Do EE analysts take sufficient account of product attributes that are inferior for EE products?
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Do EE analysts take sufficient account of product
attributes that are inferior for EE products?
Sébastien Houde∗
1. Overview
For most energy intensive durables, gains in energy efficiency can be achieved by movements
along the production frontier. As a result, the most energy efficient products may differ
from other products along several dimensions, in addition of energy operating costs. When
it occurs, the adoption of an energy efficient product raises an opportunity cost (Allcott
and Greenstone 2012; Gillingham and Palmer 2013). For analysts interested in the Energy
Efficiency Gap, the existence of such opportunity cost poses challenges from a measurement
and welfare standpoint.
This note first describes how this opportunity cost biases the measurement of the EE
Gap, and reviews how this issue has been accounted for in empirical work. Issues related to
welfare are then discussed.
The main conclusions are the following. First, applied econometricians have recently
proposed various empirical strategies to account for the existence of unobserved product
attributes. These strategies have been applied mostly to the car market. The conclusions are
mixed. Some studies suggest that the undervaluation of fuel economy is less pronounced than
some analysts have suggested, others find a considerable undervaluation of fuel economy. It is
unclear whether unobservables are an important source of bias. The second conclusion is that
while the recent empirical strategies are well-suited to address issues related to measurement,
they do not translate well to the task of conducting welfare analysis. Going forward, the
main challenges for analysts will be to develop empirical techniques that can be applied to
estimate the welfare of energy efficiency policies, and do not suffer from an important bias
due to unobserved product attributes.
∗
University of Maryland. Email: shoude@umd.edu2
2. Implications for Measurement of the EE Gap
Empirical evidence of the size of the Energy Efficiency Gap falls broadly in two categories:
econometric analysis and cost-effectiveness analysis. From an econometric standpoint, the
presence of an inferior product attribute unobserved by the analyst but valued by the con-
sumers raises a problem of omitted variable bias. Ignoring this problem leads to overestimate
the relative benefits of adopting a more energy efficient technology, and thus to overestimate
the size of the Gap.
Cost-effectiveness analyses have largely neglected this problem. These studies focus on
comparing the upfront costs and energy operating costs of different technologies, but do
not consider other attributes. The low rate of adoption of energy efficient technologies can
therefore only be rationalized by applying a high discount rate to future energy savings.
An early example of such studies is Ruderman, Levine, and McMahon (1987). More recent
applications of such approach includes the well-publicized McKinsey’s curves.2
Econometric analyses have dealt with this problem with various degrees of sophistication.
An obvious solution to this problem is to add product attributes as additional covariates.
In practice, econometricians rarely observe all product attributes valued by consumers. The
existence of unobserved attributes is therefore always a potential problem.
The first generation of studies looking at consumers’ valuation of energy efficiency focussed
on integrating the usage and purchase decisions into a single estimation framework, but did
not address endogeneity problems due to unobservable attributes. For instance, both Haus-
man (1979) and Dubin and McFadden (1984), the seminal papers for the discrete-continuous
framework, rely on cross-sectional variations in energy prices and include only a limited num-
ber of product attributes. Most of the recent papers using the discrete-continuous framework
rely on a similar data and identification strategy, and therefore do not account for unobserv-
able attributes. West (2004); Small and Van Dender (2007); Bento, Goulder, Jacobsen, and
von Haefen (2009); Jacobsen (2013); Gillingham (2013), all studies applied to the car mar-
ket, define demand using a subset of car characteristics, and do not use instruments. These
studies, however, rely on different source of variation to identify consumers’ valuation of fuel
economy. West (2004); Bento, Goulder, Jacobsen, and von Haefen (2009); Jacobsen (2013)
2
The consulting company McKinsey & Co have developed greenhouse gas abatement cost curved for
several countries. The methodology to estimate the abatement potential of various energy efficiency tech-
nologies relies on the cost-effectiveness approach. See the following website for McKinsey’s reports: http :
