2.60(-) 3.23 2.25 2.27 

 All 1.52(+) 1.27 1.28(-) 1.42 

Competitive Joint 6.09 6.00 4.85 4.73 

 Solo 3.65 3.24 3.39 3.18 

 All 4.80(+) 3.54 3.87 3.95 

Aggregate Joint 3.78 3.88 3.21(+) 3.01 

 Solo 1.58 1.58 1.61 1.38 

Bidders/Lease 

 All 2.38(+) 1.85 2.01 1.98 

Competitiveb Joint 2,410(+) 831 942 999 

MLOT/Lease, $000 Solo 1,520(+) 666 705 623 

 All 1,940(+) 684 784 809 

Noncompetitive Joint 1,710(+) 448 635 551 

 Solo 974(+) 396 393(+) 293 

 All 1,210(+) 402 446 378 

Competitive Joint 4,500(+) 1,550 1,650 1,750 

 Solo 2,690(+) 1,190 1,240 1,120 

 All 3,540(+) 1,230 1,370 1,430 

Aggregate Joint 2,660(+) 708 1,010 909 

 Solo 1,350(+) 602 608(+) 439 

High Bids/Lease, $000 

 

 All 1,830(+) 614 710 610 

a

Differences significant at the .95 level for values for Group A and Group B leases are indicated with bold 

typeface, (+) indicates the value for A is significantly larger and (-) indicating significantly smaller, regular 

typeface indicates no difference between values for the two groups. 

b

 At least two bids are required for any “money to be left on the table.” 
8

Table ES2 

Comparison of OCS Lease Sales by Context, Outcome, and Perioda for Majors and Non-Majors 

 1983-1989 1990-1999 

Variable Structure Conduct Majors 

NonMajors Majors NonMajors 

Bids/lease Competitive Joint 2.46(-) 2.83 2.66 2.82 

 Solo 2.50 2.64 2.67 2.74 

 All 2.50(-) 2.73 2.66 2.77 

 Aggregate Joint 1.51 1.60 1.49(-) 1.82 

 Solo 1.32 1.39 1.38(-) 1.47 

 All 1.34(-) 1.47 1.39(-) 1.57 

Bidders/Lease Noncompetitive Joint 2.07(-) 2.78 2.09(-) 2.45 

 All 1.09(-) 1.66 1.15(-) 1.35 

 Competitive Joint 4.65(-) 6.01 4.53(-) 5.06 

 Solo 3.57 3.88 3.36 3.37 

 All 3.74(-) 4.91 3.58(-) 4.06 

 Aggregate Joint 2.97(-) 3.84 2.82(-) 3.62 

 Solo 1.55 1.68 1.53(-) 1.64 

 All 1.69(-) 2.55 1.73(-) 2.23 

MLOT/Lease, $000 Competitive Joint 2,073 1,647 997 837 

 Solo 1,728(+) 1,172 767(+) 607 

 All 1,780 1,401 810 702 

High Bonus/Lease, $000 Noncompetitive Joint 1,344 1,364 587 676 

 Solo 907(-) 1,224 419(+) 325 

 All 944(-) 1,276 442 410 

 Competitive Joint 3,825 3,389 1,553 1,592 

 Solo 2,957(+) 2,292 1,300 1,137 

 All 3,090 2,822 1,348 1,324 

 Aggregate Joint 2,208 2,026 875 1,088 

 Solo 1,342 1,475 619 545 

 All 1,428(+) 1,696 657 705 

a

 Significant differences in lease attributes at 95 percent level for leases involving any major firm are indicated 

accordingly in the table using (+) or (-) superscripted signs. (+) indicates the mean value for majors is significantly 

larger and (-) indicates the value is significantly smaller than the mean value for leases involving non-majors only. 

Restricted Joint Bidders List: The principal MMS policy or regulation intended to promote 

competition (or inhibit collusion) in the lease market is the Restricted Joint Bidders List. 

Companies whose global petroleum production is in excess of 1.6 million barrels of oil 

equivalent per day are listed and prohibited from submitting bids jointly in OCS lease sales.1

 

 

1

 The Act, apparently, does not prohibit firms on list from joint operations, only joint bidding. If a firm on the list 

wants to acquire a share of a lease which is owned or partly owned by another firm also on the list, it makes a 

request to MMS to do so which MMS forwards to the U.S. Department of Justice for review. According to MMS 

personnel, such requests are neither very frequent nor extremely rare occurrences, yet few are denied. 
9

Using global production to determine who the restricted bidders will be, results in a different list 

than if production in the Gulf of Mexico were to be used. Texaco, for example, dropped off the 

list in 1989 apparently because its global production fell below the 1.6 million barrel floor. 

However, in the Gulf of Mexico, Texaco’s production was three or four times as large as British 

Petroleum’s, which appeared on the list beginning in 1988. Similarly, Conoco, which is the 

fourth largest cumulative producer in the Gulf, and was a top-5 producer during the 1970s and 

1980s, never has been listed as a restricted bidder. 

The effects of the mergers can be clearly seen. 

• Before the merger, BP’s share had been growing steadily, but after its merger with 

Amoco it jumped from 5.2 to 7.8 percent in 1999. 

• When BP acquired ARCO and its E&P subsidiary Vastar, BP’s share increased to 10.5 

percent making it the second largest producer in the Gulf. 

• Exxon and Mobil both experienced declining production shares prior to their merger, and 

the post merger increase for Exxon/Mobil was more modest—4.1 percent to 7 percent. 

Although changes in production shares of this magnitude warrant analysis, and it seems 

paradoxical that the prohibition against joint bidding does not appear to have been a factor 

considered in the review of the mergers among firms appearing on the list, it is important to keep 

an accurate sense of scale. In a relative sense, changes in the shares of BP/Amoco/Vastar and 

Exxon/Mobil are dominated by the near doubling of Shell’s share from about 11.5 to 21.2 

percent over the period. As Shell’s share of Gulf production nearly doubled, its absolute 

production increased from 120.796 to 292.129 million barrels. Previous to their mergers, the 

production levels of the other restricted bidders were stable or were declining. Thus, both of the 

major mergers observable resulted in much less concentration of production than did the payoff 

from non-merger-participant Shell’s very successful investment in the deep Gulf. 

There are significant differences observable among firms appearing on the List during the 1983 

to 1999 study period in their use of joint bidding. 

• BP, Exxon and Shell use joint bids less extensively than the average. 

o Only 12.5 percent of BP’s high bids were made jointly and bonuses paid with 

those bids amounted to less than 10 percent of BP’s total for the period. 

o For Exxon the comparable percentages were about 25 percent for high bids and 

19 percent for bonuses paid. 

o Of Shell’s market-leading 1,824 high bids only about 15 percent were made 

jointly and bonuses paid with those bids amounted to about 22 percent of the 

firm’s total. 

• Amoco and Texaco are of special interest because they were restricted bidders for 

relatively short increments of the 1983 to 1999 period--one year for Amoco and six for 

Texaco. When not on the list they were free to, and did, bid jointly with the firms listed. 
10

o Texaco led the restricted bidder’s group in the proportion of joint bids with about 

56 percent. 

o Amoco was second to Texaco with about 42 percent of its high bids made jointly. 

o Although an active joint bidder, Amoco had partnered neither with BP nor Vastar 

prior to their merger. 

• Texaco and Chevron, in contrast to BP and Amoco, had an extensive record of joint 

bidding that closely preceded their merger. 

o Texaco, prior to their merger, cooperated with Chevron in 163 joint bids, of which 

136 were winning bids. This amounted to about 28 percent of Texaco’s total of 

493 joint winning bids. 

o Further, all of the joint Texaco/Chevron bids took place in the last four years of 

the study period with 158 of the 163 bids submitted during the three sales held in 

1996 and 1997. 

Many considerations of technology, strategy and corporate planning, globally as well as in the 

Gulf of Mexico, may have influenced the Chevron/Texaco merger decision, but this episode of 

collaboration is notable. 

An extensive analysis of joint bidding by the two firms is beyond the scope of the study, but the 

context in which it took place is important. In 1995 there were 1,012 leases in water depth of 

800 meters or more in effect. In 1996 that number increased to 1,491 and in 1997 to 3,002—a 

nearly three-fold increase. Further, by 2001, deepwater-leases had only increased to 3,424 (Baud, 

et al., 2002:14) Thus, a desire to broaden their participation in this “leasing-up” of the deepwater 

Gulf, may have been a factor in Chevron/Texaco joint-bidding offensive. There also may have 

been elements of “catch-up” or “keep-up” involved. 

Table ES3, following the same format and conventions of the previous two tables, compares the 

bidding context and outcomes for leases won by restricted bidders, (or joint bids including 

restricted bidders) with other leases. 

• A pattern of fewer bids per lease and fewer bidders per lease for leases won by restricted 

bidders is evident over the entire period and becomes more pervasive in the later 1990 to 

1999 period. 

• For the outcome variables, in the earlier period three of the significant differences are 

positive and three are negative. In the latter period, there are also six significantly 

different categories but all of them are negative. 
11

Table ES3 

Bidding by Context, Outcome and Period for Leases Won by Restricted and Nonrestricted 

Biddersa

1983-1989 1990-1999 1983-1999 

Variable Structure Conduct Restricted 

NonRestricted Restricted NonRestricted Restricted NonRestricted 

Joint 2.86 2.76 2.49(-) 2.80 2.77 2.79 

Solo 2.40(-) 2.64 2.53(-) 2.75 2.47(-) Competitive 2.72 

All 2.59 2.70 2.52(-) 2.77 2.56(-) 2.75 

Joint 1.62 1.60 1.26(-) 1.72 1.48(-) 1.67 

Solo 1.25(-) 1.41 1.29(-) 1.46 1.27(-) 1.45 

Bids/ 

Lease 

Aggregate 

All 1.35(-) 1.49 1.29(-) 1.54 1.32(-) 1.53 

Joint 2.60(-) 2.70 2.09(-) 2.31 2.38(-) Non- 2.49 

competitive All 1.38(-) 1.68 1.20(-) 1.34 1.29(-) 1.46 

Joint 6.24 6.13 4.06(-) 4.90 5.70 5.38 

Solo 3.41(-) 3.84 3.08(-) 3.45 3.24(-) Competitive 3.56 

All 4.57 4.99 3.25(-) 4.02 3.97(-) 4.35 

Joint 3.80 3.87 2.44(-) 3.35 3.28(-) 3.58 

Solo 1.44(-) 1.72 1.40(-) 1.65 1.42(-) 1.67 

Bidders/ 

Lease 

Aggregate 

All 2.09(-) 2.64 1.58(-) 2.16 1.84(-) 2.33 

Joint 4,705(+) 2,316 936 923 3,773(+) 1,465 

Solo 1,650 1,483 614 726 1,097 946 

MLOT/ 

Lease, 

$000 

Competitive 

All 2,897(+) 1,903 667 804 1,899(+) 1,170 

Joint 2,001 1,685 332(-) 691 1,280 1,140 

Solo 807(-) 1,187 385 385 589 629 Noncompetitive All 1,090(-) 1,385 376(-) 464 733 786 

Joint 7,567(+) 4,301 1,357 1,646 6,032(+) 2,679 

Competitive Solo 2,763 2,729 1,090 1,274 1,871 1,697 

All 4,725(+) 3,522 1,134(-) 1,421 3,117(+) 2,121 

Joint 3,844(+) 2,579 513(-) 1,074 2,572(+) 1,719 

Solo 1,164(-) 1,575 520(-) 620 828 907 

High 

Bonus/ 

Lease, 

$000 

Aggregate 

All 1,901 2,005 519(-) 757 1,224 1,187 

a

 Significant differences in lease attributes at 95 percent level for leases involving any restricted bidder are indicated 

in the table using (+) or (-) superscripted signs. A (+) indicates the mean value for restricted is significantly larger 

and a (-) that the value is significantly smaller than the mean value for leases involving unrestricted only. 

• This result is consistent with the argument that the leases won by the large firms on the 

Restricted Bidders List, were subject to fewer bids by fewer bidders and were won by 

significantly lower bids than leases won by firms that did not include restricted bidders. 

• This conclusion does not account for other factors that may influence lease values, which 

are addressed in the econometric analyses reported in Table ES3. 
12

The iterative seemingly unrelated regression (SUR) procedure in the QMS Eviews program 

(version 4.0) was applied to the data to estimate a log-linear specification of the relationship 

between lease value and its determinants. The log-linear specification yields parameter estimates 

that are interpretable as the relative change in the dependent variable with respect to an absolute 

change in the independent variable. In general, the model is consistent with or “explains” about 

50 percent of the expected variation in the relative value of high bonus bids as indicated by the 

R2 statistics. Conversely, by implication, there are other variables not included in this model that 

may “explain” another 50 percent of the variation expected in the relative magnitude of high 

bonus bids. 

Econometric Modeling of the Value of Leases: We made use of two simple econometric-model 

frameworks to explore the patterns and trends in the value of OCS leases. The first model 

estimates the relationship between the value of leases and postulated explanatory factors by 

using each of the merger and acquisition (M&A) experience categories (Groups A, B, and C, 

discussed previously) to estimate separate regression equations with coefficients measuring their 

influence on bidding. Statistical tests then are applied to see which factors consistently influence 

the value of high bids on the three groups of leases. The second model or approach uses a 

“major/non-major” or “restricted bidder/nonrestricted bidder” delineation as proxies for firm size 

with combinations of type of bid and existence of bidding competition as eight separate 

interactive variables, which are combined with merger and acquisition history or experience and 

other explanatory variables in the regression equation. 

Both approaches are derivative of the same basic conceptual framework, which specifies that the 

value of the high or winning bonus bid (HB) is a function of three sets of factors: economics, 

structure, and conduct. This specification has been used successfully several times in the past to 

analyze lease auction market performance. 

In general, the econometric results confirm the following expectations: 

• Greater competition is associated with higher mean values of high bonus bids. As 

competition increases, the relative change in the mean value of high bonus bids will 

increase. 

• The economic environment measured in terms of the rising crude petroleum prices does 

not lead to a significantly higher mean value of high bids for most leases as would have 

been expected. However, another measure of perceptions of the economic environment, 

the carry-over effects of the collapse of crude oil prices in 1986 which brought about 

major strategic and structural changes in the global E&P industry, does consistently 

indicate a significant reduction in the mean value of high bonus bids for leases in 

comparison to the pre-1986 mean value of high bids. 

• The mean value of high bonus bids for leases with at least two bids (competitive) is 

greater than the mean value of single-bid leases (noncompetitive). 

• The mean value of high bonus bids for deepwater leases is significantly higher than 

shallow water leases. 
13

• Joint venturing is associated significantly with higher winning bonus bids than is the case 

for bids by solo ventures. 

To decipher and analyze the potential effects of merger and acquisitions on the mean value of 

high bonus bids through its determinants--intensity of competition, extent of competition, type of 

bid, lease location/water depth, economics, and structural changes in the E&P industry, a Wald 

coefficient restriction test was applied to the regression to ascertain whether the coefficients 

measuring the relationship between the explanatory variables and the average high bid for the 

three groups are significantly different from one another. 

The Wald test results indicated that: 

• The intensity of competition for a lease has a positive, strong, and significant effect on 

the magnitude of the winning bid, but it is not affected by M&A experience, i.e., it affects 

all three groups to the same degree. 

• As with the intensity of competition variable, the relationship between high bids and 

whether or not there are competing bidders is strong and significant but the same for all 

three groups. 

• The high bonus bid for deepwater leases over the leases on the shelf is not significantly 

affected by M&A bidders’ participation in the high bonus bid. Again, it affects all three 

groups to the same degree. 

• The involvement of bidders ranking as the top four firms in cumulative production 

significantly leads to a lower relative value of high bids in the Group B leases. Whereas 

the expected mean value of high bonus bids for Groups A and C leases involving bidders 

ranked among the top four is not significantly different from other bidders, i.e., non-topfour bidders. 

• The value of high bonus bids has dropped significantly since the collapse of world crude 

oil prices. The effect is strong in all three groups but is less so for leases in which 

participating firms had been involved in or would become involved in M&A activities. 

• The parameters designated as fixed effects (intercepts) are significantly different in 

magnitude, thus suggesting that the relative change in the mean value of high bonus bids 

for OCS leases, ceteris paribus, was on average relatively smaller for leases in which 

participating bidders include a firm or firms involved in M&A, when other factors have 

been accounted for. This result could be interpreted as suggesting these firms were able 

to exercise some degree of oligopsony power as a consequence of M&A activities, or as 

suggesting that this group of firms possessed better information or had more experience, 

which enabled them to bid more efficiently. Since the competitive measures indicate no 

significant difference among the three categories, the second interpretation is more 

consistent with our data. 
14

The second econometric formulation or approach is similar to the one used in Mead et al. (1985) 

in which different possible categories of leases were assembled based on: 1) type of bid (joint or 

solo), 2) extent or existence of competition in bidding, and 3) firm size, but using the major/nonmajor classification as a proxy. The three categories were used to define a set of interactive 

dummy variables: 

• MJC: High bonus bids which involved major oil and gas operators, bidding jointly for 

competitive leases (D1). 

• MJN: High bonus bids which involved major oil and gas operators, bidding jointly for 

noncompetitive leases (D2). 

• MSC: High bonus bids which involved major oil and gas operators, bidding solo for 

competitive leases (D3). 

• MSN: High bonus bids which involved major oil and gas operators, bidding solo for 

noncompetitive leases (D4). 

• NMJC: High bonus bids involving non-major oil and gas operators only, bidding jointly 

for competitive leases (D5). 

• NMJN: High bonus bids involving non-major oil and gas operators, bidding jointly for 

noncompetitive leases (D6). 

• NMSC: High bonus bids involving non-major oil and gas operators, bidding solo for 

competitive leases (D7). 

• NMSN: High bonus bids involving non-major oil and gas operators, bidding solo for 

noncompetitive leases. 

Given our study objectives, the most relevant results from this study using this approach are: 

 

1) The inverse relationship between DMA (M&A history and experience), and the average 

level of high bids, which supports the previous findings that firms with history of M&A, 

on average, submit lower winning bids (other factors taken into account) than firms that 

have not. 

2) Comparisons of the differences among coefficients of the interactive dummy variables, 

D1 through D7, which shows that: 

• All of the interactive dummy variables for majors have larger coefficients than the 

corresponding combination including only non-majors, i.e., D1 (MJC)>D5 (NMJC), 

D2 > D6, D3>D7. However the differences are significant only for solo bids, both 

those that are competitive and those that are not. This suggests that if firm size, as 

measured by the major/non-major classification, has any effect on bidding it is a 

positive effect. 
15

• The fact that there is no significant difference between joint bids involving majors 

and joint bids not involving majors, i.e., MJC and NMJC, or MJN and NMJN, 

supports skepticism about the rationale for the Restricted-Bidders-List approach for 

discouraging anti-competitive behavior through restrictions on joint-bidding. 

• Within both major and non-major classifications, competitive bids are consistently 

and significantly higher than noncompetitive bids, reinforcing the results of previous 

sections.

Following the same format and method, but substituting the firms designated by MMS as 

restricted bidders for the EIA’s “major” category and all firms not so designated for the “nonmajor” category, gives results that are quite similar. This is not surprising since the unit of 

analysis, or observation, is the individual lease. Although the number of firms designated as 

restricted bidders is small, they account for a substantial proportion of the leases acquired by the 

“major” category.

The implications drawn from the analysis based on the major/non-major classification hold for 

the restricted/unrestricted bidder classification as well. 

• Comparisons of the coefficients of the interactive variables for restricted and 

unrestricted bidders support the previous conclusion that if firm size has any effect on 

the value of the average high bid, it is a positive one. 

• Comparisons of coefficients for joint bidding indicate no significant difference 

between restricted and unrestricted bidders for both competitive and noncompetitive 

bids and seem to directly challenge the rationale for the Restricted Bidders List. 

The only difference in the parameter estimates between the major/non-major and 

restricted/unrestricted categories was that the crude oil price index coefficient had a positive 

sign, as postulated, in the former but a negative sign in the latter case. The post price collapse 

variable, which we previously suggested was probably a more relevant and direct reflection of 

perceptions of the economic expectations, however, had the same sign in both categories. 

