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

To: "''james.tamm@dot.gov' (james.tamm@dot.gov)'" <james.tamm@dot.gov>, "'rebecca.yoon@dot.gov'" 

<rebecca.yoon@dot.gov>, Ed Nam/AA/USEPA/US@EPA, Robin Moran/AA/USEPA/US@EPA, 

"'Timothy.Mullins@dot.gov'" <Timothy.Mullins@dot.gov>, William Charmley/AA/USEPA/US@EPA, 

"'Steve.Wood@dot.gov'" <Steve.Wood@dot.gov> 

Date: 08/21/2012 07:11 PM 

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

 

Please find attached additional interagency working comments received on draft final rule “Joint Rulemaking to 

Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy 

Standards” under EO 12866 and EO 13563 review. They are for your consideration and are subject to further 

policy review. 

 

Thanks. 

Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review.pdf 

 
From: Robin Moran/AA/USEPA/US 

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

Cc: Ed Nam/AA/USEPA/US@EPA, "''james.tamm@dot.gov' (james.tamm@dot.gov)'" <james.tamm@dot.gov>, 

"'rebecca.yoon@dot.gov'" <rebecca.yoon@dot.gov>, William Charmley/AA/USEPA/US@EPA 

Date: 08/22/2012 03:17 PM 

Subject: Re: Summary of interagency working comments received on draft final rule under EO 12866 review 

Chandana, 

Attached is EPA's response to the interagency comments on vehicle sales and employment received last night. 

Robin Moran 

Senior Policy Advisor 

Assessment and Standards Division 

U.S. EPA, Office of Transportation and Air Quality 

2000 Traverwood Dr. 

Ann Arbor, MI 48105 

(734) 214-4781 

(734) 214-4821 (fax) 

Response to Interagency Comments received 081912.docx 

Preamble III.H.11_12 8_22_12.docx 

RIA Chp 8 08 22 2012.docx 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

1

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

review of the draft final 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 provided the following suggested edits and comments on EPA’s 

Preamble discussion of Sales as well as EPA RIA’s chapter on sales and employment 

effects. 

*** 

EPA Preamble Language: 

1. What are the Estimated Cost, Economic, and Other Impacts of the Rule? 

1. U.S. Vehicle Sales Impacts and Affordability of New Vehicles 

a. Vehicle Sales Impacts 

Predicting the effects of this rule on vehicle sales entails comparing two effects. On the one 

hand, the vehicles designed to meet the standards 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 due to significant fuel savings, which could encourage sales. Which 

of these effects dominates for potential vehicle buyers when they are considering a purchase will 

determine the effect on sales. Assessing the net effect of these two competing effects is 

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

to which manufacturers and dealers reflect technology costs in the purchase price. The empirical 

literature does not provide clear evidence on how much of the value of fuel savings consumers 

consider at the time of purchase. It also generally does not speak to the efficiency of 

manufacturing and dealer pricing decisions. Thus, we do not provide quantified estimates of 

potential sales impacts in this final rule. However, as discussed later in this section, there are 

several reasons why EPA believes this rulemaking will lead to an increase in vehicle sales, 

though we do not quantify a specific impact.

An additional source of uncertainty in the analysis is understanding what would happen 

in the absence of this rule. Standard economic theory would suggest that, if automakers could 

profitably increase sales by adding more fuel-saving technologies to their vehicles, then 

manufacturers’ profit motives would lead them to voluntarily add those technologies in the 

absence of this rule. As discussed in Preamble Section III.D.1, we project, based on historical 

patterns, that auto makers would not go beyond the MY 2016 standards in the absence of this 

rule. Yet, if consumers consider just over three years’ worth of fuel savings in their vehicle 

purchase decisions and our assumptions about technology costs and future gas prices are correct, 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

2

the payback period analysis in Section III.H.5 suggests that sales would increase in response to 

this rule. Are these analyses in conflict?

Although it is possible we assume that manufacturers could would not find it profitable 

add at least some of the vehicleadditional fuel-saving technologies in the absence of the rule (see 

Section III.H.1.a for a discussion), there are also a number of reasonsmay be the potential for 

potential increases in vehicle sales as a result of the rule rule notwithstanding our assumption of 

a flat baseline. These explanations focus on conditions where the rule stimulates forces 

investments that would not happen in the rule’s absence. The explanations posed below raise 

possibilities that the rule, by requiring all automakers to meet the standards, may lead to 

mutually beneficial outcomes that might not happen in the absence of the rule. Consumers 

would then have the opportunity to purchase vehicles that would not be available in the absence 

of the rule; if consumers consider at least as many years of fuel savings when buying new 

vehicles as the payback period for the new technologies, and if manufacturers nonetheless would 

not have produced these vehicles in the absence of the rule, positive sales impacts could occur as 

a result of these final standards. The three possibilities we suggest for such outcomes are 

promotion of social learning, reduction of risk and uncertainty for manufacturers, and promotion 

of innovation. 

i. Social Learning 

For many years, fuel economy standards did not change (see Preamble III.D.1).1

 As 

discussed in Preamble III.H.1.a, consumers may not have focused on fuel economy, or may have 

found it difficult to do calculations involving the tradeoffs between fuel economy and increased 

vehicle costs, or may not have found vehicles with their preferred combination of fuel economy 

and other features. In recent years, though, fuel economy standards have started to increase.2

 In 

addition, high fuel prices have helped to focus consumer attention toward vehicle fuel economy. 

Finally, the recently revised fuel economy label, with prominent information on fuel savings, are 

starting to appear on new vehicles. These factors may contribute to consumers gaining 

experience with the benefits that accrue to them from owning and operating vehicles with greater 

fuel efficiency. Consumer households that include vehicles with a fairly wide range of fuel 

economy have an opportunity to learn about the value of fuel economy on their own. Consumer 

demand may be shifting towards such vehicles, not only because of higher fuel prices but also if 

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

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

continue before and during the model years affected by this rule. 

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

required to be on all new vehicles in MY 2013, may increase sales by hastening this very type of 

                                                            

1

 Car CAFE standards did not change from MYs 1990 through 2010. Truck CAFE standards did not change from 

MYs 1996 through 2004, and changed only 0.5 mpg cumulatively from MYs 1991 through 2004. See "Summary of 

Fuel Economy Performance," March 12, 2012, DOT/NHTSA, http://www.nhtsa.gov/fuel-economy. 2

 Truck CAFE standards began to rise in MY 2005 and have risen every year since. Car CAFE standards began to 

rise in MY 2011. Ibid. 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

3

consumer learning. As more consumers experience the savings in time and expense from owning 

more fuel efficient vehicles, demand may shift yet further in the direction of the vehicles with 

improved fuel economy and reduced GHG emissions mandated under the rule. This social 

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

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

vehicles may be more salient to individuals who own them, which might cause their subsequent 

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

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

marketing and advertising, and other forms of communications. Third, as more motorists 

experience the time and fuel savings associated with greater fuel efficiency, the price of used 

cars may better reflect such efficiency, further reducing the cost of owning more efficient 

vehicles for the buyers of new vehicles (since the resale price may increase). If these induced 

learning effects are strong, the rule could potentially increase total vehicle sales over time. This 

effect may be speeded or slowed by other factors that enter into a consumer’s valuation of fuel 

efficiency in selecting vehicles. 

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

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

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

individual automobile manufacturer would find it profitable to move toward more efficient 

vehicles to increase consumer learning because no individual company can fully internalize the 

potential future boost to demand. If one company were to make more efficient vehicles, counting 

on consumer learning to enhance demand in the future, that company would capture only a 

fraction of the extra sales so generated, because the learning at issue is not specific to any one 

company's fleet. Many of the extra sales could accrue to that company's competitors. 

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

positive externalities (spillovers) from one company to the others.3

 These positive externalities 

may lead to benefits for manufacturers as a whole if they increase the demand for vehicles. We 

emphasize that this discussion has been tentative and qualified. It is not possible to quantify these 

learning effects years in advance, and that these effects may be speeded or slowed by other 

factors that enter into a consumer’s valuation of fuel efficiency in selecting vehicles. To be sure, 

social learning of related kinds has been identified in a number of contexts.4 We asked for 

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

findings. 76 FR 75150. We continue to explore this issue but did not receive any comments on 

                                                            

3

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

trade associations to promote industry-wide public goods. For example, merchants in a given locale may band 

together to promote tourism in that locale. Antitrust law recognizes that this type of coordination can increase 

output. 

4

 See Hunt Alcott, Social Norms and Energy Conservation, Journal of Public Economics (forthcoming 2011), 

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

%20Social%20Norms%20and%20Energy%20Conservation.pdf (Docket EPA-HQ-OAR-0799-0825); Christophe 

Chamley, Rational Herds: Economic Models of Social Learning (Cambridge, 2003) (Docket EPA-HQ-OAR-0799-

1110). 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

4

the role of social learning, except in the context (discussed in Section III.H.1.a) of the effect of 

the rule on the visibility of and status associated with owning vehicles with improved fuel 

economy. 

ii. Reduction in Risk and Uncertainty for Manufacturers 

As discussed in Preamble III.H.1.a, there appears to be a great deal of uncertainty about 

how consumers will respond to increases in fuel economy. Automakers may be cautious about 

adding more fuel-saving technology to vehicles if they are uncertain how buyers will respond. 

Even if they believe that buyers will respond positively, if a company is risk-averse, it may 

nevertheless hesitate to make the substantial major investments in new technologies and in 

research that would lead to increases in fuel economy across its fleet.5

 If a manufacturer invests 

substantially in fuel efficient technologies expecting higher consumer demand than realized, then 

the manufacturer has incurred the costs of investment but not reaped the benefits of those 

investments. On the other hand, if a manufacturer does not invest in fuel-efficient technologies, 

then the manufacturers may lose some sales in the short run if demand for fuel economy is 

higher than expected, but it still retains the option of investing in fuel-efficient technologies in 

the longer run. If its investments proved unsuccessful, the company might face substantial 

losses. Even if the probability of being unsuccessful is low, the manufacturer may nevertheless 

perceive the losses in that scenario as a substantial risk. If the investment proved successful, the 

company would, of course, take market share from other companies – but, assuming that there 

are not brand-loyalty or other advantages to being first to the market with new fuel-saving 

technologies, only until the other auto companies caught up. In other words, for a risk-averse 

company, being a first mover may appear to have a greater downside risk than upside risk, even 

if the investment, on an expected-value basis, would pay off. If all companies are risk-averse, 

then they may all seek a strategy of waiting for some other company to be the first mover. In 

this case, caution about these major investments may lead to slow lack of adoption of new 

technologies, in the absence of the rulemaking, consistent with the flat baseline assumption. 

This rulemaking, by requiring that all companies act at the same time, removes the scenario of 

one company bearing all the risk. 

In addition, there may be risk aversion on the consumer side. The simultaneous 

investment by all companies may also encourage consumer confidence in the new technologies. 

If only one company adopted new technologies, early adopters might gravitate toward that 

company, but early adopters tend to be a relatively small portion of the public. More cautious 

buyers, who are likely to be more numerous, might wait for greater information before moving 

away from well-known technologies. If all companies adopt advanced technologies at the same 

time, though, potential buyers may perceive the new technologies as the new norm rather than as 

a risky innovation. They will then be more willing to move to the new technologies. As some 

                                                            

5

 Sunding, David, and David Zilberman, “The Agricultural Innovation Process: Research and Technology Adoption 

in a Changing Agricultural Sector,” Chapter 4 in Handbook of Agricultural Economics, Volume 1, edited by B. 

Gardner and G. Rausser (Elsevier, 2001) show how delaying adoption of a new technology in order to gain more 

information may be a more profitable activity than adopting a technology, even if it has positive net benefits, when a 

potential adopter is risk-averse. 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

5

commenters have pointed out, simultaneous action required by the rule may change buyers’ 

expectations (their reference points) for fuel economy, and investing in more fuel economy may 

seem less risky than in the absence of the rule. 

The rule, then, may reduce manufacturers’ risk of making significant investments in fuelsaving technologies by providing a level playing field forrequiring that all companies produce 

more fuel vehicles. Under this outcome, it is possible for the rule to facilitate investment that 

would not happen in the absence of the rule, and vehicle sales could increase as a result of the 

rule. 

iii. Promotion of Innovation 

Research among multiple parties can be a synergistic process: ideas by one researcher 

may stimulate new ideas by others, and more and better results occur than if the one researcher 

operated in isolation. One function that standards can serve is to promote research into low-CO2

technologies that would not take place in the absence of the standards. Because all companies 

(both auto firms and auto suppliers) will have incentives to find better, less expensive ways of 

meeting the standards in this rule, the possibilities for synergistic interactions may increase. 

Thus, the rule, by focusing all companies on finding more efficient ways of achieving the 

standards, may lead to better outcomes than if any one company operated on its own. 

An additional aspect of the standards is the possibility of greater standardization. As 

more companies adopt new technologies, the incentives increase for additional suppliers and 

more availability of after-market replacement parts; these suppliers would be likely to find ways 

to increase compatibility across vehicle types. For example, though electric vehicles (EVs) are 

not expected to be more than a few percent of the vehicles produced in response to this rule, their 

adoption depends on such factors as batteries and charging methods that are compatible across 

different companies. In the absence of the rule, fewer companies would be pursuing this 

technology, and it would be considered a specialty product; the incentives to coordinate might be 

low. If EVs become more common, though, compatible infrastructure and batteries may become 

more desirable, as potential buyers are likely to be encouraged toward this technology if they can 

easily find places to charge batteries. 

Thus, the rule may direct and promote innovation and standardization that would not 

happen in the absence of this rule. Such changes could reduce the cost increases associated with 

the rule and improve the qualities of the technologies, which could result in an increase in 

vehicle sales. Further, the certainty of the regulations reduces the costs of meeting them, because 

there will be more economies of scale and more learning curve benefits due to greater 

cumulative production of fuel-efficient technologies. 

In these three plausible explanations for how the rule could increase vehicle sales, we 

have focused on ways that the rule could promote coordinated actions on the part of automakers 

and suppliers that lead to mutual benefit, but that might not happen in the absence of the rule. 

These actions could lead to automakers developing technologies that they would not have 

Comment [A1]: Commenters note that this

discussion is confusing.  Commenters believe that

manufacturers may not be collaborating on research

even due to this rule, and it is likely each

manufacturer will be conducting its own proprietary

research in an effort to lower their own costs of

meeting the standards.  They might not have any

incentive to share these innovations with their

competitors.

Comment [A2]: Commenters request clarity on

why there would be incentives to coordinate?  

Commenters note that unless the manufacturers

are colluding, they should each have an incentive to

keep propriety technologies such as batteries for

themselves so that they can keep their own costs

low relative to competitors.   

Comment [A3]: Commenters note that absent

the rule, a manufacturer acting alone could achieve

economies of scale if consumers want to buy more

fuel efficient cars.  Commenters request clarity on

whether this is just a reiteration of the risk aversion

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

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

6

developed in the absence of the rule. Consumers may respond positively to the vehicles resulting 

from this coordinated action. 

Several commenters requested that we conduct a quantitative vehicle sales analysis. As 

discussed in the proposal, in previous rulemakings, EPA and NHTSA conducted vehicle sales 

analyses by comparing the up-front costs of the vehicles with the present value of five years’ 

worth of fuel savings; the direction of vehicle sales would depend on whether up-front costs 

exceeded fuel savings (in which case sales would be expected to decline), or vice versa (in which 

case sales would be expected to increase).6

 Some commenters specifically requested that we use 

the method found in the MYs 2012-16 rule; some specifically supported the five-year payback 

period; others argued for the importance of conducting the analysis without recommending 

methods. Ceres estimates that the rule will increase vehicle sales by 4.7 percent; the Defour 

Group provided estimates that the rule will decrease vehicle sales by 6-10 percent. The 

differences in the results appear to depend on the cost estimates used and on assumptions made 

about how vehicle buyers think about fuel savings when deciding on vehicle purchases. The 

Defour Group, for instance, uses cost estimates of about $3000 per vehicle based on summing 

costs (but not benefits) across multiple rules (an estimate we consider to be unfounded for 

reasons explained in this section below and also in TSD Chapter 3.1.2) and the assumption that 

consumers consider only 25 percent of fuel savings in their vehicle purchase decisions (see 

discussion in Section III.H.1.a). Other commenters wanted specific information on the effect of 

the rule on vehicle costs and whether consumers will be willing to buy the new vehicles, while 

consumer and environmental organizations indicated that consumers want more fuel-efficient 

vehicles, even if the up-front costs of the vehicles increase. The costs of the rule are discussed in 

Preamble Section III.H.2. As discussed in Preamble Section III.H.1.a, we do not at this point 

have sufficient confidence in the estimates of the role of fuel economy in consumers’ vehicle 

purchases to come to definitive conclusions about the impacts of the rule on vehicle sales. We 

do not, however, consider this uncertainty grounds for delaying the rule, as one comment 

suggested. The midterm evaluation provides an opportunity to revisit the impact of the rule on 

vehicle sales and consumer acceptance of the new technologies. 

This rule takes effect for MY 2017-2025. In the intervening years, it is possible that the 

assumptions underlying a quantitative analysis, as well as market conditions, might change. As 

the United Auto Workers points out, the state of the economy is a major, if not the primary, 

determinant of total vehicle sales. The impact of the rule on sales may therefore depend, among 

other factors, on changes in the state of the economy. Other commenters discussed the 

importance of consumer confidence, fuel prices, and even of publicity over fuel prices, in 

consumers’ interest in additional fuel economy. Sales could be negatively affected if gasoline 

prices are lower than expected or technology costs are higher than expected. In these cases, it is 

possible that the standards could require manufacturers to produce cars with higher levels of fuel 

economy than consumers would wish to buy. On the other hand, manufacturers’ marketing of 

                                                            

6

 For instance, see U.S. Environmental Protection Agency (April 2010). “Final Rulemaking to Establish LightDuty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards: Regulatory 

Impact Analysis.” EPA-420-R-10-009, Chapter 8.1.1, pp. 8-1 to 8-4. 

Comment [A4]: Commenters request EPA to

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

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

7

increased fuel economy levels is also likely to play a role in consumer response to these vehicles. 

EPA agrees that these factors are important, but we are not sufficiently confident in quantitative 

estimates of the impacts of those factors to develop numerical estimates. We instead provide this 

qualitative assessment to highlight the factors important for understanding the effects of this rule 

on vehicle sales. 

As several commenters point out, the effect of this rule on the use and scrappage of older 

vehicles will be related to its effects on new vehicle prices, the fuel efficiency of new vehicle 

models, the fuel efficiency of used vehicles, and the total sales of new vehicles. If the value of 

fuel savings resulting from improved fuel efficiency to the typical potential buyer of a new 

vehicle outweighs the average increase in new models’ prices, sales of new vehicles could rise, 

the used vehicle market may increase in volume as new vehicle buyers sell their older vehicles, 

and scrappage rates of used vehicles may increase slightly. This will cause both an influx of 

more efficient vehicles into the used vehicle market and an increase in the turnover of the vehicle 

fleet (i.e., the retirement of used vehicles and their replacement by new models), thus 

accentuating the anticipated effect of the rule on fleet-wide fuel consumption and CO2

emissions. However, if potential buyers value future fuel savings resulting from the increased 

fuel efficiency of new models at less than the increase in their average selling price, sales of new 

vehicles will decline, the used vehicle market may decrease in volume as people hold onto their 

vehicles longer, and there will be a reduction in the rate at which used vehicles are retired from 

service. These effects will partly reduce the anticipated effects of this rule on fuel use and 

emissions. Because we do not have good estimates of the relationships between the new and used 

vehicle markets, we have not attempted to estimate explicitly the effects of the rule on the used 

vehicle market, scrappage of older vehicles, and the turnover of the vehicle fleet. 

Consumer, environmental, and investor organizations, the United Auto Workers, as well 

as a citizens’ campaign suggested that the rule will help the domestic auto industry, including the 

domestic supply base, compete in the global marketplace, through its encouragement of 

advanced technologies that may be useful in meeting emissions standards and consumer 

demands in foreign markets. We agree that this is likely, as generally the emission standards 

established in this rule are similar in stringency to emissions and fuel economy standards being 

considered by Japan, the European Union, South Korea, Canada, China and other international 

markets. Global manufacturers also design vehicles using a common platform in order to reduce 

costs. Vehicles built on these common platforms are sold in many markets around the world. To 

the extent the domestic OEMs and suppliers can focus their limited research and product 

development efforts on the same technologies for the U.S. market as for international markets, 

this should enable the companies to compete more effectively outside the U.S. Comment [A5]: Commneters request EPA to

delete this paragraph.  Commenters note that this is

speculative.  The required standards may exceed

standards adopted by other countries in the future.  

This could hurt manufacturers’ ability to compete

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

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

8

2 Vehicle Sales and Employment Impacts 

1. Vehicle Sales Impacts 

1. How Vehicle Sales Impacts were Estimated for this Rule 

Predicting the effects of this rule on vehicle sales entails comparing two competing effects. On 

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

itself, 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. As discussed in 

Preamble III.H.1.a, there are many competing hypotheses for why private markets are not 

providing what appear to be cost-effective energy-saving technologies, for vehicles as well as for 

other energy-conservation technologies. There are few empirical studies testing these 

hypotheses, though. As discussed more fully in Preamble III.H.11.a, it is possible that enacting 

this rule can contribute to increased vehicle sales by encouraging coordination among 

automakers to improve fuel economy that would not happen in the absence of the rule. The 

empirical literature does not provide clear evidence on how much of the value of fuel savings 

consumers consider at the time of vehicle purchase. It also generally does not speak to the 

efficiency of manufacturing and dealer pricing decisions. Thus, we do not provide quantified 

estimates of potential sales impacts. 

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

up-front costs of the vehicles with the present value of five years’ worth of fuel savings. We 

assumed that the costs for the fuel-saving technologies would be passed along fully to vehicle 

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

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

pay, the increase in insurance premiums, the increase in loan payments that buyers would face, 

and a higher resale value, with all of these factors due to the higher up-front cost of the vehicle. 

Those calculations resulted in an adjusted increase in costs to consumers. We then assumed that 

consumers considered the present value of five years of fuel savings in their vehicle purchase, 

which is consistent with the length of a typical new light-duty vehicle loan, and is similar to the 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

9

average time that a new vehicle purchaser holds onto the vehicle.7 The present value of fuel 

savings was subtracted from technology costs to get a net effect on vehicle cost of ownership. 

We then used a short-run demand elasticity of -1 to convert a change in price into a change in 

quantity demanded of vehicles.8

 An elasticity of -1 means that a 1% increase in price leads to a 

1% reduction in quantity sold. 

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

MY 2017-2025. In the intervening years, it is possible that the assumptions underlying this 

analysis, as well as market conditions, might change. Instead, Chapter 5.5 includes a payback 

period analysis to estimate the number of years of fuel savings needed to recover the up-front 

costs of the new technologies. In other words, the payback period identifies the break-even point 

for new vehicle buyers. As discussed there, the payback period is 3.2 – 3.4 years for new 

vehicles, and even shorter for used vehicles (just over 1 year for a 5-year-old MY 2025 vehicle). 

That chapter also includes an assessment of the lifetime costs and benefits that accrue to a 

vehicle owner; the net savings are about $5,000 with a 3 percent discount rate, and over $3,000 

with a 7 percent discount rate. 

2. Consumer Vehicle Choice Modeling9

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

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

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

have identified. The evidence from consumer vehicle choice models indicates a huge range of 

estimates for consumers’ willingness to pay for additional fuel economy. Because consumer 

surplus estimates from consumer vehicle choice models depend critically on this value, we 

would consider any consumer surplus estimates of the effect of our rule from such models to be 

unreliable. In addition, the predictive ability of consumer vehicle choice models has not been 

tested. 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 

                                                             7

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

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

depending on discount rate and model year). 8

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

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

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

over time, as well as how to find good estimates for the long –run elasticity. 9

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

10

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 and (as described below) to explore the use of consumer choice modeling but, given the 

known limitations and uncertainties of vehicle choice models, EPA has not conducted an 

analysis using these models for this rule. 

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

the vehicles they produce. These changes will affect the cost of manufacturing the vehicle; as a 

result, the prices of the vehicles will also change. 

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

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

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

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

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

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

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

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

vehicle characteristics are likely to influence consumers on multiple market scales: the total 

number of new vehicles sold; the mix of new vehicles sold; and the effects of the sales on the 

used vehicle market. 

