1 See Koomey (1990), Lovins and Lovins (1991), Ayers (1993), Jaffe andStavins (1993), and DeCanio (1993) for an introduction to this literature. The paradox was one of the central themes of a special issue of Energy Policy devoted to ‘Markets for energy efficiency’ in October 1994 (Huntington et al, 1994). Certain individual articles from that special issue are cited in the text. For studies examining the potentialfailure of firms to achieve ‘general’ production efficiency, see the extensive empirical literature launched by Leibenstein’s (1966) original article on X-efficiency.

2 Train (1985) surveys this literature. See also Howarth and Sanstad (1995) and DeCanio and Laitner (1997). Energy Policy . Vol. 26, No. 5, pp. 441

— 454, 1998 ( 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0301-4215/98 $ 19.00 # 0.00 

### PII: S0301-4215(97)00152-3 

# The efficiency paradox: bureaucratic and organizational barriers to profitable energy-saving investments

Stephen J DeCanio **Department of Economics, Uni**v**ersity of California, Santa Barbara, CA 93106, U.S.A.**

The paradox of why profitable energy-saving investments are not undertaken continues to provoke debate. The underlying phenomenon might be called the ‘efficiency paradox,’ because it represents a case in which business firms, which are often presumed (or taken axiomatically) to be economically efficient, make decisions that do not maximize profits. New data from one of the US Environmental Protection Agency’s voluntary pollution prevention programs enables this paradox to be explored statistically. The data show that both the level and variation in returns to lighting upgrade investments cannot be explained by standard economic models. Substantial gains in energy efficiency can be achieved without sacrificing profitability. Both economic and organizational factors account for some of the variation in observed returns to these investments, but the results suggest a need for improved and more comprehensive theories of the investment behavior of firms and other organizations. ( 1998 Elsevier Science Ltd. All rights reserved. Keywords : Climate policy; Energy efficiency; Investment behavior; Theory of the firm



## The efficiency paradox

A strong empirical anomaly stands at the center of the study of the economics of energy efficiency. For both firms and consumers, there is abundant evidence that highly profitable energy-saving opportunities exist, 1 yet the technologies embodying these opportunities have not spread universally throughout the economy. In the caseof consumers, the failure to adopt some of these technologies has been accounted for by unrealistically high implicit discount rates in standard investment models. 2 In the case of firms, which will be the focus of the present paper, the evidence is even more puzzling. For while it may be plausible to argue that consumers face credit rationing, or have shorter or longer time horizons based on their income and personal circumstances, these limitationsshould not apply to firms. The neoclassical profit-maximizing firm should be risk neutral because shareholders can diversify their portfolios, and should invest in allprojects having a positive net present value, using a discount rate for each project suitable for that project’sriskiness. Yet undeniable evidence exists that some energy-saving investments, using proven, low-risk technologies, show rates of return well in excess of 30% or more. Why have not such improvements been adopted by all firms?Economists generally are skeptical that systematic unexploited profit opportunities exist (Schelling, 1992; Sutherland, 1991, 1994; Palmer and Simpson, 1993; see references in Jaffe et al, 1995). The researchers who have articulated this skepticism most clearly have done so in an open-minded way

—they are interested in possible theoretical justifications for the continued existence of profitable energy-saving opportunities, and care about thestrength of empirical evidence for and against such possibilities. Perhaps more common among economists is theattitude that such systematically unexploited profit opportunities are impossible a priori . It is often argued suchfailures of profit maximization are only apparent, because ‘transactions costs’ and other hidden costs reduce the seemingly high returns that can be realized from

4413 See Sanstad and Howarth (1994) for a good conceptual discussion of transactions costs in this context. 4 For the ‘Porter hypothesis,’ see Porter (1990), Moore and Miller (1994), UNEP (1994), Repetto (1995), and Porter and van der Linde (1995a, b). For the benefits of environmental activism on the part of management, see Romm (1994). For a skeptical view, see Palmer et al (1995).

5 Definitive description of the various programs can be obtained bycontacting the EPA’s Atmospheric Pollution Prevention Division directly. A good introduction to Green Lights is given in Kwartin and Weisberg (1992). 

### energy-saving investments. 3 A third type of doubt is more subtle. This line of thinking admits the possibility of a genuine failure of maximization, but holds that it should not be of policy concern because any activismdesigned to counteract maximization failures in one dimension may cause firms to miss other, even more profitable opportunities. Social policy designed to improve energy efficiency might be better directed at improving profitability by, for example, controlling health care or personnel turnover costs. It is not really a scientific argument to contend that unexploited profit opportunities cannot exist because firms are, by definition, profit-maximizing entities. (Thismight be called the ‘strong profit maximization assumption.’) Hypotheses or conjectures cannot be scientific unless they are vulnerable to falsification by evidence (Popper, 1934, 1962). Furthermore, the strong profit maximization assumption is contradicted by the very existence of firms themselves. If profit maximization were ubiquitous or automatic, there would be no management function, no market for corporate control, and no excess returns to entrepreneurial skill. Businesses would run themselves, requiring no more than a mechanical sort of coordination. In contrast to this view, the modern theory of the firm is preoccupied with questions of management’s role in overcoming problems of agency, information, and incentive compatibility (Alchian and Woodward, 1988; Williamson, 1985).

The transactions cost or hidden cost argument is more difficult to address. If transactions costs are by definitionunmeasurable, then there is no way that empirical evidence can be brought to bear and the appeal to transactions costs becomes as tautological as the strong profit maximization assumption. On the other hand, while transactions costs are in principle observable, real-world information about them is not plentiful. Koomey and Sanstad (1994) present four examples of cases in which hidden costs do not appear to be the explanation of slow diffusion of energy-efficient technologies. They recognizethe need for more empirical investigation, however, stating that ‘‘[i]t is our judgment

2 that resolution of this debate ultimately depends upon careful quantitative analysis of the engineering and economic characteristicsof specific technologies, as well as similarly careful assessment of policies directed toward energy efficiency’’ (p. 826). The argument that potential energy-saving profitsexist but are not the best unexploited opportunities available to firms may be true, but a prescription of policy passivity does not follow. According to this argument, firms are faced with an embarrassment of riches in terms of opportunities to increase productivity. Even though moving in one particular direction (say, towards greater energy efficiency) would be Pareto-improving

—the firm would become more profitable at no tangible cost to consumers or taxpayers

—policy should not be invoked tobring about that outcome because other Pareto-improving possibilities might thereby be foreclosed. This amounts to a situation in which ‘the best is the enemy of the good.’ But this argument also embodies unwarranted inferences. Without specifying why the (unobserved) other profit opportunities are also overlooked or avoidedby firms, it is not logically valid to conclude that energysaving investments would preclude them. If energy-saving profits are missed because of managerial slack,skewed incentives, or other types of organizational dysfunction, then reform of internal procedures that would allow (or require) managers to realize the profits from energy saving could have positive spillover effects forother aspects of the firm’s performance. Concurrent benefits to environmentally induced innovation are onepossible theoretical basis for Porter’s intriguing hypothesis 4 that certain kinds of environmental regulations

—or environmental consciousness in general

—can have positive rather than negative effects on the ‘competitiveness’ of firms.

