Subject: Summary of additional draft interagency working comments received on draft rule under EO 12866 

review 

Date: 11/15/2011 05:52 PM 

From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop.gov> 

To: William Charmley/AA/USEPA/US@EPA, "'Steve.Wood@dot.gov'" <Steve.Wood@dot.gov>, 

"'Neil.Eisner@dot.gov'" <Neil.Eisner@dot.gov>, "''james.tamm@dot.gov' (james.tamm@dot.gov)'" 

<james.tamm@dot.gov>, "'rebecca.yoon@dot.gov'" <rebecca.yoon@dot.gov>, Ed 

Nam/AA/USEPA/US@EPA, Robin Moran/AA/USEPA/US@EPA, Alex 

Barron/DC/USEPA/US@EPA, Ann Wolverton/DC/USEPA/US@EPA, "Larry.Blincoe@dot.gov" 

<Larry.Blincoe@dot.gov> 

 

 

Please find attached additional draft interagency working comments received on draft NPRM under EO 

12866 and EO 13563 review. They are for your consideration and are subject to further policy review. 

 

Thanks. 

Chandana 

Summary of interagency working comments received on draft rule under EO 12866a.pdf 

 
Subject: Summary of additional draft interagency working comments received on draft rule under EO 12866 

review 

Date: 11/15/2011 06:14 PM 

From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop.gov> 

To: William Charmley/AA/USEPA/US@EPA, "'Steve.Wood@dot.gov'" <Steve.Wood@dot.gov>, 

"'Neil.Eisner@dot.gov'" <Neil.Eisner@dot.gov>, "''james.tamm@dot.gov' (james.tamm@dot.gov)'" 

<james.tamm@dot.gov>, "'rebecca.yoon@dot.gov'" <rebecca.yoon@dot.gov>, Ed 

Nam/AA/USEPA/US@EPA, Robin Moran/AA/USEPA/US@EPA, Alex 

Barron/DC/USEPA/US@EPA, Ann Wolverton/DC/USEPA/US@EPA, "Larry.Blincoe@dot.gov" 

<Larry.Blincoe@dot.gov> 

 

Please find attached additional draft interagency working comments received on draft NPRM under EO 

12866 and EO 13563 review. They are for your consideration and are subject to further policy review. 

 

Thanks. 

Chandana 

Summary of interagency working comments received on draft rule under EO 12866.doc 

 
Subject: Summary of interagency working comments received in draft NPRM under EO 12866 review 

Date: 11/15/2011 07:55 PM 

From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop.gov> 

To: William Charmley/AA/USEPA/US@EPA 

Cc: Robin Moran/AA/USEPA/US@EPA, Ed Nam/AA/USEPA/US@EPA, Jim Tamm 

<james.tamm@dot.gov>, Rebecca Yoon <rebecca.yoon@dot.gov>, Ann 

Wolverton/DC/USEPA/US@EPA 

Please find attached additional draft interagency working comments received on 

draft NPRM under EO 12866 and EO 13563 review. They are for your consideration 

and are subject to further policy review. 

Thanks. 

Chandana 

Summary of interagency working comments received on draft NPRM under EO 12866 review.doc 

 
Subject: Summary of interagency working comments received in draft NPRM under EO 12866 review 

Date: 11/15/2011 10:49 PM 

From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop.gov> 

To: William Charmley/AA/USEPA/US@EPA 

Cc: Robin Moran/AA/USEPA/US@EPA, Ed Nam/AA/USEPA/US@EPA, 'Jim Tamm' 

<james.tamm@dot.gov>, 'Rebecca Yoon' <rebecca.yoon@dot.gov>, Ann 

Wolverton/DC/USEPA/US@EPA 

Please find attached additional draft interagency working comments received on 

draft NPRM under EO 12866 and EO 13563 review. They are for your consideration 

and are subject to further policy review. 

Thanks. 

Chandana 

Summary of Interagency Working Comments received on draft rule under EO 12866.pdf 

 
Subject: Summary of interagency working comments received in draft NPRM under EO 12866 review 

Date: 11/15/2011 11:47 PM 

From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop.gov> 

To: William Charmley/AA/USEPA/US@EPA, Robin Moran/AA/USEPA/US@EPA, Ed 

Nam/AA/USEPA/US@EPA, 'Jim Tamm' <james.tamm@dot.gov>, 'Rebecca Yoon' 

<rebecca.yoon@dot.gov>, Ann Wolverton/DC/USEPA/US@EPA 

Please find attached additional draft interagency working comments received on 

draft NPRM under EO 12866 and EO 13563 review. They are for your consideration 

and are subject to further policy review. 

Thanks. 

Chandana 

Summary of interagency working comments received on draft NPRM under EO 12866 review.pdf 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-1 

Below is a summary of interagency comments received during EO 12866 and EO 13563 

review of the draft rule “Joint Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas 

Emission Standards and Corporate Average Fuel Economy Standards”. They are for your 

consideration and are subject to further policy review. 

*** 

Chapter 8: Vehicle Sales and Employment Impacts 

8.1 Vehicle Sales Impacts 

8.1.1 Vehicle Sales Impacts and Payback Period 

Predicting the effects of this rule on vehicles entails comparing two competing effects. 

On the one hand, as a result of this rule, the vehicles will become more expensive, which 

would, by itself, be expected to discourage sales. On the other hand, the vehicles will have 

improved fuel economy and thus lower operating costs, producing lower total costs over the 

life of vehicles, which makes them more attractive to consumers. Which of these effects 

dominates for potential vehicle buyers when they are considering a purchase will determine 

the effect on sales. However, assessing the net effect of these two competing effects is 

complex and uncertain, as it rests on how consumers value fuel savings at the time of 

purchase and the extent to which manufacturers and dealers reflect them in the purchase price. 

There are reasons to believe that many consumers do not fully take account of the 

value of fuel savings at the time of purchase (such as lack of salience and focus on the shortterm, as discussed in detail below), and as we have stated, the proposed rule is expected to 

produce significant net benefits for consumers. But the empirical literature leaves many open 

questions. It does not give clear evidence about the effect of the proposed rule on sales, nor 

does it speak to the possibility and consequences of consumer learning over time. Thus, for 

the proposal we do not provide quantified estimates of potential sales impacts. Rather, we 

solicit comment on the issues raised here and on methods for estimating the effect of this rule 

on vehicle sales. 

If consumers do not consider net costs – if they do not accurately compare the value 

of fuel savings with the increased cost of fuel economy technology in their vehicle purchase 

decisions -- there may be a net decrease in sales for 2017 and beyond. If auto makers have 

accurately gauged how consumers consider fuel economy when purchasing vehicles and have 

provided the levels of improvement that consumers desire, then consumers should not be 

expected to want to purchase the more fuel-efficient vehicles beyond what manufacturers may 

offer in the future absent this rule. On certain assumptions, auto makers would be expected to 

provide as much fuel economy as consumers want, and vehicle sales would be expected to 

decrease, not to increase, as a result of the rule. We invite comments on this possibility. 
Chapter 8 

8-2 

If, on the other hand, the rule motivates an increase in consumer demand for fuel 

economy, perhaps as a result of learning, then it could lead to production of more desirable 

vehicles and a commensurate increase in sales. We invite comment on consumer learning and 

related possible reasons for positive effects on consumer demand. 

For years, consumers have been learning about the benefits that may accrue to them 

from owning and operating vehicles with greater fuel efficiency. Consumer demand has thus 

shifted towards such vehicles, not only because of higher fuel prices but also because many 

consumers are learning about the value of purchases based not only on initial costs but also on 

the total cost of owning and operating a vehicle over its lifetime. This type of learning is 

expected to continue before and during the model years affected by this rule, particularly 

given the new fuel economy labels that clarify potential economic effects and should therefore 

reinforce that learning. 

Today’s proposed rule, combined with the new and easier-to-understand fuel economy 

label required to be on all new vehicles beginning in 2012, may increase sales above baseline 

levels by hastening this very type of consumer learning. As more consumers experience, as a 

result of the rule, the savings in time and expense from owning more fuel efficient vehicles, 

demand may shift yet further in the direction of the vehicles mandated under the rule. This 

social learning can take place both within and across households, as consumers learn from one 

another. There are three possible effects here. 

First and most directly, the time and fuel savings associated with operating more fuel 

efficient vehicles will be more salient to individuals who own them, causing their subsequent 

purchase decisions to shift closer to minimizing the total cost of ownership over the lifetime 

of the vehicle. Second, this appreciation may spread across households through word of 

mouth and other forms of communications. Third, as more motorists experience the time and 

fuel savings associated with greater fuel efficiency, the price of used cars will better reflect 

such efficiency, further reducing the cost of owning more efficient vehicles for the buyers of 

new vehicles (since the resale price will increase). 

If these induced learning effects are strong, the rule could potentially increase total 

vehicle sales over time. These increased sales would not occur in the model years first 

affected by the rule, but they could occur once the induced learning takes place . It is not 

possible to quantify these learning effects years in advance and that effect may be speeded or 

slowed by other factors that enter into a consumer’s valuation of fuel efficiency in selecting 

vehicles. 

The possibility that the rule will (after a lag for consumer learning) increase sales need 

not rest on the assumption that automobile manufacturers are failing to pursue profitable 

opportunities to supply the vehicles that consumers demand. In the absence of the rule, no 

individual automobile manufacturer may find it profitable to move toward the more efficient 

vehicles mandated under the rule. In particular, no individual company can fully internalize 

the future boost to demand resulting from the rule. If one company were to make more 

efficient vehicles, counting on consumer learning to enhance demand in the future, that 

company would capture only a fraction of the extra sales so generated, because the learning at 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-3 

issue is not specific to any one company's fleet. Many of the extra sales would accrue to that 

company's competitors. 

In other words, consumer learning about the benefits of fuel efficient vehicles involves 

positive externalities (spillovers) from one company to the others. [Footnote: Industrywide 

positive spillovers of this type are hardly unique to this situation. In many industries, 

companies form trade associations to promote industry-wide public goods. For example, 

merchants in a given locale may band together to promote tourism in that locale. Antitrust law 

recognizes that this type of coordination can increase output.] These positive externalities may 

lead to benefits for manufacturers as a whole. We emphasize that this discussion has been 

tentative and qualified. To be sure, social learning of related kinds has been identified in a 

number of contexts. [Footnote: See Hunt Alcott, Social Norms and Energy Conservation, 

Journal of Public Economics (forthcoming 2011), available at 

http://web.mit.edu/allcott/www/Allcott%202011%20JPubEc%20-

%20Social%20Norms%20and%20Energy%20Conservation.pdf; Christophe Chamley, 

Rational Herds: Economic Models of Social Learning (Cambridge, 2003).] Comments are 

invited on the discussion offered here, with particular reference to any relevant empirical 

findings.” 

We note as well that automakers are currently putting more fuel-saving technologies 

into vehicles and are advertising based in part on the fuel efficiency of their vehicles. We 

cannot assess whether these improvements in fuel economy are due to higher gasoline prices 

in recent years, to increased consumer demand for fuel economy, to auto makers realizing that 

fuel economy is a valuable attribute to consumers, or to EPA’s and NHTSA’s recent 

rulemakings on fuel economy and greenhouse gases. If adding more fuel economy to 

vehicles, even with the added costs, does make the vehicles more attractive to consumers, 

then we would expect auto makers to add this fuel economy in the absence of the rule. If so, 

both the benefits and costs estimated for this rule are overstated. 

In addition to uncertainty over the expected offerings of fuel economy in new vehicles 

in the absence of this rule, we note the uncertainty over the role of fuel economy in vehicle 

purchase decisions. As discussed in Chapter 8.1.2.5 below, how consumers consider fuel 

economy when buying cars is not well understood; it is likely to vary substantially among 

individuals, and possibly across vehicle classes. In addition, consumer interest in fuel 

economy may change over time, in response to changes in fuel prices, to marketing efforts by 

automakers, to more experience with fuel-efficient vehicles, and (as noted) to better 

understanding of the costs associated with fuel savings, not to mention changing market 

conditions and other shifts in the U.S. economy that could affect vehicle demand and national 

driving behavior. As noted in Preamble III.H.1.a, EPA and NHTSA recently revised the fuel 

economy label for new vehicles, to improve consumer information on fuel economy. We do 

not have data on the effect of the label on consumer vehicle purchase decisions. If consumers 

put more emphasis on fuel economy in their purchase decisions, then automakers would be 

expected to respond to that interest independent of the standards proposed here, and an 

increase in vehicle sales resulting from improved fuel economy would be a result of market 
Chapter 8 

8-4 

forces instead of this rulemaking. Preamble III.D. discusses EPA’s reasoning in assuming 

that the reference fleet will comply with the standards for MY 2016 vehicles but will not 

over-comply. Adding to the uncertainty is the dynamic effect that the proposed rule may have 

on other relevant aspects of the vehicle sales market aside from purchase price. For example, 

the proposed rule may induce technological change that would not have occurred otherwise, 

and introduce consumers to new vehicle attributes that impact vehicle demand in a positive 

way. Similarly, improvements in vehicle fuel economy might accompany other changes in 

vehicle characteristics that are relevant for purchase decisions. While these factors are always 

present, they are particularly acute in the current context, in which the rule will not take effect 

until 2017. 

In previous rulemakings, EPA and NHTSA conducted vehicle sales analyses by 

comparing the up-front costs of the vehicles with the present value of five years’ worth of fuel 

savings. We assumed that the costs for the fuel-saving technologies would be passed along 

fully to vehicle buyers in the vehicle prices. The up-front vehicle costs were adjusted to take 

into account several factors that would affect consumer costs: the increased sales tax that 

consumers would pay, the increase in insurance premiums, the increase in loan payments that 

buyers would face, and a higher resale value, with all of these factors due to the higher upfront cost of the vehicle. Those calculations resulted in an adjusted increase in costs to 

consumers. We then assumed that consumers considered the present value of five years of 

fuel savings in their vehicle purchase, which is consistent with the length of a typical new 

light-duty vehicle loan, and is similar to the average time that a new vehicle purchaser holds 

onto the vehicle.A The present value of fuel savings was subtracted from technology costs to 

get a net effect on vehicle cost of ownership. We then used a short-run demand elasticity of -

1 to convert a change in price into a change in quantity demanded of vehicles.B

 An elasticity 

of -1 means that a 1% increase in price leads to a 1% reduction in quantity sold. In the 

vehicle sales analyses, if five years of fuel savings outweighed the adjusted technology costs, 

then vehicle sales were predicted to increase; if the fuel savings were smaller than the 

adjusted technology costs, sales would decrease, compared to a world without the standards. 

We do not here present a vehicle sales analysis using this approach. This rule takes 

effect for MY 2017-2025. If consumers take into account less than the break-even number of 

years in their buying decisions, then vehicle sales could decrease. In all cases, we would 

expect the impact of this rule on vehicle sales to be modest relative to other factors 

influencing future vehicle sales such as fuel prices and overall economic conditions, which 

are strong determinants of overall sales in any given year. 

 

A In this proposal, the 5-year payback assumption corresponds to an assumption that vehicle buyers take into 

account between 30 and 50 percent of the present value of lifetime vehicle fuel savings (with the variation 

depending on discount rate, model year, and car vs. truck). 

B

 For a durable good such as an auto, the elasticity may be smaller in the long run: though people may be able to 

change the timing of their purchase when price changes in the short run, they must eventually make the 

investment. We request comment on whether or when a long-run elasticity should be used for a rule that phases 

in over time, as well as how to find good estimates for the long –run elasticity. 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-5 

The calculation of the payback period is discussed in DRIA Chapter 5.3. Table 1 

shows the estimated payback period for MY 2021 and 2025. We present MY 2021 because it 

is the last year before the mid-term review impacts, if any, will take place, and MY 2025 

because it is the last year of the program. The payback period in 2021 is shorter than that in 

2025, because the technologies required to meet the proposed MY 2021 standards are more 

cost-effective than those for MY 2025. In all cases, the payback periods are less than the 5-

year payback period that EPA and NHTSA have previously used in the vehicle sales analyses. 

Given the relatively short time frames for the payback periods, these results suggest that 

vehicle sales could increase as a result of improved fuel economy. Alternatively, if 

consumers have shorter time frames for payback periods, then the rule may have a negative 

impact on vehicle sales. In either situation we would expect the impact of this rule on sales to 

be modest. As noted above, though, it is possible that some or all of this increase in fuel 

economy would occur in the absence of the rule, and it is also possible that consumers may 

revise how they think about fuel economy in their vehicle purchase decisions. 

Table 1 Estimated Payback Period for Model Years 2021 and 2025 (Years) 

Model 

Year 

Estimated 

Payback Period 

for Cash 

Purchase, 3% 

Discount Rate 

Estimated 

Payback Period 

for Cash 

Purchase, 7% 

Discount Rate 

Estimated 

Payback Period 

for Purchase on 

Credit, 3% 

Discount Rate 

Estimated 

Payback Period 

for Purchase on 

Credit, 7% 

Discount Rate 

2021 2.7 2.9 2.9 2.8 

2025 3.7 3.9 3.9 3.9 

We welcome comments on all aspects of this discussion, including assumptions on 

consumer and producer behavior; how consumers value future fuel savings at the time of 

purchase; factors that should be taken into account when assessing the impact of fuel 

economy standard on aggregate vehicle sales; and sources of uncertainty in estimating vehicle 

sales impacts. We welcome comments on the methodology described here for quantitative 

estimates of the effects of this proposal on sales and its appropriateness for this rulemaking; 

we also welcome proposals for other methods. 

We here provide further detail on some of the assumptions included in the payback 

analysis. We seek comment on all these factors as well. The analysis starts with the increase 

in costs estimated by the OMEGA model. We assume that these costs are fully passed along 

to consumers. This assumption is appropriate for cost increases in perfectly competitive 

markets. In less than perfectly competitive markets, though, it is possible that the cost 

increase is split between consumers and automakers, and possibly suppliers, and the price 
Chapter 8 

8-6 

may not increase as much as costs.1

 Thus, the assumption of full cost pass-through is possibly 

an overestimate. 

The next step in the analysis is to adjust this cost increase for other effects on the 

consumer. The higher vehicle price is likely to lead to an increase in sales tax, insurance, and 

vehicle financing costs. 

The increase in insurance costs is estimated from the average value of collision plus 

comprehensive insurance as a proportion of average new vehicle price. Collision plus 

comprehensive insurance is the portion of insurance costs that depend on vehicle value. The 

Insurance Information Institute2

 provides the average value of collision plus comprehensive 

insurance in 2008 as $432. The average value of a new vehicle in 2008, according to the U.S. 

Department of Energy, was $23,334.3

 Dividing the insurance cost by the average price of a 

new vehicle gives the proportion of comprehensive plus collision insurance as 1.85 percent of 

the price of a vehicle. If this same proportion holds for the increase in price of a vehicle, then 

insurance costs should go up by 1.85 percent of the increase in vehicle cost. We use 

information on depreciation of vehicle value from the same U.S. Department of Energy report 

to estimate a reduction in insurance costs, due to reduction in the estimated value of the 

vehicle, over 9 years. 

Calculating the average increase in sales tax starts with the vehicle sales tax for each 

state in 2006, the most recent source identified.4

 The sales tax per state was then multiplied 

by the 2010 population of the state;5

 those values were summed and divided by total U.S. 

population, to give a population-weighted sales tax. That estimate of the state sales taxes for 

vehicles in the U.S. is 5.3 percent. This value is assumed to be a one-time cost incurred when 

the vehicle is purchased. 

As of July, 2011, the national average interest rate for a 5 year new car loan was 5.51 

percent.6

 We use this loan rate to calculate the increase in vehicle costs due to financing a 

loan. 

8.1.2 Consumer Vehicle Choice ModelingC

An alternative to the vehicle sales analysis discussed above is the use of consumer 

vehicle choice models. In this section we describe some of the consumer vehicle choice 

models EPA has reviewed in the literature, and we describe the models’ results and 

limitations that we have identified. The evidence from consumer vehicle choice models 

indicates a huge range of estimates for consumers’ willingness to pay for additional fuel 

economy. Because consumer surplus estimates from consumer vehicle choice models depend 

critically on this value, we would consider any consumer surplus estimates of the effect of our 

rule from such models to be unreliable. In addition, the predictive ability of consumer vehicle 

choice models may be limited. While vehicle choice models are based on sales of existing 

 

C

 This section is drawn heavily from Helfand, Gloria, and Ann Wolverton, “Evaluating the Consumer Response 

to Fuel Economy: A Review of the Literature.” International Review of Environmental and Resource 

Economics 5 (2011): 103-146. 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-7 

vehicles, vehicle models are likely to change, both independently and in response to this rule. 

The models may not predict well in response to these changes. Instead, we compare the value 

of the fuel savings associated with this rule with the increase in technology costs. EPA will 

continue its efforts to review the literature, but, given the known limitations and uncertainties 

of vehicle choice models, EPA has not conducted an analysis using these models for this 

proposal. 

This rule will lead automakers to change characteristics – in particular, the fuel 

economy -- of the vehicles they produce. These changes will affect the cost of manufacturing 

the vehicle; as a result, the prices of the vehicles will also change. 

In response to these changes, the number and types of vehicles sold is likely to change. 

When consumers buy vehicles, they consider both their personal characteristics (such as age, 

family composition, income, and their vehicle needs) and the characteristics of vehicles (e.g., 

vehicle size, fuel economy, and price). In response to the changes in vehicle characteristics, 

consumers will reconsider their purchases. Increases in fuel economy are likely to be 

attractive to consumers, but increases in price, as well as any detrimental changes in other 

vehicle characteristics, may be deterrents to purchase. As a result, consumers may choose a 

different vehicle than they would have purchased in the absence of the rule. The changes in 

prices and vehicle characteristics are likely to influence consumers on multiple market scales: 

the total number of new vehicles sold; the mix of new vehicles sold; and the effects of the 

sales on the used vehicle market. 

Consumer vehicle choice modeling (CCM) is a method used to predict what vehicles 

consumers will purchase based on vehicle characteristics and prices. In principle, it should 

produce more accurate estimates of compliance costs compared to models that hold fleet mix 

constant, since it predicts changes in the fleet mix that can affect compliance costs. It can also 

be used to measure changes in consumer surplus, the benefit that consumers perceive from a 

good over and above the purchase price. (Consumer surplus is the difference between what 

consumers would be willing to pay for a good, represented by the demand curve, and the 

amount they actually pay. For instance, if a consumer were willing to pay $30,000 for a new 

vehicle, but ended up paying $25,000, the $5000 difference is consumer surplus.) 

A number of consumer vehicle choice models have been developed. They vary in the 

methods used, the data sources, the factors included in the models, the research questions they 

are designed to answer, and the results of the models related to the effects of fuel economy on 

consumer decisions. This section will give some background on these differences among the 

models. 

8.1.2.1 Methods 

Consumer choice models (CCMs) of vehicle purchases typically use a form of discrete 

choice modeling. Discrete choice models seek to explain discrete rather than continuous 

decisions. 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. Discrete decisions can take only a 
Chapter 8 

8-8 

limited set of values. The decision to purchase a vehicle, for instance, can only take two 

values, yes or no. Vehicle purchases are typically modeled as discrete choices, where the 

choice is whether to purchase a specified vehicle. The result of these models is a prediction 

of the probability that a consumer will purchase a specified vehicle. A minor variant on 

discrete choice models estimates the market share (a continuous variable between 0 and 1) for 

each vehicle. Because the market share is, essentially, the probability that consumers will 

purchase a specific vehicle, these approaches are similar in process; they differ mostly in the 

kinds of data that they use. 

The primary methods used to model vehicle choices are nested logit and mixed logit.D 

In a nested logit, the model is structured in layers. For instance, the first layer may be the 

choice of whether to buy a new or used vehicle. Given 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. Examples of 

nested logit models include Goldberg,7

 Greene et al.,8 and McManus.9

In a mixed logit, personal characteristics of consumers play a larger role than in nested 

logit. While nested logit can look at the effects of a change in average consumer 

characteristics, mixed logit allows consideration of the effects of the distribution of consumer 

characteristics. As a result, mixed logit can be used to examine the distributional effects on 

various socioeconomic groups, which nested logit is not designed to do. Examples of mixed 

logit models include Berry, Levinsohn, and Pakes,10 Bento et al.,11 and Train and Winston.12 

While discrete choice modeling appears to be the primary method for consumer choice 

modeling, others (such as Kleit13 and Austin and Dinan14) have used a matrix of demand 

elasticities to estimate the effects of changes in cost. The discrete choice models can produce 

such elasticities. Kleit as well as Austin and Dinan used the elasticities from an internal GM 

vehicle choice model. 

8.1.2.2 Data Sources 

The predictions of vehicle purchases from CCMs are based on consumer and vehicle 

characteristics. The CCMs identify the effects of changing the characteristics on the purchase 

decisions. These effects are typically called the parameters or coefficients of the models. For 

instance, the model parameters might predict that an increase in a person’s income of 10% 

would increase the probability of her purchasing vehicle A by 5%, and decrease the 

probability of her purchasing vehicle B by 10%. 

The parameters in CCMs can be developed either from original data sources 

(estimated models), or using values taken from other studies (calibrated models). 

Estimated models use datasets on consumer purchase patterns, consumer 

characteristics, and vehicle characteristics to develop their original sets of parameters. The 

 

D Logit refers to a statistical analysis method used for analyzing the factors that affect discrete choices (i.e., 

yes/no decisions or the choice among a countable number of options). 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-9 

datasets used in these studies sometimes come from surveys of individuals’ behaviors.15 

Because they draw on the behavior of individuals, they provide what is sometimes called 

micro-level data. Other studies, that estimate market shares instead of discrete purchase 

decisions, use aggregated data that can cover long time periods.16 

Calibrated models rely on existing studies for their parameters. Researchers may draw 

on results from a number of estimated models, or even from research other than CCM, to 

choose the parameters of the models. The Fuel Economy Regulatory Analysis Model 

developed for the Energy Information Administration17 and the New Vehicle Market Model 

developed by NERA Economic Consulting18 are examples of calibrated models. 

8.1.2.3 Factors Included in the Models 

Consumer choice models vary in their complexity and levels of analysis. Some focus 

only on the new vehicle market;19 others consider the choice between new vehicles and an 

outside good (possibly including a used vehicle);20 others explicitly consider the relationship 

between the new and used vehicle markets.21 Some models include consideration of vehicle 

miles traveled,22 though most do not. 

The models vary in their inclusion of both consumer and vehicle information. One 

model includes only vehicle price and the distribution of income in the population influencing 

choice;23 others include varying numbers and kinds of vehicle and consumer attributes. 

Some models include only the consumer side of the vehicle market;24 others seek to 

represent both consumer and producer decisions.25 Models that include only the consumer 

side are suitable for reflecting consumer choices, but they do not allow for revisions of 

vehicle characteristics in response to consumer preferences. Including producer behavior 

allows for vehicle characteristics such as price and fuel economy to be the result of market 

forces rather than characteristics of the existing fleet. For instance, in the context of 

“feebates” (subsidizing fuel-efficient cars with revenue collected by taxing fuel-inefficient 

vehicles), Greene et al. estimated that 95% of the increase in fuel economy was due to 

addition of technology rather than changes in vehicles sold.26 Including auto maker response 

is a complex exercise. Auto makers are commonly considered to have market power; they 

can influence the prices that consumers pay to increase their profits. As a result, the price 

increases that consumers face may reflect strategic factors that could make them higher or 

lower than the technology costs. In addition, auto makers may seek to influence consumer 

preferences through marketing and advertising.27 Even those vehicle choice models that 

include a producer model may not include much detail, due to computational limits: it is 

unusual for models to allow both buyers and producers to choose one vehicle characteristic, 

much less multiple characteristics.28 

8.1.2.4 Research Questions for the Models 

Consumer choice models have been developed to analyze many different research and 

policy questions. In part, these models have been developed to advance the state of economic 
Chapter 8 

8-10 

modeling. The work of Berry, Levinsohn, and Pakes,29 for instance, is often cited outside the 

motor vehicle context for its incorporation of multiple new modeling issues into its 

framework. In addition, because the vehicle sector is a major part of the U.S. economy and 

involved in many public policy discussions, research questions cover a wide gamut. These 

topics have included the effects of voluntary export restraints on Japanese vehicles compared 

to tariffs and quotas,30 the market acceptability of alternative-fuel vehicles,31 the effects of 

introduction and exit of vehicles from markets,32 causes of the decline in market shares of 

U.S. automakers,33 and the effects of gasoline taxes34 and “feebates”35 (subsidizing fuelefficient cars with revenue collected by taxing fuel-inefficient vehicles). 

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions 

Consumer vehicle choice models typically consider the effect of fuel economy on 

vehicle purchase decisions. It can appear in various forms. 

Some models36 incorporate fuel economy through its effects 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 perhaps other operating costs) associated with a 

more fuel-efficient vehicle. Those savings are considered 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 estimate 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 Kurani37 question this assumption; they find, in fact, that 

consumers do not make this calculation when they purchase a vehicle. The question remains, 

then, how or whether consumers take fuel economy into account when they purchase their 

vehicles. 

Most estimated consumer choice models, instead of making assumptions about how 

consumers incorporate fuel economy into their decisions, use data on consumer behavior to 

identify that effect. In some models, miles per gallon is one of the vehicle characteristics 

included to explain purchase decisions. Other models use fuel consumption per mile, the 

inverse of miles per gallon, as a measure:38 since consumers pay for gallons of fuel, then this 

measure can assess fuel savings relatively directly.39 Yet other models multiply fuel 

consumption per mile by the cost of fuel to get the cost of driving a mile,40 or they divide fuel 

economy by fuel cost to get miles per dollar.41 It is worth noting that these last two measures 

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.E

 On the 

one hand, while this assumption does not rely on as complex a calculation as the present value 

of fuel savings that Turrentine and Kurani examined, it suggests a calculating consumer. On 

the other hand, using a form of cost per mile is a way to recognize the role of fuel prices in 

 

E

 Likewise, these measures assume consumers respond the same way to increases and decreases in cost per mile 

of driving, as well as if those increases and decreases are large shocks rather than small, gradual changes. The 

issue of potential asymmetric consumer response to increased fuel efficiency compared to other types of changes 

to the cost of driving also arises and is discussed in the context of the VMT rebound effect (see Section III.H.4 of 

the Preamble and Chapter 4.2.5.2 of the TSD). 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-11 

consumers’ purchase of fuel economy: recent research42 presents results that higher fuel 

prices play a major role in that decision. 

Greene and Liu,43 in a paper published in 1988, reviewed 10 papers using consumer 

vehicle choice models and estimated for each one how much consumers would be willing to 

pay at time of purchase to reduce vehicle operating costs by $1 per year. They found that 

people were willing to pay between $0.74 and $25.97 for a $1 decrease in annual operating 

costs for a vehicle. This is clearly a very wide range: while the lowest estimate suggests that 

people are not willing to pay $1 once to get $1 per year reduced costs of operating their 

vehicles, the maximum suggests a willingness to pay 35 times as high. For comparison, the 

present value of saving $1 per year for 15 years at a 3% discount rate is $11.94, while a 7% 

discount rate produces a present value of $8.78. While this study is quite old, it suggests that, 

at least as of that time, consumer vehicle choice models produced widely varying estimates of 

the value of reduced vehicle operating costs. 

A newer literature review from David Greene44 suggests continued lack of 

convergence on the value of increased fuel economy to consumers. Of 27 studies, willingness 

to pay for fuel economy as a percent of the expected value of fuel savings varied from highly 

positive to highly negative. Significant numbers of studies found that consumers overvalued 

fuel economy, undervalued fuel economy, or roughly valued fuel economy correctly relative 

to fuel savings. Part of the difficulty may be, as these papers note, that fuel economy may be 

correlated (either positively or negatively) with other vehicle attributes, such as size, power, 

or quality, not all of which may be included in the analyses; as a result, “fuel economy” may 

in fact represent several characteristics at the same time. Indeed, Gramlich45 includes both 

fuel cost (dollars per mile) and miles per gallon in his analysis, with the argument that miles 

per gallon measures other undesirable quality attributes, while fuel cost picks up the 

consumer’s demand for improved fuel economy. Greene finds that, while some of the 

variation may be explainable due to issues in some of the studies, the variation shows up in 

studies that appear to be well conducted. As a result, further work needs to be conducted 

before it is possible to identify the role of fuel economy in consumer purchase decisions. 

Some studies46 argue that automakers could increase profits by increasing fuel 

economy because the amount that consumers are willing to pay for increased fuel economy 

outweighs the costs of that improvement. Other studies47 have found that increasing fuel 

economy standards imposes welfare losses on consumers and producers, because consumers 

should already be buying as much fuel economy as they want. In the course of reaching this 

result, though, at least one of these studies48 notes that its baseline model implies that 

consumers are willing to buy more fuel economy than producers have provided; they have to 

adjust their model to eliminate these “negative-cost” fuel economy improvements. 

The models do not appear to yield very consistent results on the role of fuel economy 

in consumer and producer decisions. 
Chapter 8 

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8.1.2.6 Why Consumers May Not BuyFuel Economy that Pays for 

Itself 

A detailed and wide ranging literature attempts to explain why consumers appear to 

under-invest in energy saving technologies that – at least using a present value calculation 

based on engineering estimates – appear to pay for themselves. Existing research does not 

provide a definitive answer to this question. Potential explanations are bounded by two 

scenarios. On the one hand, purely private benefits of fuel economy (fuel savings, time 

savings, increases in driving time) must be accompanied by private losses of the same 

magnitude. However, if there is no such private loss, or if it is small or insignificant, then 

there is a market or behavioral failure. 

On the consumer side, this disconnect between net present value estimates of energyconserving cost savings and what consumers actually spend on energy conservation is often 

referred to as the Energy Paradox,49 since consumers appear to undervalue a wide range of 

investments in energy conservation. There are many possible explanations for the paradox 

discussed in the literature.50 Some explanations point to costs or aspects of consumer 

decision-making unaccounted for in a simple present value calculation, while others point to 

potential behavioral or market failures. There is little empirical literature to help the analyst 

determine which combination of hypothesis offers the most credible explanation. Possibilities 

include: 

 Consumers put little weight on benefits from fuel economy in the future and show 

high discount rates; 

 Consumers do not find the benefits from fuel economy to be sufficiently salient at 

the time of purchase, even if it would be in consumers’ economic interest to take 

account of those benefits; 

 Consumers consider other attributes more important than fuel economy at the time 

of vehicle purchase, especially if fuel economy is a relatively “shrouded” attribute; 

 Consumers have difficulty in calculating expected fuel savings; 

 Consumers may use imprecise rules of thumb when deciding how much fuel 

economy to purchase; 

 Consumers may give particular weight to the losses associated with upfront costs, 

and less so to the costs over time (a version of the phenomenon of “myopic loss 

aversion”); 

 Consumers might associate higher fuel economy with inexpensive, less well 

designed vehicles; 

 Fuel savings in the future are uncertain, while at the time of purchase the increased 

costs of fuel-saving technologies are certain and immediate; 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

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 Consumers may not be able to find the vehicles they want with improved fuel 

economy; 

 The level of cost savings may be affected by the underlying reasons for the gap: 

factors such as transactions costs and differences in quality may not be adequately 

measured;51

 There is likely to be variation among consumers in the benefits they get from 

improved fuel economy, due to different miles driven and driving styles. 

8.1.2.7 Modeling Electric Vehicles and Other New Vehicles 

Modeling the introduction of new vehicles can be a greater challenge than modeling 

the existing vehicle market, because the modeler does not have data on how many of the new 

vehicles consumers buy. Nevertheless, it can be possible to estimate the effects of new 

vehicle introduction by identifying characteristics for the new vehicles and using those in a 

vehicle choice model. For instance, as discussed above, the models can estimate effects on 

the vehicle market when vehicles change their fuel economy or price. If the model 

incorporates other vehicle attributes important to the new vehicles, such as size, performance, 

or range, then the effect of the introduction can be modeled by applying the parameters for 

those features to the new vehicle characteristics. 

As discussed above, some models rely on vehicle price as the primary or only 

explanatory variable. Even in these models, it is possible, with some additional information, 

to consider the effects of new vehicle introduction. The first step is to find a vehicle similar 

on as many dimensions as possible to the new vehicle. For instance, if the change is to create 

an electric vehicle (EV) version of an existing model, then the existing model serves as the 

base vehicle. Next, it is necessary to measure the changes in vehicle attributes of interest to 

potential vehicle buyers. For an EV, changes in vehicle driving range and cost of fueling may 

be two such attributes. The next requirement is information on the value to consumers of the 

attributes that change between the new and the base vehicle. Multiplying the value for that 

attribute by the change in the attribute provides an estimate of the benefit or cost associated 

with changing that characteristic. That amount can then be added to or subtracted from the 

vehicle purchase price to give an adjusted purchase price reflecting the changed characteristic. 

This procedure is just an extension of the approach, discussed above, used to incorporate fuel 

economy improvements into vehicle choice models, by calculating future fuel savings and 

subtracting them (either in whole or a fraction) from vehicle purchase price. 

Incorporating new vehicles into a vehicle choice model, then, requires estimates of the 

changes in key attributes from conventional vehicles, and estimates of the value, also called 

the willingness to pay (WTP), that consumers put on those attributes. 

Electric vehicles (EVs) will have a number of changes in vehicle characteristics from 

any baseline model. EVs are likely to have a smaller driving range between refuelings than 

conventional vehicles, due to the large battery capacity needed to increase range. The ability 
Chapter 8 

8-14 

to recharge at home may be a convenient, desirable feature for people who have garages with 

electric hookups, but not for people who park on the street. If an infrastructure develops for 

recharging vehicles with the convenience approaching that of buying gasoline, range or home 

recharging may become less of a barrier to purchase. The reduced tailpipe emissions and 

reduced noise may be attractive features to some consumers.F

 They may have different 

performance or storage capacity. If sufficient data were available, the changes in these 

attributes, combined with WTP for each of the attributes, could be used to adjust the purchase 

price of the baseline vehicle to estimate consumers’ WTP for the electric version of a vehicle. 

Greene (2001), for instance, used this approach for a model that simulates choice, not only for 

EVs, but also for other alternative-fuel vehicles.52 In that model, he considers only one base 

vehicle, a passenger car, but considers the effect on WTP of fuel cost per mile, range, 

acceleration, and several other vehicle attributes. 

Vehicle driving range has received attention because of the current paucity of 

recharging infrastructure: if the driver of an EV gets low on fuel, it may be difficult to find a 

place to recharge. Because range appears to be a major factor in EV acceptability, it is 

starting to draw attention in the research community. 

In several studies, researchers have used stated preference conjoint analysis to 

estimate the effect of vehicle range on consumer vehicle choice. In a conjoint analysis, 

consumers are given a choice between several vehicles with different attributes. One choice 

might be, for instance, between a baseline car and another car with higher range and a higher 

purchase price. The choices that consumers make (e.g., how much higher does the purchase 

price have to be for the consumer not to choose more range?) provide data that can be used to 

estimate the role of vehicle attributes in the consumer’s choice. Stated preference analysis is 

sometimes considered less reliable than actual market behavior, because what people say they 

will do in hypothetical situations may not match what they would do in actual situations. On 

the other hand, stated preference methods can be used to study goods where market data do 

not exist, such as future market products undergoing development (marketing studies often 

use stated preference methods), or environmental goods. Because electric vehicles are not in 

widespread enough use for market studies, stated preference studies are, at this point, one of 

the few options to examine consumer behavior relating to these vehicles. 

Table 8.1-2 summarizes results from several conjoint studies that include the effects of 

extending range (in the table, from 150 to 300 miles, to present standardized results). 

Variation of results in the table is from income or other demographic factors, not from 

confidence intervals. The results suggest that the value of additional range varies among 

consumers, and the amount of that variation is changing (perhaps shrinking) in more recent 

studies. 

 

F

 For instance, Hidrue et al. (Hidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. 

“Willingness to Pay for Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 

686-705) find that some consumers are willing to pay $5100 more for vehicles with 95% lower emissions than 

the vehicles they otherwise aim to purchase. 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

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Table 8.1-2Willingness to Pay for Increasing Range Calculated from Various Studies 

Study (Date) Value of extending range 

from 150 to 300 miles (dollar 

year) 

Value of additional 

range in 2009$a 

Bunch et al. (1993)b $7,600 (1991$) $11,100 

Kavelek (1996) for California 

Energy Commissionc 

$2600 - $41,900 (1993$) $3700 - $58,700 

Resource Systems Group (2009) for 

California Energy Commissiond 

$2900 - $7500 (2009$) $2900 - $7500 

Hess et al. (2009), using the same 

data as Resource Systems Group 

(2009)e 

$2400 - $8500 (2009$) $2400 - $8500 

Hidrue et al. (2011)f $3776 - $10,399 (2009$) $3776 - $10,399 

a

Values adjusted to 2009$ using the Bureau of Economic Analysis GDP deflator. 

b

Bunch, David S., Mark Bradley, Thomas F. Golob, and Ryuichi Kitamura. “Demand for Clean-Fuel Vehicles 

in California: A Discrete-Choice Stated Preference Pilot Project.” Transportation Research Part A 27A(3) 

(1993): 237-253. The value of range was, in their model, assumed to be the same for all people. 

c

Kavelek, Chris. “CALCARS: The California Conventional and Alternative Fuel Response Simulator.” 

Demand Analysis Office, California Energy Commission, April 1996. The variation in values is due to 

willingness to pay (WTP) varying by income levels and for one-car and two-car households. The coefficient on 

range for one-car households was not statistically significantly different from zero (t-statistic = 1.5), but it was 

for 2-car households (t-statistic = 3.02). The minima and maxima presented here represent the values across 

both ownership and income categories. 

d

Resource Systems Groups, Inc. “Transportation Fuel Demand Forecast Household and Commercial Fleet 

Survey Task 8 Report: Logistic Regression Analysis and Results.” Prepared for California Energy Commission, 

June 2009. 

e

Hess, S., T. Adler, M. Fowler and A. Bahreinian “The Use of Cross-nested Logit Models for Multi-Dimensional 

Choice Processes: The Case of the Demand for Alternative Fuel Vehicles,” Proceedings of the 2009 European 

Transport Conference, Leiden, Netherlands, 2009. This study uses the same data as the Resource Systems 

Group study. The coefficient on range was not statistically significantly different from zero in these regressions: 

t-statistics varied from 1.29 to 1.52. The variation in values is due to willingness to pay (WTP) varying by 

income levels and statistical specification. The minima and maxima presented here represent the values across 

both income categories and specifications. 

f

Hidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. “Willingness to Pay for 

Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 686-705. The range of 
Chapter 8 

8-16 

values is due to the model separating consumers into “gasoline vehicle-oriented” and “electric vehicle-oriented” 

groups. The EV-oriented group has higher WTP for additional range. 

Driving range may be a major factor in consumers’ decisions on EVs, but it is not the 

only attribute that may be important to potential buyers (e.g., as noted, Hidrue et al. find that 

some consumers appear willing to pay substantially for reduced tailpipe emissions). A model 

that does not incorporate the other factors important to consumers’ decisions may not perform 

well in predicting vehicle purchases. In addition, as mentioned above, and as seen in Table 

8.1-2, it is likely that the WTP values for attributes of EVs will change over time, particularly 

if EVs are used more widely, the infrastructure to fuel the vehicles becomes more accessible, 

and consumers develop more familiarity and understanding of the vehicles. Thus, challenges 

associated with predicting market shares for EVs are even more serious than those already 

serious challenges associated with predicting market shares for conventional vehicles. 

8.1.2.8 EPA Exploration of Vehicle Choice Modeling 

In order to develop greater understanding of these models, EPA is in the process of 

developing a vehicle choice model. In its current form, the model assumes that the vehicle 

fleet and all characteristics of each vehicle, except vehicle prices and fuel economy, stay the 

same. The model will predict changes in the vehicle fleet, at the individual-configuration 

level and at more aggregated levels, in response to changes in vehicle fuel economy and price. 

The draft EPA model uses a nested logit structure common in the vehicle choice 

modeling literature, as discussed above in Chapter 8.1.2.1. “Nesting” refers to the decisiontree structure of the model, and “logit” refers to the fact that the choices are discrete (i.e., 

yes/no decisions about which vehicles to purchase, instead of continuous values). 

The nesting involves a hierarchy of choices. In its current form, at the initial decision 

node, consumers choose between buying a new vehicle or not. Conditional on choosing a 

new vehicle, consumers then choose between passenger vehicles, cargo vehicles, and ultraluxury vehicles. The next set of choices subdivides each of these categories into vehicle type 

(e.g., standard car, minivan, SUV, etc.). Next, the vehicle types are divided into classes 

(small, medium, and large SUVs, for instance), and then, at the bottom, are the individual 

vehicle configurations. 

At this bottom level, vehicles that are similar to each other (such as standard 

subcompacts, or prestige large vehicles) end up in the same “nest.” Substitution within a nest 

is considered much more likely than substitution across nests, because the vehicles within a 

nest are more similar to each other than vehicles in different nests. For instance, a person is 

more likely to substitute between a Chevrolet Aveo and a Toyota Yaris than between an Aveo 

and a pickup truck. In addition, substitution is greater at low decision nodes (such as 

individual configurations) than at higher decision nodes (such as the buy/no buy decision), 

because there are more choices at lower levels than at higher levels. 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

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Parameters for the model (including demand elasticities and the value of fuel economy 

in purchase decisions) are being selected based on a review of values found in the literature 

on vehicle choice modeling. As discussed above, a number of studies have estimated these 

parameters. Those estimates, combined with some theoretical requirements,G assist in 

assigning values for the parameters. The model will allow individual users to change those 

parameters. 