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

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

accurate estimates of total compliance costs compared to models that hold fleet mix constant, 

since it predicts changes in the fleet mix that can affect total 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 would be called 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. 
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a. 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.10 In a 

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

whether to buy a new or used vehicle. Given that the person chooses a new vehicle, the second 

layer may be whether to buy a car or a truck. Given that the person chooses a car, the third layer 

may be the choice among an economy, midsize, or luxury car. Examples of nested logit models 

include Goldberg,i

 Greene et al.,ii and McManus.iii

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,iv Bento et al.,v

 and Train and Winston.vi 

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

modeling, others (such as Kleitvii and Austin and Dinanviii) 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. 

b. 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 

                                                             10 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). 
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12

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.ix 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.x

 

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 Administrationxi and the New Vehicle Market Model developed by NERA 

Economic Consultingxii are examples of calibrated models. 

c. Factors Included in the Models 

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

the new vehicle market;xiii others consider the choice between new vehicles and an outside good 

(possibly including a used vehicle);xiv others explicitly consider the relationship between the new 

and used vehicle markets.xv Some models include consideration of vehicle miles traveled,xvi

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

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

both consumer and producer decisions.xix 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. 
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13

estimated that 95% of the increase in fuel economy was due to addition of technology rather than 

changes in vehicles sold.xx 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.xxi 

Even those vehicle choice models that include a producer model may not include much detail, 

due to computational limits: it is unusual for models to allow both buyers and producers to 

choose one vehicle characteristic, much less multiple characteristics.xxii 

d. 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,xxiii for instance, is often cited outside the 

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

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

public policy discussions, research questions cover a wide gamut. These topics have included 

the effects of voluntary export restraints on Japanese vehicles compared to tariffs and quotas,xxiv

the market acceptability of alternative-fuel vehicles,xxv the effects of introduction and exit of 

vehicles from markets,xxvi causes of the decline in market shares of U.S. automakers,xxvii and the 

effects of gasoline taxesxxviii and “feebates”xxix (subsidizing fuel-efficient cars with revenue 

collected by taxing fuel-inefficient vehicles). 

e. The Effect of Fuel Economy on Consumer Decisions 

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

purchase decisions. Fuel economy can appear in various forms in these models. 

Some modelsxxx 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 fuelefficient 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. 

The vehicle sales method described in Chapter 8.1.1 uses a variant on this approach, in which it 

is assumed that consumers consider some fraction of future fuel savings. Turrentine and 

Kuranixxxi question this assumption; they find, in fact, that consumers do not make this 
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14

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.xxxii Since consumers pay for gallons of fuel, then this measure can assess fuel 

savings relatively directly.xxxiii Yet other models multiply fuel consumption per mile by the cost 

of fuel to get the cost of driving a mile,xxxiv or they divide fuel economy by fuel cost to get miles 

per dollar.xxxv 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.11 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 

researchxxxvi presents results that higher fuel prices play a major role in that decision. 

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

choice models and estimated for each one how much consumers would be willing to pay at time 

of purchase to reduce vehicle operating costs by $1 per year. They found that people were 

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

vehicle. This is clearly a very wide range: while the lowest estimate suggests that people are not 

willing to pay $1 once to get $1 per year reduced costs of operating their vehicles, the maximum 

suggests a willingness to pay 35 times as high. For comparison, the present value of saving $1 

per year for 15 years at a 3% discount rate is $11.94, while a 7% discount rate produces a present 

value of $8.78. While this study is quite old, it suggests that, at least as of that time, consumer 

vehicle choice models produced widely varying estimates of the value of reduced vehicle 

operating costs. 

A newer literature review from David Greenexxxviii 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, 

                                                             11 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 of the TSD). 
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15

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, Gramlichxxxix includes both fuel cost (dollars per mile) 

and miles per gallon in his analysis, with the argument that miles per gallon measures other 

undesirable quality attributes, while fuel cost picks up the consumer’s demand for improved fuel 

economy. Greene finds that, while some of the variation may be explainable due to issues in 

some of the studies, the variation shows up in studies that appear to be well conducted. As a 

result, further work needs to be conducted before it is possible to identify the role of fuel 

economy in consumer purchase decisions. 

Some studiesxl 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 studiesxli 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 studiesxlii 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. 

f.Why Market Outcomes May Not Reflect Full Appreciation for Fuel Economy 

that Pays for Itself 

A detailed and wide ranging literature attempts to explain why market outcomes for energyusing products appear to reflect under-investment in energy saving technologies that – at least 

using a present value calculation based on engineering estimates – appear to pay for themselves. 

Existing research does not provide a definitive answer to this question. Potential explanations are 

bounded by two scenarios. On the one hand, purely private benefits of fuel economy (fuel 

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

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

there is a market or behavioral failure. 

This disconnect between net present value estimates of energy-conserving cost savings and what 

consumers actually spend on energy conservation is often referred to as the Energy Paradox,xliii

since consumers appear to undervalue a wide range of investments in energy conservation. 

There are many possible explanations for the paradox discussed in the literature.xliv Some 
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16

explanations point to costs or aspects of consumer decision-making unaccounted for in a simple 

present value calculation, while others point to potential behavioral or market failures. There is 

little empirical literature to help the analyst determine which combination of hypothesis offers 

the most credible explanation. Some possibilities include: 

 Consumers might be “myopic” and hence undervalue future fuel savings in their 

purchasing decisions. 

 Consumers might lack the information necessary to estimate the value of future fuel 

savings, or not have a full understanding of this information even when it is 

presented. 

 Consumers may be accounting for uncertainty in future fuel savings when comparing 

upfront cost to future returns. 

 Consumers may consider fuel economy after other vehicle attributes and, as such, not 

optimize the level of this attribute (instead “satisficing” – that is, selecting a vehicle 

that is acceptable rather than optimal -- or selecting vehicles that have some 

sufficient amount of fuel economy). 

 Consumers might be especially averse to the short-term losses associated with the 

higher prices of energy efficient products relative to the future fuel savings (the 

behavioral phenomenon of “loss aversion”). 

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

vehicles. 

 When buying vehicles, consumers may focus on visible attributes that convey status, 

such as size, and pay less attention to attributes such as fuel economy that do not 

visibly convey status. 

 Even if consumers have relevant knowledge, selecting a vehicle is a highly complex 

undertaking, involving many vehicle characteristics. In the face of such a 

complicated choice, consumers may use simplified decision rules. 

 In the case of vehicle fuel efficiency, and perhaps as a result of one or more of the 

foregoing factors, consumers may have relatively few choices to purchase vehicles 

with greater fuel economy once other characteristics, such as vehicle class, are 

chosen.12 

                                                             12 For instance, in MY 2010, the range of fuel economy (combined city and highway) available among all 

listed 6-cylinder minivans was 18 to 20 miles per gallon. With a manual-transmission 4-cylinder minivan, it is 
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17

The extent to which fuel economy is optimized relative to other, potentially more salient vehicle 

attributes (such as engine horsepower and seating capacity) in market outcomes for new vehicles 

remains an important area of uncertainty. There are significant challenges involved in 

effectively interpreting and anticipating consumer preferences for various vehicle attributes and 

amenities. There are significant lead times to market, potential return to scale limits on the range 

of options provided for a given attribute or amenity, market transaction frictional factors, and 

other factors inherent to the nature of these costly durable goods which may contribute to 

imperfect satisfaction of market demand for fuel economy among a highly heterogeneous 

customer base. Both sides of the market would be expected to attempt to maximize the utility 

they gain from these transactions; they presumably rely heavily in their calculations on the 

uncertain benefits of savings from fuel economy improvements, and yet market outcomes may 

still appear to reflect potential foregone opportunities to increase utility. We remain interested in 

these market dynamics, their underlying causes, and their potential significance for assessing the 

potential incremental effects of pollution control standards. 

g. Electric Vehicles and Other New Vehicles 

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

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

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

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

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

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

vehicle attributes important to the new vehicles, such as size, performance, or range, then the 

effect of the introduction can be modeled by applying the parameters for those features to the 

new vehicle characteristics. 

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

variable. Even in these models, it is possible, with some additional information, to consider the 

effects of new vehicle introduction. The first step is to find a vehicle similar on as many 

dimensions as possible to the new vehicle. For instance, if the change is to create an electric 

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

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

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

attributes. The next requirement is information on the value to consumers of the attributes that 

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

                                                                                                                                                                                               

possible to get 24 mpg. See http://www.fueleconomy.gov, which is jointly maintained by the U.S. Department of 

Energy and the EPA. 
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18

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.13 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.xlv In that model, he considers only one base 

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

acceleration, and several other vehicle attributes. 

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

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

recharge. Because range appears to be a major factor in EV acceptability -- indeed, a factor in 

the development of plug-in hybrid-electric vehicles is responding to this concern -- 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, 

                                                             13 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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19

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 2.1-1.1Table 8.1-1 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 2.1-1.1 Willingness 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 2010$a 

Bunch et al. (1993)b $7,600 (1991$) $11,300 

Kavelek (1996) for California Energy 

Commissionc 

$2600 - $41,900 (1993$) $3700 - $59,400 

Resource Systems Group (2009) for 

California Energy Commissiond 

$2900 - $7500 (2009$) $2900 - $7600 

Hess et al. (2009), using the same data 

as Resource Systems Group (2009)e 

$2400 - $8500 (2009$) $2400 - $8600 

Hidrue et al. (2011)f $3800 - $10,400 (2009$) $3800 - $10,500 a

Values adjusted to 2010$ using the Bureau of Economic Analysis GDP deflator. b

Bunch, David S., Mark Bradley, Thomas F. Golob, and Ryuichi Kitamura. “Demand for CleanFuel Vehicles in California: A Discrete-Choice Stated Preference Pilot Project.” Transportation 

Research Part A 27A(3) (1993): 237-253. The value of range was, in their model, assumed to be 

the same for all people. 

c

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

Simulator.” Demand Analysis Office, California Energy Commission, April 1996. The variation 

in values is due to willingness to pay (WTP) varying by income levels and for one-car and twoFormatted: Font: Times New Roman, 12 pt
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20

car households. The coefficient on range for one-car households was not statistically 

significantly different from zero (t-statistic = 1.5), but it was for 2-car households (t-statistic = 

3.02). The minima and maxima presented here represent the values across both ownership and 

income categories. 

d

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

Commercial Fleet Survey Task 8 Report: Logistic Regression Analysis and Results.” Prepared 

for California Energy Commission, June 2009. 

e

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

Multi-Dimensional Choice Processes: The Case of the Demand for Alternative Fuel Vehicles,” 

Proceedings of the 2009 European Transport Conference, Leiden, Netherlands, 2009. This 

study uses the same data as the Resource Systems Group study. The coefficient on range was 

not statistically significantly different from zero in these regressions: t-statistics varied from 

1.29 to 1.52. The variation in values is due to willingness to pay (WTP) varying by income 

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

across both income categories and specifications. 

f

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

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

(2011): 686-705. The range of values is due to the model separating consumers into “gasoline 

vehicle-oriented” and “electric vehicle-oriented” groups. The EV-oriented group has higher 

WTP for additional range. 

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 2.1-1.1Table 

8.1-1, 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. 

h. EPA Exploration of Vehicle Choice Modeling 

In order to develop greater understanding of these models, EPA is developing a vehicle choice 

model, although not for this rulemaking. In its current form, the model assumes that the vehicle 

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

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

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

The EPA model uses a nested logit structure common in the vehicle choice modeling literature, 

as discussed above in Chapter a8.1.2.1. “Nesting” refers to the decision-tree structure of the 

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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 ultra-luxury 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) were 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,14 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 21.2.g8.1.2.7: the value that the consumer places on fuel 

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

of the vehicle. In practice, implementing this calculation involves calculating the change in 

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

consistent with those in OMEGA. As discussed in Chapter 21.2.e8.1.2.5, there is no consensus 

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

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

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

in the analysis. 

                                                             14 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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22

The vehicle choice model takes as inputs an initial fleet of vehicles (including the initial sales 

and fuel economy) in the absence of standards, the cost of technologies added to each vehicle to 

comply with standards, and the change in fuel economy. With the initial sales mix, for each 

vehicle, the model calculates a vehicle-specific constant that summarizes the value of all 

attributes of the vehicle other than price and fuel economy. This constant ensures that the model 

will predict changes in consumer response that would result only from changes in price and fuel 

economy. This constant substitutes for estimating the effects of changes in all other vehicle 

characteristics; the underlying assumption is that these other vehicle characteristics do not 

change.15 For instance, it assumes that a Ford Escape will not change in size, power, or 

accessories; the only changes will be to its cost and its fuel economy. 

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

passed through as an increase in vehicle price, and some years of fuel savings (which the 

modeler may select) 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. 

The model has undergone peer review.xlvi The reviewers were generally supportive of the model 

structure and parameters, with two major qualifications. 

First, peer reviewers recommended that the model should interact closely with OMEGA, EPA’s 

technology cost and effectiveness model, and its appropriate use may depend on that interaction. 

For instance, it is possible that the predicted changes in fleet mix will lead to predictions of 

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

vehicles sold changed from the initial levels. To correct this problem, it is necessary to feed the 

new fleet mix back into OMEGA (which calculates costs and compliance) and get a new set of 

output, which is then fed back into the vehicle choice model. OMEGA increases technologies, 

and thus costs, to improve compliance; those adjustments would then again affect vehicle 

demand. We expect that this iterative process would converge to a fleet mix that would meet 

standards. Performing this iteration requires development of an interface between the vehicle 

choice model and OMEGA to ensure accurate transmission of data between the models. At this 

time, the vehicle choice model takes output from OMEGA, but the results of the modeling do not 

feed easily back into OMEGA. Building this interface is an expected part of our future modeling 

work. 

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

agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions. 
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Second, the peer review raised the issue of the uncertainties surrounding the model parameters 

and suggested the development of capacity to conduct uncertainty analysis. As discussed in 

Section e8.1.2.5, the role of fuel economy in consumer decisions is one source of uncertainty; the 

price slopes used for the different nests are also not known with certainty. EPA agrees that use 

of the model should involve, at the least, sensitivity analysis over key parameters, and we plan to 

investigate greater incorporation of uncertainty analysis in the model. 

We note as well that the current model does not take EVs or other alternatively fueled vehicles 

into account. As discussed in Section g8.1.2.7, the values for willingness to pay for features 

such as range and different refueling infrastructures appear to be subject to great uncertainty. 

EPA’s current model does not include these vehicles, because we are seeking to gain experience 

and confidence with the modeling where it is likely to work best before we investigate modeling 

where more uncertainty is involved. The incorporation of new vehicles of any kind in the 

modeling is another area for future work. 

As discussed in Preamble Section III.H.1, EPA is not using its preliminary consumer choice 

model in this rulemaking because we believe it needs further development and testing before we 

have confidence in its use. As the peer reviewers noted, it has not yet been integrated with 

OMEGA, an important step for ensuring that changes in the vehicle fleet estimated by the model 

will result in a fleet compliant with the standards. In addition, concerns remain that vehicle 

choice models have rarely been validated against real-world data. In response to these concerns, 

we would expect any use of the model to involve, at the least, a number of sensitivity analyses to 

examine the robustness of results to key parameters. We will continue model development and 

testing to understand better the results and limitations of using the model. 

i.Summary and Additional Considerations 

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

for regulatory analysis, helping to answer some of the central questions about relevant effects on 

consumer welfare. In practice, the advantages depend on the success of models in predicting 

changes in fleet size and mix. 

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

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

a policy can improve other estimates of policy impacts, such as the change in total vehicle 

emissions or vehicle miles traveled. The predictive ability of models, though, is not proven. 

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

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

characteristics. These willingness-to-pay values may, however, be sensitive to the ways they are 
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24

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. 

Consumer choice modeling has the potential to improve estimates of the compliance costs of a 

rule. Consumers can either accept the new costs and vehicle characteristics, or they can change 

which vehicles they buy. Using a vehicle choice model is likely to reduce estimated compliance 

costs: because the model allows consumers to choose among accepting the new vehicle, buying 

a different vehicle, or not buying a vehicle, the model assumes that consumers have additional 

options, which improves their welfare relative to the assumption that consumers will not change 

their buying behavior. 

An additional complication associated with consumer choice modeling is accurate prediction of 

producers’ responses to the rule. While it is possible to include auto makers’ decisions (for 

instance, on setting prices) into vehicle choices, computational limits affect the richness of these 

models. In addition, the pricing paths predicted by these models have not, to our knowledge, 

been tested against actual behavior; and auto makers may not pass along vehicle costs in the 

same way in the future as they have in the past. 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 rule take into account the costs to hold other vehicle attributes, such 

as size and performance, constant. In addition, the analysis assumes that the full technology 

costs are passed along to vehicle buyers. With these assumptions, because welfare losses are 

monetary estimates of how much buyers would have to be compensated to be made as well off as 

in the absence of the change,16 the price increase measures the loss to the buyer.17 Assuming that 

                                                            

16 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. 
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25

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 rule 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 further 

development of its vehicle choice model for use in the future. 

3. Impact of the Rule on Affordability of Vehicles and Low-Income Households 

Because this rule is expected to increase the up-front costs of vehicles, with the fuel savings that 

recover those costs coming in over time, questions have arisen about the effects of this rule on 

whether access to credit may limit consumers’ ability to purchase new vehicles, on low-income 

households, and on the availability of low-priced vehicles. Section III.H.11.b. of the Preamble 

discusses these issues in the context of public comments received on the rule; here we provide 

some background and information on sources of data in that discussion. 

When a lender is deciding whether to issue a loan to a prospective vehicle buyer, the amount of 

the vehicle loan and the person’s income are two major factors in the loan application. If lenders 

in fact restrict themselves to consideration of only those two factors, then the higher up-front 

costs of the new vehicles subject to this rule would reduce buyers’ abilities to get loans. The fuel 

savings would not come into play to counter-balance this cost, even though, as shown in the 

payback period analysis (RIA Chapter 5.5), the fuel savings exceed the increased loan payments 

from the first month of the loan. Thus, if lenders do not take fuel savings into account in 

providing loans, people who are borrowing near the limit of their abilities to borrow will either 

have to change what vehicles they buy, or not buy vehicles at all. 

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

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

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

Thus, the increase in price that the buyer faces would be the upper bound of loss of consumer welfare, unless there 

are other changes to the vehicle due to the fuel economy improvements that make the vehicle less desirable to 

buyers. 
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On the other hand, some evidence suggests that the loan market may evolve to take fuel savings 

into greater account in the lending decision. Some lenders currently give discounts for loans to 

purchase more fuel-efficient vehicles.xlvii An internet search on the term “green auto loan” 

produced more than 50 lending institutions that provide reduced loan rates for more fuel-efficient 

vehicles.xlviii There is no reason to expect that the number of lenders instituting this policy will 

decrease in the future as fuel efficiency increases beyond the levels seen today. Indeed, Iit is 

possible (though unknown at this time) that the auto loan market may evolve to include further 

consideration of fuel savings, as those savings play a significant factor in offsetting the increase 

in up-front costs of vehicles. 

It is possible that future trends in the auto loan market may affect future vehicle sales. It is also 

possible that some people who have significant debt loads may not be able to get financing for 

some of these new vehicles; they may have to buy different vehicles (including used vehicles) or 

delay purchase. For others who borrow on credit, though, as discussed in RIA Chapter 5.5, the 

fuel savings are expected to outweigh the increased loan costs from the time of vehicle purchase. 

The rule thus may make vehicles more affordable to the public, by reducing consumers’ 

vulnerability to fuel price jumps. EPA does not deny the possibility that the market for new 

vehicle loans may be affected by this rule, but we do not at this time have sufficient evidence to 

project whether that market will evolve to help or hinder sales of new, more fuel-efficient 

vehicles. 

The effects of this rule on low-income households depends on its impacts, not only in the new 

vehicle market, but also in the used vehicle market. Two sources of information on vehicle 

ownership by income are the 2010 Consumer Expenditure Survey (CES) conducted by the 

Bureau of Labor Statistics,xlix and the 2007 Survey of Consumer Finances (SCF) conducted by 

the Federal Reserve System.l

 The Consumer Expenditure Survey data indicate that, though the 

average household spent more on vehicle purchases ($2,588) than on gasoline and oil ($2,132), 

households in the bottom 40 percent of income spent more on fuel ($1,304) than on vehicles 

($1,106); in addition, they spent more on used vehicles ($756) than on new vehicles ($330). 

Households in the lowest 20 percent of income spent only $127 on new vehicles, $497 on used 

vehicles, and $1,009 on fuel. These data suggest that the used-vehicle market is more important 

for low-income households than the new-vehicle market, and that they are more vulnerable to 

changes in fuel prices than they are to changes in vehicle prices. The Survey of Consumer 

Finances asks households about purchase information in a number of categories, including 

vehicles. For the 2007 survey, we identified the households in the survey who had bought MY 

2007 or 2008 vehicles, and further looked at the income categories for those consumers. Those 

with income less than $35,200 (the maximum income for those in the bottom 40 percent of 

income in the CES) bought about 17 percent of new vehicles; those with income below $18,400 

(the bottom quintile) bought fewer than 2 percent of new vehicles. These data further support 
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27

the idea that low-income households are more affected by the impact of the rule on the usedvehicle market than on the new-vehicle market. 

The effect of this rule on the used vehicle market will be related to its effects on new vehicle 

prices, the fuel efficiency of new vehicle models, the fuel efficiency of used vehicles, and the 

total sales of new vehicles. If the value of fuel savings resulting from improved fuel efficiency 

to the typical potential buyer of a new vehicle outweighs the average increase in new models’ 

prices, sales of new vehicles could rise, and the used vehicle market may increase in volume as 

new vehicle buyers sell their older vehicles. In this case, low-income households are likely to 

benefit from the increased availability of used vehicles. However, if potential buyers value 

future fuel savings resulting from the increased fuel efficiency of new models at less than the 

increase in their average selling price, sales of new vehicles will decline, and the used vehicle 

market may decrease in volume as people hold onto their vehicles longer. In this case, lowincome households are likely to face increased costs due to reduced availability of used vehicles. 

Because, as discussed in 8.1.1, we have not estimated the effects of the rule on the new vehicle 

market, and because we do not have a good model of the relationship between the new and used 

vehicle markets, we do not have estimates of the impact of this rule on the used vehicle 

market. However, due to the significant effect of the rule on fuel savings, especially for used 

vehicles (see RIA Chapter 5.5), we expect low-income households to benefit from the more 

rapid payback period for used vehicles, though some of this benefit may be affected by the net 

effect of this rule on the prices and availability of used vehicles.

The low-priced vehicle segment of the market may also deserve consideration, because it may be 

an entry point for first-car buyers. Vehicles in the low-priced (economy-class) segment will bear 

technology costs needed to meet the new standards, but it is not known how manufacturers will 

decide to pass on these costs across their vehicle fleets, including in the low-priced vehicle 

segment. If manufacturers decide to pass on the full cost of compliance in this segment, then it is 

possible that consumers who might barely afford new vehicles may be priced out of the newvehicle market or may not have access to loans. As discussed above, the rule’s impacts on 

availability of loans is unclear, because some lenders do factor fuel economy into their loans, and 

it is possible that this trend may expand. In addition, auto makers have some flexibility in how 

both technologies and price changes are applied to these vehicles; auto makers have ways to 

keep some vehicles in the low-priced vehicle segment if they so choose. Though the rule is 

expected to increase the prices of these vehicles, the degrees of price increase and the impacts of 

the price increases, especially when combined with the fuel savings that will accompany these 

changes, are much less clear. 

In summary, we recognize that this rule may have impacts on consumers’ access to loans for new 

vehicles, on low-income households, and on the market for low-priced vehicles; less clear are the 
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28

directions of these effects. Lenders who only consider consumers’ debt-to-income ratios may 

reduce consumers’ abilities to purchase more expensive vehicles, but some lenders already take 

the fuel efficiency of vehicles into account. Low-income households will benefit from reduced 

fuel costs; Wwe do not estimate the direction of effects of this rule on used vehicle prices, which 

are more relevant for low-income households than effects on new vehicles. The effects of this 

rule on low-priced vehicles depends on how manufacturers add technologies and price vehicles 

in this segment; they have flexibility to keep some vehicles in this segment if they so wish. 

2. Employment Impacts 

1. Introduction 

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

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

concern in the current economic climate of sizeable unemployment. When President Obama 

requested that the agencies develop this program, he sought a program that would “strengthen 

the [auto] industry and enhance job creation in the United States.”li,18 The recently issued 

Executive Order 13563, “Improving Regulation and Regulatory Review” (January 18, 2011), 

states, “Our regulatory system must protect public health, welfare, safety, and our environment 

while promoting economic growth, innovation, competitiveness, and job creation” (emphasis 

added). EPA is accordingly providing partial estimates of the effects of this rule on domestic 

employment in the auto manufacturing and parts sectors, while qualitatively discussing how it 

may affect employment in other sectors more generally. 

This rule is expected to affect employment in the United States through the regulated sector – the 

auto manufacturing industry – and through several related sectors, specifically, industries that 

supply the auto manufacturing industry (e.g., vehicle parts), auto dealers, the fuel refining and 

supply sectors, and the general retail sector. According to the U.S. Bureau of Labor Statistics, in 

2010, about 677,000 people in the U.S. were employed in Motor Vehicle and Parts 

Manufacturing Sector (NAICS 3361, 3362, and 3363). About 129,000 people in the U.S. were 

employed in the Automobile and Light Truck Manufacturing Sector (NAICS 33611) in 

December 2010; this is the directly regulated sector, since it encompasses the auto manufacturers 

that are responsible for complying with the standards.lii Changes in light duty vehicle sales, 

discussed in Chapter 21.18.1.1, could affect employment for auto dealers. The employment 

effects of this rule are expected to expand to relatedbeyond the regulated sectors. Though some 

                                                             18 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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of the parts used to achieve the standards are likely to be built by auto manufacturers themselves, 

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

also be affected by changes in vehicle sales. As discussed in Chapter 5.4 of this RIA, this rule 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 rule 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. In such a period, both positive and negative employment 

effects are possible.19 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.liii In 

the longer run, the net effect on employment is more difficult to predict and will depend on the 

way in which the related industries respond to the regulatory requirements. As Schmalansee and 

Stavins note, it is possible that the magnitude of the effect on employment could vary over time, 

region, and sector, and positive effects on employment in some regions or sectors could be offset 

by negative effects in other regions or sectors. For this reason, they urge caution in reporting 

partial employment effects since it can “paint an inaccurate picture of net employment impacts if 

not placed in the broader economic context.” 

