can free information really accelerate technology diffusion?

Can Free Information Really AccelerateTechnology Diffusion?

RICHARD D. MORGENSTERN and SAADEH AL-JURF

ABSTRACT

Significant environmental benefits are often associated with the rapid diffusion of new energy-savingtechnologies. Over the past decade, electric and gas utilities, as well as the federal government, have providedfree technical information and, in some cases, direct cash subsidies, to owners of existing commercial buildingsto stimulate investment in specific energy-saving technologies. Yet little is known about the effectiveness ofthe information component of these programs. Can information itself, without explicit cash subsidies, actuallyincrease investment in new technologies? To examine these issues, a model of retrofit investment in high-efficiency lighting technologies is developed. Estimates are based on a sample of commercial buildings, ratherthan the more common comparison of program participants to a synthetic pairing with another population.The principal finding is that information programs appear to make a significant contribution to the diffusionof high-efficiency lighting in commercial office buildings. Additionally, there is some evidence that the programsare more effective in encouraging retrofits by those who have already invested in advanced lighting technologiesthan for first-time purchasers. 1999 Elsevier Science Inc.

IntroductionPublic policies which accelerate the development and dissemination of new technol-

ogies may, over the long term, be one of the most important tools for environmentalprotection. Such policies have the potential to lessen the trade-off between convention-ally measured economic well-being and environmental quality. For example, they maylower the costs of reducing greenhouse gas emissions.

While it is politically expedient to support policies and programs that lower eco-nomic costs by appealing to advanced technologies, it is less clear how, even afterapparently cost-effective technologies are developed, they diffuse throughout the econ-omy. Because such technologies are typically embodied in new investment capital, willfirms routinely and promptly make these new investments? Can public policies acceleratethis process? If so, how? This research examines the effectiveness of one particularpolicy, namely, the provision of free technical information by electric utilities aboutenvironmentally friendly technologies. “Free” means that the recipient incurs no directcosts to obtain the information.

RICHARD D. MORGENSTERN is Visiting Scholar, Resources for the Future, and Associate AssistantAdministrator for Policy, U.S. Environmental Protection Agency (on leave), Washington, DC.

SAADEH AL-JURF is a student at the University of Virginia School of Law, Charlottesville, Virginia.Address correspondence to Dr. R. D. Morgenstern, Resources for the Future, 1616 P Street, N.W.,

Washington, DC.

Technological Forecasting and Social Change 61, 13–24 (1999) 1999 Elsevier Science Inc. All rights reserved. 0040-1625/99/$–see front matter655 Avenue of the Americas, New York, NY 10010 PII S0040-1625(98)00059-6

14 R. D. MORGENSTERN AND S. AL-JURF

Over the past decade electric utilities, along with gas utilities and federal and stategovernments, have been providing information to potential buyers to stimulate privateinvestment in certain energy conservation technologies. Utility-sponsored demand-sidemanagement (DSM) programs, which often combine free technical information andcash subsidies, have been available to both commercial and residential customers inmany states since the mid-1980s.1 High-efficiency lighting is the most popular elementof these programs. The “Green Lights” program, sponsored by the U.S. EnvironmentalProtection Agency (EPA), has provided similar information, albeit not tailored to site-specific conditions, to corporations and other institutions since 1991.

Although many recipients of free technical information do retrofit advanced lightingtechnologies, little is known about whether they are motivated solely by this informationor by other factors as well. The most transparent way to measure the relation betweentechnology adoption and the provision of free technical information would be to focuson two groups of buildings, identical in all respects, including building user characteris-tics, only one of which (randomly) receives the free information. One could then examinewhether there is a difference in the rates of technology adoption between the twogroups of buildings. Because the groups would be otherwise identical (due to randomiza-tion), this would yield an unbiased estimate of the effect of information programs ontechnology adoption.

Unfortunately, survey data on such randomized samples of buildings and buildingsusers are generally unavailable. Program evaluations typically compare program partici-pants to some synthetic control group. Given the inherent difficulties of finding a suitablecontrol group the results of such studies are frequently called into question.