The fuel economy of a vehicle is used to adjust the price of the vehicle, using a 

version of the procedure discussed in Chapter 8.1.2.7: the value that the consumer places on 

fuel economy is multiplied by the change in fuel economy and incorporated into the “effective 

price” of the vehicle. In practice, implementing this calculation involves calculating the 

change in expenditures on fuel based on schedules of VMT, vehicle survival, and fuel prices 

in the future consistent with those in OMEGA. As discussed in Chapter 8.1.2.5, there is no 

consensus value for consumers’ willingness to pay for improved fuel economy: estimates 

vary tremendously. The model assumes that consumers will use some years of discounted 

fuel savings, with the modeler able to input both the number of years and the discount rate to 

be used in the analysis. 

The vehicle choice model will take as inputs an initial fleet of vehicles (including the 

initial sales and fuel economy) in the absence of standards, the cost of technologies added to 

each vehicle to comply with standards, and the change in fuel economy. With the initial sales 

mix, for each vehicle, the model calculates a vehicle-specific constant that summarizes the 

value of all attributes of the vehicle other than price and fuel economy. This constant ensures 

that the model will predict changes in consumer response that would result only from changes 

in price and fuel economy. This constant substitutes for estimating the effects of changes in 

all other vehicle characteristics; the underlying assumption is that these other vehicle 

characteristics do not change.H For instance, it assumes that a Ford Escape will not change in 

size, power, or accessories; the only changes will be to its cost and its fuel economy. 

The model assumes that the increase in vehicle cost associated with increased 

technology is fully passed through as an increase in vehicle price, and some years of fuel 

savings offset this price increase. It then calculates changes in total fleet size and in sales 

mix, at the individual-configuration level and at the level of vehicle class, due to the changes 

in fuel economy and vehicle prices. It also calculates changes in consumer surplus associated 

with the changes in fuel economy and vehicle prices. 

 

G The theory of nested logit requires that the price slopes (the change in utility as vehicle full price changes, a 

measure of consumer responsiveness to price changes) must be higher in absolute value for lower nests. This 

condition reflects the point, discussed above, that substitution is greater at lower decision notes than at higher 

ones. 

H As explained in Section III.D of the preamble, as part of the technology cost analysis for the proposed rule, the 

agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions. 
Chapter 8 

8-18 

It is possible that the predicted changes in fleet mix will lead to predictions of vehicle 

sales for auto makers that do not meet the proposed standards, because the mix and volume of 

vehicles sold changed from the initial levels. To correct this problem, it would be necessary 

to feed the new fleet mix into OMEGA (which calculates costs and compliance) and get a 

new set of output, which could then be fed back into the vehicle choice model. OMEGA 

would increase technologies, and thus costs, to improve compliance; those adjustments would 

then again affect vehicle demand. We expect that this iterative process would converge to a 

fleet mix that would meet standards. Performing this iteration requires development of an 

interface between the vehicle choice model and OMEGA to ensure accurate transmission of 

data between the models. At this time, the vehicle choice model takes output from OMEGA, 

but the results of the modeling do not feed easily back into OMEGA. Building this interface 

is an expected part of our future modeling work. 

The model is still undergoing development; EPA will seek peer review on it before it 

is utilized. In addition, concerns remain over the ability of any vehicle choice model to make 

reasonable predictions of the response of the vehicle fleet to changes in prices and vehicle 

characteristics. EPA seeks comments on the use of vehicle choice modeling for predicting 

changes in fleet mix due to policies, and on methods to test the ability of a vehicle choice 

model to produce reasonable estimates of changes in fleet mix. 

8.1.2.9 Summary and Additional Considerations 

Consumer vehicle choice modeling in principle can provide a great deal of useful 

information for regulatory analysis, helping to answer some of the central questions about 

relevant effects on consumer welfare. In practice, the advantages depend on the success of 

models in predicting changes in fleet size and mix. 

First, consumer vehicle choice modeling has the potential to describe more accurately 

the impact of a policy, by identifying market shifts. More accurate description of the market 

resulting from a policy can improve other estimates of policy impacts, such as the change in 

total vehicle emissions or vehicle miles traveled. The predictive ability of models, though, is 

not proven. 

Vehicle choice models can incorporate the effects on consumer decisions of changes 

in vehicle characteristics, if there are estimates of the value that consumers put on changes in 

those characteristics. These willingness-to-pay values may, however, be sensitive to the ways 

they are estimated, as indicated in the discussion of the value that consumers place on fuel 

economy in their purchase decisions. Especially for characteristics associated with advanced 

technology vehicles, such as EVs, the willingness-to-pay values may change over time as 

consumers develop more experience with the vehicles and these characteristics. Models 

based on current estimates may not predict well for the future. 

The modeling may improve estimates of the compliance costs of a rule. Consumers 

can either accept the new costs and vehicle characteristics, or they can change which vehicles 

they buy. Using a vehicle choice model is likely to reduce compliance costs: because the 

model allows consumers to choose among accepting the new vehicle, buying a different 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-19 

vehicle, or not buying a vehicle, consumers have additional options, which improves their 

welfare relative to the assumption that consumers will not change their buying behavior. . 

An additional complication associated with consumer choice modeling is accurate 

prediction of producers’ responses to the rule. While it is possible to include auto makers’ 

decisions (for instance, on setting prices) into vehicle choices, computational limits affect the 

richness of these models. Technology costs, while an accurate measure of the opportunity 

cost of resources to society, may overestimate or underestimate the effect on the prices that 

consumers face. 

Consumer choice models can be used to calculate consumer surplus impacts on 

vehicle purchase decisions. Because these values are based on the estimates of changes in 

vehicle sales and fleet mix, consumer surplus measures may not be accurate if the changes in 

vehicle sales and fleet mix are not well estimated. 

Principles of welfare analysis can be useful for understanding the role of consumer 

vehicle choice models in benefit-cost analysis. In particular, except for EVs, the technology 

cost estimates developed in this proposal take into account the costs to hold other vehicle 

attributes, such as size and performance, constant. In addition, the analysis assumes that the 

full technology costs are passed along to vehicle buyers. With these assumptions, because 

welfare losses are monetary estimates of how much buyers would have to be compensated to 

be made as well off as in the absence of the change,I

 the price increase measures the loss to 

the buyer.J

 Assuming that the full technology cost gets passed along to the buyer as an 

increase in price, the technology cost thus measures the welfare loss to the buyer. Increasing 

fuel economy would have to lead to other changes in the vehicles that buyers find undesirable 

for there to be additional losses not included in the technology costs. 

Given the current limitations in modeling the role of fuel economy in vehicle purchase 

decisions, and limitations in modeling market responses to the new regulations, in this 

proposal EPA holds constant the vehicle fleet size and mix in its calculations of the impacts of 

this rule, and compares the fuel and other savings that consumers will receive with the 

technology costs of the vehicles. EPA continues to explore options for including consumer 

 

I

 This approach describes the economic concept of compensating variation, a payment of money after a change 

that would make a consumer as well off after the change as before it. A related concept, equivalent variation, 

estimates the income change that would be an alternative to the change taking place. The difference between 

them is whether the consumer’s point of reference is her welfare before the change (compensating variation) or 

after the change (equivalent variation). In practice, these two measures are typically very close together. 

J

 Indeed, it is likely to be an overestimate of the loss to the buyer, because the buyer has choices other than 

buying the same vehicle with a higher price; she could choose a different vehicle, or decide not to buy a new 

vehicle. The buyer would choose one of those options only if the alternative involves less loss than paying the 

higher price. Thus, the increase in price that the buyer faces would be the upper bound of loss of consumer 

welfare, unless there are other changes to the vehicle due to the fuel economy improvements that make the 

vehicle less desirable to buyers. 
Chapter 8 

8-20 

and producer choice in modeling the impacts of fuel economy-related regulations. This effort 

includes further review of existing consumer vehicle choice models, the estimates of 

consumers’ willingness to pay for increased fuel economy, and overall effects on consumer 

welfare, as well as EPA’s exploration of a vehicle choice model for use in the future. 

8.2 Employment Impacts 

8.2.1 Introduction 

Although analysis of employment impacts is not part of a cost-benefit analysis (except 

to the extent that labor costs contribute to costs), employment impacts of federal rules are of 

particular concern in the current economic climate of sizeable unemployment. When 

President Obama requested that the agencies develop this program, he sought a program that 

would “strengthen the [auto] industry and enhance job creation in the United States.”53,K The 

recently issued Executive Order 13563, “Improving Regulation and Regulatory Review” 

(January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety, 

and our environment while promoting economic growth, innovation, competitiveness, and job 

creation” (emphasis added). EPA is accordingly providing partial estimates of the effects of 

this proposal on domestic employment in the auto manufacturing and parts sectors, while 

qualitatively discussing how it may affect employment in other sectors more generally. 

This proposal is expected to affect employment in the United States through the 

regulated sector – the auto manufacturing industry – and through several related sectors, 

specifically, industries that supply the auto manufacturing industry (e.g., vehicle parts), auto 

dealers, the fuel refining and supply sectors, and the general retail sector. According to the 

U.S. Bureau of Labor Statistics, in 2010, about 677,000 people in the U.S. were employed in 

Motor Vehicle and Parts Manufacturing Sector (NAICS 3361, 3362, and 3363). About 

129,000 people in the U.S. were employed in the Automobile and Light Truck Manufacturing 

Sector (NAICS 33611) in December 2010; this is the directly regulated sector, since it 

encompasses the auto manufacturers that are responsible for complying with the proposed 

standards.54 Changes in light duty vehicle sales, discussed in Chapter 8.1.1, could affect 

employment for auto dealers. Though some of the parts used to achieve the proposed 

standards are likely to be built by auto manufacturers themselves, the auto parts 

manufacturing sector also plays a significant role in providing those parts, and will also be 

affected by changes in vehicle sales. As discussed in Chapter 5.4 of the RIA, this proposal is 

expected to reduce the amount of fuel these vehicles use, and thus affect the petroleum 

refinery and supply industriesWhen the economy is at full employment, an environmental 

regulation is unlikely to have much impact on net overall U.S. employment; instead, labor 

would primarily be shifted from one sector to another. These shifts in employment impose an 

opportunity cost on society, approximated by the wages of the employees, as regulation 

diverts workers from other activities in the economy. In this situation, any effects on net 

 

K The May 21, 2010 Presidential Memorandum also requested that EPA and NHTSA, in developing the 

technical assessment to inform the rulemaking process (which was issued by the agencies and CARB on 

September 30, 2010), include, among other things, the “impacts on jobs and the automotive manufacturing base 

in the United States.” 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

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8-21 

employment are likely to be transitory as workers change jobs (e.g., some workers may need 

to be retrained or require time to search for new jobs, while shortages in some sectors or 

regions could bid up wages to attract workers). 

On the other hand, if a regulation comes into effect during a period of high 

unemployment, a change in labor demand due to regulation may affect net overall U.S. 

employment because the labor market is not in equilibrium. In such a period, both positive 

and negative employment effects are possible. (Cite Masur and Posner, 

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1920441) Schmalansee and Stavins point 

out that net positive employment effects are possible in the near term when the economy is at 

less than full employment due to the potential hiring of idle labor resources by the regulated 

sector to meet new requirements (e.g., to install new equipment) and new economic activity in 

sectors related to the regulated sector.55 In the longer run, the net effect on employment is 

more difficult to predict and will depend on the way in which the related industries respond to 

the regulatory requirements. As Schmalansee and Stavins note, it is possible that the 

magnitude of the effect on employment could vary over time, region, and sector, and positive 

effects on employment in some regions or sectors could be offset by negative effects in other 

regions or sectors. For this reason, they urge caution in reporting partial employment effects 

since it can “paint an inaccurate picture of net employment impacts if not placed in the 

broader economic context.” 

Given the time frame of this proposed rule, it is possible that the state of the overall 

economy may have improved to the point where the only effect of changes in employment 

associated with the standards proposed here will be to shift workers from one sector to 

another. For example, to the extent that regulation increases the number of workers in the 

regulated sector (e.g., because of higher costs of producing vehicles with higher fuel 

economy), the opportunity cost associated with those shifts in employment will be captured in 

labor costs, as regulation diverts workers from other activities in the economy. In this 

situation, any effects on net employment are likely to be transitory as workers change jobs 

(e.g., some workers may need to be retrained or require time to search for new jobs, while 

shortages in some sectors or regions could bid up wages to attract workers). It is also possible 

that the state of the economy will be such that positive or negative employment effects will 

occur. 

A number of different approaches have been used in published literature to conduct 

employment analysis. This section describes some of the common methods, as well as some 

of their limitations. 

8.2.2 Approaches to Quantitative Employment Analysis 

Measuring the employment impacts of a policy depend on a number of inputs and 

assumptions. For instance, as discussed, assumptions about the overall state of 

unemployment in the economy play a major role in measured job impacts. The inputs to the 

models commonly are the changes in quantities or expenditures in the affected sectors; model 
Chapter 8 

8-22 

results may vary in different studies depending on the assumptions about the levels of those 

inputs, and which sectors receive those changes. Which sectors are included in the study can 

also affect the results. For instance, a study of this program that looks only at employment 

impacts in the refinery sector may find negative effects, because consumers will purchase less 

gasoline; a study that looks only at the auto parts sector, on the other hand, may find positive 

impacts, because the program will require redesigned or additional parts for vehicles. In both 

instances, these would only be partial perspectives on the overall change in national 

employment due to Federal regulation. 

8.2.2.1 Conceptual Framework for Employment Impacts in the 

Regulated Sector 

One study by Morgenstern, Pizer, and Shih56 provides a retrospective look at the 

impacts of regulation in employment in the regulated sectors by estimating the effects on 

employment of spending on pollution abatement for four highly polluting/regulated U.S. 

industries (pulp and paper, plastics, steel, and petroleum refining) using data for six years 

between 1979 and 1991. The paper provides a theoretical framework that can be useful for 

examining the impacts of a regulatory change on the regulated sector in the medium to longer 

term. In particular, it identifies three separate ways that employment levels may change in the 

regulated industry in response to a new (or more stringent) regulation. 

 Demand effect: higher production costs due to the regulation will lead to higher 

market prices; higher prices in turn reduce demand for the good, reducing the 

demand for labor to make that good. In the authors’ words, the “extent of this 

effect depends on the cost increase passed on to consumers as well as the demand 

elasticity of industry output.” 

 Cost effect: as costs go up, plants add more capital and labor (holding other 

factors constant), with potentially positive effects on employment. In the authors’ 

words, as “production costs rise, more inputs, including labor, are used to produce 

the same amount of output.” 

 Factor shift effect: post-regulation production technologies may be more or less 

labor-intensive (i.e., more/less labor is required per dollar of output). In the 

authors’ words, “environmental activities may be more labor intensive than 

conventional production,” meaning that “the amount of labor per dollar of output 

will rise,” though it is also possible that “cleaner operations could involve 

automation and less employment, for example.” 

According to the authors, the “demand effect” is expected to have a negative effect on 

employment,L

 the “cost effect” to have a positive effect on employment, and the “factor shift 

 

L

 As will be discussed below, the demand effect in this proposal is potentially an exception to this rule. While 

the vehicles become more expensive, they also produce reduced fuel expenditures; the reduced fuel costs provide 

a countervailing impact on vehicle sales. As discussed in Preamble Section III.H.1, this possibility that vehicles 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-23 

effect” to have an ambiguous effect on employment. Without more information with respect 

to the magnitudes of these competing effects, it is not possible to predict the total effect 

environmental regulation will have on employment levels in a regulated sector. 

Morgenstern et al. conclude that increased abatement expenditures generally have not 

caused a significant change in employment in those sectors. More specifically, their results 

show that, on average across the industries studied, each additional $1 million spent on 

pollution abatement results in a (statistically insignificant) net increase of 1.5 jobs. 

This approach to employment analysis has the advantage of carefully controlling for 

many possibly confounding effects in order to separate the effect of changes in regulatory 

costs on employment. It was, however, conducted for only four sectors. It could also be very 

difficult to update the study for other sectors, because one of the databases on which it relies, 

the Pollution Abatement Cost and Expenditure survey, has been conducted infrequently since 

1994, with the last survey conducted in 2005. The empirical estimates provided by 

Morgenstern et al. are not relevant to the case of fuel economy standards, which are very 

different from the pollution control standards on industrial facilities that were considered in 

that study. In addition, it does not examine the effects of regulation on employment in sectors 

related to but outside of the regulated sector. Nevertheless, the theory that Morgenstern et al. 

developed continues to be useful in this context. 

The following discussion of additional methodologies draws from Berck and 

Hoffmann’s review of employment models.57 

8.2.2.2 Computable General Equilibrium (CGE) Models 

Computable general equilibrium (CGE) models are often used to assess the impacts of 

policy. These models include a stylized representation of supply and demand curves for all 

major markets in the economy. The labor market is commonly included. CGE models are 

very useful for looking at interaction effects of markets: “they allow for substitution among 

inputs in production and goods in consumption.” Thus, if one market experiences a change, 

such as a new regulation, then the effects can be observed in all other markets. As a result, 

they can measure the employment changes in the economy due to a regulation. Because they 

usually assume equilibrium in all markets, though, they typically lack involuntary 

unemployment. If the total amount of labor changes, it is due to people voluntarily entering 

or leaving the workforce. As a result, these models may not be appropriate for measuring 

effects of a policy on unemployment, because of the assumption that there is no involuntary 

unemployment. In addition, because of the assumptions of equilibrium in all markets and 

forward-looking consumers and firms, they are designed for examining the long-run effects of 

a policy but may offer little insight into its short-run effects. 

 

may become more attractive to consumers after the program poses a conundrum: why have interactions between 

vehicle buyers and producers not provided these benefits without government intervention? 
Chapter 8 

8-24 

8.2.2.3 Input-Output (IO) Models 

Input-output models represent the economy through a matrix of coefficients that 

describe the connections between supplying and consuming sectors. In that sense, like CGE 

models, they describe the interconnections of the economy. These interconnections look at 

how changes in one sector ripple through the rest of the economy. For instance, a 

requirement for additional technology for vehicles requires additional steel, which requires 

more workers in both the auto and steel sectors; the additional workers in those sectors then 

have more money to spend, which leads to more employment in retail sectors. These are 

known as “multiplier” effects, because an initial impact in one sector gets multiplied through 

the economy. Unlike CGE models, input-output models have fixed, linear relationships 

among the sectors (e.g., substitution among inputs or goods is not allowed), and quantity 

supplied need not equal quantity demanded. In particular, these models do not allow for price 

changes – an increase in the demand for labor or capital does not result in a change in its price 

to help reallocate it to its best use. As a result, these models cannot capture opportunity costs 

from using resources in one area of the economy over another. The multipliers take an initial 

impact and can increase it substantially. 

IO models are commonly used for regional analysis of projects. In a regional analysis, 

the markets are commonly considered small enough that wages and prices are determined 

outside the region, and any excess supply or demand is due to exports and imports (or, in the 

case of labor, emigration or immigration). For national-level employment analysis, the use of 

input-output models requires the assumption that workers flow into or out of the labor market 

perfectly freely. Wages do not adjust; instead, people join into or depart from the labor pool 

as production requires them. For other markets as well, there is no substitution of less 

expensive inputs for more expensive ones. As a result, IO models provide an upper bound on 

employment impacts. As Berck and Hoffmann note, “For the same reason, they can be 

thought of as simulating very short-run adjustment,” in contrast to the CGE’s implicit 

assumption of long-run adjustment. Changes in production processes, introductions of new 

technologies, or learning over time due to new regulatory requirements are also generally not 

captured by IO models, as they are calibrated to already established relationships between 

inputs and outputs. 

8.2.2.4 Hybrid Models 

As Berck and Hoffmann note, input-output models and CGE models “represent a 

continuum of closely related models.” Though not separately discussed by Berck and 

Hoffmann, some hybrid models combine some of the features of CGE models (e.g., prices 

that can change) with input-output relationships. For instance, a hybrid model may include 

the ability to examine disequilibrium phenomena, such as labor being at less than full 

employment. Hybrid models depend on assumptions about how adjustments in the economy 

occur. CGE models characterize equilibria but say little about the pathway between them, 

while IO models assume that adjustments are largely constrained by previously defined 

relationships; the effectiveness of hybrid models depends on their success in overcoming the 

limitations of each of these approaches. Hybrid models could potentially be used to model 

labor market impacts of various vehicle policy options although a number of judgments need 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-25 

to be made about the appropriate assumptions underlying the model as well the empirical 

basis for the modeling results. 

8.2.2.5 Single Sectors 

It is possible to conduct a bottom-up analysis of the partial effect of regulation on 

employment in a single sector by estimating the change in output or expenditures in a sector 

and multiplying it by an estimate of the number of workers per unit of output or expenditures, 

under the assumption that labor demand is proportional to output or expenditures. As Berck 

and Hoffmann note, though, “Compliance with regulations may create additional jobs that are 

not accounted for.” While such an analysis can approximate the effects in that one sector in a 

simple way, it also may miss important connections to related sectors. 

8.2.2.6 Ex-Post Econometric Studies 

A number of ex-post econometric analyses examine the net effect of regulation on 

employment in regulated sectors. Morgenstern, Pizer, and Shih (2002), discussed above, and 

Berman and Bui (2001) are two notable examples that rely on highly disaggregated 

establishment-level time series data to estimate longer-run employment effects.58 While often 

a sophisticated treatment of the issues analyzed, these studies commonly analyze specific 

scenarios or sectors in the past; care needs to be taken in extrapolating their results to other 

scenarios and to the future. For instance, neither of these two studies examines the auto 

industry and are therefore of limited applicability in this context. 

8.2.2.7 Summary 

All methods of estimating employment impacts of a regulation have advantages and 

limitations. CGE models may be most appropriate for long-term impacts, but the usual 

assumption of equilibrium in the employment market means that it is not useful for looking at 

changes in overall employment: overall levels are likely to be premised on full employment. 

IO models, on the other hand, may be most appropriate for small-scale, short-term effects, 

because they assume fixed relationships across sectors and do not require market equilibria. 

Hybrid models, which combine some features of CGEs with IO models, depend upon key 

assumptions and economic relationships that are built into them. Single-sector models are 

simple and straightforward, but they are often based on the assumptions that labor demand is 

proportional to output, and that other sectors are not affected. Finally, econometric models 

have been developed to evaluate the longer-run net effects of regulation on sector 

employment, though these are ex-post analyses commonly of specific sectors or situations, 

and the results may not have direct bearing for the regulation being reviewed. 

8.2.3 Employment analysis of this proposal 

As mentioned above, this program is expected to affect employment in the regulated 

sector (auto manufacturing) and other sectors directly affected by the proposal: auto parts 

suppliers, auto dealers, the fuel supply market (which will face reduced petroleum production 
Chapter 8 

8-26 

due to reduced fuel demand but which may see additional demand for electricity or other 

fuels), and consumers (who will face higher vehicle costs and lower fuel expenditures). In 

addition, as the discussion above suggests, each of these sectors could potentially have ripple 

effects in the rest of the economy. These ripple effects depend much more heavily on the 

state of the macroeconomy than do the direct effects. At the national level, employment may 

increase in one industry or region and decrease in another, with the net effect being smaller 

than either individual-sector effect. EPA does not attempt to quantify the net effects of the 

regulation on overall national employment. 

The discussion that follows provides a partial, bottom-up quantitative estimate of the 

effects of this proposal on the regulated sector (the auto industry; for reasons discussed below, 

we include some quantitative assessment of effects on suppliers to the industry although they 

are not regulated directly). It also includes qualitative discussion of the effects of the proposal 

on other sectors. Focusing quantification of employment impacts on the regulated sector has 

some advantages over quantifying all impacts. First, the analysis relies on data generated as 

part of the rulemaking process, which focuses on the regulated sector; as a result, what is 

presented here is based on internally consistent assumptions and estimates made in this 

proposal. Secondly, as discussed above, net effects on employment in the economy as a 

whole depend heavily on the overall state of the economy when this rule has its effects. 

Focusing on the regulated sector provides insight into employment effects in that sector 

without having to make assumptions about the state of the economy when this rule has its 

impacts. We include a qualitative discussion of employment effects on other sectors to 

provide a broader perspective on the impacts of this rule. 

As noted above, in a full-employment economy, any changes in employment will 

result from people changing jobs or voluntarily entering or exiting the workforce. In a fullemployment economy, employment impacts of this proposal will change employment in 

specific sectors, but it will have small, if any, effect on aggregate employment. This rule 

would take effect in model years 2017 through 2025; by then, the current high unemployment 

may be moderated or ended. For that reason, this analysis does not include multiplier effects, 

but instead focuses on employment impacts in the most directly affected industries. Those 

sectors are likely to face the most concentrated employment impacts. The agencies seek 

comment on other sectors that are likely to be significantly affected and thus warrant further 

analysis in the final rulemaking analysis. 

8.2.3.1 Employment Impacts in the Auto Industry 

If vehicle sales decrease, employment associated with these activities will 

unambiguously decrease. The sign of demand effect will depend on which of these effects 

dominates. Because, as described in Chapter 8.1, we have not quantified the impact on sales 

for this proposal, we do not quantify the demand effect. 

8.2.3.1.1 The Cost Effect

Employment may increase because manufacturers need additional people to 

implement the new or additional technologies needed for vehicles to comply with the 

proposed standards. 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-27 

One way to gauge this the cost effect, is to use the ratio of workers to each $1 million 

of expenditures in that sector. The use of these ratios has both advantages and limitations. It 

is often possible to estimate these ratios for quite specific sectors of the economy: for 

instance, it is possible to estimate the average number of workers in the light-duty vehicle 

manufacturing sector per $1 million spent in the sector, rather than use the ratio from another, 

more aggregated sector, such as motor vehicle manufacturing. As a result, it is not necessary 

to extrapolate employment ratios from possibly unrelated sectors. On the other hand, these 

estimates are averages for the sectors, covering all the activities in those sectors; they may not 

be representative of the labor required when expenditures are required on specific activities, 

as the factor shift effect (discussed below) indicates. For instance, the ratio for the motor 

vehicle manufacturing sector represents the ratio for all vehicle manufacturing, not just for 

fuel efficiency improvements. In addition, these estimates do not include changes in sectors 

that supply these sectors, such as steel or electronics producers. They thus may best be 

viewed as the effects on employment in the auto sector due to the changes in expenditures in 

that sector, rather than as an assessment of all employment changes due to these changes in 

expenditures. 

Some of the costs of this proposal will be spent directly in the auto manufacturing 

sector, but some of the costs will be spent in the auto parts manufacturing sector. Because we 

do not have information on the proportion of expenditures in each sector, we separately 

present the ratios for both the auto manufacturing sector and the auto parts manufacturing 

sector. These are not additive, but should instead be considered as a range of estimates for the 

cost effect, depending on which sector adds technologies to the vehicles to comply with the 

regulation. 

We use several public sources for estimates of employment per $1 million 

expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment 

Requirements Matrix (ERM),59 which provides direct estimates of the employment per $1 

million in sales of goods in 202 sectors. The most recent estimates, used here, are from 2008 

(adjusted to 2009$). The tables used here are adjusted to remove the employment effects of 

imports through use of the ratio of domestic production to domestic sales, described above, of 

0.667. The values reported are for Motor Vehicle Manufacturing (NAICS 3361) and Motor 

Vehicle Parts Manufacturing (NAICS 3363). 

The Annual Survey of Manufactures60 (ASM) provides another source of estimates 

based on a sample of 50,000 establishments out of a universe of 346,000 manufacturing 

establishments. It includes more sectoral detail than the BLS ERM: for instance, while the 

ERM includes the Motor Vehicle Manufacturing sector, the ASM has detail at the 6-digit 

NAICS code level (e.g., automobile manufacturing vs. light truck and utility vehicle 

manufacturing). While the ERM provides direct estimates of employees/$1 million in 

expenditures, the ASM separately provides number of employees and value shipments; the 

direct employment estimates here are the ratio of those values. The data in the ASM are 

updated annually, except for years when the full Economic Census occurs. The tables 

presented here use data from 2009. As with the ERM, we adjust for the ratio of domestic 
Chapter 8 

8-28 

production to domestic sales. The values reported are for Motor Vehicle Manufacturing 

(NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), 

and Motor Vehicle Parts Manufacturing (NAICS 3363). 

The Economic Census includes all large companies and a sample of smaller ones. The 

ASM is a subset of the Economic Census; though the Census itself is more complete, it is 

conducted only every 5 years, while the ASM is annual. The values presented here use data 

from 2007 (adjusted to 2009$), with the domestic production-to-sales adjustment. The values 

reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty 

Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing 

(NAICS 3363). 

Table 8.2-1 provides the values, either given (BLS) or calculated (ASM, Economic 

Census) for employment per $1 million of expenditures, all based on 2009 dollars, though the 

underlying data come from different years (which may account for some of the differences). 

The different data sources provide similar magnitudes for the estimates for the sectors. Parts 

manufacturing appears to be more labor-intensive than vehicle manufacturing; light-duty 

vehicle manufacturing appears to be slightly less labor-intensive than motor vehicle 

manufacturing as a whole. 

Table 8.2-1 Employment per $1 Million Expenditures (2009$) in the Motor Vehicle 

Manufacturing Sector* 

Source Sector Ratio of 

workers per 

$1 million 

expenditures

Ratio of workers per $1 

million expenditures, 

adjusted for domestic vs. 

foreign production 

BLS ERM Motor Vehicle Mfg 0.834 0.556

ASM Motor Vehicle Mfg 0.824 0.549

ASM Light Duty Vehicle 

Mfg 

0.757 0.505

Economic 

Census 

Motor Vehicle Mfg 0.674 0.449

Economic 

Census 

Light Duty Vehicle 

Mfg 

0.610 0.407

BLS ERM Motor Vehicle Parts 

Mfg 

3.073 2.049

ASM Motor Vehicle Parts 

Mfg 

3.093 2.063

Economic 

Census 

Motor Vehicle Parts 

Mfg 

2.749 1.833
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-29 

BLS ERM refers to the U.S. Bureau of Labor Statistics’ Employment Requirement Matrix. ASM refers to the 

U.S. Census Bureau’s Annual Survey of Manufactures. Economic Census refers to the U.S. Census Bureau’s 

Economic Census.

Over time, the amount of labor needed in the auto industry has changed: automation 

and improved production methods have led to significant productivity increases. The BLS 

ERM, for instance, provided estimates that, in 1993, 1.52 workers were needed per $1 million 

of 2000$, but only 0.83 workers by 2008 (in 2000$).61 Because the ERM is available 

annually for 1993-2008, we used these data to estimate productivity improvements over time. 

We regressed logged ERM values on year (to estimate percent change per year) for both the 

Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing sectors. The results 

suggest a 4.4 percent per year productivity improvement in the Motor Vehicle Manufacturing 

Sector, and a 3.6 percent per year improvement in the Motor Vehicle Parts Manufacturing 

Sector. We then used the regression relationship to project the ERM through 2025. In the 

results presented below, these projected values (adjusted to 2009$) were used directly for the 

BLS ERM estimates. For the ASM, we used the ratio of the projected value in the future to 

the projected value in 2009 (the base year for the ASM); for the Economic Census estimates, 

we used the ratio of the projected value in the future to the projected value in 2007 (the base 

year for that estimate). As noted above, we adjusted the estimate of workers per vehicle for 

the demand effect in Chapter Error! Reference source not found., above, using the 

productivity improvement for the Motor Vehicle Manufacturing sector; because the estimate 

of workers per vehicle for the demand effect was based on data from 2001 to 2010, we used 

the projected value of the ERM in 2005 as the denominator of the ratio. This is a simple way 

to examine the relationship between labor required and expenditure and we seek comment on 

refining this method. 

Table 8.2-2 shows the cost estimates developed for this rule, discussed in Chapter 5. 

The maximum value in Table 8.2-2 for employment impacts per $1 million expenditures 

(after accounting for the share of domestic production) is 2.049 in 2009 if all the additional 

costs are in the parts sector; the minimum value is 0.407 in 2009, if all the additional costs are 

in the light-duty vehicle manufacturing sector: that is, the range of employment impacts is 

between 0.4 and 2 additional jobs per $1 million expenditures in the sector in 2009. The 

results in Table 8.2-2 include the productivity adjustment described above. 

While we estimate employment impacts beginning with the first year of the standard 

(2017), some of these job gains may occur earlier as auto manufacturers and parts suppliers 

hire staff in anticipation of compliance with the standard. Also, because of the uncertainty 

associated of projecting average labor required associated with the cost of production, we 

limit the projection to 2020. 

Table 8.2-2 Employment due to Cost Effect in the Motor Vehicle Manufacturing Sector 

Year Costs (before 

adjustment for 

domestic proportion 

Minimum 

employment effect 

(if all expenditures 

Maximum 

employment effect 

(if all expenditures 
Chapter 8 

8-30 

of production) 

($Millions)

are in the parts 

sector) 

are in the light 

duty vehicle mfg 

sector) 

2017 $ 2,300 600 3,600 

2018 $ 4,656 1,200 7,000 

2019 $ 6,507 1,600 9,400 

2020 $ 8,467 1,900 11,800 

 

8.2.3.1.2 The Factor Shift Effect 

The factor shift effect looks at the effects on employment due to changes in labor 

intensity associated with a regulation. As noted above, the estimates of the cost effect assume 

constant labor per $1 million in expenditures, though the new technologies may be either 

more or less labor-intensive than the existing ones. An estimate of the factor shift effect 

would either increase or decrease the estimate used for the cost effect. 

We are not quantifying the factor shift effect here, for lack of data on the labor 

intensity of all the possible technologies that manufacturers could use to comply with the 

proposed standards. For a subset of the technologies, though, EPA-sponsored research 

(discussed in Chapter 3.2.1.1 of the Joint TSD) which compared new technologies to existing 

ones at the level of individual components provides some insights into the factor shift effect. 

The comparison involved tearing down the selected technologies to their individual 

components and looking at the differences in materials and labor needs in moving from the 

conventional to the new technologies.62 For instance, the analysis compared all the parts and 

labor associated with an 8-speed automatic transmission to those needed for a 6-speed 

automatic transmission. 

Because labor cost was one of the sources of differences between the technologies, it 

is possible, for those technologies, to see whether labor needs increase or decrease with the 

switch to technologies that might contribute to compliance with the proposed standards. An 

increase in labor cost for the new technology indicates an increase in the labor needed for the 

new technology compared to the baseline technology. For instance, an 8-speed transmission 

requires $15.11 more in labor costs than a 6-speed transmission (as accounted for in EPA’s 

cost estimates for the proposed rule). Dividing the labor cost by a wage per hour estimate 

provides an estimate of the additional hours (and thus the additional labor) needed for the new 

technology compared to the baseline technology. As with labor cost, an increase in labor 

hours per technology indicates greater employment needs for the new technologies. For this 

conversion, a weighted average wage rate (90 percent of the average wage in the Motor 

Vehicle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle 

Manufacturing Sector) of $46.36/hour in 2015, using 2008 dollars (the unit of analysis for the 

Comment [A1]: Interagency commenters 

recommend agencies only go to 2020 becusae there 

will be a mid-term review. 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-31 

FEV study). For the change from a 6-speed to an 8-speed transmission, we thus estimate an 

additional 0.33 hours of labor per transmission. 

8.2.4 Effects on Employment for Auto Dealers 

The effects of the proposed standards on employment for auto dealers depend 

principally on the effects of the standards on light duty vehicle sales. In addition, auto dealers 

may be affected by changes in maintenance and service costs. Increases in those costs are 

likely to increase labor demand in dealerships. 

Although this proposal predicts very small penetration of advanced technology 

vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. 

As discussed in Chapter 8.1.2.7, consumers may find some characteristics of electric vehicles 

and plug-in hybrid electric vehicles, such as the ability to fuel with electricity rather than 

gasoline, attractive; they may find other characteristics, such as the limited range for electric 

vehicles, undesirable. As a result, some consumers will find that EVs will meet their needs, 

but other buyers will choose more conventional vehicles. Auto dealers may play a major role 

in explaining the merits and disadvantages of these new technologies to vehicle buyers. There 

may be a temporary need for increased employment to train sales staff in the new 

technologies as the new technologies become available. 

8.2.5 Effects on Employment in the Auto Parts Sector 

As discussed in the context of employment in the auto industry, some vehicle parts are 

made in-house by auto manufacturers; others are made by independent suppliers who are not 

directly regulated, but who will be affected by the proposed standards as well. The additional 

expenditures on technologies are expected to have a positive effect on employment in the 

parts sector as well as the manufacturing sector; the breakdown in employment between the 

two sectors is difficult to predict. The effects on the parts sector also depend on the effects of 

the proposed standards on vehicle sales and on the labor intensity of the new technologies, 

qualitatively in the same ways as for the auto manufacturing sector. 

8.2.6 Effects on Employment for Fuel Suppliers 

In addition to the effects on the auto manufacturing and parts sectors, these rules will 

result in changes in fuel use that lower GHG emissions. Fuel saving, principally reductions in 

liquid fuels such as gasoline and diesel, will affect employment in the fuel suppliers industry 

sectors throughout the supply chain, from refineries to gasoline stations. To the extent that 

the proposed standards result in increased use of electricity or other new fuels, employment 

effects will result from providing these fuels and developing the infrastructure to supply them 

to consumers. 

Expected petroleum fuel consumption reductions can be found in Chapter 5.3. While 

this reduced consumption represents fuel savings for purchasers of fuel, it represents a loss in 

value of output for the petroleum refinery industry, fuel distributors, and gasoline stations. 
Chapter 8 

8-32 

The loss of expenditures to petroleum fuel suppliers throughout the petroleum fuel supply 

chain, from the petroleum refiners to the gasoline stations, is likely to result in reduced 

employment in these sectors. 

This rule is also expected to lead to increases in electricity consumption by vehicles, 

as discussed in Chapter 5.3. This new fuel may require additional infrastructure, such as 

electricity charging locations. Providing this infrastructure will require some increased 

employment. In addition, the generation of electricity is likely to require some additional 

labor. We have insufficient information at this time to predict whether the increases in labor 

associated with increased infrastructure provision and generation for electricity will be greater 

or less than the employment reductions associated with reduced demand for petroleum fuels. 

8.2.7 Summary 

The primary employment effects of this proposal are expected to be found throughout 

several key sectors: auto manufacturers, auto dealers, auto parts manufacturing, and fuel 

production and supplyThese proposed standards initially take effect in model year 2017, a 

time period sufficiently far in the future that the current sustained high unemployment at the 

national level may be moderated or ended. In an economy with full employment, the primary 

employment effect of a rulemaking is likely to be to move employment from one sector to 

another, rather than to increase or decrease employment. For that reason, we focus our partial 

quantitative analysis on employment in the regulated sector, to examine the impacts on that 

sector directly. We discuss the likely direction of other impacts in the regulated sector as well 

as in other directly related sectors, but we do not quantify those impacts, because they are 

more difficult to quantify with reasonable accuracy, particularly so far into the future. 

References 

1

 See, for instance, Gron, Ann, and Deborah Swenson, 2000. “Cost Pass-Through in the U.S. 

Automobile Market,” Review of Economics and Statistics 82: 316-324 (Docket EPA-HQOAR-2010-0799). 

2

 Insurance Information Institute, 2008, “Average Expenditures for Auto Insurance By State, 

2007-2008,” http://www.iii.org/media/facts/statsbyissue/auto/, accessed 8/22/11 (Docket 

EPA-HQ-OAR-2010-0799). 

3

 U.S. Department of Energy, 2011. “Transportation and the Economy,” Chapter 10 in 

“Transportation Energy Data Book,” http://cta.ornl.gov/data/tedb30/Edition30_Chapter10.pdf, 

accessed 8/22/11, Table 11 (Docket EPA-HQ-OAR-2010-0799). 

4

 Solheim, Mark, 2006 “State Car Tax Rankings,” 

http://www.kiplinger.com/features/archives/2006/04/cartax.html, accessed April 23, 2009 

(Docket EPA-HQ-OAR-2009-0472-0010) (Docket EPA-HQ-OAR-2010-0799). 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-33 

5

 U.S. Census Bureau, "Table 2. Resident Population of the 50 States, the District of 

Columbia, and Puerto Rico: 2010 Census" http://2010.census.gov/news/presskits/apportionment/apport.html . 

6

“National Auto Loan Rates for July 21, 2011,” 

http://www.bankrate.com/finance/auto/national-auto-loan-rates-for-july-21-2011.aspx, 

accessed 7/26/11 (Docket EPA-HQ-OAR-2010-0799). 

7

 Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-

951 (Docket EPA-HQ-OAR-2010-0799); Goldberg, Pinelopi Koujianou, “The Effects of the 

Corporate Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics 

46(1) (March 1998): 1-33 (Docket EPA-HQ-OAR-2010-0799). 

8

 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy 

Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared 

by the Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. 

DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., Philip 

D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A 

Study of Incentives for Increased Fuel Economy,” Energy Policy 33 (2005): 757-775 

(Docket EPA-HQ-OAR-2010-0799). 

9

 McManus, Walter M., “Can Proactive Fuel Economy Strategies Help Automakers Mitigate 

Fuel-Price Risks?” University of Michigan Transportation Research Institute, September 14, 

2006 (Docket EPA-HQ-OAR-2010-0799). 

10 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799); Berry, Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand 

Systems from a Combination of Micro and Macro Data: The New Car Market,” Journal of 

Political Economy 112(1) (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799). 

11 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799). 

12 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799). 

13 Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) 

Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-2010-

0799). 
Chapter 8 

8-34 

14 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher 

CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and 

Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799). 

15 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and 

Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American 

Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Train, 

Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share 

of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 

(Docket EPA-HQ-OAR-2010-0799). 

16 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

17 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy 

Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared 

by the Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. 

DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-0799). 

18 NERA Economic Consulting, “Evaluation of NHTSA’s Benefit-Cost Analysis of 2011-

2015 CAFE Standards,” 2008, available at 

http://www.heartland.org/policybot/results/23495/Evaluation_of_NHTSAs_BenefitCost_Anal

ysis_Of_20112015_CAFE_Standards.html (Docket EPA-HQ-OAR-2010-0799). 

19 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799). 

20 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

21 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and 

Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American 

Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799). 

22 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, 

and Li 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 (Docket EPA-HQ-OAR-2010-

0799). 

23 NERA Economic Consulting, “Appendix B: New Vehicle Market Model,” “Impacts of the 

California Greenhouse Gas Emission Standards on Motor Vehicle Sales,” comments 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-35 

submitted to the U.S. Environmental Protection Agency at Regulations.gov, document 

number EPA-HQ-OAR-2006-0173-9053.1 (Docket EPA-HQ-OAR-2010-0799). 

24 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799) 

25 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

26 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and 

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 

(2005): 757-775 (Docket EPA-HQ-OAR-2010-0799). 

27 Rex, Emma, and Henrikke Baumann (2007). “Beyond ecolabels: what green marketing 

can learn from conventional marketing.” Journal of Cleaner Production 15: 567-576 (Docket 

EPA-HQ-OAR-2010-0799). 

28 Whitefoot, Kate, Meredith Fowlie, and Steven Skerlos, “Product Design Responses to 

Industrial Policy: Evaluating Fuel Economy Standards Using an Engineering Model of 

Endogenous Product Design,” Energy Institute at Haas Working Paper 214, University of 

California Energy Institute, February 2011 (Docket EPA-HQ-OAR-2010-0799). 

29 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

30 Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-

951 (Docket EPA-HQ-OAR-2010-0799). 

31 Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with 

Flexible Substitution Patterns,” Journal of Econometrics 89 (1999): 109-129 (Docket EPAHQ-OAR-2010-0799); Brownstone, David, David S. Bunch, and Kenneth Train, “Joint Mixed 

Logit Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” 

Transportation Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2010-0799); 

Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” 

prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 

(Docket EPA-HQ-OAR-2010-0799); Greene, David L., K. G. Duleep, and Walter McManus, 

“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, August 

2004 (Docket EPA-HQ-OAR-2010-0799). 
Chapter 8 

8-36 

32 Petrin, Amil, “Quantifying the Benefits of New Products: The Case of the Minivan,” 

Journal of Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799); 

Berry, Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand Systems 

from a Combination of Micro and Macro Data: The New Car Market,” Journal of Political 

Economy 112 (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799). 

33 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48 (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799). 

34 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, 

and Li 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 (Docket EPA-HQ-OAR-2010-

0799). 

35 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and 

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 

(2005): 757-775 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, and Li 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 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., “Feebates, 

Footprints and Highway Safety,” Transportation Research Part D 14 (2009): 375-384. 

36 E.g., Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of 

Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental 

Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799). 

37 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy 

Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2010-0799). 

38 E.g., Espey, Molly, and Santosh Nair, 2005. “Automobile Fuel Economy: What Is It 

Worth?” Contemporary Economic Policy 23: 317-323 (Docket EPA-HQ-OAR-2010-0799). 

39 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 

1593-1594 (Docket EPA-HQ-OAR-2010-0799). 

40 E.g., Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-

951 (Docket EPA-HQ-OAR-2010-0799). 

41 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-37 

42 Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. “Pain at the Pump: 

How Gasoline Prices Affect Automobile Purchasing in New and Used Markets,” working 

paper, http://web.mit.edu/knittel/www/papers/gaspaper_latest.pdf (accessed 11/1/11) (Docket 

EPA-HQ-OAR-2010-0799); Li, Shanjun, Christopher Timmins, and Roger H. von Haefen, 

2009. “How Do Gasoline Prices Affect Fleet Fuel Economy?” American Economic Journal: 

Economic Policy 1(2): 113-137; Congressional Budget Office (2008) (Docket EPA-HQOAR-2010-0799); Congressional Budget Office (2008). Effects of Gasoline Prices on 

Driving Behavior and Vehicle Markets. The Congress of the United States, Pub. No. 2883 

(Docket EPA-HQ-OAR-2010-0799); West. Sarah (2007). “The Effect of Gasoline Prices on 

the Demand for Sport Utility Vehicles.” Working Paper. Macalester College, 

http://www.macalester.edu/~wests/SarahWestMEA2007.pdf, accessed 1/25/10 (Docket EPAHQ-OAR-2010-0799). 

43 Greene, David L., and Jin-Tan Liu, 1988. “Automotive Fuel Economy Improvements and 

Consumers’ Surplus,” Transportation Research Part A 22A (3): 203-218 (Docket EPA-HQOAR-2010-0799). 