It is assumed that the official unemployment rate will have declined to 5.3 percent by the time by 

the time this rule takes effect and so the effect of the regulation on labor will be to shift workers 

from one sector to another.20 Those shifts in employment impose an opportunity cost on society, 

approximated by the wages of the employees, as regulation diverts workers from other activities 

in the economy. In this situation, as discussed above, any effects on net employment are likely to 

                                                             19 Masur and Posner, available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1920441 

20 Office of Management and Budget, “Fiscal Year 2012 Mid-Session Review: Budget of the U.S. 

Government.” http://www.whitehouse.gov/sites/default/files/omb/budget/fy2012/assets/12msr.pdf , p. 10. 
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be transitory. It is also possible that the state of the economy will be such that positive or 

negative employment effects will occur. 

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

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

limitations. 

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. 

a. Conceptual Framework for Employment Impacts in the Regulated Sector 

One study by Morgenstern, Pizer, and Shihliv provides a retrospective look at the impacts of 

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

spending on pollution abatement for four highly polluting/regulated U.S. industries (pulp and 

paper, plastics, steel, and petroleum refining) using data for six years between 1979 and 1991. 

The paper provides a theoretical framework that can be useful for examining the impacts of a 

regulatory change on the regulated sector in the medium to longer term. In particular, it 

identifies three separate ways that employment levels may change in the regulated industry in 

response to a new (or more stringent) regulation. 

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

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

labor to make that good. In the authors’ words, the “extent of this effect depends on 

the cost increase passed on to consumers as well as the demand elasticity of industry 

output.” 

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

constant), with potentially positive effects on employment. In the authors’ words, as 
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“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 laborintensive (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,21 the “cost effect” to have a positive effect on employment, and the “factor shift

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

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

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

The authors conclude that increased abatement expenditures generally have not caused a 

significant change in employment in those sectors. More specifically, their results show that, on 

average across the industries studied, each additional $1 million spent on pollution abatement 

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

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

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

employment. It was, however, conducted for only four sectors. It could also be very difficult to 

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

Abatement Cost and Expenditure survey, has been conducted infrequently since 1994, with the 

last survey conducted in 2005. The empirical estimates provided by Morgenstern et al. are not 

relevant to the case of fuel economy standards, which are very different from the pollution 

control standards on industrial facilities that were considered in that study. In addition, it does 

not examine the effects of regulation on employment in sectors related to but outside of the 

regulated sector. Nevertheless, the theory that Morgenstern et al. developed continues to be 

useful in this context. 

The following discussion of additional methodologies draws from Berck and Hoffmann’s review 

of employment models.lv 

                                                             21 As will be discussed below, the demand effect in this rule 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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b. 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 shortrun effects. 

c. 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, inputoutput 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 inputoutput models requires the assumption that workers flow into or out of the labor market perfectly 
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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. 

d. 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 inputoutput 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. 

e. 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. 

f.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.lvi While often a sophisticated treatment of 
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34

the issues analyzed, these studies commonly analyze specific scenarios or sectors in the past; 

care needs to be taken in extrapolating their results to other scenarios and to the future. For 

instance, neither of these two studies examines the auto industry and are therefore of limited 

applicability in this context. 

g. 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. 

3. Employment Analysis of This Rule 

As mentioned above, this program is expected to affect employment in the regulated sector (auto 

manufacturing) and in other sectors directly affected by the rule: 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 rule on the regulated sector (i.e., the auto industry; for reasons discussed below, we include 

some quantitative assessment of effects on suppliers to the auto industry although suppliers are 

not regulated directly). It also includes qualitative discussion of the effects of the rule on other 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

35

sectors. Focusing quantification of employment impacts on the regulated sector has some 

advantages over quantifying all impacts. First, tThe 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 rule. Second, as 

discussed above, net effects on employment in the economy as a whole depend heavily on the 

overall state of the economy when this rule has its effects. Focusing on the regulated sector 

provides insight into employment effects in that sector without having to make assumptions 

about the state of the economy when this rule has its impacts. We include a qualitative 

discussion of employment effects on other sectors to provide a broader perspective on the 

impacts of this rule. 

As noted above, in a full-employment economy, any changes in employment will result from 

people changing jobs or voluntarily entering or exiting the workforce. In a full-employment 

economy, employment impacts of this rule will change employment in specific sectors, but it 

will have small, if any, effect on aggregate employment. This rule would take effect in model 

years 2017 through 2025; by then, the current high unemployment may be moderated or ended. 

For that reason, this analysis does not include multiplier effects, but instead focuses on 

employment impacts in the most directly affected industries. Those sectors are likely to face the 

most concentrated employment impacts. 

a. Employment Impacts in the Auto Industry 

Following the Morgenstern et al. conceptual framework for the impacts of regulation on 

employment in the regulated sector, we consider three effects for the auto sector: the demand 

effect, the cost effect, and the factor shift effect. However, we are only able to offer quantitative 

estimates for the cost effect. We note that these estimates, based on extrapolations from current 

data, become more uncertain as time goes on. 

i. The Demand Effect 

The demand effect depends on the effects of this rule 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 rule will increase vehicle costs; by itself, this effect would reduce vehicle 

sales. On the other hand, this rule will reduce the fuel costs of operating the vehicle; by itself, 

this effect would increase vehicle sales, especially if potential buyers have an expectation of 

higher fuel prices. The sign of demand effect will depend on which of these effects dominates. 
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36

Because, as described in Chapter 8.1, we have not quantified the impact on sales for this rule, we 

do not quantify the demand effect. 

b. 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 auto manufacturers installing

new or additional technologies needed for vehicles to comply with the standards. 

One way to estimate the cost effect, given the cost estimates for complying with the rule, is to 

use the ratio of workers to each $1 million of expenditures in that sector. The use of these ratios 

has both advantages and limitations. It is often possible to estimate these ratios for quite specific 

sectors of the economy: for instance, it is possible to estimate the average number of workers in 

the light-duty vehicle manufacturing sector per $1 million spent in the sector, rather than use the 

ratio from another, more aggregated sector, such as motor vehicle manufacturing. As a result, it 

is not necessary to extrapolate employment ratios from possibly unrelated sectors. On the other 

hand, these estimates are averages for the sectors, covering all the activities in those sectors; they 

may not be representative of the labor required when expenditures are required on specific 

activities, as the factor shift effect (discussed below) indicates. For instance, the ratio for the 

motor vehicle manufacturing sector represents the ratio for all vehicle manufacturing, not just for 

fuel efficiency improvements. In addition, these estimates do not include changes in sectors that 

supply these sectors, such as steel or electronics producers. They thus may best be viewed as the 

effects on employment in the 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 rule 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 sources for estimates of employment per $1 million expenditures. The U.S. 

Bureau of Labor Statistics (BLS) provides its Employment Requirements Matrix (ERM),lvii

which provides direct estimates of the employment per $1 million in sales of goods in 202 

sectors. The estimates used here, updated from the NPRM, are from 2010 (adjusted to 2010$). 

Not all expenditures are for domestically produced vehicles, however. To estimate the 

proportion of domestic expenditures affected by the rule, we use data from Ward’s Automotive 

Group for total car and truck production in the U.S. compared to total car and truck sales in the 
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37

U.S.lviii For the period 2001-2010, the proportion is 66.7%. We thus weight sales by this factor 

to get an estimate of the effect on employment in the motor vehicle manufacturing sector due to 

this rule. 

The Annual Survey of Manufactureslix (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 of 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 2010 (also updated from the 

NPRM). As with the ERM, we adjust for the ratio of domestic production to domestic sales. 

The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light 

Duty Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing 

(NAICS 3363). 

The Economic Census includes all large companies and a sample of smaller ones. The ASM is a 

subset of the Economic Census; though the Census itself is more complete, it is conducted only 

every 5 years, while the ASM is annual. The values presented here use data from 2007 (adjusted 

to 2010$), 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 2.2-2.1Table 8.2-2 provides the values, either given (BLS) or calculated (ASM, Economic 

Census) for employment per $1 million of expenditures, all based on 2010 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 2.2-2.1 Employment per $1 Million Expenditures (2010$) 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 

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12 pt
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BLS ERM Motor Vehicle Mfg 0.770 0.514

ASM Motor Vehicle Mfg 0.655 0.437

ASM Light Duty Vehicle 

Mfg 

0.609 0.406

Economic 

Census 

Motor Vehicle Mfg 0.665 0.443

Economic 

Census 

Light Duty Vehicle 

Mfg 

0.602 0.402

BLS ERM Motor Vehicle Parts 

Mfg 

2.614 1.743

ASM Motor Vehicle Parts 

Mfg 

2.309 1.540

Economic 

Census 

Motor Vehicle Parts 

Mfg 

2.712 1.809

BLS ERM refers to the U.S. Bureau of Labor Statistics’ Employment Requirement Matrix. 

ASM refers to the U.S. Census Bureau’s Annual Survey of Manufactures. Economic Census 

refers to the U.S. Census Bureau’s Economic Census.

Over time, the amount of labor needed in the auto industry has changed: automation and 

improved production methods have led to significant productivity increases. The BLS ERM, for 

instance, provided estimates that, in 1993, 1.64 workers in the Motor Vehicle Manufacturing 

sector were needed per $1 million of 2005$, but only 0.86 workers by 2010 (in 2005$). Because 

the ERM is available annually for 1993-2010, 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 3.9 percent per year productivity improvement in the Motor 

Vehicle Manufacturing Sector, and a 3.8 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 2010$) 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 2010 (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). This is a simple way to examine the relationship between 

labor required and expenditure. 

Table 2.2-2.2Table 8.2-3 shows the cost estimates developed for this rule, discussed in Chapter 

5. The maximum value in Table 2.2-2.1Table 8.2-2 for employment impacts per $1 million 

Comment [A6]: Commenters request EPA to

clarify with additional information. Commenters do

not find it very clear.

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Formatted: Font: (Default) Times New Roman,

12 pt
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39

expenditures (after accounting for the share of domestic production) is 1.809 in 2010 if all the 

additional costs are in the parts sector; the minimum value is 0.402 in 2010, 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 2010. 

The results in Table 2.2-2.2Table 8.2-3 include the productivity adjustment described above. 

While we estimate employment impacts, measured in job-years, beginning with the first year of 

the standard (2017), some of these employment gains may occur earlier as auto manufacturers 

and parts suppliers hire staff in anticipation of compliance with the standard. A job-year is a 

way to calculate the amount of work needed to complete a specific task. For example, a job-year 

is one year of work for one person.

Table 2.2-2.2 Employment per $1 Million in the Motor Vehicle Manufacturing Sector, in 

job-years 

Year Costs (before 

adjustment for 

domestic proportion of 

production) 

($Millions)

Minimum 

employment effect 

(if all expenditures 

are in the parts 

sector)

Maximum 

employment effect (if 

all expenditures are in 

the light duty vehicle 

mfg sector)

2017 $ 2,435 700 3,200 

2018 $ 4,848 1,300 6,200 

2019 $ 6,818 1,700 8,400 

2020 $ 8,858 2,100 10,500 

2021 $ 12,400 2,900 14,200 

2022 $ 18,323 4,100 20,200 

2023 $ 23,734 5,100 25,200 

2024 $ 29,101 6,000 29,700 

2025 $ 31,678 6,300 31,100 

We note that the cost effect depends only on technology costs, not vehicle sales. It is therefore 

not sensitive to assumptions about how consumers consider fuel savings at the time of vehicle 

purchase. 

i. 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 

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40

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 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. 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 standards. An increase in labor cost for 

the new technology indicates an increase in the labor needed for the new technology compared to 

the baseline technology. For instance, an 8-speed transmission requires $15.11 more in labor 

costs than a 6-speed transmission (as accounted for in EPA’s cost estimates for the rule). 

Dividing the labor cost by a wage per hour estimate provides an estimate of the additional hours 

(and thus the additional labor) needed for the new technology compared to the baseline 

technology. As with labor cost, an increase in labor hours per technology indicates greater 

employment needs for the new technologies. For this conversion, a weighted average wage rate 

(90 percent of the average wage in the Motor Vehicle Parts Manufacturing sector, and 10 percent 

of the average wage in the Motor Vehicle Manufacturing Sector) of $46.36/hour in 2015, using 

2008 dollars (the unit of analysis for the FEV study). For the change from a 6-speed to an 8-

speed transmission, we thus estimate an additional 0.33 hours of labor per transmission. 

Table 2.2-2.3Table 8.2-4 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 rule, 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 in this program. As a result, these results cannot be considered definitive 

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Comment [A7]: Commenters note that this is

confusing.  Commenters request clarity on whether

these are all increases in labor relative to capital by

deploying more fuel efficient technologies?  Are

there any technologies that have the opposite

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

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41

evidence that the factor shift effect increases employment for this rule. We therefore do not 

quantify the factor shift effect for this rule.

Table 2.2-2.3 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

ii. Summary of Employment Effects in the Auto Sector 

While we are not able to quantify the demand or factor shift effects, the cost effect results show 

that the employment effects of the increased spending in the regulated sector (and, possibly, the 

parts sector) are expected to be positive and on the order of a few thousand in the initial years of 

the program. As noted above, motor vehicle and parts manufacturing sectors employed about 

677,000 people in 2010, with automobile and light truck manufacturing accounting for about 

129,000 of that total. 

4. Effects on Employment for Auto Dealers 

The effects of the standards on employment for auto dealers depend principally on the effects of 

the standards on light duty vehicle sales: increases in sales are likely to contribute to 

employment at dealerships, while reductions in sales are likely to have the opposite effect. 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, and reductions are likely to 

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

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42

Although this rule predicts very small penetration of plug-in hybrid and electric vehicles, the 

uncertainty on consumer acceptance of such technology vehicles is even greater. As discussed in 

Chapter 21.2.g8.1.2.7, consumers may find some characteristics of electric vehicles and plug-in 

hybrid electric vehicles, such as the ability to fuel with electricity rather than gasoline, attractive; 

they may find other characteristics, such as the limited range for electric vehicles, undesirable. 

As a result, some consumers will find that EVs will meet their needs, but other buyers will 

choose more conventional vehicles. Auto dealers may play a major role in explaining the merits 

and disadvantages of these new technologies to vehicle buyers. There may be a temporary need 

for increased employment to train sales staff in the new technologies as the new technologies 

become available. 

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 inhouse by auto manufacturers; others are made by independent suppliers who are not directly 

regulated, but who will be affected by the 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 standards on vehicle 

sales and on the labor intensity of the new technologies, qualitatively in the same ways as for the 

auto manufacturing sector. 

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 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 RIA Chapter 5.4. 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 RIA Chapter 5.4. This new fuel may require additional infrastructure, such as electricity 
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EO 13563 review. They are for your consideration and are subject to further policy review. 

43

charging locations. Providing this infrastructure, as well as infrastructure for other alternative 

fuels (such as CNG), will require some increased employment. In addition, the generation of 

electricity is likely to require some additional labor. We have insufficient information at this 

time to predict whether the increases in labor associated with increased infrastructure provision 

and generation for electricity will be greater or less than the employment reductions associated 

with reduced demand for petroleum fuels. 

7. Effects on Employment due to Impacts on Consumer Expenditures 

As a result of these 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.5 and 

Chapter 5.5 of this RIA); indeed, people who finance their vehicles are expected to find that their 

fuel savings per month exceed the increase in the loan cost (though this depends on the particular 

loan rate a consumer receives). As a result, consumers will have additional money to spend on 

other goods and services, though, for those who do not finance their vehicles, it will occur after 

the initial payback period. These increased expenditures will support employment in those 

sectors where consumers spend their savings. 

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

employment by that time, any change in consumer expenditures would primarily represent a shift 

in employment among sectors. If, on the other hand, the economy still has substantial 

unemployment, these expenditures would contribute to employment through increased consumer 

demand. 

8.7. Summary 

The primary employment effects of this rule are expected to be found throughout several key 

sectors: auto manufacturers, auto dealers, auto parts manufacturing, fuel production and supply, 

and consumers. These standards initially take effect in model year 2017, a time period 

sufficiently far in the future that the current sustained high unemployment at the national level 

may be moderated or ended. In an economy with full employment, the primary employment 

effect of a rulemaking is likely to be to move employment from one sector to another, rather than 

to increase or decrease employment. For that reason, we focus our partial quantitative analysis 

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

discuss the likely direction of other impacts in the regulated sector as well as in other directly 

related sectors, but we do not quantify those impacts, because they are more difficult to quantify 

with reasonable accuracy, particularly so far into the future. 

For the regulated sector, the cost effect is expected to increase employment by 700 – 3,200 jobyears in 2017, depending on the share of that employment that is in the auto manufacturing 
Summary of interagency working comments received on draft rule under EO 12866 and 

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44

sector compared to the auto parts manufacturing sector. As mentioned above, some of these job 

gains may occur earlier as auto manufacturers and parts suppliers hire staff to prepare to comply 

with the standard. The demand effect depends on changes in vehicle sales, which are not 

quantified for this rule. Though we do not have estimates of the factor shift effect for all 

potential compliance technologies, the evidence which we do have for some technologies 

suggests that many of the technologies will have increased labor needs. 

Effects in other sectors that are predicated on vehicle sales are also ambiguous. Changes in 

vehicle sales are expected to affect labor needs in auto dealerships and in parts manufacturing. 

Increased expenditures for auto parts are expected to require increased labor to build parts, 

though this effect also depends on any changes in the labor intensity of production; as noted, the 

subset of potential compliance technologies for which data are available show increased labor 

requirements. Reduced petroleum fuel production implies less employment in the petroleum 

sectors, although there could be increases in employment related to providing infrastructure for 

alternative fuels such as electricity and CNG. Finally, consumer spending is expected to affect 

employment through changes in expenditures in general retail sectors; net fuel savings by 

consumers are expected to increase demand (and therefore employment) in other sectors.

                                                            

i

 Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International Markets: 

The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-951 (Docket 

EPA-HQ-OAR-2010-0799-0665); Goldberg, Pinelopi Koujianou, “The Effects of the Corporate 

Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics 46(1) (March 

1998): 1-33 (Docket EPA-HQ-OAR-2010-0799-0666). 

ii 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-2010-0799-0715); 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-2010-0799-0686). 

iii McManus, Walter M., “Can Proactive Fuel Economy Strategies Help Automakers Mitigate 

Fuel-Price Risks?” University of Michigan Transportation Research Institute, September 14, 

2006 (Docket EPA-HQ-OAR-2010-0799-0687). 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

45

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

Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-0688); Berry, 

Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand Systems from a 

Combination of Micro and Macro Data: The New Car Market,” Journal of Political Economy 

112(1) (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799-0689). 

v

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

Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes” American Economic Review 

99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690). 

vi Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market 

Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-

1496 (Docket EPA-HQ-OAR-2010-0799-0691). 

vii Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) Standard,” 

Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-2010-0799-0826). 

viii Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher 

CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and 

Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799-0692). 

ix E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger 

H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690); Train, Kenneth E., 

and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. 

Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket 

EPA-HQ-OAR-2010-0799-0691). 

x

 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-

0688). 

xi 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-2010-0799-0715). 

xii 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-2010-0799-0693). 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

46

                                                                                                                                                                                                xiii E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799-0691). 

xiv E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-

0688). 

xv E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger 

H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690). 

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

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690); Feng, Yi, Don 

Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau 

of Economic Analysis Working Paper 11553, available at 

http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010-

0799-0694). 

xvii 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-2010-0799-0716). 

xviii E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799-0691) 

xix E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-

0688). 

xx Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and 

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 

(2005): 757-775 (Docket EPA-HQ-OAR-2010-0799-0686). 

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

learn from conventional marketing.” Journal of Cleaner Production 15: 567-576 (Docket EPAHQ-OAR-2010-0799-0695). 

xxii Whitefoot, Kate, Meredith Fowlie, and Steven Skerlos, “Product Design Responses to 

Industrial Policy: Evaluating Fuel Economy Standards Using an Engineering Model of 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

47

                                                                                                                                                                                               

Endogenous Product Design,” Energy Institute at Haas Working Paper 214, University of 

California Energy Institute, February 2011 (Docket EPA-HQ-OAR-2010-0799). 

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

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-

0688). 

xxiv Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-951 

(Docket EPA-HQ-OAR-2010-0799-0665). 

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

Substitution Patterns,” Journal of Econometrics 89 (1999): 109-129 (Docket EPA-HQ-OAR2010-0799-0697); Brownstone, David, David S. Bunch, and Kenneth Train, “Joint Mixed Logit 

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

Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2010-0799-0698); 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-2010-0799-0717); Greene, David L., K. G. Duleep, and Walter McManus, “Future 

Potential of Hybrid and Diesel Powertrains in the U.S. Light-Duty Vehicle Market,” prepared by 

the Oak Ridge National Laboratory for the U.S. Department of Energy, August 2004 (Docket 

EPA-HQ-OAR-2010-0799-0718). 

xxvi Petrin, Amil, “Quantifying the Benefits of New Products: The Case of the Minivan,” Journal 

of Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799-0699); Berry, 

Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand Systems from a 

Combination of Micro and Macro Data: The New Car Market,” Journal of Political Economy 

112 (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799-0689). 

xxvii Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48 (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799-0691). 

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

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690); Feng, Yi, Don 

Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau 

of Economic Analysis Working Paper 11553, available at 

http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010-

0799-0694). 

xxix Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and 

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

48

                                                                                                                                                                                               

(2005): 757-775 (Docket EPA-HQ-OAR-2010-0799-0686); Feng, Yi, Don Fullerton, and Li 

Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic 

Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 

5/12/09 (Docket EPA-HQ-OAR-2010-0799-0694); Greene, David L., “Feebates, Footprints and 

Highway Safety,” Transportation Research Part D 14 (2009): 375-384 (Docket EPA-HQ-OAR2010-0799-0685). 

xxx E.g., Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of 

Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics 

and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799-0692). 

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

Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2010-0799-0661). 

xxxii E.g., Espey, Molly, and Santosh Nair, 2005. “Automobile Fuel Economy: What Is It 

Worth?” Contemporary Economic Policy 23: 317-323 (Docket EPA-HQ-OAR-2010-0799-

0700). 

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

1593-1594 (Docket EPA-HQ-OAR-2010-0799-0662). 

xxxiv E.g., Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-951 

(Docket EPA-HQ-OAR-2010-0799-0665). 

xxxv E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-

0688). 

xxxvi Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. “Pain at the Pump: 

How Gasoline Prices Affect Automobile Purchasing in New and Used Markets,” working paper, 

http://web.mit.edu/knittel/www/papers/gaspaper_latest.pdf (accessed 11/1/11) (Docket EPAHQ-OAR-2010-0799-0667); 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 (Docket EPA-HQ-OAR-2010-0799-0701); Congressional 

Budget Office (2008) (Docket EPA-HQ-OAR-2010-0799); Congressional Budget Office 

(2008). Effects of Gasoline Prices on Driving Behavior and Vehicle Markets. The Congress of 

the United States, Pub. No. 2883 (Docket EPA-HQ-OAR-2010-0799-0719); West. Sarah (2007). 

“The Effect of Gasoline Prices on the Demand for Sport Utility Vehicles.” Working Paper. 

Macalester College, http://www.macalester.edu/~wests/SarahWestMEA2007.pdf, accessed 

1/25/10 (Docket EPA-HQ-OAR-2010-0799-0702). 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

49

                                                                                                                                                                                                xxxvii Greene, David L., and Jin-Tan Liu, 1988. “Automotive Fuel Economy Improvements and 

Consumers’ Surplus,” Transportation Research Part A 22A (3): 203-218 (Docket EPA-HQOAR-2010-0799-0703). 

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

EPA Report EPA-420-R-10-008 (Docket EPA-HQ-OAR-2010-0799). 

xxxix Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S. 

Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 

(Docket EPA-HQ-OAR-2010-0799-0711). 

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

Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket 

EPA-HQ-OAR-2010-0799-0704); McManus, Walter, 2007. “The Impact of Attribute-Based 

Corporate Average Fuel Economy (CAFE) Standards: Preliminary Results.” Ann Arbor, MI: 

University of Michigan Transportation Research Institute, Report No. UMTRI-2007-31 (Docket 

EPA-HQ-OAR-2010-0799-0705). 

xli For instance, Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy 

(CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2010-0799-0672); Austin, David, and Terry Dinan, “Clearing the Air: The Costs and 

Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of 

Environmental Economics and Management 50 (2005): 562- (Docket EPA-HQ-OAR-2010-

0799-0692); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost 

of the Corporate Average Fuel Economy Standard,” Resources for the Future Paper RFF DP 10-

50 (December 2010), http://www.rff.org/RFF/Documents/RFF-DP-10-50.pdf) (Docket EPAHQ-OAR-2010-0799-0706); Jacobsen, Mark, 2010. “Evaluating U.S. Fuel Economy Standards 

in a Model with Producer and Household Heterogeneity,” working paper, at 

http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf (accessed 11/1/11) (Docket EPA-HQOAR-2010-0799-0829) . 

xlii Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher 

CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and 

Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799-0692). 

xliii IEA. 2007. “Mind the Gap: Quantifying Principal-Agent Problems in Energy Efficiency.” 