The present article is based on a random sample of buildings—in this case aDepartment of Energy survey of commercial buildings—that is not specifically directedat participants in energy conservation programs. In the absence of a set of randomizedbuildings users, we construct a model of technology adoption. This model incorporatesother factors besides information programs which affect the investment decision. Howmany firms, for example, would adopt the technologies anyway, perhaps because theyalso receive subsidies, face high electricity prices, are intensive appliance users, or havea preference for high tech solutions, but decide to accept the free information becauseit is available? Failure to account for such factors may cause us to overestimate theinfluence of information programs on technology adoption. Additionally, to test thehypothesis that the acceptance of technical information is itself an endogenous determi-nant of technology retrofit, they are modeled as jointly determined dichotomous vari-ables. Our findings lend support to the notion that the free dissemination of technicalinformation can speed the diffusion of new energy-saving technologies.

Literature ReviewResearch by economists on the subject of technology diffusion dates back more

than four decades. The single most important conclusion of this work is that diffusionof new, economically superior technologies is a gradual rather than instantaneous process(for literature reviews, see [1, 2]). Diffusion is often portrayed as a classic sigmoid curveover time (i.e., the rate of adoption begins slowly, speeds up, and eventually slowsdown). One justification for the sigmoid curve is that due to a lack of knowledge orconfidence in its performance, the probability that a non-user will adopt a new technology

1 DSM programs provide site-specific information to customers on performance, costs, and availabilityof energy-saving equipment; some also provide financial assistance to those undertaking retrofits.

FREE INFORMATION AND TECHNOLOGY DIFFUSION 15

increases with its growing popularity. From this intuition, it makes sense that the rateof adoption will be slow in the beginning (before it becomes popular) and in the end(when there are few non-users).

The pioneering work of Griliches [3] established that the diffusion of a new technol-ogy can be understood in an economic framework by allowing it to be partly determinedby the (expected) economic returns to adopting the technology. Mansfield [4] demon-strated that the rate of diffusion can depend on the size of adopting forms, the perceivedrisk of the new technology, and the size of the required investment. Such factors donot, however, distinguish the rate of adoption by different firms. Individual firms areconsidered to be homogeneous with respect to their rate of adoption.

An alternative approach would focus on inherent differences or heterogeneityamong firms [5–7]. In such a model the gradual diffusion process depends on differencesamong potential users that affects the value of the innovation to them (e.g., the costsof learning about a new technology, adapting existing processes, and acquiring andoperating the equipment). One can think of there being a threshold above which itpays to adopt the new technology and below which it does not. Over time, the cost ofthe innovation may fall and/or the quality may improve, thereby lowering the threshold.Use of this approach depends on the ability to identify differences that affect a firm’svaluation of the innovation (for environmental applications using both homogeneousand heterogeneous models, see [8–11]).

The empirical literature on the effectiveness of utility-sponsored DSM programsdraws, in part, on the studies of technology diffusion. Most studies focus on programsinvolving direct subsidies and compare them to some synthetic control group (see,for example, 12–22]). Those that also include information programs generally fail todistinguish between utility customers accepting information only and those acceptingexplicit subsidies. In contrast, the present study focuses on electric utility-sponsoredinformation-only programs that are designed to lower the cost of learning about newenergy-saving technologies. With a random sample of buildings we apply a simpleeconomic model that focuses on the heterogeneity of users.

Model and DataEconomic models of firm decision making are typically based on rational choice.

Firms are assumed to make decisions which minimize the present discounted value ofexpected net costs. In the case of retrofit investment in high-efficiency lighting, theprimary benefit is the reduction in electricity costs. The firm chooses whether or notto adopt the new technology by calculating the difference between the present discountedvalue of the operating savings versus the installed costs of each proposed investment.Technology adoption occurs when expected energy savings associated with adoptionare calculated to be greater than or equal to the expected installation and equipmentcost of the new lighting technologies. Although we do not have building-level informationon these energy savings or equipment/installation costs, we do know whether or notthe new technologies were adopted during a specified period. Thus we use a stylizedreduced form probit model of technology adoption:

Pr(Yi 5 1) 5 F[S(XS,i) 2 C(Xc,i)] (1)

where F is a cumulative standard normal density function, S(Xs,i) is a proxy for operatingsavings from reduced electricity costs, and C(Xc,i) is a proxy for the installed cost ofthe new technology. Xc,i and Xs,i are data vectors determining operating savings andinstalled costs, respectively.


