Bunching With the Stars:
How Firms Respond to Product Certification
Sébastien Houde∗
September 15, 2012
Preliminary and Incomplete, Do Not Circulate
Abstract
The Energy Star certification is a voluntary labeling program managed by the US Environmental
Protection Agency (EPA) that favors the adoption of energy efficient products. This paper first
shows that Energy Star influences firms decisions. Focusing on the refrigerator market, firms offer
products that bunch exclusively at the minimum and Energy Star standards. I also find some
evidence that consumers’ valuation of the Energy Star certification is factored in pricing decisions.
The second part of this paper uses an oligopoly model calibrated for the US refrigerator market
to investigate firms’ product lines and pricing decisions under various scenarios. I show that in a
world without Energy Star firms might discriminate less in the energy efficiency dimension. This
has important implications in determining the welfare effects of the program, and ultimately for
the design of voluntary energy efficiency standards.
∗
I am indebted to Jim Sweeney, Wes Hartmann, Jon Levin and John Weyant for all their helpful advice
in guiding me in this project.
Department of Management Science and Engineering, Stanford University, Stanford, CA 94305. Email:
shoude@stanford.edu.
2
The economics of energy efficiency policies is controversial. While engineering calculations have
repeatedly suggested that the full technical potential for energy efficiency is far to be realized,
economists have pointed out that moving closer to the full potential is desirable to the extent that
it delivers net benefits to society. The point of contention is that many practitioners and researchers
believe that the observed investments in energy efficiency are systematically below their socially
optimal levels, a phenomenon known as the energy efficiency gap. It is however still much debated
how big is the gap, why it arises, and whether it commands policy interventions.
Among the policy tools proposed to address the energy efficiency gap, labels and voluntary
certification programs are among the most popular. In several countries, it is mandatory to sell
appliances with labels that displays energy information. The design of these labels vary widely,
but often complex energy information is summarized with a star system that groups products in
different efficiency classes (e.g., in Europe, India, Australia, and Japan). In the US and Canada,
the Energy Star program uses such design to identify the most energy efficient products within
product classes.
Summarizing complex energy information with a simple graphical display has an obvious benefit,
it eases consumers’ burden of processing information, which should increase the likelihood that
consumers will account for energy efficiency in their purchasing decision. Certification systems
that rely on coarse categorization bring however a number of perverse incentives. For instance,
they make consumers insensitive to variations within category. In the energy context, this means
that consumer may invest too much or too little in energy efficiency. Certification system may
also distort equilibrium outcomes. If consumers rely heavily on a given certification, firms should
respond by making product lines and pricing decisions that are consistent with consumers’ beliefs.
Whether consumers are better off when the market is distorted by a certification remains uncertain.
The goal of this paper is to first show how firms account for certification in their decisions to
offer energy efficient products. The focus is on the Energy Star program. Using data from the
US refrigerator market, I show that firms account for Energy Star in both their product lines
and pricing decisions. I then propose a model that rationalizes firms’ responses and show that a
simple static multi-product oligopoly model can explain various stylistic facts. After estimating the
primitives of the model, I simulate various counterfactual scenarios that inform about the value of
certification. In particular, I conduct a welfare analysis of the Energy Star certification program.
In addition of bunching strongly at the minimum and Energy Star standards, I show that firms
are charging higher markups on Energy Star refrigerators. Using various strategies, I find that the
3
additional markup on Energy Star models is on the order of 2-3%. This estimate comes remarkably
close of my estimate of consumers’ willingness to pay for Energy Star (Houde 2012).
For my preferred estimate of firms’ marginal cost of providing energy efficiency, I find that
without Energy Star, firms will offer products that bunch almost exclusively at the minimum
energy efficiency standard. Energy Star thus leads to significant energy savings. Interestingly, the
largest benefits associated with Energy Star might however not come from the reduction in the
environmental externalities due to electricity generation. The interactions between Energy Star
and firms’ market power are at the source of the most important welfare gains.
1. Related Literature
forthcoming
2. A Primer on Energy Star and the US Refrigerator Market
The Energy Star certification program was established by the US EPA in 1992. The goal of
the program is to favor the adoption of energy efficient durables by residential and commercial
consumers. The EPA sets voluntary energy efficiency standards and certifies products that meet
or exceed the standards. Qualifying products can be labeled with an Energy Star logo (Figure 1).
The logo does not provide detailed information about energy efficiency. The program complements
minimum energy efficiency standards and the mandatory energy label EnergyGuide, which provides
detailed technical information on energy consumption and costs (Figure 1). While minimum energy
efficiency standards place a lower bound on efficiency, Energy Star aims to induce more efficient
purchases by providing simple and salient information to consumers.
The Energy Star standards for refrigerators are defined relative to the minimum energy efficiency
standards. Since April 2008, full-size refrigerators must consume 20% or less electricity than the
minimum energy efficiency standards to be certified Energy Star. The minimum energy efficiency
standards for refrigerators vary as a function of size, door style (single door, top-freezer, bottomfreezer and side-by-side), and attributes (ice-maker and defrost technology). Table 1 summarizes
the criteria for minimum and Energy Star standards.
The periodic revisions of the Energy Star standards is a crucial feature of the program. The
EPA revises the stringency of the standards using various criteria, such as the proportion of Energy
Star products offered on the market, the market share of Energy Star products and the availability
4
of new technologies (McWhinney, Fanara, Clark, Hershberg, Schmeltz, and Roberson 2005). The
stringency of the standards is ultimately determined by US EPA upon consultation with different
stakeholders, such as manufacturers, part providers, retailers, analysts and environmental groups.
Revisions in standards are usually announced by US EPA about one year in advance. When a
revised Energy Star standard comes into effect, the EPA requires that manufacturers and retailers
remove the Energy Star label on certified products that do not meet the more stringent standard.
For full-size refrigerators, the Energy Star standard has been revised in 2001, 2004 and 2008. The
minimum energy efficiency standard for refrigerators was last revised in 2001. The Department of
Energy (DOE) has announced that a revised version of the minimum standard will be implemented
in 2014 (Table 1). The new Energy Star standard has yet to be announced.
An important institutional feature of the US electricity markets is that several electric utilities
are subject to regulations that incentivize them to promote energy efficiency measures to consumers.
Rebate programs tied to the purchase of Energy Star products have been arguably one of the
most popular energy efficiency measures pursued by US electric utilities. Thus, apart from the
informational component, financial incentives may play a role in favoring the adoption of Energy
Star products (Datta and Gulati 2009). I account for this in my empirical strategy and I distinguish
between the information effect and the rebate effect associated to the program.
The refrigerator market, like other appliance markets, is an important contributor to the manufacturing and retailing sectors of the US economy. Almost all American households own at least
one refrigerator, and the fraction of households owning two or more refrigerators has been steadily
increasing. In 2010, 9.01 million of full-size refrigerators were shipped to the US market (AHAM,
DOE).
An important feature of the US refrigerator market for the present analysis is that it has becomes increasingly concentrated in recent years. As of 2008, the market was dominated by three
manufacturers: Electrolux, General Electric (GE), and Whirpool. Together they held about 85% of
the market share for full-size refrigerators (Table 2). This high concentration is notably the result of
several mergers and acquisitions that have taken place since the early eighties. Figure 2 summarizes
the market structure; the notable mergers and acquisitions are depicted by circles and the most
popular brands and their relationship with the main three manufacturers are shown in the dotted
boxes. A particular institutional detail of the market is that manufacturers compete under various
brand names, and some brands, such as Kenmore, are not own by a particular manufacturer. This
feature of the market is believed to be an important factor that limits manufacturers’ market power.
5
The distribution of products across brands is still however fairly concentrated, especially in recent
years; most products are offered by the major brands associated to the top three manufacturers
and Kenmore (Table 3).
Beyond its significance for the US economy and its interesting market structure, the refrigerator
market also provides a good laboratory to develop and test my framework, because modeling
the purchase decision for refrigerators has a particular methodological appeal. Refrigerators are
one of the few energy intensive durables for which the utilization decision does not need to be
explicitly modeled to have an accurate estimate of how much each consumer will pay to operate
its refrigerator. For this particular good, the utilization decision is then not, presumably, at the
source of important unobserved heterogeneity.
3. Preliminary Evidence
This section investigates whether firms, namely manufacturers, brand managers, and retailers account for the Energy Star program when deciding which products to offer and how to set prices.
Using different data sources, I provide evidence that firms participating in US the refrigerator
market are well aware of the Energy Star program, and ultimately the program is an important
determinant of equilibrium outcomes.
3.1. Data
The California Energy Commission (CEC), the Federal Trade Commission (FTC) and the EPA
maintain databases for the US appliance market.1 I use these three data sources to reconstruct the
choice set for the whole US refrigerator market and the timing of product entry and exit for the
period 2003-2010.
Using the data from a large retailer (see appendix for a full description), I also look at the
pricing strategies of manufacturers and retailer(s). For each refrigerator model in the sample, I
observe three types of price: the manufacturer’s suggested retail price (MSRP), the promotional
price, which is set by the retailer, and the manufacturer price, which is the price paid by the retailer
to the manufacturers.
1
The three agencies provide similar data, but with some important differences. The CEC has data for the
Californian refrigerator market for the period 1978-2011. The FTC has data for the whole US refrigerator
market for the period 2003-2011. The EPA has data for the whole US refrigerator market and information
about which models are certified Energy Star.
6
3.2. Product Line
In several appliance markets, there is clear evidence that manufacturers and brand managers2
account for Energy Star when deciding the energy efficiency levels of their products.3 This is
illustrated by Figure 3, which shows the distribution of refrigerator models in the energy efficiency
dimension for the year 2010. Refrigerator models offered either just meet the federal minimum
standard or the Energy Star standard; few models are located between the standards, or exceed
the Energy Star threshold. As a result, the distribution bunches strongly at the minimum standard
and the Energy Star standard.
Looking at the dynamics in the choice set, the data also suggest that manufacturers have the
ability to change their product lines quickly, and adjust to revision in Energy Star within one to two
years. Figure 4 shows how product lines evolved during the period 2003 to 2010 for one particular
type of refrigerator, the full-size bottom-freezer without an ice-maker. We observe that following
the revision of the Energy Star standard in 2004 and 2008, manufacturers responded not only by
offering new models that met the revised standard, but also by discontinuing models that were
decertified.4 Note that the 2008 revision was announced exactly one year in advance by the EPA,
and most manufacturers were able to offer products that meet the more stringent standard within
a year. In 2010, about eighteen months after the standard was revised, most decertified products
had exited the market.
Altogether, the distribution of refrigerators in the energy efficiency dimension, and adjustments
to revisions in the Energy Star standard show that manufacturers and brand managers optimize
their product lines to account for Energy Star. As I will show, these decisions are consistent
with profit maximizing behaviors where firms differentiate their products to extract the maximum
surplus from consumers. I next turn to the analysis of pricing decisions, and provide further
evidence consistent with this theory.
3.3. Pricing
If a fraction of consumers value Energy Star products, and products can be differentiated, firms
may be able to charge larger markups on Energy Star products. I now investigate how firms
2
Because some brands are not owned by any manufacturers, product line decisions can be attributed to
manufacturers and brand managers.
3
Sallee (2011) provides such evidence for several appliance markets. He also shows that certification has
a similar effect in the building market.
4
This pattern holds for other types of full-size refrigerators, as well.
7
set prices to account for Energy Star, and provide estimates of the size of markups associated
to certification. I propose three strategies, each relying on a different quasi-experiment. First, I
propose a matching estimator that exploits the existence of refrigerator models that have similar
attributes, but different energy efficiency levels. Second, I look at the prices of refrigerators before
and after the 2008 revision in the Energy Star and use a difference-in-difference estimator to arrive
at the change in price due to the decertification. The third strategy is similar to the second, but
relies on a different natural experiment: in 2010, the EPA decertified twenty-one refrigerator models
because of a problematic test procedure.
Matching Estimator. In the US refrigerator market, manufacturers commonly offer product lines
with several models. Within a product line, models may be differentiated with respect to their color,
their door material (stainless or not), the ice-maker option, and their energy efficiency level. To
isolate the effect of Energy Star on markups, I exploit this particularity of the market, and propose
a matching estimator that compares, within product line, the markups of refrigerators that differ
with respect to their energy efficiency level and Energy Star certification, but have otherwise similar
attributes.
In particular, I match all refrigerators that have a similar design (top freezer, bottom-freezer and
side-by-side), brand, size, door material (stainless or not), ice-maker option, and defrost technology.
Matched refrigerators may differ in color and energy efficiency levels; all other observed attributes
are identical. In my sample, I observe 265 refrigerators that have at least one match, and I was
able to recover 56 product lines where at least one model is certified Energy Star and one model is
not certified.