Paris, France: International Energy Agency (Docket EPA-HQ-OAR-2010-0799-0720); Jaffe, 

Adam B., Richard G. Newell, and Robert N. Stavins (2001). "Energy Efficient Technologies and 

Climate Change Policies: Issues and Evidence." In Climate Change Economics and Policy, 

Toman, Michael A., ed., Washington, D.C.: Resources for the Future. p.171-181 (Docket EPAHQ-OAR-2010-0799-0828); Metcalf, Gilbert E., and Kevin A. Hassett (1999). "Measuring the 

Energy Savings From Home Improvement Investments: Evidence From Monthly Billing Data." 

The Review of Economics and Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010-0799-
Summary of interagency working comments received on draft rule under EO 12866 and 

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

50

                                                                                                                                                                                               

0659); Tietenberg, T. (2009). “Reflections – Energy Efficiency Policy: Pipe Dream or Pipeline 

to the Future?” Review of Environmental Economics and Policy 3(2): 304-320 (Docket EPAHQ-OAR-2010-0799-0707). 

xliv Helfand, Gloria, and Ann Wolverton (2011). “Evaluating the Consumer Response to Fuel 

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

Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799-0652). 

xlv Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” 

prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 

(Docket EPA-HQ-OAR-2010-0799-0717). 

xlvi U.S. Environmental Protection Agency. “Peer Review for the Consumer Vehicle Choice 

Model and Documentation.” Office of Transportation and Air Quality, Assessment and 

Standards Division, EPA-420-R-12-013, April 2012 (Docket EPA-HQ-OAR-2010-0799). 

xlvii See, for instance, Ladika, Susan (2009). “’Green’ auto loans offer lower rates,” 

Bankrate.com, http://www.bankrate.com/finance/auto/green-auto-loans-offer-lower-rates-1.aspx 

, accessed 2/28/12 (Docket EPA-HQ-OAR-2010-0799). 

xlviii Helfand, Gloria (2012). “Memorandum: Lending institutions that provide discounts for 

more fuel-efficient vehicles.” Assessment and Standards Division, Office of Transportation and 

Air Quality, U.S. Environmental Protection Agency, Docket EPA-HQ-OAR-0799 (Docket EPAHQ-OAR-2010-0799). 

xlix U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey 2010. 

http://www.bls.gov/cex/#tables. This analysis uses the table Quintiles of Income before Taxes, 

accessed 6/14/12 (Docket EPA-HQ-OAR-2010-0799). 

l

 Federal Reserve System, Survey of Consumer Finances 2007. 

http://www.federalreserve.gov/econresdata/scf/scfindex.htm. We applied the weighting scheme 

for the data, to make the data representative of the population as a whole, provided in the 

documentation for the results presented here (Docket EPA-HQ-OAR-2010-0799). 

li 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 

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

August 9, 2011. 

liii Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis of 

EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011 

(Docket EPA-HQ-OAR-2010-0799-0676). 
Summary of interagency working comments received on draft rule under EO 12866 and 

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

51

                                                                                                                                                                                                liv Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. “Jobs Versus the 

Environment: An Industry-Level Perspective.” Journal of Environmental Economics and 

Management 43 (2002): 412-436 (Docket EPA-HQ-OAR-2010-0799-0677). 

lv Berck, Peter, and Sandra Hoffmann. “Assessing the Employment Impacts of Environmental 

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

(Docket EPA-HQ-OAR-2010-0799-0678). 

lvi Berman, Eli, and Linda T. Bui, (2001) “Environmental Regulation and Labor Demand: Evidence from the

South Coast Air Basin,” Journal of Public Economics, 79, 265 – 295 (Docket EPA‐HQ‐OAR‐2010‐0799‐

0679).

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

lviii U.S. Vehicle Production by Manufacturer, UsaPr05.xls, and U.S. Vehicle Sales, UsaSa01.xls, 

accessed July 25, 2011 at http://subscribers.wardsauto.com/refcenter/ , ©Copyright 2011, 

Ward's Automotive Group, a division of Penton Media Inc. Redistribution prohibited. 

lix http://www.census.gov/manufacturing/asm/index.html 
Deliberative Material - EPA Staff Draft 

Page numbers here refer to the pages on the Summary of interagency working comments. If we have 

not commented on a proposed edit, we have adopted it. 

Preamble:

p. 1: Rather than delete the sentence at the end of the first paragraph, we have revised it to correspond 

to other statements that raise the possibility of increased vehicle sales. 

p. 2: We have done some edits on the comments to make them more accurate (provided that we 

understand the intended meanings behind the suggested edits). 

 E.g., we do not assume that it is unprofitable to add additional fuel-saving technologies. As 

discussed below, for instance, a risk-averse firm may choose not to adopt technologies that are 

expected to be profitable because they are uncertain. 

 The rule does not “force” investments by all manufacturers. Though we expect all 

manufacturers to make some investments, they may also buy credits as an alternative compliance path. 

pp. 2-3: We have kept the footnotes. They are factual support for the statements in the text. 

Comment [A1]: Commenters note that this discussion is confusing. Commenters believe that 

manufacturers may not be collaborating on research even due to this rule, and it is likely each 

manufacturer will be conducting its own proprietary research in an effort to lower their own costs of 

meeting the standards. They might not have any incentive to share these innovations with their 

competitors. 

EPA response. EPA has added additional text to clarify this issue, and we will provide specific examples 

of collaboration among auto firms. The comment from the interagency is not technically correct. It is 

common for auto firms to collaborate on research activities. GM/Ford co-developed the current 

generation of 6-speed transmissions used by both companies. GM/Chrysler/BMW/Daimler had a multiyear development program to perform the research and initial development of the 2-mode hybrid 

system. Last year Ford and Toyota announced a new partnership to develop hybrids for pickup trucks. 

EPA will not delete this text as the basis 

EPA text edits

"Research among multiple parties can be a synergistic process: ideas by one researcher may stimulate 

new ideas by others, and more and better results occur than if the one researcher operated in isolation.1

 

Collaboration between automotive companies or automotive suppliers does occur; for example, in 2011 

Toyota and Ford announced a new effort to collaborate on the development of hybrid technology for 

 

1

 Powell, Walter W., and Eric Giannella, “Collective Invention and Inventor Networks,” Chapter 13 in Handbook of 

the Economics of Innovation, Volume 1, edited by B. Hall and N. Rosenberg (Elsevier, 2010) discuss how a 

“collective momentum” has led uncoordinated research efforts among a diverse set of players to develop

advances in a number of technologies (such as electricity and telephones). They contrast this view of technological 

innovation with that of proprietary research in corporate laboratories, where the research is part of a corporate 

strategy. Such momentum may result in part from alignment of economic, social, political, and other goals.

Deleted: ,
pickup trucks.2

 Another example was the four year joint development effort between General Motors 

and Ford for from 2002-2006 for the development of a new six-speed automatic transmission.3" 

Comment [A2]: Commenters request clarity on why there would be incentives to coordinate? 

Commenters note that unless the manufacturers are colluding, they should each have an incentive to 

keep propriety technologies such as batteries for themselves so that they can keep their own costs low 

relative to competitors. 

EPA response. EPA has added additional text to clarify this issue, pointing out the “network 

externalities” associated with development of infrastructure for electric vehicles. 

EPA text edits:

“An additional aspect of the standards is the possibility of greater standardization. As more companies

adopt new technologies, the incentives increase for additional suppliers and more availability of aftermarket replacement parts; these suppliers would be likely to find ways to increase compatibility across 

vehicle types. For example, though electric vehicles (EVs) are not expected to be more than a few 

percent of the vehicles produced in response to this rule, their adoption depends on such factors as 

batteries and charging methods that are compatible across different companies. These are examples of 

“network externalities,” where use of a technology by one party has greater benefits if more people are 

also using the technology. In this case, just as the ability to buy gasoline from any station facilitates 

owning a gasoline-based vehicle, the ability to recharge an EV or get replacement parts easily facilitates 

ownership of an EV. In the absence of the rule, fewer companies would be pursuing this technology, 

and it would be considered a specialty product; the incentives to coordinate might be low.” 

Comment [A3]: Commenters note that absent the rule, a manufacturer acting alone could achieve 

economies of scale if consumers want to buy more fuel efficient cars. Commenters request clarity on 

whether this is just a reiteration of the risk aversion argument? 

EPA Response. The deleted sentence “Further, the certainty. . .” was taken from interagency 

comments provided on NHTSA’s analysis on 8/18/12. We adopted it (as well as other statements) in 

response to the request to make conforming changes. EPA agrees with this statement. Manufacturer 

learning-by-doing is a function of cumulative production. To the extent that auto suppliers, who will 

manufacture much of the technology that will be used by auto OEMS, see higher volumes due to 

multiple auto companies using a given technology, costs will decrease as a result of those higher 

volumes. The same can be said for economies of scale. It is not automatic that an auto company on its 

own will achieve economies of scale for a given technology. Hybrids is an excellent example. The 

majority of the components in a hybrid system are often not manufactured by the OEM, but by 

suppliers. It is unlikely that any given manufacturer will see very large %’s of hybrids, but collectively the 

industry could produce volumes on the order of 1 million per year or more – and many of the 

components will be manufactured by suppliers, who can then achieve economies of scale, that would 

not occur if the technology was only being pursued by a single firm. 

p. 5-6: We are maintaining the paragraph as a summary of the points made immediately above, which 

we believe is useful to the reader. 

 

2

 "Ford and Toyota to Work Together on Hybrid System for Trucks", New York Times, August 22, 2011. 

3

 "Ford, GM Launch Joint 6-speed Automatic," Wards Automotive, August 31, 2006.

Deleted: u
Comment [A4]: Commenters request EPA to include citations. 

EPA Response. We have added the references. 

Comment [A5]: Commneters request EPA to delete this paragraph. Commenters note that this is 

speculative. The required standards may exceed standards adopted by other countries in the future. 

This could hurt manufacturers’ ability to compete there. 

EPA Response. This paragraph is not speculative. The standards contained in this final rule are of 

similar stringency to the standards in Europe and Japan for this time frame.4

 Canada is in the process of 

proposing the EPA/NHTSA standards. U.S. firms have limited R&D budgets, and many of those firms 

compete in international markets (Delphi, BorgWarner, Honeywell, Goodyear, etc., as well as Ford, GM, 

and Chrysler). These standards can enable those firms to compete more effectively, compared to a 

scenario where the U.S. GHG/CAFE standards were significantly less stringent than other major market 

standards, which would be the case if these standards are not in place. We have amended the 

paragraph to note that this effect should be true for “all global automakers.” 

RIA:

p. 8: We have maintained the sentence because it is an accurate reflection of the discussion in the 

Preamble, though we have modified the word “would” to “might.” 

p. 26: We have maintained most of the deleted paragraph. In the Preamble, it is the summary of the 

impacts we expect on this rule. (Most of the rest of the Preamble discussion is response to comments.) 

We therefore maintain it in the RIA as well. We have deleted the last sentence. 

p. 27: We disagree with the proposed deletion. We think it is an accurate and appropriately qualified 

statement of the effect of the rule on low-income households. This issue received public comment, and 

it is important that we provide a clear response on this issue. 

p. 28: We disagree with the proposed deletion. It is an accurate statement: fuel savings will benefit

low-income households. In the previous sentence, we acknowledge uncertainty over the effects of the 

rule on consumers’ ability to purchase vehicles. 

p. 28 and following: we disagree with the request to remove discussion of the impact of consumer fuel 

savings on employment. The language was included in the proposal, and we received a number of 

public comments on it. We consider the discussion to be factual and balanced. We note that some of 

the deleted language (e.g., p. 34: “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”) is the same as language included in NHTSA’s discussion since OMB 

submission and received no comments in interagency comments to NHTSA received on 8/18/12. 

Comment [A6]: Commenters request EPA to clarify with additional information. Commenters do not 

find it very clear. 

 

4

 See the International council for Clean Transportation, “Global Comparison of Light-Duty Vehicle Fuel

Economy/GHG Emissions Standards,” Update June 2012. 

http://theicct.org/sites/default/files/ICCT_PVStd_June2012_v0.pdf 
EPA Response. This is the same procedure used in the NPRM (with the spreadsheet included in the 

docket), though with some additional data. We ran a “semilog” regression: that is, we took the natural 

logarithm of the ERM values and ran an ordinary least squares regression with logged ERM as the 

dependent variable, and an intercept and year (as a time trend) as the independent variables. We used

the semilog functional form because the coefficient on year can be interpreted directly as the percent 

change per year. It also had excellent fit and results that are highly statistically significant. Below are 

results for the two sectors: 

Motor Vehicle Manufacturing

SUMMARY OUTPUT 

Regression Statistics 

Multiple R 0.969586

R Square 0.940098

Adjusted R 

Square 0.936354

Standard Error 0.053978

Observations 18

ANOVA 

 df SS MS F 

Significance 

F 

Regression 1 0.731617 0.731617 251.1025 3.35E-11

Residual 16 0.046618 0.002914

Total 17 0.778235 

 Coefficients 

Standard 

Error t Stat P-value Lower 95% 

Upper 

95% 

Intercept 77.94398 4.908248 15.8802 3.24E-11 67.53896 88.34901

Year -0.03886 0.002452 -15.8462 3.35E-11 -0.04406 -0.03366

Motor Vehicle Parts 

Manufacturing

SUMMARY OUTPUT 

Regression Statistics 

Multiple R 0.969434

R Square 0.939802

Adjusted R 

Square 0.93604

Standard Error 0.053274

Observations 18

ANOVA 

 df SS MS F 

Significance 

F 

Regression 1 0.708938 0.708938 249.7916 3.48E-11

Residual 16 0.04541 0.002838

Total 17 0.754347 
 Coefficients 

Standard 

Error t Stat P-value Lower 95% 

Upper 

95% 

Intercept 77.98493 4.844236 16.0985 2.64E-11 67.71561 88.25425

Year -0.03825 0.00242 -15.8048 3.48E-11 -0.04338 -0.03312

We have revised the text to read: “We regressed logged ERM values on year for both the Motor Vehicle 

Manufacturing and Motor Vehicle Parts Manufacturing sectors. We used this approach because the 

coefficient describing the relationship between time and productivity is a direct measure of the percent 

change in productivity per year. The results suggest a 3.9 percent per year productivity improvement in 

the Motor Vehicle Manufacturing Sector, and a 3.8 percent per year improvement in the Motor Vehicle 

Parts Manufacturing Sector. We then used the equation resulting from the regression to project the 

ERM through 2025. ” 

Comment [A7]: Commenters note that this is confusing. Commenters request clarity on whether these 

are all increases in labor relative to capital by deploying more fuel efficient technologies? Are there any 

technologies that have the opposite effect? 

EPA Response. These values are not increases in labor relative to capital, but rather a direct measure of 

labor, as stated in the text. As discussed in the text, the FEV analysis provides the change in costs on an 

extremely detailed basis for the change from a baseline technology to a new technology. Those costs 

include labor costs. We then used the hourly wage in the FEV analysis to convert these to hours per 

part. Thus, they are a direct measure of the change in labor associated with the change in the 

technology. This material is identical to what was presented in the NPRM; the UAW provided 

supportive comments stating this was useful information. We note in the text that these are “a subset 

of the technologies; ” “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 rule.” It is possible that other technologies will have the opposite effect; as noted, we do not have 

data for all the technologies that might be used for compliance with this rule, and we therefore do not 

quantify this effect. 

Deleted: (to estimate percent change per year) 

Deleted: relationship 
III. EPA MYs 2017-2025 Light-Duty Vehicle Greenhouse Gas Emissions Standards 

H. What are the Estimated Cost, Economic, and Other Impacts of the Rule? 

11. U.S. Vehicle Sales Impacts and Affordability of New Vehicles 

a. Vehicle Sales Impacts 

Predicting the effects of this rule on vehicle sales entails comparing two effects. On the 

one hand, the vehicles designed to meet the standards 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 due to significant fuel savings, which could encourage 

sales. Which of these effects dominates for potential vehicle buyers when they are considering a 

purchase will determine the effect on sales. Assessing the net effect of these two competing 

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

the extent to which manufacturers and dealers reflect technology costs in the purchase price. The 

empirical literature does not provide clear evidence on how much of the value of fuel savings 

consumers consider at the time of purchase. It also generally does not speak to the efficiency of 

manufacturing and dealer pricing decisions. Thus, we do not provide quantified estimates of 

potential sales impacts in this final rule. However, as discussed later in this section, there are 

several reasons why EPA believes this rulemaking could lead to an increase in vehicle sales, 

though we do not quantify a specific impact. 

An additional source of uncertainty in the analysis is understanding what would happen 

in the absence of this rule. Standard economic theory would suggest that, if automakers could 

profitably increase sales by adding more fuel-saving technologies to their vehicles, then 

manufacturers’ profit motives would lead them to voluntarily add those technologies in the 

absence of this rule. As discussed in Preamble Section III.D.1, we project, based on historical 

patterns, that auto makers would not go beyond the MY 2016 standards in the absence of this 

rule. Yet, if consumers consider just over three years’ worth of fuel savings in their vehicle 

purchase decisions and our assumptions about technology costs and future gas prices are correct, 

the payback period analysis in Section III.H.5 suggests that sales would increase in response to 

this rule. 

Although it is possible that manufacturers would not find it profitable to add at least some 

of the vehicle technologies in the absence of the rule (see Section III.H.1.a for a discussion), 

there may be the potential for increases in vehicle sales as a result of the rule. These 

explanations focus on conditions where the rule stimulates investments that would not happen in 

the rule’s absence. The explanations posed below raise possibilities that the rule, by requiring all 

automakers to meet the standards, may lead to mutually beneficial outcomes that might not 

happen in the absence of the rule. Consumers would then have the opportunity to purchase 

vehicles that would not be available in the absence of the rule; if consumers consider at least as 

Deleted: will 

Deleted: Are these analyses in conflict?

Deleted: could 

Deleted: are also a number of reasons for 

Deleted: notwithstanding our assumption of a flat 

baseline
Page 2 of 26

many years of fuel savings when buying new vehicles as the payback period for the new 

technologies, and if manufacturers nonetheless would not have produced these vehicles in the 

absence of the rule, positive sales impacts could occur as a result of these final standards. The 

three possibilities we suggest for such outcomes are promotion of social learning, reduction of 

risk and uncertainty for manufacturers, and promotion of innovation. 

i. Social Learning 

For many years, fuel economy standards did not change (see Preamble III.D.1).1 As 

discussed in Preamble III.H.1.a, consumers may not have focused on fuel economy, or may have 

found it difficult to do calculations involving the tradeoffs between fuel economy and increased 

vehicle costs, or may not have found vehicles with their preferred combination of fuel economy 

and other features. In recent years, though, fuel economy standards have started to increase.2 In 

addition, high fuel prices have helped to focus consumer attention toward vehicle fuel economy. 

Finally, the recently revised fuel economy label, with prominent information on fuel savings, are 

starting to appear on new vehicles. These factors may contribute to consumers gaining 

experience with the benefits that accrue to them from owning and operating vehicles with greater 

fuel efficiency. Consumer households that include vehicles with a fairly wide range of fuel 

economy have an opportunity to learn about the value of fuel economy on their own. Consumer 

demand may be shifting towards such vehicles, not only because of higher fuel prices but also if 

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

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

continue before and during the model years affected by this rule. 

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

required to be on all new vehicles in MY 2013, may increase sales by hastening this very type of 

consumer learning. As more consumers experience the savings in time and expense from owning 

more fuel efficient vehicles, demand may shift yet further in the direction of the vehicles with 

improved fuel economy and reduced GHG emissions mandated under the rule. This social 

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

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

vehicles may be more salient to individuals who own them, which might cause their subsequent 

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

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

marketing and advertising, and other forms of communications. Third, as more motorists 

experience the time and fuel savings associated with greater fuel efficiency, the price of used 

cars may better reflect such efficiency, further reducing the cost of owning more efficient 

 

1

 Car CAFE standards did not change from MYs 1990 through 2010. Truck CAFE standards did not change from 

MYs 1996 through 2004, and changed only 0.5 mpg cumulatively from MYs 1991 through 2004. See "Summary of 

Fuel Economy Performance," March 12, 2012, DOT/NHTSA, http://www.nhtsa.gov/fuel-economy. 

2

 Truck CAFE standards began to rise in MY 2005 and have risen every year since. Car CAFE standards began to 

rise in MY 2011. Ibid.
Page 3 of 26

vehicles for the buyers of new vehicles (since the resale price may increase). If these induced 

learning effects are strong, the rule could potentially increase total vehicle sales over time. This 

effect may be speeded or slowed by other factors that enter into a consumer’s valuation of fuel 

efficiency in selecting vehicles. 

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

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

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

individual automobile manufacturer would find it profitable to move toward more efficient 

vehicles to increase consumer learning because no individual company can fully internalize the 

potential future boost to demand. If one company were to make more efficient vehicles, counting 

on consumer learning to enhance demand in the future, that company would capture only a 

fraction of the extra sales so generated, because the learning at issue is not specific to any one 

company's fleet. Many of the extra sales could accrue to that company's competitors. 

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

positive externalities (spillovers) from one company to the others.3

 These positive externalities 

may lead to benefits for manufacturers as a whole if they increase the demand for vehicles. We 

emphasize that this discussion has been tentative and qualified. It is not possible to quantify these 

learning effects years in advance, and these effects may be speeded or slowed by other factors 

that enter into a consumer’s valuation of fuel efficiency in selecting vehicles. To be sure, social 

learning of related kinds has been identified in a number of contexts.4

 We asked for comments 

on the discussion offered here, with particular reference to any relevant empirical findings. 76 

FR 75150. We continue to explore this issue but did not receive any comments on the role of 

social learning, except in the context (discussed in Section III.H.1.a) of the effect of the rule on 

the visibility of and status associated with owning vehicles with improved fuel economy. 

ii. Reduction in Risk and Uncertainty for Manufacturers

As discussed in Preamble III.H.1.a, there appears to be a great deal of uncertainty about 

how consumers will respond to increases in fuel economy. Automakers may be cautious about 

adding more fuel-saving technology to vehicles if they are uncertain how buyers will respond. 

Even if they believe that buyers will respond positively, if a company is risk-averse, it may 

nevertheless hesitate to make the substantial major investments in new technologies and in 

 

3

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

trade associations to promote industry-wide public goods. For example, merchants in a given locale may band 

together to promote tourism in that locale. Antitrust law recognizes that this type of coordination can increase 

output. 

4

 See Hunt Alcott, Social Norms and Energy Conservation, Journal of Public Economics (forthcoming 2011), 

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

%20Social%20Norms%20and%20Energy%20Conservation.pdf (Docket EPA-HQ-OAR-0799-0825); Christophe 

Chamley, Rational Herds: Economic Models of Social Learning (Cambridge, 2003) (Docket EPA-HQ-OAR-0799-

1110). 
Page 4 of 26

research that would lead to increases in fuel economy across its fleet.5 If a manufacturer invests 

substantially in fuel efficient technologies expecting higher consumer demand than realized, then 

the manufacturer has incurred the costs of investment but not reaped the benefits of those 

investments. On the other hand, if a manufacturer does not invest in fuel-efficient technologies, 

then the manufacturers may lose some sales in the short run if demand for fuel economy is 

higher than expected, but it still retains the option of investing in fuel-efficient technologies in 

the longer run. If its investments proved unsuccessful, the company might face substantial 

losses. Even if the probability of being unsuccessful is low, the manufacturer may nevertheless 

perceive the losses in that scenario as a substantial risk. If the investment proved successful, the 

company would, of course, take market share from other companies – but, assuming that there 

are not brand-loyalty or other advantages to being first in the market with new fuel-saving 

technologies, only until the other auto companies caught up. In other words, for a risk-averse 

company, being a first mover may appear to have a greater downside risk than upside risk, even 

if the investment, on an expected-value basis, would pay off. If all companies are risk-averse, 

then they may all seek a strategy of waiting for some other company to be the first mover. In 

this case, caution about these major investments may lead to lack of adoption of new 

technologies in the absence of the rulemaking, consistent with the flat baseline assumption. This 

rulemaking, by requiring that all companies act at the same time, removes the scenario of one 

company bearing all the risk. 

In addition, there may be risk aversion on the consumer side. The simultaneous 

investment by all companies may also encourage consumer confidence in the new technologies. 

If only one company adopted new technologies, early adopters might gravitate toward that 

company, but early adopters tend to be a relatively small portion of the public. More cautious 

buyers, who are likely to be more numerous, might wait for greater information before moving 

away from well-known technologies. If all companies adopt advanced technologies at the same 

time, though, potential buyers may perceive the new technologies as the new norm rather than as 

a risky innovation. They will then be more willing to move to the new technologies. As some 

commenters have pointed out, simultaneous action required by the rule may change buyers’ 

expectations (their reference points) for fuel economy, and investing in more fuel economy may 

seem less risky than in the absence of the rule. 

The rule, then, may reduce manufacturers’ risk of making significant investments in fuelsaving technologies by requiring that all companies produce more fuel-efficient vehicles. Under 

this outcome, it is possible for the rule to facilitate investment that would not happen in the 

absence of the rule, and vehicle sales could increase as a result of the rule. 