These issues have obvious policy relevance. The Parties to the Framework Convention on Climate Change appear to be moving towards some protocol involving greenhouse gas emissions reductions, although the exact outcome of the negotiations that will take place in Kyoto in December 1997 remains unclear as of this writing. The extent of voluntary efforts to reduce emissions such as those outlined in the United States’ Climate ChangeAction Plan (Clinton and Gore, 1993) is in part a function of whether firms and individuals find it privately beneficial to reduce emissions. A number of voluntary pollution prevention programs were launched by the US Environmental Protection Agency in the 1990s, and the environmental impact of those programs ultimately willdepend on how many profitable green investment opportunities exist. Furthermore, evaluation of the economicconsequences of policies to reduce greenhouse gas emissions depends on the availability of unrealized energysaving opportunities. If enough such opportunities can be found, the scope of ‘no regrets’ emissions reduction options is large.

One by-product of the EPA’s voluntary pollution-prevention initiatives is that these programs have begun togenerate a great deal of new information on the dimensions of the efficiency paradox. The flagship of these programs, Green Lights, has been in operation since 1991, and has compiled a large quantity of detailed,micro-level data on lighting upgrades made by participating companies. 5 These data, covering thousands of 442 **The e** ffi **ciency paradox: S J DeCanio**6 Green Lights technologies often enhance the quality of light while reducing electricity consumption. The Green Lights program requires participating organizations to consider only lighting retrofits that maintain at least as high a quality of lighting as the older technologies they replace.

7 Sutherland (1991) makes this prediction explicitly in his discussion of what the Capital Asset Pricing Model (CAPM) implies for returns to energy-efficient investments by firms and households: ‘According to the CAPM, the critical components of an energy-efficiency investment by a corporation are that capital budgeting decisions are reflected in equity values and that equities are traded on a market where their unsystematic risk is reduced through diversification. The model yields testable hypotheses about required discount rates on energy-efficiency investments....When such investments are undertaken by publicly held corporations, the required rate of return will be comparable to that of any other asset in the same risk ( b) class....The average rate of return on business investments is just over 10%. [The average rate of return on common stocks from 1926

— 1982 was 11.58%—see Sharpe (1985, pp.9 and 10).] The CAPM implies that such a yield would be an approximate average for energy-efficiency investments by businesses’’ (p. 29).

8 Other requirements include signing of a Memorandum of Understanding (MOU) spelling out the commitment to upgrade at least 90% of eligible floorspace within 5 yrs, provided the IRR on the upgrades is at least the prime rate # 6% and there is no degradation in lighting quality. The original ‘prime # 6%’ rule has been relaxed to be a flat 20% IRR (U.S. Environmental Protection Agency, 1995). 

### lighting investment projects undertaken by over 1400 participating firms, provides a unique source of statisticalinformation on the economics of a large class of energysaving investments. The present paper represents an effort to bring some of this data to bear on the efficiency paradox. The data show that although obvious economic factors such as electricity prices and intensity of usage clearly do influence the profitability of energy-saving lighting investments, an entirely separate set of factors is simultaneously at work. These other determinants of investment behavior and outcomes can best be described as bureaucratic or organizational barriers to economicoptimization. The Green Lights data provide strong statistical confirmation of the need to take account of more than simple profit maximization in the theory of the firm.



## Predictions of economic theory

If conventional economic factors of cost and risk were the only things influencing companies’ decisions to invest in Green Lights technologies, a number of predictions could be made regarding the financial characteristics and performance of the investments. According to basicfinance theory, the marginal return on investment projects should be equal to the return available on other projects of comparable risk. The ‘cost of capital’ used to decide whether to undertake an investment should not be the average cost of capital to the particular firm making the investment, but the return available on investments of equal riskiness across firms and industries. The risk of an investment is specific to the investment, not to the firm. The energy-efficient lighting technologies promoted by Green Lights are by now generic

—energy-efficient fluorescent lamps used in conjunction with electronic ballasts, occupancy sensors, and task lighting are typical. 6 These technologies are well-tested, they have been installed successfully in all types of buildings and climates, and the equipment is produced by market-leading firms, so the investments are subject to practically no technological risk. The electricity savings are immediate, easy tomeasure, and predictable, so the investments have virtually no financial risk. These considerations suggest that the returns on Green Lights investments as measured by their internal rates of return (IRRs) should be low and uniform across firms. 7 Furthermore, the profitability of the investments should not be associated in any systematic way with internal organization of the firms, the means by which the investments are implemented, thefirms’ line of business, size, or any other such characteristics. Of course, a number of purely economic factors should influence the profitability of energy-saving investments. The potential for cost saving is greater the higher the price of electricity and the more time (measured in hours) a particular set of lighting fixtures is operated over the year. Similarly, the presence or absence of utility rebates is likely to influence the decisions of firms that might beeligible for the rebates. Economic theory might also suggest that the most profitable investments would be made first as a new technology becomes available, and that returns would decline over time as the most attractive investment opportunities were skimmed off. One of the requirements for firms participating in the Green Lights program is that they submit simple annualreports to EPA on the progress of their lighting upgrades. 8 EPA maintains this information in a large relational database. This database contains information thatcan be used to test these theoretical predictions. In addition to firm-specific information, each project is described in detail as to its specific characteristics and thecharacteristics of the facility in which it is placed. Standard multiple regression analysis is a suitable techniquefor isolating the separate effects of these different variables on the profitability of the investments.

The strategy employed in the analysis is to identify as many ‘economic’ and ‘organizational’ factors as possible that might influence the profitability of the investments. Regression analysis is then carried out using each set of factors separately, and combining both sets of factors.What matters is how much variation in returns is observed over the sample, and how much the different kinds of factors are able to account for variations in the returns. The direction of influence of the explanatory variables is also of interest. In some cases, the signs of coefficients canbe predicted by theory, but in the case of the ‘organizational’ variables, the direction of influence is not alwaysobvious (partly because the theory of organizational influences on investment outcomes is much less developedthan the economic theory). Methodologically, the approach is not to search out only those variables that are statistically ‘significant’ while discarding those that are **The e** ffi **ciency paradox: S J DeCanio** 4439 A recent enhancement to the conventional NPV analysis suggeststaking account of the lost option value of waiting to make an investment that is irreversible. This possibility is important in situations ofsubstantial technological uncertainty or potentially large price movements (as in the case of deciding when to purchase a personal computer) (Dixit and Pindyck, 1994). Hassett and Metcalf (1993) have applied this idea to the problem of diffusion of energy-efficient technologies. To the extent the option value modification of the conventional NPV analysis is applicable to the Green Lights data, it should affect the entire sample. If uncertainty about energy prices, equipment costs, and technical change is felt uniformly throughout the market for lighting upgrades (which is a reasonable assumption), the prediction of uniformity of the returns is unchanged, although the expected level of the returns mightbe somewhat higher than it would be without taking the option component into account. In any event, Sanstad, et al. (1995) have shownthat the empirical magnitude of the option effect in the case of energyefficient investments must be small.