Summary and Conclusions: Neither aggregate measures used to analyze concentrated market 

and industry structures, nor patterns of joint bidding among firms active in the offshore Gulf of 

Mexico suggest either a decrease or a deficiency in the competitiveness of the lease sales held by 

the U.S. Minerals Management Service. Whether leases acquired at the sales, or production by 

firms bidding for leases, are used as the base of the concentration measures, they indicate a 

competitive industry bidding for leases in a competitive market. 

The effects of mergers are not reflected in the trends observable in these measures over time. 

More recent mega-mergers are only reflected in the last two or three years of the data series 

analyzed, but their effects do not change the quantitative measures of the concentration of the 

industry or lease market. Comparing major and non-major companies as defined by EIA criteria 

does not indicate significant differences. Similarly, an analysis of patterns of bidding by those 
16

firms on MMS’ Restricted Bidders List does not suggest noncompetitive behavior, although an 

analysis of the criteria for compiling it raises questions about the list’s relevance and rationale. 

More inclusive econometric approaches led to findings that are consistent with other studies and 

theoretical expectations. As competition (ex post) increases, the average value of the high bonus 

bids increases, regardless of the mergers and acquisition experience of the participants winning 

the lease. Similarly, joint bidding for leases does not lead to anticompetitive bidding outcomes, 

again regardless of the M&A status of the bidders. 

However, taking account of the effects of the other intervening factors expected to affect the 

value of high bids, the econometric analysis suggests that bidders with M&A experience paid 

less than bidders without M&A experience for OCS leases. In the first econometric approach the 

size of firms, per se, had a statistically significant negative affect on average lease value only for 

leases won by firms with M&A experience. When major/non-major or restricted/nonrestricted 

bidders were used as proxies for firm size the effect was either positive or statistically 

insignificant. 

 
17

1. Introduction 

Over the past century, the structure, composition and organization of the oil and gas industry 

have changed repeatedly, as have the industry’s broader technological, political and economic 

context and culture. The petroleum industry, as we know it now, was created when Standard Oil 

was conceived and grew to a near monopoly. Since then, the industry's major transformations 

include: 

• The break-up of Standard Oil by the Anti-trust Division of the U.S. Justice 

Department; 

• The era of controlled or administered prices by the "seven sisters" and the Texas 

Railroad Commission from the Second World War to the embargo by the Arab Oil 

Exporting Countries in 1973; 

• The rise of the national oil companies in the 1960s and 1970s, and the evolution of 

transparent and open, if at times cloudy, markets for oil and gas; and, 

• The price collapse and subsequent restructuring of the mid-1980s. 

Some expect the recent consolidation of most of the largest firms in the industry, the so-called 

mega-mergers, to result in change and reorganization comparable in magnitude and consequence 

to these earlier episodes. The wave of mega-mergers is too recent, however, to be able to subject 

this conjecture to testing with data. Further, concurrently with the mega-mergers, there have 

been major changes in the industry's external environment. Many petroleum producing countries 

are trying to entice the international majors, large independents and in some cases their own 

private domestic companies into participating in the development of reserves that formerly were 

open only to their national oil and gas companies. Those pursing these strategies include 

countries previously unfriendly to global capitalism, such as India, Iran, Iraq, Libya, Russia and 

the other former countries of the Soviet Union. Even in the U.S., the development of the “Deep 

Gulf of Mexico” in the late 1990s, which was a sea change in the technology and economics of 

the industry, was led by a non-U.S. firm, Shell, with British Petroleum also a major participant. 

Thus, although the new "mega-mergers" have created some of the largest privately owned 

corporate entities in the world, the rise of national oil companies and other changes in the 

industry’s external environment have diminished the dominance of privately owned, domestic, 

companies. 

Acknowledging these inherent limitations, this study examines empirically some of the possible 

impacts of corporate consolidation and reorganization on petroleum resource allocation in the 

Gulf of Mexico OCS region. The study applies theoretical and empirical tools in anti-trust, market 

and industrial structure, organization and performance to describe the changes that have taken 

place--especially among firms active in the Gulf of Mexico OCS. The study relies on information 

on OCS leases including the sale date of each lease, lease number and location, the number of 

bids submitted, the name of bidders, the dollar value of each bid, the value of the winning bids, 
18

drilling and production history, and royalty and rental payments for the right of private firms to 

explore, develop and produce oil from the OCS. 

All OCS leases have been classified into three ownership categories on the basis of whether the 

high (that is successful or winning) bonus bid was made by a firm involved prior to lease sale in 

mergers and acquisition activity, or, in the case of joint bids, where at least one bidder had such a 

history. Winning bids by firms without M&A history formed the second category and bids by 

firms without “M&A” experience at the time of the bid but were involved in such activity later, 

made up the third category. The data on leases were also classified into several lease categories 

on the basis of firm size, bidding arrangement, and bidding structure. 

Two other dimensions of the industry’s organization were also analyzed. The Minerals 

Management Service’s own Restricted Bidders List was analyzed and comparisons were made 

between “major” and “independent” or “non-major companies” (where “major” was defined as 

those included in the Energy Information Administration’s Financial Reporting System—

essentially companies owning at least one percent of global oil or natural gas reserves or 

production) in an effort to see how the size of the firm may affect attributes of OCS lease sales. 

Two statistical approaches were used. The first approach was a descriptive analysis of the data 

and the testing of differences in the mean of some lease attributes, which characterize the 

structure and nature of the lease market, and lease ownership. The other approach was an 

econometric analysis of the leasing records, specifically, the relationship between the values of 

high bonus bids and competition, bidding arrangement, industry economics and other variables. 

The econometric approach allows for the evaluation of the variability of high bids for leases in 

terms of the perceived quality of the lease or tract, the size of firms bidding, the bidding 

methods--solo or joint, the location in terms of water depth and state jurisdiction, and firm 

characteristics. 

The report is organized into five sections and an executive summary. The following section 

presents a brief overview of the changes in the petroleum industry corporate structure, the 

motivation and objectives of the project as well an overview of the statistical methodology. 

Section three presents a descriptive analysis of the structure, conduct and of the market for OCS 

oil and gas leases since area-wide leasing began in 1983. Several attributes of the market for oil 

and gas leases are presented and analyzed to determine the impacts of mergers and acquisitions 

on the lease bonus value on the Gulf of Mexico OCS. The fourth section develops, estimates 

and exercises a regression model describing the relationship between the value of high bonus 

bids for OCS oil and gas leases to crude petroleum price index, intensity of competition, 

bidding strategy, geology and other factors. The final section presents the principal conclusions 

and recommendations for future research areas on oil and gas leasing on the OCS.
19

2. OCS Lease Market Structure, Conduct and Performance 

2.1 Background on OCS Leasing 

The OCS Land Act of 1953, an act of the United States Congress, established the procedure for 

transferring the right of private firms to explore, develop and produce petroleum resources in the 

U.S. Gulf of Mexico Outer Continental Shelf (OCS). The transfer mechanism has mostly been 

by sealed-bid auction conducted periodically by the Minerals Management Service (MMS), an 

agency of the U.S. Department of the Interior. Initially, under the auction process, firms made 

cash bonus offers for tracts that were nominated by them. The Land Act gave a mandate to 

MMS that the transfer of petroleum leases on the Gulf OCS to private firms should either be by 

the bonus bidding system with a fixed royalty rate or through a royalty bidding system with a 

fixed bonus payment. 

The final decision to award a lease to a firm or firms depends to large extent on whether the 

value of the high bid exceeds a predetermined reservation price established by MMS for the 

lease (Gilley and Karels, 1981). Thus, if a lease is awarded, the firm with the highest offer for a 

nominated tract among all the accepted bids is granted the right to develop the particular lease 

for an initial cash bonus. The stipulations for annual rental payment for the lease and a fixed 

royalty rate are also stated at the time of the award (Rockwood, 1983). The firm or firms 

winning the lease must drill within five years to avoid forfeiting the lease. Rental payments are 

paid annually until the lease is surrendered or production begins. Royalty payments, a fixed 

fraction of the revenue from the production area, are made to MMS when production begins. 

The lease is usually automatically renewed for as long as there is production on the lease (Porter, 

1995). 

The bidding process described above is called cash bonus bidding. Critics of this bidding process 

have argued over the years that the large and unconditional upfront cash bonus payments for 

leases were barriers to entry and anticompetitive. Rockwood (1983) indicates that such 

arguments persuaded the U.S. Congress to pass amendments to the original OCS Land Act of 

1953 in 1978. The amendments led to a provision that at least 20 percent and at most 60 percent 

of acreage offering must be leased using leasing systems other than the conventional bonus 

bidding system. That legislation expired in 1983 even as the new leasing policy for OCS leases 

became effective in April 1983. 

The new policy is called area-wide leasing within a planning area and allows firms to bid for any 

OCS tracts that have not been previously leased (Moody and Kruvant, 1990). Leases that are 

repossessed or confiscated for lack of activity after five years of the initial sale, in most cases, 

are also available. The immediate result of this change in policy was a large increase in the 

number of leases or tracts awarded at each subsequent sale. The average number of tracts 

awarded increased from about 300 under the nomination process to an average of more than 

5000 under the area-wide leasing procedure within the first few sales. 

The question of which leasing program maximizes the U.S. government’s chances of a fair market 

value for the right to drill for and produce oil and gas on public lands continues to provoke 

debate. In the 1980s, several studies analyzed alternative bidding methods for OCS oil and gas 

leases (Teisberg, 1980; Gilley and Karels, 1981; Rockwood, 1983; Mead and Sorensen, 1980 and 
20

Mead et al., 1985). These studies concluded that the cash bonus bidding system was the most 

effective means of allocating petroleum resources for exploration and exploitation. The studies, 

however, were conducted using data on leases obtained from 1954-1977 under the nomination 

and tract selection arrangement (Saidi and Marsden, 1992), and the structure of the U.S. oil and 

gas industry has also changed significantly since that time. It has been reported that many small 

independent oil and gas operators have become active in the Gulf OCS lease sales and 

development (Iledare et al., 1995). Moreover, the type of bids that can be submitted has also 

changed. The Energy Policy and Conservation Act (PL 94-163), enacted in December 1975, 

forbade some of the major oil and gas producers from bidding jointly for leases on the OCS so as 

to reduce the potential for anti-competitive effects (Millsaps and Ott, 1981). 

2.2 Competitive Structure of the Market for OCS Leases 

Annual and cumulative production by the top-30 operators, in each category, since offshore 

development began in the late 1940s is depicted in Figures 2.1 and 2.2. They indicate important 

elements of both stability and change as the industry evolved. 

• Chevron, Shell, and ExxonMobil, who are leading oil and gas producers in the Gulf of 

Mexico today, and are responsible for a corresponding substantial share of cumulative 

production, provide stability. 

• But comparing annual and cumulative production also reveals significant changes in the 

offshore industry. For example: 

o BP is a top-five producer today, but as a relative newcomer to the Gulf of Mexico 

still ranks only 23rd in cumulative production. 

o Conoco, an offshore pioneer, ranks 26th in annual production but still is among the 

top-five in cumulative production. 

o Seven of the today’s top-30 annual producers have not produced enough to be 

ranked amongst the top-30 cumulative producers. 

o In 1980 the combined production by all non-top-30 producers ranked 16th in terms 

of annual production. Today the combined group ranks second. 

Mergers, acquisitions, divestitures, changing corporate strategies, good and bad investment 

decisions, technological innovation, international politics, global economics and luck are 

reflected in the changes in the industry illustrated, but the extreme ups-and-downs in the annual 

production series indicate a history of substantial change in internal organizational as well as 

external circumstances. 

Has the nature or intensity of competition among firms for leases and production changed as the 

offshore industry evolved? 
21

1947

1949

1951

1953

1955

1957

1959

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

Shell

Others

Chevron U.S.A. Inc.

ExxonMobil Oil Corporation

BP

Unocal

Vastar

El Paso

Texaco

Amerada Hess Corporation

Apache Corporation

Amoco Production Company

Devon

Kerr-McGee

Newfield Exploration Company

Marathon

Mobil

Samedan Oil Corporation

Stone

Walter Oil & Gas Corporation

Dominion Exploration & Production, Inc.

RME Petroleum Company

PennzEnergy

Ocean Energy, Inc.

Murphy Exploration & Production Company

Forest Oil Corporation

Conoco Inc.

EOG Resources, Inc.

OXY USA Inc.

Freeport-McMoRan

0

50

100

150

200

250

300

Annual Production (mil. bbl)

Figure 2.1: Annual Oil and Gas Production by Firms from the Gulf of Mexico OCS 

 Measured in MMBarrels of Oil Equivalent, 1947-2001. 

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

1974

1972

1970

1968

1966

1964

1962

1960

1958

1956

1954

1952

1950

1948

Chevron U.S.A. Inc. Shell ExxonMobil Oil Corporation Conoco Inc. Others Mobil

Texaco Tenneco Oil Company Union Exploration Partners, Ltd. PennzEnergy Marathon Unocal Amoco Production Company ARCO Kerr-McGee Placid Oil Company Phillips

El Paso Forest Oil Corporation Odeco Oil & Gas Company Dominion Exploration & Production, Inc. ORYX ENERGY COMPANYBP OXY USA Inc. Superior Oil Company Samedan Oil Corporation Freeport-McMoRan Vastar Amerada Hess Corporation

Walter Oil & Gas Corporation

0

1000

2000

3000

4000

5000

6000

Cumulative Production (mil. bbl)

Figure 2.2: Cumulative Oil and Gas Production by Firms from the Gulf of Mexico OCS 

 Measured in Barrels of Oil Equivalent, 1947-2001. 
22

The degree or extent of competition for OCS oil and gas leases can be characterized by the 

number and relative size of firms in the lease auction market, the number of bids or bidders per 

lease and other related attributes and measures. The larger the number of participants, the more 

competitive is the lease auction market. But since participants can bid alone (solo bids) or 

together (joint bids), the number of participants by itself is not an unambiguous indicator of the 

degree or nature of competition. 

Whether joint bidding increases or decreases competition for leases is still a debatable question. 

Joint bidding may serve to introduce new firms and allow smaller firms to participate, thus 

promoting competition, or it may diminish competition by allowing firms who are potential 

competitors to cooperate or collude. Moreover, a principal policy MMS uses to promote 

competition or limit market power is to prohibit the largest firms from submitting joint bids 

together. 

An ex post indicator of competition is the extent to which leases are concentrated. If leases are 

won or held predominately by a few firms, the implication is that competition has been less 

effective or intense than if leases were distributed more evenly or broadly. This latter way of 

measuring competitiveness has been used traditionally by economists to study competition and 

market structure, and we will begin by applying it to the market or auction for leases in the Gulf 

of Mexico. 

Table 2.1 presents trends in the commonly used indicators of industry structure and 

competitiveness, concentration ratios, the number of firms bidding for OCS leases and the 

Herfindahl Index (HHI). 

Number of Firms participating in Lease Sales: The number of firms participating in lease sales 

varies from sale to sale as is illustrated in the second column of Table 2.1. The steepest 

contraction of competitors was in 1986 when the world oil price collapsed, creating considerable 

uncertainty in all energy markets. With that exception, however, the number of participants 

usually has been within a range of roughly 65 to 95 firms annually. The number of firms that 

participated in at least one sale during the entire period is much larger, about 290. 

Concentration Ratio: A traditional measure used to study industrial organization and anti-trust 

trends and issues is the share of the industry owned by the largest four, eight or twenty firms. In 

our case there is a question of how “share” ought to be defined and measured. The most direct 

way is probably, simply, as the share of ownership of oil and gas leases in the OCS awarded to 

the top four or the top eight firms, which is shown in Table 2.1. To calculate the ratios, leases 

won at the annual bidding process on the OCS were arranged by firm in a descending order and a 

lease share was calculated for each firm in each sale-year.2

 

 

2

 Jointly-owned leases were allocated to firms according to their percentage share of the lease (a convention 

followed throughout the study). 
23

Table 2.1 

Measures of Competitiveness Based on Leases Acquired, 1983 to 1999 

 

Year 

Number of

Firms Percent of Leases Held by HHI 

 Top 4 Top 8 Top 20 

1983 92 28.5 44.8 71.4 367 

1984 81 28.8 46.0 73.4 381 

1985 74 38.9 53.6 76.1 552 

1986 45 49.3 66.3 89.3 870 

1987 68 45.8 63.9 84.8 829 

1988 92 42.7 56.5 79.0 908 

1989 87 45.4 57.1 77.6 865 

1990 94 36.8 49.1 72.2 430 

1991 83 28.6 46.7 70.0 373 

1992 63 24.9 40.7 71.7 335 

1993 65 32.1 49.2 74.9 425 

1994 87 24.8 41.6 64.9 308 

1995 86 30.2 45.6 70.8 371 

1996 91 34.6 52.6 74.8 456 

1997 101 32.6 49.6 71.5 453 

1998 87 34.2 56.5 77.8 486 

1999 72 30.0 45.9 74.4 411 

1983-1990 176 33.0 46.8 68.9 480 

1990-1999 207 25.0 40.9 64.9 307 

1983-1999 290 26.5 41.7 63.2 337 

The four-firm ratio is the sum of the market shares of the top four firms, while the eight-firm 

concentration ratio is the sum of market shares for the top eight firms. In 1986, the top four 

winners of OCS leases accounted for 49.3 percent, the highest share during the 1983 – 1999 

period. By 1999, the share of the top four firms was only 30 percent, about a four percent 

deviation from the overall average share over the period. The share of the top eight firms also 

declined from a high of 66.3 percent in 1986 to a low of 40.7 in 1992, a decline that is slightly 

more than 25 percent. 

The shares of leases won by the top four and the top eight firms seem to be quite stable over the 

period, with the exception of the first four or five years after the crude oil prices collapsed in 

1986. The increasing shares observed from1985 to 1988 probably reflected the exodus of 

smaller firms from lease sales in response to the collapse of crude oil prices. 

Herfindahl Index: Concentration ratios by themselves do not reveal the relative sizes of the 

firms within the groups nor within the rest of the market. A deviation from competitive behavior 
24

and performance may occur if there are significant differences in the relative market shares. The 

Herfindahl index (HHI) measures both the absolute and the relative concentration of the market 

shares. The HHI is defined as the sum of the squares of the market shares of all participating 

firms in the lease auction market in a given period or year—considering the size of all 

participants, not just the largest four or largest eight. 

Usually, the industry or market competitiveness is broadly characterized using the magnitude of 

the HHI index. A market that has an HHI of less than 1,000 is less concentrated and can be 

described as being competitive. Dougher (1987) argues that a market is moderately concentrated 

if its HHI lies between 1000 and 1,800, or highly concentrated when HHI is greater than 1,800. 

Application of this categorization to lease sales from 1983-1999 suggests that the market for 

OCS oil and gas leases is not concentrated. In fact, the lease market has become less 

concentrated with time. Table 2.1 shows that the HHI in 1988 was 908, the closest to 1000 level 

during the period. By 1999, the estimated HHI was 411, a decline of nearly 500 units. The 

average HHI for the 1983 to 1990 period was 480, and it fell to 307 during the 1990-1999 

interval. 

Leases are held for varying terms. Some are turned back after their initial 5-year term, others are 

held as long as there is production or other activity such as pumping. Thus the stock or inventory 

of active leases that a firm controls will differ from the number of leases it acquires. The 

concentration ratios calculated on the basis of currently active leases are: 34 percent of active 

leases were held by the top four firms, 48 percent by the largest eight, and 71 percent for the 

largest twenty firms.3

 Although these are current rather than average or cumulative measures, 

they are only marginally larger than the averages for the 1990 to 1999 period shown in Table 2.1

of 25 percent, 41 percent and 65 percent, respectively. The HHI for active leases of 406 is also 

reasonably close to the 1990 to 1999 HHI of 307. 

Leases or Production as the Basis for Measurement: Implicit in some analysis and regulation of 

the petroleum industry is the idea that the relevant indicator of potential market power is 

production. Table 2.2 reports the same data as is shown in Table 2.1, except that the 

concentration ratios and HHI index are calculated with production rather than leases acquired. 

Comparing the tables indicates that measures based on leases acquired are both more variable 

and indicate less concentration than do the same measures based on production. The average 

HHI value over the 1983 to 1999 period was 624 using production, contrasted to an HHI of 337 

using leases acquired. Similarly the average percentage of production accounted for by the top 

20 firms over the period was 84 percent as opposed to 63 percent of the leases acquired. 