This specification assumes that the probability of technology adoption rises withthe modeled net savings (i.e., the difference between S and C). Actual adoption alsodepends on an unobserved disturbance. Specifying S(Xs,i) and C(Xc,i) as linear functionswe estimate the coefficients (as and ac) using maximum likelihood.2 To test the hypothesisthat the acceptance of technical information is an endogenous (as opposed to exogen-eous) determinant of technology retrofit, they can both be modeled as jointly determineddichotomous variables (see Appendix 1).

Our data set contains several variables, Xc,i, relevant to electricity savings: theaverage price paid by the building for electricity, the number of hours per week thebuilding operates, building size, and whether or not time of day pricing is applied. Asproxies for the cost of adopting the new technologies, Xs,i, we have information on theage of the building and the prevailing wage rate in the area. Although a utility-sponsoredinformation program would not be expected to reduce the direct cost of a new technol-ogy, it is posited to reduce certain “trial and error” costs associated with finding andassessing the new technologies. Both the provision of free information and the presenceof time of day pricing are postulated to directly increase the probability of technologyadoption. They are both defined as simple categorical variables (1 5 yes; 0 5 otherwise).

Our principal data set is the Department of Energy’s 1992 Commercial BuildingsEnergy Consumption Survey (CBECS) [23], a probability sample, representing the 4.8million commercial buildings in the United States as of spring 1992. Information in thissurvey is drawn from building owners/managers/tenants as well as from local utilities.3

In order to focus on retrofits and avoid the possibility that the high-efficiency lightingwas installed at the time of new construction, the sample is limited to buildings con-structed prior to 1986. And because of the varied patterns of energy demand in differenttypes of commercial buildings (e.g., restaurants versus warehouses), the sample is furtherrestricted to office buildings, the largest and probably most homogenous category ofcommercial buildings.

The three most common lighting upgrades, compact fluorescents, occupancy sen-sors, and specular reflectors, are selected as indicators of high-efficiency lighting forthis study. Three dichotomous variables are defined according to whether lesser orgreater amounts of advanced lighting technologies are present in the building. TECH1indicates whether a building contains one or more of the three technologies; TECH2and TECH3 indicate whether a building contains at least two, or all three technologies,respectively. As shown in Table 1, 64.3% of the commercial office buildings had oneor more of these technologies in place, 27.5% had two or more, and 6.5% had all three.

CBECS asks about DSM participation over the three previous years and categorizespositive responses into “site-specific information,” and “financial assistance.”4 Thesetwo types of DSM assistance involve very different activities. Because receipt of financial

2 All continuous variables are modeled in logs.3 Cross-checking information from two separate sources is a desirable but unusual practice. In CBECS

the building respondents (owners/managers/tenants) reported only about one-fourth as much participation inDSM programs as did utility respondents. The authors of the CBECS indicate that one of the reasons thisdiscrepancy may arise is that the utilities had more detailed records of DSM participation [23]. Examinationof the individual responses indicated that the discrepancies went in both directions: that is, utilities reportedbuildings had participated in DSM programs when the building respondent indicated they had not participated,and vice-versa. Accordingly, on the assumption that errors of omission were more likely than errors ofcommission, participation in DSM programs is defined on the basis of a positive indication from either theutility or the building respondent or both.

4 There was also a category for “general information.” However, that is most likely a bill insert and notpart of any systematic transfer of information.


