Russell G. Thompson
University of Houston
The 1980s were turbulent years for publicly traded oil companies. High real oil prices typified the early 1980s, whereas low real oil prices typified the early 1990s.
In recent years, the large publicly traded oil companies have been restructuring and downsizing to improve efficiency. Non-U.S. exploration expenditures, relative to U.S. outlays, have been rising; however, earnings from the new non-U.S. investments have not yet materialized.
The former OGJ4OO has become the OGJ300. As reported earlier this year, "In the past 7 years, profits for U.S. companies have been depressed...The number of public companies operating in the U.S. is declining as the industry becomes more concentrated (OGJ, Sept. 20, p. 47)."
This theme also typified the Fortune 500, where several questions were highlighted: "Is cost cutting all there is to corporate life? Will job destruction be the dominant theme for the 500 from now on? Will today's brutal competitive environment condemn many of the 500 to middling profits (Fortune, April 19)?"
Typically, the large, publicly traded oil companies rank high in the Fortune 500. Downsizing by prominent firms was significant from 1991 to 1992. For example, 11 large oil companies eliminated around 31,000 jobs in 1992 (Table 1). Very likely, the ripple effects of these layoffs alone were around 100,000 jobs in total across the economy.
Newly developed decision theory forces one to question the widely held singular focus on efficiency because improving efficiency will not necessarily improve profits.1
This is especially likely in the oil industry, where price volatility is the norm. Because its products are so basic, its price volatility typically ripples widely throughout the economy.
In light of this, efficiency and profitability in the oil industry require separate treatment. More specifically, the efficient are not necessarily the most profitable; conversely, the most profitable are not necessarily the most efficient.
Such a decoupling of efficiency and profitability requires a totally new look at business strategy. In the face of highly variable prices, firms can no longer depend on the long-accepted duality norm between profits and efficiency.
MODELING PERSPECTIVE
Thompson and Thrall's profit-oriented modeling perspective, cited above, seems ideally suited as a complement to efficiency in analysis of the exploration and production activities of firms in the oil and gas industry.
Managers generally know a lot about how much they are going to spend for exploration, how many oil and gas reserves will be brought into the year, and what their planned production levels will be.
Further, they may have pretty good ideas about what their stockholders' equity will be and the likely market value of their firms. However, managers have experienced great difficulty in anticipating well the prices of crude and natural gas, yields of their firms' stocks, and opportunity costs for their stockholders' equity.
As in the efficiency modeling approach by Charnes et al.,2 3 4 the Thompson-Thrall profit modeling approach takes the inputs and outputs about which a lot is known as data-y's and x's. It takes the prices about which little is known as the unknown variables-u's, v's. It may be applied to any firm.
MODELING APPLICATION
Applications of this approach might be made for any number of years. They might also be made internal to any firm to evaluate different entities of relevance; e.g., divisions, regions, and fields.
The worldwide oil and gas modeling application here has three inputs and four outputs.
The outputs:
y1 is the firm's liquids production.
y2 is the firm's natural gas production.
y3 is the firm's market value.
The inputs:
x1 is the firm's capital and exploration spending.
x2 is the firm's stockholders' equity.
x3 is the firm's liquids reserves.
x4 is the firm's natural gas reserves.
This modeling format was applied to the 11 publicly traded oil companies identified in Table 1. All 11 of these firms reported not only the outputs and inputs above but also profits and market values. The years of application were 1990, 1991, and 1992.
The data for these outputs and inputs are presented in Table 2. The source of these input-output data were the OGJ300 and Fortune 500, as reported for 1990 in 1991, 1991 in 1992, and 1992 in 1993.