10 These practices are confirmed in DeCanio (1994) and by numerous interviews with executives in firms participating in the Green Lightsprogram. IRR is prominent in the analysis partly because of its importance in the MOU and in the reporting done by Green Lights participants.
11 In very few cases are disposal costs taken into account and in any case such costs are typically very small.

12 For infinitely long-lived projects, if C 0 is the cost of the project, i is the risk-adjusted discount rate appropriate for computing present value, and r is the IRR, then it is easy to show that

NP »" C 0 M [r/(1 # r)][(1 # i)/i ]

! 1 N
This equality yields the condition that NP »' 0 if and only if r ' i . If the project’s lifetime is finite, a little algebra shows that this condition still holds. 13 For reasonably long equipment lifetimes, the variations in IRR associated with different lifetimes are much smaller than observed variations in the paybacks. For example, if the payback is 3 yrs and the lifetime is 7 yrs, the IRR is 45%, while if the lifetime is 10 yrs, the IRR is 49%. If the payback is 2 yrs, the IRR is 99% whether the lifetime is 7 or 10 yrs. 

### not; it is often the case (and especially so here) that asmuch can be learned from variables that do not contribute much to the explanation of the variation in returns as from those that do.Before proceeding with the statistical tests, it is necessary to address the issue of how to measure the investments’ returns. Financial theorists are virtually unanimous that net present value (NPV) is the preferred way to evaluate the profitability of any investment. The NPV is the discounted sum of net cash flows, using a discount rate equal to the rate of return obtainable on other projects of comparable risk. 9 On the other hand, thenearly universal practice of firms contemplating or carrying out lighting upgrades is to use measures such as simple payback and internal rate of return (IRR) to evaluate the prospective investments. 10 Fortunately, in the case of uncomplicated investments of this type, the different methods of calculating returns are functionally equivalent. Lighting upgrades typically involve an up-front cost, followed by a steady stream of positive net revenues as the electricity and maintenance savings are realized. 11

In this case, the troublesome problems of multiple roots that can arise when there are negative cash flows in separate periods do not plague the IRR, and investments with an IRR greater than the risk-adjusted discounted rate will also show a positive NPV. 12 It is also the case that the simple payback is closely related to the IRR. If the cost savings continue indefinitely, the IRR is the solution of the equation:

0 "! C 0 # S 0 # S 1 /(1 # r) # S 2/(1 # r) 2 #

2 (1) where C 0 is the initial investment, Sk is the cost saving in
year k, and r is the IRR. Elementary algebra shows that if the cost savings are uniform over time (Sk " S 0 for all k), then

C 0/S 0 " (1 # r)/r (2)

where C 0/S 0 is the simple payback (in ys). Now, in actuality, the cost savings do not continue indefinitely, but only over the lifetime of the new equipment. In that case,the relationship between IRR and payback is only slightly more complicated. If equipment lasts through period

n and there is no disposal cost, the relationship between payback and IRR is given by

C 0/S 0 " (1 # r)/r

! 1/[r (1 # r)/] (3)

Let f (r) denote the right-hand side of Equation (3) for any particular n . The derivatives of f (r) exist, so it can be expanded as a Taylor series around r " 1. Thus, the payback can be expressed as a linear function of the powers of (r

! 1):

C 0/S 0 " f(1) # f@ (1)(r

! 1) # (1/2!)fA (1)(r

! 1) 2

# (1/3!)fA@ (1)(r

! 1) 3 #

2 (4) This Taylor series expansion will converge so long as D r

! 1 D ( 1 (or equivalently, the internal rate of return lies between zero and 200%). Equations (3) and (4) show that, for any particular lifetime, the simple payback and the IRR are functionally equivalent. Thus, nothing will be lost by conducting the statistical analysis in terms of the simple payback for these investments. 13 Indeed, it will become apparent from the data that there are advantages in using the payback for the hypothesis tests.



## Statistical analysis

The data on the Green Lights investments that have been reported to the EPA do not confirm the hypothesis that only economic factors affect the profitability of lighting upgrade investments. While economic variables do tend to influence the profitability of the investments in the expected way, a large number of organizational and institutional factors also move paybacks. Paybacks and IRRs are neither uniform across firms nor close to the risk-adjusted discount rate that would constitute theappropriate ‘hurdle rate’ for investments under conventional investment analysis. To begin, let us examine the simple statistics on paybacks and IRRs reported by the firms in the sample. 444 **The e** ffi **ciency paradox: S J DeCanio**14 Observations judged by the EPA’s Green Lights staff to ‘need clarification’ are excluded from this analysis and from that which follows. The reports submitted to EPA by Green Lights participants are screened asthey are received, and if the data contain obvious internal inconsistencies they are flagged. About 244 project observations (roughly 7%) fell into this category. Note that the database is continuously growing and evolving, and that not all errors are necessarily caught by the EPA’s review process. The exclusion of outliers is more subjective, but in this investigator’s judgment, projects with paybacks greater than 10.5 yrswere ‘uneconomic’ and were either misreported or were being undertaken for reasons unrelated to profit calculations. There were 444payback observations that fit into this category, and these were excluded from the calculations of summary statistics. If they are included, the median payback rises only to 3.1 yrs. (Truly outlandish outliers bias the mean; the largest reported ‘payback’ is 25,639 yrs!) Similarly, for the IRR univariate statistics, values greater than 200% were excluded. If they are included, the median is 37% rather than 36%.

15 The projects were all completed between January 1991 (the date of the beginning of the Green Lights program) and January 1995 (the date as of which the continuously evolving EPA database was ‘frozen’ for purposes of this analysis). 16 The fit of the regression of payback on calculated IRR is not perfect because only the first six terms of the Taylor series expansion were used and because equipment lifetimes differ. Exact numerical values of the coefficients of Equation (4) were computed in Mathcad 4.0 (1993) usingthe Maple symbolic computation engine, for a typical equipment lifetime of 7 yrs. The results were unchanged if a 10 yrs lifetime is assumed in computing the Taylor series coefficients and the first ten terms of Equation (4) are included.

17 This has been confirmed by the author in interviews with Green Lights participants. It would be correct if the projects had an infinite lifetime and discounting of the savings began in the initial year. 