Using a production base yields higher concentration ratios and HHI values, but neither measure 

reaches levels that have traditionally been used to delineate a concentrated industry. Although 

the average HHI index for the production based measure almost doubles, it still falls well short 

of the 1000 value commonly used to divide competitive from noncompetitive markets. 

 

3

 The authors calculated these figures in a separate document, using MMS data. 
25

Table 2.2 

Measures of Competitiveness Based on Production in the Gulf of Mexico OCS 

Year # Firms Top 4 Top 8 Top 20 HHI 

1983 55 39 64 93 643 

1984 58 38 63 92 629 

1985 60 39 65 93 651 

1986 64 39 64 93 641 

1987 67 40 65 93 662 

1988 70 39 64 93 650 

1989 76 39 62 91 623 

1990 87 43 66 91 688 

1991 94 45 68 90 730 

1992 97 45 69 90 747 

1993 101 45 70 89 760 

1994 104 43 66 87 708 

1995 109 42 64 86 672 

1996 109 45 63 84 701 

1997 111 44 61 82 668 

1998 111 43 60 80 650 

1999 105 43 61 83 674 

1983-1990 100 39 64 92 637 

1990-1999 196 44 63 83 671 

1983-1999 222 42 61 84 624 

If production is used to make the calculations, there is also a question of whether production in 

the Gulf of Mexico (GOM), U.S. or domestic production or global production is the appropriate 

measure to use. Some regulation, including MMS’ own Restricted Joint Bidders List, uses global 

production as an indicator of potential market power. 

Table 2.3 shows the GOM production, GOM leases, U.S. production, and global production for 

each of the companies that ranked in the top 50 in either GOM production or leases on a 

cumulative basis during the 1990s. 

The firm-by-firm summary shown in Table 2.3 underscores the wide variation in type of firm 

and extent of participation in the GOM lease market. Shell, the largest GOM producer during the 

period was also the largest global producer, but ranked behind ARCO and Exxon and only 

slightly ahead of Chevron and Texaco in U.S. production. At the other end of the distribution 

were small companies like King Ranch operating only in the GOM but also large international 

companies like Petrobras with small GOM production but larger global production than some 

U.S. majors. 
26

Table 2.3 

Gulf of Mexico (GOM) Production and Leases, U.S. Production and Global Production for the 

Top 50 Firms in Either GOM Production or Leases, 1990 to 1999 

Totals: 1990-1999 

Company 

U.S. GOM 

MMBOE 

U.S. GOM, 

Leases Acquired 

WORLD 

MMBOE 

U.S. 

MMBOE 

Agip 71 55 3,365 71 

Amerada 205 207 1,287 498 

Amoco 341 243 3,916 1,972 

Anadarko 50 71 397 384 

Apache 124 45 439 382 

ARCO 182 109 3,546 2,867 

Basin 11 33 24 24 

BHP 20 207 100 81 

BP 315 418 6,449 1,953 

British-Borneo 20 64 32 20 

Burlington 47 79 525 459 

Chevron 1,449 494 5,150 2,669 

Chieftain 2 57 44 41 

CNG 187 77 77 77 

Coastal 69 72 295 295 

Conoco 382 268 2,101 862 

Cxy 29 62 541 43 

Elf 55 62 3,599 55 

Energy Development 44 30 106 100 

Enron 94 115 403 354 

Enserch 33 60 174 175 

Exxon 595 354 8,415 3,126 

Fina . 41 114 114 

Forcenergy 54 26 52 52 

Forest 121 27 90 71 

Freeport 84 10 51 51 

Hall-Houston 84 28 84 84 

Houston Exploration 37 63 13 13 

Kerr-McGee 199 161 515 305 

King Ranch 8 34 8 8 

Louisiana L&E 63 82 218 150 

Marathon 328 110 1,284 328 

Mobil 629 341 5,248 1,758 

Murphy 146 115 332 151 

Nerco 55 15 55 55 

Newfield 160 33 75 74 

Nippon 5 38 5 5 

Norcen 96 45 2 24 

Ocean Energy 35 24 131 99 
27

Table 2.3: Gulf of Mexico (GOM) Production and Leases, U.S. Production and Global 

Production for the Top 50 Firms in Either GOM Production or Leases, 1990 to 1999 (continued) 

Totals: 1990-1999 

Company 

U.S. GOM 

MMBOE 

U.S. GOM, 

Leases Acquired 

WORLD 

MMBOE 

U.S. 

MMBOE 

Oxy 162 18 1,374 624 

Pennzoil 372 96 478 460 

Petrobras 4 35 3,311 4 

Petsec 24 35 9 9 

Phillips 113 89 1,838 1,017 

Pogo 44 31 127 117 

Samedan 187 104 301 275 

Santa Fe 62 65 346 308 

Seagull 59 89 171 138 

Seneca 41 42 19 19 

Shell 1,579 789 12,504 2,703 

Sonat 101 64 302 302 

Statoil 4 35 7 7 

Sun . 138 481 . 

Texaco 547 404 4,235 2,640 

Umc 16 36 57 41 

Union Pacific 87 68 912 819 

Unocal 511 288 1,935 1,058 

Vastar 212 242 212 212 

Walter 159 52 159 159 

Zilkha 62 348 62 62 

As illustrated in Figure 2.3, the “GOM-top-50” accounted for slightly more than 90 percent of 

GOM production over the 1990 through 1999 interval, and almost 55 percent of total U.S. 

production. However, they only accounted for 22.4 percent of global production during the same 

period.

90.3

21.4

54.9

0

20

40

60

80

100

Percent

GOM World U.S.

Figure 2.3: Share of Production from the U.S. GOM, U.S. and the World 

 for the Top 50 Firms Operating in the U.S. Gulf of Mexico OCS 
28

Table 2.4 compares the top four, top eight and top twenty concentration ratios and HHI indexes 

based on GOM production and U.S. production on an annual basis for the 1990s. As might be 

expected, the ratios and index based on the total U.S. petroleum production are somewhat below 

the ratios and index calculated with GOM production, but the differences are not large. 

Table 2.5 combines the two bases of measurement for the GOM (leases acquired and 

production) by showing concentration ratios measured as the share of leases acquired by the top 

four, top eight and top twenty firms, but using their production over the preceding 10 year period 

to define who the top four, etc., were. 

Comparing Tables 2.1 and 2.2 indicates both less year-to-year variation and more concentration 

if production, rather than leases acquired, is used as the basis of the measurement. When the two 

approaches are combined as in Table 2.5 with leases acquired by the top four, eight, or twenty 

firms ranked in terms of production during the preceding ten years, measures of concentration 

fall and year-to-year variation in measurements increases markedly. This is not unexpected since 

firms, especially larger firms who have been active in the Gulf in the previous ten years, 

accumulate inventories of leases that can be either increased or drawn down if circumstances 

change. 

Table 2.6 lists the number of leases acquired and total barrels of oil equivalent produced in the 

Gulf of Mexico OCS during the 1990 to 1999 period for the top twenty firms in both categories. 

Shell and Chevron are one and two in both categories, and the first fifteen or so ranks correspond 

fairly closely. However, there are also some significant differences. BP is ranked eleventh in 

production but third in leases won. Zilkha obtained 348 leases during the period which was the 

sixth highest, but it was only 37th in production.4

 At the other extreme, Newfield was 19th in 

production but only 39th in leases acquired. 

Differences among firms resulting from timing and strategy are evident in both rankings. The 

leasing of most of the deep tracts in the Gulf occurred in the 1995-1997 lease sales. In 1995 there 

were 1012 active leases in water deeper than 800 meters. By 1998 this number had almost tripled 

to 3002.5 Some firms acquired leases very aggressively and successfully but production will lag 

leasing by at least five or six years. Conversely, other firms such as Newfield have followed a 

strategy of acquiring properties by means other than lease sales. 

The last two columns in Table 2.6 indicate the number of times the firms listed were among the 

top 20 firms in the production or leasing categories from 1990-1999. The irregular nature of the 

industry over the short or intermediate term is indicated by fact that three of the firms ranked as 

top ten producers over the period were ranked as top 20 producers in only five or fewer years. 

 

4

 Zilkha Energy Company’s history illustrates one of the difficulties in analyzing the lease market’s performance—

the frequency of changes in ownership and control. A very active participant in the 1990s, Zilkha was purchased by 

Sonat, another large independent, for $1Billion in stock in January of 1998. Sonat was then purchased by El Paso 

Energy Corp. for $6 Billion in October of 1999. 

5

 Baud, R.D. et al. Deepwater Gulf of Mexico 2002, p.14. 
29

Table 2.4 

Concentration Ratios (shown as percentages), 

HHI Indices and Total Production for the U.S. & the Gulf of Mexico OCS 

OCS U.S. 

Year Top 4 Top 8 Top 20 HHI Top 4 Top 8 Top 20 HHI 

1990 43 66 91 688 34 61 82 521 

1991 45 68 90 730 34 60 82 516 

1992 45 69 90 747 33 59 82 499 

1993 45 70 89 760 31 56 79 461 

1994 43 66 87 708 31 56 79 454 

1995 42 64 86 672 31 55 78 446 

1996 45 63 84 701 30 53 76 429 

1997 44 61 82 668 30 53 76 425 

1998 43 60 81 650 35 55 76 472 

1999 43 61 83 674 38 56 77 522 

Table 2.5

Share of Leases Acquired by Top 4, 8, and 20 Producing Firmsa

Year No. Firms Top 4 Top 8 Top 20 HHI 

1983 55 20 31 57 643 

1984 58 20 29 51 629 

1985 60 30 40 60 651 

1986 64 45 50 75 641 

1987 67 36 52 78 662 

1988 70 36 46 67 650 

1989 76 35 41 54 623 

1990 87 19 26 47 688 

1991 94 27 40 49 730 

1992 97 17 24 31 747 

1993 101 11 19 27 760 

1994 104 16 20 28 708 

1995 109 27 34 45 672 

1996 109 24 37 46 701 

1997 111 28 39 56 668 

1998 111 30 54 66 650 

1999 105 5 13 49 674 

1983-1999 222 25 37 53 637 

1983-1990 100 28 39 60 671 

1990-1999 196 24 35 49 624 

a Ranking is based on immediate past ten years’ production. 
30

Table 2.6 

 Top 20 Firms in Total Oil and Gas Production or 

Total Leases Acquired in the Gulf of Mexico, 1990-1999 

Company Rank Production Leases FT20 

 Production Leases MMBOE Number Production Leases 

Shell 1 1 1,579 789 10 10 

Chevron 2 2 1,449 494 8 8 

Mobil 3 7 629 341 9 9 

Exxon 4 5 595 354 7 7 

Texaco 5 4 547 404 9 9 

Unocal 6 8 511 288 5 5 

Conoco 7 9 382 268 6 6 

Pennzoil 8 21 372 96 4 0 

Amoco 9 10 341 243 7 7 

Marathon 10 18 328 110 4 4 

BP 11 3 315 418 7 7 

Vastar 12 11 212 242 6 6 

Amerada 13 13 205 207 9 9 

Kerr-McGee 14 14 199 161 7 7 

Samedan 15 20 187 104 5 5 

CNG 16 26 187 77 4 0 

ARCO 17 19 182 109 4 4 

Oxy 18 71 162 18 0 0 

Newfield 19 53 160 33 0 0 

Walter 20 39 159 52 0 0 

Murphy 21 16 146 115 0 5 

Enron 28 17 94 115 0 6 

Zilkha 37 6 62 348 0 6 

BHP 68 12 20 207 0 7 

Sun - 15 0 138 0 7 

Number of Bids per Lease: Table 2.7 presents two attributes commonly used to describe the 

degree of competition in lease auction markets, the number of bids per lease and number of 

bidders per lease. 
31

Table 2.7

Measures of the Intensity of Competition for OCS Leases 

 All Bids 

Competitive Bids 

(More than One Bid) 

Year Bids/Lease Bidders/Lease Bids/Lease Bidders/Lease 

1983 1.64 3.51 2.86 6.31 

1984 1.49 3.01 2.59 5.58 

1985 1.39 2.58 2.56 4.98 

1986 1.17 1.68 2.13 2.74 

1987 1.33 1.68 2.49 3.17 

1988 1.36 1.79 2.67 3.76 

1989 1.39 1.82 2.66 3.84 

1990 1.55 2.02 2.76 3.83 

1991 1.35 1.85 2.49 3.62 

1992 1.31 1.91 2.33 3.67 

1993 1.28 1.58 2.52 3.52 

1994 1.48 2.09 2.63 3.89 

1995 1.42 1.98 2.61 3.78 

1996 1.50 2.07 2.71 3.83 

1997 1.64 2.26 2.85 4.03 

1998 1.40 1.88 2.89 4.17 

1999 1.25 1.62 2.51 3.46 

1983-1989 1.44 2.41 2.66 4.85 

1990-1999 1.48 2.01 2.73 3.89 

1983-1999 1.46 2.17 2.70 4.26 

On average there were more bids per lease in the 1990s than in the 1980s. But there also were 

fewer participants in the bidding process on a per lease basis. This is true whether the bids were 

competitive or noncompetitive. The average number of bids per lease increased from 1.44 for the 

period 1983-1989 to 1.48 for the period 1990-1999. However, the mean value for the number of 

bidders decreased from 2.41 to 2.01 during the same periods. For leases with at least two bids per 

lease, i.e., competitive bids, the overall average number of bids per lease for the period 1983-

1999, was 2.70. This measure also increased from the 1983-1989 average of 2.66 to an average 

of 2.73 between 1990 and 1999. 

A pictorial view of the overall trend in bids per lease, which incorporates estimated values from 

previous studies, is presented in Figure 2.4. The number of bids per lease was 2.72 for 1954-

1966 and 3.90 for 1966-1977 (Mead and Sorensen, 1980). Erickson and Spann (1974) also 

estimated the average number of bids per tract over the period 1954-1973 for the entire U.S. 

OCS as 3.56 and Saidi and Marsden (1992) estimated bids per lease for the period 1973-1977 as 

2.96. These estimates were made prior to 1983 when area-wide leasing policy began on the OCS. 
32

2.73 2.66

2.96

3.90

3.56

2.72

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

1954-1966 1954-1973 1967-1977 1973-1977 1983-1989 1990-1999

Bids per Lease

 Figure 2.4: Average Number of Bids Per Lease in Different Periods. 

Competition measured in terms of bids per lease on the OCS, on average, appears to be 

significantly less intense during the period 1983-1999 compared to earlier history. The reason for 

the overall decline in bids per lease is evident in the frequency distribution of bids per lease 

presented in Table 2.8. On average, the proportion of leases with just one bid from 1983 to 1999 

was 75 percent. The proportion of leases receiving at least three bids was less than 10 percent 

during this period. Erickson and Spann (1974)6 reported that nearly 70 percent of leases had 

three or more bids during the period 1969-1973. 

2.3 Bidding Arrangements for OCS Leases 

Firms seeking the right to explore and develop petroleum resources on the Gulf OCS participate 

in lease sales either by bidding alone in solo ventures or by bidding as a partner with other firms 

through joint ventures. By definition, joint ventures reduce the number of potential bidders. Thus 

they might be considered to tend to reduce competition in the lease market. But, joint venturing 

may also increase the number of actual bidders by facilitating the entry of relatively smaller 

operators into offshore E&P ventures, ventures that are by most standards risky and capital 

intensive. Joint ventures allow for the pooling of resources together to meet the minimum capital 

requirement for acquiring petroleum leases. Thus, it has been suggested that the incidence of 

joint ventures and the number of bidders or bids should be positively correlated (Erickson and 

Spann, 1974; Saidi and Marsden, 1992; and Markham, 1970). 

 

Of the 20, 361 bids submitted from 1983-1999 for the right to explore and develop 14,300 leases 

in the Gulf of Mexico OCS, joint ventures accounted for 32 percent. Nearly 50 percent of the 

20,361 bids were for leases that attracted at least two bids (competitive). Joint ventures were 

involved in 38.4 percent of bids submitted for competitive leases. In contrast, less than 28 

percent of the single-bid leases (non competitive) were made jointly. 

 

6

 See Erickson and Spann (1974) Table 2, Page 1701. 
33

Table 2.8 

Percentage Frequency Distribution of Leases by the Number of Bids 

 Number of Bids/Lease 

Year 1 2 3 4 5 6 >6 

1983 65.8 19.8 6.9 3.6 2.1 0.9 1.0 

1984 69.2 20.7 5.3 2.8 1.4 0.2 0.4 

1985 75.1 17.7 3.2 2.3 1.2 0.2 0.3 

1986 85.2 12.9 1.9 0.0 0.0 0.0 0.0 

1987 77.9 15.3 4.3 1.9 0.0 0.4 0.1 

1988 78.2 14.8 3.1 2.5 0.6 0.3 0.4 

1989 76.6 15.7 4.7 1.6 0.3 0.4 0.7 

1990 69.1 18.5 6.6 3.6 0.9 0.4 0.9 

1991 76.4 16.0 5.0 1.8 0.7 0.0 0.2 

1992 76.9 17.0 4.7 1.4 0.0 0.0 0.0 

1993 81.6 13.1 2.2 2.2 0.6 0.3 0.0 

1994 70.8 19.1 5.1 3.2 1.0 0.2 0.5 

1995 73.7 17.5 5.3 1.6 0.9 0.5 0.5 

1996 70.9 17.0 7.8 2.0 1.3 0.6 0.4 

1997 65.4 18.8 8.8 3.8 1.5 0.7 1.0 

1998 78.8 13.4 3.4 1.8 1.3 0.3 0.9 

1999 83.6 11.7 2.2 1.4 1.1 0.0 0.0 

1983-89 75.4 16.7 4.2 2.1 0.8 0.3 0.4 

1990-99 74.7 16.2 5.1 2.3 0.9 0.3 0.4 

1983-99 75.0 16.4 4.7 2.2 0.9 0.3 0.4 

The decline in the share of bids submitted as joint ventures for both the competitive and 

noncompetitive leases may be linked to the restriction imposed on some major oil companies 

from being co-bidders in any joint bids7. This restriction will be discussed in a subsequent 

section of this chapter. However, as the proportion of bids by joint ventures declined through 

time, the proportion of high bonus bids awarded to joint venture bidders also declined. In 1983, 

for example, about 64 percent of leases awarded in the OCS were to joint bidders compared to 

the less than 30 percent, on average, within the last three years (Table 2.9). This is comparable 

to the trend in the proportion of all bids submitted as joint ventures over the same period. During 

the first three years of lease sales after area-wide leasing began, roughly 56 percent of all bids 

submitted on the OCS were accounted for by joint ventures. In contrast, the proportion of bids 

accounted for through joint bidding from 1997-1999 was less than 30 percent on average. 

Overall, Erickson and Spann (1974) reported that joint ventures as a fraction of total bids 

increased significantly over time during the period from 1954–1977. 

 

7 The Energy Policy Conservation Act, enacted in 1976, banned companies from submitting bids jointly in federal 

OCS lease sales, if their global petroleum production is in excess of 1.6 millions barrels of oil equivalent per day 

(Moody and Kruvant, 1988; Sullivan et al., 1980) The list of restricted joint bidders is published in the U.S. Federal 

Register. 
34

Table 2.9 

Percent Share of Bids that were Joint Venture Bids 

Year Single Bids At Least Two Bids 

 All Bids 

High 

Bids All Bids High Bids 

1983 63.9 62.0 66.0 64.4 

1984 53.5 49.3 60.9 59.2 

1985 46.1 45.6 49.3 52.1 

1986 30.4 29.7 26.5 17.4 

1987 18.8 19.6 21.4 28.7 

1988 23.9 22.0 29.7 30.8 

1989 22.8 19.0 32.1 31.3 

1990 22.5 20.6 27.6 30.7 

1991 28.2 27.4 32.9 36.4 

1992 33.6 31.1 41.2 40.8 

1993 19.2 18.4 28.9 39.4 

1994 31.2 28.9 36.3 36.8 

1995 27.8 26.0 31.6 30.8 

1996 28.5 30.0 30.3 38.8 

1997 29.1 29.6 30.7 35.1 

1998 27.7 25.5 33.8 35.2 

1999 23.6 22.8 31.8 39.0 

1983-89 37.1 35.3 40.8 40.6 

1990-99 27.1 26.0 32.5 36.3 

1983-99 31.2 29.9 35.9 38.1 

2.4 Characterizing the Market for OCS Leases 

The Value of High Bonus Bid: The value of high bonus bids provides a measure of the initial 

cash payment to the government for granting firms the right to explore in the OCS region per 

lease awarded. As such it is an important variable to monitor and understand. It is not, of course, 

the only or necessarily the most important indicator of the performance of the lease market. The 

fundamental goal of the leasing program is to facilitate the efficient exploration and development 

of the nation’s petroleum resources. High bonus bids may be either “too high” or “too low” to 

accomplish this goal (McDonald, 1979). 

