THE IMPACT OF THE ENERGY STAR LABEL ON CONSUMER DECISION-MAKING
Anshuman Sahoo, Department of Management Science & Engineering, Stanford University, 415 742 8604, asahoo@stanford.edu1
Nik Sawe, Emmett Interdisciplinary Program in Environment and Resources, Stanford University, 650 814 4648, sawe@stanford.edu
Overview
The residential sector accounts for about 22% of U.S. energy consumption, and changes in residential appliance
stock have the potential to meaningfully shift U.S. energy demand. The Energy Star certification program of the
U.S. Environmental Protection Agency attempts to help consumers identify energy efficient appliances. Standard
economic theory suggests that such certification should not alter consumers’ tradeoffs between costs and benefits of
alternative appliance products; instead, it should only reduce the search costs for efficient alternatives. However,
recent analyses of consumer appliance choice suggest that Energy Star certification may affect certain consumer
subpopulations differently; in particular, it may disrupt rational economic calculations for some individuals while
complementing them in others (Houde, 2012). This work aims to articulate potential differences between these
consumer populations by (1) implementing a stated choice experiment to test the presence of heterogeneous
responses to Energy Star certification, (2) examining the evidence for heterogeneity across products of different
price scales, and (3) exploring the association of demographic, psychological and financial co-variates with
heterogeneous choice model coefficients. Our initial data appear consistent with heterogeneous responses to Energy
Star certification for both high-cost (i.e., refrigerator) and low-cost (i.e., light bulb) goods. The forthcoming output
of our work, a predictive mapping from easily observed co-variates to Energy Star response modality, could
facilitate the sort of matches between social programs and consumers envisioned by Manksi (2005).
Methodology
We employ a five step process. Our first two steps involved the collection of stated choice data. Pilot
participants were recruited via the Amazon Mechanical Turk platform and data gathered using the Qualtrics survey
tool.2 This yielded a sample of 103 respondents and prior estimates for the coefficients of a choice model. We then
used these priors to redesign the choice sets for a full-scale stated choice exercise. This full-scale exercise will be
implemented through a link on the Stanford Appliance Calculator, a website utility which allows consumers to
research refrigerators on the basis of price, consumption, size, and Energy Star presence. Individuals navigate to this
tool by entering search terms on Google which indicate that the user is likely in the market for appliances. Our stated
choice experiment integrates two separate experiments: it first asks participants to select the light bulb they would
purchase from a choice set of three alternatives and then to perform the same task for refrigerators. In each
experiment, we included as many choice sets as necessary to accommodate the smallest orthogonal design implied
by the number of alternatives, attributes, and attribute levels. For our refrigerator and light bulb experiments, the
smallest orthogonal design required 256 and 128 choice sets, respectively, and we partitioned these into 16 and 8
blocks, respectively. Thus, each participant selected light bulbs from 16 different choice sets and refrigerators from
16 different choice sets. We used Ngene 1.1.1 to generate these experimental choice sets.
Our third step was to collect and process demographic, psychological, and financial data. Each participant in the
stated choice experiment filled out a 129-item questionnaire after completing the choice tasks described above.
Selection of these items was motivated by a set of hypotheses about how the underlying covariates could shape
comparative static predictions for coefficient values. We employed procedures from the economics and psychology
literatures to consolidate the responses to these questions into standard metrics (e.g., 21 questions about preferences
for a smaller immediate reward versus a larger delayed reward implied one exponential and one hyperbolic discount
rate). After this consolidation, our data included 33 demographic, psychological, and financial variables.
1
We gratefully acknowledge the financial support of the Precourt Energy Efficiency Center at Stanford University
and thank John Beshears, Brian Knutson and James Sweeney for their very helpful comments on experimental
design. We also thank Taurean Butler and Natalie Luu for their research assistance.
2
This abstract is based on results from pilot data, but we do not intend to use these data or results for publication or
presentation; instead, we would present results from our full scale stated choice experiment.
In a subsequent step, we estimate mixed logit models for refrigerator and light bulb choices, following the
random parameter selection, empirical distribution selection and simulated maximum likelihood procedures
suggested by Hensher and Greene (2003). This process provides us with distributions for the coefficients of the
mixed logit choice model and, by extension, of the willingness-to-pay (WTP) for the various attributes of the
alternatives. Our pilot data allowed us to estimate mixed logit models without any latent classes, but our full data set
will allow model estimation with both random parameters and latent classes.
Our final step combines the distributions of the coefficients of Energy Star certification with (1) the
distributions of the coefficients on other attributes (e.g., purchase price and consumption) and the covariance
matrices specific to each class and (2) the collected demographic, psychological, and financial data. The former
allow us to interpret how members of each class use Energy Star certification, consumption, and price information.
The latter allow us to characterize differences in the types of consumers who are part of a given class.
Results
Pilot data suggest that individuals respond heterogeneously to Energy Star presence. Specifically, we reject null
hypotheses of fixed coefficients on Energy Star presence, consumption and ice-maker presence for refrigerators and
of fixed coefficients on Energy Star presence, consumption, warmth and purchase price for CFL light bulbs. Figure
1 plots kernel density estimates of the Energy Star coefficients on refrigerator and CFL light bulbs. Despite the tight
bounds implied by these one-class kernel estimations, significant interactions with other attributes suggest that a
relaxation of the one-class estimation could reveal additional heterogeneity across classes. This encourages future
work to identify latent classes in which the joint distributions of attribute coefficients differ within classes.
Figure 1. Kernel density estimations of coefficients on the Energy Star attribute for CFL bulbs and refrigerators
Our future work is motivated by our desire to understand whether Energy Star presence drives different
decision-making modalities. This evidence of heterogeneous coefficient values does not itself imply differences in
decision-making modalities as it could simply reflect different expectations of future energy prices or utility from
the appliance services. Our estimation of latent class mixed logit models and the resultant distributions of
coefficients within the different classes will offer evidence for or against differences in decision-making modalities.
Conclusions
Our initial data suggest heterogeneity in preferences for the Energy Star logo across low and high price
products. These data are consistent with the existence of deeper heterogeneity in decision-making modalities. The
identification of different modalities and the psychological, demographic, and financial covariates associated with
them is the subject of current work with a larger stated choice sample (n = 1,000, in collection).
References
Hensher, D. and W. Greene (2003), “The Mixed Logit Model: The State of Practice,” Transportation, 30(2),
133 – 176.
Houde, S. (2012), “How Consumers Respond to Product Certification: A Welfare Analysis of the Energy Star
Program,” Mimeo.
Manski, C.F. (2005), Social Choice with Partial Knowledge of Treatment Response, Princeton U.P., Princeton.