### o nivariate statistics : payback and IRR

Excluding outliers and internally inconsistent observations, 14 the average payback of 3673 Green Lights projects reported to EPA as of the beginning of 1995 was 3.3 yr, with a standard deviation of 2.3 yr, and a median of 2.8 yr. 15 The IRR of 3,394 projects (excluding outliers)during the same period had a mean of 44.7% with a standard deviation of 33.9%, and a median of 36.0%. In both cases, the mean is larger than the median because the distributions have a long upper tail. These paybacks and IRRs imply that Green Lights projects are, on the whole, far more profitable than any plausible risk-adjusted cost of capital for comparable projects. (A project with a 3 yrs payback and 7 yrs lifetime has an IRR of 45%; if the payback is 2 yrs, the IRR is 99%, ignoring the tax savingassociated with depreciation.) The variation in profitability appears to be much larger than would be predicted by economic theory.Figures 1 and 2 show histogram plots of the frequencies of the paybacks and IRRs, by 1 yr and ten percentage point intervals. The histograms are single-peaked and skewed to the right, with a modal payback of 2 yr. The spike at the upper tail of the IRR distribution is an artifact of inclusion of all the IRRs between 100% and 200% in the topmost category.

It is also of interest to see how much of the wide dispersion of paybacks is attributable to differencesacross firms. To examine this question, univariate statistics of the deviations of the paybacks from their company averages were computed. (Most of the firms contributing data had multiple projects.) The average of these payback deviations was zero by construction, but the standard deviation of their distribution was 1.7 yr. This standard deviation is 74% of the standard deviation of the raw payback distribution. Differences in project outcomes within firms, rather than across firms, constitute most of the variation in the reported payback data. Some important economic variables (such as the price of electricity and the fraction of time the lights are on) do vary across projects, so to pursue an explanation of the causes of the variation in profits it is necessary to proceed to a multiple regression analysis that can control for multiple influences on the paybacks. Before doing that, however, it is useful to examine what the data show about the theoretical relationship between the payback and IRR discussed above.

Given the payback and project lifetime, the IRR can be computed using any standard statistical or spreadsheet software. There should be a direct equivalence between the computed IRR and the IRR reported by participating companies. At the same time, either the computed IRR or the reported IRR should ‘explain’ essentially all of the variance of paybacks, if an equation such as Equation (4) is implemented as a regression. Instead, the relationship between the computed IRR and the reported IRR is only partial, and while the computed IRR is equivalent to the payback when Equation (4) is estimated, the reported IRR is not. When the extreme outliers are excluded (paybacks greater than 10.5 yrs and IRRs greater than 200%), a regression of reported IRR on computed IRR has an adjusted R 2 of only 0.431 ( n " 3,067). If the payback is regressed on the first six terms of the Taylor series expansion shown in Equation (4), the calculated IRR explains 93% of the variance, while the reported IRR explains only 51% of the variance. 16 This same disparityin explanatory power persists in regressions of the payback on the first six terms of Equation (4) in which the coefficients of the powers of (r

! 1) are not constrained toequal the values derived from the Taylor series expansion. In these unconstrained regressions, the adjusted R 2 is 0.962 for calculated IRR and only 0.527 for thereported IRR. While some discrepancies between reported IRRs and paybacks might be expected, it is anomalous that the calculated IRR (computed using a single lifetime for all projects) explains the payback so much better than the reported IRR. Clearly, the IRRs supplied by the Green Lights participants are being computed or reported erroneously. This is so despite the fact that one of the main conditions for participation in Green Lights has been that companies make all lighting upgrades meeting a specific IRR criterion. Part of the ‘lore’ of the Green Lights program is that some companies fill in an ‘IRR’ simply as equal to the reciprocal of the payback. 17 Other peculiarities undoubtedly occur; only about 10% of the Green Lights participants report their data using **The e** ffi **ciency paradox: S J DeCanio** 445

### Figure 1 Frequency distribution of paybacks (outliers excluded)

18 Personal communication from Green Lights personnel. 19 Several dummy variables representing different classes of Green Lights ‘Ally’ corporations were combined into a single dummy for allies. See Table 1.

### the software provided by the EPA, software that automatically computes the IRR correctly. 18

Regression analysis

To test the relative influence of economic and organizational factors on the profitability of lighting upgradeprojects, a standard multiple regression analysis was performed using the variables collected by EPA or computed from these data. Of course, none of the data wasproduced by companies that are not Green Lights participants. If the Green Lights companies showed lowreturns from their lighting upgrade projects, this potential selection bias might constitute a statistical problem. (One might suspect that companies were trying to please EPA by making uneconomic investments, even though the Green Lights program does not require that.) But since the returns are so much higher than market levels, the potential selection bias does not affect the analysis.

There are two stages at which judgments had to bemade regarding the variables. First was in selecting variables to include in the regressions, and second was in classifying the variables as ‘economic’ or ‘organizational.’ No post hoc decisions were made to exclude variables from the regressions. Plausible candidate variables were calculated and included; no ‘significance threshold’ was employed to drop variables from the regressions. 19 The regressions reported below are therefore free of the source of much of the skepticism that surrounds reported statistical results

—the data were not massaged to produce ‘statistically significant’ estimates.Classification of variables as ‘economic’ or ‘organizational’ is perhaps more open to question. However, even if a decision regarding the classification of one or several variables were incorrect, there can be no doubt that (1) a large number of variables of all types are statistically significant explanatory variables for the payback, and (2) taken as a whole, even the extensive list of variables that were chosen does not explain a very large proportion ofthe total variation. In any case, organizational and institutional factors appear to be at least as important aseconomic ones in explaining the performance of the lighting upgrade investments. This finding has implicationsfor the way firms are modeled and described. The variables used in the regression analysis are defined and classified in Table 1. 446 **The e** ffi **ciency paradox: S J DeCanio**

### Figure 2 Frequency distribution of IRRs (outliers excluded)



### Discussion : the variables and their classification

Both the date of the project’s completion and the time elapsed from when the company joined Green Lights tothe date of the project’s completion are included as economic variables. It is expected that the sign of the time elapsed variable (time lag) would be positive, because after a company joins Green Lights it seems likely that the most attractive project’s would be undertaken first. This in turn suggests that the longer a company has been in the Green Lights program, the longer would be the paybacks on its projects. It may also be the case that ascompanies gain experience with Green Lights investments, they relax their internal hurdle rates or accept longer paybacks. The date of completion of projects (aside from a company’s time in the program) could havea positive or negative coefficient. This project date variable is included as a control variable to account for the possibility that early Green Lights investments mightshow higher than marginal returns because the technology was relatively new. This possibility would manifest itself as a positive coefficient on the project date variable (lower payback and higher IRR for earlier projects). On the other hand, the coefficient of this variable could be negative if technical progress and economies of scale associated with market penetration reduce the cost of the energy-efficient equipment over time, so that profitability goes up (and paybacks go down) as time goes on. The coefficients of electricity prices and hours of lighting operation should have negative signs because increases in either of them would shorten the payback, other things equal. One would also expect the payback to be longer on projects with guaranteed savings (because ofthe reduced risk of such projects). According to the Hassett and Metcalf model in which uncertainty in prices and costs delays investment by raising the hurdle rate, ‘‘[n]o investment occurs during periods in which energy prices are stable or falling, while investment occurs once prices rise sufficiently to clear the hurdle rate’’ (1993, p. 713). To test this idea, the 5-yr rate of change of energy prices was included as an explanatory variable, with the expectation that this variable should have a positive coefficient. (The hurdle rate decreases

— paybacks lengthen and more projects are undertaken

— the more rapidly electricity prices have been rising.)

