Directory UMM :Data Elmu:jurnal:J-a:Journal of Energy Finance & Development:Vol4.Issue2.Jan1999:

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Journal of Energy Finance and Development 4 (1999) 1–27

Evaluating the exploration efficiency of oil and gas

firms using SFAS 69 supplemental disclosures

Charles E. Boynton IV

_{, Jeffery P. Boone}

_{, Teddy L. Coe}

_*

a_{University of North Texas, Institute of Petroleum Accounting,} P.O. Box 305460, Denton, TX 76203-5460, USA

b_{Mississippi State University, Drawer EF, Mississippi State, MS 39762}

Received October 1998; accepted May 1999

Abstract

Investors and other users of financial statements often analyze financial statement informa-tion to evaluate the explorainforma-tion efficiency of oil and gas firms. One approach commonly employed is to calculate an average per-unit cost of finding and developing oil and gas reserves using data disclosed by oil and gas firms in the footnotes to their financial statements. These average finding costs ratios, while widely used, are by no means universally accepted as providing meaningful insight into the exploration efficiency and potential profitability of an oil and gas firm. In fact, a number of financial analysts who specialize in oil and gas firms have argued that these finding costs ratios in fact provide no useful insights into how well a company has done. The purpose of our paper is to evaluate the usefulness of these finding costs measures as indicators of exploration efficiency and potential profitability. Our approach involves comparing the statistical association between various finding costs measures to a benchmark measure of exploration efficiency derived from a Cobb-Douglas regression. We also compare these finding costs measures to two commonly used financial statement measures of profitability—return on sales and return on assets—to evaluate whether finding costs are useful as indicators of profitability. Our results indicate that finding costs ratios calculated from readily available financial statement data provide useful insight into both exploration efficiency and the potential profitability of an oil and gas firm. Our findings are important because they provide empirical evidence useful in resolving a debate within the financial analyst community concerning the utility of these finding costs ratios. 1999 Elsevier Science Inc. All rights reserved.

Keywords:Oil and gas accounting; Financial statement analysis; Finding costs

* Corresponding author. Tel.: 940-565-3170; fax: 940-369-8839.

E-mail address: [email protected] (T.L. Coe)

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2 Boynton et al. / Journal of Energy Finance and Development 4 (1999) 1–27 1. Introduction

This paper uses supplemental disclosures required by Statement of Financial Accounting Standards No. 69 (SFAS 69)1_{to evaluate exploration efficiency of oil and}

gas firms. We do this because many users of financial statements calculate the average per-unit cost of finding and developing oil and gas reserves using data disclosed by oil and gas firms in the footnotes to their financial statements. However, while popular in use, this data is by no means universally accepted as providing meaningful insight into the exploration efficiency and potential profitability of an oil and gas firm. In fact, a number of financial analysts who specialize in oil and gas firms have argued that these finding costs ratios provide no insight into how well a company has done. The purpose of our paper is to evaluate the usefulness of these finding costs measures as indicators of exploration efficiency and potential profitability.

Our results indicate that finding costs ratios calculated from readily available finan-cial statement data provide useful insight into both exploration efficiency and the potential profitability of an oil and gas firm. Our findings are important because they provide empirical evidence useful in resolving a debate within the financial analyst community concerning the utility of these finding costs ratios.

In particular, our goal is to determine, first, the usefulness of finding costs ratios as measures of exploration efficiency, and second, the usefulness of finding costs ratios as indicators of potential profitability. Specifically, this paper examines the statistical association between rank on each of four finding costs ratios, rank on each of two operating profit ratios, and rank on a Cobb-Douglas regression derived index of exploration efficiency (EFF). By construction, higher rank on EFF is an indicator of exploration efficiency relative to lower rank on EFF. Each of the measures compared is calculated using supplemental disclosure financial and quantity data required by SFAS 69 for 119 publicly owned oil and gas exploration firms for 1988–1992. By comparing ranks on measures rather than the values of the measures, we eliminate issues of differences in scale inherent in the measures.

We measure the statistical association between rank on our Cobb-Douglas regres-sion derived index of exploration efficiency (EFF) and rank on the four finding costs ratios in two ways. First, we calculate Spearman rank correlations. Second, to determine if the association is stronger or weaker for any of the four finding costs ratios in a statistically significant sense, we use regression of a firm’s rank on EFF on the firm’s rank on the various ratios. In regression it is possible to impose joint restrictions on two or more variables at once and to test the restrictions. We use regression to test the restriction that the estimated association with EFF is the same for all four finding costs ratios, that is, that any difference is not statistically significant.

We expect a negative association between rank on EFF and rank on finding costs ratios. In other words, we expect firms that rank high on EFF will rank low on any finding costs ratio and be judged more efficient in exploration, and that firms that rank high on finding costs ratios will rank low on EFF and be judged less efficient in exploration. The expected association between finding costs ratios and EFF would be consistent with finding costs ratios functioning as measures of exploration efficiency.

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We also measure the Spearman rank correlations between two operating profit ratios (return on oil and gas revenues and return on oil and gas assets) and each of the four finding costs ratios. We expect a negative association between rank on operating profit ratios and rank on finding costs ratios, that is, that more profitable firms have lower finding costs. We are particularly interested in determining if the association between the operating profit ratios and the finding costs ratios is stronger or weaker for any of the four finding costs ratios in a statistically significant sense. We therefore supplement the rank correlation analysis with rank regression of the operating profit ratios on rank on the finding costs ratios to test for any significant differences in the association for different finding costs ratios with the operating profit ratios.

We also measure the Spearman rank correlations between EFF and each of the two operating profit ratios. We expect a positive association between rank on EFF and rank on operating profit ratios, that is, more efficient firms in exploration are more profitable overall.

The balance of our paper is organized as follows: In section 2 we describe the selection of our sample of 119 publicly owned oil and gas firms with data for 1988–1992. In section 2.1 we define the four finding costs ratios that we examine. In section 2.2, we define the two operating profit ratios we use. In section 2.3, we develop our Cobb-Douglas regression derived index of exploration efficiency (EFF) including the coefficient estimates of the Cobb-Douglas production model. The estimated EFF for 1989–1992 for each of the 119 firms is presented.

In section 3 we present descriptive statistics for key data items for the 119-firm sample as a whole and for the full cost and successful efforts subsets.2_{In section 3.1}

we present Spearman rank correlations between each of the four finding costs ratios and EFF for the 119-firm sample as a whole and for the full cost and successful efforts subsets. In section 3.2 we present Spearman rank correlations between each of the four finding costs ratios and the two operating profit ratios for the 119-firm sample as a whole and for the full cost and successful efforts subsets. In section 3.3 we present Spearman rank correlations between each of the two operating profit ratios and EFF for the 119-firm sample as a whole and for the full cost and successful efforts subsets. In section 3.4 we present the rank regression results comparing the four finding costs ratios as a set to EFF. In section 3.5 we present the rank regression results comparing the four finding costs ratios as a set to each of the two operating profit ratios. Section 4 summarizes our findings.

2. Sample selection of 119 publicly owned oil and gas firms with data for 1988–1992

This paper grows out of research performed under a grant (EIA Financial Assistance Instrument DE-FG01-92EI23624) from the Energy Information Administration (EIA) of the U.S. Department of Energy to the Institute of Petroleum Accounting (IPA) at the University of North Texas.3 _{As part of the grant, EIA furnished IPA with a}

restricted-use copy of a database prepared by EIA from proprietary databases to which EIA was a subscriber.4_{SFAS 69 supplemental disclosures data for publicly held}

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4 Boynton et al. / Journal of Energy Finance and Development 4 (1999) 1–27

for a fee, in a computer-accessible proprietary database, from Arthur Andersen LLP, Houston, Texas.

The 119-firm sample for this paper consists of all publicly owned firms listed on the EIA restricted-use database with non-missing values5 _{for each of the five years}

between 1988–1992 for the following SFAS 69 data items: additions and extensions to proved oil reserves worldwide (thousands of barrels), total proved oil reserves worldwide (thousands of barrels), total oil production worldwide (thousands of bar-rels), additions and extensions to proved gas reserves worldwide (millions of cubic feet), total proved gas reserves worldwide (millions of cubic feet), total gas production worldwide (millions of cubic feet), exploration expenditures worldwide (thousands of dollars), development expenditures worldwide (thousands of dollars), total revenues from oil and gas operations worldwide (thousands of dollars), total oil and gas assets (capitalized costs) worldwide (thousands of dollars), total oil and gas net income worldwide (thousand of dollars), total depreciation, depletion, and amortization (DDA) worldwide (thousands of dollars), and total exploration expense worldwide (thousands of dollars).

2.1. Finding costs ratios

A finding costs ratio is calculated by dividing an exploration-related expenditure for a period by an estimate of the quantity of oil and gas discovered during the same period (Gaddis et al., 1992). The financial expenditure and quantity data are supplemental disclosures required under SFAS 69. Conventionally, and for this paper, oil and gas quantities are aggregated by converting 6,000 cubic feet (6 mcf) of gas to one barrel of oil equivalent (1 BOE), a conversion based on the energy content of oil and gas.6

Finding costs ratios are a popular but not universally accepted means of evaluating the performance of oil and gas exploration firms.7_{Many firms now disclose their own}

calculation of finding costs ratios. The1999 PricewaterhouseCoopers Survey of U.S.