Figure 5 compares the (average) percentage markups of Energy Star and non-Energy Star models
for each product line. Markups are computed using two approaches. The first approach compares
the percentage difference between the manufacturers’ suggested retail prices (MSRPs) and the
the prices that manufacturers sell their products to retail stores and brand managers, which are
referred as the manufacturer prices. The second approach compares the promotional prices with
the manufacturer prices. For each refrigerator models, the markups vary over time because MSRPs
and promotional prices vary from week to week. However, for each product, I observe only one
manufacturer price. To construct Figure 5, I compute the average markups over the period 20082010, for all models that are certified Energy Star within a product line, and all models that are
not certified.
8
We observe that Energy Star models tend to have higher markups (more points above the 45◦
line), especially when markups are computed with promotional prices. Regressing the percentage
markups on a product line fixed effect, and a dummy variable that takes the value one if a product
is certified Energy Star and zero otherwise, I find that the markups on Energy Star refrigerators
are 1.9 percentage points higher (p = 0.025), when MSRP are used to define markups, and 3.0
percentage points higher (p = 0.001), when promotional prices are used (Table 5). In sum, the
matching estimator suggests that manufacturers and retailers charge larger markups on Energy
Star models.
2008 Revision of the Energy Star Standard. When the EPA revises the Energy Star standard,
it requires that manufacturers and retailers remove the Energy Star label on certified products that
do not meet the more stringent standard. Following the 2008 revision, it is then possible to observe
the same refrigerator model being sold with and without the Energy Star label. In 2008, there were
2,762 full-size refrigerator models offered on the US market, 1,278 of which were decertified.5
My sample contains 1,196 refrigerator models that lost their certification in 2008. Figure 6
compares the average normalized prices (MSRP and promotional price) of models that lost their
certification to prices of models that were not certified Energy Star as of January 1st 2008 (N=482),
and prices of Energy Star models that did not loose their certification following the revision in
standard (N=227). Normalized prices are computed by dividing the price of each refrigerator
model by its average price before the revision of the Energy Star standard, i.e., the first seventeen
weeks of 2008. Only models that entered the market before the standard are revised are then
considered. Figure 6 plots the mean and the standard errors from a regression spline where the
normalized price is fitted on week fixed effects and allows for a discontinuity in the seventeenth
week.
For both MSRP and promotional price, there is no clear evidence that the prices of decertified
models changed around the time of the revision. In the post-revision period, we however observe
that the MSRPs of non-Energy Star models, and Energy Star models that remained certified have
a strong upward trend cumulating with a large price increase in the first week of the year 2009.
The prices of decertified Energy Star models have a similar trend, but it is much less pronounced.
In relative terms, decertified models thus became less expensive in the post-revision period. The
5This
number was obtained from the US EPA. According to the FTC data, there were 2,693 full-size
refrigerators offered on the US market.
9
trends for promotional prices are similar. In addition, we also observe that the promotional prices
of decertified models are much less variable relatively to other models.
To estimate the size of the price change, I use the following difference-in-difference estimator:
Pj,t
(1)
= αT af tert + βDecertif iedj + ρT af tert × Decertif iedj + j,t
P̄T af terj =0
where the dummy variable T af tert takes the value one for all weeks after the decertification and is
zero otherwise, and the dummy variable Decertif iedj takes the value one for refrigerator models
that lost their Energy Star certification. PT af terj =0 is the average price (MSRP or promotional) for
the period before the decertification (T af tert = 0). The quantity of interest is ρ, the difference-indifference estimator.
I also estimate a more flexible version of the model that allows for week fixed effects:
X
Pj,t
αi W eeki,t + j,t
= ρEnergyStarj,t +
(2)
P̄T af terj =0
i
where the dummy variable EnergyStarj,t takes the value one if refrigerator model j is certified
Energy Star in week t and is zero otherwise.
Table 6 presents the estimates. According to the simpler difference-in-difference estimator, the
suggested retail price of decertified models decreases by 2.5%. For promotional price, the decrease
is 1.9%, and is not statistically different from zero. Controlling for week fixed effects, the decrease
in MSRP is 0.3%, and the decrease in promotional prices is not statistically significant. These
estimates should be interpreted with caution. The validity of the difference-in-difference estimator
requires that both types of refrigerator models be subject to similar time trends in the period
before the revision. Figure 6, however, suggest that prices of non-Energy Star models had started
increasing before the revision, but decertified models were not subject to such a trend.
To summarize, both manufactures and retailers appear to have responded to change in decertification. Perhaps surprisingly, there is limited evidence that they responded by decreasing the prices
of decertified models in the post-revision period. Instead, manufacturers appear to have responded
by increasing the prices of other models, which led to a relative price decrease for decertified models.
Promotional prices follow similar trends, it also appears that retailers responded by excluding the
decertified models from their dynamic pricing strategy.
2010 Decertification. In January 2010, the EPA found that a number of Energy Star refrigerator
models had undergone problematic testing procedures. As a result, their electricity consumption
was underestimated (ref). The EPA concluded that these refrigerator models were not meeting
10
the Energy Star standard, and issued a public statement that 21 refrigerator models would be
decertified. I observe 16 of these refrigerators in my sample.
Figure 7 shows, respectively, the average normalized suggested retail and promotional prices
for refrigerator models that lost their certification, and for models that were certified Energy Star
as of January 1st 2009. I now use the models that keep their Energy Star certification as the
counterfactual. Clearly, we see that following the decertification, prices (MSRP and promotional)
dropped sharply. Using the same difference-in-difference estimators as above, the size of the decrease
is 6.5% for the suggested retail price (Model V) and 2.5% (Model VI) for promotional price. Using
the estimator that controls for weekly fixed effect (Models VII and VIII), the size of the estimates
is about half, respectively 3.6% and 1.2%, but still statistically significant. Overall, the estimates
suggest that the manufacturers and retailers responded to the decertification by lowering prices.
3.4. Discussions
In the US refrigerator market, product lines and price patterns indicate that manufacturers, brand
managers, and retailers account for Energy Star. From the present analysis, five stylistic facts
emerge:
(1) manufacturers/brand managers offer refrigerator models that just meet the Energy Star
standard or the minimum standard;
(2) after the 2008 revision, decertified models exited the market within 18 months;
(3) Energy Star models have higher markups;
(4) in 2008, decertification did not lead to an absolute price decrease, but a relative price
decrease; and
(5) in 2010, decertification led to an absolute price decrease.
Altogether, these suggest that firms strategically optimize product lines and prices. In particular,
these facts are consistent with the hypothesis that firms have some market power, which allows
them to differentiated their products and maintain positive markups. Interestingly, the size of the
markup associated to Energy Star is consistent with my estimate of consumers’ valuation of the
Energy Star label. In my demand estimation, I found that consumers value Energy Star models
beyond energy savings, which translates to a willingness to pay for Energy Star that is consistent
11
with a price premium of 1.5% to 3%.6 The matching estimator suggests that markups on Energy
Star models is about 1.9-3.0% higher than non Energy Star models, and the 2010 decertification
led to a price decreasing of about 1.2% to 3.6%.
Higher markups for Energy Star models implies that firms extract part of the consumer surplus
brought by better energy efficiency. Consumers then face a longer payback period to recoup their
investment with energy savings. For instance, if we compare the refrigerator models used for the
matching estimator, the difference in prices for Energy Star and non-Energy Star models is about
$125 (for MSRP, Table 6). For this price differential, assuming a discount rate of 7%, and an
average electricity price of $0.11, the paypack period for an Energy Star refrigerator model is 9.3
year. If we assume that the markup for Energy Star models leads to an increase in price of 3
percentage points relative to price of non Energy Star models, removing this additional markup
would decrease the payback period by 4.2 years.
One element particularly puzzling among the five stylistic facts is the apparent inconsistency in
the price dynamics following the revision of the Energy Star standard in 2008, and the decertification
of the 21 models in 2010. It seems at first intuitive to think that if consumers value the Energy
Star label itself removing the label should lead to a price decrease. However, if what matters for
firms’ market shares is the relative change in prices, increasing the prices of non-decertified models
can be equivalent to a decrease in prices in decertified models. In theory, both stylized facts can
then be reconciled.
A market equilibrium where the prices of non-decertified models increase, i.e., where all prices
are weakly larger, should however lead to larger profits than a market equilibrium where prices are
weakly smaller. Therefore, if in 2008, firms were able to adjust to the decertification by increasing
prices, they should have done similarly in 2010. It should however be noted that in 2008 a large
number of models got decertified (1,278 models). In 2010, only 21 models lost their certification.
To support a new market equilibrium with higher prices should require an important change in
market conditions, which the 2008 decertification might have provided. The 2010 decertification
was a marginal event, affecting only two brands, and thus probably of not enough importance to
affect the market equilibrium.
6My
demand estimation suggests that the Energy Star label influences the probability of selling a given
refrigerator model by 7-15%. For an own price elasticity of about 6, a 2% decrease in price will lead to a
12% increase in sale.
12
4. Model
This section presents a model of firms’ product lines and pricing decisions focusing on how firms
strategically determine energy efficiency levels and prices of refrigerators. To rationalize the stylize
facts reported above, a necessary feature of the model is to allow for product differentiation and
imperfect competition. I then propose to characterize the US refrigerator market with a static
multi-product oligopoly. The model aims to represent a medium-run equilibrium (12-18 months)
where the decisions to enter and exit the market, and to determine the size and characteristics of
product lines are taken as given. My approach closely follows Bento, Goulder, Jacobsen, and von
Haefen (2009), Klier and Linn (2012), and Whitefoot, Fowlie, and Skerlos (2011), who investigate
how car manufacturers respond to mandatory fuel economy standards. The present model, however,
has a different purpose and looks at the role of voluntary standards. Additionally, a number of
institutional features specific to the refrigerator market motivate particular modeling assumptions.
4.1. Assumptions
Assumption 1: Firms are Brand Managers. An important feature of the refrigerator market
is that manufacturers offer similar products under different brand names. Offering products under
different brand names raises a number of issues for manufacturers, such as product cannibalization,
and double marginalization.
To circumvent the difficulties associated with the manufacturers’ decision to rebrand products,
I will focus on modeling the behavior of brand managers. I will assume that each brand manager represents a firm that aims to maximize the profit of his own brand. In this context, the
product line decision consists of acquiring refrigerator models through procurement contracts with
manufacturers, and brand managers’ costs are simply the prices they pay to manufacturers.
Assumption 2: Costs of Providing Energy Efficiency are Separable. A second important
feature of the refrigerator market is that, within a relatively short time, manufacturers can change
the energy efficiency level of their refrigerators, with little impact on their overall design. This has
been demonstrated by the various revisions in the Energy Star standard, which has shown that
manufacturers managed to offer new products that were more energy efficient, but were otherwise
similar to previous generations.7 I take this as evidence that the cost of providing energy efficiency
7Interestingly,
when the EPA announced in April 2007 that the Energy Star standard would be revised in
April 2008, all but one manufacturer notified the EPA that they would not be able to offer new refrigerator
models on time to meet the revised standard (ref). Ultimately, most manufacturers were, however, able to
13
are separable from the cost of providing other attributes. I will further assume that the costs faced
by brand managers, i.e., the manufacturers’ prices, reflect this assumption.
4.2. A Static Multi-Product Oligopoly Model
Consider that they are K brands, and brand manager k offers Jk refrigerator models. Each brand
manager maximizes his profit by choosing the energy efficient levels, the vector fk = {fk1 , ..., fkJk },
and the prices, the vector pk = {pk1 , ..., pkJk }, of his Jk refrigerator models, taking the strategies
of his competitors as given. Firms face a population of consumers, where the demand for each
product j is a function of consumers’ valuation of energy efficiency and Energy Star.
The problem of the brand manager k consists in solving:
(3)
max
fk ={fk1 ,...,fkJk },
pk ={pk1 ,...,pkJk }
=
Jk
X
(pkj − ckj (fkj )) · Qkj (f, p|θ, τ )
j=1
where f = {f1 , ..., fK }, p = {p1 , ..., pK }, ckj (fkj ) is the brand-model specific cost of model j
offered by brand k, and Qkj (f, p|θ, τ ) is the demand. The demand is a function of all prices and
energy efficiency levels, and depends on consumers’ valuation of energy efficiency (θ) and Energy
Star certification (τ ).
The first order conditions for firm k are given by:
Qkl (f ∗ , p∗ |θ, τ ) +
Jk
X
∗
(p∗kj − ckj (fkj
)) ·
j=1
∂Qkj (f ∗ , p∗ |θ, τ )
= 0,
∂p∗kl
offer new models meeting the 2008 Energy Star standard within a year of the date that the EPA made the
announcement.