44 Greene, David L. (2010). “How Consumers Value Fuel Economy: A Literature Review.” 

EPA Report EPA-420-R-10-008 (Docket EPA-HQ-OAR-2010-0799). 

45 Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S. 

Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 

11/1/11 (Docket EPA-HQ-OAR-2010-0799). 

46 Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile 

Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket 

EPA-HQ-OAR-2010-0799); McManus, Walter, 2007. “The Impact of Attribute-Based 

Corporate Average Fuel Economy (CAFE) Standards: Preliminary Results.” Ann Arbor, MI: 

University of Michigan Transportation Research Institute, Report No. UMTRI-2007-31 

(Docket EPA-HQ-OAR-2010-0799). 

47 For instance, Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy 

(CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2010-0799); Austin, David, and Terry Dinan, “Clearing the Air: The Costs and 

Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of 

Environmental Economics and Management 50 (2005): 562- (Docket EPA-HQ-OAR-2010-

0799); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of 

the Corporate Average Fuel Economy Standard,” Resources for the Future Paper RFF DP 10-

50 (December 2010), http://www.rff.org/RFF/Documents/RFF-DP-10-50.pdf) (Docket EPAHQ-OAR-2010-0799)Jacobsen, Mark, 2010. “Evaluating U.S. Fuel Economy Standards in a 

Model with Producer and Household Heterogeneity,” working paper, at 

http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf (accessed 11/1/11) (Docket EPA-HQOAR-2010-0799) . 
Chapter 8 

8-38 

48 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher 

CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and 

Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799). 

49 IEA. 2007. “Mind the Gap: Quantifying Principal-Agent Problems in Energy Efficiency.” 

Paris, France: International Energy Agency (Docket EPA-HQ-OAR-2010-0799); Jaffe, Adam 

B., Richard G. Newell, and Robert N. 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 (Docket 

EPA-HQ-OAR-2010-0799); Metcalf, Gilbert E., and Kevin A. Hassett (1999). "Measuring 

the Energy Savings From Home Improvement Investments: Evidence From Monthly Billing 

Data." The Review of Economics and Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010-

0799); Tietenberg, T. (2009). “Reflections – Energy Efficiency Policy: Pipe Dream or 

Pipeline to the Future?” Review of Environmental Economics and Policy 3(2): 304-320 

(Docket EPA-HQ-OAR-2010-0799). 

50 Helfand, Gloria, and Ann Wolverton (2011). “Evaluating the Consumer Response to Fuel 

Economy: A Review of the Literature.” International Review of Environmental and 

Resource Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799). 

51 Jaccard, Mark. “Paradigms of Energy Efficiency’s Cost and their Policy Implications: 

Déjà Vu All Over Again.” Modeling the Economics of Greenhouse Gas Mitigation: 

Summary of a Workshop, K. John Holmes, Rapporteur. National Academies Press, 2010. 

http://www.nap.edu/openbook.php?record_id=13023&page=42 (Docket EPA-HQ-OAR2010-0799) 

52 Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” 

prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 

(Docket EPA-HQ-OAR-2010-0799). 

53 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency 

Standards. The White House, Office of the Press Secretary, May 21, 2010. 

http://www.whitehouse.gov/the-press-office/presidential-memorandum-regarding-fuelefficiency-standards 

54 U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed 

on August 9, 2011. 

55 Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis 

of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011 

(Docket EPA-HQ-OAR-2010-0799). 

56 Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. “Jobs Versus the 

Environment: An Industry-Level Perspective.” Journal of Environmental Economics and 

Management 43 (2002): 412-436 (Docket EPA-HQ-OAR-2010-0799). 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-39 

57 Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental 

and Natural Resource Policy.” Environmental and Resource Economics 22 (2002): 133-156 

(Docket EPA-HQ-OAR-2010-0799). 

58 Berman, Eli, and Linda T. Bui, (2001) “Environmental Regulation and Labor Demand: 

Evidence from the South Coast Air Basin,” Journal of Public Economics, 79, 265 – 295 

(Docket EPA-HQ-OAR-2010-0799).

59 http://www.bls.gov/emp/ep_data_emp_requirements.htm 

60 http://www.census.gov/manufacturing/asm/index.html 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-1 

Below is a summary of interagency comments received during EO 12866 and EO 13563 

review of the draft rule “Joint Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas 

Emission Standards and Corporate Average Fuel Economy Standards”. They are for your 

consideration and are subject to further policy review. 

*** 

Chapter 8: Vehicle Sales and Employment Impacts 

8.1 Vehicle Sales Impacts 

8.1.1 Vehicle Sales Impacts and Payback Period 

Predicting the effects of this rule on vehicles entails comparing two competing effects. 

On the one hand, as a result of this rule, the vehicles will become more expensive, which 

would, by itself, be expected to discourage sales. On the other hand, the vehicles will have 

improved fuel economy and thus lower operating costs, producing lower total costs over the 

life of vehicles, which makes them more attractive to consumers. Which of these effects 

dominates for potential vehicle buyers when they are considering a purchase will determine 

the effect on sales. However, assessing the net effect of these two competing effects is 

complex and uncertain, as it rests on how consumers value fuel savings at the time of 

purchase and the extent to which manufacturers and dealers reflect them in the purchase price. 

There are reasons to believe that many consumers do not fully take account of the 

value of fuel savings at the time of purchase (such as lack of salience and focus on the shortterm, as discussed in detail below), and as we have stated, the proposed rule is expected to 

produce significant net benefits for consumers. But the empirical literature leaves many open 

questions. It does not give clear evidence about the effect of the proposed rule on sales, nor 

does it speak to the possibility and consequences of consumer learning over time. Thus, for 

the proposal we do not provide quantified estimates of potential sales impacts. Rather, we 

solicit comment on the issues raised here and on methods for estimating the effect of this rule 

on vehicle sales. 

If consumers do not consider net costs – if they do not accurately compare the value 

of fuel savings with the increased cost of fuel economy technology in their vehicle purchase 

decisions -- there may be a net decrease in sales for 2017 and beyond. If auto makers have 

accurately gauged how consumers consider fuel economy when purchasing vehicles and have 

provided the levels of improvement that consumers desire, then consumers should not be 

expected to want to purchase the more fuel-efficient vehicles beyond what manufacturers may 

offer in the future absent this rule. On certain assumptions, auto makers would be expected to 

provide as much fuel economy as consumers want, and vehicle sales would be expected to 

decrease, not to increase, as a result of the rule. We invite comments on this possibility. 
Chapter 8 

8-2 

If, on the other hand, the rule motivates an increase in consumer demand for fuel 

economy, perhaps as a result of learning, then it could lead to production of more desirable 

vehicles and a commensurate increase in sales. We invite comment on consumer learning and 

related possible reasons for positive effects on consumer demand. 

For years, consumers have been learning about the benefits that may accrue to them 

from owning and operating vehicles with greater fuel efficiency. Consumer demand has thus 

shifted towards such vehicles, not only because of higher fuel prices but also because many 

consumers are learning about the value of purchases based not only on initial costs but also on 

the total cost of owning and operating a vehicle over its lifetime. This type of learning is 

expected to continue before and during the model years affected by this rule, particularly 

given the new fuel economy labels that clarify potential economic effects and should therefore 

reinforce that learning. 

Today’s proposed rule, combined with the new and easier-to-understand fuel economy 

label required to be on all new vehicles beginning in 2012, may increase sales above baseline 

levels by hastening this very type of consumer learning. As more consumers experience, as a 

result of the rule, the savings in time and expense from owning more fuel efficient vehicles, 

demand may shift yet further in the direction of the vehicles mandated under the rule. This 

social learning can take place both within and across households, as consumers learn from one 

another. There are three possible effects here. 

First and most directly, the time and fuel savings associated with operating more fuel 

efficient vehicles will be more salient to individuals who own them, causing their subsequent 

purchase decisions to shift closer to minimizing the total cost of ownership over the lifetime 

of the vehicle. Second, this appreciation may spread across households through word of 

mouth and other forms of communications. Third, as more motorists experience the time and 

fuel savings associated with greater fuel efficiency, the price of used cars will better reflect 

such efficiency, further reducing the cost of owning more efficient vehicles for the buyers of 

new vehicles (since the resale price will increase). 

If these induced learning effects are strong, the rule could potentially increase total 

vehicle sales over time. These increased sales would not occur in the model years first 

affected by the rule, but they could occur once the induced learning takes place . It is not 

possible to quantify these learning effects years in advance and that effect may be speeded or 

slowed by other factors that enter into a consumer’s valuation of fuel efficiency in selecting 

vehicles. 

The possibility that the rule will (after a lag for consumer learning) increase sales need 

not rest on the assumption that automobile manufacturers are failing to pursue profitable 

opportunities to supply the vehicles that consumers demand. In the absence of the rule, no 

individual automobile manufacturer may find it profitable to move toward the more efficient 

vehicles mandated under the rule. In particular, no individual company can fully internalize 

the future boost to demand resulting from the rule. If one company were to make more 

efficient vehicles, counting on consumer learning to enhance demand in the future, that 

company would capture only a fraction of the extra sales so generated, because the learning at 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-3 

issue is not specific to any one company's fleet. Many of the extra sales would accrue to that 

company's competitors. 

In other words, consumer learning about the benefits of fuel efficient vehicles involves 

positive externalities (spillovers) from one company to the others. [Footnote: Industrywide 

positive spillovers of this type are hardly unique to this situation. In many industries, 

companies form trade associations to promote industry-wide public goods. For example, 

merchants in a given locale may band together to promote tourism in that locale. Antitrust law 

recognizes that this type of coordination can increase output.] These positive externalities may 

lead to benefits for manufacturers as a whole. We emphasize that this discussion has been 

tentative and qualified. To be sure, social learning of related kinds has been identified in a 

number of contexts. [Footnote: See Hunt Alcott, Social Norms and Energy Conservation, 

Journal of Public Economics (forthcoming 2011), available at 

http://web.mit.edu/allcott/www/Allcott%202011%20JPubEc%20-

%20Social%20Norms%20and%20Energy%20Conservation.pdf; Christophe Chamley, 

Rational Herds: Economic Models of Social Learning (Cambridge, 2003).] Comments are 

invited on the discussion offered here, with particular reference to any relevant empirical 

findings.” 

We note as well that automakers are currently putting more fuel-saving technologies 

into vehicles and are advertising based in part on the fuel efficiency of their vehicles. We 

cannot assess whether these improvements in fuel economy are due to higher gasoline prices 

in recent years, to increased consumer demand for fuel economy, to auto makers realizing that 

fuel economy is a valuable attribute to consumers, or to EPA’s and NHTSA’s recent 

rulemakings on fuel economy and greenhouse gases. If adding more fuel economy to 

vehicles, even with the added costs, does make the vehicles more attractive to consumers, 

then we would expect auto makers to add this fuel economy in the absence of the rule. If so, 

both the benefits and costs estimated for this rule are overstated. 

In addition to uncertainty over the expected offerings of fuel economy in new vehicles 

in the absence of this rule, we note the uncertainty over the role of fuel economy in vehicle 

purchase decisions. As discussed in Chapter 8.1.2.5 below, how consumers consider fuel 

economy when buying cars is not well understood; it is likely to vary substantially among 

individuals, and possibly across vehicle classes. In addition, consumer interest in fuel 

economy may change over time, in response to changes in fuel prices, to marketing efforts by 

automakers, to more experience with fuel-efficient vehicles, and (as noted) to better 

understanding of the costs associated with fuel savings, not to mention changing market 

conditions and other shifts in the U.S. economy that could affect vehicle demand and national 

driving behavior. As noted in Preamble III.H.1.a, EPA and NHTSA recently revised the fuel 

economy label for new vehicles, to improve consumer information on fuel economy. We do 

not have data on the effect of the label on consumer vehicle purchase decisions. If consumers 

put more emphasis on fuel economy in their purchase decisions, then automakers would be 

expected to respond to that interest independent of the standards proposed here, and an 

increase in vehicle sales resulting from improved fuel economy would be a result of market 
Chapter 8 

8-4 

forces instead of this rulemaking. Preamble III.D. discusses EPA’s reasoning in assuming 

that the reference fleet will comply with the standards for MY 2016 vehicles but will not 

over-comply. Adding to the uncertainty is the dynamic effect that the proposed rule may have 

on other relevant aspects of the vehicle sales market aside from purchase price. For example, 

the proposed rule may induce technological change that would not have occurred otherwise, 

and introduce consumers to new vehicle attributes that impact vehicle demand in a positive 

way. Similarly, improvements in vehicle fuel economy might accompany other changes in 

vehicle characteristics that are relevant for purchase decisions. While these factors are always 

present, they are particularly acute in the current context, in which the rule will not take effect 

until 2017. 

In previous rulemakings, EPA and NHTSA conducted vehicle sales analyses by 

comparing the up-front costs of the vehicles with the present value of five years’ worth of fuel 

savings. We assumed that the costs for the fuel-saving technologies would be passed along 

fully to vehicle buyers in the vehicle prices. The up-front vehicle costs were adjusted to take 

into account several factors that would affect consumer costs: the increased sales tax that 

consumers would pay, the increase in insurance premiums, the increase in loan payments that 

buyers would face, and a higher resale value, with all of these factors due to the higher upfront cost of the vehicle. Those calculations resulted in an adjusted increase in costs to 

consumers. We then assumed that consumers considered the present value of five years of 

fuel savings in their vehicle purchase, which is consistent with the length of a typical new 

light-duty vehicle loan, and is similar to the average time that a new vehicle purchaser holds 

onto the vehicle.A

 The present value of fuel savings was subtracted from technology costs to 

get a net effect on vehicle cost of ownership. We then used a short-run demand elasticity of -

1 to convert a change in price into a change in quantity demanded of vehicles.B An elasticity 

of -1 means that a 1% increase in price leads to a 1% reduction in quantity sold. In the 

vehicle sales analyses, if five years of fuel savings outweighed the adjusted technology costs, 

then vehicle sales were predicted to increase; if the fuel savings were smaller than the 

adjusted technology costs, sales would decrease, compared to a world without the standards. 

We do not here present a vehicle sales analysis using this approach. This rule takes 

effect for MY 2017-2025. If consumers take into account less than the break-even number of 

years in their buying decisions, then vehicle sales could decrease. In all cases, we would 

expect the impact of this rule on vehicle sales to be modest relative to other factors 

influencing future vehicle sales such as fuel prices and overall economic conditions, which 

are strong determinants of overall sales in any given year. 

 

A

 In this proposal, the 5-year payback assumption corresponds to an assumption that vehicle buyers take into 

account between 30 and 50 percent of the present value of lifetime vehicle fuel savings (with the variation 

depending on discount rate, model year, and car vs. truck). 

B

 For a durable good such as an auto, the elasticity may be smaller in the long run: though people may be able to 

change the timing of their purchase when price changes in the short run, they must eventually make the

investment. We request comment on whether or when a long-run elasticity should be used for a rule that phases 

in over time, as well as how to find good estimates for the long –run elasticity. 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-5 

The calculation of the payback period is discussed in DRIA Chapter 5.3. Table 1 

shows the estimated payback period for MY 2021 and 2025. We present MY 2021 because it 

is the last year before the mid-term review impacts, if any, will take place, and MY 2025 

because it is the last year of the program. The payback period in 2021 is shorter than that in 

2025, because the technologies required to meet the proposed MY 2021 standards are more 

cost-effective than those for MY 2025. In all cases, the payback periods are less than the 5-

year payback period that EPA and NHTSA have previously used in the vehicle sales analyses. 

Given the relatively short time frames for the payback periods, these results suggest that 

vehicle sales could increase as a result of improved fuel economy. Alternatively, if 

consumers have shorter time frames for payback periods, then the rule may have a negative 

impact on vehicle sales. In either situation we would expect the impact of this rule on sales to 

be modest. As noted above, though, it is possible that some or all of this increase in fuel 

economy would occur in the absence of the rule, and it is also possible that consumers may 

revise how they think about fuel economy in their vehicle purchase decisions. 

Table 1 Estimated Payback Period for Model Years 2021 and 2025 (Years) 

Model 

Year 

Estimated 

Payback Period 

for Cash 

Purchase, 3% 

Discount Rate 

Estimated 

Payback Period 

for Cash 

Purchase, 7% 

Discount Rate 

Estimated 

Payback Period 

for Purchase on 

Credit, 3% 

Discount Rate 

Estimated 

Payback Period 

for Purchase on 

Credit, 7% 

Discount Rate 

2021 2.7 2.9 2.9 2.8

2025 3.7 3.9 3.9 3.9

We welcome comments on all aspects of this discussion, including assumptions on 

consumer and producer behavior; how consumers value future fuel savings at the time of 

purchase; factors that should be taken into account when assessing the impact of fuel 

economy standard on aggregate vehicle sales; and sources of uncertainty in estimating vehicle 

sales impacts. We welcome comments on the methodology described here for quantitative 

estimates of the effects of this proposal on sales and its appropriateness for this rulemaking; 

we also welcome proposals for other methods. 

We here provide further detail on some of the assumptions included in the payback 

analysis. We seek comment on all these factors as well. The analysis starts with the increase 

in costs estimated by the OMEGA model. We assume that these costs are fully passed along 

to consumers. This assumption is appropriate for cost increases in perfectly competitive 

markets. In less than perfectly competitive markets, though, it is possible that the cost 

increase is split between consumers and automakers, and possibly suppliers, and the price 
Chapter 8 

8-6 

may not increase as much as costs.1 Thus, the assumption of full cost pass-through is possibly 

an overestimate. 

The next step in the analysis is to adjust this cost increase for other effects on the 

consumer. The higher vehicle price is likely to lead to an increase in sales tax, insurance, and 

vehicle financing costs. 

The increase in insurance costs is estimated from the average value of collision plus 

comprehensive insurance as a proportion of average new vehicle price. Collision plus 

comprehensive insurance is the portion of insurance costs that depend on vehicle value. The 

Insurance Information Institute2 provides the average value of collision plus comprehensive 

insurance in 2008 as $432. The average value of a new vehicle in 2008, according to the U.S. 

Department of Energy, was $23,334.3 Dividing the insurance cost by the average price of a 

new vehicle gives the proportion of comprehensive plus collision insurance as 1.85 percent of 

the price of a vehicle. If this same proportion holds for the increase in price of a vehicle, then 

insurance costs should go up by 1.85 percent of the increase in vehicle cost. We use 

information on depreciation of vehicle value from the same U.S. Department of Energy report 

to estimate a reduction in insurance costs, due to reduction in the estimated value of the 

vehicle, over 9 years. 

Calculating the average increase in sales tax starts with the vehicle sales tax for each 

state in 2006, the most recent source identified.4

 The sales tax per state was then multiplied 

by the 2010 population of the state;5 those values were summed and divided by total U.S.

population, to give a population-weighted sales tax. That estimate of the state sales taxes for 

vehicles in the U.S. is 5.3 percent. This value is assumed to be a one-time cost incurred when 

the vehicle is purchased. 

As of July, 2011, the national average interest rate for a 5 year new car loan was 5.51 

percent.6 We use this loan rate to calculate the increase in vehicle costs due to financing a 

loan. 

8.1.2 Consumer Vehicle Choice ModelingC

An alternative to the vehicle sales analysis discussed above is the use of consumer 

vehicle choice models. In this section we describe some of the consumer vehicle choice 

models EPA has reviewed in the literature, and we describe the models’ results and 

limitations that we have identified. The evidence from consumer vehicle choice models 

indicates a huge range of estimates for consumers’ willingness to pay for additional fuel 

economy. Because consumer surplus estimates from consumer vehicle choice models depend 

critically on this value, we would consider any consumer surplus estimates of the effect of our 

rule from such models to be unreliable. In addition, the predictive ability of consumer vehicle 

choice models may be limited. While vehicle choice models are based on sales of existing 

 

C

 This section is drawn heavily from Helfand, Gloria, and Ann Wolverton, “Evaluating the Consumer Response 

to Fuel Economy: A Review of the Literature.” International Review of Environmental and Resource 

Economics 5 (2011): 103-146. 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-7 

vehicles, vehicle models are likely to change, both independently and in response to this rule. 

The models may not predict well in response to these changes. Instead, we compare the value 

of the fuel savings associated with this rule with the increase in technology costs. EPA will 

continue its efforts to review the literature, but, given the known limitations and uncertainties 

of vehicle choice models, EPA has not conducted an analysis using these models for this 

proposal. 

This rule will lead automakers to change characteristics – in particular, the fuel 

economy -- of the vehicles they produce. These changes will affect the cost of manufacturing 

the vehicle; as a result, the prices of the vehicles will also change. 

In response to these changes, the number and types of vehicles sold is likely to change. 

When consumers buy vehicles, they consider both their personal characteristics (such as age, 

family composition, income, and their vehicle needs) and the characteristics of vehicles (e.g., 

vehicle size, fuel economy, and price). In response to the changes in vehicle characteristics, 

consumers will reconsider their purchases. Increases in fuel economy are likely to be 

attractive to consumers, but increases in price, as well as any detrimental changes in other 

vehicle characteristics, may be deterrents to purchase. As a result, consumers may choose a 

different vehicle than they would have purchased in the absence of the rule. The changes in 

prices and vehicle characteristics are likely to influence consumers on multiple market scales: 

the total number of new vehicles sold; the mix of new vehicles sold; and the effects of the 

sales on the used vehicle market. 

Consumer vehicle choice modeling (CCM) is a method used to predict what vehicles 

consumers will purchase based on vehicle characteristics and prices. In principle, it should 

produce more accurate estimates of compliance costs compared to models that hold fleet mix 

constant, since it predicts changes in the fleet mix that can affect compliance costs. It can also 

be used to measure changes in consumer surplus, the benefit that consumers perceive from a 

good over and above the purchase price. (Consumer surplus is the difference between what 

consumers would be willing to pay for a good, represented by the demand curve, and the 

amount they actually pay. For instance, if a consumer were willing to pay $30,000 for a new 

vehicle, but ended up paying $25,000, the $5000 difference is consumer surplus.) 

A number of consumer vehicle choice models have been developed. They vary in the 

methods used, the data sources, the factors included in the models, the research questions they 

are designed to answer, and the results of the models related to the effects of fuel economy on 

consumer decisions. This section will give some background on these differences among the 

models. 

8.1.2.1 Methods 

Consumer choice models (CCMs) of vehicle purchases typically use a form of discrete 

choice modeling. Discrete choice models seek to explain discrete rather than continuous 

decisions. 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. Discrete decisions can take only a 
Chapter 8 

8-8 

limited set of values. The decision to purchase a vehicle, for instance, can only take two 

values, yes or no. Vehicle purchases are typically modeled as discrete choices, where the 

choice is whether to purchase a specified vehicle. The result of these models is a prediction 

of the probability that a consumer will purchase a specified vehicle. A minor variant on 

discrete choice models estimates the market share (a continuous variable between 0 and 1) for 

each vehicle. Because the market share is, essentially, the probability that consumers will 

purchase a specific vehicle, these approaches are similar in process; they differ mostly in the 

kinds of data that they use. 

The primary methods used to model vehicle choices are nested logit and mixed logit.D

 

In a nested logit, the model is structured in layers. For instance, the first layer may be the 

choice of whether to buy a new or used vehicle. Given 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. Examples of 

nested logit models include Goldberg,7

 Greene et al.,8 and McManus.9

In a mixed logit, personal characteristics of consumers play a larger role than in nested 

logit. While nested logit can look at the effects of a change in average consumer 

characteristics, mixed logit allows consideration of the effects of the distribution of consumer 

characteristics. As a result, mixed logit can be used to examine the distributional effects on 

various socioeconomic groups, which nested logit is not designed to do. Examples of mixed 

logit models include Berry, Levinsohn, and Pakes,10 Bento et al.,11 and Train and Winston.12 

While discrete choice modeling appears to be the primary method for consumer choice 

modeling, others (such as Kleit13 and Austin and Dinan14) have used a matrix of demand 

elasticities to estimate the effects of changes in cost. The discrete choice models can produce 

such elasticities. Kleit as well as Austin and Dinan used the elasticities from an internal GM 

vehicle choice model. 

8.1.2.2 Data Sources 

The predictions of vehicle purchases from CCMs are based on consumer and vehicle 

characteristics. The CCMs identify the effects of changing the characteristics on the purchase 

decisions. These effects are typically called the parameters or coefficients of the models. For 

instance, the model parameters might predict that an increase in a person’s income of 10% 

would increase the probability of her purchasing vehicle A by 5%, and decrease the 

probability of her purchasing vehicle B by 10%. 

The parameters in CCMs can be developed either from original data sources 

(estimated models), or using values taken from other studies (calibrated models). 

Estimated models use datasets on consumer purchase patterns, consumer 

characteristics, and vehicle characteristics to develop their original sets of parameters. The 

 

D

 Logit refers to a statistical analysis method used for analyzing the factors that affect discrete choices (i.e., 

yes/no decisions or the choice among a countable number of options). 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-9 

datasets used in these studies sometimes come from surveys of individuals’ behaviors.15

 

Because they draw on the behavior of individuals, they provide what is sometimes called 

micro-level data. Other studies, that estimate market shares instead of discrete purchase 

decisions, use aggregated data that can cover long time periods.16

 

Calibrated models rely on existing studies for their parameters. Researchers may draw 

on results from a number of estimated models, or even from research other than CCM, to 

choose the parameters of the models. The Fuel Economy Regulatory Analysis Model 

developed for the Energy Information Administration17 and the New Vehicle Market Model 

developed by NERA Economic Consulting18 are examples of calibrated models. 

8.1.2.3 Factors Included in the Models 

Consumer choice models vary in their complexity and levels of analysis. Some focus 

only on the new vehicle market;19 others consider the choice between new vehicles and an 

outside good (possibly including a used vehicle);20 others explicitly consider the relationship 

between the new and used vehicle markets.21 Some models include consideration of vehicle 

miles traveled,22 though most do not. 

The models vary in their inclusion of both consumer and vehicle information. One 

model includes only vehicle price and the distribution of income in the population influencing 

choice;23 others include varying numbers and kinds of vehicle and consumer attributes. 

Some models include only the consumer side of the vehicle market;24 others seek to 

represent both consumer and producer decisions.25 Models that include only the consumer 

side are suitable for reflecting consumer choices, but they do not allow for revisions of 

vehicle characteristics in response to consumer preferences. Including producer behavior 

allows for vehicle characteristics such as price and fuel economy to be the result of market 

forces rather than characteristics of the existing fleet. For instance, in the context of 

“feebates” (subsidizing fuel-efficient cars with revenue collected by taxing fuel-inefficient 

vehicles), Greene et al. estimated that 95% of the increase in fuel economy was due to 

addition of technology rather than changes in vehicles sold.26 Including auto maker response 

is a complex exercise. Auto makers are commonly considered to have market power; they 

can influence the prices that consumers pay to increase their profits. As a result, the price 

increases that consumers face may reflect strategic factors that could make them higher or 

lower than the technology costs. In addition, auto makers may seek to influence consumer 

preferences through marketing and advertising.27 Even those vehicle choice models that 

include a producer model may not include much detail, due to computational limits: it is 

unusual for models to allow both buyers and producers to choose one vehicle characteristic, 

much less multiple characteristics.28 

8.1.2.4 Research Questions for the Models 

Consumer choice models have been developed to analyze many different research and 

policy questions. In part, these models have been developed to advance the state of economic 
Chapter 8 

8-10 

modeling. The work of Berry, Levinsohn, and Pakes,29 for instance, is often cited outside the 

motor vehicle context for its incorporation of multiple new modeling issues into its 

framework. In addition, because the vehicle sector is a major part of the U.S. economy and 

involved in many public policy discussions, research questions cover a wide gamut. These 

topics have included the effects of voluntary export restraints on Japanese vehicles compared 

to tariffs and quotas,30 the market acceptability of alternative-fuel vehicles,31 the effects of 

introduction and exit of vehicles from markets,32 causes of the decline in market shares of 

U.S. automakers,33 and the effects of gasoline taxes34 and “feebates”35 (subsidizing fuelefficient cars with revenue collected by taxing fuel-inefficient vehicles). 

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions 

Consumer vehicle choice models typically consider the effect of fuel economy on 

vehicle purchase decisions. It can appear in various forms. 

Some models36 incorporate fuel economy through its effects 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 perhaps other operating costs) associated with a 

more fuel-efficient vehicle. Those savings are considered 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 estimate 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 Kurani37 question this assumption; they find, in fact, that

consumers do not make this calculation when they purchase a vehicle. The question remains, 

then, how or whether consumers take fuel economy into account when they purchase their 

vehicles. 

Most estimated consumer choice models, instead of making assumptions about how 

consumers incorporate fuel economy into their decisions, use data on consumer behavior to 

identify that effect. In some models, miles per gallon is one of the vehicle characteristics 

included to explain purchase decisions. Other models use fuel consumption per mile, the 

inverse of miles per gallon, as a measure:38 since consumers pay for gallons of fuel, then this 

measure can assess fuel savings relatively directly.

39 Yet other models multiply fuel 

consumption per mile by the cost of fuel to get the cost of driving a mile,40 or they divide fuel 

economy by fuel cost to get miles per dollar.41 It is worth noting that these last two measures 

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.E

 On the 

one hand, while this assumption does not rely on as complex a calculation as the present value 

of fuel savings that Turrentine and Kurani examined, it suggests a calculating consumer. On 

the other hand, using a form of cost per mile is a way to recognize the role of fuel prices in 

 

E

 Likewise, these measures assume consumers respond the same way to increases and decreases in cost per mile 

of driving, as well as if those increases and decreases are large shocks rather than small, gradual changes. The 

issue of potential asymmetric consumer response to increased fuel efficiency compared to other types of changes 

to the cost of driving also arises and is discussed in the context of the VMT rebound effect (see Section III.H.4 of 

the Preamble and Chapter 4.2.5.2 of the TSD). 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-11 

consumers’ purchase of fuel economy: recent research42 presents results that higher fuel 

prices play a major role in that decision. 

Greene and Liu,43 in a paper published in 1988, reviewed 10 papers using consumer 

vehicle choice models and estimated for each one how much consumers would be willing to 

pay at time of purchase to reduce vehicle operating costs by $1 per year. They found that 

people were willing to pay between $0.74 and $25.97 for a $1 decrease in annual operating 

costs for a vehicle. This is clearly a very wide range: while the lowest estimate suggests that 

people are not willing to pay $1 once to get $1 per year reduced costs of operating their 

vehicles, the maximum suggests a willingness to pay 35 times as high. For comparison, the 

present value of saving $1 per year for 15 years at a 3% discount rate is $11.94, while a 7% 

discount rate produces a present value of $8.78. While this study is quite old, it suggests that, 

at least as of that time, consumer vehicle choice models produced widely varying estimates of 

the value of reduced vehicle operating costs. 

A newer literature review from David Greene44 suggests continued lack of 

convergence on the value of increased fuel economy to consumers. Of 27 studies, willingness 

to pay for fuel economy as a percent of the expected value of fuel savings varied from highly 

positive to highly negative. Significant numbers of studies found that consumers overvalued 

fuel economy, undervalued fuel economy, or roughly valued fuel economy correctly relative 

to fuel savings. Part of the difficulty may be, as these papers note, that fuel economy may be 

correlated (either positively or negatively) with other vehicle attributes, such as size, power, 

or quality, not all of which may be included in the analyses; as a result, “fuel economy” may 

in fact represent several characteristics at the same time. Indeed, Gramlich45 includes both 

fuel cost (dollars per mile) and miles per gallon in his analysis, with the argument that miles 

per gallon measures other undesirable quality attributes, while fuel cost picks up the 

consumer’s demand for improved fuel economy. Greene finds that, while some of the 

variation may be explainable due to issues in some of the studies, the variation shows up in 

studies that appear to be well conducted. As a result, further work needs to be conducted 

before it is possible to identify the role of fuel economy in consumer purchase decisions. 

Some studies46 argue that automakers could increase profits by increasing fuel 

economy because the amount that consumers are willing to pay for increased fuel economy 

outweighs the costs of that improvement. Other studies47 have found that increasing fuel 

economy standards imposes welfare losses on consumers and producers, because consumers 

should already be buying as much fuel economy as they want. In the course of reaching this 

result, though, at least one of these studies48 notes that its baseline model implies that 

consumers are willing to buy more fuel economy than producers have provided; they have to 

adjust their model to eliminate these “negative-cost” fuel economy improvements. 

The models do not appear to yield very consistent results on the role of fuel economy 

in consumer and producer decisions. 
Chapter 8 

8-12 

8.1.2.6 Why Consumers May Not BuyFuel Economy that Pays for

Itself 

A detailed and wide ranging literature attempts to explain why consumers appear to 

under-invest in energy saving technologies that – at least using a present value calculation 

based on engineering estimates – appear to pay for themselves. Existing research does not 

provide a definitive answer to this question. Potential explanations are bounded by two 

scenarios. On the one hand, purely private benefits of fuel economy (fuel savings, time 

savings, increases in driving time) must be accompanied by private losses of the same 

magnitude. However, if there is no such private loss, or if it is small or insignificant, then 

there is a market or behavioral failure. 

On the consumer side, this disconnect between net present value estimates of energyconserving cost savings and what consumers actually spend on energy conservation is often 

referred to as the Energy Paradox,49 since consumers appear to undervalue a wide range of 

investments in energy conservation. There are many possible explanations for the paradox 

discussed in the literature.50 Some explanations point to costs or aspects of consumer 

decision-making unaccounted for in a simple present value calculation, while others point to 

potential behavioral or market failures. There is little empirical literature to help the analyst 

determine which combination of hypothesis offers the most credible explanation. Possibilities 

include: 

• Consumers put little weight on benefits from fuel economy in the future and show 

high discount rates; 

• Consumers do not find the benefits from fuel economy to be sufficiently salient at 

the time of purchase, even if it would be in consumers’ economic interest to take 

account of those benefits; 

• Consumers consider other attributes more important than fuel economy at the time 

of vehicle purchase, especially if fuel economy is a relatively “shrouded” attribute; 

• Consumers have difficulty in calculating expected fuel savings; 

• Consumers may use imprecise rules of thumb when deciding how much fuel 

economy to purchase; 

• Consumers may give particular weight to the losses associated with upfront costs, 

and less so to the costs over time (a version of the phenomenon of “myopic loss 

aversion”); 

• Consumers might associate higher fuel economy with inexpensive, less well 

designed vehicles; 

• Fuel savings in the future are uncertain, while at the time of purchase the increased 

costs of fuel-saving technologies are certain and immediate; 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-13 

• Consumers may not be able to find the vehicles they want with improved fuel 

economy; 

• The level of cost savings may be affected by the underlying reasons for the gap: 

factors such as transactions costs and differences in quality may not be adequately 

measured;51

• There is likely to be variation among consumers in the benefits they get from 

improved fuel economy, due to different miles driven and driving styles. 

8.1.2.7 Modeling Electric Vehicles and Other New Vehicles 

Modeling the introduction of new vehicles can be a greater challenge than modeling 

the existing vehicle market, because the modeler does not have data on how many of the new 

vehicles consumers buy. Nevertheless, it can be possible to estimate the effects of new 

vehicle introduction by identifying characteristics for the new vehicles and using those in a 

vehicle choice model. For instance, as discussed above, the models can estimate effects on 

the vehicle market when vehicles change their fuel economy or price. If the model 

incorporates other vehicle attributes important to the new vehicles, such as size, performance, 

or range, then the effect of the introduction can be modeled by applying the parameters for 

those features to the new vehicle characteristics. 

As discussed above, some models rely on vehicle price as the primary or only 

explanatory variable. Even in these models, it is possible, with some additional information, 

to consider the effects of new vehicle introduction. The first step is to find a vehicle similar 

on as many dimensions as possible to the new vehicle. For instance, if the change is to create 

an electric vehicle (EV) version of an existing model, then the existing model serves as the 

base vehicle. Next, it is necessary to measure the changes in vehicle attributes of interest to 

potential vehicle buyers. For an EV, changes in vehicle driving range and cost of fueling may 

be two such attributes. The next requirement is information on the value to consumers of the 

attributes that change between the new and the base vehicle. Multiplying the value for that 

attribute by the change in the attribute provides an estimate of the benefit or cost associated 

with changing that characteristic. That amount can then be added to or subtracted from the 

vehicle purchase price to give an adjusted purchase price reflecting the changed characteristic. 

This procedure is just an extension of the approach, discussed above, used to incorporate fuel 

economy improvements into vehicle choice models, by calculating future fuel savings and 

subtracting them (either in whole or a fraction) from vehicle purchase price. 

Incorporating new vehicles into a vehicle choice model, then, requires estimates of the 

changes in key attributes from conventional vehicles, and estimates of the value, also called 

the willingness to pay (WTP), that consumers put on those attributes. 

Electric vehicles (EVs) will have a number of changes in vehicle characteristics from 

any baseline model. EVs are likely to have a smaller driving range between refuelings than 

conventional vehicles, due to the large battery capacity needed to increase range. The ability 
Chapter 8 

8-14 

to recharge at home may be a convenient, desirable feature for people who have garages with 

electric hookups, but not for people who park on the street. If an infrastructure develops for 

recharging vehicles with the convenience approaching that of buying gasoline, range or home 

recharging may become less of a barrier to purchase. The reduced tailpipe emissions and 

reduced noise may be attractive features to some consumers.F They may have different 

performance or storage capacity. If sufficient data were available, the changes in these 

attributes, combined with WTP for each of the attributes, could be used to adjust the purchase 

price of the baseline vehicle to estimate consumers’ WTP for the electric version of a vehicle. 

Greene (2001), for instance, used this approach for a model that simulates choice, not only for 

EVs, but also for other alternative-fuel vehicles.52 In that model, he considers only one base 

vehicle, a passenger car, but considers the effect on WTP of fuel cost per mile, range, 

acceleration, and several other vehicle attributes.

Vehicle driving range has received attention because of the current paucity of 

recharging infrastructure: if the driver of an EV gets low on fuel, it may be difficult to find a 

place to recharge. Because range appears to be a major factor in EV acceptability, it is 

starting to draw attention in the research community. 

In several studies, researchers have used stated preference conjoint analysis to 

estimate the effect of vehicle range on consumer vehicle choice. In a conjoint analysis, 

consumers are given a choice between several vehicles with different attributes. One choice 

might be, for instance, between a baseline car and another car with higher range and a higher 

purchase price. The choices that consumers make (e.g., how much higher does the purchase 

price have to be for the consumer not to choose more range?) provide data that can be used to 

estimate the role of vehicle attributes in the consumer’s choice. Stated preference analysis is 

sometimes considered less reliable than actual market behavior, because what people say they 

will do in hypothetical situations may not match what they would do in actual situations. On 

the other hand, stated preference methods can be used to study goods where market data do 

not exist, such as future market products undergoing development (marketing studies often 

use stated preference methods), or environmental goods. Because electric vehicles are not in 

widespread enough use for market studies, stated preference studies are, at this point, one of 

the few options to examine consumer behavior relating to these vehicles. 

Table 8.1-2 summarizes results from several conjoint studies that include the effects of 

extending range (in the table, from 150 to 300 miles, to present standardized results). 

Variation of results in the table is from income or other demographic factors, not from 

confidence intervals. The results suggest that the value of additional range varies among 

consumers, and the amount of that variation is changing (perhaps shrinking) in more recent 

studies. 

 

F

 For instance, Hidrue et al. (Hidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. 

“Willingness to Pay for Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 

686-705) find that some consumers are willing to pay $5100 more for vehicles with 95% lower emissions than 

the vehicles they otherwise aim to purchase. 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-15 

Table 8.1-2Willingness to Pay for Increasing Range Calculated from Various Studies 

Study (Date) Value of extending range 

from 150 to 300 miles (dollar 

year) 

Value of additional 

range in 2009$a 

Bunch et al. (1993)b $7,600 (1991$) $11,100

Kavelek (1996) for California 

Energy Commissionc 

$2600 - $41,900 (1993$) $3700 - $58,700

Resource Systems Group (2009) for 

California Energy Commissiond 

$2900 - $7500 (2009$) $2900 - $7500

Hess et al. (2009), using the same 

data as Resource Systems Group 

(2009)e 

$2400 - $8500 (2009$) $2400 - $8500

Hidrue et al. (2011)f $3776 - $10,399 (2009$) $3776 - $10,399

aValues adjusted to 2009$ using the Bureau of Economic Analysis GDP deflator. 

bBunch, David S., Mark Bradley, Thomas F. Golob, and Ryuichi Kitamura. “Demand for Clean-Fuel Vehicles

in California: A Discrete-Choice Stated Preference Pilot Project.” Transportation Research Part A 27A(3) 

(1993): 237-253. The value of range was, in their model, assumed to be the same for all people. 

cKavelek, Chris. “CALCARS: The California Conventional and Alternative Fuel Response Simulator.” 

Demand Analysis Office, California Energy Commission, April 1996. The variation in values is due to 

willingness to pay (WTP) varying by income levels and for one-car and two-car households. The coefficient on 

range for one-car households was not statistically significantly different from zero (t-statistic = 1.5), but it was 

for 2-car households (t-statistic = 3.02). The minima and maxima presented here represent the values across 

both ownership and income categories. 

dResource Systems Groups, Inc. “Transportation Fuel Demand Forecast Household and Commercial Fleet 

Survey Task 8 Report: Logistic Regression Analysis and Results.” Prepared for California Energy Commission, 

June 2009. 

eHess, S., T. Adler, M. Fowler and A. Bahreinian “The Use of Cross-nested Logit Models for Multi-Dimensional 

Choice Processes: The Case of the Demand for Alternative Fuel Vehicles,” Proceedings of the 2009 European 

Transport Conference, Leiden, Netherlands, 2009. This study uses the same data as the Resource Systems 

Group study. The coefficient on range was not statistically significantly different from zero in these regressions: 

t-statistics varied from 1.29 to 1.52. The variation in values is due to willingness to pay (WTP) varying by 

income levels and statistical specification. The minima and maxima presented here represent the values across 

both income categories and specifications. 

fHidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. “Willingness to Pay for

Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 686-705. The range of 
Chapter 8 

8-16 

values is due to the model separating consumers into “gasoline vehicle-oriented” and “electric vehicle-oriented” 

groups. The EV-oriented group has higher WTP for additional range. 

Driving range may be a major factor in consumers’ decisions on EVs, but it is not the 

only attribute that may be important to potential buyers (e.g., as noted, Hidrue et al. find that 

some consumers appear willing to pay substantially for reduced tailpipe emissions). A model 

that does not incorporate the other factors important to consumers’ decisions may not perform 

well in predicting vehicle purchases. In addition, as mentioned above, and as seen in Table 

8.1-2, it is likely that the WTP values for attributes of EVs will change over time, particularly 

if EVs are used more widely, the infrastructure to fuel the vehicles becomes more accessible, 

and consumers develop more familiarity and understanding of the vehicles. Thus, challenges 

associated with predicting market shares for EVs are even more serious than those already 

serious challenges associated with predicting market shares for conventional vehicles. 

8.1.2.8 EPA Exploration of Vehicle Choice Modeling 

In order to develop greater understanding of these models, EPA is in the process of 

developing a vehicle choice model. In its current form, the model assumes that the vehicle 

fleet and all characteristics of each vehicle, except vehicle prices and fuel economy, stay the 

same. The model will predict changes in the vehicle fleet, at the individual-configuration 

level and at more aggregated levels, in response to changes in vehicle fuel economy and price. 

The draft EPA model uses a nested logit structure common in the vehicle choice 

modeling literature, as discussed above in Chapter 8.1.2.1. “Nesting” refers to the decisiontree structure of the model, and “logit” refers to the fact that the choices are discrete (i.e., 

yes/no decisions about which vehicles to purchase, instead of continuous values). 

The nesting involves a hierarchy of choices. In its current form, at the initial decision 

node, consumers choose between buying a new vehicle or not. Conditional on choosing a 

new vehicle, consumers then choose between passenger vehicles, cargo vehicles, and ultraluxury vehicles. The next set of choices subdivides each of these categories into vehicle type 

(e.g., standard car, minivan, SUV, etc.). Next, the vehicle types are divided into classes 

(small, medium, and large SUVs, for instance), and then, at the bottom, are the individual 

vehicle configurations. 

At this bottom level, vehicles that are similar to each other (such as standard 

subcompacts, or prestige large vehicles) end up in the same “nest.” Substitution within a nest 

is considered much more likely than substitution across nests, because the vehicles within a 

nest are more similar to each other than vehicles in different nests. For instance, a person is 

more likely to substitute between a Chevrolet Aveo and a Toyota Yaris than between an Aveo 

and a pickup truck. In addition, substitution is greater at low decision nodes (such as 

individual configurations) than at higher decision nodes (such as the buy/no buy decision), 

because there are more choices at lower levels than at higher levels. 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

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Parameters for the model (including demand elasticities and the value of fuel economy 

in purchase decisions) are being selected based on a review of values found in the literature 

on vehicle choice modeling. As discussed above, a number of studies have estimated these 

parameters. Those estimates, combined with some theoretical requirements,G assist in 

assigning values for the parameters. The model will allow individual users to change those 

parameters. 

The fuel economy of a vehicle is used to adjust the price of the vehicle, using a 

version of the procedure discussed in Chapter 8.1.2.7: the value that the consumer places on 

fuel economy is multiplied by the change in fuel economy and incorporated into the “effective 

price” of the vehicle. In practice, implementing this calculation involves calculating the 

change in expenditures on fuel based on schedules of VMT, vehicle survival, and fuel prices 

in the future consistent with those in OMEGA. As discussed in Chapter 8.1.2.5, there is no 

consensus value for consumers’ willingness to pay for improved fuel economy: estimates 

vary tremendously. The model assumes that consumers will use some years of discounted 

fuel savings, with the modeler able to input both the number of years and the discount rate to 

be used in the analysis. 

