1 See Koomey (1990), Lovins and Lovins (1991), Ayers (1993), Jaffe and

Stavins (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 potential

failure of firms to achieve ‘general’ production efficiency, see the extensive empirical literature launched by Leibenstein’s (1966) original article

on X-efficiency.

2Train (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, University 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 case

of 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 limitations

should not apply to firms. The neoclassical profit-maximizing firm should be risk neutral because shareholders

can diversify their portfolios, and should invest in all

projects having a positive net present value, using a discount rate for each project suitable for that project’s

riskiness. 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 the

strength of empirical evidence for and against such possibilities. Perhaps more common among economists is the

attitude that such systematically unexploited profit opportunities are impossible a priori. It is often argued such

failures of profit maximization are only apparent, because ‘transactions costs’ and other hidden costs reduce

the seemingly high returns that can be realized from

441
3 See Sanstad and Howarth (1994) for a good conceptual discussion of

transactions costs in this context.

4For 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 by

contacting 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 activism

designed 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. (This

might 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 definition

unmeasurable, 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 recognize

the need for more empirical investigation, however, stating that ‘‘[i]t is our judgment2that resolution of this

debate ultimately depends upon careful quantitative

analysis of the engineering and economic characteristics

of specific technologies, as well as similarly careful assessment of policies directed toward energy efficiency’’ (p. 826).

The argument that potential energy-saving profits

exist 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 to

bring 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 avoided

by 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 for

other aspects of the firm’s performance. Concurrent benefits to environmentally induced innovation are one

possible theoretical basis for Porter’s intriguing hypothesis4 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 Change

Action 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 will

depend on how many profitable green investment opportunities exist. Furthermore, evaluation of the economic

consequences 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 to

generate 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 efficiency 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 statistical

information 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 economic

optimization. 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 basic

finance 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 to

measure, 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, the

firms’ 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 be

eligible 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 annual

reports to EPA on the progress of their lighting upgrades.8 EPA maintains this information in a large relational database. This database contains information that

can 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 the

characteristics of the facility in which it is placed. Standard multiple regression analysis is a suitable technique

for 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 can

be predicted by theory, but in the case of the ‘organizational’ variables, the direction of influence is not always

obvious (partly because the theory of organizational influences on investment outcomes is much less developed

than the economic theory). Methodologically, the approach is not to search out only those variables that are

statistically ‘significant’ while discarding those that are

The efficiency paradox: S J DeCanio 443
9 A recent enhancement to the conventional NPV analysis suggests

taking account of the lost option value of waiting to make an investment that is irreversible. This possibility is important in situations of

substantial 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 might

be somewhat higher than it would be without taking the option component into account. In any event, Sanstad, et al. (1995) have shown

that the empirical magnitude of the option effect in the case of energyefficient investments must be small.

10These practices are confirmed in DeCanio (1994) and by numerous

interviews with executives in firms participating in the Green Lights

program. 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.

12For infinitely long-lived projects, if C0 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»"C0M[r/(1#r)][(1#i)/i]!1N

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.

13For 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 as

much 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, the

nearly 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"!C0#S

0

#S

1

/(1#r)#S

2

/(1#r)2#2 (1)

where C0 is the initial investment, S

k is the cost saving in

year k, and r is the IRR. Elementary algebra shows that if

the cost savings are uniform over time (S

k

"S0 for all k),

then

C0/S

0

"(1#r)/r (2)

where C0/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

C0/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):

C0/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

Dr!1D(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 the

appropriate ‘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 efficiency paradox: S J DeCanio
14Observations 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 as

they 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 yrs

were ‘uneconomic’ and were either misreported or were being undertaken for reasons unrelated to profit calculations. There were 444

payback 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%.

15The 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).

16The 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) using

the 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.

17This 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.

onivariate 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 saving

associated 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 differences

across 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 R2 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 disparity

in 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 to

equal the values derived from the Taylor series expansion. In these unconstrained regressions, the adjusted

R2 is 0.962 for calculated IRR and only 0.527 for the

reported 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 efficiency paradox: S J DeCanio 445
Figure 1 Frequency distribution of paybacks (outliers excluded)

18Personal 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 upgrade

projects, a standard multiple regression analysis was performed using the variables collected by EPA or computed from these data. Of course, none of the data was

produced by companies that are not Green Lights participants. If the Green Lights companies showed low

returns 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 be

made 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 of

the total variation. In any case, organizational and institutional factors appear to be at least as important as

economic ones in explaining the performance of the lighting upgrade investments. This finding has implications

for the way firms are modeled and described. The variables used in the regression analysis are defined and

classified in Table 1.

446 The efficiency 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 to

the 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 as

companies 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 have

a positive or negative coefficient. This project date variable is included as a control variable to account for the

possibility that early Green Lights investments might

show 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 of

the 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 can

be calculated without detailed knowledge of the particular components of cost. These dummy variables were

included as economic variables because companies’ practices in analyzing projects vary widely. Some carry out

meticulous accounting of all cost factors; others are much

less careful. These dummy variables (especially the ‘administrative cost,’ ‘survey/consulting cost,’ and ‘other

The efficiency paradox: S J DeCanio 447
Table 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 of

companies 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 efficiency paradox: S J DeCanio
Table 2a Regression of payback on economic and organizational variables

Parameter Standard T for H0:

Variable estimate error parameter 5 0 Prob > D TD

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 R2"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 the

variables; 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 R2s. The regression

results are taken directly from the output of PRODAS

4.0, the statistical package that was used to estimate the

regressions (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 similar

across specifications. There is no case of a variable’s coefficient being statistically significant in more than one of the

equations 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 R2 of the regression containing all the

variables is 0.24, which is unquestionably statistically

The efficiency paradox: S J DeCanio 449
Table 2b Regression of payback on economic variables only

Parameter Standard T for H0:

Variable estimate error parameter 5 0 Prob > D TD

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 R2 "0.162136.

Table 2c Regression of payback on organizational variables only

Parameter Standard T for H0:

Variable estimate error parameter 5 0 Prob > D TD

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 R2"0.130872.

significant (probability value less than 0.0001) but not

particularly large. The regression with only economic

variables has an R2 of 0.16, while that including only the

organizational and institutional variables has an R2 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 efficiency 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.

22This finding does not imply that energy-saving technologies spread

more 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 has

a 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 savings

guarantee 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 in

energy-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 as

expected. 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 the

lighting 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 than

smaller 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 on

the 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 these

dummy 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 and

a 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 81

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 and

educational 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 efficiency paradox: S J DeCanio 451
Table 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 H0:

Variable estimate error parameter 5 0 Prob > D TD

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 R2"0.068999.

regression analysis by taking the payback deviations as

the 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 of

the 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 as

a 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 efficiency 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 H0:

Variable estimate error parameter 5 0 Prob > D TD

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 R2"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 TD

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 R2"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 explain

either 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 efficiency paradox: S J DeCanio 453
and they vary systematically according to a number of

non-economic characteristics. In general, the data reinforce the view that there is a large potential for profitable

energy-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 while

reducing pollution. Substantial progress in cutting greenhouse gas emissions, as well as emissions contributing to

acid 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 mathematically

elegant constructs rather than established facts of organizational behavior, and resistance to empirical findings

replicated 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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