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

review 

Sent: Friday, November 11, 2011 11:44 AM

From: Achanta, Chandana L. 

To: 'goo.michael@epamail.epa.gov'; 'kathryn.thomson@dot.gov'

Cc: Theroux, Richard P.; Mancini, Dominic J.; Fitzpatrick, Michael A.; Guzy, Gary S.; Bordoff, Jason E.

Hello Michael, Katie- 

 

Please find attached additional 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 and EO 13563 review.docx 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

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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. 

Interagency commenters recommend that the agencies replace current EPA Chapter 8 with the 

following working draft language and make consistent changes throughout the Preamble, 

NHTSA RIA, and TSD chapters. 

*** 

Chapter 8 Vehicle Sales, Employment, and Economy-Wide 

Impacts 

1.1 Vehicle Sales Impacts 

1.1.1 The Effect of Purchase Price and Fuel Savings Changes on Vehicle Sales 

An initial consideration of the Proposed Rule on vehicle sales impacts must compare 

two competing effects. On the one hand, as a result of this rule, the vehicles will become 

more expensive, which would, by itself, discourage sales. On the other hand, the vehicles will 

have improved fuel economy and thus lower operating costs, which makes them more 

attractive to consumers. The net effect will depend on the extent to which consumers are 

willing to pay for fuel economy. As discussed in Preamble III.H of the Proposed Rule, there 

is considerable uncertainty in the economics literature about the extent to which consumers 

fully value fuel savings from increased fuel economy. The effect of this proposed regulation 

on vehicle sales will depend on whether the value placed on the increased fuel economy is 

greater or less than the increase in vehicle prices. Of course, there are two possibilities. 

Consumers will value the fuel economy improvements at amounts that are either greater than 

or less than the corresponding increase in vehicle price. For the first group of consumers, the 

standard assumption that producers act to maximize profits implies these vehicles will be 

produced without a regulation, since consumers would be willing to pay the higher price and 

it would be profitable for the manufacturers to produce such vehicles. On the other hand, 

consumers who value the fuel economy improvements at less than the corresponding increase 

in vehicle price would be less likely to buy such vehicles and a requirement that they be 

produced nonetheless would generally lead to a decrease in sales. This is because for these 

consumers the difference between the value of greater fuel economy and the higher sales 

prces will be perceived as a pure price increase. 

An illustrative example might help. Suppose that a particular improvement in fuel 

economy causes a vehicle’s production cost to rise by $1,000; and suppose that the auto 

market were competitive, so that the price rose by the same amount. Consider three 

scenarios, with different values of fuel savings. First, suppose that consumers valued the 

Comment [A1]: Interagency commenters note 

that this may be more complicated than presented in

the original example. The original example assumed

imperfect competition in the case of high consumer 

value of fuel savings, but perfect competition in the 

case of low consumer value. Interagency 

commenters suggest that it might be easier to use 

one market power assumption, and in that case 

perfect competition might be more intuitive. 

However, interagency commenters recommend that 

agencies drop this example. 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

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corresponding fuel savings at moire tha$1,500. In this case, one would expect ordinary 

market forces to result in these vehicles being produced in the absence of regulation, since 

consumers would pay the price of increase of $1,000 and still be better off than before.In this 

case, the regulation would have no effect on fuel economy or on vehicle sales. In a second 

scenario, if consumers valued the fuel savings at exactly $1,000, the regulation might induce 

manufacturers to build more fuel-efficient cars, but it would would have no impact on sales. 

Because consumer willingness to pay would increase by exactly the amount of the cost, 

producers could raise prices to just cover their costs, and the quantity demanded would be the 

same as before the improvement in fuel economy. Finally, if consumers valued the fuel 

savings at $750, it would not be in manufacturers’ interest to provide them. A requirement 

that they be produced would lead to cars being produced with higher fuel economy. 

However, while their price would rise by $1,000, they would be worth only an additional 

$750. Consumers would perceive the $250 difference as a pure price increase and sales 

would decline, with the magnitude of the decline depending on the price elasticity of vehicle 

demand. In reality, consumers will vary in their valuation of fuel savings, for a variety of 

reasons, including miles driven, local gasoline prices, willingness to trade off future savings 

against initial costs (measured by the rate at which consumers discount future fuel savings), 

and so on. Producer decisions should take such consumer heterogeneity into account. 

Nonetheless, the effect of fuel economy standards on sales will depend on how consumers 

value future fuel savings relative to the increase in cost. 