//www.mckinsey.com/clients ervice/sustainability/latestt hinking/greenhouseg asa batementc ostc urves.3 rely on cross-sectional variation of gasoline prices. Small and Van Dender (2007); Gillingham (2013) use richer data with cross-sectional and temporal variation. Rapson (2012) is one of the few example of a recent study using the discrete-continuous framework applied to the appliance market (air conditioner). He also exploits temporal variation, but by bringing dynamics into the framework. He does not address the problem of unobserved attributes. Roth (2013) is one of the few studies that uses instruments in this framework to account for unobserved attributes. Whether it impacts significantly the results remain unclear. The two challenges for accounting for unobserved product attributes are the lack of rich panel data and the use of instruments in a non-linear framework. The solution to the latter problem came in the early nineties with the instrumental variable estimator proposed by Berry, Levinsohn, and Pakes (1995). BLP’s approach was especially designed to deal with cross-sectional data where the analyst observes a few attributes, but other attributes, possi- bly correlated with prices, remain unobserved. BLP’s instruments for a particular product consist of a combination of the non-price attributes of other products offer by the same firm, and the characteristics of all other products located in the same class. The validity of these instruments is, however, questionable, especially for the purpose of estimating consumers’ valuation of energy efficiency. If energy efficiency (fuel economy) is observed, but correlated with inferior but unobservable attributes, these instruments violate the exclusion restriction. This may explain why some of BLP’s results are not well-behaved; they found a negative willingness to pay for fuel economy. There are several alternatives to the BLP’s instruments. Three recent papers that propose approaches that aim to explicitly account for unobservables correlated with fuel economy are Verboven (2002), Klier and Linn (2012), and Whitefoot, Fowlie, and Skerlos (2011). Using European data, Verboven (2002) exploits the fact that in Europe consumers can choose between a diesel or gasoline engine for the exact same vehicle. Using variation in engine type within each model, he can control for all other attributes, and identify consumers’ valuation for fuel economy. His results suggest that consumers discount future operating with a (implicit) discount rate of about 11.5%. Klier and Linn (2012) also rely on engine platform for identification, but exploit a dif- ferent source variation. Manufacturers commonly offer vehicles in different product classes, with similar engine platform. This suggests that vehicles with a similar engine platform have similar attributes in terms of fuel economy, but may differ along several other unob- served dimensions. Klier and Linn (2012) thus propose to use as instruments the product
4 characteristics of other models located in different vehicle classes, but that share a similar engine platform. When comparing results using these instruments to BLP’s instrument, they found that the coefficient for operating cost is negative, and of similar magnitude for both approaches. The coefficients for price and power, however, differ between the two ap- proaches. Because the WTP for fuel economy is some ratio of the coefficients for operating cost and price, their instrumental strategy impacts the measurement of the Gap. However, it is unclear if inferior product attributes correlated with energy efficiency play a role here. In fact, their results suggest that it may not be an important source of bias on the coefficient related to fuel economy. Whitefoot, Fowlie, and Skerlos (2011) also rely on BLP’s estimation framework, but pro- pose a different strategy for instruments. Using a detailed engineering model, they simulate the production frontiers of a large number of vehicle models, which allow them to identify the trade-off between fuel economy and other attributes. They select as instruments attributes that the engineering model show to not be correlated with fuel economy. Their instruments consist essentially to design features that are determined over the long-run. Their estimates suggest that consumers’ value fuel economy, on average, but the undervaluation is consider- able. They found that the WTP for a $1100 savings in discounted lifetime operating costs is about $50. Roth (2013) uses similar instruments. Preliminary results (to not be quoted) suggest an important undervaluation of fuel economy. These results contrast with other recent estimates for the US car market. Whether these discrepancies can be attributed to the presence of unobserved product attributes is unclear. There are a number of recent studies (Li, Timmins, and von Haefen 2009; Busse, Knittel, and Zettelmeyer 2013; Allcott and Wozny 2012; Klier and Linn 2010) that take a more reduced-form approach and found no evidence of consumers’ undervaluation of fuel economy or modest undervaluation. The strength of these studies is that they rely on rich panel data, which allows the analysts to control for many unobservables. The car market has, however, a number of particularities that make difficult to completely rule out the effect of unobservables. For instance, manufacturers make small change to each vehicle model offered every year, the same models manufactured in different years are therefore not entirely comparable. Analysts have accounted for this problem by using a model-year fixed effects, and exploiting short-term variation in gasoline prices within the year. Another challenge comes from the fact that in the US, prices, car options, and accessories are often negotiated
5
for each consumer. This implies that firms have the ability to respond to local and short-
term shocks by adjusting prices or offering different option packages. As I discuss below,
these short-term responses are the ones that pose the greatest threat to internal validity,
even with rich panel data.