Petroleum Accounting Practices reports that 34 of the 39 responding independent

producers and 5 of the 6 responding major producers calculate finding costs ratios for internal use and that 18 of the 39 responding independent producers and 3 of the 6 responding major producers disclose finding costs ratios externally (Pricewaterhouse-Coopers and the University of North Texas Institute of Petroleum Accounting, 1999). Disagreements exist as to the appropriate exploration-related expenditures to in-clude in finding costs ratios (e.g., SFAS 69 exploration expenditures only, the sum of SFAS 69 exploration and development expenditures, or the sum of SFAS 69 explora-tion and development expenditures and acquisiexplora-tion expenditures, i.e., expenditures to purchase reserves), the length of the period to use (e.g., 1, 3, or 5 years), and the composition of the quantity estimates to be used (for example, SFAS 69 additions and extensions to proved reserves only, the sum of SFAS 69 additions and extensions to proved reserves and revisions to proved reserves [see Clinch and Magliolo, 1992, for a discussion of the value relevance of revisions to proven reserves], or the sum of SFAS 69 additions and extensions to proved reserves, revisions to proved reserves, and proved reserves acquired, i.e., purchased). This paper will examine four of the possible

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variations using two definitions of exploration-related expenditures (SFAS 69 explora-tion expenditures only and the sum of SFAS 69 exploraexplora-tion and development expendi-tures), two definitions of period (1 year and 3 years), and one definition of quantity (SFAS 69 additions and extensions to proved reserves only). We believe that these four will provide insight into the usefulness of finding costs ratios as measures of exploration efficiency and the effect of varying the definition of the finding costs ratio.

The four finding costs ratios that we study are:

• FC1 5 exploration expenditures worldwide for year t (thousands of dollars) divided by oil and gas additions and extensions to proved reserves worldwide for yeart(thousands of BOE).

• FC35total exploration expenditures worldwide for three years, years t, t21, and t 2 2 (thousands of dollars) divided by total oil and gas additions and extensions to proved reserves worldwide for yearst,t21, andt22 (thousands of BOE).

• FC1D5total of exploration expenditures and development expenditures world-wide for year t (thousands of dollars) divided by oil and gas additions and extensions to proved reserves worldwide for yeart(thousands of BOE). • FC3D5total of exploration expenditures and development expenditures

world-wide for three years, years t,t21,t22 (thousands of dollars) divided by total oil and gas additions and extensions to proved reserves worldwide for years t,

t21, t22 (thousands of BOE).

The 3-year finding costs ratios, FC3 and FC3D, use data from both the current year and the prior 2 years in each observation. Using 5 years of data (1988–1992), we have 3 years for which 3-year ratios may be observed (1990–1992) for each firm, or 357 total observations for each 3-year ratio for the 119-firm sample. To compare 1-year finding costs ratios, FC1 and FC1D, with the 3-year ratios, we also observe the 1-year ratios for 1990–1992 for each firm for a total of 357 observations for each 1-year ratio for the 119 firm sample.

2.2. Operating profit ratios

The SFAS 69 supplemental disclosure data also permits the calculation of conven-tional operating profit ratios for the oil and gas component of a firm. The operating profit ratios that we study are:

• ROS5total net income from oil and gas operations worldwide for yeart (thou-sands of dollars) divided by total oil and gas revenues worldwide for year t

(thousands of dollars).

• ROA 5 total net income from oil and gas operations worldwide for year t

(thousands of dollars) divided by total oil and gas assets (“capitalized costs”) worldwide for yeart(thousands of dollars).

As mentioned above, using 5 years of data (1988–1992), we have 3 years for which 3-year finding costs ratios may be observed (1990–1992) for each firm, or 357 total observations for each 3-year ratio for the 119-firm sample. For comparability, we also

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observe the 1-year finding costs ratios for 1990–1992 for each firm for a total of 357 observations for each 1-year ratio. Similarly, for comparability, we observe the 1-year operating profit ratios, ROS and ROA , for 1990–1992 for each firm for a total of 357 observations for each 1-year ratio.

2.3. Cobb-Douglas regression derived index of exploration efficiency (EFF)

Statistical regression using a Cobb-Douglas production model offers yet another means of evaluating efficiency in firms in a wide variety of industries.8_{For our purposes,}

a firm is treated as “producing” oil and gas discoveries (the output) from various resources such as exploration and development expenditures (the inputs). A simple Cobb-Douglas production model relates output Yifor firmito inputsX1andX2over

tperiods as follows:9

Yi5 Ai* X

1 * X2b2 *h (1)

such that, by taking natural logarithms, one arrives at the regression equation lnYi5 ai1 b1lnX11 b2lnX21 e (2)

where

ai5lnAi (3)

and

e 5 lnh (4)

Assuming the requirements for regression are met (for example, thateis a normally distributed error term), the regression coefficients may be estimated. In a Cobb-Douglas production model, if the sum of the b (slope) coefficients equals 1, the production model has constant returns to scale; if less than 1, decreasing returns to scale; and if greater than 1, increasing returns to scale (Chiang, 1984, p. 414). If there are constant returns to scale, size by itself does not affect efficiency.

For given quantities ofX1 and X2 and given b (slope) coefficients, the estimated

magnitude of the multiplierAi will proportionately affect the estimated level of Yi.

The multiplierAiis an efficiency parameter (Chiang, 1984, p. 416). The firm-specific

intercept in the regression equation, ai, is the natural logarithm of the firm-specific

multiplierAi. The estimate of the firm-specific multiplierAiis the natural anti-log of

the estimate of the firm-specific interceptai. If all firms are assumed in the estimation

to share common beta (slope) coefficients, then for any fixed quantities of the inputs, the ratio of the expected output for any two firms will be in the ratio of the estimated firm-specific multipliers for the firms. We follow the practice of comparing all firms to the most efficient firm (Cornwell et al., 1990). If Firm 1 has the largest estimated firm-specific multiplier of all firms, dividing each firm’s estimated multiplier by the estimated multiplier of Firm 1 will produce an efficiency index expressing each firm’s productivity in terms of Firm 1. Firm 1 will have an index of 1.00. If another firm, say Firm 2, has an index of, say, 0.625, then Firm 2 is expected to have output of only 62.5% of that of Firm 1 from the same inputs. Firm 2 may be said to be 62.5% as

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efficient as Firm 1. We refer to our Cobb-Douglas regression derived index as “EFF.” By construction, higher rank on EFF is a sign of exploration efficiency relative to lower rank on EFF.

We employ the following Cobb-Douglas production model with firm-specific inter-cepts, year specific interinter-cepts, and year specific slopes10_{to model the relation between}

oil and gas discovered (the output or dependent variable) and resources employed (the inputs or independent variables):

LNADD 5INTERCEP 1 diFIRMi1 g89DUM89 1 g90DUM90

1 g91DUM91 1 b1LNEXP 1 b11LNEXP89 1 b12LNEXP90

1 b13LNEXP91 1 b2LNDEV1 b21LNDEV891 b22LNDEV90

1 b23LNDEV91 1 b3LNOUT1 b31LNOUT89 1 b32LNOUT90

1 b33LNOUT91 1 b4LAGEXP1 b41LNLEXP89

1 b42LNLEXP90 1 b43LNLEXP91 1 b5LAGDEV

1 b51LNLDEV89 1 b52LNLDEV90 1 b53LNLDEV91 1 e (5) where

LNADD 5 natural logarithm of SFAS 69 additions and extensions to proved

reserves in thousands of BOE (converting 6 mcf of natural gas to 1 BOE).

INTERCEP 5the intercept for the regression equation and the estimate of the

firm-specific intercept a1for Firm 1.

FIRMi 5dummy variable equal to 1 for Firm iobservation and equal to zero

otherwise (fori52–119); the sum ofINTERCEPand the coefficient estimate forFIRMi,di, is the estimate of the firm-specific interceptai

for Firm i.

DUMyy 5 dummy variable equal to 1 for year 19yy observation and equal to zero otherwise (yy 589, 90, or 91).

LNEXP 5natural logarithm of SFAS 69 exploration expenditures in thousands

of dollars.

LNEXPyy 5natural logarithm of SFAS 69 exploration expenditures in thousands

of dollars for year 19yy observation and equal to zero otherwise (yy5

89, 90, or 91).

LNDEV 5natural logarithm of SFAS 69 development expenditures in thousands

of dollars.

LNDEVyy 5natural logarithm of SFAS 69 development expenditures in thousands

of dollars for year 19yy observation and equal to zero otherwise (yy5

89, 90, or 91).

LNOUT 5natural logarithm of SFAS 69 oil and gas production in thousands of

BOE.

