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Does Better Information Lead to Better Choices?
Evidence from Energy-Efficiency Labels
Lucas W. Davis, Gilbert E. Metcalf
Abstract: Information provision is a key element of government energy-efficiency
policy, but the information that is provided is often too coarse to allow consumers to
make efficient decisions. An important example is the ubiquitous yellow “EnergyGuide” label, which is required by law to be displayed on all major appliances sold
in the United States. These labels report energy cost information based on average
national usage and energy prices. We conduct an online stated-choice experiment to
measure the potential welfare benefits from labels tailored to each household’s state of
residence. We find that state-specific labels lead to significantly better choices. Consumers choose to invest about the same amount overall in energy efficiency, but
the allocation is much better with more investment in high-usage high-price states
and less investment in low-usage low-price states.
JEL Codes: D12, H49, Q41, Q48
Keywords: Energy demand, Energy efficiency, EnergyGuide, Inattention, Information provision
INF O RM A TI ON PROV ISI ON IS A K EY element of government energy-efficiency policy. An important example is the ubiquitous yellow “EnergyGuide” label, which is
required by law to be displayed on all major appliances sold in the United States.
Lucas W. Davis (corresponding author) is at the Haas School of Business, University of California, Berkeley; Energy Institute at Haas; and National Bureau of Economic Research (ldavis
@haas.berkeley.edu). Gilbert E. Metcalf is at the Department of Economics, Tufts University,
and National Bureau of Economic Research (gmetcalf@tufts.edu). We are grateful to Hunt
Allcott, Stefano DellaVigna, Matthew Harding, Sébastien Houde, Kelsey Jack, and seminar
participants at Iowa State, Illinois, University of California, Berkeley, Harvard, and the NBER
for helpful comments. We also thank Poom Nukulkij at GfK for his work on the experiment.
Data collected by Time-sharing Experiments for the Social Sciences, NSF grant 0818839,
Jeremy Freese and James Druckman, principal investigators. This RCT was registered in the
American Economic Association Registry for randomized control trials under trial number 560.
The authors have not received any financial compensation for this project nor do they have any
financial relationships that relate to this research.
Received March 30, 2015; Accepted February 9, 2016; Published online July 14, 2016.
JAERE, volume 3, number 3. © 2016 by The Association of Environmental and Resource Economists.
All rights reserved. 2333-5955/2016/0303-0003$10.00
http://dx.doi.org/10.1086/686252
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Journal of the Association of Environmental and Resource Economists
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Similarly, new cars and trucks sold in the United States must display information
about vehicle fuel efficiency and an estimate of annual gasoline expenditures. Over
40 countries worldwide have some sort of energy-efficiency labeling requirements
(CLASP 2014).
This information is intended to help consumers make better decisions. However,
in many cases government-mandated labels do not provide accurate information necessary for consumers to make efficient decisions. In particular, most labels report
only very coarse information based on national average energy prices and typical national usage. In practice, energy prices and typical usage vary substantially, so the labels provide information that is highly inaccurate for many consumers.
The objective of our project is to evaluate the potential welfare benefits from providing more accurate information. We focus on room air conditioners because they
are a particularly lucid example. Within the lower 48 US states we show that annual cooling hours range by a 9∶1 ratio, while electricity prices vary by more than a
2∶1 ratio. As a result, typical operating costs vary widely, from $28 per year in Washington state, to $316 per year in Florida. Despite these very large differences in operating costs, consumers in all states see the exact same EnergyGuide label.
We designed and implemented an online stated-choice experiment to measure
how consumer decisions would change with information tailored to each household’s
state of residence. We find that better labels indeed lead to better choices. When
presented with more accurate information, the average energy efficiency of selected
air conditioners stays about the same, but the allocation is much better. Households
facing low energy prices and low expected usage invest less in energy efficiency, while
households facing high energy prices and high expected usage invest more. This reallocation leads to lower lifetime costs—defined as the sum of purchase price plus
present discounted value of energy costs—for both types of households.
The implied aggregate savings are substantial. State-specific labels decrease lifetime cost by an average of $11.60 per air conditioner. While small as a percentage of
the average lifetime cost (just under 1%), absolute savings can be large when aggregated over the large number of air conditioners sold each year. US consumers purchase more than 4 million room air conditioners each year, so the implied aggregate
cost savings exceed $50 million annually.1 Moreover, our results suggest that statespecific labels would improve decision making not just for room air conditioners, but
for a whole host of residential appliances.
1. Our estimate of the aggregate cost savings assumes that our survey choice alternatives
are representative of air conditioners available on the market. As discussed below, we went to
considerable effort to try to span efficiency and purchase price ranges relevant for room air
conditioners. Our estimates are based on a representative and quite typical air conditioner size
(10,000 BTU). Our cost savings estimates should be interpreted in light of this assumption.
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Does Better Information Lead to Better Choices?
Davis and Metcalf
591
We then provide additional analysis and evidence aimed at better understanding
the mechanisms underlying our results. We find that immediately after the experiment most participants are unable to correctly answer basic questions about the information they have just seen. Most do not know whether the labels they just saw
were based on national or state energy prices, nor do they know how energy prices
or appliance usage in their state compares to the national average.