This is true even if the policy perspective is limited to government revenues rather than national 

economic output or efficiency. Over the 1982 to 2000 period the federal treasury received about 

$57 billion in offshore royalties and rental payments and about $24 billion in bonus payments 

from lease sales.8

 More recently this difference has grown. 

 

8

 These totals include payments for non-oil and gas minerals; they are not significant enough to bias the comparison. 
35

In the year 2000, for example, offshore oil and gas royalties, plus rental payments on active 

leases on which production has not yet begun, amounted to about $4.3 billion which is almost ten 

times as much as the $442 million in total bonus bid payments which were received from lease 

sales in that year. Thus, if bidding at lease sales were so intense that exploration and 

development were discouraged it is possible that royalties would fall enough to reduce total OCS 

revenues going to the Treasury. 

• The total value of bonus bids paid during the 1983 to 1999 period was $16.720 billion 

and the mean value of high bonus bids per lease was about $1.12 million. 

• When leases were won through competitive joint venture bidding, the average value of 

the high bonus bid per lease was $3.29 million, more than twice the value for 

noncompetitive, joint venture, high bonus bids, which was $1.18 million. 

• The average high bonus bid for competitive solo venture bids for the period 1983-1999 

was $1.75 million in comparison to an average high bid value of $0.614 million for 

noncompetitive solo ventures.9

 

Thus, consistently, the data show higher bonus bids for competitive leases than for 

noncompetitive leases, irrespective of the type of bid (Tables 2.10 and 2.11). It is important to 

note that a joint venture bid on a lease is counted as one bid irrespective of the number of 

participating partners in the winning joint venture. 

The relationship between the average value of high bonus bids and number of bids is illustrated 

in Figure 2.5. The value of the high bonus bids, on average, tends to increase with the number of 

bids per lease. 

0

2

4

6

8

10

012345678

Number of Bids per Lease

High Bonus /Lease

(Thousand Dollars)

 Figure 2.5: Trend in the Value of the High Bonus Bids, 1983-1999. 

 

9

 The authors calculated these figures in a separate document, using MMS data. 
36

Table 2.10 

Average Value of High Bonus Bids, 

1983 to 1999 (in thousands of dollars) 

 Noncompetitive Bids Competitive Bids 

YEAR Solo Joint All Solo Joint All 

1983 3,023 2,608 2,771 6,357 9,060 8,099 

1984 1,717 1,998 1,843 4,043 4,308 4,200 

1985 1,558 1,858 1,688 3,595 5,051 4,354 

1986 1,182 1,258 1,206 1,844 2,234 1,912 

1987 572 864 621 1,153 1,876 1,360 

1988 376 417 384 1,145 1,303 1,194 

1989 383 731 436 1,111 1,375 1,194 

1990 449 455 450 1,282 1,190 1,254 

1991 363 486 393 1,040 881 982 

1992 283 323 294 726 888 792 

1993 262 429 285 635 950 759 

1994 270 507 331 1,202 1,120 1,172 

1995 288 323 296 1,051 972 1,027 

1996 321 478 363 1,035 1,129 1,071 

1997 397 692 476 1,228 1,623 1,366 

1998 547 1,046 662 2,053 4,531 2,925 

1999 482 1,010 586 1,216 2,013 1,527 

A pictorial view of the trend in the index of mean value of high bonus bids for the period is 

presented in Figure 2.6. The index is defined as the value of high bonus bids per lease in each 

year divided by the mean value from 1983-1999. 

0.00

0.50

1.00

1.50

2.00

2.50

1983 1985 1987 1989 1991 1993 1995 1997

Year

Mean Value Index

Mean index 1983-99 =1 Mean Index 1983-89 Mean Index 1990-99

 Figure 2.6: Trend in the Index of the Annual Average Value of High Bids. 
37

Table 2.11 lists the top 20 firms, in either production or leases acquired, and their average high 

bids for leases over the 1990 to 1999 time period. Average high bids are shown for all leases 

(total in column 4) shallow leases (less than 800 meters water depth), deepwater leases (more 

than 800 meters), competitive leases (two or more bids submitted) and noncompetitive leases 

(acquired with a solo bid). The variation observed among firms seems large, especially at the 

extremes of the distributions. For all bids, the average ranges from Marathon’s high of 

$1,418,000 to a low of $186,000 for Zilkha—a ratio of 7.6. Further, neither of these firms were 

small or marginal participants in the lease market. Marathon ranked 10th in production and 18th in 

leases acquired while Zilkha was the sixth highest in terms of leases and 37th in production. It 

might be expected that the variation among leases would narrow if leases were subdivided into 

shallow/deep and competitive/noncompetitive categories, but the high/low ratios actually 

increase. 

A more comprehensive measure of the relative variation of the distributions can be made by 

expressing the standard deviation of the series as a proportion, or percentage, of the mean of the 

distribution. By this measurement only the competitive series is less varied than the total series. 

The standard deviation as a percent of the mean is: 

• 46 percent for the all bids considered as a single series, 

• 54 percent for the shallow bids, 

• 86 percent for deep water bids, 

• 51 percent for noncompetitive bids, and 

• 41 percent for competitive bids.10

In addition to the categorization of leases listed in the columns of Table 2.11, corporate strategy, 

development strategy, experience, development objectives, and a host of other factors are related 

to the differences in average high bids among the firms listed in the table. Identifying and 

incorporating such factors into our analysis in a comprehensive way exceeds the scope of this 

project. However, we try to facilitate the comparison of patterns in bidding results among firms 

by showing the normalized standard deviation or “Z-score” for each of the average high bid 

series in Table 2.12. The order in which firms are listed in Table 2.12 was by the firm’s Z-score 

for all high bids (column 6). Figures 2.7-2.11 illustrate the ranking for each of the five 

categories in the table. 

The Z-score was calculated for the top 50 firms in either leases or production, resulting in a 

sample of 63 firms. A Z-score of zero means the average high bid for the firm lies on the average 

of all the firms in the distribution, a Z-score of + 1.00 means the firm’s average was one standard 

deviation above the mean of the distribution, a –1.87 means the firm’s average was 1.87 standard 

deviations below the mean of the distribution. 

 

10 The means and standard deviations used were calculated for the “top fifty” sample shown in table 2.12, using both 

leases and production top 50s rankings gives a combined sample of 63 firms. 
38

Table 2.11 

 Top 20 Firms in Total Oil and Gas Production or 

Total Leases Acquired, Average High Bid/Lease for Total, Shallow, Deep, 

Competitive and Noncompetitive Leases, 1990-1999 

Company Production Leases Average High Bid (in thousands of dollars) 

MMBOE Number Total Shallow Deep At Least 

Two Bids Single Bids 

Shell 1,579 789 512 557 568 1,162 404 

Chevron 1,449 494 364 337 480 1,285 274 

Mobil 629 341 367 247 495 896 320 

Exxon 595 354 487 657 478 1,045 373 

Texaco 547 404 446 535 657 1,257 429 

Unocal 511 288 886 406 1,386 2,408 615 

Conoco 382 268 440 297 611 948 420 

Pennzoil 372 96 323 366 475 682 293 

Amoco 341 243 382 544 466 1,013 321 

Marathon 328 110 1,418 1,078 2,354 2,295 1,635 

BP 315 418 548 656 579 1,210 439 

Vastar 212 242 845 707 1,432 1,926 461 

Amerada 205 207 725 944 1,066 1,685 608 

Kerr-McGee 199 161 1,009 1,508 1,193 1,894 1,105 

Samedan 187 104 469 638 1,050 1,663 494 

CNG 187 77 757 829 1,520 1,691 513 

ARCO 182 109 556 630 - 1,254 360 

Oxy 162 18 1,281 175 1,713 2,183 571 

Newfield 160 33 359 376 - 571 289 

Walter 159 52 441 482 519 859 357 

Murphy 146 115 888 922 881 1,407 624 

Enron 94 115 651 614 940 1,090 489 

Zilkha 62 348 186 186 213 301 174 

BHP 20 207 513 664 457 1,006 328 

Sun 0 138 1,064 578 1,557 1,680 751 
39

Table 2.12

 Top 50 Firms in Either Production or Leases Acquired from 1990 to 1999 Ranked by 

Z-Scores for Average High Bids, with Z-Scores for Total, Shallow, Deep, 

Competitive, and Noncompetitive Average High Bids 

 Production Leases Rank Z-Score for Average High Bid 

Company MMBOE Number BOE Leases Total Shallow Deep Competitive 

NonCompetitive 

Marathon 328 110 10 18 3.20 1.15 1.32 1.79 4.29 

Anadarko 50 71 42 28 2.96 5.55 -0.60 4.14 0.66 

Statoil 4 35 122 47 2.84 0.79 1.44 4.08 

Elf 55 62 40 34 1.94 0.14 0.75 1.66 1.77 

Oxy 162 18 18 71 1.91 -1.37 0.65 1.58 0.22 

Kerr-McGee 199 161 14 14 1.52 2.35 0.10 1.05 2.27 

Basin 11 33 90 50 1.15 1.55 0.35 0.53 

British-Borneo 20 64 67 32 0.91 0.82 0.18 1.10 0.49 

Sun 138 15 0.73 -0.24 0.48 0.66 0.91 

Unocal 511 288 6 8 0.63 -0.73 0.30 2.00 0.39 

Vastar 212 242 12 11 0.62 0.12 0.35 1.11 -0.19 

CNG 187 77 16 26 0.57 0.46 0.44 0.68 0.00 

Burlington 47 79 43 25 0.56 -0.02 0.05 0.88 0.45 

Amerada 205 207 13 13 0.50 0.78 -0.04 0.67 0.37 

Agip 71 55 33 38 0.44 0.60 -0.05 1.02 -0.17 

Freeport 84 10 31 93 0.39 0.76 -0.38 0.06 

Ocean Energy 35 24 49 65 0.38 -0.57 0.33 0.44 0.24 

Murphy 146 115 21 16 0.25 0.72 -0.23 0.16 0.43 

Petsec 24 35 64 48 0.22 0.66 -0.30 0.73 

Forest 121 27 24 62 0.12 0.20 -0.75 0.05 0.12 

Union Pacific 87 68 29 29 0.07 0.24 -0.08 -0.27 -0.16 

Odeco 75 12 32 86 0.06 0.42 -0.31 0.16 

Enserch 33 60 52 36 0.02 -0.10 -0.26 0.64 -0.33 

Forcenergy 54 26 41 63 0.01 -0.58 0.94 0.45 -0.73 

Phillips 113 89 25 23 -0.01 0.68 -0.48 0.00 0.14 

Energy Devel 44 30 44 58 -0.04 0.20 0.27 -0.43 -0.06 

Umc 16 36 79 45 -0.06 0.44 -0.70 -0.79 0.64 

Seneca 41 42 46 42 -0.07 0.28 0.10 -0.49 
40

Table 2.12: Top 50 Firms in Either Production or Leases Acquired from 1990 to 1999 

Ranked by Z-scores for Average High Bids, with Z-scores for Total, Shallow, Deep, 

Competitive, and Noncompetitive Average High Bids (continued) 

 Production Leases Rank Z-Score for Average High Bid 

Company MMBOE Number BOE Leases Total Shallow Deep Competitive 

Non 

Competitive 

Pogo 44 31 45 55 -0.13 -0.46 1.78 -0.16 -0.36 

Apache 124 45 23 41 -0.18 0.17 -0.39 -0.45 

Santa Fe 62 65 36 30 -0.19 -0.14 -0.16 -0.63 0.09 

Samedan 187 104 15 20 -0.21 -0.08 -0.05 0.63 -0.07 

Norcen 96 45 27 40 -0.23 0.00 0.23 -0.40 -0.47 

Louisiana L&E 63 82 35 24 -0.36 -0.01 -0.46 -0.58 -0.37 

Hall-Houston 84 28 30 61 -0.37 -0.03 -0.77 -0.26 

Enron 94 115 28 17 -0.39 -0.14 -0.17 -0.43 -0.09 

Fina 41 43 -0.39 -0.15 -0.43 -0.81 -0.07 

ARCO 182 109 17 19 -0.44 -0.10 -0.12 -0.58 

Seagull 59 89 38 22 -0.45 -0.77 5.63 -0.21 -0.19 

Texaco 547 404 5 4 -0.47 -0.36 -0.47 -0.12 -0.32 

Nippon 5 38 116 44 -0.54 -0.45 -0.08 -1.08 -0.01 

BP 315 418 11 3 -0.56 -0.03 -0.55 -0.20 -0.28 

Chieftain 2 57 147 37 -0.59 -0.36 0.22 -0.30 -0.85 

Shell 1,579 789 1 1 -0.62 -0.30 -0.56 -0.29 -0.41 

Petrobras 4 35 121 46 -0.62 -0.46 -0.36 -0.71 -0.69 

Conoco 382 268 7 9 -0.63 -1.03 -0.52 -0.69 -0.35 

King Ranch 8 34 100 49 -0.64 -0.28 -1.11 -0.40 

Nerco 55 15 39 80 -0.69 -0.36 -1.10 -0.16 

BHP 20 207 68 12 -0.76 0.00 -0.68 -0.58 -0.70 

Exxon 595 354 4 5 -0.80 -0.02 -0.66 -0.51 -0.53 

Amoco 341 243 9 10 -0.82 -0.34 -0.67 -0.57 -0.73 

Walter 159 52 20 39 -0.83 -0.51 -0.61 -0.85 -0.59 

Sonat 101 64 26 31 -0.83 -0.90 -0.46 0.42 -0.54 

Houston Expl 37 63 47 33 -0.91 -0 .58 -0.84 -0.50 -0.87 

Chevron 1,449 494 2 2 -0.96 -0.92 -0.65 -0.07 -0.91 

Mobil 629 341 3 7 -1.01 -1.17 -0.64 -0.78 -0.74 

Pennzoil 372 96 8 21 -1.09 -0.84 -0.66 -1.17 -0.84 

Coastal 69 72 34 27 -1.09 -0.76 -0.74 -0.95 -0.77 

Newfield 160 33 19 53 -1.13 -0.81 -1.38 -0.85 

Cxy 29 62 57 35 -1.22 -0.99 -0.29 -1.18 -1.14 

Zilkha 62 348 37 6 -1.64 -1.34 -0.94 -1.87 -1.29 
41

 Figure 2.7: Z-Scores for Average High Bids (Shallow Water).
42

 Figure 2.8: Z-Scores for Average High Bids (Deep Water). 
43

Figure 2.9: Z-Scores for Average High Bids (Competitive Bids). 
44

 Figure 2.10: Z-Scores for Average High Bids (Single Bids). 
45

 Figure 2.11: Z-Scores for Average High Bids (All Bids).
46

Table 2.12 inspires several observations--although all of them need further analysis at a more 

detailed level. 

• The number of firms that appear to bid either higher or lower than the norm is quite 

small. Keeping in mind that all the observations are winning bids, “high-winning bids,” 

i.e. bids that are significantly higher than the average winning bid, might suggest worse 

than average bidding (“worse” from the perspective of the shareholders of the bidders but 

“better” for the government’s revenues) and are referred to here as “too-high-winning 

bids.” Conversely “too-low-winning bids” indicate better than average bidding 

performance from the standpoint of the firm but are, in a similar sense, “too-low” from 

the standpoint of the government. Using 1.5 standard deviations as an admittedly ad hoc 

criterion for making such a determination: 

o Six firms, Marathon, Anadarko, Statoil, Elf, Oxy, and Kerr-McGee, might be 

termed “too-high-winning” bidders and one firm, Zilkha, a “too-low-winning” 

bidder for all leases considered together. 

o Three firms were “too-high-winning” bidders for shallow leases and two for deep 

leases. No firm met the criteria for “too-low-winning” bidder in either category. 

o Five firms were “too-high-winning” bidders for competitive leases and four for 

noncompetitive leases. 

o One firm was a “too-low-winning bidder” for competitive leases and no firms met 

the criteria for noncompetitive leases. 

• Only a few firms appear to meet the criteria for “too-high-winning” bidder or “too-lowwinning” bidder consistently: 

o Marathon’s average high bid was more than one standard deviation above the 

mean in each of the five categories and exceeded the 1.5 criteria in three of them. 

o Elf and Kerr-McGee also exceeded the criteria in three of five categories. 

o Anadarko, Statoil and Oxy met fell into the “too-high-winning” bidder category 

for two of the five series. 

o Zilka was the only firm to earn the more desirable “too-low-winning” bidder it 

did this in two categories. 

• The firms listed on MMS’ Restricted Bidders List, discussed later in this chapter, all fall 

in the lower end of each of the five series, but only Mobil was (barely) more than one 

standard deviation below the mean in any series. 

• The timing of the “leasing-up” of the Deep Gulf may be reflected in the relationships in 

Table 2.12. Larger firms such as those on the Restricted Bidders List dominated lease 

sales in the 1992-96 period. Participation by smaller majors and larger independents grew 

significantly in 1997-99 period. If the perception had spread that opportunities to 

participate in deepwater activity were becoming scarce, average bids may have been 

elevated later in the period. 
47

3. Mergers and Acquisitions, Firm Size and the Market for OCS Leases

Implicit in the concern about possible effects of mergers and acquisitions on the leasing of oil 

and gas tracts in the Gulf of Mexico is apprehension that firms participating in the process may 

become large enough to exercise market power for their own advantage. However, mergers and 

acquisitions are not the only or necessarily most important route to large size. As emphasized 

previously, the growth of Shell’s share of production has been a more quantitatively important 

factor in changing the structure of the offshore industry output than the mega-mergers. Further, 

smaller independents have been at least as active participants in merger and acquisition activity 

as the majors whose mega-mergers have attracted attention in recent years. In other words, 

merger and acquisition experience and the size of firms are separable characteristics. 

The objective in this chapter is to determine whether the attributes of leases won by firms who 

have a history of mergers and acquisitions and leases won by firms of different sizes are related 

to the structure, conduct and performance of the market for oil and gas leases in the Gulf of 

Mexico OCS region. 

3.1 Effects of Mergers and Acquisitions 

This section analyzes the lease records from 1983-1999 to investigate changing patterns of 

ownership in the E&P industry on the structure, conduct, and performance of the market for oil 

and gas leases in the Gulf of Mexico OCS region. The primary approach is a descriptive analysis 

of the data and empirical testing of the differences in the mean values of lease statistics that 

characterized the OCS lease auction market and the conduct of participating bidders. 

To do this, all OCS leases from 1989-1999 were classified into three categories. 

Group A: Leases with high bids (solo or joint) by firms that were not involved in mergers 

and acquisitions from 1983-1999. This represents the control group in the analysis. 

 

Group B: Leases won by firms involved in mergers and acquisitions prior to the time of 

lease sales during the period 1983-1999. Joint ventures leases, which involve other firms 

without such experience, are also included in this category. 

Group C: Leases with winning bids submitted solely or jointly by firms that were 

candidates for M&A between 1983-1999, but M&A had not occurred prior to the time 

of lease sales. As with Group B, joint ventures leases, which involved firms from Group 

A, were included. 

Table 3.1 presents aggregate statistics on oil and gas leases in the Gulf of Mexico OCS. The 

summary information is organized by category of leases as described above and by period. Two 

sub-periods 1983-1989 and 1990-1999 have been selected arbitrarily, but they clearly show 

major movement among the three categories over the period. 
48

Table 3.1 

Aggregate Values of OCS Lease Attributes 

OCS Auction Attributes Period Group A Group B Group C Aggregate 

 

Number of Operators 1983-89 188 51 92 191 

 1990-99 222 100 55 223 

Percent Change 18 96 (40) 17 

Number of Leases 1983-89 4,796 145 604 5,545 

 1990-99 7,121 1,115 165 8,401 

Percent Change 48 670 (73) 52 

Number of Bids 1983-89 6,894 213 859 7,966 

 1990-99 10,642 1,535 218 12,395 

Percent Change 54 621 (75) 56 

Number of Bidders 1983-89 11,455 268 1,656 13,369 

 1990-99 14,401 2,203 291 16,895 

Percent Change 26 722 (82) 26 

Total Bonus Exposed, $MM 1983-89 12,155 124 2,688 14,937 

 1990-99 7,207 960 135 8,302 

Percent Change (69) 674 (95) (44) 

Total High Bid Bonus, $MM 1983-89 8,796 89 1,997 10,882 

 1990-99 5,048 681 111 5,839 

Percent Change (43) 665 (94) (46) 

 

More specifically, Group A is the largest category and dominates the aggregate measures over 

both periods, but its growth is relatively modest. Group B starts from a much smaller relative 

base, but grows much more dramatically in each attribute category, while, conversely, Group C 

declines much more dramatically as firms shift from Group C to Group B throughout the period. 