As noted above and in Preamble III.H, there is considerable evidence that consumers 

undervalue the fuel savings from increased fuel economy. A simple way to capture this is by 

viewing consumers as applying a high discount rate to future fuel savings [cite literature]. For 

a consumer with an effective discount rate of 15%, the value of fuel savings – corresponding 

to the consumer’s willingness to pay for increased fuel economy – would therefore be 

calculated by discounting the first five years of fuel savings at 15% and summing them. For 

instance, the 2017 fuel savings per vehicle are the present value in year 2017 of fuel savings 

estimated for 2017 through 2021, using the assumed discount rate that reflects consumer 

behavior. 

To better understand the factors that can determine the effect of fuel economy 

standards on vehicle sales, we conduct a simple sales impact analysis. As a benchmark for 

our analysis, we maintain the assumption used in the compliance modeling (discussed in the 

Preamble XX and in Chapter YY of this DRIA) that vehicle sales are unaffected by the 

proposed rule. This approach allows us to ask: What is the effective consumer discount rate 

for which the increased cost of greater fuel economy just equals the consumer’s value for fuel 

savings, so that total vehicle sales are constant? 

For the cost of improved fuel economy, we use the increase in cost estimated by the 

OMEGA model.A

 To calculate fuel savings, we start by assuming that the consumer holds 

 

A

 The higher vehicle price is likely to lead to an increase in sales tax, insurance, and vehicle financing costs, as 

well as increases in the resale value of the vehicle. However, for simplicity we ignore those in this analysis. We 

point out that to the extent they are included, they should be discounted using the same effective discount rate 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

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onto this vehicle for 5 years and then sells it. This period 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; it has also been used by NHTSA in previous CAFE 

rulemakings.B We request comment on whether there are more appropriate time periods to 

consider for this calculation. 

We can perform this analysis for an average vehicle in any given model-year. Chapter 

XX of this DRIA discusses the source of aggregate fuel savings, in dollars, for cars and trucks 

for each model year by year. These values are divided by the total number of the vehicles 

produced to get per-vehicle savings per year for the first five years of the vehicle’s life. This 

method ignores the few vehicles of the new model year that are scrapped. Because 

incorporating scrappage would reduce the denominator, and thus increase per-vehicle fuel 

savings, it underestimates per-vehicle fuel savings by a small amount. 

For example, consider model year 2025. In that year, the average increase in vehicle 

cost associated with improved fuel economy is $2100, while average fuel savings are about 

$600 per year (Preamble XXX and DRIA Chapter YYY). Under the assumption that 

consumer consider five years of fuel savings, the “breakeven discount rate” is 15.5% . In 

other words, if the entire increased cost of $2100 is passed onto consumer in the form of a 

higher initial purchase price, consumer willingness to pay for the resulting fuel savings would 

just balance out the initial cost if they apply a discount rate of 15.5% to the annual fuel 

savings for the first five years. Put yet another way, a discount rate of 15.5% is consistent 

with the following assumptions: (i) consumers do not fully value fuel savings; (ii) producers 

seek to maximize profits; and (iii) fuel economy standards will change the vehicle mix 

without affecting total vehicle sales. 

We request comment on whether those parameters above are representative of current 

market conditions. For example, what discount rate is appropriate for consumer decision 

making on durable goods? The agency requests that commenters point to additional studies 

or data sources that can be used in updated those assumptions. 

The breakeven discount rate calculated above can serve as a benchmark in evaluating 

the effect of the regulation on sales under other assumptions about consumer behavior. In 

theory, if consumers apply a lower effective discount rate, so that they are willing to pay more 

for fuel economy, sales would increase following an improvement in fuel economy. 

 

used to discount fuel savings. That significantly complicates the analysis. These factors weigh against each 

other: the higher sales tax, insurance, and financing costs increase costs to consumers; the higher resale value 

allows consumers to recover a portion of these costs. 

B

 As discussed further in Chapter 8.1.2.5, there is not a consensus in the literature on how consumers consider 

fuel economy in their vehicle purchases. Results are inconsistent, possibly due to fuel economy not being a 

major focus of many of the studies, and possibly due to sensitivity of results to modeling and data used. A 

survey by Greene (Greene, David L. “How Consumers Value Fuel Economy: A Literature Review.” EPA 

Report EPA-420-R-10-008, March 2010 (Docket EPA-HQ-OAR-2009-0472-11575)) finds that estimates in the 

literature of the value that consumers place on fuel economy when buying a vehicle range from negative – 

consumers would pay to reduce fuel economy – to more than 1000 times the value of fuel savings. 

