Subject: Summary of additional draft interagency working com ments received on draft rule under EO 12866 review Date: 11/15/2011 05:52 PM From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop .gov> To: William Charmley/AA/USEPA/US@EPA, "'Steve.Wood@dot. gov'" <Steve.Wood@dot.gov>,

"'Neil.Eisner@dot.gov'" <Neil.Eisner@dot.gov>, "''j ames.tamm@dot.gov' (james.tamm@dot.gov)'" <james.tamm@dot.gov>, "'rebecca.yoon@dot.gov'" <reb ecca.yoon@dot.gov>, Ed Nam/AA/USEPA/US@EPA, Robin Moran/AA/USEPA/US@EPA, A lex Barron/DC/USEPA/US@EPA, Ann Wolverton/DC/USEPA/US@E PA, "Larry.Blincoe@dot.gov" <Larry.Blincoe@dot.gov>

Please find attached additional draft interagency w orking comments received on draft NPRM under EO 12866 and EO 13563 review. They are for your consid eration and are subject to further policy review.

Thanks.
Chandana

Summary of interagency working comments received on draft rule under EO 12866a.pdfSubject: Summary of additional draft interagency working com ments received on draft rule under EO 12866 review Date: 11/15/2011 06:14 PM From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop .gov> To: William Charmley/AA/USEPA/US@EPA, "'Steve.Wood@dot. gov'" <Steve.Wood@dot.gov>,

"'Neil.Eisner@dot.gov'" <Neil.Eisner@dot.gov>, "''j ames.tamm@dot.gov' (james.tamm@dot.gov)'" <james.tamm@dot.gov>, "'rebecca.yoon@dot.gov'" <reb ecca.yoon@dot.gov>, Ed Nam/AA/USEPA/US@EPA, Robin Moran/AA/USEPA/US@EPA, A lex Barron/DC/USEPA/US@EPA, Ann Wolverton/DC/USEPA/US@E PA, "Larry.Blincoe@dot.gov" <Larry.Blincoe@dot.gov>

Please find attached additional draft interagency w orking comments received on draft NPRM under EO 12866 and EO 13563 review. They are for your consid eration and are subject to further policy review.

Thanks.
Chandana

Summary of interagency working comments received on draft rule under EO 12866.docSubject: Summary of interagency working comments received in draft NPRM under EO 12866 review

Date: 11/15/2011 07:55 PM From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop .gov> To: William Charmley/AA/USEPA/US@EPA Cc: Robin Moran/AA/USEPA/US@EPA, Ed Nam/AA/USEPA/US@EPA , Jim Tamm

<james.tamm@dot.gov>, Rebecca Yoon <rebecca.yoon@do t.gov>, Ann Wolverton/DC/USEPA/US@EPA

Please find attached additional draft interagency w orking comments received on draft NPRM under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

Thanks.

Chandana

Summary of interagency working comments received on draft NPRM under EO 12866 review.docSubject: Summary of interagency working comments received in draft NPRM under EO 12866 review

Date: 11/15/2011 10:49 PM From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop .gov> To: William Charmley/AA/USEPA/US@EPA Cc: Robin Moran/AA/USEPA/US@EPA, Ed Nam/AA/USEPA/US@EPA , 'Jim Tamm'

<james.tamm@dot.gov>, 'Rebecca Yoon' <rebecca.yoon@ dot.gov>, Ann Wolverton/DC/USEPA/US@EPA

Please find attached additional draft interagency w orking comments received on draft NPRM under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

Thanks.

Chandana

Summary of Interagency Working Comments received on draft rule under EO 12866.pdfSubject: Summary of interagency working comments received in draft NPRM under EO 12866 review

Date: 11/15/2011 11:47 PM From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop .gov> To: William Charmley/AA/USEPA/US@EPA, Robin Moran/AA/US EPA/US@EPA, Ed

Nam/AA/USEPA/US@EPA, 'Jim Tamm' <james.tamm@dot.gov >, 'Rebecca Yoon' <rebecca.yoon@dot.gov>, Ann Wolverton/DC/USEPA/US@E PA

Please find attached additional draft interagency w orking comments received on draft NPRM under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

Thanks.

Chandana

Summary of interagency working comments received on draft NPRM under EO 12866 review.pdfSummary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-1 Below is a summary of interagency comments received during EO 12866 and EO 13563 review of the draft rule “Joint Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards”. They are for your consideration and are subject to further policy review.



### ***

Chapter 8: Vehicle Sales and Employment Impacts

8.1 Vehicle Sales Impacts

8.1.1 Vehicle Sales Impacts and Payback Period

Predicting the effects of this rule on vehicl es entails comparing two competing effects. On the one hand, as a result of this rule, th e vehicles will become more expensive, which would, by itself, be expected to discourage sa les. On the other hand, the vehicles will have improved fuel economy and thus lower operating costs, producing lower total costs over the life of vehicles, which makes them more attractive to consumers. Which of these effects dominates for potential vehicle buyers when th ey are considering a purchase will determine the effect on sales. However, assessing the net effect of these two competing effects is complex and uncertain, as it rests on how consumers value fuel savings at the time of purchase and the extent to which manufacturers and dealers reflect them in the purchase price.

There are reasons to believe that many consumers do not fully take account of thevalue of fuel savings at the time of purchase (such as lack of salience and focus on the shortterm, as discussed in detail below), and as we have stated, the proposed rule is expected to produce significant net benefits for consumers. But the empirical literature leaves many open questions. It does not give clear evidence about the effect of the proposed rule on sales, nor does it speak to the possibility and consequences of consumer learning over time. Thus, for the proposal we do not provide quantified esti mates of potential sales impacts. Rather, we solicit comment on the issues raised here and on methods for estimating the effect of this rule
on vehicle sales.

If consumers do not consider net costs – if they do not accurately compare the value of fuel savings with the increased cost of fu el economy technology in their vehicle purchase decisions -- there may be a net decrease in sales for 2017 and beyond. If auto makers have accurately gauged how consumers consider fuel economy when purchasing vehicles and have provided the levels of improvement that consumers desire, then consumers should not be expected to want to purchase the more fuel-e fficient vehicles beyond what manufacturers may offer in the future absent this rule. On certain assumptions, auto makers would be expected to provide as much fuel economy as consumers want, and vehicle sales would be expected to decrease, not to increase, as a result of the rule. We invite comments on this possibility.Chapter 8

8-2 If, on the other hand, the rule motivates an increase in consumer demand for fuel economy, perhaps as a result of learning, then it could lead to production of more desirable vehicles and a commensurate increase in sales. We invite comment on consumer learning and related possible reasons for positive effects on consumer demand.

For years, consumers have been learning about the benefits that may accrue to them from owning and operating vehicles with greater fuel efficiency. Consumer demand has thus shifted towards such vehicles, not only because of higher fuel prices but also because many consumers are learning about the value of purch ases based not only on initial costs but also on the total cost of owning and operating a vehicle over its lifetime. This type of learning is expected to continue before and during the model years affected by this rule, particularly given the new fuel economy labels that clarify potential economic effects and should therefore reinforce that learning.

Today’s proposed rule, combined with the new and easier-to-understand fuel economy label required to be on all new vehicles beginning in 2012, may increase sales above baseline levels by hastening this very type of consumer learning. As more consumers experience, as a result of the rule, the savings in time and ex pense from owning more fuel efficient vehicles, demand may shift yet further in the direction of the vehicles mandated under the rule. This social learning can take place both within and acr oss households, as consumers learn from one another. There are three possible effects here.

First and most directly, the time and fuel sa vings associated with operating more fuel efficient vehicles will be more salient to individuals who own them, causing their subsequent purchase decisions to shift closer to minimizing the total cost of ownership over the lifetime of the vehicle. Second, this appreciation may spread across households through word of mouth and other forms of communications. Third, as more motorists experience the time and fuel savings associated with greater fuel effici ency, the price of used cars will better reflect such efficiency, further reducing the cost of owning more efficient vehicles for the buyers of new vehicles (since the resale price will increase).

If these induced learning effects are strong, the rule could potentially increase total vehicle sales over time. These increased sales would not occur in the model years first affected by the rule, but they could occur once the induced learning takes place . It is not possible to quantify these learning effects years in advance and that effect may be speeded or slowed by other factors that enter into a consume r’s valuation of fuel efficiency in selecting
vehicles.

The possibility that the rule will (after a la g for consumer learning) increase sales need not rest on the assumption that automobile manufacturers are failing to pursue profitable opportunities to supply the vehicles that consumers demand. In the absence of the rule, no individual automobile manufacturer may find it profitable to move toward the more efficient vehicles mandated under the rule. In particular, no individual company can fully internalize the future boost to demand resulting from the rule. If one company were to make more efficient vehicles, counting on consumer learning to enhance demand in the future, that company would capture only a fraction of the extra sales so generated, because the learning atSummary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-3 issue is not specific to any one company's fleet. Many of the extra sales would accrue to that company's competitors.

In other words, consumer learning about the be nefits of fuel efficient vehicles involves positive externalities (spillovers) from one comp any to the others. [Footnote: Industrywide positive spillovers of this type are hardly unique to this situation. In many industries, companies form trade associations to promote industry-wide public goods. For example, merchants in a given locale may band together to promote tourism in that locale. Antitrust law recognizes that this type of coordination can increase output.] These positive externalities may lead to benefits for manufacturers as a whole. We emphasize that this discussion has been tentative and qualified. To be sure, social lear ning of related kinds has been identified in a number of contexts. [Footnote: See Hunt Alcott, Social Norms and Energy Conservation, Journal of Public Economics (forthcoming 2011), available at http://web.mit.edu/allcott/www/ Allcott%202011%20JPubEc%20- %20Social%20Norms%20and%20Energy%20Conservation.pdf; Christophe Chamley, Rational Herds: Economic Models of Social Learning (Cambridge, 2003).] Comments are invited on the discussion offered here, with pa rticular reference to any relevant empirical findings.”

We note as well that automakers are currently putting more fuel-saving technologies into vehicles and are advertising based in part on the fuel efficiency of their vehicles. We cannot assess whether these improvements in fuel economy are due to higher gasoline prices in recent years, to increased consumer demand for fuel economy, to auto makers realizing that fuel economy is a valuable attribute to consumers, or to EPA’s and NHTSA’s recent rulemakings on fuel economy and greenhouse gases. If adding more fuel economy to vehicles, even with the added costs, does make the vehicles more attractive to consumers, then we would expect auto makers to add this fuel economy in the absence of the rule. If so,
both the benefits and costs estima ted for this rule are overstated.

In addition to uncertainty over the expected offerings of fuel economy in new vehicles in the absence of this rule, we note the uncertainty over the role of fuel economy in vehicle purchase decisions. As discussed in Chapter 8.1.2.5 below, how consumers consider fuel economy when buying cars is not well understood; it is likely to vary substantially among individuals, and possibly across vehicle classe s. In addition, consumer interest in fuel economy may change over time, in response to changes in fuel prices, to marketing efforts by automakers, to more experience with fuel-efficient vehicles, and (as noted) to better understanding of the costs associated with fuel savings, not to mention changing market conditions and other shifts in the U.S. economy that could affect vehicle demand and national driving behavior. As noted in Preamble III.H.1.a, EPA and NHTSA recently revised the fuel economy label for new vehicles, to improve consumer information on fuel economy. We do not have data on the effect of the label on consumer vehicle purchase decisions. If consumers put more emphasis on fuel economy in their pur chase decisions, then automakers would be expected to respond to that interest independent of the standards proposed here, and an increase in vehicle sales resulting from improved fuel economy would be a result of marketChapter 8

8-4 forces instead of this rulemaking. Preamble III.D. discusses EPA’s reasoning in assuming that the reference fleet will comply with the standards for MY 2016 vehicles but will not over-comply. Adding to the uncertainty is the dynamic effect that the proposed rule may have on other relevant aspects of the vehicle sales ma rket aside from purchase price. For example, the proposed rule may induce technological change that would not have occurred otherwise, and introduce consumers to new vehicle attributes that impact vehicle demand in a positive way. Similarly, improvements in vehicle fuel economy might accompany other changes in vehicle characteristics that are relevant for purc hase decisions. While these factors are always present, they are particularly acute in the curren t context, in which the rule will not take effect
until 2017.

In previous rulemakings, EPA and NHTSA conducted vehicle sales analyses by comparing the up-front costs of the vehicles with the present value of five years’ worth of fuel savings. We assumed that the costs for the fuel-saving technologies would be passed along fully to vehicle buyers in the vehicle prices. Th e 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 thatbuyers would face, and a higher resale value, with all of these factors due to the higher upfront cost of the vehicle. Those calculations resulted in an adjusted increase in costs to consumers. We then assumed that consumers considered the present value of five years of fuel savings in their vehicle purchase, which is consistent with the length of a typical new light-duty vehicle loan, and is similar to the average time that a new vehicle purchaser holds
onto the vehicle. A The present value of fuel savings was subtracted from technology costs to get a net effect on vehicle cost of ownership. We then used a short-run demand elasticity of - 1 to convert a change in price into a change in quantity demanded of vehicles. B An elasticity of -1 means that a 1% increase in price leads to a 1% reduction in quantity sold. In the vehicle sales analyses, if five years of fuel savings outweighed the adjusted technology costs, then vehicle sales were predic ted to increase; if the fuel savings were smaller than the adjusted technology costs, sales would decrease, compared to a world without the standards.

We do not here present a vehicle sales analys is using this approach. This rule takes
effect for MY 2017-2025. If consumers take into account less than the break-even number of years in their buying decisions, then vehicle sales could decrease. In all cases, we would expect the impact of this rule on vehicle sales to be modest relative to other factors influencing future vehicle sales such as fuel prices and overall economic conditions, which are strong determinants of overall sales in any given year.

A In this proposal, the 5-year payback assumption corresp onds to an assumption that vehicle buyers take into account between 30 and 50 percent of the present value of lifetime vehicle fuel savings (with the variation depending on discount rate, model year, and car vs. truck).

B For a durable good such as an auto, the elasticity may be smaller in the long run: though people may be able to change the timing of their purchase when price changes in the short run, they must eventually make the investment. We request comment on whether or when a long-run elasticity should be used for a rule that phases in over time, as well as how to find good estimates for the long –run elasticity.Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-5 The calculation of the payback period is discussed in DRIA Chapter 5.3. Table 1 shows the estimated payback period for MY 2021 and 2025. We present MY 2021 because it is the last year before the mid-term review impacts, if any, will take place, and MY 2025 because it is the last year of the program. The payback period in 2021 is shorter than that in 2025, because the technologies required to meet the proposed MY 2021 standards are more cost-effective than those for MY 2025. In all cases, the payback periods are less than the 5- year payback period that EPA and NHTSA have previously used in the vehicle sales analyses. Given the relatively short time frames for th e payback periods, these results suggest that vehicle sales could increase as a result of improved fuel economy. Alternatively, if consumers have shorter time frames for payback periods, then the rule may have a negative impact on vehicle sales. In either situation we would expect the impact of this rule on sales to be modest. As noted above, though, it is possibl e that some or all of this increase in fuel economy would occur in the absence of the rule, and it is also possible that consumers may revise how they think about fuel economy in their vehicle purchase decisions.

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

Model
Year Estimated Payback Period
for Cash Purchase, 3%
Discount Rate Estimated Payback Period
for Cash Purchase, 7%
Discount Rate Estimated Payback Period
for Purchase on

Credit, 3%
Discount Rate Estimated Payback Period
for Purchase on

Credit, 7%
Discount Rate

2021 2.7 2.9 2.9 2.8

2025 3.7 3.9 3.9 3.9

We welcome comments on all aspects of this discussion, including assumptions on consumer and producer behavior; how consumers value future fuel savings at the time of purchase; factors that should be taken into account when assessing the impact of fuel economy standard on aggregate vehicle sales; and sources of uncertainty in estimating vehicle sales impacts. We welcome comments on the methodology described here for quantitative estimates of the effects of this proposal on sa les and its appropriateness for this rulemaking; we also welcome proposals for other methods.

We here provide further detail on some of the assumptions included in the payback analysis. We seek comment on all these factors as well. The analysis starts with the increase in costs estimated by the OMEGA model. We assume that these costs are fully passed along to consumers. This assumption is appropriate for cost increases in perfectly competitive markets. In less than perfectly competitive markets, though, it is possible that the cost increase is split between consumers and automakers, and possibly suppliers, and the priceChapter 8

8-6 may not increase as much as costs. 1 Thus, the assumption of full cost pass-through is possibly
an overestimate.

The next step in the analysis is to adjust this cost increase for other effects on the consumer. The higher vehicle price is likely to lead to an increase in sales tax, insurance, and vehicle financing costs.

The increase in insurance costs is estimated from the average value of collision plus comprehensive insurance as a proportion of average new vehicle price. Collision plus comprehensive insurance is the portion of insurance costs that depend on vehicle value. The
Insurance Information Institute 2 provides the average value of collision plus comprehensive insurance in 2008 as $432. The average value of a new vehicle in 2008, according to the U.S. Department of Energy, was $23,334. 3 Dividing the insurance cost by the average price of a new vehicle gives the proportion of comprehensive plus collision insurance as 1.85 percent of the price of a vehicle. If this same proportion holds for the increase in price of a vehicle, then insurance costs should go up by 1.85 percent of the increase in vehicle cost. We use information on depreciation of vehicle value from the same U.S. Department of Energy report
to estimate a reduction in in surance costs, due to reduction in the estimated value of the vehicle, over 9 years.

Calculating the average increase in sales tax starts with the vehicle sales tax for each state in 2006, the most recent source identified. 4 The sales tax per state was then multiplied by the 2010 population of the state; 5 those values were summed and divided by total U.S. population, to give a population-weighted sales tax. That estimate of the state sales taxes for vehicles in the U.S. is 5.3 percent. This valu e is assumed to be a one-time cost incurred when the vehicle is purchased.

As of July, 2011, the national average interest rate for a 5 year new car loan was 5.51 percent. 6 We use this loan rate to calculate the increase in vehicle costs due to financing a
loan.

8.1.2 Consumer Vehicle Choice Modeling C

An alternative to the vehicle sales analysis discussed above is the use of consumer
vehicle choice models. In this section we de scribe 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 consum ers’ willingness to pay for additional fuel economy. Because consumer surplus estimate s from consumer vehicl e choice models depend critically on this value, we would consider any consumer surplus estimates of the effect of our rule from such models to be unreliable. In addition, the predictive ability of consumer vehicle choice models may be limited. While vehicle choice models are based on sales of existing

C This section is drawn heavily from Helfand, Gloria, and Ann Wolverton, “Evaluating the Consumer Response to Fuel Economy: A Review of the Literature.” International Review of Environmental and Resource Economics 5 (2011): 103-146.Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-7 vehicles, vehicle models are likely to change, both independently and in response to this rule. The models may not predict well in response to these changes. Instead, we compare the value of the fuel savings associated with this rule with the increase in technology costs. EPA will
continue its efforts to review the literature, but, given the know n limitations and uncertainties of vehicle choice models, EPA has not conducted an analysis using these models for this proposal.

This rule will lead automakers to change characteristics – in particular, the fuel economy -- of the vehicles they produce. These changes will affect the cost of manufacturing the vehicle; as a result, the prices of the vehicles will also change.

In response to these changes, the number and types of vehicles sold is likely to change. When consumers buy vehicles, they consider both their personal characteristics (such as age, family composition, income, and their vehicle needs) and the characteristics of vehicles (e.g., vehicle size, fuel economy, and price). In resp onse 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 charact eristics and prices. In principle, it should produce more accurate estimates of compliance costs compared to models that hold fleet mix constant, since it predicts changes in the fleet mix that can affect compliance costs. It can also be used to measure changes in consumer surplus, the benefit that consumers perceive from a good over and above the purchase price. (Consumer surplus is the difference between what consumers would be willing to pay for a good, represented by the demand curve, and the amount they actually pay. For instance, if a consumer were willing to pay $30,000 for a new vehicle, but ended up paying $25,000, the $5000 difference is consumer surplus.)

A number of consumer vehicle choice models have been developed. They vary in the methods used, the data sources, the factors included in the models, the research questions they are designed to answer, and the results of the models related to the effects of fuel economy on consumer decisions. This section will give some background on these differences among the
models.

8.1.2.1 Methods

Consumer choice models (CCMs) of vehicle purchases typically use a form of discrete choice modeling. Discrete choice models seek to explain discrete rather than continuous decisions. An example of a continuous decision is how many pounds of food a farm might grow: the pounds of food can take any numerical value. Discrete decisions can take only aChapter 8

8-8 limited set of values. The decision to purchase a vehicle, for instance, can only take two values, yes or no. Vehicle purchases are typically modeled as discrete choices, where the choice is whether to purchase a specified vehicle. The result of these models is a prediction of the probability that a consumer will purchase a specified vehicle. A minor variant on discrete choice models estimates the market share (a continuous variable between 0 and 1) for each vehicle. Because the market share is, es sentially, the probability that consumers will purchase a specific vehicle, these approaches are similar in process; they differ mostly in the kinds of data that they use.

The primary methods used to model vehicle choices are nested logit and mixed logit. D In a nested logit, the model is structured in layers. For instance, the first layer may be the choice of whether to buy a new or used vehicle. Given that the person chooses a new vehicle, the second layer may be whether to buy a car or a truck. Given that the person chooses a car, the third layer may be the choice among an economy, midsize, or luxury car. Examples of nested logit models include Goldberg, 7 Greene et al., 8 and McManus. 9

In a mixed logit, personal characteristics of consumers play a larger role than in nested logit. While nested logit can look at the effects of a change in average consumer characteristics, mixed logit allows consideration of the effects of the distribution of consumer characteristics. As a result, mixed logit can be used to examine the distributional effects on various socioeconomic groups, which nested logit is not designed to do. Examples of mixed logit models include Berry, Levinsohn, and Pakes, 10 Bento et al., 11 and Train and Winston. 12

While discrete choice modeling appears to be the primary method for consumer choice modeling, others (such as Kleit 13 and Austin and Dinan 14 ) 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 a nd 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 effect s 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 purcha sing 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 charact eristics to develop their original sets of parameters. The

D Logit refers to a statistical analysis method used for analyzing the factors that a ffect discrete choices (i.e., yes/no decisions or the choice among a countable number of options).Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-9 datasets used in these studies sometimes come from surveys of individuals’ behaviors. 15 Because they draw on the behavior of individuals, they provide what is sometimes called micro-level data. Other studies, that estimate market shares instead of discrete purchase decisions, use aggregated data that can cover long time periods. 16

Calibrated models rely on ex isting 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. Th e Fuel Economy Regulatory Analysis Model developed for the Energy Information Administration 17 and the New Vehicle Market Model developed by NERA Economic Consulting 18 are examples of calibrated models.

8.1.2.3 Factors Included in the Models

Consumer choice models vary in their complexity and levels of analysis. Some focus only on the new vehicle market; 19 others consider the choice between new vehicles and an outside good (possibly including a used vehicle); 20 others explicitly consider the relationship
between the new and used vehicle markets. 21 Some models include consideration of vehicle miles traveled, 22 though most do not.

The models vary in their inclusion of both consumer and vehicle information. One model includes only vehicle price and the distribution of income in the population influencing choice; 23 others include varying numbers and kinds of vehicle and consumer attributes.

Some models include only the consumer side of the vehicle market; 24 others seek to represent both consumer and producer decisions. 25 Models that include only the consumer side are suitable for reflecting consumer choices, but they do not allow for revisions of vehicle characteristics in response to consumer preferences. Including producer behavior allows for vehicle characteristics such as price and fuel economy to be the result of market forces rather than characteristics of the existing fleet. For instance, in the context of “feebates” (subsidizing fuel-eff icient cars with revenue collected by taxing fuel-inefficient vehicles), Greene et al. estima ted that 95% of the increase in fuel economy was due to addition of technology rather than changes in vehicles sold. 26 Including auto maker response is a complex exercise. Auto makers are commonly considered to have market power; they can influence the prices that consumers pay to increase their profits. As a result, the price increases that consumers face may reflect strategic factors that could make them higher or lower than the technology costs. In addition , auto makers may seek to influence consumer preferences through marketing and advertising. 27 Even those vehicle choice models that include a producer model may not include much detail, due to computational limits: it is unusual for models to allow both buyers and producers to choose one vehicle characteristic, much less multiple characteristics. 28

8.1.2.4 Research Questions for the Models

Consumer choice models have been developed to analyze many different research and policy questions. In part, these models have been developed to advance the state of economicChapter 8

8-10 modeling. The work of Berry, Levinsohn, and Pakes, 29 for instance, is often cited outside the motor vehicle context for its incorporation of multiple new modeling issues into its framework. In addition, because the vehicle sector is a major part of the U.S. economy and involved in many public policy discussions, research questions cover a wide gamut. These topics have included the effects of voluntary export restraints on Japanese vehicles compared to tariffs and quotas, 30 the market acceptability of alternative-fuel vehicles, 31 the effects of introduction and exit of vehicles from markets, 32 causes of the decline in market shares of U.S. automakers, 33 and the effects of gasoline taxes 34 and “feebates” 35 (subsidizing fuelefficient cars with revenue collected by taxing fuel-inefficient vehicles).

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions

Consumer vehicle choice models typically consider the effect of fuel economy on vehicle purchase decisions. It can appear in various forms.

Some models 36 incorporate fuel economy through its effects on the cost of owning a vehicle. With assumptions on the number of mile s traveled per year and the cost of fuel, it is possible to estimate the fuel savings (and pe rhaps other operating costs) associated with a more 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 esti mate the fuel savings that they expect to receive from a more fuel-efficient vehicle and consider the savings equivalent to a reduction in purchase price. Turrentine and Kurani 37 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 pe r gallon is one of the vehicle characteristics included to explain purchase decisions. Other models use fuel consumption per mile, the inverse of miles per gallon, as a measure: 38 since consumers pay for gallons of fuel, then this measure can assess fuel savings relatively directly. 39 Yet other models multiply fuel consumption per mile by the cost of fuel to get the cost of driving a mile, 40 or they divide fuel economy by fuel cost to get miles per dollar. 41 It is worth noting that these last two measures assume that consumers respond the same way to an increase in fuel economy as they do to a decrease in the price of fuel when each has the same effect on cost per mile driven. E On the one hand, while this assumption does not rely on as complex a calculation as the present value of fuel savings that Turrentine and Kurani examined, it suggests a calculating consumer. On the other hand, using a form of cost per mile is a way to recognize the role of fuel prices in

E Likewise, these measures assume consumers respond the same way to increases and decreases in cost per mile of driving, as well as if those increases and decreases are large shocks rather than small, gradual changes. The issue of potential asymmetric consumer response to increased fuel efficiency compared to other types of changes to the cost of driving also arises and is discussed in th e context of the VMT rebound effect (see Section III.H.4 of the Preamble and Chapter 4.2.5.2 of the TSD).Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-11 consumers’ purchase of fuel economy: recent research 42 presents results that higher fuel prices play a major role in that decision.

Greene and Liu, 43 in a paper published in 1988, reviewed 10 papers using consumer
vehicle choice models and estimated for each one how much consumers would be willing to pay at time of purchase to reduce vehicle operating costs by $1 per year. They found that people were willing to pay between $0.74 and $25.97 for a $1 decrease in annual operating costs for a vehicle. This is clearly a very wide range: while the lowest estimate suggests that people are not willing to pay $1 once to get $1 per year reduced costs of operating their vehicles, the maximum suggests a willingness to pay 35 times as high. For comparison, the present value of saving $1 per year for 15 years at a 3% discount rate is $11.94, while a 7% discount rate produces a present value of $8.78. While this study is quite old, it suggests that, at least as of that time, consumer vehicle choi ce models produced widely varying estimates of the value of reduced vehicle operating costs.

A newer literature revi ew from David Greene 44 suggests continued lack of convergence on the value of increased fuel economy to consumers. Of 27 studies, willingness to pay for fuel economy as a percent of the expected value of fuel savings varied from highly positive to highly negative. Significant numbers of studies found that consumers overvalued fuel economy, undervalued fuel economy, or roughly valued fuel economy correctly relative to fuel savings. Part of the difficulty may be, as these papers note, that fuel economy may be correlated (either positively or negatively) with other vehicle attributes, such as size, power, or quality, not all of which may be included in the analyses; as a result, “fuel economy” may in fact represent several characteristics at the same time. Indeed, Gramlich 45 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 studies 46 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 studies 47 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 studies 48 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 “neg ative-cost” fuel economy improvements.

The models do not appear to yield very consistent results on the role of fuel economy in consumer and producer decisions.Chapter 8

8-12 8.1.2.6 Why Consumers May Not BuyFuel Economy that Pays for
Itself

A detailed and wide ranging li terature attempts to explain why consumers appear to under-invest in energy saving technologies that – at least using a present value calculation based on engineering estimates – appear to pay for themselves. Existing research does not provide a definitive answer to this question. Potential explanations are bounded by two scenarios. On the one hand, purely private benefits of fuel economy (fuel savings, time savings, increases in driving time) must be accompanied by private losses of the same magnitude. However, if there is no such private loss, or if it is small or insignificant, then
there is a market or behavioral failure.

On the consumer side, this disconnect between net present value estimates of energyconserving cost savings and what consumers actually spend on energy conservation is often referred to as the Energy Paradox, 49 since consumers appear to undervalue a wide range of investments in energy conservation. There are many possible explanations for the paradox
discussed in th e literature. 50 Some explanations point to costs or aspects of consumer decision-making unaccounted for in a simple pr esent value calculation, while others point to potential behavioral or market failures. There is little empirical literature to help the analyst determine which combination of hypothesis offers the most credible explanation. Possibilities
include:

 Consumers put little weight on benefits fro m fuel economy in the future and show high discount rates;

 Consumers do not find the benefits from fuel economy to be sufficiently salient at the time of purchase, even if it would be in consumers’ economic interest to take account of those benefits;

 Consumers consider other attributes more important than fuel economy at the time of vehicle purchase, especially if fuel economy is a relatively “shrouded” attribute;

 Consumers have difficulty in calculating expected fuel savings;

 Consumers may use imprecise rules of thumb when deciding how much fuel economy to purchase;

 Consumers may give particular weight to the losses associated with upfront costs, and less so to the costs over time (a version of the phenomenon of “myopic loss aversion”);

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

 Fuel savings in the future are uncertain, while at the time of purchase the increased costs of fuel-saving technologies are certain and immediate;Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-13  Consumers may not be able to find the vehicles they want with improved fuel

economy;

 The level of cost savings may be affected by the underlying reasons for the gap: factors such as transactions costs and differences in quality may not be adequately measured; 51

 There is likely to be variation among consumers in the benefits they get from improved fuel economy, due to different miles driven and driving styles.

8.1.2.7 Modeling Electric Vehicles and Other New Vehicles

Modeling the introduction of new vehicles can be a greater challenge than modeling the existing vehicle market, because the modeler does not have data on how many of the new vehicles consumers buy. Nevertheless, it can be possible to estimate the effects of new vehicle introduction by identifying characteristics for the new vehicles and using those in a vehicle choice model. For instance, as discussed above, the models can estimate effects on the vehicle market when vehicles change their fuel economy or price. If the model incorporates other vehicle attributes important to the new vehicles, such as size, performance, or range, then the effect of the introduction can be modeled by applying the parameters for
those features to the new vehicle characteristics.

As discussed above, some models rely on vehicle price as the primary or only explanatory variable. Even in these models, it is possible, with some additional information, to consider the effects of new vehicle introduction. The first step is to find a vehicle similar on as many dimensions as possible to the new vehicle. For instance, if the change is to create an electric vehicle (EV) version of an existing model, then the existing model serves as the base vehicle. Next, it is necessary to measure the changes in vehicle attributes of interest to potential vehicle buyers. For an EV, changes in vehicle driving range and cost of fueling may be two such attributes. The next requirement is information on the value to consumers of the attributes that change between the new and the base vehicle. Multiplying the value for that attribute by the change in the attribute provides an estimate of the benefit or cost associated with changing that characteristic. That amount can then be added to or subtracted from the vehicle purchase price to give an adjusted purchas e 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 vehi cles, 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 abilityChapter 8

8-14 to recharge at home may be a convenient, desirable feature for people who have garages with electric hookups, but not for people who park on the street. If an infrastructure develops for recharging vehicles with the convenience approaching that of buying gasoline, range or home recharging may become less of a barrier to purchase. The reduced tailpipe emissions and reduced noise may be attractive features to some consumers. F They may have different performance or storage capacity. If sufficient data were available, the changes in these attributes, combined with WTP for each of the attributes, could be used to adjust the purchase price of the baseline vehicle to estimate consumers’ WTP for the electric version of a vehicle. Greene (2001), for instance, used this approach for a model that simulates choice, not only for EVs, but also for other alternative-fuel vehicles. 52 In that model, he considers only one base vehicle, a passenger car, but considers the ef fect on WTP of fuel cost per mile, range, acceleration, and several other vehicle attributes.

Vehicle driving range has received attention because of the current paucity of recharging infrastructure: if the driver of an EV gets low on fuel, it may be difficult to find a place to recharge. Because range appears to be a major factor in EV acceptability, it is starting to draw attention in the research community.

In several studies, researchers have used stated preference conjoint analysis to estimate the effect of vehicle range on consumer vehicle choice. In a conjoint analysis, consumers are given a choice between several vehicles with different attributes. One choice might be, for instance, between a baseline car and another car with higher range and a higher purchase price. The choices that consumers make (e.g., how much higher does the purchase price have to be for the consumer not to choose more range?) provide data that can be used to
estimate the role of vehicle attributes in the c onsumer’s choice. Stated preference analysis is sometimes considered less reliable than actual market behavior, because what people say they will do in hypothetical situations may not match what they would do in actual situations. On the other hand, stated preference methods can be used to study goods where market data do not exist, such as future market products undergoing development (marketing studies often use stated preference methods), or environmental goods. Because electric vehicles are not in widespread enough use for market studies, stated preference studies are, at this point, one of the few options to examine consumer behavior relating to these vehicles.

Table 8.1-2 summarizes results from several conjoint studies that include the effects of extending range (in the table, from 150 to 3 00 miles, to present standardized results). Variation of results in the table is from income or other demographic factors, not from confidence intervals. The results suggest that the value of additional range varies among consumers, and the amount of that variation is changing (perhaps shrinking) in more recent
studies.

F For instance, Hidrue et al. (Hidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. “Willingness to Pay for Electric Vehicles and their Attri butes.” Resource and Energy Economics 33(3) (2011): 686-705) find that some consumers are willing to pay $5 100 more for vehicles with 95% lower emissions than the vehicles they otherwise aim to purchase.Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-15 Table 8.1-2Willingness to Pay for Increasing Range Calculated from Various Studies

Study (Date) Value of extending range from 150 to 300 miles (dollar year) Value of additional range in 2009$ a

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

Kavelek (1996) for California Energy Commission c $2600 - $41,900 (1993$) $3700 - $58,700

Resource Systems Group (2009) for California Energy Commission d $2900 - $7500 (2009$) $2900 - $7500

Hess et al. (2009), using the same data as Resource Systems Group (2009) e $2400 - $8500 (2009$) $2400 - $8500

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

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

b Bunch, David S., Mark Brad ley, Thomas F. Golob, and Ryuichi Kitamura. “Demand for Clean-Fuel Vehicles
in California: A Discrete-Choice Stated Preference Pilot Project.” Transportation Research Part A 27A(3) (1993): 237-253. The value of range was, in their model, assumed to be the same for all people.

c Kavelek, Chris. “CALCARS: The California Conventional and Alternative Fuel Response Simulator.” Demand Analysis Office, California Energy Commissi on, 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 signific antly 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 Kempt on, and Meryl P. Gardner. “Willingness to Pay for
Electric Vehicles and their Attributes .” Resource and Energy Economics 33(3) (2011): 686-705. The range ofChapter 8

8-16 values is due to the model separating consumers into “g asoline 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 potentia l buyers (e.g., as noted, Hidrue et al. find that some consumers appear willing to pay substantia lly 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 addi tion, as mentioned above, and as seen in Table 8.1-2, it is likely that the WTP values for attri butes 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 predicti ng market shares for conventional vehicles.

8.1.2.8 EPA Exploration of Vehicle Choice Modeling

In order to develop greater understanding of these models, EPA is in the process of developing a vehicle choice model. In its current form, the model assumes that the vehicle fleet and all characteristics of each vehicle, except vehicle prices and fuel economy, stay the same. The model will predict changes in the vehicle fleet, at the individual-configuration level and at more aggregated levels, in response to changes in vehicle fuel economy and price.

The draft EPA model uses a nested logit structure common in the vehicle choicemodeling literature, as discussed above in Chapter 8.1.2.1. “Nesting” refers to the decisiontree structure of the model, and “logit” refers to the fact that the choices are discrete (i.e., yes/no decisions about which vehicles to purchase, instead of continuous values).

The nesting involves a hierarchy of choices. In its current form, at the initial decision node, consumers choose between buying a new vehicle or not. Conditional on choosing anew 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 substitutio n across nests, because the vehicles within a
nest are more similar to each other than vehicles in different nests. For instance, a person is more likely to substitute between a Chevrolet Aveo and a Toyota Yaris than between an Aveo and a pickup truck. In addition, substitution is greater at low decision nodes (such as individual configurations) than at higher decision nodes (such as the buy/no buy decision), because there are more choices at lower levels than at higher levels.Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-17 Parameters for the model (including demand elasticities and the value of fuel economy in purchase decisions) are being selected based on a review of values found in the literature on vehicle choice modeling. As discussed above, a number of studies have estimated these parameters. Those estimates, combined with some theoretical requirements, G assist in assigning values for the parameters. The model will allow individual users to change those parameters.

The fuel economy of a vehicle is used to adjust the price of the vehicle, using a version of the procedure discussed in Chapter 8.1.2.7: the value that the consumer places on fuel economy is multiplied by the change in fuel economy and incorporated into the “effective price” of the vehicle. In practice, implementing this calculation involves calculating the change in expenditures on fuel based on schedules of VMT, vehicle survival, and fuel prices in the future consistent with those in OMEGA. As discussed in Chapter 8.1.2.5, there is no consensus value for consumers’ willingness to pay for improved fuel economy: estimates vary tremendously. The model assumes that consumers will use some years of discounted fuel savings, with the modeler able to input both the number of years and the discount rate to be used in the analysis.