14
∗
ES
∗
1{π(fkl , fk,−l
, p∗k ) > π(fkl
, fk,−l
, p∗k )|∀fkl }×

Qkl (f ∗ , p∗ |θ, τ )
∗)
dckl (fkl
dfkl
−
Jk
X
∗
(p∗kj − ckj (fkj
)) ·
∂Qkj
(f ∗ , p∗ |θ, τ )
j=1
∂fkl

= 0 ,
∗
∗
1{π(fkl , fk,−l
, p∗k ) ≤ π(f ES , fk,−l
, p∗k )|∀fkl }×
h
i
∗
fkl
= f ES ,
for all l ∈ Jk and k
where f ES is the energy efficiency level corresponding to the Energy Star standard, and f ∗ and
p∗ correspond to the Nash equilibrium that solves the system of 3 × J1 × J2 × ... JK equations.
Note that the second and third conditions arise because the demand function is not continuous at
f ES ; the derivative of the profit with respect to energy efficiency level fkl is then not defined at
this point. This discontinuity at the Energy Star threshold implies that the firm may find optimal
to not equate the marginal cost of providing energy efficiency level fkl with its marginal benefit.
Whether firms find optimal to offer products that meet the Energy Star standard depends crucially on consumers’ valuation of energy efficiency, the marginal cost of providing energy efficiency,
and firms’ market power. Moreover, heterogeneity in consumers’ valuation of energy efficiency is
also an important element that will determine whether firms can differentiate their products with
respect to energy efficiency. To illustrate how these different elements come into play, I first solve
the model analytically for a simple case: a monopoly facing only two consumer types. The monopoly case allows me to provide a graphical analysis, which will be extended to explain the various
stylistic facts previously reported.
4.3. Illustration: Monopoly with Two Consumer Types
Consider a market with only one firm selling to two potential consumers, each with different valuation of energy efficiency. Consumer i’s utility of purchasing refrigerator j is given by:
(4)
Uij = δj + θi fj − pj
where δj is the overall quality of the product, θi = {θL , θH } corresponds to consumer i’s valuation
of energy efficiency, f , and p is the purchase price.
15
The monopolist must choose the energy efficiency level and price of each refrigerator model that
he offers. Assume that the quality is the same for all models, i.e., δj = δ, and is fixed. With
only one consumer type, the monopolist will offer one model with an energy efficiency level such
that the marginal cost of providing energy efficiency equals the consumer’s marginal valuation of
energy efficiency. Formally, if the cost of providing energy efficiency is c(f ), the monopolist sets
f s.t. c0 (f ) = θ. He then sets a price to extract the maximum surplus from the consumer, i.e.,
Uj (p) = 0 ⇒ p = δj + θfj .
With two types of consumers, offering two different models might be optimal. The monopolist
faces the canonical screening problem; he can sell two different products if the individual rationality
(IR) constraints and incentive compatibility (IC) constraints are respected. Formally, the problem
of the monopolist is:
(5)
max
fH ,fL ,pH ,pL
pH + pL − c(fL ) − c(fH )
s.t.
IRH: δ + θH fH − pH ≥ 0
IRL: δ + θL fL − pL ≥ 0
ICH: θH fH − pH ≥ θH fL − pL
ICL: θL fL − pL ≥ θL fH − pH
It can be shown that having θH > θL , ensures that the incentive compatibility constraint of the
low type (ICL) is non-binding at the optimum. Moreover, if ICL is not binding at the optimum,
the individual rationality constraint of the low type (IRL) must be binding, otherwise the firm
could increase profit by slightly increasing the price pL . By a similar argument, the incentive
compatibility constraint of the high type (ICH) must be binding at the optimal, i.e., the consumer
with a high valuation of energy efficiency may have an incentive to purchase the product offered to
the consumer with a low valuation of energy efficiency (all proofs can be found in appendix). The
firm must then distort the prices and energy efficiency levels to ensure that the high type consumer
does not find optimal to purchase a less energy efficient product.
Using the fact that IRL and ICH are binding, we can solve for prices as a function of energy
efficiency levels, and the relaxed form of the monopolist’s problem is given by:
16
max θH (fH − fL ) + 2δ + 2θL fL − c(fL ) − c(fH )
(6)
fH ,fL
The first order conditions yield:
∗
θH = c0 (fH
)
2θL − θH = c0 (fL∗ ),
and the optimal prices are given by:
p∗L = δ + θL fL∗
p∗H = θH (fH − fL ) + δ + θL fL
At the optimal solution, the firm will distort the energy efficiency level of the less energy efficient
product only. If the cost function is increasing with respect to energy efficiency, the monopolist
will have to reduce fL to screen between consumers. Relative to the social optimum, the low type
consumer then purchases less energy efficient products.
Interestingly, if θL is small and θH is large, there might not be a solution where fL∗ is positive.
In such case, the firm might simply set fL∗ = 0, or offer only one product (pooling equilibrium).
In a market without Energy Star, two possible equilibria might then be sustained: a separating
equilibrium corresponding to the solution of 6 (Figure 8(a))8, or a pooling equilibrium where f ∗
solves c0 (f ∗ ) = θL and p∗ = δ + θL f ∗ (Figure 8(b)).
When the market is subject to Energy Star, this complicates the matter. The Energy Star
program creates a discontinuity in the valuation of energy efficiency, which increase the number
of possible equilibria. To model Energy Star, I assume that consumer i’s utility of purchasing
refrigerator j is given by:
(7)
Uij = δj + θi fj + τ Dj − pj
where Dj = 1 if the refrigerator is certified Energy Star. The parameter τ subsumes different
behavioral effects; it might capture the effect of rebates, bias in the perception of the quality of
8A
special case of the separating equilibrium exists when fL∗ = 0
17
Energy Star products, warm glow effect, or energy savings associated with Energy Star products. I
will be agnostic on the causes of the discontinuity, and simply assume that both consumers respond
to the certification in the same manner, i.e., τ = τ L = τ H . Graphically, this means that the demand
curves have a notch of size τ at the Energy Star standard: f ES .
With Energy Star, the monopolist might find profitable to depart from the optimality conditions
dictated by the first order conditions, and will offer products that bunch at the Energy Star standard. In particular, it can be shown (see appendix) that for each energy efficiency level that solves
the problem 6, there is a range of values [f i , f ES ], i = {L, H} for which it is always optimal for
the firm to depart from the optimality conditions and bunch at the Energy Star standard. When
Energy Star is in effect, there are now four possible equilibria. In addition to the separating and
pooling equilibria that can be sustained without Energy Star, a separating equilibrium with one
product bunching at Energy Star (e.g., Figure 9(a)), or a pooling equilibria with both products
bunching at Energy Star can arise.
Before using the model to explain the stylized facts, I make a small detour to discuss how
competition will impact the equilibrium outcomes.
4.4. Oligopoly Case
When more than one firm is active in the market, firms can set prices above their marginal costs as
long as they have some market power. Therefore, if products can be differentiated, notably because
of brands, positive markups can be sustained in equilibrium. Rearranging the first order condition
for price, we obtain the classic formula for the Lerner index applied to the multi-product oligopoly
case (Tirole, 1988):
(8)
X (pkj − c(fkj )) Qkj kl,kj
1
pkl − c(fkl )
=
−
pkl
kl,kl j6=l
Rkl kl,kl
where kl,kl ≡ −(∂Qkl /∂pkl )(pkl /Qkl ) is the own elasticity of demand, kl,kj ≡ −(∂Qkj /∂pkl )(pkl /Qkj )
is the cross-elasticity of demand of good kj w.r.t. price kl, and Rkl ≡ pkl Qkl is the revenue associated to good kl. Note that when all products offered by a given firm are substitutes, the
cross-elasticity of demand is negative, kl,kj < 0. A firm with multiple competing products will
then want to charge higher markups.
18
The effect of competition from other firms can be formalized via the size of the own elasticity of
demand. When the market has multiple firms, each firm will face a lower residual demand, which
will lead to a larger own elasticity of demand. As expected, competitive forces will then lead to
lower markups.
Whether firms will still find optimal to differentiate their products in the energy efficiency
dimension under competition is uncertain. Clearly, if the market were perfectly competitive, firms
could only charge prices that are equal to marginal costs. In such case, firms will see no benefits
in differentiating their products in the energy efficiency dimension. Bunching at the minimum and
Energy Star standards could then only be rationalized by arguing that standards are focal points
that firms converge to.9
Returning to the two consumer types example, when subject to competition, firms will not be
able to extract the full surplus of the low valuation consumer (i.e., IRL will not be binding). In a
separating equilibrium, both prices will be lower, and consumers will be better off.
4.5. Explaining the Stylized Facts
Bunching At the Standards. In the US refrigerator market, we observe a special case of a
separating equilibrium, where some products bunch at the minimum energy efficiency standard,
but most products just meet the Energy Star standard. This equilibrium is consistent with a
market where there is a share of consumers that do not place a high value on energy efficiency, but
others value energy efficiency, and especially Energy Star, highly (e.g., Figure 9(b)). Market power
combined with consumer heterogeneity can thus explain bunching at the standards.
Higher Markups for Energy Star Models. The fact that we observe models bunching at the
standards corresponds to a separating equilibrium and reveals that the profit margins on Energy
Star models are at least as large as the margins on non-Energy Star models. Otherwise, this would
lead to the following contradiction. If firms were making larger profits on low efficiency models,
they could increase their profits by offering only such models. This would correspond to a pooling
9Note
that the analysis of the perfect competition case can lead to sharp predictions if we assume that
firms face a small fixed cost to maintain each product on the market. In this scenario, the equilibrium prices
will be set equal to total costs, and only products that maximize the consumer surplus would be sold. This is
due to fact that firms would undercut each others by changing prices and energy efficiency levels to capture
the market, and ultimately only products that benefit the most to consumers will remain. In particular,
only products that maximize the difference between firms’ total costs and consumers’ valuation of energy
efficiency will be offered.
19
equilibrium with bunching at the minimum standard. Remember that consumers that value highly
energy efficiency would purchase a low efficiency model if Energy Star models were not offered. This
is true because, in equilibrium, their incentive compatibility constraint (ICH) must be binding, i.e.,
they are indifferent between low efficiency and high efficiency models. In sum, higher markups on
Energy Star models is a necessary condition to support a separating equilibrium.
Decertified Products Exit the Market. When the Energy Star standard is revised and a
product looses it Energy Star certification, the discontinuity in the demand curve due to Energy
Star will be at the new Energy Star threshold (Figures 10). For models that just meet the old
standard, this may imply that the product has no potential market, unless prices are reduced
(Figure 10(a)), or the consumer surplus associated with this product becomes much smaller (Figure
11(b)).
Even after adjusting prices, it still unlikely that it would be optimal for the firm to maintain a
significant number of models that meet the previous standard. As shown earlier, it can be shown
that there is a range of values [f i , f ES ], i = {L, H} for which the firm will always prefer to offer an
Energy Star model, instead of maintain an interior solution. Given that change in the Energy Star
standard usually follows small increment, i.e., 5%, models that meet the old standard are likely to
fall in the range [f i , f ES ].
Decertification Leads to a Price Decrease. If following its decertification, a product does
not meet the individual rationality constraints of any agents, it will have no potential market and
remain unsold (Figure 10(a)). To liquidate its remaining inventory, its price will have to decrease
so that at least the high valuation consumers may want to purchase the product.
Decertification Leads to a Price Increase. When markups are not too large, decertified models
may still bring a positive surplus for some consumers, even before prices are adjusted (Figure 11(b)).
Post-certification, it is however unlikely that a separating equilibrium could be sustained without
some re-optimization in prices. Following the decertification, consumers with a high valuation
of energy efficiency will have a lower surplus associated with the decertified models, and might
thus prefer to purchase a low efficiency model. Note that an increase in the equilibrium prices
of the low efficiency models could ensure that the decertified models keep their market, and a
separating equilibrium would be sustained. Following the 2008 revision, we observe something akin
of this scenario. One question however remains. Why were firms able to increase prices of the low
efficiency models in the post-revision period? If firms could increase prices of low efficiency models,
they should have done so in the pre-revision period. To sustain a separating equilibrium, firms
20
should always try to set the highest price possible for the low efficiency models. Doing so, allow
them to relax the incentive compatibility constraint of the high valuation consumers, and charge
larger prices to them.
To understand the price dynamics in post-revision period, we need also to account for the fact
that firms also offered new models that meet the revised standards. To ensure that the new products
has a market, firms needed to set prices so that the new models brought surplus that were at least
as large that the ones brought by the lower efficiency models and the decertified models. In sum,
the addition of new models led to new conditions for a separating equilibrium, which may have
required an increase in the price of lower efficiency models.
5. Estimation
In this section, I estimate the various primitives the model, namely the cost functions, and the
demand system. The focus is however mostly on the cost estimation. The demand estimation is
discussed in great length in a companion paper (Houde, 2012).