The vehicle choice model will take as inputs an initial fleet of vehicles (including the 

initial sales and fuel economy) in the absence of standards, the cost of technologies added to 

each vehicle to comply with standards, and the change in fuel economy. With the initial sales 

mix, for each vehicle, the model calculates a vehicle-specific constant that summarizes the 

value of all attributes of the vehicle other than price and fuel economy. This constant ensures 

that the model will predict changes in consumer response that would result only from changes 

in price and fuel economy. This constant substitutes for estimating the effects of changes in 

all other vehicle characteristics; the underlying assumption is that these other vehicle 

characteristics do not change.H

 For instance, it assumes that a Ford Escape will not change in 

size, power, or accessories; the only changes will be to its cost and its fuel economy. 

The model assumes that the increase in vehicle cost associated with increased 

technology is fully passed through as an increase in vehicle price, and some years of fuel 

savings offset this price increase. It then calculates changes in total fleet size and in sales 

mix, at the individual-configuration level and at the level of vehicle class, due to the changes 

in fuel economy and vehicle prices. It also calculates changes in consumer surplus associated 

with the changes in fuel economy and vehicle prices. 

 

G

 The theory of nested logit requires that the price slopes (the change in utility as vehicle full price changes, a 

measure of consumer responsiveness to price changes) must be higher in absolute value for lower nests. This 

condition reflects the point, discussed above, that substitution is greater at lower decision notes than at higher 

ones. 

H

 As explained in Section III.D of the preamble, as part of the technology cost analysis for the proposed rule, the 

agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions. 
Chapter 8 

8-18 

It is possible that the predicted changes in fleet mix will lead to predictions of vehicle 

sales for auto makers that do not meet the proposed standards, because the mix and volume of 

vehicles sold changed from the initial levels. To correct this problem, it would be necessary 

to feed the new fleet mix into OMEGA (which calculates costs and compliance) and get a 

new set of output, which could then be fed back into the vehicle choice model. OMEGA 

would increase technologies, and thus costs, to improve compliance; those adjustments would 

then again affect vehicle demand. We expect that this iterative process would converge to a 

fleet mix that would meet standards. Performing this iteration requires development of an 

interface between the vehicle choice model and OMEGA to ensure accurate transmission of 

data between the models. At this time, the vehicle choice model takes output from OMEGA, 

but the results of the modeling do not feed easily back into OMEGA. Building this interface 

is an expected part of our future modeling work. 

The model is still undergoing development; EPA will seek peer review on it before it 

is utilized. In addition, concerns remain over the ability of any vehicle choice model to make 

reasonable predictions of the response of the vehicle fleet to changes in prices and vehicle 

characteristics. EPA seeks comments on the use of vehicle choice modeling for predicting 

changes in fleet mix due to policies, and on methods to test the ability of a vehicle choice 

model to produce reasonable estimates of changes in fleet mix. 

8.1.2.9 Summary and Additional Considerations 

Consumer vehicle choice modeling in principle can provide a great deal of useful 

information for regulatory analysis, helping to answer some of the central questions about 

relevant effects on consumer welfare. In practice, the advantages depend on the success of 

models in predicting changes in fleet size and mix. 

First, consumer vehicle choice modeling has the potential to describe more accurately 

the impact of a policy, by identifying market shifts. More accurate description of the market 

resulting from a policy can improve other estimates of policy impacts, such as the change in 

total vehicle emissions or vehicle miles traveled. The predictive ability of models, though, is 

not proven. 

Vehicle choice models can incorporate the effects on consumer decisions of changes 

in vehicle characteristics, if there are estimates of the value that consumers put on changes in 

those characteristics. These willingness-to-pay values may, however, be sensitive to the ways 

they are estimated, as indicated in the discussion of the value that consumers place on fuel 

economy in their purchase decisions. Especially for characteristics associated with advanced 

technology vehicles, such as EVs, the willingness-to-pay values may change over time as 

consumers develop more experience with the vehicles and these characteristics. Models 

based on current estimates may not predict well for the future. 

The modeling may improve estimates of the compliance costs of a rule. Consumers 

can either accept the new costs and vehicle characteristics, or they can change which vehicles 

they buy. Using a vehicle choice model is likely to reduce compliance costs: because the 

model allows consumers to choose among accepting the new vehicle, buying a different 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-19 

vehicle, or not buying a vehicle, consumers have additional options, which improves their 

welfare relative to the assumption that consumers will not change their buying behavior. . 

An additional complication associated with consumer choice modeling is accurate 

prediction of producers’ responses to the rule. While it is possible to include auto makers’ 

decisions (for instance, on setting prices) into vehicle choices, computational limits affect the 

richness of these models. Technology costs, while an accurate measure of the opportunity 

cost of resources to society, may overestimate or underestimate the effect on the prices that 

consumers face. 

Consumer choice models can be used to calculate consumer surplus impacts on 

vehicle purchase decisions. Because these values are based on the estimates of changes in 

vehicle sales and fleet mix, consumer surplus measures may not be accurate if the changes in 

vehicle sales and fleet mix are not well estimated.

Principles of welfare analysis can be useful for understanding the role of consumer 

vehicle choice models in benefit-cost analysis. In particular, except for EVs, the technology 

cost estimates developed in this proposal take into account the costs to hold other vehicle 

attributes, such as size and performance, constant. In addition, the analysis assumes that the 

full technology costs are passed along to vehicle buyers. With these assumptions, because 

welfare losses are monetary estimates of how much buyers would have to be compensated to 

be made as well off as in the absence of the change,

I

 the price increase measures the loss to 

the buyer.J

 Assuming that the full technology cost gets passed along to the buyer as an 

increase in price, the technology cost thus measures the welfare loss to the buyer. Increasing 

fuel economy would have to lead to other changes in the vehicles that buyers find undesirable 

for there to be additional losses not included in the technology costs. 

Given the current limitations in modeling the role of fuel economy in vehicle purchase 

decisions, and limitations in modeling market responses to the new regulations, in this 

proposal EPA holds constant the vehicle fleet size and mix in its calculations of the impacts of 

this rule, and compares the fuel and other savings that consumers will receive with the 

technology costs of the vehicles. EPA continues to explore options for including consumer 

 

I

 This approach describes the economic concept of compensating variation, a payment of money after a change 

that would make a consumer as well off after the change as before it. A related concept, equivalent variation, 

estimates the income change that would be an alternative to the change taking place. The difference between 

them is whether the consumer’s point of reference is her welfare before the change (compensating variation) or 

after the change (equivalent variation). In practice, these two measures are typically very close together. 

J

 Indeed, it is likely to be an overestimate of the loss to the buyer, because the buyer has choices other than 

buying the same vehicle with a higher price; she could choose a different vehicle, or decide not to buy a new 

vehicle. The buyer would choose one of those options only if the alternative involves less loss than paying the 

higher price. Thus, the increase in price that the buyer faces would be the upper bound of loss of consumer 

welfare, unless there are other changes to the vehicle due to the fuel economy improvements that make the 

vehicle less desirable to buyers. 
Chapter 8 

8-20 

and producer choice in modeling the impacts of fuel economy-related regulations. This effort 

includes further review of existing consumer vehicle choice models, the estimates of 

consumers’ willingness to pay for increased fuel economy, and overall effects on consumer 

welfare, as well as EPA’s exploration of a vehicle choice model for use in the future. 

8.2 Employment Impacts 

8.2.1 Introduction 

Although analysis of employment impacts is not part of a cost-benefit analysis (except 

to the extent that labor costs contribute to costs), employment impacts of federal rules are of 

particular concern in the current economic climate of sizeable unemployment. When 

President Obama requested that the agencies develop this program, he sought a program that 

would “strengthen the [auto] industry and enhance job creation in the United States.”53,K The 

recently issued Executive Order 13563, “Improving Regulation and Regulatory Review” 

(January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety, 

and our environment while promoting economic growth, innovation, competitiveness, and job 

creation” (emphasis added). EPA is accordingly providing partial estimates of the effects of 

this proposal on domestic employment in the auto manufacturing and parts sectors, while 

qualitatively discussing how it may affect employment in other sectors more generally. 

This proposal is expected to affect employment in the United States through the 

regulated sector – the auto manufacturing industry – and through several related sectors, 

specifically, industries that supply the auto manufacturing industry (e.g., vehicle parts), auto 

dealers, the fuel refining and supply sectors, and the general retail sector. According to the 

U.S. Bureau of Labor Statistics, in 2010, about 677,000 people in the U.S. were employed in 

Motor Vehicle and Parts Manufacturing Sector (NAICS 3361, 3362, and 3363). About 

129,000 people in the U.S. were employed in the Automobile and Light Truck Manufacturing 

Sector (NAICS 33611) in December 2010; this is the directly regulated sector, since it 

encompasses the auto manufacturers that are responsible for complying with the proposed 

standards.54 Changes in light duty vehicle sales, discussed in Chapter 8.1.1, could affect 

employment for auto dealers. Though some of the parts used to achieve the proposed 

standards are likely to be built by auto manufacturers themselves, the auto parts 

manufacturing sector also plays a significant role in providing those parts, and will also be 

affected by changes in vehicle sales. As discussed in Chapter 5.4 of the RIA, this proposal is 

expected to reduce the amount of fuel these vehicles use, and thus affect the petroleum 

refinery and supply industriesWhen the economy is at full employment, an environmental 

regulation is unlikely to have much impact on net overall U.S. employment; instead, labor 

would primarily be shifted from one sector to another. These shifts in employment impose an 

opportunity cost on society, approximated by the wages of the employees, as regulation 

diverts workers from other activities in the economy. In this situation, any effects on net 

 

K

 The May 21, 2010 Presidential Memorandum also requested that EPA and NHTSA, in developing the 

technical assessment to inform the rulemaking process (which was issued by the agencies and CARB on 

September 30, 2010), include, among other things, the “impacts on jobs and the automotive manufacturing base 

in the United States.” 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-21 

employment are likely to be transitory as workers change jobs (e.g., some workers may need 

to be retrained or require time to search for new jobs, while shortages in some sectors or 

regions could bid up wages to attract workers). 

On the other hand, if a regulation comes into effect during a period of high 

unemployment, a change in labor demand due to regulation may affect net overall U.S. 

employment because the labor market is not in equilibrium. In such a period, both positive 

and negative employment effects are possible. (Cite Masur and Posner, 

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1920441) Schmalansee and Stavins point 

out that net positive employment effects are possible in the near term when the economy is at 

less than full employment due to the potential hiring of idle labor resources by the regulated 

sector to meet new requirements (e.g., to install new equipment) and new economic activity in 

sectors related to the regulated sector.55 In the longer run, the net effect on employment is 

more difficult to predict and will depend on the way in which the related industries respond to 

the regulatory requirements. As Schmalansee and Stavins note, it is possible that the 

magnitude of the effect on employment could vary over time, region, and sector, and positive 

effects on employment in some regions or sectors could be offset by negative effects in other 

regions or sectors. For this reason, they urge caution in reporting partial employment effects 

since it can “paint an inaccurate picture of net employment impacts if not placed in the 

broader economic context.” 

Given the time frame of this proposed rule, it is possible that the state of the overall 

economy may have improved to the point where the only effect of changes in employment 

associated with the standards proposed here will be to shift workers from one sector to 

another. For example, to the extent that regulation increases the number of workers in the 

regulated sector (e.g., because of higher costs of producing vehicles with higher fuel 

economy), the opportunity cost associated with those shifts in employment will be captured in 

labor costs, as regulation diverts workers from other activities in the economy. In this 

situation, any effects on net employment are likely to be transitory as workers change jobs 

(e.g., some workers may need to be retrained or require time to search for new jobs, while 

shortages in some sectors or regions could bid up wages to attract workers). It is also possible 

that the state of the economy will be such that positive or negative employment effects will 

occur. 

A number of different approaches have been used in published literature to conduct 

employment analysis. This section describes some of the common methods, as well as some 

of their limitations. 

8.2.2 Approaches to Quantitative Employment Analysis 

Measuring the employment impacts of a policy depend on a number of inputs and 

assumptions. For instance, as discussed, assumptions about the overall state of 

unemployment in the economy play a major role in measured job impacts. The inputs to the 

models commonly are the changes in quantities or expenditures in the affected sectors; model 
Chapter 8 

8-22 

results may vary in different studies depending on the assumptions about the levels of those 

inputs, and which sectors receive those changes. Which sectors are included in the study can 

also affect the results. For instance, a study of this program that looks only at employment 

impacts in the refinery sector may find negative effects, because consumers will purchase less 

gasoline; a study that looks only at the auto parts sector, on the other hand, may find positive 

impacts, because the program will require redesigned or additional parts for vehicles. In both 

instances, these would only be partial perspectives on the overall change in national 

employment due to Federal regulation. 

8.2.2.1 Conceptual Framework for Employment Impacts in the 

Regulated Sector 

One study by Morgenstern, Pizer, and Shih56 provides a retrospective look at the 

impacts of regulation in employment in the regulated sectors by estimating the effects on 

employment of spending on pollution abatement for four highly polluting/regulated U.S. 

industries (pulp and paper, plastics, steel, and petroleum refining) using data for six years 

between 1979 and 1991. The paper provides a theoretical framework that can be useful for 

examining the impacts of a regulatory change on the regulated sector in the medium to longer 

term. In particular, it identifies three separate ways that employment levels may change in the 

regulated industry in response to a new (or more stringent) regulation. 

• Demand effect: higher production costs due to the regulation will lead to higher 

market prices; higher prices in turn reduce demand for the good, reducing the 

demand for labor to make that good. In the authors’ words, the “extent of this 

effect depends on the cost increase passed on to consumers as well as the demand 

elasticity of industry output.” 

• Cost effect: as costs go up, plants add more capital and labor (holding other 

factors constant), with potentially positive effects on employment. In the authors’ 

words, as “production costs rise, more inputs, including labor, are used to produce 

the same amount of output.” 

• Factor shift effect: post-regulation production technologies may be more or less 

labor-intensive (i.e., more/less labor is required per dollar of output). In the 

authors’ words, “environmental activities may be more labor intensive than 

conventional production,” meaning that “the amount of labor per dollar of output 

will rise,” though it is also possible that “cleaner operations could involve 

automation and less employment, for example.” 

According to the authors, the “demand effect” is expected to have a negative effect on 

employment,L

 the “cost effect” to have a positive effect on employment, and the “factor shift 

 

L

 As will be discussed below, the demand effect in this proposal is potentially an exception to this rule. While 

the vehicles become more expensive, they also produce reduced fuel expenditures; the reduced fuel costs provide 

a countervailing impact on vehicle sales. As discussed in Preamble Section III.H.1, this possibility that vehicles 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-23 

effect” to have an ambiguous effect on employment. Without more information with respect 

to the magnitudes of these competing effects, it is not possible to predict the total effect 

environmental regulation will have on employment levels in a regulated sector. 

Morgenstern et al. conclude that increased abatement expenditures generally have not 

caused a significant change in employment in those sectors. More specifically, their results 

show that, on average across the industries studied, each additional $1 million spent on 

pollution abatement results in a (statistically insignificant) net increase of 1.5 jobs. 

This approach to employment analysis has the advantage of carefully controlling for 

many possibly confounding effects in order to separate the effect of changes in regulatory 

costs on employment. It was, however, conducted for only four sectors. It could also be very 

difficult to update the study for other sectors, because one of the databases on which it relies, 

the Pollution Abatement Cost and Expenditure survey, has been conducted infrequently since 

1994, with the last survey conducted in 2005. The empirical estimates provided by 

Morgenstern et al. are not relevant to the case of fuel economy standards, which are very 

different from the pollution control standards on industrial facilities that were considered in 

that study. In addition, it does not examine the effects of regulation on employment in sectors 

related to but outside of the regulated sector. Nevertheless, the theory that Morgenstern et al. 

developed continues to be useful in this context. 

The following discussion of additional methodologies draws from Berck and 

Hoffmann’s review of employment models.57

 

8.2.2.2 Computable General Equilibrium (CGE) Models 

Computable general equilibrium (CGE) models are often used to assess the impacts of 

policy. These models include a stylized representation of supply and demand curves for all 

major markets in the economy. The labor market is commonly included. CGE models are 

very useful for looking at interaction effects of markets: “they allow for substitution among 

inputs in production and goods in consumption.” Thus, if one market experiences a change, 

such as a new regulation, then the effects can be observed in all other markets. As a result, 

they can measure the employment changes in the economy due to a regulation. Because they 

usually assume equilibrium in all markets, though, they typically lack involuntary 

unemployment. If the total amount of labor changes, it is due to people voluntarily entering 

or leaving the workforce. As a result, these models may not be appropriate for measuring 

effects of a policy on unemployment, because of the assumption that there is no involuntary 

unemployment. In addition, because of the assumptions of equilibrium in all markets and 

forward-looking consumers and firms, they are designed for examining the long-run effects of 

a policy but may offer little insight into its short-run effects. 

 

may become more attractive to consumers after the program poses a conundrum: why have interactions between 

vehicle buyers and producers not provided these benefits without government intervention? 
Chapter 8 

8-24 

8.2.2.3 Input-Output (IO) Models 

Input-output models represent the economy through a matrix of coefficients that 

describe the connections between supplying and consuming sectors. In that sense, like CGE 

models, they describe the interconnections of the economy. These interconnections look at 

how changes in one sector ripple through the rest of the economy. For instance, a 

requirement for additional technology for vehicles requires additional steel, which requires 

more workers in both the auto and steel sectors; the additional workers in those sectors then 

have more money to spend, which leads to more employment in retail sectors. These are 

known as “multiplier” effects, because an initial impact in one sector gets multiplied through 

the economy. Unlike CGE models, input-output models have fixed, linear relationships 

among the sectors (e.g., substitution among inputs or goods is not allowed), and quantity 

supplied need not equal quantity demanded. In particular, these models do not allow for price 

changes – an increase in the demand for labor or capital does not result in a change in its price 

to help reallocate it to its best use. As a result, these models cannot capture opportunity costs 

from using resources in one area of the economy over another. The multipliers take an initial 

impact and can increase it substantially. 

IO models are commonly used for regional analysis of projects. In a regional analysis, 

the markets are commonly considered small enough that wages and prices are determined 

outside the region, and any excess supply or demand is due to exports and imports (or, in the 

case of labor, emigration or immigration). For national-level employment analysis, the use of 

input-output models requires the assumption that workers flow into or out of the labor market 

perfectly freely. Wages do not adjust; instead, people join into or depart from the labor pool 

as production requires them. For other markets as well, there is no substitution of less 

expensive inputs for more expensive ones. As a result, IO models provide an upper bound on 

employment impacts. As Berck and Hoffmann note, “For the same reason, they can be 

thought of as simulating very short-run adjustment,” in contrast to the CGE’s implicit 

assumption of long-run adjustment. Changes in production processes, introductions of new 

technologies, or learning over time due to new regulatory requirements are also generally not 

captured by IO models, as they are calibrated to already established relationships between 

inputs and outputs. 

8.2.2.4 Hybrid Models 

As Berck and Hoffmann note, input-output models and CGE models “represent a 

continuum of closely related models.” Though not separately discussed by Berck and 

Hoffmann, some hybrid models combine some of the features of CGE models (e.g., prices 

that can change) with input-output relationships. For instance, a hybrid model may include 

the ability to examine disequilibrium phenomena, such as labor being at less than full 

employment. Hybrid models depend on assumptions about how adjustments in the economy 

occur. CGE models characterize equilibria but say little about the pathway between them, 

while IO models assume that adjustments are largely constrained by previously defined 

relationships; the effectiveness of hybrid models depends on their success in overcoming the 

limitations of each of these approaches. Hybrid models could potentially be used to model 

labor market impacts of various vehicle policy options although a number of judgments need 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-25 

to be made about the appropriate assumptions underlying the model as well the empirical 

basis for the modeling results. 

8.2.2.5 Single Sectors 

It is possible to conduct a bottom-up analysis of the partial effect of regulation on 

employment in a single sector by estimating the change in output or expenditures in a sector 

and multiplying it by an estimate of the number of workers per unit of output or expenditures, 

under the assumption that labor demand is proportional to output or expenditures. As Berck 

and Hoffmann note, though, “Compliance with regulations may create additional jobs that are 

not accounted for.” While such an analysis can approximate the effects in that one sector in a 

simple way, it also may miss important connections to related sectors. 

8.2.2.6 Ex-Post Econometric Studies 

A number of ex-post econometric analyses examine the net effect of regulation on 

employment in regulated sectors. Morgenstern, Pizer, and Shih (2002), discussed above, and 

Berman and Bui (2001) are two notable examples that rely on highly disaggregated 

establishment-level time series data to estimate longer-run employment effects.58 While often 

a sophisticated treatment of the issues analyzed, these studies commonly analyze specific 

scenarios or sectors in the past; care needs to be taken in extrapolating their results to other 

scenarios and to the future. For instance, neither of these two studies examines the auto 

industry and are therefore of limited applicability in this context. 

8.2.2.7 Summary 

All methods of estimating employment impacts of a regulation have advantages and 

limitations. CGE models may be most appropriate for long-term impacts, but the usual 

assumption of equilibrium in the employment market means that it is not useful for looking at 

changes in overall employment: overall levels are likely to be premised on full employment. 

IO models, on the other hand, may be most appropriate for small-scale, short-term effects, 

because they assume fixed relationships across sectors and do not require market equilibria. 

Hybrid models, which combine some features of CGEs with IO models, depend upon key 

assumptions and economic relationships that are built into them. Single-sector models are 

simple and straightforward, but they are often based on the assumptions that labor demand is 

proportional to output, and that other sectors are not affected. Finally, econometric models 

have been developed to evaluate the longer-run net effects of regulation on sector 

employment, though these are ex-post analyses commonly of specific sectors or situations, 

and the results may not have direct bearing for the regulation being reviewed. 

8.2.3 Employment analysis of this proposal 

As mentioned above, this program is expected to affect employment in the regulated 

sector (auto manufacturing) and other sectors directly affected by the proposal: auto parts 

suppliers, auto dealers, the fuel supply market (which will face reduced petroleum production 
Chapter 8 

8-26 

due to reduced fuel demand but which may see additional demand for electricity or other 

fuels), and consumers (who will face higher vehicle costs and lower fuel expenditures). In 

addition, as the discussion above suggests, each of these sectors could potentially have ripple 

effects in the rest of the economy. These ripple effects depend much more heavily on the 

state of the macroeconomy than do the direct effects. At the national level, employment may 

increase in one industry or region and decrease in another, with the net effect being smaller 

than either individual-sector effect. EPA does not attempt to quantify the net effects of the 

regulation on overall national employment. 

The discussion that follows provides a partial, bottom-up quantitative estimate of the 

effects of this proposal on the regulated sector (the auto industry; for reasons discussed below, 

we include some quantitative assessment of effects on suppliers to the industry although they 

are not regulated directly). It also includes qualitative discussion of the effects of the proposal 

on other sectors. Focusing quantification of employment impacts on the regulated sector has 

some advantages over quantifying all impacts. First, the analysis relies on data generated as 

part of the rulemaking process, which focuses on the regulated sector; as a result, what is 

presented here is based on internally consistent assumptions and estimates made in this 

proposal. Secondly, as discussed above, net effects on employment in the economy as a 

whole depend heavily on the overall state of the economy when this rule has its effects. 

Focusing on the regulated sector provides insight into employment effects in that sector 

without having to make assumptions about the state of the economy when this rule has its 

impacts. We include a qualitative discussion of employment effects on other sectors to 

provide a broader perspective on the impacts of this rule. 

As noted above, in a full-employment economy, any changes in employment will 

result from people changing jobs or voluntarily entering or exiting the workforce. In a fullemployment economy, employment impacts of this proposal will change employment in 

specific sectors, but it will have small, if any, effect on aggregate employment. This rule 

would take effect in model years 2017 through 2025; by then, the current high unemployment 

may be moderated or ended. For that reason, this analysis does not include multiplier effects, 

but instead focuses on employment impacts in the most directly affected industries. Those 

sectors are likely to face the most concentrated employment impacts. The agencies seek 

comment on other sectors that are likely to be significantly affected and thus warrant further 

analysis in the final rulemaking analysis. 

8.2.3.1 Employment Impacts in the Auto Industry 

If vehicle sales decrease, employment associated with these activities will 

unambiguously decrease. The sign of demand effect will depend on which of these effects 

dominates. Because, as described in Chapter 8.1, we have not quantified the impact on sales 

for this proposal, we do not quantify the demand effect. 

8.2.3.1.1 The Cost Effect

Employment may increase because manufacturers need additional people to 

implement the new or additional technologies needed for vehicles to comply with the 

proposed standards. 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-27 

One way to gauge this the cost effect, is to use the ratio of workers to each $1 million 

of expenditures in that sector. The use of these ratios has both advantages and limitations. It 

is often possible to estimate these ratios for quite specific sectors of the economy: for 

instance, it is possible to estimate the average number of workers in the light-duty vehicle 

manufacturing sector per $1 million spent in the sector, rather than use the ratio from another, 

more aggregated sector, such as motor vehicle manufacturing. As a result, it is not necessary 

to extrapolate employment ratios from possibly unrelated sectors. On the other hand, these 

estimates are averages for the sectors, covering all the activities in those sectors; they may not 

be representative of the labor required when expenditures are required on specific activities, 

as the factor shift effect (discussed below) indicates. For instance, the ratio for the motor 

vehicle manufacturing sector represents the ratio for all vehicle manufacturing, not just for 

fuel efficiency improvements. In addition, these estimates do not include changes in sectors 

that supply these sectors, such as steel or electronics producers. They thus may best be 

viewed as the effects on employment in the auto sector due to the changes in expenditures in 

that sector, rather than as an assessment of all employment changes due to these changes in 

expenditures. 

Some of the costs of this proposal will be spent directly in the auto manufacturing 

sector, but some of the costs will be spent in the auto parts manufacturing sector. Because we 

do not have information on the proportion of expenditures in each sector, we separately 

present the ratios for both the auto manufacturing sector and the auto parts manufacturing 

sector. These are not additive, but should instead be considered as a range of estimates for the 

cost effect, depending on which sector adds technologies to the vehicles to comply with the 

regulation. 

We use several public sources for estimates of employment per $1 million 

expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment 

Requirements Matrix (ERM),59 which provides direct estimates of the employment per $1 

million in sales of goods in 202 sectors. The most recent estimates, used here, are from 2008 

(adjusted to 2009$). The tables used here are adjusted to remove the employment effects of 

imports through use of the ratio of domestic production to domestic sales, described above, of 

0.667. The values reported are for Motor Vehicle Manufacturing (NAICS 3361) and Motor 

Vehicle Parts Manufacturing (NAICS 3363). 

The Annual Survey of Manufactures60 (ASM) provides another source of estimates 

based on a sample of 50,000 establishments out of a universe of 346,000 manufacturing 

establishments. It includes more sectoral detail than the BLS ERM: for instance, while the 

ERM includes the Motor Vehicle Manufacturing sector, the ASM has detail at the 6-digit 

NAICS code level (e.g., automobile manufacturing vs. light truck and utility vehicle 

manufacturing). While the ERM provides direct estimates of employees/$1 million in 

expenditures, the ASM separately provides number of employees and value shipments; the 

direct employment estimates here are the ratio of those values. The data in the ASM are 

updated annually, except for years when the full Economic Census occurs. The tables 

presented here use data from 2009. As with the ERM, we adjust for the ratio of domestic 
Chapter 8 

8-28 

production to domestic sales. The values reported are for Motor Vehicle Manufacturing 

(NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), 

and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Economic Census includes all large companies and a sample of smaller ones. The 

ASM is a subset of the Economic Census; though the Census itself is more complete, it is 

conducted only every 5 years, while the ASM is annual. The values presented here use data 

from 2007 (adjusted to 2009$), with the domestic production-to-sales adjustment. The values 

reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty 

Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing 

(NAICS 3363). 

Table 8.2-1 provides the values, either given (BLS) or calculated (ASM, Economic 

Census) for employment per $1 million of expenditures, all based on 2009 dollars, though the 

underlying data come from different years (which may account for some of the differences). 

The different data sources provide similar magnitudes for the estimates for the sectors. Parts 

manufacturing appears to be more labor-intensive than vehicle manufacturing; light-duty 

vehicle manufacturing appears to be slightly less labor-intensive than motor vehicle 

manufacturing as a whole. 

Table 8.2-1 Employment per $1 Million Expenditures (2009$) in the Motor Vehicle 

Manufacturing Sector* 

Source Sector Ratio of 

workers per 

$1 million 

expenditures

Ratio of workers per $1 

million expenditures, 

adjusted for domestic vs. 

foreign production 

BLS ERM Motor Vehicle Mfg 0.834 0.556

ASM Motor Vehicle Mfg 0.824 0.549

ASM Light Duty Vehicle 

Mfg

0.757 0.505

Economic 

Census 

Motor Vehicle Mfg 0.674 0.449

Economic 

Census 

Light Duty Vehicle 

Mfg

0.610 0.407

BLS ERM Motor Vehicle Parts 

Mfg

3.073 2.049

ASM Motor Vehicle Parts 

Mfg

3.093 2.063

Economic 

Census 

Motor Vehicle Parts 

Mfg

2.749 1.833
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-29 

BLS ERM refers to the U.S. Bureau of Labor Statistics’ Employment Requirement Matrix. ASM refers to the 

U.S. Census Bureau’s Annual Survey of Manufactures. Economic Census refers to the U.S. Census Bureau’s 

Economic Census.

Over time, the amount of labor needed in the auto industry has changed: automation 

and improved production methods have led to significant productivity increases. The BLS 

ERM, for instance, provided estimates that, in 1993, 1.52 workers were needed per $1 million 

of 2000$, but only 0.83 workers by 2008 (in 2000$).61 Because the ERM is available 

annually for 1993-2008, we used these data to estimate productivity improvements over time. 

We regressed logged ERM values on year (to estimate percent change per year) for both the 

Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing sectors. The results 

suggest a 4.4 percent per year productivity improvement in the Motor Vehicle Manufacturing 

Sector, and a 3.6 percent per year improvement in the Motor Vehicle Parts Manufacturing 

Sector. We then used the regression relationship to project the ERM through 2025. In the 

results presented below, these projected values (adjusted to 2009$) were used directly for the 

BLS ERM estimates. For the ASM, we used the ratio of the projected value in the future to 

the projected value in 2009 (the base year for the ASM); for the Economic Census estimates, 

we used the ratio of the projected value in the future to the projected value in 2007 (the base 

year for that estimate). As noted above, we adjusted the estimate of workers per vehicle for 

the demand effect in Chapter Error! Reference source not found., above, using the 

productivity improvement for the Motor Vehicle Manufacturing sector; because the estimate 

of workers per vehicle for the demand effect was based on data from 2001 to 2010, we used 

the projected value of the ERM in 2005 as the denominator of the ratio. This is a simple way 

to examine the relationship between labor required and expenditure and we seek comment on 

refining this method. 

Table 8.2-2 shows the cost estimates developed for this rule, discussed in Chapter 5. 

The maximum value in Table 8.2-2 for employment impacts per $1 million expenditures 

(after accounting for the share of domestic production) is 2.049 in 2009 if all the additional 

costs are in the parts sector; the minimum value is 0.407 in 2009, if all the additional costs are 

in the light-duty vehicle manufacturing sector: that is, the range of employment impacts is 

between 0.4 and 2 additional jobs per $1 million expenditures in the sector in 2009. The 

results in Table 8.2-2 include the productivity adjustment described above. 

While we estimate employment impacts beginning with the first year of the standard 

(2017), some of these job gains may occur earlier as auto manufacturers and parts suppliers 

hire staff in anticipation of compliance with the standard. Also, because of the uncertainty 

associated of projecting average labor required associated with the cost of production, we 

limit the projection to 2020. 

Table 8.2-2 Employment due to Cost Effect in the Motor Vehicle Manufacturing Sector 

Year Costs (before 

adjustment for 

domestic proportion 

Minimum 

employment effect 

(if all expenditures 

Maximum 

employment effect 

(if all expenditures 
Chapter 8 

8-30 

of production) 

($Millions)

are in the parts 

sector) 

are in the light 

duty vehicle mfg 

sector) 

2017 $ 2,300 600 3,600 

2018 $ 4,656 1,200 7,000 

2019 $ 6,507 1,600 9,400 

2020 $ 8,467 1,900 11,800 

8.2.3.1.2 The Factor Shift Effect 

The factor shift effect looks at the effects on employment due to changes in labor 

intensity associated with a regulation. As noted above, the estimates of the cost effect assume 

constant labor per $1 million in expenditures, though the new technologies may be either 

more or less labor-intensive than the existing ones. An estimate of the factor shift effect 

would either increase or decrease the estimate used for the cost effect. 

We are not quantifying the factor shift effect here, for lack of data on the labor 

intensity of all the possible technologies that manufacturers could use to comply with the 

proposed standards. For a subset of the technologies, though, EPA-sponsored research 

(discussed in Chapter 3.2.1.1 of the Joint TSD) which compared new technologies to existing 

ones at the level of individual components provides some insights into the factor shift effect. 

The comparison involved tearing down the selected technologies to their individual 

components and looking at the differences in materials and labor needs in moving from the 

conventional to the new technologies.62 For instance, the analysis compared all the parts and 

labor associated with an 8-speed automatic transmission to those needed for a 6-speed 

automatic transmission. 

Because labor cost was one of the sources of differences between the technologies, it 

is possible, for those technologies, to see whether labor needs increase or decrease with the 

switch to technologies that might contribute to compliance with the proposed standards. An 

increase in labor cost for the new technology indicates an increase in the labor needed for the 

new technology compared to the baseline technology. For instance, an 8-speed transmission 

requires $15.11 more in labor costs than a 6-speed transmission (as accounted for in EPA’s 

cost estimates for the proposed rule). Dividing the labor cost by a wage per hour estimate 

provides an estimate of the additional hours (and thus the additional labor) needed for the new 

technology compared to the baseline technology. As with labor cost, an increase in labor 

hours per technology indicates greater employment needs for the new technologies. For this 

conversion, a weighted average wage rate (90 percent of the average wage in the Motor 

Vehicle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle 

Manufacturing Sector) of $46.36/hour in 2015, using 2008 dollars (the unit of analysis for the 

Comment [A1]: Interagency commenters 

recommend agencies only go to 2020 becusae there 

will be a mid-term review. 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-31 

FEV study). For the change from a 6-speed to an 8-speed transmission, we thus estimate an 

additional 0.33 hours of labor per transmission. 

8.2.4 Effects on Employment for Auto Dealers 

The effects of the proposed standards on employment for auto dealers depend 

principally on the effects of the standards on light duty vehicle sales. In addition, auto dealers 

may be affected by changes in maintenance and service costs. Increases in those costs are 

likely to increase labor demand in dealerships. 

Although this proposal predicts very small penetration of advanced technology 

vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. 

As discussed in Chapter 8.1.2.7, consumers may find some characteristics of electric vehicles 

and plug-in hybrid electric vehicles, such as the ability to fuel with electricity rather than 

gasoline, attractive; they may find other characteristics, such as the limited range for electric 

vehicles, undesirable. As a result, some consumers will find that EVs will meet their needs, 

but other buyers will choose more conventional vehicles. Auto dealers may play a major role 

in explaining the merits and disadvantages of these new technologies to vehicle buyers. There 

may be a temporary need for increased employment to train sales staff in the new 

technologies as the new technologies become available. 

8.2.5 Effects on Employment in the Auto Parts Sector 

As discussed in the context of employment in the auto industry, some vehicle parts are 

made in-house by auto manufacturers; others are made by independent suppliers who are not 

directly regulated, but who will be affected by the proposed standards as well. The additional 

expenditures on technologies are expected to have a positive effect on employment in the 

parts sector as well as the manufacturing sector; the breakdown in employment between the 

two sectors is difficult to predict. The effects on the parts sector also depend on the effects of 

the proposed standards on vehicle sales and on the labor intensity of the new technologies, 

qualitatively in the same ways as for the auto manufacturing sector. 

8.2.6 Effects on Employment for Fuel Suppliers 

In addition to the effects on the auto manufacturing and parts sectors, these rules will 

result in changes in fuel use that lower GHG emissions. Fuel saving, principally reductions in 

liquid fuels such as gasoline and diesel, will affect employment in the fuel suppliers industry 

sectors throughout the supply chain, from refineries to gasoline stations. To the extent that 

the proposed standards result in increased use of electricity or other new fuels, employment 

effects will result from providing these fuels and developing the infrastructure to supply them 

to consumers. 

Expected petroleum fuel consumption reductions can be found in Chapter 5.3. While 

this reduced consumption represents fuel savings for purchasers of fuel, it represents a loss in 

value of output for the petroleum refinery industry, fuel distributors, and gasoline stations. 
Chapter 8 

8-32 

The loss of expenditures to petroleum fuel suppliers throughout the petroleum fuel supply 

chain, from the petroleum refiners to the gasoline stations, is likely to result in reduced 

employment in these sectors. 

This rule is also expected to lead to increases in electricity consumption by vehicles, 

as discussed in Chapter 5.3. This new fuel may require additional infrastructure, such as 

electricity charging locations. Providing this infrastructure will require some increased 

employment. In addition, the generation of electricity is likely to require some additional 

labor. We have insufficient information at this time to predict whether the increases in labor 

associated with increased infrastructure provision and generation for electricity will be greater 

or less than the employment reductions associated with reduced demand for petroleum fuels. 

8.2.7 Summary 

The primary employment effects of this proposal are expected to be found throughout 

several key sectors: auto manufacturers, auto dealers, auto parts manufacturing, and fuel 

production and supplyThese proposed standards initially take effect in model year 2017, a 

time period sufficiently far in the future that the current sustained high unemployment at the 

national level may be moderated or ended. In an economy with full employment, the primary 

employment effect of a rulemaking is likely to be to move employment from one sector to 

another, rather than to increase or decrease employment. For that reason, we focus our partial 

quantitative analysis on employment in the regulated sector, to examine the impacts on that 

sector directly. We discuss the likely direction of other impacts in the regulated sector as well 

as in other directly related sectors, but we do not quantify those impacts, because they are 

more difficult to quantify with reasonable accuracy, particularly so far into the future. 

References 

1

 See, for instance, Gron, Ann, and Deborah Swenson, 2000. “Cost Pass-Through in the U.S. 

Automobile Market,” Review of Economics and Statistics 82: 316-324 (Docket EPA-HQOAR-2010-0799). 

2

 Insurance Information Institute, 2008, “Average Expenditures for Auto Insurance By State, 

2007-2008,” http://www.iii.org/media/facts/statsbyissue/auto/, accessed 8/22/11 (Docket 

EPA-HQ-OAR-2010-0799). 

3

 U.S. Department of Energy, 2011. “Transportation and the Economy,” Chapter 10 in 

“Transportation Energy Data Book,” http://cta.ornl.gov/data/tedb30/Edition30_Chapter10.pdf, 

accessed 8/22/11, Table 11 (Docket EPA-HQ-OAR-2010-0799). 

4

 Solheim, Mark, 2006 “State Car Tax Rankings,” 

http://www.kiplinger.com/features/archives/2006/04/cartax.html, accessed April 23, 2009 

(Docket EPA-HQ-OAR-2009-0472-0010) (Docket EPA-HQ-OAR-2010-0799). 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-33 

5

 U.S. Census Bureau, "Table 2. Resident Population of the 50 States, the District of 

Columbia, and Puerto Rico: 2010 Census" http://2010.census.gov/news/presskits/apportionment/apport.html . 

6

“National Auto Loan Rates for July 21, 2011,” 

http://www.bankrate.com/finance/auto/national-auto-loan-rates-for-july-21-2011.aspx, 

accessed 7/26/11 (Docket EPA-HQ-OAR-2010-0799). 

7

 Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-

951 (Docket EPA-HQ-OAR-2010-0799); Goldberg, Pinelopi Koujianou, “The Effects of the 

Corporate Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics

46(1) (March 1998): 1-33 (Docket EPA-HQ-OAR-2010-0799). 

8

 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy 

Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared 

by the Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. 

DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., Philip 

D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A 

Study of Incentives for Increased Fuel Economy,” Energy Policy 33 (2005): 757-775 

(Docket EPA-HQ-OAR-2010-0799). 

9

 McManus, Walter M., “Can Proactive Fuel Economy Strategies Help Automakers Mitigate 

Fuel-Price Risks?” University of Michigan Transportation Research Institute, September 14, 

2006 (Docket EPA-HQ-OAR-2010-0799). 

10 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799); Berry, Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand 

Systems from a Combination of Micro and Macro Data: The New Car Market,” Journal of 

Political Economy 112(1) (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799). 

11 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799). 

12 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799). 

13 Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) 

Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-2010-

0799). 
Chapter 8 

8-34 

14 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher 

CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and 

Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799). 

15 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and 

Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American 

Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Train, 

Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share 

of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 

(Docket EPA-HQ-OAR-2010-0799). 

16 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

17 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy 

Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared 

by the Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. 

DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-0799). 

18 NERA Economic Consulting, “Evaluation of NHTSA’s Benefit-Cost Analysis of 2011-

2015 CAFE Standards,” 2008, available at 

http://www.heartland.org/policybot/results/23495/Evaluation_of_NHTSAs_BenefitCost_Anal

ysis_Of_20112015_CAFE_Standards.html (Docket EPA-HQ-OAR-2010-0799). 

19 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799). 

20 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

21 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and 

Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American 

Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799). 

22 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, 

and Li 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 (Docket EPA-HQ-OAR-2010-

0799). 

23 NERA Economic Consulting, “Appendix B: New Vehicle Market Model,” “Impacts of the 

California Greenhouse Gas Emission Standards on Motor Vehicle Sales,” comments 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

8-35 

submitted to the U.S. Environmental Protection Agency at Regulations.gov, document 

number EPA-HQ-OAR-2006-0173-9053.1 (Docket EPA-HQ-OAR-2010-0799). 

24 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799) 

25 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

26 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and 

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 

(2005): 757-775 (Docket EPA-HQ-OAR-2010-0799). 

27 Rex, Emma, and Henrikke Baumann (2007). “Beyond ecolabels: what green marketing 

can learn from conventional marketing.” Journal of Cleaner Production 15: 567-576 (Docket 

EPA-HQ-OAR-2010-0799). 

28 Whitefoot, Kate, Meredith Fowlie, and Steven Skerlos, “Product Design Responses to 

Industrial Policy: Evaluating Fuel Economy Standards Using an Engineering Model of 

Endogenous Product Design,” Energy Institute at Haas Working Paper 214, University of 

California Energy Institute, February 2011 (Docket EPA-HQ-OAR-2010-0799). 

29 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

30 Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-

951 (Docket EPA-HQ-OAR-2010-0799). 

31 Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with 

Flexible Substitution Patterns,” Journal of Econometrics 89 (1999): 109-129 (Docket EPAHQ-OAR-2010-0799); Brownstone, David, David S. Bunch, and Kenneth Train, “Joint Mixed 

Logit Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” 

Transportation Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2010-0799); 

Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” 

prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 

(Docket EPA-HQ-OAR-2010-0799); Greene, David L., K. G. Duleep, and Walter McManus, 

“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, August 

2004 (Docket EPA-HQ-OAR-2010-0799). 
Chapter 8 

8-36 

32 Petrin, Amil, “Quantifying the Benefits of New Products: The Case of the Minivan,” 

Journal of Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799); 

Berry, Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand Systems 

from a Combination of Micro and Macro Data: The New Car Market,” Journal of Political 

Economy 112 (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799).

33 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48 (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799). 

34 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, 

and Li 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 (Docket EPA-HQ-OAR-2010-

0799). 

35 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and 

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 

(2005): 757-775 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, and Li 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 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., “Feebates, 

Footprints and Highway Safety,” Transportation Research Part D 14 (2009): 375-384. 

36 E.g., Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of 

Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental 

Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799). 

37 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy 

Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2010-0799). 

38 E.g., Espey, Molly, and Santosh Nair, 2005. “Automobile Fuel Economy: What Is It 

Worth?” Contemporary Economic Policy 23: 317-323 (Docket EPA-HQ-OAR-2010-0799). 

39 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 

1593-1594 (Docket EPA-HQ-OAR-2010-0799). 

40 E.g., Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-

951 (Docket EPA-HQ-OAR-2010-0799). 

41 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

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42 Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. “Pain at the Pump: 

How Gasoline Prices Affect Automobile Purchasing in New and Used Markets,” working 

paper, http://web.mit.edu/knittel/www/papers/gaspaper_latest.pdf (accessed 11/1/11) (Docket 

EPA-HQ-OAR-2010-0799); Li, Shanjun, Christopher Timmins, and Roger H. von Haefen, 

2009. “How Do Gasoline Prices Affect Fleet Fuel Economy?” American Economic Journal: 

Economic Policy 1(2): 113-137; Congressional Budget Office (2008) (Docket EPA-HQOAR-2010-0799); Congressional Budget Office (2008). Effects of Gasoline Prices on 

Driving Behavior and Vehicle Markets. The Congress of the United States, Pub. No. 2883

(Docket EPA-HQ-OAR-2010-0799); West. Sarah (2007). “The Effect of Gasoline Prices on 

the Demand for Sport Utility Vehicles.” Working Paper. Macalester College, 

http://www.macalester.edu/~wests/SarahWestMEA2007.pdf, accessed 1/25/10 (Docket EPAHQ-OAR-2010-0799). 

43 Greene, David L., and Jin-Tan Liu, 1988. “Automotive Fuel Economy Improvements and 

Consumers’ Surplus,” Transportation Research Part A 22A (3): 203-218 (Docket EPA-HQOAR-2010-0799). 

44 Greene, David L. (2010). “How Consumers Value Fuel Economy: A Literature Review.” 

EPA Report EPA-420-R-10-008 (Docket EPA-HQ-OAR-2010-0799). 

45 Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S. 

Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 

11/1/11 (Docket EPA-HQ-OAR-2010-0799). 

46 Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile 

Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket 

EPA-HQ-OAR-2010-0799); McManus, Walter, 2007. “The Impact of Attribute-Based 

Corporate Average Fuel Economy (CAFE) Standards: Preliminary Results.” Ann Arbor, MI: 

University of Michigan Transportation Research Institute, Report No. UMTRI-2007-31 

(Docket EPA-HQ-OAR-2010-0799). 