Li, Timmins, and von Haefen (2009) uses a partial adjustment model and regress quantiles
of fuel economy in each MSA on gasoline prices, lagged quantities, and other controls. In the
preferred specification, fixed effects for each quantile-product class, year, and year-product
class are included. These fixed effects capture firms’ long and medium-run responses, but
not the short term response. If firms have the ability to respond to short-term variation in
gasoline prices by changing the options and accessories offered,3 or by simply changing rela-
tive prices, this will imply that there are unobserved quantile-time specific effects correlated
with changes in gasoline prices.
Busse, Knittel, and Zettelmeyer (2013) presents two sets of regressions, one that relates
vehicle prices to gasoline prices, and a second that relates sales to gasoline prices. The price
regressions include several fixed effects. They report results that include region specific time
fixed effects, and car-type fixed effects. Their definition of car-type is fairly disaggregated
and consists of the interaction of make, model, model year, trim level, doors, body type,
displacement, cylinders, and transmission. With these fixed effect, their identification relies
on within year variation in gasoline prices. Their specification should then control well for
unobserved product attributes that firms determine in the long and medium-run. However,
as in Li, Timmins, and von Haefen (2009), firms’ short term response to gasoline prices could
be a source of bias. If in regions subject to high gasoline prices (higher than average, to be
more precise), dealers offer more generous options for vehicles with high MPG, the relative
prices for these vehicles would appear to respond less to change in gasoline prices. For their
quantity regressions, they rely on a similar strategy than Li, Timmins, and von Haefen (2009),
but with a finer level of disaggregation (sales are aggregated at the dealer level, instead of
MSA). They regress MPG quartiles on gasoline price, and other controls. Note that the
fact that they find that transaction prices respond to short-term variations in gasoline prices
should imply that the relative prices of different MPG quartiles are correlated with variation
in gasoline prices. These shocks are not accounted for in their quantity regressions, and
should bias the coefficient on gasoline prices toward zero. If dealers’ options also respond to
3
I conjecture that in period of high gasoline prices, dealers might offer upgrades on options, especially
for less fuel efficient vehicles to compensate for their high operating costs. Firms’ ability to exercise quality
discrimination plays an important role here.6
gasoline prices, this would also bias this coefficient toward zero. Whether these effects are
empirically important is unclear. Busse, Knittel, and Zettelmeyer (2013)’s results suggest
that consumers do not undervalue fuel economy on average, which suggests that the bias
induced by firms’ short-term response is small.
Klier and Linn (2010) rely on US national sales data aggregated at the model and month
level for the period 1970-2005. Using model-year fixed effects, they exploit monthly variation
is gasoline prices to estimate consumers’ valuation of fuel economy. Their approach is subject
to a similar critic than above, and do not account for firms’ short-term response correlated
with movements in gasoline prices. They however provide some robustness tests4 that suggest
that these are not an important source of bias, which is somewhat consistent with the findings
of Busse, Knittel, and Zettelmeyer (2013).