The total number of firms11 that submitted or participated in at least one high bonus bid (joint or 

solo) for leases on the OCS from 1983 to 1999 was 414. These firms exposed themselves to 

about $23,240 billion for the right to own and develop 13,946 leases awarded from 1983 to1999. 

The surrendered cash value of the high bonus bids for the period was $16.721 billion. This 

represents 72 percent of the total bonus exposed during the period. 

From 1983-1989, 191 operators surrendered $10.882 billion for high bonus bids against the total 

bid bonus value of $14,937 billion (73 percent of total bonus bid value). Whereas the total value 

of high bonus bids submitted by 223 operators was about $5.839 billion in the period 1990-1999, 

a decline of nearly 46 percent from the total bonus in the 1980s. 

 

11 The number of “firms” is more precisely, the number of different MMS operator identification numbers assigned 

to at least one winning bid. Different identification numbers may be assigned to the same commercial entity as a 

consequence of changes in legal or internal organization of the entity or because of administrative procedures 

implemented or followed by MMS. All identification numbers associated with the commercial entity are classified 

consistently in the three groups. 
49

It should be noted that this drop occurred despite the noticeable increase in the number of bids 

submitted and the expansion in the number of participating bidders. The fraction of the total bid 

bonus value that was surrendered from 1990 to 1999, in the aggregate, was 70.3 percent, a 2.6 

percent drop from what was surrendered from 1983-1989. 

Table 3.2 presents sample statistics measuring the structure, conduct and performance of the 

market for OCS oil and gas lease sales from 1983-1999. Table 3.3 shows the same variables for 

groups A and B separated into the 1983 to 1989 and 1990 to 1999 time periods. The empirical 

determination of whether there is any difference in two comparable estimates of a random 

variable requires a specification of two opposing hypotheses: the null hypotheses of interest and 

the alternative hypothesis. The null hypothesis represents an assumption that ordinary sampling 

variability does sufficiently explain the observed differences between point estimates of samples 

drawn from the same population. 

The alternative proposition, of course, is that the differences in point estimate cannot be 

attributed to sample variability but that some other unspecified causes are present. If such a 

difference is due to the former, then we can conclude that the two estimates are not significantly 

different, thereby failing to reject the null hypothesis at a given significant level. The 

significance level provides an estimated probability that the null hypothesis is true and can also 

mean that the alternative hypothesis is false (Brennan and Caroll, 1987). 

Using these statistical concepts on hypotheses and the test of significance of sample statistics, the 

effects of mergers and acquisitions are analyzed in Table 3.3 for each of the variables 

summarized in Table 3.2. The null hypothesis is that there is no difference between the value of 

the variable for Group A and Group B leases. Each hypothesis is empirically tested for 

verification or negation depending on whether the sample statistics indicate a level of statistical 

significance. The level of significance before failing to reject the null hypotheses has been 

specified at five percent, meaning that the chances of the alternative hypothesis being false is set 

at less than 95 percent. 

The pattern apparent in Table 3.3 is that bids per lease and bidders per lease variables show little 

consistent, significant difference in either period between Group A and Group B leases. However 

the financial variables measuring the average value of the high bid and the average amount of 

“money left on the table,” are consistently and significantly larger for the control group (Group 

A) than for the merger and acquisition group (Group B) in the earlier 1983 to 1989 period. But, 

the differences between Group A and Group B shrink to statistical insignificance in the later 

1990 to 1999 period. As shown in Table 3.1, Group B is much smaller than Group A in the 1983 

to 1989 period, and increased much more rapidly. 

This same pattern of differences between the two groups eroding from the earlier to later period 

is reflected in Table 3.4 which compares the frequency of joint bidding in the acquisition of the 

Group A and Group B leases. Joint venturing was much more commonly used in bidding on 

leases in Group A in the earlier period, for both bids and winning or high bids, but was used 

relatively more for Group B leases in the latter period. 
50

Table 3.2 

Measures of the Competitive Structure, Type of Bid and Value of Mean High Bids for OCS Oil 

and Gas Leases by Lease Category Groups A, B and C, 1983-1999 

Variable Structure Type Group A Group B Group C OCS Total 

Joint 2.78 2.87 2.75 2.78 

Competitive Solo 2.66 2.61 2.54 2.65 

All 2.70 2.72 2.66 2.70 

Joint 1.63 1.55 1.65 1.63 

Solo 1.40 1.30 1.30 1.39 

Bids/Lease 

Aggregate 

All 1.47 1.39 1.44 1.46 

Joint 2.43 2.32 3.03 2.46 Noncompetitive All 1.38 1.40 1.71 1.40 

Joint 5.43 4.77 6.20 5.44 

Competitive Solo 3.48 3.19 3.74 3.47 

All 4.23 3.89 5.13 4.26 

Joint 3.50 3.04 4.21 3.51 

Solo 1.60 1.41 1.53 1.58 

Bidders/Lease 

Aggregate 

All 2.16 1.96 2.62 2.17 

Joint 1,636 993 5,335 1,887 

Solo 977 632 1,828 989 MLOT/Lease, 

$ Thousanda Competitive 

All 1,230 793 3,813 1,347 

Joint 1,180 547 2,048 1,177 

Noncompetitive Solo 611 308 1,188 614 

All 761 381 1,488 768 

Joint 2,993 1,739 7,893 3,291 

Competitive Solo 1,722 1,138 3,249 1,746 

All 2,211 1,406 5,879 2,363 

Joint 1,825 901 4,225 1,920 

Solo 879 464 1,591 879 

High Bonus/Lease, 

$ Thousand 

Aggregate 

All 1,160 611 2,662 1,199 

a

MLOT means the total money left on the table, which measures the difference between the value of the high bonus 

bid and the value of the next highest bid for the lease. 
51

Table 3.3 

Measures of the Competitive Structure, Type of Bid and Value of Mean High Bids 

for OCS Oil and Gas Leases by Lease Category Groups A and B, 

 1983-1989 and 1990-1999 

 1983-1989 1990-1999 

Variable Structure Type of Bid Group Aa Group B Group A Group B 

Competitive Joint 2.78 3.00 2.78 2.86 

 Solo 2.54 2.82 2.71 2.56 

 All 2.65 2.84 2.73 2.71 

Aggregate Joint 1.60 1.47 1.66 1.56 

 Solo 1.34 1.47 1.44(+) 1.27 

Bids/Lease 

 All 1.43 1.47 1.49 1.38 

Noncompetitive Joint 2.60(-) 3.23 2.25 2.27 

 All 1.52(+) 1.27 1.28(-) 1.42 

Competitive Joint 6.09 6.00 4.85 4.73 

 Solo 3.65 3.24 3.39 3.18 

 All 4.80(+) 3.54 3.87 3.95 

Aggregate Joint 3.78 3.88 3.21(+) 3.01 

 Solo 1.58 1.58 1.61 1.38 

Bidders/Lease 

 All 2.38(+) 1.85 2.01 1.98 

Competitiveb Joint 2,410(+) 831 942 999 

MLOT/Lease, $ Thousand Solo 1,520(+) 666 705 623 

 All 1,940(+) 684 784 809 

Noncompetitive Joint 1,710(+) 448 635 551 

 Solo 974(+) 396 393(+) 293 

 All 1,210(+) 402 446 378 

Competitive Joint 4,500(+) 1,550 1,650 1,750 

 Solo 2,690(+) 1,190 1,240 1,120 

 All 3,540(+) 1,230 1,370 1,430 

Aggregate Joint 2,660(+) 708 1,010 909 

 Solo 1,350(+) 602 608(+) 439 

High Bids/Lease, $Thousand 

 

 All 1,830(+) 614 710 610 

a

 Differences significant at the .95 level for values for Group A and Group B leases are indicated with bold 

typeface, (+) indicates the Value for A is significantly larger and (-) indicating significantly smaller, regular 

type face indicates no difference between values for the two groups. 

b

 At least two bids are required for any “money to be left on the table.” 
52

Table 3.4 

Frequency Distribution of Bidding by Joint Venturing 

Type Bids Period Group A Group B Group C Aggregate 

1983-89 39.7 13.6 48.3 39.9 

Total 1990-99 26.8 33.7 21.1 27.6

 

1983-89 36.4 11.7 43.8 36.6 

All Bids 

High 1990-99 25.5 36.5 20.0 26.9 

 

1983-89 47.1 15.2 58.0 47.4

Total 1990-99 31.0 34.8 28.1 31.4

 

1983-89 47.0 10.8 56.8 47.1

Competitive Bids 

(At Least Two) 

High 1990-99 33.8 49.6 33.3 35.4

Table 3.5 compares the average value of joint and solo bids among the groups for the two 

periods we have used in the analysis. 

To generalize, the variables we have examined do not show consistent differences between 

Group A and Group B throughout the time period. Significant differences exist in the earlier 

period but as Group B grows, they vanish in the latter one. 

In summary, the results of the sample means equality testing of the lease attributes reported in 

Tables 3.3 and 3.4 point to the following probable effects of mergers and acquisitions on the 

market for OCS oil and gas leases: 

• The effects of M&A on bids per lease (measures of the extent or degree of competition 

for leases), in the aggregate, were statistically insignificant in the 1980s but highly 

significant in the 1990s for all leases (noncompetitive and competitive leases). 

• M&A did limit significantly the average number of bidders in the 1980s, but no such 

effect was evident in the 1990s for all leases (noncompetitive and competitive leases). 

• The effects of M&A on the mean values of high bids for competitive--solo ventures and 

joint ventures--leases from 1983-1989 are statistically significant, but insignificant 

during the 1990-1999 sub-period. 

• M&A effects on the mean values of noncompetitive solo ventures are statistically 

significant in both sub-periods, whereas the effects of M&A on the mean values of high 

bids for leases that are noncompetitive joint-venture bids in the two sub-periods are 

insignificant at 95 percent confidence interval. 
53

Table 3.5 

Mean Value of High Bids by 

Structure, Conduct and Lease Category ($ Million) 

Structure Conduct Period Group A Group B 

1983-89 4.500 1.550 

JV 1990-99 1.650 1.750

1983-89 2.690 1.190 

SV 1990-99 1.240 1.120 

1983-89 3.540 1.230 

Competitive 

All 1990-99 1.370 1.430 

 

1983-89 1.710 0.447

JV 1990-99 0.634 0.551

1983-89 0.974 0.396

SV 1990-99 0.393 0.293

1983-89 1.210 0.402 

Noncompetitive 

All 1990-99 0.446 0.377 

3.2 Comparing the Lease Records of Majors and Non-Majors 

Oil and gas operators operating in the U.S. Gulf of Mexico are classified for the analysis in this 

section using the criteria established by the U.S. EIA in its classification of Financial Reporting 

System (FRS) companies. The major energy companies are designated as majors if they have at 

least one percent of U.S. crude oil natural gas liquids (NGL) reserves or production, or at least 

one percent of U.S. natural gas reserves or production (U.S. Dept. of Energy, Energy Information 

Administration, 2002)12. MMS also used a major/non-major classification in some publications, 

which is more selective than this classification. The majors defined by MMS are basically the 

firms on its Restricted Bidders List (Shell, Exxon-Mobil, BP-Amoco-Arco, Chevron) plus 

Texaco. 

Table 3.6 presents the value of lease attributes summarizing the group effects of firms of 

different sizes on lease market structure and performance from 1983-1999. The columns show 

the mean values or magnitudes for those leases in which the winning bids were submitted by: 1) 

majors submitting solo bids or joint bid with other majors, 2) joint bids submitted by groups 

including both majors and non-majors, and 3) non-majors submitting solo bids or joint bids with 

other non-majors. 

 

12 The U.S. based companies that respond to the Financial Reporting System (FRS) for EIA-28 in 2000 with 

operations on the Gulf of Mexico OCS are Arco, BP Amoco, Amerada, Anadarko, Burlington, Chevron, Coastal, 

Conoco, Diamond, El Paso, Enron, Exxon, Fina, Kerr McGee, Marathon, Mobil, Penzoil, Phillips, Shell, Texaco, 

Union Pacific, Unocal, Valero, Williams. 
54

Table 3.6 

Comparison of OCS Lease Attributes by Structure and Conduct, 1983-1999 

Lease Attributes Structure Conduct Majors Majors and 

Non-majors Non-majors 

Bids/Lease Competitive Joint 2.59 2.83 2.82 

 Solo 2.61 2.71 

Total Bonus/Lease, $ 000 Noncompetitive Joint 793 1,558 1,001 

 Solo 612 617 

 Competitive Joint 1,392 2,867 1,293 

 Solo 1,110 881 

Bidders/Lease Noncompetitive Joint 2.09 2.54 2.61 

 Competitive Joint 4.57 5.97 5.39 

 Solo 3.44 3.51 

High Bonus/Lease, $ 000 Noncompetitive Joint 793 1,558 1,001 

 Solo 612 617 

 Compétitive Joint 2,280 5,162 2,218 

 Solo 1,923 1,465 

Intensity of Competition: Data in Table 3.6 show a higher degree of competition for leases in 

which non-majors were involved either through joint or solo bidding arrangement than those that 

involved only majors under the same bidding environment from 1983-1999. Statistical tests of 

the equality of the means confirmed that the number of bids per lease for leases involving nonmajor operators (2.83 or 2.82) is significantly greater than for leases involving majors only 

(2.59) when bidding jointly13. The result of the tests also lead to reject the null of hypothesis that 

when bidding arrangement is by solo ventures, the number of bids per lease involving only 

majors equal to that involving non-majors only. 

There were also more bidders (number of firms participating) per lease for leases involving nonmajors irrespective of whether the bids for the leases are competitive or non competitive as long 

as the bidding arrangement is joint venturing. Testing the significance of the differences in the 

means of the categories in Table 3.6, however, does not confirm the above conjecture when 

bidding is competitive and the bidding arrangement in the winning bid was a solo venture. 

Leases involving majors and non-majors as partners in the high bids for all performance 

attributes reveal significantly lower mean values when compared with the mean values of the 

 

13 We evaluated the differences between the average performance indicators for restricted and nonrestricted 

companies using the standard t-test. The null hypothesis is the hypothesis that the means for the two groups 

are not different, i.e., the difference between the means is not greater than zero, at a given significance level (we 

used 0.1 or 90% probability). Whenever the null hypothesis is rejected, this indicates that there is an observable 

statistical difference between the means for the two groups. In the table of the t-test results, negative one (-1) 

indicates that the mean for the restricted companies is larger than that for the nonrestricted companies, while the 

positive one (1) indicate the reverse condition. 
55

lease attributes for all categories of leases won by majors or non-major operators. In other 

words, the value of the high bonus bids for leases involving both majors and non-majors as 

partners were significantly higher than for leases involving only the majors or non-majors. 

Data comparing lease market performance attributes for the two sub-periods for majors and nonmajors are presented in Table 3.7. In all cases, the expected value of the average bonus per lease, 

the average money left on the table (MLOT), and that of the high bonus per lease declined 

significantly in the 1990-99 sub-period relative to the mean values in the 1983-89 periods. The 

average decline in bonus exposed per lease as well as the average bonus per lease paid in the 

1990s by both the majors and non-majors alike and for all possible categories of leases were 

more than 50 percent the mean values in the 1980s (See Figure 3.1). The drastic decline could 

be attributed to changes in oil and gas market conditions in terms of oil price stability other 

things being equal. The price of oil was considerably less attractive in the post price crash era in 

1986 than they were in the early 1980s. Figure 3.2 shows the percent change in the high bonus 

bids by structure and conduct between 1980s to the 1990s. 

The average number of bidders per lease won by majors through joint venturing in the 1980s, on 

average, was 2.97 compared to an average of 2.82 in the 1990s, a drop of about five percent. 

When all the leases won by non-majors bidding jointly were evaluated, the average number of 

bidders per lease in the 1980s and the 1990s was 3.84 and 3.62, respectively. This is a decline of 

about 5.73 percent. However, the number of high bids submitted per lease by major oil and gas 

producers through joint ventures decreased very marginally by just 1.32 percent in the 1990s 

from 1.51 in the 1980s. Whereas the number of high bids per lease by non-majors bidding 

jointly increased by 2.54 percent between the 1980s and 1990s. 

Majors on average paid significantly less than non-major operators for noncompetitive solo 

leases in the 1980s, but majors paid significantly more in the 1990s. However, majors bidding 

jointly with other majors paid neither significantly more or less than non-major operators bidding 

jointly for noncompetitive leases in both sub-periods. When leases receiving at least two bids 

were evaluated, statistical tests of the equality of the means suggested no evidence that majors 

bidding jointly with other majors paid significantly more or less than non-major operators 

bidding jointly in both sub-periods. The tests also revealed a statistically significant difference 

between the bonus the majors and non-majors paid in the 1980s for competitive leases when the 

bid type is solo venture, but no statistical difference was evident in the 1990s for the same 

category of leases.

In a statistical sense, the significant differences in lease attributes, between majors as defined in 

this section, are mixed depending on the classification and bid arrangement for leases, and time 

period. The values of lease attributes for majors and non-majors are a lot closer in the 1990s than 

in the 1980s. 
56

Table 3.7 

Comparison of OCS Lease Attributes by Structure, Conduct, and Perioda

 1983-1989 1990-1999 

Variable Structure Conduct Majors 

NonMajors Majors NonMajors 

Bids/lease Competitive Joint 2.46(-) 2.83 2.66 2.82 

 Solo 2.50 2.64 2.67 2.74 

 All 2.50(-) 2.73 2.66 2.77 

 Aggregate Joint 1.51 1.60 1.49(-) 1.82 

 Solo 1.32 1.39 1.38(-) 1.47 

 All 1.34(-) 1.47 1.39(-) 1.57 

Bidders/Lease Noncompetitive Joint 2.07(-) 2.78 2.09(-) 2.45 

 All 1.09(-) 1.66 1.15(-) 1.35 

 Competitive Joint 4.65(-) 6.01 4.53(-) 5.06 

 Solo 3.57 3.88 3.36 3.37 

 All 3.74(-) 4.91 3.58(-) 4.06 

 Aggregate Joint 2.97(-) 3.84 2.82(-) 3.62 

 Solo 1.55 1.68 1.53(-) 1.64 

 All 1.69(-) 2.55 1.73(-) 2.23 

MLOT/Lease, $000 Competitive Joint 2,073 1,647 997 837 

 Solo 1,728(+) 1,172 767(+) 607 

 All 1,780 1,401 810 702 

High Bonus/Lease, $000 Noncompetitive Joint 1,344 1,364 587 676 

 Solo 907(-) 1,224 419(+) 325 

 All 944(-) 1,276 442 410 

 Competitive Joint 3,825 3,389 1,553 1,592 

 Solo 2,957(+) 2,292 1,300 1,137 

 All 3,090 2,822 1,348 1,324 

 Aggregate Joint 2,208 2,026 875 1,088 

 Solo 1,342 1,475 619 545 

 All 1,428(+) 1,696 657 705 

 

a

 Significant differences in lease attributes at 95 percent level for leases involving any major firm are indicated 

accordingly in the table using (+) or (-) superscripted signs. (+) indicates the mean value for majors is significantly 

larger and (-) indicates the value is significantly smaller than the mean value for leases involving non-majors only. 
57

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

TotalBonus M LOT High Bonus

Percent Decl

Majors Nmajors

Figure 3.1: Percent Decline in Aggregate Lease Market Performance Attributes 

 Between the 1980s and 1990s. 