The vehicle choice model will take as inputs an initial fleet of vehicles (including the initial sales and fuel economy) in the absence of standards, the cost of technologies added to each vehicle to comply with standards, and the change in fuel economy. With the initial sales mix, for each vehicle, the model calculates a vehicle-specific constant that summarizes the value of all attributes of the vehicle other than price and fuel economy. This constant ensures that the model will predict changes in consumer response that would result only from changes in price and fuel economy. This constant substitutes for estimating the effects of changes in all other vehicle characteristics; the underlying assumption is that these other vehicle characteristics do not change. H For instance, it assumes that a Ford Escape will not change in size, power, or accessories; the only changes will be to its cost and its fuel economy.

The model assumes that the increase in vehicle cost associated with increased technology is fully passed through as an increas e in vehicle price, and some years of fuel savings offset this price increase. It then cal culates 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 cal culates changes in consumer surplus associated with the changes in fuel economy and vehicle prices.

G The theory of nested logit requires that the price slopes (the change in utility as vehicle full price changes, a measure of consumer responsiveness to price changes) must be higher in absolute value for lower nests. This condition reflects the point, discussed above, that substitutio n is greater at lower decision notes than at higher

ones.
H As explained in Section III.D of the preamble, as part of the technology cost analysis for the proposed rule, the agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions.Chapter 8

8-18 It is possible that the predicted changes in fleet mix will lead to predictions of vehicle sales for auto makers that do not meet the proposed standards, because the mix and volume of vehicles sold changed from the initial levels. To correct this problem, it would be necessary to feed the new fleet mix into OMEGA (which calculates costs and compliance) and get a new set of output, which could then be fed back into the vehicle choice model. OMEGA would increase technologies, and thus costs, to improve compliance; those adjustments would then again affect vehicle demand. We expect th at this iterative process would converge to a fleet mix that would meet standards. Performing this iteration requires development of an
interface between the vehicle choice model and OMEGA to ensure accurate transmission of data between the models. At this time, the vehicle choice model takes output from OMEGA, but the results of the modeling do not feed easily back into OMEGA. Building this interface is an expected part of our future modeling work.

The model is still undergoing development; EP A will seek peer review on it before it is utilized. In addition, concerns remain over the ability of any vehicle choice model to make reasonable predictions of the response of the vehicle fleet to changes in prices and vehicle
characteristics. EPA seeks comments on the us e of vehicle choice modeling for predicting changes in fleet mix due to policies, and on methods to test the ability of a vehicle choice model to produce reasonable estimates of changes in fleet mix.

8.1.2.9 Summary and Additional Considerations

Consumer vehicle choice modeling in principle can provide a great deal of useful information for regulatory analysis, helping to answer some of the central questions about relevant effects on consumer welfare. In practice, the advantages depend on the success of models in predicting changes in fleet size and mix.

First, consumer vehicle choice modeling has the potential to describe more accurately the impact of a policy, by identifying market sh ifts. 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 willing ness-to-pay values may change over time as consumers develop more experience with the vehicles and these characteristics. Models based on current estimates may not predict well for the future.

The modeling may improve estimates of the compliance costs of a rule. Consumers can either accept the new costs and vehicle characteristics, or they can change which vehicles they buy. Using a vehicle choice model is li kely to reduce compliance costs: because the model allows consumers to choose among accepting the new vehicle, buying a differentSummary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-19 vehicle, or not buying a vehicle, consumers have additional options, which improves their welfare relative to the assumption that consumers will not change their buying behavior. .

An additional complication associated with consumer choice modeling is accurate prediction of producers’ responses to the rule. While it is possible to include auto makers’ decisions (for instance, on setting prices) into vehicle choices, computational limits affect the richness of these models. Technology costs, while an accurate measure of the opportunity cost of resources to society, may overestimate or underestimate the eff ect on the prices that
consumers face.

Consumer choice models can be used to calculate consumer surplus impacts on vehicle purchase decisions. Because these valu es are based on the estimates of changes in vehicle sales and fleet mix, consumer surplus measures may not be accurate if the changes in
vehicle sales and fleet mix are not well estimated.

Principles of welfare analysis can be useful for understanding the role of consumer vehicle choice models in benefit-cost analysis. In particular, except for EVs, the technology cost estimates developed in this proposal take into account the costs to hold other vehicle attributes, such as size and performance, constant. In addition, the analysis assumes that the full technology costs are passed along to vehicle buyers. With these assumptions, because welfare losses are monetary estimates of how much buyers would have to be compensated to be made as well off as in the absence of the change, I the price increase measures the loss to the buyer. J Assuming that the full technology cost gets passed along to the buyer as an increase in price, the technology cost thus measures the welfare loss to the buyer. Increasing fuel economy would have to lead to other changes in the vehicles that buyers find undesirable for there to be additional losses not included in the technology costs.

Given the current limitations in modeling the role of fuel economy in vehicle purchase decisions, and limitations in modeling market responses to the new regulations, in this proposal EPA holds constant the vehicle fleet size and mix in its calculations of the impacts of this rule, and compares the fuel and other savings that consumers will receive with the technology costs of the vehicles. EPA continues to explore options for including consumer

I This approach describes the economic concept of compensating variation, a payment of money after a change that would make a consumer as well off after the change as before it. A related conc ept, 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 he r welfare before the change (compensating variation) or after the change (equivalent variation). In practice, these two measures are typically very close together.

J Indeed, it is likely to be an overestimate of the loss to the buyer, because the buyer has choices other than buying the same vehicle with a higher price; she could c hoose a different vehicle, or decide not to buy a new vehicle. The buyer would choose one of those options only if the alternative involves less loss than paying the higher price. Thus, the increase in price that the buyer faces would be the upper bound of loss of consumer welfare, unless there are other changes to the vehicle due to the fuel economy improvements that make the vehicle less desirable to buyers.Chapter 8

8-20 and producer choice in modeling the impacts of fuel economy-related regulations. This effort includes further review of existing consumer vehicle choice models, the estimates of consumers’ willingness to pay for increased fu el economy, and overall effects on consumer welfare, as well as EPA’s exploration of a vehicle choice model for use in the future.

8.2 Employment Impacts

8.2.1 Introduction

Although analysis of employment impacts is not part of a cost-benefit analysis (except to the extent that labor costs contribute to costs), employment impacts of federal rules are of particular concern in the current economic climate of sizeable unemployment. When President Obama requested that the agencies develop this program, he sought a program that would “strengthen the [auto] industry and enhance job creation in the United States.” 53,K The recently issued Executive Order 13563, “Improving Regulation and Regulatory Review” (January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety, and our environment while promoting economic growth, innovation, competitiveness, and job creation” (emphasis added). EPA is accordingly providing partial estimates of the effects of this proposal on domestic employment in the auto manufacturing and parts sectors, while qualitatively discussing how it may affect empl oyment in other sect ors more generally.

This proposal is expected to affect em ployment in the United States through the regulated sector – the auto manufacturing industry – and through several related sectors, specifically, industries that supply the auto manufacturing industry (e.g., vehicle parts), auto dealers, the fuel refining and supply sectors, and the general retail sector. According to the U.S. Bureau of Labor Statistics, in 2010, about 677,000 people in the U.S. were employed in Motor Vehicle and Parts Manufacturing Sector (NAICS 3361, 3362, and 3363). About 129,000 people in the U.S. were employed in the Automobile and Light Truck Manufacturing Sector (NAICS 33611) in December 2010; this is the directly regulated sector, since it encompasses the auto manufacturers that are responsible for complying with the proposed
standards. 54 Changes in light duty vehicle sales, discussed in Chapter 8.1.1, could affect employment for auto dealers. Though some of the parts used to achieve the proposed standards are likely to be built by auto manufacturers themselves, the auto parts manufacturing sector also plays a significant ro le in providing those parts, and will also be affected by changes in vehicle sales. As discussed in Chapter 5.4 of the RIA, this proposal is expected to reduce the amount of fuel these vehicles use, and thus affect the petroleum refinery and supply industriesWhen the economy is at full employment, an environmental regulation is unlikely to have much impact on net overall U.S. employment; instead, labor would primarily be shifted from one sector to another. These shifts in employment impose an opportunity cost on society, approximated by the wages of the employees, as regulation diverts workers from other activities in the economy. In this situation, any effects on net

K The May 21, 2010 Presidential Memorandum also requested that EPA and NHTSA, in developing the technical assessment to inform the rulemaking process (which was issued by the agencies and CARB on September 30, 2010), include, among other things, the “impacts on jobs and the automotive manufacturing base
in the United States.”Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-21 employment are likely to be transitory as workers change jobs (e.g., some workers may need to be retrained or require time to search fo r new jobs, while shortages in some sectors or regions could bid up wages to attract workers).

On the other hand, if a regulation comes into effect during a period of high unemployment, a change in labor demand due to regulation may affect net overall U.S. employment because the labor market is not in equilibrium. In such a period, both positive and negative employment effects are possible. (Cite Masur and Posner, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1920441) Schmalansee and Stavins point out that net positive employment effects are possible in the near term when the economy is at less than full employment due to the potential hiring of idle labor resources by the regulated sector to meet new requirements (e.g., to install new equipment) and new economic activity in sectors related to the regulated sector. 55 In the longer run, the net effect on employment is more difficult to predict and will depend on the way in which the related industries respond to the regulatory requirements. As Schmalansee and Stavins note, it is possible that the magnitude of the effect on employment could vary over time, region, and sector, and positive effects on employment in some regions or sectors could be offset by negative effects in other regions or sectors. For this reason, they urge caution in reporting partial employment effects since it can “paint an inaccurate picture of net employment impacts if not placed in the
broader economic context.”

Given the time frame of this proposed rule, it is possible that the state of the overall economy may have improved to the point where the only effect of changes in employment associated with the standards proposed here will be to shift workers from one sector to another. For example, to the extent that regulation increases the number of workers in the regulated sector (e.g., because of higher costs of producing vehicles with higher fuel economy), the opportunity cost associated with those shifts in employment will be captured in labor costs, as regulation diverts workers from other activities in the economy. In this situation, any effects on net employment are likely to be transitory as workers change jobs (e.g., some workers may need to be retrained or require time to search for new jobs, while shortages in some sectors or regions could bid up wages to attract workers). It is also possible that the state of the economy will be such th at 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; modelChapter 8

8-22 results may vary in different studies depending on the assumptions about the levels of those inputs, and which sectors receive those changes. Which sectors are included in the study can also affect the results. For instance, a study of this program that looks only at employment impacts in the refinery sector may find negati ve effects, because cons umers 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 redesign ed 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 Shih 56 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 th e 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 activitie s 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 th at “cleaner operations could involve automation and less employment, for example.”

According to the authors, the “demand effect” is expected to have a negative effect on employment, L the “cost effect” to have a positive effect on employment, and the “factor shift

L As will be discussed below, the demand effect in this proposal is potentially an exception to this rule. While the vehicles become more expensive, they also produce reduced fuel expenditures; the reduced fuel costs provide a countervailing impact on vehicle sales. As discussed in Preamble Section III.H.1, this possibility that vehiclesSummary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-23 effect” to have an ambiguous effect on employment. Without more information with respect to the magnitudes of these competing effects, it is not possible to predict the total effect environmental regulation will have on empl oyment levels in a regulated sector.

Morgenstern et al. conclude that increased abatement expenditures generally have not caused a significant change in employment in those sectors. More specifically, their results show that, on average across th e industries studied, each additional $1 million spent on pollution abatement results in a (statistically insignificant) net increase of 1.5 jobs.

This approach to employment analysis has the advantage of carefully controlling for many possibly confounding effects in order to separate the effect of changes in regulatory costs on employment. It was, however, conducted for only four sectors. It could also be very difficult to update the study for other sectors, because one of the databases on which it relies, the Pollution Abatement Cost and Expenditure survey, has been conducted infrequently since 1994, with the last survey conducted in 2005. The empirical estimates provided by Morgenstern et al. are not relevant to the case of fuel economy standards, which are very different from the pollution control standards on industrial facilities that were considered in that study. In addition, it does not examine the effects of regulation on employment in sectors related to but outside of the regulated sector. Nevertheless, the theory that Morgenstern et al. developed continues to be useful in this context.

The following discussion of additional methodologies draws from Berck and Hoffmann’s review of employment models. 57

8.2.2.2 Computable General Equi librium (CGE) Models

Computable general equilibrium (CGE) models are often used to assess the impacts of policy. These models include a stylized representation of supply and demand curves for all major markets in the economy. The labor market is commonly included. CGE models are very useful for looking at interaction effects of markets: “they allow for substitution among inputs in production and goods in consumption.” Thus, if one market experiences a change, such as a new regulation, then the effects can be observed in all other markets. As a result, they can measure the employment changes in the economy due to a regulation. Because they usually assume equilibrium in all markets, though, they typically lack involuntary unemployment. If the total amount of labor changes, it is due to people voluntarily entering or leaving the workforce. As a result, these models may not be appropriate for measuring effects of a policy on unemployment, because of the assumption that there is no involuntary unemployment. In addition, because of the assumptions of equilibrium in all markets and forward-looking consumers and firms, they are designed for examining the long-run effects of a policy but may offer little insight into its short-run effects.

may become more attractive to consumers after the prog ram poses a conundrum: why have interactions between vehicle buyers and producers not provided these benefits without government intervention?Chapter 8

8-24 8.2.2.3 Input-Output (IO) Models

Input-output models represent the economy through a matrix of coefficients that describe the connections between supplying and consuming sectors. In that sense, like CGE models, they describe the interconnections of the economy. These interconnections look at how changes in one sector ripple through the rest of the economy. For instance, a requirement for additional technology for vehicles requires additional steel, which requires more workers in both the auto and steel sectors; the additional workers in those sectors then have more money to spend, which leads to more employment in retail sectors. These are known as “multiplier” effects, because an initial impact in one sector gets multiplied through the economy. Unlike CGE models, input-output models have fixed, linear relationships among the sectors (e.g., substitution among inputs or goods is not allowed), and quantity supplied need not equal quantity demanded. In particular, these models do not allow for price changes – an increase in the demand for labor or capital does not result in a change in its price to help reallocate it to its best use. As a re sult, these models cannot capture opportunity costs from using resources in one area of the economy over another. The multipliers take an initial impact and can increase it substantially.

IO models are commonly used for regional analysis of projects. In a regional analysis, the markets are commonly considered small enough that wages and prices are determined outside the region, and any excess supply or demand is due to exports and imports (or, in the case of labor, emigration or immigration). For national-level employment analysis, the use of input-output models requires the assumption that workers flow into or out of the labor market perfectly freely. Wages do not adjust; instead, people join into or depart from the labor pool as production requires them. For other markets as well, there is no substitution of less expensive inputs for more expensive ones. As a result, IO models provide an upper bound on employment impacts. As Berck and Hoffmann note, “For the same reason, they can be thought of as simulating very short-run adjustment,” in contrast to the CGE’s implicit assumption of long-run adjustment. Changes in production processes, introductions of new technologies, or learning over time due to new regulatory requirements are also generally not captured by IO models, as they are calibrated to already established relationships between inputs and outputs.

8.2.2.4 Hybrid Models

As Berck and Hoffmann note, input-output models and CGE models “represent a continuum of closely related models.” Though not separately discussed by Berck and Hoffmann, some hybrid models combine some of the features of CGE models (e.g., prices that can change) with input-output relationships. For instance, a hybrid model may include the ability to examine disequilibrium phenomena, such as labor being at less than full employment. Hybrid models depend on assumptions about how adjustments in the economy occur. CGE models characterize equilibria but say little about the pathway between them, while IO models assume that adjustments are largely constrained by previously defined relationships; the effectiveness of hybrid models depends on their success in overcoming the limitations of each of these approaches. Hybr id models could potentially be used to model labor market impacts of various vehicle policy options although a number of judgments needSummary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-25 to be made about the appropriate assumptions underlying the model as well the empirical basis for the modeling results.

8.2.2.5 Single Sectors

It is possible to conduct a bottom-up analysis of the partial effect of regulation on employment in a single sector by estimating the change in output or expenditures in a sector and multiplying it by an estimate of the number of workers per unit of output or expenditures, under the assumption that labor demand is proportional to output or expenditures. As Berck and Hoffmann note, though, “Compliance with regulations may create additional jobs that are not accounted for.” While such an analysis can approximate the effects in that one sector in a simple way, it also may miss import ant connections to related sectors.

8.2.2.6 Ex-Post Econometric Studies

A number of ex-post econometric analyses examine the net effect of regulation on employment in regulated sectors. Morgenstern, Pizer, and Shih (2002), discussed above, and Berman and Bui (2001) are two notable examples that rely on highly disaggregated establishment-level time series data to estimate longer-run employment effects. 58 While often a sophisticated treatment of the issues anal yzed, 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 approp riate 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 appr opriate for small-scale, short-term effects, because they assume fixed relationships across s ectors and do not require market equilibria. Hybrid models, which combine some features of CGEs with IO models, depend upon key assumptions and economic relationships that are built into them. Single-sector models are simple and straightforward, but they are often based on the assumptions that labor demand is proportional to output, and that other sectors are not affected. Finally, econometric models have been developed to evaluate the longer-run net effects of regulation on sector employment, though these are ex-post analyses commonly of specific sectors or situations, and the results may not have direct bearing for the regulation being reviewed.

8.2.3 Employment analysis of this proposal

As mentioned above, this program is expected to affect employment in the regulated sector (auto manufacturing) and other sectors directly affected by the proposal: auto parts suppliers, auto dealers, the fuel supply market (which will face reduced petroleum productionChapter 8

8-26 due to reduced fuel demand but which may see additional demand for electricity or other fuels), and consumers (who will face higher vehi cle 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 par tial, bottom-up quantitative estimate of the effects of this proposal on the regulated sector (the auto industry; for reasons discussed below, we include some quantitative assessment of effects on suppliers to the industry although they are not regulated directly). It also includes qualitative discussion of the effects of the proposal on other sectors. Focusing quantification of employment impacts on the regulated sector has some advantages over quantifying all impacts. First, the analysis relies on data generated as part of the rulemaking process, which focuses on the regulated sector; as a result, what is presented here is based on internally consiste nt assumptions and estimates made in this proposal. Secondly, as discussed above, net effects on employment in the economy as a whole depend heavily on the overall state of the economy when this rule has its effects. Focusing on the regulated sector provides insight into employment effects in that sector without having to make assump tions 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 willresult from people changing jobs or voluntarily entering or exiting the workforce. In a fullemployment economy, employment impacts of this proposal will change employment in specific sectors, but it will have small, if any, effect on aggregate employment. This rule would take effect in model years 2017 through 2025; by then, the current high unemployment may be moderated or ended. For that reason, this analysis does not include multiplier effects, but instead focuses on employment impacts in the most directly affected industries. Those sectors are likely to face the most concentrat ed employment impacts. The agencies seek comment on other sectors that are likely to be significantly affected and thus warrant further analysis in the final rulemaking analysis.

8.2.3.1 Employment Impacts in the Auto Industry

If vehicle sales decrease, employment associated with these activities will unambiguously decrease. The sign of demand effect will depend on which of these effects dominates. Because, as described in Chapter 8.1, we have not quantified the impact on sales for this proposal, we do not quantify the demand effect.

8.2.3.1.1 The Cost Effect

Employment may increase because manufacturers need additional people to implement the new or additional technologies needed for vehicles to comply with the proposed standards.Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-27 One way to gauge this the cost effect, is to use the ratio of workers to each $1 million of expenditures in that sector. The use of these ratios has both advantages and limitations. It is often possible to estimate these ratios fo r 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 rati o for all vehicle manufacturing, not just for fuel efficiency improvements. In addition, th ese estimates do not include changes in sectors that supply these sectors, such as steel or electronics producers. They thus may best be viewed as the effects on employment in the auto sector due to the changes in expenditures in that sector, rather than as an assessment of all employment changes due to these changes in expenditures.

Some of the costs of this proposal will be spent directly in the auto manufacturing sector, but some of the costs will be spent in the auto parts manufacturing sector. Because we do not have information on the proportion of expenditures in each sector, we separately present the ratios for both the auto manufacturing sector and the auto parts manufacturing sector. These are not additive, but should instead be considered as a range of estimates for the cost effect, depending on which sector adds tech nologies to the vehicles to comply with the regulation.

We use several public sources for estimates of employment per $1 million expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment Requirements Matrix (ERM), 59 which provides direct estimates of the employment per $1 million in sales of goods in 202 sectors. The most recent estim ates, used here, are from 2008 (adjusted to 2009$). The tables used here are adjusted to remove the employment effects of imports through use of the ratio of domestic production to domestic sales, described above, of 0.667. The values reported are for Motor Vehicle Manufacturing (NAICS 3361) and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Annual Survey of Manufactures 60 (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 de tail 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 di rect estimates of employees/$1 million in expenditures, the ASM separately provides number of employees and value shipments; the direct employment estimates here are the ratio of those values. The data in the ASM are updated annually, except for years when the full Economic Census occurs. The tables presented here use data from 2009. As with the ERM, we adjust for the ratio of domesticChapter 8

8-28 production to domestic sales. The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Economic Census includes all large comp anies and a sample of smaller ones. The ASM is a subset of the Economic Census; though the Census itself is more complete, it is conducted only every 5 years, while the ASM is annual. The values presented here use data from 2007 (adjusted to 2009$), with the domestic production-to-sales adjustment. The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing (NAICS 3363).

Table 8.2-1 provides the values, either given (BLS) or calculated (ASM, Economic Census) for employment per $1 million of expenditures, all based on 2009 dollars, though the underlying data come from different years (which may account for some of the differences). The different data sources provide similar magnitudes for the estimates for the sectors. Parts manufacturing appears to be more labor-intensive than vehicle manufacturing; light-duty vehicle manufacturing appears to be slightly less labor-intensive than motor vehicle manufacturing as a whole.

Table 8.2-1 Employment per $1 Million Ex penditures (2009$) in the Motor Vehicle Manufacturing Sector*

Source Sector Ratio of workers per

$1 million expenditures Ratio of workers per $1 million expenditures, adjusted for domestic vs. foreign production BLS ERM Motor Vehicle Mfg 0.834 0.556

ASM Motor Vehicle Mfg 0.824 0.549

ASM Light Duty Vehicle Mfg 0.757 0.505

Economic
Census Motor Vehicle Mfg 0.674 0.449

Economic
Census Light Duty Vehicle Mfg 0.610 0.407

BLS ERM Motor Vehicle Parts Mfg 3.073 2.049

ASM Motor Vehicle Parts Mfg 3.093 2.063

Economic
Census Motor Vehicle Parts Mfg 2.749 1.833Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-29 BLS ERM refers to the U.S. Bureau of Labor Statistics’ Employment Requirement Matrix. ASM refers to the U.S. Census Bureau’s Annual Survey of Manufactures. Economic Census re fers to the U.S. Census Bureau’s
Economic Census.

Over time, the amount of labor needed in the auto industry has changed: automation and improved production methods have led to significant productivity increases. The BLS ERM, for instance, provided estimates that, in 1993, 1.52 workers were needed per $1 million of 2000$, but only 0.83 workers by 2008 (in 2000$). 61 Because the ERM is available annually for 1993-2008, we used these data to estimate productivity improvements over time. We regressed logged ERM values on year (to estimate percent change per year) for both the Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing sectors. The results suggest a 4.4 percent per year productivity improvement in the Motor Vehicle Manufacturing Sector, and a 3.6 percent per year improvement in the Motor Vehicle Parts Manufacturing Sector. We then used the regression relationship to project the ERM through 2025. In the results presented below, these projected values (adjusted to 2009$) were used directly for the BLS ERM estimates. For the ASM, we used the ratio of the projected value in the future to the projected value in 2009 (the base year for the ASM); for the Economic Census estimates, we used the ratio of the projected value in the future to the projected value in 2007 (the base year for that estimate). As noted above, we adjusted the estimate of workers per vehicle for the demand effect in Chapter Error! Reference source not found. , above, using the productivity improvement for the Motor Vehicle Manufacturing sector; because the estimate of workers per vehicle for the demand effect was based on data from 2001 to 2010, we used the projected value of the ERM in 2005 as the de nominator of the ratio. This is a simple way to examine the relationship between labor required and expenditure and we seek comment on refining this method.

Table 8.2-2 shows the cost estimates developed for this rule, discussed in Chapter 5. The maximum value in Table 8.2-2 for empl oyment impacts per $1 million expenditures (after accounting for the share of domestic pro duction) is 2.049 in 2009 if all the additional costs are in the parts sector; the minimum value is 0.407 in 2009, if all the additional costs are in the light-duty vehicle manufacturing sector: that is, the range of employment impacts is between 0.4 and 2 additional jobs per $1 million expenditures in the sector in 2009. The results in Table 8.2-2 include the productivity adjustment described above.

While we estimate employment impacts beginning with the first year of the standard (2017), some of these job gains may occur earlier as auto manufacturers and parts suppliers hire staff in anticipation of compliance with the standard. Also, because of the uncertainty associated of projecting average labor required associated with the cost of production, we limit the projection to 2020.

Table 8.2-2 Employment due to Cost Effect in the Motor Vehicle Manufacturing Sector

Year Costs (before adjustment for domestic proportion Minimum employment effect (if all expenditures Maximum employment effect (if all expendituresChapter 8

8-30 of production)

($Millions) are in the parts sector) are in the light duty vehicle mfg sector) 2017 $ 2,300 600 3,600 2018 $ 4,656 1,200 7,000 2019 $ 6,507 1,600 9,400

2020 $ 8,467 1,900 11,800

8.2.3.1.2 The Factor Shift Effect

The factor shift effect looks at the effects on employment due to changes in labor intensity associated with a regulation. As noted above, the estimates of the cost effect assume constant labor per $1 million in expenditures, though the new technologies may be either more or less labor-intensive than the existing one s. An estimate of the factor shift effect
would either increase or decrease the estimate used for the cost effect.

We are not quantifying the factor shift effect here, for lack of data on the labor intensity of all the possible technologies that manufacturers could use to comply with the proposed standards. For a subset of the technologies, though, EPA-sponsored research (discussed in Chapter 3.2.1.1 of the Joint TSD) which compared new technologies to existing ones at the level of individual components provides some insights into the factor shift effect.

The comparison involved tearing down the selected technologies to their individual components and looking at the differences in materials and labor needs in moving from the conventional to the new technologies. 62 For instance, the analysis compared all the parts and labor associated with an 8-speed automatic transmission to those needed for a 6-speed
automatic transmission.

Because labor cost was one of the sources of differences between the technologies, it is possible, for those technologies, to see whether labor needs increase or decrease with the switch to technologies that migh t contribute to compliance with the proposed standards. An increase in labor cost for the new technology indicates an increase in the labor needed for the new technology compared to the baseline technology. For instance, an 8-speed transmission requires $15.11 more in labor costs than a 6-speed transmission (as accounted for in EPA’s cost estimates for the proposed rule). Dividing the labor cost by a wage per hour estimate provides an estimate of the additional hours (and thus the additional labor) needed for the new technology compared to the baseline technology. As with labor cost, an increase in labor hours per technology indicates greater employment needs for the new technologies. For this conversion, a weighted average wage rate (90 percent of the average wage in the Motor Vehicle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle Manufacturing Sector) of $46.36/hour in 2015, using 2008 dollars (the unit of analysis for the Comment [A1]: Interagency commenters recommend agencies only go to 2020 becusae there
will be a mid-term review.Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-31 FEV study). For the change from a 6-speed to an 8-speed transmission, we thus estimate an additional 0.33 hours of labor per transmission.

8.2.4 Effects on Employment for Auto Dealers

The effects of the proposed standards on employment for auto dealers depend principally on the effects of the standards on light duty vehicle sales. In addition, auto dealers may be affected by changes in maintenance and service costs. Increases in those costs are likely to increase labor demand in dealerships.

Although this proposal predicts very small penetration of advanced technology vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. As discussed in Chapter 8.1.2.7, consumers may find some characteristics of electric vehicles and plug-in hybrid electric vehicles, such as the ability to fuel with electricity rather than gasoline, attractive; they may find other characteristics, such as the limited range for electric vehicles, undesirable. As a result, some consum ers will find that EVs will meet their needs, but other buyers will choose more conventional vehicles. Auto dealers may play a major role in explaining the merits and disadvantages of these new technologies to vehicle buyers. There may be a temporary need for increased employment to train sales staff in the new technologies as the new technologies become available.

8.2.5 Effects on Employment in the Auto Parts Sector

As discussed in the context of employment in the auto industry, some vehicle parts are made in-house by auto manufacturers; others are made by independent suppliers who are not directly regulated, but who will be affected by the proposed standards as well. The additional expenditures on technologies are expected to have a positive effect on employment in the parts sector as well as the manufacturing sector; the breakdown in employment between the two sectors is difficult to predict. The effects on the parts sector also depend on the effects of the proposed standards on vehicle sales and on the labor intensity of the new technologies, qualitatively in the same ways as for the auto manufacturing sector.

8.2.6 Effects on Employment for Fuel Suppliers

In addition to the effects on the auto manufacturing and parts sectors, these rules will result in changes in fuel use that lower GHG emissions. Fuel saving, principally reductions in liquid fuels such as gasoline and diesel, will af fect employment in the fuel suppliers industry sectors throughout the supply chain, from refineries to gasoline stations. To the extent that the proposed standards result in increased use of electricity or other new fuels, employment effects will result from providing these fuels and developing the infrastructure to supply them

to consumers.

Expected petroleum fuel consumption reductions can be found in Chapter 5.3. While this reduced consumption represents fuel savings for purchasers of fuel, it represents a loss in value of output for the petroleum refinery industry, fuel distributors, and gasoline stations.Chapter 8

8-32 The loss of expenditures to petroleum fuel suppliers throughout the petroleum fuel supply chain, from the petroleum refiners to the gasoli ne stations, is likely to result in reduced employment in these sectors.

This rule is also expected to lead to increases in electricity consumption by vehicles, as discussed in Chapter 5.3. This new fuel may require additional infrastructure, such as electricity charging locations. Providing this infrastructure will require some increased employment. In addition, the generation of elec tricity 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 associat ed with reduced demand for petroleum fuels.

8.2.7 Summary

The primary employment effects of this proposal are expected to be found throughout several key sectors: auto manufacturers, auto dealers, auto parts manufacturing, and fuel production and supplyThese proposed standards initially take effect in model year 2017, a time period sufficiently far in the future that the current sustained high unemployment at the national level may be moderated or ended. In an economy with full employment, the primary employment effect of a rulemaking is likely to be to move employment from one sector to another, rather than to increase or decrease employment. For that reason, we focus our partial quantitative analysis on employment in the regulated sector, to examine the impacts on that sector directly. We discuss the likely direction of other impacts in the regulated sector as well as in other directly related sectors, but we do not quantify those impacts, because they are more difficult to quantify with reasonable accuracy, particularly so far into the future.

References

1 See, for instance, Gron, Ann, and Deborah Swenson, 2000. “Cost Pass-Through in the U.S.Automobile Market,” Review of Economics and Statistics 82: 316-324 (Docket EPA-HQOAR-2010-0799).

2 Insurance Information Institute, 2008, “Average Expenditures for Auto Insurance By State, 2007-2008,” http://www. iii.org/media/facts/statsbyissue/ auto/, accessed 8/22/11 (Docket EPA-HQ-OAR-2010-0799).

3 U.S. Department of Energy, 2011. “Transportation and the Economy,” Chapter 10 in “Transportation Energy Data Book,” http://cta.ornl.gov/data/tedb30/Edition30_Chapter10.pdf, accessed 8/22/11, Table 11 (Docket EPA-HQ-OAR-2010-0799).

4 Solheim, Mark, 2006 “State Car Tax Rankings,” http://www.kiplinger.com/feat ures/archives/2006/04/cartax.html, accessed April 23, 2009 (Docket EPA-HQ-OAR-2009-0472-0010) (Docket EPA-HQ-OAR-2010-0799).Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-33 5 U.S. Census Bureau, "Table 2. Resident Population of the 50 States, the District ofColumbia, and Puerto Rico: 2010 Census" http://2010.census.gov/news/presskits/apportionment/apport.html .

6 “National Auto Loan Rates for July 21, 2011,” http://www.bankrate.com/finan ce/auto/national-auto-loan-ra tes-for-july-21-2011.aspx, accessed 7/26/11 (Docket EPA-HQ-OAR-2010-0799).

7 Goldberg, Pinelopi Koujianou, “Product Di fferentiation and Oligopoly in International Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799); Goldberg, Pinelopi Koujianou, “The Effects of the Corporate Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics 46(1) (March 1998): 1-33 (Docket EPA-HQ-OAR-2010-0799).

8 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799).

9 McManus, Walter M., “Can Proactive Fuel Ec onomy Strategies Help Automakers Mitigate Fuel-Price Risks?” University of Michigan Transportation Research Institute, September 14, 2006 (Docket EPA-HQ-OAR-2010-0799).

10 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010- 0799); Berry, Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market,” Journal of Political Economy 112(1) (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799).

11 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Im pacts of Gasoline Taxes” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799).

12 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

13 Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-2010- 0799).Chapter 8

8-34 14 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

15 E.g., Bento, Antonio M., Lawrence H. Goulde r, Emeric Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

16 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010- 0799).

17 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-0799).

18 NERA Economic Consulting, “Evaluation of NHTSA’s Benefit-Cost Analysis of 2011- 2015 CAFE Standards,” 2008, available at http://www.heartland.org/ policybot/results/23495/Evalua tion_of_NHTSAs_Be nefitCost_Anal ysis_Of_20112015_CAFE_Standards.html (Docket EPA-HQ-OAR-2010-0799).

19 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

20 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010- 0799).

21 E.g., Bento, Antonio M., Lawrence H. Goulde r, Emeric Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799).

22 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010- 0799).

23 NERA Economic Consulting, “Appendix B: New Vehicle Market Model,” “Impacts of the California Greenhouse Gas Emission Standards on Motor Vehicle Sales,” commentsSummary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-35 submitted to the U.S. Environmental Prot ection Agency at Regulations.gov, document number EPA-HQ-OAR-2006-0173-9053.1 (Docket EPA-HQ-OAR-2010-0799).

24 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799)

25 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010- 0799).

26 Greene, David L., Philip D. Pa tterson, Margaret Singh, and Ji a Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799).

27 Rex, Emma, and Henrikke Baumann (2007). “Beyond ecolabels: what green marketing can learn from conventional marketing.” Journal of Cleaner Production 15: 567-576 (Docket EPA-HQ-OAR-2010-0799).

28 Whitefoot, Kate, Meredith Fowlie, and Stev en Skerlos, “Product Design Responses to Industrial Policy: Evaluating Fuel Economy Standards Using an Engineering Model of Endogenous Product Design,” Energy Institute at Haas Working Paper 214, University of California Energy Institute, February 2011 (Docket EPA-HQ-OAR-2010-0799).

29 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010- 0799).

30 Goldberg, Pinelopi Koujianou, “Product Di fferentiation and Oligopoly in International Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799).

31 Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with Flexible Substitution Patterns,” Journal of Econometrics 89 (1 999): 109-129 (Docket EPAHQ-OAR-2010-0799); Brownstone, David, David S. Bunch, and Kenneth Train, “Joint Mixed Logit Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” Transportation Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2010-0799); Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., K. G. Duleep, and Walter McManus, “Future Potential of Hybrid and Diesel Powertrains in the U.S. Light-Duty Vehicle Market,” prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, August 2004 (Docket EPA-HQ-OAR-2010-0799).Chapter 8

8-36 32 Petrin, Amil, “Quantifying the Benefits of New Products: The Case of the Minivan,” Journal of Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799); Berry, Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market,” Journal of Political Economy 112 (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799).

33 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48 (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

34 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010- 0799).

35 Greene, David L., Philip D. Pa tterson, Margaret Singh, and Ji a Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., “Feebates, Footprints and Highway Safety,” Transportation Research Part D 14 (2009): 375-384.

36 E.g., Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

37 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2010-0799).

38 E.g., Espey, Molly, and Santosh Nair, 2005. “Automobile Fuel Economy: What Is It Worth?” Contemporary Economic Policy 23: 317-323 (Docket EPA-HQ-OAR-2010-0799).

39 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 1593-1594 (Docket EPA-HQ-OAR-2010-0799).

40 E.g., Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799).

41 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010- 0799).Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-37 42 Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. “Pain at the Pump:
How Gasoline Prices Affect Automobile Purc hasing in New and Us ed Markets,” working paper, http://web.mit.edu/knitte l/www/papers/gaspaper_latest.pdf (accessed 11/1/11) (Docket EPA-HQ-OAR-2010-0799); Li, Shanjun, Christopher Timmins, and Roger H. von Haefen, 2009. “How Do Gasoline Prices Affect Fleet Fuel Economy?” American Economic Journal:Economic Policy 1(2): 113-137; Congressional Budget Office (2008) (Docket EPA-HQOAR-2010-0799); Congressional Budget Office (2008). Effects of Gasoline Prices on Driving Behavior and Vehicle Markets. The Congress of the United States, Pub. No. 2883 (Docket EPA-HQ-OAR-2010-0799); West. Sarah (2007). “The Effect of Gasoline Prices on the Demand for Sport Utility Vehicles.” Working Paper. Macalester College, http://www.macalester.edu/~wes ts/SarahWestMEA2007.pdf, accessed 1/25/10 (Docket EPAHQ-OAR-2010-0799).

43 Greene, David L., and Jin-Tan Liu, 1988. “Automotive Fuel Economy Improvements andConsumers’ Surplus,” Transportation Research Part A 22A (3): 203-218 (Docket EPA-HQOAR-2010-0799).

44 Greene, David L. (2010). “How Consumers Value Fuel Economy: A Literature Review.” EPA Report EPA-420-R-10-008 (Docket EPA-HQ-OAR-2010-0799).

45 Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S. Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket EPA-HQ-OAR-2010-0799).

46 Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket EPA-HQ-OAR-2010-0799); McManus, Walter, 2007. “The Impact of Attribute-Based Corporate Average Fuel Economy (CAFE) Standards: Preliminary Results.” Ann Arbor, MI: University of Michigan Transportation Research Institute, Report No. UMTRI-2007-31 (Docket EPA-HQ-OAR-2010-0799).