 

5

 Sunding, David, and David Zilberman, “The Agricultural Innovation Process: Research and Technology Adoption 

in a Changing Agricultural Sector,” Chapter 4 in Handbook of Agricultural Economics, Volume 1, edited by B. 

Gardner and G. Rausser (Elsevier, 2001) show how delaying adoption of a new technology in order to gain more 

information may be a more profitable activity than adopting a technology, even if it has positive net benefits, when a 

potential adopter is risk-averse. 

Deleted: slow 

Deleted: ,

Deleted: providing a level playing field for 
Page 5 of 26

iii. Promotion of Innovation 

Research among multiple parties can be a synergistic process: ideas by one researcher 

may stimulate new ideas by others, and more and better results occur than if the one researcher 

operated in isolation.6 Collaboration between automotive companies or automotive suppliers 

does occur; for example, in 2011 Toyota and Ford announced a new effort to collaborate on the 

development of hybrid technology for pickup trucks.7 Another example was the four year joint 

development effort between General Motors and Ford for from 2002-2006 for the development 

of a new six-speed automatic transmission.8 One function that standards can serve is to promote

research into low-CO2 technologies that would not take place in the absence of the standards. 

Because all companies (both auto firms and auto suppliers) will have incentives to find better, 

less expensive ways of meeting the standards in this rule, the possibilities for synergistic 

interactions may increase. Thus, the rule, by focusing all companies on finding more efficient 

ways of achieving the standards, may lead to better outcomes than if any one company operated 

on its own. 

An additional aspect of the standards is the possibility of greater standardization. As 

more companies adopt new technologies, the incentives increase for additional suppliers and 

more availability of after-market replacement parts; these suppliers would be likely to find ways 

to increase compatibility across vehicle types. For example, though electric vehicles (EVs) are 

not expected to be more than a few percent of the vehicles produced in response to this rule, their 

adoption depends on such factors as batteries and charging methods that are compatible across 

different companies. These are examples of “network externalities,” where use of a technology 

by one party has greater benefits if more people are also using the technology. In this case, just 

as the ability to buy gasoline from any station facilitates owning a gasoline-based vehicle, the 

ability to recharge an EV or get replacement parts easily facilitates ownership of an EV. In the 

absence of the rule, fewer companies would be pursuing this technology, and it would be 

considered a specialty product; the incentives to coordinate might be low. If EVs become more 

common, though, compatible infrastructure and batteries may become more desirable, as 

potential buyers are likely to be encouraged toward this technology if they can easily find places 

to charge batteries. 

Thus, the rule may direct and promote innovation and standardization that would not 

happen in the absence of this rule. Such changes could reduce the cost increases associated with 

 

6

 Powell, Walter W., and Eric Giannella, “Collective Invention and Inventor Networks,” Chapter 13 in Handbook of 

the Economics of Innovation, Volume 1, edited by B. Hall and N. Rosenberg (Elsevier, 2010) (EPA Docket EPAHQ-OAR-2010-0799) discuss how a “collective momentum” has led uncoordinated research efforts among a diverse 

set of players to develop advances in a number of technologies (such as electricity and telephones). They contrast 

this view of technological innovation with that of proprietary research in corporate laboratories, where the research 

is part of a corporate strategy. Such momentum may result in part from alignment of economic, social, political, and 

other goals.

7

"Ford and Toyota to Work Together on Hybrid System for Trucks", New York Times, August 22, 2011. (EPA 

Docket EPA-HQ-OAR-2010-0799).

8

 "Ford, GM Launch Joint 6-speed Automatic," Wards Automotive, August 31, 2006.
Page 6 of 26

the rule and improve the qualities of the technologies, which could result in an increase in 

vehicle sales. Further, the certainty of the regulations reduces the costs of meeting them, because 

there will be more economies of scale and more learning curve benefits due to greater 

cumulative production of fuel-efficient technologies. 

In summary, in these three plausible explanations for how the rule could increase vehicle 

sales, we have focused on ways that the rule could promote coordinated actions on the part of 

automakers and suppliers that lead to mutual benefit, but that might not happen in the absence of 

the rule. These actions could lead to automakers developing technologies that they would not 

have developed in the absence of the rule. Consumers may respond positively to the vehicles 

resulting from this coordinated action. 

Several commenters requested that we conduct a quantitative vehicle sales analysis. As 

discussed in the proposal, in previous rulemakings, EPA and NHTSA conducted vehicle sales 

analyses by comparing the up-front costs of the vehicles with the present value of five years’ 

worth of fuel savings; the direction of vehicle sales would depend on whether up-front costs 

exceeded fuel savings (in which case sales would be expected to decline), or vice versa (in which 

case sales would be expected to increase).9 Some commenters specifically requested that we use 

the method found in the MYs 2012-16 rule; some specifically supported the five-year payback 

period; others argued for the importance of conducting the analysis without recommending 

methods. Ceres estimates that the rule will increase vehicle sales by 4.7 percent;10 the Defour 

Group provided estimates that the rule will decrease vehicle sales by 6-10 percent.11 The 

differences in the results appear to depend on the cost estimates used and on assumptions made 

about how vehicle buyers think about fuel savings when deciding on vehicle purchases. The 

Defour Group, for instance, uses cost estimates of about $3000 per vehicle based on summing 

costs (but not benefits) across multiple rules (an estimate we consider to be unfounded for 

reasons explained in this section below and also in TSD Chapter 3.1.2) and the assumption that 

consumers consider only 25 percent of fuel savings in their vehicle purchase decisions (see 

discussion in Section III.H.1.a). Other commenters wanted specific information on the effect of 

the rule on vehicle costs and whether consumers will be willing to buy the new vehicles, while 

consumer and environmental organizations indicated that consumers want more fuel-efficient 

vehicles, even if the up-front costs of the vehicles increase. The costs of the rule are discussed in

Preamble Section III.H.2. As discussed in Preamble Section III.H.1.a, we do not at this point 

have sufficient confidence in the estimates of the role of fuel economy in consumers’ vehicle 

 

9

 For instance, see U.S. Environmental Protection Agency (April 2010). “Final Rulemaking to Establish LightDuty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards: Regulatory 

Impact Analysis.” EPA-420-R-10-009, Chapter 8.1.1, pp. 8-1 to 8-4. 

10 Comments on this rule from Ceres, Docket EPA-HQ-OAR-2010-0799-9475, referring to a forthcoming report, 

Citi Investment Research and Analysis, “U.S. Autos and Auto Parts: Fuel Economy Focus: Industry Perspectives 

on 2020,” April 3, 2012, Docket EPA-HQ-OAR-2010-0799.

11 Walton, Thomas F., and Dean Drake, Defour Group, LLC. “Comments on the Notice of Proposed Rulemaking

and Preliminary Regulatory Impact Analysis for MY 2017 to 2025 Fuel Economy Standards.” February 13, 2012. 

Docket EPA-HQ-OAR-2010-0799-9319-A1.
Page 7 of 26

purchases to come to definitive conclusions about the impacts of the rule on vehicle sales. We 

do not, however, consider this uncertainty grounds for delaying the rule, as one comment 

suggested. The midterm evaluation provides an opportunity to revisit the impact of the rule on 

vehicle sales and consumer acceptance of the new technologies. 

This rule takes effect for MY 2017-2025. In the intervening years, it is possible that the 

assumptions underlying a quantitative analysis, as well as market conditions, might change. As 

the United Auto Workers points out, the state of the economy is a major, if not the primary, 

determinant of total vehicle sales. The impact of the rule on sales may therefore depend, among 

other factors, on changes in the state of the economy. Other commenters discussed the 

importance of consumer confidence, fuel prices, and even of publicity over fuel prices, in 

consumers’ interest in additional fuel economy. Sales could be negatively affected if gasoline 

prices are lower than expected or technology costs are higher than expected. In these cases, it is 

possible that the standards could require manufacturers to produce cars with higher levels of fuel 

economy than consumers would wish to buy. On the other hand, manufacturers’ marketing of 

increased fuel economy levels is also likely to play a role in consumer response to these vehicles. 

EPA agrees that these factors are important, but we are not sufficiently confident in quantitative 

estimates of the impacts of those factors to develop numerical estimates. We instead provide this 

qualitative assessment to highlight the factors important for understanding the effects of this rule 

on vehicle sales. 

As several commenters point out, the effect of this rule on the use and scrappage of older 

vehicles will be related to its effects on new vehicle prices, the fuel efficiency of new vehicle 

models, the fuel efficiency of used vehicles, and the total sales of new vehicles. If the value of 

fuel savings resulting from improved fuel efficiency to the typical potential buyer of a new 

vehicle outweighs the average increase in new models’ prices, sales of new vehicles could rise, 

the used vehicle market may increase in volume as new vehicle buyers sell their older vehicles, 

and scrappage rates of used vehicles may increase slightly. This will cause both an influx of 

more efficient vehicles into the used vehicle market and an increase in the turnover of the vehicle 

fleet (i.e., the retirement of used vehicles and their replacement by new models), thus 

accentuating the anticipated effect of the rule on fleet-wide fuel consumption and CO2

emissions. However, if potential buyers value future fuel savings resulting from the increased 

fuel efficiency of new models at less than the increase in their average selling price, sales of new 

vehicles will decline, the used vehicle market may decrease in volume as people hold onto their 

vehicles longer, and there will be a reduction in the rate at which used vehicles are retired from 

service. These effects will partly reduce the anticipated effects of this rule on fuel use and 

emissions. Because we do not have good estimates of the relationships between the new and used 

vehicle markets, we have not attempted to estimate explicitly the effects of the rule on the used 

vehicle market, scrappage of older vehicles, and the turnover of the vehicle fleet. 

Consumer, environmental, and investor organizations, the United Auto Workers, as well 

as a citizens’ campaign suggested that the rule will help the domestic auto industry, including the 
Page 8 of 26

domestic supply base, compete in the global marketplace, through its encouragement of 

advanced technologies that may be useful in meeting emissions standards and consumer 

demands in foreign markets. We agree that this is likely for all global automakers, as generally 

the emission standards established in this rule are similar in stringency to emissions and fuel 

economy standards being considered by Japan, the European Union, South Korea, Canada, China 

and other international markets. Global manufacturers also design vehicles using a common 

platform in order to reduce costs. Vehicles built on these common platforms are sold in many 

markets around the world. To the extent the domestic OEMs and suppliers can focus their 

limited research and product development efforts on the same technologies for the U.S. market 

as for international markets, this should enable the companies to compete more effectively 

outside the U.S. 

Chapter 8 of EPA’s RIA has further discussion of methods for examining the effects of 

this rule on vehicle sales. 

b. Impact of the Rule on Affordability of Vehicles and Low-Income Households 

Several organizations provided comments about the effect of the rule on the affordability 

of new vehicles, as well as the impacts of the rule specifically on low-income households. 

Comments from Consumer Federation of America (CFA) and 23 other consumer groups, 

as well as Consumers Union (CU) and several environmental organizations, argued that lowincome households will benefit from the rule. These commenters cite Bureau of Labor Statistics 

data that low-income households spend more on fuel than they do on new vehicles each year and 

are thus more vulnerable to fuel costs. CU comments that low-income households pay a 

disproportionately large portion of their income on fuel and are thus most vulnerable to price 

spikes in gasoline. CFA reported that in 2010, households with incomes below $20,000 spent 

7.3 times as much on gasoline as on new car payments, compared with 1.2 times as much for 

households with incomes above $70,000. This commenter believes that consumers will benefit 

greatly from the fuel savings that come with improved fuel economy. These organizations note 

that low-income households account for a very small portion of new car buyers, since they 

primarily purchase used cars, and are therefore less affected by the up-front costs of the more 

efficient vehicles than those who buy new vehicles. CU further comments that Consumers 

Reports survey data show that low-income households support improved fuel economy. In a 

recent survey, 71% of low-income households responded that they expect to choose a model 

with better fuel economy, compared to 59% of moderate and high-income respondents. In 

addition, 79% of low-income respondents to the survey reported that they were willing to pay 

extra for a more fuel efficient vehicle if they can recover the additional cost through lower fuel 

costs within five years, compared to 86% of moderate and high-income respondents. 

In addition, these commenters agreed with EPA’s assessment in the NPRM that 

consumers who buy their vehicles with loans save more in fuel each month than they do in 
Page 9 of 26

increased loan payments. CU points out that this is especially true for buyers of future, more 

fuel-efficient used vehicles: the increase in up-front cost is much lower on a used vehicle, due to 

depreciation, while the fuel economy of the vehicle is unlikely to change over time. Because 

low-income households disproportionately buy used vehicles, they will benefit from this more 

rapid cost recovery. Because most of the increased vehicle cost depreciates after five years, the 

payback period for improved fuel economy in used MY 2017 and later vehicles will be shorter 

than the payback period for these vehicles when newly purchased (under two years for some 

examples). EPA agrees that more efficient vehicles will reduce operating costs for buyers of 

used vehicles as well as new vehicles, because the fuel-saving technologies maintain their 

effectiveness over time; indeed, GHG standards continue to apply in-use. As shown in RIA 

Chapter 5.5, our estimate of the payback period for five-year-old MY 2025 vehicles is 

approximately 1.1 years, less than the payback period of about 3.2-3.4 years for new MY 2025 

vehicles. We also note that depreciation rates may be affected by the rule: increases in 

reliability would decrease depreciation, and decreases in reliability would increase depreciation. 

Finally, CU points out that some auto lenders take into consideration the fuel economy of new 

vehicles, and offer discounted rates for more efficient vehicles.12 As discussed further below, 

EPA also finds that a number of financial institutions give a discount on loans for more fuelefficient vehicles. 

The National Automobile Dealers Association (NADA) and the Institute for Energy 

Research emphasized that the increase in the up-front vehicle costs would be a factor in 

consumers’ abilities to purchase. In particular, they stated that, if vehicle buyers are not able to 

get loans for vehicles that have become more expensive as a result of new standards, because 

they cannot get access to credit for the additional cost, then they will be unable to participate in 

the new vehicle market even if the new vehicles offer significant fuel savings. This argument is 

based on the statement from NADA that auto lenders do not take into account the fuel economy 

of the vehicles when they are deciding on providing loans; the lenders consider only consumers’ 

debt-to-income ratios. NADA provided an analysis that concludes that 6.8 million licensed 

drivers may no longer have access to new vehicles. According to NADA’s analysis, this 

estimate is the number of licensed drivers who live in the 3.1 – 4.2 million households that could 

borrow $11,750, the loan amount for the least expensive new vehicle in 2011 after a $1000 down 

payment, but could not borrow $14,750.13 This difference of $3,000 is meant to represent what 

NADA views as the cost increase of new fuel economy standards, which EPA believes is 

incorrect and responds to further below. 

 In assessing these comments, EPA finds that the NADA study does not provide a usable 

estimate of those consumers in the market for new vehicles who might have trouble getting 

 

12 See, for instance, Ladika, Susan (2009). “’Green’ auto loans offer lower rates,” Bankrate.com, 

http://www.bankrate.com/finance/auto/green-auto-loans-offer-lower-rates-1.aspx , accessed 2/28/12. 

13 Wagner, D., P. Nusinovich, and E. Plaza-Jennings, National Automobile Dealers Association (February 13, 2012). 

“The Effect of Proposed MY 2017-2025 Corporate Average Fuel Economy (CAFE) Standards on the New Vehicle 

Market Population.” Docket EPA-HQ-OAR-0799. 
Page 10 of 26

loans, and is not a usable estimate of the impacts of the rule on the new vehicle market. Because 

the NADA study does not separate consumers who might consider new vehicles from consumers 

who are not in the market for new vehicles, the 6.8 million licensed driver figure significantly 

overestimates any impact of this rule on the new vehicle market. 

The NADA study suffers from a number of inaccuracies and weaknesses. First, it is 

important to understand what NADA’s 6.8 million estimate actually represents. NADA simply 

looked at the 113 million households in the U.S. who could afford to borrow $11,750 and 

estimated which ones of those could not afford to take out a loan of $14,750.14 NADA’s analysis 

unfortunately neglects a fundamental factor that could make this analysis relevant to this 

rulemaking – how many of those households would in fact even be in the market for a new 

vehicle. EPA believes that the vast majority of these households would not be in the market for 

new vehicles (for context, the total new vehicle market is estimated to be 17.2 million vehicles in 

2025; see TSD Chapter 1.3.2.1). As documented by many other commenters and as can be 

found in the Federal Reserve Board’s Survey of Consumer Finances,15 low-income households 

account for a very small portion of new car buyers, since they primarily purchase used cars. 

Thus, the NADA estimate is severely flawed and does not contribute usable information to 

identify the impacts of this rule on the vehicle market or on low-income households. 

Second, the NADA estimate is based, not on people who are considering purchasing new 

vehicles, but on the number of licensed drivers in households in the U.S. who could theoretically 

qualify to borrow $11,750 but not $14,750, based purely on debt-to-income ratio.16 Even 

accepting NADA’s study at face value, the relevant unit for the financial decision would be the 

number of households – not every licensed driver in a low income household would purchase a 

separate vehicle. The number of households in the NADA study is 3.1 to 4.2 million, already 

far lower than the estimate of 6.8 million drivers. 

Third, NADA’s assumption of a $3,000 cost increase per vehicle is based on summing 

the costs of MY 2011, MY 2012-16, and MY 2017-25 rules. This estimate does not correspond 

to EPA’s estimate, an average cost of about $1,800 per vehicle by MY2025, in several ways. 

For analyzing the effects of this rulemaking, it is appropriate to focus on the costs and benefits 

associated with this rulemaking, not those of previous rulemakings. The impacts of the other 

 

14 The Bureau of Labor Statistics’ Consumer Expenditure Survey, on which the Wagner et al. paper is based, 

measures 121,000 households in the U.S. in 2010. Wagner et al. find that “an estimated 93% of all consumer units 

have a financial profile that would allow them to meet the 40% maximum debt to income ratio after purchasing the 

current minimum cost new vehicle ($12,750).” (See footnote 13, p. 4.) Ninety-three percent of 121 million 

households is about 113 million households; Wagner et al.’s estimate of 3.1 to 4.2 million of those who can borrow 

$11,750 but not $14,750 is 2.8 to 3.7 percent of that total. 

15 In the Federal Reserve Board’s 2007 Survey of Consumer Finances, households with income below $35,200

(about the lower 40% of population by income) bought about 17% of new vehicles; those in the bottom quintile of 

income bought fewer than 2% of new vehicles. See Federal Reserve Board, 2007 Survey of Consumer Finances, 

http://www.federalreserve.gov/econresdata/scf/scf_2007.htm. 

16 As noted, these amounts are based on the cost of the least expensive vehicle in 2011, with $1000 down payment, 

with the assumption that it will become $3000 more expensive as the result of three rulemakings, for MYs 2011, 

2012-16, and 2017-25 (see Wagner et al., footnote 13). 

Deleted: 507

Deleted: 507
Page 11 of 26

rules are included in the reference case for this rule. The NADA cost estimate, based on a MY 

2011 vehicle, appears to double-count MY 2011 costs, because those should already be included 

in the price of the MY 2011 vehicle used in its study. Further, the costs of meeting MY 2016 

standards in 2025 are expected to be lower than the costs of meeting those standards in 2016, the 

value used by NADA, due to manufacturer learning. Moreover, EPA’s costs estimates are based 

on industry-wide averages, not applicable to specific vehicle models. As discussed further 

below, impacts of the rule on the prices of low-price vehicles may well be less than these 

averages. 

Fourth, the estimate does not take into account, as pointed out by CU and as EPA has 

documented, that some lenders currently give discounts for loans to purchase more fuel-efficient 

vehicles.17 It is possible (though unknown at this time) that the auto loan market may evolve to 

include further consideration of fuel savings, as those savings play a significant factor in 

offsetting the increase in up-front costs of vehicles. 

Fifth, the NADA analysis is based on the cost of the least expensive vehicle in the MY 

2011 market, but the market size for low-priced vehicles is only about one-tenth the size of 

NADA’s estimate of 6.8 million affected people. The agencies’ baseline estimates of the vehicle 

fleet in 2025 finds that total sales of vehicles costing less than $15,000 (a price point that low 

income consumers in the new car market would most likely be pursuing) in the absence of the 

rule are estimated to be well below 1 million in MY 2025; there is also no relationship between 

the NADA estimate and the potential impact of this rule on sales of low-priced vehicles. 

Sixth, if NADA’s estimate reflected a measurable effect of the rule, that effect would be 

reflected in a commensurate reduction in vehicle sales. Yet there is no connection between any 

vehicle sales estimates provided in comments on this rule and the NADA estimate. As discussed 

in section III.H.11.a, many commenters predict an increase in vehicle sales as a result of the rule, 

though others predict decreases.18 However, even the most negative estimate provided in public 

comments of the GHG rule’s impact on vehicle sales, from the Defour Group (which we address 

in detail in Section III.H.11.a), is a reduction of 1.8 million vehicles. The NADA estimate 

appears significantly overstated even compared to this commenter’s most negative estimate of 

vehicle sales impacts. 

For these reasons, we find the NADA study does not provide a usable estimate of 

consumers in the market for new vehicles who might have trouble getting new vehicle loans, nor 

do we find it a usable estimate of the impacts of the rule on the new vehicle market. 

 

17 See footnote12, above. An internet search on the term “green auto loan” produced more than 50 lending 

institutions that provide reduced rates for more efficient vehicles. See Helfand, Gloria (2012). “Memorandum: 

Lending institutions that provide discounts for more fuel-efficient vehicles.” Assessment and Standards Division, 

Office of Transportation and Air Quality, U.S. Environmental Protection Agency, Docket EPA-HQ-OAR-0799. 

18 We note that the role of vehicle financing in vehicle purchase decisions is not a separate factor in typical studies 

of the determinants of vehicle sales. Estimates of vehicle sales in the literature, which commonly are dependent on 

both up-front vehicle costs and fuel costs, implicitly account for effects of the loan market. 

Deleted: There is no reason to expect that the 

number of lenders instituting this policy will 

decrease in the future as fuel efficiency increases

beyond the levels seen today. 

Deleted: ndeed, i

Deleted: 506
Page 12 of 26

It is possible that future trends in the auto loan market may affect future vehicle sales. It 

is also possible that some people who have significant debt loads may not be able to get 

financing for some of these new vehicles; they may have to buy different vehicles (including 

used vehicles) or delay purchase. For others who borrow on credit, though, as discussed in 

Section III.H.5, the fuel savings are expected to outweigh the increased loan costs from the time 

of vehicle purchase. As some comments suggest, the rule thus may make vehicles more 

affordable to the public, by reducing consumers’ vulnerability to fuel price jumps. 

Some comments raised concerns about the impacts of the rule specifically on low-priced 

vehicles. EPA agrees that vehicles in the low-priced (economy-class) segment will bear 

technology costs needed to meet the new standards, but it is not known how manufacturers will 

decide to pass on these costs across their vehicle fleets, including in the low-priced vehicle 

segment. If manufacturers decide to pass on the full cost of compliance in this segment, then it is 

possible that consumers who might barely afford new vehicles may be priced out of the newvehicle market or may not have access to loans. As just discussed, the rule’s impacts on 

availability of loans are unclear, because some lenders do factor fuel economy into their loans, 

and it is possible that this trend may expand. In addition, as the Union of Concerned Scientists 

comments, auto makers have some flexibility in how both technologies and price changes are 

applied to these vehicles; auto makers have ways to keep some vehicles in the low-priced vehicle 

segment if they so choose. Though the rule is expected to increase the prices of these vehicles, 

the degrees of price increase and the impacts of the price increases, especially when combined 

with the fuel savings that will accompany these changes, are much less clear. 

The Defour Group suggests that the standards are regressive, with adverse impacts falling 

disproportionately on low-income households, and possibly limiting their ability to obtain 

employment because of limited mobility. The commenter’s regressivity assessment is based on a 

study of a non-footprint-based fuel economy program;

19 the disproportionate impact on lowincome households is based on the increased prices of used vehicles and the shift toward smaller 

vehicles. As discussed above in Section III.H.11.a, EPA finds that the impact on the used 

vehicle market depends on the impact of the rule on new vehicle sales, which we have not 

quantified. Because the footprint-based standard reduces incentives to downsize vehicles, we do 

not accept the conclusion that the rule will result in buyers of used vehicles getting smaller ones 

with a consequent welfare loss. For these reasons, the regressivity finding from Jacobsen’s paper 

is not applicable to the effects of this rule. 

In summary, the net effect of the rule on low-income households depends on several 

factors: the way that manufacturers choose to translate cost increases into price increases; the 

effects on sales of used vehicles, which depend on the effects on sales of new vehicles; the fuel 

savings that the new (and used) vehicles will provide; and any effects on access to credit for new 

 

19 Jacobsen, Mark. “Evaluating U.S. Fuel Economy Standards In a Model with Producer and Household 

Heterogeneity.” Working paper, University of California, San Diego, September 2010. Docket EPA-HQ-OAR0799-0829. 

Deleted: EPA does not deny the possibility that the 

market for new vehicle loans may be affected by this 

rule, but we do not at this time have sufficient 

evidence to project whether that market will evolve

to help or hinder sales of new, more fuel-efficient

vehicles.
Page 13 of 26

and used vehicles. For reasons outlined above, we do not at this time have quantitative 

assessments of how these effects interact and affect low-income households. However, due to 

the significant effect of the rule on fuel savings, especially for used vehicles (see RIA Chapter 

5.5), we expect low-income households to benefit from the more rapid payback period for used 

vehicles, though some of this benefit may be affected by the net effect of this rule on the prices 

and availability of used vehicles, which we have not estimated. 