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Joint Solo Joint Solo

Noncom petitive Com petitive

Percent Decl

Majors Nonmajors

Figure 3.2: Percent Decline in Average High Bonus Bids by Lease Structure and Conduct 

 Between the 1980s and 1990s. 
58

3.3 The Restricted Joint Bidders and OCS Lease Market 

The principal MMS policy or regulation intended to promote competition (or inhibit collusion) in 

the lease market is the Restricted Joint Bidders List. It is compiled and published twice a year 

and applies to all lease sales held during the subsequent six-month period or until the next list is 

issued. The Energy Policy Conservation Act that was enacted in 1976 requires the list. Relevant 

provisions in the Act ban companies whose global petroleum production is in excess of 1.6 

million barrels of oil equivalent per day from submitting bids jointly in federal OCS lease sales 

(Moody and Kruvant, 1988; Sullivan et al., 1980).14 

The list of restricted joint bidders is published in the U.S. Federal Register. Table 3.8 shows the 

companies listed since 1983.15 The table indicates the principal firm and the Roman numeral 

assigned by MMS when the list was published. Mergers or acquisitions among firms listed are 

indicated by the use of the same numeral. Thus, after 1999, the merger between Mobil and 

Exxon, who had been numerals III and I respectively, is indicated by assigning Numeral I to both 

firms. Within the parentheses in each cell in the table is the percentage share of Gulf of Mexico 

production measured in oil equivalent for the year in question. 

Using global production as a criteria: Comparing the shares of Gulf production in Table 3.8

suggests that using global production to determine the restricted bidders results in a different list 

than if production in the Gulf of Mexico were to be used. Texaco, for example, dropped off the 

list in 1989 apparently because its global production fell below the 1.6 million barrel floor. 

However in the Gulf of Mexico, Texaco’s production was three or four times as large as British 

Petroleum’s, which appeared on the list beginning in 1988. Similarly, Conoco, which is the 

fourth largest cumulative producer in the Gulf (see Figure 2.1 above), and was a top-5 producer 

during the 1970s and 1980s, never has been listed as a restricted bidder. 

Traditional practice and theory in dealing with anti-trust or monopolistic concerns is to apply the 

analysis to the relevant market or region in which the competition takes place. As a recent study 

of mergers in the refining industry said, “As its decision to approve the Phillips Tosco merger 

indicates, the FTC [Federal Trade Commission] focuses on conditions in specific geographic 

markets, not national concentration statistics. Any assessment of the impact of recent mergers on 

competition must also consider regional impacts rather than simply changes in national 

aggregates.”16

 

14 The Act, apparently, does not prohibit firms on list from joint operations, only joint bidding. If a firm on the list 

wants to acquire a share of a lease which is owned or partly owned by another firm also on the list, it makes a 

request to MMS to do so which MMS forwards to the U.S. Department of Justice for review. According to MMS 

personnel, such requests are neither very frequent nor extremely rare occurrences, but those that we talked with did 

not recall any such requests being denied by MMS or the Department of Justice. 

15 Subsidiaries registered under different names, usually identifiable, are listed in the notices in the Federal Register.

The listing in the table includes all those listed. 

16 Petroleum Industry Research Foundation, Refining Concentration and Industry Dynamics, NY: 2002, p.3. 
59

Table 3.8 

 List of Restricted Joint Bidders 1983 to 2001: 

Category and Share of Total Gulf of Mexico Production 

 Firm/ 

Period 

Chevron Exxon Mobil Shell BP Amoco Vastar Texaco 

5/1/01 V I I II III III III 

11/1/00 (9.9) I (7.0) I II (21.2) III (10.5) III III (3.4) 

5/1/00 I I II III III 

11/1/99 (9.7) I (4.1) III (2.0) II (19.8) IV (7.8) IV (3.2) (3.7) 

5/1/99 I III II IV 

11/1/98 (9.9) I (4.4) III (3.2) II (18.0) IV (3.9) (3.1) (2.8) (4.2) 

5/1/98 I III II IV 

11/1/97 (10.2) I (5.2) III (3.8) II (15.1) IV (2.2) (3.0) (2.5) (4.5) 

5/1/97 I III II IV 

11/1/96 (10.0) I (5.3) III (4.7) II (14.0) IV (2.0) (3.1) (2.8) (4.9) 

5/1/96 I III II IV 

11/1/95 (10.7) I (5.3) II (5.5) II (13.3) IV (1.7) (3.1) (3.1) (4.6) 

5/1/95 I III II IV 

11/1/94 I (12.2) II (5.9) IV (6.1) III (11.2) V (1.4) (3.3) (2.5) (4.6) 

5/1/94 I II IV III V 

11/1/93 I (13.2) II (5.1) IV (6.5) III (10.1) V (1.5) (3.6) (4.8) 

5/1/93 I II IV III V 

11/1/92 I (14.5) II (4.8) IV (6.8) III (10.0) V (1.3) (3.2) (4.7) 

5/1/92 I II IV III V 

11/1/91 I (14.6) II (5.2) IV (6.9) III (9.0) V (1.3) (3.2) (5.0) 

5/1/91 I II IV III V 

11/1/90 I (16.0) II (5.8) IV (5.6) III (8.2) V (1.9) (3.3) (4.9) 

5/1/90 I II IV III V 

11/1/89 I (13.6) II (6.9) IV (7.8) III (9.4) V (1.2) (3.8) (4.7) 

5/1/89 I II IV III V 

11/1/88 I (10.9) II (7.0) V (7.0) IV (10.4) VI (0.9) (3.8) III (5.3) 

5/1/88 I II V IV III 

11/1/87 I (11.2) II (6.8) V (6.3) IV (12.2) (0.6) (3.3) III (4.8) 

5/1/87 I II V IV III 

11/1/86 I (11.5) II (7.6) V (4.8) IV (13.1) (0.3) (3.1) III (5.8) 

5/1/86 I II V IV III 

11/1/85 I (11.6) II (7.6) V (4.2) IV (12.2) (0.3) (2.7) III (7.0) 

5/1/85 I II III V IV 

11/1/84 I (11.8) II (7.2) III (4.5) V (11.5) (0.1) (2.5) IV (6.5) 

5/1/84 I II III V IV 

11/1/83 I (12.3) II (6.7) III (5.0) V (12.7) (2.4) IV (5.6) 

5/1/83 I II III V IV 
60

Chevron was not listed between 1995 and 2001 despite the fact that it had been the largest Gulf 

producer in 1995 and was the second largest producer during the 1995 to 2000 period. Company 

reports indicate that Chevron’s global production exceeded the 1.6 million barrel floor and its 

absence from the list appears to be the result of a reporting or clerical mix-up. Further, not being 

listed as a restricted bidder does not appear to have affected its joint-bidding behavior. Only one 

joint-bid (made with BP in 1996), appears to have been submitted, which would have not been 

permitted were Chevron listed on the Restricted Bidders List. However, the fact that the mix-up 

persisted for five years suggests the list has not played a major role in lease sales or bidding for 

either the firms or the agency. 

Reliance on joint bidding by restricted bidders: The potential relative importance of the 

Restricted Bidders List can be measured by comparing the number of high bids made and 

bonuses paid by those on the list and the number of high joint bids and bonuses paid to the 

respective totals for all firms over the period.17 

• Of the 13,946 leases awarded between 1983 and 1999, 6,554 or about 47 percent went to 

those firms on the Restricted Bidders List. Of those leases, 1,915 or about 14 percent 

involved joint bids with other firms. 

• The bonus paid for leases won by firms on the restricted list bidding jointly with others 

was $1.955 billion, which was about 12 percent of the $16,721 billion of total bonuses 

paid for leases. 

Total bonuses paid by restricted bidders amounted to $5,982 billion so joint bids accounted for 

about 33 percent of their bonus payments. 

It is speculative to estimate how many more joint bids and, potentially, fewer competitive bids 

would have resulted if the Restricted Bidders List were not in force; however, clearly the 

restricted bidders are a very important part of the lease market and their use of joint bids even 

under the joint bidder restrictions is not negligible. 

Mergers among restricted bidders: Figure 3.3 illustrates the year-to-year changes in the share of 

Gulf of Mexico production for the firms on the Restricted Bidders List from 1982 - 2001.

The effects of the Exxon/Mobil and BP/Amoco/ARCO (Vastar) mergers can be clearly seen. 

• Before the merger, BP’s share had been growing steadily, but after its merger with 

Amoco it jumped from 5.2 to 7.8 percent in 1999. 

 

• When BP acquired ARCO and its E&P subsidiary Vastar, BP’s share increased to 10.5 

percent making it the second largest producer in the Gulf. 

• Exxon and Mobil both experienced declining production shares prior to their merger and 

the post merger increase for Exxon/Mobil was more modest—4.1 to 7 percent. 

 

17 For our analysis and exposition we include all firms that were listed as restricted bidders during the period even 

though some such as Amoco, Chevron, and Texaco were not on the list for the all of the years in period. 
61

0.0

10.0

20.0

30.0

40.0

50.0

60.0

1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

Shell

Vastar

Am oco

BP

M obil

Exxon

Chevron

Texaco

 Figure 3.3: Share of GOM Production by MMS’ Restricted Joint Bidders, 1982-2000. 

0

100

200

300

400

500

600

700

800

1983 1985 1987 1989 1991 1993 1995 1997 1999

MMBOE

Shell

Vastar

Amoco

BP

Mobil

Exxon

Texaco

Chevron

 Figure 3.4: Total Production for MMS’ Restricted Bidders in the GOM, 1983-2000. 
62

Although changes in production shares of this magnitude warrant analysis, it is important to keep 

an accurate sense of scale. In a relative sense, changes in the shares of BP/Amoco/Vastar and 

Exxon/Mobil are dominated by the doubling of Shell’s share from about 10 to 20 percent of Gulf 

production over the period, also illustrated in Figure 3.3. As its share of Gulf production nearly 

doubled from 11.5 to 21.2 percent, its absolute production increased from 120.796 to 292.129 

million barrels. Without the mergers, the production levels of the other restricted bidders were 

stable or were declining. Figure 3.3 presents the trend in total petroleum production for MMS’ 

restricted joint bidders from 1983-1999. 

Thus, both of the major mergers observable resulted in much less concentration of production 

than did the payoff from non-merger-participant Shell’s very successful investment in the deep 

Gulf. 

Bidding Patterns and Trends: Looking at the behavior of the restricted joint bidders individually 

also helps characterize the lease market. Table 3.9 shows the number and the value of the joint 

bids won by the restricted bidders and other Gulf of Mexico OCS producers with whom they 

submitted ten or more bids during the study period. 

• Texaco was the most frequent partner in joint bids, participating in 225 winning bids 

during the period. 

• Conoco (207), Amoco (197), Chevron (148) and Unocal (132) were the other members of 

the five most frequent joint winning bidders. 

• Conoco was the only firm to partner with each of the restricted bidders. 

• Amoco submitted joint winning bids with all of the restricted bidders except their 

eventual merger mate BP, and Elf, although only participating in 50 winning bids, 

partnered with every firm except Texaco. 

• Several firms, such as AGIP, Apache, Ensearch, Marathon and Phillips participated in 

joint bidding primarily with a single firm. 

Figure 3.5 illustrates the major differences in the relative use of joint bids among the firms 

appearing on the Restricted Bidders List. 

• BP, Exxon and Shell use joint bids less extensively than the average. 

o Only 12.5 percent of BP’s high bids were made jointly and bonuses paid with 

those bids amounted to less than 10 percent of BP’s total. 

o For Exxon, the comparable percentages were about 25 percent for high bids and 

19 percent for bonuses paid. 

o Of Shell’s market-leading 1,824 high bids only about 15 percent were made 

jointly and bonuses paid with those bids amounted to about 22 percent of the 

firm’s total. 
63

Table 3.9 

Number of High Joint Bids by Firms on the Restricted 

Bidders List and Other Firms: 1983 to 1999 

Amoco BP Chevron Exxon Mobil Shell Texaco Vastar 

Shell-71 BHP-58 Texaco-136 Amoco-55 Phillips-69 FL Explor. 

-84 

Chevron-136 Texaco17 

Exxon-55 Conoco12 BHP-28 Conoco48 Sohio-35 Flour-84 Unocal-92 Elf-15 

Tenneco-45 Samedan-25 Sohio-20 Amoco-32 Conoco-79 Uni.Explor-55 

Texaco-32 Unocal-20 Getty-18 KerrMcGee-27 Apache-72 Pogo-42 

Mobil-32 Kerr-McGee-18 Amerada18 Ensearch18 Amoco-71 Amoco-32 

Conoco-19 Bechtel-10 Elf-16 Agip-13 Texaco-29 Conoco-31 

Sun-18 Aminoil12 NW 

Mutual-11 

 Marathon-29 

Pennzoil-18 Texaco-11 Shell-29 

Brit.-

Borneo-13 

 Tricentral-27 

Elf-10 Brit.-Borneo20 

 Samedan-17 

 Vastar-17 

 Mobil-11 

 Getty-10 

 

Note: From lease sale summaries, only firms with ten or more joint bids with a restricted bidder 

 reported. 

31

25

35

15 12

42

33

55

29 33

19 22

10

25

60

33 31

65

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Chevron

Exxon

Mobil

Shell

BP

Amoco

Vastar

Texaco

Total 

Bids Bonuses

Figure 3.5: Percent of JV High Bids and JV Bonuses for MMS’ Restricted 

 Bidders, 1983-2000. 
64

• Both Chevron and Mobil were close to the average for the group in terms of the 

proportion of the their bids that were made jointly, but for Mobil bonuses paid with joint 

bids amounted to 66 percent of the total bonuses they paid, higher than any other firm on 

the list. 

• Amoco and Texaco are of special interest because they were restricted bidders for 

relatively short increments of the 1983 to 1999 period--one year for Amoco and six for 

Texaco. When not on the list they were free to, and did, bid jointly with the firms listed. 

Both firms were large and significant participants in the lease market throughout the 

study period. 

o Amoco was second only to Texaco with about 42 percent of its high bids made 

jointly, but was below the average for the group in terms of bonuses paid at 31 

percent. 

o Texaco led the group in the proportion of joint bids with about 56 percent and was 

second to Mobil in bonuses paid at 60 percent. 

o Although an active joint bidder, Amoco had partnered neither with BP nor Vastar 

prior to their merger. 

• Texaco and Chevron, in contrast to BP and Amoco, had an extensive record of joint 

bidding that closely preceded their merger. 

o Texaco, prior to their merger, cooperated with Chevron in 163 joint bids, of which 

136 were winning bids. This amounted to about 28 percent of Texaco’s total of 

493 joint winning bids. 

o Further, all of the joint Texaco/Chevron bids took place in the last four years of 

the study period with 158 of the 163 bids submitted during the three sales held in 

1996 and 1997. 

Many considerations of technology, strategy and corporate planning, globally as well as in the 

Gulf of Mexico, may have influenced the Chevron/Texaco merger decision, but this episode of 

collaboration is notable. 

Figure 3.6 shows the location of Texaco/Chevron’s joint bids for the lease sales held in the 1996 

through 1999 period. It suggests that the bids made at the 1996-1997 sales were distinct 

initiatives. Most of the bids in 1998 and 1999, in contrast, appear to be efforts to fill-in or expand 

lease holdings from earlier sales. 

The two sales in 1996 and the sale in 1997 were the first wave of the “leasing-up” of the 

Deepwater Gulf. In 1995 1,012 leases in water depth of 800 meters or more were in effect. In 

1996 that number increased to 1,491 and in 1997 to 3,002—a nearly three-fold increase. Further, 

by 2001, deepwater-leases had only increased to 3,424 (Baud et al., 2002). 

The desire to broaden their participation in this “leasing-up” of the deepwater Gulf of Mexico, 

may have been a causal factor in Chevron/Texaco joint-bidding offensive. There may also have 

been an element of “catch-up” or “keep-up” involved as well. 

Lease Records and Attributes Table 3.10 presents lease attributes summarizing the group effects 

of MMS’ joint bidding restrictions on lease market structure and performance from 1983-1999. 
65

The columns show the mean values or magnitudes for those leases in which the winning bids 

were submitted by: 1) restricted bidders bidding solo or joint with other non restricted bidders 

and 2) non restricted bidders submitting solo bids or joint bids with other non restricted bidders 

in two sub-periods 1983-1989 and 1990-1999, and the entire period, 1983-1999. 

The degree of competition measured in terms of bid per lease or bidders per lease as reflected in 

Table 3.10 was less in the 1990s for leases in which restricted bidders were involved than when 

nonrestricted bidders were involved. Also in the 1990s, the value of high bonus per lease was 

significantly less for leases won by restricted bidders than for those won by non restricted 

bidders, irrespective of whether the bidding structure is competitive or non competitive. And for 

all categories of lease structure and bidding arrangement, there is no statistical evidence to 

suggest that average money left on the table (MLOT) per lease involving restricted bidders is 

different from MLOT for not involving the other group. 
66

 Figure 3.6: Location of Texaco/Chevron’s Joint Bids, 1996-1999. 
67

Table 3.10 

Lease Attributes for Restricted and Unrestricted Biddersa

 

a

 Significant differences in lease attributes at 95 percent level for leases involving any restricted bidder are indicated 

in the table using (+) or (-) superscripted signs. A (+) indicates the mean value for majors is significantly larger and 

a (-) that the value is significantly smaller than the mean value for leases involving non-majors only. 

1983-1989 1990-1999 1983-1999 

Variable Structure Conduct Restricted 

NonRestricted Restricted NonRestricted Restricted NonRestricted 

Joint 2.86 2.76 2.49(-) 2.80 2.77 2.79 

Solo 2.40(-) 2.64 2.53(-) 2.75 2.47(-) Competitive 2.72 

All 2.59 2.70 2.52(-) 2.77 2.56(-) 2.75 

Joint 1.62 1.60 1.26(-) 1.72 1.48(-) 1.67 

Solo 1.25(-) 1.41 1.29(-) 1.46 1.27(-) 1.45 

Bids/Lease 

Aggregate 

All 1.35(-) 1.49 1.29(-) 1.54 1.32(-) 1.53 

Joint 2.60(-) 2.70 2.09(-) 2.31 2.38(-) 2.49 Noncompetitive All 1.38(-) 1.68 1.20(-) 1.34 1.29(-) 1.46 

Joint 6.24 6.13 4.06(-) 4.90 5.70 5.38 

Solo 3.41(-) 3.84 3.08(-) 3.45 3.24(-) Competitive 3.56 

All 4.57 4.99 3.25(-) 4.02 3.97(-) 4.35 

Joint 3.80 3.87 2.44(-) 3.35 3.28(-) 3.58 

Solo 1.44(-) 1.72 1.40(-) 1.65 1.42(-) 1.67 

Bidders/Lease 

Aggregate 

All 2.09(-) 2.64 1.58(-) 2.16 1.84(-) 2.33 

Joint 4,705(+) 2,316 936 923 3,773(+) 1,465 

Solo 1,650 1,483 614 726 1,097 946 MLOT/Lease, 

$000 Competitive 

All 2,897(+) 1,903 667 804 1,899(+) 1,170 

Joint 2,001 1,685 332(-) 691 1,280 1,140 

Solo 807(-) Noncompetitive 1,187 385 385 589 629 

All 1,090(-) 1,385 376(-) 464 733 786 

Joint 7,567(+) 4,301 1,357 1,646 6,032(+) 2,679 

Competitive Solo 2,763 2,729 1,090 1,274 1,871 1,697 

All 4,725(+) 3,522 1,134(-) 1,421 3,117(+) 2,121 

Joint 3,844(+) 2,579 513(-) 1,074 2,572(+) 1,719 

Solo 1,164(-) 1,575 520(-) 620 828 907 

High 

Bonus/Lease, 

$000 

Aggregate 

All 1,901 2,005 519(-) 757 1,224 1,187 
68

3.4 Conclusions 

 

Neither aggregate measures used by economist to analyze competitive and noncompetitive 

market and industry structures, nor patterns of joint bidding and cooperation among firms active 

in the offshore Gulf of Mexico indicate or suggest a deficiency in the competitiveness of the 

lease sales held by MMS. Whether the measures are applied to the leases acquired at the sales, or 

to production by firms bidding for leases, they indicate a competitive industry bidding for leases 

in a competitive market. 

The effects of mergers are not reflected in the trends observable in these measures over time. 

Although the more recent mega-mergers are only incorporated in the last two or three years of 

the data series analyzed, their effects will not change the quantitative measures of the 

competitiveness of the industry or lease market. The relative share of BP has increased 

dramatically as a result of the merger of BP, Amoco, Arco/Vastar, but by far the largest change 

in market share in the industry was the result of the growth of production by non-merger 

participant Shell. Other merger participants were experiencing stable or declining share of Gulf 

production, and their mergers did not affect overall competitiveness. 