47 For instance, Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy(CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2010-0799); Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and Management 50 (2005): 562- (Docket EPA-HQ-OAR-2010- 0799); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of the Corporate Average Fuel Economy Standard,” Resources for the Future Paper RFF DP 10- 50 (December 2010), http://www.rff.org/R FF/Documents/RFF-DP- 10-50.pdf) (Docket EPAHQ-OAR-2010-0799)Jacobsen, Mark, 2010. “Evaluating U.S. Fuel Economy Standards in a Model with Producer and Household Heterogeneity,” working paper, athttp://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf (accessed 11/1/11) (Docket EPA-HQOAR-2010-0799) .Chapter 8

8-38 48 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

49 IEA. 2007. “Mind the Gap: Quantifying Principal-Agent Problems in Energy Efficiency

. ” Paris, France: International Energy Agency (Docket EPA-HQ-OAR-2010-0799); Jaffe, Adam B., Richard G. Newell, and Robert N. Stavins (2001). "Energy Efficient Technologies and Climate Change Policies: Issues and Evidence." In Climate Change Economics and Policy, Toman, Michael A., ed., Washington, D.C.: Resources for the Future. p.171-181 (Docket EPA-HQ-OAR-2010-0799); Metcalf, Gilbert E., and Kevin A. Hassett (1999). "Measuring the Energy Savings From Home Improvement Investments: Evidence From Monthly Billing
Data." The Review of Economics and Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010- 0799); Tietenberg, T. (2009). “Reflections – Energy Efficiency Policy: Pipe Dream or Pipeline to the Future?” Review of Environmental Economics and Policy 3(2): 304-320 (Docket EPA-HQ-OAR-2010-0799).

50 Helfand, Gloria, and Ann Wolverton (2011). “Evaluating the Consumer Response to Fuel Economy: A Review of the Literature.” International Review of Environmental and Resource Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799).

51 Jaccard, Mark. “Paradigms of Energy Effici ency’s Cost and their Policy Implications: Déjà Vu All Over Again.” Modeling the Economics of Greenhouse Gas Mitigation: Summary of a Workshop, K. John Holmes, Rapporteur. National Academies Press, 2010. http://www.nap.edu/openbo ok.php?record_id=13023&pa ge=42 (Docket EPA-HQ-OAR2010-0799)

52 Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799).

53 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency Standards. The White House, Office of the Press Secretary, May 21, 2010. http://www.whitehouse.gov/the- press-office/presidential- memorandum-regarding-fuelefficiency-standards

54 U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed on August 9, 2011.

55 Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011 (Docket EPA-HQ-OAR-2010-0799).

56 Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. “Jobs Versus the Environment: An Industry-Level Perspective.” Journal of Environmental Economics and Management 43 (2002): 412-436 (Docket EPA-HQ-OAR-2010-0799).Summary of interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. These are for your consideration and are subject to further review.

8-39 57 Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental and Natural Resource Policy.” Environmental and Resource Economics 22 (2002): 133-156 (Docket EPA-HQ-OAR-2010-0799).

58 Berman, Eli, and Linda T. Bui, (2001) “Environmental Regulation and Labor Demand: Evidence from the South Coast Air Basin,” Journal of Public Economics , 79, 265 – 295 (Docket EPA-HQ-OAR-2010-0799)

.

59 http://www.bls.gov/emp/ep _data_emp_requirements.htm

60 http://www.census .gov/manufacturing/asm/index.htmlSummary of interagency working comments received on draft NPRM under EO 12866 and

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

8-1 Below is a summary of interagency comments received during EO 12866 and EO 13563 review of the draft rule “Joint Rulemaking to Estab lish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Econo my Standards”. They are for your consideration and are subject to further policy rev iew.



### ***

Chapter 8: Vehicle Sales and Employment Impacts

8.1 Vehicle Sales Impacts

8.1.1 Vehicle Sales Impacts and Payback Period

Predicting the effects of this rule on vehicles ent ails comparing two competing effects. On the one hand, as a result of this rule, the vehi cles will become more expensive, which would, by itself, be expected to discourage sales. On the other hand, the vehicles will have improved fuel economy and thus lower operating cost s, producing lower total costs over the life of vehicles, which makes them more attractive to consumers. Which of these effects dominates for potential vehicle buyers when they ar e considering a purchase will determine the effect on sales. However, assessing the net eff ect of these two competing effects is complex and uncertain, as it rests on how consumers value fuel savings at the time of purchase and the extent to which manufacturers and dealers reflect them in the purchase price.

There are reasons to believe that many consumers do not fully take account of the value of fuel savings at the time of purchase (such as lack of salience and focus on the shortterm, as discussed in detail below), and as we have stated, the proposed rule is expected to produce significant net benefits for consumers. But the empirical literature leaves many open questions. It does not give clear evidence about th e effect of the proposed rule on sales, nor does it speak to the possibility and consequences o f consumer learning over time. Thus, for the proposal we do not provide quantified estimates of potential sales impacts. Rather, we
solicit comment on the issues raised here and on me thods for estimating the effect of this rule
on vehicle sales.

If consumers do not consider net costs – if they do not accurately compare the value of fuel savings with the increased cost of fuel eco nomy technology in their vehicle purchase decisions -- there may be a net decrease in sales f or 2017 and beyond. If auto makers have accurately gauged how consumers consider fuel econo my when purchasing vehicles and have provided the levels of improvement that consumers d esire, then consumers should not be expected to want to purchase the more fuel-efficien t vehicles beyond what manufacturers may
offer in the future absent this rule. On certain a ssumptions, auto makers would be expected to provide as much fuel economy as consumers want, and vehicle sales would be expected to decrease, not to increase, as a result of the rule. We invite comments on this possibility.Chapter 8

8-2

If, on the other hand, the rule motivates an increa se in consumer demand for fuel economy, perhaps as a result of learning, then it c ould lead to production of more desirable
vehicles and a commensurate increase in sales. We invite comment on consumer learning and related possible reasons for positive effects on co nsumer demand.

For years, consumers have been learning about the b enefits that may accrue to them from owning and operating vehicles with greater fue l efficiency. Consumer demand has thus shifted towards such vehicles, not only because of higher fuel prices but also because many consumers are learning about the value of purchases based not only on initial costs but also on the total cost of owning and operating a vehicle ov er its lifetime. This type of learning is expected to continue before and during the model ye ars affected by this rule, particularly given the new fuel economy labels that clarify pote ntial economic effects and should therefore reinforce that learning.

Today’s proposed rule, combined with the new and ea sier-to-understand fuel economy label required to be on all new vehicles beginning in 2012, may increase sales above baseline levels by hastening this very type of consumer lear ning. As more consumers experience, as a result of the rule, the savings in time and expense from owning more fuel efficient vehicles, demand may shift yet further in the direction of th e vehicles mandated under the rule. This social learning can take place both within and acro ss households, as consumers learn from one another. There are three possible effects here.

First and most directly, the time and fuel savings associated with operating more fuel
efficient vehicles will be more salient to individu als who own them, causing their subsequent purchase decisions to shift closer to minimizing th e total cost of ownership over the lifetime of the vehicle. Second, this appreciation may spre ad across households through word of mouth and other forms of communications. Third, as more motorists experience the time and fuel savings associated with greater fuel efficienc y, the price of used cars will better reflect such efficiency, further reducing the cost of ownin g more efficient vehicles for the buyers of new vehicles (since the resale price will increase)

.
If these induced learning effects are strong, the r ule could potentially increase total
vehicle sales over time. These increased sales wou ld not occur in the model years first affected by the rule, but they could occur once the induced learning takes place . It is not possible to quantify these learning effects years i n advance and that effect may be speeded or slowed by other factors that enter into a consumer’ s valuation of fuel efficiency in selecting
vehicles.

The possibility that the rule will (after a lag for consumer learning) increase sales need not rest on the assumption that automobile manufact urers are failing to pursue profitable opportunities to supply the vehicles that consumers demand. In the absence of the rule, no individual automobile manufacturer may find it prof itable to move toward the more efficient vehicles mandated under the rule. In particular, n o individual company can fully internalize the future boost to demand resulting from the rule. If one company were to make more efficient vehicles, counting on consumer learning t o enhance demand in the future, that company would capture only a fraction of the extra sales so generated, because the learning atSummary of interagency working comments received on draft NPRM under EO 12866 and

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

8-3 issue is not specific to any one company's fleet. Many of the extra sales would accrue to that company's competitors.

In other words, consumer learning about the benefit s of fuel efficient vehicles involves positive externalities (spillovers) from one compan y to the others. [Footnote: Industrywide positive spillovers of this type are hardly unique to this situation. In many industries, companies form trade associations to promote indust ry-wide public goods. For example, merchants in a given locale may band together to pr omote tourism in that locale. Antitrust law recognizes that this type of coordination can incre ase output.] These positive externalities may
lead to benefits for manufacturers as a whole. We emphasize that this discussion has been tentative and qualified. To be sure, social learnin g of related kinds has been identified in a number of contexts. [Footnote: See Hunt Alcott, Soc ial Norms and Energy Conservation, Journal of Public Economics (forthcoming 2011), ava ilable at http://web.mit.edu/allcott/www/Allcott%202011%20JPu bEc%20- %20Social%20Norms%20and%20Energy%20Conservation.pdf ; Christophe Chamley, Rational Herds: Economic Models of Social Learning (Cambridge, 2003).] Comments are invited on the discussion offered here, with partic ular reference to any relevant empirical findings.”

We note as well that automakers are currently putti ng more fuel-saving technologies into vehicles and are advertising based in part on the fuel efficiency of their vehicles. We cannot assess whether these improvements in fuel ec onomy are due to higher gasoline prices in recent years, to increased consumer demand for f uel economy, to auto makers realizing that fuel economy is a valuable attribute to consumers, or to EPA’s and NHTSA’s recent rulemakings on fuel economy and greenhouse gases. If adding more fuel economy to vehicles, even with the added costs, does make the vehicles more attractive to consumers, then we would expect auto makers to add this fuel e conomy in the absence of the rule. If so,
both the benefits and costs estimated for this rule are overstated.

In addition to uncertainty over the expected offeri ngs of fuel economy in new vehicles in the absence of this rule, we note the uncertaint y over the role of fuel economy in vehicle purchase decisions. As discussed in Chapter 8.1.2. 5 below, how consumers consider fuel economy when buying cars is not well understood; it is likely to vary substantially among individuals, and possibly across vehicle classes. In addition, consumer interest in fuel economy may change over time, in response to change s in fuel prices, to marketing efforts by automakers, to more experience with fuel-efficient vehicles, and (as noted) to better understanding of the costs associated with fuel sav ings, not to mention changing market conditions and other shifts in the U.S. economy tha t could affect vehicle demand and national driving behavior. As noted in Preamble III.H.1.a, EPA and NHTSA recently revised the fuel economy label for new vehicles, to improve consumer information on fuel economy. We do
not have data on the effect of the label on consume r vehicle purchase decisions. If consumers put more emphasis on fuel economy in their purchase decisions, then automakers would be expected to respond to that interest independent of the standards proposed here, and an increase in vehicle sales resulting from improved f uel economy would be a result of marketChapter 8

8-4

forces instead of this rulemaking. Preamble III.D. discusses EPA’s reasoning in assuming that the reference fleet will comply with the stand ards for MY 2016 vehicles but will not over-comply. Adding to the uncertainty is the dynam ic effect that the proposed rule may have on other relevant aspects of the vehicle sales mark et aside from purchase price. For example, the proposed rule may induce technological change t hat would not have occurred otherwise,
and introduce consumers to new vehicle attributes t hat impact vehicle demand in a positive way. Similarly, improvements in vehicle fuel econo my might accompany other changes in vehicle characteristics that are relevant for purch ase decisions. While these factors are always present, they are particularly acute in the current context, in which the rule will not take effect
until 2017.

In previous rulemakings, EPA and NHTSA conducted ve hicle sales analyses by comparing the up-front costs of the vehicles with t he present value of five years’ worth of fuel savings. We assumed that the costs for the fuel-sa ving 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 cons umer costs: the increased sales tax that consumers would pay, the increase in insurance prem iums, 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 resu lted in an adjusted increase in costs to
consumers. We then assumed that consumers consider ed the present value of five years of fuel savings in their vehicle purchase, which is co nsistent with the length of a typical new light-duty vehicle loan, and is similar to the aver age 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 qua ntity demanded of vehicles. B An elasticity of -1 means that a 1% increase in price leads to a 1% reduction in quantity sold. In the vehicle sales analyses, if five years of fuel savin gs outweighed the adjusted technology costs, then vehicle sales were predicted to increase; if t he fuel savings were smaller than the adjusted technology costs, sales would decrease, co mpared to a world without the standards.

We do not here present a vehicle sales analysis usi ng this approach. This rule takes
effect for MY 2017-2025. If consumers take into a ccount less than the break-even number of years in their buying decisions, then vehicle sales could decrease. In all cases, we would expect the impact of this rule on vehicle sales to be modest relative to other factors influencing future vehicle sales such as fuel price s and overall economic conditions, which are strong determinants of overall sales in any giv en year.

A In this proposal, the 5-year payback assumption co rresponds to an assumption that vehicle buyers take into account between 30 and 50 percent of the present va lue of lifetime vehicle fuel savings (with the vari ation depending on discount rate, model year, and car vs. truck).

B For a durable good such as an auto, the elasticity may be smaller in the long run: though people may be able to change the timing of their purchase when price chan ges 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 tha t phases in over time, as well as how to find good estimates for the long –run elasticity.Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-5 The calculation of the payback period is discussed in DRIA Chapter 5.3. Table 1 shows the estimated payback period for MY 2021 and 2025. We present MY 2021 because it is the last year before the mid-term review impacts , if any, will take place, and MY 2025 because it is the last year of the program. The pa yback period in 2021 is shorter than that in 2025, because the technologies required to meet the proposed MY 2021 standards are more
cost-effective than those for MY 2025. In all case s, the payback periods are less than the 5- year payback period that EPA and NHTSA have previou sly used in the vehicle sales analyses. Given the relatively short time frames for the payb ack periods, these results suggest that vehicle sales could increase as a result of improve d fuel economy. Alternatively, if consumers have shorter time frames for payback peri ods, then the rule may have a negative impact on vehicle sales. In either situation we wo uld expect the impact of this rule on sales to be modest. As noted above, though, it is possible that some or all of this increase in fuel economy would occur in the absence of the rule, and it is also possible that consumers may revise how they think about fuel economy in their v ehicle purchase decisions.

Table 1 Estimated Payback Period for Model Years 20 21 and 2025 (Years)

Model
Year Estimated Payback Period
for Cash Purchase, 3%
Discount Rate Estimated

Payback Period
for Cash Purchase, 7%
Discount Rate Estimated

Payback Period
for Purchase on Credit, 3%
Discount Rate Estimated

Payback Period
for Purchase on Credit, 7%
Discount Rate

2021 2.7 2.9 2.9 2.8

2025 3.7 3.9 3.9 3.9

We welcome comments on all aspects of this discussi on, including assumptions on consumer and producer behavior; how consumers value future fuel savings at the time of purchase; factors that should be taken into account when assessing the impact of fuel economy standard on aggregate vehicle sales; and so urces of uncertainty in estimating vehicle sales impacts. We welcome comments on the methodol ogy described here for quantitative estimates of the effects of this proposal on sales and its appropriateness for this rulemaking; we also welcome proposals for other methods.

We here provide further detail on some of the assum ptions included in the payback analysis. We seek comment on all these factors as well. The analysis starts with the increase in costs estimated by the OMEGA model. We assume t hat these costs are fully passed along to consumers. This assumption is appropriate for c ost increases in perfectly competitive markets. In less than perfectly competitive market s, though, it is possible that the cost increase is split between consumers and automakers, and possibly suppliers, and the priceChapter 8

8-6

may not increase as much as costs. 1 Thus, the assumption of full cost pass-through is possibly
an overestimate.

The next step in the analysis is to adjust this cos t increase for other effects on the consumer. The higher vehicle price is likely to le ad to an increase in sales tax, insurance, and vehicle financing costs.

The increase in insurance costs is estimated from t he average value of collision plus comprehensive insurance as a proportion of average new vehicle price. Collision plus comprehensive insurance is the portion of insurance costs that depend on vehicle value. The
Insurance Information Institute 2 provides the average value of collision plus compr ehensive insurance in 2008 as $432. The average value of a new vehicle in 2008, according to the U.S. Department of Energy, was $23,334. 3 Dividing the insurance cost by the average price of a new vehicle gives the proportion of comprehensive p lus collision insurance as 1.85 percent of the price of a vehicle. If this same proportion ho lds for the increase in price of a vehicle, then insurance costs should go up by 1.85 percent of the increase in vehicle cost. We use information on depreciation of vehicle value from t he same U.S. Department of Energy report to estimate a reduction in insurance costs, due to reduction in the estimated value of the vehicle, over 9 years.

Calculating the average increase in sales tax start s with the vehicle sales tax for each state in 2006, the most recent source identified. 4 The sales tax per state was then multiplied by the 2010 population of the state; 5 those values were summed and divided by total U.S. population, to give a population-weighted sales tax . That estimate of the state sales taxes for vehicles in the U.S. is 5.3 percent. This value is assumed to be a one-time cost incurred when the vehicle is purchased.

As of July, 2011, the national average interest rat e for a 5 year new car loan was 5.51 percent. 6 We use this loan rate to calculate the increase i n vehicle costs due to financing a
loan.

8.1.2 Consumer Vehicle Choice Modeling C

An alternative to the vehicle sales analysis discus sed 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 d escribe 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 c onsumer vehicle choice models depend critically on this value, we would consider any con sumer surplus estimates of the effect of our
rule from such models to be unreliable. In additio n, the predictive ability of consumer vehicle choice models may be limited. While vehicle choice models are based on sales of existing

C This section is drawn heavily from Helfand, Gloria , and Ann Wolverton, “Evaluating the Consumer Respo nse to Fuel Economy: A Review of the Literature.” Int ernational Review of Environmental and Resource
Economics 5 (2011): 103-146.Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-7 vehicles, vehicle models are likely to change, both independently and in response to this rule. The models may not predict well in response to thes e changes. Instead, we compare the value of the fuel savings associated with this rule with the increase in technology costs. EPA will continue its efforts to review the literature, but, given the known limitations and uncertainties of vehicle choice models, EPA has not conducted an analysis using these models for this proposal.

This rule will lead automakers to change characteri stics – in particular, the fuel economy -- of the vehicles they produce. These cha nges will affect the cost of manufacturing the vehicle; as a result, the prices of the vehicle s 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 the ir 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 respon se to the changes in vehicle characteristics, consumers will reconsider their purchases. Increas es 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 purch ase. 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 in fluence consumers on multiple market scales: the total number of new vehicles sold; the mix of n ew 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 characteri stics and prices. In principle, it should produce more accurate estimates of compliance costs compared to models that hold fleet mix constant, since it predicts changes in the fleet mi x that can affect compliance costs. It can also be used to measure changes in consumer surplus, the benefit that consumers perceive from a good over and above the purchase price. (Consumer surplus is the difference between what consumers would be willing to pay for a good, repre sented by the demand curve, and the amount they actually pay. For instance, if a consu mer were willing to pay $30,000 for a new vehicle, but ended up paying $25,000, the $5000 dif ference is consumer surplus.)

A number of consumer vehicle choice models have bee n developed. They vary in the methods used, the data sources, the factors include d in the models, the research questions they are designed to answer, and the results of the mode ls related to the effects of fuel economy on consumer decisions. This section will give some ba ckground 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 ex plain 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 va lue. Discrete decisions can take only aChapter 8

8-8

limited set of values. The decision to purchase a vehicle, for instance, can only take two values, yes or no. Vehicle purchases are typically modeled as discrete choices, where the choice is whether to purchase a specified vehicle. The result of these models is a prediction of the probability that a consumer will purchase a specified vehicle. A minor variant on discrete choice models estimates the market share ( a continuous variable between 0 and 1) for each vehicle. Because the market share is, essenti ally, the probability that consumers will purchase a specific vehicle, these approaches are s imilar in process; they differ mostly in the kinds of data that they use.

The primary methods used to model vehicle choices a re nested logit and mixed logit. D In a nested logit, the model is structured in layer s. For instance, the first layer may be the choice of whether to buy a new or used vehicle. Gi ven that the person chooses a new vehicle, the second layer may be whether to buy a car or a t ruck. Given that the person chooses a car, the third layer may be the choice among an economy, midsize, or luxury car. Examples of nested logit models include Goldberg, 7 Greene et al., 8 and McManus. 9

In a mixed logit, personal characteristics of consu mers 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 o f the effects of the distribution of consumer characteristics. As a result, mixed logit can be u sed to examine the distributional effects on various socioeconomic groups, which nested logit is not designed to do. Examples of mixed logit models include Berry, Levinsohn, and Pakes, 10 Bento et al., 11 and Train and Winston. 12

While discrete choice modeling appears to be the pr imary method for consumer choice modeling, others (such as Kleit 13 and Austin and Dinan 14 ) 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 Din an 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 a n increase in a person’s income of 10% would increase the probability of her purchasing ve hicle 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 othe r studies (calibrated models).

Estimated models use datasets on consumer purchase patterns, consumer characteristics, and vehicle characteristics to dev elop their original sets of parameters. The

D Logit refers to a statistical analysis method used for analyzing the factors that affect discrete cho ices (i.e., yes/no decisions or the choice among a countable nu mber of options).Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-9 datasets used in these studies sometimes come from surveys of individuals’ behaviors. 15 Because they draw on the behavior of individuals, t hey provide what is sometimes called micro-level data. Other studies, that estimate mar ket shares instead of discrete purchase decisions, use aggregated data that can cover long time periods. 16

Calibrated models rely on existing studies for thei r parameters. Researchers may draw on results from a number of estimated models, or ev en from research other than CCM, to choose the parameters of the models. The Fuel Econ omy Regulatory Analysis Model developed for the Energy Information Administration 17 and the New Vehicle Market Model developed by NERA Economic Consulting 18 are examples of calibrated models.

8.1.2.3 Factors Included in the Models

Consumer choice models vary in their complexity and levels of analysis. Some focus only on the new vehicle market; 19 others consider the choice between new vehicles an d an outside good (possibly including a used vehicle); 20 others explicitly consider the relationship
between the new and used vehicle markets. 21 Some models include consideration of vehicle miles traveled, 22 though most do not.

The models vary in their inclusion of both consumer and vehicle information. One model includes only vehicle price and the distribut ion of income in the population influencing choice; 23 others include varying numbers and kinds of vehicl e and consumer attributes.

Some models include only the consumer side of the v ehicle market; 24 others seek to represent both consumer and producer decisions. 25 Models that include only the consumer side are suitable for reflecting consumer choices, but they do not allow for revisions of vehicle characteristics in response to consumer pre ferences. Including producer behavior allows for vehicle characteristics such as price an d 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 re venue 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 vehic les sold. 26 Including auto maker response is a complex exercise. Auto makers are commonly co nsidered to have market power; they can influence the prices that consumers pay to incr ease their profits. As a result, the price increases that consumers face may reflect strategic factors that could make them higher or lower than the technology costs. In addition, auto makers may seek to influence consumer preferences through marketing and advertising. 27 Even those vehicle choice models that include a producer model may not include much detai l, due to computational limits: it is unusual for models to allow both buyers and produce rs to choose one vehicle characteristic, much less multiple characteristics. 28

8.1.2.4 Research Questions for the Models

Consumer choice models have been developed to analy ze many different research and policy questions. In part, these models have been developed to advance the state of economicChapter 8

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modeling. The work of Berry, Levinsohn, and Pakes, 29 for instance, is often cited outside the motor vehicle context for its incorporation of mult iple 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, researc h questions cover a wide gamut. These topics have included the effects of voluntary expor t restraints on Japanese vehicles compared to tariffs and quotas, 30 the market acceptability of alternative-fuel vehic les, 31 the effects of introduction and exit of vehicles from markets, 32 causes of the decline in market shares of U.S. automakers, 33 and the effects of gasoline taxes 34 and “feebates” 35 (subsidizing fuelefficient cars with revenue collected by taxing fue l-inefficient vehicles).

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions

Consumer vehicle choice models typically consider t he effect of fuel economy on vehicle purchase decisions. It can appear in vario us forms.

Some models 36 incorporate fuel economy through its effects on th e cost of owning a vehicle. With assumptions on the number of miles t raveled 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 con sidered to reduce the cost of owning a vehicle: effectively, they reduce the purchase pri ce. This approach relies on the assumption that, when purchasing vehicles, consumers can estim ate the fuel savings that they expect to
receive from a more fuel-efficient vehicle and cons ider the savings equivalent to a reduction in purchase price. Turrentine and Kurani 37 question this assumption; they find, in fact, that consumers do not make this calculation when they pu rchase a vehicle. The question remains, then, how or whether consumers take fuel economy in to account when they purchase their
vehicles.

Most estimated consumer choice models, instead of m aking assumptions about how consumers incorporate fuel economy into their decis ions, use data on consumer behavior to identify that effect. In some models, miles per ga llon is one of the vehicle characteristics included to explain purchase decisions. Other mode ls use fuel consumption per mile, the inverse of miles per gallon, as a measure: 38 since consumers pay for gallons of fuel, then thi s measure can assess fuel savings relatively directly

. 39 Yet other models multiply fuel consumption per mile by the cost of fuel to get the cost of driving a mile, 40 or they divide fuel economy by fuel cost to get miles per dollar. 41 It is worth noting that these last two measures assume that consumers respond the same way to an in crease in fuel economy as they do to a decrease in the price of fuel when each has the sam e effect on cost per mile driven. E On the one hand, while this assumption does not rely on as complex a calculation as the present value of fuel savings that Turrentine and Kurani examined , it suggests a calculating consumer. On the other hand, using a form of cost per mile is a way to recognize the role of fuel prices in

E Likewise, these measures assume consumers respond the same way to increases and decreases in cost per mile of driving, as well as if those increases and decre ases are large shocks rather than small, gradual ch anges. The issue of potential asymmetric consumer response to increased fuel efficiency compared to other types o f changes to the cost of driving also arises and is discussed in the context of the VMT rebound effect (see Sect ion III.H.4 of the Preamble and Chapter 4.2.5.2 of the TSD).Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-11 consumers’ purchase of fuel economy: recent resear ch 42 presents results that higher fuel prices play a major role in that decision.

Greene and Liu, 43 in a paper published in 1988, reviewed 10 papers u sing consumer
vehicle choice models and estimated for each one ho w 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 r ange: 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. W hile this study is quite old, it suggests that, at least as of that time, consumer vehicle choice m odels produced widely varying estimates of the value of reduced vehicle operating costs.

A newer literature review from David Greene 44 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 expecte d value of fuel savings varied from highly positive to highly negative. Significant numbers o f studies found that consumers overvalued fuel economy, undervalued fuel economy, or roughly valued fuel economy correctly relative to fuel savings. Part of the difficulty may be, as these papers note, that fuel economy may be correlated (either positively or negatively) with o ther 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 sa me time. Indeed, Gramlich 45 includes both fuel cost (dollars per mile) and miles per gallon i n his analysis, with the argument that miles per gallon measures other undesirable quality attri butes, while fuel cost picks up the consumer’s demand for improved fuel economy. Green e 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 res ult, further work needs to be conducted before it is possible to identify the role of fuel economy in consumer purchase decisions.

Some studies 46 argue that automakers could increase profits by in creasing fuel economy because the amount that consumers are willi ng to pay for increased fuel economy outweighs the costs of that improvement. Other stu dies 47 have found that increasing fuel economy standards imposes welfare losses on consume rs and producers, because consumers should already be buying as much fuel economy as th ey want. In the course of reaching this result, though, at least one of these studies 48 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-cos t” fuel economy improvements.

The models do not appear to yield very consistent r esults on the role of fuel economy in consumer and producer decisions.Chapter 8

8-12 8.1.2.6 Why Consumers May Not BuyFuel Economy that Pays for
Itself

A detailed and wide ranging literature attempts to explain why consumers appear to under-invest in energy saving technologies that – a t 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. Poten tial explanations are bounded by two scenarios. On the one hand, purely private benefit s of fuel economy (fuel savings, time savings, increases in driving time) must be accompa nied by private losses of the same magnitude. However, if there is no such private los s, or if it is small or insignificant, then
there is a market or behavioral failure.

On the consumer side, this disconnect between net p resent value estimates of energyconserving cost savings and what consumers actually spend on energy conservation is often referred to as the Energy Paradox, 49 since consumers appear to undervalue a wide range of investments in energy conservation. There are many possible explanations for the paradox
discussed in the literature. 50 Some explanations point to costs or aspects of co nsumer 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 th e most credible explanation. Possibilities
include:

• Consumers put little weight on benefits from fuel e conomy in the future and show high discount rates;

• Consumers do not find the benefits from fuel econom y to be sufficiently salient at the time of purchase, even if it would be in consum ers’ economic interest to take account of those benefits;

• Consumers consider other attributes more important than fuel economy at the time of vehicle purchase, especially if fuel economy is a relatively “shrouded” attribute;

• Consumers have difficulty in calculating expected f uel savings;

• Consumers may use imprecise rules of thumb when dec iding how much fuel economy to purchase;

• Consumers may give particular weight to the losses associated with upfront costs, and less so to the costs over time (a version of th e phenomenon of “myopic loss aversion”);

• Consumers might associate higher fuel economy with inexpensive, less well designed vehicles;

• Fuel savings in the future are uncertain, while at the time of purchase the increased costs of fuel-saving technologies are certain and i mmediate;Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-13 • Consumers may not be able to find the vehicles they want with improved fuel

economy;

• The level of cost savings may be affected by the un derlying reasons for the gap:
factors such as transactions costs and differences in quality may not be adequately measured; 51

• There is likely to be variation among consumers in the benefits they get from improved fuel economy, due to different miles drive n and driving styles.

8.1.2.7 Modeling Electric Vehicles and Other New Vehicles

Modeling the introduction of new vehicles can be a greater challenge than modeling the existing vehicle market, because the modeler do es not have data on how many of the new vehicles consumers buy. Nevertheless, it can be po ssible 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 a bove, 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 b e modeled by applying the parameters for
those features to the new vehicle characteristics.

As discussed above, some models rely on vehicle pri ce 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 vehicl e. For instance, if the change is to create an electric vehicle (EV) version of an existing mod el, 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 ve hicle driving range and cost of fueling may be two such attributes. The next requirement is in formation 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 a n 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) fr om vehicle purchase price.

Incorporating new vehicles into a vehicle choice mo del, then, requires estimates of the changes in key attributes from conventional vehicle s, 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 chang es in vehicle characteristics from any baseline model. EVs are likely to have a small er driving range between refuelings than conventional vehicles, due to the large battery cap acity needed to increase range. The abilityChapter 8

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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 th e street. If an infrastructure develops for recharging vehicles with the convenience approachin g 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 co nsumers. F They may have different performance or storage capacity. If sufficient dat a were available, the changes in these attributes, combined with WTP for each of the attri butes, could be used to adjust the purchase price of the baseline vehicle to estimate consumers ’ WTP for the electric version of a vehicle. Greene (2001), for instance, used this approach for a model that simulates choice, not only for EVs, but also for other alternative-fuel vehicles. 52 In that model, he considers only one base vehicle, a passenger car, but considers the effect on WTP of fuel cost per mile, range, acceleration, and several other vehicle attributes.

Vehicle driving range has received attention becaus e 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 m ajor factor in EV acceptability, it is starting to draw attention in the research communit y.

In several studies, researchers have used stated pr eference conjoint analysis to estimate the effect of vehicle range on consumer ve hicle choice. In a conjoint analysis, consumers are given a choice between several vehicl es 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 mor e range?) provide data that can be used to
estimate the role of vehicle attributes in the cons umer’s choice. Stated preference analysis is
sometimes considered less reliable than actual mark et behavior, because what people say they will do in hypothetical situations may not match wh at they would do in actual situations. On the other hand, stated preference methods can be us ed to study goods where market data do not exist, such as future market products undergoin g development (marketing studies often use stated preference methods), or environmental go ods. Because electric vehicles are not in widespread enough use for market studies, stated pr eference studies are, at this point, one of the few options to examine consumer behavior relati ng to these vehicles.

Table 8.1-2 summarizes results from several conjoin t studies that include the effects of extending range (in the table, from 150 to 300 mile s, 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 chan ging (perhaps shrinking) in more recent
studies.

F For instance, Hidrue et al. (Hidrue, Michael K., G eorge R. Parsons, Willett Kempton, and Meryl P. Gar dner. “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 pa y $5100 more for vehicles with 95% lower emissions than the vehicles they otherwise aim to purchase.Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-15 Table 8.1-2Willingness to Pay for Increasing Range Calculated from Various Studies

Study (Date) Value of extending range from 150 to 300 miles (dollar

year) Value of additional

range in 2009$ a

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

Kavelek (1996) for California Energy Commission c $2600 - $41,900 (1993$) $3700 - $58,700

Resource Systems Group (2009) for California Energy Commission d $2900 - $7500 (2009$) $2900 - $7500

Hess et al. (2009), using the same data as Resource Systems Group (2009) e $2400 - $8500 (2009$) $2400 - $8500

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

a Values adjusted to 2009$ using the Bureau of Econom ic Analysis GDP deflator.

b Bunch, David S., Mark Bradley, Thomas F. Golob, and Ryuichi Kitamura. “Demand for Clean-Fuel Vehicles
in California: A Discrete-Choice Stated Preference Pilot Project.” Transportation Research Part A 27A(3) (1993): 237-253. The value of range was, in their model, assumed to be the same for all people.

c Kavelek, Chris. “CALCARS: The California Conventi onal and Alternative Fuel Response Simulator.” Demand Analysis Office, California Energy Commissio n, April 1996. The variation in values is due to willingness to pay (WTP) varying by income levels a nd for one-car and two-car households. The coeffic ient 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 min ima 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 Comm ission,
June 2009.

e Hess, S., T. Adler, M. Fowler and A. Bahreinian “Th e Use of Cross-nested Logit Models for Multi-Dimens ional
Choice Processes: The Case of the Demand for Altern ative Fuel Vehicles,” Proceedings of the 2009 European Transport Conference , Leiden, Netherlands, 2009. This study uses the s ame data as the Resource Systems Group study. The coefficient on range was not stat istically significantly different from zero in thes e regressions:
t-statistics varied from 1.29 to 1.52. The variati on in values is due to willingness to pay (WTP) var ying by income levels and statistical specification. The m inima and maxima presented here represent the value s across both income categories and specifications.

f Hidrue, Michael K., George R. Parsons, Willett Kemp ton, and Meryl P. Gardner. “Willingness to Pay for
Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 686-705. The range ofChapter 8

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values is due to the model separating consumers int o “gasoline vehicle-oriented” and “electric vehicle -oriented” groups. The EV-oriented group has higher WTP for a dditional range.

Driving range may be a major factor in consumers’ d ecisions on EVs, but it is not the only attribute that may be important to potential b uyers (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 importa nt to consumers’ decisions may not perform well in predicting vehicle purchases. In addition, as mentioned above, and as seen in Table 8.1-2, it is likely that the WTP values for attribu tes 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 understa nding of the vehicles. Thus, challenges associated with predicting market shares for EVs ar e even more serious than those already serious challenges associated with predicting marke t shares for conventional vehicles.

8.1.2.8 EPA Exploration of Vehicle Choice Modeling

In order to develop greater understanding of these models, EPA is in the process of developing a vehicle choice model. In its current form, the model assumes that the vehicle fleet and all characteristics of each vehicle, exce pt vehicle prices and fuel economy, stay the same. The model will predict changes in the vehicl e fleet, at the individual-configuration level and at more aggregated levels, in response to changes in vehicle fuel economy and price.

The draft EPA model uses a nested logit structure c ommon in the vehicle choice modeling literature, as discussed above in Chapter 8.1.2.1. “Nesting” refers to the decisiontree structure of the model, and “logit” refers to the fact that the choices are discrete (i.e., yes/no decisions about which vehicles to purchase, instead of continuous values).

The nesting involves a hierarchy of choices. In it s 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 passenge r vehicles, cargo vehicles, and ultraluxury vehicles. The next set of choices subdivide s each of these categories into vehicle type (e.g., standard car, minivan, SUV, etc.). Next, th e 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 ac ross nests, because the vehicles within a
nest are more similar to each other than vehicles i n 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 g reater at low decision nodes (such as individual configurations) than at higher decision nodes (such as the buy/no buy decision),
because there are more choices at lower levels than at higher levels.Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-17 Parameters for the model (including demand elastici ties and the value of fuel economy in purchase decisions) are being selected based on a review of values found in the literature on vehicle choice modeling. As discussed above, a number of studies have estimated these parameters. Those estimates, combined with some th eoretical requirements, G assist in assigning values for the parameters. The model wil l 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 ec onomy and incorporated into the “effective price” of the vehicle. In practice, implementing t his calculation involves calculating the change in expenditures on fuel based on schedules o f VMT, vehicle survival, and fuel prices
in the future consistent with those in OMEGA. As d iscussed in Chapter 8.1.2.5, there is no consensus value for consumers’ willingness to pay f or improved fuel economy: estimates vary tremendously. The model assumes that consumer s will use some years of discounted fuel savings, with the modeler able to input both t he number of years and the discount rate to be used in the analysis.

The vehicle choice model will take as inputs an ini tial fleet of vehicles (including the initial sales and fuel economy) in the absence of s tandards, the cost of technologies added to each vehicle to comply with standards, and the chan ge in fuel economy. With the initial sales mix, for each vehicle, the model calculates a vehic le-specific constant that summarizes the value of all attributes of the vehicle other than p rice and fuel economy. This constant ensures that the model will predict changes in consumer res ponse that would result only from changes in price and fuel economy. This constant substitut es for estimating the effects of changes in all other vehicle characteristics; the underlying a ssumption is that these other vehicle characteristics do not change. H For instance, it assumes that a Ford Escape will not change in size, power, or accessories; the only changes will be to its cost and its fuel economy.

The model assumes that the increase in vehicle cost associated with increased technology is fully passed through as an increase i n vehicle price, and some years of fuel savings offset this price increase. It then calcul ates changes in total fleet size and in sales mix, at the individual-configuration level and at t he level of vehicle class, due to the changes in fuel economy and vehicle prices. It also calcul ates changes in consumer surplus associated with the changes in fuel economy and vehicle prices

.

