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s Behavioral Study of Appliance Efficiency Decisions Energy consumption is determined in part by consumers' choices about purchasing, and then about using new and replacement technology. Choices about the energy effici- ency of appliances are a significant instance of technol- ogy choice for several reasons. A large part of the long- run ability of consumers to modify energy consumption patterns is embodied in the energy-using technology they choose and is fixed for the lifetime of that equipment. Two ways of affecting consumer choice, standards for appliance efficiency and incentives for selected technol- ogies, are often considered or used as energy policy tools. And consumer information and marketing programs may also be effective tools for energy management by cor- recting consumers' misperceptions and encouraging purchase of more efficient appliances. Thus, wherever possible, energy policy analyses and forecasting systems should incorporate adequate descriptions of equipment technology and of choices about the purchase and use of technologies. To capture the effects of information and consumer percep- tion that may be important in the design of policies and programs, these descriptions should incorporate a sub- stantive account of the behavioral process by which con- sumers assess the alternatives and choose among technolo- gies. Many current policy simulation models assume that appliance technology is chosen using a criterion of life- cycle cost minimization. This assumption excludes con- sideration of factors that may be behaviorally important, including appliance capacity, safety, convenience, brand- name loyalty, marketing of different models, dealer char- acteristics, and so forth. It also ignores what may be an important interaction between efficiency and utiliza- tion decisions: consumers may use more efficient appli- 75
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76 ances more heavily, and may anticipate this when assessing technological alternatives. Furthermore, even if the life-cycle cost criterion were an acceptable description of behavior, its realization in models now often fails to include factors that may also be important, such as con- sumer expectations about future fuel prices and attitudes toward risk. In examining appliance choice, this chapter differs from Chapters 2, 3, and 4--each of which explored a class of factors that influence demand--by looking at a class of demand-related behavior that may be influenced by any of the factors discussed in the previous chapters--prices, incentives, and information. This chapter, then, is closer to the typical work of energy demand analysts: focuses on energy demand in a particular sector of the energy economy. We first consider some of the issues raised by current models of appliance choice and identify particular areas where there are gaps in available infor- mation and in behavioral theory. We then outline the experiments and field data collection efforts that would be required for an ideal and comprehensive behavioral tuav Of cho ices of appliance technoloav. ~ ~ ~ Finally, we discuss a few practical data collection efforts that would provide useful incremental information on appliance choice. ISSUES AND LIMITATIONS OF CURRENT ANALYSIS There are a very limited number of major empirical bases for formal modeling of appliance fuel choices, efficiency choices, and decisions about utilization. There are a few market studies of the initial costs and technological characteristics of purchased appliances: A ~ room air con- ditioners (e.g., Hausman, 1979; Brownstone, 1980); heating and central air conditioning systems (e.g., Dubin and McFadden, 1984); and refrigerators (e.g., Meter and Whittier, 1983). There are also laboratory bench studies and engineering projections of the costs of appliances of various efficiencies (Hirst and Carney, 1978) and thermal models used to calculate the sizes and levels of use of heating and cooling systems in buildings with well- specified structural characteristics. In addition, there are statistical analyses of appliance purchases or hold- ings using household survey data--the National Interim Energy Consumption Survey and the Residential Energy Con- sumption Survey (RECS) of the Energy Information Adminis-
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77 "ration; the Annual Housing Survey; and national surveys conducted by the Midwest Research Institute (MRI), the Washington Center for Metropolitan Studies, and numerous utilities--or state and regional cross-sectional, time- series data. However, none of these data sources contains sufficient information on appliance efficiencies to permit direct study of choices about efficiency.) Consequently, attention has concentrated on appliance fuel choices. One research approach has been to assume some version of the life-cycle cost-minimization hypothesis and to develop estimates of the implied discount rates that accord with observed choices (e.g., Hirst and Carney, 1978). Another approach has been to fit discrete choice models that allow that consumers sometimes do not minimize cost, possibly because they respond to factors other than initial and operating costs (see McFadden, Puig, and Kirschner, 1977; Berkovec, Hausman, and Rust, 1983; Dubin and McFadden, 1984). The latter studies permit testing of the life-cycle cost-minimization hypothesis, albeit in models in which the functional forms of equations are chosen for convenience rather than on the basis of data or established theory. These studies generally reject the cost-minimization hypothesis in its simpler forms. Fur- ther, the choice models they estimate meet some of the criteria for behavioral explanation. For example, esti- mated parameters are reasonably stable across data sets, and parameters estimated in some of the studies have gen- erated accurate accounts of fuel shares among appliances purchased in different regions and time periods (Goett and McFadden, 1984). But these models are still considerably short of an adequate behavioral theory, even for fuel choice. The models offer inadequate treatment of consumer information and expectations, a critical limitation in light of the policy need to predict the effects of marketing programs. There is considerable error in measuring consumer charac- teristics and appliance attributes. Finally, the models contain a large number of parameters and impose restric- tions that are neither implied by behavioral theory nor supported empirically. This does not in itself imply that these models are useless for policy purposes. Even imper- fect models may help account for physical and economic 1A partial exception is the study by Hausman (1979) of room air conditioner choice that used MRI data on indivi- dually metered appliances in a small sample of households.