A comparison of major and non-major companies as defined by EIA criteria did not indicate any 

differences between the groups or their response to market structure. Similarly, an analysis of 

patterns of bidding by those firms on MMS’ Restricted Bidders List does not indicate 

noncompetitive behavior. Chevron and Texaco submitted 136 joint winning bids in the four 

years immediately preceding their merger, but other instances of concentrated joint bidding 

among other firms did not lead to mergers. Further, the years in question were peak years in the 

leasing of the deepwater Gulf and joint bidding may have been a strategy to spread investment 

budgets and avoid being left out of the rush to lease. In the aggregate, however, joint bid 

restriction by some operators, seem to have reduced bidding effectiveness, especially, in the 

1990s. 
69

4. Econometric Analysis of the Market for OCS Leases 

4.1 Model Specification 

In this section we make use of two simple econometric-modeling frameworks to explore the 

patterns and trends in the value of OCS leases. 

The first model estimates the relationship between the value of leases and postulated explanatory 

factors by using each of the merger and acquisition (M&A) experience categories (Groups A, B, 

and C, discussed previously) to estimate separate regression coefficients measuring their 

influence on bidding. Statistical tests then are applied to see which factors consistently influence 

the value of high bids on the three groups of leases. 

The second model or approach uses a “major/non-major” or “restricted bidder/unrestricted 

bidder” delineation as a proxy for firm size, with variations of the type of bid and existence of 

bidding competition to form eight separate interactive variables. These are combined with 

merger and acquisition history or experience and other explanatory variables in a single 

regression equation. 

Both approaches are derivative of the same basic conceptual framework, which specifies that the 

value of the high or winning bonus bid (HB) is a function of three sets of factors: economics, 

structure, and conduct. This specification has been used successfully several times in the past to 

analyze lease auction market performance (Dougher, 1987; Giley and Karels, 1981; Markham, 

1970; Porter, 1995). 

Symbolically, the general specification that has been used takes the form (Dougher, 1987; Giley 

and Karels, 1981; Markham, 1970): 

 

HB f V S C Z = (,,,) (1) 

 

Where: 

• HB = the magnitude of the high bonus bid for a lease. This is the dependent variable in 

our model. It is the highest bid received and accepted by MMS for the right to develop 

the lease. 

• V represents the set of factors, which capture the expected gross value of the lease, herein 

referred to as “the economic factor.” Some proxies that have been used in the past 

include: 

o Crude oil price index or current crude oil price is used as proxy for expected value 

of the lease in the absence of the present value of actual and future production 

data. 

o The number of bidders on a lease is used as a proxy for the perceived quality of 

the lease. 
70

o The number of wells drilled within 24 months has also been used to symbolize the 

perceived quality of leases offered.18 

• S defines factors that characterize the degree of competition in the leasing program. 

o A proxy for the intensity of competition is the number of bids or the number of 

bidders. It is expected that greater competition as reflected in more bids per lease 

is positively associated with the expected value of the high bonus bid. 

• C is another set of factors that serve as proxies for the behavior of bidders in terms of the 

types of bids--joint ventures or solo, and whether the bidding was competitive, i.e. 

whether more than one bid for the lease was submitted. 

o It is expected that if the incidence of joint ventures is anticompetitive, then its 

effect on the high bonus bid will be negative. If it is not negative, then the null 

hypothesis that joint venturing is anticompetitive can be rejected. The economic 

implication would be that, joint venturing is undertaken for information-, 

expertise-, or risk-sharing purposes rather than as an attempt at acquiring and 

applying oligopsonistic market power in the lease auctions. 

• Z represents other variables postulated to be relevant to the value of the lease such as 

water depth (deep water or the shelf), the size and experience of the firm, as well as timeassociated events such as the Deep Water Royalty Relief Act the U.S. Congress passed in 

1995, or the collapsed of the world oil price in 1986. 

o For deepwater leases, two factors are at work. On the one hand, other things being 

equal, one might expect the magnitude of the high bonus bid to decrease with 

water depth because of higher development costs. However, technical progress 

has significantly reduced finding and development costs. Along with the 

perception of abundant reserves, and very high production rates in deep water, 

this may compensate for higher development costs and lead to higher bids for 

deepwater reservoirs. 

o There are reasons to expect the large firms that pioneered offshore production and 

have long experience in developing and producing offshore have some advantages 

in the bidding for OCS leases. This hypothesis will be examined by including a 

dummy variable to proxy the size of the firm that wins and develops the lease. If 

a lease is awarded to a firm or firms ranked among the top four in total production 

within the previous ten years, the dummy variable has a value of one; otherwise, 

the dummy variable has a value of zero. 

o The historical content of lease offerings may also affect the value of the high 

bonus bid. For example, the collapse of the global crude oil prices in 1986 was a 

catastrophic economic event for those in the industry that may have negatively 

affected the value of high bids in subsequent years. Similarly, the passage of the 

Deepwater Royalty Relief Acts in by the U.S. Congress in 1999 may have had a 

 

18 Although we have tried to use previous studies as guides, for some variables, such as this one, the data either are 

not available or making them consistent with our data is beyond the scope of the study. 
71

positive effect not only on net development costs but also on future expectations 

about the returns from developing offshore leases. 

An estimable form of the functional specification in equation (1), assuming a log-linear 

relationship between the high bonus bid and its determinants, takes the form: 

ln HB NBD CPP DJ DC DZ DD DY i i i i i i i i iy

yy

it =+ + + + + + + + αα α α α α α α ε 01 2 3 4 5 6 ∑

 (2) 

Where: 

NBD is the number of bids submitted per lease. It measures the intensity of competition 

for a lease. A positive relationship with the value of high bid is anticipated suggesting 

that the high bid value will increase as the number of bids increases, ceteris paribus. 

CPP is crude petroleum price index with 1982 as the base year. It represents a measure 

of the perceptions or expectations prevailing in the economic environment within which 

the bidding process operated. 

DJ is a dummy variable included to capture the effect of the type of high bid (whether it 

was a solo or a joint bid) on the value of high bid. DJ equals one if the lease was won 

through joint bid arrangement, or zero if it was won by solo bid. The inclusion of DJ in 

the model will facilitate the testing of a simple null hypothesis of whether joint bidding 

reflects any anticompetitive behavior in the bidding process from 1983-1999. 

DZ is the firm size dummy variable that has the value of one if a solo bidder which 

ranked among the top four firms in cumulative production submitted the high bid to win 

the lease or if any of the bidders in a joint bid that won the lease was among the top four 

firms. Again the top four ranking is as defined earlier by total production in the previous 

ten years. Including a firm size variable enables us to reevaluate two alternative 

hypotheses that were postulated and tested in the early 1980s (Erickson and Spann, 1974; 

Giley and Karels, 1981; Markham, 1970; Mead et al., 1985). The null hypothesis is that 

large firms possess market power and pay less than smaller firms for leases of similar 

perceived quality. A significant and negative coefficient of the firm size dummy in our 

estimation of equation (2) will support this proposition. If, however, the coefficient is 

positive and significant, then the alternative hypothesis that large firms have financial or 

informational advantages that make it profitable to submit higher bids, all other factors 

accounted for cannot be rejected. 

DD is the dummy variable for average water depth. If the average water depth is greater 

than 200 meters, the lease is classified as a deepwater for the purpose of this analysis.

19

 

DD is defined such that the dummy variable is equal to one if deeper than 200 meters and 

zero otherwise. Current technical advances tend to favor higher expectations for returns 

from deepwater leases as technical progress continues to drive down the cost of 

 

19 MMS’s deepwater classification for ecological purposes begins at 400 meters. Other depths such as 200 meters 

and 800 meters are used for various regulatory purposes (MMS WebPages, January 1999). 
72

exploration, completion, and production. Thus as drilling progresses into deepwater 

frontiers, we may experience higher bids. 

DY is the dummy variable for the time or historical dimension. As indicated previously, 

this dummy can help to identify in specific terms the relative impact over time of a 

particular event. For example, the Deep Water Royalty Relief Act, passed in 1995 has led 

to, or is associated with, the subsequent, significant, increase in deepwater activity in the 

OCS. What happened to the value of high bids beyond the passage of this Act can be 

inferred from the sign of the coefficient of dummy variable capturing the time effect. 

Table 4.1 provides the definitions of the regression variables and hypothesized signs of each 

parameter estimate in equation 2. 

4.2 Model Estimation and Regression Results 

To estimate equation (2), a consistent database on OCS leases from published and unpublished 

data sources for the period 1983-1999 was constructed. The database is limited to this period to 

cover only the period when the area-wide-leasing policy was operational. The lease data 

attributes were arranged for each lease and by category of leases as previously defined. 

The iterative seemingly unrelated regression (SUR) procedure in the QMS Eviews program 

(version 4.0) was applied to the data to estimate a log-linear specification of equation (1). SUR is 

employed in this study because there is a possibility of contemporaneous correlation among 

disturbances across different individual observations. The SUR estimator is different from OLS 

(ordinary least squares). Using SUR is advantageous because it is more efficient than OLS, and 

the gain in efficiency is positively related to the extent of correlation of the disturbances. 

The log-linear specification yields parameter estimates that are interpretable as the relative 

change in the dependent variable with respect to an absolute change in the independent variable. 

Table 4.2 presents the results of the SUR estimation of the linearized function underlying 

equation (1) using panel data. In general, nearly all the independent variables are statistically 

significant and have the expected signs. 

The model is consistent with or “explains” about 50 percent of the expected variation in the 

relative value of high bonus bids as indicated by the R2 statistics. Conversely, by implication, 

there are other variables not included in this model that may “explain” another 50 percent of the 

variation expected in the relative magnitude of high bonus bids. However, given that all the 

included variables are significant and the other regression statistics do not indicate any 

significant problem with the model’s theoretical specification, the inference seems reasonable 

that this model captures some of the important determining factors affecting high bonus bids in 

the Gulf of Mexico OCS. 
73

Table 4.1 

 Variable Definitions and Hypothesized Signs of the Parameters 

Determinants Variable Name & Symbol Parameter Symbol 

Expected 

Sign 

 Intercepts, C α 0i

Intensity of Competition Number of Bids or Bidders, NBD α1i + 

Perceived Value of the 

Lease Crude Oil Price Index, CPP α 2i + 

Bidding Arrangement Joint Ventures Dummy, DJ α 3i + 

Bidding Structure Competition Dummy, DC α 4i −

Lease Location Deep Water Dummy, DD α 5i + 

Firm Size Top 4 Firms’ Dummy, DZ α 6i + 

Post Royalty Relief Act Lease Dynamics 1996-1999 α 7i + 

Post 1986 Price Effect Lease Dynamics 1987-1999 α 8i −

Table 4.2 

Estimated Model of the Value of High Bonus Bids on the Gulf OCS, 1983-1999 

Determinants 

i=1 

Group A 

i=2 

Group B 

i=3 

Group C 

Expected 

Sign 

Fixed Effects 13.6751* 13.1264* 12.1700* 

Intensity of Competition, NBD 0.3280* 0.3712* 0.3451* + 

Economic Environment, CPP 0.0001 0.0027 0.0189* + 

Bidding Arrangement, DJ 0.2603* 0.1826* -0.0038 + 

Bidding Structure, DC 0.3711* 0.4223* 0.3836* + 

Lease Location, DD 0.1083* 0.0943*** 0.1374** + 

Firm Size or Experience, DZ 0.0131 -0.2426* 0.3848* −

Post Royalty Relief Act, DY (95-99) 0.0840* 0.0967*** -0.1912 + 

Post 1986 Price Effect, DY (86-99) -1.6101* -1.3312* -1.0549* −

i=1, 2, 3 for Group A, B, C leases, respectively and *, **, *** Denotes significance at the 0.01, 0.05, 0.l0 levels, 

respectively. 

The number of bids (NBD), which measures the degree or intensity of competition, has a positive 

and significant effect on the value of high bids according to our regression results. This effect, in 

addition to the positive coefficient on DJ, provides significant evidence of competitiveness in the 

OCS leasing program. 
74

As stated earlier the expected gross value of lease may be captured by the expected price of 

crude petroleum (as measured in crude oil price index). The sign of the estimated coefficients of 

the proxy for economic effect in the regression results although positive, is statistically 

insignificant for categories A and B. 

Any inference that economic conditions or expectations are not related to the bidding for leases 

should be tempered by the relationship measured by the variable DY. The dummy variable for 

time, DY, shows all yearly dummies except those defined for the period from 1983 to 1985 and 

indicates a different pattern of effect on the value of high bonus bids than the periods after 1986 

(the base year). Specifically, the results imply that bonus bids after 1986 tends to be lower than 

in the pre-1986 magnitude, taking all other influences captured in the regression equation into 

consideration. This is certainly consistent with the conventional wisdom among oil and gas 

industry participants and analysts who believe strongly that firms became much more cautious 

about long-term investment decisions after the collapse of the oil market in 1986. 

The sign on the dummy variable DJ capturing the type of the high bid (whether joint or solo) is 

positive and significant. Thus, the null hypothesis that joint bidding depresses high bids in lease 

sales is rejected. The results suggest that the incidence of joint bidding during the period of our 

analysis (1983-1999) shows no anti-competitive behavior or pattern during OCS lease sales.

The lease location or water depth variable, DD, in our regression indicates whether the fact that 

the lease is 200 meters or more below the surface affects the value of the high bid. It might be 

expected that the sign on this variable would be positive--deepwater leases increase development 

costs and lead to lower bids, ceteris paribus. On the other hand, all other things still being equal, 

if success rates and the size of discoveries are higher in deepwater than on the shelf, firms may 

bid higher for deepwater leases. The result here is consistent with the latter case, that is, the 

greater the water depth, the higher the bonus bids. 

The coefficients for the firm-size variable DZ are mixed among the three groups. Although the 

DZ variable, which measures the relationship between large firms and high bids is statistically 

significant for Groups B and C, it is negative for Group B and positive for Group C. Further it is 

not significant for Group A. This indicates that larger firms with merger and acquisition histories 

and experience tend to make lower winning bids in comparison to other firms with similar 

histories.

In general, the results in Table 4.2 confirm the following expectations: 

• Greater competition is associated with higher mean values of high bonus bids. As 

competition increases, the relative change in the mean value of high bonus bids increases. 

• The economic environment measured in terms of the rising crude petroleum prices does 

not lead to a significantly higher mean value of high bids for most leases, as most 

probably would have expected. However, another measure of perceptions of the 

economic environment, the carry-over effects of the collapse of crude oil prices in 1986 

which brought about major strategic and structural changes in the global E&P industry, 

does consistently indicate a significant reduction in the mean value of high bonus bids for 
75

leases in comparison to the pre-1986 mean value of high bids. A more descriptively 

accurate way to view this relationship may be that the run-up in world oil prices in the 

beginning of the 1980s has an aberration based on unrealistic expectations. After the 

price collapse, expectations resumed more realistic and modest levels. 

• The mean value of high bonus bids for leases with at least two bids (competitive) is 

greater than the mean value of single-bid leases (noncompetitive). 

• The mean value of high bonus bids for deepwater leases is significantly higher than 

shallow water leases. 

• Joint venturing is associated significantly with higher winning bonus bids than is the case 

for bids by solo ventures. 

4.3 Effects of Mergers and Acquisitions on the Value of OCS Leases 

To decipher the potential effects of merger and acquisitions on the mean value of high bonus 

bids through its determinants--intensity of competition, extent of competition, type of bid, lease 

location/water depth, economics, and structural changes in the E&P industry--a Wald coefficient 

restriction test was applied to the regression results in Table 4.2. 

A Wald test ascertains if the coefficients measuring the relationship between the explanatory 

variables and the average high bid for the three groups are significantly different from one 

another. The objective of employing the test here is to ascertain which influences on high bids 

seem to be affected by M&A experience and which do not. 

Subsequently, equation (2) was estimated again, using the same data set but, with restrictions 

imposed on the parameters measuring the relationship of the change in high bid value to changes 

the variables that the Wald test indicated were not significantly different from each other for the 

three groups—specifically, intensity of competition, extent of competition, and lease 

location/water depth. Symbolically, the new regression equation specified that: 

α1i = α1 for i = 1, 2, 3, α 4i = α4 for i = 1, 2, 3, andα 5i = α5 for i = 1, 2, 3. 

The restricted regression model results are presented in Table 4.3. It should be noted that the 

signs of the estimated model parameters did not change after imposing the Wald restrictions. 

Neither were there any changes with respect to the results of our tests of significance of the 

estimates nor the adjusted R2

. 

 

The following observations or statistical inferences are plausible concerning the overall effects of 

mergers and acquisitions on the market for OCS oil and gas leases using the results reported in 

Table 4.3: 
76

Table 4.3 

Estimated Model of the Value of High Bonus Bids with Coefficient Restrictions 

Determinants 

i=1 

Group A 

i=2 

Group B 

i=3 

Group C Expected 

Sign 

Fixed Effects 13.6714* 13.1628* 12.1758* 

Intensity of Competition, NBD 0.3323* 0.3323* 0.3323* + 

Economic Environment, CPP 0.0000 0.0027 0.0192* + 

Bidding Type, DJ 0.2589* 0.1981* 0.0028 + 

Bidding Competition, DC 0.3771* 0.3771* 0.3771* + 

Lease Location, DD 0.1093* 0.1093* 0.1093* + 

Firm Size, DZ 0.0138 -0.2409* 0.3801* −

Post Royalty Relief Act, DY (95-99) 0.0831* 0.0990 -0.1810 + 

Post 1986 Price Effect, DY (86-99) -1.6115* -1.3233* -1.0412* −

• Mergers and acquisitions, as indicated by differences between the coefficients for Group 

B and Groups A and C, do not significantly alter the overall expectation that intensity of 

competition for OCS leases leads to a higher mean value of winning bonus bids, ceteris 

paribus. In other words, the relative change in the mean value of high bonus bids per unit 

change in the number of bids per lease is not significantly affected when firms that have 

been involved in M&A participated in the high bonus bids. The intensity of competition 

for a lease has a positive, strong, and significant effect on the magnitude of the winning 

bid, but it is not affected by M&A experience, i.e., it affects all three groups to the same 

degree. 

• The percent increase in the mean value of high bonus bids for leases that received at least 

two bids (competitive leases) over noncompetitive leases is not significantly affected by 

M&A experience. As with the intensity of competition variable, the relationship between 

high bids and whether or not there are competing bidders is strong and significant but the 

same for all three groups. 

• The increase in the relative mean value of the high bonus bid for deep water leases over 

the leases in the shelf is about 11 percent and this is not significantly affected by M&A 

bidders’ participation in the high bonus bid. Again, it affects all three groups to the same 

degree. 

• The involvement of bidders ranking as the top four firms in cumulative production 

significantly leads to a lower relative value of high bids in the Group B leases. Whereas 

the expected mean value of high bonus bids for Group A and C leases involving bidders 

ranked among the top four is not significantly different from other bidders, i.e., non-topfour bidders. 
77

• On average, the relative value of high bonus bids has dropped significantly since the 

collapse of world crude oil prices and the decline is significantly different among the 

three groups. The effect is strong in all three groups but is less so for leases in which 

participating firms had been involved in or would become involved in M&A activities. 

• The parameters designated as fixed effects (intercepts) are significantly different in 

magnitude, thus suggesting that the relative change in the mean value of high bonus bids 

for OCS leases, ceteris paribus, was on average relatively smaller for leases in which 

participating bidders include a firm or firms involved in M&A plan over the entire period 

1983-1999, when other factors have been accounted for. This result could be interpreted 

as suggesting these firms were able to exercise some degree of oligopsony power as a 

consequence of M&A activities, or as suggesting that this group of firms possessed better 

information or had more experience, which enabled them to bid more efficiently. Since 

the competitive measures indicate no significant difference among the three categories, 

the second interpretation is more consistent with our data. 

4.4 Firms of Different Sizes and the Value of OCS Oil and Gas Leases 

Effects of Majors and Non-majors: The purpose of this section is to evaluate the same 

relationships discussed in the previous section, but to use the distinction between “major” and 

“non-major” or the distinction between “restricted bidders” and “unrestricted bidders” rather 

than M&A experience as a tool to do so. Although a different formulation and estimation of 

equation (1) is adopted, we are concerned with the same measures of the behavior and 

performance of firms in OCS oil and gas lease auction markets as we were previously. For 

example, did major oil operators on the OCS traditionally pay more for competitive joint venture 

leases than for competitive solo venture leases? Or do majors pay less than non-majors for 

competitive joint venture leases? 