TABLE 1Variable Means and Standard Deviations (SD)

Variable Mean SD

Adopted lighting technology (TECH1) 0.643 (0.479)Adopted lighting technology (TECH2) 0.275 (0.447)Adopted lighting technology (TECH3) 0.065 (0.247)Wages (dollars/hour) 13.814 (2.709)Electricity price (P, cents/kwh) 7.931 (3.270)Hours 90.200 (44.195)Year of construction 1967.0 (21.441)Size of building (in thousands of square feet) 73.428 (52.599)Time of day pricing (TOD) 0.124 (0.329)Infromation provided (INFO) 0.209 (0.407)Owner occupied 0.733 (0.443)Heating-cooling DSM 0.272 (0.445)Annual electricity bill (cents/square feet) 1.572 (1.104)Sample size 990

Source: [23].TECH1 One if at least one of three lighting technologies in use. Zero otherwise.TECH2 One if at least two of three lighting technologies in use. Zero otherwise.TECH3 One if all three lighting technologies in use. Zero otherwise.

assistance is predicated on actually installing specified equipment, one cannot use thisinformation to examine the effect that accepting financial assistance has on the decisionto retrofit lighting systems. In contrast, receipt of site-specific information does not bindthe recipient to any action. Although the building owner/tenant has to request or atleast accept the offer of site-specific information from the utility, there is no requirementto actually install the equipment. Accordingly, the sample is defined to include onlythose buildings which received site-specific information from the utility but did notreceive financial assistance.5 All other variable definitions are relatively straightforward.6

Table 2 shows the distribution of technology adoption outcomes depending onwhether they received information from the local utility. In this and subsequent tablesall data are weighted by building size. Thus the percentages shown here representfractions of the total square footage in the sample, not the proportion of buildingstructures. Of those receiving information from the utility, 20.7% have none of thethree technologies, 36.5% have one, 29.6% have two, and 13.2% have all three technolo-gies. Among those not receiving utility-supplied information, a higher percentage havenone or one of the technologies and a lower percentage have two or more of thetechnologies. Although these tabulations do not account for any of the economic anduse factors likely to effect the probability of technology adoption, they do suggest twotentative conclusions: (1) information programs appear to increase the probability of

5 To test for the possibility that eliminating those buildings which received financial assistance (n 5 340)introduces selectivity bias into the sample, the models presented in Table 3 and Appendix 1 were re-estimatedon the full sample by coding “financial assistance” as if it were the same as “information.” The basic results wereunchanged; although, as expected, the coefficient on the newly defined information variable was slightly larger.

6 Average electricity prices are defined as the annual electricity bill divided by the annual kilowatt-hours,as reported by the utility. The presence of time of day pricing (as reported by the utility) is defined as adichotomous variable. As shown in Table 1, it is used in 12.4% of the buildings. Average hourly wages in theregion are constructed by dividing annual earnings of electrical equipment installers by their hours workedper year. Wages were averaged for metropolitan and non-metropolitan statistical areas in each census division(Census Bureau’s Data Extraction System).


TABLE 2Technology Adoption According to Information Provided: Gross Data

Percent not Percent adopting Percent adopting Percent adoptingadopting any of one of three two of three all three

three technologies technologies technologies technologies

INFO provided 20.7 36.5 29.6 13.2INFO not provided 39.7 36.9 18.6 4.8INFO relative to 219.0 20.4 11.0 8.4

no INFO

Source: [23].

high-efficiency lighting and (2) such programs seem to shift non-adopters to the two ormore technologies category, leaving the percent adopting only one technology virtuallyunchanged. The model estimates presented in the next section assess whether thesetentative conclusions withstand further scrutiny.

ResultsUsing TECH1, TECH2, and TECH3 as (alternative) dependent variables, the

maximum likelihood results for the exogenous model are presented in Table 3. Mostof the independent variables are significant in at least one of the equations and thecoefficients generally conform to expectations. The coefficients on average electricityprices are positive and significant in all technology adoption equations. The coefficientsfor time of day pricing are also positive in all equations and significant in two of thethree equations. As expected, buildings that operate longer hours per week and thosewhich were constructed more recently have a greater likelihood of adopting high-efficiency lighting technologies, although the coefficients on these variables are not

TABLE 3Probit Results: Technology Adoption with Exogenous Program Participation

Exogenous program participation

TECH1 TECH2 TECH3Independent variables Technology adoption Technology adoption Technology adoption

Intercept 251.871* 228.455 87.061(30.436) (35.585) (57.704)

Wages (dollars/hour) 0.659*** 20.299 21.459***(0.222) (0.225) (0.466)