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Table 1

Cobb-Douglas Model Regression Estimates, Related Test of Coefficient Restrictions, and Calculated Exploration Efficiencies for 119 Oil and Gas Firms for 1989–1992

Estimated 2-tailed

Variable coefficient t statistic significance level

Intercept 21.259 20.909 0.364

LNEXP 0.260 1.790 0.074

LNDEV 0.395 2.796 0.006

LNOUT 0.293 0.987 0.324

LAGEXP 0.007 0.040 0.968

LAGDEV 0.087 0.412 0.681

DUM89 0.890 1.554 0.121

DUM90 0.987 1.675 0.095

DUM91 0.444 0.815 0.416

LNEXP89 20.169 20.811 0.418

LNEXP90 0.191 0.962 0.337

LNEXP91 0.378 1.789 0.075

LNDEV89 0.284 1.117 0.265

LNDEV90 20.150 20.572 0.567

LNDEV91 20.145 20.575 0.566

LNOUT89 20.292 21.405 0.161

LNOUT90 20.425 21.987 0.048

LNOUT91 20.412 21.996 0.047

LNLEXP89 0.180 0.814 0.416

LNLEXP90 20.038 20.199 0.842

LNLEXP91 20.022 20.109 0.913

LNLDEV89 20.100 20.412 0.681

LNLDEV90 0.331 1.266 0.207

LNLDEV91 0.140 0.531 0.596

R-square value is .91; adjusted r-square value is .87.

BOE for year 19yy observation and equal to zero otherwise (yy 5

89, 90, or 91).

LAGEXP 5natural logarithm of SFAS 69 exploration expenditures for prior year

in thousands of dollars.

LNLEXPyy 5natural logarithm of SFAS 69 exploration expenditures for prior year

in thousands of dollars for year 19yy observation and equal to zero otherwise (yy589, 90, or 91).

LAGDEV 5 natural logarithm of SFAS 69 development expenditures for prior

year in thousands of dollars.

LNLDEVyy5 natural logarithm of SFAS 69 development expenditures for prior

year in thousands of dollars for year 19yy observation and equal to zero otherwise (yy 589, 90, or 91).

Table 1 presents the estimated coefficients for the Cobb-Douglas regression for our sample of 119 oil and gas firms for 1989–1992. The model uses data from both the current year and the prior year in each observation. Using 5 years of data (1988– 1992), we have 4 years of observations (1989–1992) for each firm or 476 total

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observa-Boynton et al. / Journal of Energy Finance and Development 4 (1999) 1–27 9 Table 2

Test of Coefficient Restrictions

F statistic p value Test SCALE8992: Joint test that the coefficients on LNEXP, LNDEV, LNOUT,

LAGEXP and LAGDEV sum to 1; that the coefficients on LNEXP91, LNDEV91, LNOUT91, LAGEXP91 and LAGDEV91 sum to 0; that the coefficients on LNEXP90, LNDEV90, LNOUT90, LAGEXP90 and LAGDEV90 sum to 0; and that the coefficients on LNEXP89, LNDEV89,

LNOUT89, LAGEXP89 and LAGDEV89 sum to 0. 1.09 .36

Test EXP: Joint test that the coefficients on LNEXP89, LNEXP90, LNEXP91 are

all 0. 3.03 .03

Test DEV: Joint test that the coefficients on LNDEV89, LNDEV90, LNDEV91

are all 0. 1.15 .33

Test OUT: Joint test that the coefficients on LNOUT89, LNOUT90, LNOUT91

are all 0. 1.72 .16

tions for the 119-firm sample for the regression. The R2 _{value of .91 (adjusted R}2

value of .87) suggests that the model captures much of the complexity of the exploration process. The model explains approximately 91% of the variation in LNADD, the natural logarithm of the additions and extension to proved reserves.

Table 2 presents tests of various restrictions on the estimation of the Cobb-Douglas regression. One cannot reject at normal levels of statistical significance the restriction that the sum of the beta (slope) coefficients for each year equals 1, suggesting that there are constant returns to scale (Kmenta, 1986, pp. 412–422). (Test SCALE8992 has a p5.36..10.) One can reject the restriction that the coefficients onLNEXP

in each year, 1989–1992, are equal, suggesting that in one year a significant difference occurs in the coefficient onLNEXP and is allowed in the model by the use of year-specificbs (slopes). (Test EXP has a p 5.03,.05.) However, one cannot reject at normal levels of statistical significance the restriction that the estimated coefficients

on LNDEV in each year are equal. (Test DEV has a p 5 .33 . .10.) The same is

true for the estimated coefficients onLNOUT. (Test OUT has a p5 .16..10). Table 3 presents the Cobb-Douglas regression derived index of exploration effi-ciency (EFF) for each of the 119 firms in our sample ranked on EFF. The measure EFF for each firm is the estimate of the firm-specific multiplier for each firm divided by the largest such estimated multiplier.11 _{The measure EFF for a firm is a single}

value for 1989–1992 and represents the average efficiency of a firm during the 4-year estimation period compared to the average efficiency of the firm that was most efficient. By construction, higher rank on EFF is a sign of exploration efficiency relative to lower rank on EFF. In section 3, we compare rank on EFF with rank on each of four finding costs ratios and rank on each of two operating profit ratios.

3. Descriptive statistics for sample of 119 publicly owned oil and gas firms for 1990–1992

Table 4 A presents descriptive statistics for 18 data items for our sample of 119 oil and gas firms for the three years, 1990–1992. Table 4, B and C, presents the descriptive

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Table 3

Exploration Efficiency Index EFF for 119 Oil and Gas Firms for 1989–1992

Rank Name Symbol EFF Rank Name Symbol EFF

1 Alexander Energy Corp AEOK 1.00000 31 Barnwell Industries BRN 0.42459

2 Washington Energy Co. WEG 0.92696 32 Phillips Petroleum Co. P 0.41382

3 Barrett Resources Corp. BARC 0.89308 33 National Fuel Gas Co. NFG 0.40788

4 The Phoenix Resource Companies, Inc. PHNI 0.87786 34 Kerr-McGee Corp. KMG 0.40097

5 Brown (Tom), Inc. TMBR 0.85837 35 Maxus Energy Corp. MXS 0.37695

6 Garnet Resources Corp. GARN 0.83043 36 Union Pacific Corp. UNP 0.37253

7 Sage Energy Co. 6041C 0.83031 37 Unit Corp. UNT 0.37222

8 Southwestern Energy Co. SWN 0.82081 38 Edisto Resources Corp. EDS 0.35675

9 Burlington Resources, Inc. BR 0.81865 39 Noble Affiliates, Inc. NBL 0.35392

10 Swift Energy Co. SFY 0.81599 40 Columbia Gas System CG 0.35330

11 Energen Corp. EGN 0.76212 41 Consolidated Natural Gas Co. CNG 0.35111

12 Cabot Corp. CBT 0.75797 42 Apache Corp. APA 0.34999

13 American National Petroleum ANPC 0.68602 43 Nicor, Inc. GAS 0.34841

14 Nahama & Weagant Energy Co. NAWE 0.68435 44 Sonat, Inc. SNT 0.34648

15 Tesoro Petroleum Corp. TSO 0.64229 45 Societe National Elf Aquitaine ELF 0.34209

16 Hallwood Energy Corp. HEP 0.62813 46 Primeenergy Corp. PNRG 0.33986

17 KCS Energy, Inc. KCSE 0.59825 47 North Canadian Oils, Ltd. NCD 0.33873

18 Mitchell Energy & Development MND 0.56897 48 Helmerich & Payne HP 0.32884

19 CMS Energy Corp. CMS 0.56859 49 Oneok, Inc. OKE 0.32434

20 Presidio Oil Company PRS.B 0.56369 50 Prairie Oil Royalties Co., Ltd. POY 0.32210

21 Unocal Corp. UCL 0.55532 51 American Exploration Co. AX 0.32169

22 Questar Corp. STR 0.55394 52 Canadian Occidental Petroleum CXY 0.32090

23 New London, Inc. NLON 0.49489 53 Wiser Oil Co. WISE 0.31890

24 Oryx Energy Co. ORX 0.46870 54 Snyder Oil Corp. SNY 0.31853

25 Montana Power Co. MTP 0.45498 55 Mesa, Inc. MXP 0.31793

26 Triton Energy Corp. OIL 0.45251 56 Ashland Oil, Inc. ASH 0.31407

27 Coastal Corp. CGP 0.43367 57 Bellwether Exploration Co. BELW 0.30875

28 Basic Earth Science Systems 3BSIC 0.43242 58 Royal Dutch/Shell GRP Comb. RDSC.CM 0.29707 29 Enron Oil & Gas Co. EOG 0.42855 59 Equitable Resources, Inc. EQT 0.29583

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Table 3 (continued)