Like Klier and Linn (2010), Allcott and Wozny (2012) use data aggregated at the model
and month level for the whole US. They regress vehicle prices on car operating costs and
other controls. As the studies above, firms’ short-term response might be a source of bias.
In one robustness test, they found that adding a set of car characteristics as controls do not
change substantially their estimates, which suggests that unobservable characteristics have
also a small effect. The observable characteristics that they consider are: horsepower, curb
weight, wheelbase, anti-lock brakes, stability control, and traction control. The first three of
these features should not vary much in the short-term, the latter three may. This robustness
test thus provides a somewhat convincing evidence that firm’s short-term response may not
be an important source of bias.
In sum, the particularities of the car market make difficult to exclude any form of endo-
geneity bias due to unobservable characteristics. Even with rich panel data, firms’ short-term
response is, in theory, a source of bias. Empirically, this does not appear to be a major prob-
lem. In the absence of good panel data, various instrumental variable strategies have been
proposed. The results from these studies, with few exceptions, do not match the ones relying
on panel data. It is however unclear whether unobserved attributes are an explanation for
these discrepancies. One could be tempted to attribute these discrepancies to differences
in modeling approaches, and the reduced-form vs. structural paradigms, but again this is
unclear if this plays a role here. The fact that the identification of structural demand models
4
They redo the estimation by excluding US manufacturers, which tend to be more likely to offer dealer
rebates. This robustness test thus investigates the role of a short-term price response.7 has typically relied on cross-sectional variation in gasoline prices, which tend to be weak, might be simply the explanation. In other markets, there is little evidence on how unobserved product characteristics can be a problem. For the appliance market, one advantage over the car market is that prod- ucts have a longer product life, and there is few opportunities to negotiate over add-ons and options. Using panel data and fixed effects should then provide a good way to control for un- observables. However, electricity prices tend to vary much less overtime than gasoline prices. Their main source of variation is therefore across regions, which raises other challenges, such as the presence of region-specific unobservables. Houde (2011) exploits variation in electric- ity prices across regions to estimate consumers’ valuation of refrigerator operating costs, and found that consumers respond to electricity prices, but tend to undervalue operating costs, on average. Another avenue to control for unobserved product characteristics is to rely on carefully designed choice experiments. This approach has been used by several researchers. Two recent studies that rely on this approach are Newell and Siikamäki (2013) and Allcott and Taubinsky (2013). Newell and Siikamäki (2013)’ choice experiment measures the effect of different energy labels, and focuses on water heaters. They control for unobserved product attributes by specifically stating to the survey participants that all the options were identical with respect to non-energy attributes. Their choice sets have variation in prices, operating costs, and CO2 emissions, in addition of energy information. Note that by creating vari- ation in prices and operating costs, it is not necessary to force the options to match with respect to non-energy attributes. In particular, experimental variation in operating costs is sufficient to distinguish consumers’ valuation of energy efficiency from other attributes, which could be simply captured by a product fixed effect. The main rationale to hold con- stant these attributes should then be to gain efficiency. In Allcott and Taubinsky (2013), survey participants were asked to choose between CFL and incandescent lightbulbs. Their experimental variation is with respect to the purchase price only. Therefore, their choice experiment does not allow them to distinguish how consumers value operating costs from all the other attributes specific to each lightbulb technology.