5.1. Cost Estimation
A common approach to estimate firm costs is to use equilibrium conditions and observed prices to
infer cost functions. Using the first order condition of the oligopoly problem with respect to price,
we can solve for the costs c(fkl ), ∀kl, given an estimate of the demand function. Because we have
Jk products, and c(fkl ) costs to estimate, no estimation is formally required, and all c(fkl ) can
simply be found by solving a system of linear equations.
Once c(fkl ) are obtained, we can regress the estimates on a vector of product attributes, to determine how the costs change as a function of energy efficiency. This two-step estimation procedure
then allow to recover the marginal cost of providing energy efficiency.
My data however allow me taking a more straightforward estimation strategy, given that I
directly observe manufacturer prices, which are the costs faced by brand managers. Using that
information, I propose two additional approaches to estimate the marginal cost of providing energy
efficiency. I first revisit the matching estimator presented earlier. I also propose a simple hedonic
model that regresses manufacturer prices on observed attributes.
21
5.1.1. Matching Estimator Revisited
As discussed earlier, it is common for refrigerator manufacturers to offer product lines with several
models that are quite similar and have few different attributes, such as their color, their finish
(stainless or not) and their energy efficiency level. In particular, my sample contains 56 product
lines with very similar refrigerator models that have different energy efficiency levels.
I thus first estimate the cost of providing energy efficiency using variation in energy efficiency
level within product line. For all refrigerator models matched (N=265), I regress the log of the
weekly manufacturer price on a pair fixed effect, dummies for color, and on a proxy for energy
efficiency:
(9)
ln(pricej,r,t ) = α + γj,j 0 +
K
X
γ k Cjk + φfj + j,r,t ,
k
where γj,j 0 is a product line fixed effect that is common to the matched refrigerator models j and j 0 ,
and Cjk are dummy variables that is equal to one if refrigerator j is of a given color. The proxy for
energy efficiency, fj , is defined as the inverse of the annual electricity consumption. Note that the
log-linear specification implies that the cost of providing energy efficiency is increasing and convex
in fj if φ > 0, given that
∂c(fj )
∂fj )
= φc(fj ) and
∂ 2 c(fj )
∂ 2 fj )
= φ2 c(fj ).
5.1.2. Hedonic Regressions
The second estimator uses information about observed attributes, but does not control for product
line fixed effects. Note that I am observing one manufacturer price for each model in my sample.
This approach allows me to use a large number of refrigerator models (N=3424). On the other
hand, identification relies on cross-sectional variation, and is thus subject to an omitted variable
bias. The extent of this problem is hard to assess, but it should be a source of concern. In my
sample, not all models have the same information about attributes because manufacturers use
different ways to describe their products. Keeping this caveat in mind, the second estimator that
I propose is:
(10)
ln(pricej,r,t ) = α + βXj + φfj + j,r,t ,
22
where Xj is a vector of attributes, other than energy efficiency.10 I first estimate equation 10 by
OLS. I also use a more flexible specification, and estimate a generalized additive model (Ref), where
the attribute size enters equation 10 with an unknown function that is estimated non-parametrically.
For all estimators, I focus on estimating the average marginal cost of providing energy efficiency.
Results. Table 7 presents the estimates for the various specifications. The first model presents the
estimate of the marginal cost of providing energy efficiency obtained using the two-step estimation
approach, where the costs were first estimated using the equilibrium conditions. The estimate of
φ is positive, suggesting that costs are increasing and convex in energy efficiency. The matching
estimator produces an estimate of similar magnitude, but he hedonic approach yields an estimate
that is about four times smaller. Using the generalized additive model to control for size does not
change the results substantially. The estimates from the hedonic models are also only marginally
statistically significant. The matching estimator explains most of the variance (R2 = 96.2%). The
hedonic model has a lower but still large, R2 of 87.4%.
Figures 11(a) and 11 show respectively the predicted costs using the hedonic approach and the
the two-step estimator. The hedonic approach does well on average, but the two-step estimator
tends to over-estimate the costs, especially for large values.
5.2. Demand Estimation
To account for heterogeneity in the way consumers value energy efficiency, I propose a framework that explicitly models consumers’ decision to collect and process energy information. In
this framework, heterogeneity in the costs of collecting and processing energy information leads to
heterogeneity in the heuristics consumers use to trade-off energy efficiency with other attributes.
The proposed framework as a number of good properties. First, it is fully consistent with
utility maximization, which allows me to conduct welfare analysis and quantify the change in
consumer surplus. Second the structure of the model provides a theory of choice with and without
Energy Star. The model thus allows me to simulate a counterfactual scenario without certification.
Third, the model explicitly addresses the selection issue that arises in the estimation of consumers’
10The
following attributes are considered: dummies for refrigerator type (top-freezer, side-by-side or
bottom-freezer), size interacted with dummies for refrigerator type, size interacted with dummies for overall
quality (low: price <$1,000, medium: price >$1,000 & price <$2,800, high: price >$2,800), dummy for
ice-maker, dummy for defrost technology (automatic vs. manual), dummy for water dispenser, dummy for
an advance cooling technology, dummy for an air filtration technology, dummy for indoor lighting using
LED, and dummies for brand.
23
valuation of energy efficiency. As shown by Bento et al. 2011, unobserved heterogeneity biases
estimates of valuation of energy efficiency when left unaccounted for. My model is akin of the
Heckman’s selection model, where the first step, the decision to get informed about energy efficiency,
represents a latent decision to care or not about energy efficiency (the selection decision). The model
proceeds as follows.
Consider a discrete choice model where consumer i receives utility level Uij from purchasing
an energy intensive durable j. The value of option j is a function of its quality (δj ), price (Pj ),
expectation of annual energy cost (Cj ) and an idiosyncratic taste parameter (ij ):
Uij = δj − ηPj − θCj + ij
(11)
In equation (11), the parameter η is the consumer’s sensitivity to prices and corresponds to the
marginal utility of income, while θ is the sensitivity to energy cost.
Cj is the product of energy price and energy consumption.11 I assume that with limited information a consumer will have few ways to make an accurate forecast of Cj for each option j in his
choice set. Before collecting information, the consumer’s knowledge about Cj is then imperfect,
and is modeled with a prior distribution F , where Cj ∼ F , ∀j. The consumer can collect energy
information and learn the value of Cj for each j. In the context of purchasing a refrigerator, the
process of information acquisition and learning may consist of taking the time to look at the EnergyGuide label, understand the various pieces of information, look up the electricity prices, and
perform mental calculations to compute energy costs.
Consumer’s purchasing decision is modeled as a two-step process. To begin, the consumer
observes the quality δj and the price Pj for each refrigerator. Then, the consumer can collect
and process energy information at a cost K. By doing so, the consumer observes a realization cj
from F for each j. If the consumer does not collect/process information, no learning occurs and
the consumer only knows the average energy cost for all refrigerator models, which is given by
E[Cj ] =
R
C
cf (c)dc = C, where f (c) is the probability density of F . Afterwards, the idiosyncratic
taste parameters ij are realized12. Finally, the consumer decides which refrigerator to purchase.
11I
assume that the consumer’s expectations about prices and utilization are constant, which is a reasonable assumption for my empirical application.
12As it is customary in the literature (e.g. Rust (1986)), I assume that the idiosyncratic taste parameters,
ij , are only realized at the time of making a purchase, but the consumer has a prior on their distribution.
24
Consumer i will search for energy information if the following inequality holds:
(12)
−K + E,C max{Uij (δj , ηPj , Cj , ij )} ≥ E max{EC [Uij (δj , ηPj , Cj , ij )]}
j
j
When the consumer does not collect and process energy information, the expected energy costs
are the same for all products and do not influence the purchase decision. So the model offers a
simple way to capture consumer inattention to energy costs. I next discuss how the impact of the
Energy Star certification can be modeled in this framework.
Energy Star as a Heuristic. The Energy Star certification partly informs about energy costs
and can serve as a heuristic to compare products in a binary manner along the energy dimension.
I model its effect as follows. Prior to collecting information, the consumer has some beliefs about
energy costs and Energy Star. As before, the consumer’s belief about the energy cost of the
j-th refrigerator is given by the prior distribution F , ∀j. Uncertainty related to the Energy Star
certification stems from the fact that the Energy Star label carries no information about its meaning.
A priori, the consumer is uncertain about the meaning of the Energy Star certification, but can
learn it by searching for additional information. Moreover, I also assume that the consumer is
also uncertain about whether a product is certified Energy Star or not. With probability q, the
consumer believes that product j is certified.
If the consumer knows the meaning of the Energy Star certification, he will then know that the
energy costs of Energy Star products are below a particular threshold corresponding to the Energy
Star standard. Denote this threshold by S. If the consumer does not collect energy information,
his belief about the meaning of Energy Star, S, is given by a prior distribution G. If the consumer
learns the meaning of the certification, he observes a realization s from G. Define the indicator
variable Dj that takes the value one if product j is certified Energy Star and zero otherwise. For
a given realization s, the expected energy cost of an Energy Star product j is:
Z s
1
cf (c)dc ≡ C0 (s),
(13)
E[Cj (s)|Dj = 1] = E[Cj |Cj ≤ s] =
F (s) c
where c is the lower bound of the support of F . Similarly, the expected energy cost of a non-Energy
Star product j 0 is given by:
(14)
1
E[Cj 0 (s)|Dj = 0] = E[Cj 0 |Cj 0 > s] =
1 − F (s)
Z c
cf (c)dc ≡ C1 (s),
s
where c is the higher bound of the support of F .
The timing is now as follows. The consumer first chooses his effort level for collecting and
processing information, defined by the variable e, which now takes three values. A consumer that
25
selects e = l, does not collect energy related information. At e = m, the consumer only collects
information related to Energy Star, and learns the meaning of the Energy Star and which products
are certified. Finally, for e = h, the consumer collects and processes enough information to form
expectations about the energy costs associated with each option. After choosing e, the idiosyncratic
taste parameters ij are realized and the consumer decides which refrigerator to purchase.
The optimal level of effort is given by the following optimization problem:
(15)
max V(e)
e∈{l,m,h}
V(e) = −K(e) + E,D,S,C max{Uij (δj , Pj , Cj (S), Dj , ij )}|I(e) ,
j
where K(e) is the information acquisition cost that varies with e and I(e) represents consumer’s
knowledge about energy costs at the time of purchase.
To estimate the model, I assume that the idiosyncratic taste parameters are extreme value
distributed. In addition, I assume that the information acquisition costs have an unobservable
idiosyncratic component that is also Type I extreme value distributed. For a level of effort, e, the
cost for consumer i is given by:
Ki (e) = K e + β e Xi + νie ,
(16)
where, Xi is a vector of demographics, and the constant K e and the vector of coefficients β e
parameterize the average information acquisition cost. νi,e is a mean zero stochastic component of
the cost and gives rise to closed form solutions for the probability of choosing effort e:
(17)
exp (−K e − β e Xi + E,C,S [maxj {Uijrt }|I(e)])
Qi (e) = P
k
k
k exp (−K − β Xi + E,C,S [maxj {Uijrt }|I(k)])
At the moment of choosing the effort level, consumer i is uncertain about the electricity costs
(Cj ), the meaning of the Energy Star label (S), the certification (Dj ), and the idiosyncratic taste
parameters. Because is extreme value distributed, the expectation in (17) simplifies to:
(18)


E,D,S,C max{Uijrt }|I(e) = ED,S,C log 
j
J
X


Uijrt } |I(e)
j
To evaluate the expectation in 18, I specify beliefs about electricity costs and Energy Star
such that consumers have rational expectations. I will assume that consumers take the average
electricity price in their county as given and have a prior on the annual amount of kWh consumed by
26
refrigerators. The prior consists of a distribution F normally distributed with a mean that matches
the empirical distribution of electricity consumption of all the refrigerator models available on the
US market, for the trimester the purchase was made. Note that because the choice set changes
between trimesters, notably due to the revision in Energy Star standards, I effectively allow the
prior on electricity consumption to vary across time. The variance of the normal distribution F
will be estimated.
Beliefs about the meaning of Energy Star, G, is modeled with a flat prior. Specifically, the
Energy Star threshold (previously noted S) is uniformly distributed, with a support centered at
the mean of F , the prior for electricity consumption.
Ex ante, consumers are also uncertain about whether a product is certified or not. I set the
prior for the probability that product j is certified Energy Star, q, equal to the share of products
that are certified Energy Star in their choice set, i.e., their local store. Therefore, the probability
that consumers believe that N products are Energy Star certified among the J products available
is given by a binomial distribution with mean Jq. I thus effectively assume that consumers have
rational expectations with respect to the number of Energy Star models in their choice set.