Comment [A2]: Interagency commenters note 

that it may be preferable to consider another year,

e.g., 2017. 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

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However, such an effect should already be reflected in the baseline without regulation (i.e., 

producers should already be producing the vehicles that consumers desire to purchase); 

otherwise, such an outcome would be inconsistent with the standard assumption that 

producers maximize profits. 

If consumers apply an even higher effective discount rate in valuing fuel savings [as 

some studies of consumer durables suggest], then consumer valuation of potential fuel savings 

would be less than the cost of the increased technology, and sales would be expected to 

decline. The magnitude of this impact would depend on the net cost of improved fuel 

economy, after accounting for fuel savings, as well as on the elasticity of demand. A standard 

value for the elasticity of demand for new vehicles of -1:1

 that is, an increase of 1% in the 

price of a new vehicle will lead to a 1% reduction in new vehicle sales. This elasticity is 

generally considered to be a short-run elasticity, reflecting the immediate impacts of an 

immediate price change on vehicle sales. 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. 

Using a smaller elasticity would reduce the magnitude of the estimates presented here for 

vehicle sales, but it would not change the direction. A short-run elasticity is more valid for 

initial responses to changes in price, but, over time, a long-run elasticity may better reflect 

behavior; thus, the results presented for the initial years of the program may be more 

appropriate for modeling with the short-run elasticity than the later years of the program. A 

search of the literature has not found studies more recent than the 1970s that specifically 

investigate long-run elasticities.C This change in consumer expenditure as a percent of the 

average price of a new vehicle, with the elasticity of demand of -1, is the negative of the 

percent change in vehicle purchases. 

Evaluating the impact of the fuel economy rule on vehicle sales also depends on a 

number of assumptions that become less certain the further into the future they are applied. 

For example, consumers are heterogeneous in their behavior: some types of consumers may 

value fuel economy highly, while others give it less weight. Thus the effect of fuel economy 

on sales will depend on the distribution of consumer types in the population. For vehicle 

sales, these include the future path of gasoline prices, changes in future cost of fuel-saving 

technologies, changing market conditions, and other shifts in the U.S. economy that could 

affect vehicle demand and national driving behavior. 

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. 

 

C

 E.g., Hymans, Saul H. “Consumer Durable Spending: Explanation and Prediction.” Brookings Papers on

Economic Activity 1 (1970): 173-206. 

http://www.brookings.edu/~/media/Files/Programs/ES/BPEA/1970_2_bpea_papers/1970b_bpea_hymans_ackley

_juster.pdf finds a short-run elasticity of auto expenditures (not sales) with respect to price of 0.78 50 1.17, and a 

long-run elasticity of 0.3 to 0.46. 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

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Similarly, improvements in vehicle economy might accompany other changes in vehicle 

characteristics that are relevant for purchase decisions. 

 The agency requests comment on how future uncertainty about economic conditions 

my present unique difficulties in conducting a sales impact analysis for this rulemaking. 

While these factors are always present, they are particularly acute in the current 

context, in which the rule will not take effect until 2017. As a result, we are not seeking to 

quantify the impacts of this proposed rule on vehicle sales. We welcome comments on all 

aspects of this discussion, including assumptions on consumer 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; sources of 

uncertainty in estimating vehicle sales impacts. We also welcome quantitative estimates of 

the effects on sales, accompanied by detailed descriptions of the methodologies used. 

1.1.2 Consumer Vehicle Choice ModelingD

The vehicle sales analysis outlined above focuses on the comparison between the 

increased purchase price associated with improved fuel economy and the value of the 

resulting fuel savings. An alternative approach 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., 

 

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

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

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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. 

1.1.2.1 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 

 

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 and EO 

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

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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,2 Greene et al.,3 and McManus.4

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,5

 Bento et al.,6 and Train and Winston.7 

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

modeling, others (such as Kleit8 and Austin and Dinan9) 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. 

1.1.2.2 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.10 

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.11

 

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 Administration12 and the New Vehicle Market Model 

developed by NERA Economic Consulting13 are examples of calibrated models. 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

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1.1.2.3 Factors Included in the Models 

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

only on the new vehicle market;14 others consider the choice between new vehicles and an 

outside good (possibly including a used vehicle);15 others explicitly consider the relationship 

between the new and used vehicle markets.16 Some models include consideration of vehicle 

miles traveled,17 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;18 others include varying numbers and kinds of vehicle and consumer attributes. 