47 For instance, Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy 

(CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2010-0799); Austin, David, and Terry Dinan, “Clearing the Air: The Costs and 

Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of 

Environmental Economics and Management 50 (2005): 562- (Docket EPA-HQ-OAR-2010-

0799); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of 

the Corporate Average Fuel Economy Standard,” Resources for the Future Paper RFF DP 10-

50 (December 2010), http://www.rff.org/RFF/Documents/RFF-DP-10-50.pdf) (Docket EPAHQ-OAR-2010-0799)Jacobsen, Mark, 2010. “Evaluating U.S. Fuel Economy Standards in a 

Model with Producer and Household Heterogeneity,” working paper, at 

http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf (accessed 11/1/11) (Docket EPA-HQOAR-2010-0799) . 
Chapter 8 

8-38 

48 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher 

CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and 

Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

49 IEA. 2007. “Mind the Gap: Quantifying Principal-Agent Problems in Energy Efficiency.” 

Paris, France: International Energy Agency (Docket EPA-HQ-OAR-2010-0799); Jaffe, Adam 

B., Richard G. Newell, and Robert N. 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 (Docket 

EPA-HQ-OAR-2010-0799); Metcalf, Gilbert E., and Kevin A. Hassett (1999). "Measuring 

the Energy Savings From Home Improvement Investments: Evidence From Monthly Billing 

Data." The Review of Economics and Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010-

0799); Tietenberg, T. (2009). “Reflections – Energy Efficiency Policy: Pipe Dream or 

Pipeline to the Future?” Review of Environmental Economics and Policy 3(2): 304-320 

(Docket EPA-HQ-OAR-2010-0799). 

50 Helfand, Gloria, and Ann Wolverton (2011). “Evaluating the Consumer Response to Fuel 

Economy: A Review of the Literature.” International Review of Environmental and 

Resource Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799). 

51 Jaccard, Mark. “Paradigms of Energy Efficiency’s Cost and their Policy Implications: 

Déjà Vu All Over Again.” Modeling the Economics of Greenhouse Gas Mitigation: 

Summary of a Workshop, K. John Holmes, Rapporteur. National Academies Press, 2010. 

http://www.nap.edu/openbook.php?record_id=13023&page=42 (Docket EPA-HQ-OAR2010-0799) 

52 Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” 

prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 

(Docket EPA-HQ-OAR-2010-0799). 

53 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency 

Standards. The White House, Office of the Press Secretary, May 21, 2010. 

http://www.whitehouse.gov/the-press-office/presidential-memorandum-regarding-fuelefficiency-standards 

54 U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed 

on August 9, 2011. 

55 Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis 

of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011 

(Docket EPA-HQ-OAR-2010-0799). 

56 Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. “Jobs Versus the 

Environment: An Industry-Level Perspective.” Journal of Environmental Economics and 

Management 43 (2002): 412-436 (Docket EPA-HQ-OAR-2010-0799).
Summary of interagency working comments received on draft NPRM under EO 12866 and 

EO 13563 review. These are for your consideration and are subject to further review. 

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57 Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental 

and Natural Resource Policy.” Environmental and Resource Economics 22 (2002): 133-156 

(Docket EPA-HQ-OAR-2010-0799). 

58 Berman, Eli, and Linda T. Bui, (2001) “Environmental Regulation and Labor Demand: 

Evidence from the South Coast Air Basin,” Journal of Public Economics, 79, 265 – 295 

(Docket EPA-HQ-OAR-2010-0799).

59 http://www.bls.gov/emp/ep_data_emp_requirements.htm 

60 http://www.census.gov/manufacturing/asm/index.html

Deleted: ¶
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Below is a summary of interagency comments received during EO 12866 and EO 13563 review of the draft rule “Joint 

Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel 

Economy Standards”. They are for your consideration and are subject to further policy review. 

Interagency commenters recommend the following embedded edits to EPA’s RIA discussion of Vehicle sales and 

employment: 

*** 

Chapter 8: Vehicle Sales and Employment Impacts 

8.1 Vehicle Sales Impacts 

8.1.1 Vehicle Sales Impacts and Payback Period 

Predicting the effects of this rule on vehicles entails comparing two competing effects. 

On the one hand, as a result of this rule, the vehicles will become more expensive, which 

would, by itself, be expected to discourage sales. On the other hand, the vehicles will have 

improved fuel economy and thus lower operating costs, producing lower total costs over the 

life of vehicles, which makes them more attractive to consumers. Which of these effects 

dominates for potential vehicle buyers when they are considering a purchase will determine 

the effect on sales. However, assessing the net effect of these two competing effects is 

complex and uncertain, as it rests on how consumers value fuel savings at the time of 

purchase and the extent to which manufacturers and dealers reflect them in the purchase price. 

The empirical literature does not provide clear evidence on whether consumers fully consider 

the value of fuel savings at the time of purchase. It also generally does not speak to the 

efficiency of manufacturing and dealer pricing decisions. Thus, for the proposal we do not 

provide quantified estimates of potential sales impacts. Rather, we solicit comment on the 

issues raised here and on methods for estimating the effect of this rule on vehicle sales. 

For years, consumers have been gaining experience with the benefits that accrue to 

them from owning and operating vehicles with greater fuel efficiency. Many households 

already own vehicles with a fairly wide range of fuel economy, and thus already have an 

opportunity to learn about the value of fuel economy on their own. Among two-vehicle 

households, for example, the least fuel-efficient vehicle averages just over 22 mpg (EPA test 

rating), and the range between this and the fuel economy of their other vehicle averages 

nearly 7 mpg. Among households that own 3 or more vehicles, the typical range of the fuel 

economy they offer is much wider. Consumer demand may have shifted towards such 

vehicles, not only because of higher fuel prices but also if many consumers are learning about 

the value of purchases based not only on initial costs but also on the total cost of owning and 

operating a vehicle over its lifetime. This type of learning should continue before and during 

the model years affected by this rule, particularly given the new fuel economy labels that 

clarify potential economic effects and should therefore reinforce that learning.. 

Today’s proposed rule, combined with the new and easier-to-understand fuel economy 

label required to be on all new vehicles beginning in 2012, may increase sales above baseline 

levels by hastening this very type of consumer learning. As more consumers experience, as a 
Summary of interagency working comments received in draft NPRM under EO 12866 

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8-2 

result of the rule, the savings in time and expense from owning more fuel efficient vehicles, 

demand may shift yet further in the direction of the vehicles mandated under the rule. This 

social learning can take place both within and across households, as consumers learn from one 

another. 

First and most directly, the time and fuel savings associated with operating more fuel 

efficient vehicles may be more salient to individuals who own them, which might cause their 

subsequent purchase decisions to shift closer to minimizing the total cost of ownership over 

the lifetime of the vehicle. 

Second, this appreciation may spread across households through word of mouth and 

other forms of communications. 

Third, as more motorists experience the time and fuel savings associated with greater 

fuel efficiency, the price of used cars will better reflect such efficiency, further reducing the 

cost of owning more efficient vehicles for the buyers of new vehicles (since the resale price 

will increase). 

If these induced learning effects are strong, the rule could potentially increase total 

vehicle sales over time. It is not possible to quantify these learning effects years in advance 

and that effect may be speeded or slowed by other factors that enter into a consumer’s 

valuation of fuel efficiency in selecting vehicles. The possibility that the rule will (after a lag 

for consumer learning) increase sales need not rest on the assumption that automobile 

manufacturers are failing to pursue profitable opportunities to supply the vehicles that 

consumers demand. In the absence of the rule, no individual automobile manufacturer would 

find it profitable to move toward the more efficient vehicles mandated under the rule. In 

particular, no individual company can fully internalize the future boost to demand resulting 

from the rule. If one company were to make more efficient vehicles, counting on consumer 

learning to enhance demand in the future, that company would capture only a fraction of the 

extra sales so generated, because the learning at issue is not specific to any one company's 

fleet. Many of the extra sales would accrue to that company's competitors. 

In other words, consumer learning about the benefits of fuel efficient vehicles involves 

positive externalities (spillovers) from one company to the others. [Footnote: Industrywide 

positive spillovers of this type are hardly unique to this situation. In many industries, 

companies form trade associations to promote industry-wide public goods. For example, 

merchants in a given locale may band together to promote tourism in that locale. Antitrust law 

recognizes that this type of coordination can increase output.] These positive externalities may 

lead to benefits for manufacturers as a whole. We emphasize that this discussion has been 

tentative and qualified. To be sure, social learning of related kinds has been identified in a 

number of contexts. [Footnote: See Hunt Alcott, Social Norms and Energy Conservation, 

Journal of Public Economics (forthcoming 2011), available at 

http://web.mit.edu/allcott/www/Allcott%202011%20JPubEc%20-
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8-3 

%20Social%20Norms%20and%20Energy%20Conservation.pdf; Christophe Chamley, 

Rational Herds: Economic Models of Social Learning (Cambridge, 2003).] Comments are 

invited on the discussion offered here, with particular reference to any relevant empirical 

findings.” 

We do not here present a vehicle sales analysis using this approach. This rule takes 

effect for MY 2017-2025. In the intervening years, it is possible that the assumptions 

underlying this analysis, as well as market conditions, might change. Instead, we present a 

payback period analysis to estimate the number of years of fuel savings needed to recover the 

up-front costs of the new technologies. In other words, the payback period identifies the 

break-even point for new vehicle buyers. The calculation of the payback period is discussed 

in DRIA Chapter 5.3. Table 1 shows the estimated payback period for MY 2021 and 2025. 

We present MY 2021 because it is the last year before the mid-term review impacts, if any, 

will take place, and MY 2025 because it is the last year of the program. The payback period 

in 2021 is shorter than that in 2025, because the technologies required to meet the proposed 

MY 2021 standards are more cost-effective than those for MY 2025. In all cases, the payback 

periods are less than 4 years. 

Table 1 Estimated Payback Period for Model Years 2021 and 2025 (Years) 

Model 

Year 

Estimated 

Payback Period 

for Cash 

Purchase, 3% 

Discount Rate 

Estimated 

Payback Period 

for Cash 

Purchase, 7% 

Discount Rate 

Estimated 

Payback Period 

for Purchase on 

Credit, 3% 

Discount Rate 

Estimated 

Payback Period 

for Purchase on 

Credit, 7% 

Discount Rate 

2021 2.7 2.9 2.9 2.8

2025 3.7 3.9 3.9 3.9

We welcome comments on all aspects of this discussion, including the full range of 

considerations and assumptions which influence market behavior and outcomes; and 

associated uncertainties. We welcome comments on the methodology described here for 

quantitative estimates of the effects of this proposal on sales and its appropriateness for this 

rulemaking; we also welcome proposals for other methods. 

We here provide further detail on some of the assumptions included in the payback 

analysis. We seek comment on all these factors as well. The analysis starts with the increase 

in costs estimated by the OMEGA model. We assume that these costs are fully passed along 

to consumers. This assumption is appropriate for cost increases in perfectly competitive 

markets. In less than perfectly competitive markets, though, it is possible that the cost 
Summary of interagency working comments received in draft NPRM under EO 12866 

review. These are for your consideration and are subject to further policy review. 

8-4 

increase is split between consumers and automakers, and possibly suppliers, and the price 

may not increase as much as costs.1 Thus, the assumption of full cost pass-through is possibly 

an overestimate. 

The next step in the analysis is to adjust this cost increase for other effects on the 

consumer. The higher vehicle price is likely to lead to an increase in sales tax, insurance, and 

vehicle financing costs. 

The increase in insurance costs is estimated from the average value of collision plus 

comprehensive insurance as a proportion of average new vehicle price. Collision plus 

comprehensive insurance is the portion of insurance costs that depend on vehicle value. The 

Insurance Information Institute2 provides the average value of collision plus comprehensive 

insurance in 2008 as $432. The average value of a new vehicle in 2008, according to the U.S. 

Department of Energy, was $23,334.3

 Dividing the insurance cost by the average price of a 

new vehicle gives the proportion of comprehensive plus collision insurance as 1.85 percent of 

the price of a vehicle. If this same proportion holds for the increase in price of a vehicle, then 

insurance costs should go up by 1.85 percent of the increase in vehicle cost. We use 

information on depreciation of vehicle value from the same U.S. Department of Energy report 

to estimate a reduction in insurance costs, due to reduction in the estimated value of the 

vehicle, over 9 years. 

Calculating the average increase in sales tax starts with the vehicle sales tax for each 

state in 2006, the most recent source identified.4

 The sales tax per state was then multiplied 

by the 2010 population of the state;5

 those values were summed and divided by total U.S.

population, to give a population-weighted sales tax. That estimate of the state sales taxes for 

vehicles in the U.S. is 5.3 percent. This value is assumed to be a one-time cost incurred when 

the vehicle is purchased. 

As of July, 2011, the national average interest rate for a 5 year new car loan was 5.51 

percent.6 We use this loan rate to calculate the increase in vehicle costs due to financing a 

loan. 

8.1.2 Consumer Vehicle Choice ModelingA

An alternative to the vehicle sales analysis discussed above is the use of consumer 

vehicle choice models. In this section we describe some of the consumer vehicle choice 

models EPA has reviewed in the literature, and we describe the models’ results and 

limitations that we have identified. The evidence from consumer vehicle choice models 

indicates a huge range of estimates for consumers’ willingness to pay for additional fuel 

 

A

 This section is drawn heavily from Helfand, Gloria, and Ann Wolverton, “Evaluating the Consumer Response 

to Fuel Economy: A Review of the Literature.” International Review of Environmental and Resource 

Economics 5 (2011): 103-146. 
Summary of interagency working comments received in draft NPRM under EO 12866 

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8-5 

economy. Because consumer surplus estimates from consumer vehicle choice models depend 

critically on this value, we would consider any consumer surplus estimates of the effect of our 

rule from such models to be unreliable. In addition, the predictive ability of consumer vehicle 

choice models may be limited. While vehicle choice models are based on sales of existing 

vehicles, vehicle models are likely to change, both independently and in response to this rule. 

The models may not predict well in response to these changes. Instead, we compare the value 

of the fuel savings associated with this rule with the increase in technology costs. EPA will 

continue its efforts to review the literature, but, given the known limitations and uncertainties 

of vehicle choice models, EPA has not conducted an analysis using these models for this 

proposal. 

This rule will lead automakers to change characteristics – in particular, the fuel 

economy -- of the vehicles they produce. These changes will affect the cost of manufacturing 

the vehicle; as a result, the prices of the vehicles will also change. 

In response to these changes, the number and types of vehicles sold is likely to change. 

When consumers buy vehicles, they consider both their personal characteristics (such as age, 

family composition, income, and their vehicle needs) and the characteristics of vehicles (e.g., 

vehicle size, fuel economy, and price). In response to the changes in vehicle characteristics, 

consumers will reconsider their purchases. Increases in fuel economy are likely to be 

attractive to consumers, but increases in price, as well as any detrimental changes in other 

vehicle characteristics, may be deterrents to purchase. As a result, consumers may choose a 

different vehicle than they would have purchased in the absence of the rule. The changes in 

prices and vehicle characteristics are likely to influence consumers on multiple market scales: 

the total number of new vehicles sold; the mix of new vehicles sold; and the effects of the 

sales on the used vehicle market. 

Consumer vehicle choice modeling (CCM) is a method used to predict what vehicles 

consumers will purchase based on vehicle characteristics and prices. In principle, it should 

produce more accurate estimates of compliance costs compared to models that hold fleet mix 

constant, since it predicts changes in the fleet mix that can affect compliance costs. It can also 

be used to measure changes in consumer surplus, the benefit that consumers perceive from a 

good over and above the purchase price. (Consumer surplus is the difference between what 

consumers would be willing to pay for a good, represented by the demand curve, and the 

amount they actually pay. For instance, if a consumer were willing to pay $30,000 for a new 

vehicle, but ended up paying $25,000, the $5000 difference is consumer surplus.) 

A number of consumer vehicle choice models have been developed. They vary in the 

methods used, the data sources, the factors included in the models, the research questions they 

are designed to answer, and the results of the models related to the effects of fuel economy on 

consumer decisions. This section will give some background on these differences among the 

models. 
Summary of interagency working comments received in draft NPRM under EO 12866 

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8.1.2.1 Methods 

Consumer choice models (CCMs) of vehicle purchases typically use a form of discrete 

choice modeling. Discrete choice models seek to explain discrete rather than continuous 

decisions. 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. Discrete decisions can take only a 

limited set of values. The decision to purchase a vehicle, for instance, can only take two 

values, yes or no. Vehicle purchases are typically modeled as discrete choices, where the 

choice is whether to purchase a specified vehicle. The result of these models is a prediction 

of the probability that a consumer will purchase a specified vehicle. A minor variant on 

discrete choice models estimates the market share (a continuous variable between 0 and 1) for 

each vehicle. Because the market share is, essentially, the probability that consumers will 

purchase a specific vehicle, these approaches are similar in process; they differ mostly in the 

kinds of data that they use. 

The primary methods used to model vehicle choices are nested logit and mixed logit.B

 

In a nested logit, the model is structured in layers. For instance, the first layer may be the 

choice of whether to buy a new or used vehicle. Given 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. Examples of 

nested logit models include Goldberg,7 Greene et al.,8 and McManus.9

In a mixed logit, personal characteristics of consumers play a larger role than in nested 

logit. While nested logit can look at the effects of a change in average consumer 

characteristics, mixed logit allows consideration of the effects of the distribution of consumer 

characteristics. As a result, mixed logit can be used to examine the distributional effects on 

various socioeconomic groups, which nested logit is not designed to do. Examples of mixed 

logit models include Berry, Levinsohn, and Pakes,10 Bento et al.,11 and Train and Winston.12

 

While discrete choice modeling appears to be the primary method for consumer choice 

modeling, others (such as Kleit13 and Austin and Dinan14) have used a matrix of demand 

elasticities to estimate the effects of changes in cost. The discrete choice models can produce 

such elasticities. Kleit as well as Austin and Dinan used the elasticities from an internal GM 

vehicle choice model. 

8.1.2.2 Data Sources 

The predictions of vehicle purchases from CCMs are based on consumer and vehicle 

characteristics. The CCMs identify the effects of changing the characteristics on the purchase 

decisions. These effects are typically called the parameters or coefficients of the models. For 

 

B

 Logit refers to a statistical analysis method used for analyzing the factors that affect discrete choices (i.e., 

yes/no decisions or the choice among a countable number of options). 
Summary of interagency working comments received in draft NPRM under EO 12866 

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8-7 

instance, the model parameters might predict that an increase in a person’s income of 10% 

would increase the probability of her purchasing vehicle A by 5%, and decrease the 

probability of her purchasing vehicle B by 10%. 

The parameters in CCMs can be developed either from original data sources 

(estimated models), or using values taken from other studies (calibrated models). 

Estimated models use datasets on consumer purchase patterns, consumer 

characteristics, and vehicle characteristics to develop their original sets of parameters. The 

datasets used in these studies sometimes come from surveys of individuals’ behaviors.15

 

Because they draw on the behavior of individuals, they provide what is sometimes called 

micro-level data. Other studies, that estimate market shares instead of discrete purchase 

decisions, use aggregated data that can cover long time periods.16

 

Calibrated models rely on existing studies for their parameters. Researchers may draw 

on results from a number of estimated models, or even from research other than CCM, to 

choose the parameters of the models. The Fuel Economy Regulatory Analysis Model 

developed for the Energy Information Administration17 and the New Vehicle Market Model 

developed by NERA Economic Consulting18 are examples of calibrated models. 

8.1.2.3 Factors Included in the Models 

Consumer choice models vary in their complexity and levels of analysis. Some focus 

only on the new vehicle market;19 others consider the choice between new vehicles and an 

outside good (possibly including a used vehicle);20 others explicitly consider the relationship 

between the new and used vehicle markets.21 Some models include consideration of vehicle 

miles traveled,22 though most do not. 

The models vary in their inclusion of both consumer and vehicle information. One 

model includes only vehicle price and the distribution of income in the population influencing 

choice;23 others include varying numbers and kinds of vehicle and consumer attributes. 

Some models include only the consumer side of the vehicle market;24 others seek to 

represent both consumer and producer decisions.25 Models that include only the consumer 

side are suitable for reflecting consumer choices, but they do not allow for revisions of 

vehicle characteristics in response to consumer preferences. Including producer behavior 

allows for vehicle characteristics such as price and fuel economy to be the result of market 

forces rather than characteristics of the existing fleet. For instance, in the context of 

“feebates” (subsidizing fuel-efficient cars with revenue collected by taxing fuel-inefficient 

vehicles), Greene et al. estimated that 95% of the increase in fuel economy was due to 

addition of technology rather than changes in vehicles sold.26 Including auto maker response 

is a complex exercise. Auto makers are commonly considered to have market power; they 

can influence the prices that consumers pay to increase their profits. As a result, the price 

increases that consumers face may reflect strategic factors that could make them higher or 

lower than the technology costs. In addition, auto makers may seek to influence consumer 
Summary of interagency working comments received in draft NPRM under EO 12866 

review. These are for your consideration and are subject to further policy review. 

8-8 

preferences through marketing and advertising.27 Even those vehicle choice models that 

include a producer model may not include much detail, due to computational limits: it is 

unusual for models to allow both buyers and producers to choose one vehicle characteristic, 

much less multiple characteristics.28

 

8.1.2.4 Research Questions for the Models 

Consumer choice models have been developed to analyze many different research and 

policy questions. In part, these models have been developed to advance the state of economic 

modeling. The work of Berry, Levinsohn, and Pakes,29 for instance, is often cited outside the 

motor vehicle context for its incorporation of multiple new modeling issues into its 

framework. In addition, because the vehicle sector is a major part of the U.S. economy and 

involved in many public policy discussions, research questions cover a wide gamut. These 

topics have included the effects of voluntary export restraints on Japanese vehicles compared 

to tariffs and quotas,30 the market acceptability of alternative-fuel vehicles,31 the effects of 

introduction and exit of vehicles from markets,32 causes of the decline in market shares of 

U.S. automakers,33 and the effects of gasoline taxes34 and “feebates”35 (subsidizing fuelefficient cars with revenue collected by taxing fuel-inefficient vehicles). 

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions 

Consumer vehicle choice models typically consider the effect of fuel economy on 

vehicle purchase decisions. It can appear in various forms. 

Some models36 incorporate fuel economy through its effects 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 perhaps other operating costs) associated with a 

more fuel-efficient vehicle. Those savings are considered 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 estimate 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 Kurani37 question this assumption; they find, in fact, that

consumers do not make this calculation when they purchase a vehicle. The question remains, 

then, how or whether consumers take fuel economy into account when they purchase their 

vehicles. 

Most estimated consumer choice models, instead of making assumptions about how 

consumers incorporate fuel economy into their decisions, use data on consumer behavior to 

identify that effect. In some models, miles per gallon is one of the vehicle characteristics 

included to explain purchase decisions. Other models use fuel consumption per mile, the 

inverse of miles per gallon, as a measure:38 since consumers pay for gallons of fuel, then this 

measure can assess fuel savings relatively directly.

39 Yet other models multiply fuel 

consumption per mile by the cost of fuel to get the cost of driving a mile,40 or they divide fuel 

economy by fuel cost to get miles per dollar.41 It is worth noting that these last two measures 

assume that consumers respond the same way to an increase in fuel economy as they do to a 
Summary of interagency working comments received in draft NPRM under EO 12866 

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8-9 

decrease in the price of fuel when each has the same effect on cost per mile driven.C On the 

one hand, while this assumption does not rely on as complex a calculation as the present value 

of fuel savings that Turrentine and Kurani examined, it suggests a calculating consumer. On 

the other hand, using a form of cost per mile is a way to recognize the role of fuel prices in 

consumers’ purchase of fuel economy: recent research42 presents results that higher fuel 

prices play a major role in that decision. 

Greene and Liu,43 in a paper published in 1988, reviewed 10 papers using consumer 

vehicle choice models and estimated for each one how much consumers would be willing to 

pay at time of purchase to reduce vehicle operating costs by $1 per year. They found that 

people were willing to pay between $0.74 and $25.97 for a $1 decrease in annual operating 

costs for a vehicle. This is clearly a very wide range: while the lowest estimate suggests that 

people are not willing to pay $1 once to get $1 per year reduced costs of operating their 

vehicles, the maximum suggests a willingness to pay 35 times as high. For comparison, the 

present value of saving $1 per year for 15 years at a 3% discount rate is $11.94, while a 7% 

discount rate produces a present value of $8.78. While this study is quite old, it suggests that, 

at least as of that time, consumer vehicle choice models produced widely varying estimates of 

the value of reduced vehicle operating costs. 

A newer literature review from David Greene44 suggests continued lack of 

convergence on the value of increased fuel economy to consumers. Of 27 studies, willingness 

to pay for fuel economy as a percent of the expected value of fuel savings varied from highly 

positive to highly negative. Significant numbers of studies found that consumers overvalued 

fuel economy, undervalued fuel economy, or roughly valued fuel economy correctly relative 

to fuel savings. Part of the difficulty may be, as these papers note, that fuel economy may be 

correlated (either positively or negatively) with other vehicle attributes, such as size, power, 

or quality, not all of which may be included in the analyses; as a result, “fuel economy” may 

in fact represent several characteristics at the same time. Indeed, Gramlich45 includes both 

fuel cost (dollars per mile) and miles per gallon in his analysis, with the argument that miles 

per gallon measures other undesirable quality attributes, while fuel cost picks up the 

consumer’s demand for improved fuel economy. Greene finds that, while some of the 

variation may be explainable due to issues in some of the studies, the variation shows up in 

studies that appear to be well conducted. As a result, further work needs to be conducted 

before it is possible to identify the role of fuel economy in consumer purchase decisions. 

Some studies46 argue that automakers could increase profits by increasing fuel 

economy because the amount that consumers are willing to pay for increased fuel economy 

 

C

 Likewise, these measures assume consumers respond the same way to increases and decreases in cost per mile 

of driving, as well as if those increases and decreases are large shocks rather than small, gradual changes. The 

issue of potential asymmetric consumer response to increased fuel efficiency compared to other types of changes 

to the cost of driving also arises and is discussed in the context of the VMT rebound effect (see Section III.H.4 of 

the Preamble and Chapter 4.2.5.2 of the TSD). 
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outweighs the costs of that improvement. Other studies47 have found that increasing fuel 

economy standards imposes welfare losses on consumers and producers, because consumers 

should already be buying as much fuel economy as they want. In the course of reaching this 

result, though, at least one of these studies48 notes that its baseline model implies that 

consumers are willing to buy more fuel economy than producers have provided; they have to 

adjust their model to eliminate these “negative-cost” fuel economy improvements. 

The models do not appear to yield very consistent results on the role of fuel economy 

in consumer and producer decisions. 

8.1.2.6 Why Market Outcomes May Not Reflect Full Appreciation for 

Fuel Economy which Pays for Itself 

A detailed and wide ranging literature attempts to explain why market outcomes for 

energy-using products appear to reflect under-investment in energy saving technologies that – 

at least using a present value calculation based on engineering estimates – appear to pay for 

themselves. Existing research does not provide a definitive answer to this question. Potential 

explanations are bounded by two scenarios. On the one hand, purely private benefits of fuel 

economy (fuel savings, time savings, increases in driving time) must be accompanied by 

private losses of the same magnitude. However, if there is no such private loss, or if it is small 

or insignificant, then there is a market or behavioral failure. 

This disconnect between net present value estimates of energy-conserving cost savings 

and what consumers actually spend on energy conservation is often referred to as the Energy 

Paradox,49 since consumers appear to undervalue a wide range of investments in energy 

conservation. There are many possible explanations for the paradox discussed in the 

literature.50 Some explanations point to costs or aspects of consumer decision-making 

unaccounted for in a simple present value calculation, while others point to potential 

behavioral or market failures. There is little empirical literature to help the analyst determine 

which combination of hypothesis offers the most credible explanation. Some possibilities 

include: 

• Consumers put little weight on benefits from fuel economy in the future and show 

high discount rates; 

• Consumers do not find the benefits from fuel economy to be sufficiently salient at 

the time of purchase, even if it would be in consumers’ economic interest to take 

account of those benefits; 

• Consumers consider other attributes more important than fuel economy at the time 

of vehicle purchase, especially if fuel economy is a relatively “shrouded” attribute; 

• Consumers have difficulty in calculating expected fuel savings; 
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• Consumers may use imprecise rules of thumb when deciding how much fuel 

economy to purchase; 

• Consumers might associate higher fuel economy with inexpensive, less well 

designed vehicles; 

• Fuel savings in the future are uncertain, while at the time of purchase the increased 

costs of fuel-saving technologies are certain and immediate; 

• Consumers may not be able to find the vehicles they want with improved fuel 

economy; 

• The level of cost savings may be affected by the underlying reasons for the gap: 

factors such as transactions costs and differences in quality may not be adequately 

measured;51

• There is likely to be variation among consumers in the benefits they get from 

improved fuel economy, due to different miles driven and driving styles. 

• Consumers may give particular weight to the losses associated with upfront costs, 

and less so to the costs over time (a version of the phenomenon of “myopic loss 

aversion”); 

 The extent to which fuel economy is optimized relative to other, potentially more 

salient vehicle attributes (such as engine horsepower and seating capacity) in market 

outcomes for new vehicles remains an important area of uncertainty. There are significant 

challenges involved in effectively interpreting and anticipating consumer preferences for 

various vehicle attributes and amenities. There are significant lead times to market, potential 

return to scale limits on the range of options provided for a given attribute or amenity, market 

transaction frictional factors, and other factors inherent to the nature of these costly durable 

goods which may contribute to imperfect satisfaction of market demand for fuel economy 

among a highly heterogeneous customer base. . Both sides of the market would be expected 

to attempt to maximize the utility they gain from these transactions, they presumably rely 

heavily in their calculations on the uncertain benefits of savings from fuel economy 

improvements, and yet market outcomes may still appear to reflect potential foregone 

opportunities to increase utility. We remain interested in these market dynamics, their 

underlying causes, and their potential significance for assessing the potential incremental 

effects of pollution control standards. We welcome comments on any aspect of this 

discussion. 

8.1.2.7 Modeling Electric Vehicles and Other New Vehicles 

Modeling the introduction of new vehicles can be a greater challenge than modeling 

the existing vehicle market, because the modeler does not have data on how many of the new 

vehicles consumers buy. Nevertheless, it can be possible to estimate the effects of new 
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8-12 

vehicle introduction by identifying characteristics for the new vehicles and using those in a 

vehicle choice model. For instance, as discussed above, the models can estimate effects on 

the vehicle market when vehicles change their fuel economy or price. If the model 

incorporates other vehicle attributes important to the new vehicles, such as size, performance, 

or range, then the effect of the introduction can be modeled by applying the parameters for 

those features to the new vehicle characteristics. 

As discussed above, some models rely on vehicle price as the primary or only 

explanatory variable. Even in these models, it is possible, with some additional information, 

to consider the effects of new vehicle introduction. The first step is to find a vehicle similar 

on as many dimensions as possible to the new vehicle. For instance, if the change is to create 

an electric vehicle (EV) version of an existing model, then the existing model serves as the 

base vehicle. Next, it is necessary to measure the changes in vehicle attributes of interest to 

potential vehicle buyers. For an EV, changes in vehicle driving range and cost of fueling may 

be two such attributes. The next requirement is information on the value to consumers of the 

attributes that change between the new and the base vehicle. Multiplying the value for that 

attribute by the change in the attribute provides an estimate of the benefit or cost associated 

with changing that characteristic. That amount can then be added to or subtracted from the 

vehicle purchase price to give an adjusted purchase price reflecting the changed characteristic. 

This procedure is just an extension of the approach, discussed above, used to incorporate fuel 

economy improvements into vehicle choice models, by calculating future fuel savings and 

subtracting them (either in whole or a fraction) from vehicle purchase price. 

Incorporating new vehicles into a vehicle choice model, then, requires estimates of the 

changes in key attributes from conventional vehicles, and estimates of the value, also called 

the willingness to pay (WTP), that consumers put on those attributes. 

Electric vehicles (EVs) will have a number of changes in vehicle characteristics from 

any baseline model. EVs are likely to have a smaller driving range between refuelings than 

conventional vehicles, due to the large battery capacity needed to increase range. The ability 

to recharge at home may be a convenient, desirable feature for people who have garages with 

electric hookups, but not for people who park on the street. If an infrastructure develops for 

recharging vehicles with the convenience approaching that of buying gasoline, range or home 

recharging may become less of a barrier to purchase. The reduced tailpipe emissions and 

reduced noise may be attractive features to some consumers.D

 They may have different 

performance or storage capacity. If sufficient data were available, the changes in these 

attributes, combined with WTP for each of the attributes, could be used to adjust the purchase 

price of the baseline vehicle to estimate consumers’ WTP for the electric version of a vehicle. 

 

D

 For instance, Hidrue et al. (Hidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. 

“Willingness to Pay for Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 

686-705) find that some consumers are willing to pay $5100 more for vehicles with 95% lower emissions than 

the vehicles they otherwise aim to purchase. 
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Greene (2001), for instance, used this approach for a model that simulates choice, not only for 

EVs, but also for other alternative-fuel vehicles.52 In that model, he considers only one base 

vehicle, a passenger car, but considers the effect on WTP of fuel cost per mile, range, 

acceleration, and several other vehicle attributes.

Vehicle driving range has received attention because of the current paucity of 

recharging infrastructure: if the driver of an EV gets low on fuel, it may be difficult to find a 

place to recharge. Because range appears to be a major factor in EV acceptability, it is 

starting to draw attention in the research community. 

In several studies, researchers have used stated preference conjoint analysis to 

estimate the effect of vehicle range on consumer vehicle choice. In a conjoint analysis, 

consumers are given a choice between several vehicles with different attributes. One choice 

might be, for instance, between a baseline car and another car with higher range and a higher 

purchase price. The choices that consumers make (e.g., how much higher does the purchase 

price have to be for the consumer not to choose more range?) provide data that can be used to 

estimate the role of vehicle attributes in the consumer’s choice. Stated preference analysis is 

sometimes considered less reliable than actual market behavior, because what people say they 

will do in hypothetical situations may not match what they would do in actual situations. On 

the other hand, stated preference methods can be used to study goods where market data do 

not exist, such as future market products undergoing development (marketing studies often 

use stated preference methods), or environmental goods. Because electric vehicles are not in 

widespread enough use for market studies, stated preference studies are, at this point, one of 

the few options to examine consumer behavior relating to these vehicles. 

Table 8.1-2 summarizes results from several conjoint studies that include the effects of 

extending range (in the table, from 150 to 300 miles, to present standardized results). 

Variation of results in the table is from income or other demographic factors, not from 

confidence intervals. The results suggest that the value of additional range varies among 

consumers, and the amount of that variation is changing (perhaps shrinking) in more recent 

studies. 

Table 8.1-2Willingness to Pay for Increasing Range Calculated from Various Studies 

Study (Date) Value of extending range 

from 150 to 300 miles (dollar 

year) 

Value of additional 

range in 2009$a 

Bunch et al. (1993)b $7,600 (1991$) $11,100

Kavelek (1996) for California $2600 - $41,900 (1993$) $3700 - $58,700
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8-14 

Energy Commissionc

Resource Systems Group (2009) for 

California Energy Commissiond 

$2900 - $7500 (2009$) $2900 - $7500

Hess et al. (2009), using the same 

data as Resource Systems Group 

(2009)e 

$2400 - $8500 (2009$) $2400 - $8500

Hidrue et al. (2011)f $3776 - $10,399 (2009$) $3776 - $10,399

aValues adjusted to 2009$ using the Bureau of Economic Analysis GDP deflator. 

bBunch, David S., Mark Bradley, Thomas F. Golob, and Ryuichi Kitamura. “Demand for Clean-Fuel Vehicles

in California: A Discrete-Choice Stated Preference Pilot Project.” Transportation Research Part A 27A(3) 

(1993): 237-253. The value of range was, in their model, assumed to be the same for all people. 

cKavelek, Chris. “CALCARS: The California Conventional and Alternative Fuel Response Simulator.” 

Demand Analysis Office, California Energy Commission, April 1996. The variation in values is due to 

willingness to pay (WTP) varying by income levels and for one-car and two-car households. The coefficient on 

range for one-car households was not statistically significantly different from zero (t-statistic = 1.5), but it was 

for 2-car households (t-statistic = 3.02). The minima and maxima presented here represent the values across 

both ownership and income categories. 

dResource Systems Groups, Inc. “Transportation Fuel Demand Forecast Household and Commercial Fleet 

Survey Task 8 Report: Logistic Regression Analysis and Results.” Prepared for California Energy Commission, 

June 2009. 

eHess, S., T. Adler, M. Fowler and A. Bahreinian “The Use of Cross-nested Logit Models for Multi-Dimensional 

Choice Processes: The Case of the Demand for Alternative Fuel Vehicles,” Proceedings of the 2009 European 

Transport Conference, Leiden, Netherlands, 2009. This study uses the same data as the Resource Systems 

Group study. The coefficient on range was not statistically significantly different from zero in these regressions: 

t-statistics varied from 1.29 to 1.52. The variation in values is due to willingness to pay (WTP) varying by 

income levels and statistical specification. The minima and maxima presented here represent the values across 

both income categories and specifications. 

fHidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. “Willingness to Pay for

Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 686-705. The range of 

values is due to the model separating consumers into “gasoline vehicle-oriented” and “electric vehicle-oriented” 

groups. The EV-oriented group has higher WTP for additional range. 

Driving range may be a major factor in consumers’ decisions on EVs, but it is not the 

only attribute that may be important to potential buyers (e.g., as noted, Hidrue et al. find that 

some consumers appear willing to pay substantially for reduced tailpipe emissions). A model 

that does not incorporate the other factors important to consumers’ decisions may not perform 

well in predicting vehicle purchases. In addition, as mentioned above, and as seen in Table 

8.1-2, it is likely that the WTP values for attributes of EVs will change over time, particularly 

if EVs are used more widely, the infrastructure to fuel the vehicles becomes more accessible, 

and consumers develop more familiarity and understanding of the vehicles. Thus, challenges 

associated with predicting market shares for EVs are even more serious than those already 

serious challenges associated with predicting market shares for conventional vehicles. 
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8.1.2.8 EPA Exploration of Vehicle Choice Modeling 

In order to develop greater understanding of these models, EPA is in the process of 

developing a vehicle choice model. In its current form, the model assumes that the vehicle 

fleet and all characteristics of each vehicle, except vehicle prices and fuel economy, stay the 

same. The model will predict changes in the vehicle fleet, at the individual-configuration 

level and at more aggregated levels, in response to changes in vehicle fuel economy and price. 

The draft EPA model uses a nested logit structure common in the vehicle choice 

modeling literature, as discussed above in Chapter 8.1.2.1. “Nesting” refers to the decisiontree structure of the model, and “logit” refers to the fact that the choices are discrete (i.e., 

yes/no decisions about which vehicles to purchase, instead of continuous values). 

The nesting involves a hierarchy of choices. In its current form, at the initial decision 

node, consumers choose between buying a new vehicle or not. Conditional on choosing a 

new vehicle, consumers then choose between passenger vehicles, cargo vehicles, and ultraluxury vehicles. The next set of choices subdivides each of these categories into vehicle type 

(e.g., standard car, minivan, SUV, etc.). Next, the vehicle types are divided into classes 

(small, medium, and large SUVs, for instance), and then, at the bottom, are the individual 

vehicle configurations. 

At this bottom level, vehicles that are similar to each other (such as standard 

subcompacts, or prestige large vehicles) end up in the same “nest.” Substitution within a nest 

is considered much more likely than substitution across nests, because the vehicles within a 

nest are more similar to each other than vehicles in different nests. For instance, a person is 

more likely to substitute between a Chevrolet Aveo and a Toyota Yaris than between an Aveo 

and a pickup truck. In addition, substitution is greater at low decision nodes (such as 

individual configurations) than at higher decision nodes (such as the buy/no buy decision), 

because there are more choices at lower levels than at higher levels. 

Parameters for the model (including demand elasticities and the value of fuel economy 

in purchase decisions) are being selected based on a review of values found in the literature 

on vehicle choice modeling. As discussed above, a number of studies have estimated these 

parameters. Those estimates, combined with some theoretical requirements,E

 assist in 

assigning values for the parameters. The model will allow individual users to change those 

parameters. 

 

E

 The theory of nested logit requires that the price slopes (the change in utility as vehicle full price changes, a 

measure of consumer responsiveness to price changes) must be higher in absolute value for lower nests. This 

condition reflects the point, discussed above, that substitution is greater at lower decision notes than at higher 

ones. 
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The fuel economy of a vehicle is used to adjust the price of the vehicle, using a 

version of the procedure discussed in Chapter 8.1.2.7: the value that the consumer places on 

fuel economy is multiplied by the change in fuel economy and incorporated into the “effective 

price” of the vehicle. In practice, implementing this calculation involves calculating the 

change in expenditures on fuel based on schedules of VMT, vehicle survival, and fuel prices 

in the future consistent with those in OMEGA. As discussed in Chapter 8.1.2.5, there is no 

consensus value for consumers’ willingness to pay for improved fuel economy: estimates 

vary tremendously. The model assumes that consumers will use some years of discounted 

fuel savings, with the modeler able to input both the number of years and the discount rate to 

be used in the analysis. 

The vehicle choice model will take as inputs an initial fleet of vehicles (including the 

initial sales and fuel economy) in the absence of standards, the cost of technologies added to 

each vehicle to comply with standards, and the change in fuel economy. With the initial sales 

mix, for each vehicle, the model calculates a vehicle-specific constant that summarizes the 

value of all attributes of the vehicle other than price and fuel economy. This constant ensures 

that the model will predict changes in consumer response that would result only from changes 

in price and fuel economy. This constant substitutes for estimating the effects of changes in 

all other vehicle characteristics; the underlying assumption is that these other vehicle 

characteristics do not change.F

 For instance, it assumes that a Ford Escape will not change in 

size, power, or accessories; the only changes will be to its cost and its fuel economy. 

The model assumes that the increase in vehicle cost associated with increased 

technology is fully passed through as an increase in vehicle price, and some years of fuel 

savings offset this price increase. It then calculates changes in total fleet size and in sales 

mix, at the individual-configuration level and at the level of vehicle class, due to the changes 

in fuel economy and vehicle prices. It also calculates changes in consumer surplus associated 

with the changes in fuel economy and vehicle prices. 

It is possible that the predicted changes in fleet mix will lead to predictions of vehicle 

sales for auto makers that do not meet the proposed standards, because the mix and volume of 

vehicles sold changed from the initial levels. To correct this problem, it would be necessary 

to feed the new fleet mix into OMEGA (which calculates costs and compliance) and get a 

new set of output, which could then be fed back into the vehicle choice model. OMEGA 

would increase technologies, and thus costs, to improve compliance; those adjustments would 

then again affect vehicle demand. We expect that this iterative process would converge to a 

fleet mix that would meet standards. Performing this iteration requires development of an 

interface between the vehicle choice model and OMEGA to ensure accurate transmission of 

data between the models. At this time, the vehicle choice model takes output from OMEGA, 

 

F

 As explained in Section III.D of the preamble, as part of the technology cost analysis for the proposed rule, the 

agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions. 
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but the results of the modeling do not feed easily back into OMEGA. Building this interface 

is an expected part of our future modeling work. 

The model is still undergoing development; EPA will seek peer review on it before it 

is utilized. In addition, concerns remain over the ability of any vehicle choice model to make 

reasonable predictions of the response of the vehicle fleet to changes in prices and vehicle 

characteristics. EPA seeks comments on the use of vehicle choice modeling for predicting 

changes in fleet mix due to policies, and on methods to test the ability of a vehicle choice 

model to produce reasonable estimates of changes in fleet mix. 

8.1.2.9 Summary and Additional Considerations 

Consumer vehicle choice modeling in principle can provide a great deal of useful 

information for regulatory analysis, helping to answer some of the central questions about 

relevant effects on consumer welfare. In practice, the advantages depend on the success of 

models in predicting changes in fleet size and mix. 

First, consumer vehicle choice modeling has the potential to describe more accurately 

the impact of a policy, by identifying market shifts. More accurate description of the market 

resulting from a policy can improve other estimates of policy impacts, such as the change in 

total vehicle emissions or vehicle miles traveled. The predictive ability of models, though, is 

not proven. 

Vehicle choice models can incorporate the effects on consumer decisions of changes 

in vehicle characteristics, if there are estimates of the value that consumers put on changes in 

those characteristics. These willingness-to-pay values may, however, be sensitive to the ways 

they are estimated, as indicated in the discussion of the value that consumers place on fuel 

economy in their purchase decisions. Especially for characteristics associated with advanced 

technology vehicles, such as EVs, the willingness-to-pay values may change over time as 

consumers develop more experience with the vehicles and these characteristics. Models 

based on current estimates may not predict well for the future. 

The modeling may improve estimates of the compliance costs of a rule. Consumers 

can either accept the new costs and vehicle characteristics, or they can change which vehicles 

they buy. Using a vehicle choice model is likely to reduce compliance costs: because the 

model allows consumers to choose among accepting the new vehicle, buying a different 

vehicle, or not buying a vehicle, consumers have additional options, which improves their 

welfare relative to the assumption that consumers will not change their buying behavior. . 

An additional complication associated with consumer choice modeling is accurate 

prediction of producers’ responses to the rule. While it is possible to include auto makers’ 

decisions (for instance, on setting prices) into vehicle choices, computational limits affect the 

richness of these models. Technology costs, while an accurate measure of the opportunity 

cost of resources to society, may overestimate or underestimate the effect on the prices that 

consumers face. 
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Consumer choice models can be used to calculate consumer surplus impacts on 

vehicle purchase decisions. Because these values are based on the estimates of changes in 

vehicle sales and fleet mix, consumer surplus measures may not be accurate if the changes in 

vehicle sales and fleet mix are not well estimated.