Rank Name Symbol EFF Rank Name Symbol EFF

61 USX Corporation MROX.CM 0.29095 91 Ranger Oil, Ltd. RGO 0.19019

62 Atlantic Richfield Co. ARC 0.28984 92 Amoco Corp. AN 0.18496

63 Bow Valley Industries, Ltd. OBVI 0.27717 93 Numac Oil & Gas, Ltd. NMC 0.18303

64 Anadarko Petroleum Corp. APC 0.27217 94 Devon Energy Corp. DVN 0.18143

65 Enserch Corp. ENS 0.26874 95 Convest Energy Partners-LP COV 0.17948

66 Wainoco Oil Corp. WOL 0.26385 96 Patrick Petroleum Co. PPC 0.17302

67 Box Energy Corp. BOXXA 0.26177 97 Mobil Corp. MOB 0.17030

68 Seagull Energy Corp. SGO 0.25807 98 Exxon Corp. XON 0.16911

69 Murphy Oil Corp. MUR 0.25759 99 Maynard Oil Co. MOIL 0.16332

70 BHP Broken Kill Proprietary BHP 0.25632 100 Du Pont (E.I.)-Conoco, Ltd. DD 0.16139

71 Fina, Inc. FI 0.25276 101 Gulf Canada Resources, Ltd.-ORD GOU 0.15659

72 Dekalb Energy Co., -CL B ENRGB 0.25008 102 Chevron Corp. CHV 0.15193

73 British Petroleum PLC-ADR BP 0.24863 103 Howell Corp. HWL 0.14846

74 Wilshire Oil of Texas WOC 0.24604 104 Plains Resources, Inc. PLX 0.14026

75 Home Oil Company, Ltd. HO 0.24085 105 British Gas BRG 0.13826

76 Penn Virginia Corp. PVIR 0.23664 106 Repsol, S.A. REP 0.13396

77 Total TOT 0.23192 107 Equity Oil Co. EQTY 0.13218

78 Sceptre Resources, Ltd. SRL 0.23123 108 Sun Co., Inc. SUN 0.12979

79 MDU Resources Group, Inc. MDU 0.22787 109 Amerada Hess Corp. AHC 0.12931

80 Crystal Oil Co. COR 0.22422 110 Brock Exploration Corp. BKE 0.12758

81 Global Natural Resources GNR 0.22204 111 Santa Fe Energy Resources SFR 0.12055 82 McFarland Energy, Inc. MCFE 0.22124 112 Allegheny & Western Energy ALGH 0.10401 83 Louisiana Land & Exploration LLX 0.22002 113 Vintage Petroleum, Inc. VPI 0.07687

84 Pennzoil Co. PZL 0.21823 114 Hondo Oil & Gas Co. HOG 0.07574

85 Canadian Pacific, Ltd. CP 0.21755 115 Hadson Energy Resources Corp. HERC 0.07085

86 Occidental Petroleum Corp. OXY 0.21479 116 Forest Oil Corp. FOIL 0.06588

87 Texaco, Inc. TX 0.21024 117 Resource America, Inc. REXID 0.04697

88 Plains Petroleum Corp. PLP 0.19496 118 Union Texas Petroleum Holdings, Inc. UTH 0.02497

89 Coho Resources, Inc. COHO 0.19178 119 Beard Oil Co. BOC 0.01570

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statistics for the 53 full cost firms and the 65 successful efforts firms, respectively.12

The 18 data items are:

1. ADD5SFAS 69 additions and extensions to proved reserves in thousands of BOE (converting 6 mcf of natural gas to 1 BOE).

2. EXP 5SFAS 69 exploration expenditures in thousands of dollars. 3. DEV5SFAS 69 development expenditures in thousands of dollars. 4. OUT5SFAS 69 oil and gas production in thousand of dollars.

5. EFF 5the Cobb-Douglas regression derived index of exploration efficiency. 6. FC1 5 exploration expenditures worldwide for year t (thousands of dollars)

divided by oil and gas additions and extensions to proved reserves worldwide for yeart (thousands of BOE).

7. FC35 total exploration expenditures worldwide for three years, years t, t 2

1, and t22 (thousands of dollars) divided by total oil and gas additions and extensions to proved reserves worldwide for yearst,t21, andt22 (thousands of BOE).

8. FC1D5total of exploration expenditures and development expenditures world-wide for year t (thousands of dollars) divided by oil and gas additions and extensions to proved reserves worldwide for year t(thousands of BOE). 9. FC3D5total of exploration expenditures and development expenditures

world-wide for three years, yearst,t21,t22 (thousands of dollars) divided by total oil and gas additions and extensions to proved reserves worldwide for years t,

t2 1, t22 (thousands of BOE).

10. ROS 5 total net income from oil and gas operations worldwide for year t

(thousands of dollars) divided by total oil and gas revenues worldwide for year

t(thousands of dollars).

11. ROA 5 total net income from oil and gas operations worldwide for year t

(thousands of dollars) divided by total oil and gas assets (“capitalized costs”) worldwide for year t(thousands of dollars).

12. SDDA15total of oil and gas DDA (depreciation, depletion, and amortization) and exploration expense worldwide for year t (thousands of dollars) divided by total oil and gas revenues worldwide for year t(thousands of dollars).13

13. SDDA25total oil and gas DDA and exploration expense worldwide for year

t(thousands of dollars) divided by total oil and gas assets (“capitalized costs”) worldwide for year t(thousands of dollars).13

14. WWTOTREV5 total oil and gas revenues worldwide for year t (thousands of dollars).

15. WWNETCST 5 total oil and gas assets (“capitalized costs”) worldwide for year t(thousands of dollars).

16. WWRESPFT 5 total net income from oil and gas operations worldwide for year t(thousands of dollars).

17. WWDEPDEP5total of oil and gas DDA (depreciation, depletion, and amorti-zation) worldwide for year t (thousands of dollars).

18. WWEXPEXP 5 total oil and gas exploration expense worldwide for year t

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Table 4 A indicates that the mean (median) oil and gas annual revenues (WWTO-TREV) for the 119 firms for the three years 1990–1992 was $989.9 million ($77.4 million). Table 4, B and C, indicates that the mean (median) for the 53 full-costs firms was $93.5 million ($43.4 million) and for the 65 successful efforts firms was $1.705 billion ($306.1 million). The largest 1-year revenues for a full cost firm was $690.2 million and $18.9 billion for a successful efforts firm. Mean (median) oil and gas assets (capitalized costs) (WWNETCST) for the 119 firms was $2.0 billion ($256.9 million), $390.1 million ($136.5 million) for the 53 full cost firms, and $3.3 billion ($766.6 million) for the 65 successful efforts firms. Mean (median) exploration expenditures (EXP) for the 119 firms was $110.5 million ($11.0 million), $18.2 million ($6.6 million) for the 53 full cost firms, and $184.6 million ($31.8 million) for the 65 successful efforts firms. Mean (median) development expenditures (DEV) for the 119 firms was $230.4 million ($22.7 million), $25.9 million ($10.2 million) for the 53 full cost firms, and $392.7 million ($56.4 million) for the 65 successful efforts firms.

Turning to performance measures, the mean (median) EFF for the 119 firms was .35 (.29), .40 (.34) for the 53 full cost firms, and .30 (.26) for the 65 successful efforts firms. The mean (median) ROS for the 119 firms was 12.8% (16.7%), 12.4% (21.8%) for the 53 full cost firms, and 13.0% (13.9%) for the 65 successful efforts firms. The mean (median) ROA for the 119 firms was 7.3% (6.1%), 5.7% (5.8%) for the 53 full cost firms, and 8.7% (6.5%) for the 65 successful efforts firms.

3.1. Spearman rank correlations between the four finding costs ratios and EFF

Table 5 A presents Spearman rank correlation coefficients between each of the 9 ratios (EFF, FC1, FC3, FC1D, FC3D, ROS, ROA, SDDA1, SDDA2), and Table 5, B and C, presents the correlations for the 53 full cost firms and the 65 successful efforts firms, respectively.

As expected, rank on each of the finding costs ratios, FC1, FC3, FC1D, and FC3D, is negatively correlated with rank on EFF. The rank correlations for the sample of 119 oil and gas firms are 2.61, 2.66, 2.66, and 2.81, respectively (see Table 5 A, row 1, columns 2, 3, 4, and 5). The rank correlations for the 53 full cost firms are

2.48, 2.53, 2.57, and 2.78 (Table 5 B). The rank correlations for the 65 successful efforts firms are2.70,2.74,2.70, and2.83 (Table 5 C). All of the rank correlations are significantly different from zero (p5.0001). By construction, higher rank on EFF is a sign of exploration efficiency relative to lower rank on EFF. The strong negative rank correlations that we find suggest that firms that rank low on finding costs ratios rank high on EFF, and firms that rank high on finding costs ratios rank low on EFF. The observed correlations between finding costs ratios and EFF are consistent with finding costs ratios functioning as a measure of exploration efficiency. In section 3.4, we will use rank regression to test if the four estimated rank correlation for the 119 firms are in fact different from each other in a statistically significant sense.