Some models include only the consumer side of the vehicle market;19 others seek to 

represent both consumer and producer decisions.20 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.21 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.22 Even those vehicle choice models that 

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

market forces to determine multiple vehicle characteristics is still under development.23 

1.1.2.4 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,24 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 a 

stakeholder 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,25 the market acceptability of alternative-fuel vehicles,26 the effects of 

introduction and exit of vehicles from markets,27 causes of the decline in market shares of 

U.S. automakers,28 and the effects of gasoline taxes29 and “feebates”30 (subsidizing fuelefficient cars with revenue collected by taxing fuel-inefficient vehicles). 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

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1.1.2.5 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 models31 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 Kurani32 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:33 since consumers pay for gallons of fuel, then this 

measure can assess fuel savings relatively directly.

34 Yet other models multiply fuel 

consumption per mile by the cost of fuel to get the cost of driving a mile,35 or they divide fuel 

economy by fuel cost to get miles per dollar.36 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 research37 presents results that higher fuel 

prices play a major role in that decision. 

Greene and Liu,38 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 

 

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

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

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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 review from David Greene39 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, Gramlich40 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 one value that represents the role of fuel economy in consumer 

purchase decisions. 

Some studies41 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 studies42 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 studies43 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. 

1.1.2.6 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, 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

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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.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.44 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.

 

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. 
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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 1.1-1 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 1.1-1Willingness 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
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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 

1.1-1, 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. 

1.1.2.7 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 
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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 1.1.2.1. “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,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 1.1.2.6: 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 1.1.2.5, there is no 

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

 

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. 
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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 from OMEGA the initial fleet of vehicles 

(including the initial sales and fuel economy), the cost of technologies added to each vehicle, 

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 the 

baseline vehicle fleet when the baseline fleet characteristics are used as inputs. 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. 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 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 vehicle sales for auto 

makers that do not meet the proposed standards. 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 iteration 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. 

1.1.2.8 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 
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2-16 

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. The fact that consumers have additional options suggests that compliance costs are 

likely to be lower through incorporation of a consumer choices than through use of a 

technology-cost model alone. 

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 
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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 

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. 

1.2 Employment Impacts 

1.2.1 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 “strenghten the [auto] industry and enhance job creation in the United States.”45,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 estimates of the effects of this 

proposal on domestic employment. 

 

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. 

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.” 
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This proposal could 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.46 

Changes in light duty vehicle sales, discussed in Chapter Error! Reference source not 

found., could affect employment for auto dealers. The employment effects of this rule could 

also 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. As discussed in 

Chapter 5.3 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. 

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. 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.47 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.” 
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Given the time frame of this proposed rule, we adopt the default presumption that the 

unemployment rate will have declined to 5.3 percent by the time by the time this rule takes 

effect (using official government forecasts), so that the effect of the regulation on labor will 

be to shift workers from one sector to another.48 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). 

Nonetheless, it may be of interest to consider the qualitative impacts of the proposed 

rule on employment in various sectors. 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. 

1.2.2 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. 

1.2.2.1 Conceptual Framework for Employment Impacts in the 

Regulated Sector 

One study by Morgenstern, Pizer, and Shih49 provides a retrospective look at the 

impacts of regulation in employment in the regulated sectors. 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 
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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 

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. estimated 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 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. In addition, it does not examine the effects of 

regulation on employment in sectors related to but outside of the regulated sector. 

In addition, 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 

 

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 

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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standards on industrial facilities that were considered in that study. 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.50

 

1.2.2.2 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. 

1.2.2.3 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 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

2-22 

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. 

1.2.2.4 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. 

1.2.2.5 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. 

1.2.2.6 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.51 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 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

2-23 

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. 

1.2.2.7 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. 

1.2.3 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 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

2-24 

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 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. 

1.2.3.1 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 demand and cost effects. We note that these estimates, based on 

extrapolations from current data, become more uncertain as time goes on. 

1.2.3.1.1 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 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. As noted earlier in this chapter, the 

assumption that consumer willingness-to-pay tend to undervalue improved fuel economy, 

along with standard assumptions about producer behavior, would suggest that vehicle sales 

will decrease, but the magnitude of any such effect is highly uncertain. For that reason, we 

are also not seeking to quantify the demand effect.

1.2.3.1.2 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. 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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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),52 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 Manufactures53 (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 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

2-26 

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 1.2-1 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 1.2-1 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
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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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 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$, but only 0.83 workers by 2008 (in 2000$).54 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 1.2.3.1.1, 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. 