The dummy variables indicating whether companies reported particular components of cost could have been included in either group of variables. The total cost of projects is reported, however, so the actual payback canbe calculated without detailed knowledge of the particular components of cost. These dummy variables wereincluded as economic variables because companies’ practices in analyzing projects vary widely. Some carry out meticulous accounting of all cost factors; others are muchless careful. These dummy variables (especially the ‘administrative cost,’ ‘survey/consulting cost,’ and ‘other **The e** ffi **ciency paradox: S J DeCanio** 447Table 1 Definitions and classification of variables

Economic variables Organizational variables

Variable Definition Variable Definition

projdate Date of completion of the project ftsq Total square footage of company timelag Interval between date the company allydum Dummy variable " 1 if company is joined Green Lights and Green Lights Ally (a utility, completion of the project manufacturer, or distributor of lighting upgrade equipment) rebrat Ratio of utility rebate to total citydum Dummy variable " 1 if ‘‘company’’ project cost is a city lhthour Number of hours per year the statedum Dummy variable " 1 if ‘‘company’’ project’s fixtures are turned on is a state. etot91 State average 1991 electricity price, pugdum Dummy variable " 1 if company for the project facility’s location belongs to Partner User Group (EIA, 1993) getot91 5-yr rate of growth of state sadum1-7 Survey/Analysis dummy variables: electricity price (ending 1991), for 0: in-house personnel the project facility’s location, 1: independent consultant computed from data in (EIA, 2: electrical contractor 1993) 3: utility representative 4: equipment supplier 5: lighting mgt. company 6: energy services company 7: GL Surveyor Ally sgdum Dummy variable " 1 if the project epdum1-3 Equipment provider dummy variables had a savings guarantee 0: lighting equipment supplier 1: lighting mgt. company 2: utility 3: contractor ncdum Dummy variable " 1 for project imdum1-2 Installation method dummy variables part of new construction 0: in-house staff 1: contractor 2: utility fmdum1-5 Financing method dummy variables 0: internal funds 1: conventional loan 2: utility 3: lease/lease-purchase 4: shared savings 5: other admdum Dummy variable " 1 if company facware Dummy variable " 1 if warehouse reported administrative costs scdum Dummy variable " 1 if company facindu Dummy variable " 1 if industrial reported survey and consulting costs facility matdum Dummy variable " 1 if company facreta Dummy variable " 1 if retail reported materials costs ildum Dummy variable " 1 if company facheal Dummy variable " 1 if health reported installation and labor costs facility admdum Dummy variable " 1 if company faclodg Dummy variable " 1 if lodging reported administrative costs drdum Dummy variable " 1 if company facasse Dummy variable " 1 if assembly reported disposal and removal cost othdum Dummy variable " 1 if company faceduc Dummy variable " 1 if reported ‘other’ cost educational facfood Dummy variable " 1 if food facpark Dummy variable " 1 if parking faclabo Dummy variable " 1 if laboratory facothe Dummy variable " 1 if other

20 Only 23% of the projects in the sample show a separate cost component for administrative costs, 26% show survey/consulting costs (even though one of the requirements of the Green Lights program is a survey of facilities to identify potential upgrade projects), and only 13% show ‘other’ costs. These figures might suggest that companies are lax in fully accounting for the costs of their lighting upgrade projects. However, only 68% show a separate materials cost and only 65% show installation and labor cost, so it is likely that a considerable number ofcompanies simply do not break the cost down into detailed components.

### cost’ components) may be thought of as indicating whether companies measured the transactions costs associating with this type of investment. 20 If internal transactions costs hold back energy-saving investments that appear to have attractive paybacks otherwise, all three of these dummy variables should have positive coefficients. The companies that actually take them into account will show longer paybacks for their investments 448 **The e** ffi **ciency paradox: S J DeCanio**Table 2a Regression of payback on economic and organizational variables

Parameter Standard T for H 0 : Variable estimate error parameter 5 0 Prob > D T D

intercept 18.409129 5.632343 3.268467 0.0011 ftsq 1.683178 0.323945 5.195883 0.0001 projdate

! 0.134227 0.060843

! 2.206107 0.0274 timelag 0.120635 0.054150 2.227808 0.0260 rebrat 0.001932 0.026132 0.073930 0.9411 lhthour

! 0.000276 0.000018

! 15.466211 0.0001 allydum

! 0.511660 0.111562

! 4.586334 0.0001 citydum

! 0.471560 0.198194

! 2.379282 0.0174 statedum

! 0.798722 0.263365

! 3.032753 0.0024 etot91

! 0.085093 0.010221

! 8.325637 0.0001 getot91

! 4.231010 2.926167

! 1.445922 0.1483 sadum1 0.394092 0.136035 2.896987 0.0038 sadum2 1.040542 0.192915 5.393797 0.0001 sadum3

! 0.402425 0.164679

! 2.443695 0.0146 sadum4

! 0.580790 0.161033

! 3.606651 0.0003 sadum5

! 0.659923 0.153305

! 4.304650 0.0001 sadum6

! 0.187487 0.193163

! 0.970615 0.3318 sadum7 0.324577 0.300218 1.081139 0.2797 epdum1 0.192507 0.136216 1.413243 0.1577 epdum2

! 0.612907 0.313105

! 1.957515 0.0504 epdum3 0.066990 0.112867 0.593528 0.5529 imdum1 0.507218 0.102548 4.946157 0.0001 imdum2 1.490238 0.550135 2.708861 0.0068 fmdum1

! 0.089512 0.348232

! 0.257048 0.7972 fmdum2 0.109195 0.190263 0.573915 0.5661 fmdum3 0.306692 0.229951 1.333732 0.1824 fmdum4

! 0.444386 0.359296

! 1.236823 0.2162 fmdum5 0.562363 0.255835 2.198147 0.0280 sgdum

! 0.413346 0.113430

! 3.644044 0.0003 ncdum 0.158329 0.253712 0.624049 0.5326 pugdum 0.600237 0.214608 2.796897 0.0052 facware 0.368767 0.153243 2.406420 0.0162 facindu 0.082244 0.114015 0.721339 0.4708 facreta

! 0.404517 0.178323

! 2.268451 0.0234 facheal

! 0.325364 0.187260

! 1.737499 0.0824 faclodg

! 0.343883 0.244435

! 1.406846 0.1596 facasse 0.040342 0.378517 0.106578 0.9151 faceduc 0.574585 0.160215 3.586339 0.0003 facfood

! 0.323350 0.227037

! 1.424216 0.1545 facpark 0.279277 0.506731 0.551135 0.5816 faclabo

! 0.636846 0.504487

! 1.262365 0.2069 facothe 0.364544 0.427521 0.852692 0.3939 admdum 0.050068 0.113298 0.441919 0.6586 scdum

! 0.127985 0.111499

! 1.147859 0.2511 matdum

! 0.091332 0.116610

! 0.783226 0.4335 ildum 0.343232 0.113622 3.020823 0.0025 drdum 0.386568 0.198720 1.945293 0.0518 othdum 0.663124 0.112810 5.878263 0.0001

N " 3416, Adjusted R 2
" 0.240529. 