Overall, the evidence points to people taking the information in these labels as
given without analyzing it carefully. Daniel Kahneman (2011) has referred to this
kind of decision making as WYSIATI: “What You See Is All There Is.” The content of the labels changes participants’ decisions, so it is not that they are ignoring
this information completely. But they appear not to be exerting the additional effort
that would be required to understand what this information means nor are they
spontaneously transforming this information to take local conditions into account.
Our paper differs from most previous studies of energy efficiency. While there is
an extensive theoretical and empirical literature on the economic determinants of investments in energy-efficient capital, there is little that has taken an explicit experimental approach.2 None of the work to date has focused on the efficiency cost of
inaccurate information provided to consumers as this study does.3 Our paper complements a growing broader literature that shows that customized information can
significantly improve education, health, and finance-related choices (see, e.g., Hastings and Weinstein 2008; Bertrand and Morse 2011; Kling et al. 2012; and Hoxby
and Turner 2013).
It is worth emphasizing that our evidence comes from a stated-choice experiment.
The highly stylized setting allows us to eliminate many of the factors that complicate
these decisions in real-world settings. This facilitates analysis and interpretation, but
it also may lead participants to focus more on labels than they otherwise would. One
approach to validating our results is to look for complementary evidence from actual
choices. Examining nationally representative data from air conditioner purchases, we
find no evidence of a positive correlation between operating costs and investments in
energy efficiency. Although this does not tell us how much choices would change with
2. Studies focusing on consumer choice of energy-efficient capital include Hausman (1979),
Dubin and McFadden (1984), Metcalf (1994), Revelt and Train (1998), Metcalf and Hassett
(1999), and Davis (2008), among others. See Gillingham, Newell, and Palmer (2009) and Gillingham and Palmer (2014) for recent surveys.
3. Two related studies perform online experiments using the same nationally representative panel that we employ. Newell and Siikamaki (2014) analyze optimal EnergyGuide label
design, while Allcott and Taubinsky (2015) measure the effect of information provision on
willingness to pay for energy-efficient lightbulbs. Neither study considers the role of inaccurate information provided to consumers.
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Journal of the Association of Environmental and Resource Economists
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better information, it provides some corroboration for other results in the paper about
the lack of effectiveness of current labels.
The paper proceeds as follows. Section 1 provides background information and
makes the case for why better information might matter. Section 2 describes our
online experiment. Sections 3 and 4 provide the main results and additional analysis. Section 5 offers concluding comments.
1. BACK GROUND
1.1. Previous Research
Economists have long been interested in how consumers make energy-related decisions. Hausman (1979) and Dubin and McFadden (1984) model durable good purchase decisions as a household production problem with a trade-off between purchase price and operating cost. Following these seminal studies, much of the literature
has focused on whether or not consumers undervalue operating cost when making
these trade-offs (see, e.g., Metcalf 1994; Metcalf and Hassett 1999). The most recent
evidence comes from vehicle purchases and indicates that consumers do not undervalue (Busse, Knittel, and Zettelmeyer 2013) or modestly undervalue operating costs
(Allcott and Wozny 2014).
Another recent strand in the literature has aimed at understanding specific behavioral biases in energy-related decisions. Studies by Allcott (2011a, 2013) examine “MPG Illusion,” the idea that consumers may not understand the nonlinear
relationship between miles per gallon and motor vehicle fuel consumption. Camilleri
and Larrick (2014) test whether vehicle preferences are affected by the scale in which
fuel economy information is expressed, for example, gallons per 100 miles versus gallons per 1,000 miles. Finally, Allcott and Taubinsky (2015) and Allcott and Sweeney
(forthcoming) test for biased beliefs and imperfect information by measuring the effect of information provision on demand for energy-efficient lightbulbs and hot water
heaters, respectively.
There are also studies that examine the effect of environmental messaging, such
as Energy Star Certification (e.g., Houde 2014b; Newell and Siikamaki 2014) and
“normative” letter grades for the energy-efficiency characteristics of products (e.g.,
Brounen and Kok 2011).4 The evidence shows that people respond to these nonprice
4. Newell and Siikamaki (2014) is similar to our study in that it uses an online statedchoice experiment to evaluate components of EnergyGuide labels. In addition to comparing
choices with and without Energy Star certification, they randomly include or exclude information about carbon dioxide emissions, normative letter grades, and other elements of label design. While they vary the way operating cost information is displayed, they do not vary the
operating cost information itself or explore information that is tailored to the participant’s local
usage or prices.
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Does Better Information Lead to Better Choices?
Davis and Metcalf
593
interventions, although it is not always clear if this is because they trigger “warm glow”
responses or because they are indirectly providing information about private costs.
Finally, there is a group of papers that study the effect of peer comparisons.
Learning about how your electricity consumption compares to that of your neighbors
tends to significantly reduce consumption, both in the short run and long run. See,
for example, Ayres, Raseman, and Shih (2009), Allcott (2011b), and Allcott and
Rogers (2014).