PROFIT RATIO
The Thompson-Thrall profit ratio is formed as follows for each selected firm:
PR =
u1y1 + u2y2 + u3y3
(1)
v1x1 + v2x2 + v3x3 + v4x4
where u1 is the liquids price per barrel, u2 is the natural gas price per thousand cubic feet, u3 is the stock yield, v1 is the permit price of capital and exploratory spending (taken to be one below), v2 is the opportunity cost rate of return for stockholder equity, v3 is the rental price of carrying a barrel of liquids reserves from one year to the next, and v4 is the rental price of carrying 1 Mcf of gas reserves from one year to the next. The y's and x's are defined above.
In the numerator, the benefits to the firm from upstream production are represented by u1y1 + u2y2; the benefits to the firm from yield on the firm's market value (including downstream) is represented by u3y3.
The denominator represents (i) the costs of the firm's exploration and development activities, v1x1; (ii) the opportunity cost of the stockholders' equity, v2x2, relative to a secure government investment; and (iii) the rental costs of the firm carrying its liquid and natural gas reserves from one year to the next, v3x3 + v4x4.
The maximization of equation (1), subject to constraints discussed more fully below, provides the maximum profit ratio (MPR); similarly, the minimization of equation (1) provides the minimum profit ratio (mPR).
Preliminary estimates are made to implement the methodology and illustrate a conceptual point for management consideration. Further refinements in the model might be made in additional modeling efforts to improve the results presented here.
COST NORMALIZATION CONSTRAINT
In the optimization process, costs are restricted to a unit level:
v1x1 + v2x2 + v3x3 + v4x4 = 1 (2)
This constraint works toward providing a "level playing field" for the small vs. large firms in the optimization of equation (1) above.
Bounds for the modeled price ratios were formed from the ranges of variation observed in the prices.
Net production value per barrel for liquids production worldwide reflects the range reported by Arthur Andersen in its Oil and Gas Disclosures for the 11 large companies (with an estimate for 1992). The range of net production value per thousand cubic feet for natural gas worldwide is derived from the range of net production value for liquids per barrel by use of the annual average U.S. natural gas price (wellhead) to U.S. crude oil price (imported) reported by the Monthly Energy Review.
The stock price range reflects the variation in the Standard & Poor's Industrials yields reported monthly by the Survey of Current Business.
The range of yield on stockholders' equity represents the range of rates on 3 month U.S. Treasury bills reported (in monthly terms) by the Economic Report of the President.
For the 11 companies, the range of rental rates for liquid reserves was developed from the range of reserve present values per barrel reported by Arthur Andersen in Oil and Gas Disclosures (with an estimate for 1992) and the range of Aaa bond rates reported (in monthly terms) by the Economic Report of the President. The product of the maximum values gives the largest endpoint (LEP) of the range; similarly, the product of the minimum values gives the smallest endpoint (SEP) of the range.
As for natural gas production value (net) above, the range for the natural gas reserve rental rate is derived from the range toy the liquid reserve rental rate.
Using the ranges in Table 3, the smallest lower-bound (SLB) and the largest upper-bound (LUB) were found for each ratio of modeled prices. These ratios bounded the respective modeled prices by use of inequalities.
For example, for the three outputs in 1991, the inequality bounds were set up as follows:
[See original document for statistics]
Similarly, the bounds for the other price ratios were formed from the data in Table 3. The complete set of ratio bounds for the modeled prices totals 22 pairs of inequalities.
OPTIMIZATION PROCESS
Firm by firm, the maximization (or minimization) of the PR in equation (1) finds the prices-i.e., u's and v's-maximizing (or minimizing) each firm's profit ratio subject to the cost normalization in equation (2) and the price ratio bounds as illustrated in equation (3). Operational profits terminology is used below since only the essence of profits is accounted for in the modeling.
MPR 1 shows where operational profit potential exists. MPR < 1 shows where operational losses are assured. Similarly, mPR < 1 shows where operational loss potential exists, and mPR 1 shows where operational profits are assured.
Mathematically, the MPRs and mPRs evaluate comprehensively all of the differentials embedded in the model. They do not focus selectively on any one differential. Further, all of the differentials are simultaneously considered.