### than companies that ignore them in reporting results to the EPA.

Three regressions are reported in Table 2a

—Table 2c. The first Table 2a is the regression containing all thevariables; the second Table 2b includes only the economic variables, and the third Table 2c, only the organizational variables. The variables, estimated coefficients, standard errors, t-statistics for testing the null hypothesis that the coefficient is zero, and two-tailed probability values of the t-statistics are shown for each regression, as well as the sample sizes and adjusted R 2 s. The regression results are taken directly from the output of PRODAS 4.0, the statistical package that was used to estimate theregressions (Conceptual Software, Inc., 1993). (As is typical, the statistical package prints out results to many more decimal places than are meaningful.) In most cases, the signs and statistical significance of the coefficients are similaracross specifications. There is no case of a variable’s coefficient being statistically significant in more than one of theequations but differing in sign across equations. Inferences regarding the direction of influence of individual variables are robust across the alternative specifications.

Discussion : economic variables

The adjusted R 2 of the regression containing all the variables is 0.24, which is unquestionably statistically **The e** ffi **ciency paradox: S J DeCanio** 449Table 2b Regression of payback on economic variables only

Parameter Standard T for H 0 : Variable estimate error parameter 5 0 Prob > D T D

intercept

! 18.790932 4.982124

! 3.771671 0.0002 projdate

! 0.139708 0.053514

! 2.610709 0.0091 timelag 0.199293 0.047230 4.219656 0.0001 rebrat 0.002119 0.027198 0.077908 0.9379 lhthour

! 0.000312 0.000016

! 19.672071 0.0001 etot91

! 0.057871 0.010053

! 5.756602 0.0001 getot91

! 5.888075 2.768387

! 2.126897 0.0335 sgdum

! 0.383397 0.108436

! 3.535686 0.0004 ncdum 0.155773 0.263721 0.590673 0.5548 admdum

! 0.353041 0.105480

! 3.347007 0.0008 scdum 0.020454 0.096544 0.211860 0.8322 matdum

! 0.058763 0.107730

! 0.545464 0.5855 ildum 0.327533 0.105938 3.091740 0.0020 drdum 0.344166 0.197162 1.745598 0.0810 othdum 0.656748 0.109776 5.982620 0.0001

N " 3416, Adjusted R 2
" 0.162136.

Table 2c Regression of payback on organizational variables only

Parameter Standard T for H 0 : Variable estimate error parameter 5 0 Prob > D T D

intercept 2.921188 0.092888 31.448330 0.0001 ftsq 1.784923 0.301560 5.918962 0.0001 allydum 0.002404 0.105224 0.022847 0.9818 citydum

! 0.457461 0.193611

! 2.362787 0.0182 statedum

! 0.791875 0.261349

! 3.029955 0.0025 pugdum 0.786351 0.213570 3.681930 0.0002 sadum1 0.303410 0.134581 2.254488 0.0242 sadum2 1.254777 0.192868 6.505881 0.0001 sadum3

! 0.078207 0.165407

! 0.472819 0.6364 sadum4

! 0.393841 0.161299

! 2.441682 0.0147 sadum5

! 0.456265 0.155235

! 2.939195 0.0033 sadum6

! 0.342639 0.194318

! 1.763294 0.0779 sadum7 0.283172 0.308846 0.916869 0.3593 epdum1 0.183630 0.135947 1.350750 0.1769 epdum2

! 0.538896 0.327021

! 1.647896 0.0995 epdum3

! 0.056419 0.109749

! 0.514073 0.6072 imdum1 0.610345 0.102799 5.937257 0.0001 imdum2 1.094949 0.577886 1.894750 0.0582 fmdum1

! 0.711555 0.366657

! 1.940659 0.0524 fmdum2

! 0.245331 0.187006

! 1.311888 0.1896 fmdum3

! 0.037661 0.232616

! 0.161901 0.8714 fmdum4

! 0.392971 0.366007

! 1.073670 0.2830 fmdum5 0.682502 0.265420 2.571401 0.0102 facware 0.228663 0.151942 1.504936 0.1324 facindu

! 0.259507 0.107130

! 2.422344 0.0155 facreta

! 1.301238 0.169394

! 7.681706 0.0001 facheal

! 0.404088 0.190817

! 2.117668 0.0343 faclodg

! 0.603922 0.243454

! 2.480645 0.0132 facasse 0.533698 0.399326 1.336497 0.1815 faceduc 0.672439 0.160866 4.180121 0.0001 facfood

! 1.121538 0.160896

! 6.970592 0.0001 facpark

! 0.332544 0.536365

! 0.619996 0.5353 faclabo

! 0.963760 0.530120

! 1.818004 0.0691 facothe 1.128971 0.393885 2.866246 0.0042

N " 3672, Adjusted R 2
" 0.130872.



### significant (probability value less than 0.0001) but not particularly large. The regression with only economic variables has an R 2 of 0.16, while that including only the organizational and institutional variables has an R 2 of 0.13. (Each of these regressions is statistically significant at the 0.0001 level, because of the large sample size.) Each set of variables adds to the explanation of the variance of the payback, but even when all are combined they do not account for much of that variation. Even if every variable were somehow associated with economic factors, these factors would explain less than a quarter of the variance in paybacks. The effects of a large number of individual variables are statistically discernible, however. Lighting hours and electricity prices have the expected negative impact on paybacks. Also, the coefficient of time lag is positive as 450 **The e** ffi **ciency paradox: S J DeCanio**21 Companies interviewed report almost unanimously that the presence of utility rebates is an important decision factor in their undertaking lighting efficiency investments. 22 This finding does not imply that energy-saving technologies spreadmore quickly in large organizations than in small ones. The determinants of the rate of diffusion of these technologies within and across organizations having different characteristics is a subject for further research. 

### expected; for each year a company is in the Green Lights program, the payback realized on its projects increases by about a month and a half. Project date by itself hasa negative coefficient. This may reflect continuing technological progress in lighting that has lowered the paybacks of these projects over time, or declining cost of the equipment as the market for it has grown. The savingsguarantee dummy, contrary to expectations, has a negative coefficient, and the coefficient of the new construction dummy is not statistically significant. The coefficient of the rebate rate is not statistically significant. The reason is that the total project cost and the electricity savings (which are used to compute the payback) are gross of any utility rebate; it does not appear that utilities differentially target high or low profitability projects for their DSM programs. 21 The coefficient of the 5-yr rate of growth of electricity prices is significant at the 5% level in the regression on economic variables only, but its sign is negative. It is not statistically significant (and also has a negative sign) in the regression including all variables. These data do not support the notion that investments inenergy-saving lighting equipment are stepped up (thereby driving down IRRs and increasing paybacks) after a period of electricity price increases.