G The theory of nested logit requires that the price slopes (the change in utility as vehicle full pric e 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 th an at higher

ones.
H As explained in Section III.D of the preamble, as part of the technology cost analysis for the propos ed rule, the agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions.Chapter 8

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It is possible that the predicted changes in fleet mix will lead to predictions of vehicle sales for auto makers that do not meet the proposed standards, because the mix and volume of vehicles sold changed from the initial levels. To correct this problem, it would be necessary to feed the new fleet mix into OMEGA (which calcula tes costs and compliance) and get a new set of output, which could then be fed back int o the vehicle choice model. OMEGA would increase technologies, and thus costs, to imp rove compliance; those adjustments would then again affect vehicle demand. We expect that t his iterative process would converge to a fleet mix that would meet standards. Performing th is iteration requires development of an
interface between the vehicle choice model and OMEG A to ensure accurate transmission of data between the models. At this time, the vehicle choice model takes output from OMEGA, but the results of the modeling do not feed easily back into OMEGA. Building this interface is an expected part of our future modeling work.

The model is still undergoing development; EPA will seek peer review on it before it is utilized. In addition, concerns remain over the ability of any vehicle choice model to make reasonable predictions of the response of the vehic le fleet to changes in prices and vehicle
characteristics. EPA seeks comments on the use of vehicle choice modeling for predicting changes in fleet mix due to policies, and on method s to test the ability of a vehicle choice model to produce reasonable estimates of changes in fleet mix.

8.1.2.9 Summary and Additional Considerations

Consumer vehicle choice modeling in principle can p rovide a great deal of useful information for regulatory analysis, helping to ans wer 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 pot ential to describe more accurately the impact of a policy, by identifying market shift s. 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 o n 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 va lues 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 fo r characteristics associated with advanced technology vehicles, such as EVs, the willingness-t o-pay values may change over time as consumers develop more experience with the vehicles and these characteristics. Models based on current estimates may not predict well for the future.

The modeling may improve estimates of the complianc e costs of a rule. Consumers can either accept the new costs and vehicle charact eristics, or they can change which vehicles they buy. Using a vehicle choice model is likely t o reduce compliance costs: because the model allows consumers to choose among accepting th e new vehicle, buying a differentSummary of interagency working comments received on draft NPRM under EO 12866 and

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

8-19 vehicle, or not buying a vehicle, consumers have ad ditional options, which improves their welfare relative to the assumption that consumers w ill not change their buying behavior. .

An additional complication associated with consumer choice modeling is accurate prediction of producers’ responses to the rule. Wh ile it is possible to include auto makers’ decisions (for instance, on setting prices) into ve hicle choices, computational limits affect the richness of these models. Technology costs, while an accurate measure of the opportunity cost of resources to society, may overestimate or u nderestimate the effect on the prices that
consumers face.

Consumer choice models can be used to calculate con sumer surplus impacts on vehicle purchase decisions. Because these values a re based on the estimates of changes in vehicle sales and fleet mix, consumer surplus measu res 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 un derstanding the role of consumer vehicle choice models in benefit-cost analysis. In particular, except for EVs, the technology cost estimates developed in this proposal take into account the costs to hold other vehicle attributes, such as size and performance, constant. In addition, the analysis assumes that the full technology costs are passed along to vehicle b uyers. With these assumptions, because welfare losses are monetary estimates of how much b uyers would have to be compensated to be made as well off as in the absence of the change

, I the price increase measures the loss to the buyer. J Assuming that the full technology cost gets passe d along to the buyer as an increase in price, the technology cost thus measure s 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 t he technology costs.

Given the current limitations in modeling the role of fuel economy in vehicle purchase decisions, and limitations in modeling market respo nses to the new regulations, in this proposal EPA holds constant the vehicle fleet size and mix in its calculations of the impacts of this rule, and compares the fuel and other savings that consumers will receive with the technology costs of the vehicles. EPA continues to explore options for including consumer

I This approach describes the economic concept of co mpensating variation, a payment of money after a ch ange that would make a consumer as well off after the ch ange as before it. A related concept, equivalent v ariation, estimates the income change that would be an altern ative to the change taking place. The difference b etween them is whether the consumer’s point of reference i s her welfare before the change (compensating varia tion) or after the change (equivalent variation). In practi ce, these two measures are typically very close tog ether.

J Indeed, it is likely to be an overestimate of the loss to the buyer, because the buyer has choices ot her than buying the same vehicle with a higher price; she co uld choose a different vehicle, or decide not to bu y a new vehicle. The buyer would choose one of those optio ns 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 co nsumer welfare, unless there are other changes to the vehi cle due to the fuel economy improvements that make the vehicle less desirable to buyers.Chapter 8

8-20

and producer choice in modeling the impacts of fuel economy-related regulations. This effort includes further review of existing consumer vehicl e choice models, the estimates of consumers’ willingness to pay for increased fuel ec onomy, and overall effects on consumer welfare, as well as EPA’s exploration of a vehicle choice model for use in the future.

8.2 Employment Impacts

8.2.1 Introduction

Although analysis of employment impacts is not part of a cost-benefit analysis (except to the extent that labor costs contribute to costs) , employment impacts of federal rules are of particular concern in the current economic climate of sizeable unemployment. When President Obama requested that the agencies develop this program, he sought a program that would “strengthen the [auto] industry and enhance j ob creation in the United States.” 53,K The recently issued Executive Order 13563, “Improving R egulation and Regulatory Review” (January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety,
and our environment

while promoting economic growth, innovation, compe titiveness, and job
creation ” (emphasis added). EPA is accordingly providing p artial estimates of the effects of this proposal on domestic employment in the auto ma nufacturing and parts sectors, while qualitatively discussing how it may affect employme nt in other sectors more generally.

This proposal is expected to affect employment in t he United States through the regulated sector – the auto manufacturing industry – and through several related sectors, specifically, industries that supply the auto manuf acturing 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 Aut omobile and Light Truck Manufacturing Sector (NAICS 33611) in December 2010; this is the directly regulated sector, since it encompasses the auto manufacturers that are respons ible for complying with the proposed
standards. 54 Changes in light duty vehicle sales, discussed in Chapter 8.1.1, could affect employment for auto dealers. Though some of the pa rts used to achieve the proposed standards are likely to be built by auto manufactur ers themselves, the auto parts manufacturing sector also plays a significant role in providing those parts, and will also be affected by changes in vehicle sales. As discussed in Chapter 5.4 of the RIA, this proposal is expected to reduce the amount of fuel these vehicle s use, and thus affect the petroleum refinery and supply industriesWhen the economy is a t full employment, an environmental regulation is unlikely to have much impact on net o verall U.S. employment; instead, labor would primarily be shifted from one sector to anoth er. These shifts in employment impose an opportunity cost on society, approximated by the wa ges of the employees, as regulation
diverts workers from other activities in the econom y. In this situation, any effects on net

K The May 21, 2010 Presidential Memorandum also requ ested that EPA and NHTSA, in developing the technical assessment to inform the rulemaking proce ss (which was issued by the agencies and CARB on September 30, 2010), include, among other things, t he “impacts on jobs and the automotive manufacturin g base
in the United States.”Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-21 employment are likely to be transitory as workers c hange jobs (e.g., some workers may need to be retrained or require time to search for new j obs, while shortages in some sectors or regions could bid up wages to attract workers).

On the other hand, if a regulation comes into effec t during a period of high unemployment, a change in labor demand due to regul ation may affect net overall U.S. employment because the labor market is not in equil ibrium. In such a period, both positive and negative employment effects are possible. (Cite Masur and Posner, http://papers.ssrn.com/sol3/papers.cfm?abstract_id= 1920441) Schmalansee and Stavins point out that net positive employment effects are possib le in the near term when the economy is at less than full employment due to the potential hiri ng of idle labor resources by the regulated sector to meet new requirements (e.g., to install n ew equipment) and new economic activity in sectors related to the regulated sector. 55 In the longer run, the net effect on employment i s more difficult to predict and will depend on the wa y in which the related industries respond to the regulatory requirements. As Schmalansee and St avins note, it is possible that the magnitude of the effect on employment could vary ov er time, region, and sector, and positive effects on employment in some regions or sectors co uld be offset by negative effects in other regions or sectors. For this reason, they urge cau tion in reporting partial employment effects since it can “paint an inaccurate picture of net em ployment impacts if not placed in the
broader economic context.”

Given the time frame of this proposed rule, it is p ossible that the state of the overall economy may have improved to the point where the on ly effect of changes in employment associated with the standards proposed here will be to shift workers from one sector to another. For example, to the extent that regulatio n increases the number of workers in the regulated sector (e.g., because of higher costs of producing vehicles with higher fuel economy), the opportunity cost associated with thos e shifts in employment will be captured in labor costs, as regulation diverts workers from oth er 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 req uire time to search for new jobs, while shortages in some sectors or regions could bid up w ages to attract workers). It is also possible that the state of the economy will be such that pos itive 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 o f 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, assumptio ns about the overall state of unemployment in the economy play a major role in me asured job impacts. The inputs to the models commonly are the changes in quantities or ex penditures in the affected sectors; modelChapter 8

8-22

results may vary in different studies depending on the assumptions about the levels of those inputs, and which sectors receive those changes. W hich 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 ef fects, 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 redesigne d 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 Shih 56 provides a retrospective look at the impacts of regulation in employment in the regulate d sectors by estimating the effects on employment of spending on pollution abatement for f our highly polluting/regulated U.S. industries (pulp and paper, plastics, steel, and pe troleum refining) using data for six years between 1979 and 1991. The paper provides a theore tical 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 st ringent) regulation.

• Demand effect : higher production costs due to the regulation wi ll 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 co nsumers as well as the demand elasticity of industry output.”

• Cost effect : as costs go up, plants add more capital and labo r (holding other factors constant), with potentially positive effect s on employment. In the authors’ words, as “production costs rise, more inputs, incl uding labor, are used to produce the same amount of output.”

• Factor shift effect : post-regulation production technologies may be m ore or less labor-intensive (i.e., more/less labor is required per dollar of output). In the authors’ words, “environmental activities may be mo re labor intensive than conventional production,” meaning that “the amount of labor per dollar of output will rise,” though it is also possible that “cleane r operations could involve automation and less employment, for example.”

According to the authors, the “demand effect” is ex pected to have a negative effect on employment, L

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

L As will be discussed below, the demand effect in t his proposal is potentially an exception to this ru le. While the vehicles become more expensive, they also produ ce reduced fuel expenditures; the reduced fuel cost s provide a countervailing impact on vehicle sales. As discu ssed in Preamble Section III.H.1, this possibility that vehiclesSummary of interagency working comments received on draft NPRM under EO 12866 and

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

8-23 effect” to have an ambiguous effect on employment. Without more information with respect to the magnitudes of these competing effects, it is not possible to predict the total effect environmental regulation will have on employment le vels in a regulated sector.

Morgenstern et al. conclude that increased abatemen t 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 ins ignificant) net increase of 1.5 jobs.

This approach to employment analysis has the advant age of carefully controlling for many possibly confounding effects in order to separ ate the effect of changes in regulatory costs on employment. It was, however, conducted fo r only four sectors. It could also be very difficult to update the study for other sectors, be cause 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 i ndustrial facilities that were considered in that study. In addition, it does not examine the e ffects of regulation on employment in sectors related to but outside of the regulated sector. Ne vertheless, the theory that Morgenstern et al. developed continues to be useful in this context.

The following discussion of additional methodologie s draws from Berck and Hoffmann’s review of employment models. 57

8.2.2.2 Computable General Equilibrium (CGE) Models

Computable general equilibrium (CGE) models are oft en used to assess the impacts of policy. These models include a stylized representa tion 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 m arkets: “they allow for substitution among inputs in production and goods in consumption.” Th us, if one market experiences a change, such as a new regulation, then the effects can be o bserved in all other markets. As a result, they can measure the employment changes in the econ omy 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 model s 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 assumpti ons of equilibrium in all markets and forward-looking consumers and firms, they are desig ned for examining the long-run effects of a policy but may offer little insight into its shor t-run effects.

may become more attractive to consumers after the p rogram poses a conundrum: why have interactions be tween vehicle buyers and producers not provided these ben efits without government intervention?Chapter 8

8-24 8.2.2.3 Input-Output (IO) Models

Input-output models represent the economy through a matrix of coefficients that describe the connections between supplying and cons uming sectors. In that sense, like CGE models, they describe the interconnections of the e conomy. These interconnections look at how changes in one sector ripple through the rest o f 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; th e additional workers in those sectors then have more money to spend, which leads to more emplo yment in retail sectors. These are known as “multiplier” effects, because an initial i mpact in one sector gets multiplied through the economy. Unlike CGE models, input-output model s 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 part icular, these models do not allow for price changes – an increase in the demand for labor or ca pital 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 ove r another. The multipliers take an initial impact and can increase it substantially.

IO models are commonly used for regional analysis o f projects. In a regional analysis, the markets are commonly considered small enough th at 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 nat ional-level employment analysis, the use of input-output models requires the assumption that wo rkers flow into or out of the labor market perfectly freely. Wages do not adjust; instead, pe ople 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 res ult, IO models provide an upper bound on employment impacts. As Berck and Hoffmann note, “F or 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 prod uction processes, introductions of new technologies, or learning over time due to new regu latory requirements are also generally not captured by IO models, as they are calibrated to al ready 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 s eparately discussed by Berck and Hoffmann, some hybrid models combine some of the fe atures 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, su ch as labor being at less than full employment. Hybrid models depend on assumptions ab out 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 d epends on their success in overcoming the limitations of each of these approaches. Hybrid mo dels could potentially be used to model labor market impacts of various vehicle policy opti ons although a number of judgments needSummary of interagency working comments received on draft NPRM under EO 12866 and

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

8-25 to be made about the appropriate assumptions underl ying 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 t he partial effect of regulation on employment in a single sector by estimating the cha nge 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 proportio nal to output or expenditures. As Berck and Hoffmann note, though, “Compliance with regulat ions may create additional jobs that are not accounted for.” While such an analysis can app roximate 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 th e 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 lo nger-run employment effects. 58 While often a sophisticated treatment of the issues analyzed, t hese 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 r egulation have advantages and limitations. CGE models may be most appropriate fo r 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 appropria te for small-scale, short-term effects, because they assume fixed relationships across sect ors 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 bui lt into them. Single-sector models are simple and straightforward, but they are often base d 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 commo nly of specific sectors or situations, and the results may not have direct bearing for the regulation being reviewed.

8.2.3 Employment analysis of this proposal

As mentioned above, this program is expected to aff ect employment in the regulated sector (auto manufacturing) and other sectors direc tly affected by the proposal: auto parts suppliers, auto dealers, the fuel supply market (wh ich will face reduced petroleum productionChapter 8

8-26

due to reduced fuel demand but which may see additi onal 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 e ffects depend much more heavily on the state of the macroeconomy than do the direct effect s. 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, bot tom-up quantitative estimate of the effects of this proposal on the regulated sector (t he 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 qual itative discussion of the effects of the proposal on other sectors. Focusing quantification of emplo yment impacts on the regulated sector has some advantages over quantifying all impacts. Firs t, the analysis relies on data generated as part of the rulemaking process, which focuses on th e regulated sector; as a result, what is presented here is based on internally consistent as sumptions and estimates made in this proposal. Secondly, as discussed above, net effect s on employment in the economy as a whole depend heavily on the overall state of the ec onomy when this rule has its effects. Focusing on the regulated sector provides insight i nto 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 em ployment effects on other sectors to provide a broader perspective on the impacts of thi s rule.

As noted above, in a full-employment economy, any c hanges in employment will result from people changing jobs or voluntarily ent ering or exiting the workforce. In a fullemployment economy, employment impacts of this prop osal will change employment in specific sectors, but it will have small, if any, e ffect 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 a nalysis does not include multiplier effects, but instead focuses on employment impacts in the mo st directly affected industries. Those sectors are likely to face the most concentrated em ployment impacts. The agencies seek comment on other sectors that are likely to be sign ificantly affected and thus warrant further analysis in the final rulemaking analysis.

8.2.3.1 Employment Impacts in the Auto Industry

If vehicle sales decrease, employment associated wi th these activities will unambiguously decrease. The sign of demand effect will depend on which of these effects dominates. Because, as described in Chapter 8.1, we have not quantified the impact on sales for this proposal, we do not quantify the demand ef fect.

8.2.3.1.1 The Cost Effect

Employment may increase because manufacturers need additional people to implement the new or additional technologies needed for vehicles to comply with the proposed standards.Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-27 One way to gauge this the cost effect, is to use th e ratio of workers to each $1 million of expenditures in that sector. The use of these r atios has both advantages and limitations. It is often possible to estimate these ratios for quit e specific sectors of the economy: for instance, it is possible to estimate the average nu mber of workers in the light-duty vehicle manufacturing sector per $1 million spent in the se ctor, rather than use the ratio from another, more aggregated sector, such as motor vehicle manuf acturing. As a result, it is not necessary to extrapolate employment ratios from possibly unre lated sectors. On the other hand, these estimates are averages for the sectors, covering al l the activities in those sectors; they may not be representative of the labor required when expend itures are required on specific activities, as the factor shift effect (discussed below) indica tes. For instance, the ratio for the motor vehicle manufacturing sector represents the ratio f or all vehicle manufacturing, not just for fuel efficiency improvements. In addition, these e stimates do not include changes in sectors that supply these sectors, such as steel or electro nics producers. They thus may best be viewed as the effects on employment in the auto sec tor due to the changes in expenditures in that sector, rather than as an assessment of all em ployment changes due to these changes in expenditures.

Some of the costs of this proposal will be spent di rectly 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 expend itures 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 technol ogies to the vehicles to comply with the regulation.

We use several public sources for estimates of empl oyment per $1 million expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment Requirements Matrix (ERM), 59 which provides direct estimates of the employment per $1 million in sales of goods in 202 sectors. The most recent estimates, used here, are from 2008 (adjusted to 2009$). The tables used here are adju sted to remove the employment effects of imports through use of the ratio of domestic produc tion to domestic sales, described above, of 0.667. The values reported are for Motor Vehicle M anufacturing (NAICS 3361) and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Annual Survey of Manufactures 60 (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 t han 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 esti mates of employees/$1 million in expenditures, the ASM separately provides number of employees and value shipments; the direct employment estimates here are the ratio of t hose values. The data in the ASM are updated annually, except for years when the full Ec onomic Census occurs. The tables presented here use data from 2009. As with the ERM , we adjust for the ratio of domesticChapter 8

8-28

production to domestic sales. The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty Motor Vehic le Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Economic Census includes all large companies an d 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 annu al. The values presented here use data from 2007 (adjusted to 2009$), with the domestic pr oduction-to-sales adjustment. The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), and Moto r Vehicle Parts Manufacturing (NAICS 3363).

Table 8.2-1 provides the values, either given (BLS) or calculated (ASM, Economic Census) for employment per $1 million of expenditur es, all based on 2009 dollars, though the underlying data come from different years (which ma y account for some of the differences). The different data sources provide similar magnitud es for the estimates for the sectors. Parts manufacturing appears to be more labor-intensive th an vehicle manufacturing; light-duty vehicle manufacturing appears to be slightly less l abor-intensive than motor vehicle manufacturing as a whole.

Table 8.2-1 Employment per $1 Million Expenditures (2009$) in the Motor Vehicle Manufacturing Sector*

Source Sector Ratio of workers per $1 million expenditures Ratio of workers per $1

million expenditures, adjusted for domestic vs. foreign production BLS ERM Motor Vehicle Mfg 0.834 0.556

ASM Motor Vehicle Mfg 0.824 0.549

ASM Light Duty Vehicle Mfg 0.757 0.505

Economic
Census Motor Vehicle Mfg 0.674 0.449

Economic
Census Light Duty Vehicle Mfg 0.610 0.407

BLS ERM Motor Vehicle Parts Mfg 3.073 2.049

ASM Motor Vehicle Parts Mfg 3.093 2.063

Economic
Census Motor Vehicle Parts Mfg 2.749 1.833Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-29 BLS ERM refers to the U.S. Bureau of Labor Statisti cs’ Employment Requirement Matrix. ASM refers to t he 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 i ndustry has changed: automation and improved production methods have led to signifi cant productivity increases. The BLS ERM, for instance, provided estimates that, in 1993 , 1.52 workers were needed per $1 million of 2000$, but only 0.83 workers by 2008 (in 2000$). 61 Because the ERM is available annually for 1993-2008, we used these data to estim ate productivity improvements over time. We regressed logged ERM values on year (to estimate percent change per year) for both the Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing sectors. The results suggest a 4.4 percent per year productivity improve ment in the Motor Vehicle Manufacturing Sector, and a 3.6 percent per year improvement in t he Motor Vehicle Parts Manufacturing Sector. We then used the regression relationship t o project the ERM through 2025. In the results presented below, these projected values (ad justed to 2009$) were used directly for the BLS ERM estimates. For the ASM, we used the ratio of the projected value in the future to the projected value in 2009 (the base year for the ASM); for the Economic Census estimates, we used the ratio of the projected value in the fut ure to the projected value in 2007 (the base year for that estimate). As noted above, we adjust ed the estimate of workers per vehicle for the demand effect in Chapter Error! Reference source not found. , above, using the productivity improvement for the Motor Vehicle Manu facturing sector; because the estimate of workers per vehicle for the demand effect was ba sed on data from 2001 to 2010, we used the projected value of the ERM in 2005 as the denom inator of the ratio. This is a simple way to examine the relationship between labor required and expenditure and we seek comment on refining this method.

Table 8.2-2 shows the cost estimates developed for this rule, discussed in Chapter 5. The maximum value in Table 8.2-2 for employment imp acts per $1 million expenditures (after accounting for the share of domestic product ion) is 2.049 in 2009 if all the additional costs are in the parts sector; the minimum value is 0.407 in 2009, if all the additional costs are in the light-duty vehicle manufacturing sector: th at is, the range of employment impacts is between 0.4 and 2 additional jobs per $1 million ex penditures in the sector in 2009. The results in Table 8.2-2 include the productivity adj ustment described above.

While we estimate employment impacts beginning with the first year of the standard (2017), some of these job gains may occur earlier a s auto manufacturers and parts suppliers hire staff in anticipation of compliance with the s tandard. Also, because of the uncertainty associated of projecting average labor required ass ociated with the cost of production, we limit the projection to 2020.

Table 8.2-2 Employment due to Cost Effect in the Mo tor Vehicle Manufacturing Sector

Year Costs (before

adjustment for domestic proportion Minimum

employment effect (if all expenditures Maximum

employment effect (if all expendituresChapter 8

8-30 of production) ($Millions) are in the parts

sector) are in the light

duty vehicle mfg sector) 2017 $ 2,300 600 3,600 2018 $ 4,656 1,200 7,000 2019 $ 6,507 1,600 9,400
2020 $ 8,467 1,900 11,800

8.2.3.1.2 The Factor Shift Effect

The factor shift effect looks at the effects on emp loyment due to changes in labor intensity associated with a regulation. As noted a bove, the estimates of the cost effect assume constant labor per $1 million in expenditures, thou gh 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 man ufacturers could use to comply with the proposed standards. For a subset of the technologi es, though, EPA-sponsored research (discussed in Chapter 3.2.1.1 of the Joint TSD) whi ch 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 t echnologies to their individual components and looking at the differences in materi als and labor needs in moving from the conventional to the new technologies. 62 For instance, the analysis compared all the parts and labor associated with an 8-speed automatic transmis sion to those needed for a 6-speed
automatic transmission.

Because labor cost was one of the sources of differ ences 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 com pliance with the proposed standards. An increase in labor cost for the new technology indic ates an increase in the labor needed for the new technology compared to the baseline technology. For instance, an 8-speed transmission requires $15.11 more in labor costs than a 6-speed transmission (as accounted for in EPA’s cost estimates for the proposed rule). Dividing th e labor cost by a wage per hour estimate provides an estimate of the additional hours (and t hus 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 n eeds for the new technologies. For this conversion, a weighted average wage rate (90 percen t of the average wage in the Motor Vehicle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle Manufacturing Sector) of $46.36/hour in 2015, using 2008 dollars (the unit of analysis for the Comment [A1]: Interagency commenters recommend agencies only go to 2020 becusae there
will be a mid-term review.Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-31 FEV study). For the change from a 6-speed to an 8- speed transmission, we thus estimate an additional 0.33 hours of labor per transmission.

8.2.4 Effects on Employment for Auto Dealers

The effects of the proposed standards on employment for auto dealers depend principally on the effects of the standards on ligh t duty vehicle sales. In addition, auto dealers may be affected by changes in maintenance and servi ce costs. Increases in those costs are likely to increase labor demand in dealerships.

Although this proposal predicts very small penetrat ion of advanced technology vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. As discussed in Chapter 8.1.2.7, consumers may find some characteristics of electric vehicles and plug-in hybrid electric vehicles, such as the a bility to fuel with electricity rather than gasoline, attractive; they may find other character istics, 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 vehi cles. 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 availab le.

8.2.5 Effects on Employment in the Auto Parts Sector

As discussed in the context of employment in the au to industry, some vehicle parts are made in-house by auto manufacturers; others are mad e by independent suppliers who are not directly regulated, but who will be affected by the proposed standards as well. The additional expenditures on technologies are expected to have a positive effect on employment in the parts sector as well as the manufacturing sector; t he breakdown in employment between the two sectors is difficult to predict. The effects o n the parts sector also depend on the effects of the proposed standards on vehicle sales and on the labor intensity of the new technologies, qualitatively in the same ways as for the auto manu facturing sector.

8.2.6 Effects on Employment for Fuel Suppliers

In addition to the effects on the auto manufacturin g and parts sectors, these rules will result in changes in fuel use that lower GHG emissi ons. Fuel saving, principally reductions in liquid fuels such as gasoline and diesel, will affe ct employment in the fuel suppliers industry sectors throughout the supply chain, from refinerie s to gasoline stations. To the extent that the proposed standards result in increased use of e lectricity or other new fuels, employment effects will result from providing these fuels and developing the infrastructure to supply them

to consumers.

Expected petroleum fuel consumption reductions can be found in Chapter 5.3. While this reduced consumption represents fuel savings fo r purchasers of fuel, it represents a loss in value of output for the petroleum refinery industry , fuel distributors, and gasoline stations.Chapter 8

8-32

The loss of expenditures to petroleum fuel supplier s throughout the petroleum fuel supply chain, from the petroleum refiners to the gasoline stations, is likely to result in reduced employment in these sectors.

This rule is also expected to lead to increases in electricity consumption by vehicles, as discussed in Chapter 5.3. This new fuel may req uire additional infrastructure, such as electricity charging locations. Providing this inf rastructure will require some increased employment. In addition, the generation of electri city is likely to require some additional
labor. We have insufficient information at this ti me 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 w ith reduced demand for petroleum fuels.

8.2.7 Summary

The primary employment effects of this proposal are expected to be found throughout several key sectors: auto manufacturers, auto deale rs, auto parts manufacturing, and fuel production and supplyThese proposed standards initi ally 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 ec onomy with full employment, the primary employment effect of a rulemaking is likely to be t o move employment from one sector to another, rather than to increase or decrease employ ment. For that reason, we focus our partial quantitative analysis on employment in the regulate d sector, to examine the impacts on that sector directly. We discuss the likely direction o f other impacts in the regulated sector as well as in other directly related sectors, but we do not quantify those impacts, because they are more difficult to quantify with reasonable accuracy , particularly so far into the future.

References

1 See, for instance, Gron, Ann, and Deborah Swenson, 2000. “Cost Pass-Through in the U.S. Automobile Market,” Review of Economics and Statist ics 82: 316-324 (Docket EPA-HQOAR-2010-0799).

2 Insurance Information Institute, 2008, “Average Ex penditures for Auto Insurance By State, 2007-2008,” http://www.iii.org/media/facts/statsbyi ssue/auto/, accessed 8/22/11 (Docket EPA-HQ-OAR-2010-0799).

3 U.S. Department of Energy, 2011. “Transportation and the Economy,” Chapter 10 in “Transportation Energy Data Book,” http://cta.ornl. gov/data/tedb30/Edition30_Chapter10.pdf, accessed 8/22/11, Table 11 (Docket EPA-HQ-OAR-2010- 0799).

4 Solheim, Mark, 2006 “State Car Tax Rankings,” http://www.kiplinger.com/features/archives/2006/04/ cartax.html, accessed April 23, 2009 (Docket EPA-HQ-OAR-2009-0472-0010) (Docket EPA-HQ-O AR-2010-0799).Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-33 5 U.S. Census Bureau, "Table 2. Resident Population of the 50 States, the District of Columbia, and Puerto Rico: 2010 Census" http://2010 .census.gov/news/presskits/apportionment/apport.html .

6 “National Auto Loan Rates for July 21, 2011,” http://www.bankrate.com/finance/auto/national-auto- loan-rates-for-july-21-2011.aspx, accessed 7/26/11 (Docket EPA-HQ-OAR-2010-0799).

7 Goldberg, Pinelopi Koujianou, “Product Differentia tion and Oligopoly in International Markets: The Case of the U.S. Automobile Industry, ” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799); Goldberg, Pinel opi Koujianou, “The Effects of the Corporate Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics 46(1) (March 1998): 1-33 (Docket EPA-HQ-OAR-2010 -0799).

8 Greene, David L., K.G. Duleep, Doug Elliott, and S anjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy In formation Administration,” prepared by the Oak Ridge National Laboratory for the U.S. D epartment of Energy under contract No. DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-079 9); 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,” En ergy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799).

9 McManus, Walter M., “Can Proactive Fuel Economy St rategies Help Automakers Mitigate Fuel-Price Risks?” University of Michigan Transpor tation Research Institute, September 14, 2006 (Docket EPA-HQ-OAR-2010-0799).

10 Berry, Steven, James Levinsohn, and Ariel Pakes, “ Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799); Berry, Steven, James Levinsohn, and Ariel Pa kes, “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-2010-07 99).

11 Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes” American Economic
Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799 ).

12 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

13 Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-2 010- 0799).Chapter 8

8-34 14 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journ al of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799)

.

15 E.g., Bento, Antonio M., Lawrence H. Goulder, Emer ic 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-2010-07 99); Train, Kenneth E., and Clifford Winston, “Vehicle Choice B ehavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

16 E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).

17 Greene, David L., K.G. Duleep, Doug Elliott, and S anjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy In formation Administration,” prepared by the Oak Ridge National Laboratory for the U.S. D epartment of Energy under contract No. DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-079 9).

18 NERA Economic Consulting, “Evaluation of NHTSA’s B enefit-Cost Analysis of 2011- 2015 CAFE Standards,” 2008, available at http://www.heartland.org/policybot/results/23495/Ev aluation_of_NHTSAs_BenefitCost_Anal ysis_Of_20112015_CAFE_Standards.html (Docket EPA-H Q-OAR-2010-0799).

19 E.g., Train, Kenneth E., and Clifford Winston, “Ve hicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

20 E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).

21 E.g., Bento, Antonio M., Lawrence H. Goulder, Emer ic 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-2010-07 99).

22 Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, 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-2010-079 9); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Poll ution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5 /12/09 (Docket EPA-HQ-OAR-2010- 0799).

23 NERA Economic Consulting, “Appendix B: New Vehicl e Market Model,” “Impacts of the
California Greenhouse Gas Emission Standards on Mot or Vehicle Sales,” commentsSummary of interagency working comments received on draft NPRM under EO 12866 and

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

8-35 submitted to the U.S. Environmental Protection Agen cy at Regulations.gov, document number EPA-HQ-OAR-2006-0173-9053.1 (Docket EPA-HQ- OAR-2010-0799).

24 E.g., Train, Kenneth E., and Clifford Winston, “Ve hicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799)

25 E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).

26 Greene, David L., Philip D. Patterson, Margaret Si ngh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Incre ased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799).

27 Rex, Emma, and Henrikke Baumann (2007). “Beyond e colabels: what green marketing can learn from conventional marketing.” Journal of Cleaner Production 15: 567-576 (Docket EPA-HQ-OAR-2010-0799).

28 Whitefoot, Kate, Meredith Fowlie, and Steven Skerl os, “Product Design Responses to Industrial Policy: Evaluating Fuel Economy Standar ds Using an Engineering Model of Endogenous Product Design,” Energy Institute at Haa s Working Paper 214, University of California Energy Institute, February 2011 (Docket EPA-HQ-OAR-2010-0799).

29 Berry, Steven, James Levinsohn, and Ariel Pakes, “ Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).

30 Goldberg, Pinelopi Koujianou, “Product Differentia tion and Oligopoly in International Markets: The Case of the U.S. Automobile Industry, ” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799).

31 Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with Flexible Substitution Patterns,” Journal of Econome trics 89 (1999): 109-129 (Docket EPAHQ-OAR-2010-0799); Brownstone, David, David S. Bunc h, and Kenneth Train, “Joint Mixed Logit Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” Transportation Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2010-0799) ; Greene David L., “TAFV Alternative Fuels and Vehicl es Choice Model Documentation,” prepared by the Oak Ridge National Laboratory for t he U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., K. G. Duleep, and Walter McManus, “Future Potential of Hybrid and Diesel Powertrains in the U.S. Light-Duty Vehicle Market,” prepared by the Oak Ridge National Laboratory for t he U.S. Department of Energy, August 2004 (Docket EPA-HQ-OAR-2010-0799).Chapter 8

8-36 32 Petrin, Amil, “Quantifying the Benefits of New Pro ducts: The Case of the Minivan,” Journal of Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799 ); Berry, Steven, James Levinsohn, and Ariel Pakes, “D ifferentiated Products Demand Systems
from a Combination of Micro and Macro Data: The Ne w Car Market,” Journal of Political Economy 112 (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799).

33 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48 (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

34 Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, 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-2010-079 9); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Poll ution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5 /12/09 (Docket EPA-HQ-OAR-2010- 0799).

35 Greene, David L., Philip D. Patterson, Margaret Si ngh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Incre ased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799); Fe ng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Pollution Polici es,” National Bureau of Economic Analysis Working Paper 11553, available at http://e conweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010-0799); Gr eene, David L., “Feebates, Footprints and Highway Safety,” Transportation Rese arch Part D 14 (2009): 375-384.

36 E.g., Austin, David, and Terry Dinan, “Clearing th e Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes, ” Journal of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799)

.

37 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2010-079 9).

38 E.g., Espey, Molly, and Santosh Nair, 2005. “Autom obile Fuel Economy: What Is It Worth?” Contemporary Economic Policy 23: 317-323 ( Docket EPA-HQ-OAR-2010-0799).

39 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 1593-1594 (Docket EPA-HQ-OAR-2010-0799).

40 E.g., Goldberg, Pinelopi Koujianou, “Product Diffe rentiation and Oligopoly in International Markets: The Case of the U.S. Automobile Industry, ” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799).

41 E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-37 42 Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. “Pain at the Pump: How Gasoline Prices Affect Automobile Purchasing in New and Used Markets,” working paper, http://web.mit.edu/knittel/www/papers/gaspap er_latest.pdf (accessed 11/1/11) (Docket EPA-HQ-OAR-2010-0799); Li, Shanjun, Christopher Tim mins, and Roger H. von Haefen,
2009. “How Do Gasoline Prices Affect Fleet Fuel Ec onomy?” American Economic Journal: Economic Policy 1(2): 113-137; Congressional Budget Office (2008) (Docket EPA-HQOAR-2010-0799); Congressional Budget Office (2008) . Effects of Gasoline Prices on Driving Behavior and Vehicle Markets . The Congress of the United States, Pub. No. 2883 (Docket EPA-HQ-OAR-2010-0799); West. Sarah (2007). “The Effect of Gasoline Prices on the Demand for Sport Utility Vehicles.” Working Pa per. Macalester College, http://www.macalester.edu/~wests/SarahWestMEA2007.p df, accessed 1/25/10 (Docket EPAHQ-OAR-2010-0799).

43 Greene, David L., and Jin-Tan Liu, 1988. “Automot ive Fuel Economy Improvements and Consumers’ Surplus,” Transportation Research Part A 22A (3): 203-218 (Docket EPA-HQOAR-2010-0799).

44 Greene, David L. (2010). “How Consumers Value Fue l Economy: A Literature Review.” EPA Report EPA-420-R-10-008 (Docket EPA-HQ-OAR-2010 -0799).

45 Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S. Automobile Industry,” http://faculty.msb.edu/jpg72/ Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket EPA-HQ-OAR-2010-0799).

46 Gramlich, Jacob, “Gas Prices and Endogenous Produc t Selection in the U.S. Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_Graml ichJacob.pdf , accessed 11/1/11 (Docket EPA-HQ-OAR-2010-0799); McManus, Walter, 2007. “The Impact of Attribute-Based Corporate Average Fuel Economy (CAFE) Standards: P reliminary Results.” Ann Arbor, MI: University of Michigan Transportation Research Inst itute, Report No. UMTRI-2007-31 (Docket EPA-HQ-OAR-2010-0799).

47 For instance, Kleit, Andrew N., “Impacts of Long-R ange Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2010-0799); Austin, David, and Terry Dinan, “Cleari ng the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and Management 50 (2005): 562- (Docket EPA-HQ-OAR-2010- 0799); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of the Corporate Average Fuel Economy Standard,” Resou rces for the Future Paper RFF DP 10- 50 (December 2010), http://www.rff.org/RFF/Document s/RFF-DP-10-50.pdf) (Docket EPAHQ-OAR-2010-0799)Jacobsen, Mark, 2010. “Evaluating U.S. Fuel Economy Standards in a Model with Producer and Household Heterogeneity,” w orking paper, at http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf (a ccessed 11/1/11) (Docket EPA-HQOAR-2010-0799) .Chapter 8

8-38

48 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journ al of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

49 IEA. 2007. “Mind the Gap: Quantifying Principal-Ag ent Problems in Energy Efficiency

. ” Paris, France: International Energy Agency (Docket EPA-HQ-OAR-2010-0799); Jaffe, Adam B., Richard G. Newell, and Robert N. Stavins (2001) . "Energy Efficient Technologies and Climate Change Policies: Issues and Evidence." In C limate Change Economics and Policy

, Toman, Michael A., ed., Washington, D.C.: Resources for the Future. p.171-181 (Docket EPA-HQ-OAR-2010-0799); Metcalf, Gilbert E., and Kev in A. Hassett (1999). "Measuring the Energy Savings From Home Improvement Investment s: Evidence From Monthly Billing
Data." The Review of Economics and Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010- 0799); Tietenberg, T. (2009). “Reflections – Energ y Efficiency Policy: Pipe Dream or Pipeline to the Future?” Review of Environmental E conomics and Policy 3(2): 304-320 (Docket EPA-HQ-OAR-2010-0799).