Relative to statistics, the optimization process is altogether different. For example, the least-squares coefficients are found by optimizing across all of the data points simultaneously (in an averaging sense).
Here, the MPRs and mPRs are found by optimizing data point by data point. Roughly speaking, the aim is to extract the maximum amount of profit and loss substance from each data point (in a frontier sense).
ANALYSIS METHODS
A multivariate statistical analysis was made of the three outputs relative to the four inputs for the 11 firms in the years 1990-92.
Indicator variables were included for the years 1991 and 1992.
The results highlighted the following: The ranking of the input variables (from the most to least significant) was capital and exploratory spending, stockholders' equity, liquids reserves, and natural gas reserves.
Similarly, the ranking of the output variables was liquids production, market value, and natural gas production. For liquids output, liquids reserves were highly significant (positive); for natural gas output, natural gas reserves were highly significant (positive), followed by liquids reserves (negative) and capital and exploration spending (positive). For market value output, liquids reserves followed by stockholders' equity were highly significant (both positive).
Other variables were not significant. The partial correlation was negative and small (r = -.05) between total revenue and market value; i.e., market value includes potential beyond the present.
ANALYSIS RESULTS
In Table 4, the MPRs are presented for the 11 companies for 1990-92. Chevron, Amoco, Texaco, Arco, Phillips, Unocal, and Oryx exhibited average maximum operational profit potential across the period.
Excluding Amoco, these firms reported actual profits as a percent of total assets in all 3 years (Table 5). This actual profit ratio is only one of several commonly evaluated.
Also, Exxon, Mobil, Occidental, and Amerada Hess exhibited average assured operational losses during 1990-92. Actually, Occidental reported losses in 2 of these years, 1990 and 1992.
Further, Amoco, whose MPR was less than 1 in 1990, reported losses in 1992. There were required write-offs for accumulated health care liabilities in this period.
Strikingly, the lowest average maximum profit potential during 1990-92 was exhibited by Amerada Hess, which was appreciably less than 1. This indicator of assured operational losses was consistent with the firm's relatively low profits in 1991 and 1992. Also, Occidental's assured average operational losses were consistent with its actual losses in 1990 and 1992.
Exxon's and Mobil's assured average operational losses stand in contrast to their actual profits in all 3 years.
Perhaps this result is due to the firms' relatively large liquids and natural gas reserves relative to their rental costs in this modeling study.
In Table 6, the mPRs are presented for all 11 companies during 1990-92. Recall that mPR 1 (none in Table 6) assures operational profits and that mPR < 1 indicates operational loss potential.
Loss potential was exhibited by all 11 firms in the 3 years.
However, across firms, the increase in the average mPR in 1992 vs. 1991 and 1990 suggests the loss potential in the 1990s may be beginning to diminish. Most strikingly, the average operational profit potentials across firms (both maximum and minimum) indicate an upward trend in this industry, in contrast to the downward trend in the corresponding average annual profits as percent of total assets.
However, the average market value from 1990 to 1991, 1990 to 1992, and 1991 to 1992 showed an upward perspective over these three year-to-year comparisons (Table 7). Notably, Exxon, whose average maximum profit potential was virtually one, and Mobil, whose MPRs exhibited an upward pattern (with MPR 1 in 1992), both exhibited an upward pattern in these market value comparisons.
EFFICIENCY VS. PROFITABILITY
For a single output relative to a single input, recall that the most efficient firm obtains the greatest output from the level of input used relative to all the other firms.
Taking into account the price ratio bounds, the relative efficiencies were also computed from the same data analyzed in the profitability analysis (Table 8).
The highest average efficiency was exhibited by Unocal, and the lowest average efficiency was exhibited by Amerada Hess. Further, the five largest companies studied-Exxon, Mobil, Chevron, Amoco, and Texaco-each exhibited a decreasing trend in efficiency during 1990-92.
Also, the average annual efficiency of the 11 companies studied shows a downward trend, in the presence of the downsizing and restructuring made in the 1990s. Most strikingly, the average annual maximum and minimum profit ratios suggest the business environment for the companies studied is getting better, whereas the average annual efficiencies suggest the business environment for these companies is getting worse.