Principles of welfare analysis can be useful for understanding the role of consumer 

vehicle choice models in benefit-cost analysis. In particular, except for EVs, the technology 

cost estimates developed in this proposal take into account the costs to hold other vehicle 

attributes, such as size and performance, constant. In addition, the analysis assumes that the 

full technology costs are passed along to vehicle buyers. With these assumptions, because 

welfare losses are monetary estimates of how much buyers would have to be compensated to 

be made as well off as in the absence of the change,

G

 the price increase measures the loss to 

the buyer.H Assuming that the full technology cost gets passed along to the buyer as an 

increase in price, the technology cost thus measures the welfare loss to the buyer. Increasing 

fuel economy would have to lead to other changes in the vehicles that buyers find undesirable 

for there to be additional losses not included in the technology costs. 

Given the current limitations in modeling the role of fuel economy in vehicle purchase 

decisions, and limitations in modeling market responses to the new regulations, in this 

proposal EPA holds constant the vehicle fleet size and mix in its calculations of the impacts of 

this rule, and compares the fuel and other savings that consumers will receive with the 

technology costs of the vehicles. EPA continues to explore options for including consumer 

and producer choice in modeling the impacts of fuel economy-related regulations. This effort 

includes further review of existing consumer vehicle choice models, the estimates of 

consumers’ willingness to pay for increased fuel economy, and overall effects on consumer 

welfare, as well as EPA’s exploration of a vehicle choice model for use in the future. 

8.2 Employment Impacts 

8.2.1 Introduction 

Although analysis of employment impacts is not part of a cost-benefit analysis (except 

to the extent that labor costs contribute to costs), employment impacts of federal rules are of 

 

G

 This approach describes the economic concept of compensating variation, a payment of money after a change 

that would make a consumer as well off after the change as before it. A related concept, equivalent variation, 

estimates the income change that would be an alternative to the change taking place. The difference between 

them is whether the consumer’s point of reference is her welfare before the change (compensating variation) or 

after the change (equivalent variation). In practice, these two measures are typically very close together. 

H

 Indeed, it is likely to be an overestimate of the loss to the buyer, because the buyer has choices other than 

buying the same vehicle with a higher price; she could choose a different vehicle, or decide not to buy a new 

vehicle. The buyer would choose one of those options only if the alternative involves less loss than paying the 

higher price. Thus, the increase in price that the buyer faces would be the upper bound of loss of consumer 

welfare, unless there are other changes to the vehicle due to the fuel economy improvements that make the 

vehicle less desirable to buyers. 
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8-19 

particular concern in the current economic climate of sizeable unemployment. When 

President Obama requested that the agencies develop this program, he sought a program that 

would “strengthen the [auto] industry and enhance job creation in the United States.”53,I The 

recently issued Executive Order 13563, “Improving Regulation and Regulatory Review” 

(January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety, 

and our environment while promoting economic growth, innovation, competitiveness, and job 

creation” (emphasis added). EPA is accordingly providing partial estimates of the effects of 

this proposal on domestic employment in the auto manufacturing and parts sectors, while 

qualitatively discussing how it may affect employment in other sectors more generally. 

This proposal is expected to affect employment in the United States through the 

regulated sector – the auto manufacturing industry – and through several related sectors, 

specifically, industries that supply the auto manufacturing industry (e.g., vehicle parts), auto 

dealers, the fuel refining and supply sectors, and the general retail sector. According to the 

U.S. Bureau of Labor Statistics, in 2010, about 677,000 people in the U.S. were employed in 

Motor Vehicle and Parts Manufacturing Sector (NAICS 3361, 3362, and 3363). About 

129,000 people in the U.S. were employed in the Automobile and Light Truck Manufacturing 

Sector (NAICS 33611) in December 2010; this is the directly regulated sector, since it 

encompasses the auto manufacturers that are responsible for complying with the proposed 

standards.54 Changes in light duty vehicle sales, discussed in Chapter 8.1.1, could affect 

employment for auto dealers. Though some of the parts used to achieve the proposed 

standards are likely to be built by auto manufacturers themselves, the auto parts 

manufacturing sector also plays a significant role in providing those parts, and will also be 

affected by changes in vehicle sales. As discussed in Chapter 5.4 of the RIA, this proposal is 

expected to reduce the amount of fuel these vehicles use, and thus affect the petroleum 

refinery and supply industries. 

When the economy is at full employment, an environmental regulation is unlikely to 

have much impact on net overall U.S. employment; instead, labor would primarily be shifted 

from one sector to another. These shifts in employment impose an opportunity cost on 

society, approximated by the wages of the employees, as regulation diverts workers from 

other activities in the economy. In this situation, any effects on net employment are likely to 

be transitory as workers change jobs (e.g., some workers may need to be retrained or require 

time to search for new jobs, while shortages in some sectors or regions could bid up wages to 

attract workers). 

On the other hand, if a regulation comes into effect during a period of high 

unemployment, a change in labor demand due to regulation may affect net overall U.S. 

 

I

 The May 21, 2010 Presidential Memorandum also requested that EPA and NHTSA, in developing the technical 

assessment to inform the rulemaking process (which was issued by the agencies and CARB on September 30, 

2010), include, among other things, the “impacts on jobs and the automotive manufacturing base in the United 

States.” 
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employment because the labor market is not in equilibrium. In such a period, both positive 

and negative employment effects are possible. (Cite Masur and Posner, 

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1920441) Schmalansee and Stavins 

point out that net positive employment effects are possible in the near term when the economy 

is at less than full employment due to the potential hiring of idle labor resources by the 

regulated sector to meet new requirements (e.g., to install new equipment) and new economic 

activity in sectors related to the regulated sector.

55 In the longer run, the net effect on 

employment is more difficult to predict and will depend on the way in which the related 

industries respond to the regulatory requirements. As Schmalansee and Stavins note, it is 

possible that the magnitude of the effect on employment could vary over time, region, and 

sector, and positive effects on employment in some regions or sectors could be offset by 

negative effects in other regions or sectors. For this reason, they urge caution in reporting 

partial employment effects since it can “paint an inaccurate picture of net employment 

impacts if not placed in the broader economic context.” 

Given the time frame of this proposed rule, the state of the overall economy may have 

improved to the point where the only effect of changes in employment associated with the 

standards proposed here will be to shift workers from one sector to another. For example, to 

the extent that regulation increases the number of workers in the regulated sector (e.g., 

because of higher costs of producing vehicles with higher fuel economy), the opportunity cost 

associated with those shifts in employment will be captured in labor costs, as regulation 

diverts workers from other activities in the economy. In this situation, any effects on net 

employment are likely to be transitory as workers change jobs (e.g., some workers may need 

to be retrained or require time to search for new jobs, while shortages in some sectors or 

regions could bid up wages to attract workers). It is also possible that the state of the 

economy will be such that positive or negative employment effects will occur. 

A number of different approaches have been used in published literature to conduct 

employment analysis. This section describes some of the common methods, as well as some 

of their limitations. 

8.2.2 Approaches to Quantitative Employment Analysis 

Measuring the employment impacts of a policy depend on a number of inputs and 

assumptions. For instance, as discussed, assumptions about the overall state of 

unemployment in the economy play a major role in measured job impacts. The inputs to the 

models commonly are the changes in quantities or expenditures in the affected sectors; model 

results may vary in different studies depending on the assumptions about the levels of those 

inputs, and which sectors receive those changes. Which sectors are included in the study can 

also affect the results. For instance, a study of this program that looks only at employment 

impacts in the refinery sector may find negative effects, because consumers will purchase less 

gasoline; a study that looks only at the auto parts sector, on the other hand, may find positive 

impacts, because the program will require redesigned or additional parts for vehicles. In both 
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8-21 

instances, these would only be partial perspectives on the overall change in national 

employment due to Federal regulation. 

8.2.2.1 Conceptual Framework for Employment Impacts in the 

Regulated Sector 

One study by Morgenstern, Pizer, and Shih56 provides a retrospective look at the 

impacts of regulation in employment in the regulated sectors by estimating the effects on 

employment of spending on pollution abatement for four highly polluting/regulated U.S. 

industries (pulp and paper, plastics, steel, and petroleum refining) using data for six years 

between 1979 and 1991. The paper provides a theoretical framework that can be useful for 

examining the impacts of a regulatory change on the regulated sector in the medium to longer 

term. In particular, it identifies three separate ways that employment levels may change in the 

regulated industry in response to a new (or more stringent) regulation. 

• Demand effect: higher production costs due to the regulation will lead to higher 

market prices; higher prices in turn reduce demand for the good, reducing the 

demand for labor to make that good. In the authors’ words, the “extent of this 

effect depends on the cost increase passed on to consumers as well as the demand 

elasticity of industry output.” 

• Cost effect: as costs go up, plants add more capital and labor (holding other 

factors constant), with potentially positive effects on employment. In the authors’ 

words, as “production costs rise, more inputs, including labor, are used to produce 

the same amount of output.” 

• Factor shift effect: post-regulation production technologies may be more or less 

labor-intensive (i.e., more/less labor is required per dollar of output). In the 

authors’ words, “environmental activities may be more labor intensive than 

conventional production,” meaning that “the amount of labor per dollar of output 

will rise,” though it is also possible that “cleaner operations could involve 

automation and less employment, for example.” 

According to the authors, the “demand effect” is expected to have a negative effect on 

employment,J

 the “cost effect” to have a positive effect on employment, and the “factor shift 

effect” to have an ambiguous effect on employment. Without more information with respect 

 

J

 As will be discussed below, the demand effect in this proposal is potentially an exception to this rule. While 

the vehicles become more expensive, they also produce reduced fuel expenditures; the reduced fuel costs provide 

a countervailing impact on vehicle sales. As discussed in Preamble Section III.H.1, this possibility that vehicles 

may become more attractive to consumers after the program poses a conundrum: why have interactions between 

vehicle buyers and producers not provided these benefits without government intervention? 
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8-22 

to the magnitudes of these competing effects, it is not possible to predict the total effect 

environmental regulation will have on employment levels in a regulated sector. 

The authors conclude that increased abatement expenditures generally have not caused 

a significant change in employment in those sectors. More specifically, their results show 

that, on average across the industries studied, each additional $1 million spent on pollution 

abatement results in a (statistically insignificant) net increase of 1.5 jobs. 

This approach to employment analysis has the advantage of carefully controlling for 

many possibly confounding effects in order to separate the effect of changes in regulatory 

costs on employment. It was, however, conducted for only four sectors. It could also be very 

difficult to update the study for other sectors, because one of the databases on which it relies, 

the Pollution Abatement Cost and Expenditure survey, has been conducted infrequently since 

1994, with the last survey conducted in 2005. The empirical estimates provided by 

Morgenstern et al. are not relevant to the case of fuel economy standards, which are very 

different from the pollution control standards on industrial facilities that were considered in 

that study. In addition, it does not examine the effects of regulation on employment in sectors 

related to but outside of the regulated sector. Nevertheless, the theory that Morgenstern et al. 

developed continues to be useful in this context. 

The following discussion of additional methodologies draws from Berck and 

Hoffmann’s review of employment models.57

 

8.2.2.2 Computable General Equilibrium (CGE) Models 

Computable general equilibrium (CGE) models are often used to assess the impacts of 

policy. These models include a stylized representation of supply and demand curves for all 

major markets in the economy. The labor market is commonly included. CGE models are 

very useful for looking at interaction effects of markets: “they allow for substitution among 

inputs in production and goods in consumption.” Thus, if one market experiences a change, 

such as a new regulation, then the effects can be observed in all other markets. As a result, 

they can measure the employment changes in the economy due to a regulation. Because they 

usually assume equilibrium in all markets, though, they typically lack involuntary 

unemployment. If the total amount of labor changes, it is due to people voluntarily entering 

or leaving the workforce. As a result, these models may not be appropriate for measuring 

effects of a policy on unemployment, because of the assumption that there is no involuntary 

unemployment. In addition, because of the assumptions of equilibrium in all markets and 

forward-looking consumers and firms, they are designed for examining the long-run effects of 

a policy but may offer little insight into its short-run effects. 

8.2.2.3 Input-Output (IO) Models 

Input-output models represent the economy through a matrix of coefficients that 

describe the connections between supplying and consuming sectors. In that sense, like CGE 

models, they describe the interconnections of the economy. These interconnections look at 
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8-23 

how changes in one sector ripple through the rest of the economy. For instance, a 

requirement for additional technology for vehicles requires additional steel, which requires 

more workers in both the auto and steel sectors; the additional workers in those sectors then 

have more money to spend, which leads to more employment in retail sectors. These are 

known as “multiplier” effects, because an initial impact in one sector gets multiplied through 

the economy. Unlike CGE models, input-output models have fixed, linear relationships 

among the sectors (e.g., substitution among inputs or goods is not allowed), and quantity 

supplied need not equal quantity demanded. In particular, these models do not allow for price 

changes – an increase in the demand for labor or capital does not result in a change in its price 

to help reallocate it to its best use. As a result, these models cannot capture opportunity costs 

from using resources in one area of the economy over another. The multipliers take an initial 

impact and can increase it substantially. 

IO models are commonly used for regional analysis of projects. In a regional analysis, 

the markets are commonly considered small enough that wages and prices are determined 

outside the region, and any excess supply or demand is due to exports and imports (or, in the 

case of labor, emigration or immigration). For national-level employment analysis, the use of 

input-output models requires the assumption that workers flow into or out of the labor market 

perfectly freely. Wages do not adjust; instead, people join into or depart from the labor pool 

as production requires them. For other markets as well, there is no substitution of less 

expensive inputs for more expensive ones. As a result, IO models provide an upper bound on 

employment impacts. As Berck and Hoffmann note, “For the same reason, they can be 

thought of as simulating very short-run adjustment,” in contrast to the CGE’s implicit 

assumption of long-run adjustment. Changes in production processes, introductions of new 

technologies, or learning over time due to new regulatory requirements are also generally not 

captured by IO models, as they are calibrated to already established relationships between 

inputs and outputs. 

8.2.2.4 Hybrid Models 

As Berck and Hoffmann note, input-output models and CGE models “represent a 

continuum of closely related models.” Though not separately discussed by Berck and 

Hoffmann, some hybrid models combine some of the features of CGE models (e.g., prices 

that can change) with input-output relationships. For instance, a hybrid model may include 

the ability to examine disequilibrium phenomena, such as labor being at less than full 

employment. Hybrid models depend on assumptions about how adjustments in the economy 

occur. CGE models characterize equilibria but say little about the pathway between them, 

while IO models assume that adjustments are largely constrained by previously defined 

relationships; the effectiveness of hybrid models depends on their success in overcoming the 

limitations of each of these approaches. Hybrid models could potentially be used to model 

labor market impacts of various vehicle policy options although a number of judgments need 

to be made about the appropriate assumptions underlying the model as well the empirical 

basis for the modeling results. 
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8.2.2.5 Single Sectors 

It is possible to conduct a bottom-up analysis of the partial effect of regulation on 

employment in a single sector by estimating the change in output or expenditures in a sector 

and multiplying it by an estimate of the number of workers per unit of output or expenditures, 

under the assumption that labor demand is proportional to output or expenditures. As Berck 

and Hoffmann note, though, “Compliance with regulations may create additional jobs that are 

not accounted for.” While such an analysis can approximate the effects in that one sector in a 

simple way, it also may miss important connections to related sectors. 

8.2.2.6 Ex-Post Econometric Studies 

A number of ex-post econometric analyses examine the net effect of regulation on 

employment in regulated sectors. Morgenstern, Pizer, and Shih (2002), discussed above, and 

Berman and Bui (2001) are two notable examples that rely on highly disaggregated 

establishment-level time series data to estimate longer-run employment effects.58 While often 

a sophisticated treatment of the issues analyzed, these studies commonly analyze specific 

scenarios or sectors in the past; care needs to be taken in extrapolating their results to other 

scenarios and to the future. For instance, neither of these two studies examines the auto 

industry and are therefore of limited applicability in this context. 

8.2.2.7 Summary 

All methods of estimating employment impacts of a regulation have advantages and 

limitations. CGE models may be most appropriate for long-term impacts, but the usual 

assumption of equilibrium in the employment market means that it is not useful for looking at 

changes in overall employment: overall levels are likely to be premised on full employment. 

IO models, on the other hand, may be most appropriate for small-scale, short-term effects, 

because they assume fixed relationships across sectors and do not require market equilibria. 

Hybrid models, which combine some features of CGEs with IO models, depend upon key 

assumptions and economic relationships that are built into them. Single-sector models are 

simple and straightforward, but they are often based on the assumptions that labor demand is 

proportional to output, and that other sectors are not affected. Finally, econometric models 

have been developed to evaluate the longer-run net effects of regulation on sector 

employment, though these are ex-post analyses commonly of specific sectors or situations, 

and the results may not have direct bearing for the regulation being reviewed. 

8.2.3 Employment analysis of this proposal 

As mentioned above, this program is expected to affect employment in the regulated 

sector (auto manufacturing) and other sectors directly affected by the proposal: auto parts 

suppliers, auto dealers, the fuel supply market (which will face reduced petroleum production 

due to reduced fuel demand but which may see additional demand for electricity or other 

fuels), and consumers (who will face higher vehicle costs and lower fuel expenditures). In 

addition, as the discussion above suggests, each of these sectors could potentially have ripple 
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8-25 

effects in the rest of the economy. These ripple effects depend much more heavily on the 

state of the macroeconomy than do the direct effects. At the national level, employment may 

increase in one industry or region and decrease in another, with the net effect being smaller 

than either individual-sector effect. EPA does not attempt to quantify the net effects of the 

regulation on overall national employment. 

The discussion that follows provides a partial, bottom-up quantitative estimate of the 

effects of this proposal on the regulated sector (the auto industry; for reasons discussed below, 

we include some quantitative assessment of effects on suppliers to the industry although they 

are not regulated directly). It also includes qualitative discussion of the effects of the proposal 

on other sectors. Focusing quantification of employment impacts on the regulated sector has 

some advantages over quantifying all impacts. First, the analysis relies on data generated as 

part of the rulemaking process, which focuses on the regulated sector; as a result, what is 

presented here is based on internally consistent assumptions and estimates made in this 

proposal. Secondly, as discussed above, net effects on employment in the economy as a 

whole depend heavily on the overall state of the economy when this rule has its effects. 

Focusing on the regulated sector provides insight into employment effects in that sector 

without having to make assumptions about the state of the economy when this rule has its 

impacts. We include a qualitative discussion of employment effects on other sectors to 

provide a broader perspective on the impacts of this rule. 

As noted above, in a full-employment economy, any changes in employment will 

result from people changing jobs or voluntarily entering or exiting the workforce. In a fullemployment economy, employment impacts of this proposal will change employment in 

specific sectors, but it will have small, if any, effect on aggregate employment. This rule 

would take effect in model years 2017 through 2025; by then, the current high unemployment 

may be moderated or ended. For that reason, this analysis does not include multiplier effects, 

but instead focuses on employment impacts in the most directly affected industries. Those 

sectors are likely to face the most concentrated employment impacts. The agencies seek 

comment on other sectors that are likely to be significantly affected and thus warrant further 

analysis in the final rulemaking analysis. 

8.2.3.1 Employment Impacts in the Auto Industry 

If vehicle sales increase, then more people will be required to assemble vehicles and 

their components. If vehicle sales decrease, employment associated with these activities will 

unambiguously decrease. The sign of demand effect will depend on which of these effects 

dominates. Because, as described in Chapter 8.1, we have not quantified the impact on sales 

for this proposal, we do not quantify the demand effect. 

8.2.3.1.1 The Cost Effect

The demand effect, discussed above, measures employment changes due to new 

vehicle sales only. The cost effect measures employment impacts due to the new or 

additional technologies needed for vehicles to comply with the proposed standards. 
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One way to estimate the cost effect, given the cost estimates for complying with the 

rule, is to use the ratio of workers to each $1 million of expenditures in that sector. The use of 

these ratios has both advantages and limitations. It is often possible to estimate these ratios 

for quite specific sectors of the economy: for instance, it is possible to estimate the average 

number of workers in the light-duty vehicle manufacturing sector per $1 million spent in the 

sector, rather than use the ratio from another, more aggregated sector, such as motor vehicle 

manufacturing. As a result, it is not necessary to extrapolate employment ratios from possibly 

unrelated sectors. On the other hand, these estimates are averages for the sectors, covering all 

the activities in those sectors; they may not be representative of the labor required when 

expenditures are required on specific activities, as the factor shift effect (discussed below) 

indicates. For instance, the ratio for the motor vehicle manufacturing sector represents the 

ratio for all vehicle manufacturing, not just for fuel efficiency improvements. In addition, 

these estimates do not include changes in sectors that supply these sectors, such as steel or 

electronics producers. They thus may best be viewed as the effects on employment in the 

auto sector due to the changes in expenditures in that sector, rather than as an assessment of 

all employment changes due to these changes in expenditures. 

Some of the costs of this proposal will be spent directly in the auto manufacturing 

sector, but some of the costs will be spent in the auto parts manufacturing sector. Because we 

do not have information on the proportion of expenditures in each sector, we separately 

present the ratios for both the auto manufacturing sector and the auto parts manufacturing 

sector. These are not additive, but should instead be considered as a range of estimates for the 

cost effect, depending on which sector adds technologies to the vehicles to comply with the 

regulation. 

We use several public sources for estimates of employment per $1 million 

expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment 

Requirements Matrix (ERM),59 which provides direct estimates of the employment per $1 

million in sales of goods in 202 sectors. The most recent estimates, used here, are from 2008 

(adjusted to 2009$). The tables used here are adjusted to remove the employment effects of 

imports through use of the ratio of domestic production to domestic sales, described above, of 

0.667. The values reported are for Motor Vehicle Manufacturing (NAICS 3361) and Motor 

Vehicle Parts Manufacturing (NAICS 3363). 

The Annual Survey of Manufactures60 (ASM) provides another source of estimates 

based on a sample of 50,000 establishments out of a universe of 346,000 manufacturing 

establishments. It includes more sectoral detail than the BLS ERM: for instance, while the 

ERM includes the Motor Vehicle Manufacturing sector, the ASM has detail at the 6-digit 

NAICS code level (e.g., automobile manufacturing vs. light truck and utility vehicle 

manufacturing). While the ERM provides direct estimates of employees/$1 million in 

expenditures, the ASM separately provides number of employees and value shipments; the 

direct employment estimates here are the ratio of those values. The data in the ASM are 

updated annually, except for years when the full Economic Census occurs. The tables 

presented here use data from 2009. As with the ERM, we adjust for the ratio of domestic 
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8-27 

production to domestic sales. The values reported are for Motor Vehicle Manufacturing 

(NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), 

and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Economic Census includes all large companies and a sample of smaller ones. The 

ASM is a subset of the Economic Census; though the Census itself is more complete, it is 

conducted only every 5 years, while the ASM is annual. The values presented here use data 

from 2007 (adjusted to 2009$), with the domestic production-to-sales adjustment. The values 

reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty 

Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing 

(NAICS 3363). 

Table 8.2-1 provides the values, either given (BLS) or calculated (ASM, Economic 

Census) for employment per $1 million of expenditures, all based on 2009 dollars, though the 

underlying data come from different years (which may account for some of the differences). 

The different data sources provide similar magnitudes for the estimates for the sectors. Parts 

manufacturing appears to be more labor-intensive than vehicle manufacturing; light-duty 

vehicle manufacturing appears to be slightly less labor-intensive than motor vehicle 

manufacturing as a whole. 

Table 8.2-1 Employment per $1 Million Expenditures (2009$) in the Motor Vehicle 

Manufacturing Sector* 

Source Sector Ratio of 

workers per 

$1 million 

expenditures

Ratio of workers per $1 

million expenditures, 

adjusted for domestic vs. 

foreign production 

BLS ERM Motor Vehicle Mfg 0.834 0.556

ASM Motor Vehicle Mfg 0.824 0.549

ASM Light Duty Vehicle 

Mfg

0.757 0.505

Economic 

Census 

Motor Vehicle Mfg 0.674 0.449

Economic 

Census 

Light Duty Vehicle 

Mfg

0.610 0.407

BLS ERM Motor Vehicle Parts 

Mfg

3.073 2.049

ASM Motor Vehicle Parts 

Mfg

3.093 2.063
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Economic 

Census 

Motor Vehicle Parts 

Mfg

2.749 1.833

BLS ERM refers to the U.S. Bureau of Labor Statistics’ Employment Requirement Matrix. ASM refers to the 

U.S. Census Bureau’s Annual Survey of Manufactures. Economic Census refers to the U.S. Census Bureau’s 

Economic Census.

Over time, the amount of labor needed in the auto industry has changed: automation 

and improved production methods have led to significant productivity increases. The BLS 

ERM, for instance, provided estimates that, in 1993, 1.52 workers were needed per $1 million 

of 2000$, but only 0.83 workers by 2008 (in 2000$).61 Because the ERM is available 

annually for 1993-2008, we used these data to estimate productivity improvements over time. 

We regressed logged ERM values on year (to estimate percent change per year) for both the 

Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing sectors. The results 

suggest a 4.4 percent per year productivity improvement in the Motor Vehicle Manufacturing 

Sector, and a 3.6 percent per year improvement in the Motor Vehicle Parts Manufacturing 

Sector. We then used the regression relationship to project the ERM through 2025. In the 

results presented below, these projected values (adjusted to 2009$) were used directly for the 

BLS ERM estimates. For the ASM, we used the ratio of the projected value in the future to 

the projected value in 2009 (the base year for the ASM); for the Economic Census estimates, 

we used the ratio of the projected value in the future to the projected value in 2007 (the base 

year for that estimate). As noted above, we adjusted the estimate of workers per vehicle for 

the demand effect in Chapter Error! Reference source not found., above, using the 

productivity improvement for the Motor Vehicle Manufacturing sector; because the estimate 

of workers per vehicle for the demand effect was based on data from 2001 to 2010, we used 

the projected value of the ERM in 2005 as the denominator of the ratio. This is a simple way 

to examine the relationship between labor required and expenditure and we seek comment on 

refining this method. 

Table 8.2-2 shows the cost estimates developed for this rule, discussed in Chapter 5. 

The maximum value in Table 8.2-2 for employment impacts per $1 million expenditures 

(after accounting for the share of domestic production) is 2.049 in 2009 if all the additional 

costs are in the parts sector; the minimum value is 0.407 in 2009, if all the additional costs are 

in the light-duty vehicle manufacturing sector: that is, the range of employment impacts is 

between 0.4 and 2 additional jobs per $1 million expenditures in the sector in 2009. The 

results in Table 8.2-2 include the productivity adjustment described above. 

While we estimate employment impacts beginning with the first year of the standard 

(2017), some of these job gains may occur earlier as auto manufacturers and parts suppliers 

hire staff in anticipation of compliance with the standard. Also, because of the uncertainty 

associated of projecting average labor required associated with the cost of production, we 

limit the projection to 2020. 

Table 8.2-2 Employment due to Cost Effect in the Motor Vehicle Manufacturing Sector 
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Year Costs (before 

adjustment for 

domestic proportion 

of production) 

($Millions)

Minimum 

employment effect 

(if all expenditures 

are in the parts 

sector) 

Maximum 

employment effect 

(if all expenditures 

are in the light 

duty vehicle mfg 

sector) 

2017 $ 2,300 600 3,600 

2018 $ 4,656 1,200 7,000 

2019 $ 6,507 1,600 9,400 

2020 $ 8,467 1,900 11,800 

2021 $ 11,878 2,600 15,900 

2022 $ 19,340 4,100 25,000 

2023 $ 25,036 5,000 31,200 

2024 $ 30,738 5,900 37,000 

2025 $ 33,561 6,200 39,000 

8.2.3.1.2 The Factor Shift Effect 

The factor shift effect looks at the effects on employment due to changes in labor 

intensity associated with a regulation. As noted above, the estimates of the cost effect assume 

constant labor per $1 million in expenditures, though the new technologies may be either 

more or less labor-intensive than the existing ones. An estimate of the factor shift effect 

would either increase or decrease the estimate used for the cost effect. 

We are not quantifying the factor shift effect here, for lack of data on the labor 

intensity of all the possible technologies that manufacturers could use to comply with the 

proposed standards. For a subset of the technologies, though, EPA-sponsored research 

(discussed in Chapter 3.2.1.1 of the Joint TSD) which compared new technologies to existing 

ones at the level of individual components provides some insights into the factor shift effect. 

The comparison involved tearing down the selected technologies to their individual 

components and looking at the differences in materials and labor needs in moving from the 

conventional to the new technologies.62 For instance, the analysis compared all the parts and 

labor associated with an 8-speed automatic transmission to those needed for a 6-speed 

automatic transmission. 

Because labor cost was one of the sources of differences between the technologies, it 

is possible, for those technologies, to see whether labor needs increase or decrease with the 

switch to technologies that might contribute to compliance with the proposed standards. An 

increase in labor cost for the new technology indicates an increase in the labor needed for the 

new technology compared to the baseline technology. For instance, an 8-speed transmission 

requires $15.11 more in labor costs than a 6-speed transmission (as accounted for in EPA’s 

cost estimates for the proposed rule). Dividing the labor cost by a wage per hour estimate 

provides an estimate of the additional hours (and thus the additional labor) needed for the new 
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8-30 

technology compared to the baseline technology. As with labor cost, an increase in labor 

hours per technology indicates greater employment needs for the new technologies. For this 

conversion, a weighted average wage rate (90 percent of the average wage in the Motor 

Vehicle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle 

Manufacturing Sector) of $46.36/hour in 2015, using 2008 dollars (the unit of analysis for the 

FEV study). For the change from a 6-speed to an 8-speed transmission, we thus estimate an 

additional 0.33 hours of labor per transmission. 

Error! Reference source not found. shows the changes in labor hours in moving 

from baseline to new fuel-saving technologies for technologies in the FEV study. It indicates 

that, in switching from the baseline to the new technologies, labor use per technology 

increased: the fuel-saving technologies use more labor than the baseline technologies. For a 

subset of the technologies likely to be used to meet the standards in this proposal, then, the 

factor shift effect increases labor demand, at least in the short run; in the long run, as with all 

technologies, the cost structure is likely to change due to learning, economies of scale, etc. 

The technologies examined in this research are, however, only a subset of the technologies 

that auto makers may use to comply with the standards proposed in this program. As a result, 

these results cannot be considered definitive evidence that the factor shift effect increases 

employment for this rule. We therefore do not quantify the factor shift effect for this 

proposal.

8.2.4 Effects on Employment for Auto Dealers 

The effects of the proposed standards on employment for auto dealers depend 

principally on the effects of the standards on light duty vehicle sales. In addition, auto dealers 

may be affected by changes in maintenance and service costs. Increases in those costs are 

likely to increase labor demand in dealerships. 

Although this proposal predicts very small penetration of advanced technology 

vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. 

As discussed in Chapter 8.1.2.7, consumers may find some characteristics of electric vehicles 

and plug-in hybrid electric vehicles, such as the ability to fuel with electricity rather than 

gasoline, attractive; they may find other characteristics, such as the limited range for electric 

vehicles, undesirable. As a result, some consumers will find that EVs will meet their needs, 

but other buyers will choose more conventional vehicles. Auto dealers may play a major role 

in explaining the merits and disadvantages of these new technologies to vehicle buyers. There 

may be a temporary need for increased employment to train sales staff in the new 

technologies as the new technologies become available. 

8.2.5 Effects on Employment in the Auto Parts Sector 

As discussed in the context of employment in the auto industry, some vehicle parts are 

made in-house by auto manufacturers; others are made by independent suppliers who are not 

directly regulated, but who will be affected by the proposed standards as well. The additional 

expenditures on technologies are expected to have a positive effect on employment in the 
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8-31 

parts sector as well as the manufacturing sector; the breakdown in employment between the 

two sectors is difficult to predict. The effects on the parts sector also depend on the effects of 

the proposed standards on vehicle sales and on the labor intensity of the new technologies, 

qualitatively in the same ways as for the auto manufacturing sector. 

8.2.6 Effects on Employment for Fuel Suppliers 

In addition to the effects on the auto manufacturing and parts sectors, these rules will 

result in changes in fuel use that lower GHG emissions. Fuel saving, principally reductions in 

liquid fuels such as gasoline and diesel, will affect employment in the fuel suppliers industry 

sectors throughout the supply chain, from refineries to gasoline stations. To the extent that 

the proposed standards result in increased use of electricity or other new fuels, employment 

effects will result from providing these fuels and developing the infrastructure to supply them 

to consumers. 

Expected petroleum fuel consumption reductions can be found in Chapter 5.3. While 

this reduced consumption represents fuel savings for purchasers of fuel, it represents a loss in 

value of output for the petroleum refinery industry, fuel distributors, and gasoline stations. 

The loss of expenditures to petroleum fuel suppliers throughout the petroleum fuel supply 

chain, from the petroleum refiners to the gasoline stations, is likely to result in reduced 

employment in these sectors. 

This rule is also expected to lead to increases in electricity consumption by vehicles, 

as discussed in Chapter 5.3. This new fuel may require additional infrastructure, such as 

electricity charging locations. Providing this infrastructure will require some increased 

employment. In addition, the generation of electricity is likely to require some additional 

labor. We have insufficient information at this time to predict whether the increases in labor 

associated with increased infrastructure provision and generation for electricity will be greater 

or less than the employment reductions associated with reduced demand for petroleum fuels. 

8.2.7 Summary 

The primary employment effects of this proposal are expected to be found throughout 

several key sectors: auto manufacturers, auto dealers, auto parts manufacturing, and fuel 

production and supply. 

These proposed standards initially take effect in model year 2017, a time period 

sufficiently far in the future that the current sustained high unemployment at the national level 

may be moderated or ended. In an economy with full employment, the primary employment 

effect of a rulemaking is likely to be to move employment from one sector to another, rather 

than to increase or decrease employment. For that reason, we focus our partial quantitative 

analysis on employment in the regulated sector, to examine the impacts on that sector directly. 

We discuss the likely direction of other impacts in the regulated sector as well as in other 

directly related sectors, but we do not quantify those impacts, because they are more difficult 

to quantify with reasonable accuracy, particularly so far into the future. 
Summary of interagency working comments received in draft NPRM under EO 12866 

review. These are for your consideration and are subject to further policy review. 

8-32 

References 

1

 See, for instance, Gron, Ann, and Deborah Swenson, 2000. “Cost Pass-Through in the U.S. 

Automobile Market,” Review of Economics and Statistics 82: 316-324 (Docket EPA-HQOAR-2010-0799). 

2

 Insurance Information Institute, 2008, “Average Expenditures for Auto Insurance By State, 

2007-2008,” http://www.iii.org/media/facts/statsbyissue/auto/, accessed 8/22/11 (Docket 

EPA-HQ-OAR-2010-0799). 

3

 U.S. Department of Energy, 2011. “Transportation and the Economy,” Chapter 10 in 

“Transportation Energy Data Book,” http://cta.ornl.gov/data/tedb30/Edition30_Chapter10.pdf, 

accessed 8/22/11, Table 11 (Docket EPA-HQ-OAR-2010-0799). 

4

 Solheim, Mark, 2006 “State Car Tax Rankings,” 

http://www.kiplinger.com/features/archives/2006/04/cartax.html, accessed April 23, 2009 

(Docket EPA-HQ-OAR-2009-0472-0010) (Docket EPA-HQ-OAR-2010-0799). 

5

 U.S. Census Bureau, "Table 2. Resident Population of the 50 States, the District of 

Columbia, and Puerto Rico: 2010 Census" http://2010.census.gov/news/presskits/apportionment/apport.html . 

6

“National Auto Loan Rates for July 21, 2011,” 

http://www.bankrate.com/finance/auto/national-auto-loan-rates-for-july-21-2011.aspx, 

accessed 7/26/11 (Docket EPA-HQ-OAR-2010-0799). 

7

 Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-

951 (Docket EPA-HQ-OAR-2010-0799); Goldberg, Pinelopi Koujianou, “The Effects of the 

Corporate Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics

46(1) (March 1998): 1-33 (Docket EPA-HQ-OAR-2010-0799). 

8

 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy 

Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared 

by the Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. 

DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., Philip 

D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A 

Study of Incentives for Increased Fuel Economy,” Energy Policy 33 (2005): 757-775 

(Docket EPA-HQ-OAR-2010-0799). 
Summary of interagency working comments received in draft NPRM under EO 12866 

review. These are for your consideration and are subject to further policy review. 

8-33 

9

 McManus, Walter M., “Can Proactive Fuel Economy Strategies Help Automakers Mitigate 

Fuel-Price Risks?” University of Michigan Transportation Research Institute, September 14, 

2006 (Docket EPA-HQ-OAR-2010-0799). 

10 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799); Berry, Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand 

Systems from a Combination of Micro and Macro Data: The New Car Market,” Journal of 

Political Economy 112(1) (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799). 

11 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799). 

12 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799). 

13 Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) 

Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-2010-

0799). 

14 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher 

CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and 

Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799). 

15 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and 

Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American 

Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Train, 

Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share 

of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 

(Docket EPA-HQ-OAR-2010-0799). 

16 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

17 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy 

Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared 

by the Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. 

DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-0799). 

18 NERA Economic Consulting, “Evaluation of NHTSA’s Benefit-Cost Analysis of 2011-

2015 CAFE Standards,” 2008, available at 
Summary of interagency working comments received in draft NPRM under EO 12866 

review. These are for your consideration and are subject to further policy review. 

8-34 

http://www.heartland.org/policybot/results/23495/Evaluation_of_NHTSAs_BenefitCost_Anal

ysis_Of_20112015_CAFE_Standards.html (Docket EPA-HQ-OAR-2010-0799). 

19 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799). 

20 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

21 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and 

Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American 

Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799). 

22 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, 

and Li 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 (Docket EPA-HQ-OAR-2010-

0799). 

23 NERA Economic Consulting, “Appendix B: New Vehicle Market Model,” “Impacts of the 

California Greenhouse Gas Emission Standards on Motor Vehicle Sales,” comments 

submitted to the U.S. Environmental Protection Agency at Regulations.gov, document 

number EPA-HQ-OAR-2006-0173-9053.1 (Docket EPA-HQ-OAR-2010-0799). 

24 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799) 

25 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

26 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and 

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 

(2005): 757-775 (Docket EPA-HQ-OAR-2010-0799). 

27 Rex, Emma, and Henrikke Baumann (2007). “Beyond ecolabels: what green marketing 

can learn from conventional marketing.” Journal of Cleaner Production 15: 567-576 (Docket 

EPA-HQ-OAR-2010-0799). 
Summary of interagency working comments received in draft NPRM under EO 12866 

review. These are for your consideration and are subject to further policy review. 

8-35 

28 Whitefoot, Kate, Meredith Fowlie, and Steven Skerlos, “Product Design Responses to 

Industrial Policy: Evaluating Fuel Economy Standards Using an Engineering Model of 

Endogenous Product Design,” Energy Institute at Haas Working Paper 214, University of 

California Energy Institute, February 2011 (Docket EPA-HQ-OAR-2010-0799). 

29 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

30 Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-

951 (Docket EPA-HQ-OAR-2010-0799). 

31 Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with 

Flexible Substitution Patterns,” Journal of Econometrics 89 (1999): 109-129 (Docket EPAHQ-OAR-2010-0799); Brownstone, David, David S. Bunch, and Kenneth Train, “Joint Mixed 

Logit Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” 

Transportation Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2010-0799); 

Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” 

prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 

(Docket EPA-HQ-OAR-2010-0799); Greene, David L., K. G. Duleep, and Walter McManus, 

“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, August 

2004 (Docket EPA-HQ-OAR-2010-0799). 

32 Petrin, Amil, “Quantifying the Benefits of New Products: The Case of the Minivan,” 

Journal of Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799); 

Berry, Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand Systems 

from a Combination of Micro and Macro Data: The New Car Market,” Journal of Political 

Economy 112 (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799).

33 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48 (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799). 

34 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, 

and Li 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 (Docket EPA-HQ-OAR-2010-

0799). 

35 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and 

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 
Summary of interagency working comments received in draft NPRM under EO 12866 

review. These are for your consideration and are subject to further policy review. 

8-36 

(2005): 757-775 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, and Li 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 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., “Feebates, 

Footprints and Highway Safety,” Transportation Research Part D 14 (2009): 375-384. 

36 E.g., Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of 

Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental 

Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799). 

37 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy 

Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2010-0799). 

38 E.g., Espey, Molly, and Santosh Nair, 2005. “Automobile Fuel Economy: What Is It 

Worth?” Contemporary Economic Policy 23: 317-323 (Docket EPA-HQ-OAR-2010-0799). 

39 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 

1593-1594 (Docket EPA-HQ-OAR-2010-0799). 

40 E.g., Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-

951 (Docket EPA-HQ-OAR-2010-0799). 

41 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

42 Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. “Pain at the Pump: 

How Gasoline Prices Affect Automobile Purchasing in New and Used Markets,” working 

paper, http://web.mit.edu/knittel/www/papers/gaspaper_latest.pdf (accessed 11/1/11) (Docket 

EPA-HQ-OAR-2010-0799); Li, Shanjun, Christopher Timmins, and Roger H. von Haefen, 

2009. “How Do Gasoline Prices Affect Fleet Fuel Economy?” American Economic Journal: 

Economic Policy 1(2): 113-137; Congressional Budget Office (2008) (Docket EPA-HQOAR-2010-0799); Congressional Budget Office (2008). Effects of Gasoline Prices on 

Driving Behavior and Vehicle Markets. The Congress of the United States, Pub. No. 2883

(Docket EPA-HQ-OAR-2010-0799); West. Sarah (2007). “The Effect of Gasoline Prices on 

the Demand for Sport Utility Vehicles.” Working Paper. Macalester College, 

http://www.macalester.edu/~wests/SarahWestMEA2007.pdf, accessed 1/25/10 (Docket EPAHQ-OAR-2010-0799). 

43 Greene, David L., and Jin-Tan Liu, 1988. “Automotive Fuel Economy Improvements and 

Consumers’ Surplus,” Transportation Research Part A 22A (3): 203-218 (Docket EPA-HQOAR-2010-0799). 
Summary of interagency working comments received in draft NPRM under EO 12866 

review. These are for your consideration and are subject to further policy review. 

8-37 

44 Greene, David L. (2010). “How Consumers Value Fuel Economy: A Literature Review.” 

EPA Report EPA-420-R-10-008 (Docket EPA-HQ-OAR-2010-0799). 

45 Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S. 

Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 

11/1/11 (Docket EPA-HQ-OAR-2010-0799). 

46 Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile 

Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket 

EPA-HQ-OAR-2010-0799); McManus, Walter, 2007. “The Impact of Attribute-Based 

Corporate Average Fuel Economy (CAFE) Standards: Preliminary Results.” Ann Arbor, MI: 

University of Michigan Transportation Research Institute, Report No. UMTRI-2007-31 

(Docket EPA-HQ-OAR-2010-0799). 

47 For instance, Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy 

(CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2010-0799); Austin, David, and Terry Dinan, “Clearing the Air: The Costs and 

Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of 

Environmental Economics and Management 50 (2005): 562- (Docket EPA-HQ-OAR-2010-

0799); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of 

the Corporate Average Fuel Economy Standard,” Resources for the Future Paper RFF DP 10-

50 (December 2010), http://www.rff.org/RFF/Documents/RFF-DP-10-50.pdf) (Docket EPAHQ-OAR-2010-0799)Jacobsen, Mark, 2010. “Evaluating U.S. Fuel Economy Standards in a 

Model with Producer and Household Heterogeneity,” working paper, at 

http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf (accessed 11/1/11) (Docket EPA-HQOAR-2010-0799) . 

48 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher 

CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and 

Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

49 IEA. 2007. “Mind the Gap: Quantifying Principal-Agent Problems in Energy Efficiency.” 

Paris, France: International Energy Agency (Docket EPA-HQ-OAR-2010-0799); Jaffe, Adam 

B., Richard G. Newell, and Robert N. 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 (Docket 

EPA-HQ-OAR-2010-0799); Metcalf, Gilbert E., and Kevin A. Hassett (1999). "Measuring 

the Energy Savings From Home Improvement Investments: Evidence From Monthly Billing 

Data." The Review of Economics and Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010-

0799); Tietenberg, T. (2009). “Reflections – Energy Efficiency Policy: Pipe Dream or 

Pipeline to the Future?” Review of Environmental Economics and Policy 3(2): 304-320 

(Docket EPA-HQ-OAR-2010-0799). 
Summary of interagency working comments received in draft NPRM under EO 12866 

review. These are for your consideration and are subject to further policy review. 

8-38 

50 Helfand, Gloria, and Ann Wolverton (2011). “Evaluating the Consumer Response to Fuel 

Economy: A Review of the Literature.” International Review of Environmental and 

Resource Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799). 

51 Jaccard, Mark. “Paradigms of Energy Efficiency’s Cost and their Policy Implications: 

Déjà Vu All Over Again.” Modeling the Economics of Greenhouse Gas Mitigation: 

Summary of a Workshop, K. John Holmes, Rapporteur. National Academies Press, 2010. 

http://www.nap.edu/openbook.php?record_id=13023&page=42 (Docket EPA-HQ-OAR2010-0799) 

52 Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” 

prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 

(Docket EPA-HQ-OAR-2010-0799). 

53 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency 

Standards. The White House, Office of the Press Secretary, May 21, 2010. 

http://www.whitehouse.gov/the-press-office/presidential-memorandum-regarding-fuelefficiency-standards 

54 U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed 

on August 9, 2011. 

55 Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis 

of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011 

(Docket EPA-HQ-OAR-2010-0799). 

56 Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. “Jobs Versus the 

Environment: An Industry-Level Perspective.” Journal of Environmental Economics and 

Management 43 (2002): 412-436 (Docket EPA-HQ-OAR-2010-0799).

57 Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental 

and Natural Resource Policy.” Environmental and Resource Economics 22 (2002): 133-156 

(Docket EPA-HQ-OAR-2010-0799). 

58 Berman, Eli, and Linda T. Bui, (2001) “Environmental Regulation and Labor Demand: 

Evidence from the South Coast Air Basin,” Journal of Public Economics, 79, 265 – 295 

(Docket EPA-HQ-OAR-2010-0799).