We see what we are doing as quite different. We are not studying consumers’
undervaluation of energy costs, nor are we studying a specific behavioral bias like MPG
illusion. Moreover, we have designed our experiment explicitly to exclude any environmental messaging or peer comparisons. Instead, we are focused sharply on the quality of
the information that is publicly provided, and we want to ask whether better tailoring this information to consumers’ characteristics can lead to more efficient choices.
1.2. US Energy Labeling Requirements
EnergyGuide labels must be displayed on all major appliances sold in the United
States. As of 2015, this includes clothes washers, dishwashers, refrigerators, freezers,
televisions, water heaters, window air conditioners, central air conditioners, furnaces,
boilers, heat pumps, and pool heaters. Collectively, these appliances account for over
60% of residential energy consumption and 13% of total US energy consumption.5
Energy-efficiency labels have existed since the first energy crisis in the mid-1970s.
France mandated labels for a variety of appliances in 1976, and Japan, Canada, and
the United States followed soon after (Wiel and McMahon 2001).6 The Energy
Policy and Conservation Act of 1975 mandated labels for certain appliances beginning in 1980. Changes to the labeling program were made in the Energy Policy Act
of 1992, which gave rise to the EnergyGuide labels in their current form.
The Federal Trade Commission (FTC) is charged with enforcing these labeling
requirements. The FTC provides templates on its website for manufacturers to use
and the Energy Labeling Rule in the Code of Federal Regulations provides samples
of acceptable labels (Federal Trade Commission 2014).
Information provision requirements for vehicles are similar. Since 1977, all new
cars and trucks sold in the United States must display information about vehicle fuel
5. According to US Energy Information Administration (2014a, table A4), space heating, space cooling, water heating, refrigeration, clothes dryers, freezers, clothes washers, and
dishwashers accounted for 62% of total residential energy consumption in 2012. These end
uses represented in 2012 a total of 12.5 quadrillion Btu compared to 95.0 quadrillion Btu
from all sectors and sources in 2012.
6. Wiel and McMahon (2001) discuss the early motivation for energy labels. Thorne and
Egan (2002) conduct qualitative interviews with focus groups about alternative graphical elements and other aspects of EnergyGuide label design.
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Journal of the Association of Environmental and Resource Economists
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efficiency. Until recently, labels reported estimated city, highway, and combined fuel
efficiency in miles per gallon (MPG). Starting with model year 2013, new labels provide additional information, including estimated gallons per 100 miles, annual fuel cost,
and 5-year fuel cost savings compared to the average new vehicle. The inclusion of gallons per 100 miles brings the United States in line with the European Union, which
reports liters per 100 kilometers.
Fuel economy labels on vehicles suffer from the same problem as do appliance
labels in using national energy prices to compute fuel savings and ignoring variation
in vehicle miles traveled across the states. Paradoxically, the improvement in fuel
economy labels on motor vehicles may exacerbate losses from inaccurate information
on the labels. When labels only reported miles per gallon, consumers had to undertake significant mental computations to balance the cost savings from a more fuel
efficient vehicle against the higher purchase price (holding other attributes constant).
The current labels now report estimated 5-year cost savings for each vehicle relative
to the fleet average. Now it is more straightforward to balance cost savings from
more efficient vehicles against a higher purchase price. But cost savings can differ
significantly given differences in average gasoline prices and driving patterns across
states. Whether consumers will make those mental adjustments is not clear.
1.3. Focus on Air Conditioning
More accurate labels could be important for many different appliance types, but in
our experiment we focus specifically on room air conditioners. More than 25 million American households own one or more room air conditioners, so this is an appliance that is of large intrinsic interest.7 It is also a particularly lucid example of an
energy-efficiency investment for which consumers face a clear trade-off between purchase price and energy costs, and for which operating costs vary substantially across
states. Moreover, most consumers install room air conditioners themselves, thereby
avoiding any principal-agent problem that arises when contractors are involved in selecting and installing equipment.
More broadly, residential air conditioning is of large and growing policy interest
nationwide because of the high level of energy consumption associated with it. In the
United States, there is air conditioning in nearly 100 million homes (87% of homes),
and households spend an estimated $22 billion dollars annually on electricity for air
conditioning.8 Table 1 shows that air conditioner usage is pervasive in all parts of
7. US Department of Energy, Residential Energy Consumption Survey 2009, table HC7.1,
“Air Conditioning in U.S. Homes.”
8. Data from US Department of Energy (2009). See table HC7.1, “Air Conditioning in
U.S. Homes” and table CE3.6, “Household Site End-Use Consumption in the U.S.”
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Does Better Information Lead to Better Choices?
Davis and Metcalf
595
the country. The lowest share is in the West, where one-third of households have no
form of air conditioning. The table also illustrates considerable variation in the shares
of central versus room air conditioning among those households with air conditioning
with central air conditioning dominating in all regions except the Northeast.
Figure 1 shows annual cooling hours by state from US Department of Energy
(2014b).9 This is the number of hours per year for which a household should expect
to use an air conditioner. On av …
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