Electricity prices 0.687*** 0.465*** 1.510***(0.161) (0.136) (0.256)

Hours 0.160 0.018 0.224(0.101) (0.119) (0.242)

Year of construction 6.037 3.228 212.229(4.006) (4.683) (7.554)

Building size 0.191*** 0.227*** 0.255*(in thousands of square feet) (0.038) (0.053) (0.145)

Time of day pricing 0.657*** 0.217 0.924***(0.155) (0.137) (0.246)

Information provided 0.594*** 0.530*** 0.820***(0.116) (0.108) (0.210)

Log likelihood 2971.04 2935.10 2585.59

n 5 990 observations. Standard errors in parentheses.*** Significantly different from zero at the 0.01 level; ** at the .05 level; * at the .10 level.


TABLE 4Technology Adoption According to Information Provided: Modeled Results

Percent not Percent adopting Percent adopting Percent adoptingadopting any of one of three two of three all three

three technologies technologies technologies technologies

INFO provided 14.8 39.0 38.3 7.8INFO not provided 32.6 39.4 26.3 1.6INFO relative to

no INFO 217.8 20.4 12.0 6.2

Source: Calculated from Table 3.

significantly different from zero. Larger buildings are more likely to adopt high-efficiencylighting, at least for TECH1 and TECH2. Wages are a more complex story.7

Of key interest in this study is the information variable which is positive and highlysignificant in all three technology adoption equations. Thus, even after adjusting for acomplex set of factors that a rational model suggests would influence adoption, theprovision of technical information by local utilities remains an important determinantof technology choice.8

One way to display these results is to consider how the probability of adoptingadvanced lighting technologies varies depending on whether the firms received informa-tion from the local utility. To develop such estimates, we apply the basic model tovarious subsamples of buildings in order to compare the effect of information programson those firms which have adopted some advanced lighting technologies versus theeffect of these programs on first-time buyers.9 As shown in row one of Table 4, 14.8%of those who received information from the utility have none of the three technologies,39.0% have one, 38.3% have two, and 7.8% have all three. Among those who didnot receive utility-supplied information, a higher percentage have none or one of thetechnologies and a lower percentage have two or more of the technologies. Theseestimates, based on the economic and use factors included in the model confirm thebasic findings shown in the simple sample distributions calculated in Table 2, (i.e.,information programs appear to increase the probability of technology adoption, andthey appear to be somewhat more important in encouraging building owners to adopt

7 If labor were only a factor in the installation of the high-efficiency lighting, one would expect thecoefficient to be negative. However, since labor is also required to change lightbulbs and high-efficiency lightingtends to require less frequent bulb replacement, there is also a labor-saving element associated with high-efficiency lighting. For TECH3 the negative and highly significant coefficient indicates the first effect dominates.For TECH1 the reverse is true. This suggests that marginal labor costs for installation are a more importantfactor than marginal labor savings from reduced maintenance in the presence of a larger number of high-efficiency lighting appliances.

8 The results from the endogenous model are not statistically different from the single equation approach(see Appendix 1).

9 The probability of adopting no new technologies is derived from calculating the value of TECH1 usingaverage values for all variables except INFO, which is set equal to 0. The probability of adopting one newtechnology is derived from the same equation, where INFO 5 1, multiplied by one minus the probability ofadopting two or more technologies, given that you have already adopted one appliance. (The latter probabilityis derived by re-estimating the basic model on the subset of buildings which have adopted at least onetechnology. For that subsample, the calculated effect of the information programs is to raise the probabilityof adoption from 41.5% to 54.2%.) The probabilities of adopting two or all three technologies are derivedfrom similar calculations. For the subsample of buildings with two or three technologies in place, the effectof the information programs is to raise the probability of adoption of all three technologies from 5.9% to 16.8%.


multiple technologies than for encouraging non-adopters to purchase a single tech-nology).