In the regression containing all variables, the administrative cost and survey/consulting cost dummy variables do not show statistically significant coefficients; the other cost dummy’s coefficient is positive and significant asexpected. In the regression with only the economic variables, the administrative cost dummy variable’s coefficient is statistically significant but negative. Transactions costs, at least as measured by these variables, cannot carry the burden of explaining variations in payback, or of why these investments have not spread throughout the
economy.

Discussion : the organizational and institutional variables

As a group and in many cases individually, the organizational and institutional variables exert a statistically and substantively strong influence on the profitability of thelighting upgrade investments. Both cities and states appear to require or achieve quicker paybacks than the average for private corporations. The coefficient of the state dummy is equivalent to an average payback 10 months faster than the average payback for the private sector; for cities the payback is 6 months faster. The coefficient of total square footage is positive, suggesting that larger organizations are relatively more willing to accept longer paybacks for investments of this type thansmaller organizations. The effect is not strong quantitatively, however. At the point of means, a 10% increase in total floorspace increases the payback by only about a week. 22

Perhaps what is most striking about the organizational and institutional dummy variables is that every subset of them shows a statistically significant effect onthe payback of projects. The method of survey and analysis, the equipment provider, and the financing method all show at least one dummy with a statistically significant coefficient. In addition, the type of facility

— warehouse, industrial, retail, etc.

— matters for the payback. If only economic factors influenced profitability, none of these variables would have an effect.

There is no evident pattern in the coefficients of thesedummy variables. Relative to in-house survey and analysis (the omitted dummy), the payback is longer if the survey work is done by an independent consultant or an electrical contractor. On the other hand, survey and analysis by a utility representative, equipment supplier, or lighting management company is associated with a shorter payback, relative to surveying by in-house personnel. All of these effects are statistically significant at the 5% level, and they are economically significant (i.e.the coefficients are large) as well. Similarly, if the equipment is provided by the utility the payback is shorter,although this effect just barely falls short of being statistically significant at the 5% level (the two-tailed probability value is 0.0504). Installation by either a contractor or utility is associated with a longer payback (6 months anda year and a half, respectively), with the results statistically significant at the 1% level. Interestingly, payback does not appear to be associated with financing method, except that it tends to be about 6 months longer if financing is in the ‘other’ category. In the regression with organizational and institutional variables alone, payback is shorter by 8 1 2 months if financing is obtained through a conventional loan, although here again the coefficient just falls short of being significant at the 5% level (its probability value is 0.0524). Payback also varies according to the type of facility being upgraded, with longer paybacks (relative to office space, the omitted dummy) noted for warehouses andeducational buildings, while shorter paybacks are associated with retail and health care facilities.

Regressions with payback deviations as dependent variable

It was noted above that most of the variation in paybacks was due to variations within firms, rather than across firms. Firm effects were removed by measuring pay backs as deviations from the company averages. This method of eliminating firm-specific effects can be used in the **The e** ffi **ciency paradox: S J DeCanio** 451Table 3a Regression of payback on economic and organizational variables, excluding firm effects (Dependent variable is deviation of payback from company mean)

Parameter Standard T for H 0 : Variable estimate error parameter 5 0 Prob > D T D

intercept 11.415537 4.684624 2.436810 0.0149 ftsq

! 0.265852 0.269436

! 0.986695 0.3239 projdate

! 0.105139 0.050606

! 2.077614 0.0378 timelag 0.044218 0.045038 0.981786 0.3263 rebrat

! 0.009865 0.021735

! 0.453897 0.6499 lhthour

! 0.000149 0.000015

! 10.054269 0.0001 allydum

! 0.305764 0.092790

! 3.295224 0.0010 citydum 0.072413 0.164845 0.439281 0.6605 statedum 0.495981 0.219050 2.264233 0.0236 etot91

! 0.044196 0.008501

! 5.199067 0.0001 getot91 0.490970 2.433799 0.201730 0.8401 sadum1

! 0.118725 0.113145

! 1.049315 0.2941 sadum2 1.108230 0.160454 6.906837 0.0001 sadum3

! 0.640672 0.136969

! 4.677482 0.0001 sadum4

! 0.159761 0.133937

! 1.192809 0.2330 sadum5

! 0.239634 0.127509

! 1.879350 0.0603 sadum6

! 0.011455 0.160660

! 0.071302 0.9432 sadum7 0.114133 0.249702 0.457076 0.6476 epdum1

! 0.027601 0.113296

! 0.243615 0.8075 epdum2

! 0.184778 0.260420

! 0.709538 0.4780 epdum3

! 0.134336 0.093875

! 1.431007 0.1525 imdum1 0.140092 0.085293 1.642479 0.1006 imdum2 0.741089 0.457567 1.619630 0.1054 fmdum1 0.041083 0.289637 0.141845 0.8872 fmdum2 0.341637 0.158249 2.158860 0.0309 fmdum3

! 0.061088 0.191258

! 0.319403 0.7494 fmdum4

! 0.353891 0.298840

! 1.184217 0.2364 fmdum5

! 0.116772 0.212787

! 0.548773 0.5832 sgdum

! 0.061318 0.094344

! 0.649935 0.5158 ncdum 0.170550 0.211021 0.808211 0.4190 pugdum

! 0.190964 0.178497

! 1.069840 0.2848 facware 0.077135 0.127458 0.605177 0.5451 facindu 0.108676 0.094830 1.146004 0.2519 facreta 0.143373 0.148318 0.966661 0.3338 facheal

! 0.010998 0.155751

! 0.070611 0.9437 faclodg

! 0.223620 0.203306

! 1.099919 0.2714 facasse

! 0.043000 0.314826

! 0.136583 0.8914 faceduc

! 0.244942 0.133257

! 1.838125 0.0661 facfood 0.101722 0.188835 0.538684 0.5901 facpark

! 0.123332 0.421467

! 0.292625 0.7698 faclabo 0.010264 0.419600 0.024462 0.9805 facothe 0.256494 0.355584 0.721330 0.4708 admdum 0.281964 0.094234 2.992179 0.0028 scdum

! 0.180614 0.092738

! 1.947581 0.0515 matdum

! 0.124167 0.096989

! 1.280221 0.2006 ildum 0.256725 0.094504 2.716561 0.0066 drdum 0.319118 0.165283 1.930744 0.0536 othdum 0.069323 0.093828 0.738829 0.4601

N " 3416, Adjusted R 2
" 0.068999. 