3.2. Spearman rank correlations between the four finding costs ratios and ROS and ROA

As expected, rank on each of the finding costs ratios, FC1, FC3, FC1D, and FC3D, is negatively correlated with rank on the operating profit ratio ROS. The rank

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Table 4

Descriptive Statistics for SFAS 69 Data

Variable N Mean Std. Dev. Median Minimum Maximum

A. 119 oil and gas firms for 1990–1992

ADD 357 39303 94305 4885.000000 1.666667 864833

EXP 357 110524 251307 11000 5.000000 1559370

DEV 357 230385 581401 22737 102.000000 4023210

OUT 357 62351 159966 6147.833333 80.333333 1134833

EFF 357 0.345377 0.219230 0.293752 0.015700 1.000000

FC1 357 9.241122 66.880183 2.769773 0.034414 1227.272727

FC3 357 3.388785 4.576513 2.518210 0.209560 68.238806

FC1D 357 32.026693 269.064689 7.908745 0.612497 5030.303030

FC3D 357 9.344196 13.783221 6.699927 1.374082 223.714286

ROS 356 0.127645 0.270596 0.167005 22.074211 0.634936

ROA 357 0.073463 0.169093 0.061326 20.750000 1.638618

SDDA1 356 0.461091 0.305978 0.394080 0.044624 2.704104

SDDA2 357 0.274128 1.927235 0.148421 0 36.536437

WWOTREV 357 989881 2644562 77363 0 18908910

WWNETCST 357 2005365 4931287 256903 494.000000 31619000

WWRESPFT 357 151524 437926 13569 2363000 3127000

WWDEPDEP 357 241060 557769 36334 0 3610000

WWEXPEXP 357 85387 211191 1289.000000 0 1392990

B. 53 full cost oil and gas firms for 1990–1992

ADD 159 6909.665618 9557.865634 3084.333333 12.166667 61592

EXP 159 18159 28850 6605.000000 5.000000 182700

DEV 159 25931 35573 100226 102.000000 185600

OUT 159 7636.363732 10287 3050.333333 80.333333 56185

EFF 159 0.397588 0.235002 0.338734 0.065882 1.000000

FC1 159 3.837322 4.390481 2.303009 0.034414 28.916767

FC3 159 2.803540 2.141749 2.239956 0.209560 11.668363

FC1D 159 15.418123 34.589801 6.286123 0.876200 264.532110

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Table 4 (continued)

Variable N Mean Std. Dev. Median Minimum Maximum

ROS 158 0.123728 0.349298 0.217909 22.074211 0.434148

ROA 159 0.056923 0.122187 0.057511 20.339920 0.742964

SDDA1 158 0.491089 0.398573 0.388859 0.044624 2.704104

SDDA2 159 0.133280 0.089729 0.108068 0 0.549147

WWOTREV 159 93455 124433 43392 0 690200

WWNETCST 159 390076 560956 136500 1807.000000 2883800

WWRESPFT 159 12953 39627 7663.000000 2218900 234300

WWDEPDEP 159 45072 62413 18160 0 382100

WWEXPEXP 159 214.465409 1557.214182 0 0 12200

C. 65 Successful efforts oil and gas firms for 1990–1992

ADD 195 64499 120661 10968 1.666667 864833

EXP 195 184575 320304 31800 48.000000 1559370

DEV 195 392694 746417 56437 128.000000 4023210

OUT 195 106015 206016 20685 196.666667 1134833

EFF 195 0.304175 0.197875 0.258065 0.015700 0.877856

FC1 195 13.755537 90.261925 3.080717 0.070606 1227.272727

FC3 195 3.882917 5.843175 2.790990 0.333482 68.238806

FC1D 195 45.935088 362.555014 8.990383 0.612497 5030.303030

FC3D 195 10.784560 17.360293 7.906503 1.374082 223.714286

ROS 195 0.130122 0.187587 0.138893 20.821050 0.634936

ROA 195 0.086658 0.199735 0.064756 20.750000 1.638618

SDDA1 195 0.439605 0.202527 0.406644 0.071078 1.628491

SDDA2 195 0.390977 2.603652 0.180544 0.064443 36.536437

WWOTREV 195 1704649 3410120 306053 3182.000000 18908910

WWNETCST 195 3292641 6364926 766633 494.000000 31619000

WWRESPFT 195 261446 567464 39891 2363000 3127000

WWDEPDEP 195 397934 714796 103300 766.000000 3160000

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Table 5

Spearman Rank-Order Correlation Analysis for EFF with Finding Costs and Profitability 1990–1992

EFF FC1 FC3 FC1D FC3D ROS ROA SDDA1 SDDA2

A. All 119 firms

EFF 1.00*** 2.61*** 2.66*** 2.66*** 2.81*** .24*** .10*** 2.06*** 2.21*** FC1 1.00*** .77*** .81*** .65*** 2.32*** 2.20*** .18*** .24***

FC3 1.00*** .54*** .78*** 2.38*** 2.22*** .19*** .30***

FC1D 1.00*** .73*** 2.23*** 2.13** .11*** .18***

FC3D 1.00*** 2.27*** 2.10* .09*** .27***

ROS 1.00*** .78*** 2.63*** 2.51***

ROA 1.00*** 2.75*** 2.04 NS

SDDA1 1.00*** .35***

SDDA2 1.00***

B. Full cost firms only (53 firms)

EFF 1.00*** 2.48*** 2.53*** 2.57*** 2.78*** .21*** .17*** 2.20*** 2.18** FC1 1.00*** .73*** .77*** .59*** 2.27*** 2.28*** .25*** .08 NS

FC3 1.00*** .43*** .72*** 2.36*** 2.35*** .36*** .13*

FC1D 1.00*** .66*** 2.16** 2.15* .13 NS .05 NS

FC3D 1.00*** 2.26*** 2.21*** .25*** .20***

ROS 1.00*** .90*** 2.74*** 2.41***

ROA 1.00*** 2.77*** 2.17**

SDDA1 1.00*** .51***

SDDA2 1.00***

C. Successful efforts firms only (65 firms)

EFF 1.00*** 2.70*** 2.74*** 2.70*** 2.83*** .19*** .09 NS .06 NS 2.03 NS FC1 1.00*** .80*** .85*** .70*** 2.29*** 2.15** .11 NS .27***

FC3 1.00*** .61*** .83*** 2.35*** 2.15** .05 NS .34***

FC1D 1.00*** .76*** 2.29*** 2.14* .08 NS .10 NS

FC3D 1.00*** 2.21*** 2.08 NS 2.05 NS .13*

ROS 1.00*** .82*** 2.57*** 2.29***

ROA 1.00*** 2.74*** .08 NS

SDDA1 1.00*** .20***

SDDA2 1.00***

*, **, *** denote the significance levels of .10, .05, and .01, respectively, obtained in a 2-tailed test of the null hypothesis that the estimated parameter equals zero. NS denotes not significant.

tions for the sample of 119 oil and gas firms are2.32,2.38,2.23, and2.27, respectively (Table 5 A, column 6, rows 2, 3, 4, and 5). The rank correlations for the 53 full cost firms are 2.27,2.36, 2.16, and 2.26 (Table 5 B). The rank correlations for the 65 successful efforts firms are 2.29, 2.35, 2.19, and 2.21 (Table 5 C). All of the rank correlations are significantly different from zero (generally p5.01 or better except for FC1D for full cost firms at p5.05). By inspection, the negative rank correlations with ROS are only about one half the magnitude of the negative rank correlations with EFF. The observed correlations between finding costs ratios and ROS are consis-tent with finding costs ratios functioning as an indicator of poconsis-tential profitability. In

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section 3.5, we will use rank regression to test if the four estimated rank correlations for the 119 firms are in fact different from each other in a statistically significant sense. As expected, rank on each of the finding costs ratios, FC1, FC3, FC1D, and FC3D, is negatively correlated with rank on the operating profit ratio ROA. The rank correla-tions for the sample of 119 oil and gas firms are2.20,2.22,2.13, and2.10, respectively (Table 5 A, column 6, rows 2, 3, 4, and 5). The rank correlations for the 53 full cost firms are 2.28,2.35, 2.15, and 2.21 (Table 5 B). The rank correlations for the 65 successful efforts firms are 2.15,2.15, 2.14, and 2.08 (Table 5 C). For the 53 full cost firms, the rank correlations for ROS and for ROA are similar, but for the 65 successful efforts firms, and therefore for the full 119 firms, the negative rank correla-tion on ROA is weaker than for ROS. The observed correlacorrela-tions between finding costs ratios and ROA are consistent with finding costs ratios functioning as an indicator of potential profitability. In section 3.5, we will use rank regression to test if the four estimated rank correlations for the 119 firms are in fact different from each other in a statistically significant sense.

3.3. Spearman rank correlations between each of ROS and ROA and EFF

As expected, rank on each of the operating profit ratios, ROS and ROA, is positively correlated with rank on EFF. The rank correlations for the sample of 119 oil and gas firms are .24 and .10, respectively (Table 5 A, row 1, columns 6 and 7). The rank correlations for the 53 full cost firms are .21 and .17 (Table 5 B). The rank correlations for the 65 successful efforts firms are .19 and .09 (Table 5 C). All of the rank correlations for ROS are significantly different from zero (at p5.01 or better). The rank correlation for ROA is significantly different from zero for the 53 full cost firms (p5 .04) but not for the successful efforts firms (p 5.20) and only marginally significant for the 119 firm sample (p5 .07). The result for ROS (and for ROA for full cost firms) is consistent with profitability depending, in part, on exploration efficiency.