ConclusionsThis article examined the use of information subsidies as a means of accelerating the

diffusion of new technologies. Estimates are based on a random sample of commercialbuildings, rather than the more common comparison of program participants to asynthetic pairing with another populations. In the context of an economic model whichemphasizes heterogeneity among building users, the provision of technical informationby electric utilities is found to be a significant determinant of the adoption of high-efficiency lighting technology in commercial office buildings. Additionally, informationprograms appear to be more effective for encouraging building owners who have alreadyinvested in some advanced lighting technologies compared to first-time purchasers.These findings are based on a relatively simple specification where information programsare modeled as directly increasing the probability of technology adoption. Other specifi-cations may also be of interest (e.g., whether the information programs are particularlyeffective in the presence of high energy prices or long hours of building use).

It is instructive to make cross-comparisons among policy tools—in this case electric-ity prices and information programs—to determine their relative effectiveness in influ-encing technology choice.10 Of course, the preferred way to compare these policy toolsis in the context of a more complete economic model (e.g., one which accounts for boththe benefits and costs of the different tools). Such an accounting would recognize thatprice increases would have the additional effect of reducing electricity use in new andexisting installations as well as in nonlighting uses of electricity. Notwithstanding thosedifferences, our estimates suggest that electricity prices would have to rise by morethan 25% in order to bring about an increase in technology adoption equivalent to thatinduced by the utility-sponsored information programs.11

EPA’s Green Lights program, which has some similar features to utility-sponsoredDSM programs, measures its success in terms of the amount of its participant’s buildingspace committed to install new technology.12 It does not estimate how much retrofit islikely to have occurred in the absence of the program [24, 25]. It is certainly plausibleto interpret our findings as consistent with general claims of effectiveness made byproviders of technical information, including the Green Lights program. However, ourresults clearly suggest the importance of accounting for heterogeneity among userswhen developing quantitative estimates.

Absent detailed cost information it is not possible to address the cost-effectivenessor net economic benefits of information-based programs. However, it is clear the provi-sion of technical information has a positive effect on the decision to adopt high-efficiency

10 Changes in average prices can be brought about by some form of energy taxes (which would increaseend-use prices), or, alternatively, by policies that increase competition in the electricity industry, such asrestructuring (which would reduce end-use prices).

11 This estimate is derived from the TECH1 equation by calculating the increase in average electricityprices necessary to bring about an increase in technology adoption equivalent to the use of a utility-sponsoredinformation program.

12 Although it does not directly provide for site visits by lighting experts, Green Lights is similar to utilityDSM programs in many respects. Typically Green Lights works with managers of large amounts of commercialfloor space and provides evaluation tools for the firm to undertake a set of detailed assessments. Theseassessments, in turn, provide the basis for the firm to make its own site-specific decisions to determine whichlighting upgrades are most appropriate in particular applications.


lighting technologies, particularly so when firms had prior experience with related tech-nologies.

The authors acknowledge helpful comments from Howard Gruenspecht, WinstonHarrington, Richard Newell, William Pizer, and David Ribar.

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Received 10 July 1998; revised and accepted 12 December 1998

Appendix 1

TEST OF POSSIBILE ENDOGENEITY

A simple empirical model can be used to analyze the possibility that acceptanceof technical information is an endogeneous determinant of technology retrofit [26,27].13 Let INF*i denote the (unobserved) benefit to the ith firm of receiving technicalinformation from the utility. Assume that INF*i is a linear function of economic andinstitutional determinants. Specifically, let

INF*i 5 Zid 1 hi (A1)

where Zi. is a vector of observed variables which includes both Xi and other variables,and hi is a random error term.14 Although INF*i is not observable, it is related to INFi

(which is observable) as follows:

INFi 5 51 (if firm receives/accepts technical information) if INF*I > 00 (if firm does not receive/accept technical information) otherwise.