Finally, I need to specify learning. I will assume that learning is unbiased and realized before the
purchase decision. Upon deploying effort e = h, consumer i living in region r learns a realization cr,j
from F that corresponds to the true value of the electricity consumption (kWh/year) of refrigerator
j. Consumers will multiply this number by the average electricity price in region r, which is assumed
to be known. If e = m, consumers learn the true the meaning of Energy Star, i.e., the realization
s from G will be such that the expected electricity cost for all Energy Star refrigerator models
(Cr,1 (s)) in a region r will correspond to the true average electricity cost of Energy Star models in
this region. The same will be true for non-Energy Star models.
I assume that ex ante consumers are completely unaware of the rebate programs available in
their regions and learn the existence of the rebate program only if they collect and process energy
information, i.e., if e = m or e = h. Therefore, under this assumption the existence of a rebate program in a particular region does not influence the level of effort to collect and process information,
but does influence the purchase decision.
I introduce a constant τ that corresponds to the Energy Star perceived added quality if a
product j is certified Energy Star (Dj = 1). If consumers value Energy Star refrigerators beyond
their electricity cost savings, this will be captured by the coefficient τ . The model thus allows
me distinguishing whether consumers adopt Energy Star products purely based on financial or
27
non-financial motives. I will assume that τ is only realized at the time of making a purchase for
consumers that search for energy information (i.e., e = m or e = h), is unexpected, and differ for
e = m and e = h.
For the estimation, I approximate the expectation in (18) with respect to the distributions of
D, S and C with Monte Carlo integration. I then form the simulated likelihood and estimate the
model via maximum likelihood. In the likelihood, the probability that consumer i chooses product
j is given by:
Hijrt =
(19)
X
H̃ijrt (k) · Qi (k),
k
where H̃i,j,r,t (e) are the simulated choice probabilities conditional on the information set determined
by the level of effort, e.
Under the above assumptions about beliefs and learning, the alternative specific utility for each
level of effort is given by:
(20)
h
Uijrt
= −ηPjrt + ψRrt XDjt + τ h Djt − θCjr + γj + ijrt
m
= −ηPjrt + ψRrt XDjt + τ m Djt − θEC [CrDj (s)] + γj + ijrt
Uijrt
l
Uijrt
= −ηPjrt − θED,S,C [CrDj (S)] + γj + ijrt
Note that if e = l, the expectation ED,S,C [Cr,Dj (S)] is the same for all j, and is not influencing
the choices. The model therefore assumes that consumers that do not search for energy information
make purchase decision as if they were not paying attention to electricity costs and Energy Star.
For consumers that rely on Energy Star information (e = m), the expectation EC [Cr,Dj (s)] takes
only two possible values, and the difference EC [Cr,1 (s)] − EC [Cr,0 (s)] corresponds to the expected
electricity cost savings, in region r, associated with Energy Star. The present specification accounts
for two mechanisms by which the Energy Star certification influences choices. First, there is the
purely financial motive, i.e., the fact that Energy Star products lead to electricity cost savings.
Second, there is the effect of the label on consumers, which might both capture consumers’ willingness to pay for green products and/or higher perceived quality for energy efficient products.
If consumers do not value Energy Star products beyond their electricity cost savings, we should
expect that the parameter τ will take a value close to zero.
28
To account for additional heterogeneity, the model is estimated for three different income groups:
(income < $50, 000, income ≥ $50, 000, < $100, 000, and income ≥ $100, 000). Further details about
the estimation procedure can be found in appendix.
Results. To account for the role of income, I estimated the information acquisition model for
three different income groups. For comparison purpose, I have also estimated a simple multinomial
logit model on the same data (see appendix). Table 8 presents the results. Focusing on the price
coefficients, we observe an inverse correlation between consumers’ sensitivity to prices and income
levels, i.e., the marginal utility of income, |η|, decreases with income. Meanwhile, lower income
consumers are also less sensitive to electricity costs. The effect of the Energy Star label varies
across income levels. Consumers in the upper income group have the highest willingness to pay for
Energy Star products, irrespectively of their beliefs about electricity costs. Looking at the effort
probabilities (Table 9, constrained model), the estimated share of consumers that does not value
Energy Star or electricity costs (e = l) is 34.7%. I find that about 46.2% of the consumers behave
as if they were discounting electricity costs (e = h) and 19.0% of consumers rely on Energy Star
(e = m). These probabilities vary by demographics. Table 9 reports these probabilities for the three
income groups. Lower income group tend to have a high probability to have a low effort (e = l),
while it is the opposite for the higher income group. For the low income group, the probability
that they rely on Energy Star (e = m) is close to zero. The above numbers correspond to averages
taken over the whole population of consumers.
Overall, the information acquisition model produces estimates that are consistent with the hypothesis that there are different types of consumers with different levels of sophistication with
respect to how they account for the energy efficiency attribute. Moreover, I find evidence that consumers with different income levels value energy efficiency differently, controlling for their degree
of sophistication.
6. Counterfactual Scenarios
In this section, I first illustrate how the model can replicate some of the main stylistic facts. I then
perform sensitivity tests with respect to key parameters of the model. Finally, I simulate a number
of counterfactual scenarios that show what would happen if the Energy Star program were not in
effect, and provide estimates of the welfare effects of Energy Star.
29
For all scenarios, the size of the choice set is set equal to 75.13 To locate products in the
quality space (all non-energy attributes), I use the estimated product fixed effects, obtained from
the demand estimation, as the measure of observed quality. To create a representative choice
set, I randomly sample 75 products from the 1065 products used in the demand estimation such
that the distribution of the products in terms of brand, style, size, and energy efficiency fits the
observed distribution for the whole US market for the year 2010. This approach ensures that the
proportion of refrigerator models of a certain brand, style, size, and energy efficiency level in the
constructed choice sets matches the proportion observed in the whole US refrigerator market. Table
10 compares the distribution of the products across the main attributes for the whole US market
and the constructed choice set.
For all simulations, consumers’ purchase decisions is simulated with the information acquisition model estimated in section 5.14 Quality (i.e., the product fixed effects) and the number of
products offered by each brand is held constant. To account for uncertainty and the possibility of
multiple equilibria, Monte Carlos are performed for each scenario considered. In this context, a
Monte Carlo experiment consists in drawing the demand and cost parameters from their estimated
distributions, and simulating the model for these particular parameter values; 10 experiments are
performed for each scenario. Each experiment solves for the Nash equilibrium using the Gauss
Seidel algorithm. To account for the discontinuity created by Energy Star, a non-gradient based
optimization algorithm is used (simulated annealing).
6.1. Bunching
The first question that I ask is whether the model can replicate the strong bunching at the minimum
and Energy Star standards observed in the US refrigerator market (Figures 3 and 4). To simulate
this scenario, I set the minimum energy standard to the standard enacted in 2001 (Table 1), and
the Energy Star standard is 20% more energy efficient (the Energy Star standard since April 2008).
The results suggest that the model replicates the main pattern observed. For the year 2010, 61%
of the models on the market met the Energy Star standard (Table 10). For my preferred estimate
13In
my sample, the average number of refrigerator models offered by a store is 250, on average. I set the
size of the choice set to 75 for computational reasons. Although, I was able to solve the model for larger
choice sets (e.g., 252), the simulation results were more robust and converged faster with smaller choice sets.
Qualitatively, the results were the same with a larger choice set.
14Specifically, the information acquisition model that allows for heterogeneity with respect to income
(Table 8).
30
of the marginal cost of providing energy efficiency (φ = 181), the model predicts that, on average,
69% of the products offered should meet the Energy Star standard in equilibrium.15 Although
the model slightly over-predicts the proportion of models that meet the Energy Star standard, it
performs well, however, in predicting bunching. Next, I assess the sensitivity of the results.
Figure 12 compares the predicted distribution of energy efficiency to the distribution observed
in the constructed choice set (75 models), for different values of the parameter φ. We observe that
for lower estimates of φ, the model predictions differ substantially from the data, and the model
poorly predicts bunching. For low marginal costs, firms will tend to offer much more energy efficient
models, well above the Energy Star standard.
6.2. A World Without Energy Star
I now turn to the main counterfactual scenario at the core of the welfare analysis. I remove the
Energy Star standard and simulate the market. I simulate this scenario for three values of the
estimate of the marginal costs of providing energy efficiency.
The important result is that in a world without Energy Star, firms will offer products that
bunch almost exclusively at the minimum standard (Figure 13) for the largest value of φ. For lower
estimates, the results from the simple monopoly case extend to a more complex setting–in a world
without Energy Star, we observe differentiation along the energy efficiency dimension. Clearly, we
observe that in a world without Energy Star, firms may still offer highly energy efficient products.
Moreover, without Energy Star, product differentiation in the energy efficiency dimension increases.
The above findings have important implications for the design and the evaluation of Energy Star,
especially to estimate the energy savings associated with the program. Previous analyses of Energy
Star have assumed that in a world without Energy Star, all products would bunch at the minimum
energy efficiency standard. The present results suggest that this scenario seems likely; without a
voluntary certification, firms have few incentives to offer highly efficient refrigerator models.
6.3. Welfare Analysis
I propose a welfare measure that accounts for the change in consumer surplus, producer surplus,
and externality costs. Note that the EPA deploys substantial efforts to manage and market the
Energy Star program. In the present analysis, I will not consider the public funds required to run
15I
assume that a product bunches at a standard if it is within +/- 2.5% of the standard.
31
marketing campaigns, pay salaries of public employees and other operating expenses related to the
program, with the caveat that this could be an important cost associated with the program.
Consumer Welfare. According to the information acquisition model, it is important to note
that for consumers that do not fully process energy information or rely on Energy Star, there is a
discrepancy between the electricity costs consumers believe they would pay and the electricity costs
they effectively pay. That is, the utility they experience differs from the utility they thought they
would experience. Because the demand model estimated in Chapter 2 is a model that rationalizes
observed choices, it should be thought as a model of decision utility. Using the demand model as is
to conduct the welfare analysis will thus not reflect what consumers will truly experience ex ante.
As analysts, this raises the question of what concept of utility should be used to measure consumer
welfare. In the context of energy intensive durables, this problem has been previously pointed out
by several researchers. Similarly to Allcott and Wozny (2011), I propose a measure of consumer
surplus based on the notion of experience utility.
I first make the following assumption.
Assumption 1. If e = h, decision utility equals experience utility.
Assumption (1) simply says that under perfect information consumers experience what they
believed they will experience. Under this assumption and using the estimates obtained from the
information acquisition model, the observed component of experience utility, net of the (observed)
costs of collecting and processing information is thus:
(21)
OXUi,j,r,t = γ̂j + τ̂ h Dj,t − η̂Pj,r,t + ψ̂Rr,t XDj,t − θ̂Cj,r − K k − β k Xi ,
Note that whether the Energy Star label effect, τ h is truly experienced can be debated. If a
consumer believes that a product is of higher quality because of the Energy Star label and this
belief is never updated, the consumer may experience the perceived quality. It can also be argued
that the costs of collecting and processing information should not be treated as part of the consumer
surplus, because their identification relies on our assumption about consumers’ priors. In the present
analysis, I will thus perform sensitivity analysis and provide estimates of the welfare effects with
and without the effects of τ h and Ki (e).
To deal with the unobserved idiosyncratic taste parameters i,j,r,t and unobserved component of
the costs, νie , I will compute the expected consumer surplus (Train 2009). Note that the probability
that consumer i chooses and thus experiences product j is given by the choice probabilities in
equation (19). Following the estimation, these choice probabilities can be simulated and are our
32
best representation of how each consumer selects his favorite refrigerator. I then derive the expected
consumer surplus (ESC) by computing the expected experience utility, where the expectation is
taken with respect to the observed choice probabilities. For consumer i that lives in region r and
makes a purchase at time t, the expected consumer surplus (ESCi,r,t ) is given by:
(22)


J X
X
1
ECSi,r,t = E,ν 
Ĥi,j,r,t (k) · Q̂i (k) · (OXUi,j,r,t + i,j,r,t − νie )
η
=
j
1
η
k
J X
X
j
Ĥi,j,r,t (k) · Q̂i (k) · (OXUi,j,r,t ) ,
k
where I have used the facts that E [i,j,r,t ] = 0, Eν [Ki (e) = K e + β e Xi + νi,e ] = K e + β e Xi , and
that the unobservable components of utility do not enter the choice probabilities. I also multiply
by the inverse of the marginal utility of income to convert utils in dollars (Train 2009).
Finally, note that the measure of the consumer surplus should be interpreted as the expected
consumer surplus over the lifetime of the refrigerator. To obtain an annual measure, I compute the
immediate-annuity assuming a lifetime of 18 years, and the discount rate implied by the estimates
of marginal utility of income (η) and sensitivity to electricity costs (θ).