50 Helfand, Gloria, and Ann Wolverton (2011). “Evalu ating the Consumer Response to Fuel Economy: A Review of the Literature.” Internation al Review of Environmental and
Resource Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799).

51 Jaccard, Mark. “Paradigms of Energy Efficiency’s Cost and their Policy Implications: Déjà Vu All Over Again.” Modeling the Economics of Greenhouse Gas Mitigation: Summary of a Workshop, K. John Holmes, Rapporteur. National Academies Press, 2010. http://www.nap.edu/openbook.php?record_id=13023&pag e=42 (Docket EPA-HQ-OAR2010-0799)

52 Greene David L., “TAFV Alternative Fuels and Vehic les Choice Model Documentation,” prepared by the Oak Ridge National Laboratory for t he U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799).

53 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency Standards. The White House, Office of the Press Se cretary, May 21, 2010. http://www.whitehouse.gov/the-press-office/presiden tial-memorandum-regarding-fuelefficiency-standards

54 U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed on August 9, 2011.

55 Schmalensee, Richard, and Robert N. Stavins. “A G uide to Economic and Policy Analysis of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011 (Docket EPA-HQ-OAR-2010-0799).

56 Morgenstern, Richard D., William A. Pizer, and Jhi h-Shyang Shih. “Jobs Versus the Environment: An Industry-Level Perspective.” Jour nal of Environmental Economics and Management 43 (2002): 412-436 (Docket EPA-HQ-OAR-2010-0799).Summary of interagency working comments received on draft NPRM under EO 12866 and

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

8-39 57 Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental and Natural Resource Policy.” Environmental and Re source Economics 22 (2002): 133-156 (Docket EPA-HQ-OAR-2010-0799).

58 Berman, Eli, and Linda T. Bui, (2001) “Environment al Regulation and Labor Demand: Evidence from the South Coast Air Basin,” Journal of Public Economics , 79, 265 – 295 (Docket EPA-HQ-OAR-2010-0799)

.

59 http://www.bls.gov/emp/ep_data_emp_requirements.ht m

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

Deleted: ¶

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-1 Below is a summary of interagency comments received during EO 12866 and EO 13563 review of the draft r ule “Joint Rulemaking to Establish Light-Duty Vehicle Greenhou se Gas Emission Standards and Corporate Average Fue l Economy Standards”. They are for your consideration and are subject to further policy review.

Interagency commenters recommend the following embe dded edits to EPA’s RIA discussion of Vehicle sales and employment:
***


## Chapter 8: Vehicle Sales and Employment Impacts

8.1 Vehicle Sales Impacts

8.1.1 Vehicle Sales Impacts and Payback Period

Predicting the effects of this rule on vehicles ent ails comparing two competing effects. On the one hand, as a result of this rule, the vehi cles will become more expensive, which would, by itself, be expected to discourage sales. On the other hand, the vehicles will have improved fuel economy and thus lower operating cost s, producing lower total costs over the life of vehicles, which makes them more attractive to consumers. Which of these effects dominates for potential vehicle buyers when they ar e considering a purchase will determine the effect on sales. However, assessing the net eff ect of these two competing effects is complex and uncertain, as it rests on how consumers value fuel savings at the time of purchase and the extent to which manufacturers and dealers reflect them in the purchase price. The empirical literature does not provide clear evi dence on whether consumers fully consider the value of fuel savings at the time of purchase. It also generally does not speak to the efficiency of manufacturing and dealer pricing deci sions. Thus, for the proposal we do not provide quantified estimates of potential sales imp acts. Rather, we solicit comment on the issues raised here and on methods for estimating th e effect of this rule on vehicle sales.

For years, consumers have been gaining experience w ith the benefits that accrue to them from owning and operating vehicles with greate r fuel efficiency. Many households already own vehicles with a fairly wide range of fu el economy, and thus already have an opportunity to learn about the value of fuel econom y on their own. Among two-vehicle households, for example, the least fuel-efficient v ehicle averages just over 22 mpg (EPA test rating), and the range between this and the fuel ec onomy of their other vehicle averages nearly 7 mpg. Among households that own 3 or more vehicles, the typical range of the fuel economy they offer is much wider. Consumer demand may have shifted towards such vehicles, not only because of higher fuel prices bu t also if many consumers are learning about the value of purchases based not only on initial co sts but also on the total cost of owning and operating a vehicle over its lifetime. This type o f learning should continue before and during the model years affected by this rule, particularly given the new fuel economy labels that clarify potential economic effects and should there fore reinforce that learning..

Today’s proposed rule, combined with the new and ea sier-to-understand fuel economy label required to be on all new vehicles beginning in 2012, may increase sales above baseline levels by hastening this very type of consumer lear ning. As more consumers experience, as a

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-2 result of the rule, the savings in time and expense from owning more fuel efficient vehicles, demand may shift yet further in the direction of th e vehicles mandated under the rule. This social learning can take place both within and acro ss 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 individua ls who own them, which might cause their subsequent purchase decisions to shift closer to mi nimizing the total cost of ownership over
the lifetime of the vehicle.

Second, this appreciation may spread across househo lds through word of mouth and
other forms of communications.

Third, as more motorists experience the time and fu el savings associated with greater fuel efficiency, the price of used cars will better reflect such efficiency, further reducing the cost of owning more efficient vehicles for the buye rs of new vehicles (since the resale price will increase).

If these induced learning effects are strong, the r ule could potentially increase total vehicle sales over time. It is not possible to qua ntify these learning effects years in advance and that effect may be speeded or slowed by other f actors that enter into a consumer’s valuation of fuel efficiency in selecting vehicles. The possibility that the rule will (after a lag for consumer learning) increase sales need not rest on the assumption that automobile manufacturers are failing to pursue profitable oppo rtunities to supply the vehicles that consumers demand. In the absence of the rule, no in dividual automobile manufacturer would find it profitable to move toward the more efficien t vehicles mandated under the rule. In particular, no individual company can fully interna lize the future boost to demand resulting from the rule. If one company were to make more eff icient vehicles, counting on consumer learning to enhance demand in the future, that comp any would capture only a fraction of the extra sales so generated, because the learning at i ssue is not specific to any one company's fleet. Many of the extra sales would accrue to that company's competitors.

In other words, consumer learning about the benefit s of fuel efficient vehicles involves positive externalities (spillovers) from one compan y to the others. [Footnote: Industrywide positive spillovers of this type are hardly unique to this situation. In many industries, companies form trade associations to promote indust ry-wide public goods. For example, merchants in a given locale may band together to pr omote tourism in that locale. Antitrust law recognizes that this type of coordination can incre ase output.] These positive externalities may
lead to benefits for manufacturers as a whole. We emphasize that this discussion has been tentative and qualified. To be sure, social learnin g of related kinds has been identified in a number of contexts. [Footnote: See Hunt Alcott, Soc ial Norms and Energy Conservation, Journal of Public Economics (forthcoming 2011), ava ilable at http://web.mit.edu/allcott/www/Allcott%202011%20JPu bEc%20-

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8-3 %20Social%20Norms%20and%20Energy%20Conservation.pdf ; Christophe Chamley, Rational Herds: Economic Models of Social Learning (Cambridge, 2003).] Comments are invited on the discussion offered here, with partic ular reference to any relevant empirical findings.”

We do not here present a vehicle sales analysis usi ng 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 conditi ons, might change. Instead, we present a payback period analysis to estimate the number of y ears of fuel savings needed to recover the up-front costs of the new technologies. In other w ords, the payback period identifies the break-even point for new vehicle buyers. The calcu lation of the payback period is discussed in DRIA Chapter 5.3. Table 1 shows the estimated p ayback period for MY 2021 and 2025. We present MY 2021 because it is the last year befo re the mid-term review impacts, if any, will take place, and MY 2025 because it is the last year of the program. The payback period in 2021 is shorter than that in 2025, because the t echnologies required to meet the proposed
MY 2021 standards are more cost-effective than thos e for MY 2025. In all cases, the payback periods are less than 4 years.

Table 1 Estimated Payback Period for Model Years 20 21 and 2025 (Years)

Model
Year Estimated Payback Period
for Cash Purchase, 3%
Discount Rate Estimated

Payback Period
for Cash Purchase, 7%
Discount Rate Estimated

Payback Period
for Purchase on Credit, 3%
Discount Rate Estimated

Payback Period
for Purchase on Credit, 7%
Discount Rate

2021 2.7 2.9 2.9 2.8

2025 3.7 3.9 3.9 3.9

We welcome comments on all aspects of this discussi on, including the full range of considerations and assumptions which influence mark et behavior and outcomes; and
associated uncertainties. We welcome comments on t he methodology described here for quantitative estimates of the effects of this propo sal on sales and its appropriateness for this rulemaking; we also welcome proposals for other met hods.

We here provide further detail on some of the assum ptions included in the payback analysis. We seek comment on all these factors as well. The analysis starts with the increase in costs estimated by the OMEGA model. We assume t hat these costs are fully passed along to consumers. This assumption is appropriate for c ost increases in perfectly competitive markets. In less than perfectly competitive market s, though, it is possible that the cost

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8-4 increase is split between consumers and automakers, and possibly suppliers, and the price may not increase as much as costs. 1 

### Thus, the assumption of full cost pass-through is possibly
an overestimate.

The next step in the analysis is to adjust this cos t increase for other effects on the consumer. The higher vehicle price is likely to le ad to an increase in sales tax, insurance, and vehicle financing costs.

The increase in insurance costs is estimated from t he average value of collision plus comprehensive insurance as a proportion of average new vehicle price. Collision plus comprehensive insurance is the portion of insurance costs that depend on vehicle value. The
Insurance Information Institute 2 

### provides the average value of collision plus compr ehensive insurance in 2008 as $432. The average value of a new vehicle in 2008, according to the U.S. Department of Energy, was $23,334. 3 

### Dividing the insurance cost by the average price of a new vehicle gives the proportion of comprehensive p lus collision insurance as 1.85 percent of the price of a vehicle. If this same proportion ho lds for the increase in price of a vehicle, then insurance costs should go up by 1.85 percent of the increase in vehicle cost. We use information on depreciation of vehicle value from t he same U.S. Department of Energy report to estimate a reduction in insurance costs, due to reduction in the estimated value of the vehicle, over 9 years.

Calculating the average increase in sales tax start s with the vehicle sales tax for each state in 2006, the most recent source identified. 4 

### The sales tax per state was then multiplied by the 2010 population of the state; 5 

### those values were summed and divided by total U.S. population, to give a population-weighted sales tax . That estimate of the state sales taxes for vehicles in the U.S. is 5.3 percent. This value is assumed to be a one-time cost incurred when the vehicle is purchased.

As of July, 2011, the national average interest rat e for a 5 year new car loan was 5.51 percent. 6 

### We use this loan rate to calculate the increase i n vehicle costs due to financing a
loan.

8.1.2 Consumer Vehicle Choice Modeling A



### An alternative to the vehicle sales analysis discus sed 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 d escribe the models’ results and
limitations that we have identified. The evidence from consumer vehicle choice models indicates a huge range of estimates for consumers’ willingness to pay for additional fuel

A This section is drawn heavily from Helfand, Gloria , and Ann Wolverton, “Evaluating the Consumer Respo nse to Fuel Economy: A Review of the Literature.” Int ernational Review of Environmental and Resource
Economics 5 (2011): 103-146.

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8-5 economy. Because consumer surplus estimates from c onsumer vehicle choice models depend critically on this value, we would consider any con sumer surplus estimates of the effect of our
rule from such models to be unreliable. In additio n, the predictive ability of consumer vehicle choice models may be limited. While vehicle choice models are based on sales of existing vehicles, vehicle models are likely to change, both independently and in response to this rule. The models may not predict well in response to thes e changes. Instead, we compare the value of the fuel savings associated with this rule with the increase in technology costs. EPA will continue its efforts to review the literature, but, given the known limitations and uncertainties of vehicle choice models, EPA has not conducted an analysis using these models for this proposal.

This rule will lead automakers to change characteri stics – in particular, the fuel economy -- of the vehicles they produce. These cha nges will affect the cost of manufacturing the vehicle; as a result, the prices of the vehicle s 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 the ir 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 respon se to the changes in vehicle characteristics, consumers will reconsider their purchases. Increas es 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 purch ase. 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 in fluence consumers on multiple market scales: the total number of new vehicles sold; the mix of n ew 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 characteri stics and prices. In principle, it should produce more accurate estimates of compliance costs compared to models that hold fleet mix constant, since it predicts changes in the fleet mi x that can affect compliance costs. It can also be used to measure changes in consumer surplus, the benefit that consumers perceive from a good over and above the purchase price. (Consumer surplus is the difference between what consumers would be willing to pay for a good, repre sented by the demand curve, and the amount they actually pay. For instance, if a consu mer were willing to pay $30,000 for a new vehicle, but ended up paying $25,000, the $5000 dif ference is consumer surplus.)

A number of consumer vehicle choice models have bee n developed. They vary in the methods used, the data sources, the factors include d in the models, the research questions they are designed to answer, and the results of the mode ls related to the effects of fuel economy on consumer decisions. This section will give some ba ckground on these differences among the
models.

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8-6 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 ex plain 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 va lue. 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, essenti ally, the probability that consumers will purchase a specific vehicle, these approaches are s imilar in process; they differ mostly in the kinds of data that they use.

The primary methods used to model vehicle choices a re nested logit and mixed logit. B 

### In a nested logit, the model is structured in layer s. For instance, the first layer may be the choice of whether to buy a new or used vehicle. Gi ven that the person chooses a new vehicle, the second layer may be whether to buy a car or a t ruck. Given that the person chooses a car, the third layer may be the choice among an economy, midsize, or luxury car. Examples of nested logit models include Goldberg, 7 

### Greene et al., 8 

### and McManus. 9



### In a mixed logit, personal characteristics of consu mers 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 o f the effects of the distribution of consumer characteristics. As a result, mixed logit can be u sed to examine the distributional effects on various socioeconomic groups, which nested logit is not designed to do. Examples of mixed logit models include Berry, Levinsohn, and Pakes, 10 

### Bento et al., 11 

### and Train and Winston. 12



### While discrete choice modeling appears to be the pr imary method for consumer choice modeling, others (such as Kleit 13 

### and Austin and Dinan 14 

### ) 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 Din an used the elasticities from an internal GM
vehicle choice model.

8.1.2.2 Data Sources

The predictions of vehicle purchases from CCMs are based on consumer and vehicle characteristics. The CCMs identify the effects of changing the characteristics on the purchase decisions. These effects are typically called the parameters or coefficients of the models. For

B Logit refers to a statistical analysis method used for analyzing the factors that affect discrete cho ices (i.e., yes/no decisions or the choice among a countable nu mber of options).

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8-7 instance, the model parameters might predict that a n increase in a person’s income of 10% would increase the probability of her purchasing ve hicle 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 othe r studies (calibrated models).

Estimated models use datasets on consumer purchase patterns, consumer characteristics, and vehicle characteristics to dev elop their original sets of parameters. The
datasets used in these studies sometimes come from surveys of individuals’ behaviors. 15 

### Because they draw on the behavior of individuals, t hey provide what is sometimes called micro-level data. Other studies, that estimate mar ket shares instead of discrete purchase decisions, use aggregated data that can cover long time periods. 16



### Calibrated models rely on existing studies for thei r parameters. Researchers may draw on results from a number of estimated models, or ev en from research other than CCM, to choose the parameters of the models. The Fuel Econ omy Regulatory Analysis Model developed for the Energy Information Administration 17 

### and the New Vehicle Market Model developed by NERA Economic Consulting 18 

### are examples of calibrated models.

8.1.2.3 Factors Included in the Models

Consumer choice models vary in their complexity and levels of analysis. Some focus only on the new vehicle market; 19 

### others consider the choice between new vehicles an d an outside good (possibly including a used vehicle); 20 

### others explicitly consider the relationship
between the new and used vehicle markets. 21 

### Some models include consideration of vehicle miles traveled, 22 

### though most do not.

The models vary in their inclusion of both consumer and vehicle information. One model includes only vehicle price and the distribut ion of income in the population influencing choice; 23 

### others include varying numbers and kinds of vehicl e and consumer attributes.

Some models include only the consumer side of the v ehicle market; 24 

### others seek to represent both consumer and producer decisions. 25 

### Models that include only the consumer side are suitable for reflecting consumer choices, but they do not allow for revisions of vehicle characteristics in response to consumer pre ferences. Including producer behavior allows for vehicle characteristics such as price an d 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 re venue 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 vehic les sold. 26 

### Including auto maker response is a complex exercise. Auto makers are commonly co nsidered to have market power; they can influence the prices that consumers pay to incr ease 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

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8-8 preferences through marketing and advertising. 27 

### Even those vehicle choice models that include a producer model may not include much detai l, due to computational limits: it is unusual for models to allow both buyers and produce rs to choose one vehicle characteristic, much less multiple characteristics. 28



### 8.1.2.4 Research Questions for the Models

Consumer choice models have been developed to analy ze many different research and policy questions. In part, these models have been developed to advance the state of economic modeling. The work of Berry, Levinsohn, and Pakes, 29 

### for instance, is often cited outside the motor vehicle context for its incorporation of mult iple 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, researc h questions cover a wide gamut. These topics have included the effects of voluntary expor t restraints on Japanese vehicles compared to tariffs and quotas, 30 

### the market acceptability of alternative-fuel vehic les, 31 

### the effects of introduction and exit of vehicles from markets, 32 

### causes of the decline in market shares of U.S. automakers, 33 

### and the effects of gasoline taxes 34 

### and “feebates” 35 

### (subsidizing fuelefficient cars with revenue collected by taxing fue l-inefficient vehicles).

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions

Consumer vehicle choice models typically consider t he effect of fuel economy on vehicle purchase decisions. It can appear in vario us forms.

Some models 36 

### incorporate fuel economy through its effects on th e cost of owning a vehicle. With assumptions on the number of miles t raveled 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 con sidered to reduce the cost of owning a vehicle: effectively, they reduce the purchase pri ce. This approach relies on the assumption that, when purchasing vehicles, consumers can estim ate the fuel savings that they expect to
receive from a more fuel-efficient vehicle and cons ider the savings equivalent to a reduction in purchase price. Turrentine and Kurani 37 

### question this assumption; they find, in fact, that consumers do not make this calculation when they pu rchase a vehicle. The question remains, then, how or whether consumers take fuel economy in to account when they purchase their
vehicles.

Most estimated consumer choice models, instead of m aking assumptions about how consumers incorporate fuel economy into their decis ions, use data on consumer behavior to identify that effect. In some models, miles per ga llon is one of the vehicle characteristics included to explain purchase decisions. Other mode ls use fuel consumption per mile, the inverse of miles per gallon, as a measure: 38 

### since consumers pay for gallons of fuel, then thi s measure can assess fuel savings relatively directly

. 39 

### Yet other models multiply fuel consumption per mile by the cost of fuel to get the cost of driving a mile, 40 

### or they divide fuel economy by fuel cost to get miles per dollar. 41 

### It is worth noting that these last two measures assume that consumers respond the same way to an in crease in fuel economy as they do to a

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8-9 decrease in the price of fuel when each has the sam e effect on cost per mile driven. C 

### On the one hand, while this assumption does not rely on as complex a calculation as the present value of fuel savings that Turrentine and Kurani examined , it suggests a calculating consumer. On the other hand, using a form of cost per mile is a way to recognize the role of fuel prices in consumers’ purchase of fuel economy: recent resear ch 42 

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

Greene and Liu, 43 

### in a paper published in 1988, reviewed 10 papers u sing consumer
vehicle choice models and estimated for each one ho w 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 r ange: 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. W hile this study is quite old, it suggests that, at least as of that time, consumer vehicle choice m odels produced widely varying estimates of the value of reduced vehicle operating costs.

A newer literature review from David Greene 44 

### 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 expecte d value of fuel savings varied from highly positive to highly negative. Significant numbers o f studies found that consumers overvalued fuel economy, undervalued fuel economy, or roughly valued fuel economy correctly relative to fuel savings. Part of the difficulty may be, as these papers note, that fuel economy may be correlated (either positively or negatively) with o ther 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 sa me time. Indeed, Gramlich 45 

### includes both fuel cost (dollars per mile) and miles per gallon i n his analysis, with the argument that miles per gallon measures other undesirable quality attri butes, while fuel cost picks up the consumer’s demand for improved fuel economy. Green e 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 res ult, further work needs to be conducted before it is possible to identify the role of fuel economy in consumer purchase decisions.

Some studies 46 

### argue that automakers could increase profits by in creasing fuel economy because the amount that consumers are willi ng to pay for increased fuel economy

C Likewise, these measures assume consumers respond the same way to increases and decreases in cost per mile of driving, as well as if those increases and decre ases are large shocks rather than small, gradual ch anges. The issue of potential asymmetric consumer response to increased fuel efficiency compared to other types o f changes to the cost of driving also arises and is discussed in the context of the VMT rebound effect (see Sect ion III.H.4 of the Preamble and Chapter 4.2.5.2 of the TSD).

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8-10 outweighs the costs of that improvement. Other stu dies 47 

### have found that increasing fuel economy standards imposes welfare losses on consume rs and producers, because consumers should already be buying as much fuel economy as th ey want. In the course of reaching this result, though, at least one of these studies 48 

### 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-cos t” fuel economy improvements.

The models do not appear to yield very consistent r esults on the role of fuel economy in consumer and producer decisions.

8.1.2.6 Why Market Outcomes May Not Reflect Full Appreciati on for Fuel Economy which Pays for Itself

A detailed and wide ranging literature attempts to explain why market outcomes for energy-using products appear to reflect under-inves tment 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 d efinitive 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 d riving time) must be accompanied by private losses of the same magnitude. However, if t here is no such private loss, or if it is small or insignificant, then there is a market or behavio ral failure.

This disconnect between net present value estimates of energy-conserving cost savings and what consumers actually spend on energy conserv ation is often referred to as the Energy Paradox, 49 

### since consumers appear to undervalue a wide range of investments in energy conservation. There are many possible explanations for the paradox discussed in the
literature. 50 

### Some explanations point to costs or aspects of co nsumer decision-making unaccounted for in a simple present value calculati on, while others point to potential behavioral or market failures. There is little emp irical literature to help the analyst determine which combination of hypothesis offers the most cre dible explanation. Some possibilities
include:

• Consumers put little weight on benefits from fuel e conomy in the future and show high discount rates;

• Consumers do not find the benefits from fuel econom y to be sufficiently salient at the time of purchase, even if it would be in consum ers’ economic interest to take account of those benefits;

• Consumers consider other attributes more important than fuel economy at the time of vehicle purchase, especially if fuel economy is a relatively “shrouded” attribute;

• Consumers have difficulty in calculating expected f uel savings;

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8-11 • Consumers may use imprecise rules of thumb when dec iding how much fuel economy to purchase;

• Consumers might associate higher fuel economy with inexpensive, less well designed vehicles;

• Fuel savings in the future are uncertain, while at the time of purchase the increased costs of fuel-saving technologies are certain and i mmediate;

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

economy;

• The level of cost savings may be affected by the un derlying reasons for the gap:
factors such as transactions costs and differences in quality may not be adequately measured; 51



### • There is likely to be variation among consumers in the benefits they get from improved fuel economy, due to different miles drive n and driving styles.

• Consumers may give particular weight to the losses associated with upfront costs, and less so to the costs over time (a version of th e phenomenon of “myopic loss aversion”);

The extent to which fuel economy is optimized rela tive to other, potentially more salient vehicle attributes (such as engine horsepow er 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 ar e significant lead times to market, potential return to scale limits on the range of options prov ided for a given attribute or amenity, market transaction frictional factors, and other factors i nherent to the nature of these costly durable goods which may contribute to imperfect satisfactio n of market demand for fuel economy among a highly heterogeneous customer base. . Bot h sides of the market would be expected to attempt to maximize the utility they gain from t hese transactions, they presumably rely heavily in their calculations on the uncertain bene fits of savings from fuel economy improvements, and yet market outcomes may still app ear to reflect potential foregone opportunities to increase utility. We remain inter ested in these market dynamics, their underlying causes, and their potential significance for assessing the potential incremental effects of pollution control standards. We welcome comments on any aspect of this
discussion.

8.1.2.7 Modeling Electric Vehicles and Other New Vehicles

Modeling the introduction of new vehicles can be a greater challenge than modeling the existing vehicle market, because the modeler do es not have data on how many of the new vehicles consumers buy. Nevertheless, it can be po ssible to estimate the effects of new

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8-12 vehicle introduction by identifying characteristics for the new vehicles and using those in a vehicle choice model. For instance, as discussed a bove, 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 b e modeled by applying the parameters for
those features to the new vehicle characteristics.

As discussed above, some models rely on vehicle pri ce 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 vehicl e. For instance, if the change is to create an electric vehicle (EV) version of an existing mod el, 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 ve hicle driving range and cost of fueling may be two such attributes. The next requirement is in formation 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 a n 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) fr om vehicle purchase price.

Incorporating new vehicles into a vehicle choice mo del, then, requires estimates of the changes in key attributes from conventional vehicle s, 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 chang es in vehicle characteristics from any baseline model. EVs are likely to have a small er driving range between refuelings than conventional vehicles, due to the large battery cap acity 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 th e street. If an infrastructure develops for recharging vehicles with the convenience approachin g 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 co nsumers. D 

### They may have different performance or storage capacity. If sufficient dat a were available, the changes in these attributes, combined with WTP for each of the attri butes, could be used to adjust the purchase price of the baseline vehicle to estimate consumers ’ WTP for the electric version of a vehicle.

D For instance, Hidrue et al. (Hidrue, Michael K., G eorge R. Parsons, Willett Kempton, and Meryl P. Gar dner. “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 pa y $5100 more for vehicles with 95% lower emissions than the vehicles they otherwise aim to purchase.

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8-13 Greene (2001), for instance, used this approach for a model that simulates choice, not only for EVs, but also for other alternative-fuel vehicles. 52 

### In that model, he considers only one base vehicle, a passenger car, but considers the effect on WTP of fuel cost per mile, range, acceleration, and several other vehicle attributes.

Vehicle driving range has received attention becaus e 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 m ajor factor in EV acceptability, it is starting to draw attention in the research communit y.

In several studies, researchers have used stated pr eference conjoint analysis to estimate the effect of vehicle range on consumer ve hicle choice. In a conjoint analysis, consumers are given a choice between several vehicl es 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 mor e range?) provide data that can be used to
estimate the role of vehicle attributes in the cons umer’s choice. Stated preference analysis is
sometimes considered less reliable than actual mark et behavior, because what people say they will do in hypothetical situations may not match wh at they would do in actual situations. On the other hand, stated preference methods can be us ed to study goods where market data do not exist, such as future market products undergoin g development (marketing studies often use stated preference methods), or environmental go ods. Because electric vehicles are not in widespread enough use for market studies, stated pr eference studies are, at this point, one of the few options to examine consumer behavior relati ng to these vehicles.

Table 8.1-2 summarizes results from several conjoin t studies that include the effects of extending range (in the table, from 150 to 300 mile s, 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 chan ging (perhaps shrinking) in more recent
studies.

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

Study (Date) Value of extending range from 150 to 300 miles (dollar

year) Value of additional

range in 2009$ a



### Bunch et al. (1993) b 

### $7,600 (1991$) $11,100

Kavelek (1996) for California $2600 - $41,900 (1993 $) $3700 - $58,700

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8-14 Energy Commission c



### Resource Systems Group (2009) for California Energy Commission d 

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

Hess et al. (2009), using the same data as Resource Systems Group (2009) e 

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

Hidrue et al. (2011) f 

### $3776 - $10,399 (2009$) $3776 - $10,399

a Values adjusted to 2009$ using the Bureau of Econom ic Analysis GDP deflator.

b Bunch, David S., Mark Bradley, Thomas F. Golob, and Ryuichi Kitamura. “Demand for Clean-Fuel Vehicles
in California: A Discrete-Choice Stated Preference Pilot Project.” Transportation Research Part A 27A(3) (1993): 237-253. The value of range was, in their model, assumed to be the same for all people.

c Kavelek, Chris. “CALCARS: The California Conventi onal and Alternative Fuel Response Simulator.” Demand Analysis Office, California Energy Commissio n, April 1996. The variation in values is due to willingness to pay (WTP) varying by income levels a nd for one-car and two-car households. The coeffic ient 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 min ima 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 Comm ission,
June 2009.

e Hess, S., T. Adler, M. Fowler and A. Bahreinian “Th e Use of Cross-nested Logit Models for Multi-Dimens ional
Choice Processes: The Case of the Demand for Altern ative Fuel Vehicles,” Proceedings of the 2009 European Transport Conference , Leiden, Netherlands, 2009. This study uses the s ame data as the Resource Systems Group study. The coefficient on range was not stat istically significantly different from zero in thes e regressions:
t-statistics varied from 1.29 to 1.52. The variati on in values is due to willingness to pay (WTP) var ying by income levels and statistical specification. The m inima and maxima presented here represent the value s across both income categories and specifications.

f Hidrue, Michael K., George R. Parsons, Willett Kemp ton, 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 int o “gasoline vehicle-oriented” and “electric vehicle -oriented” groups. The EV-oriented group has higher WTP for a dditional range.



### Driving range may be a major factor in consumers’ d ecisions on EVs, but it is not the only attribute that may be important to potential b uyers (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 importa nt to consumers’ decisions may not perform well in predicting vehicle purchases. In addition, as mentioned above, and as seen in Table 8.1-2, it is likely that the WTP values for attribu tes 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 understa nding of the vehicles. Thus, challenges associated with predicting market shares for EVs ar e even more serious than those already serious challenges associated with predicting marke t shares for conventional vehicles.

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8-15 8.1.2.8 EPA Exploration of Vehicle Choice Modeling

In order to develop greater understanding of these models, EPA is in the process of developing a vehicle choice model. In its current form, the model assumes that the vehicle fleet and all characteristics of each vehicle, exce pt vehicle prices and fuel economy, stay the same. The model will predict changes in the vehicl e fleet, at the individual-configuration level and at more aggregated levels, in response to changes in vehicle fuel economy and price.

The draft EPA model uses a nested logit structure c ommon in the vehicle choice modeling literature, as discussed above in Chapter 8.1.2.1. “Nesting” refers to the decisiontree structure of the model, and “logit” refers to the fact that the choices are discrete (i.e., yes/no decisions about which vehicles to purchase, instead of continuous values).

The nesting involves a hierarchy of choices. In it s 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 passenge r vehicles, cargo vehicles, and ultraluxury vehicles. The next set of choices subdivide s each of these categories into vehicle type (e.g., standard car, minivan, SUV, etc.). Next, th e 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 ac ross nests, because the vehicles within a
nest are more similar to each other than vehicles i n 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 g reater 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 elastici ties and the value of fuel economy in purchase decisions) are being selected based on a review of values found in the literature on vehicle choice modeling. As discussed above, a number of studies have estimated these parameters. Those estimates, combined with some th eoretical requirements, E 

### assist in assigning values for the parameters. The model wil l allow individual users to change those parameters.

E The theory of nested logit requires that the price slopes (the change in utility as vehicle full pric e 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 th an at higher

ones.

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8-16 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 ec onomy and incorporated into the “effective price” of the vehicle. In practice, implementing t his calculation involves calculating the change in expenditures on fuel based on schedules o f VMT, vehicle survival, and fuel prices
in the future consistent with those in OMEGA. As d iscussed in Chapter 8.1.2.5, there is no consensus value for consumers’ willingness to pay f or improved fuel economy: estimates vary tremendously. The model assumes that consumer s will use some years of discounted fuel savings, with the modeler able to input both t he number of years and the discount rate to be used in the analysis.

The vehicle choice model will take as inputs an ini tial fleet of vehicles (including the initial sales and fuel economy) in the absence of s tandards, the cost of technologies added to each vehicle to comply with standards, and the chan ge in fuel economy. With the initial sales mix, for each vehicle, the model calculates a vehic le-specific constant that summarizes the value of all attributes of the vehicle other than p rice and fuel economy. This constant ensures that the model will predict changes in consumer res ponse that would result only from changes in price and fuel economy. This constant substitut es for estimating the effects of changes in all other vehicle characteristics; the underlying a ssumption is that these other vehicle characteristics do not change. F 

### For instance, it assumes that a Ford Escape will not change in size, power, or accessories; the only changes will be to its cost and its fuel economy.

The model assumes that the increase in vehicle cost associated with increased technology is fully passed through as an increase i n vehicle price, and some years of fuel savings offset this price increase. It then calcul ates changes in total fleet size and in sales mix, at the individual-configuration level and at t he level of vehicle class, due to the changes in fuel economy and vehicle prices. It also calcul ates changes in consumer surplus associated with the changes in fuel economy and vehicle prices

.
It is possible that the predicted changes in fleet mix will lead to predictions of vehicle sales for auto makers that do not meet the proposed standards, because the mix and volume of vehicles sold changed from the initial levels. To correct this problem, it would be necessary to feed the new fleet mix into OMEGA (which calcula tes costs and compliance) and get a new set of output, which could then be fed back int o the vehicle choice model. OMEGA would increase technologies, and thus costs, to imp rove compliance; those adjustments would then again affect vehicle demand. We expect that t his iterative process would converge to a fleet mix that would meet standards. Performing th is iteration requires development of an
interface between the vehicle choice model and OMEG A to ensure accurate transmission of data between the models. At this time, the vehicle choice model takes output from OMEGA,

F As explained in Section III.D of the preamble, as part of the technology cost analysis for the propos ed rule, the agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions.

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8-17 but the results of the modeling do not feed easily back into OMEGA. Building this interface is an expected part of our future modeling work.

The model is still undergoing development; EPA will seek peer review on it before it is utilized. In addition, concerns remain over the ability of any vehicle choice model to make reasonable predictions of the response of the vehic le fleet to changes in prices and vehicle
characteristics. EPA seeks comments on the use of vehicle choice modeling for predicting changes in fleet mix due to policies, and on method s to test the ability of a vehicle choice model to produce reasonable estimates of changes in fleet mix.

8.1.2.9 Summary and Additional Considerations

Consumer vehicle choice modeling in principle can p rovide a great deal of useful information for regulatory analysis, helping to ans wer 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 pot ential to describe more accurately the impact of a policy, by identifying market shift s. 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 o n 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 va lues 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 fo r characteristics associated with advanced technology vehicles, such as EVs, the willingness-t o-pay values may change over time as consumers develop more experience with the vehicles and these characteristics. Models based on current estimates may not predict well for the future.

The modeling may improve estimates of the complianc e costs of a rule. Consumers can either accept the new costs and vehicle charact eristics, or they can change which vehicles they buy. Using a vehicle choice model is likely t o reduce compliance costs: because the model allows consumers to choose among accepting th e new vehicle, buying a different vehicle, or not buying a vehicle, consumers have ad ditional options, which improves their welfare relative to the assumption that consumers w ill not change their buying behavior. .

An additional complication associated with consumer choice modeling is accurate prediction of producers’ responses to the rule. Wh ile it is possible to include auto makers’ decisions (for instance, on setting prices) into ve hicle choices, computational limits affect the richness of these models. Technology costs, while an accurate measure of the opportunity cost of resources to society, may overestimate or u nderestimate the effect on the prices that
consumers face.

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8-18 Consumer choice models can be used to calculate con sumer surplus impacts on vehicle purchase decisions. Because these values a re based on the estimates of changes in vehicle sales and fleet mix, consumer surplus measu res 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 un derstanding the role of consumer vehicle choice models in benefit-cost analysis. In particular, except for EVs, the technology cost estimates developed in this proposal take into account the costs to hold other vehicle attributes, such as size and performance, constant. In addition, the analysis assumes that the full technology costs are passed along to vehicle b uyers. With these assumptions, because welfare losses are monetary estimates of how much b uyers would have to be compensated to be made as well off as in the absence of the change

, G 

### the price increase measures the loss to the buyer. H 

### Assuming that the full technology cost gets passe d along to the buyer as an increase in price, the technology cost thus measure s 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 t he technology costs.

Given the current limitations in modeling the role of fuel economy in vehicle purchase decisions, and limitations in modeling market respo nses to the new regulations, in this proposal EPA holds constant the vehicle fleet size and mix in its calculations of the impacts of this rule, and compares the fuel and other savings that consumers will receive with the technology costs of the vehicles. EPA continues to explore options for including consumer and producer choice in modeling the impacts of fuel economy-related regulations. This effort includes further review of existing consumer vehicl e choice models, the estimates of consumers’ willingness to pay for increased fuel ec onomy, and overall effects on consumer welfare, as well as EPA’s exploration of a vehicle choice model for use in the future.

8.2 Employment Impacts

8.2.1 Introduction

Although analysis of employment impacts is not part of a cost-benefit analysis (except to the extent that labor costs contribute to costs) , employment impacts of federal rules are of

G This approach describes the economic concept of co mpensating variation, a payment of money after a ch ange that would make a consumer as well off after the ch ange as before it. A related concept, equivalent v ariation, estimates the income change that would be an altern ative to the change taking place. The difference b etween them is whether the consumer’s point of reference i s her welfare before the change (compensating varia tion) or after the change (equivalent variation). In practi ce, these two measures are typically very close tog ether.

H Indeed, it is likely to be an overestimate of the loss to the buyer, because the buyer has choices ot her than buying the same vehicle with a higher price; she co uld choose a different vehicle, or decide not to bu y a new vehicle. The buyer would choose one of those optio ns 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 co nsumer welfare, unless there are other changes to the vehi cle due to the fuel economy improvements that make the vehicle less desirable to buyers.

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8-19 particular concern in the current economic climate of sizeable unemployment. When President Obama requested that the agencies develop this program, he sought a program that would “strengthen the [auto] industry and enhance j ob creation in the United States.” 53,I 

### The recently issued Executive Order 13563, “Improving R egulation and Regulatory Review” (January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety,
and our environment while promoting economic growth, innovation, compe titiveness, and job
creation ” (emphasis added). EPA is accordingly providing p artial estimates of the effects of this proposal on domestic employment in the auto ma nufacturing and parts sectors, while qualitatively discussing how it may affect employme nt in other sectors more generally.