59 http://www.bls.gov/emp/ep_data_emp_requirements.htm 

60 http://www.census.gov/manufacturing/asm/index.html

61 http://www.bls.gov/emp/ep_data_emp_requirements.htm; this analysis used data for sectors 

88 (Motor Vehicle Manufacturing) and 90 (Motor Vehicle Parts Manufacturing) from “Chain-
Summary of interagency working comments received in draft NPRM under EO 12866 

review. These are for your consideration and are subject to further policy review. 

8-39 

weighted (2000 dollars) real domestic employment requirements table. . . adjusted to remove 

imports.” 

62FEV, Inc. “Light-Duty Technology Cost Analysis: Power-split and P2 HEV Case Studies,” 

Report FEV07-069-303, October 10, 2011 (Docket EPA-HQ-OAR-2010-0799). 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

1

Below is a summary of interagency working comments received during EO 12866 and EO 13563 

review of the draft rule “Joint Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas 

Emission Standards and Corporate Average Fuel Economy Standards”. They are for your 

consideration and subject to further policy review. 

1) Interagency commenters recommend that NHTSA consider the following edits to the 

maximize net benefits discussion: 

“Whether the standards maximize net benefits has thus been a touchstone, in the past for 

NHTSA’s consideration of economic practicability. Executive Order 12866, as amended 

by Executive Order 13563, states that agencies should “select, in choosing among 

alternative regulatory approaches, those approaches that maximize net benefits...,” Under 

the statute, however, NHTSA must consider situations in which economic feasibility 

constrains its choices. In this case, the NHTSA balancing of the statutory factors 

suggests that the maximum feasible stringency for this rulemaking points to another level 

besides the modeled net benefits maximum, and consideration of such a situation is well 

within the guidance provided by EO’s 12866 and 13563.[1] 

The agency's consideration of economic practicability depends on a number of factors. 

Expected availability of capital to make investments in new technologies matters; 

manufacturers’ expected ability to sell vehicles with new technologies matters; likely 

consumer choices matter; and so forth. NHTSA’s analysis of the impacts of this 

rulemaking does incorporate assumptions to capture aspects of consumer preferences, 

vehicle attributes, safety, and other factors relevant to an impact estimate. It is well 

within the agency’s discretion to deviate from a modeled net benefits maximum in the 

face of evidence of economic impracticability, if the agency concludes that the modeled 

net benefits maximum would not represent the maximum feasible level for future CAFE 

standards. Economic practicability is a complex factor, and like the other factors must 

also be considered in the context of the overall balancing and EPCA’s overarching 

purpose of energy conservation. “ 

2) Interagency commenter recommends the following edits to the flexibilities section in 

EPA Preamble: 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

2

“In the context of the increasing stringency of standards starting in MY 2017, as 

discussed, EPA is not proposing a continuation of the TLAAS. TLAAS was available to 

firms with a wide range of U.S. sales volumes (between 5,000 and 400,000 in MY2009). 

One company with U.S. sales on the order of 25,000 vehicles per year has indicated that

it believes that the CO2 standards in today’s proposal for MY2017-2025 would present 

significant technical challenges for their company, due to the relatively small volume of 

products it sells in the U.S., limited ability to average across its limited line fleet, and the 

performance-oriented nature of its vehicles. This firm indicated that absent access 

several years in advance to CO2 credits that it could purchase from other firms, this firm 

would need to change significantly the types of products it currently markets in the U.S. 

beginning in model year 2017, even if it adds substantial CO2-reducing technology to its

vehicles. EPA requests comment on the potential need to include additional flexibilities 

for companies with U.S. vehicle sales on the order of 25,000 units per year, and what 

types of additional flexibilities would be appropriate. Potential flexibilities could include 

an extension of the TLAAS program for lower volume companies, or a one-to-three year 

delay in the applicable model year standard (e.g., the proposed MY2017 standards could 

be delayed to begin in MY2018, MY2019, or MY2020). Commenters suggesting that 

additional flexibilities may be needed are encouraged to provide EPA with data 

supporting their suggested flexibilities.” 
Summary of interagency working comments received on draft rule under EO 12866 

review. These are for your consideration and are subject to policy review. 

8-1 

Below is a summary of interagency working comments received during EO 12866 and 

EO 13563 review of the draft rule “Joint Rulemaking to Establish Light-Duty Vehicle 

Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards”. They 

are for your consideration and are subject to further policy review. 

Chapter 8: Vehicle Sales and Employment Impacts 

8.1 Vehicle Sales Impacts 

8.1.1 Vehicle Sales Impacts and Payback Period 

Predicting the effects of this rule on vehicles entails comparing two competing effects. 

On the one hand, as a result of this rule, the vehicles will become more expensive, which 

would, by itself, be expected to discourage sales. On the other hand, the vehicles will have 

improved fuel economy and thus lower operating costs, producing lower total costs over the 

life of vehicles, which makes them more attractive to consumers. Which of these effects 

dominates for potential vehicle buyers when they are considering a purchase will determine 

the effect on sales. However, assessing the net effect of these two competing effects is 

complex and uncertain, as it rests on how consumers value fuel savings at the time of 

purchase and the extent to which manufacturers and dealers reflect them in the purchase price. 

The empirical literature does not provide clear evidence on whether consumers fully consider 

the value of fuel savings at the time of purchase. It also generally does not speak to the 

efficiency of manufacturing and dealer pricing decisions. Thus, for the proposal we do not 

provide quantified estimates of potential sales impacts. Rather, we solicit comment on the 

issues raised here and on methods for estimating the effect of this rule on vehicle sales. 

For years, consumers have been gaining experience with the benefits that accrue to 

them from owning and operating vehicles with greater fuel efficiency. Many households 

already own vehicles with a fairly wide range of fuel economy, and thus already have an 

opportunity to learn about the value of fuel economy on their own. Among two-vehicle 

households, for example, the least fuel-efficient vehicle averages just over 22 mpg (EPA test 

rating), and the range between this and the fuel economy of their other vehicle averages 

nearly 7 mpg. Among households that own 3 or more vehicles, the typical range of the fuel 

economy they offer is much wider. Consumer demand may have shifted towards such 

vehicles, not only because of higher fuel prices but also if many consumers are learning about 

the value of purchases based not only on initial costs but also on the total cost of owning and 

operating a vehicle over its lifetime. This type of learning should continue before and during 

the model years affected by this rule, particularly given the new fuel economy labels that 

clarify potential economic effects and should therefore reinforce that learning.. 
Chapter 8 

8-2 

Today’s proposed rule, combined with the new and easier-to-understand fuel economy 

label required to be on all new vehicles beginning in 2012, may increase sales above baseline 

levels by hastening this very type of consumer learning. As more consumers experience, as a 

result of the rule, the savings in time and expense from owning more fuel efficient vehicles, 

demand may shift yet further in the direction of the vehicles mandated under the rule. This 

social learning can take place both within and across households, as consumers learn from one 

another. 

First and most directly, the time and fuel savings associated with operating more fuel 

efficient vehicles may be more salient to individuals who own them, which might cause their 

subsequent purchase decisions to shift closer to minimizing the total cost of ownership over 

the lifetime of the vehicle. 

Second, this appreciation may spread across households through word of mouth and 

other forms of communications. 

Third, as more motorists experience the time and fuel savings associated with greater 

fuel efficiency, the price of used cars will better reflect such efficiency, further reducing the 

cost of owning more efficient vehicles for the buyers of new vehicles (since the resale price 

will increase). 

If these induced learning effects are strong, the rule could potentially increase total 

vehicle sales over time. It is not possible to quantify these learning effects years in advance 

and that effect may be speeded or slowed by other factors that enter into a consumer’s 

valuation of fuel efficiency in selecting vehicles. The possibility that the rule will (after a lag 

for consumer learning) increase sales need not rest on the assumption that automobile 

manufacturers are failing to pursue profitable opportunities to supply the vehicles that 

consumers demand. In the absence of the rule, no individual automobile manufacturer would 

find it profitable to move toward the more efficient vehicles mandated under the rule. In 

particular, no individual company can fully internalize the future boost to demand resulting 

from the rule. If one company were to make more efficient vehicles, counting on consumer 

learning to enhance demand in the future, that company would capture only a fraction of the 

extra sales so generated, because the learning at issue is not specific to any one company's 

fleet. Many of the extra sales would accrue to that company's competitors. 

In other words, consumer learning about the benefits of fuel efficient vehicles involves 

positive externalities (spillovers) from one company to the othersA. These positive 

externalities may lead to benefits for manufacturers as a whole. We emphasize that this 

discussion has been tentative and qualified. To be sure, social learning of related kinds has 

been identified in a number of contexts.1

 Comments are invited on the discussion offered 

here, with particular reference to any relevant empirical findings. 

 

A Industrywide positive spillovers of this type are hardly unique to this situation. In many industries, companies 

form trade associations to promote industry-wide public goods. For example, merchants in a given locale may 

band together to promote tourism in that locale. Antitrust law recognizes that this type of coordination can 

increase output. 
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In previous rulemakings, EPA and NHTSA conducted vehicle sales analyses by 

comparing the up-front costs of the vehicles with the present value of five years’ worth of fuel 

savings. We assumed that the costs for the fuel-saving technologies would be passed along 

fully to vehicle buyers in the vehicle prices. The up-front vehicle costs were adjusted to take 

into account several factors that would affect consumer costs: the increased sales tax that 

consumers would pay, the increase in insurance premiums, the increase in loan payments that 

buyers would face, and a higher resale value, with all of these factors due to the higher upfront cost of the vehicle. Those calculations resulted in an adjusted increase in costs to 

consumers. We then assumed that consumers considered the present value of five years of 

fuel savings in their vehicle purchase, which is consistent with the length of a typical new 

light-duty vehicle loan, and is similar to the average time that a new vehicle purchaser holds 

onto the vehicle.B

 The present value of fuel savings was subtracted from technology costs to 

get a net effect on vehicle cost of ownership. We then used a short-run demand elasticity of -

1 to convert a change in price into a change in quantity demanded of vehicles.C An elasticity 

of -1 means that a 1% increase in price leads to a 1% reduction in quantity sold. In the 

vehicle sales analyses, if five years of fuel savings outweighed the adjusted technology costs, 

then vehicle sales were predicted to increase; if the fuel savings were smaller than the 

adjusted technology costs, sales would decrease, compared to a world without the standards. 

We do not here present a vehicle sales analysis using this approach. This rule takes 

effect for MY 2017-2025. In the intervening years, it is possible that the assumptions 

underlying this analysis, as well as market conditions, might change. Instead, we present a 

payback period analysis to estimate the number of years of fuel savings needed to recover the 

up-front costs of the new technologies. In other words, the payback period identifies the 

break-even point for new vehicle buyers. The calculation of the payback period is discussed 

in DRIA Chapter 5.3. Table 1Table 1 shows the estimated payback period for MY 2021 and 

2025. We present MY 2021 because it is the last year before the mid-term review impacts, if 

any, will take place, and MY 2025 because it is the last year of the program. The payback 

period in 2021 is shorter than that in 2025, because the technologies required to meet the 

proposed MY 2021 standards are more cost-effective than those for MY 2025. In all cases, 

the payback periods are less than 4 years. 

Table 1 Estimated Payback Period for Model Years 2021 and 2025 (Years) 

Model Estimated Estimated Estimated Estimated 

 

B

 In this proposal, the 5-year payback assumption corresponds to an assumption that vehicle buyers take into 

account between 30 and 50 percent of the present value of lifetime vehicle fuel savings (with the variation 

depending on discount rate, model year, and car vs. truck). 

C

 For a durable good such as an auto, the elasticity may be smaller in the long run: though people may be able to 

change the timing of their purchase when price changes in the short run, they must eventually make the 

investment. We request comment on whether or when a long-run elasticity should be used for a rule that phases 

in over time, as well as how to find good estimates for the long –run elasticity. 
Chapter 8 

8-4 

Year Payback Period 

for Cash 

Purchase, 3% 

Discount Rate 

Payback Period 

for Cash 

Purchase, 7% 

Discount Rate 

Payback Period 

for Purchase on 

Credit, 3% 

Discount Rate 

Payback Period 

for Purchase on 

Credit, 7% 

Discount Rate 

2021 2.7 2.9 2.9 2.8

2025 3.7 3.9 3.9 3.9

We welcome comments on all aspects of this discussion, including the full range of 

considerations and assumptions which influence market behavior and outcomes; and 

associated uncertainties. We welcome comments on the methodology described here for 

quantitative estimates of the effects of this proposal on sales and its appropriateness for this 

rulemaking; we also welcome proposals for other methods. 

We here provide further detail on some of the assumptions included in the payback 

analysis. We seek comment on all these factors as well. The analysis starts with the increase 

in costs estimated by the OMEGA model. We assume that these costs are fully passed along 

to consumers. This assumption is appropriate for cost increases in perfectly competitive 

markets. In less than perfectly competitive markets, though, it is possible that the cost 

increase is split between consumers and automakers, and possibly suppliers, and the price 

may not increase as much as costs.2

 Thus, the assumption of full cost pass-through is possibly 

an overestimate. 

The next step in the analysis is to adjust this cost increase for other effects on the 

consumer. The higher vehicle price is likely to lead to an increase in sales tax, insurance, and 

vehicle financing costs. 

The increase in insurance costs is estimated from the average value of collision plus 

comprehensive insurance as a proportion of average new vehicle price. Collision plus 

comprehensive insurance is the portion of insurance costs that depend on vehicle value. The 

Insurance Information Institute3

 provides the average value of collision plus comprehensive 

insurance in 2008 as $432. The average value of a new vehicle in 2008, according to the U.S. 

Department of Energy, was $23,334.4

 Dividing the insurance cost by the average price of a 

new vehicle gives the proportion of comprehensive plus collision insurance as 1.85 percent of 

the price of a vehicle. If this same proportion holds for the increase in price of a vehicle, then 

insurance costs should go up by 1.85 percent of the increase in vehicle cost. We use 

information on depreciation of vehicle value from the same U.S. Department of Energy report 

to estimate a reduction in insurance costs, due to reduction in the estimated value of the 

vehicle, over 9 years. 

Calculating the average increase in sales tax starts with the vehicle sales tax for each 

state in 2006, the most recent source identified.5

 The sales tax per state was then multiplied 

by the 2010 population of the state;6

 those values were summed and divided by total U.S. 
Summary of interagency working comments received on draft rule under EO 12866 

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8-5 

population, to give a population-weighted sales tax. That estimate of the state sales taxes for 

vehicles in the U.S. is 5.3 percent. This value is assumed to be a one-time cost incurred when 

the vehicle is purchased. 

As of July, 2011, the national average interest rate for a 5 year new car loan was 5.51 

percent.7

 We use this loan rate to calculate the increase in vehicle costs due to financing a 

loan. 

8.1.2 Consumer Vehicle Choice ModelingD

An alternative to the vehicle sales analysis discussed above is the use of consumer 

vehicle choice models. In this section we describe some of the consumer vehicle choice 

models EPA has reviewed in the literature, and we describe the models’ results and 

limitations that we have identified. The evidence from consumer vehicle choice models 

indicates a huge range of estimates for consumers’ willingness to pay for additional fuel 

economy. Because consumer surplus estimates from consumer vehicle choice models depend 

critically on this value, we would consider any consumer surplus estimates of the effect of our 

rule from such models to be unreliable. In addition, the predictive ability of consumer vehicle 

choice models may be limited. While vehicle choice models are based on sales of existing 

vehicles, vehicle models are likely to change, both independently and in response to this rule. 

The models may not predict well in response to these changes. Instead, we compare the value 

of the fuel savings associated with this rule with the increase in technology costs. EPA will 

continue its efforts to review the literature, but, given the known limitations and uncertainties 

of vehicle choice models, EPA has not conducted an analysis using these models for this 

proposal. 

This rule will lead automakers to change characteristics – in particular, the fuel 

economy -- of the vehicles they produce. These changes will affect the cost of manufacturing 

the vehicle; as a result, the prices of the vehicles will also change. 

In response to these changes, the number and types of vehicles sold is likely to change. 

When consumers buy vehicles, they consider both their personal characteristics (such as age, 

family composition, income, and their vehicle needs) and the characteristics of vehicles (e.g., 

vehicle size, fuel economy, and price). In response to the changes in vehicle characteristics, 

consumers will reconsider their purchases. Increases in fuel economy are likely to be 

attractive to consumers, but increases in price, as well as any detrimental changes in other 

vehicle characteristics, may be deterrents to purchase. As a result, consumers may choose a 

different vehicle than they would have purchased in the absence of the rule. The changes in 

prices and vehicle characteristics are likely to influence consumers on multiple market scales: 

 

D This section is drawn heavily from Helfand, Gloria, and Ann Wolverton, “Evaluating the Consumer Response 

to Fuel Economy: A Review of the Literature.” International Review of Environmental and Resource 

Economics 5 (2011): 103-146. 
Chapter 8 

8-6 

the total number of new vehicles sold; the mix of new vehicles sold; and the effects of the 

sales on the used vehicle market. 

Consumer vehicle choice modeling (CCM) is a method used to predict what vehicles 

consumers will purchase based on vehicle characteristics and prices. In principle, it should 

produce more accurate estimates of compliance costs compared to models that hold fleet mix 

constant, since it predicts changes in the fleet mix that can affect compliance costs. It can also 

be used to measure changes in consumer surplus, the benefit that consumers perceive from a 

good over and above the purchase price. (Consumer surplus is the difference between what 

consumers would be willing to pay for a good, represented by the demand curve, and the 

amount they actually pay. For instance, if a consumer were willing to pay $30,000 for a new 

vehicle, but ended up paying $25,000, the $5000 difference is consumer surplus.) 

A number of consumer vehicle choice models have been developed. They vary in the 

methods used, the data sources, the factors included in the models, the research questions they 

are designed to answer, and the results of the models related to the effects of fuel economy on 

consumer decisions. This section will give some background on these differences among the 

models. 

8.1.2.1 Methods 

Consumer choice models (CCMs) of vehicle purchases typically use a form of discrete 

choice modeling. Discrete choice models seek to explain discrete rather than continuous 

decisions. 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. Discrete decisions can take only a 

limited set of values. The decision to purchase a vehicle, for instance, can only take two 

values, yes or no. Vehicle purchases are typically modeled as discrete choices, where the 

choice is whether to purchase a specified vehicle. The result of these models is a prediction 

of the probability that a consumer will purchase a specified vehicle. A minor variant on 

discrete choice models estimates the market share (a continuous variable between 0 and 1) for 

each vehicle. Because the market share is, essentially, the probability that consumers will 

purchase a specific vehicle, these approaches are similar in process; they differ mostly in the 

kinds of data that they use. 

The primary methods used to model vehicle choices are nested logit and mixed logit.E

 

In a nested logit, the model is structured in layers. For instance, the first layer may be the 

choice of whether to buy a new or used vehicle. Given 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. Examples of 

nested logit models include Goldberg,8 Greene et al.,9 and McManus.10

In a mixed logit, personal characteristics of consumers play a larger role than in nested 

logit. While nested logit can look at the effects of a change in average consumer 

 

E

 Logit refers to a statistical analysis method used for analyzing the factors that affect discrete choices (i.e., 

yes/no decisions or the choice among a countable number of options). 
Summary of interagency working comments received on draft rule under EO 12866 

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8-7 

characteristics, mixed logit allows consideration of the effects of the distribution of consumer 

characteristics. As a result, mixed logit can be used to examine the distributional effects on 

various socioeconomic groups, which nested logit is not designed to do. Examples of mixed 

logit models include Berry, Levinsohn, and Pakes,11 Bento et al.,12 and Train and Winston.13 

While discrete choice modeling appears to be the primary method for consumer choice 

modeling, others (such as Kleit14 and Austin and Dinan15) have used a matrix of demand 

elasticities to estimate the effects of changes in cost. The discrete choice models can produce 

such elasticities. Kleit as well as Austin and Dinan used the elasticities from an internal GM 

vehicle choice model. 

8.1.2.2 Data Sources 

The predictions of vehicle purchases from CCMs are based on consumer and vehicle 

characteristics. The CCMs identify the effects of changing the characteristics on the purchase 

decisions. These effects are typically called the parameters or coefficients of the models. For 

instance, the model parameters might predict that an increase in a person’s income of 10% 

would increase the probability of her purchasing vehicle A by 5%, and decrease the 

probability of her purchasing vehicle B by 10%. 

The parameters in CCMs can be developed either from original data sources 

(estimated models), or using values taken from other studies (calibrated models). 

Estimated models use datasets on consumer purchase patterns, consumer 

characteristics, and vehicle characteristics to develop their original sets of parameters. The 

datasets used in these studies sometimes come from surveys of individuals’ behaviors.16 

Because they draw on the behavior of individuals, they provide what is sometimes called 

micro-level data. Other studies, that estimate market shares instead of discrete purchase 

decisions, use aggregated data that can cover long time periods.17 

Calibrated models rely on existing studies for their parameters. Researchers may draw 

on results from a number of estimated models, or even from research other than CCM, to 

choose the parameters of the models. The Fuel Economy Regulatory Analysis Model 

developed for the Energy Information Administration18 and the New Vehicle Market Model 

developed by NERA Economic Consulting19 are examples of calibrated models. 

8.1.2.3 Factors Included in the Models 

Consumer choice models vary in their complexity and levels of analysis. Some focus 

only on the new vehicle market;20 others consider the choice between new vehicles and an 

outside good (possibly including a used vehicle);21 others explicitly consider the relationship 

between the new and used vehicle markets.22 Some models include consideration of vehicle 

miles traveled,23 though most do not. 
Chapter 8 

8-8 

The models vary in their inclusion of both consumer and vehicle information. One 

model includes only vehicle price and the distribution of income in the population influencing 

choice;24 others include varying numbers and kinds of vehicle and consumer attributes. 

Some models include only the consumer side of the vehicle market;25 others seek to 

represent both consumer and producer decisions.26 Models that include only the consumer 

side are suitable for reflecting consumer choices, but they do not allow for revisions of 

vehicle characteristics in response to consumer preferences. Including producer behavior 

allows for vehicle characteristics such as price and fuel economy to be the result of market 

forces rather than characteristics of the existing fleet. For instance, in the context of 

“feebates” (subsidizing fuel-efficient cars with revenue collected by taxing fuel-inefficient 

vehicles), Greene et al. estimated that 95% of the increase in fuel economy was due to 

addition of technology rather than changes in vehicles sold.27 Including auto maker response 

is a complex exercise. Auto makers are commonly considered to have market power; they 

can influence the prices that consumers pay to increase their profits. As a result, the price 

increases that consumers face may reflect strategic factors that could make them higher or 

lower than the technology costs. In addition, auto makers may seek to influence consumer 

preferences through marketing and advertising.28 Even those vehicle choice models that 

include a producer model may not include much detail, due to computational limits: it is 

unusual for models to allow both buyers and producers to choose one vehicle characteristic, 

much less multiple characteristics.29 

8.1.2.4 Research Questions for the Models 

Consumer choice models have been developed to analyze many different research and 

policy questions. In part, these models have been developed to advance the state of economic 

modeling. The work of Berry, Levinsohn, and Pakes,30 for instance, is often cited outside the 

motor vehicle context for its incorporation of multiple new modeling issues into its 

framework. In addition, because the vehicle sector is a major part of the U.S. economy and 

involved in many public policy discussions, research questions cover a wide gamut. These 

topics have included the effects of voluntary export restraints on Japanese vehicles compared 

to tariffs and quotas,31 the market acceptability of alternative-fuel vehicles,32 the effects of 

introduction and exit of vehicles from markets,33 causes of the decline in market shares of 

U.S. automakers,34 and the effects of gasoline taxes35 and “feebates”36 (subsidizing fuelefficient cars with revenue collected by taxing fuel-inefficient vehicles). 

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions 

Consumer vehicle choice models typically consider the effect of fuel economy on 

vehicle purchase decisions. It can appear in various forms. 

Some models37 incorporate fuel economy through its effects 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 perhaps other operating costs) associated with a 

more fuel-efficient vehicle. Those savings are considered 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 estimate the fuel savings that they expect to 
Summary of interagency working comments received on draft rule under EO 12866 

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8-9 

receive from a more fuel-efficient vehicle and consider the savings equivalent to a reduction 

in purchase price. Turrentine and Kurani38 question this assumption; they find, in fact, that 

consumers do not make this calculation when they purchase a vehicle. The question remains, 

then, how or whether consumers take fuel economy into account when they purchase their 

vehicles. 

Most estimated consumer choice models, instead of making assumptions about how 

consumers incorporate fuel economy into their decisions, use data on consumer behavior to 

identify that effect. In some models, miles per gallon is one of the vehicle characteristics 

included to explain purchase decisions. Other models use fuel consumption per mile, the 

inverse of miles per gallon, as a measure:39 since consumers pay for gallons of fuel, then this 

measure can assess fuel savings relatively directly.40 Yet other models multiply fuel 

consumption per mile by the cost of fuel to get the cost of driving a mile,41 or they divide fuel 

economy by fuel cost to get miles per dollar.42 It is worth noting that these last two measures 

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.F On the 

one hand, while this assumption does not rely on as complex a calculation as the present value 

of fuel savings that Turrentine and Kurani examined, it suggests a calculating consumer. On 

the other hand, using a form of cost per mile is a way to recognize the role of fuel prices in 

consumers’ purchase of fuel economy: recent research43 presents results that higher fuel 

prices play a major role in that decision. 

Greene and Liu,44 in a paper published in 1988, reviewed 10 papers using consumer 

vehicle choice models and estimated for each one how much consumers would be willing to 

pay at time of purchase to reduce vehicle operating costs by $1 per year. They found that 

people were willing to pay between $0.74 and $25.97 for a $1 decrease in annual operating 

costs for a vehicle. This is clearly a very wide range: while the lowest estimate suggests that 

people are not willing to pay $1 once to get $1 per year reduced costs of operating their 

vehicles, the maximum suggests a willingness to pay 35 times as high. For comparison, the 

present value of saving $1 per year for 15 years at a 3% discount rate is $11.94, while a 7% 

discount rate produces a present value of $8.78. While this study is quite old, it suggests that, 

at least as of that time, consumer vehicle choice models produced widely varying estimates of 

the value of reduced vehicle operating costs. 

A newer literature review from David Greene45 suggests continued lack of 

convergence on the value of increased fuel economy to consumers. Of 27 studies, willingness 

to pay for fuel economy as a percent of the expected value of fuel savings varied from highly 

 

F

 Likewise, these measures assume consumers respond the same way to increases and decreases in cost per mile 

of driving, as well as if those increases and decreases are large shocks rather than small, gradual changes. The 

issue of potential asymmetric consumer response to increased fuel efficiency compared to other types of changes 

to the cost of driving also arises and is discussed in the context of the VMT rebound effect (see Section III.H.4 of 

the Preamble and Chapter 4.2.5.2 of the TSD). 
Chapter 8 

8-10 

positive to highly negative. Significant numbers of studies found that consumers overvalued 

fuel economy, undervalued fuel economy, or roughly valued fuel economy correctly relative 

to fuel savings. Part of the difficulty may be, as these papers note, that fuel economy may be 

correlated (either positively or negatively) with other vehicle attributes, such as size, power, 

or quality, not all of which may be included in the analyses; as a result, “fuel economy” may 

in fact represent several characteristics at the same time. Indeed, Gramlich46 includes both 

fuel cost (dollars per mile) and miles per gallon in his analysis, with the argument that miles 

per gallon measures other undesirable quality attributes, while fuel cost picks up the 

consumer’s demand for improved fuel economy. Greene finds that, while some of the 

variation may be explainable due to issues in some of the studies, the variation shows up in 

studies that appear to be well conducted. As a result, further work needs to be conducted 

before it is possible to identify the role of fuel economy in consumer purchase decisions. 

Some studies47 argue that automakers could increase profits by increasing fuel 

economy because the amount that consumers are willing to pay for increased fuel economy 

outweighs the costs of that improvement. Other studies48 have found that increasing fuel 

economy standards imposes welfare losses on consumers and producers, because consumers 

should already be buying as much fuel economy as they want. In the course of reaching this 

result, though, at least one of these studies49 notes that its baseline model implies that 

consumers are willing to buy more fuel economy than producers have provided; they have to 

adjust their model to eliminate these “negative-cost” fuel economy improvements. 

The models do not appear to yield very consistent results on the role of fuel economy 

in consumer and producer decisions. 

8.1.2.6 Why Market Outcomes May Not Reflect Full Appreciation for 

Fuel Economy which Pays for Itself 

A detailed and wide ranging literature attempts to explain why market outcomes for 

energy-using products appear to reflect under-investment in energy saving technologies that – 

at least using a present value calculation based on engineering estimates – appear to pay for 

themselves. Existing research does not provide a definitive answer to this question. Potential 

explanations are bounded by two scenarios. On the one hand, purely private benefits of fuel 

economy (fuel savings, time savings, increases in driving time) must be accompanied by 

private losses of the same magnitude. However, if there is no such private loss, or if it is small 

or insignificant, then there is a market or behavioral failure. 

This disconnect between net present value estimates of energy-conserving cost savings 

and what consumers actually spend on energy conservation is often referred to as the Energy 

Paradox,50 since consumers appear to undervalue a wide range of investments in energy 

conservation. There are many possible explanations for the paradox discussed in the 

literature.51 Some explanations point to costs or aspects of consumer decision-making 

unaccounted for in a simple present value calculation, while others point to potential 

behavioral or market failures. There is little empirical literature to help the analyst determine 

which combination of hypothesis offers the most credible explanation. Some possibilities 

include: 
Summary of interagency working comments received on draft rule under EO 12866 

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8-11 

 Consumers put little weight on benefits from fuel economy in the future and show 

high discount rates; 

 Consumers do not find the benefits from fuel economy to be sufficiently salient at 

the time of purchase, even if it would be in consumers’ economic interest to take 

account of those benefits; 

 Consumers consider other attributes more important than fuel economy at the time 

of vehicle purchase, especially if fuel economy is a relatively “shrouded” attribute; 

 Consumers have difficulty in calculating expected fuel savings; 

 Consumers may use imprecise rules of thumb when deciding how much fuel 

economy to purchase; 

 Consumers might associate higher fuel economy with inexpensive, less well 

designed vehicles; 

 Fuel savings in the future are uncertain, while at the time of purchase the increased 

costs of fuel-saving technologies are certain and immediate; 

 Consumers may not be able to find the vehicles they want with improved fuel 

economy; 

 The level of cost savings may be affected by the underlying reasons for the gap: 

factors such as transactions costs and differences in quality may not be adequately 

measured;52

 There is likely to be variation among consumers in the benefits they get from 

improved fuel economy, due to different miles driven and driving styles; 

 Consumers may give particular weight to the losses associated with upfront costs, 

and less so to the costs over time (a version of the phenomenon of “myopic loss 

aversion”). 

 The extent to which fuel economy is optimized relative to other, potentially more 

salient vehicle attributes (such as engine horsepower and seating capacity) in market 

outcomes for new vehicles remains an important area of uncertainty. There are significant 

challenges involved in effectively interpreting and anticipating consumer preferences for 

various vehicle attributes and amenities. There are significant lead times to market, potential 

return to scale limits on the range of options provided for a given attribute or amenity, market 

transaction frictional factors, and other factors inherent to the nature of these costly durable 

goods which may contribute to imperfect satisfaction of market demand for fuel economy 

among a highly heterogeneous customer base. . Both sides of the market would be expected 
Chapter 8 

8-12 

to attempt to maximize the utility they gain from these transactions, they presumably rely 

heavily in their calculations on the uncertain benefits of savings from fuel economy 

improvements, and yet market outcomes may still appear to reflect potential foregone 

opportunities to increase utility. We remain interested in these market dynamics, their 

underlying causes, and their potential significance for assessing the potential incremental 

effects of pollution control standards. We welcome comments on any aspect of this 

discussion. 

8.1.2.7 Modeling Electric Vehicles and Other New Vehicles 

Modeling the introduction of new vehicles can be a greater challenge than modeling 

the existing vehicle market, because the modeler does not have data on how many of the new 

vehicles consumers buy. Nevertheless, it can be possible to estimate the effects of new 

vehicle introduction by identifying characteristics for the new vehicles and using those in a 

vehicle choice model. For instance, as discussed above, the models can estimate effects on 

the vehicle market when vehicles change their fuel economy or price. If the model 

incorporates other vehicle attributes important to the new vehicles, such as size, performance, 

or range, then the effect of the introduction can be modeled by applying the parameters for 

those features to the new vehicle characteristics. 

As discussed above, some models rely on vehicle price as the primary or only 

explanatory variable. Even in these models, it is possible, with some additional information, 

to consider the effects of new vehicle introduction. The first step is to find a vehicle similar 

on as many dimensions as possible to the new vehicle. For instance, if the change is to create 

an electric vehicle (EV) version of an existing model, then the existing model serves as the 

base vehicle. Next, it is necessary to measure the changes in vehicle attributes of interest to 

potential vehicle buyers. For an EV, changes in vehicle driving range and cost of fueling may 

be two such attributes. The next requirement is information on the value to consumers of the 

attributes that change between the new and the base vehicle. Multiplying the value for that 

attribute by the change in the attribute provides an estimate of the benefit or cost associated 

with changing that characteristic. That amount can then be added to or subtracted from the 

vehicle purchase price to give an adjusted purchase price reflecting the changed characteristic. 

This procedure is just an extension of the approach, discussed above, used to incorporate fuel 

economy improvements into vehicle choice models, by calculating future fuel savings and 

subtracting them (either in whole or a fraction) from vehicle purchase price. 

Incorporating new vehicles into a vehicle choice model, then, requires estimates of the 

changes in key attributes from conventional vehicles, and estimates of the value, also called 

the willingness to pay (WTP), that consumers put on those attributes. 

Electric vehicles (EVs) will have a number of changes in vehicle characteristics from 

any baseline model. EVs are likely to have a smaller driving range between refuelings than 

conventional vehicles, due to the large battery capacity needed to increase range. The ability 

to recharge at home may be a convenient, desirable feature for people who have garages with 

electric hookups, but not for people who park on the street. If an infrastructure develops for 

recharging vehicles with the convenience approaching that of buying gasoline, range or home 

recharging may become less of a barrier to purchase. The reduced tailpipe emissions and 
Summary of interagency working comments received on draft rule under EO 12866 

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8-13 

reduced noise may be attractive features to some consumers.G They may have different 

performance or storage capacity. If sufficient data were available, the changes in these 

attributes, combined with WTP for each of the attributes, could be used to adjust the purchase 

price of the baseline vehicle to estimate consumers’ WTP for the electric version of a vehicle. 

Greene (2001), for instance, used this approach for a model that simulates choice, not only for 

EVs, but also for other alternative-fuel vehicles.53 In that model, he considers only one base 

vehicle, a passenger car, but considers the effect on WTP of fuel cost per mile, range, 

acceleration, and several other vehicle attributes. 

Vehicle driving range has received attention because of the current paucity of 

recharging infrastructure: if the driver of an EV gets low on fuel, it may be difficult to find a 

place to recharge. Because range appears to be a major factor in EV acceptability, it is 

starting to draw attention in the research community. 

In several studies, researchers have used stated preference conjoint analysis to 

estimate the effect of vehicle range on consumer vehicle choice. In a conjoint analysis, 

consumers are given a choice between several vehicles with different attributes. One choice 

might be, for instance, between a baseline car and another car with higher range and a higher 

purchase price. The choices that consumers make (e.g., how much higher does the purchase 

price have to be for the consumer not to choose more range?) provide data that can be used to 

estimate the role of vehicle attributes in the consumer’s choice. Stated preference analysis is 

sometimes considered less reliable than actual market behavior, because what people say they 

will do in hypothetical situations may not match what they would do in actual situations. On 

the other hand, stated preference methods can be used to study goods where market data do 

not exist, such as future market products undergoing development (marketing studies often 

use stated preference methods), or environmental goods. Because electric vehicles are not in 

widespread enough use for market studies, stated preference studies are, at this point, one of 

the few options to examine consumer behavior relating to these vehicles. 

Table 8.1-2Table 8.1-3 summarizes results from several conjoint studies that include 

the effects of extending range (in the table, from 150 to 300 miles, to present standardized 

results). Variation of results in the table is from income or other demographic factors, not 

from confidence intervals. The results suggest that the value of additional range varies among 

consumers, and the amount of that variation is changing (perhaps shrinking) in more recent 

studies. 

 

G For instance, Hidrue et al. (Hidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. 

“Willingness to Pay for Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 

686-705) find that some consumers are willing to pay $5100 more for vehicles with 95% lower emissions than 

the vehicles they otherwise aim to purchase. 
Chapter 8 

8-14 

Table 8.1-24Willingness to Pay for Increasing Range Calculated from Various Studies 

Study (Date) Value of extending range 

from 150 to 300 miles (dollar 

year) 

Value of additional 

range in 2009$a 

Bunch et al. (1993)b $7,600 (1991$) $11,100

Kavelek (1996) for California 

Energy Commissionc 

$2600 - $41,900 (1993$) $3700 - $58,700

Resource Systems Group (2009) for 

California Energy Commissiond 

$2900 - $7500 (2009$) $2900 - $7500

Hess et al. (2009), using the same 

data as Resource Systems Group 

(2009)e 

$2400 - $8500 (2009$) $2400 - $8500

Hidrue et al. (2011)f $3776 - $10,399 (2009$) $3776 - $10,399

a

Values adjusted to 2009$ using the Bureau of Economic Analysis GDP deflator. 

b

Bunch, David S., Mark Bradley, Thomas F. Golob, and Ryuichi Kitamura. “Demand for Clean-Fuel Vehicles 

in California: A Discrete-Choice Stated Preference Pilot Project.” Transportation Research Part A 27A(3) 

(1993): 237-253. The value of range was, in their model, assumed to be the same for all people. 

c

Kavelek, Chris. “CALCARS: The California Conventional and Alternative Fuel Response Simulator.” 

Demand Analysis Office, California Energy Commission, April 1996. The variation in values is due to 

willingness to pay (WTP) varying by income levels and for one-car and two-car households. The coefficient on 

range for one-car households was not statistically significantly different from zero (t-statistic = 1.5), but it was 

for 2-car households (t-statistic = 3.02). The minima and maxima presented here represent the values across 

both ownership and income categories. 

d

Resource Systems Groups, Inc. “Transportation Fuel Demand Forecast Household and Commercial Fleet 

Survey Task 8 Report: Logistic Regression Analysis and Results.” Prepared for California Energy Commission, 

June 2009. 

e

Hess, S., T. Adler, M. Fowler and A. Bahreinian “The Use of Cross-nested Logit Models for Multi-Dimensional 

Choice Processes: The Case of the Demand for Alternative Fuel Vehicles,” Proceedings of the 2009 European 

Transport Conference, Leiden, Netherlands, 2009. This study uses the same data as the Resource Systems 

Group study. The coefficient on range was not statistically significantly different from zero in these regressions: 

t-statistics varied from 1.29 to 1.52. The variation in values is due to willingness to pay (WTP) varying by 

income levels and statistical specification. The minima and maxima presented here represent the values across 

both income categories and specifications. 

f

Hidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. “Willingness to Pay for 

Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 686-705. The range of 

values is due to the model separating consumers into “gasoline vehicle-oriented” and “electric vehicle-oriented” 

groups. The EV-oriented group has higher WTP for additional range. 
Summary of interagency working comments received on draft rule under EO 12866 

review. These are for your consideration and are subject to policy review. 

8-15 

Driving range may be a major factor in consumers’ decisions on EVs, but it is not the 

only attribute that may be important to potential buyers (e.g., as noted, Hidrue et al. find that 

some consumers appear willing to pay substantially for reduced tailpipe emissions). A model 

that does not incorporate the other factors important to consumers’ decisions may not perform 

well in predicting vehicle purchases. In addition, as mentioned above, and as seen in Table 

8.1-2Table 8.1-3, it is likely that the WTP values for attributes of EVs will change over time, 

particularly if EVs are used more widely, the infrastructure to fuel the vehicles becomes more 

accessible, and consumers develop more familiarity and understanding of the vehicles. Thus, 

challenges associated with predicting market shares for EVs are even more serious than those 

already serious challenges associated with predicting market shares for conventional vehicles. 

8.1.2.8 EPA Exploration of Vehicle Choice Modeling 

In order to develop greater understanding of these models, EPA is in the process of 

developing a vehicle choice model. In its current form, the model assumes that the vehicle 

fleet and all characteristics of each vehicle, except vehicle prices and fuel economy, stay the 

same. The model will predict changes in the vehicle fleet, at the individual-configuration 

level and at more aggregated levels, in response to changes in vehicle fuel economy and price. 

The draft EPA model uses a nested logit structure common in the vehicle choice 

modeling literature, as discussed above in Chapter 8.1.2.1. “Nesting” refers to the decisiontree structure of the model, and “logit” refers to the fact that the choices are discrete (i.e., 

yes/no decisions about which vehicles to purchase, instead of continuous values). 

The nesting involves a hierarchy of choices. In its current form, at the initial decision 

node, consumers choose between buying a new vehicle or not. Conditional on choosing a 

new vehicle, consumers then choose between passenger vehicles, cargo vehicles, and ultraluxury vehicles. The next set of choices subdivides each of these categories into vehicle type 

(e.g., standard car, minivan, SUV, etc.). Next, the vehicle types are divided into classes 

(small, medium, and large SUVs, for instance), and then, at the bottom, are the individual 

vehicle configurations. 

At this bottom level, vehicles that are similar to each other (such as standard 

subcompacts, or prestige large vehicles) end up in the same “nest.” Substitution within a nest 

is considered much more likely than substitution across nests, because the vehicles within a 

nest are more similar to each other than vehicles in different nests. For instance, a person is 

more likely to substitute between a Chevrolet Aveo and a Toyota Yaris than between an Aveo 

and a pickup truck. In addition, substitution is greater at low decision nodes (such as 

individual configurations) than at higher decision nodes (such as the buy/no buy decision), 

because there are more choices at lower levels than at higher levels. 

Parameters for the model (including demand elasticities and the value of fuel economy 

in purchase decisions) are being selected based on a review of values found in the literature 

on vehicle choice modeling. As discussed above, a number of studies have estimated these 
Chapter 8 

8-16 

parameters. Those estimates, combined with some theoretical requirements,H assist in 

assigning values for the parameters. The model will allow individual users to change those 

parameters. 

The fuel economy of a vehicle is used to adjust the price of the vehicle, using a 

version of the procedure discussed in Chapter 8.1.2.7: the value that the consumer places on 

fuel economy is multiplied by the change in fuel economy and incorporated into the “effective 

price” of the vehicle. In practice, implementing this calculation involves calculating the 

change in expenditures on fuel based on schedules of VMT, vehicle survival, and fuel prices 

in the future consistent with those in OMEGA. As discussed in Chapter 8.1.2.5, there is no 

consensus value for consumers’ willingness to pay for improved fuel economy: estimates 

vary tremendously. The model assumes that consumers will use some years of discounted 

fuel savings, with the modeler able to input both the number of years and the discount rate to 

be used in the analysis. 

The vehicle choice model will take as inputs an initial fleet of vehicles (including the 

initial sales and fuel economy) in the absence of standards, the cost of technologies added to 

each vehicle to comply with standards, and the change in fuel economy. With the initial sales 

mix, for each vehicle, the model calculates a vehicle-specific constant that summarizes the 

value of all attributes of the vehicle other than price and fuel economy. This constant ensures 

that the model will predict changes in consumer response that would result only from changes 

in price and fuel economy. This constant substitutes for estimating the effects of changes in 

all other vehicle characteristics; the underlying assumption is that these other vehicle 

characteristics do not change.I

 For instance, it assumes that a Ford Escape will not change in 

size, power, or accessories; the only changes will be to its cost and its fuel economy. 

The model assumes that the increase in vehicle cost associated with increased 

technology is fully passed through as an increase in vehicle price, and some years of fuel 

savings offset this price increase. It then calculates changes in total fleet size and in sales 

mix, at the individual-configuration level and at the level of vehicle class, due to the changes 

in fuel economy and vehicle prices. It also calculates changes in consumer surplus associated 

with the changes in fuel economy and vehicle prices. 

It is possible that the predicted changes in fleet mix will lead to predictions of vehicle 

sales for auto makers that do not meet the proposed standards, because the mix and volume of 

vehicles sold changed from the initial levels. To correct this problem, it would be necessary 

to feed the new fleet mix into OMEGA (which calculates costs and compliance) and get a 

new set of output, which could then be fed back into the vehicle choice model. OMEGA 

would increase technologies, and thus costs, to improve compliance; those adjustments would 

 

H The theory of nested logit requires that the price slopes (the change in utility as vehicle full price changes, a 

measure of consumer responsiveness to price changes) must be higher in absolute value for lower nests. This 

condition reflects the point, discussed above, that substitution is greater at lower decision notes than at higher 

ones. 

I

 As explained in Section III.D of the preamble, as part of the technology cost analysis for the proposed rule, the 

agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions. 
Summary of interagency working comments received on draft rule under EO 12866 

review. These are for your consideration and are subject to policy review. 

8-17 

then again affect vehicle demand. We expect that this iterative process would converge to a 

fleet mix that would meet standards. Performing this iteration requires development of an 

interface between the vehicle choice model and OMEGA to ensure accurate transmission of 

data between the models. At this time, the vehicle choice model takes output from OMEGA, 

but the results of the modeling do not feed easily back into OMEGA. Building this interface 

is an expected part of our future modeling work. 

The model is still undergoing development; EPA will seek peer review on it before it 

is utilized. In addition, concerns remain over the ability of any vehicle choice model to make 

reasonable predictions of the response of the vehicle fleet to changes in prices and vehicle 

characteristics. EPA seeks comments on the use of vehicle choice modeling for predicting 

changes in fleet mix due to policies, and on methods to test the ability of a vehicle choice 

model to produce reasonable estimates of changes in fleet mix. 