Externality Costs. To quantify the externality costs associated with the electricity generated to
operate refrigerators, I focus on the emissions of carbon dioxide (CO2 ), sulphur dioxide (SO2 ),
and nitrous oxide (N Ox ), and use emissions factors recommended by the EPA. I compute the
dollar damages associated with carbon dioxide using the recent estimates of the social cost of
carbon recommended to assess federal regulations (Greenstone, Kopits, and Wolverton 2011). For
sulphur dioxide and nitrous oxide, I rely on two sources, I consider the estimates used by the
Department of Energy in the cost-benefit analysis of the 2014 minimum energy efficiency standards
for refrigerators (DOE 2011), and the average estimates provided by Muller and Mendhelson (2012).
Table 11 presents the emission factors and the damage costs used.
I compute the externality cost associated with each scenario by taking the product of the corresponding average electricity consumption purchased, the market size, the emission factors, and
the damage costs of electricity generation. The average electricity consumption purchased is the
average of the electricity consumption of the refrigerators sold, weighted by market shares. For the
market size, I use the annual shipments of refrigerators in the US for the year 2010, which is 9,01
million units (DOE 2011).
33
Producer Surplus. For each refrigerator model in the choice set, I compute the brand manager’s
markup using the estimated cost functions. I compute the average profits by multiplying markups
with the simulated market shares. Note that I do not consider the change in manufacturers’ profits
in the producer surplus.
Rebates. I assume that when Energy Star is in effect, consumers can claim a $50 rebate for
purchasing an Energy Star refrigerator. The rebate program is offered by a government agency.
When Energy Star is not in effect, I assume that this agency will distribute the amount reserved
for the rebate program to all consumers via a lump-sum payment. The amount of the lump-sum
payment is set equal to the rebate amount ($50) times the probability that a consumer takes
advantage of the rebate amount, previously referred as π, implied by the estimates of the marginal
utility of income (η) and sensitivity to rebates (ψ).
6.3.1. Results
When Energy Star is not in effect, and firms offer products that bunch exclusively at the minimum
standard (Scenario 1), the Energy Star program leads to important energy savings. Without Energy
Star, the electricity consumption of a refrigerator purchased increases by 49 kWh/year, on average.
Assuming a market size of 9.01 million refrigerators, the upper bound on energy savings implied by
this estimate corresponds to a reduction of 441 GWh/year. Webber, Brown, and Koomey (2000)
estimated that the overall energy savings associated to the program for the refrigerator market were
about 1400 GWh/year, for the period 1997-2000. This larger estimate can be in part attributed to
the fact that refrigerators were much less energy efficient on average before 2001, the last time the
minimum energy efficiency standards for refrigerators was revised.
For the range of estimates of the marginal utility of income and sensitivity to electricity costs
that I obtain, I find that consumers are worst-off in a market with less efficient refrigerators. For the
three measures of consumers surplus reported, consumers surplus decreases when Energy Star is not
in effect. In this scenario, removing Energy Star also makes firms worst-off. Offering products that
bunch exclusively at the minimum standard leads to a large loss of profits ($159 million annually).
Under Scenario 2, removing the Energy Star program thus reduces social welfare.
Using lower estimates of the marginal cost of providing energy efficiency, the welfare effects
have a different sign. I find that without Energy Star, choices might even become more energy
efficient. This highlights an unintended consequence of the program, which I call the crowding-out
effect. When Energy Star is not in effect, products may not bunch exclusively at the minimum
34
energy efficiency standard, but instead become more differentiated. This increase in differentiation
combined with the fact that more consumers are perfectly informed induces market shares for the
most energy efficient products to increase, enough to have more energy efficient choices.
7. Conclusions
In this paper, I show that manufacturers, brand managers, and retailers, respond to the Energy
Star program. I propose a model that rationalizes the observed market outcomes and estimate the
model using data for the refrigerator market. The model can predict the observed market outcomes
quite closely. Using the estimated model, I show that if the refrigerator market were not subject
to Energy Star, products would bunch almost exclusively at the minimum standard.
References
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Working paper, Massachusetts Institute of Technology.
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Paper 204, Energy Institute at Haas.
Datta, S., and S. Gulati (2009): “Utility Rebates for Energy Star Appliances: Are they Effective?,” Working paper, University of British Columbia.
DOE (2011): “Energy Conservation Program: Energy Conservation Standards for Residential
Refrigerators, Refrigerator-Freezers, and Freezers,” Federal Register, 76(179).
Greenstone, M., E. Kopits, and A. Wolverton (2011): “Estimating the Social Cost of
Carbon for Use in U.S. Federal Rulemakings: A Summary and Interpretation,” NBER Working
Papers 16913, National Bureau of Economic Research, Inc.
Houde, S. (2012): “How Consumers Respond to Product Certification: A Welfare Analysis of
Energy Star,” Working paper.
35
Ito, K. (2010): “Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear
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Klier, T., and J. Linn (2012): “New-vehicle characteristics and the cost of the Corporate Average
Fuel Economy standard,” The RAND Journal of Economics, 43(1), 186–213.
McWhinney, M., A. Fanara, R. Clark, C. Hershberg, R. Schmeltz, and J. Roberson (2005): “ENERGY STAR Product Specification Development Framework: Using Data and
Analysis to Make Program Decisions,” Energy Policy, 33(12), 1613 – 1625.
Muller, N. Z., and R. Mendelsohn (2012): “Efficient Pollution Regulation: Getting the Prices
Right: Corrigendum (Mortality Rate Update),” American Economic Review, 102(1).
Rust, J. (1986): “Structural Estimation of Markov Decision Processes,” in Handbook of Econometrics, ed. by R. F. Engle, and D. McFadden, vol. 4 of Handbook of Econometrics, chap. 51,
pp. 3081–3143. Elsevier.
Sallee, J. M. (2011): “Rational Inattention and Energy Efficiency,” Working paper.
Train, K. (2009): Discrete Choice Methods with Simulation. Cambridge University Press.
Webber, C., R. Brown, and J. Koomey (2000): “Savings Estimates for the Energy Star
Voluntary Labeling Program,” Energy Policy, 28(15), 1137 – 1149.
Whitefoot, K., M. Fowlie, and S. Skerlos (2011): “Product Design Response to Industrial
Policy: Evaluating Fuel Economy Standards Using an Engineering Model of Endogenous Product
Design,” Haas Working Papers 214, Energy Institute at Haas.
36
8. Figures and Tables
Figure 1. Energy Star and EnergyGuide labels
37
Figure 2. The Three Main Manufacturers and their Major Brands. Circle
with dates correspond to mergers and acquisitions. Mabe and GE entered a joint venture
in 1986. Doted squares are the major brands produced by each manufacturer.
0
.1
.2
Density
.3
.4
.5
38
0
10
20
30
Energy Efficiency: Percentage Better than Minimum Standard
Figure 3. Empirical Density of Energy Efficiency for Full-Size Refrigerators,
Year 2010
Energy Star standard: 20% better than the federal minimum standard. Sources: US EPA,
Federal Trade Commission and California Energy Commission.
39
15
20
25
30
35
400 450 500 550 600
15
20
25
30
35
20
25
30
35
20
25
30
35
30
35
30
35
20
400 450 500 550 600
15
20
25
25
30
35
Standards
400 450 500 550 600
25
15
2010
400 450 500 550 600
20
25
400 450 500 550 600
15
2009
15
20
2008
400 450 500 550 600
15
15
2007
400 450 500 550 600
2006
Electricity (kwh/year)
2005
400 450 500 550 600
2004
400 450 500 550 600
2003
30
35
Minimum Energy Efficiency Standard
ENERGY STAR: 2001
ENERGY STAR: 2004
ENERGY STAR: 2008
15
20
25
30
Adjusted Volume (Cu.Ft.)
Figure 4. Choice Set for Bottom-Freezer Refrigerator without Ice-Maker:
2003-2010.
Each dot represents a refrigerator model on the market. Most refrigerator models bunch
at the minimum standard or Energy Star standard. Following revisions in the Energy Star
standard, decertified models exit the market. Sources: US EPA, Federal Trade Commission
and California Energy Commission.
35
40
Mark−Up: Promo. Price vs. Manuf. Price
% Mark−Up Energy Star
% Mark−Up Energy Star
Mark−Up: MSRP vs. Manuf. Price
45
45
% Mark−Up Non−Energy Star
% Mark−Up Non−Energy Star
Figure 5. Markups on Energy Star Models vs. Non Energy Star Models
Percentage markups for 56 product lines are compared. Each product line has at least one
Energy Star model and one non Energy Star model. Panel A uses manufacturers’ suggested
retail prices and manufacturers’ prices (retailers’ costs) to compute markups. Panel B
uses promotional prices and manufactures’ prices. Both figures shows that the percentage
markups of Energy Star models tend to be larger than the percentage markups of non
Energy Star models.
Revision Energy Star
1
Normalized Price
1.05
1.1
41
Non Energy Star Models
.95
Decertified Energy Star Models: 15% Better than Minimum
2008w1
Energy Star Models: 20% Better than Minimum
2008w27
2009w1
Weeks
2009w26
2010w1
(a) Manufacturers’ Suggested Retail Prices
.95
Normalized Price
1
1.05
1.1
Revision Energy Star
Non Energy Star Models
.9
Decertified Energy Star Models: 15% Better than Minimum
2008w1
Energy Star Models: 20% Better than Minimum
2008w27
2009w1
Weeks
2009w26
2010w1
(b) Promotional Prices
Figure 6. Prices Before and After the 2008 Revision of Energy Star
Each panel displays average normalized weekly prices, with 5% confidence intervals, of refrigerators that belong to different efficiency classes. Three efficiency classes are considered:
models that were not certified Energy Star before and after the revision (less than 15%
more efficient than the minimum standard), models that lost the Energy Star certification
(15-19% more efficient than the minimum standard), and models that met the revised standard before and after the revision (at least 20% more efficient than the minimum standard.)
The normalized price for each model is computed by divided its weekly price (MSRP or
promotional) by its average weekly price for the pre-revision period (January 1st , 2008 to
April 28th , 2008). The average normalized price and standard errors in each efficiency class
are computed by fitting a cubic spline on normalized prices. Refrigerators with no observed
sales after November 15th , 2009 are not considered. This criterion ensures that refrigerators
that were decertified but exited the market are excluded from the analysis.
42
1.05
MSRP
Energy Star Models
.85
.9
Normalized Price
.95
1
Decertified Energy Star Models
2009w13
2009w40
2010w13
2010w40
Weeks
(a) Manufacturers’ Suggested Retail Prices
1.05
Promo. Price
Energy Star Models
.95
Normalized Price
1
Decertified Energy Star Models
2009w13
2009w40
2010w13
2010w40
Weeks
(b) Promotional Prices
Figure 7. Prices Before and After the 2010 Decertification Due to Problematic Test Procedures
Each panel displays average normalized weekly prices with a 5% confidence interval. Average
normalized prices are computed using the same methodology than for Figure 6. Prices of
the 16 models that were decertified are compared to the prices of Energy Star models that
did not loose their certification.
43
p
p
H
θ f
c(f)
{f*H,p*H}
c(f)
{f*L,p*L}
H
θ f
πH
L
θ f
L
θ f
{f*, p*}
πL
H
θ =c ' ( f )
2 π0
L
θ =c ' ( f )
f
f
(a) Separating Equilibrium
(b) Pooling Equilibrium
Figure 8. Possible Equilibria without Energy Star; Monopoly Selling to Two
Consumer Types
p
H
θ f
p
H
θ f
{f*H,p*H}
τ
τ {f*H,p*H} c(f)
o
τ
{f*L,p*L}
c(f)
o
πH
L
θ f
τ
o
L
θ f
o
{f*L,p*L}
πL
πH
πL
fES
(a) Separating Equilibrium
f
fES
f
(b) Special Case, Separating Equilibrium
Figure 9. Possible Equilibria with Energy Star; Monopoly Selling to Two
Consumer Types
44
p
Decertified
Model
τ
o
H
θ f
{f*H,p*H}
c(f)
πH
p
H
θ f
Decertified
Model
L
θ f
τ
{f*L,p*L}
(a) High Markup Case
c(f)
L
θ f
o
πL
fES fES'
o
τ
o
{f*L,p*L}
τ
πL
fES fES'
f
(b) Low Markup Case
Figure 10. Effect of Decertification
f
45
Estimates
Marginal Cost ($)
Data (Manufacturer Prices)
0
200
400
600
800
1000
Product Id
(a) Estimated Marginal Costs w. Hedonic Approach vs. Observed Costs (Manufacturer
Prices)
Second Stage
Predicted
Marginal Cost ($)
Data (Manufacturer Price)
0
200
400
600
800
1000
Product Id
(b) Estimated Marginal Costs w. Two-Step Estimation vs. Observed Costs (Manufacturer Prices)
Figure 11. Predicted Costs (ĉ(f ))
0
0
.1
.05
Density
Density
.1
.2
.3
.15
46
0
10
20
30
Energy Efficiency: Percentage Better than Minimum Standard
40
0
10
20
30
Energy Efficiency: Percentage Better than Minimum Standard
40
(b) Predicted, High Marginal Cost: φ = 181
Density
.02
0
0
.01
.01
Density
.02
.03
.03
.04
.04
(a) Data
0
10
20
30
40
50
Energy Efficiency: Percentage Better than Minimum Standard
60
(c) Predicted, Medium Marginal Cost: φ = 90
0
10
20
30
40
50
60
70
Energy Efficiency: Percentage Better than Minimum Standard
80
(d) Predicted, Low Marginal Cost: φ = 40
Figure 12. Distribution of Energy Efficiency with Energy Star, Observed
and Predicted
0
.5
Density
1
1.5
2
47
0
10
20
30
Energy Efficiency: Percentage Better than Minimum Standard
40
0
.05
Density
.1
.15
.2
(a) High Marginal Cost: φ = 181
0
10
20
30
Energy Efficiency: Percentage Better than Minimum Standard
40
0
.02
Density
.04
.06
.08
(b) Medium Marginal Cost: φ = 90
0
10
20
30
40
50
60
70
Energy Efficiency: Percentage Better than Minimum Standard
80
(c) Low Marginal Cost: φ = 40
Figure 13. Distribution of Energy Efficiency without Energy Star, Predicted
48
49
Table 1. Minimum and Energy Star Standards for Full-Size Refrigerators
Refrigerator Type
Refrigerator-freezers and refrigerators other than allrefrigerators w manual defrost
All-refrigerators manual defrost
Refrigerator-freezers partial auto. defrost
Refrigerator-freezers auto. defrost w top-mounted
freezer w/o auto. icemaker
Built-in refrigerator-freezer auto.