This proposal is expected to affect employment in t he United States through the regulated sector – the auto manufacturing industry – and through several related sectors, specifically, industries that supply the auto manuf acturing 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 Aut omobile and Light Truck Manufacturing Sector (NAICS 33611) in December 2010; this is the directly regulated sector, since it encompasses the auto manufacturers that are respons ible for complying with the proposed
standards. 54 

### Changes in light duty vehicle sales, discussed in Chapter 8.1.1, could affect employment for auto dealers. Though some of the par ts used to achieve the proposed standards are likely to be built by auto manufactur ers themselves, the auto parts manufacturing sector also plays a significant role in providing those parts, and will also be affected by changes in vehicle sales. As discussed in Chapter 5.4 of the RIA, this proposal is expected to reduce the amount of fuel these vehicle s use, and thus affect the petroleum refinery and supply industries.

When the economy is at full employment, an environm ental regulation is unlikely to have much impact on net overall U.S. employment; in stead, labor would primarily be shifted from one sector to another. These shifts in employ ment 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 wo rkers may need to be retrained or require time to search for new jobs, while shortages in som e sectors or regions could bid up wages to attract workers).

On the other hand, if a regulation comes into effec t during a period of high unemployment, a change in labor demand due to regul ation may affect net overall U.S.

I The May 21, 2010 Presidential Memorandum also requ ested that EPA and NHTSA, in developing the technic al 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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8-20 employment because the labor market is not in equil ibrium. In such a period, both positive and negative employment effects are possible. (Cite Masur and Posner, http://papers.ssrn.com/sol3/papers.cfm?abstract_id= 1920441) Schmalansee and Stavins point out that net positive employment effects are possible in the near term when the economy is at less than full employment due to the potentia l hiring of idle labor resources by the regulated sector to meet new requirements (e.g., to install new equipment) and new economic activity in sectors related to the regulated sector

. 55 

### In the longer run, the net effect on employment is more difficult to predict and will de pend 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 employ ment 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 i naccurate picture of net employment impacts if not placed in the broader economic conte xt.”

Given the time frame of this proposed rule, the sta te of the overall economy may have improved to the point where the only effect of chan ges in employment associated with the standards proposed here will be to shift workers fr om one sector to another. For example, to the extent that regulation increases the number of workers in the regulated sector (e.g., because of higher costs of producing vehicles with higher fuel economy), the opportunity cost associated with those shifts in employment will be captured in labor costs, as regulation
diverts workers from other activities in the econom y. In this situation, any effects on net employment are likely to be transitory as workers c hange jobs (e.g., some workers may need to be retrained or require time to search for new j obs, 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 empl oyment effects will occur.

A number of different approaches have been used in published literature to conduct employment analysis. This section describes some o f 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, assumptio ns about the overall state of unemployment in the economy play a major role in me asured job impacts. The inputs to the models commonly are the changes in quantities or ex penditures 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. W hich 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 ef fects, 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 redesigne d or additional parts for vehicles. In both

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8-21 instances, these would only be partial perspectives on the overall change in national employment due to Federal regulation.

8.2.2.1 Conceptual Framework for Employment Impacts in the Regulated Sector

One study by Morgenstern, Pizer, and Shih 56 

### provides a retrospective look at the impacts of regulation in employment in the regulate d sectors by estimating the effects on employment of spending on pollution abatement for f our highly polluting/regulated U.S. industries (pulp and paper, plastics, steel, and pe troleum refining) using data for six years between 1979 and 1991. The paper provides a theore tical 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 st ringent) regulation.

• Demand effect : higher production costs due to the regulation wi ll 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 co nsumers as well as the demand elasticity of industry output.”

• Cost effect : as costs go up, plants add more capital and labo r (holding other factors constant), with potentially positive effect s on employment. In the authors’ words, as “production costs rise, more inputs, incl uding labor, are used to produce the same amount of output.”

• Factor shift effect : post-regulation production technologies may be m ore or less labor-intensive (i.e., more/less labor is required per dollar of output). In the authors’ words, “environmental activities may be mo re labor intensive than conventional production,” meaning that “the amount of labor per dollar of output will rise,” though it is also possible that “cleane r operations could involve automation and less employment, for example.”

According to the authors, the “demand effect” is ex pected to have a negative effect on employment, J 

### the “cost effect” to have a positive effect on emp loyment, and the “factor shift effect” to have an ambiguous effect on employment. Without more information with respect

J As will be discussed below, the demand effect in t his proposal is potentially an exception to this ru le. While the vehicles become more expensive, they also produ ce reduced fuel expenditures; the reduced fuel cost s provide a countervailing impact on vehicle sales. As discu ssed in Preamble Section III.H.1, this possibility that vehicles may become more attractive to consumers after the p rogram poses a conundrum: why have interactions be tween vehicle buyers and producers not provided these ben efits without government intervention?

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8-22 to the magnitudes of these competing effects, it is not possible to predict the total effect environmental regulation will have on employment le vels in a regulated sector.

The authors conclude that increased abatement expen ditures generally have not caused a significant change in employment in those sectors . More specifically, their results show that, on average across the industries studied, eac h 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 advant age of carefully controlling for many possibly confounding effects in order to separ ate the effect of changes in regulatory costs on employment. It was, however, conducted fo r only four sectors. It could also be very difficult to update the study for other sectors, be cause 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 i ndustrial facilities that were considered in that study. In addition, it does not examine the e ffects of regulation on employment in sectors related to but outside of the regulated sector. Ne vertheless, the theory that Morgenstern et al. developed continues to be useful in this context.

The following discussion of additional methodologie s draws from Berck and Hoffmann’s review of employment models. 57



### 8.2.2.2 Computable General Equilibrium (CGE) Models

Computable general equilibrium (CGE) models are oft en used to assess the impacts of policy. These models include a stylized representa tion 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 m arkets: “they allow for substitution among inputs in production and goods in consumption.” Th us, if one market experiences a change, such as a new regulation, then the effects can be o bserved in all other markets. As a result, they can measure the employment changes in the econ omy 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 model s 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 assumpti ons of equilibrium in all markets and forward-looking consumers and firms, they are desig ned for examining the long-run effects of a policy but may offer little insight into its shor t-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 cons uming sectors. In that sense, like CGE models, they describe the interconnections of the e conomy. These interconnections look at

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8-23 how changes in one sector ripple through the rest o f 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; th e additional workers in those sectors then have more money to spend, which leads to more emplo yment in retail sectors. These are known as “multiplier” effects, because an initial i mpact in one sector gets multiplied through the economy. Unlike CGE models, input-output model s 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 part icular, these models do not allow for price changes – an increase in the demand for labor or ca pital 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 ove r another. The multipliers take an initial impact and can increase it substantially.

IO models are commonly used for regional analysis o f projects. In a regional analysis, the markets are commonly considered small enough th at 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 nat ional-level employment analysis, the use of input-output models requires the assumption that wo rkers flow into or out of the labor market perfectly freely. Wages do not adjust; instead, pe ople 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 res ult, IO models provide an upper bound on employment impacts. As Berck and Hoffmann note, “F or 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 prod uction processes, introductions of new technologies, or learning over time due to new regu latory requirements are also generally not captured by IO models, as they are calibrated to al ready 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 s eparately discussed by Berck and Hoffmann, some hybrid models combine some of the fe atures 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, su ch as labor being at less than full employment. Hybrid models depend on assumptions ab out 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 d epends on their success in overcoming the limitations of each of these approaches. Hybrid mo dels could potentially be used to model labor market impacts of various vehicle policy opti ons although a number of judgments need to be made about the appropriate assumptions underl ying the model as well the empirical basis for the modeling results.

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8-24 8.2.2.5 Single Sectors

It is possible to conduct a bottom-up analysis of t he partial effect of regulation on employment in a single sector by estimating the cha nge 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 proportio nal to output or expenditures. As Berck and Hoffmann note, though, “Compliance with regulat ions may create additional jobs that are not accounted for.” While such an analysis can app roximate 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 th e 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 lo nger-run employment effects. 58 

### While often a sophisticated treatment of the issues analyzed, t hese 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 r egulation have advantages and limitations. CGE models may be most appropriate fo r 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 appropria te for small-scale, short-term effects, because they assume fixed relationships across sect ors 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 bui lt into them. Single-sector models are simple and straightforward, but they are often base d 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 commo nly of specific sectors or situations, and the results may not have direct bearing for the regulation being reviewed.

8.2.3 Employment analysis of this proposal

As mentioned above, this program is expected to aff ect employment in the regulated sector (auto manufacturing) and other sectors direc tly affected by the proposal: auto parts suppliers, auto dealers, the fuel supply market (wh ich will face reduced petroleum production due to reduced fuel demand but which may see additi onal demand for electricity or other fuels), and consumers (who will face higher vehicle costs and lower fuel expenditures). In addition, as the discussion above suggests, each of these sectors could potentially have ripple

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8-25 effects in the rest of the economy. These ripple e ffects depend much more heavily on the state of the macroeconomy than do the direct effect s. 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, bot tom-up quantitative estimate of the effects of this proposal on the regulated sector (t he 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 qual itative discussion of the effects of the proposal on other sectors. Focusing quantification of emplo yment impacts on the regulated sector has some advantages over quantifying all impacts. Firs t, the analysis relies on data generated as part of the rulemaking process, which focuses on th e regulated sector; as a result, what is presented here is based on internally consistent as sumptions and estimates made in this proposal. Secondly, as discussed above, net effect s on employment in the economy as a whole depend heavily on the overall state of the ec onomy when this rule has its effects. Focusing on the regulated sector provides insight i nto 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 em ployment effects on other sectors to provide a broader perspective on the impacts of thi s rule.

As noted above, in a full-employment economy, any c hanges in employment will result from people changing jobs or voluntarily ent ering or exiting the workforce. In a fullemployment economy, employment impacts of this prop osal will change employment in specific sectors, but it will have small, if any, e ffect 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 a nalysis does not include multiplier effects, but instead focuses on employment impacts in the mo st directly affected industries. Those sectors are likely to face the most concentrated em ployment impacts. The agencies seek comment on other sectors that are likely to be sign ificantly affected and thus warrant further analysis in the final rulemaking analysis.

8.2.3.1 Employment Impacts in the Auto Industry

If vehicle sales increase, then more people will be required to assemble vehicles and their components. If vehicle sales decrease, emplo yment associated with these activities will unambiguously decrease. The sign of demand effect will depend on which of these effects dominates. Because, as described in Chapter 8.1, we have not quantified the impact on sales for this proposal, we do not quantify the demand ef fect.

8.2.3.1.1 The Cost Effect

The demand effect, discussed above, measures employ ment changes due to new vehicle sales only. The cost effect measures emplo yment impacts due to the new or additional technologies needed for vehicles to comp ly with the proposed standards.

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-26 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 mil lion 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 ins tance, it is possible to estimate the average number of workers in the light-duty vehicle manufac turing sector per $1 million spent in the sector, rather than use the ratio from another, mor e 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 estima tes are averages for the sectors, covering all the activities in those sectors; they may not be re presentative of the labor required when expenditures are required on specific activities, a s the factor shift effect (discussed below) indicates. For instance, the ratio for the motor v ehicle manufacturing sector represents the ratio for all vehicle manufacturing, not just for f uel efficiency improvements. In addition, these estimates do not include changes in sectors t hat supply these sectors, such as steel or electronics producers. They thus may best be viewe d as the effects on employment in the auto sector due to the changes in expenditures in t hat sector, rather than as an assessment of all employment changes due to these changes in expe nditures.

Some of the costs of this proposal will be spent di rectly 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 expend itures 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 technol ogies to the vehicles to comply with the regulation.

We use several public sources for estimates of empl oyment per $1 million expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment Requirements Matrix (ERM), 59 

### which provides direct estimates of the employment per $1 million in sales of goods in 202 sectors. The most recent estimates, used here, are from 2008 (adjusted to 2009$). The tables used here are adju sted to remove the employment effects of imports through use of the ratio of domestic produc tion to domestic sales, described above, of 0.667. The values reported are for Motor Vehicle M anufacturing (NAICS 3361) and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Annual Survey of Manufactures 60 

### (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 t han 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 esti mates of employees/$1 million in expenditures, the ASM separately provides number of employees and value shipments; the direct employment estimates here are the ratio of t hose values. The data in the ASM are updated annually, except for years when the full Ec onomic Census occurs. The tables presented here use data from 2009. As with the ERM , we adjust for the ratio of domestic

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-27 production to domestic sales. The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty Motor Vehic le Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Economic Census includes all large companies an d 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 annu al. The values presented here use data from 2007 (adjusted to 2009$), with the domestic pr oduction-to-sales adjustment. The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), and Moto r Vehicle Parts Manufacturing (NAICS 3363).

Table 8.2-1 provides the values, either given (BLS) or calculated (ASM, Economic Census) for employment per $1 million of expenditur es, all based on 2009 dollars, though the underlying data come from different years (which ma y account for some of the differences). The different data sources provide similar magnitud es for the estimates for the sectors. Parts manufacturing appears to be more labor-intensive th an vehicle manufacturing; light-duty vehicle manufacturing appears to be slightly less l abor-intensive than motor vehicle manufacturing as a whole.

Table 8.2-1 Employment per $1 Million Expenditures (2009$) in the Motor Vehicle Manufacturing Sector*

Source Sector Ratio of workers per $1 million expenditures Ratio of workers per $1

million expenditures, adjusted for domestic vs. foreign production BLS ERM Motor Vehicle Mfg 0.834 0.556

ASM Motor Vehicle Mfg 0.824 0.549

ASM Light Duty Vehicle Mfg 0.757 0.505

Economic
Census Motor Vehicle Mfg 0.674 0.449

Economic
Census Light Duty Vehicle Mfg 0.610 0.407

BLS ERM Motor Vehicle Parts Mfg 3.073 2.049

ASM Motor Vehicle Parts Mfg 3.093 2.063

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8-28 Economic
Census Motor Vehicle Parts Mfg 2.749 1.833

BLS ERM refers to the U.S. Bureau of Labor Statisti cs’ Employment Requirement Matrix. ASM refers to t he 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 i ndustry has changed: automation and improved production methods have led to signifi cant productivity increases. The BLS ERM, for instance, provided estimates that, in 1993 , 1.52 workers were needed per $1 million of 2000$, but only 0.83 workers by 2008 (in 2000$). 61 

### Because the ERM is available annually for 1993-2008, we used these data to estim ate productivity improvements over time. We regressed logged ERM values on year (to estimate percent change per year) for both the Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing sectors. The results suggest a 4.4 percent per year productivity improve ment in the Motor Vehicle Manufacturing Sector, and a 3.6 percent per year improvement in t he Motor Vehicle Parts Manufacturing Sector. We then used the regression relationship t o project the ERM through 2025. In the results presented below, these projected values (ad justed to 2009$) were used directly for the BLS ERM estimates. For the ASM, we used the ratio of the projected value in the future to the projected value in 2009 (the base year for the ASM); for the Economic Census estimates, we used the ratio of the projected value in the fut ure to the projected value in 2007 (the base year for that estimate). As noted above, we adjust ed the estimate of workers per vehicle for the demand effect in Chapter Error! Reference source not found. , above, using the productivity improvement for the Motor Vehicle Manu facturing sector; because the estimate of workers per vehicle for the demand effect was ba sed on data from 2001 to 2010, we used the projected value of the ERM in 2005 as the denom inator of the ratio. This is a simple way to examine the relationship between labor required and expenditure and we seek comment on refining this method.

Table 8.2-2 shows the cost estimates developed for this rule, discussed in Chapter 5. The maximum value in Table 8.2-2 for employment imp acts per $1 million expenditures (after accounting for the share of domestic product ion) is 2.049 in 2009 if all the additional costs are in the parts sector; the minimum value is 0.407 in 2009, if all the additional costs are in the light-duty vehicle manufacturing sector: th at is, the range of employment impacts is between 0.4 and 2 additional jobs per $1 million ex penditures in the sector in 2009. The results in Table 8.2-2 include the productivity adj ustment described above.

While we estimate employment impacts beginning with the first year of the standard (2017), some of these job gains may occur earlier a s auto manufacturers and parts suppliers hire staff in anticipation of compliance with the s tandard. Also, because of the uncertainty associated of projecting average labor required ass ociated with the cost of production, we limit the projection to 2020.

Table 8.2-2 Employment due to Cost Effect in the Mo tor Vehicle Manufacturing Sector

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8-29 Year Costs (before

adjustment for domestic proportion of production) ($Millions) Minimum

employment effect (if all expenditures are in the parts sector) Maximum

employment effect (if all expenditures are in the light duty vehicle mfg sector) 2017 $ 2,300 600 3,600 2018 $ 4,656 1,200 7,000 2019 $ 6,507 1,600 9,400 2020 $ 8,467 1,900 11,800 2021 $ 11,878 2,600 15,900 2022 $ 19,340 4,100 25,000 2023 $ 25,036 5,000 31,200 2024 $ 30,738 5,900 37,000 2025 $ 33,561 6,200 39,000

8.2.3.1.2 The Factor Shift Effect

The factor shift effect looks at the effects on emp loyment due to changes in labor intensity associated with a regulation. As noted a bove, the estimates of the cost effect assume constant labor per $1 million in expenditures, thou gh 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 man ufacturers could use to comply with the proposed standards. For a subset of the technologi es, though, EPA-sponsored research (discussed in Chapter 3.2.1.1 of the Joint TSD) whi ch 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 t echnologies to their individual components and looking at the differences in materi als and labor needs in moving from the conventional to the new technologies. 62 

### For instance, the analysis compared all the parts and labor associated with an 8-speed automatic transmis sion to those needed for a 6-speed
automatic transmission.

Because labor cost was one of the sources of differ ences 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 com pliance with the proposed standards. An increase in labor cost for the new technology indic ates an increase in the labor needed for the new technology compared to the baseline technology. For instance, an 8-speed transmission requires $15.11 more in labor costs than a 6-speed transmission (as accounted for in EPA’s cost estimates for the proposed rule). Dividing th e labor cost by a wage per hour estimate provides an estimate of the additional hours (and t hus the additional labor) needed for the new

### Summary of interagency working comments received in draft NPRM under EO 12866

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8-30 technology compared to the baseline technology. As with labor cost, an increase in labor hours per technology indicates greater employment n eeds for the new technologies. For this conversion, a weighted average wage rate (90 percen t of the average wage in the Motor Vehicle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle Manufacturing Sector) of $46.36/hour in 2015, using 2008 dollars (the unit of analysis for the FEV study). For the change from a 6-speed to an 8- speed transmission, we thus estimate an additional 0.33 hours of labor per transmission.

Error! Reference source not found. shows the changes in labor hours in moving from baseline to new fuel-saving technologies for t echnologies in the FEV study. It indicates that, in switching from the baseline to the new tec hnologies, labor use per technology increased: the fuel-saving technologies use more l abor than the baseline technologies. For a subset of the technologies likely to be used to mee t the standards in this proposal, then, the factor shift effect increases labor demand, at leas t in the short run; in the long run, as with all technologies, the cost structure is likely to chang e due to learning, economies of scale, etc. The technologies examined in this research are, how ever, only a subset of the technologies that auto makers may use to comply with the standar ds proposed in this program. As a result,
these results cannot be considered definitive evide nce that the factor shift effect increases employment for this rule. We therefore do not quan tify the factor shift effect for this proposal.

8.2.4 Effects on Employment for Auto Dealers

The effects of the proposed standards on employment for auto dealers depend principally on the effects of the standards on ligh t duty vehicle sales. In addition, auto dealers may be affected by changes in maintenance and servi ce costs. Increases in those costs are likely to increase labor demand in dealerships.

Although this proposal predicts very small penetrat ion of advanced technology vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. As discussed in Chapter 8.1.2.7, consumers may find some characteristics of electric vehicles and plug-in hybrid electric vehicles, such as the a bility to fuel with electricity rather than gasoline, attractive; they may find other character istics, 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 vehi cles. 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 availab le.

8.2.5 Effects on Employment in the Auto Parts Sector

As discussed in the context of employment in the au to industry, some vehicle parts are made in-house by auto manufacturers; others are mad e by independent suppliers who are not directly regulated, but who will be affected by the proposed standards as well. The additional expenditures on technologies are expected to have a positive effect on employment in the

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-31 parts sector as well as the manufacturing sector; t he breakdown in employment between the two sectors is difficult to predict. The effects o n the parts sector also depend on the effects of the proposed standards on vehicle sales and on the labor intensity of the new technologies, qualitatively in the same ways as for the auto manu facturing sector.

8.2.6 Effects on Employment for Fuel Suppliers

In addition to the effects on the auto manufacturin g and parts sectors, these rules will result in changes in fuel use that lower GHG emissi ons. Fuel saving, principally reductions in liquid fuels such as gasoline and diesel, will affe ct employment in the fuel suppliers industry sectors throughout the supply chain, from refinerie s to gasoline stations. To the extent that the proposed standards result in increased use of e lectricity or other new fuels, employment effects will result from providing these fuels and developing the infrastructure to supply them

to consumers.

Expected petroleum fuel consumption reductions can be found in Chapter 5.3. While this reduced consumption represents fuel savings fo r 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 supplier s throughout the petroleum fuel supply chain, from the petroleum refiners to the gasoline stations, is likely to result in reduced employment in these sectors.

This rule is also expected to lead to increases in electricity consumption by vehicles, as discussed in Chapter 5.3. This new fuel may req uire additional infrastructure, such as electricity charging locations. Providing this inf rastructure will require some increased employment. In addition, the generation of electri city is likely to require some additional
labor. We have insufficient information at this ti me 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 w ith reduced demand for petroleum fuels.

8.2.7 Summary

The primary employment effects of this proposal are expected to be found throughout several key sectors: auto manufacturers, auto deale rs, auto parts manufacturing, and fuel production and supply.

These proposed standards initially take effect in m odel year 2017, a time period sufficiently far in the future that the current sus tained 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 empl oyment 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 th ose impacts, because they are more difficult to quantify with reasonable accuracy, particularly so far into the future.

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-32 References

1 

### See, for instance, Gron, Ann, and Deborah Swenson, 2000. “Cost Pass-Through in the U.S. Automobile Market,” Review of Economics and Statist ics 82: 316-324 (Docket EPA-HQOAR-2010-0799).

2 

### Insurance Information Institute, 2008, “Average Ex penditures for Auto Insurance By State, 2007-2008,” http://www.iii.org/media/facts/statsbyi ssue/auto/, accessed 8/22/11 (Docket EPA-HQ-OAR-2010-0799).

3 

### U.S. Department of Energy, 2011. “Transportation and the Economy,” Chapter 10 in “Transportation Energy Data Book,” http://cta.ornl. gov/data/tedb30/Edition30_Chapter10.pdf, accessed 8/22/11, Table 11 (Docket EPA-HQ-OAR-2010- 0799).

4 

### Solheim, Mark, 2006 “State Car Tax Rankings,” http://www.kiplinger.com/features/archives/2006/04/ cartax.html, accessed April 23, 2009 (Docket EPA-HQ-OAR-2009-0472-0010) (Docket EPA-HQ-O AR-2010-0799).

5 

### U.S. Census Bureau, "Table 2. Resident Population of the 50 States, the District of Columbia, and Puerto Rico: 2010 Census" http://2010 .census.gov/news/presskits/apportionment/apport.html .

6 

### “National Auto Loan Rates for July 21, 2011,” http://www.bankrate.com/finance/auto/national-auto- loan-rates-for-july-21-2011.aspx, accessed 7/26/11 (Docket EPA-HQ-OAR-2010-0799).

7 

### Goldberg, Pinelopi Koujianou, “Product Differentia tion and Oligopoly in International Markets: The Case of the U.S. Automobile Industry, ” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799); Goldberg, Pinel opi Koujianou, “The Effects of the Corporate Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics 46(1) (March 1998): 1-33 (Docket EPA-HQ-OAR-2010 -0799).

8 

### Greene, David L., K.G. Duleep, Doug Elliott, and S anjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy In formation Administration,” prepared by the Oak Ridge National Laboratory for the U.S. D epartment of Energy under contract No. DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-079 9); 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,” En ergy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799).

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-33 9 

### McManus, Walter M., “Can Proactive Fuel Economy St rategies Help Automakers Mitigate Fuel-Price Risks?” University of Michigan Transpor tation Research Institute, September 14, 2006 (Docket EPA-HQ-OAR-2010-0799).

10 

### Berry, Steven, James Levinsohn, and Ariel Pakes, “ Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799); Berry, Steven, James Levinsohn, and Ariel Pa kes, “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-2010-07 99).

11 

### Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes” American Economic
Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799 ).

12 

### Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

13 

### Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-2 010- 0799).

14 

### Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journ al of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799)

.

15 

### E.g., Bento, Antonio M., Lawrence H. Goulder, Emer ic 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-2010-07 99); Train, Kenneth E., and Clifford Winston, “Vehicle Choice B ehavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

16 

### E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).

17 

### Greene, David L., K.G. Duleep, Doug Elliott, and S anjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy In formation Administration,” prepared by the Oak Ridge National Laboratory for the U.S. D epartment of Energy under contract No. DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-079 9).

18 

### NERA Economic Consulting, “Evaluation of NHTSA’s B enefit-Cost Analysis of 2011- 2015 CAFE Standards,” 2008, available at

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-34 http://www.heartland.org/policybot/results/23495/Ev aluation_of_NHTSAs_BenefitCost_Anal ysis_Of_20112015_CAFE_Standards.html (Docket EPA-H Q-OAR-2010-0799).

19 

### E.g., Train, Kenneth E., and Clifford Winston, “Ve hicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

20 

### E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).

21 

### E.g., Bento, Antonio M., Lawrence H. Goulder, Emer ic 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-2010-07 99).

22 

### Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, 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-2010-079 9); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Poll ution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5 /12/09 (Docket EPA-HQ-OAR-2010- 0799).

23 

### NERA Economic Consulting, “Appendix B: New Vehicl e Market Model,” “Impacts of the
California Greenhouse Gas Emission Standards on Mot or Vehicle Sales,” comments submitted to the U.S. Environmental Protection Agen cy at Regulations.gov, document number EPA-HQ-OAR-2006-0173-9053.1 (Docket EPA-HQ- OAR-2010-0799).

24 

### E.g., Train, Kenneth E., and Clifford Winston, “Ve hicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799)

25 

### E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).

26 

### Greene, David L., Philip D. Patterson, Margaret Si ngh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Incre ased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799).

27 

### Rex, Emma, and Henrikke Baumann (2007). “Beyond e colabels: what green marketing can learn from conventional marketing.” Journal of Cleaner Production 15: 567-576 (Docket EPA-HQ-OAR-2010-0799).

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-35 28 

### Whitefoot, Kate, Meredith Fowlie, and Steven Skerl os, “Product Design Responses to Industrial Policy: Evaluating Fuel Economy Standar ds Using an Engineering Model of Endogenous Product Design,” Energy Institute at Haa s Working Paper 214, University of California Energy Institute, February 2011 (Docket EPA-HQ-OAR-2010-0799).

29 

### Berry, Steven, James Levinsohn, and Ariel Pakes, “ Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).

30 

### Goldberg, Pinelopi Koujianou, “Product Differentia tion and Oligopoly in International Markets: The Case of the U.S. Automobile Industry, ” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799).

31 

### Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with Flexible Substitution Patterns,” Journal of Econome trics 89 (1999): 109-129 (Docket EPAHQ-OAR-2010-0799); Brownstone, David, David S. Bunc h, and Kenneth Train, “Joint Mixed Logit Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” Transportation Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2010-0799) ; Greene David L., “TAFV Alternative Fuels and Vehicl es Choice Model Documentation,” prepared by the Oak Ridge National Laboratory for t he U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., K. G. Duleep, and Walter McManus, “Future Potential of Hybrid and Diesel Powertrains in the U.S. Light-Duty Vehicle Market,” prepared by the Oak Ridge National Laboratory for t he U.S. Department of Energy, August 2004 (Docket EPA-HQ-OAR-2010-0799).

32 

### Petrin, Amil, “Quantifying the Benefits of New Pro ducts: The Case of the Minivan,” Journal of Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799 ); Berry, Steven, James Levinsohn, and Ariel Pakes, “D ifferentiated Products Demand Systems
from a Combination of Micro and Macro Data: The Ne w Car Market,” Journal of Political Economy 112 (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799).

33 

### Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48 (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

34 

### Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, 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-2010-079 9); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Poll ution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5 /12/09 (Docket EPA-HQ-OAR-2010- 0799).

35 

### Greene, David L., Philip D. Patterson, Margaret Si ngh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Incre ased Fuel Economy,” Energy Policy 33

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-36 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799); Fe ng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Pollution Polici es,” National Bureau of Economic Analysis Working Paper 11553, available at http://e conweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010-0799); Gr eene, David L., “Feebates, Footprints and Highway Safety,” Transportation Rese arch Part D 14 (2009): 375-384.

36 

### E.g., Austin, David, and Terry Dinan, “Clearing th e Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes, ” Journal of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799)

.

37 

### Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2010-079 9).

38 

### E.g., Espey, Molly, and Santosh Nair, 2005. “Autom obile Fuel Economy: What Is It Worth?” Contemporary Economic Policy 23: 317-323 ( Docket EPA-HQ-OAR-2010-0799).

39 

### Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 1593-1594 (Docket EPA-HQ-OAR-2010-0799).

40 

### E.g., Goldberg, Pinelopi Koujianou, “Product Diffe rentiation and Oligopoly in International Markets: The Case of the U.S. Automobile Industry, ” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799).

41 

### E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).

42 

### Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. “Pain at the Pump: How Gasoline Prices Affect Automobile Purchasing in New and Used Markets,” working paper, http://web.mit.edu/knittel/www/papers/gaspap er_latest.pdf (accessed 11/1/11) (Docket EPA-HQ-OAR-2010-0799); Li, Shanjun, Christopher Tim mins, and Roger H. von Haefen,
2009. “How Do Gasoline Prices Affect Fleet Fuel Ec onomy?” American Economic Journal: Economic Policy 1(2): 113-137; Congressional Budget Office (2008) (Docket EPA-HQOAR-2010-0799); Congressional Budget Office (2008) . Effects of Gasoline Prices on Driving Behavior and Vehicle Markets . The Congress of the United States, Pub. No. 2883 (Docket EPA-HQ-OAR-2010-0799); West. Sarah (2007). “The Effect of Gasoline Prices on the Demand for Sport Utility Vehicles.” Working Pa per. Macalester College, http://www.macalester.edu/~wests/SarahWestMEA2007.pd f, accessed 1/25/10 (Docket EPAHQ-OAR-2010-0799).

43 

### Greene, David L., and Jin-Tan Liu, 1988. “Automot ive Fuel Economy Improvements and Consumers’ Surplus,” Transportation Research Part A 22A (3): 203-218 (Docket EPA-HQOAR-2010-0799).

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-37 44 

### Greene, David L. (2010). “How Consumers Value Fue l Economy: A Literature Review.” EPA Report EPA-420-R-10-008 (Docket EPA-HQ-OAR-2010 -0799).

45 

### Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S. Automobile Industry,” http://faculty.msb.edu/jpg72/ Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket EPA-HQ-OAR-2010-0799).

46 

### Gramlich, Jacob, “Gas Prices and Endogenous Produc t Selection in the U.S. Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_Graml ichJacob.pdf , accessed 11/1/11 (Docket EPA-HQ-OAR-2010-0799); McManus, Walter, 2007. “The Impact of Attribute-Based Corporate Average Fuel Economy (CAFE) Standards: P reliminary Results.” Ann Arbor, MI: University of Michigan Transportation Research Inst itute, Report No. UMTRI-2007-31 (Docket EPA-HQ-OAR-2010-0799).

47 

### For instance, Kleit, Andrew N., “Impacts of Long-R ange Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2010-0799); Austin, David, and Terry Dinan, “Cleari ng the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and Management 50 (2005): 562- (Docket EPA-HQ-OAR-2010- 0799); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of the Corporate Average Fuel Economy Standard,” Resou rces for the Future Paper RFF DP 10- 50 (December 2010), http://www.rff.org/RFF/Document s/RFF-DP-10-50.pdf) (Docket EPAHQ-OAR-2010-0799)Jacobsen, Mark, 2010. “Evaluating U.S. Fuel Economy Standards in a Model with Producer and Household Heterogeneity,” w orking paper, at http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf (ac cessed 11/1/11) (Docket EPA-HQOAR-2010-0799) .

48 

### Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journ al of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

49 

### IEA. 2007. “Mind the Gap: Quantifying Principal-Ag ent Problems in Energy Efficiency

. ” Paris, France: International Energy Agency (Docket EPA-HQ-OAR-2010-0799); Jaffe, Adam B., Richard G. Newell, and Robert N. Stavins (2001) . "Energy Efficient Technologies and Climate Change Policies: Issues and Evidence." In C limate Change Economics and Policy

, Toman, Michael A., ed., Washington, D.C.: Resources for the Future. p.171-181 (Docket EPA-HQ-OAR-2010-0799); Metcalf, Gilbert E., and Kev in A. Hassett (1999). "Measuring the Energy Savings From Home Improvement Investment s: Evidence From Monthly Billing
Data." The Review of Economics and Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010- 0799); Tietenberg, T. (2009). “Reflections – Energ y Efficiency Policy: Pipe Dream or Pipeline to the Future?” Review of Environmental E conomics and Policy 3(2): 304-320 (Docket EPA-HQ-OAR-2010-0799).

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-38 50 

### Helfand, Gloria, and Ann Wolverton (2011). “Evalu ating the Consumer Response to Fuel Economy: A Review of the Literature.” Internation al Review of Environmental and
Resource Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799).

51 

### Jaccard, Mark. “Paradigms of Energy Efficiency’s Cost and their Policy Implications: Déjà Vu All Over Again.” Modeling the Economics of Greenhouse Gas Mitigation: Summary of a Workshop, K. John Holmes, Rapporteur. National Academies Press, 2010. http://www.nap.edu/openbook.php?record_id=13023&pag e=42 (Docket EPA-HQ-OAR2010-0799)

52 

### Greene David L., “TAFV Alternative Fuels and Vehic les Choice Model Documentation,” prepared by the Oak Ridge National Laboratory for t he U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799).

53 

### President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency Standards. The White House, Office of the Press Se cretary, May 21, 2010. http://www.whitehouse.gov/the-press-office/presiden tial-memorandum-regarding-fuelefficiency-standards

54 

### U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed on August 9, 2011.

55 

### Schmalensee, Richard, and Robert N. Stavins. “A G uide to Economic and Policy Analysis of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011 (Docket EPA-HQ-OAR-2010-0799).

56 

### Morgenstern, Richard D., William A. Pizer, and Jhi h-Shyang Shih. “Jobs Versus the Environment: An Industry-Level Perspective.” Jour nal of Environmental Economics and Management 43 (2002): 412-436 (Docket EPA-HQ-OAR-2010-0799).

57 

### Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental and Natural Resource Policy.” Environmental and Re source Economics 22 (2002): 133-156 (Docket EPA-HQ-OAR-2010-0799).

58 

### Berman, Eli, and Linda T. Bui, (2001) “Environment al Regulation and Labor Demand: Evidence from the South Coast Air Basin,” Journal of Public Economics , 79, 265 – 295 (Docket EPA-HQ-OAR-2010-0799)

.

59 

### http://www.bls.gov/emp/ep_data_emp_requirements.ht m

60 

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

61 

### http://www.bls.gov/emp/ep_data_emp_requirements.ht m; this analysis used data for sectors 88 (Motor Vehicle Manufacturing) and 90 (Motor Vehi cle Parts Manufacturing) from “Chain-

### Summary of interagency working comments received in draft NPRM under EO 12866

review. These are for your consideration and are su bject to further policy review.

8-39 weighted (2000 dollars) real domestic employment re quirements table. . . adjusted to remove imports.”

62 

### FEV, Inc. “Light-Duty Technology Cost Analysis: P ower-split and P2 HEV Case Studies,” Report FEV07-069-303, October 10, 2011 (Docket EPA- HQ-OAR-2010-0799).Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

1 

### Below is a summary of interagency working comments received duri ng EO 12866 and EO 13563 review of the draft rule “Joint Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fu el Economy Standards”. They are for your consideration and subject to further policy review.

1) Interagency commenters recommend that NHTSA consider the following edits to the

maximize net benefits discussion:

“Whether the standards maximize net benefits ha s thus been a touchstone, in the past for NHTSA’s consideration of economic practicab ility. Executive Order 12866, as amended by Executive Order 13563, states that agen cies should “select, in choosing among alternative regulatory approaches, those appr oaches that maximize net benefits...,” Under
the statute, however, NHTSA must consider situations in whic h economic feasibility

constrains its choices. In this case, the NHTSA balanci ng of the statutory factors suggests that the maximum feasible stringency for this rulemaking points to another level
besides the modeled net benefits maximum, a nd consideration of such a situation is well within the guidance provided by EO’s 12866 and 13563.[1]

The agency's consideration of economic prac ticability depends on a number of factors. Expected availability of capital to make investments in new technologies matters; manufacturers’ expected ability to sell vehicles with new technologies matters; likely
consumer choices matter; and so forth. NHT SA’s analysis of th e impacts of this rulemaking does incorporate assumptions to cap ture aspects of consumer preferences, vehicle attributes, safety, and other factors relevant to an impact estimate. It is well within the agency’s discretion to deviate fr om a modeled net benefits maximum in the face of evidence of economic impracticability, if the agency concludes that the modeled

net benefits maximum would not represent the maximum feasible level for future CAFE standards. Economic practicabil ity is a complex factor, and like the other factors must

also be considered in the context of th e overall balancing and EPCA’s overarching purpose of energy conservation. “

2) Interagency commenter recommends the followi ng edits to the flexibilities section in

EPA Preamble:Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

2 

### “In the context of the incr easing stringency of standard s starting in MY 2017, as discussed, EPA is not proposing a continuati on of the TLAAS. TLAAS was available to firms with a wide range of U.S. sales volumes (between 5,000 and 400,000 in MY2009). One company with U.S. sales on the order of 25,000 vehicles per year has indicated that

it believe s that the CO2 standards in today’s pr oposal for MY2017-2025 would present significant technical ch allenges for their company, due to the relatively small volume of products it sells in the U.S., limited ability to average across its limited line fleet, and the performance-oriented nature of its vehicles. This firm indi cated that absent access several years in advance to CO2 credits that it could purchase from other firms, this firm would need to change significantly the types of products it currently market s in the U.S. beginning in model year 2017, even if it add s substantial CO2

- reducing technology to its vehicles. EPA requests comment on the potentia l need to include additional flexibilities for companies with U.S. vehicle sales on the order of 25,000 units per year, and what types of additional flexibilities would be appropr iate. Potential flexibilities could include an extension of the TLAAS program for lower volume companies, or a one-to-three year delay in the applicable model year standa rd (e.g., the proposed MY2017 standards could be delayed to begin in MY2018, MY2019, or MY2020). Commenters suggesting that additional flexibilities may be needed ar e encouraged to provide EPA with data supporting their suggested flexibilities.”