In addition, the net effect of the rule on low-priced vehicles is difficult to assess; though 

we expect the prices of these vehicles to increase, it is also possible that auto makers may find 

ways to preserve the entry-vehicle segment, by adding less additional technology to these 

vehicles or through pricing strategies. The net effect of the rule on access to credit is also 

difficult to assess: though some consumers may find themselves credit-constrained, some auto 

lenders are already giving interest rate discounts for more fuel-efficient vehicles, and the loan 

market may continue to evolve. 

12. Employment Impacts 

a. Introduction 

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

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

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

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

“strengthen the [auto] industry and enhance job creation in the United States.”20 The recently 

issued Executive Order 13563, “Improving Regulation and Regulatory Review” (January 18, 

2011), states, “Our regulatory system must protect public health, welfare, safety, and our 

environment while promoting economic growth, innovation, competitiveness, and job creation” 

(emphasis added). EPA is accordingly providing partial estimates of the effects of this rule on 

domestic employment in the auto manufacturing and parts sectors, while qualitatively discussing 

how it may affect employment in other sectors more generally. Several commenters specifically 

pointed to the desirability of our conducting employment analyses, to provide insights into the 

effects of the rule on economic recovery and the health of the auto industry; we did not receive 

comments opposed to the inclusion of employment impacts. 

This rule is expected to affect employment in the United States through the regulated 

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

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

refining and supply sectors, and the general retail sector. According to the U.S. Bureau of Labor 

Statistics, in 2010, about 677,000 people in the U.S. were employed in Motor Vehicle and Parts 

 

20 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency Standards. The White House, 

Office of the Press Secretary, May 21, 2010. http://www.whitehouse.gov/the-press-office/presidentialmemorandum-regarding-fuel-efficiency-standards 
Page 14 of 26

Manufacturing Sector (NAICS 3361, 3362, and 3363). About 129,000 people in the U.S. were 

employed specifically in the Automobile and Light Truck Manufacturing Sector (NAICS 33611), 

the directly regulated sector, since it encompasses the auto manufacturers that are responsible for 

complying with the standards.21 The employment effects of this rule are expected to expand 

beyond the regulated sector. Though some of the parts used to achieve the standards are likely to 

be built by auto manufacturers themselves, the auto parts manufacturing sector also plays a 

significant role in providing those parts, and will also be affected by changes in vehicle sales. 

Changes in light duty vehicle sales, discussed in Section III.H.11, could affect employment for 

auto dealers. As discussed in Section III.H.4, this rule 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 rule 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. In such a period, both positive and 

negative employment effects are possible.22 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.23 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.”

 

21 U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed on August 9, 2011. 

22 Masur and Posner, 2011. “Regulation, Unemployment, and Cost-Benefit Analysis.” 

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1920441 (Docket EPA-HQ-OAR-2010-0799-1222). 

23 Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis of EPA’s Transport 

Rule.” White paper commissioned by Excelon Corporation, March 2011 (Docket EPA-HQ-OAR-2010-0799-0676). 
Page 15 of 26

It is assumed that the official unemployment rate will have declined to 5.3 percent by the 

time by the time this rule takes effect and so the effect of the regulation on labor will be to shift 

workers from one sector to another.24 Those shifts in employment impose an opportunity cost on 

society, approximated by the wages of the employees, as regulation diverts workers from other 

activities in the economy. In this situation, any effects on net employment are likely to be 

transitory as workers change jobs (e.g., some workers may need to be retrained or require time to 

search for new jobs, while shortages in some sectors or regions could bid up wages to attract 

workers). It is also possible that the state of the economy will be such that positive or negative 

employment effects will occur. 

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. 

The NPRM included a discussion of different methods for conducting employment 

analysis (see the discussion in RIA Chapter 8.2.2), including computable general equilibrum 

models, input-output models, hybrid models, and single-sector models, and requested comment 

on those methods. See 76 FR 75155-156. That discussion noted that all potential methods of 

estimating employment impacts of a rule have advantages and limitations. We did not receive 

comments about methods, except for some support for EPA’s approach in the NPRM, and some 

support (discussed further below) for including multiplier impacts. 

We received a number of comments (from the Defour Group and from some private 

individuals) asserting that there will be decreases in employment as a result of the costs of the 

rule, and a number of comments (from the United Auto Workers, environmental organizations, 

sustainable business groups, some private individuals, and others) asserting increases in 

employment, based on the development of advanced technologies and the reduction in net costs 

due to fuel savings. An assessment by the Defour Group predicts a loss of 155,000 jobs in 

 

24 Office of Management and Budget, “Fiscal Year 2012 Mid-Session Review: Budget of the U.S. Government.” 

http://www.whitehouse.gov/sites/default/files/omb/budget/fy2012/assets/12msr.pdf , p. 10. 
Page 16 of 26

manufacturing and supply, plus another 50,000 in distribution.25 A study by Ceres predicts job 

gains of 43,000 in the auto industry and 484,000 economy-wide.26 Some comments cite a study 

by the Natural Resources Defense Council, National Wildlife Federation, and United Auto 

Workers that 150,000 auto workers already are working to supply clean, fuel-efficient 

technologies.27 The differences in results for quantitative employment impacts are due to factors 

such as those discussed above. Estimates of decreases in employment commonly come from 

studies that use cost estimates higher than those of EPA, and sometimes lower benefits estimates, 

resulting in reductions in vehicle sales. For instance, some comments from individuals cite the 

National Automobile Dealers Association and Center for Automotive Research for cost estimates 

of $5000 to $6000 per vehicle, much higher than those estimated in Section III.H.2; EPA does 

not endorse those alternative cost estimates, as discussed in Section 18.2 of the Response to 

Comments. The NADA estimates inappropriately include the costs of other rulemakings and use 

indirect cost estimates which we consider inappropriate (see TSD Chapter 3.1.2.2). The Center 

for Automotive Research estimates do not take into account expected technological advances, do 

not reflect the use of air conditioning credits in this rule, and calculate costs from a baseline of 

2008 instead of MY 2016 standards. Those studies commonly look at the employment 

associated with vehicle sales, but not the employment associated with producing the technologies 

needed to comply with the standards, or changes in labor intensity of production. Analyses that 

find increases in employment commonly start with increased vehicle sales as a result of the rule, 

and take into consideration the employment effects associated with additional technologies. In 

both cases, “multiplier” effects, which extend employment impacts beyond the auto sector to 

impacts on suppliers, other sectors, and expenditure changes by workers, lead to large estimates, 

either positive or negative, of the employment effects of the rule. We received the suggestion to 

include in our analysis an alternative scenario where there is less than full employment; the 

implication of less than full employment is that multiplier effects are more likely. We also 

requested comment on other sectors that warranted consideration in this rule, 76 FR 75157, but 

we did not receive suggestions. 

After considering these comments, EPA is continuing with the employment approach in 

the NPRM, though with some updating of quantitative impacts in the auto sector. For impacts in 

the auto sector, EPA uses a conceptual framework that identifies employment impacts due to 

changes in vehicle sales, changes in costs, and changes in the labor intensity of production. For 

impacts in related sectors, EPA presents qualitative discussions. We do not quantify multiplier 

 

25 Walton, Thomas F., and Dean Drake, Defour Group LLC (February 13, 2012). “Comments on the Notice of 

Proposed Rulemaking and Preliminary Regulatory Impact Analysis for MY 2017 to 2025 Fuel Economy Standards.” 

Docket EPA-HQ-OAR-2010-0799-9319. 

26 Management Information Services, Inc. (July 2011). “More Jobs per Gallon: How Strong Fuel Economy/GHG 

Standards Will Fuel American Jobs.” Boston, MA: Ceres. Docket EPA-HQ-OAR-2010-0799-0709. 

27 Natural Resources Defense Council, National Wildlife Federation, and United Auto Workers (August 2011). 

“Supplying Ingenuity: U.S. Suppliers of Clean, Fuel-Efficient Vehicle Technologies.” 

http://www.nrdc.org/transportation/autosuppliers/files/SupplierMappingReport.pdf (Docket EPA-HQ-OAR-2010-

0799-). 
Page 17 of 26

effects, due to uncertainty over the state of the economy at the time this rule takes effect as well 

as the market evolutions that are likely to occur between now and implementation. 

b. Conceptual Framework for Employment Impacts in the Regulated Sector 

A study by Morgenstern, Pizer, and Shih28 provides a retrospective look at the impacts of 

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

spending on pollution abatement for four highly polluting/regulated U.S. industries (pulp and 

paper, plastics, steel, and petroleum refining) using data for six years between 1979 and 1991. 

The paper provides a theoretical framework that can be useful for examining the impacts of a 

regulatory change on the regulated sector in the medium to longer term. In particular, it 

identifies three separate ways that employment levels may change in the regulated industry in 

response to a new (or more stringent) regulation. 

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

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

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

on the cost increase passed on to consumers as well as the demand elasticity of 

industry output.” 

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

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

“production costs rise, more inputs, including labor, are used to produce the same 

amount of output.” 

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

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

words, “environmental activities may be more labor intensive than conventional 

production,” meaning that “the amount of labor per dollar of output will rise,” though 

it is also possible that “cleaner operations could involve automation and less 

employment, for example.” 

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

employment,29 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. 

 

28 Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. “Jobs Versus the Environment: An IndustryLevel Perspective.” Journal of Environmental Economics and Management 43 (2002): 412-436 (Docket EPA-HQOAR-2010-0799-1011). 

29 As will be discussed below, the demand effect 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? 
Page 18 of 26

The authors conclude that increased abatement expenditures generally have not caused a 

significant change in employment in those sectors. More specifically, their results show that, on 

average across the industries studied, each additional $1 million spent on pollution abatement 

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

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

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

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

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

Pollution Abatement Cost and Expenditure survey, has been conducted infrequently since 1994, 

with the last survey conducted in 2005. The empirical estimates provided by Morgenstern et al. 

are not relevant to the case of fuel economy standards, which are very different from the 

pollution control standards on industrial facilities that were considered in that study. In addition, 

it does not examine the effects of regulation on employment in sectors related to but outside of 

the regulated sector. Nevertheless, the theory that Morgenstern et al. developed continues to be 

useful in this context for examining the impacts of the rule on the auto sector. 

c. Employment Analysis of This Rule 

As mentioned above, this program is expected to affect employment in the regulated 

sector (auto manufacturing) and other sectors directly affected by the rule: auto parts suppliers, 

auto dealers, and 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).

Changes in consumer expenditures due to higher vehicle costs and lower fuel expenses will also 

affect employment. 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 rule 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 rule on other 

sectors. Focusing quantification of employment impacts on the regulated sector has some 

advantages over quantifying all impacts. 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 rule. Focusing on the 

regulated sector provides insight into employment effects in that sector without having to make 

assumptions about the state of the economy when this rule has its impacts. We include a 

Deleted: , and

Deleted: (who will face 

Deleted: d

Deleted: itures

Deleted: )

Deleted: First, t

Deleted: 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. 
Page 19 of 26

qualitative discussion of employment effects in 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 rule 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. 

i. Employment Impacts in the Auto Industry

Following the Morgenstern et al. conceptual framework for the impacts of regulation on 

employment in the regulated sector, we consider three effects for the auto sector: the demand 

effect, the cost effect, and the factor shift effect. However, we are only able to offer quantitative

estimates for the cost effect. We note that these estimates, based on extrapolations from current 

data, become more uncertain as time goes on. 

(1) The Demand Effect 

The demand effect depends on the effects of this rule 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 rule will increase vehicle costs; by itself, this effect would reduce vehicle 

sales. On the other hand, this rule will reduce the fuel costs of operating the vehicle; by itself, 

this effect would increase vehicle sales, especially if potential buyers have an expectation of 

higher fuel prices. The sign of the demand effect will depend on which of these effects 

dominates. This issue is discussed further in Sections III.H.1 and III.H.11. Some comments 

encouraged us to quantify this effect, once we quantified estimates for vehicle sales. Because, as 

described in Section III.H.11, we have not quantified the impact on sales for this rule, we do not 

quantify the demand effect. 

(2) The Cost Effect 

The demand effect measures employment changes due to new vehicle sales only. The 

cost effect measures employment impacts due to the development, manufacturing, and 

installation by auto suppliers and manufacturers of the new or additional technologies needed for 

vehicles to comply with the standards. As RIA Chapter 8.2.3.1.2 explains, we estimate the cost 

effect by multiplying the costs of rule compliance by ratios of workers to each $1 million of 

Deleted: ¶
Page 20 of 26

expenditures in that sector. The magnitude and relative size of these ratios depends on the 

sectors’ labor intensity of the production process. Several commenters mentioned the 

importance of this rule in encouraging employment related to the technologies expected to be 

used to comply with this rule. We received no comments criticizing the approach used here; the 

UAW commended EPA for it. 

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; 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. 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 rule 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) Employment Requirements Matrix (ERM);30 the 

Census Bureau’s Annual Survey of Manufactures31 (ASM); and the Census Bureau’s Economic 

Census. RIA Chapter 8.2.3.1.2 provides details on all these sources. The ASM and the 

Economic Census have more sectoral detail than the ERM; we provide estimates for both Motor 

Vehicle Manufacturing and Light Duty Vehicle Manufacturing sectors for comparison purposes. 

For all of these, we adjust for the ratio of domestic production to domestic sales (as supported by 

a commenter). The maximum value for employment impacts per $1 million expenditures (after 

accounting for the share of domestic production) in 2010 was estimated to be 1.809 if all the 

additional costs are in the parts sector; the minimum value is 0.402, 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. 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. As 

discussed in the RIA, trends in the BLS ERM are used to estimate productivity improvements 

 

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

31 http://www.census.gov/manufacturing/asm/index.html
Page 21 of 26

over time that are used to adjust these ratios over time. Table III-1 shows the cost estimates 

developed for this rule, discussed in Section III.H.2. Multiplying those cost estimates by the 

maximum and minimum values for the cost effect (maximum using the Economic Census ratio if 

all additional costs are in the parts sector, and minimum using the Economic Census ratio for the 

light-duty sector if all additional costs are borne by auto manufacturers) provides the cost effect 

employment estimates. This is a simple way to examine the relationship between labor required 

and expenditure. 

While we estimate employment impacts, in job-years, beginning with the first year of the 

standard (2017), some of these employment gains may occur earlier as auto manufacturers and 

parts suppliers hire staff in anticipation of compliance with the standard. A job-years is a way to 

calculate the amount of work needed to complete a specific task. For example, a job-year is one 

year of work for one person, or 6 months of work for 2 people. 

Table III-1 Employment Effects due to Increased Expenditures on Vehicles and Parts, in 

job-years 

Year 

Costs (before 

adjustment for 

domestic 

proportion of 

production) 

($Millions) 

Minimum employment 

effect if all expenditures 

are in light duty vehicle 

mfg sector 

Maximum employment 

effect if all expenditures 

are in the parts sector 

2017 $ 2,435 700 3,200 

2018 $ 4,848 1,300 6,200 

2019 $ 6,818 1,700 8,400 

2020 $ 8,858 2,100 10,500 

2021 $ 12,400 2,900 14,200 

2022 $ 18,323 4,100 20,200 

2023 $ 23,734 5,100 25,200 

2024 $ 29,101 6,000 29,700 

2025 $ 31,678 6,300 31,100 

(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 standards. As 

discussed in RIA Chapter 8.2.3.1.3, for a subset of the technologies, EPA-sponsored research 

(discussed in Chapter 3.1.1.1 of the Joint TSD), which compared new technologies to existing 

Deleted: Table III-107

Deleted: 107
Page 22 of 26

ones at the level of individual components, found that labor use for those new technologies 

increased: those new fuel-saving technologies use more labor than the baseline technologies. 

For instance, switching from a conventional mid-size vehicle to a hybrid version of that vehicle 

involves an additional $395.85 in labor costs, which we estimate to require an additional 8.6 

hours per vehicle.32 For a subset of the technologies likely to be used to meet the standards in 

this rule, 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. 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. Comments supported this 

approach and encouraged development of these estimates for more technologies. Because of the 

complexity of the estimation process, we are not presenting additional estimates in the RIA. 

(4) Summary of Employment Effects in the Auto Sector 

While we are not able to quantify the demand or factor shift effects, the cost effect results 

show that the employment effects of the increased spending in the regulated sector (and, 

possibly, the parts sector) are expected to be positive and on the order of a few thousand in the 

initial years of the program. As noted above, the motor vehicle and parts manufacturing sectors 

employed about 677,000 people in 2010, with automobile and light truck manufacturing 

accounting for about 129,000 of that total. 

ii. Effects on Employment for Auto Dealers 

The effects of the standards on employment for auto dealers depend principally on the 

effects of the standards on light duty vehicle sales: increases in sales are likely to contribute to 

employment at dealerships, while reductions in sales are likely to have the opposite effect. 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, and reductions are likely to 

decrease labor demand. 

The Defour Group as part of its employment estimate (discussed in III.H.12.a) expressed 

concern about employment in this sector, due to the potential impacts of the rule on vehicle 

sales; they provide an estimate of 35,000 jobs lost at auto dealers due to their predicted sales 

reductions for MY 2025.33 As discussed in III.H.11, we do not at this point provide a 

quantitative estimate of the effects of this rule on vehicle sales. The National Automobile 

Dealers Association encouraged additional information to help consumers better understand the 

 

32 FEV, Inc. “Light-Duty Technology Cost Analysis, Power-Split and P2 HEV Case Studies.” EPA Report EPA420-R-11_015, November 2011 (Docket EPA-HQ-OAR-2010-0799-1101). 

33 See footnote 25. Deleted: 519
Page 23 of 26

benefits of investing in improved fuel economy, and noted the information provided by the new 

fuel economy label developed by the agencies.34

 

Although this rule predicts very small penetration of plug-in hybrids and electric 

vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. 

As discussed in Section III.H.1.b, consumers may find some characteristics of electric vehicles 

and plug-in hybrid electric vehicles, such as the ability to fuel with electricity rather than 

gasoline, attractive; they may find other characteristics, such as the limited range for electric 

vehicles, undesirable. As a result, some consumers will find that EVs will meet their needs, but 

other buyers will choose more conventional vehicles. Auto dealers may play a major role in 

explaining the merits and disadvantages of these new technologies to vehicle buyers. There may 

be a temporary need for increased employment to train sales staff in the new technologies as the 

new technologies become available. We agree with the comment that consumer information has 

the potential to play an important role in consumer acceptance of vehicles subject to this rule. 

iii. 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 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 standards on vehicle 

sales and on the labor intensity of the new technologies, qualitatively in the same ways as for the 

auto manufacturing sector. The United Auto Workers, Blue-Green Alliance, environmental 

organizations, and various others specifically noted the employment gains associated with 

development and use of these advanced technologies.

iv. 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 

standards result in increased use of electricity, natural gas, or other 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 Section III.H.4. While 

those figures represent fuel savings for purchasers of fuel, it represents a loss in value of output 

for the petroleum refinery industry, fuel distribution, and gasoline stations. The loss of 

 

34 Information on the label may be found at http://www.epa.gov/otaq/carlabel/index.htm . 
Page 24 of 26

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. Comments from the United Auto Workers (UAW), Blue-Green Alliance, environmental 

organizations, and Investor Network on Climate Risk suggested that, because other sectors are 

more labor-intensive than gasoline production and sales, reducing expenditures on gasoline and 

making them available for other consumer goods may increase employment. EPA has not 

estimated this effect. 

This rule is also expected to lead to increases in electricity consumption by vehicles, as 

discussed in Section III.H.4. 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 will also require some additional labor. 

We have insufficient information at this time to predict whether the increases in labor associated 

with increased infrastructure provision and fuel generation for these newer fuels will be greater 

or less than the employment reductions associated with reduced demand for petroleum fuels. 

v. Effects on Employment due to Impacts on Consumer Expenditures 

As a result of these 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 Section III.H.5); indeed, 

people who finance their vehicles are expected to find that their fuel savings per month exceed 

the increase in the loan cost (except at very high interest rate levels). As a result, consumers will

have additional money to spend on other goods and services (for those consumers who pay cash 

for 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 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. 

Environmental organizations, CFA, the National Association of Clean Air Agencies, 

American Council for an Energy-Efficient Economy (ACEEE), UAW, Business for Innovative 

Climate & Energy Policy (BICEP), Ceres, and some private citizens suggested in written 

comments and in public hearings that this rule would increase economic growth in the U.S. The 

Center for Biological Diversity, International Council for Clean Transportation, Natural 

Resources Defense Council, and Union of Concerned Scientists (UCS) recommended that EPA 

include an analysis of the economy-wide impacts of the rule, including impacts on U.S. gross 

domestic product (GDP) and consumption patterns. ACEEE, Ceres, BICEP, and UCS suggested 

that fuel savings from the rule would allow consumers to increase their spending on other goods 

and services in more productive sectors of the economy, which would likely increase GDP and 

consumption in the U.S. CFA specifically recommended that EPA use a GDP multiplier 
Page 25 of 26

approach that recognizes that national output would increase from the rule as a result of reducing 

U.S. oil imports. Ceres, BICEP, UCS, and the National Wildlife Federation cited a report for 

Ceres by Management Information Services, Inc. that found that a 4% annual improvement in 

fuel economy would increase U.S. gross economic output by $21.3 billion, personal income by 

$14.2 billion, and revenue for federal, state, and local governments by $12.7 billion in 2030.35 

On the other hand, other private citizens suggested the economy could be harmed as a result of 

this rule, but did not offer any specific data to support the claim. Analyzing the economy-wide 

impacts from this rule is challenging due to the inherent uncertainty in projecting a myriad of 

economic parameters into the future (e.g., levels of employment of labor and capital, the 

structure of the economy, prices of goods and services) and determining an appropriate economic 

framework to model (e.g., supply equaling demand in all markets and specific forms of market 

interactions). EPA has not been able to identify a widely agreed upon methodology and thus we 

continue to not quantify the impacts of the rule on overall economic patterns in the U.S. 

d. Summary 

The primary employment effects of this rule are expected to be found throughout several 

key sectors: auto manufacturers, auto dealers, auto parts manufacturing, fuel production and 

supply, and consumers. 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 partial quantitative analysis 

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

discuss the likely direction of other impacts in the regulated sector as well as in other directly 

related sectors, but we do not quantify those impacts, because they are more difficult to quantify 

with reasonable accuracy, particularly so far into the future. 

For the regulated sector, we have not quantified the demand effect. The cost effect is 

expected to increase employment by 700 – 3,200 job-years in 2017 depending on the share of 

that employment that is in the auto manufacturing sector compared to the auto parts 

manufacturing sector. As mentioned above, some of these job gains may occur earlier as auto 

manufacturers and parts suppliers hire staff to prepare to comply with the standard. 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. 

Changes in vehicle sales are expected to affect labor needs in auto dealerships and in 

parts manufacturing. Increased expenditures for auto parts are expected to require increased 

labor to build parts, though this effect also depends on any changes in the labor intensity of 

 

35 Management Information Services, Inc., July 2011, “More Jobs Per Gallon: How Strong Fuel Economy/GHG 

Standards Will Fuel American Jobs”, A Ceres Report, Washington DC. 
Page 26 of 26

production; as noted, the subset of potential compliance technologies for which data are available 

show increased labor requirements. Reduced fuel production implies less employment in the 

petroleum sectors. Finally, consumer spending is expected to affect employment through 

changes in expenditures in general retail sectors; net fuel savings by consumers are expected to 

increase demand (and therefore employment) in other sectors. 
MY 2017 and Later - Regulatory Impact Analysis 

8-1 

Chapter 8: Vehicle Sales and Employment Impacts 

8.1 Vehicle Sales Impacts 

8.1.1 How Vehicle Sales Impacts were Estimated for this Rule 

Predicting the effects of this rule on vehicle sales entails comparing two competing 

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

which would, by itself, 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. As discussed in Preamble III.H.1.a, there are many competing hypotheses for 

why private markets are not providing what appear to be cost-effective energy-saving 

technologies, for vehicles as well as for other energy-conservation technologies. There are 

few empirical studies testing these hypotheses, though. As discussed more fully in Preamble 

III.H.11.a, it is possible that enacting this rule can contribute to increased vehicle sales by 

encouraging coordination among automakers to improve fuel economy that might not happen 

in the absence of the rule. The empirical literature does not provide clear evidence on how 

much of the value of fuel savings consumers consider at the time of vehicle purchase. It also 

generally does not speak to the efficiency of manufacturing and dealer pricing decisions. 

Thus, we do not provide quantified estimates of potential sales impacts. 

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

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

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

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

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

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

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

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

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

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

onto the vehicle.A

 The present value of fuel savings was subtracted from technology costs to 

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

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

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

 

A

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

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

discount rate and model year). 

B

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

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

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

in over time, as well as how to find good estimates for the long –run elasticity. 