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78 constraints and may identify variables to which consumer- response is sensitive. However, policy analysts should avoid making assertions that are not supported by behav- · . . ioral evidence, and improved congruence between nolicv models and behavioral theory should be a constant objec- tive. In short, existing empirical studies of appliance tech- nology and fuel choice have important limitations, and there are virtually no studies of choices of appliance , ~ efficiency. This state of knowledge is partly due to lack ot data and partly to weaknesses of theory. Questions on four topics must be addressed if analysis of appliance efficiency is to improve: (1) the analytic representation of appliances; (2) analytic representation of energy price expectations; (3) determinants of appliance use; and (4) the behavioral processes that affect consumers' decisions. Asking these questions focuses attention on the assump- tions and limitations of the hypothesis of life-cycle cost-minimization as a foundation for policy modeling. Analytic Representation of Appliances Many questions need to be addressed in considering how appliances are represented analytically: How is the set of feasible technological alternatives defined? Is there a continuum of idealized alternatives or a finite list of brands and models? IS efficiency to be measured by manu- facturer's rating, bench testing, or field testing? What dimensions of technology other than fuel and efficiency-- such as capacity, noise level, size, durability, safety, convenience, and so forth--need to be included as behav- iorally relevant attributes? How is the initial price of equipment defined: by list price, observed transaction price, or engineering calculation? If engineering studies are used to estimate the costs of alternative designs, what relationship holds between fabrication cost and prices? How should the prices of appliances supplied in new construction be imputed in life-cYcle cost calcula- · . . . tlons: by using equipment costs to contractors or by regression analysis of building prices as a function of attributes of the equipment in the building? Most current models are derived from the early model of Hirst and Carney (1978), which considers discrete fuel alternatives and represents the trade-off of efficiency and initial cost in terms of only three parameters. Appliance attributes such as capacity are incorporated
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79 into the analyses only at fixed levels; the model does not subject them to behavioral control. The estimates of technical efficiency are determined from engineering principles, and there is only limited validation of the link from idealized fabrication cost to market price. Future refinements of such models might include: repre- sentation of efficiency alternatives in terms of the brands and models actually available to consumers; direct measurement of purchase prices; representation of effici- encies as rated by manufacturers; field testing of real- ized efficiencies; measurement of attributes other than purchase price and efficiency; data collection on the degree of discounting by distributors from list prices, particularly in new construction; and measurement of the "perceived value" of equipment alternatives in new con- struction by hedonic regression. 2 Analytic Representation of Energy Price Expectations In considering energy price expectations, two major ques- tions stand out: How are future energy prices, as antici- pated by consumers, to be estimated? Which assumption is most justified for behavioral models of expectations: static prices at current levels, perfectly anticipated prices, or prices changing at historical trend rates? Most current studies assume a static expectation that current real fuel prices will continue to prevail in the future. Recent econometric investigations of expectation formation (primarily of financial markets, for which con- siderable data are available to market participants and to observers) suggest that consumers are less naive and use information to form "models" of future market behavior (Eichenbaum and Hansen, 1983; Mishkin, 1983). The limited evidence on energy markets is consistent with an adaptative-expectations hypothesis for gasoline prices (see Chapter 2), but this conclusion may not be applicable to electricity and natural gas markets, in which con- 2 In this procedure, detailed data on actual equipment choices or laboratory simulations of equipment choices are used to regress prices paid for appliances on a number of appliance characteristics measured or simulated in the study. The resulting regression equation repre- sents the "perceived value" of each of the character- istics.