The average annual market value chances for the 11 firms lend support to the operational profit indication.
COMPARISONS
By firm, the average MPRs and the average mPRs were positively and highly correlated (Fig. 1, r = 0.96). Similarly, the average MPRs were positively correlated with the average efficiencies (Fig. 2, r = 0.9). However the most efficient firm ranked fourth in maximum profit potential.
Figs. 3, 4, and 5 represent an attempt to relate the measures obtained to real world measures of profits as percent of total assets (P/TA%). Of course, the relatively small number of firms and small number of years analyzed need to be kept in mind.
Putting things in an odds framework seems meaningful. The following is evident: (i) Fig. 3 suggests the odds of a firm having 2.3% or greater profits, given an average MPR 1, is 7 out of 8 (placing Exxon in the average MPR 1 grouping); (ii) Fig. 4 suggests the odds of a firm having 2.3% or greater profits, given an average MPR 0.4, is 8 out of 10; and (iii) Fig. 5 suggests the odds of a firm having 2.3% or greater profits, given an average efficiency of 0.8 or greater, is 7 out of 8.
Notably, the firms in the comparison sets are not necessarily the same.
The input-output data base was extended to include Coastal, Ashland, Pennzoil, Kerr-McGee, Mitchell, Murphy, and Louisiana Land & Exploration. Much similarity existed between those results and the ones presented here.
SUMMARY AND CONCLUSIONS
Both theory and practice show the importance of evaluating profits and efficiency as separable concepts.
Different perspectives should be expected, as found in this study. Industry's singular efficiency focus may be risky in widespread layoffs and investment restructuring.
Yes, there appears to be much more to corporate life than cost-cutting. No, job destruction does not have to be the dominant theme in the rest of the 1990s. Yes, managers need to refocus their strategies; they need to use profit norms in addition to efficiency norms.
The efficiency, norm, which has been the economic guiding light in past, more tranquil price environments, is no longer sufficient for success in today's volatile price environment. Variation in the prices is too great to be ignored in decisions affecting thousands of jobs and large capital investments.
Advanced profit ratio methods provide a sound way for managers to improve their firms' possibly middling profits and weak competitive positions.
REFERENCES
- Thompson, R.G., and Thrall, R.M., "Polyhedral Assurance Regions with Linked Constraints," to appear in Advances in Computational Economics, Boston: Kluwer Academic Pub., Vol. 4, 1994. See also Rice University Working Paper No. 92, October 1992.
- Charnes, A., Cooper, W.W., and Rhodes, E., "Measuring the Efficiency of Decision Making Units," European Journal of Operational Research, Vol. 2, 1978, pp. 429-444.
- Charnes, A., and Cooper, W.W., "Preface to Topics in Data Envelopment Analysis," Normative Analysis in Policy Decisions, Public and Private, Annals of Operations Research, Basel: J.C. Baltzer AG, Vol. 2, 1985, pp. 59-94.
- Charnes, A., Cooper, W.W., and Thrall, R.M., "A Structure for Classifying and Characterizing Efficiencies and Inefficiencies in Data Envelopment Analysis," The Journal of Productivity Analysis, Vol. 2, 1991, pp. 197-237.
Thompson, R.G., Singleton, F.D. Jr., Smith, B.A., and Thrall, R.M., "Comparative Site Evaluations for Locating High Energy Lab in Texas," TIMS Interfaces, 1986, pp. 1380-1395.
Thompson, R.G., Langemeir, L., Lee, C-T, Lee, E., and Thrall, R.M., "The Role of Multiplier Bounds in Efficiency Analysis with an Application to Kansas Farming," Journal of Econometrics, Vol. 46, No. 1/2, Oct./Nov. 1990, pp. 93-108.
Copyright 1993 Oil & Gas Journal. All Rights Reserved.