3.4. Rank regression results comparing finding costs ratios FC1, FC3, FC1D, and FC3D to EFF

We wish to compare the association between finding costs ratios FC1, FC3, FC1D, and FC3D and EFF in such a way as to determine if the association is stronger or weaker for any of the four finding costs ratios in a statistically significant sense. The four estimated rank correlations between finding costs ratios FC1, FC3, FC1D, and FC3D and EFF presented in section 3.1 (Table 5 A, row 1, columns 2, 3, 4, and 5) do appear to differ some (2.61 for FC1,2.66 for FC3,2.66 for FC1D, and2.81 for FC3D). A test of statistical difference between any two correlations is possible using a transformation of the paired correlations to a z score followed by a t test.14_{We wish}

to compare all four correlations jointly. Use of multiple t tests to test the four correla-tions two at a time may lead to an error in estimating the statistical significance level of the tests and may lead to finding a difference in the correlations when none exists at the level desired. In regression it is possible to construct tests that impose restrictions on two or more variables at once and to test the restriction with an F test (Kmenta,

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Table 6

Rank Regression Analysis of Association between EFF, Profitability, and Finding Costs Dependent

variable Intercept FC1 FC3 FC1D FC3D R2

EFF .808*** 2.615***

(33.505) (214.695) .379

EFF .831*** 2.662***

(36.173) (216.577) .437

EFF .833*** 2.665***

(36.458) (216.769) .443

EFF .908*** 2.816***

(51.245) (226.500) .665

ROS .649*** 2.298***

(22.222) (25.871) .089

ROS .687*** 2.374***

(24.191) (27.577) .140

ROS .601*** 2.202***

(20.070) (23.890) .041

ROS .632*** 2.264***

(21.424) (25.155) .070

ROA .592*** 2.181***

(19.695) (23.473) .033

ROA .606*** 2.210***

(20.285) (24.051) .044

ROA .553*** 2.103**

(18.191) (21.960) .011

ROA .547*** 2.092*

(17.980) (21.736) .008

*, **, *** denote the significance levels of .10, .05, and .01, respectively, obtained in a 2-tailed test of the null hypothesis that the estimated parameter equals zero; t statistics in parentheses.

1986, pp. 412–422). We use regression to test the restriction that the estimated associa-tion with EFF is the same for all four finding costs ratios.

The regressions are rank regressions. The original value of the variables in an observa-tion has been replaced by the rank of that value in the sample divided byn11 for a sample of sizen. The transformed values for each variable range from 1/(n 1 1) to

n/(n11). Each variable after transformation has a uniform distribution on the zero-one interval with mean 1/2 and variance 1/12 (for large n and subject to rounding errors). The fact that the transformed variables in the rank regressions have equal means and equal variances is important. In a simple regression of one variable, Y, on another,X, if the two variables have equal variances then theb(slope) coefficient onXwill be equal to the correlation coefficient betweenXandY.15_{If the two variables}

have the same mean, the regression intercept will be that mean time 1 minus the estimatedb. Finally, for any regression of Yon one X, the regression R2_{will be the}

square of the correlation ofXand Y.

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on each of four independent variables, FC1, FC3, FC1D, and FC3D, taken one at a time for a total of 12 regressions for the purpose of developing our analysis. Table 7 presents three stacked regressions (one for of each EFF, ROS, and ROA as dependent variables) on all four finding costs ratios. Each stacked regression is used to test an equality restriction on the estimated bs (slopes) on the finding costs ratios. Table 8 reports the rejection or non-rejection of the equality restriction.

Row 1 of Table 6 presents the results of regressing rank on EFF on rank on FC1. The estimatedb (slope) coefficient is 2.615, which is also the estimated correlation and which is within rounding error of the earlier estimated rank correlation of2.61. The intercept is .808, which is approximately .5[1 2(2.615)]. The R2_{is .379, which}

is approximately the square of 2.615. In row 2 of Table 6, the estimated b (slope) coefficient on FC3 is 2.662, which is also the estimated correlation and is within rounding error of the prior estimate of2.66. The intercept is .831, which is approxi-mately .5[12 (2.662)]. The R2_{is .437, which is approximately the square of} ₂_.662.

In row 3 of Table 6, the estimatedb (slope) coefficient on FC1D is 2.665, which is also the estimated correlation and is within rounding error of the prior estimate of

2.66. The intercept is .833, which is approximately .5[1 2 (2.665)]. The R2 _{is .443,}

which is approximately the square of2.665. In row 4 of Table 6 the estimatedb(slope) coefficient for FC3D is2.816, which is also the estimated correlation and is within rounding error of the prior estimate of2.81. The intercept is .908, which is approxi-mately .5[12(2.816)]. The R2_{is .665, which is approximately the square of} ₂_.816.

Row 1 of Table 7 presents a stacked regression of EFF on all four finding costs ratios in a single regression for the purpose of testing an equality restriction on the estimatedbs (slopes) on each finding costs ratios (Kmenta, 1986, pp. 412–422). The stacked regression in row 1 of Table 7 has four times as many observations as any of the regressions in rows 1–4 of Table 6. Each observation in the stacked regression is one of the original observations from the regressions in rows 1–4 of Table 6 expanded, so that it includes, in addition to the original dependent variable and the original independent variable, the other three independent variables but with zero values for those additional variables. Arbitrarily, the intercept is assigned to the FC1 observa-tions, and zero-one dummy variables are assigned to each of the three other sets of observations: RIFC3 for FC3 observation, RIFC1D for FC1D observations, and RIFC3D for FC3D observations.

Theb(slope) coefficients for FC1, FC3, FC1D, and FC3D in row 1 of Table 7 are the same as in rows 1–4 of Table 6, respectively. The intercept is the same as the intercept for FC1 in row 1 of Table 6. The sum of the intercept and the coefficient on RIFC3 is the same as the intercept for FC3 in row 2 of Table 6. The sum of the intercept and the coefficient on RIFC1D is the same as the intercept for FC1D in row 3 of Table 6. The sum of the intercept and the coefficient on RIFC3D is the same as the intercept for FC3D in row 4 of Table 6. The R2 _{for row 1 of Table 7 is}

the average of the R2_{s in rows 1–4 on Table 6.}

Table 8 reports the rejection or non-rejection of the equality restriction on the stacked regression presented in Table 7. The restriction requires that the four bs (slopes) be estimated as if equal and that the four intercepts also be estimated as if

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Table 7

Stacked Rank Regression Analysis for the Purpose of Testing an Equality Restriction on the Estimated Coefficients on the Finding costs Ratios—Estimated Regression Coefficients

Intercept RIFC3 RIFC1D RIFC3D RFC1 RFC3 RFC1D RFC3D R2

EFF .808*** .023 .025 .100*** 2.615*** 2.662*** 2.665*** 2.816***

(36.648) (.737) (.800) (3.202) (216.074) (217.263) (217.375) (221.292) .481

ROS .649*** .038 2.048 2.017 2.298*** 2.374*** 2.202*** 2.264***

(22.174) (.916) (21.150) (2.406) (25.859) (27.346) (23.982) (25.197) .085

ROA .592*** .015 2.039 2.045 2.181*** 2.210*** 2.103** 2.092*

(19.605) (.340) (2.910) (21.047) (23.458) (24.009) (21.973) (21.750) .024 *, **, *** denote the significance levels of .10, .05, and .01, respectively, obtained in a 2-tailed test of the null hypothesis that the estimated parameter equals zero; t statistic in parentheses.

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Test of Equality Restrictions

F Test statistic for dependent variable

Restriction tested EFF ROS ROA

Joint test that (1) the coefficients on RFC1, RFC3, RFC1D, and RFC3D are equal

and (2) that the coefficients on RIFC3, RIFC1D, and RIFC3D are all zero 2.58** .99 .62 ** denotes the significance level of .05, obtained in a 2-tailed test of the null hypothesis that the estimated parameter equals zero.

equal, the latter by specifying that the coefficients on RIFC3, RIFC1D, and RIFC3D be estimated as if equal to zero. The restriction is rejected for EFF at the .05 level (see column 2 in Table 8). If the restriction is imposed, the R2_{will be reduced from}

.481 to.475,16 _{and the estimated common correlation will be}₂_.689.17

By inspection, the bs (slopes) of FC1, FC3, and FC1D are “close” at2.62,2.66, and2.66 to the estimated common correlation of2.69 under the restriction. However, based on the F test, the b of 2.82 for FC3D is significantly different from (higher than) that estimated common correlation of2.69 under the restriction and from the

bs for the other three finding costs ratios. For the 119-firm sample, FC1, FC3, FC1D, and FC3D are all negatively rank correlated with EFF and useful as measures of exploration efficiency, but rank on FC3D is significantly more useful than rank on the others.

3.5. Rank regression results comparing finding costs ratios FC1, FC3, FC1D, and FC3D to operating profit ratios ROS and ROA

Table 6 presents (see rows 5, 6, 7, and 8) the regression of ROS on each of the finding costs ratios taken one at a time for the purpose of developing our analysis. Row 2 of Table 7 presents a stacked regression including all four finding costs ratios in a single regression for the purpose of testing an equality restriction on the estimated

bs (slopes) on each finding costs ratios. Table 8 reports the rejection or non-rejection of the equality restriction. The restriction requires that the fourbs (slopes) be estimated as if equal and that the four intercepts also be estimated as if equal, the latter by specifying that the coefficients on RIFC3, RIFC1D, and RIFC3D be estimated as if equal to zero. The restriction is not rejected at normal levels of statistical significance for ROS (see column 3 in Table 8). If the restriction is imposed, the R2_{will be reduced}

from .085 to.081,18 _{and the estimated common correlation will be} ₂_.285.19

For the 119-firm sample, FC1, FC3, FC1D, and FC3D are all negatively correlated with ROS, but no finding costs measure has a higher significance. Each of the finding costs ratios is useful as an indicator of potential profitability.