(A2)

The error terms ei and hi are assumed to be distributed bivariate standard normal withmeans and standard deviations as follows:15

3ei

hi4 z N13004,3

1 r

142. (A3)

Equation A1 with the distribution assumption (A3) specify the probability of technologyadoption as a probit model with information as an endogenous determinant. The purposeof the distribution assumption in equation A3 is to allow for the measurement ofcorrelation (r). (The case of r 5 0 is the model used in the text, i.e., the acceptanceof information is exogenous to the decision to retrofit.)The effect of information on technology adoption is only identified subject to exclusionor covariance restrictions, in this case on the vector Xi. The variables which are excludedfrom Xi should be theoretically and statistically related to obtaining information fromthe utility but unrelated to technology adoption. In fact, it is both logically and empiricallydifficult to identify factors that influence one but not the other. Notwithstanding, thisresearch considers three such variables—a dichotomous variable indicating whether ornot the building has participated in utility-sponsored DSM programs on heating and

13 The theoretical foundations of this model are found in Maddala [26]. An interesting empirical (albeitnon-energy) example can be found in Ribar [27]. Train [20] constructs an econometric model to deal with theendogenous choice issue by assuming a nested logit distribution.

14 The determination of TECH* and INFO* are not treated symmetrically (INFO* does not includeTECH as an explanatory variable). Unfortunately, the likelihood function for the symmetric specification doesnot, in general, integrate to one [26].

15 Maximum likelihood estimation of this specification is straightforward. However, the coefficients anderror variances in equations 9 and 11 are only identified up to their proportions, b/se, A/se, and d/sh. Weapply the standard normalization se 5 sh 5 1.


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cooling; a dichotomous variable indicating whether or not the building is owner occupied;and a variable for the electricity bill (per square foot) of the entire building (not justthe lighting system).16

The endogenous results, which are similar to the exogenous model in many respects,are shown in the Table A1. Despite the theoretical appeal of the endogenous specifica-tion, the empirical grounds for rejecting the exogenous model are not strong.17,18

16 Participation in another utility-sponsored DSM program is taken as an indicator that the buildingowner is familiar with DSM programs. Assuming the firms’ experience with the other program was positive,one would expect a positive sign on this variable (for a positive assessment of DSM programs see Hirst [15,16]; for a contrary view see Wirl [22]). Owner occupancy is a more complicated factor. Inasmuch as owner-occupants are more likely to reap the full benefits from lighting retrofits, one would expect a positive sign onthis variable. If, however, many owner occupants had already retrofit their lighting systems, then they mightbe expected to participate less often in utility DSM programs than non-owner occupants. Electricity expendituresper square foot is also a complicated factor. High electricity expenditures per square foot, even if it is unrelatedto lighting, could represent a wake-up call to acquire information about lighting retrofits. On the other hand,low expenditures per square foot in a building which did not already use efficient lighting could indicate astrong preference for energy efficiency and thus could account for an interest in technical information onlighting retrofits. None of these three variables is assumed to directly affect the probability of lighting retrofit.(Some might challenge that assumption, for example, owner occupancy may also affect the ability to capturesavings from retrofitting lighting systems.) Several other types of DSM programs were also examined forinclusion as independent variables in this equation. However, none altered the basic results reported here andnone were statistically significant.

17 The excluded variables in the endogenous model are highly significant and of the expected signs in allthree equations. However, the values of the correlation coefficient which measure endogeneity (r) are all quitesmall (0.112, 20.004, and 0.227, respectively). In no case is the t-value of these correlation coefficients greaterthan 0.9. Similarly, statistical comparison of the log likelihood ratios indicates there are no significant differencesbetween the exogenous and the endogenous specifications. These findings suggest, contrary to expectations,extremely weak evidence to support use of the endogenous model. Note also that the only other variablewhich is statistically significant in the estimation of DSM program participation is electricity prices and it hasa negative coefficient. Sensitivity analysis showed that estimating this equation separately without the threeexcluded variables also produced a significant and negative coefficient. The most obvious explanation for thisfinding is that many buildings in areas with high electricity prices had previously installed high-efficiencylighting and were thus less likely to get involved in DSM lighting programs.

18 One possible explanation for the lack of statistical significance between the exogenous and endogenousmodels is that some firms have a preference for high technology solutions and that preference is an importantdeterminant of both the decision to adopt high-efficiency lighting and to participate in the lighting DSMprogram. To check for the possibility of such a misspecification, a total of 22 different measures of “hightechnology” equipment, relating to energy management equipment, heating and cooling, shell measures, andothers were examined as possible variables. Few of these variables were significant and none of them statisticallyaltered the size or significance of the coefficient on the information variable.

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