defrost w topmounted freezer w/o auto. icemaker
Refrigerator-freezers auto. defrost w top-mounted
freezer w auto. icemaker w/o through-the-door ice
Built-in refrigerator-freezers auto. defrost w topmounted freezer w auto. icemaker w/o through-thedoor ice
All-refrigeratorsŮauto. defrost
Built-in All-refrigerators auto. defrost
Refrigerator-freezers auto. defrost w side-mounted
freezer w/o auto. ice-maker
Built-In Refrigerator-freezers auto. defrost w sidemounted freezer w/o auto. icemaker
Refrigerator-freezers auto. defrost w side-mounted
freezer w auto. icemaker w/o through-the-door ice
Built-In Refrigerator-freezers auto. defrost w sidemounted freezer w auto. icemaker w/o through-thedoor ice
Refrigerator-freezers auto. defrost w bottom-mounted
freezer w/o auto. ice-maker
Built-In Refrigerator-freezers auto. defrost w bottommounted freezer w/o auto. icemaker
Refrigerator-freezers auto. defrost w bottom-mounted
freezer w auto. ice-maker w/o through-the-door ice
Built-In Refrigerator-freezers auto. defrost w bottommounted freezer w auto. icemaker w/o through-thedoor ice
Refrigerator-freezer auto. defrost w bottom-mounted
freezer w through-the-door ice
Built-in refrigerator-freezer auto. defrost w bottommounted freezer w through-the-door ice
Refrigerator-freezers auto. defrost w top-mounted
freezer w through-the-door ice
Refrigerator-freezers auto. defrost w side-mounted
freezer w through-the-door ice
Built-In Refrigerator-freezers auto. defrost w sidemounted freezer w through-the-door ice
Minimum Energy Efficiency Standard
Effective
Effective
Effective
January 1993
January 2001
Starting 2014
13.5AV+299
8.82AV+248.4
7.99AV + 225.0
10.4AV+398
16.0AV+355
NA
NA
8.82AV+248.4
9.80AV+276.0
6.79AV + 193.6
7.99AV + 225.0
8.07AV + 233.7
NA
NA
9.15AV + 264.9
NA
NA
8.07AV + 317.7
NA
NA
9.15AV + 348.9
NA
NA
11.8AV+501
NA
NA
4.91AV+507.5
7.07AV + 201.6
8.02AV + 228.5
8.51AV + 297.8
NA
NA
10.22AV + 357.4
NA
NA
8.51AV + 381.8
NA
NA
10.22AV + 441.4
16.5AV+367
4.60AV+459.0
8.85AV + 317.0
NA
NA
9.40AV + 336.9
NA
NA
8.85AV + 401.0
NA
NA
9.40AV + 420.9
NA
5.0AV+539.0
9.25AV + 475.4
NA
NA
9.83AV + 499.9
17.6AV+391
10.20AV+356.0
8.40AV + 385.4
16.3AV+527
10.10AV+406.0
8.54AV + 432.8
NA
NA
10.25AV + 502.6
Note: AV: adjusted volume measured in cubic feet. For all-refrigerator, AV is calculated as follows: AV = 1.63
x total refrigerator volume. For refrigerator-freezer, AV is calculated as follows: AV= fresh volume + 1.63 x
total freezer volume.
Energy Star
Effective
April 2008
-20 %
50
Table 2. Market Share by Manufacturer
Manufacturer
GE
Electrolux
Whirlpool
Maytag
(Amana)
Haier
W.C. Wood
Other
1995
Market Share
2000 2005
2008
35%
17%
27%
10%
10%
0%
0%
1%
34%
21%
24%
14%
5%
0%
0%
2%
29%
25%
25%
11%
0%
2%
1%
7%
27%
23%
33%
6%
1%
10%
Source: Appliance Magazine; data compiled by
the Department of Energy.
Table 3. Full-Size Refrigerator Models Offered by Brand, Californian Market
Brand
Manufacturer(s)
Kenmore
GE, Electrolux,
Whirlpool
GE
Whirlpool
Whirlpool
Whirlpool
General Electric
Kitchen Aid
Amana
Maytag
Whirlpool
Frigidaire
White-Westing.
LG
Others
Total
Electrolux
Electrolux
1995
Nb of Model
Models Share
2000
Nb of Model
Models Share
2005
Nb of Model
Models Share
2008
Nb of Model
Models Share
2010
Nb of Model
Models Share
130
0.08
241
0.14
569
0.17
875
0.17
975
0.19
219
91
131
0.13
0.05
0.08
122
70
70
0.07
0.04
0.04
125
92
72
187
95
294
0.07
0.05
0.04
0.11
0.05
0.17
163
210
119
534
355
455
0.05
0.06
0.03
0.16
0.10
0.13
409
300
157
606
528
621
0.08
0.06
0.03
0.12
0.10
0.12
286
328
175
477
597
782
0.05
0.06
0.03
0.09
0.11
0.15
0.30
1
165
1369
5185
0.03
0.27
1
197
1267
5227
0.04
0.24
1
828
1798
0.50
1
632
1859
0.36
1
1028
3702
Source: California Energy Commission (CEC) Appliance Database. Only full-size refrigerators models on the Californian market for each year are considered. Model shares correspond to the number of models, non-sales weighted,
offered by each brand. Manufacturer(s) for each brand as of January 2012.
51
Table 4. Summary Statistics: Product Lines Used for Matching Estimator
Nb of Product Lines
Nb of Products
Percentage Energy Star
MSRP Non Energy Star Models (mean, std)
MSRP Energy Star Models (mean, std)
Promo. Price Non Energy Star Models (mean, std)
Promo. Price Energy Star Models (mean, std)
Percentage Stainless Steel
Percentage with Icemaker
Nb Top Freezer
Nb Bottom Freezer
Nb Side-by-Side
56
265
65%
$1209
$1315
$1122
$1238
20%
68%
164
49
52
$755
$744
$708
$719
Table 5. Matching Estimator: Difference in Markups Energy Star vs. Non
Energy Star
Dependent Variable
EnergyStarj = 1
Product Line FE
Week FE
Markup
Markup
Markup
vs. Manuf.
Price)
(Promo
Price
vs.
Manuf.
Price)
(MSRP
vs. Manuf.
Price)
(Promo
Price
vs.
Manuf.
Price)
0.019∗
(0.0082)
Yes
No
0.030∗∗
(0.0089)
Yes
No
0.019∗
(0.0082)
Yes
Yes
Markup
(MSRP
0.030∗∗
(0.0088)
Yes
Yes
Nb of Product Lines
56
56
56
56
Nb of Observations
Adjusted R2
17009
0.547
17009
0.496
17009
0.548
17009
0.505
Note: For all regressions, the explanatory variables are product line fixed effects and a
dummy variable that takes the value 1 if product j is certified Energy Star. The first
column shows that within a given product line, the markup between the manufacturer
suggested retail price (MSRP) and manufacturer price paid by the retailer is 7.4 percentage point higher. The second column shows that the markup between the promotional
price and the manufacturer price is 11 percent point higher. Adding week fixed effects
has a marginal impact on the estimates. Clustered standard errors in parentheses. ∗
p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
52
Table 6. Price Change After Decertification of Energy Star Models
Dependent
Variable:
Pt
P̄T af ter =0
j
T af tert = 1×
Decertif iedj = 1
T af tert = 1
Decertif iedj = 1
2008 Revision in Energy Star Standard
MSRP
Promo.
MSRP
Promo.
(I)
(II)
(III)
(IV)
-0.025∗∗∗
(0.0015)
0.035∗∗∗
(0.0011)
-0.00064
(0.0012)
Week FE
Nb of Models
Nb of Decertified
Models
Nb of
Observations
Adjusted R2
-0.065∗∗∗
(0.0040)
-0.018∗∗∗
(0.00058)
-0.000055
(0.0029)
-0.019
(0.044)
0.044∗
(0.017)
-0.00027
(0.020)
EnergyStarj = 1
2010 Revision in Certification
MSRP
Promo. Price MSRP Promo.
(V)
(VI)
(VII)
(VIII)
-0.0031∗∗∗
-0.015
-0.025∗∗∗
(0.0057)
0.0052∗∗∗
(0.00082)
0.00053
(0.0041)
0.036∗∗∗
0.012∗∗∗
No
No
(0.00062)
Yes
(0.0097)
Yes
No
No
(0.0020)
Yes
(0.0028)
Yes
1,905
1,196
1,905
1,196
1,905
1,196
1,905
1,196
1,515
16
1,515
16
1,515
16
1,515
16
80,237
80,237
80,237
80,237
52,759
52,759
52,759
52,759
0.034
0.000
0.015
0.000
0.031
0.001
0.064
0.098
Note: For all regressions, the dependent variable is the weekly price divided by the average price before
the decertification took place. For the first four specifications, only refrigerator models that were not
certified Energy Star (control) and had lost their Energy Star certification (treatment) are considered.
For the fifth to eighth specifications, only refrigerator models that were certified Energy Star as of January
1st are considered. For 2008, I assume that the decertification of Energy Star models occurred in the 17th
week. For 2010, I assume that the decertification occurred in the 5th . The dummy variable T af tert takes
the value one for all weeks after the decertification and zero otherwise. The dummy variable Decertif iedj
takes the value one for refrigerator models that lost their Energy Star certification.
53
Table 7. Estimates: Marginal Cost of Providing Energy Efficiency
Model 1
2nd Stage
Eq. Conditions
Model 2
Pair FE
Model 3
Hedonic
GAM
Model 4
Hedonic w.
181.3
(85.3)
0.127
(.026)
171.4
38.4
-
41.8
(20.3)
0.057
(0.003)
40.1
26.8
-
Product Line FE
No
Yes
No
No
Product Attributes
Yes
No
Yes
Yes
999
0.877
265
0.962
3424
0.870
3424
0.874
Dependent Variable:
log(Manufacturer Price)
Efficiency (1/kWh): φ̂
Adjusted Volume (cu.ft.)
Nb of Models
R2
Note: All specification shows that the manufacturer prices are increasing with energy efficiency
level. Energy efficiency is the inverse of the annual electricity consumption of a refrigerator
(1/kwh). Model 1 uses the estimated manufacturers prices obtained from the first order
conditions of the oligopoly model as a dependant variable. Only refrigerator models used for
the demand estimation are considered. Models 2-4 uses the manufacturer prices observed in
the data. Model 2 uses only products that belong to similar product lines and control for
product line FE. Models 3 and 4 uses all refrigerator models observed in the sample suring
the period 2008-2010. Models 1, 3, and 4 use the following regressors to control for product
attributes: dummies for refrigerator type (top-freezer, side-by-side or bottom-freezer), size
(adjusted volume) interacted with dummies for refrigerator type, size interacted with dummies
for overall quality (low: price <$1,000, medium: price >$1,000 & price <$2,800, high: price
>$2,800), dummy for ice-maker, dummy for defrost technology (automatic vs. manual),
dummy for water dispenser, dummy for an advance cooling technology, dummy for an air
filtration technology, dummy for indoor lighting using LED, and dummies for brand. Model 4
uses a generalized additive model to control for the effect of size (adjusted volume), and thus
model the effect of size with a flexible non-parametric function.