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-1 Below is a summary of interagency work ing comments received during EO 12866 and
EO 13563 review of the draft rule “Joint Ru lemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporat e Average Fuel Economy Standards”. They are for your consideration and are su bject to further policy review.



## Chapter 8: Vehicle Sales and Employment Impacts

8.1 Vehicle Sales Impacts

8.1.1 Vehicle Sales Impacts and Payback Period

Predicting the effects of this rule on vehicles entails compar ing two competing effects. On the one hand, as a result of this rule, the vehicles will become more expensive, which would, by itself, be expected to discourage sale s. On the other hand, th e vehicles will have improved fuel economy and thus lower operating costs, producing lower total costs over the life of vehicles, which makes them more attrac tive to consumers. Which of these effects dominates for potential vehicle buyers when th ey are considering a purchase will determine the effect on sales. However, assessing the net effect of these tw o competing effects is complex and uncertain, as it rests on how c onsumers value fuel savings at the time of purchase and the extent to which manufacturers and dealers reflect th em in the purchase price. The empirical literature does not provide clear evidence on whether consumers fully consider the value of fuel savings at th e time of purchase. It also generally does not speak to the efficiency of manufacturing and dealer prici ng decisions. Thus, for the proposal we do not provide quantified estimates of potential sales impacts. Rather, we solicit comment on the
issues raised here and on met hods for estimating the effect of this rule on vehicle sales.

For years, consumers have been gaining expe rience with the benefits that accrue to them from owning and operating vehicles with greater fuel efficiency. Many households already own vehicles with a fairly wide range of fuel economy, and thus already have an opportunity to learn about the value of fuel economy on their own. Among two-vehicle households, for example, the least fuel-efficient vehicle averages just over 22 mpg (EPA test rating), and the range between this and the fu el economy of their other vehicle averages nearly 7 mpg. Among households that own 3 or more vehicles, the typical range of the fuel economy they offer is much wider. Consum er demand may have shifted towards such vehicles, not only because of higher fuel prices but also if many consumers are learning about the value of purchases based not only on initial costs but also on the to tal 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, particul arly given the new fuel economy labels that clarify potential economic effects and shoul d therefore reinforce that learning..

### Chapter 8

8-2 Today’s proposed rule, combined with th e new and easier-to-u nderstand fuel economy label required to be on all new vehicles begi nning in 2012, may increase sales above baseline levels by hastening this very t ype of consumer learning. As mo re consumers experience, as a result of the rule, the savings in time and expe nse from owning more fuel efficient vehicles, demand may shift yet further in the direction of the vehicles mandated under the rule. This social learning can take place both within and across households, as consumers learn from one
another.

First and most directly, the time and fuel sa vings associated with operating more fuel efficient vehicles may be more salient to in dividuals who own them, which might cause their subsequent purchase decisions to shift closer to minimizing th e total cost of ownership over
the lifetime of the vehicle.

Second, this appreciation may spread acr oss households through word of mouth and
other forms of communications.

Third, as more motorists experience the time and fuel savings associated with greater fuel efficiency, the price of used cars will bette r reflect such efficiency, further reducing the cost of owning more efficient vehicles for the buyers of new vehicles (s ince the resale price will increase).

If these induced learning effects are str ong, the rule could pote ntially increase total vehicle sales over time. It is not possible to quantify these learning effects years in advance and that effect may be speeded or slowed by other factors that enter into a consumer’s valuation of fuel efficiency in selecting vehicles . The possibility that th e rule will (after a lag for consumer learning) increase sales need not rest on the assumption that automobile manufacturers are failing to pursue profitable opportunities to supply the vehicles that consumers demand. In the absence of the rule, no individual automob ile manufacturer would find it profitable to move toward the more efficient vehicles mandated under the rule. In particular, no individual company can fully internalize the future boost to demand resulting from the rule. If one company were to make more efficient vehicles, counting on consumer learning to enhance demand in the future, that company would capture on ly a fraction of the extra sales so generated, because the learning at issue is not specific to any one company's fleet. Many of the extra sales would accr ue to that company's competitors.

In other words, consumer learning about the be nefits of fuel effici ent vehicles involves positive externalities (spillovers) from one company to the others A 

### . These positive externalities may lead to benef its for manufacturers as a whol e. We emphasize that this discussion has been tentative and qualified. To be sure, social learning of related kinds has
been identified in a number of contexts. 1 

### Comments are invited on the discussion offered here, with particular reference to any relevant empirical findings.

A Industrywide positive spillovers of this type are hardly unique to this situation. In many industries, companies form trade associations to promote industry-wide public goods. For example, merchants in a given locale may band together to promote tourism in that locale. Antitr ust law recognizes that this type of coordination can increase output.

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-3 In previous rulemakings, EPA and NHTS A 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 ve hicle prices. The up-fr ont 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 pr emiums, the increase in loan payments that buyers would face, and a higher resale value, w ith all of these factors due to the higher upfront cost of the vehicle. Those calculations re sulted in an adjusted increase in costs to
consumers. We then assumed that consumers c onsidered the present value of five years of fuel savings in their vehicle purchase, which is consistent with the length of a typical new light-duty vehicle loan, and is similar to the average time that a new vehicle purchaser holds
onto the vehicle. B 

### The present value of fuel savings was subtracted from technology costs to get a net effect on vehicle cost of ownership. We then used a short-r un demand elasticity of - 1 to convert a change in price into a ch ange in quantity demanded of vehicles. C 

### An elasticity of -1 means that a 1% increase in price leads to a 1% reductio n in quantity sold. In the vehicle sales analyses, if five years of fuel savings outweighed the ad justed technology costs, then vehicle sales were predicted to increase; if the fuel savings were smaller than the adjusted technology costs, sales would decrease, compared to a world without the standards.

We do not here present a vehicle sales analys is using this approach. This rule takes effect for MY 2017-2025. In the intervening ye ars, it is possible that the assumptions underlying this analysis, as well as market conditions, might change. Instead, we present a payback period analysis to estimate the number of years of fuel savings needed to recover the up-front costs of the new technologies. In other words, the payback period identifies the break-even point for new vehicle buyers. The ca lculation of the payback period is discussed in DRIA Chapter 5.3. Table 1 Table 1 shows the estimated payback period for MY 2021 and 2025. We present MY 2021 because it is the last year before the mid-term review impacts, if any, will take place, and MY 2025 because it is the last year of the program. The payback period in 2021 is shorter than that in 2025, becau se the technologies required to meet the proposed MY 2021 standards are more cost-effec tive than those for MY 2025. In all cases, the payback periods are less than 4 years.

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

Model Estimated Estimated Estimated Estimated

B In this proposal, the 5-year payback assumption corres ponds to an assumption that vehicle buyers take into account between 30 and 50 percent of the present value of lifetime vehicl e fuel savings (with the variation depending on discount rate, model year, and car vs. truck).

C For a durable good such as an auto, the elasticity may be smaller in the long run: though people may be able to change the timing of their purchase wh en price changes in the short run, they must eventually make the investment. We request comment on whether or when a long-run elasticity should be used for a rule that phases in over time, as well as how to find good estimates for the long –run elasticity.

### Chapter 8

8-4 Year Payback Period
for Cash Purchase, 3%
Discount Rate Payback Period
for Cash Purchase, 7%
Discount Rate Payback Period
for Purchase on Credit, 3%
Discount Rate Payback Period
for Purchase on Credit, 7%
Discount Rate

2021 2.7 2.9 2.9 2.8

2025 3.7 3.9 3.9 3.9

We welcome comments on all aspects of this discussion, including the full range of considerations and assumptions which infl uence market behavior and outcomes; and
associated uncertainties. We welcome co mments on the methodology described here for quantitative estimates of the effects of this pr oposal on sales and its appropriateness for this rulemaking; we also welcome proposals for other methods.

We here provide further detail on some of the assumptions included in the payback analysis. We seek comment on all these factors as well. The analysis starts with the increase in costs estimated by the OMEGA model. We a ssume that these costs are fully passed along to consumers. This assumption is appropriate for cost increases in perfectly competitive markets. In less than perfectly competitiv e markets, though, it is possible that the cost increase is split between consumers and automakers, and possibly suppliers, and the price may not increase as much as costs. 2 

### Thus, the assumption of fu ll cost pass-through is possibly
an overestimate.

The next step in the analysis is to adjust this cost increase for other effects on the consumer. The higher vehicle pri ce is likely to lead to an incr ease in sales tax, insurance, and vehicle financing costs.

The increase in insurance costs is estimated from the average value of collision plus comprehensive insurance as a proportion of average new vehicle price. Collision plus comprehensive insurance is the portion of insu rance costs that depend on vehicle value. The
Insurance Information Institute 3 

### provides the average value of collision plus comprehensive insurance in 2008 as $432. The average value of a new vehicle in 2008, according to the U.S. Department of Energy, was $23,334. 4 

### Dividing the insurance cost by the average price of a new vehicle gives the proportion of comprehensiv e plus collision insura nce as 1.85 percent of the price of a vehicle. If this same proportion ho lds for the increase in pr ice of a vehicle, then insurance costs should go up by 1.85 percent of the increase in vehicle cost. We use information on depreciation of vehicle value from the same U.S. Department of Energy report to estimate a reduction in insurance costs, due to reduction in the estimated value of the vehicle, over 9 years.

Calculating the average increase in sales tax starts with the vehi cle sales tax for each state in 2006, the most recent source identified. 5 

### The sales tax per state was then multiplied by the 2010 population of the state; 6 

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

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-5 population, to give a population-weighted sales ta x. That estimate of the state sales taxes for vehicles in the U.S. is 5.3 percent. This valu e is assumed to be a one-time cost incurred when the vehicle is purchased.

As of July, 2011, the national average interest rate for a 5 year new car loan was 5.51 percent. 7 

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

8.1.2 Consumer Vehicle Choice Modeling D



### An alternative to the vehicle sales analysis discussed above is the use of consumer
vehicle choice models. In this section we de scribe some of the consumer vehicle choice
models EPA has reviewed in the literature, and we descri be the models’ results and
limitations that we have identi fied. The evidence from consumer vehicle choice models indicates a huge range of estimates for consum ers’ willingness to pay for additional fuel economy. Because consumer surplus estimates from consumer vehicle choice models depend critically on this value, we woul d consider any consumer surplus estimates of the effect of our
rule from such models to be unreliable. In addition, the predictive abil ity of consumer vehicle choice models may be limited. While vehicle ch oice models are based on sales of existing vehicles, vehicle models are like ly to change, both independently and in response to this rule. The models may not predict well in response to these changes. Instead, we compare the value of the fuel savings associated with this rule with the increase in technology costs. EPA will
continue its efforts to review the literature , but, given the known lim itations and uncertainties of vehicle choice models, EPA has not conducted an analysis using these models for this proposal.

This rule will lead automakers to change characteristics – in particular, the fuel economy -- of the vehicles they produce. Thes e 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 type s 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 need s) and the characterist ics of vehicles (e.g., vehicle size, fuel economy, and price). In resp onse 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 pur chase. As a result, consumers may choose a different vehicle than they woul d have purchased in the absence of the rule. The changes in prices and vehicle characteristics are likely to influence consumers on multiple market scales:

D This section is drawn heavily from Helfand, Gloria , and Ann Wolverton, “Evaluating the Consumer Response to Fuel Economy: A Review of the Literature.” International Review of Environmental and Resource
Economics 5 (2011): 103-146.

### Chapter 8

8-6 the total number of new vehicles sold; the mix of new vehicles sold; and the effects of the
sales on the used vehicle market.

Consumer vehicle choice modeling (CCM) is a method used to predict what vehicles consumers will purchase based on vehicle characte ristics and prices. In principle, it should produce more accurate estimates of compliance costs compared to models that hold fleet mix constant, since it predicts changes in the fleet mix that can affect compliance costs. It can also be used to measure changes in consumer surplu s, the benefit that consumers perceive from a good over and above the purchase price. (Consumer surplus is the difference between what consumers would be willing to pay for a good, represented by the demand curve, and the amount they actually pay. For instance, if a consumer were willing to pay $30,000 for a new vehicle, but ended up paying $25,000, the $5000 difference is consumer surplus.)

A number of consumer vehicle choice models have been developed. They vary in the methods used, the data sources, the factors includ ed in the models, the research questions they are designed to answer, and the results of the mo dels related to the eff ects 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 pur chases typically use a form of discrete choice modeling. Discrete choice models seek to explain discrete rath er than continuous decisions. An example of a continuous deci sion is how many pounds of food a farm might grow: the pounds of food can ta ke any numerical value. Disc rete decisions can take only a limited set of values. The decision to purchas e a vehicle, for instance, can only take two values, yes or no. Vehicle purchases are typica lly 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 purcha se a specified vehicle. A minor variant on
discrete choice models estimates the market shar e (a continuous variable between 0 and 1) for each vehicle. Because the market share is, e ssentially, the probability that consumers will purchase a specific vehicle, these approaches are similar in process; they differ mostly in the kinds of data that they use.

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

### In a nested logit, the model is structured in layers. For instance, the first layer may be the choice of whether to buy a new or used vehicle. Given that the person chooses a new vehicle, the second layer may be whether to buy a car or a truck. Given that the person chooses a car, the third layer may be the choice among an econo my, midsize, or luxury car. Examples of nested logit models include Goldberg, 8 

### Greene et al., 9 

### and McManus. 10



### In a mixed logit, personal characteristics of c onsumers play a larger role than in nested logit. While nested logit can look at the effects of a change in average consumer

E Logit refers to a statistical analysis method used for analyzing the factors that affect discrete choices (i.e., yes/no decisions or the choice among a countable number of options).

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-7 characteristics, mixed logit allows consideration of the effects of the di stribution of consumer characteristics. As a result, mixed logit can be used to examine the distributional effects on various socioeconomic groups, which nested log it is not designed to do. Examples of mixed logit models include Berry, Levinsohn, and Pakes, 11 

### Bento et al., 12 

### and Train and Winston. 13



### While discrete choice modeling appears to be the primary method for consumer choice modeling, others (such as Kleit 14 

### and Austin and Dinan 15 

### ) have used a matrix of demand
elasticities to estimate th e 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 effect s of changing the characteristics on the purchase decisions. These effects are typically called the pa rameters or coefficients of the models. For instance, the model parameters might predict th at an increase in a person’s income of 10% would increase the probability of her pur chasing vehicle A by 5%, and decrease the probability of her purchasing vehicle B by 10%.

The parameters in CCMs can be develope d either from original data sources (estimated models), or usi ng values taken from other st udies (calibrated models).

Estimated models use datasets on consumer purchase patterns, consumer characteristics, and vehicle characteristics to de velop their original sets of parameters. The
datasets used in these studies sometimes co me from surveys of individuals’ behaviors. 16 

### Because they draw on the behavior of individuals, they provide what is sometimes called micro-level data. Other studies, that estimate market shares instead of discrete purchase decisions, use aggregated data th at can cover long time periods. 17



### Calibrated models rely on existing studies fo r 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 Administration 18 

### and the New Vehicle Market Model developed by NERA Economic Consulting 19 

### are examples of calibrated models.

8.1.2.3 Factors Included in the Models

Consumer choice models vary in their comple xity and levels of an alysis. Some focus only on the new vehicle market; 20 

### others consider the choice between new vehicles and an outside good (possibly in cluding a used vehicle); 21 

### others explicitly consider the relationship
between the new and used vehicle markets. 22 

### Some models include consideration of vehicle miles traveled, 23 

### though most do not.

### Chapter 8

8-8 The models vary in their inclusion of bot h consumer and vehicle information. One model includes only vehicle price and the distri bution of income in the population influencing choice; 24 

### others include varying numbers and kinds of vehicle and consumer attributes.

Some models include only the consum er side of the vehicle market; 25 

### others seek to represent both consumer and producer decisions. 26 

### Models that include only the consumer side are suitable for reflecting consumer choi ces, 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 characteris tics of the existing fleet. For instance, in the context of “feebates” (subsidizing fuel-efficient cars with revenue collected by taxing fuel-inefficient vehicles), Greene et al. estimated that 95% of the increase in fuel economy was due to addition of technology rather than changes in vehicles sold. 27 

### Including auto maker response is a complex exercise. Auto makers are commonly considered to have market power; they can influence the prices that consumers pay to increase their profits. As a result, the price increases that consumers face may reflect strate gic factors that could make them higher or lower than the technology costs. In addition, auto makers may seek to influence consumer preferences through mark eting and advertising. 28 

### Even those vehicle choice models that include a producer model may not include much detail, due to computational limits: it is unusual for models to allow both buyers and pro ducers to choose one ve hicle characteristic, much less multiple characteristics. 29



### 8.1.2.4 Research Questions for the Models

Consumer choice models have been develope d to analyze many different research and policy questions. In part, these models have be en developed to advan ce the state of economic modeling. The work of Berry, Levinsohn, and Pakes, 30 

### for instance, is often cited outside the motor vehicle context for its incorporation of multiple new modeling issues into its framework. In addition, because the vehicle sect or is a major part of the U.S. economy and involved in many public policy di scussions, research questions cover a wide gamut. These topics have included the effects of voluntary export restraints on Japanese vehicles compared to tariffs and quotas, 31 

### the market acceptability of alternative-fuel vehicles, 32 

### the effects of introduction and exit of vehicles from markets, 33 

### causes of the decline in market shares of U.S. automakers, 34 

### and the effects of gasoline taxes 35 

### and “feebates” 36 

### (subsidizing fuelefficient cars with revenue collected by taxing fuel-ineffi cient vehicles).

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions

Consumer vehicle choice models typically consider the effect of fuel economy on vehicle purchase decisions. It can appear in various forms.

Some models 37 

### 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 perh aps other operating costs) associated with a more fuel-efficient vehicle. Those savings ar e considered to reduce the cost of owning a vehicle: effectively, they reduce the purchase pr ice. This approach relies on the assumption that, when purchasing vehicles, consumers can estimate the fuel savings that they expect to

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-9 receive from a more fuel-efficient vehicle and co nsider the savings equi valent to a reduction in purchase price. Turrentine and Kurani 38 

### question this assumption; they find, in fact, that
consumers do not make this calculation when th ey purchase a vehicle. The question remains, then, how or whether consumers take fuel ec onomy 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 de cisions. Other models use fuel consumption per mile, the inverse of miles per gallon, as a measure: 39 

### since consumers pay for gallons of fuel, then this
measure can assess fuel savi ngs relatively directly. 40 

### Yet other models multiply fuel consumption per mile by the cost of fuel to get the cost of driving a mile, 41 

### or they divide fuel economy by fuel cost to get miles per dollar. 42 

### It is worth noting that these last two measures assume that consumers respond the same way to an increase in fuel economy as they do to a decrease in the price of fuel when each has the same effect on cost per mile driven. F 

### On the one hand, while this assumption does not rely on as complex a calculation as the present value of fuel savings that Turrentine and Kurani exam ined, 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 research 43 

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

Greene and Liu, 44 

### in a paper published in 1988, reviewed 10 papers using consumer
vehicle choice models and estimated for each one how much consumers would be willing to pay at time of purchase to re duce vehicle operating costs by $1 per year. They found that people were willing to pay between $0.74 a nd $25.97 for a $1 decrease in annual operating costs for a vehicle. This is clearly a very wi de range: while the lowest estimate suggests that people are not willing to pay $1 once to get $1 per year reduced cost s 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 year s 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 choi ce models produced widely varying estimates of the value of reduced vehicle operating costs.

A newer literature review from David Greene 45 

### 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 expe cted value of fuel savi ngs varied from highly

F Likewise, these measures assume consumers respond the same way to increases and decreases in cost per mile of driving, as well as if those increas es and decreases are large shocks rather than small, gradual changes. The issue of potential asymmetric consumer response to increa sed fuel efficiency compared to other types of changes to the cost of driving also arises and is discussed in th e context of the VMT rebound effect (see Section III.H.4 of the Preamble and Chapter 4.2.5.2 of the TSD).

### Chapter 8

8-10 positive to highly negative. Significant numbers of studies found that consumers overvalued fuel economy, undervalued fuel economy, or roughl y 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 ne gatively) with other vehicle attr ibutes, such as size, power, or quality, not all of which may be included in the analyses; as a re sult, “fuel economy” may in fact represent several characteristic s at the same time. Indeed, Gramlich 46 

### includes both fuel cost (dollars per mile) and miles per gall on 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 so me of the studies, the variation shows up in studies that appear to be we ll 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 studies 47 

### argue that automakers could incr ease 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 studies 48 

### 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 co urse of reaching this result, though, at leas t one of these studies 49 

### 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 “neg ative-cost” fuel economy improvements.

The models do not appear to yield very cons istent results on the role of fuel economy in consumer and producer decisions.

8.1.2.6 Why Market Outcomes May Not Reflect Full Appreciation for Fuel Economy which Pays for Itself

A detailed and wide ranging literature atte mpts 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 th e 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 cons ervation is often referred to as the Energy Paradox, 50 

### since consumers appear to undervalue a wide range of inve stments in energy conservation. There are many possible expl anations for the paradox discussed in the
literature. 51 

### Some explanations point to costs or aspects of consumer decision-making unaccounted for in a simple present value ca lculation, while others point to potential
behavioral or market failures. There is little empirical literatu re to help the analyst determine which combination of hypothesis offers the most credible explanati on. Some possibilities
include:

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-11  Consumers put little weight on benefits fr om fuel economy in the future and show high discount rates;

 Consumers do not find the benefits from fuel economy to be sufficiently salient at the time of purchase, even if it would be in consumers’ economic interest to take account of those benefits;

 Consumers consider other attributes more important than fuel economy at the time of vehicle purchase, especia lly if fuel economy is a rela tively “shrouded” attribute;

 Consumers have difficulty in calcul ating expected fuel savings;

 Consumers may use imprecise rules of thumb when deciding how much fuel economy to purchase;

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

 Fuel savings in the future are uncertain, wh ile at the time of purchase the increased costs of fuel-saving technologies are certain and immediate;

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

economy;

 The level of cost savings may be affect ed by the underlying reasons for the gap:
factors such as transactions costs and diffe rences in quality may not be adequately measured; 52



###  There is likely to be variation among cons umers in the benefits they get from improved fuel economy, due to different miles driven and driving styles;

 Consumers may give particular weight to th e losses associated with upfront costs, and less so to the costs over time (a version of the phenomenon of “myopic loss aversion”).

The extent to which fuel economy is optim ized relative to ot her, 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 interpreti ng and anticipating cons umer preferences for various vehicle attributes and amenities. There are significant lead times to market, potential return to scale limits on the range of options pr ovided for a given attribute or amenity, market transaction frictional factors, a nd other factors inherent to the nature of these costly durable goods which may contribute to imperfect sati sfaction of market demand for fuel economy among a highly heterogeneous customer base. . Both sides of the market would be expected

### Chapter 8

8-12 to attempt to maximize the utility they gain fr om these transactions, they presumably rely heavily in their calculations on the uncerta in 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 significan ce for assessing the potential incremental effects of pollution control standards. We welcome comments on any aspect of this
discussion.

8.1.2.7 Modeling Electric Vehicles and Other New Vehicles

Modeling the introduction of new vehicles can be a greater challenge than modeling the existing vehicle market, because the modele r 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 ve hicles and using those in a vehicle choice model. For instance, as disc ussed 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, w ith some additional information,
to consider the effects of new vehicle introductio n. The first step is to find a vehicle similar on as many dimensions as possible to the new vehicl e. For instance, if the change is to create an electric vehicle (EV) version of an existi ng model, then the existi ng model serves as the base vehicle. Next, it is necessary to measure the changes in vehi cle attributes of interest to potential vehicle buyers. For an EV, changes in vehicle driving range an d 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 a dded to or subtracted from the vehicle purchase price to give an adjusted purch ase price reflecting the changed characteristic. This procedure is just an exte nsion 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 fr action) from vehicle purchase price.

Incorporating new vehicles into a vehicle c hoice model, then, requires estimates of the changes in key attributes from conventional vehi cles, 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 ca pacity needed to increase range. The ability to recharge at home may be a convenient, desi rable feature for people who have garages with electric hookups, but not for peopl e who park on the street. If an infrastructure develops for recharging vehicles with the convenience appro aching that of buying gasoline, range or home recharging may become less of a barrier to pur chase. The reduced tailpipe emissions and

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-13 reduced noise may be attractive features to some consumers. G 

### They may have different performance or storage capacity. If sufficien t data were available, the changes in these attributes, combined with WTP for each of the attr ibutes, 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 a lternative-fuel vehicles. 53 

### In that model, he considers only one base vehicle, a passenger car, but considers the effect on WTP of fuel cost per mile, range, acceleration, and several other vehicle attributes.

Vehicle driving range has received attent ion because of the current paucity of recharging infrastructure: if th e driver of an EV gets low on fuel, it may be difficult to find a place to recharge. Because range appears to be a major factor in EV acceptability, it is starting to draw attention in the research community.

In several studies, researchers have used stated preference conjoint analysis to estimate the effect of vehicle range on consum er vehicle choice. In a conjoint analysis, consumers are given a choice between several vehi cles with different at tributes. One choice might be, for instance, between a baseline car and another car with hi gher 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 attri butes 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 wh at 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 environmenta l goods. Because electric vehicles are not in widespread enough use for market studies, stated preference studies are, at this point, one of the few options to examine consumer behavior relating to these vehicles.

Table 8.1-2 Table 8.1

- 3 summarizes results from several conjoint studies that include the effects of extending range (in the table, fr om 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, Wille tt Kempton, and Meryl P. Gardner. “Willingness to Pay for Electric Vehicles and thei r Attributes.” Resource and Energy Economics 33(3) (2011): 686-705) find that some consumers are willing to pay $5100 more for vehicles with 95% lower emissions than the vehicles they otherwise aim to purchase.

### Chapter 8

8-14 Table 8.1- 2 4 Willingness to Pay for Increasing Rang e Calculated from Various Studies

Study (Date) Value of extending range from 150 to 300 miles (dollar year) Value of additional range in 2009$ a



### Bunch et al. (1993) b 

### $7,600 (1991$) $11,100

Kavelek (1996) for California Energy Commission c 

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

Resource Systems Group (2009) for California Energy Commission d 

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

Hess et al. (2009), using the same data as Resource Systems Group (2009) e 

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

Hidrue et al. (2011) f 

### $3776 - $10,399 (2009$) $3776 - $10,399

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

b Bunch, David S., Mark Bradley, Thomas F. Golob, and Ryuichi Kitamura. “Demand for Clean-Fuel Vehicles
in California: A Discrete-Choice Stated Preferen ce Pilot Project.” Transp ortation 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 Conven tional 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 fo r one-car and two-car househo lds. 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 Fu el 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-nest ed Logit Models for Multi-Dimensional
Choice Processes: The Case of the De mand 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 differe nt 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 specifica tion. 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 thei r Attributes.” Resource and Energy Economics 33(3) (2011): 686-705. The range of values is due to the model separating consumers into “g asoline vehicle-oriented” and “electric vehicle-oriented” groups. The EV-oriented group has higher WTP for additional range.

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-15 Driving range may be a major factor in cons umers’ decisions on EVs, but it is not the only attribute that may be important to potential buyers (e.g., as noted, Hi drue et al. find that some consumers appear willing to pay substantia lly for reduced tailpipe emissions). A model that does not incorporate the ot her factors important to consumers’ decisions may not perform well in predicting vehicle purchases. In addi tion, as mentioned above, and as seen in Table
8.1-2 T able 8.1

- 3 , it is likely that the WTP values for attributes of EVs will change over time, particularly if EVs are used more widely, the in frastructure to fuel th e vehicles becomes more accessible, and consumers develop more familiarity and understanding of the vehicles. Thus, challenges associated with predicting market sh ares for EVs are even more serious than those already serious challenges associated with pred icting market shares for conventional vehicles.

8.1.2.8 EPA Exploration of Vehicle Choice Modeling

In order to develop greater understanding of these models, EPA is in the process of developing a vehicle choice model. In its curr ent form, the model assumes that the vehicle fleet and all characteristics of each vehicle, ex cept 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 le vels, in response to changes in vehicle fuel economy and price.

The draft EPA model uses a nested log it structure common in the vehicle choice modeling literature, as discussed above in Ch apter 8.1.2.1. “Nesting” refers to the decisiontree structure of the model, and “logit” refers to the fact that the choices are discrete (i.e., yes/no decisions about which vehicles to pur chase, 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 pa ssenger 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 substituti on 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 add ition, substitution is greater at low decision nodes (such as individual configurations) than at higher de cision nodes (such as the buy/no buy decision),
because there are more choices at lowe r levels than at higher levels.

Parameters for the model (including demand el asticities and the value of fuel economy in purchase decisions) are being selected based on a review of values found in the literature on vehicle choice modeling. As discussed above , a number of studies have estimated these

### Chapter 8

8-16 parameters. Those estimates, combined with some theoretical requirements, H 

### assist in assigning values for the parameters. The mode l will allow individual users to change those parameters.

The fuel economy of a vehicle is used to adjust the price of the vehicle, using a version of the procedure discussed in Chapter 8.1.2.7: the value that the consumer places on fuel economy is multiplied by the change in fuel economy and incorporated into the “effective price” of the vehicle. In practice, implementing this calc ulation 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 OME GA. 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 c onsumers will use some years of discounted fuel savings, with the modeler able to input bot h the number of years a nd the discount rate to be used in the analysis.

The vehicle choice model will take as inputs an initial fleet of ve hicles (including the
initial sales and fuel eco nomy) in the absence of standards, the cost of tec hnologies 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 ve hicle-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 consum er response that would result only from changes in price and fuel economy. This constant substitutes for estima ting the effects of changes in all other vehicle characteristics; the underl ying 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 wi ll be to its cost and its fuel economy.

The model assumes that the increase in ve hicle cost associated with increased technology is fully passed through as an increase in vehicle price, and some years of fuel savings offset this price increase . It then calculates changes in total fleet size and in sales mix, at the individual-configurati on level and at the level of vehi cle class, due to the changes in fuel economy and vehicle prices . It also calculates changes in consumer surplus associated with the changes in fuel economy and vehicle prices.

It is possible that the predicted changes in fleet mix will lead to predictions of vehicle sales for auto makers that do not meet the propos ed standards, because the mix and volume of vehicles sold changed from the initial levels. To correct this problem , it would be necessary to feed the new fleet mix into OMEGA (which calculates costs and compliance) and get a new set of output, which could then be fed b ack into the vehicle choice model. OMEGA would increase technologies, and thus costs, to improve compliance; those adjustments would

H The theory of nested logit requires that the price slope s (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 substitu tion is greater at lower decision notes than at higher

ones.
I As explained in Section III.D of the preamble, as part of the technology cost analysis for the proposed rule, the agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions.

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-17 then again affect vehicle demand. We expect th at this iterative process would converge to a
fleet mix that would meet standards. Perfor ming this iteration requires development of an
interface between the vehicle c hoice 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 OM EGA. Building this interface is an expected part of our future modeling work.

The model is still undergoing development; EP A will seek peer review on it before it is utilized. In addition, concerns remain over th e ability of any vehicle choice model to make reasonable predictions of the response of the ve hicle fleet to changes in prices and vehicle
characteristics. EPA seeks comments on the use of vehicle choice modeling for predicting changes in fleet mix due to policies, and on me thods to test the abil ity of a vehicle choice model to produce reasonable estimat es of changes in fleet mix.

8.1.2.9 Summary and Additional Considerations

Consumer vehicle choice modeling in princi ple can provide a great deal of useful information for regulatory analysis, helping to answer some of the cen tral questions about relevant effects on consumer welfare. In pr actice, the advantages depend on the success of models in predicting changes in fleet size and mix.

First, consumer vehicle choice modeling has th e potential to describe more accurately the impact of a policy, by identifying market shif ts. More accurate desc ription 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 travele d. 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 discus sion of the value that consumers place on fuel economy in their purchase decisions. Especially for characteristics asso ciated with advanced technology vehicles, such as EVs, the willingne ss-to-pay values may change over time as consumers develop more experience with the ve hicles and these characteristics. Models based on current estimates may not predict well for the future.

The modeling may improve estimates of the compliance costs of a rule. Consumers can either accept the new costs and vehicle charac teristics, or they can change which vehicles they buy. Using a vehicle choice model is likel y to reduce compliance costs: because the model allows consumers to choose among accep ting the new vehicle, buying a different vehicle, or not buying a vehicle, consumers have additional options, which improves their welfare relative to the assumption that consum ers will not change their buying behavior. .

### Chapter 8

8-18 An additional complication associated with consumer choice modeling is accurate prediction of producers’ responses to the rule. While it is pos sible to include auto makers’ decisions (for instance, on setting prices) into ve hicle choices, computational limits affect the richness of these models. Technology costs, while an accurate measure of the opportunity cost of resources to society, may overestimate or underestimate the eff ect on the prices that
consumers face.

Consumer choice models can be used to calculate consumer surplus impacts on vehicle purchase decisions. Because these valu es 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 usef ul for understanding the role of consumer
vehicle choice models in benef it-cost analysis. In particular, except for EVs, the technology cost estimates developed in this proposal take into account the costs to hold other vehicle attributes, such as size and perf ormance, 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 m easures 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 meas ures the welfare loss to the buyer. Increasing fuel economy would have to lead to other change s in the vehicles that buyers find undesirable
for there to be additional losses not included in the technology costs.

Given the current limitations in modeling the role of fuel economy in vehicle purchase decisions, and limitations in modeling market responses to the new regulations, in this proposal EPA holds constant the ve hicle 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 continue s to explore options for including consumer and producer choice in modeling the impacts of fuel economy-related regul ations. This effort includes further review of existing consumer vehicle choice models, the estimates of consumers’ willingness to pay for increased fuel economy, and overall effects on consumer welfare, as well as EPA’s exploration of a ve hicle choice model for use in the future.

J This approach describes the economic concept of compensating variation, a payment of money after a change that would make a consumer as well off after the change as before it. A related concept, equivalent variation, estimates the income change that w ould be an alternative to the change taking place. The difference between them is whether the consumer’s point of reference is he r welfare before the change (compensating variation) or after the change (equivalent variation). In practice, th ese 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 im provements that make the vehicle less desirable to buyers.

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-19 8.2 Employment Impacts

8.2.1 Introduction

Although analysis of employment impacts is not part of a cost-benefit analysis (except
to the extent that labor costs contribute to co sts), employment impacts of federal rules are of particular concern in the current economic climate of sizeable unemployment. When President Obama requested that the agencies de velop this program, he sought a program that would “strengthen the [auto] industry and enhance job creation in the United States.” 54,L 

### The recently issued Executive Order 13563, “Improving Regulation and Regulatory Review” (January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety,
and our environment while promoting economic growth, i nnovation, competitiveness, and job
creation ” (emphasis added). EPA is accordingly provi ding partial estimates of the effects of this proposal on domestic employment in the auto manufacturing and parts sectors, while qualitatively discussing how it may affect em ployment in other sectors more generally.

This proposal is expected to affect empl oyment in the United States through the regulated sector – the auto manufacturing i ndustry – and through seve ral related sectors, specifically, industries that suppl y the auto manufacturing industry (e.g., vehicle parts), auto dealers, the fuel refining and supply sectors, and the general re tail sector. According to the U.S. Bureau of Labor Statistics, in 2010, a bout 677,000 people in the U.S. were employed in Motor Vehicle and Parts Manufacturing S ector (NAICS 3361, 3362, and 3363). About 129,000 people in the U.S. were employed in th e Automobile and Light Truck Manufacturing Sector (NAICS 33611) in December 2010; this is the directly regulat ed sector, since it encompasses the auto manufacturers that are re sponsible for complying with the proposed
standards. 55 

### Changes in light duty vehicle sales, discussed in Chapter 8.1.1, could affect employment for auto dealers. The employment effects of this rule are expected to expand beyond the regulated sector. Though some of the parts used to achieve the proposed standards are likely to be built by auto manufacturers themselves, the auto parts manufacturing sector also plays a significant ro le in providing those parts, and will also be affected by changes in vehicle sales. As discu ssed in Chapter 5.4 of the RIA, this proposal is expected to reduce the amount of fuel these vehicles use, an d thus affect the petroleum refinery and supply industries. Finally, since the net reduction in cost as sociated with this proposal is expected to lead to lower househol d expenditures on fuel net of vehicle costs,
consumers then will have additional discretiona ry income that can be spent on other goods
and services.

L The May 21, 2010 Presidential Memorandum also reque sted that EPA and NHTSA, in developing the technical assessment to inform the rulemaking process (which wa s issued by the agencies and CARB on September 30, 2010), include, among other things, the “impacts on jobs and the automotive manufacturing base in the United
States.”

### Chapter 8

8-20 When the economy is at full employment, an environmental regulation is unlikely to have much impact on net overall U.S. employme nt; 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 empl oyees, as regulation di verts workers from other activities in the economy. In this situation, any effects on net employment are likely to be transitory as worker s 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 come s 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 n ear term when the economy is at less than full employment due to the potential hiring of id le labor resources by th e regulated sector to meet new requirements (e.g., to install new equi pment) and new economic activity in sectors related to the regulated sector. 56 

### In the longer run, the net effect on employment is more difficult to predict and will depend on the way in which the related industries respond to the regulatory requirements. As Schmalansee and St avins 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 effect s in other regions or sectors. For this reason, they urge caution in reporting partial employme nt effects since it can “paint an inaccurate picture of net employment impacts if not placed in the broader economic
context.”

It is assumed that the official unemployment rate will have declined to 5.3 percent by the time by the time this rule takes effect and so the effect of the regulation on labor will be to
shift workers from one sector to another (from Fiscal Year 2012 Mid-Session Review: http://www.whitehouse.gov/sites/default/file s/omb/budget/fy2012/assets/12msr.pdf) . Those shifts in employment impose an opportunity cost on society, approximated by the wages of the employees, as regulation diverts workers from other activities in the economy. In this situation, any effects on net employment are likely to be transitory as workers change jobs (e.g., some workers may need to be retrained or require time to search for new jobs, while shortages in some sectors or regions c ould bid up wages to attract workers).