8.1.2.9 Summary and Additional Considerations 

Consumer vehicle choice modeling in principle can provide a great deal of useful 

information for regulatory analysis, helping to answer some of the central questions about 

relevant effects on consumer welfare. In practice, the advantages depend on the success of 

models in predicting changes in fleet size and mix. 

First, consumer vehicle choice modeling has the potential to describe more accurately 

the impact of a policy, by identifying market shifts. More accurate description of the market 

resulting from a policy can improve other estimates of policy impacts, such as the change in 

total vehicle emissions or vehicle miles traveled. The predictive ability of models, though, is 

not proven. 

Vehicle choice models can incorporate the effects on consumer decisions of changes 

in vehicle characteristics, if there are estimates of the value that consumers put on changes in 

those characteristics. These willingness-to-pay values may, however, be sensitive to the ways 

they are estimated, as indicated in the discussion of the value that consumers place on fuel 

economy in their purchase decisions. Especially for characteristics associated with advanced 

technology vehicles, such as EVs, the willingness-to-pay values may change over time as 

consumers develop more experience with the vehicles and these characteristics. Models 

based on current estimates may not predict well for the future. 

The modeling may improve estimates of the compliance costs of a rule. Consumers 

can either accept the new costs and vehicle characteristics, or they can change which vehicles 

they buy. Using a vehicle choice model is likely to reduce compliance costs: because the 

model allows consumers to choose among accepting the new vehicle, buying a different 

vehicle, or not buying a vehicle, consumers have additional options, which improves their 

welfare relative to the assumption that consumers will not change their buying behavior. . 
Chapter 8 

8-18 

An additional complication associated with consumer choice modeling is accurate 

prediction of producers’ responses to the rule. While it is possible to include auto makers’ 

decisions (for instance, on setting prices) into vehicle choices, computational limits affect the 

richness of these models. Technology costs, while an accurate measure of the opportunity 

cost of resources to society, may overestimate or underestimate the effect on the prices that 

consumers face. 

Consumer choice models can be used to calculate consumer surplus impacts on 

vehicle purchase decisions. Because these values are based on the estimates of changes in 

vehicle sales and fleet mix, consumer surplus measures may not be accurate if the changes in 

vehicle sales and fleet mix are not well estimated. 

Principles of welfare analysis can be useful for understanding the role of consumer 

vehicle choice models in benefit-cost analysis. In particular, except for EVs, the technology 

cost estimates developed in this proposal take into account the costs to hold other vehicle 

attributes, such as size and performance, constant. In addition, the analysis assumes that the 

full technology costs are passed along to vehicle buyers. With these assumptions, because 

welfare losses are monetary estimates of how much buyers would have to be compensated to 

be made as well off as in the absence of the change,J

 the price increase measures the loss to 

the buyer.K Assuming that the full technology cost gets passed along to the buyer as an 

increase in price, the technology cost thus measures the welfare loss to the buyer. Increasing 

fuel economy would have to lead to other changes in the vehicles that buyers find undesirable 

for there to be additional losses not included in the technology costs. 

Given the current limitations in modeling the role of fuel economy in vehicle purchase 

decisions, and limitations in modeling market responses to the new regulations, in this 

proposal EPA holds constant the vehicle fleet size and mix in its calculations of the impacts of 

this rule, and compares the fuel and other savings that consumers will receive with the 

technology costs of the vehicles. EPA continues to explore options for including consumer 

and producer choice in modeling the impacts of fuel economy-related regulations. This effort 

includes further review of existing consumer vehicle choice models, the estimates of 

consumers’ willingness to pay for increased fuel economy, and overall effects on consumer 

welfare, as well as EPA’s exploration of a vehicle choice model for use in the future. 

 

J

 This approach describes the economic concept of compensating variation, a payment of money after a change 

that would make a consumer as well off after the change as before it. A related concept, equivalent variation, 

estimates the income change that would be an alternative to the change taking place. The difference between 

them is whether the consumer’s point of reference is her welfare before the change (compensating variation) or 

after the change (equivalent variation). In practice, these two measures are typically very close together. K Indeed, it is likely to be an overestimate of the loss to the buyer, because the buyer has choices other than 

buying the same vehicle with a higher price; she could choose a different vehicle, or decide not to buy a new 

vehicle. The buyer would choose one of those options only if the alternative involves less loss than paying the 

higher price. Thus, the increase in price that the buyer faces would be the upper bound of loss of consumer 

welfare, unless there are other changes to the vehicle due to the fuel economy improvements that make the 

vehicle less desirable to buyers. 
Summary of interagency working comments received on draft rule under EO 12866 

review. These are for your consideration and are subject to policy review. 

8-19 

8.2 Employment Impacts 

8.2.1 Introduction 

Although analysis of employment impacts is not part of a cost-benefit analysis (except 

to the extent that labor costs contribute to costs), employment impacts of federal rules are of 

particular concern in the current economic climate of sizeable unemployment. When 

President Obama requested that the agencies develop this program, he sought a program that 

would “strengthen the [auto] industry and enhance job creation in the United States.”54,L The 

recently issued Executive Order 13563, “Improving Regulation and Regulatory Review” 

(January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety, 

and our environment while promoting economic growth, innovation, competitiveness, and job 

creation” (emphasis added). EPA is accordingly providing partial estimates of the effects of 

this proposal on domestic employment in the auto manufacturing and parts sectors, while 

qualitatively discussing how it may affect employment in other sectors more generally. 

This proposal is expected to affect employment in the United States through the 

regulated sector – the auto manufacturing industry – and through several related sectors, 

specifically, industries that supply the auto manufacturing industry (e.g., vehicle parts), auto 

dealers, the fuel refining and supply sectors, and the general retail sector. According to the 

U.S. Bureau of Labor Statistics, in 2010, about 677,000 people in the U.S. were employed in 

Motor Vehicle and Parts Manufacturing Sector (NAICS 3361, 3362, and 3363). About 

129,000 people in the U.S. were employed in the Automobile and Light Truck Manufacturing 

Sector (NAICS 33611) in December 2010; this is the directly regulated sector, since it 

encompasses the auto manufacturers that are responsible for complying with the proposed 

standards.55 Changes in light duty vehicle sales, discussed in Chapter 8.1.1, could affect 

employment for auto dealers. The employment effects of this rule are expected to expand 

beyond the regulated sector. Though some of the parts used to achieve the proposed 

standards are likely to be built by auto manufacturers themselves, the auto parts 

manufacturing sector also plays a significant role in providing those parts, and will also be 

affected by changes in vehicle sales. As discussed in Chapter 5.4 of the RIA, this proposal is 

expected to reduce the amount of fuel these vehicles use, and thus affect the petroleum 

refinery and supply industries. Finally, since the net reduction in cost associated with this 

proposal is expected to lead to lower household expenditures on fuel net of vehicle costs, 

consumers then will have additional discretionary income that can be spent on other goods 

and services. 

 

L

 The May 21, 2010 Presidential Memorandum also requested that EPA and NHTSA, in developing the technical 

assessment to inform the rulemaking process (which was issued by the agencies and CARB on September 30, 

2010), include, among other things, the “impacts on jobs and the automotive manufacturing base in the United 

States.” 
Chapter 8 

8-20 

When the economy is at full employment, an environmental regulation is unlikely to 

have much impact on net overall U.S. employment; instead, labor would primarily be shifted 

from one sector to another. These shifts in employment impose an opportunity cost on 

society, approximated by the wages of the employees, as regulation diverts workers from 

other activities in the economy. In this situation, any effects on net employment are likely to 

be transitory as workers change jobs (e.g., some workers may need to be retrained or require 

time to search for new jobs, while shortages in some sectors or regions could bid up wages to 

attract workers). 

On the other hand, if a regulation comes into effect during a period of high 

unemployment, a change in labor demand due to regulation may affect net overall U.S. 

employment because the labor market is not in equilibrium. In such a period, both positive 

and negative employment effects are possible.M Schmalansee and Stavins point out that net 

positive employment effects are possible in the near term when the economy is at less than 

full employment due to the potential hiring of idle labor resources by the regulated sector to 

meet new requirements (e.g., to install new equipment) and new economic activity in sectors 

related to the regulated sector.56 In the longer run, the net effect on employment is more 

difficult to predict and will depend on the way in which the related industries respond to the 

regulatory requirements. As Schmalansee and Stavins note, it is possible that the magnitude 

of the effect on employment could vary over time, region, and sector, and positive effects on 

employment in some regions or sectors could be offset by negative effects in other regions or 

sectors. For this reason, they urge caution in reporting partial employment effects since it can 

“paint an inaccurate picture of net employment impacts if not placed in the broader economic 

context.” 

It is assumed that the official unemployment rate will have declined to 5.3 percent by 

the time by the time this rule takes effect and so the effect of the regulation on labor will be to 

shift workers from one sector to another (from Fiscal Year 2012 Mid-Session Review: 

http://www.whitehouse.gov/sites/default/files/omb/budget/fy2012/assets/12msr.pdf) . Those 

shifts in employment impose an opportunity cost on society, approximated by the wages of 

the employees, as regulation diverts workers from other activities in the economy. In this 

situation, any effects on net employment are likely to be transitory as workers change jobs 

(e.g., some workers may need to be retrained or require time to search for new jobs, while 

shortages in some sectors or regions could bid up wages to attract workers). 

Given the time frame of this proposed rule, the state of the overall economy may have 

improved to the point where the only effect of changes in employment associated with the 

standards proposed here will be to shift workers from one sector to another. For example, to 

the extent that regulation increases the number of workers in the regulated sector (e.g., 

because of higher costs of producing vehicles with higher fuel economy), the opportunity cost 

associated with those shifts in employment will be captured in labor costs, as regulation 

diverts workers from other activities in the economy. In this situation, any effects on net 

employment are likely to be transitory as workers change jobs (e.g., some workers may need 

 

M Masur and Posner, available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1920441 
Summary of interagency working comments received on draft rule under EO 12866 

review. These are for your consideration and are subject to policy review. 

8-21 

to be retrained or require time to search for new jobs, while shortages in some sectors or 

regions could bid up wages to attract workers). It is also possible that the state of the 

economy will be such that positive or negative employment effects will occur. 

A number of different approaches have been used in published literature to conduct 

employment analysis. This section describes some of the common methods, as well as some 

of their limitations. 

8.2.2 Approaches to Quantitative Employment Analysis 

Measuring the employment impacts of a policy depend on a number of inputs and 

assumptions. For instance, as discussed, assumptions about the overall state of 

unemployment in the economy play a major role in measured job impacts. The inputs to the 

models commonly are the changes in quantities or expenditures in the affected sectors; model 

results may vary in different studies depending on the assumptions about the levels of those 

inputs, and which sectors receive those changes. Which sectors are included in the study can 

also affect the results. For instance, a study of this program that looks only at employment 

impacts in the refinery sector may find negative effects, because consumers will purchase less 

gasoline; a study that looks only at the auto parts sector, on the other hand, may find positive 

impacts, because the program will require redesigned or additional parts for vehicles. In both 

instances, these would only be partial perspectives on the overall change in national 

employment due to Federal regulation. 

8.2.2.1 Conceptual Framework for Employment Impacts in the 

Regulated Sector 

One study by Morgenstern, Pizer, and Shih57 provides a retrospective look at the 

impacts of regulation in employment in the regulated sectors by estimating the effects on 

employment of spending on pollution abatement for four highly polluting/regulated U.S. 

industries (pulp and paper, plastics, steel, and petroleum refining) using data for six years 

between 1979 and 1991. The paper provides a theoretical framework that can be useful for 

examining the impacts of a regulatory change on the regulated sector in the medium to longer 

term. In particular, it identifies three separate ways that employment levels may change in the 

regulated industry in response to a new (or more stringent) regulation. 

 Demand effect: higher production costs due to the regulation will lead to higher 

market prices; higher prices in turn reduce demand for the good, reducing the 

demand for labor to make that good. In the authors’ words, the “extent of this 

effect depends on the cost increase passed on to consumers as well as the demand 

elasticity of industry output.” 

 Cost effect: as costs go up, plants add more capital and labor (holding other 

factors constant), with potentially positive effects on employment. In the authors’ 
Chapter 8 

8-22 

words, as “production costs rise, more inputs, including labor, are used to produce 

the same amount of output.” 

 Factor shift effect: post-regulation production technologies may be more or less 

labor-intensive (i.e., more/less labor is required per dollar of output). In the 

authors’ words, “environmental activities may be more labor intensive than 

conventional production,” meaning that “the amount of labor per dollar of output 

will rise,” though it is also possible that “cleaner operations could involve 

automation and less employment, for example.” 

According to the authors, the “demand effect” is expected to have a negative effect on 

employment,N the “cost effect” to have a positive effect on employment, and the “factor shift 

effect” to have an ambiguous effect on employment. Without more information with respect 

to the magnitudes of these competing effects, it is not possible to predict the total effect 

environmental regulation will have on employment levels in a regulated sector. 

The authors conclude that increased abatement expenditures generally have not caused 

a significant change in employment in those sectors. More specifically, their results show 

that, on average across the industries studied, each additional $1 million spent on pollution 

abatement results in a (statistically insignificant) net increase of 1.5 jobs. 

This approach to employment analysis has the advantage of carefully controlling for 

many possibly confounding effects in order to separate the effect of changes in regulatory 

costs on employment. It was, however, conducted for only four sectors. It could also be very 

difficult to update the study for other sectors, because one of the databases on which it relies, 

the Pollution Abatement Cost and Expenditure survey, has been conducted infrequently since 

1994, with the last survey conducted in 2005. The empirical estimates provided by 

Morgenstern et al. are not relevant to the case of fuel economy standards, which are very 

different from the pollution control standards on industrial facilities that were considered in 

that study. In addition, it does not examine the effects of regulation on employment in sectors 

related to but outside of the regulated sector. Nevertheless, the theory that Morgenstern et al. 

developed continues to be useful in this context. 

The following discussion of additional methodologies draws from Berck and 

Hoffmann’s review of employment models.58 

8.2.2.2 Computable General Equilibrium (CGE) Models 

Computable general equilibrium (CGE) models are often used to assess the impacts of 

policy. These models include a stylized representation of supply and demand curves for all 

 

N As will be discussed below, the demand effect in this proposal is potentially an exception to this rule. While 

the vehicles become more expensive, they also produce reduced fuel expenditures; the reduced fuel costs provide 

a countervailing impact on vehicle sales. As discussed in Preamble Section III.H.1, this possibility that vehicles 

may become more attractive to consumers after the program poses a conundrum: why have interactions between 

vehicle buyers and producers not provided these benefits without government intervention? 
Summary of interagency working comments received on draft rule under EO 12866 

review. These are for your consideration and are subject to policy review. 

8-23 

major markets in the economy. The labor market is commonly included. CGE models are 

very useful for looking at interaction effects of markets: “they allow for substitution among 

inputs in production and goods in consumption.” Thus, if one market experiences a change, 

such as a new regulation, then the effects can be observed in all other markets. As a result, 

they can measure the employment changes in the economy due to a regulation. Because they 

usually assume equilibrium in all markets, though, they typically lack involuntary 

unemployment. If the total amount of labor changes, it is due to people voluntarily entering 

or leaving the workforce. As a result, these models may not be appropriate for measuring 

effects of a policy on unemployment, because of the assumption that there is no involuntary 

unemployment. In addition, because of the assumptions of equilibrium in all markets and 

forward-looking consumers and firms, they are designed for examining the long-run effects of 

a policy but may offer little insight into its short-run effects. 

8.2.2.3 Input-Output (IO) Models 

Input-output models represent the economy through a matrix of coefficients that 

describe the connections between supplying and consuming sectors. In that sense, like CGE 

models, they describe the interconnections of the economy. These interconnections look at 

how changes in one sector ripple through the rest of the economy. For instance, a 

requirement for additional technology for vehicles requires additional steel, which requires 

more workers in both the auto and steel sectors; the additional workers in those sectors then 

have more money to spend, which leads to more employment in retail sectors. These are 

known as “multiplier” effects, because an initial impact in one sector gets multiplied through 

the economy. Unlike CGE models, input-output models have fixed, linear relationships 

among the sectors (e.g., substitution among inputs or goods is not allowed), and quantity 

supplied need not equal quantity demanded. In particular, these models do not allow for price 

changes – an increase in the demand for labor or capital does not result in a change in its price 

to help reallocate it to its best use. As a result, these models cannot capture opportunity costs 

from using resources in one area of the economy over another. The multipliers take an initial 

impact and can increase it substantially. 

IO models are commonly used for regional analysis of projects. In a regional analysis, 

the markets are commonly considered small enough that wages and prices are determined 

outside the region, and any excess supply or demand is due to exports and imports (or, in the 

case of labor, emigration or immigration). For national-level employment analysis, the use of 

input-output models requires the assumption that workers flow into or out of the labor market 

perfectly freely. Wages do not adjust; instead, people join into or depart from the labor pool 

as production requires them. For other markets as well, there is no substitution of less 

expensive inputs for more expensive ones. As a result, IO models provide an upper bound on 

employment impacts. As Berck and Hoffmann note, “For the same reason, they can be 

thought of as simulating very short-run adjustment,” in contrast to the CGE’s implicit 

assumption of long-run adjustment. Changes in production processes, introductions of new 

technologies, or learning over time due to new regulatory requirements are also generally not 
Chapter 8 

8-24 

captured by IO models, as they are calibrated to already established relationships between 

inputs and outputs. 

8.2.2.4 Hybrid Models 

As Berck and Hoffmann note, input-output models and CGE models “represent a 

continuum of closely related models.” Though not separately discussed by Berck and 

Hoffmann, some hybrid models combine some of the features of CGE models (e.g., prices 

that can change) with input-output relationships. For instance, a hybrid model may include 

the ability to examine disequilibrium phenomena, such as labor being at less than full 

employment. Hybrid models depend on assumptions about how adjustments in the economy 

occur. CGE models characterize equilibria but say little about the pathway between them, 

while IO models assume that adjustments are largely constrained by previously defined 

relationships; the effectiveness of hybrid models depends on their success in overcoming the 

limitations of each of these approaches. Hybrid models could potentially be used to model 

labor market impacts of various vehicle policy options although a number of judgments need 

to be made about the appropriate assumptions underlying the model as well the empirical 

basis for the modeling results. 

8.2.2.5 Single Sectors 

It is possible to conduct a bottom-up analysis of the partial effect of regulation on 

employment in a single sector by estimating the change in output or expenditures in a sector 

and multiplying it by an estimate of the number of workers per unit of output or expenditures, 

under the assumption that labor demand is proportional to output or expenditures. As Berck 

and Hoffmann note, though, “Compliance with regulations may create additional jobs that are 

not accounted for.” While such an analysis can approximate the effects in that one sector in a 

simple way, it also may miss important connections to related sectors. 

8.2.2.6 Ex-Post Econometric Studies 

A number of ex-post econometric analyses examine the net effect of regulation on 

employment in regulated sectors. Morgenstern, Pizer, and Shih (2002), discussed above, and 

Berman and Bui (2001) are two notable examples that rely on highly disaggregated 

establishment-level time series data to estimate longer-run employment effects.59 While often 

a sophisticated treatment of the issues analyzed, these studies commonly analyze specific 

scenarios or sectors in the past; care needs to be taken in extrapolating their results to other 

scenarios and to the future. For instance, neither of these two studies examines the auto 

industry and are therefore of limited applicability in this context. 

8.2.2.7 Summary 

All methods of estimating employment impacts of a regulation have advantages and 

limitations. CGE models may be most appropriate for long-term impacts, but the usual 

assumption of equilibrium in the employment market means that it is not useful for looking at 

changes in overall employment: overall levels are likely to be premised on full employment. 

IO models, on the other hand, may be most appropriate for small-scale, short-term effects, 

because they assume fixed relationships across sectors and do not require market equilibria. 
Summary of interagency working comments received on draft rule under EO 12866 

review. These are for your consideration and are subject to policy review. 

8-25 

Hybrid models, which combine some features of CGEs with IO models, depend upon key 

assumptions and economic relationships that are built into them. Single-sector models are 

simple and straightforward, but they are often based on the assumptions that labor demand is 

proportional to output, and that other sectors are not affected. Finally, econometric models 

have been developed to evaluate the longer-run net effects of regulation on sector 

employment, though these are ex-post analyses commonly of specific sectors or situations, 

and the results may not have direct bearing for the regulation being reviewed. 

8.2.3 Employment analysis of this proposal 

As mentioned above, this program is expected to affect employment in the regulated 

sector (auto manufacturing) and other sectors directly affected by the proposal: auto parts 

suppliers, auto dealers, the fuel supply market (which will face reduced petroleum production 

due to reduced fuel demand but which may see additional demand for electricity or other 

fuels), and consumers (who will face higher vehicle costs and lower fuel expenditures). In 

addition, as the discussion above suggests, each of these sectors could potentially have ripple 

effects in the rest of the economy. These ripple effects depend much more heavily on the 

state of the macroeconomy than do the direct effects. At the national level, employment may 

increase in one industry or region and decrease in another, with the net effect being smaller 

than either individual-sector effect. EPA does not attempt to quantify the net effects of the 

regulation on overall national employment. 

The discussion that follows provides a partial, bottom-up quantitative estimate of the 

effects of this proposal on the regulated sector (the auto industry; for reasons discussed below, 

we include some quantitative assessment of effects on suppliers to the industry although they 

are not regulated directly). It also includes qualitative discussion of the effects of the proposal 

on other sectors. Focusing quantification of employment impacts on the regulated sector has 

some advantages over quantifying all impacts. First, the analysis relies on data generated as 

part of the rulemaking process, which focuses on the regulated sector; as a result, what is 

presented here is based on internally consistent assumptions and estimates made in this 

proposal. Secondly, as discussed above, net effects on employment in the economy as a 

whole depend heavily on the overall state of the economy when this rule has its effects. 

Focusing on the regulated sector provides insight into employment effects in that sector 

without having to make assumptions about the state of the economy when this rule has its 

impacts. We include a qualitative discussion of employment effects on other sectors to 

provide a broader perspective on the impacts of this rule. 

As noted above, in a full-employment economy, any changes in employment will 

result from people changing jobs or voluntarily entering or exiting the workforce. In a fullemployment economy, employment impacts of this proposal will change employment in 

specific sectors, but it will have small, if any, effect on aggregate employment. This rule 

would take effect in model years 2017 through 2025; by then, the current high unemployment 

may be moderated or ended. For that reason, this analysis does not include multiplier effects, 

but instead focuses on employment impacts in the most directly affected industries. Those 
Chapter 8 

8-26 

sectors are likely to face the most concentrated employment impacts. The agencies seek 

comment on other sectors that are likely to be significantly affected and thus warrant further 

analysis in the final rulemaking analysis. 

8.2.3.1 Employment Impacts in the Auto Industry 

Following the Morgenstern et al. conceptual framework for the impacts of regulation 

on employment in the regulated sector, we consider three effects for the auto sector: the 

demand effect, the cost effect, and the factor shift effect. However, we are only able to offer 

quantitative estimates for the cost effect. We note that these estimates, based on 

extrapolations from current data, become more uncertain as time goes on. 

8.2.3.1.1 The Demand Effect 

The demand effect depends on the effects of this proposal on vehicle sales. If vehicle 

sales increase, then more people will be required to assemble vehicles and their components. 

If vehicle sales decrease, employment associated with these activities will unambiguously 

decrease. Unlike in Morgenstern et al.’s study, where the demand effect unambiguously 

decreased employment, there are countervailing effects in the vehicle market due to the fuel 

savings resulting from this program. On one hand, this proposal will increase vehicle costs; 

by itself, this effect would reduce vehicle sales. On the other hand, this proposal will reduce 

the fuel costs of operating the vehicle; by itself, this effect would increase vehicle sales, 

especially if potential buyers have an expectation of higher fuel prices. The sign of demand 

effect will depend on which of these effects dominates. Because, as described in Chapter 8.1, 

we have not quantified the impact on sales for this proposal, we do not quantify the demand 

effect. 

8.2.3.1.2 The Cost Effect

The demand effect, discussed above, measures employment changes due to new 

vehicle sales only. The cost effect measures employment impacts due to the new or 

additional technologies needed for vehicles to comply with the proposed standards. 

One way to estimate the cost effect, given the cost estimates for complying with the 

rule, is to use the ratio of workers to each $1 million of expenditures in that sector. The use of 

these ratios has both advantages and limitations. It is often possible to estimate these ratios 

for quite specific sectors of the economy: for instance, it is possible to estimate the average 

number of workers in the light-duty vehicle manufacturing sector per $1 million spent in the 

sector, rather than use the ratio from another, more aggregated sector, such as motor vehicle 

manufacturing. As a result, it is not necessary to extrapolate employment ratios from possibly 

unrelated sectors. On the other hand, these estimates are averages for the sectors, covering all 

the activities in those sectors; they may not be representative of the labor required when 

expenditures are required on specific activities, as the factor shift effect (discussed below) 

indicates. For instance, the ratio for the motor vehicle manufacturing sector represents the 

ratio for all vehicle manufacturing, not just for fuel efficiency improvements. In addition, 

these estimates do not include changes in sectors that supply these sectors, such as steel or 

electronics producers. They thus may best be viewed as the effects on employment in the 
Summary of interagency working comments received on draft rule under EO 12866 

review. These are for your consideration and are subject to policy review. 

8-27 

auto sector due to the changes in expenditures in that sector, rather than as an assessment of 

all employment changes due to these changes in expenditures. 

Some of the costs of this proposal will be spent directly in the auto manufacturing 

sector, but some of the costs will be spent in the auto parts manufacturing sector. Because we 

do not have information on the proportion of expenditures in each sector, we separately 

present the ratios for both the auto manufacturing sector and the auto parts manufacturing 

sector. These are not additive, but should instead be considered as a range of estimates for the 

cost effect, depending on which sector adds technologies to the vehicles to comply with the 

regulation. 

We use several public sources for estimates of employment per $1 million 

expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment 

Requirements Matrix (ERM),60 which provides direct estimates of the employment per $1 

million in sales of goods in 202 sectors. The most recent estimates, used here, are from 2008 

(adjusted to 2009$). The tables used here are adjusted to remove the employment effects of 

imports through use of the ratio of domestic production to domestic sales, described above, of 

0.667. The values reported are for Motor Vehicle Manufacturing (NAICS 3361) and Motor 

Vehicle Parts Manufacturing (NAICS 3363). 

The Annual Survey of Manufactures61 (ASM) provides another source of estimates 

based on a sample of 50,000 establishments out of a universe of 346,000 manufacturing 

establishments. It includes more sectoral detail than the BLS ERM: for instance, while the 

ERM includes the Motor Vehicle Manufacturing sector, the ASM has detail at the 6-digit 

NAICS code level (e.g., automobile manufacturing vs. light truck and utility vehicle 

manufacturing). While the ERM provides direct estimates of employees/$1 million in 

expenditures, the ASM separately provides number of employees and value shipments; the 

direct employment estimates here are the ratio of those values. The data in the ASM are 

updated annually, except for years when the full Economic Census occurs. The tables 

presented here use data from 2009. As with the ERM, we adjust for the ratio of domestic 

production to domestic sales. The values reported are for Motor Vehicle Manufacturing 

(NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), 

and Motor Vehicle Parts Manufacturing (NAICS 3363). 

The Economic Census includes all large companies and a sample of smaller ones. The 

ASM is a subset of the Economic Census; though the Census itself is more complete, it is 

conducted only every 5 years, while the ASM is annual. The values presented here use data 

from 2007 (adjusted to 2009$), with the domestic production-to-sales adjustment. The values 

reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty 

Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing 

(NAICS 3363). 

Table 8.2-1Table 8.2-3 provides the values, either given (BLS) or calculated (ASM, 

Economic Census) for employment per $1 million of expenditures, all based on 2009 dollars, 
Chapter 8 

8-28 

though the underlying data come from different years (which may account for some of the 

differences). The different data sources provide similar magnitudes for the estimates for the 

sectors. Parts manufacturing appears to be more labor-intensive than vehicle manufacturing; 

light-duty vehicle manufacturing appears to be slightly less labor-intensive than motor vehicle 

manufacturing as a whole. 

Table 8.2-13 Employment per $1 Million Expenditures (2009$) in the Motor Vehicle 

Manufacturing Sector* 

Source Sector Ratio of 

workers per 

$1 million 

expenditures

Ratio of workers per $1 

million expenditures, 

adjusted for domestic vs. 

foreign production 

BLS ERM Motor Vehicle Mfg 0.834 0.556 

ASM Motor Vehicle Mfg 0.824 0.549 

ASM Light Duty Vehicle 

Mfg

0.757 0.505 

Economic 

Census 

Motor Vehicle Mfg 0.674 0.449 

Economic 

Census 

Light Duty Vehicle 

Mfg

0.610 0.407 

BLS ERM Motor Vehicle Parts 

Mfg

3.073 2.049 

ASM Motor Vehicle Parts 

Mfg

3.093 2.063 

Economic 

Census 

Motor Vehicle Parts 

Mfg

2.749 1.833 

BLS ERM refers to the U.S. Bureau of Labor Statistics’ Employment Requirement Matrix. ASM refers to the 

U.S. Census Bureau’s Annual Survey of Manufactures. Economic Census refers to the U.S. Census Bureau’s 

Economic Census.

Over time, the amount of labor needed in the auto industry has changed: automation 

and improved production methods have led to significant productivity increases. The BLS 

ERM, for instance, provided estimates that, in 1993, 1.52 workers were needed per $1 million 

of 2000$, but only 0.83 workers by 2008 (in 2000$).62 Because the ERM is available 

annually for 1993-2008, we used these data to estimate productivity improvements over time. 

We regressed logged ERM values on year (to estimate percent change per year) for both the 

Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing sectors. The results 

suggest a 4.4 percent per year productivity improvement in the Motor Vehicle Manufacturing 

Sector, and a 3.6 percent per year improvement in the Motor Vehicle Parts Manufacturing 

Sector. We then used the regression relationship to project the ERM through 2025. In the 

results presented below, these projected values (adjusted to 2009$) were used directly for the 

BLS ERM estimates. For the ASM, we used the ratio of the projected value in the future to 
Summary of interagency working comments received on draft rule under EO 12866 

review. These are for your consideration and are subject to policy review. 

8-29 

the projected value in 2009 (the base year for the ASM); for the Economic Census estimates, 

we used the ratio of the projected value in the future to the projected value in 2007 (the base 

year for that estimate). As noted above, we adjusted the estimate of workers per vehicle for 

the demand effect in Chapter 8.2.3.1.1, above, using the productivity improvement for the 

Motor Vehicle Manufacturing sector; because the estimate of workers per vehicle for the 

demand effect was based on data from 2001 to 2010, we used the projected value of the ERM 

in 2005 as the denominator of the ratio. This is a simple way to examine the relationship 

between labor required and expenditure and we seek comment on refining this method. 

Table 8.2-2Table 8.2-4 shows the cost estimates developed for this rule, discussed in 

Chapter 5. The maximum value in Table 8.2-2Table 8.2-4 for employment impacts per $1 

million expenditures (after accounting for the share of domestic production) is 2.049 in 2009 

if all the additional costs are in the parts sector; the minimum value is 0.407 in 2009, if all the 

additional costs are in the light-duty vehicle manufacturing sector: that is, the range of 

employment impacts is between 0.4 and 2 additional jobs per $1 million expenditures in the 

sector in 2009. The results in Table 8.2-2Table 8.2-4 include the productivity adjustment 

described above. 

While we estimate employment impacts beginning with the first year of the standard 

(2017), some of these job gains may occur earlier as auto manufacturers and parts suppliers 

hire staff in anticipation of compliance with the standard. 

Table 8.2-24 Employment due to Cost Effect in the Motor Vehicle Manufacturing Sector 

Year 

Costs (before 

adjustment for 

domestic proportion of 

production) ($Millions) 

Minimum 

employment effect (if 

all expenditures are in 

the parts sector) 

Maximum employment 

effect (if all expenditures 

are in the light duty 

vehicle mfg sector) 

2017 $ 2,300 600 3,600 

2018 $ 4,656 1,200 7,000 

2019 $ 6,507 1,600 9,400 

2020 $ 8,467 1,900 11,800 

2021 $ 11,878 2,600 15,900 

2022 $ 19,340 4,100 25,000 

2023 $ 25,036 5,000 31,200 

2024 $ 30,738 5,900 37,000 

2025 $ 33,561 6,200 39,000 

8.2.3.1.3 The Factor Shift Effect 

The factor shift effect looks at the effects on employment due to changes in labor 

intensity associated with a regulation. As noted above, the estimates of the cost effect assume 

constant labor per $1 million in expenditures, though the new technologies may be either 
Chapter 8 

8-30 

more or less labor-intensive than the existing ones. An estimate of the factor shift effect 

would either increase or decrease the estimate used for the cost effect. 

We are not quantifying the factor shift effect here, for lack of data on the labor 

intensity of all the possible technologies that manufacturers could use to comply with the 

proposed standards. For a subset of the technologies, though, EPA-sponsored research 

(discussed in Chapter 3.2.1.1 of the Joint TSD) which compared new technologies to existing 

ones at the level of individual components provides some insights into the factor shift effect. 

The comparison involved tearing down the selected technologies to their individual 

components and looking at the differences in materials and labor needs in moving from the 

conventional to the new technologies.63 For instance, the analysis compared all the parts and 

labor associated with an 8-speed automatic transmission to those needed for a 6-speed 

automatic transmission. 

Because labor cost was one of the sources of differences between the technologies, it 

is possible, for those technologies, to see whether labor needs increase or decrease with the 

switch to technologies that might contribute to compliance with the proposed standards. An 

increase in labor cost for the new technology indicates an increase in the labor needed for the 

new technology compared to the baseline technology. For instance, an 8-speed transmission 

requires $15.11 more in labor costs than a 6-speed transmission (as accounted for in EPA’s 

cost estimates for the proposed rule). Dividing the labor cost by a wage per hour estimate 

provides an estimate of the additional hours (and thus the additional labor) needed for the new 

technology compared to the baseline technology. As with labor cost, an increase in labor 

hours per technology indicates greater employment needs for the new technologies. For this 

conversion, a weighted average wage rate (90 percent of the average wage in the Motor 

Vehicle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle 

Manufacturing Sector) of $46.36/hour in 2015, using 2008 dollars (the unit of analysis for the 

FEV study). For the change from a 6-speed to an 8-speed transmission, we thus estimate an 

additional 0.33 hours of labor per transmission. 

Table 8.2-3Table 8.2-5 shows the changes in labor hours in moving from baseline to 

new fuel-saving technologies for technologies in the FEV study. It indicates that, in 

switching from the baseline to the new technologies, labor use per technology increased: the 

fuel-saving technologies use more labor than the baseline technologies. For a subset of the 

technologies likely to be used to meet the standards in this proposal, then, the factor shift 

effect increases labor demand, at least in the short run; in the long run, as with all 

technologies, the cost structure is likely to change due to learning, economies of scale, etc. 

The technologies examined in this research are, however, only a subset of the technologies 

that auto makers may use to comply with the standards proposed in this program. As a result, 

these results cannot be considered definitive evidence that the factor shift effect increases 

employment for this rule. We therefore do not quantify the factor shift effect for this 

proposal.

Table 8.2-35 Estimated Change in Labor for Selected Compliance Technologies 

Technology FEV Vehicle Class Labor Total Hours/ 
Summary of interagency working comments received on draft rule under EO 12866 

review. These are for your consideration and are subject to policy review. 

8-31 

Case 

Study 

Costs Costs Technology 

Downsized 

Turbo GDI 4 

0101 Compact C $72.58 $537.70 1.57 

Downsized 

Turbo GDI V6 

0102 Mid/Large C $25.76 $87.38 0.56 

Downsized 

Turbo GDI V6 

0104 SUV/Trucks $84.19 $789.53 1.82 

Electric A/C 

compressor 

0602 $4.68 $167.54 0.10 

Power split 

hybrid 

0502 Mid/Large C $395.85 $3,435.01 8.54 

6- to 8-speed 

transmission 

0803 Mid/Large C $15.11 $61.84 0.33 

8.2.3.1.4 Summary of Employment Effects in the Auto Sector 

While we are not able to quantify the demand or factor shift effects, the cost effect 

results show that the employment effects of the increased spending in the regulated sector 

(and, possibly, the parts sector) are expected to be positive and on the order of a few thousand 

in the initial years of the program. As noted above, motor vehicle and parts manufacturing 

sectors employed about 677,000 people in 2010, with automobile and light truck 

manufacturing accounting for about 129,000 of that total. 

8.2.4 Effects on Employment for Auto Dealers 

The effects of the proposed standards on employment for auto dealers depend 

principally on the effects of the standards on light duty vehicle sales. In addition, auto dealers 

may be affected by changes in maintenance and service costs. Increases in those costs are 

likely to increase labor demand in dealerships. 

Although this proposal predicts very small penetration of advanced technology 

vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. 

As discussed in Chapter 8.1.2.7, consumers may find some characteristics of electric vehicles 

and plug-in hybrid electric vehicles, such as the ability to fuel with electricity rather than 

gasoline, attractive; they may find other characteristics, such as the limited range for electric 

vehicles, undesirable. As a result, some consumers will find that EVs will meet their needs, 

but other buyers will choose more conventional vehicles. Auto dealers may play a major role 

in explaining the merits and disadvantages of these new technologies to vehicle buyers. There 

may be a temporary need for increased employment to train sales staff in the new 

technologies as the new technologies become available. 
Chapter 8 

8-32 

8.2.5 Effects on Employment in the Auto Parts Sector 

As discussed in the context of employment in the auto industry, some vehicle parts are 

made in-house by auto manufacturers; others are made by independent suppliers who are not 

directly regulated, but who will be affected by the proposed standards as well. The additional 

expenditures on technologies are expected to have a positive effect on employment in the 

parts sector as well as the manufacturing sector; the breakdown in employment between the 

two sectors is difficult to predict. The effects on the parts sector also depend on the effects of 

the proposed standards on vehicle sales and on the labor intensity of the new technologies, 

qualitatively in the same ways as for the auto manufacturing sector. 

8.2.6 Effects on Employment for Fuel Suppliers 

In addition to the effects on the auto manufacturing and parts sectors, these rules will 

result in changes in fuel use that lower GHG emissions. Fuel saving, principally reductions in 

liquid fuels such as gasoline and diesel, will affect employment in the fuel suppliers industry 

sectors throughout the supply chain, from refineries to gasoline stations. To the extent that 

the proposed standards result in increased use of electricity or other new fuels, employment 

effects will result from providing these fuels and developing the infrastructure to supply them 

to consumers. 

Expected petroleum fuel consumption reductions can be found in Chapter 5.3. While 

this reduced consumption represents fuel savings for purchasers of fuel, it represents a loss in 

value of output for the petroleum refinery industry, fuel distributors, and gasoline stations. 

The loss of expenditures to petroleum fuel suppliers throughout the petroleum fuel supply 

chain, from the petroleum refiners to the gasoline stations, is likely to result in reduced 

employment in these sectors. 

This rule is also expected to lead to increases in electricity consumption by vehicles, 

as discussed in Chapter 5.3. This new fuel may require additional infrastructure, such as 

electricity charging locations. Providing this infrastructure will require some increased 

employment. In addition, the generation of electricity is likely to require some additional 

labor. We have insufficient information at this time to predict whether the increases in labor 

associated with increased infrastructure provision and generation for electricity will be greater 

or less than the employment reductions associated with reduced demand for petroleum fuels. 

8.2.7 Effects on Employment due to Impacts on Consumer Expenditures 

As a result of these proposed standards, consumers will pay a higher up-front cost for 

the vehicles, but they will recover those costs in a fairly short payback period (see Preamble 

Section III.H.10.b); indeed, people who finance their vehicles are expected to find that their 

fuel savings per month exceed the increase in the loan cost (though this depends on the 

particular loan rate a consumer receives). As a result, consumers will have additional money 

to spend on other goods and services, though, for those who do not finance their vehicles, it 

will occur after the initial payback period. These increased expenditures will support 

employment in those sectors where consumers spend their savings. 
Summary of interagency working comments received on draft rule under EO 12866 

review. These are for your consideration and are subject to policy review. 

8-33 

These increased expenditures will occur in 2017 and beyond. If the economy returns 

to full employment by that time, any change in consumer expenditures would primarily 

represent a shift in employment among sectors. If, on the other hand, the economy still has 

substantial unemployment, these expenditures would contribute to employment through 

increased consumer demand. 

8.2.8 Summary 

The primary employment effects of this proposal are expected to be found throughout 

several key sectors: auto manufacturers, auto dealers, auto parts manufacturing, fuel 

production and supply, and consumers. 

These proposed standards initially take effect in model year 2017, a time period 

sufficiently far in the future that the current sustained high unemployment at the national level 

may be moderated or ended. In an economy with full employment, the primary employment 

effect of a rulemaking is likely to be to move employment from one sector to another, rather 

than to increase or decrease employment. For that reason, we focus our partial quantitative 

analysis on employment in the regulated sector, to examine the impacts on that sector directly. 

We discuss the likely direction of other impacts in the regulated sector as well as in other 

directly related sectors, but we do not quantify those impacts, because they are more difficult 

to quantify with reasonable accuracy, particularly so far into the future. 

For the regulated sector, the cost effect is expected to increase employment by 600 – 

3,600 workers in 2017, depending on the share of that employment that is in the auto 

manufacturing sector compared to the auto parts manufacturing sector. As mentioned above, 

some of these job gains may occur earlier as auto manufacturers and parts suppliers hire staff 

to prepare to comply with the standard. The demand effect is ambiguous and depends on 

changes in vehicle sales, which are not quantified for this proposal. Though we do not have 

estimates of the factor shift effect for all potential compliance technologies, the evidence 

which we do have for some technologies suggests that many of the technologies will have 

increased labor needs. 

Effects in other sectors that are predicated on vehicle sales are also ambiguous. 

Changes in vehicle sales are expected to affect labor needs in auto dealerships and in parts 

manufacturing. Increased expenditures for auto parts are expected to require increased labor 

to build parts, though this effect also depends on any changes in the labor intensity of 

production; as noted, the subset of potential compliance technologies for which data are 

available show increased labor requirements. Reduced fuel production implies less 

employment in the petroleum sectors. Finally, consumer spending is expected to affect 

employment through changes in expenditures in general retail sectors; net fuel savings by 

consumers are expected to increase demand (and therefore employment) in other sectors.
Chapter 8 

8-34 

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Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share 

of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 

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17 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

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18 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy 

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19 NERA Economic Consulting, “Evaluation of NHTSA’s Benefit-Cost Analysis of 2011-

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20 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799). 

21 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

22 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and 

Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American 

Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799). 

23 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, 

and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of 

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http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010-

0799). 

24 NERA Economic Consulting, “Appendix B: New Vehicle Market Model,” “Impacts of the 

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25 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799) 

26 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

27 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and 

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 

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30 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

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31 Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

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33 Petrin, Amil, “Quantifying the Benefits of New Products: The Case of the Minivan,” 

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34 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48 (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799). 

35 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, 

and Li 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 (Docket EPA-HQ-OAR-2010-

0799). 

36 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and 

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 

(2005): 757-775 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, and Li Gan, 
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“Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic 

Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , 

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Footprints and Highway Safety,” Transportation Research Part D 14 (2009): 375-384. 

37 E.g., Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of 

Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental 

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38 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy 

Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2010-0799). 

39 E.g., Espey, Molly, and Santosh Nair, 2005. “Automobile Fuel Economy: What Is It 

Worth?” Contemporary Economic Policy 23: 317-323 (Docket EPA-HQ-OAR-2010-0799). 

40 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 

1593-1594 (Docket EPA-HQ-OAR-2010-0799). 

41 E.g., Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-

951 (Docket EPA-HQ-OAR-2010-0799). 

42 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

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43 Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. “Pain at the Pump: 

How Gasoline Prices Affect Automobile Purchasing in New and Used Markets,” working 

paper, http://web.mit.edu/knittel/www/papers/gaspaper_latest.pdf (accessed 11/1/11) (Docket 

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45 Greene, David L. (2010). “How Consumers Value Fuel Economy: A Literature Review.” 

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review. These are for your consideration and are subject to policy review. 

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46 Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S. 

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47 Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile 

Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket 

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48 For instance, Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy 

(CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2010-0799); Austin, David, and Terry Dinan, “Clearing the Air: The Costs and 

Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of 

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0799); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of 

the Corporate Average Fuel Economy Standard,” Resources for the Future Paper RFF DP 10-

50 (December 2010), http://www.rff.org/RFF/Documents/RFF-DP-10-50.pdf) (Docket EPAHQ-OAR-2010-0799)Jacobsen, Mark, 2010. “Evaluating U.S. Fuel Economy Standards in a 

Model with Producer and Household Heterogeneity,” working paper, at 

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49 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher 

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50 IEA. 2007. “Mind the Gap: Quantifying Principal-Agent Problems in Energy Efficiency.” 

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51 Helfand, Gloria, and Ann Wolverton (2011). “Evaluating the Consumer Response to Fuel 

Economy: A Review of the Literature.” International Review of Environmental and 

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52 Jaccard, Mark. “Paradigms of Energy Efficiency’s Cost and their Policy Implications: 

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53 Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” 

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54 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency 

Standards. The White House, Office of the Press Secretary, May 21, 2010. 

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55 U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed 

on August 9, 2011. 

56 Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis 

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57 Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. “Jobs Versus the 

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58 Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental 

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59 Berman, Eli, and Linda T. Bui, (2001) “Environmental Regulation and Labor Demand: 

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60 http://www.bls.gov/emp/ep_data_emp_requirements.htm 

61 http://www.census.gov/manufacturing/asm/index.html 

62 http://www.bls.gov/emp/ep_data_emp_requirements.htm; this analysis used data for sectors 

88 (Motor Vehicle Manufacturing) and 90 (Motor Vehicle Parts Manufacturing) from “Chainweighted (2000 dollars) real domestic employment requirements table. . . adjusted to remove 

imports.” 

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