Table 6 presents (see rows 9, 10, 11, and 12) the regression of ROA on each of the finding costs ratios taken one at a time for the purpose of developing our analysis. Row 3 of Table 7 presents a stacked regression, including all four finding costs ratios

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in a single regression for the purpose of testing an equality restriction on the estimated

bs (slopes) on each finding costs ratios. Table 8 reports the rejection or non-rejection of the equality restriction. The restriction requires that the fourbs (slopes) be estimated as if equal and that the four intercepts also be estimated as if equal, the latter by specifying that the coefficients on RIFC3, RIFC1D, and RIFC3D be estimated as if equal to zero. The restriction is not rejected at normal levels of statistical significance for ROA (see column 3 in Table 8). If the restriction is imposed, the R2_{will be reduced}

from .024 to .022,20 _{and the estimated common correlation will be}₂_.147.21

For the 119-firm sample, FC1, FC3, FC1D, and FC3D are all negatively correlated with ROA, but no finding costs measure has a higher significance. Each of the finding costs ratios is useful as an indicator of potential profitability.

4. Summary of findings

We used SFAS 69 data for 1988–1992 for 119 publicly owned oil and gas firms to evaluate the exploration efficiency of the firms as measured by EFF, a Cobb-Douglas regression derived index of exploration efficiency. We use the statistical association between EFF and four finding costs ratios, FC1, FC3, FC1D, and FC3D, to evaluate the usefulness of finding costs ratios as measures of exploration efficiency. FC1 and FC3 are 1- and 3-year measures that do not include development expenditures; FC1D and FC3D are 1- and 3-year measures that do include development expenditures. We measured the statistical association in two ways. First, we calculated Spearman rank correlations. Second, we used rank regression to test the restriction that the estimated association with EFF is the same for all four finding costs ratios, i.e., any difference is not statistically significant. We compared ranks on measures rather than the values of the measures to eliminate issues of differences in scale inherent in the measures. By construction, higher rank on EFF is an indicator of exploration efficiency relative to lower rank on EFF.

We found a strong negative correlation between rank on the four finding costs ratios and rank on EFF for the sample of 119 firms. The Spearman rank correlations for FC1, FC3, FC1D, and FC3D are 2.61, 2.66, 2.66, and 2.81, respectively. We used rank regression of EFF on FC1, FC3, FC1D, and FC3D to test a restriction to a common correlation (calculated as 2.69) and were able to reject that restriction. For the 119-firm sample, FC1, FC3, FC1D, and FC3D are all negatively rank correlated with EFF and useful as measures of exploration efficiency, but rank on FC3D is significantly more useful than rank on the others.

We also compared rank on FC1, FC3, FC1D, and FC3D with rank on two operating profit ratios, return on oil and gas revenues (ROS) and return on oil and gas assets (ROA), to determine the usefulness of the finding costs ratios as indicators of potential profitability. We used rank regression of ROS on FC1, FC3, FC1D, and FC3D to test a restriction to a common correlation (calculated as2.28) and were not able to reject that restriction. For the 119-firm sample, FC1, FC3, FC1D, and FC3D are all negatively correlated with ROS, but no finding costs measure has a higher significance. We also used rank regression of ROA on FC1, FC3, FC1D, and FC3D to test a restriction to

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Boynton et al. / Journal of Energy Finance and Development 4 (1999) 1–27 23

a common correlation (calculated as2.15) and were not able to reject that restriction. For the 119-firm sample, FC1, FC3, FC1D, and FC3D are all negatively correlated with ROA, but no finding costs measure has a higher significance. The results are similar with ROS and ROA but weaker for ROA. Each of the finding costs ratios is useful as an indicator of potential profitability.

In summary, our results indicate that finding costs ratios calculated from readily available financial statement data provide useful insight into both exploration effi-ciency and the potential profitability of an oil and gas firm. Our findings are important because they provide empirical evidence useful in resolving a debate within the finan-cial analyst community concerning the utility of these finding costs ratios.

Acknowledgments

This paper grows out of research under a grant (EIA Financial Assistance Instru-ment DE-FG01-92EI23624) from the Energy Information Administration (EIA) of the U.S. Department of Energy to the Institute of Petroleum Accounting (IPA) at the University of North Texas. The authors wish to express their gratitude to EIA for the direct financial support received, for the database EIA made available, and for the exchange of ideas that occurred.

Notes

1. Financial Accounting Standards Board (1982). The introductory summary to SFAS 69 states, in part:

Publicly traded enterprises with significant oil and gas activities, when presenting a complete set of annual financial statements, are to disclose the following as supplementary information, but not as part of the financial statements:

1.1. Proved oil and gas reserve quantities.

1.2. Capitalized costs relating to oil and gas producing activities. 1.3. Costs incurred in oil and gas property acquisition, exploration, and

development activities.

1.4. Results of operations for oil and gas producing activities.

1.5. A standardized measure of discounted future net cash flows relating to proved oil and gas reserve quantities.

SFAS 69 was issued November 1982 effective for fiscal years beginning on or after December 15, 1982. SFAS 69 superseded SFAS 19 (issued December 1977 effective for fiscal years beginning after December 15, 1978) and SFAS 25 (issued February 1979 effective for fiscal years beginning after December 15, 1978). SFAS 19 eliminated the full cost method of accounting for oil and gas exploration and required the successful efforts method of accounting. SFAS 19 also required disclosure of reserve quantities, costs incurred, and capitalized

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24 Boynton et al. / Journal of Energy Finance and Development 4 (1999) 1–27

costs for oil and gas producing activities. SFAS 25 suspended the effective date of parts of SFAS 19, including the requirement of the successful efforts method of accounting. SFAS 25 permitted the required disclosures to be made as supplementary information outside the financial statements. SFAS 69 limited the supplementary information disclosure requirements to the annual financial statements of publicly traded enterprises. SFAS 69 added as a required disclo-sure a standardized meadisclo-sure of discounted future net cash flows . For a more detailed history, see Brock et al. (1996, pp. 55–59).

2. Of the 119 firms in our sample, 65 used the successful efforts accounting method, and 53 used the full cost accounting method. Our database did not indicate which method was used by one firm. Full cost firms report no exploration expense. Full cost firms capitalize all exploration expenditures and then expense these expenditures as depreciation, depletion, and amortization (DDA). Suc-cessful efforts firms report exploration expenditures associated with sites that are not economically productive (“dry holes”) as exploration expense and capi-talize the expenditure associated with productive wells and then expense these capitalized costs as DDA. A full cost firm may report exploration expense if it owns an interest in an affiliate using the successful efforts method.

3. The authors wish to express their gratitude to EIA for the direct financial support received, for the database EIA made available, and for the exchange of ideas that occurred.

4. The database was provided to IPA as a research grant contractor to EIA, and use of the database was restricted to research related to the grant. Neither IPA nor the authors of this paper are authorized to distribute the EIA database to others.

5. One firm retained in the sample in 1-year reported non-zero amounts for oil and gas production quantities, additions and extensions quantities, exploration and development expenditures, and assets (capitalized costs) but zero amounts for oil and gas revenues, net income, depreciation, depletion, and amortization, and exploration expense. See Table 3.

6. The use of 6 mcf51 BOE is not universal in financial analysis. The conversion may also be based on relative oil and gas prices. White et al. (1997, p. 373, ftn. 8) state: “In recent years, in the United States, gas has sold at a lower price than its energy equivalent would suggest. Thus, many analysts use a ratio of 1:10 to combine oil and gas reserves.”

7. See for example:

After reserve replacement ratios, finding costs are the most critical internal factor for the oil companies. (Randol, 1993, p. 17)

Wall Street performs a big disservice to the institutional investment community by placing undeserved emphasis on reserve replacement ratios and finding costs as measures of how well a company has done. Oil compa-nies do not pay particular attention to these numbers, and no major oil company bases its own internal economics on them. (Mayer, 1993, p. 29)

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Boynton et al. / Journal of Energy Finance and Development 4 (1999) 1–27 25

Although annual finding costs are volatile, over longer time periods they measure management’s proficiency in discovering reserves. (White et al., 1997, p. 372)

The industry has relentlessly been attacking its cost structure since 1986 with everything from high technology to joint ventures. . . . [B]etween 1981 and 1996 the cost of finding and developing a barrel of crude came down from $21 to under $5, with much of the reduction increasingly coming from technological innovation. (Yergin, 1998, p. A-22)

8. See Cornwell et al. (1990) for an efficiency analysis using the model applied to the airline industry. See Reiss (1990) for an investment analysis using the model to analyze changing investment in reserves by oil and gas exploration firms in response to changing prices and financial constraints.

9. For discussion of regression using a Cobb-Douglas production model, see Chiang (1984, pp. 414–431), Greene (1990, pp. 216–217, 253–257, and 266), Griffiths et al. (1993, pp. 384–394 and 721–726), Hsiao (1986, pp. 26–28), and Kmenta (1986, pp. 511–517). For a discussion of regression models using firm-specific intercepts, year-firm-specific intercepts, and year-firm-specific slopes, also known as panel models, fixed effects models, analysis of covariance models, binary variable models, or dummy variable models. See Greene (1990, pp. 239–251), Griffiths et al. (1993, pp. 411–430), Hsiao (1986, pp. 1–32), and Kmenta (1986, pp. 460–473).