Given the time frame of this proposed rule, the state of the overall economy may have improved to the point where the only effect of ch anges in employment associated with the standards proposed here w ill be to shift workers from one sector to another . For example, to the extent that regulation in creases the number of workers in the regulated sector (e.g., because of higher costs of produ cing vehicles with higher fuel economy), t h e opportunity cost
associated with tho se shifts in employment will be captured in labor costs, as regulation
diverts workers from other ac tivities in the economy. In this situation, any effects on net employment are likely to be transitory as workers change jobs (e.g., some workers may need

M Masur and Posner, available at http://pap ers.ssrn.com/sol3/paper s.cfm?abstract_id=1920441

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-21 to be retrained or require time to search for new jobs, while shortage s 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 ne gative employment effects will occur.

A number of different approaches have b een 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 discusse d, 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 assumpti ons about the levels of those inputs, and which sectors receive those changes. Which sectors are incl uded 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 Shih 57 

### 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 abatemen t for four highly polluting/regulated U.S. industries (pulp and paper, plas tics, steel, and petroleum refini ng) using data for six years between 1979 and 1991. The paper provides a theo retical 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 ne w (or more stringent) regulation.

 Demand effect : higher production costs due to th e 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 th e authors’ words, the “extent of this effect depends on the cost increase passe d on to consumers as well as the demand elasticity of i ndustry output.”

 Cost effect : as costs go up, plants add more capital and labor (holding other factors constant), with potentially positive effects on employment. In the authors’

### Chapter 8

8-22 words, as “production costs rise, more i nputs, including labor, are used to produce the same amount of output.”

 Factor shift effect : post-regulation production tec hnologies may be more or less labor-intensive (i.e., more/less labor is required per dollar of output). In the authors’ words, “environmental activities may be more labor intensive than conventional production,” meaning that “the amount of labor per dollar of output will rise,” though it is also possible that “cleaner operations could involve automation and less employment, for example.”

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

### the “cost effect” to have a positive e ffect on employment, and the “factor shift effect” to have an ambiguous effect on employme nt. 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 em ployment levels in a regulated sector.

The authors conclude that increased abatem ent expenditures generally have not caused a significant change in employment in those se ctors. More specifica lly, their results show that, on average across the industries studie d, 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 caref ully controlling for many possibly confounding effects in order to sepa rate the effect of changes in regulatory costs on employment. It was, however, conducted fo r 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 su rvey, has been conducted infrequently since 1994, with the last survey conducted in 2005. The empirical estimates provided by Morgenstern et al. are not rele vant to the case of fuel econom y 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 regul ated sector. Nevertheless, the theory that Morgenstern et al. developed continues to be useful in this context.

The following discussion of additional methodologies draws from Berck and Hoffmann’s review of employment models. 58



### 8.2.2.2 Computable General Equilibrium (CGE) Models

Computable general equilibrium (CGE) models are often used to assess the impacts of policy. These models include a stylized representation of supply and demand curves for all

N As will be discussed below, the demand effect in this proposal is potentially an exception to this rule. While the vehicles become more expensive, they also produce reduced fuel expenditures; the reduced fuel costs provide a countervailing impact on vehicle sales. As discussed in Preamble Section II I.H.1, this possibility that vehicles may become more attractive to consum ers after the program poses a conundrum : why have interactions between vehicle buyers and producers not provided these benefits without government intervention?

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-23 major markets in the economy. The labor ma rket is commonly included. CGE models are very useful for looking at inte raction 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 th e 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 ch anges, it is due to pe ople voluntarily entering or leaving the workforce. As a result, thes e 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 a ssumptions 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 inte rconnections look at how changes in one sector ripple through th e rest of the economy. For instance, a requirement for additional technology for vehicles requires additional steel, which requires
more workers in both the auto a nd 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, b ecause 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 input s or goods is not allowed), and quantity supplied need not equal quantity demanded. In pa rticular, 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 anal ysis 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, pe ople join into or depart from the labor pool as production requires them. For other market s 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 Hoffma nn note, “For the same reason, they can be thought of as simulating very short-run adjust ment,” in contrast to the CGE’s implicit assumption of long-run adjustment. Changes in production processes, introductions of new technologies, or learning over time due to new regulatory requirements are also generally not

### Chapter 8

8-24 captured by IO models, as they are calibrated to already established relationships between inputs and outputs.

8.2.2.4 Hybrid Models

As Berck and Hoffmann note, input-output models and CGE models “represent a continuum of closely related models.” T hough 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-out put relationships. For instan ce, a hybrid model may include the ability to examine disequilibrium phenomen a, such as labor being at less than full employment. Hybrid models depend on assump tions 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 ar e 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 potential ly be used to model labor market impacts of various vehicle polic y 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 eff ect 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 de mand is proportional to output or expenditures. As Berck and Hoffmann note, though, “Compliance with regul ations may create additional jobs that are not accounted for.” While such an analysis can ap proximate 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 eff ect of regulation on employment in regulated sectors. Morgenster n, Pizer, and Shih (2002), discussed above, and Berman and Bui (2001) are two notable exam ples that rely on highly disaggregated establishment-level time series data to estimate longer-run employment effects. 59 

### While often a sophisticated treatment of the issues anal yzed, these studies co mmonly analyze specific scenarios or sectors in the past ; care needs to be taken in extr apolating their results to other scenarios and to the future. For instance, ne ither 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 imp acts of a regulation have advantages and limitations. CGE models may be most approp riate for long-term impacts, but the usual assumption of equilibrium in the employment mark et 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 appropr iate for small-scale, short-term effects, because they assume fixed relationships across s ectors and do not require market equilibria.

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-25 Hybrid models, which combine some features of CGEs with IO models, depend upon key assumptions and economic relationships that ar e built into them. Single-sector models are simple and straightforward, but they are often based on the assu mptions that labor demand is proportional to output, and that ot her sectors are not affected. Finally, econometric models have been developed to evaluate the longe r-run net effects of regulation on sector employment, though these are ex-post analyses commonly of specific se ctors or situations, and the results may not have direct bear ing for the regulation being reviewed.

8.2.3 Employment analysis of this proposal

As mentioned above, this program is expect ed to affect employment in the regulated sector (auto manufacturi ng) and other sectors directly aff ected by the proposal: auto parts suppliers, auto dealers, the fuel supply market (which will face reduced petroleum production due to reduced fuel demand but which may s ee additional demand for electricity or other fuels), and consumers (who will face higher vehicl e costs and lower fuel expenditures). In addition, as the discussion above su ggests, each of these sectors could potentially have ripple
effects in the rest of the economy. These ripp le effects depend much more heavily on the state of the macroeconomy than do the direct eff ects. At the national level, employment may increase in one industry or regi on and decrease in another, with the net effect being smaller
than either individual-sector e ffect. EPA does not attempt to quantify the net effects of the regulation on overall national employment.

The discussion that follows provides a partia l, bottom-up quantitative estimate of the effects of this proposal on the regulated sector (the auto industry; for reasons discussed below, we include some quantitative assessment of e ffects on suppliers to the industry although they are not regulated directly ). It also includes qualitative disc ussion of the effects of the proposal on other sectors. Focusing quan tification of employment impact s on the regulated sector has some advantages over quantifying a ll 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 consis tent assumptions and estimates made in this proposal. Secondly, as discussed above, net effects on employment in the economy as a whole depend heavily on the ove rall state of the economy when th is rule has its effects. Focusing on the regulated sector provides insi ght into employment effects in that sector without having to make assumptions about the st ate of the economy when this rule has its impacts. We include a qualitative discussi on of employment effect s on other sectors to provide a broader perspective on the impacts of this rule.

As noted above, in a full-employment ec onomy, any changes in employment will result from people changing jobs or voluntarily entering or exiti ng the workforce. In a fullemployment economy, employment impacts of this proposal will change employment in specific sectors, but it will have small, if a ny, effect on aggregate employment. This rule would take effect in model years 2017 thr ough 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 th e most directly affected industries. Those

### Chapter 8

8-26 sectors are likely to face the most concentrated employment impacts. The agencies seek
comment on other sectors that are likely to be significantly aff ected and thus warrant further analysis in the final rulemaking analysis.

8.2.3.1 Employment Impacts in the Auto Industry

Following the Morgenstern et al. conceptual framework for the impacts of regulation on employment in the regulated sector, we consider three eff ects for the auto sector: the demand effect, the cost effect, a nd the factor shift effect. Howe ver, we are only able to offer quantitative estimates for the cost effect . We note that these estimates, based on extrapolations from current data, beco me more uncertain as time goes on.

8.2.3.1.1 The Demand Effect

The demand effect depends on the effects of th is proposal on vehicle sales. If vehicle sales increase, then more people will be require d to assemble vehicles and their components. If vehicle sales decrease, empl oyment associated with these activities will unambiguously decrease. Unlike in Morgenstern et al.’ s study, where the demand effect unambiguously decreased employment, there are countervailing effects in the vehicle market due to the fuel savings resulting from this program. On one ha nd, this proposal will increase vehicle costs; by itself, this effect would reduce vehicle sale s. On the other hand, this proposal will reduce the fuel costs of operating the vehicle; by itsel f, this effect would increase vehicle sales, especially if potential buyers have an expectation of higher fu el prices. The sign of demand effect will depend on which of these effects dom inates. Because, as described in Chapter 8.1, we have not quantified the impact on sales fo r this proposal, we do not quantify the demand
effect.

8.2.3.1.2 The Cost Effect

The demand effect, discussed above, meas ures employment changes due to new vehicle sales only. The cost effect measur es employment impacts due to the new or additional technologies needed for vehicles to comply with the proposed standards.

One way to estimate the cost effect, given the cost estimates for complying with the rule, is to use the ratio of workers to each $1 m illion 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 in stance, it is possible to estimate the average number of workers in the light-duty vehicle ma nufacturing sector per $1 million spent in the sector, rather than use the rati o 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 othe r 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 mo tor vehicle manufacturing sector represents the ratio for all vehicle manufacturing, not just fo r fuel efficiency improvements. In addition, these estimates do not include changes in sectors that supply these sectors, such as steel or electronics producers. They thus may best be viewed as the effects on employment in the

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-27 auto sector due to the changes in expenditures in that sector, rather than as an assessment of all employment changes due to th ese changes in expenditures.

Some of the costs of this proposal will be spent directly in the auto manufacturing sector, but some of the costs will be spent in the auto parts manufacturing sector. Because we do not have information on the proportion of e xpenditures in each sector, we separately present the ratios for both the auto manufactur ing 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 tech nologies to the vehicles to comply with the regulation.

We use several public sources for estimates of employment per $1 million expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment Requirements Matrix (ERM), 60 

### which provides direct estimates of the employment per $1 million in sales of goods in 202 sectors. The most recent estimates, used here, are from 2008 (adjusted to 2009$). The tables used here are adjusted to remove the employment effects of imports through use of the ratio of domestic pro duction to domestic sale s, described above, of 0.667. The values reported are for Motor Ve hicle Manufacturing (NAICS 3361) and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Annual Survey of Manufactures 61 

### (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 deta il 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 manufact uring vs. light truck and utility vehicle manufacturing). While the ERM provides di rect estimates of employees/$1 million in expenditures, the ASM separately provides num ber of employees and value shipments; the direct employment estimates here are the ratio of those values. The data in the ASM are updated annually, except for years when the full Economic Census occurs. The tables presented here use data from 2009. As with th e ERM, we adjust for the ratio of domestic production to domestic sales. The values reported are for Moto r Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty Motor Vehicle Manufact uring (NAICS 33611),
and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Economic Census includes all large compan ies and a sample of smaller ones. The ASM is a subset of the Economic Census; though the Census itself is more complete, it is conducted only every 5 years, while the ASM is annual. The values presented here use data from 2007 (adjusted to 2009$), with the domestic production-to-sales adjustment. The values reported are for Motor Vehicl e Manufacturing (NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing (NAICS 3363).

Table 8.2-1 Table 8.2

- 3 provides the values, either gi ven (BLS) or calculated (ASM, Economic Census) for employment per $1 million of expenditures, all based on 2009 dollars,

### Chapter 8

8-28 though the underlying data come from different years (which may account for some of the differences). The different da ta sources provide similar magn itudes for the estimates for the sectors. Parts manufacturing appears to be mo re labor-intensive than vehicle manufacturing; light-duty vehicle manufacturing ap pears to be slightly less labor-intensive than motor vehicle manufacturing as a whole.

Table 8.2- 1 3 Employment per $1 Million Expendi tures (2009$) in the Motor Vehicle Manufacturing Sector*

Source Sector Ratio of workers per

$1 million expenditures Ratio of workers per $1 million expenditures, adjusted for domestic vs. foreign production BLS ERM Motor Vehicle Mfg 0.834 0.556

ASM Motor Vehicle Mfg 0.824 0.549

ASM Light Duty Vehicle Mfg 0.757 0.505

Economic
Census Motor Vehicle Mfg 0.674 0.449

Economic
Census Light Duty Vehicle Mfg 0.610 0.407

BLS ERM Motor Vehicle Parts Mfg 3.073 2.049

ASM Motor Vehicle Parts Mfg 3.093 2.063

Economic
Census Motor Vehicle Parts Mfg 2.749 1.833

BLS ERM refers to the U.S. Bureau of Labor Statistic s’ Employment Requirement Matrix. ASM refers to the U.S. Census Bureau’s Annual Survey of Manufactures. Economic Census refers to the U.S. Census Bureau’s
Economic Census.



### Over time, the amount of labor needed in the auto industry has changed: automation and improved production methods have led to significant productivity increases. The BLS ERM, for instance, provided estimates that, in 1993, 1.52 workers were needed per $1 million of 2000$, but only 0.83 workers by 2008 (in 2000$). 62 

### Because the ERM is available annually for 1993-2008, we used these data to estimate productivity improvements over time. We regressed logged ERM values on year (to es timate percent change per year) for both the Motor Vehicle Manufacturing and Motor Vehicle Parts Manufactur ing sectors. The results suggest a 4.4 percent per year productivity improvement in the Motor Vehicle Manufacturing Sector, and a 3.6 percent per year improvement in the Motor Vehicl e Parts Manufacturing
Sector. We then used the regression relationship to proj ect the ERM through 2025. In the results presented below, these pr ojected values (adjusted to 2009$) were used directly for the BLS ERM estimates. For the ASM, we used the ra tio of the projected va lue in the future to

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-29 the projected value in 2009 (the base year fo r the ASM); for the Economic Census estimates, we used the ratio of the projec ted value in the future to the projected value in 2007 (the base year for that estimate). As noted above, we adjusted the estimate of workers per vehicle for the demand effect in Chapter 8.2.3.1.1, above, us ing the productivity improvement for the Motor Vehicle Manufacturing sector; because the estimate of workers per vehicle for the
demand effect was based on data from 2001 to 2010, we used the projected value of the ERM
in 2005 as the denominator of the ratio. This is a simple way to examine the relationship between labor required and expenditure and we seek comment on refining this method.

Table 8.2-2 Table 8.2

- 4 shows the cost estimates devel oped for this rule, discussed in Chapter 5. The maximum value in Table 8.2-2 Table 8.2

- 4 for employment impacts per $1 million expenditures (after accounting for the share of domestic production) is 2.049 in 2009
if all the additional costs are in the parts sector; the minimum value is 0.407 in 2009, if all the additional costs are in the light -duty vehicle manufacturing sect or: that is, the range of employment impacts is between 0.4 and 2 additi onal jobs per $1 million expenditures in the
sector in 2009. The results in Table 8.2-2 Table 8.2

- 4 include the produc tivity adjustment
described above.

While we estimate employment impacts beginni ng with the first year of the standard (2017), some of these job gains may occur earl ier as auto manufacturers and parts suppliers hire staff in anticipation of compliance with the standard.

Table 8.2- 2 4 Employment due to Cost Effect in the Motor Vehicle Manufacturing Sector

Year Costs (before adjustment for domestic proportion of production) ($Millions) Minimum employment effect (if all expenditures are in the parts sector) Maximum employment effect (if all expenditures are in the light duty vehicle mfg sector) 2017 $ 2,300 600 3,600 2018 $ 4,656 1,200 7,000 2019 $ 6,507 1,600 9,400 2020 $ 8,467 1,900 11,800 2021 $ 11,878 2,600 15,900 2022 $ 19,340 4,100 25,000 2023 $ 25,036 5,000 31,200 2024 $ 30,738 5,900 37,000 2025 $ 33,561 6,200 39,000

8.2.3.1.3 The Factor Shift Effect

The factor shift effect looks at the eff ects on employment due to changes in labor intensity associated with a regulation. As noted above, the estimates of the cost effect assume constant labor per $1 million in expenditures, though the new technologies may be either

### Chapter 8

8-30 more or less labor-intensive th an the existing ones. An estimat e of the factor shift effect
would either increase or decrease th e estimate used for the cost effect.

We are not quantifying the fact or shift effect here, for lack of data on the labor intensity of all the possible te chnologies that manufacturers c ould use to comply with the proposed standards. For a subset of the technologies, though, EP A-sponsored research (discussed in Chapter 3.2.1.1 of the Joint TSD) which compared new t echnologies to existing
ones at the level of individual co mponents provides some insights in to the factor shift effect.

The comparison involved tearing down the se lected technologies to their individual components and looking at the differences in ma terials and labor needs in moving from the conventional to the new technologies. 63 

### For instance, the analysis compared all the parts and labor associated with an 8-speed automatic transmission to those needed for a 6-speed
automatic transmission.

Because labor cost was one of the sources of differences between the technologies, it is possible, for those technologi es, to see whether labor needs increase or decrease with the switch to technologies that might contribute to compliance with the proposed standards. An increase in labor cost for the new technology indi cates an increase in the labor needed for the new technology compared to the baseline techno logy. For instance, an 8-speed transmission requires $15.11 more in labor costs than a 6-sp eed transmission (as accounted for in EPA’s cost estimates for the proposed rule). Dividi ng the labor cost by a wage per hour estimate provides an estimate of the additional hours (and t hus 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 grea ter employment needs for the new technologies. For this conversion, a weighted average wage rate (90 percent of th e average wage in the Motor Vehicle Parts Manufacturing sector , and 10 percent of the averag e 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 8.2-3 Table 8.2

- 5 shows the changes in labor hour s in moving from baseline to new fuel-saving technologies for technologies in the FEV study. It indicates that, in switching from the baseline to th e new technologies, labor use pe r technology increased: the fuel-saving technologies use more labor than th e baseline technologies. For a subset of the technologies likely to be used to meet the sta ndards in this proposal, then, the factor shift effect increases labor demand, at least in th e 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 ar e, however, only a subset of the technologies that auto makers may use to comply with the st andards proposed in this program. As a result,
these results cannot be considered definitive ev idence that the factor shift effect increases employment for this rule. We therefore do not quantify the factor shift effect for this proposal.

Table 8.2- 3 5 Estimated Change in Labor for Selected Compliance Technologies

Technology FEV Vehicle Class Labor Total Hours/

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-31 Case Study Costs Costs Technology

Downsized
Turbo GDI 4 0101 Compact C $72.58 $537.70 1.57

Downsized
Turbo GDI V6 0102 Mid/Large C $25.76 $87.38 0.56

Downsized
Turbo GDI V6 0104 SUV/Trucks $84.19 $789.53 1.82

Electric A/C

compressor 0602 $4.68 $167.54 0.10

Power split hybrid 0502 Mid/Large C $395.85 $3,435.01 8.54

6- to 8-speed
transmission 0803 Mid/Large C $15.11 $61.84 0.33

8.2.3.1.4 Summary of Employment Effects in the Auto Sector

While we are not able to quantify the demand or factor shift effects, the cost effect results show that the employment effects of th e 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 pr ogram. As noted above, motor vehicle and parts manufacturing sectors employed about 677,000 people in 2010, with automobile and light truck manufacturing accounting for about 129,000 of that total.

8.2.4 Effects on Employment for Auto Dealers

The effects of the proposed standards on employment for auto dealers depend principally on the effects of the standards on light duty vehicle sales. In addition, auto dealers may be affected by changes in maintenance and service costs. Increases in those costs are likely to increase labor demand in dealerships.

Although this proposal predicts very sma ll penetration of advanced technology vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. As discussed in Chapter 8.1.2.7, consumers may find some characteristics of electric vehicles and plug-in hybrid electric vehicles, such as the ability to fuel with electricity rather than gasoline, attractive; they may find other character istics, such as the limited range for electric vehicles, undesirable. As a result, some cons umers will find that EVs w ill 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 empl oyment to train sales staff in the new technologies as the new technol ogies become available.

### Chapter 8

8-32 8.2.5 Effects on Employment in the Auto Parts Sector

As discussed in the context of employment in the auto industry, some vehicle parts are made in-house by auto manufacturers; others ar e made by independent suppliers who are not directly regulated, but who will be affected by the proposed standards as well. The additional expenditures on technologies are expected to have a positive effect on employment in the parts sector as well as the manufacturing se ctor; the breakdown in employment between the two sectors is difficult to predict. The effects on the parts sector also depend on the effects of the proposed standards on vehicle sales and on the labor intensity of the new technologies, qualitatively in the same ways as for the auto manufacturing sector.

8.2.6 Effects on Employment for Fuel Suppliers

In addition to the effects on the auto manuf acturing and parts sectors, these rules will result in changes in fuel use that lower GHG em issions. Fuel saving, prin cipally reductions in liquid fuels such as gasoline and diesel, will a ffect employment in the fuel suppliers industry sectors throughout the supply chain, from refineries to gasoline st ations. To the extent that the proposed standards result in increased use of electricity or other new fuels, employment effects will result from providing these fuels and developing the infrastructure to supply them

to consumers.

Expected petroleum fuel consumption reduc tions can be found in Chapter 5.3. While this reduced consumption represents fuel savings for purchasers of fuel, it represents a loss in value of output for the petroleum refinery indus try, fuel distributors, and gasoline stations. The loss of expenditures to petroleum fuel s uppliers throughout the petroleum fuel supply chain, from the petroleum refiners to the gaso line stations, is likely to result in reduced employment in these sectors.

This rule is also expected to lead to in creases in electricity consumption by vehicles, as discussed in Chapter 5.3. This new fuel ma y require additional infr astructure, such as electricity charging locations. Providing this infrastructure will require some increased employment. In addition, the generation of elect ricity 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 provisi on and generation for elect ricity will be greater or less than the employment reductions associ ated with reduced demand for petroleum fuels.

8.2.7 Effects on Employment due to Impacts on Consumer Expenditures

As a result of these proposed standards, consumers will pay a higher up-front cost for the vehicles, but they will recover those costs in a fairly short payback period (see Preamble Section III.H.10.b); indeed, people who finance thei r 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 in itial payback period. These in creased expenditures will support employment in those sectors where consumers spend their savings.

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-33 These increased expenditures will occur in 2017 and beyond. If the economy returns to full employment by that time, any change in consumer expenditures would primarily represent a shift in employment among sector s. If, on the other hand, the economy still has substantial unemployment, these expenditures would contribute to employment through
increased consumer demand.

8.2.8 Summary

The primary employment effects of this proposal are expected to be found throughout several key sectors: auto manufacturers, auto dealers, auto parts manufacturing, fuel production and supply, and consumers.

These proposed standards in itially 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 w ith full employment, the primary employment effect of a rulemaking is likely to be to move employment from one sect or to another, rather than to increase or decrease employment. Fo r that reason, we focus our partial quantitative analysis on employment in the re gulated 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, pa rticularly so far into the future.

For the regulated sector, the cost effect is expected to increase employment by 600 – 3,600 workers in 2017, depending on the share of that employment that is in the auto manufacturing sector compared to the auto parts manufacturi ng sector. As mentioned above, some of these job gains may occur earlier as au to manufacturers and part s suppliers hire staff to prepare to comply with the standard. The demand effect is ambiguous and depends on changes in vehicle sales, which are not quantif ied for this proposal. Though we do not have
estimates of the factor shift effect for all potential compliance tec hnologies, the evidence which we do have for some technologies suggest s that many of the technologies will have
increased labor needs.

Effects in other sectors that are predi cated 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 depe nds on any changes in the labor intensity of production; as noted, the subset of potential compliance tech nologies for which data are available show increased labor requireme nts. Reduced fuel production implies less employment in the petroleum sectors. Finall y, consumer spending is expected to affect employment through changes in expenditures in general retail sectors; net fuel savings by consumers are expected to increase demand (and therefore employment) in other sectors.

### Chapter 8

8-34 References

1 

### See Hunt Alcott, Social Norms and Ener gy Conservation, Journal of Public Economics (forthcoming 2011), available at http ://web.mit.edu/allcott/www/Allcott%20201
1%20JPubEc%20-%20 Social% 20Norms% 20and%20Energy%20Conservation.pdf; Christophe Chamley, Rational Herds: Economic Models of Social Learning (Cambridge, 2003).

2 

### See, for instance, Gron, Ann, and Deborah Swenson, 2000. “Cost Pass-Through in the U.S. Automobile Market,” Review of Economics and Statisti cs 82: 316-324 (Docket EPA-HQOAR-2010-0799).

3 

### Insurance Information Institute, 2008, “Average Expenditures for Auto Insurance By State, 2007-2008,” http://www.iii.org/media/facts/stat sbyissue/auto/, acce ssed 8/22/11 (Docket EPA-HQ-OAR-2010-0799).

4 

### U.S. Department of Energy, 2011. “Transportation and the Economy,” Chapter 10 in “Transportation Energy Data Book,” http: //cta.ornl.gov/data/tedb30/Edition30_Chapter10.pdf, accessed 8/22/11, Table 11 (D ocket EPA-HQ-OAR-2010-0799).

5 

### Solheim, Mark, 2006 “State Car Tax Rankings,” http://www.kiplinger.com/features/archive s/2006/04/cartax.html, accessed April 23, 2009 (Docket EPA-HQ-OAR-2009-0472-0010) (Docket EPA-HQ-OAR-2010-0799).

6 

### U.S. Census Bureau, "Table 2. Resident P opulation of the 50 Stat es, the District of Columbia, and Puerto Rico: 2010 Cens us" http://2010.census.gov/news/presskits/apportionment/apport.html .

7 

### “National Auto Loan Rates for July 21, 2011,” http://www.bankrate.com/finance/auto/nati onal-auto-loan-rates- for-july-21-2011.aspx, accessed 7/26/11 (Docket EPA-HQ-OAR-2010-0799).

8 

### Goldberg, Pinelopi Koujianou, “Product Di fferentiation and Oligopol y in International Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799); Goldberg, Pi nelopi 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).

9 

### Greene, David L., K.G. Duleep, Doug E lliott, and Sanjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the En ergy Information Administration,” prepared by the Oak Ridge National Laboratory for the U. S. Department of Energy under contract No. DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR -2010-0799); Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feeb ates, Rebates, and Ga s-Guzzler Taxes: A

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

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10 

### McManus, Walter M., “Can Proactive Fuel Economy Strategies Help Automakers Mitigate Fuel-Price Risks?” University of Michigan Tr ansportation Research Institute, September 14, 2006 (Docket EPA-HQ-OAR-2010-0799).

11 

### Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010- 0799); Berry, Steven, James Levinsohn, and Arie l Pakes, “Differentiated Products Demand Systems from a Combination of Micro and Macr o Data: The New Car Market,” Journal of Political Economy 112(1) (2004): 68-105 (D ocket EPA-HQ-OAR-2010-0799).

12 

### Bento, Antonio M., Lawrence H. Goulder, Emer ic Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoli ne Taxes” American Economic
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13 

### Train, Kenneth E., and Clifford Winston, “V ehicle Choice Behavior and the Declining
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14 

### 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).

15 

### Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher
CAFE Standards and Increased Gasoline Taxe s,” Journal of Environmental Economics and Management 50 (2005): 562-582 (Doc ket EPA-HQ-OAR-2010-0799).

16 

### E.g., Bento, Antonio M., Lawrence H. Goulde r, Emeric Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Effici ency Impacts of Gasoline Taxes,” American
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17 

### E.g., Berry, Steven, James Levinsohn, and Arie l Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010- 0799).

18 

### Greene, David L., K.G. Duleep, Doug E lliott, and Sanjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the En ergy Information Administration,” prepared by the Oak Ridge National Laboratory for the U. S. Department of Energy under contract No. DE-AC0500OR22725, 2005 (Doc ket EPA-HQ-OAR-2010-0799).

### Chapter 8

8-36 19 

### NERA Economic Consulting, “Evaluation of NHTSA’s Benefit-Cost Analysis of 2011- 2015 CAFE Standards,” 2008, available at http://www.heartland.org/policybot/results/2349 5/Evaluation_of_NHTSA s_BenefitCost_Anal ysis_Of_20112015_CAFE_Standards.html (Docket EPA-HQ-OAR-2010-0799).

20 

### E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining
Market Share of U.S. Automakers ,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

21 

### E.g., Berry, Steven, James Levinsohn, and Arie l Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010- 0799).

22 

### E.g., Bento, Antonio M., Lawrence H. Goulde r, Emeric Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Effici ency Impacts of Gasoline Taxes,” American
Economic Review 99(3) 2009): 667-699 (D ocket EPA-HQ-OAR-2010-0799).

23 

### Bento, Antonio M., Lawrence H. Goulder, Emer ic Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic
Review 99(3) 2009): 667-699 (Docket EPA- HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010- 0799).

24 

### NERA Economic Consulting, “Appendix B: New Vehicle Market Model,” “Impacts of the
California Greenhouse Gas Emission Standard s on Motor Vehicle Sales,” comments submitted to the U.S. Environmental Protection Agency at Regulations.gov, document number EPA-HQ-OAR-2006-0173-9053.1 (Docket EPA-HQ-OAR-2010-0799).

25 

### E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining
Market Share of U.S. Automakers ,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799)

26 

### E.g., Berry, Steven, James Levinsohn, and Arie l Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010- 0799).

27 

### Greene, David L., Philip D. Patterson, Margar et Singh, and Jia Li, “F eebates, 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).

28 

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### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-37 Endogenous Product Design,” Energy Institute at Haas Working Paper 214, University of California Energy Institute, Februa ry 2011 (Docket EPA-HQ-OAR-2010-0799).

30 

### 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).

31 

### Goldberg, Pinelopi Koujianou, “Product Di fferentiation and Oligopol y in International Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799).

32 

### Brownstone, David, and Kenneth Train, “F orecasting New Product Penetration with Flexible Substitution Patterns,” Journal of Econometrics 89 (1999): 109-129 (Docket EPAHQ-OAR-2010-0799); Brownstone, Da vid, David S. Bunch, and Ke nneth Train, “Joint Mixed Logit Models of Stated and Revealed Pref erences for Alternative-Fuel Vehicles,” Transportation Research Part B 34 (2000): 315-338 (D ocket EPA-HQ-OAR-2010-0799); Greene David L., “TAFV Alternative Fuels a nd Vehicles Choice M odel Documentation,” prepared by the Oak Ridge Nati onal Laboratory for the U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., K. G. Duleep, and Walter McManus, “Future Potential of Hybrid and Diesel Powert rains in the U.S. Light-Duty Vehicle Market,” prepared by the Oak Ridge Nati onal Laboratory for the U.S. Department of Energy, August 2004 (Docket EPA-HQ-OAR-2010-0799).

33 

### Petrin, Amil, “Quantifying the Benefits of New Products: The Case of the Minivan,” Journal of Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799); Berry, Steven, James Levinsohn, and Ariel Pake s, “Differentiated Products Demand Systems
from a Combination of Micro and Macro Data: The New Car Market,” Journal of Political Economy 112 (2004): 68-105 (Doc ket EPA-HQ-OAR-2010-0799).

34 

### Train, Kenneth E., and Clifford Winston, “V ehicle Choice Behavior and the Declining
Market Share of U.S. Automakers ,” International Economic Review 48 (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

35 

### Bento, Antonio M., Lawrence H. Goulder, Emer ic Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic
Review 99(3) 2009): 667-699 (Docket EPA- HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010- 0799).

36 

### Greene, David L., Philip D. Patterson, Margar et Singh, and Jia Li, “F eebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799) ; Feng, Yi, Don Fullerton, and Li Gan,

### Chapter 8

8-38 “Vehicle Choices, Miles Driven and Polluti on Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.t amu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010- 0799); Greene, David L., “Feebates, Footprints and Highway Safety,” Transportation Research Part D 14 (2009): 375-384.

37 

### E.g., Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gaso line Taxes,” Journal of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

38 

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39 

### E.g., Espey, Molly, and Santosh Nair, 2005. “A utomobile Fuel Economy: What Is It Worth?” Contemporary Economic Policy 23: 317-323 (Docket EP A-HQ-OAR-2010-0799).

40 

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41 

### E.g., Goldberg, Pinelopi Koujianou, “Product Diffe rentiation and Oligop oly in International Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799).

42 

### E.g., Berry, Steven, James Levinsohn, and Arie l Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010- 0799).

43 

### Busse, Meghan, Christopher R. Knittel, and Fl orian Zettelmeyer, 2009. “Pain at the Pump:
How Gasoline Prices Affect Automobile Purc hasing in New and Used Markets,” working paper, http://web.mit.edu/knittel/www/papers/g aspaper_latest.pdf (acce ssed 11/1/11) (Docket EPA-HQ-OAR-2010-0799); Li, Shanjun, Christophe r Timmins, and Roger H. von Haefen,
2009. “How Do Gasoline Prices Affect Fleet Fu el Economy?” American Economic Journal: Economic Policy 1(2): 113-137; Congre ssional Budget Office (2008) (Docket EPA-HQOAR-2010-0799); Congressional Budget Office ( 2008). Effects of Gasoline Prices on Driving Behavior and Vehicle Markets . The Congress of the United States, Pub. No. 2883 (Docket EPA-HQ-OAR-2010-0799); West. Sarah (2007) . “The Effect of Gasoline Prices on the Demand for Sport Utility Vehicles.” Working Paper. Macalester College, http://www.macalester.edu/~wests/Sarah WestMEA2007.pdf, accessed 1/25/10 (Docket EPAHQ-OAR-2010-0799).

44 

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### Greene, David L. (2010). “How Consumers Va lue Fuel Economy: A Literature Review.” EPA Report EPA-420-R-10-008 (D ocket EPA-HQ-OAR-2010-0799).

### Summary of interagency working comment s received on draft rule under EO 12866 review. These are for your considera tion and are subject to policy review.

8-39 46 

### Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S. Automobile Industry,” http://faculty.ms b.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket EP A-HQ-OAR-2010-0799).

47 

### Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile Industry,” http://faculty.msb.edu/jpg72/Auto s_GramlichJacob.pdf , accessed 11/1/11 (Docket EPA-HQ-OAR-2010-0799); McManus, Walter, 2007. “The Impact of Attribute-Based Corporate Average Fuel Economy (C AFE) Standards: Preliminar y Results.” Ann Arbor, MI: University of Michigan Transportation Re search Institute, Report No. UMTRI-2007-31 (Docket EPA-HQ-OAR-2010-0799).

48 

### For instance, Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 2 79-294 (Docket EPA-HQ-OAR2010-0799); Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards a nd Increased Gasoline Taxes,” Journal of Environmental Economics and Management 50 (2005): 562- (Docket EPA-HQ-OAR-2010- 0799); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of the Corporate Average Fuel Economy Standard,” Resources for the Future Paper RFF DP 10- 50 (December 2010), http://www.rff.org/RFF/Do cuments/RFF-DP-10-50.pdf) (Docket EPAHQ-OAR-2010-0799)Jacobsen, Mark, 2010. “Evaluati ng U.S. Fuel Economy Standards in a
Model with Producer and Household He terogeneity,” working paper, at http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAF E.pdf (accessed 11/1/11) (Docket EPA-HQOAR-2010-0799) .

49 

### Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher
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50 

### IEA. 2007. “Mind the Gap: Quantifying Princi pal-Agent Problems in Energy Efficiency

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, Toman, Michael A., ed., Washington, D.C.: Resources for the Future. p.171-181 (Docket EPA-HQ-OAR-2010-0799); Metcalf, Gilbert E., and Kevin A. Hassett (1999). "Measuring the Energy Savings From Home Improvement Investments: Evidence From Monthly Billing
Data." The Review of Economics and Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010- 0799); Tietenberg, T. (2009). “Reflections – Energy Efficiency Polic y: Pipe Dream or Pipeline to the Future?” Review of Environmental Economics and Policy 3(2): 304-320 (Docket EPA-HQ-OAR-2010-0799).

51 

### Helfand, Gloria, and Ann Wolverton (2011). “Evaluating the Consumer Response to Fuel Economy: A Review of the Literature.” International Review of Environmental and
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### Chapter 8

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### Jaccard, Mark. “Paradigms of Energy Effici ency’s Cost and their Policy Implications: Déjà Vu All Over Again.” Modeling the Economics of Greenhouse Gas Mitigation: Summary of a Workshop, K. John Holmes, Rappo rteur. National Academies Press, 2010. http://www.nap.edu/openbook.php?record_id= 13023&page=42 (Docket EPA-HQ-OAR2010-0799)

53 

### Greene David L., “TAFV Alternative Fuels and Vehicles Choice M odel Documentation,” prepared by the Oak Ridge Nati onal Laboratory for the U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799).

54 

### 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/pr esidential-memorandum-regarding-fuelefficiency-standards

55 

### U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed on August 9, 2011.

56 

### Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011 (Docket EPA-HQ-OAR-2010-0799).

57 

### 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 (Doc ket EPA-HQ-OAR-2010-0799).

58 

### Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental and Natural Resource Policy.” Environmen tal and Resource Economics 22 (2002): 133-156 (Docket EPA-HQ-OAR-2010-0799).

59 

### 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)

.

60 

### http://www.bls.gov/emp/ep_da ta_emp_requirements.htm

61 

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

62 

### http://www.bls.gov/emp/ep_data_emp_requirements .htm; this analysis used data for sectors 88 (Motor Vehicle Manufacturing) and 90 (Motor Vehicle Parts Manufac turing) from “Chainweighted (2000 dollars) real domestic employment requirements table. . . adjusted to remove imports.”

63 

### FEV, Inc. “Light-Duty Technology Cost Analys is: Power-split and P2 HEV Case Studies,” Report FEV07-069-303, October 10, 2011 (Docket EPA-HQ-OAR-2010-0799).