The formulation or approach used in this section is similar to the one used in Mead et al. (1985) 

in which different possible categories of leases were selected based on the size of bidders 

involved in the high bonus bids, the lease type and bidding arrangement. Thus, using Mead’s 

approach, a set of interactive dummy variables was employed to define eight distinct 

combinations of: 1) type of bid (joint or solo), 2) extent or existence of competition in bidding, 

and 3) firm size, but using the major/non-major classification as a proxy. 

Using this format, the eight possible lease categories are: 

• MJC: High bonus bids which involved major oil and gas operators, bidding jointly for 

competitive leases. 

• MJN: High bonus bids which involved major oil and gas operators, bidding jointly for 

noncompetitive leases. 

• MSC: High bonus bids which involved major oil and gas operators, bidding solo for 

competitive leases. 
78

• MSN: High bonus bids which involved major oil and gas operators, bidding solo for 

noncompetitive leases. 

• NMJC: High bonus bids involving non-major oil and gas operators only, bidding jointly 

for competitive leases. 

• NMJN: High bonus bids involving non-major oil and gas operators, bidding jointly for 

noncompetitive leases. 

• NMSC: High bonus bids involving non-major oil and gas operators, bidding solo for 

competitive leases. 

• NMSN: High bonus bids involving non-major oil and gas operators, bidding solo for 

noncompetitive leases. 

The estimated results of Equation 2 using covariance-pooling techniques (CAT) and different 

base cases are reported in Tables 4.4 and 4.5. The estimated equation includes new sets of 

dummy variables used as proxies for the eight categories of leases representing the different set 

of lease combinations, which were listed above. The CAT estimation techniques allow for crosssectional differences or inter-temporal differences with restrictive assumptions that some of the 

slope coefficients are equal and stochastic, and that at least one of the intercepts is different and 

stochastic. 

In general, the base case results presented in Table 4.4 are consistent with and strengthen the 

results from the SUR formulation based summarized in Tables 4.2 and 4.3. Specifically, the 

CAT results using non-major, noncompetitive solo venture bids as the base case also show: 

 

• As competition increases, the relative change in (the natural logarithm of) the mean value 

of high bonus bids also increases.

• The diminished expectations that followed the collapse of crude oil prices in 1986, 

depresses the mean value of high bonus bids for leases in a manner that is very consistent 

with the SUR results. 

• The mean value of high bonus bids for deep-water leases is significantly higher than for 

shallow water leases, ceteris paribus. 

• A negative or inverse relationship between the value of the high bids and bidders who 

have been involved in mergers and acquisitions. 

 

• A positive effect from the Deepwater Royalty Relief Acts of 1995. 

The only difference between the SUR results reported in Section 4 and the CAT results, is a 

small positive effect of rising crude petroleum prices on the mean value high bonus bids of leases 

as would have been expected, but only at a 90 percent confidence level. 
79

Table 4.4 

Estimated CAT Model of the Relative Change in the 

Value of High Bonus Bids with NMSN as the Base Casea

Determinants 

Parameter 

Estimate Std. Error 

A priori 

Sign 

Intercept 13.4846* 0.0732 

Intensity of Competition, NBD 0.3326* 0.0111 + 

Economic Environment, CPP 0.0015*** 0.0008 + 

Interactive D1 Variable, MJC 0.7117* 0.0403 

Interactive D2 Variable, MJN 0.3430* 0.0278 

Interactive D3 Variable, MSC 0.5169* 0.0344 

Interactive D4 Variable, MSN 0.1432* 0.0227 

Interactive D5 Variable, NMJC 0.6887* 0.0426 

Interactive D6 Variable, NMJN 0.3385* 0.0322 

Interactive D7 Variable, NMSC 0.3898* 0.0388 

Deepwater Lease Dummy, DDP 0.0820* 0.0168 + 

Merger & Acquisition Dummy, DMA -0.2177* 0.0263 

Post Royalty Relief Act, DY (95-99) 0.1000* 0.0176 + 

Post 1986 Price Collapse, DY (86-99) -1.5807* 0.0323 −

*, **, *** Denotes significance at the 0.01, 0.05, 0.l0 levels, respectively. 

a

The base case represents leases won through noncompetitive solo venture bids by firms classified as nonmajor oil and gas operators only (NMSN). 

 

Table 4.5 

Estimated Model of the Relative Change in the 

Value of High Bonus Bids with Varying Base Case1

 MJC MJN MSC MSN NMJC NMJN NMSC NMSN 

MJC -0.369* -0.195* -0.568* -0.023 -0.373* -0.322* -0.712* 

MJN 0.369* 0.174* -0.200* 0.345* -0.005 0.047 -0.343* 

MSC 0.195* -0.174* -0.374* 0.172* -0.178* -0.127* -0.517* 

MSN 0.568* 0.200* 0.374* 0.545* 0.195* 0.247* -0.143* 

NMJC 0.023 -0.346* -0.172* -0.545* -0.350* -0.299* -0.689* 

NMJN 0.373* 0.005 0.178* -0.195* 0.350* 0.051 -0.338* 

NMSC 0.322* -0.047 0.127* -0.247* 0.299* -0.051 -0.390* 

NMSN 0.712* 0.343* 0.517* 0.143* 0.689* 0.338* 0.390* 

*, **, *** Denotes significance at the 0.01, 0.05, 0.l0 levels, respectively. 

1

The varying base case represents each possible lease combinations on the basis of the classification of the 

firms involved in the winning bids (M or NM), the bidding type (J or S) or bidding structure (C or N). 
80

Given our objectives in this report, the most relevant results are: 

• The inverse relationship between DMA (M&A history and experience), and the average 

level of high bids, which supports the previous findings that firms with a history of 

M&A, on average, submit lower winning bids (other factors taken into account) than 

firms that have not. 

• Comparisons of the coefficients and the comparisons of the differences among 

coefficients of the interactive dummy variables, D1 through D7. 

The comparisons of the coefficients is facilitated by Table 4.5 which presents the relative 

differences in the expected lease value in a relative term between lease categories with varying 

base case specification. The numbers reported under the columns in Table 4.5 are the parameter 

estimates of the effect of the interactive dummy represented in the column on the relative change 

in the mean value of the high bonus with respect to the base case lease category represented by 

each row in the table. The relative confidence levels for ascertaining the statistical significance 

of the differences among the variables are also shown in the table. 

Comparisons of interest include: 

• All of the interactive dummy variables for majors have larger coefficients than the 

corresponding combination including only non-majors, i.e., D1 (MJC)>D5 (NMJC), D2 

> D6, D3>D7. However the differences are significant (as indicated in Table 4.5) only 

for solo bids, both those that are competitive and those that are not. This suggests that if 

firm size, as measured by the major/non-major classification, has any effect on bidding it 

is a positive effect. 

• The fact that there is no significant difference between joint bids involving majors and 

joint bids not involving majors, i.e., MJC and NMJC, or MJN and NMJN, supports 

skepticism about the rationale for the Restricted Bidders List approach for discouraging 

anti-competitive behavior through restrictions on joint bidding discussed in the second 

chapter of the report. 

• Within both the major and the non-major classifications, competitive bids are consistently 

and significantly higher than noncompetitive bids, reinforcing the results of previous 

sections. 

Effects of Joint Bid Restrictions: In this section, we will evaluate the effect of joint bid 

restriction on the market for OCS oil and gas leases using equation (2). A new set of databases is 

constructed for this purpose using MMS’ Restricted Bidders List from 1983-1999, lease structure 

(ex post) and bidding arrangement or type. As previously stated, we are again concerned with 

the same measures of the behavior and performance of firms in OCS oil and gas lease auction 

markets. For example, was the value of leases involving restricted joint bidders on the OCS 

traditionally more than leases involving nonrestricted bidders? Or do restricted bidders pay less 

than non restricted for joint venture leases irrespective of the ex post lease structure? 
81

A set of interactive dummy variables was again employed to define eight distinct combinations 

of: 1) type of bid (joint or solo), 2) extent or existence of competition in bidding, and 3) firm 

size, but using the restricted/nonrestricted classification as a proxy. 

Using this format, the eight possible lease categories are: 

• RJC: High bonus bids which involved restricted bidders, bidding jointly for competitive 

leases (leases which received at least two bids). 

• RJN: High bonus bids which involved restricted bidders, bidding jointly for 

noncompetitive leases (leases which received only one bid). 

• RSC: High bonus bids which involved restricted bidders, bidding solo for leases receiving 

at least two bids (competitive leases). 

• RSN: High bonus bids which involved restricted bidders, bidding solo for leases receiving 

at least two bids (noncompetitive leases). 

• UJC: High bonus bids involving unrestricted bidders, bidding jointly for competitive 

leases (leases which received at least two bids). 

• UJN: High bonus bids involving unrestricted bidders, bidding jointly for noncompetitive 

leases (leases which received only one bid). 

• USC: High bonus bids involving unrestricted bidders, bidding solo for leases receiving at 

least two bids (competitive leases). 

• USN: High bonus bids involving non-major oil and gas operators, bidding solo for leases 

receiving at least two bids (noncompetitive leases). 

In general, the base case results presented in Table 4.6 using covariance-pooling techniques 

(CAT) restricted/unrestricted bidder classification of leases are similar to the results summarized 

in Table 4.4. The only noticeable exception is the negative and statistically significant index of 

economic environment, which is contrary to our a priori positive expectations. Although this 

index has an expected positive sign in the two previous results, it was only marginally significant 

at the 10 percent level. 

The CAT results using unrestricted, noncompetitive solo venture bids as the base case also show: 

 

• As competition increases, the relative change in (the natural logarithm of) the mean value 

of high bonus bids also increases.

• The diminished expectations that followed the collapse of crude oil prices in 1986, 

depresses the mean value of high bonus bids for leases in a manner that is consistent with 

earlier results.
82

Table 4.6 

Estimated CAT Model of the Relative Change in the 

Value of High Bonus Bids with USN as the Base Casea

Determinants 

Parameter 

Estimate Std. Error 

A priori 

Sign 

Intercept 13.9397* 0.0844 

Intensity of Competition, NBD 0.3324* 0.0114 + 

Economic Environment, CPP -0.0028* 0.0009 + 

Interactive D1 Variable, RJC 0.6706* 0.0437 

Interactive D2 Variable, RJN 0.1472* 0.0353 

Interactive D3 Variable, RSC 0.4944* 0.0358 

Interactive D4 Variable, RSN 0.0764* 0.0225 

Interactive D5 Variable, UJC 0.6325* 0.0374 

Interactive D6 Variable, UJN 0.3338* 0.0235 

Interactive D7 Variable, USC 0.3617* 0.0337 

Deepwater Dummy, DDP 0.1000* 0.0171 + 

Merger & Acquisition Dummy, DMA -0.1574* 0.0235 

Post Royalty Relief Act, DY (95-99) 0.0445* 0.0179 + 

Post 1986 Price Collapse, DY (86-99) -1.704* 0.0378 −

*, **, *** Denotes significance at the 0.01, 0.05, 0.l0 levels, respectively. 

a

The base case represents leases won through noncompetitive solo venture bids by firms classified as 

unrestricted bidders only (USN). 

• The mean value of high bonus bids for deep-water leases is significantly higher than for 

shallow water leases, ceteris paribus. 

 

• A negative or inverse relationship between the value of the high bids and bidders who 

have been involved in mergers and acquisitions. 

 

• A positive effect from the Deepwater Royalty Relief Acts of 1995. 

Given our objectives of this report, the econometric analysis in this section using restricted and 

unrestricted classification still confirms a statistically significant inverse relationship between 

DMA (M&A history and experience), and the average level of high bids. The results support the 

previous findings that firms with history of M&A, on average, submit lower winning bids (other 

factors taken into account) than firms that have not. 

Table 4.7 presents the relative differences in the expected lease value in a relative term between 

lease categories with varying base case specification. As previously stated, the numbers reported 

under the columns in Table 4.7 are measures of the effect of the interactive dummy represented 

in the column with respect to the base case lease category represented by each row in the table. 

The relative confidence levels for ascertaining the statistical significance of the differences 

among the variables are also shown in the table. 
83

Table 4.7 

Estimated Model of the Relative Change in the 

Value of High Bonus Bids with Varying Base Case1

 RJC RJN RSC RSN UJC UJN USC USN 

RJC -0.523* -0.159* -0.594* -0.038 -0.337* -0.331* -0.671* 

RJN 0.523* 0.347* -0.071*** 0.485* 0.187* 0.215* -0.147* 

RSC 0.159* -0.347* -0.418* 0.138* -0.161* -0.172* -0.494* 

RSN 0.594* 0.071*** 0.418* 0.556* 0.257* 0.285* -0.076* 

UJC 0.038 -0.485* -0.138* -0.556* -0.299* -0.269* -0.633* 

UJN 0.337* -0.187* 0.161* -0.257* 0.299* 0.032 -0.334* 

USC 0.331* -0.215* 0.172* -0.285* 0.269* -0.032 -0.362* 

USN 0.671* 0.147* 0.494* 0.076* 0.633* 0.334* 0.362* 

*, **, *** Denotes significance at the 0.01, 0.05, 0.l0 levels, respectively. 

1

The varying base case (row) represents each possible lease combinations on the basis of the classification 

of the firms involved in the winning bids (U or R), the bidding type (J or S) or bidding structure (C or N). 

It can be inferred from the table that: 

All of the interactive dummy variables for majors have larger coefficients than the corresponding 

combination including only non-majors, i.e., D1 (MJC)>D5 (NMJC), D2 > D6, D3>D7. 

However the differences are significant (as indicated in Table 4.5) only for solo bids, both those 

that are competitive and those that are not. This suggests that if firm size, as measured by the 

major/non-major classification, has any effect on bidding it is a positive effect. 

• There is no significant difference between competitive joint bids involving restricted 

bidders and joint bids not involving restricted bidders, i.e., RJC and UJC. However, when 

joint bidding is for leases classified ex post as noncompetitive, the bids are significantly 

less if restricted bidders are involved, i.e. RJN < UJN.

• Within both the restricted and the unrestricted firm classifications, competitive bids are 

consistently and significantly higher than noncompetitive bids. 

• Within both the restricted and the unrestricted firm classifications, joint venture bids for 

under comparative ex post bidding structure (competitive or non competitive) are 

consistently and significantly higher than solo venture bids, reinforcing the results of 

previous sections (RJC>RSC, RJN > RSN, UJC>USC, and UJN>USN). 

These results suggest some skepticism about the rationale for the Restricted Bidders List 

approach for discouraging anti-competitive behavior through restrictions on joint bidding 

discussed in the second chapter of the report. 
84

5. Conclusions and Implications 

The core of our study is an analysis of leasing of offshore oil and gas property under the 

jurisdiction of the Minerals Management Service since the adoption of area-wide leasing in 

1983. Our study is a broad application of statistical measurements and techniques to a large 

number of transactions involving a complex and evolving set of firms either acting alone or 

jointly with other firms, over a time period of major changes in the industry and its economic, 

political and technological context. It is an analytical overview, not a dispositive analysis. 

None of the measures of industrial concentration indicate that the characteristics of the offshore 

lease auction/market exceed criteria used to characterize concentrated or potentially 

uncompetitive industries. Econometric analyses following different approaches and formulations 

do not reveal a negative association between the size of firms and average winning bids. The 

association is either statistically insignificant or positive, suggesting larger firms submit higher 

average bids when other contextual variables are incorporated into the analysis. 

However the variables measuring whether or not firms have participated in mergers or 

acquisitions before the time of the lease sale, consistently show a modest negative relationship 

between average high bids and merger and acquisition experience or history. Such an association 

could be interpreted as evidence that firms who have participated in mergers or acquisitions 

have, in some way, either acquired a degree of oligopsonistic market power or tend to be better 

informed or more experienced and, thus, more efficient bidders. We find the latter a more 

plausible conjecture. 

Although we have identified and characterized some patterns of bidding on a less aggregated 

basis, an adequate analysis of this subject exceeds the scope of, and resources available for, the 

project. However, the weight of the evidence in our view is that the lease market is competitive 

and that there are no apparent barriers to its efficient operation. 

A related implication of our analysis is that Restricted Bidders List that MMS is required to 

prepare does not appear to serve a useful public policy purpose. The criteria used to compile the 

list are arbitrary and the list has not been used in other legal or regulatory determinations by 

MMS or the Department of Justice. Joint bidding is consistently associated with higher average 

winning bids and seems to be consistent with, and perhaps enhanced by competition in the lease 

market. 
85

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Dougher, R. 1987. Market Shares and Individual Company Data for the U.S. Energy 

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Erickson, E.W. and R.M. Spann. 1974. An Analysis of the Competitive Effects of Joint 

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Bell Journal of Economics 12(2):637-648. 

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 Independents on Petroleum Development. The Energy Journal 16(2):59-76. 

Markham, J.W. 1970. The Competitive Effects of Joint Bidding by Oil Companies for 

 Offshore Leases. In: Markham, J.W. and G.F. Papanek, eds. Industrial Organization and 

 Economic Development: In Honor of E.S. Mason. Boston, MA: Houghton Mifflin. 

McDonald, S.L. 1979. The Leasing of Federal Lands for Fossil Fuels Production. 

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Mead, W.J., A. Moseidjord, D. Mauraoka, and P.E. Sorensen. 1985. Offshore Lands: Oil and 

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Mead, W.J. and P.E. Sorensen. 1980. Competition and Performance in OCS Oil and Gas Lease 

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Millsaps, S.W. and M. Ott. 1981. Information and Bidding Behavior by Major Oil 

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Moody, C.E. and W.J. Kruvant. 1990. OCS Leasing Policy and Lease Prices. Land 

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Moody, C.E. and W.J. Kruvant. 1988. Joint Bidding, Entry, and the Price of OCS Leases. 

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Petroleum Industry Research Foundation. 2002. Refining Concentration and Industry Dynamics. 

 New York: Petroleum Industry Research Foundation, Inc. (PIRINC). 

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Rockwood, A. 1983. The Impact of Joint Ventures on the Market for OCS Oil and Gas 

 Leases. Journal of Industrial Economics 31(4):453-468.

Saidi, R. and J.R. Marsden. 1992. Number of Bids, Number of Bidders and Bidding 

 Behavior in Outer Continental Shelf Oil Lease Auctions. European Journal of 

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Sullivan, B. and P. Kobrin. 1980. The Joint Bidding Ban: Pro- and Anti-Competitive 

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 33(1):1-19. 

Teisberg, T.J. 1980. Federal Management of Energy and Mineral Resources on the Public 

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Printing Office. 
The Department of the Interior Mission

As the Nation's principal conservation agency, the Department of the Interior has responsibility 

for most of our nationally owned public lands and natural resources. This includes fostering 

sound use of our land and water resources; protecting our fish, wildlife, and biological diversity; 

preserving the environmental and cultural values of our national parks and historical places; 

and providing for the enjoyment of life through outdoor recreation. The Department assesses 

our energy and mineral resources and works to ensure that their development is in the best 

interests of all our people by encouraging stewardship and citizen participation in their care. 

The Department also has a major responsibility for American Indian reservation communities 

and for people who live in island territories under U.S. administration. 

The Minerals Management Service Mission 

As a bureau of the Department of the Interior, the Minerals Management Service's (MMS) 

primary responsibilities are to manage the mineral resources located on the Nation's Outer 

Continental Shelf (OCS), collect revenue from the Federal OCS and onshore Federal and Indian 

lands, and distribute those revenues. 

Moreover, in working to meet its responsibilities, the Offshore Minerals Management Program 

administers the OCS competitive leasing program and oversees the safe and environmentally 

sound exploration and production of our Nation's offshore natural gas, oil and other mineral 

resources. The MMS Minerals Revenue Management meets its responsibilities by ensuring the 

efficient, timely and accurate collection and disbursement of revenue from mineral leasing and 

production due to Indian tribes and allottees, States and the U.S. Treasury. 

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1) being 

responsive to the public's concerns and interests by maintaining a dialogue with all potentially 

affected parties and (2) carrying out its programs with an emphasis on working to enhance the 

quality of life for all Americans by lending MMS assistance and expertise to economic 

development and environmental protection.