Table 1.2-2 shows the cost estimates developed for this rule, discussed in Chapter 5. 

The maximum value in Table 1.2-2 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 1.2-2 include the productivity adjustment described 

above.

Table 1.2-2 Employment per $1 Million 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)
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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2017 $ 1,997 500 3,100 

2018 $ 4,179 1,000 6,200 

2019 $ 6,255 1,500 9,000 

2020 $ 8,369 1,900 11,600 

2021 $ 11,875 2,600 15,900 

2022 $ 17,374 3,700 22,500 

2023 $ 22,690 4,600 28,300 

2024 $ 28,151 5,400 33,900

2025 $ 33,566 6,200 39,000 

1.2.3.1.3 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 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.55 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. 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 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

2-29 

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 1.2-3 shows the changes in labor hours in moving from baseline to new fuelsaving 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 1.2-3 Estimated Change in Labor for Selected Compliance Technologies

Technology FEV 

Case 

Study 

Vehicle Class Labor 

Costs 

 Total 

Costs 

 Hours/ 

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

1.2.4 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. 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

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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 1.1.2.6, 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. 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. 

1.2.5 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. 

1.2.6 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 also 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. 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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2-31 

1.2.7 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. 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. 

These increased expenditures will occur in 2017 and beyond. If the economy returns 

to full employment by that time, any change in consumer expenditures (as discussed in the 

economy-wide impact analysis in section 8.3) 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. 

1.2.8 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. We emphasize that the discussion here is a partial 

view of these effects. Whether the economy is in full employment will determine whether the 

discussed changes in employment in these sectors is offset in other sectors of the economy. 

The extent to which employment in the sectors discussed above responds to this program will 

be influenced by how consumers consider fuel savings in their vehicle purchase decisions, 

changes in vehicle manufacturing technologies and locations, and other factors. 

This rule initially takes 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 limited quantitative analysis on 

employment in the regulated sector, to examine the impacts on that sector directly. We 

discuss the likely direction of impacts in directly related sectors, but we do not quantify those 

impacts, because employment impacts outside the regulated sector 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 500 – 

2,900 workers in 2017, depending on the share of that employment that is in the auto 

manufacturing sector compared to the auto parts manufacturing sector. 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. 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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2-32 

Increased vehicle sales are expected to lead to increased labor needs in auto 

dealerships. Increased vehicle sales and 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 fuel provision sectors. Finally, net cost savings to consumers are 

expected to increase employment through their increased expenditures in general retail 

sectors. 

References 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

33 

2 Small Business Flexibility Analysis 

 

1

 Kleit A.N., 1990, “The Effect of Annual Changes in Automobile Fuel Economy Standards,” 

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McCarthy, Patrick S., 1996. “Market Price and Income Elasticities of New Vehicle Demands.” 

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in the US,” Journal of Industrial Economics 46(1): 1-33 (Docket EPA-HQ-OAR-2009-0472-

0017); Greene, David L., “Feebates, Footprints and Highway Safety,” Transportation Research 

Part D 14 (2009): 375-384 (Docket EPA-HQ-OAR-2009-0472-0019). 

2

 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-2009-0472-0021); 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-2009-0472-0017). 

3

 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. DEAC0500OR22725, 2005 (Docket EPA-HQ-OAR-2009-0472-0022); 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-HQOAR-2009-0472-0023). 

4

 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-2009-0472-0024). 

5

 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” 

Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2009-0472-0025); 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-2009-0472-0026). 

6

 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. von 

Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes: An Econometrically Based 

Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 (Docket EPAHQ-OAR-2009-0472-0029). 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

34 

 

7

 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-2009-0472-0030). 

8

 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-2009-0472-0031). 

9

 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-2009-0472-0032). 

10 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: An Econometrically 

Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 (Docket 

EPA-HQ-OAR-2009-0472-0029); 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-2009-0472-0030). 

11 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-2009-0472-

0025). 

12 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. DEAC0500OR22725, 2005 (Docket EPA-HQ-OAR-2009-0472-0022). 

13 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_Analysi

s_Of_20112015_CAFE_Standards.html (Docket EPA-HQ-OAR-2009-0472-0033). 

14 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-2009-0472-0030). 

15 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-2009-0472-

0025). 