8
Table 1. Review of Methods and Results: Consumers’ Valuation of Energy
Efficient
Reference Method Product Market Result
Cross-Section, no IV
Hausman (1979) DC micro room ac US, R, 1976 14-25%
Dubin & McFadden (1984) DC micro heating US, R, 1975 20.5%
Dreyfus & Viscusi (1995) H agg cars US, R, 1988 11-17%
Verboven (2002) D agg cars EU, R, 1991-94 r=11.5%
Bento et al. (2009) DC micro cars US, R, 2001 Expected Sign
Jacobsen (2013) DC micro cars US, R, 2001 Expected Sign
West (2004) DC micro cars US, R, 1997 Expected Sign
Rapson (2012) DCD micro ac US, R, 1990-2005 Expected Sign
Cross-Section with IV
BLP
BLP (1995) D agg cars US, R, 1971-90 Unexpected Sign
Petrin (2002) D agg cars US, R, 1981-93 Unexpected Sign
BLP (2004) D micro cars US, R, 1993 Expected Sign
BLP (2004) D micro cars US, 2000 r > 1000%
Train & Winston (2007) D micro cars US, R, 2000 -
Linn & Klier (2012) D Agg cars US, 2000-08 -
Whitefoot et al. (2012) D micro cars US, R, 2006 $50 WTP for $1100 savings
Roth (2013) DC micro cars US, R, 2000-06 Expected Sign
Panel Data
Small & Van Dender (2007) DC Agg cars US, R,
Li et al. (2009) FE Agg cars US, 1997-2005
Linn & Klier (2013) FE Agg cars US, 1970-2008
Buse et al. (2013) FE Agg cars US,
Allcott & Wozny (2013) FE Agg cars US, 1970-2008
Houde (2013) FE Agg, D micro refrigerators US, R, 2007-11
Gillingham (2012) DC micro, no IV cars US, R, Expected Sign
Choice Experiment
Train & Atherton (1995) DC refrigerators, ac US, R, 1994 r=30-36%
Newell and Siikamäki (2013) D micro water heaters US, r, 2010 γ = 1.04
Allcott (2013) FE lightbulbs US, R, 2012 $1.51 WTP for $40 savings
Notes: The following nomenclature is used to identify the estimation method: DC refers to a discrete-continuous
choice model, D refers to a discrete choice model, DCD refers to a discrete-continuous dynamic choice model, H refers
to an hedonic regression, and FE refers to a fixed effect strategy. Whether the estimation is performed with aggregate
data (agg) or micro data (micro) is also identified. The market specifies the region, the time period, and whether the
data cover the residential (R), commercial (C), or industrial sector (I). All studies reviewed focus on the commercial
sector.9
3. Implications for Welfare
In a perfectly competitive market and in the absence of externalities, the fact that providing
energy efficiency induces movements along the product frontier is not a market failure, and
therefore not a rationale for policy intervention. This hold even if these movements are
not observed by the analyst. The trade-off between energy efficiency and other attributes,
however, raises welfare issues when second-best policies, such as standards and subsidies, are
used to promote the adoption of energy efficient technologies.
Put simply, second-best policies distort the provision of non-energy attributes, which
may decrease or increase welfare. The main challenge in conducting welfare analysis of
subsidies and standards is to identify these trade-offs and estimate how consumers value
these attributes. We should discuss three cases. First, the trade-offs are between energy
efficiency and an attribute that is observed by both the analyst and the consumer. Second,
the attribute is observed by the consumer, but not the analyst. Third, the attribute is
observed by the analyst, but not the consumer.
In theory, the solution to the first case is simple. It requires estimating the willingness to
pay for energy efficiency and the other attribute. The optimal standard/subsidy should then
be a function of these demand parameters. In practice, this is however hard to accomplish.
Identifying consumer preferences for non-energy attributes is quite challenging given that
these attributes tend to not have credible source of variation and instruments are hard to
find. Size is a good example of such attribute. Clearly, manufacturers can meet energy
efficiency standards by trading-off energy consumption with size, an attribute presumably
valued by consumers. How should we estimate consumers’ valuation of an incremental change
in size of a Porche, or refrigerator, is however unclear.
In the rulemaking process for setting standards, this type of trade-off is accounted for
in a rather ad hoc manner. When it comes to set new standards, OMB OIRA performs
a so-called screening analysis. The screening analysis consists of a qualitative assessment
conducted by engineers with the aims of identifying the main trade-offs that manufacturers
could rely on to meet the standards. Once these traded-offs are identified, standards are set
for different classes of products to try to mitigate manufacturers’ incentives to move along
the production frontier. For instance, for clothes washers, standards were differentiated for
front load and top load washers so that manufacturers did not favor one particular design to
meet the minimum standards. For refrigerators, standards are defined for eighteen different10
product classes. CAFE and appliance standards are also defined with respect to size for
similar reasons. Once each product class is defined, the standard for a specific class is
set using a cost-effectiveness criterion. That is, the imposition of the standard should not
increase the cost to the consumers more than the discounted energy savings brought by the
standard. This criterion leaves some of the welfare effects unaccounted for because it neglects
the distortions in non-energy related attributes.
The fact that the willingness to pay for non-energy attributes might be difficult to estimate
should however not be a determent to use a more welfare-consistent criterion to set standards.
In future research, it could be interesting to compare the different criterions, even with less
than perfect estimates of consumer preferences.
When energy efficiency is traded-off with attributes that are not unobserved by the ana-
lyst, the challenges to conduct welfare analysis are more serious. One potential avenue would
be to recover a distribution of unobservables, and use that distribution to provide bounds
on the welfare effects.
The third case provides a puzzle for the application of standard welfare economics. If
the analyst is aware that manufacturers trade-off some attributes with energy efficiency,
but these trade-offs are not observed by the consumers, how should we proceed? As an
example, consider the case where refrigerator manufacturers met the more stringent Energy
Star standard in 2008 by offering the exact same refrigerator models, with the only exception
that the quality of the interior lighting was slightly reduced. Few consumers might have been
aware of this subtle change, although they might value high quality lighting for refrigerators.
If we inform consumers about this change, would it induce a welfare loss? If so, this suggests
that the regulator should remain silent. If the changes in quality were more substantial, but
still hard to perceive for consumers, should the regulator intervene? How the line should be
drawn?
4. Summary and Recommendations
Much of the analysis and regulatory process for energy efficiency policies rely on the con-
cept of cost-effectiveness. From a measurement and welfare standpoint, cost-effectiveness is
a problematic criterion because it does not account for the role of non-energy attributes.
Applied econometricians have recognized this problem, and proposed various strategies to
account for this problem.11
In the car market, it remains unclear how the effects of unobserved product attributes are
a source of bias. There is an important discrepancy in the estimate of the size of the Energy
Efficiency Gap between structural and reduced-form approaches. It seems, however, unlikely
that unobserved product attributes explain this difference. With few exceptions, structural
models have not exploited rich source of variation in gasoline prices. One avenue for future
research would be to attempt to reconciliate the estimates by performing a structural and
reduced-form estimation using the same dataset.
A second avenue for future research would be to provide new estimates outside of the
car market. I have argued that accounting for unobserved product attributes might easier
for appliances given the nature of the market, i.e., longer product life, and the posted-price
retail environment. For consumer electronics, the short product life, like for the car market,
is a challenge.
The most important challenges might, however, be in conducting welfare analysis account-
ing for the various trade-offs between energy efficiency and other attributes. For attributes
that are observed by both the consumer and the analyst, the challenge is mainly in re-
covering credible estimates of consumer preferences. For unobserved product attributes, the
challenges are more conceptual, and might deserve a departure from the standard framework
for welfare economics.
To conclude, there is one element that has not been discussed in this note, but that
relates to non-energy product attributes, which is the role of imperfect competition. In
market subject to imperfect competition, firms might distort the quality of their products
to screen consumers with high and low valuation of energy efficiency. This a classic market
failure that justifies a policy intervention. Markets for energy intensive durables tend to
be fairly concentrated, which might raise this issue. Another avenue for future research
would then to investigate whether energy efficient products systematically differ from other
products because of firms’ exercise of market power.12
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