### regression analysis by taking the payback deviations asthe dependent variable, using the same sets of independent variables as shown in Table 2a

—Table 2c. The results are given in Table 3a

—Table 3c. These results generally confirm the previous findings. As expected, with firm effects eliminated the total fraction of variance explained is smaller in all three regressions. (Again, because of the large sample size, each of the regressions is statistically significant as a whole.) In all cases but the coefficient of statedum, if the coefficient is significant in both regressions including all the variables, it has the same sign in both. It is also to be expected that a greater proportion ofthe coefficients of the economic variables than the organizational variables remain statistically significant when firm effects are eliminated; one of the things that differs across firms is the way they are organized. This too is confirmed in the regressions of Table 3a

—Table 3c. It is worth noting that the projdate variable’s coefficient is negative in Table 3a and Table 3b but that timelag is not statistically significant. When firm effects are eliminated, there is a tendency for paybacks to decline over time. This strengthens the inference that across the market asa whole, the declining costs of the investments as economies of scale in production are achieved and the technology advances outweigh the tendency for the most attractive projects to be undertaken first. 452 **The e** ffi **ciency paradox: S J DeCanio**Table 3b Regression of payback on economic variables only, excluding firm effects (Dependent variable is deviation of payback from company average)

Parameter Standard T for H 0 : Variable estimate error parameter 5 0 Prob > D T D

intercept 12.037629 3.996156 3.012302 0.0026 projdate

! 0.114439 0.042923

! 2.666128 0.0077 timelag 0.043852 0.037883 1.157579 0.2471 rebrat

! 0.014237 0.021815

! 0.652608 0.5141 lhthour

! 0.000144 0.000013

! 11.315046 0.0001 etot91

! 0.036680 0.008063

! 4.548962 0.0001 getot91 1.764719 2.220521 0.794732 0.4268 sgdum

! 0.179559 0.086977

! 2.064450 0.0391 ncdum 0.198149 0.211530 0.936741 0.3490 admdum 0.298421 0.084605 3.527220 0.0004 scdum

! 0.225660 0.077438

! 2.914091 0.0036 matdum

! 0.122663 0.086410

! 1.419549 0.1558 ildum 0.315767 0.084973 3.716089 0.0002 drdum 0.224575 0.158144 1.420068 0.1557 othdum

! 0.046185 0.088051

! 0.524522 0.5999

N " 3416, Adjusted R 2
" 0.044794.

Table 3c Regression of payback on organizational variables only, excuding firm effects (Dependent variable is deviation of payback from company average)

Parameter Standard T For H0 : Variable estimate error parameter 5 0 Prob > D T D

intercept

! 0.051133 0.074956

! 0.682175 0.4952 ftsq

! 0.491134 0.243343

! 2.018276 0.0436 allydum

! 0.048435 0.084910

! 0.570426 0.5684 citydum 0.062521 0.156234 0.400172 0.6891 statedum 0.620871 0.210895 2.943982 0.0033 pugdum

! 0.127624 0.172340

! 0.740537 0.4590 sadum1

! 0.202619 0.108600

! 1.865741 0.0622 sadum2 1.277060 0.155635 8.205500 0.0001 sadum3

! 0.516647 0.133475

! 3.870751 0.0001 sadum4

! 0.096792 0.130160

! 0.743640 0.4571 sadum5

! 0.155912 0.125266

! 1.244641 0.2133 sadum6

! 0.123088 0.156804

! 0.784978 0.4325 sadum7 0.002811 0.249223 0.011280 0.9910 epdum1

! 0.078790 0.109702

! 0.718218 0.4727 epdum2

! 0.234722 0.263889

! 0.889475 0.3738 epdum3

! 0.207852 0.088562

! 2.346960 0.0190 imdum1 0.224712 0.082954 2.708886 0.0068 imdum2 0.599816 0.466324 1.286265 0.1984 fmdum1

! 0.305789 0.295873

! 1.033513 0.3014 fmdum2 0.164626 0.150904 1.090932 0.2754 fmdum3

! 0.242570 0.187709

! 1.292264 0.1963 fmdum4

! 0.247184 0.295349

! 0.836922 0.4027 fmdum5

! 0.132641 0.214180

! 0.619296 0.5358 facware 0.015896 0.122609 0.129645 0.8969 facindu

! 0.096617 0.086449

! 1.117618 0.2638 facreta

! 0.179272 0.136693

! 1.311497 0.1898 facheal

! 0.045172 0.153980

! 0.293361 0.7693 faclodg

! 0.292921 0.196454

! 1.491036 0.1360 facasse 0.290531 0.322235 0.901613 0.3673 faceduc

! 0.124126 0.129810

! 0.956208 0.3390 facfood

! 0.144091 0.129834

! 1.109804 0.2672 facpark

! 0.438743 0.432819

! 1.013688 0.3108 faclabo

! 0.057765 0.427780

! 0.135034 0.8926 facothe 1.085818 0.317845 3.416191 0.0006

N " 3672, Adjusted R 2
" 0.029602.



## Summary and conclusions

The data on the profitability of lighting upgrades collected by the EPA as part of the Green Lights program strongly support the conclusion that organizational and institutional factors are important determinants of firms’investment behavior and outcomes. While economic forces also play a role, economics alone cannot explaineither the level of or the variation in returns to energyefficient lighting investments. These returns are higher than the cost of capital for projects of comparable risk, **The e** ffi **ciency paradox: S J DeCanio** 453

### and they vary systematically according to a number ofnon-economic characteristics. In general, the data reinforce the view that there is a large potential for profitableenergy-saving investments that is not being realized because of impediments that are internal to private and public-sector organizations. Several policy implications can be drawn from these results. Voluntary programs such as Green Lights have the potential to improve corporate performance whilereducing pollution. Substantial progress in cutting greenhouse gas emissions, as well as emissions contributing toacid rain and smog formation, is possible without incurring large (or any) net cost to the economy. But the findings reported here should have an impact on the way economists think about corporate performance and the environment more generally. At some point, economists’ unwillingness to acknowledge evidence of shortcomings in corporate decision processes becomes itself a barrier to progress. Dogmatic adherence to simplistic theories of the firm, choosing models that embody mathematicallyelegant constructs rather than established facts of organizational behavior, and resistance to empirical findingsreplicated again and again across a wide range of circumstances, do not serve the advancement of scientific knowledge. Those who study the actual workings of firms know that corporate cultures exhibit a rich diversity of forms and show large variations in performance. Energy and policy economists who wish to contribute to the solution of global environmental problems and expand the frontiers of knowledge should heed this evidence and adjust their models accordingly.



## Acknowledgement

This work is supported in part by a grant from the United States Environmental Protection Agency. Capable research assistance was provided by Paymon Aliabadi and William E. Watkins. I appreciate helpful comments by Maria Tickoff, Alan Miller, H.E. Frech III, Clement Krouse, and Jeffrey Williams on earlier drafts. The conclusions and responsibility for any errors are those of the author alone.



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### 454 **The e** ffi **ciency paradox: S J DeCanio**