16 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: An Econometrically 

Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 (Docket 

EPA-HQ-OAR-2009-0472-0029). 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

35 

 

17 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes: An Econometrically 

Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 (Docket 

EPA-HQ-OAR-2009-0472-0029); 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-HQOAR-2009-0472-0034). 

18 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-HQOAR-2006-0173-9053.1 (Docket EPA-HQ-OAR-2009-0472-0035). 

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-2009-0472-0030). 

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-2009-0472-

0025). 

21 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-2009-0472-0023). 

22 Rex, Emma, and Henrikke Baumann (2007). “Beyond ecolabels: what green marketing can 

learn from conventional marketing.” Journal of Cleaner Production 15: 567-576. 

23 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. 

24 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2009-0472-

0025). 

25 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-2009-0472-0021). 

26 Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with Flexible 

Substitution Patterns,” Journal of Econometrics 89 (1999): 109-129 (Docket EPA-HQ-OAR2009-0472-0036); Brownstone, David, David S. Bunch, and Kenneth Train, “Joint Mixed Logit 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

36 

 

Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” Transportation 

Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2009-0472-00357); 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-HQOAR-2009-0472-0038); 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-2009-0472-0039). 

27 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-2009-0472-0040); 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-2009-0472-0026). 

28 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-2009-0472-0030). 

29 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes: An Econometrically 

Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 (Docket 

EPA-HQ-OAR-2009-0472-0029); 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-HQOAR-2009-0472-0034). 

30 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-2009-0472-0023); 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-2009-0472-0034); Greene, David L., “Feebates, Footprints and 

Highway Safety,” Transportation Research Part D (2009) (in press) (Docket EPA-HQ-OAR2009-0472-0019). 

31 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-2009-0472-0032). 

32 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy 

Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2009-0472-0041). 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

37 

 

33 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-2009-0472-0042). 

34 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 1593-

1594 (Docket EPA-HQ-OAR-2009-0472-0043). 

35 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-2009-0472-0021). 

36 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-2009-0472-

0025). 

37 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://www.econ.ucdavis.edu/faculty/knittel/papers/gaspaper_latest.pdf (accessed 6/16/09) 

(Docket EPA-HQ-OAR-2009-0472-0044); 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). Effects of 

Gasoline Prices on Driving Behavior and Vehicle Markets. The Congress of the United States, 

Pub. No. 2883; 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. 

38 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-2009-0472-0045). 

39 Greene, David L. (2010). “How Consumers Value Fuel Economy: A Literature Review.” 

EPA Report EPA-420-R-10-008. (Docket EPA-HQ-OAR-2009-0472-11575). 

40 Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile 

Industry,” http://www.econ.yale.edu/seminars/apmicro/am08/gramlich-081216.pdf , accessed 

5/11/09 (Docket EPA-HQ-OAR-2009-0472-0046). 

41 Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile 

Industry,” http://www.econ.yale.edu/seminars/apmicro/am08/gramlich-081216.pdf , accessed 

5/11/09 (Docket EPA-HQ-OAR-2009-0472-0046); 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. UMTRI2007-31 (Docket EPA-HQ-OAR-2009-0472-0054). 
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

38 

 

42 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-OAR2009-0472-0031); 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-2009-

0472-0032); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost 

of the Corporate Average Fuel Economy Standard,” Federal Reserve Bank of Chicago WP 2008-

13, at http://www.chicagofed.org/publications/workingpapers/wp2008_13.pdf (accessed 5/6/09) 

(Docket EPA-HQ-OAR-2009-0472-0047); Jacobsen, Mark, 2008. “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 5/6/09) (Docket EPA-HQ-OAR2009-0472-0048). 

43 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-2009-0472-0032). 

44 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-2009-0472-0038). 

45 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency Standards. 

The White House, Office of the Press Secretary, May 21, 2010. http://www.whitehouse.gov/thepress-office/presidential-memorandum-regarding-fuel-efficiency-standards 

46 U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed on 

August 9, 2011. 

47 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. 

48 Fiscal Year 2012 Mid-Session Review, available at 

http://www.whitehouse.gov/sites/default/files/omb/budget/fy2012/assets/12msr.pdf

49 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. 

50 Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental 

and Natural Resource Policy.” Environmental and Resource Economics 22 (2002): 133-156. 

51 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.
Summary of interagency working comments received on draft rule under EO 12866 and EO 

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

39 

 

52 http://www.bls.gov/emp/ep_data_emp_requirements.htm 

53 http://www.census.gov/manufacturing/asm/index.html

54 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.” 

55 FOOTNOTE FOR FEV TEARDOWN WORK