Deleted: would 
Chapter 8 

8-2 

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

effect for MY 2017-2025. In the intervening years, it is possible that the assumptions 

underlying this analysis, as well as market conditions, might change. Instead, Chapter 5.5 

includes a payback period analysis to estimate the number of years of fuel savings needed to 

recover the up-front costs of the new technologies. In other words, the payback period 

identifies the break-even point for new vehicle buyers. As discussed there, the payback 

period is 3.2 – 3.4 years for new vehicles, and even shorter for used vehicles (just over 1 year 

for a 5-year-old MY 2025 vehicle). That chapter also includes an assessment of the lifetime 

costs and benefits that accrue to a vehicle owner. 

8.1.2 Consumer Vehicle Choice ModelingC

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

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

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

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

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

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

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

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

choice models has not been tested. 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 and (as described below) to explore the 

use of consumer choice modeling but, given the known limitations and uncertainties of 

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

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

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

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

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

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

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

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

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

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

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

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

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

 

C

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

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

Economics 5 (2011): 103-146. 

Deleted: ; the net savings are about $5,000 with a 

3 percent discount rate, and over $3,000 with a 7 

percent discount rate
MY 2017 and Later - Regulatory Impact Analysis 

8-3 

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 total compliance costs compared to models that hold fleet 

mix constant, since it predicts changes in the fleet mix that can affect total 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 would be 

called consumer surplus.) 

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

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

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

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

models. 

8.1.2.1 Methods 

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

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

decisions. An example of a continuous decision is how many pounds of food a farm might 

grow: the pounds of food can take any numerical value. Discrete decisions can take only a 

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

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

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

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

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

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

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

kinds of data that they use. 

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

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

choice of whether to buy a new or used vehicle. Given that the person chooses a new vehicle, 

the second layer may be whether to buy a car or a truck. Given that the person chooses a car, 

the third layer may be the choice among an economy, midsize, or luxury car. Examples of 

nested logit models include Goldberg,1

 Greene et al.,2 and McManus.3

 

D

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

yes/no decisions or the choice among a countable number of options). 
Chapter 8 

8-4 

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,4

 Bento et al.,5 and Train and Winston.6 

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

modeling, others (such as Kleit7 and Austin and Dinan8) have used a matrix of demand 

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

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

vehicle choice model. 

8.1.2.2 Data Sources 

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

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

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

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

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

probability of her purchasing vehicle B by 10%. 

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

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

Estimated models use datasets on consumer purchase patterns, consumer 

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

datasets used in these studies sometimes come from surveys of individuals’ behaviors.9 

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.10

 

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 Administration11 and the New Vehicle Market Model 

developed by NERA Economic Consulting12 are examples of calibrated models. 

8.1.2.3 Factors Included in the Models 

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

only on the new vehicle market;13 others consider the choice between new vehicles and an 

outside good (possibly including a used vehicle);14 others explicitly consider the relationship 

between the new and used vehicle markets.15 Some models include consideration of vehicle 

miles traveled,16 though most do not. 
MY 2017 and Later - Regulatory Impact Analysis 

8-5 

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

Some models include only the consumer side of the vehicle market;18 others seek to 

represent both consumer and producer decisions.19 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.20 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.21 Even those vehicle choice models that 

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

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

much less multiple characteristics.22 

8.1.2.4 Research Questions for the Models 

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

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

modeling. The work of Berry, Levinsohn, and Pakes,23 for instance, is often cited outside the 

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

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

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

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

to tariffs and quotas,24 the market acceptability of alternative-fuel vehicles,25 the effects of 

introduction and exit of vehicles from markets,26 causes of the decline in market shares of 

U.S. automakers,27 and the effects of gasoline taxes28 and “feebates”29 (subsidizing fuelefficient cars with revenue collected by taxing fuel-inefficient vehicles). 

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions 

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

vehicle purchase decisions. Fuel economy can appear in various forms in these models. 

Some models30 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 
Chapter 8 

8-6 

receive from a more fuel-efficient vehicle and consider the savings equivalent to a reduction 

in purchase price. The vehicle sales method described in Chapter 8.1.1 uses a variant on this 

approach, in which it is assumed that consumers consider some fraction of future fuel savings. 

Turrentine and Kurani31 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.32 Since consumers pay for gallons of fuel, then this 

measure can assess fuel savings relatively directly.

33 Yet other models multiply fuel 

consumption per mile by the cost of fuel to get the cost of driving a mile,34 or they divide fuel 

economy by fuel cost to get miles per dollar.35 It is worth noting that these last two measures 

assume that consumers respond the same way to an increase in fuel economy as they do to a 

decrease in the price of fuel when each has the same effect on cost per mile driven.E On the 

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

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

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

consumers’ purchase of fuel economy: recent research36 presents results that higher fuel 

prices play a major role in that decision. 

Greene and Liu,37 in a paper published in 1988, reviewed 10 papers using consumer 

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

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

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

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

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

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

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

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

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

the value of reduced vehicle operating costs. 

A newer literature review from David Greene38 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 

 

E

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

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

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

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

the Preamble and Chapter 4.2.5 of the TSD). 
MY 2017 and Later - Regulatory Impact Analysis 

8-7 

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, Gramlich39 includes both 

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

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

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

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

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

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

Some studies40 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 studies41 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 studies42 notes that its baseline model implies that 

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

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

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

in consumer and producer decisions. 

8.1.2.6 Why Market Outcomes May Not Reflect Full Appreciation for 

Fuel Economy that Pays for Itself 

A detailed and wide ranging literature attempts to explain why market outcomes for 

energy-using products appear to reflect under-investment in energy saving technologies that – 

at least using a present value calculation based on engineering estimates – appear to pay for 

themselves. Existing research does not provide a definitive answer to this question. Potential 

explanations are bounded by two scenarios. On the one hand, purely private benefits of fuel 

economy (fuel savings, time savings, increases in driving time) must be accompanied by 

private losses of the same magnitude. However, if there is no such private loss, or if it is small 

or insignificant, then there is a market or behavioral failure. 

This disconnect between net present value estimates of energy-conserving cost savings 

and what consumers actually spend on energy conservation is often referred to as the Energy 

Paradox,43 since consumers appear to undervalue a wide range of investments in energy 

conservation. There are many possible explanations for the paradox discussed in the 

literature.44 Some explanations point to costs or aspects of consumer decision-making 

unaccounted for in a simple present value calculation, while others point to potential 

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

which combination of hypothesis offers the most credible explanation. Some possibilities 

include: 

• Consumers might be “myopic” and hence undervalue future fuel savings in their 

purchasing decisions. 
Chapter 8 

8-8 

• Consumers might lack the information necessary to estimate the value of future 

fuel savings, or not have a full understanding of this information even when it is 

presented. 

• Consumers may be accounting for uncertainty in future fuel savings when 

comparing upfront cost to future returns. 

• Consumers may consider fuel economy after other vehicle attributes and, as such, 

not optimize the level of this attribute (instead “satisficing” – that is, selecting a 

vehicle that is acceptable rather than optimal -- or selecting vehicles that have 

some sufficient amount of fuel economy). 

• Consumers might be especially averse to the short-term losses associated with the 

higher prices of energy efficient products relative to the future fuel savings (the 

behavioral phenomenon of “loss aversion”). 

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

designed vehicles. 

• When buying vehicles, consumers may focus on visible attributes that convey 

status, such as size, and pay less attention to attributes such as fuel economy that 

do not visibly convey status. 

• Even if consumers have relevant knowledge, selecting a vehicle is a highly 

complex undertaking, involving many vehicle characteristics. In the face of such a 

complicated choice, consumers may use simplified decision rules. 

• In the case of vehicle fuel efficiency, and perhaps as a result of one or more of the 

foregoing factors, consumers may have relatively few choices to purchase vehicles 

with greater fuel economy once other characteristics, such as vehicle class, are 

chosen.F

 

The extent to which fuel economy is optimized relative to other, potentially more 

salient vehicle attributes (such as engine horsepower and seating capacity) in market 

outcomes for new vehicles remains an important area of uncertainty. There are significant 

challenges involved in effectively interpreting and anticipating consumer preferences for 

various vehicle attributes and amenities. There are significant lead times to market, potential 

return to scale limits on the range of options provided for a given attribute or amenity, market 

transaction frictional factors, and other factors inherent to the nature of these costly durable 

goods which may contribute to imperfect satisfaction of market demand for fuel economy 

among a highly heterogeneous customer base. Both sides of the market would be expected to 

 

F

 For instance, in MY 2010, the range of fuel economy (combined city and highway) available among all listed 

6-cylinder minivans was 18 to 20 miles per gallon. With a manual-transmission 4-cylinder minivan, it is 

possible to get 24 mpg. See http://www.fueleconomy.gov, which is jointly maintained by the U.S. Department 

of Energy and the EPA. 
MY 2017 and Later - Regulatory Impact Analysis 

8-9 

attempt to maximize the utility they gain from these transactions; they presumably rely 

heavily in their calculations on the uncertain benefits of savings from fuel economy 

improvements, and yet market outcomes may still appear to reflect potential foregone 

opportunities to increase utility. We remain interested in these market dynamics, their 

underlying causes, and their potential significance for assessing the potential incremental 

effects of pollution control standards. 

8.1.2.7 Electric Vehicles and Other New Vehicles 

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

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

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

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

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

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

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

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

those features to the new vehicle characteristics. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

electric hookups, but not for people who park on the street. If an infrastructure develops for 
Chapter 8 

8-10 

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.45 In that model, he considers only one base 

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

acceleration, and several other vehicle attributes.

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

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

place to recharge. Because range appears to be a major factor in EV acceptability -- indeed, a 

factor in the development of plug-in hybrid-electric vehicles is responding to this concern -- it 

is starting to draw attention in the research community. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Table 8.1-1 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. 

 

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. 

Deleted: Table 8.1-1
MY 2017 and Later - Regulatory Impact Analysis 

8-11 

Table 8.1-1 Willingness 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 2010$a 

Bunch et al. (1993)b$7,600 (1991$) $11,300

Kavelek (1996) for California 

Energy Commissionc 

$2600 - $41,900 (1993$) $3700 - $59,400

Resource Systems Group (2009) for 

California Energy Commissiond 

$2900 - $7500 (2009$) $2900 - $7600

Hess et al. (2009), using the same 

data as Resource Systems Group 

(2009)e 

$2400 - $8500 (2009$) $2400 - $8600

Hidrue et al. (2011)f$3800 - $10,400 (2009$) $3800 - $10,500

aValues adjusted to 2010$ using the Bureau of Economic Analysis GDP deflator. 

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

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

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

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

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

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

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

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

both ownership and income categories. 

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

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

June 2009. 

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

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

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

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

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

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

both income categories and specifications. 

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

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

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

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

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

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

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

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

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

8.1-1, 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. 

Deleted: Table 8.1-1
Chapter 8 

8-12 

8.1.2.8 EPA Exploration of Vehicle Choice Modeling 

In order to develop greater understanding of these models, EPA is developing a 

vehicle choice model, although not for this rulemaking. In its current form, the model 

assumes that the vehicle fleet and all characteristics of each vehicle, except vehicle prices and 

fuel economy, stay the same. The model will predict changes in the vehicle fleet, at the 

individual-configuration level and at more aggregated levels, in response to changes in 

vehicle fuel economy and price. 

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

literature, as discussed above in Chapter 8.1.2.1. “Nesting” refers to the decision-tree 

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) were 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 8.1.2.7: the value that the consumer places on 

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

 

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. 
MY 2017 and Later - Regulatory Impact Analysis 

8-13 

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

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

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

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

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

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

be used in the analysis. 

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

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

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

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

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

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

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

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

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

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

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

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

savings (which the modeler may select) 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. 

The model has undergone peer review.46 The reviewers were generally supportive of 

the model structure and parameters, with two major qualifications. 

First, peer reviewers recommended that the model should interact closely with 

OMEGA, EPA’s technology cost and effectiveness model, and its appropriate use may 

depend on that interaction. For instance, it is possible that the predicted changes in fleet mix 

will lead to predictions of vehicle sales for auto makers that do not meet the standards, 

because the mix and volume of vehicles sold changed from the initial levels. To correct this 

problem, it is necessary to feed the new fleet mix back into OMEGA (which calculates costs 

and compliance) and get a new set of output, which is then fed back into the vehicle choice 

model. OMEGA increases technologies, and thus costs, to improve compliance; those 

adjustments would then again affect vehicle demand. We expect that this iterative process 

would converge to a fleet mix that would meet standards. Performing this iteration requires 

development of an interface between the vehicle choice model and OMEGA to ensure 

accurate transmission of data between the models. At this time, the vehicle choice model 

takes output from OMEGA, but the results of the modeling do not feed easily back into 

OMEGA. Building this interface is an expected part of our future modeling work. 

 

I

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

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

8-14 

Second, the peer review raised the issue of the uncertainties surrounding the model 

parameters and suggested the development of capacity to conduct uncertainty analysis. As 

discussed in Section 8.1.2.5, the role of fuel economy in consumer decisions is one source of 

uncertainty; the price slopes used for the different nests are also not known with certainty. 

EPA agrees that use of the model should involve, at the least, sensitivity analysis over key 

parameters, and we plan to investigate greater incorporation of uncertainty analysis in the 

model. 

We note as well that the current model does not take EVs or other alternatively fueled 

vehicles into account. As discussed in Section 8.1.2.7, the values for willingness to pay for 

features such as range and different refueling infrastructures appear to be subject to great 

uncertainty. EPA’s current model does not include these vehicles, because we are seeking to 

gain experience and confidence with the modeling where it is likely to work best before we 

investigate modeling where more uncertainty is involved. The incorporation of new vehicles 

of any kind in the modeling is another area for future work. 

As discussed in Preamble Section III.H.1, EPA is not using its preliminary consumer 

choice model in this rulemaking because we believe it needs further development and testing 

before we have confidence in its use. As the peer reviewers noted, it has not yet been 

integrated with OMEGA, an important step for ensuring that changes in the vehicle fleet 

estimated by the model will result in a fleet compliant with the standards. In addition, 

concerns remain that vehicle choice models have rarely been validated against real-world 

data. In response to these concerns, we would expect any use of the model to involve, at the 

least, a number of sensitivity analyses to examine the robustness of results to key parameters. 

We will continue model development and testing to understand better the results and 

limitations of using the model. 

8.1.2.9 Summary and Additional Considerations 

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

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

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

models in predicting changes in fleet size and mix. 

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

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

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

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

not proven. 

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

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

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

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

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

technology vehicles, such as EVs, the willingness-to-pay values may change over time as 
MY 2017 and Later - Regulatory Impact Analysis 

8-15 

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

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

Consumer choice modeling has the potential to improve estimates of the compliance 

costs of a rule. Consumers can either accept the new costs and vehicle characteristics, or they 

can change which vehicles they buy. Using a vehicle choice model is likely to reduce 

estimated compliance costs: because the model allows consumers to choose among accepting 

the new vehicle, buying a different vehicle, or not buying a vehicle, the model assumes that 

consumers have additional options, which improves their welfare relative to the assumption 

that consumers will not change their buying behavior. 

An additional complication associated with consumer choice modeling is accurate 

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

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

richness of these models. In addition, the pricing paths predicted by these models have not, to 

our knowledge, been tested against actual behavior; and auto makers may not pass along 

vehicle costs in the same way in the future as they have in the past. 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 rule take into account the costs to hold other vehicle 

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

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

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

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

J

 the price increase measures the loss to 

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

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

 

J

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

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

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

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

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

K

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

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

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

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

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

vehicle less desirable to buyers. 
Chapter 8 

8-16 

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 rule 

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 further development of its vehicle choice model for use in the future. 

8.1.3 Impact of the Rule on Affordability of Vehicles and Low-Income Households 

Because this rule is expected to increase the up-front costs of vehicles, with the fuel 

savings that recover those costs coming in over time, questions have arisen about the effects 

of this rule on whether access to credit may limit consumers’ ability to purchase new vehicles, 

on low-income households, and on the availability of low-priced vehicles. Section III.H.11.b. 

of the Preamble discusses these issues in the context of public comments received on the rule; 

here we provide some background and information on sources of data in that discussion. 

When a lender is deciding whether to issue a loan to a prospective vehicle buyer, the 

amount of the vehicle loan and the person’s income are two major factors in the loan 

application. If lenders in fact restrict themselves to consideration of only those two factors, 

then the higher up-front costs of the new vehicles subject to this rule would reduce buyers’ 

abilities to get loans. The fuel savings would not come into play to counter-balance this cost, 

even though, as shown in the payback period analysis (RIA Chapter 5.5), the fuel savings 

exceed the increased loan payments from the first month of the loan. Thus, if lenders do not 

take fuel savings into account in providing loans, people who are borrowing near the limit of 

their abilities to borrow will either have to change what vehicles they buy, or not buy vehicles 

at all. 

On the other hand, some evidence suggests that the loan market may evolve to take 

fuel savings into greater account in the lending decision. Some lenders currently give 

discounts for loans to purchase more fuel-efficient vehicles.47 An internet search on the term 

“green auto loan” produced more than 50 lending institutions that provide reduced loan rates 

for more fuel-efficient vehicles.48 It is possible (though unknown at this time) that the auto 

loan market may evolve to include further consideration of fuel savings, as those savings play 

a significant factor in offsetting the increase in up-front costs of vehicles. 

It is possible that future trends in the auto loan market may affect future vehicle sales. 

It is also possible that some people who have significant debt loads may not be able to get 

financing for some of these new vehicles; they may have to buy different vehicles (including 

used vehicles) or delay purchase. For others who borrow on credit, though, as discussed in 

RIA Chapter 5.5, the fuel savings are expected to outweigh the increased loan costs from the 

Deleted: There is no reason to expect that the 

number of lenders instituting this policy will 

decrease in the future as fuel efficiency increases

beyond the levels seen today. 

Deleted: ndeed, i
MY 2017 and Later - Regulatory Impact Analysis 

8-17 

time of vehicle purchase. The rule thus may make vehicles more affordable to the public, by 

reducing consumers’ vulnerability to fuel price jumps. 

The effects of this rule on low-income households depends on its impacts, not only in 

the new vehicle market, but also in the used vehicle market. Two sources of information on 

vehicle ownership by income are the 2010 Consumer Expenditure Survey (CES) conducted 

by the Bureau of Labor Statistics,49 and the 2007 Survey of Consumer Finances (SCF) 

conducted by the Federal Reserve System.50 The Consumer Expenditure Survey data indicate 

that, though the average household spent more on vehicle purchases ($2,588) than on gasoline 

and oil ($2,132), households in the bottom 40 percent of income spent more on fuel ($1,304) 

than on vehicles ($1,106); in addition, they spent more on used vehicles ($756) than on new 

vehicles ($330). Households in the lowest 20 percent of income spent only $127 on new 

vehicles, $497 on used vehicles, and $1,009 on fuel. These data suggest that the used-vehicle 

market is more important for low-income households than the new-vehicle market, and that 

they are more vulnerable to changes in fuel prices than they are to changes in vehicle prices. 

The Survey of Consumer Finances asks households about purchase information in a number 

of categories, including vehicles. For the 2007 survey, we identified the households in the 

survey who had bought MY 2007 or 2008 vehicles, and further looked at the income 

categories for those consumers. Those with income less than $35,200 (the maximum income 

for those in the bottom 40 percent of income in the CES) bought about 17 percent of new 

vehicles; those with income below $18,400 (the bottom quintile) bought fewer than 2 percent 

of new vehicles. These data further support the idea that low-income households are more 

affected by the impact of the rule on the used-vehicle market than on the new-vehicle market. 

The effect of this rule on the used vehicle market will be related to its effects on new 

vehicle prices, the fuel efficiency of new vehicle models, the fuel efficiency of used vehicles, 

and the total sales of new vehicles. If the value of fuel savings resulting from improved fuel 

efficiency to the typical potential buyer of a new vehicle outweighs the average increase in 

new models’ prices, sales of new vehicles could rise, and the used vehicle market may 

increase in volume as new vehicle buyers sell their older vehicles. In this case, low-income 

households are likely to benefit from the increased availability of used vehicles. However, if 

potential buyers value future fuel savings resulting from the increased fuel efficiency of new 

models at less than the increase in their average selling price, sales of new vehicles will 

decline, and the used vehicle market may decrease in volume as people hold onto their 

vehicles longer. In this case, low-income households are likely to face increased costs due to 

reduced availability of used vehicles. Because, as discussed in 8.1.1, we have not estimated 

the effects of the rule on the new vehicle market, and because we do not have a good model of 

the relationship between the new and used vehicle markets, we do not have estimates of the 

impact of this rule on the used vehicle market. However, due to the significant effect of the 

rule on fuel savings, especially for used vehicles (see RIA Chapter 5.5), we expect lowincome households to benefit from the more rapid payback period for used vehicles, though 

some of this benefit may be affected by the net effect of this rule on the prices and availability 

of used vehicles.

The low-priced vehicle segment of the market may also deserve consideration, 

because it may be an entry point for first-car buyers. Vehicles in the low-priced (economyclass) segment will bear technology costs needed to meet the new standards, but it is not 

Deleted: EPA does not deny the possibility that the 

market for new vehicle loans may be affected by this 

rule, but we do not at this time have sufficient 

evidence to project whether that market will evolve

to help or hinder sales of new, more fuel-efficient

vehicles.
Chapter 8 

8-18 

known how manufacturers will decide to pass on these costs across their vehicle fleets, 

including in the low-priced vehicle segment. If manufacturers decide to pass on the full cost 

of compliance in this segment, then it is possible that consumers who might barely afford new 

vehicles may be priced out of the new-vehicle market or may not have access to loans. As 

discussed above, the rule’s impacts on availability of loans is unclear, because some lenders 

do factor fuel economy into their loans, and it is possible that this trend may expand. In 

addition, auto makers have some flexibility in how both technologies and price changes are 

applied to these vehicles; auto makers have ways to keep some vehicles in the low-priced 

vehicle segment if they so choose. Though the rule is expected to increase the prices of these 

vehicles, the degrees of price increase and the impacts of the price increases, especially when 

combined with the fuel savings that will accompany these changes, are much less clear. 

In summary, we recognize that this rule may have impacts on consumers’ access to 

loans for new vehicles, on low-income households, and on the market for low-priced vehicles; 

less clear are the directions of these effects. Lenders who only consider consumers’ debt-toincome ratios may reduce consumers’ abilities to purchase more expensive vehicles, but some 

lenders already take the fuel efficiency of vehicles into account. Low-income households will 

benefit from reduced fuel costs; we do not estimate the direction of effects of this rule on used 

vehicle prices, which are more relevant for low-income households than effects on new 

vehicles. The effects of this rule on low-priced vehicles depends on how manufacturers add 

technologies and price vehicles in this segment; they have flexibility to keep some vehicles in 

this segment if they so wish. 

8.2 Employment Impacts 

8.2.1 Introduction 

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

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

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

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

would “strengthen the [auto] industry and enhance job creation in the United States.”51,L The 

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

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

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

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

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

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

This rule is expected to affect employment in the United States through the regulated 

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

 

L

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

assessment to inform the rulemaking process (which was issued by the agencies and CARB on September 30, 

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

States.” 
MY 2017 and Later - Regulatory Impact Analysis 

8-19 

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 standards.52 Changes in 

light duty vehicle sales, discussed in Chapter 8.1.1, could affect employment for auto dealers. 

The employment effects of this rule are expected to expand beyond the regulated sector. 

Though some of the parts used to achieve the standards are likely to be built by auto 

manufacturers themselves, the auto parts manufacturing sector also plays a significant role in 

providing those parts, and will also be affected by changes in vehicle sales. As discussed in 

Chapter 5.4 of this RIA, this rule 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 rule 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. In such a period, both positive 

and negative employment effects are possible.M Schmalansee and Stavins point out that net 

positive employment effects are possible in the near term when the economy is at less than 

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

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

related to the regulated sector.53 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.” 

 

M

 Masur and Posner, available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1920441 
Chapter 8 

8-20 

It is assumed that the official unemployment rate will have declined to 5.3 percent by 

the time by the time this rule takes effect and so the effect of the regulation on labor will be to 

shift workers from one sector to another.N

 Those shifts in employment impose an opportunity 

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

from other activities in the economy. In this situation, as discussed above, any effects on net 

employment are likely to be transitory. It is also possible that the state of the economy will be 

such that positive or negative employment effects will occur. 

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

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

of their limitations. 

8.2.2 Approaches to Quantitative Employment Analysis 

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

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

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

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

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

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

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

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

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

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

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

employment due to Federal regulation. 

8.2.2.1 Conceptual Framework for Employment Impacts in the 

Regulated Sector 

One study by Morgenstern, Pizer, and Shih54 provides a retrospective look at the 

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

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

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

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

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

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

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

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

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

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

 

N

 Office of Management and Budget, “Fiscal Year 2012 Mid-Session Review: Budget of the U.S. Government.” 

http://www.whitehouse.gov/sites/default/files/omb/budget/fy2012/assets/12msr.pdf , p. 10. 
MY 2017 and Later - Regulatory Impact Analysis 

8-21 

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,O

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

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

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

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

The authors conclude that increased abatement expenditures generally have not caused 

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

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

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

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

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

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

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

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

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

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

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

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

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

developed continues to be useful in this context. 