10. The use of year-specific intercepts and year-specific slopes permits the model to adjust for industry-wide changes year by year including shocks such as wars, major price changes, and changes in technology.

11. The natural anti-log of the estimate of the firm-specific intercept is the estimate of the firm-specific multiplier. The estimate of the firm-specific intercept for Firm 1 is the estimated intercept (INTERCEP) for the model. For Firmi(i5

2–119), the estimate of the firm-specific interceptaiis the sum ofINTERCEP

and di, the coefficient of the dummy variable FIRMi. The 118-firm dummy

coefficients are not shown in Table 1 to save space.

12. Of the 119 firms in our sample, 65 used the successful efforts accounting method, and 53 used the full cost accounting method. Our database did not indicate which method was used by one firm.

13. SDDA1 and SDDA2 are operating expense ratios defined to parallel the defini-tions of the two operating profit ratios, ROS and ROA. We add oil and gas depreciation, depletion, and amortization (DDA) and exploration expenses together in order to minimize the difference between the successful efforts and full cost accounting methods.

14. See Hogg and Craig (1978, p. 303).

Z 51/2ln[(11r1)/(12r1)] 21/2ln[(11r2)/(12r2)]

√

1/(n 23)

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26 Boynton et al. / Journal of Energy Finance and Development 4 (1999) 1–27

covariance of Xand Ydivided by the variance ofX.The correlation between

XandYis defined as the covariance ofXandYdivided by the product of the standard deviations of X and Y, that is by the square root of the product of the variances ofXandY.The mean ofYminusbtimes the mean ofXequals the intercept. R2 _{defined in terms of the total sums of squares variation may}

be decomposed into the square of the covariance of X and Y divided by the product of the variances ofXandY.See Kmenta (1986, pp. 212–215, 240–241, and 301–302).

16. The estimated common correlation is estimated using data not presented in Table 7. The numerator of the F test for the tested restriction is .1107 and the degrees of freedom are 6. The product, .6642, is the reduction in the sums of squares for the model and increase in the error sums of squares as a result of the restriction. The change in the R2 _{is the reduction in sums of squares for}

the model, .6642, divided by the total sums of squares 116.84257, or .0057, reducing the R2_{from .4809 to .4752. See Kmenta (1986, pp. 412–422).}

17. The estimated common correlation under the restriction will be the square root of the restricted R2_{. The negative square root is used because the association}

is negative.

18. The estimated common correlation is estimated using data not presented in Table 7. The numerator of the F test for the tested restriction is .0747, and the degrees of freedom are 6. The product, .4482, is the reduction in the sums of squares for the model and increase in the error sums of squares as a result of the restriction. The change in the R2 _{is the reduction in sums of squares for}

the model, .4482, divided by the total sums of squares 116.68338, or .0038, reducing the R2_{from .0848 to .0810. See Kmenta (1986, pp. 412–422).}

19. The estimated common correlation under the restriction will be the square root of the restricted R2_{. The negative square root is used because the association}

is negative.

20. The estimated common correlation is estimated using data not presented in Table 7. The numerator of the F test for the tested restriction is .0494, and the degrees of freedom are 6. The product, .2964, is the reduction in the sums of squares for the model and increase in the error sums of squares as a result of the restriction. The change in the R2 _{is the reduction in sums of squares for}

the model, .2964, divided by the total sums of squares 116.42724, or .0025, reducing the R2_{from .0241 to .0216. See Kmenta (1986, pp. 412–422).}

21. The estimated common correlation under the restriction will be the square root of the restricted R2_{. The negative square root is used because the association}

is negative.

References

Brock, H., Jennings, D., & Feiten, J. (1996).Petroleum Accounting: Principles, Procedures, & Issues.4th ed. Denton, TX: Professional Development Institute.

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in a single regression for the purpose of testing an equality restriction on the estimated bs (slopes) on each finding costs ratios. Table 8 reports the rejection or non-rejection of the equality restriction. The restriction requires that the fourbs (slopes) be estimated as if equal and that the four intercepts also be estimated as if equal, the latter by specifying that the coefficients on RIFC3, RIFC1D, and RIFC3D be estimated as if equal to zero. The restriction is not rejected at normal levels of statistical significance for ROA (see column 3 in Table 8). If the restriction is imposed, the R2_{will be reduced} from .024 to .022,20 _{and the estimated common correlation will be}_2.147.21

4. Summary of findings

(2)

Acknowledgments

Notes

1. Financial Accounting Standards Board (1982). The introductory summary to SFAS 69 states, in part:

1.1. Proved oil and gas reserve quantities.

1.2. Capitalized costs relating to oil and gas producing activities. 1.3. Costs incurred in oil and gas property acquisition, exploration, and

development activities.

1.4. Results of operations for oil and gas producing activities.

1.5. A standardized measure of discounted future net cash flows relating to proved oil and gas reserve quantities.

(3)

3. The authors wish to express their gratitude to EIA for the direct financial support received, for the database EIA made available, and for the exchange of ideas that occurred.

7. See for example:

After reserve replacement ratios, finding costs are the most critical internal factor for the oil companies. (Randol, 1993, p. 17)

(4)

Although annual finding costs are volatile, over longer time periods they measure management’s proficiency in discovering reserves. (White et al., 1997, p. 372)

and di, the coefficient of the dummy variable FIRMi. The 118-firm dummy

coefficients are not shown in Table 1 to save space.

12. Of the 119 firms in our sample, 65 used the successful efforts accounting method, and 53 used the full cost accounting method. Our database did not indicate which method was used by one firm.

14. See Hogg and Craig (1978, p. 303).

Z 51/2ln[(11r1)/(12r1)] 21/2ln[(11r2)/(12r2)]

√

1/(n 23)

(5)

covariance of Xand Ydivided by the variance ofX.The correlation between XandYis defined as the covariance ofXandYdivided by the product of the standard deviations of X and Y, that is by the square root of the product of the variances ofXandY.The mean ofYminusbtimes the mean ofXequals the intercept. R2 _{defined in terms of the total sums of squares variation may} be decomposed into the square of the covariance of X and Y divided by the product of the variances ofXandY.See Kmenta (1986, pp. 212–215, 240–241, and 301–302).

17. The estimated common correlation under the restriction will be the square root of the restricted R2_{. The negative square root is used because the association} is negative.

19. The estimated common correlation under the restriction will be the square root of the restricted R2_{. The negative square root is used because the association} is negative.

21. The estimated common correlation under the restriction will be the square root of the restricted R2_{. The negative square root is used because the association} is negative.

References

Brock, H., Jennings, D., & Feiten, J. (1996).Petroleum Accounting: Principles, Procedures, & Issues.4th ed. Denton, TX: Professional Development Institute.

(6)

Clinch, G., & Magliolo, J. (1992). Market perceptions of reserves disclosures under SFAS No. 69.The Accounting Review 67, 843–861.

Chiang, A. (1984).Fundamental Methods of Mathematical Economics, 3rd ed. New York: McGraw-Hill Book Company.

Cornwell, C., Schmidt, P., & Sickles, R. (1990). Production frontiers with cross-sectional and time series variation in efficiency levels.Journal of Econometrics 46, 185–200.

Financial Accounting Standards Board. (1982). Statement of Financial Accounting Standard No. 69: Disclosures about Oil and Gas Producing Activities. Norwalk, Connecticut: Financial Accounting Standards Board.

Gaddis, D., Brock, H., & Boynton, C. (1992). Pros, cons of techniques used to calculate finding costs. Oil and Gas Journal (June 1) 90, 93–95.

Greene, W. (1990).Econometric Analysis.New York: Macmillian Publishing Company.

Griffiths, W., Hill, C., & Judge, G. (1993). Learning and Practicing Econometrics. New York: John Wiley & Sons.

Hogg, R., & Craig, A. (1978).Introduction to Mathematical Statistics. 4th ed. New York: Macmillian Publishing Company.

Hsiao, C. (1986).Analysis of Panel Data.Econometric Society Monograph No. 11. New York: Cambridge University Press.

Kmenta, J. (1986).Elements of Econometrics.2nd ed. New York: Macmillian Publishing Company. Mayer, M. (1993). Interpreting the oil industry numbers. In T. Petrie (Ed.),The Oil and Gas Industries

(pp. 23–43). Charlottesville, VA: AIMR Publications.

PricewaterhouseCoopers and the University of North Texas Institute of Petroleum Accounting (1999). 1999 PricewaterhouseCoopers Survey of U.S. Petroleum Accounting Practices, University of North Texas: Institute of Petroleum Accounting.

Randol, W. (1993). Factors affecting oil industry dynamics. In T. Petrie (Ed.),The Oil and Gas Industries (pp. 13–22). Charlottesville, VA: AIMR Publications.

Reiss, P. (1990). Economic and financial determinants of oil and gas exploration activity. In G. Hubbard (Ed.),Asymmetric Information, Corporate Finance, and Investment(pp. 181–206). National Bureau of Economic Research. Chicago: University of Chicago Press.

White, G., Sondhi, A., & Fried, D. (1994).The Analysis and Use of Financial Statements, 2nd ed. New York: John Wiley & Sons.

Directory UMM :Data Elmu:jurnal:J-a:Journal of Energy Finance & Development:Vol4.Issue2.Jan1999:

Evaluating the exploration efficiency of oil and gas

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