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80 sumers' decisions are less routine and information is less systematic. Still, the importance of expectations for policy affecting appliance purchases makes the behavioral process of forming expectations a critical topic for study. Determinants of Appliance Use In studying appliance use analysts should address at least two major questions: How is anticipated future use of appliances estimated? If the level of use is not assumed to be fixed and beyond behavioral control, how does one incorporate feedback from appliance operating costs into level of use in estimating life-cycle cost? Current models using the criterion of life-cycle cost- minimization assume that, at least from the standpoint of a purchaser at the time of an appliance choice, the inten- sity of appliance use is fixed and beyond behavioral con- trol. While some of the models allow for changes in use once appliances are in place, this approach implies com- plete lack of planning by consumers. The possibility of feedback from anticipated operating cost to utilization to choice of technology poses a fundamental problem for the life-cycle cost-minimization hypothesis, since it implies that consumers may trade initial costs against the benefits of greater future utilization: for example, by buying an energy-efficient air conditioner, a household may be able to afford to keep cool more of the time. Assessing this possibility requires study of the joint choice of technology and utilization, with a theory of choice that allows trade-offs and with survey data that encompass both purchases and subsequent appliance use. Consumers' Decision Processes Analysts should address several questions on decision processes: How are future costs to be discounted under the cost-minimization hypothesis? How are appliance durability and consumers' replacement strategies taken into account? Are there perceptual elements that lead to different discount factors in fuel choice and in effici- ency choice? What is the impact of uncertainty about appliance performance or about resale market value? What is the impact of credit constraints?
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81 A number of studies have estimated discount rates by assuming the weights given to initial and operating costs in discrete choice models of appliance fuel choice. These studies have usually ignored the effects of expectations about fuel prices, interest rates, and equipment life and of feedback from utilization. The empirical results are quite erratic, reflecting these factors as well as prob- lems in accurately measuring initial and operating costs and limitations of the life-cycle cost-minimization hypothesis as a behavioral model. Refinements require a recognition that the life-cycle cost-minimization criterion must be modified under at least three conditions: (1) when the alternatives vary not only in energy efficiency but in service attributes, such as capacity and convenience; t2) when there is feed- back from anticipated levels of appliance use; (3) when consumer behavior is likely to be more complex than the criterion suggests. Some of the objectives of a behav- ioral theory may be achievable by adopting a psychophysi- cal version of the traditional economic model of prefer- ence maximization, in which consumer behavior is assumed to vary around optimal choice according to some mathe- matical distribution. This is a relatively mechanical approach to accounting for deviations from life-cycle cost minimization. It is easily adapted to the task of quanti- tative simulation (see Thurstone, 1927; Tver sky, 1972; Luce, 1977; McFadden, 1981), but it does not provide a useful framework for studying the relationships of infor- mation processing to choice or the social aspects of the transmission or use of information. A second approach is to conduct experimental or mar- keting studies of specific behavioral phenomena. Two examples are a controlled field experiment to test the effects of alternative cable television advertising "treatments" on consumer awareness of appliance costs and the use of focused group discussions to investigate the structure of consumers' information networks. Such studies can potentially provide information critical to the design of marketing programs for energy-efficient appliances. However, they are not well suited to develop- ing general purpose quantitative policy simulations. THE IDEAL DATA FOR BEHAVIORAL STUDY OF APPLIANCE CHOICES A comprehensive behavioral study of choices affecting appliance efficiency would concurrently examine the
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82 effects on appliance choice of fuel, efficiency, expected utilization levels, and a range of features of appliances and their marketing. It would also address the efforts of government and utility companies to influence consumer choice. This comprehensiveness imposes requirements on both data collection and on the method of analysis. The ideal data base must contain descriptions of all appliance alternatives, chosen and unchosen, as well as of con- sumers' actual choices and their subsequent intensities of use of the appliances. The efficiencies of alternative appliances should be measured both as perceived by con- sumers (e.g., manufacturers' labels, the content of adver- tising, and attitudes about the appliances) and in terms of field performance. The initial prices of the appli- ances should be measured as actual transaction costs. Appliance use should be observed under a sufficient vari- ety of conditions to reliably assess behavioral response and that information should be used in describing any feedback from utilization to appliance choice. To carry out the collection of such data in a con- trolled setting, with availability and attributes of appliances, prices, and other factors under experimental control, would be a formidable undertaking. Less defini- tive, but nevertheless informative studies, could be car- ried out with field data from natural experiments. Col- lection of such data by the U. S. Department of Energy would require a different orientation from that of annual surveys such as RECS that provide census-like data on energy consumption. Such studies would necessarily be smaller and more specialized, with intensive data collec- tion. Most of the elements of such surveys are already in use by various electric utilities, which collect appliance serial numbers, develop panel surveys, and attach meters to individual appliances. The U. S. Bureau of Labor Statistics routinely collects data on appliance transaction prices and uses hedonic regression techniques to relate price to equipment attributes. Without a national framework for simultaneous collection of such data, however, it is unlikely that enough standardization and comprehensiveness will be achieved to give good an- swers to questions about how consumers make choices about the energy efficiency of purchased appliances. Any general analysis of these questions based on field surveys will certainly leave some key questions unan- swered. Specialized experiments and marketing studies may shed light on specific behavioral phenomena that are critical to particular policy problems and may help make