54
Table 8. Heterogeneity w.r.t. Income
All
Income
Income
<$50,000 ≥$50,000 &
<$100,000
Income
≥$100,000
Multinomial Logit
Price (η̂)
-0.459
(0.005)
Energy Star (τ̂ )
.155
(0.029)
Rebate (ψ̂)
0.060
(0.036)
Electricity Costs (θ̂) -2.267
(0.146)
-0.589
(0.008)
.056
(0.032)
0.053
(0.048)
-0.513
(0.160)
-0.483
(0.007)
.114
(0.038)
0.085
(0.043)
-1.343
(0.197)
-0.404
(0.006)
.187
(0.038)
-0.013
(0.048)
-2.045
(0.191)
-0.574
(0.001)
-.036
(0.020)
.261
(0.096)(0
0.023
(0.173)
-3.441
(0.211)
-0.480
(0.001)
.085
(0.031)
.286
.074)
0.110
(0.057)
-3.090
(0.175)
-0.400
(0.001)
.125
(0.027)
.497
(0.037)
0.010
(0.051)
-3.410
(0.183)
Information Acquisition Model
Price (η̂)
-0.460
(0.001)
h
Energy Star (τ̂ )
.079
(0.020)
Energy Star (τ̂ m )
.778
(0.154)
Rebate (ψ̂)
0.096
(0.045)
Electricity Costs (θ̂) -3.870
(0.100)
Note: The top panel corresponds to the estimates of the multinomial logit
estimated for different income groups. The estimates in the first column are
from the base model with all income groups, and are presented for comparison purpose. The bottom panel presents the estimates from the information
acquisition model.
55
Table 9. Effort Choice Probabilities: Information Acquisition Model
All
e=h
e=m
Income
<$50,000
e=l
e=h
e=m
e=l
Income
≥$50,000 &
<$100,000
e=h e=m e=l
Income
≥$100,000
e=h
.614
-
.431
17.1
7.3
.268 .301
21.8
-1.5
¯
2 ×kW h
.564
5.8
1.0
.362
.073
0.0
-0.2
¯
2 ×kW h
.473 .135 .392
-0.2 -27.9
5.4
-2.4
¯h
1.8 ×kW
.595
0.0
5.1
.229
.176
-0.1
1.2
¯h
4.5 ×kW
e=m
e=l
Unconstrained Model
Q(e)
K(e) ($)
VoI ($)
σbelief s
.528
123.4
78.2
.241 .231 .384
.002
6.3
-311.7 -3824.3
-12.5
11.6
-35.4
¯
¯h
2 ×kW h
2 ×kW
Constrained Model: Variance of Beliefs Estimated
Q(e)
K(e) ($)
VoI ($)
σbelief s
.462
.190 .347 .218
.194
.588
-244.0 -184.7
-174.5 -170.5
331.7
65.9
1.2
-27.6
¯h
¯h
4.2 ×kW
0.3 ×kW
2
Note: In the unconstrained model the variance of the prior beliefs (σbelief
s ) for the distribution of electricity consumption (referred as F ) is set exogenously. The prior F is assumed to be normally distributed with a mean equals
to the empirical mean of electricity consumption of all refrigerators in the sample, the standard deviation is set to
be equal to two times the empirical mean. Information acquisition costs (K(e)) are unconstrained, i.e., they can
take positive or negative values. Costs are positive for the model estimated on transactions with all households,
and the two upper income groups, this violates the rational model. This suggests that the variance of the beliefs
is too small to rationalize the choice probabilities. In the constrained model, the behavioral parameters that enter
the purchase probabilities are set equal to the estimates obtained in the unconstrained model and treated as data,
costs are estimated but are constrained to be negative, and the standard deviation of the prior beliefs is estimated.
The standard deviation is set equal to a scaling factor times the empirical mean of electricity consumption of all
refrigerators in the sample. The last row of the table presents the estimates of the scaling factor. For all models,
the information costs vary as a function of demographic information: income, education, age of the head of the
household and family size. The choice probability for each effort level are computed for all consumers in the subsample. Only the mean across the whole sub-sample is reported.The value of information (VoI) for e = h is given
by E[maxj Uj |I(e = h)] − E[maxj Uj |I(e = l)], and for e = m by E[maxj Uj |I(e = m)] − E[maxj Uj |I(e = l)]. The
value of information is positive for e = h, but tend to be negative for e = m.
56
Table 10. Summary Statistics: US and Constructed Choice Sets
Attribute
US Market
2010
(1,580 Models)
Nb of Models Model Share (%)
Constructed
Choice Set
(75 Models)
Nb of Models
Model Share (%)
Brand
Frigidaire
GE
Kenmore
Whirlpool
268
313
237
292
16.96
19.81
15.00
18.48
6
13
31
7
10.00
17.34
41.34
9.33
Others
470
29.75
18
24.00
1,063
517
67.28
32.72
37
38
49.33
50.67
472
597
511
29.87
37.78
32.34
12
19
44
16.00
25.33
58.67
Energy Star Certification
No
623
Decertified Model
21
Yes
957
39.43
1.30
60.64
22
53
29.23
70.67
Size
AV< 23.5 cu. ft.
AV≥ 23.5 cu. ft.
Refrigerator Style
Top Freezer
Side-by-Side
Bottom-Freezer
For the US market, only full-size refrigerators that were on the market for the
year 2010 are considered. Sources: California Energy Commission, Federal Trade
Commission, and US EPA.
57
Table 11. Emission Factors and Externality Costs
Non-baseload Output Emission Rates (U.S. Average)
Pollutant
Estimate
Source
CO2
1,583 lb/MWh
CH4a
35.8 lb/GWh
N 2Oa
19.9 lb/GWh
USEPA, eGRID2007
SO2
6.13 lb/MWh
N Ox
2.21 lb/MWh
Damage Cost (2008 $)
Pollutant Low Estimate High Estimate Source
CO2
$21.8/t
$67.1/t
Greenstone, Kopits, and Wolverton (2011)
SO2
$2,060/t
$6,700/t
low: Muller and Mendelsohn (2012), high: USEPAb
N Ox
$380/t
$4,591/t
low: Muller and Mendelsohn (2012), high: DOEc
Note: (a) Externality costs associated to CH4 and N 2O are assumed to be the same than
for CO2. CH4 and N 2O are converted in CO2 equivalent using estimates of global warming
potential (GWP). The GWP used for CH4 is 25, and the GWP used for N 2O is 298. Source:
IPCC Fourth Assessment Report: Climate Change 2007. (b) Estimate used in the illustrative
analysis of the 2012 regulatory impact analysis for the proposed standards for electric utility
generating units. (c) Higher value of the estimate used in the Federal Rule for new minimum
energy-efficiency standards for refrigerators (1904-AB79).
58
Table 12. The Effects of Removing the Energy Star Standard
Scenario 1
w Firms’ Responses
Oligopoly Model
Scenario 2
w Firms’ Responses
Oligopoly Model
Scenario 3
w Firms’ Responses
Oligopoly Model
Avg kWh Purchased (kWh/year)
∆withoutES−withES
Mean
Std
49.13
27.96
∆withoutES−withES
Mean
Std
-6.57
5.09
∆withoutES−withES
Mean
Std
-1.93
9.22
Externality Cost Low Damage (M$/year)
10.68
6.08
-1.43
1.11
-0.42
2.00
Externality Cost High Damage (M$/year)
35.16
20.01
-4.70
3.64
-1.38
6.60
Avg Price Paid ($)
14.24
30.94
3.74
5.09
10.07
3.81
Consumer Surplus (M$/year)
-96.65
55.74
-69.79
12.17
-92.35
9.45
Consumer Surplus w/o
Search Cost (M$/year)
Consumer Surplus w/o Search Cost
& w/o Label Effect (τ ) (M$/year)
Profits (M$/year)
-64.71
51.24
-23.80
5.35
-35.48
8.79
-59.91
48.88
-12.25
5.05
-22.84
8.68
-159.31
225.23
63.04
43.85
159.23
34.38
Welfare Low Damage (M$/year)
-266.64
181.08
-5.32
36.87
67.30
32.68
Welfare High Damage (M$/year)
-291.12
176.28
-2.05
37.46
68.26
31.83
Welfare Low Damage, w/o
-234.69
183.63
40.67
42.39
124.17
29.48
Search Cost (M$/year)
Welfare High Damage, w/o
-259.17
178.58
43.94
43.32
125.13
28.80
Search Cost (M$/year)
Welfare Low Damage, w/o Search Cost
-229.89
184.54
52.22
42.72
136.81
29.42
& w/o Label Effect (τ ) (M$/year)
Welfare High Damage, w/o Search Cost
-254.37
179.31
55.50
43.63
137.77
28.71
& w/o Label Effect (τ ) (M$/year)
Note: For each scenario, 10 simulations of the demand model were performed. Each simulation takes a
random draw of parameter values from the estimated distributions. For each simulation, the differences
between the metrics obtained for the state of the world without Energy Star and the state of the world with
Energy Star are computed. For each scenario, the first and second columns report respectively the mean
and the standard deviation of the difference in various metrics for the 10 simulations. The externality
costs are computed for two estimates of the dollar value of the damage associated to electricity generation.
The market size for refrigerators is assumed to be 9.01 millions. The consumer surplus is converted to
an annual measure by assuming that consumers will own (and believe they will own) their refrigerators
for 18 years. The total welfare is the sum of the total consumer surplus, externality costs, and produced
surplus. The last four rows report different estimates of the welfare effects. The welfare estimates without
search costs exclude the cost of collecting and processing information from the consumer surplus. The
last two rows present welfare estimates where both the search costs and the label effect, captured by the
parameter τ are excluded. All dollar figures are in 2008 dollars.
59
Appendix A. Proofs: Screening Problem
forthcoming
Appendix B. Data Cleaning and Manipulation
Demand Estimation. The primary data for the demand estimation were provided by a large appliance
retailer. The retailer offers a large selection of refrigerator models, has at least one brick-and-mortar store
in each US state and a national online store. The main data consist of all transactions where a full-size
refrigerator was bought. The data cover the period 2008-2010. For each transaction, I observe the date,
the model of the refrigerator, attributes, the suggested retail price, the promotional price, the manufacturer
price,16 taxes paid, and the zipcode of the store where the transaction was made. For a subset of transactions,
I also observe consumer demographics, such as household size, income, education, homeownership, housing
type and age of the head of the household. I restrict attention to transactions with demographic information
and that can be attributable to households that have to pay for their electricity bills. In particular, I only
consider transactions made by homeowners living in single family housing units that bought no more than
one refrigerator in any given year. This rules out heterogeneity in the sensitivity to energy costs due to the
split incentive problem (Blumstein, Krieg, Schipper, and York 1980), i.e., the fact that some consumers of
energy intensive durables do not pay for energy costs. For the structural estimation, a random sub-sample
of the transactions that fit the above criteria is used. I next discuss the construction of the key variables
used in the estimation, and their main sources of variation.
To perform the estimation of the demand model, a random sub-sample of the transactions is used. The
sub-sample is constructed as follow.
First, the sub-sample is drawn from the set of transactions that fit the following criteria (the restricted
sample):
• transactions made by consumers that are homeowners;
• transactions made by consumers living in single family housing units; and
• transactions made by consumers that made no more that one refrigerator purchase in any given
year.
Second, I employ the following stratified sampling method to create the sub-sample. For a given targeted
sample size, I sample transactions in each state so that the state market shares in the sub-sample are equal
to the state market shares in the restricted sample. Sampling is done at the store level. That is, I first
randomly select a store in each state and then keep a number of transactions from this store, also randomly
selected, to match state market shares. If needed, I sample additional stores until I match state market
shares.
The main motivation to sample at the store level is to restrict the number of choice sets that need to
be imputed. Moreover, if one is concerned about endogeneity problems that could be eliminated with store
fixed effects, the present approach allows me to limit the number of fixed effects to estimate.
Average Electricity Prices. The use of average electricity prices is partly motivated by recent empirical
evidence (Borenstein (2010), Ito (2010)) that suggests that electricity consumers may in fact respond to
variation in average prices, more than marginal prices. In the present case, the use of average electricity
16
The manufacturer price does not vary across time, and corresponds to the manufacturer price the retailer
paid when a given refrigerator model entered the market.
60
prices is also dictated by the fact that household’s location is not perfectly known. Therefore, it is impossible
to match households with their exact electricity tariff and infer marginal price.
Average electricity prices at the county level are computed as follow. Using form EIA-861 of the Energy
Information Administration, I compute the average residential electric price for each electric utility operating
in the US for the years 2008. I then match electric utility territories with each of the county where I sampled
at least one store. For counties with only one electric utility, I use the average electricity price for this
particular utility. For counties with several electric utilities, I take the arithmetic mean of each utility
average price to construct the county level price. Ideally, we would like to weight prices by the number of
consumers served by each utility. However, this information is not available at the county level.
Appendix C. Demand Estimation
forthcoming