The following discussion of additional methodologies draws from Berck and 

Hoffmann’s review of employment models.55

 

 

O

 As will be discussed below, the demand effect in this rule 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? 
Chapter 8 

8-22 

8.2.2.2 Computable General Equilibrium (CGE) Models 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

8.2.2.3 Input-Output (IO) Models 

Input-output models represent the economy through a matrix of coefficients that 

describe the connections between supplying and consuming sectors. In that sense, like CGE 

models, they describe the interconnections of the economy. These interconnections look at 

how changes in one sector ripple through the rest of the economy. For instance, a

requirement for additional technology for vehicles requires additional steel, which requires 

more workers in both the auto and steel sectors; the additional workers in those sectors then 

have more money to spend, which leads to more employment in retail sectors. These are 

known as “multiplier” effects, because an initial impact in one sector gets multiplied through 

the economy. Unlike CGE models, input-output models have fixed, linear relationships 

among the sectors (e.g., substitution among inputs or goods is not allowed), and quantity 

supplied need not equal quantity demanded. In particular, these models do not allow for price 

changes – an increase in the demand for labor or capital does not result in a change in its price 

to help reallocate it to its best use. As a result, these models cannot capture opportunity costs 

from using resources in one area of the economy over another. The multipliers take an initial 

impact and can increase it substantially. 

IO models are commonly used for regional analysis of projects. In a regional analysis, 

the markets are commonly considered small enough that wages and prices are determined 

outside the region, and any excess supply or demand is due to exports and imports (or, in the 

case of labor, emigration or immigration). For national-level employment analysis, the use of 

input-output models requires the assumption that workers flow into or out of the labor market 

perfectly freely. Wages do not adjust; instead, people join into or depart from the labor pool 

as production requires them. For other markets as well, there is no substitution of less 

expensive inputs for more expensive ones. As a result, IO models provide an upper bound on 

employment impacts. As Berck and Hoffmann note, “For the same reason, they can be 

thought of as simulating very short-run adjustment,” in contrast to the CGE’s implicit 

assumption of long-run adjustment. Changes in production processes, introductions of new 

technologies, or learning over time due to new regulatory requirements are also generally not 
MY 2017 and Later - Regulatory Impact Analysis 

8-23 

captured by IO models, as they are calibrated to already established relationships between 

inputs and outputs. 

8.2.2.4 Hybrid Models 

As Berck and Hoffmann note, input-output models and CGE models “represent a 

continuum of closely related models.” Though not separately discussed by Berck and 

Hoffmann, some hybrid models combine some of the features of CGE models (e.g., prices 

that can change) with input-output relationships. For instance, a hybrid model may include 

the ability to examine disequilibrium phenomena, such as labor being at less than full 

employment. Hybrid models depend on assumptions about how adjustments in the economy 

occur. CGE models characterize equilibria but say little about the pathway between them, 

while IO models assume that adjustments are largely constrained by previously defined 

relationships; the effectiveness of hybrid models depends on their success in overcoming the 

limitations of each of these approaches. Hybrid models could potentially be used to model 

labor market impacts of various vehicle policy options although a number of judgments need 

to be made about the appropriate assumptions underlying the model as well the empirical 

basis for the modeling results. 

8.2.2.5 Single Sectors 

It is possible to conduct a bottom-up analysis of the partial effect of regulation on 

employment in a single sector by estimating the change in output or expenditures in a sector 

and multiplying it by an estimate of the number of workers per unit of output or expenditures, 

under the assumption that labor demand is proportional to output or expenditures. As Berck 

and Hoffmann note, though, “Compliance with regulations may create additional jobs that are 

not accounted for.” While such an analysis can approximate the effects in that one sector in a 

simple way, it also may miss important connections to related sectors. 

8.2.2.6 Ex-Post Econometric Studies 

A number of ex-post econometric analyses examine the net effect of regulation on 

employment in regulated sectors. Morgenstern, Pizer, and Shih (2002), discussed above, and 

Berman and Bui (2001) are two notable examples that rely on highly disaggregated 

establishment-level time series data to estimate longer-run employment effects.56 While often 

a sophisticated treatment of the issues analyzed, these studies commonly analyze specific 

scenarios or sectors in the past; care needs to be taken in extrapolating their results to other 

scenarios and to the future. For instance, neither of these two studies examines the auto 

industry and are therefore of limited applicability in this context. 

8.2.2.7 Summary 

All methods of estimating employment impacts of a regulation have advantages and 

limitations. CGE models may be most appropriate for long-term impacts, but the usual 

assumption of equilibrium in the employment market means that it is not useful for looking at 

changes in overall employment: overall levels are likely to be premised on full employment. 

IO models, on the other hand, may be most appropriate for small-scale, short-term effects, 

because they assume fixed relationships across sectors and do not require market equilibria. 
Chapter 8 

8-24 

Hybrid models, which combine some features of CGEs with IO models, depend upon key 

assumptions and economic relationships that are built into them. Single-sector models are 

simple and straightforward, but they are often based on the assumptions that labor demand is 

proportional to output, and that other sectors are not affected. Finally, econometric models 

have been developed to evaluate the longer-run net effects of regulation on sector 

employment, though these are ex-post analyses commonly of specific sectors or situations, 

and the results may not have direct bearing for the regulation being reviewed. 

8.2.3 Employment Analysis of This Rule 

As mentioned above, this program is expected to affect employment in the regulated 

sector (auto manufacturing) and in other sectors directly affected by the rule: auto parts 

suppliers, auto dealers, and 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). Changes in consumer expenditures due to higher vehicle costs and lower fuel 

expenses will also affect employment. 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 rule on the regulated sector (i.e., the auto industry; for reasons discussed below, 

we include some quantitative assessment of effects on suppliers to the auto industry although 

suppliers are not regulated directly). It also includes qualitative discussion of the effects of 

the rule 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 rule. Second, as discussed above, net effects on employment in the economy as a whole 

depend heavily on the overall state of the economy when this rule has its effects. Focusing on 

the regulated sector provides insight into employment effects in that sector without having to 

make assumptions about the state of the economy when this rule has its impacts. We include 

a qualitative discussion of employment effects on other sectors to provide a broader 

perspective on the impacts of this rule. 

As noted above, in a full-employment economy, any changes in employment will 

result from people changing jobs or voluntarily entering or exiting the workforce. In a fullemployment economy, employment impacts of this rule will change employment in specific 

sectors, but it will have small, if any, effect on aggregate employment. This rule would take 

effect in model years 2017 through 2025; by then, the current high unemployment may be 

moderated or ended. For that reason, this analysis does not include multiplier effects, but 

instead focuses on employment impacts in the most directly affected industries. Those sectors 

are likely to face the most concentrated employment impacts. 

Deleted: ,

Deleted: and 

Deleted: (who will face 

Deleted: expenditures)
MY 2017 and Later - Regulatory Impact Analysis 

8-25 

8.2.3.1 Employment Impacts in the Auto Industry 

Following the Morgenstern et al. conceptual framework for the impacts of regulation 

on employment in the regulated sector, we consider three effects for the auto sector: the 

demand effect, the cost effect, and the factor shift effect. However, we are only able to offer 

quantitative estimates for the cost effect. We note that these estimates, based on 

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

8.2.3.1.1 The Demand Effect 

The demand effect depends on the effects of this rule 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 rule will increase vehicle costs; by itself, this 

effect would reduce vehicle sales. On the other hand, this rule will reduce the fuel costs of 

operating the vehicle; by itself, this effect would increase vehicle sales, especially if potential 

buyers have an expectation of higher fuel prices. The sign of demand effect will depend on 

which of these effects dominates. Because, as described in Chapter 8.1, we have not 

quantified the impact on sales for this rule, we do not quantify the demand effect. 

8.2.3.2 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 development, 

manufacturing, and installation by auto suppliers and manufacturers of the new or additional 

technologies needed for vehicles to comply with the standards. 

One way to estimate the cost effect, given the cost estimates for complying with the 

rule, is to use the ratio of workers to each $1 million of expenditures in that sector. The use of 

these ratios has both advantages and limitations. It is often possible to estimate these ratios 

for quite specific sectors of the economy: for instance, it is possible to estimate the average 

number of workers in the light-duty vehicle manufacturing sector per $1 million spent in the 

sector, rather than use the ratio from another, more aggregated sector, such as motor vehicle 

manufacturing. As a result, it is not necessary to extrapolate employment ratios from possibly 

unrelated sectors. On the other hand, these estimates are averages for the sectors, covering all 

the activities in those sectors; they may not be representative of the labor required when 

expenditures are required on specific activities, as the factor shift effect (discussed below) 

indicates. For instance, the ratio for the motor vehicle manufacturing sector represents the 

ratio for all vehicle manufacturing, not just for fuel efficiency improvements. In addition, 

these estimates do not include changes in sectors that supply these sectors, such as steel or 

electronics producers. They thus may best be viewed as the effects on employment in the 

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. 
Chapter 8 

8-26 

Some of the costs of this rule 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 sources for estimates of employment per $1 million expenditures. The 

U.S. Bureau of Labor Statistics (BLS) provides its Employment Requirements Matrix 

(ERM),57 which provides direct estimates of the employment per $1 million in sales of goods 

in 202 sectors. The estimates used here, updated from the NPRM, are from 2010 (adjusted to 

2010$). Not all expenditures are for domestically produced vehicles, however. To estimate 

the proportion of domestic expenditures affected by the rule, we use data from Ward’s 

Automotive Group for total car and truck production in the U.S. compared to total car and 

truck sales in the U.S.58 For the period 2001-2010, the proportion is 66.7%. We thus weight 

sales by this factor to get an estimate of the effect on employment in the motor vehicle 

manufacturing sector due to this rule. 

The Annual Survey of Manufactures59 (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 of 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 2010 (also updated from the NPRM). As with the ERM, we 

adjust for the ratio of domestic production to domestic sales. The values reported are for 

Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty Motor Vehicle 

Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing (NAICS 3363). 

The Economic Census includes all large companies and a sample of smaller ones. The 

ASM is a subset of the Economic Census; though the Census itself is more complete, it is 

conducted only every 5 years, while the ASM is annual. The values presented here use data 

from 2007 (adjusted to 2010$), with the domestic production-to-sales adjustment. The values 

reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty 

Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing 

(NAICS 3363). 

Table 8.2-2 provides the values, either given (BLS) or calculated (ASM, Economic 

Census) for employment per $1 million of expenditures, all based on 2010 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. 

Deleted: Table 8.2-2
MY 2017 and Later - Regulatory Impact Analysis 

8-27 

Table 8.2-2 Employment per $1 Million Expenditures (2010$) 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 0.770 0.514

ASM Motor Vehicle Mfg 0.655 0.437

ASM Light Duty Vehicle 

Mfg

0.609 0.406

Economic 

Census 

Motor Vehicle Mfg 0.665 0.443

Economic 

Census 

Light Duty Vehicle 

Mfg

0.602 0.402

BLS ERM Motor Vehicle Parts 

Mfg

2.614 1.743

ASM Motor Vehicle Parts 

Mfg

2.309 1.540

Economic 

Census 

Motor Vehicle Parts 

Mfg

2.712 1.809

BLS ERM refers to the U.S. Bureau of Labor Statistics’ Employment Requirement Matrix. ASM refers to the 

U.S. Census Bureau’s Annual Survey of Manufactures. Economic Census refers to the U.S. Census Bureau’s 

Economic Census.

Over time, the amount of labor needed in the auto industry has changed: automation 

and improved production methods have led to significant productivity increases. The BLS 

ERM, for instance, provided estimates that, in 1993, 1.64 workers in the Motor Vehicle 

Manufacturing sector were needed per $1 million of 2005$, but only 0.86 workers by 2010 (in 

2005$). Because the ERM is available annually for 1993-2010, we used these data to 

estimate productivity improvements over time. We regressed logged ERM values on year for 

both the Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing sectors. We 

used this approach because the coefficient describing the relationship between time and 

productivity is a direct measure of the percent change in productivity per year. The results 

suggest a 3.9 percent per year productivity improvement in the Motor Vehicle Manufacturing 

Sector, and a 3.8 percent per year improvement in the Motor Vehicle Parts Manufacturing 

Sector. We then used the equation resulting from the regression to project the ERM through 

2025. In the results presented below, these projected values (adjusted to 2010$) 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 2010 (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). This is a simple way to examine the relationship 

between labor required and expenditure. 

Deleted: (to estimate percent change per year) 

Deleted: relationship 
Chapter 8 

8-28 

Table 8.2-3 shows the cost estimates developed for this rule, discussed in Chapter 5. 

The maximum value in Table 8.2-2 for employment impacts per $1 million expenditures

(after accounting for the share of domestic production) is 1.809 in 2010 if all the additional 

costs are in the parts sector; the minimum value is 0.402 in 2010, 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 2010. The 

results in Table 8.2-3 include the productivity adjustment described above. 

While we estimate employment impacts, measured in job-years, beginning with the 

first year of the standard (2017), some of these employment gains may occur earlier as auto 

manufacturers and parts suppliers hire staff in anticipation of compliance with the standard. 

A job-year is a way to calculate the amount of work needed to complete a specific task. For 

example, a job-year is one year of work for one person. 

Table 8.2-3 Employment per $1 Million in the Motor Vehicle Manufacturing Sector, in 

job-years 

Year Costs (before 

adjustment for 

domestic proportion 

of production) 

($Millions) 

Minimum 

employment effect 

(if all expenditures 

are in the parts 

sector) 

Maximum 

employment effect (if 

all expenditures are 

in the light duty 

vehicle mfg sector) 

2017 $ 2,435 700 3,200 

2018 $ 4,848 1,300 6,200 

2019 $ 6,818 1,700 8,400 

2020 $ 8,858 2,100 10,500 

2021 $ 12,400 2,900 14,200 

2022 $ 18,323 4,100 20,200 

2023 $ 23,734 5,100 25,200 

2024 $ 29,101 6,000 29,700 

2025 $ 31,678 6,300 31,100 

We note that the cost effect depends only on technology costs, not vehicle sales. It is 

therefore not sensitive to assumptions about how consumers consider fuel savings at the time 

of vehicle purchase. 

8.2.3.2.1 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 

standards. For a subset of the technologies, though, EPA-sponsored research (discussed in 

Deleted: Table 8.2-2
MY 2017 and Later - Regulatory Impact Analysis 

8-29 

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. 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 standards. An increase in 

labor cost for the new technology indicates an increase in the labor needed for the new 

technology compared to the baseline technology. For instance, an 8-speed transmission 

requires $15.11 more in labor costs than a 6-speed transmission (as accounted for in EPA’s 

cost estimates for the rule). Dividing the labor cost by a wage per hour estimate provides an 

estimate of the additional hours (and thus the additional labor) needed for the new technology 

compared to the baseline technology. As with labor cost, an increase in labor hours per 

technology indicates greater employment needs for the new technologies. For this 

conversion, a weighted average wage rate (90 percent of the average wage in the Motor 

Vehicle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle 

Manufacturing Sector) of $46.36/hour in 2015, using 2008 dollars (the unit of analysis for the 

FEV study). For the change from a 6-speed to an 8-speed transmission, we thus estimate an 

additional 0.33 hours of labor per transmission. 

Table 8.2-4 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 rule, 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 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 rule. 

Table 8.2-4 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 0104 SUV/Trucks $84.19 $789.53 1.82 
Chapter 8 

8-30 

Turbo GDI V6 

Electric A/C 

compressor 

0602 $4.68 $167.54 0.10

Power split 

hybrid 

0502 Mid/Large C $395.85 $3,435.01 8.54 

6- to 8-speed 

transmission 

0803 Mid/Large C $15.11 $61.84 0.33

8.2.3.2.2 Summary of Employment Effects in the Auto Sector 

While we are not able to quantify the demand or factor shift effects, the cost effect 

results show that the employment effects of the increased spending in the regulated sector 

(and, possibly, the parts sector) are expected to be positive and on the order of a few thousand 

in the initial years of the program. As noted above, motor vehicle and parts manufacturing 

sectors employed about 677,000 people in 2010, with automobile and light truck 

manufacturing accounting for about 129,000 of that total. 

8.2.4 Effects on Employment for Auto Dealers 

The effects of the standards on employment for auto dealers depend principally on the 

effects of the standards on light duty vehicle sales: increases in sales are likely to contribute 

to employment at dealerships, while reductions in sales are likely to have the opposite effect. 

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, and reductions are 

likely to decrease labor demand. 

Although this rule predicts very small penetration of plug-in hybrid and electric 

vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. 

As discussed in Chapter 8.1.2.7, consumers may find some characteristics of electric vehicles 

and plug-in hybrid electric vehicles, such as the ability to fuel with electricity rather than 

gasoline, attractive; they may find other characteristics, such as the limited range for electric 

vehicles, undesirable. As a result, some consumers will find that EVs will meet their needs, 

but other buyers will choose more conventional vehicles. Auto dealers may play a major role 

in explaining the merits and disadvantages of these new technologies to vehicle buyers. There 

may be a temporary need for increased employment to train sales staff in the new 

technologies as the new technologies become available. 

8.2.5 Effects on Employment in the Auto Parts Sector 

As discussed in the context of employment in the auto industry, some vehicle parts are 

made in-house by auto manufacturers; others are made by independent suppliers who are not 

directly regulated, but who will be affected by the 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 standards on vehicle sales and on the labor intensity of the new technologies, qualitatively 

in the same ways as for the auto manufacturing sector. 
MY 2017 and Later - Regulatory Impact Analysis 

8-31 

8.2.6 Effects on Employment for Fuel Suppliers 

In addition to the effects on the auto manufacturing and parts sectors, these rules will 

result in changes in fuel use that lower GHG emissions. Fuel saving, principally reductions in 

liquid fuels such as gasoline and diesel, will affect employment in the fuel suppliers industry 

sectors throughout the supply chain, from refineries to gasoline stations. To the extent that 

the 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 RIA Chapter 5.4. 

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 RIA Chapter 5.4. This new fuel may require additional infrastructure, such as 

electricity charging locations. Providing this infrastructure, as well as infrastructure for other 

alternative fuels (such as CNG), will require some increased employment. In addition, the 

generation of electricity is likely to require some additional labor. We have insufficient 

information at this time to predict whether the increases in labor associated with increased 

infrastructure provision and generation for electricity will be greater or less than the 

employment reductions associated with reduced demand for petroleum fuels. 

8.2.7 Effects on Employment due to Impacts on Consumer Expenditures 

As a result of these 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.5 and Chapter 5.5 of this RIA); indeed, people who finance their vehicles are 

expected to find that their fuel savings per month exceed the increase in the loan cost (though 

this depends on the particular loan rate a consumer receives). As a result, consumers will 

have additional money to spend on other goods and services, though, for those who do not 

finance their vehicles, it will occur after the initial payback period. These increased 

expenditures will support employment in those sectors where consumers spend their savings. 

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

to full employment by that time, any change in consumer expenditures would primarily 

represent a shift in employment among sectors. If, on the other hand, the economy still has 

substantial unemployment, these expenditures would contribute to employment through 

increased consumer demand. 

8.2.8 Summary 

The primary employment effects of this rule are expected to be found throughout 

several key sectors: auto manufacturers, auto dealers, auto parts manufacturing, fuel 

production and supply, and consumers. These standards initially take effect in model year 
Chapter 8 

8-32 

2017, a time period sufficiently far in the future that the current sustained high unemployment 

at the national level may be moderated or ended. In an economy with full employment, the 

primary employment effect of a rulemaking is likely to be to move employment from one 

sector to another, rather than to increase or decrease employment. For that reason, we focus 

our partial quantitative analysis on employment in the regulated sector, to examine the 

impacts on that sector directly. We discuss the likely direction of other impacts in the 

regulated sector as well as in other directly related sectors, but we do not quantify those 

impacts, because they are more difficult to quantify with reasonable accuracy, particularly so 

far into the future. 

For the regulated sector, the cost effect is expected to increase employment by 700 – 

3,200 job-years in 2017, depending on the share of that employment that is in the auto 

manufacturing sector compared to the auto parts manufacturing sector. As mentioned above, 

some of these job gains may occur earlier as auto manufacturers and parts suppliers hire staff 

to prepare to comply with the standard. The demand effect depends on changes in vehicle 

sales, which are not quantified for this rule. Though we do not have estimates of the factor 

shift effect for all potential compliance technologies, the evidence which we do have for some 

technologies suggests that many of the technologies will have increased labor needs. 

Effects in other sectors that are predicated on vehicle sales are also ambiguous. 

Changes in vehicle sales are expected to affect labor needs in auto dealerships and in parts 

manufacturing. Increased expenditures for auto parts are expected to require increased labor 

to build parts, though this effect also depends on any changes in the labor intensity of 

production; as noted, the subset of potential compliance technologies for which data are 

available show increased labor requirements. Reduced petroleum fuel production implies less 

employment in the petroleum sectors, although there could be increases in employment 

related to providing infrastructure for alternative fuels such as electricity and CNG. Finally, 

consumer spending is expected to affect employment through changes in expenditures in 

general retail sectors; net fuel savings by consumers are expected to increase demand (and 

therefore employment) in other sectors.
MY 2017 and Later - Regulatory Impact Analysis 

8-33 

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Adam B., Richard G. Newell, and Robert N. Stavins (2001). "Energy Efficient Technologies 

and Climate Change Policies: Issues and Evidence." In Climate Change Economics and 

Policy, Toman, Michael A., ed., Washington, D.C.: Resources for the Future. p.171-181 

(Docket EPA-HQ-OAR-2010-0799-0828); Metcalf, Gilbert E., and Kevin A. Hassett (1999). 
Chapter 8 

8-38 

"Measuring the Energy Savings From Home Improvement Investments: Evidence From 

Monthly Billing Data." The Review of Economics and Statistics 81(3): 516-528 (Docket 

EPA-HQ-OAR-2010-0799-0659); Tietenberg, T. (2009). “Reflections – Energy Efficiency 

Policy: Pipe Dream or Pipeline to the Future?” Review of Environmental Economics and 

Policy 3(2): 304-320 (Docket EPA-HQ-OAR-2010-0799-0707). 

44 Helfand, Gloria, and Ann Wolverton (2011). “Evaluating the Consumer Response to Fuel 

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

Resource Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799-0652). 

45 Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” 

prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 

(Docket EPA-HQ-OAR-2010-0799-0717). 

46 U.S. Environmental Protection Agency. “Peer Review for the Consumer Vehicle Choice 

Model and Documentation.” Office of Transportation and Air Quality, Assessment and 

Standards Division, EPA-420-R-12-013, April 2012 (Docket EPA-HQ-OAR-2010-0799). 

47 See, for instance, Ladika, Susan (2009). “’Green’ auto loans offer lower rates,” 

Bankrate.com, http://www.bankrate.com/finance/auto/green-auto-loans-offer-lower-rates1.aspx , accessed 2/28/12 (Docket EPA-HQ-OAR-2010-0799). 

48 Helfand, Gloria (2012). “Memorandum: Lending institutions that provide discounts for 

more fuel-efficient vehicles.” Assessment and Standards Division, Office of Transportation 

and Air Quality, U.S. Environmental Protection Agency, Docket EPA-HQ-OAR-0799 

(Docket EPA-HQ-OAR-2010-0799). 

49 U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey 2010. 

http://www.bls.gov/cex/#tables. This analysis uses the table Quintiles of Income before 

Taxes, accessed 6/14/12 (Docket EPA-HQ-OAR-2010-0799). 

50 Federal Reserve System, Survey of Consumer Finances 2007. 

http://www.federalreserve.gov/econresdata/scf/scfindex.htm. We applied the weighting 

scheme for the data, to make the data representative of the population as a whole, provided in 

the documentation for the results presented here (Docket EPA-HQ-OAR-2010-0799). 

51 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency 

Standards. The White House, Office of the Press Secretary, May 21, 2010. 

http://www.whitehouse.gov/the-press-office/presidential-memorandum-regarding-fuelefficiency-standards 

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

on August 9, 2011. 

53 Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis 

of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011 

(Docket EPA-HQ-OAR-2010-0799-0676). 
MY 2017 and Later - Regulatory Impact Analysis 

8-39 

54 Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. “Jobs Versus the 

Environment: An Industry-Level Perspective.” Journal of Environmental Economics and 

Management 43 (2002): 412-436 (Docket EPA-HQ-OAR-2010-0799-0677). 

55 Berck, Peter, and Sandra Hoffmann. “Assessing the Employment Impacts of 

Environmental and Natural Resource Policy.” Environmental and Resource Economics 22 

(2002): 133-156 (Docket EPA-HQ-OAR-2010-0799-0678). 

56 Berman, Eli, and Linda T. Bui, (2001) “Environmental Regulation and Labor Demand: 

Evidence from the South Coast Air Basin,” Journal of Public Economics, 79, 265 – 295 

(Docket EPA-HQ-OAR-2010-0799-0679).

57 http://www.bls.gov/emp/ep_data_emp_requirements.htm; see the “Cost Effect Employment 

Impacts Final Rulemaking” spreadsheet, Docket EPA-HQ-OAR-2010-0799. 

58 U.S. Vehicle Production by Manufacturer, UsaPr05.xls, and U.S. Vehicle Sales, 

UsaSa01.xls, accessed July 25, 2011 at http://subscribers.wardsauto.com/refcenter/ , 

©Copyright 2011, Ward's Automotive Group, a division of Penton Media Inc. Redistribution 

prohibited. Docket EPA-HQ-OAR-2010-0799. 

59 http://www.census.gov/manufacturing/asm/index.html; see the “Cost Effect Employment 

Impacts Final Rulemaking” spreadsheet, Docket EPA-HQ-OAR-2010-0799.