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83 sense of the relationships observed in field data. For example, research by cognitive psychologists shows that people's choices can depend on the way in which the alternatives are presented to them (Tversky and Kahneman, 1981). Small-scale experiments could be conducted to explore the implications of this phenomenon for the mar- keting of energy efficiency in appliances. With regard to methods of analysis, there will always be a conflict between researchers who hesitate to extrapo- late from weak empirical evidence and policy analysts who seek the least inadequate method of extrapolating as far as is required by an immediate question or problem. Given the current state of behavioral theory, the limits to experimentation with human subjects, and the problems of understanding complex behavior by use of survey data, any practical policy tool will almost inevitably severely compromise scientific standards. Consequently, policy modeling should be a continuing process of scientific attack, invalidation, and improvement of interim simula- tion tools. With specific reference to the problem of analyzing appliance efficiency standards, an initial goal might be to carry out the data collection and analysis necessary to describe efficiency choice at the level of empirical precision that has already been achieved for fuel choice, while at the same time developing the experi- ments and behavioral knowledge necessary to refine the descriptions of both fuel choice and efficiency choice. PRACTICAL ALTERNATIVES FOR DATA COLLECTION Most of the quantitative policy models currently in use at the U. S. Department of Energy and elsewhere are based on the assumption that equipment efficiency choices are governed by life-cycle cost-minimization criteria or on some variant that admits exceptions in which additional aspects, such as capacity, may influence choice. The most narrowly defined cost-minimization models would be rejected by most social scientists as behaviorally unrealistic, particularly because of their insensitivity to such factors as consumer information, which may be important foci of policy. As a result, most existing models would be judged inaccurate for forecasting. Such models, if modified to make them consistent with classical psychophysical and economic ~laws" and fitted to survey data, may offer reasonably accurate bases for simulations of policies that operate primarily through economic vari-
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84 ables, but even these models still have to be validated for such applications. One task for those people who challenge current models is to propose improved alternatives that are practical enough to be adopted by policy makers and planners. A recommendation to scrap the current generation of models may be sound, but is unlikely to be followed (as indicated in Chapter 1). What may be easier is to move away from the current models toward directed experiments and field studies when possible and to continue to try to improve the behavioral foundations of current simulations. One way to implement such an approach would be for the U.S. Department of Energy (DOE) to cooperate with the private sector whenever feasible to promote standardization and sharing of data sources and to further develop data col- lection methods, such as automatic metering of individual appliances. Such coordination might involve industry-wide groups such as the Electric Power Research Institute and the Gas Research Institute. The DOE could also cooperate with other federal agencies in data collection. It would be helpful, for example, if DOE promoted joint collection with the Bureau of Labor Statistics (BLS) of data on the transaction prices of appliances and encouraged BLS to refine and publish hedonic regressions accounting for housing prices as a function of building attributes. The DOE should also review its use of large-scale policy- simulation models, adopt an ongoing program of upgrading and evaluation, and redirect policy analysis of appliance choice toward using specialized experiments and surveys as much as possible. The central element in a program of data collection on appliance efficiency would be a household survey that covers appliance choice, including fuel, efficiency, and other features, and that subsequently meters the chosen appliance. The Electric Power Research Institute is developing the technology for the last of these, appliance metering. A random survey sample could be drawn from the population with further selection of a sample of recent appliance purchasers. Alternatively, information could be collected retrospectively on old appliances, although this possibility is limited by the ability to obtain retrospective data on price and rated efficiency. Another design would measure appliance use among a panel of pur- chasers. This last approach would require careful statis- tical analysis and would lack useful observations on utilization before appliance purchase. Measurement of appliance efficiencies and ratings could be carried out
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as in a manner similar to Consumer Reports bench studies or EPA fuel efficiency measurements for automobiles; some utilities are now collecting such data with the coopera- tion of manufacturers, using appliance serial numbers. By cooperating with the private sector, DOE may be able to improve its own capacity to forecast the quantitative effects of energy policies on appliance efficiencies.
Representative terms from entire chapter: