Evaluating the Consumer Response to Fuel Economy: A Review of the Literature

Gloria Helfand and Ann Wolverton



### Working Paper Series

Working Paper # 09-04 August, 2009

U.S. Environmental Protection Agency National Center for Environmental Economics 1200 Pennsylvania Avenue, NW (MC 1809) Washington, DC 20460 http://www.epa.gov/economicsEvaluating the Consumer Response to Fuel Economy: A Review of the Literature

Gloria Helfand and Ann Wolverton

NCEE W orking Paper Series Working Paper # 09-04 August, 2009

DISCLAIMER The views expressed in this paper are those of the author(s) and do not necessarily represent those of the U.S. Environmental Protection Agency. In addition, although the research described in this paper may have been funded entirely or in part by the U.S. Environmental Protection Agency, it has not been subjected to the Agency's required peer and policy review. No official Agency endorsement should be inferred.Evaluating the Consumer Response to Fuel Economy: A Review of the Literature

By Gloria Helfand and Ann Wolverton, 1

U.S Environmental Protection Agency

Abstract In modeling how the U.S. market responds to changes in national fuel economy standards, the question of how consumers ev aluate trade-offs between the cost of consuming more fuel economy than they woul d otherwise choose and the expected fuel savings that result is potentially quite impor tant. Consumer vehicle choice models are a means to predict the change in vehicle purchase patterns, as well as the effects of these changes on compliance costs and consumer surplu s. This paper survey s the literature on consumer choice models and finds a wide range in methods and results. A large puzzle raised is whether automakers build into their vehicles as much fuel economy as consumers are willing to purchase. This paper examines possible reasons why there may be a gap between the amount consumers ar e willing to pay for fuel economy and the amount that automakers provide.

Key Words: Consumer behavior , vehicle purchase decisions NCEE Subject Area Classifica tion: Transportation, benef it-cost analysis, m odeling

1 Authors can be contacted via email at helfand.gloria@epa.gov and wolverton.ann@epa.gov . The views exp ressed in this paper are those of the authors and do not necessarily represent those of the U.S. Environmental Protection Agency. This paper has not been subjected to EPA’s formal review process and therefore does not represent official policy or views.

11. Introduc tion Thus far, at the national level the pr imary means of reducing greenhouse gas (GHG) emissions from vehicles has been via increasing the stringency of Corporate Average Fuel Economy (CAFE) standards. 2 Because the major source of GHG emissions from vehicles is burning fuel, reducing fuel consumption will reduce emissions. 3

Consumers have reasons to be interested in the fuel economy of the vehicles they purchase apart from what is induced by gove rnment regulation: An increase in fuel economy reduces the private cost of driving. Hi gher fuel prices do seem to trigger efforts on the part of consumers to reduce their ga soline consumption. However, there still appear to be many low cost opportunities to reduce fuel consumption that are not undertaken, in spite of the likelihood that they will pay off in the end. Possible explanations for why consumers may under-val ue energy efficiency include uncertainty, risk aversion, lack of intere st in fuel savings, bounded ratio nality, confusing information, loss aversion, and unavailability of preferred choices. From a public policy perspective, it is unclear whether these problems justify a dditional fuel economy requirements. Some consumers are likely to appreciate the additional fuel savings that they did not believe would actually occur; other consumers are likely to be made worse off by incurring the

additional costs for an attribute of little interest to them. In regulatory analyses, the U.S. Envir onmental Protection Agency (EPA) and the National Highway Traffic Safety Administratio n (NHTSA) often assume that the market

2 CAFE standards require the sales-weighted fuel economy of its fleet to achieve minimum levels 3 Government intervention in markets is commonly justified by market failures, such as reducing the environmental externalities associated with vehicle tailpipe emissions. These types of benefits from such intervention are in addition to any deficiencies on the part of consumers and producers that have been the subject of this review. The effect of fuel economy regulations on consumer and producer surplus would need to be combined with these external effects to estimate the total benefits and costs of fuel economy regulation.

2shares of th e vehicle fleet stay constant. For rules that lead to small changes in vehicle attributes and prices – that is, changes th at consumers are unlikely to notice – this procedure is not a bad approximation, since th e changes may not affect consumer vehicle purchase patterns. Proposals to increase fu el economy substantially in the next few years, on the other hand, may change both vehi cle attributes and the price of vehicles enough that consumers may rearrange what vehicles they purchase. Consumer vehicle choice models are ways to estimate the change s in fleet mix. In addition, the models can be used to estimate changes in consumer surplus associated with these changes. This paper reviews the literature on the role of fuel economy in consumers’ vehicle purchase decisions. S ection 2 reviews the state-of-the-art in consumer vehicle choice modeling, with a focus on the role of fuel economy in these models. Sections 3 and 4 examine consumer and producer behavi or in the market for fuel economy. Is consumers’ valuation of fuel economy compar able to the value of the savings they get from lower fuel use? If not, why might th ey not pay for money-saving improvements? And if consumers actually would buy vehicles with greater fuel economy, then why hasn’t competition among automakers to captur e greater market share led to the supply of those vehicles? Finally, this paper assess es the potential contri butions of consumer vehicle choice modeling to regulatory analys is, as well as some of their limitations.

2. State of the Art of Cons umer Vehicle Choice Modeling Consumer vehicle choice models (CCMs) are one tool used by analysts to examine how consumers weigh different attrib utes when deciding what vehicles they buy. The models typically take into consideration both consum er characteristics (such as income, family size, and age) and vehicle ch aracteristics (includi ng a vehicle’s power,

3price, and fuel econom y). Once estimated, these models are often used to examine how a consumer’s vehicle purchase decision is affect ed by a change in a particular vehicle or personal characteristic. While the focus is on the consumer, many of these models also specify aspects of the auto producers’ side, in recognition that it is the interaction between producers and consumers that le ads to observed market outcomes. Reflecting the complexity of consumers’ decisions, CCMs involve a number of modeling and data choices. This section di scusses the range of CCMs that have been developed in recent decades. It reviews the methods, data sources, characteristics included in the models, and research questions asked. As the analysis shows, it has been

and continues to be a rich area of resear ch, because of the significance of the auto industry in the U.S. economy, large fluctuati ons in fuel prices over time, and a multitude of technical challenges. A review of the literature also suggests that consumer vehicle choice models have produced widely varying estimates of the role of fuel economy in consumers’ purchase decisions.



### 2a. Methods

Consumer choice models (CCMs) of vehicle purchases typically model choices as

unordered and discrete ra ther than continuous. 4 The choice is typically defined as whether to purchase a specified vehicle. The result of these models is an estimate of the probability that a consumer will purchase a specified vehicle. A minor variant on

discrete choice models estimates the market share for each vehicle. Because the market share is, essentially, the probability that consumers will purchase a specific vehicle, these

4 An example of a continuous decision is how many pounds of food a farm might grow: the pounds of food can take any numerical value.

4approaches are sim ilar in process; they differ mo stly in the kinds of da ta that they use and in the interpretation of results. The primary methods used to model vehi cle choices are nested logit and mixed logit. 5 The nested logit structures consumer choices into “nests” or layers. For instance, the first layer may be the choice of whet her to buy a new vehicl e or some other good, including a used vehicle; gi ven that the person chooses a new vehicle, the second layer may be whether to buy a car or a truck; given that the person chooses a car, the third layer may be the choice among an economy, midsize, or luxury car. Th e analyst restricts substitution patterns among altern atives by the way the data ar e organized into nests. There is no definitive way to test whether one nesting structure is superior to another, however, which means that the question of how to best model consumer decisions may remain unsettled even when different nesting st ructures result in signi ficant differences in results. It is also important to keep in mind that the complexity of the model increases rapidly with the number of nests, which can make it challenging to make the model converge to a unique solution. Examples of nested logit models include Goldberg (1995, 1998), Greene, Duleep, Elliott, and Ah mad (2005), and McManus (2006). A mixed logit relaxes the restrictions on su bstitution patterns imposed in a nested logit structure by allowing for random vari ation in tastes. The addition of a random variable as an explanatory va riable can capture the effect s of unobserved characteristics that are randomly distributed in the populat ion. Multiplying a (different) random variable by another variable in the anal ysis (such as a consumer’s in come) similarly is intended to capture variation across the population in the eff ects of that variable on consumer

5 Discrete-choice equations often model the choice as Yes = 1, No = 0. A common tool for estimating discrete choices is a logit; its defining characteristics include a dependent (endogenous) variable whose values are between 0 and 1, and a sp ecific distribution for the error term.

5purchases. For instance, if there is variati on in h ow people’s income affects their choices – some people, for instance, buy luxury cars wh en they get wealthier, while others do not change their choices – the mixed logit struct ure allows for this variation. Because a mixed logit approach allows unobserved person al characteristics of consumers to enter the model, it allows the analyst to evaluate the effects of a change in one factor on a range of consumers that respond in different ways (for instance, only some individuals change their purchases when their income ch anges). Nested logit can only look at the effects of a change in average consumer char acteristics (how, on average, income affects consumer purchases). Examples of mixed l ogit models include Berry et al. (1995, 2004), Bento et al. (2005), and Train and Winston (2007). 6 While discrete choice modeling appears to be the primary method for consumer choice modeling, others (such as Kleit (2004 ), Austin and Dinan (2005), and McManus and Kleinbaum (2009)) have used a matrix of demand elasticities to estimate the effects of changes in cost. At times these are borro wed from other studies that have estimated them using discrete choice models (e.g. fr om Train and Winston 2007). In addition, Bordley (1993) proposes a method for calculati ng cross-price elastici ties (the effect of increasing the price of one vehi cle on sales of other vehicles ) using own-price elasticities combined with information on the vehicles pe ople would have bought if their first-choice vehicle was not available; the second-choice information provides specific evidence on the substitutions that people would have made . GM has collected information on second choices in surveys to vehicle buyers for a numb er of years. Kleit (2004) uses GM data

6 Train (2003) is one source for further explanation of these methods.

6and an appr oach similar to Bordley to estimate elasticities that he then uses in model simulations; Austin and Dinan base their consumer model on Kleit’s elasticities. In addition to the modeling approach, seve ral other methodological issues arise in

the context of consumer vehicle choice modeling. One problem is the possible endogeneity of some explanator y variables, especi ally vehicle price. If consumers’ choices of vehicles affect the price charged for the vehicle, or if the price of a vehicle is

correlated with vehicle characte ristics not otherwise included in the analysis (for instance, if the distinction between luxury cars and ot hers is omitted), th en the analysis may produce biased - that is inaccura te - estimates of the role of price in consumers’ purchase decisions. As a result, some studies (Ber ry et al. 1995, Goldberg 1995, Klier and Linn 2008, Allcott and Muehlegger 2008) use instrumental variable methods to correct for endogeneity. 7 The results in these papers indicate that ignoring the e ndogeneity leads to different results than in their full models. In Berry et al., for instance, the effect of miles per dollar (miles per gallon divided by fuel pr ice) on vehicle purchase is smaller once the model is corrected for endogene ity. The full model (including random coefficients) also

finds that variation around mile s per dollar is an important f actor: that is, some people find high miles per dollar a desirable factor in their vehicle pur chase decision, while others find it a disincentive for purchase. Thus, model specification can affect the results and their interpretations. More recent literature addresses endoge neity in vehicle choice and technology decisions in novel ways. Bento et al. (2005) and Jacobsen (2008) incorporate, not only the decision of what new vehicle to purch ase, but also the decision on how long and

7 Berry et al. and Goldberg are notable as well for their incorporation of oligopolistic behavior on the part of automakers, a relatively common practice afterwards (e.g., Bento et al. 2005; Austin and Dinan 2005, Allcott and Muehlegger 2008).

7whether to hold onto a used vehicle, and how many miles to drive a vehicle each year. Market prices for the vehicles are also treated en dogenously in the models. For instance, in Klier and Linn (2008) the fact that a manufacturer often uses the same engine platform

across different vehicle models is used to develop an instrument for fuel economy, since

vehicles in different classes that use the sa me engine tend to exhi bit similar power and fuel economy characteristics. Likewise, A llcott and Muehlegger (2008) combine macroand micro-level data to resolve the problem of endogenous fuel ec onomy, prices, power, and weight. In their model, vehicle manufac turers adjust vehicle characteristics in response to both consumer demand and ot her manufacturers’ vehicle plans.



### 2b. Data Sources

CCMs that model how consumers choose a vehicle for purchase commonly rely on detailed information on the consumer, vehicl e purchased, and other vehicles available to the consumer at the time of purchase that were not chosen. The CCMs identify the effects of changing either consumer or vehicle characteristics on a household’s purchasing decision. These effect s are typically calle d the parameters or coefficients of the models. For instance, the model parameters may predict that a 10 percent increase in a household’s disposable income would increas e the probability of purchasing vehicle A by 5%, and decrease the probability of purchas ing vehicle B by 10%. The parameters in CCMs can be developed either from estim ations based on original data sources (estimated models) or values taken from other studies (calibrated models).

Estimated models use datasets on c onsumer purchase patterns, consumer characteristics, and vehicle ch aracteristics to develop origin al sets of parameters. The

datasets used in these studies sometimes co me from surveys of individuals’ behaviors

8(e.g., Bento et al., 2005; Train and W inston, 20 07). Because they draw on the behaviorof individual households, they provide what is sometimes called micro-level data. Microlevel data can be very valuable because of the high level of detail about the role of households’ traits in their pu rchase decisions. Other stud ies estimate market shares instead of discrete purchase decisions using aggregated data that can cover long time periods (e.g., Berry et al., 1995). Aggregate da ta tend to be more easily accessible than micro data, which often rely on surveys that may not be made publicly available. In addition, the ability to look at data over long time periods allows for examination of trends or changes in tastes. It is also possible to co mbine micro-level data with aggregated data (Berry et al. 2004, Allcott and Muehlegger 2008). Each of these estimated CCMs provides information about the factors that influence vehicle choice. The quality of the results depends on the quality of the underlying data and the appropriate use of statistical methods. Calibrated models rely on existing studies for their parameters. Researchers may

draw on results from estimated CCMs or other research to choose the parameters of the models. Kleit (2004) develops a set of elasticities, based on survey data that included information on purchasers’ sec ond choices as well as their actual choices; Austin and Dinan (2005) use these elasticit ies in their analysis. Th e Fuel Economy Regulatory Analysis Model developed for the Energy In formation Administration (Greene, Duleep, Elliott, and Ahmad, 2005) and the New Ve hicle Market Model developed by NERA Economic Consulting (2008) are other examples of calibrated models. The quality of calibrated models depends on the parameter estimates they use. The advantage of

9calib rated models is that they can use best estimates from the range of existing studies, though they do not themselves provide new estimates of the parameters.



### 2c. Factors Included in the Models

Consumer choice models have been devel oped to analyze many different research and policy questions. In part, these models have been de veloped to advance the state of economic modeling: the work of Berry et al . (1995), for instance, is often cited outside the motor vehicle context for its incorporati on of multiple new modeling issues into its framework. In addition, because the vehicle s ector is a major part of the U.S. economy and a stakeholder in many public policy disc ussions, research ques tions cover a wide gamut. These topics have included the effect s of voluntary export rest raints on Japanese vehicles compared to tariffs and quotas (Goldberg 1995), the market acceptability of alternative-fuel vehicles (Brownstone a nd Train 1999; Brownstone, Bunch, and Train 2000; Greene 2001; Greene et al., 2004), the effects of introduction and removal of vehicles from markets (Petri n 2002; Berry et al. 2004), causes of the decline in market shares of U.S. automakers (Train and Wi nston 2007), and the effects of gasoline taxes (Bento et al., 2005, Feng et al. 2005) and “feebat es” (subsidizing fuel-e fficient cars with revenue collected by taxing fuel-inefficient vehicles) (Greene, Patterson, Singh, and Li, 2005; Feng et al. 2005; Gr eene et al. 2009a). The research question of interest affect s the structure of the consumer choice model. Some studies focus only on the ne w vehicle market (e.g., Train and Winston, 2007); others consider the choice between ne w vehicles and an outside good (possibly including a used vehicle) (Ber ry et al., 1995); others explicit ly consider the relationship between the new and used vehicle markets (e .g., Bento et al., 2005). Some models also

10consider vehicle m iles traveled (e.g., Gol dberg, 1998; Bento et al ., 2005; Feng et al., 2005), though most do not. A model focused on ma rket share, such as Train and Winston (2007), does not consider the choi ce of whether or not to buy a vehicle, or whether to buy a new or used vehicle. This approach is sufficient, for instance, to focus on the question of why there has been a decline in market shares of domestic U.S. auto producers, but it would not be appropriate for estimating the e ffect of a policy on total vehicle sales. Some models (e.g., Berry et al., 1995; Goldbe rg, 1995) include a generic “outside good” as a way to incorporate the decision on whet her to buy a new vehicle; this approach can estimate total market size, but it cannot look at the effect of change d new vehicle sales on the used vehicle market. Bento et al. (2005) is unusual in its incorporation of different vehicle vintages as well as consumers’ choices on miles driven, so that it can examine the effect of policies on both the used vehi cle market and total miles driven. In a statistical analysis, it is important to include all the variables in a demand model that are likely to affect a consumer’s choices. Otherwise, it is possible that the relationship between an included variable a nd the consumer’s choice will be incorrect, through what is termed “omitted variable bias .” For instance, a vehicle’s size may be related to its price: all else equal, larger vehi cles tend to be more expensive. If size is not included in the model, then the coefficient on pr ice will also measure, in part, the effect of a vehicle’s size. As a result, the model cannot predict the effect s of a price change separately from the effects of changing size. For this reason, the re sults from consumer vehicle choice models may be difficult to in terpret. Gramlich (2008) includes variables for both dollars per mile and miles per gallon (mpg) in his regression. The coefficient on dollars per mile is negative, indicating that pe ople are less likely to choose a vehicle that

11is expens ive to drive per mile, all else equal; this result is expected. He also finds, though, that the coefficient on mpg is negative: all else equal, pe ople prefer low-mpg vehicles. He interprets this finding as an indication that miles per gallon measures some omitted vehicle characteristics: for instance, perhaps high-mpg vehicles are, on average,

not as well made or not as l uxurious as low-mpg vehicles. In practice, because it is impossible to identify all the factors that influence people’s vehicle choices, res earchers try to capture the ke y variables, and hope that

omitted variables do not bias other coefficients. 8 The choice of key variables depends on the research question on which the model focuse s. For instance, to incorporate vehicle miles traveled into her model, Goldberg ( 1998) uses a more aggregated version of the model in Goldberg (1995): she does not in clude individual model characteristics, but rather only vehicle classes differentiati ng between foreign and domestic. NERA Economic Consulting has used different specific ations of its New Vehi cle Market Model: in its July 2008 comments on the National Highway Traffic Safety Administration’s proposed fuel economy standards, it included on ly price as a dependent variable; in its April 2009 study, it includes price divided by income as a dependent variable. Both models are calibrated, with alternative-specif ic coefficients picki ng up all other vehicle attributes besides price; income is addr essed as a random vari able with a known

distribution. Most other models include a wide range of consumer and vehicle characteristics, such as vehicle size, fuel economy, and family characteristics.

8 Mixed logit models seek to account for omitted variables through the use of random variables. This method assumes that the omitted variables are not associated with the error term -- for instance, that an omitted characteristic such as the quality of the stereo system is distributed randomly among vehicles in the population. If, however, the quality of the stereo system is associated with, e.g., the price of the vehicle, then the mixed logit will still lead to biased coefficients.

12

### 2d. Results from CCMs on Fuel Economy

In consumer vehicle choice models, th e effect of fuel economy on vehicle purchase decisions can appear in various form s. Some models (e .g., Austin and Dinan, 2005) incorporate fuel economy through its effect s on the cost of owning a vehicle. With assumptions on the number of miles traveled per year and the cost of fuel, it is possible to estimate the fuel savings (and pe rhaps other operating costs) associated with a more fuelefficient vehicle. Those savings are consider ed to reduce the cost of owning a vehicle: effectively, they reduce the purchase price. This approach relies on the assumption that, when purchasing vehicles, consumers can estimat e the fuel savings that they expect to

receive from a more fuel-efficient vehicle and consider the savings equivalent to a reduction in purchase price. Turrentine and Ku rani (2007) question this assumption; they

find that consumers do not make this calcu lation when they purchase a vehicle. The question remains, then, how or whether cons umers take fuel economy into account when they purchase their vehicles. See Section 3 of this paper for further discussion. Instead of making assumptions about how consumers incorporate fuel economy into their decisions, most estimated consum er choice models use data on consumer behavior to identify that effect . In some models, a vehicle’s miles per gallon is included to explain purchase decisi ons. Other models (e.g., Espe y and Nair, 2 005; Train and Winston, 2007) use fuel consumption per m ile, the inverse of miles per gallon, as a measure: since consumers pay for gallons of fuel, then this measure can assess fuel savings relatively directly (Larrick and So ll, 2008). Yet other models multiply fuel consumption per mile by the cost of fuel to get the price of driving a mile (e.g., Goldberg, 1995), or they divide fuel economy by fuel consumption to get miles per dollar (e.g.,

13Berry et al., 1995; Petrin, 2004). It is worth noting that thes e last two m easures assume that consumers respond the same way to an increase in fuel economy as they do to a decrease in the price of fuel when each has the same effect on cost per mile driven: in other words, it assumes that consumers are i ndifferent to the source of fuel savings. Among others, Greene et al. (2009b) and Klier and Linn (2008) note that many improvements in the efficiency of engine t echnology have led, not to increases in fuel economy, but to increases in power and vehi cle weight. Klier and Linn, as well as Allcott and Muehlegger (2008), seek to build into their models the tradeoffs among fuel economy, power, and weight. Klier and Linn fi nd that their results are sensitive to the way that these tradeoffs are incorporated. It is possible, then, that the effect of fuel economy on consumers’ choices may be sensitive to model specification. If so, then models may not predict consumer choices or preferences accurately without careful attention to these relationships. Greene and Liu (1988) review 10 papers using consumer vehicle choice models

and estimated for each one how much consum ers would be willing to pay at time of purchase to reduce vehicle operating costs by $1 per year. They find that people were willing to pay between $0.74 and $25.97 for a $1 decrease in annual operating costs of a vehicle. This is clearly a ve ry wide range: the lowest estimate suggests that people are not willing to pay $1 once to get $1 per year re duced costs of operating their vehicle; the maximum suggests a willingness to pay 35 times as much, almost twice as much as the undiscounted fuel savings over the typical 15-yea r lifetime of a vehicle. For comparison, the present value of saving $1 per year for 15 years at a 3% disc ount rate is $11.94, while a 7% discount rate produces a present value of $8.78. While th is study is quite old, it

14suggests that, at least as of that tim e, c onsumer vehicle choice models produced widely varying estimates of the value of reduced vehicle operating costs. More recent studies suggest continued disa greement on the value of increased fuel economy to consumers. For instance, some papers (Goldberg 1995; Berry et al., 1995) find that the role of fuel cost (price per ga llon divided by miles per gallon, or the cost of driving one mile) in purchase decisions is lower for larger vehicles, while Gramlich (2008) finds that owners of large, fuel-inefficient vehicles have the greatest willingness to pay for improved fuel economy. Part of the difficulty may be, as these papers note, that fuel economy may be correlated (either posit ively or negatively) with other vehicle attributes, such as size, power, or qualit y, not all of which may be included in the analyses; as a result, “fuel ec onomy” may in fact represent se veral characteristics at the same time. Indeed, as noted above, Gramlich includes both fuel cost (dollars per mile) and miles per gallon in his analysis, with the argument that miles per gallon is positively correlated with unobserved undesirable attributes, while fuel cost picks up the consumer’s demand for improved fuel economy. Busse et al. (2009) focus solely on the re lationship between th e price of gasoline and the fuel economy of vehicles sold, and find a very strong correlation. This result is consistent with consumers basing their expecta tion of future fuel price on the current fuel price (known as a random walk expectation) . They find that the new and used vehicle markets adjust differently: with new vehicles , prices of vehicles change relatively little when fuel prices rise, but hi gh fuel prices lead to much higher market shares for more

fuel-efficient vehicles. In the used vehi cle market, on the other hand, prices of high miles-per-gallon (mpg) vehicles rise, and those of low-mpg vehicles fall, when fuel prices

15rise. These results suggest that consum er s pay attention to the fuel economy of the vehicles that they purchase, both in the new and used vehicle markets, though the two markets respond in different ways. Prelimin ary results from West (2008) suggest that consumers pay more attention to past gasoline prices than they do to present prices when making vehicle purchase decisions, suggesting that accounting for medium or long term behavioral response may matter for the accurate modeling of vehicle purchase decisions. Not many studies in this literature direct ly report willingness to pay for fuel economy. Given the methods utilized – nested and mixed logit – these values are

difficult to back out of the mode ls without having direct access to the detailed results. A few studies report their findings on this parameter. Espey and Nair (2005) find, using data from model year 2001, that consumer s might be willing to pay roughly $500 for a 1- mpg increase in city driv ing, approximately $250 for a 1-mpg increase in highway driving, or approximately $600 for an increas e in combined fuel economy; they argue that these values approximately correspond to the fuel savings that consumers might expect over the lifetime of the vehicle. Mc Manus (2006) finds, in 2005, that consumers were willing to pay $578 for a 1-mpg increa se in fuel economy. Gramlich (2008) reports

his results in terms of an increase from 25 mpg to 30 mpg; his estimates of willingness to pay range between $4100 for this 5-mpg increm ent (for luxury cars, when gasoline costs $2/gallon) to $20,600 (for SUVs when gasoline costs $3.50/gallon).

2e. Assumptions of Market Efficiency in CCM

Most consumer choice models assume that consumers are the best judges of how to improve their own welfare. If consumers wa nt more fuel economy, they will seek it in the vehicles they buy; automakers, sensitive to consumer desires, will provide more fuel

16econom y in their vehicles if the cost of th e additional fuel economy is less than what consumers are willing to pay for it. Consider the following example. Improved fuel economy reduces operating co sts per mile. To minimize the costs of owning and operating a vehicle, a person can calculate the expected fuel savings per year from a more fuel-efficient vehicle and compare it to the additional cost of that vehicle. For instance, consider a vehicle that gets 20 miles per gallon (mpg), and an otherwise identical vehicle that gets 25 mpg. If a person drives 12,000 miles per year, the first vehicle uses 12,000/20 = 600 gallons per year, whil e the latter uses 12,000/25 = 480 gallons per year, a savings of 120 gallons. At a gasoline price of $2.50 per gallon, those savings are worth $300 per year. Over the 14- year median lifespan of a vehicle, the present value of those savings is $3,388 with a 3% discount rate; at a 7% discount rate, they are worth $2,624. In principle, then, a consumer should be willing to spend at least $2,600 to buy a 25 mpg vehicle instead of a 20 m pg vehicle. If the costs of improving the fuel economy in the vehicle cost less th an $2,600, the automakers should be willing to

include it in the vehicle. Most consumer choice models (e.g., Kl eit 2004; Austin and Dinan 2005; Klier and Linn 2008; Jacobsen 2008) start with th e assumption that the market for fuel economy works efficiently, absent an accounting for externalities: that is, producers are offering as much fuel economy on vehicles as consumers are willi ng to buy. If this assumption is true, then consumers are not willing to pay for addi tional fuel economy, and new requirements will make them wors e off. A few papers (Gramlich 2008; McManus 2006; McManus and Kleinbaum, 2009) explicitly find that both automakers

and consumers would be better off with in creased fuel economy; and Austin and Dinan

17note that they have to adjust their model to elim inate such gains. If the amount that consumers are willing to pay to get addi tional fuel economy exceeds the costs to automakers of that addition, then both woul d be better off with more fuel-efficient vehicles. In other studies, consumers app ear not to buy fuel economy for which savings in gasoline expenses would easily cover the co sts. Possible reasons why this could occur

are discussed in Section 3.



### 2f. Potential contributions of consumer vehicle choice modeling to regulatory analysis

In modeling the impacts of vehicle regulation, EPA and NHTSA typically assume

that the fleet mix – the market shares of specific vehicles – stays constant in response to new regulation. There may be overall impacts on the number of new ve hicle sales, due to changes in vehicle costs, but, in the abse nce of consumer choice modeling, they are unable to predict changes in market shares. For small changes in vehicle prices and attributes, the market shares may not adjust very much, and the omission of consumer vehicle choice modeling may not miss notable market effects. However, recently

discussed increases in fuel ec onomy standards and the costs a ssociated with them may be significant enough to lead to more subs tantial effects on the fleet mix.

Consumer vehicle choice models allow for the possibility that the fleet mix will respond to changes in costs a nd vehicle attributes. In re sponse to new fleet-wide fuel economy requirements, automakers may sell more existing high-mpg vehicles -- for instance, by increasing the prices of low- mpg vehicles and lowe ring those of high-mpg vehicles – or they can add technology to th eir vehicles that will improve fuel economy

18standards. Autom akers are expected to pur sue the combination of changes in vehicle prices and additions of techno logies that they expect to maximize their profits. If inducing consumers to buy a different vehi cle is cheaper than applying a given technology, then manufacturers should do so; not accounting for such a response could

overstate the costs of the rule. Work currently underway by Resources for the Future is aimed at shedding light

on whether it is much more cos tly to rely solely on the application of technologies to the vehicle to meet a given fuel standard than wh en consumer behavior is taken into account. Initial work used a market-share model, wh ich, unlike a true demand model, does not examine the effect on total fleet sales. One result from this work (Coleman and Harrington 2008) is that doubling fuel econom y improvement appears to more than double the average compliance cost when cons umer demand is fixed; when consumers are allowed to adjust, however, the average compliance cost is much closer to a linear function, reflecting consumers’ tendencies to respond to increases in vehicle prices by shifting to less expensive models. Also, since regulations can change the structure of the automobile market and the types of vehi cles produced, consumer response may matter when evaluating the costs associated with di fferent forms of the standard (e.g., whether the standard varies for different auto produ cers based on the characteristics of the vehicles they produce). Work currently underway has evolved to modeling the vehicle market in a true demand modeling framew ork with an outside good available for purchase (e.g., a used vehicle). The producer si de of the market is also modeled as an oligopolistic market in the newer work.

19An addition al output from consumer choice modeling is the effect of changes on consumer surplus. Consumer surplus is the benefit that a consumer gets over and above the market price paid for the good. For instan ce, if a consumer is willing to pay up to $30,000 for a car but is able to negotiate a price of $25,000, the $5000 difference is consumer surplus. Because consumer choice models are able to estimate the maximum willingness to pay for a vehicle, these models are capable of calculating effects on consumer surplus. Current modeling by EP A and NHTSA of changes in fuel economy standards, for instance, compares the fuel savings with the technol ogy costs of additional fuel economy. This approach does not take into account changes in consumer satisfaction as they trade off higher prices with better fuel economy and potentially buy a vehicle (or no vehicle) with a different set of attributes compared to what they would have purchased in the absence of higher standards. Consumer surplus estimated from

consumer choice models could measure thes e combined effects and provide a more encompassing benefits estimation.

Because consumer choice models – and econom etric models in general -- rely on historical data to predict responses, they ar e most useful for evaluating the effects of small changes. They are not necessarily well suited to predicting larger scale compositional changes in the vehicle fleet. Berry et al. (1995) note, for instance, that their model predicted fuel economy changes re asonably well after the oil shock of 1973, but that its predictive ability declined markedly starting in 1976, due to wide introduction

of new vehicles into the market. Given th at vehicle models and characteristics may change substantially within only a few year s’ time -- in respons e to changing market conditions or new legal settings -- consumer choice models used to predict responses

20severa l years into the future may be more us eful when makes and models are aggregated by class of vehicle than when they are treated individually. In summary, consumer vehicle choice mode ls are a continuing area of research and development. Existing models vary in a number of dimensions, in part because of

different research intentions behind the models and in part because different methods can be used to analyze similar questions. Th ere has been relatively little systematic comparison of consumer choice models: while Greene and Liu (1988) compare the value of reducing vehicle operating co sts across a number of mode ls, the study has not been updated despite substantial modeling developments that have occurred since that time. It appears that the studies conducted since Gr eene and Liu continue to show notable disparities in results. In particular, ther e are significant differences across models in predicting whether consumers and automakers will benefit from additional fuel economy improvements to their vehicles. The lack of consensus makes it difficult to determine the effects of new fuel economy regulations. The following section reviews the evidence on why consumers, at the time of vehicle purchase, might not spend as much on fuel economy as an objective evaluation of the re sulting financial savings would suggest.

3. Do Consumers Value Fuel Economy “Correctly?” EPA and NHTSA analyses of increasing th e fuel econom y standard for light duty vehicles routinely find that there are a myri ad of relatively low-cost technologies available, and that these technologies are exp ected to result in fuel savings to consumers that more than make up for the up-front cost of the technology over the lifetime of the vehicle. The quandary, then, is why does the market not alr eady take advantage of these low cost technologies? Why aren’t consumers demanding these vehicle improvements

21and m anufacturers supplying them when they appear to “pay for themselves” even in the absence of regulation? This disconnect between net pres ent value estimates of energyconserving cost savings and what consum ers actually spend on energy conservation is often referred to as the Energy Paradox (IEA 2007; Jaffe, Newell, and Stavins 2001; Metcalf and Hassett 1999; Tiet enberg 2009), sin ce consumers appear to routinely undervalue a wide range of investments in en ergy conservation; it has been most studied in consumers’ decisions related to energy-sa ving products and appliances, such as light bulbs and washing machines. Possible expl anations for the paradox include: consumers who put little weight on the future; consumer di sinterest in fuel economy; the inability to find a specific vehicle with improved fuel ec onomy; the difficulty in calculating expected fuel savings; bounded rationality; uncertain fuel savings contrasted with certain and immediate increased costs; loss aversion; and variation in consumer benefits from improved fuel economy. We discuss each of these in turn. Recognizing that consumers do not buy as much energy conservation as a simple present value calculation would suggest, gove rnment agencies have assumed consumers have very high discount rates absent gove rnment intervention. For instance, when modeling consumers’ choices of appliances, the U.S. Department of Energy’s Energy Information Agency (1996) has used discount rates as high as 111 percent for water heaters and 120 percent for electr ic clothes dryers in their an alyses. Kubik (2006) offers

some evidence for the notion that consumer s are impatient or myopic (e.g., use a high discount rate) when it comes to fuel saving returns from automobile purchases. On average, consumers from the Kubik survey in dicate that fuel savings would have to pay back the additional cost in 2.9 years to pe rsuade them to buy a higher fuel-economy

22vehicle, ev en when evidence shows that c onsumers tend to hold ont o their vehicles for longer (on average, 5 years). 9 Rubin and Fin (2009) also find some evidence that

consumers undervalue the stream of future fuel savings that would result from improvements in fuel economy. While this explanation points to similarly heavy discounting of future returns in fuel savings , it says little about the underlying reasons why consumers may be doing so. Various e xplanations have been proposed for the reluctance of the private market to invest in energy efficiency. It is worth noting that little empirical evidence exists to support one hy pothesis over another.

If consumers are well informed but relative ly indifferent to increased fuel savings compared to improvements in other vehicle at tributes they may purchase, then it is possible that no market failure exists. If so, then seemingly low cost opportunities to improve fuel economy simply do not pay for themselves in spite of appearances that they should. The American Council for an Energy-Efficient Economy (ACEEE) (2007) finds in interviews with consumers that, while fu el economy is mentioned as a concern when making a vehicle purchase, it ranks below reliab ility, price, features, and safety in order of importance. Busse et al. (2009) provide evidence that the role of fuel economy in people’s vehicle purchases varies with fuel prices: high fuel pr ices lead to increased sales of high-mpg new vehicles, and higher prices for high-mpg used vehicles. West (2008) offers preliminary evidence that consumers pa y more attention to past gasoline prices than present ones when making vehicle purch ase decisions, while the Congressional Budget Office (CBO) (2008) notes that the long er higher gasoline prices are maintained, the more likely that they will affect consumer s expectations about future gasoline prices

9 Dasgupta et. al (2007) find that consumers are also myopic when it co mes to the decision of whether to lease or buy a new vehicle, prefer ring contracts with lower payment streams even when they imply an overall higher cost.

23and thus consum ers’ longer term decisions, su ch as which vehicle to purchase. It is worth noting that the degree to which demand fo r fuel economy varies with fuel prices or

other related characteristics is only beginning to be studied. As mentioned above, the same technology that can be used to improve fuel economy can also be used to improve vehicl e performance or increase vehicle size. If

consumers value these other attributes more highly than they do fuel economy, then it makes sense that these technologies are applied to that end. Hi storical trends point to this occurrence in passenger cars. Fuel economy st andards for these vehicles were constant over a roughly 20-year time period. Over the same time period, almost all technology was applied for the purposes of increasing acce leration, weight and automatic transmission instead of for improving vehicle fuel economy. Greene et al. (2009b) calculate that the average 2006 passenger car would have achieve d 38 miles per gallon instead of 29 miles per gallon had these technologi es been applied exclusively toward fuel economy improvements. That said, the relative pref erence for performance over fuel economy still does not explain the seeming paradox that fuel savings appears to exceed the cost of adding additional fuel economy to the vehicl e. One would expect from economic theory

that consumers would continue to demand fu el economy improvements until the benefits of a marginal improvement just met the cost. Another reason why consumers may appear relatively indifferent to fuel economy compared to other attributes is that a vehi cle is actually a bundle of attributes sold together. While it is common to vary some individual attributes when contemplating a particular vehicle – for instance, a cons umer can often choose between manual and

automatic transmission for the same make a nd model -- fuel economy is not usually one

24of the m; 10 there is no independent ability, for instance, to spend an additional $500 to improve the fuel economy on a particular vehi cle. Instead, consumers must trade off between fuel economy and other attributes incl uded in the bundle, several of which are likely to differ simultaneously across automake rs and models in the same vehicle class (Greene et al. 2009b). In fact, there is some evidence that consumers associate better gas mileage with smaller, lighter cars, and that, as a result, they expect to pay less, not more, for such a vehicle (Nye 2002). This result is co nsistent with Gramlich’s (2008) finding that increasing mpg, holding cost of travel per mile constant , decreases the likelihood that a consumer will buy a particular vehicle. He attributes this to mpg representing, in consumers’ minds, something other than fuel economy. Recent evidence points out that consumer s may not understand how to calculate fuel savings correctly when making vehicle purchase decisions and thus are not making

well informed decisions. Sanstad and Howa rth (1994) explain this phenomenon with the concept of “bounded rationality,” that consum ers optimize their behavior without full information by resorting to imprecise but conve nient rules of thumb. This allows for the possibility that consumers may make mistakes when evaluating fuel economy relative to

other vehicle attributes availa ble for purchase. Turrentine and Kurani (2007) present the results of a survey of 57 households in north ern California, asking a variety of questions related to the role of fuel economy in thei r vehicle purchasing decisions. They find no

evidence that households track and analyze fuel costs related to automobile or gasoline purchases over time. Households appear awar e of the cost of gasoline today but do not incorporate this cost into th eir budgets. As a result, when asked to consider how much

10 There are a couple exceptions. For instance, one can purchase a hybrid or non-hybrid version of the Honda Civic.

25they would be willing to pay f or their prefe rred vehicle with a 50% improvement in fuel economy, the majority of households could not answer, seemed to guess at a number, or made basic errors in estimating gasoline savi ngs over time when constructing an answer. Larrick and Soll (2008) argue that measuri ng fuel economy in mpg is one source of

consumer miscalculations. Potential fuel sa vings are non-linearly associated with mpg but linearly associated with ga llons per mile (the inverse of mpg). Thus, while reducing fuel use by one gallon per hundred miles is inde pendent of the effici ency of a vehicle – one gallon is one gallon – improving a 10 mpg ve hicle by 1 mpg results in more than ten times higher gasoline savings than improvi ng a 35 mpg vehicle 1 mpg. Through a series of surveys, they find that consumers regularly and incorrectly assume that mpg is directly

associated with the amount of gasoline consumed. As a result, participants appeared to substantially undervalue improvements in fuel efficiency for relati vely fuel-inefficient vehicles and to overvalue those improvement s for relatively fuel-efficient vehicles. 11 Another potential explanation for th e Energy Paradox is the notion of irreversibility in investment (Metcalf and Rosenthal 1995). The argument is as follows:

 Energy conservation investments require up-front capital investments that are “sunk” and therefore essentially irreversib le – they have a low salvage value if

the return never materializes.

 The return (e.g. the reduction in opera ting costs) is uncertain due to fluctuations in (and an inability to anticipate) energy prices.

11 In their experiment, participants were asked to assess fuel savings in a gallons-per-mile framework or in a mpg framework. A substantially larger number were able to identify a more fuel efficient option with gallons per mile than when the same scenario was expr essed in mpg. The authors argue for reporting fuel efficiency in gallons per mile, or per hundred miles, to aid consumer s in accurately assessing the savings from investing in incr eased fuel efficiency

26 Purchasers have flexibility with regard to the tim ing of their investment. They can always postpone it until they have more and better information on potential returns.

These characteristics imply that a more cautious approach to investment is optimal for the consumer, and that the required rate of return for the investment is often substantially higher than the underlying discount rate applied by consumers. Specifically, Metcalf and Rose nthal show, for two energy efficiency examples, that estimated discount rates are biased upward when irreversibility and uncertainty are ignored. They estimate that the consumer discount rate is somewh ere between 5 and 10 percent for these examples – previous studi es found much higher discount rates. Thus, they argue that apparently high discount rates appear to be at least partially at tributable to not accounting for uncertainty and irreversibility. Looking specifically at the automotive ma rket, Greene et al. (2009b) put forward a theory related to that of Metcalf and Rose nthal (1995). They argue for uncertainty with regard to fuel savings combined with lo ss aversion as the main explanation for why consumers do not adopt energy efficiency im provements in this market. Loss aversion

means that consumers are more sensitive to lo sses than to gains. Empirical evidence points to a loss aversion of 2: “the disutility of giving some thing up is twice as great as the utility of acquiring it” (Benartzi and Thaler 1995; Tversky and Kahneman 1991). Thus, because consumers evaluate decisions not in terms of thei r impact on uncertain future wealth, but in terms of potential cha nges from present, known wealth, they have a preference for maintaining the status quo -- avoiding increased ve hicle costs in the present -- and would have to be offered a premium over the expected value of fuel

27savings to invest in the new vehicle technology. W hen taki ng these aspects of consumer decision making into account, Greene et al. (2009b) find that the expected net present value of increasing the fuel economy of a passenger car from 28 to 35 miles per gallon falls from $405 when calculated using standard methods to very close to zero. In their results, uncertainty regarding the future price of gasoline is less important than uncertainty with regard to vehicle lifetime. While the median lifetime of a passenger vehicle is 14 years, this implies that half of the vehicles on the road will not last that long

due to mechanical failure or accidents. Bern atzi and Thaler (1995) also point out that the willingness of an investor to take on a risk is related to the time horizon of the investor. The shorter the time horizon, the less likely they are to invest in th e risky asset. This argument could apply in the context of vehi cle purchases, given the short payback period and uncertainty with regard to vehicle lifetime. 12 Dasgupta et. al (2007) provide

evidence that consumers are aware of differen ces in relative risk across vehicles: they find that consumers are more likely to lease than buy a vehicle with higher maintenance costs because it provides them with the option to return it before those costs become too high. Another significant source of uncertainty is the fuel economy that consumers will actually achieve. If consumers believe that the fuel economy that they will get is lower than the vehicle’s rated value, then their future gains are overestimated. Engineering

returns estimated in a controlled or lab se tting often do not materialize – and are not expected – in a household context (Metcalf and Hassett 1999). For instance, EPA states on its fuel economy sticker that actually fuel economy is likely to vary from EPA

12 Bernatzi and Thaler (1995) examin e myopic loss aversion in the contex t of the “equity premium puzzle”: relatively risky stocks have outperformed bonds by a far larger margin than expected, even though investors have relatively low values of relative risk aversion.

28estim ates with maintenance and use (e.g., house holds that drive with underinflated tires or in a particularly aggressive manner woul d not get the same fu el savings as those predicted in a lab setting). Gr eene et al. (2006) find empirical evidence that th is is indeed the case. Relying on self-repor ted data, they find that EPA fuel economy estimates listed on labels at the time of purchase are unbiased , b ut that they are also highly inaccurate

indicato

of the actual discount rate s that people have,

and thu rs of the actual fuel economy achieved. 13 Another alternative explana tion for the Energy Paradox th at is discussed in the

literature is variation across consumers in th eir discount rates, e xpected uses of the product, the lifespans of the investment, th e purchase prices, and other costs. This variation can lead to differences in the expect ed value that individual purchasers attach to energy-efficient products. For example, it may not make sense fo r someone who rarely uses an air conditioner to spend more money on an energy-efficient model: they will not recoup their investment through energy savings. In a study of consum er air conditioning purchases, such a person may appear instea d to have a very high discount rate. Averaging the discount rates that people appe ar to have, without accounting for consumer heterogeneity, may lead to overestimates s exaggerate the Energy Paradox. In sum, the Energy Paradox asks why consumers do not buy as much energy conservation as a simple present value calcu lation would suggest. Possible explanations for the paradox include: consumers who put little weight on the future; consumer disinterest in fuel economy; the inability to find a specific vehicle with improved fuel economy; the difficulty in calculating expected fuel savings; bounded rationality;

13 Using drivers’ own estimates of th e fraction of driving done in city versus highway driving conditions improved the ability to predict fuel economy for a particular vehicle by 19 percent.

29uncertain f uel savings contrasted with ce rtain and immediate increased costs; loss aversion; and variation in cons umer benefits from improved fuel economy. It is worth noting, as discussed above, that the empirical evidence from econome trically estimated consumer choice models varies widely regard ing the existence of an Energy Paradox in fuel economy. Greene and Liu (1988) find widely varying estimates of the amount consumers are willing to pay for increased fu el economy. More recently, Espey and Nair (2005) argue that consumers appear to pur chase approximately the cost-minimizing amount of fuel economy. The wide variation in estimates sugg ests that the value of

increas

ch as re duced air pollution, that may outweigh any private loss in consumer welfare. ed fuel economy to consumers deserves increased research. Another question arising from this discussi on is its implications for policy. Price volatility and uncertainty about the future are natural occurrences in markets, and consumers regularly face decisions under these circumstances. Does a requirement for improved fuel economy that pays for itsel f under reasonable conditions, but that consumers might not buy in the absence of that requirement, make consumers better off? On the one hand, it is likely to give consumer s actual savings in cost s that, because of the complexity of the calculation and uncertain ties about future payback, they did not appreciate at the time of purchase; the requirement may compensate for a boundedly rational but not globally rational decision. On the other hand, in this situation the government is imposing a requirement that consumers may not want; fuel economy may not be important at the time of purchase for a variety of reasons. It is also important to keep in mind that there may be other policy justifications -- not discussed here -- for decreasing fuel consumption, su

304. Reasons why producers may not provide as much fuel efficiency as consumers want to buy The empirical literature has spent very litt le time focusing on the efficiency of the producer side of the new vehicle market; it is typically assumed that automakers are taking advantage of all profit-making opportun ities. However, the seeming disconnect between the existence of low cost opportunitie s to improve fuel economy and the failure of the market to invest in such improveme nts may not be entirely due to errors in consumer decision making. Manufacturers also pl ay a role, particularly in how they view consumers and what they would be willing to pay for various attributes. The following discussion posits several hypotheses on the auto makers’ role in the market for fuel economy. Little research has been done to da te to investigate their relative merits. As previously mentioned, several recent analyses (Greene et al. 2009a; Klier and Linn 2008) argue that improvement s in engine efficiency have been channeled into other vehicle characteristics, such as power and acceleration, rather than fuel economy. If producers are maximizing profits, then they should add improvements in efficiency to both fuel economy and to other vehicle characteristics until the cost of additional energy efficiency just equals the amount that consumers are willing to pay for the additional fuel economy and for the additional acceleration or power. If producers have been channeling the increased engine efficiency into accelera tion and power rather than fuel economy, then they must expect that consumers are willing to pay more fo r increased acceleration and power than they are for increased fuel economy. One possible explanation for seemingly suboptimal behavior on the part of automakers is analogous to the effect of risk aver sion on consumers. If large auto

31m anufacturers are risk averse, then investments viewed as in herently more risky are less likely to be undertaken. Since th e fuel savings that accrue to consumers in the future are uncertain, it is less likely that manufacturers will invest in fuel economy (Greene et al. 2009b), and more likely that these technologies will be put towards attributes for which consumers have shown a strong and consiste nt preference and from which consumers will get immediate and certain benefits, such as performance and weight. As we mentioned earlier, automakers also bundle attributes. To control the number of variations of any given make an d model, they only provide consumers with choices over certain vehicle attributes, those for which they believe consumer preferences are highest and heterogeneous. These choice s may include, for instance, manual vs. automatic transmissions or different body styl es. Different fuel economy options for a given make and model is not typically offere d as a choice. Automakers may consider

choices in other attributes to be more salientto consumers at the time of vehicle purchase

than the choice of additional fuel econom y. Thus, consumers may not have the opportunity to select a particular model w ith additional fuel economy due to producers’

desire to control the number of vari ations of models that they build. Vehicle design and development also can take several years. In response to regulations and to market conditions, automakers may want to change the attributes of the vehicles that they sell, but those changes cannot happen as quickly as fluctuations in fuel price. As a result, the fuel economy that au tomakers include in their vehicles may lag unexpected changes in the market by several ye ars. A period of relatively stable and low gasoline prices, such as the 1990s, did not encourage development of new high-mpg vehicles; when prices rose sharply in 2008, vehicle manuf acturers could not suddenly

32im prove the fuel economy of the vehicles that they offered. In addition, because it takes time and significant cost to change the vehicl e that a manufacturing facility produces, many automakers struggled to increase the capacity to produce their existing high-mpg vehicles. Gramlich (2008) attempts to exp licitly account for the disconnect between the timing of an automaker’s production decision and the market conditions at the time of sale. He models the supply de cision in two distinct stages . In the first stage, the

automaker decides on vehicle ch aracteristics based on current gasoline prices, costs, and demand information. However, sales do not oc cur until the second stage (in his model, one year later). Gasoline prices are allo wed to change between the first and second stages, but automakers are already committe d to producing a vehicle with the set of characteristics decided upon in the first stage. Automakers can only adjust the prices of the vehicles they sell to consum ers based on the new information. Train and Winston (2007) argue that U.S. manufacturers may have been slow to respond to changes in consumer preferences. This is a key reason why they have seen

their market share consiste ntly decline since the 1970s. They reject alternative hypotheses, such as brand loyalty, dealership networks, and health care and other legacy costs. According to their re sults, U.S. manufacturers had provided relatively less value relative to the cost for fuel consumption, hor sepower, and other attributes, compared to Japanese manufacturers. The general slowness of U.S. manufacturers to respond to consumer desires may have contributed to sl ow response to changes in consumer demand brought about by much higher fuel prices. McManus and Kleinbaum (2009) argue that U.S. manufacturers have underestimated the value of fuel economy to consumers; in particular, they argue that

33GM regularly applied a lower value to the w illin gness to pay for fuel economy than was estimated by its own researchers. Given wh at may have been a sub-optimal nature of decision-making among U.S. manufacturers, they find that tightening fuel economy standards could result in increased profits for U.S. automakers: consumers may be willing to pay for additional fuel economy improvements, and the sales for those vehicles could actually increase relative to those of Japanese automakers. This result is somewhat sensitive to the value that consumers place on fuel economy; if consumers value fuel economy at half of what is deemed “rational, ” then profits for U.S. manufacturers could decline. Automakers’ possible underestimation of consumers’ valuation of fuel economy may be partially explained, as discussed above , by risk aversion. It is also possible that

U.S. manufacturers made a ra tional decision to specialize in larger, less fuel efficient vehicles and that this became a large disadva ntage as fuel prices increased, given their inability to quickly change th e vehicles that they produce. In sum, possible non-profit-maximizi ng behavior on the part of auto

manufacturers has received even less atten tion than the consumers’ Energy Paradox. Possible reasons for the alleged non-profit-ma ximizing behavior include risk aversion; the decision to focus, in designing model va riations, on characterist ics other than fuel economy; time lags inherent in the productio n process; and internal decision-making processes that have not been responsive to changes in consumer preferences. As with consumers, uncertainty is a no rmal part of markets, and ri sk aversion is one way of coping with it: though risk averse behavior is likely to reduce average profits, it should also reduce the variance of profits and thus make a company more stable. Economic theory predicts that manufacturers have st rong incentives to unde rtake all profitable

34product developm ent; the market would be e xpected to reward companies that make correct predictions, and punish producers wh ose products do not adequately fulfill

consumers’ desires. 14 The state of major change in th e auto industry in 2009 is due both to short-term shocks associated with high gasoline prices in 2007 and 2008 and the subsequent recession, and to longer-lasting ma rket forces that has led to industry restructuring. The relative pr oportions of these changes is cl early a topic of great debate. In the same way that the implications of how consumers evaluate tradeoffs when making vehicle choices are uncle ar, it is difficult to reach clear policy conclusions regarding possible sub-optimality in how pr oducer make vehicle production decisions. On the one hand, many policymakers are reluctant to let the market provide harsh discipline for non-profit-maximiz ing behavior in the auto industry, due to the importance of the industry and the large effects on work ers and other affiliated industries, especially in a recession. On the other hand, if auto makers, who have a profit motive to make vehicles that people want, ha ve trouble finding the best se t of attributes to offer to consumers, are government agencies lik ely to have better insights?

5. Conclusion In regulatory analyses, EPA and NHTSA often hold the m arket shares of the

vehicle fleet constant. For rule s that lead to small changes in vehicle attribut es and prices – that is, changes that consumers are unlikely to notice – this procedure is not a bad approximation, since the changes may not affect consumer vehicle purchase patterns. Proposals to increase fuel economy substantiall y in the next few years, on the other hand, may change both vehicle attributes and the price of vehicles enough that consumers may

14 Of course, GM and Chrysler recently have gone through bankruptcy proceedings.

35rearrange w hat vehicles they purchase. Consumer vehicle choice models are ways to estimate the changes in fleet mix. In addition, the models can be used to estimate changes in consumer surplus associated with these changes. As this review has shown, a number of researchers have developed consumer vehicle choice models. These models vary along a number of dimensions, including analytical methods, data sources, and research questions addressed. Of particular interest for fuel economy-related rules, the models a ppear to produce widely varying estimates of the value that consumers place on fuel economy. This value plays a major role in the issues surrounding consumer vehicle choice modeling: its ma gnitude affects the estimates of consumer surplus as well as debates over the efficiency of the market for vehicle fuel economy. As discussed, this parameter deserves additional research. Consumer vehicle choice modeling has the potential to provide useful information for regulatory analysis of the e ffects of vehicle changes. To make the modeling useful in practice, it is important to identify the factors in the m odels around which there appears to be consensus, as well as the important factor s that lead to variation in model results. Even with the ability to m odel consumer choice, the literature still leaves open the question of how consumers value fuel econo my, and why their willingness to pay for more of it does not appear to equal the expected value of the fuel savings. In the general context of energy efficiency, this obs ervation is known as the Energy Paradox. Explanations, which typically center on why consumers may under-value energy efficiency, include uncertainty, risk aversion, lack of interest in fuel savings, bounded rationality, confusing information, loss aversion , and unavailability of preferred choices. From a public policy perspectiv e, it is an open question wh ether these problems justify

36additional fuel economy requirem ents. Some consumers are likely to appreciate the additional fuel savings that they did not be lieve would actually occur; other consumers are likely to be made worse off by incurring th e additional costs for an attribute of little

interest to them.

Much less research exists on whether or why producers might provide less fuel economy than consumers are willing to buy. Some possible explanations include risk aversion, investing in characte ristics other than fuel economy, time lags in responding to market changes, and flawed decision-making processes within the companies. Economic theory argues that markets should discipline companies that do not provide the products people want at suitable prices; whether gove rnment intervention is warranted if auto companies make mistakes is a current policy debate. Consumer decisions on vehicle purchases involve complex tradeoffs among many dimensions. For environmental and energy polic y, the dimension of most interest is fuel economy of vehicles. This review has shown that consumers pay attention to the fuel economy of the vehicles that they buy, but that there is still much to be learned about how to model the role of fuel economy in consumers’ decisions.

37References ACEEE (2007). “Environmental Performance La bels for Vehicles: Context and Findings of Market Research for the U.S. Environmental Protection Agency.” Report T071. Allcott, H., and E. Muehlegger (2008). “Refining Equilibria in Counterfactual Simulations: An Application to Corporate Average Fuel Economy Standards.” Working paper, available at http://www.nber.org/ ~confer/2008/si2008/EEE/allcott.pdf, accessed 7/24/09. Austin, D., and T. Dinan (2005). “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes.” Journal of Environmental Economics and Management 50: 562-582. Bento, A., L. Goulder, E. Henry, M. Jacobs en, and R. von Haefen (2005). “Distributional and Efficiency Impacts of Gasoline Taxes: An Econometrically Based Multi-Market Study,” American Economic Review 95(2): 282-287. Benartzi, S. and R. Thaler (1995). “Myopi c Loss Aversion and the Equity Premium Puzzle.” Quarterly Journal of Economics 110(1): 73-92. Berry, S., J. Levinsohn, and A. Pakes (1995). “Automobile Pr ices in Market Equilibrium,” Econometrica 63(4): 841-940. Berry, S., J. Levinsohn, and A. Pakes (2004) . “Differentiated Products Demand Systems from a Combination of Micro and Macro Da ta: The New Car Market,” Journal of Political Economy 112(1): 68-105. Bordley, R. (1993). “Estimating Automotive El asticities from Segment Elasticities and First Choice/Second Choice Data,” Review of Economics and Statistics 75(3): 455-462. Brownstone, D., D. Bunch, and K. Train (2000 ). “Joint Mixed Log it Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” Transporta tion Research Part B 34: 315-338 Brownstone, D., and K. Train (1999). “F orecasting New Product Penetration with Flexible Substitution Patterns,” Journal of Econometrics 89: 109-129. Busse, M., C. Knittel, and F. Zettelmeyer (2009). “Pain at the Pump: How Gasoline Prices Affect Automobile Purchasing in New and Used Mark ets,” Working paper (accessed 6/30/09), available at http://www.econ.ucdavis.edu/faculty/ knittel/papers/gaspaper_latest.pdf, Congressional Budget Office (2008). Effects of Gasoline Prices on Driving Behavior and Vehicle Markets

.

38Colem an, C., and W. Harrington (2008). “A Model of New Vehicle Purchases by Households

. ” Final Interim Report for EPA. September. Dasgupta, S., S. Siddarth, and J. Silva-Risso (2007). “To Lease or to Buy? A Structural Model of a Consumer’s Vehicle and Contract Choice Decisions.” Journal of Marketing Research 44: 490 – 502. Energy Information Administration, U.S. De partment of Energy (1996). Issues in Midterm Analysis and Forecasting 1996, DOE/EIA-0607(96), Washington, D.C., http://www.osti.gov/bridge/purl.cover. jsp?purl=/366567-BvCFp0/webviewable/, accessed 7/7/09. Espey, M., and S. Nair (2005). “Automobile Fuel Economy: What is it Worth?” Contemporary Economic Policy 23(3): 317-323. Feng, Y., D. Fullerton, and L. Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/ w11553.pdf , accessed 5/12/09. Goldberg, P. (1995). “Product Differentiation and Oligopoly in Internationa l Markets: The Case of the U.S. Automobile In dustry,” Econometrica 63(4): 891-951. Goldberg, P. (1998). “The Effects of the Cor porate Average Fuel Efficiency Standards in the U.S.” Journal of Industrial Economics 46(1): 1-33. Gramlich, J., “Gas Prices and Endogenous Pr oduct Selection in the U.S. Automobile Industry,” http://www.econ.yale.edu/semi nars/apmicro/am08/gramlich-081216.pdf , accessed 5/11/09. Greene, D. (2009a). “Feebates, Footprints a nd Highway Safety,” Transportation Research Part D 14: 375-384. Greene D. (2001). “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” prepared by the Oak Ri dge National Laboratory for the U.S. Department of Energy. Greene, D., K. Duleep, D. Elliott, and S. Ahmad (2005). “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy Info rmation Administration,” prepared by the Oak Ridge National Labor atory for the U.S. Department of Energy under contract No. DE-AC0500OR22725. Greene, D., K. Duleep, and W. McManus (2004) . “Future Potential of Hybrid and Diesel Powertrains in the U.S. Light -Duty Vehicle Market ,” prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy.

39Greene, D., J. Germ an, and M. Delucchi ( 2009b). “Fuel Economy: The Case for Market Failure” in Reducing Climate Impacts in the Transportation Sector , Sperling, D., and J. Cannon, eds. Springer Science. Greene, D., L. Goeltz, J. Hopson, and E. Tw orek (2006). “Analysis of In-Use Fuel Economy Shortfall by Means of Voluntar ily Reported Fuel Economy Estimates.” Transportation Research Record No. 1983. Transportation Research Board, Washington, DC. Greene, D., and J. Liu (1988). “Autom otive Fuel Economy Improvements and Consumers’ Surplus.” Transportation Research Part A 22A(3): 203-218.Greene, D., P. Patterson, M. Singh, and J. Li (2005). “Feebates, Rebates, and GasGuzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33: 757-775. IEA. 2007. “Mind the Gap: Quantifying Princi pal-Agent Problems in Energy Efficiency

. ” Paris, France: Interna tional Energy Agency. Jacobsen, M. “Evaluating U.S. Fuel Econom y Standards In a Model with Producer and Household Heterogeneity,” http://www.econ.ucsd.edu/~m3jacobs/J acobsen_CAFE.pdf, accessed 5/11/09. Jaffe, A, R. Newell, and R. Stavins (2001). "Energy Efficient Technologies and Climate Change Policies: Issues and Evidence." In Climate Change Economics and Policy , Toman, Michael A., ed., Washington, D.C. : Resources for the Future. p.171 - 181. Kleit, A. (2004). “Impacts of Long-Range Increases in the Fuel Economy (CAFE) Standard.” Economic Inquiry 42(2): 279-294. Klier, T., and J. Linn (2008). “New Vehicle Characteristics and the Cost of the Corporate Average Fuel Economy Sta ndard,” working paper. http://www.chicagofed.org/public ations/workingpapers/wp2008_13.pdf Kubik, M. (2006). Consumer Views on Tran sportation and Energy. Second Edition. Technical Report: National Re newable Energy Laboratory. Larrick, R. P., and J.B. Soll (2008). “The MPG illusion

. ” Science 320: 1593-1594. McManus, W. (2006). “Can Proactive Fuel Economy Strategies Help Automakers Mitigate Fuel-Price Risks?” University of Michigan Transportation Research Institute. McManus, W. (2007). “The Impact of Attri bute-Based Corporate Average Fuel Economy (CAFE) Standards: Preliminary Findings.” University of Michigan Transportation Research Institute paper UMTRI-2007-31.

40McManus, W. and R. Kleinbaum (2009). “Fixin g Detroit: How Far, How Fast, How Fuel Efficient.“ Working Paper, Transportation Rese arch Institute, University of Michigan. Metcalf, G., and K. Hassett (1999). "M easuring the Energy Savings From Home Improvement Investments: Evidence From Monthly Billing Data." The Review of Economics and Statistics 81(3): 516-528. Metcalf, G., and D. Rosenthal (1995). "The ‘New’ View of Investment Decisions and Public Policy Analysis: An Application to Gr een Lights and Cold Refr igerators,’’ Journal of Policy Analysis and Management 14: 517–531. NERA Economic Consulting (2009). “Append ix B: New Vehicle Market Model,” “Impacts of the California Greenhouse Gas Em ission Standards on Motor Vehicle Sales,” comments submitted to the U.S. Environmenta l Protection Agency at Regulations.gov, document number EPA-HQ-OAR-2006-0173-9053.1. NERA Economic Consulting (2008). “Evaluatio n of NHTSA’s Benefit- Cost Analysis of 2011-2015 CAFE Standards,” available at http://www.heartland.org/policybot/results/ 23495/Evaluation_of_N HTSAs_BenefitCost_ Analysis_Of_20112015_CAFE_Standards.html. Nye, R. (2002). Qualitative Research Report: ORNL/NTRC Focus Groups,” conducted by the Looking Glass Group. Knoxville, TN. Petrin, A. (2002). “Quantifying the Benefits of New Products: The Case of the Minivan,” Journal of Political Economy 110: 705-729. Rubin, J., and Q. Fin (2009). “Hedonic Price Model for Light-Duty Vehicles: Consumers’ Valuation of Automotive Fuel Ec onomy.” Working Paper, University of Maine. Sanstad, A., and R. Howarth (1994). “’Norm al’ Markets, Market Imperfections, and Energy Efficiency.” Energy Policy 22(10): 811-818. Tietenberg, T. (2009). “Refl ections – Energy Efficiency Policy: Pipe Dream or Pipeline to the Future?” Review of Environmental Economics and Policy. Vol.3, 2: 304 -320. Train, K. (2003). Discrete Choice Methods with Simulation. New York: Cambridge University Press. Train, K., and Clifford Winston (November 2007). “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48: 1469- 1496. Turrentine, T. and K. Kurani (2007). “Car Buyers and Fuel Economy?” Energy Policy 35: 1213-1223.

4142 Tversky and Kahneman (1991). “Loss Aver sion in Riskless Choice: A Reference Dependent Model.” The Quarterly Jour nal of Economics. V. 106, 4: 1039 – 61. Welch, D. “CAFE: A Prize for Making Gas- Guzzlers?” Business Week April 16, 2009, http://www.businessweek.com/magaz ine/content/09_17/b4128048030307.htm?chan=ma gazine+channel_what's+next , accessed 7/7/09. West, S. (2008). “The Effect of Gasolin e Prices on the Demand for Sport Utility Vehicles.” Working Paper. Macalester College.