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

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

From: Achanta, Chandana L.
To: 'goo.michael@epamail.epa.gov'; 'kathryn.thomson@do t.gov'
Cc: Theroux, Richard P.; Mancini, Dominic J.; Fitzpatr ick, Michael A.; Guzy, Gary S.; Bordoff, Jason E.



### Hello Michael, Katie-

Please find attached additional interagency working comments received on draft NPRM under EO 12866 and EO 13563 review. They are for your consideratio n, and are subject to further policy review.

Thanks.
Chandana

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

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

2-1 Below is a summary of interagency working comments received during EO 12866 and EO 13563 review of the draft rule “Joint Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Ave rage Fuel Economy Standards”. They are for your consideration and are subject to furth er policy review.

Interagency commenters recommend that the agencies replace current EPA Chapter 8 with the following working draft language and make consisten t changes throughout the Preamble, NHTSA RIA, and TSD chapters.



## ***

Chapter 8 Vehicle Sales, Employment, and Economy-Wi de Impacts

1.1 Vehicle Sales Impacts

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

An initial consideration of the Proposed Rule on ve hicle sales impacts must compare two competing effects. On the one hand, as a resul t of this rule, the vehicles will become more expensive, which would, by itself, discourage sales. On the other hand, the vehicles will have improved fuel economy and thus lower operating costs, which makes them more attractive to consumers. The net effect will depen d on the extent to which consumers are willing to pay for fuel economy. As discussed in P reamble III.H of the Proposed Rule, there is considerable uncertainty in the economics litera ture about the extent to which consumers fully value fuel savings from increased fuel econom y. The effect of this proposed regulation on vehicle sales will depend on whether the value p laced on the increased fuel economy is greater or less than the increase in vehicle prices . Of course, there are two possibilities. Consumers will value the fuel economy improvements at amounts that are either greater than or less than the corresponding increase in vehicle price. For the first group of consumers, the standard assumption that producers act to maximize profits implies these vehicles will be produced without a regulation, since consumers woul d be willing to pay the higher price and it would be profitable for the manufacturers to pro duce such vehicles. On the other hand, consumers who value the fuel economy improvements a t less than the corresponding increase in vehicle price would be less likely to buy such v ehicles and a requirement that they be produced nonetheless would generally lead to a decr ease in sales. This is because for these consumers the difference between the value of great er fuel economy and the higher sales prces will be perceived as a pure price increase.

An illustrative example might help. Suppose that a particular improvement in fuel economy causes a vehicle’s production cost to rise by $1,000; and suppose that the auto market were competitive, so that the price rose by the same amount. Consider three scenarios, with different values of fuel savings. First, suppose that consumers valued the Comment [A1]: Interagency commenters note that this may be more complicated than presented in the original example. The original example assumed imperfect competition in the case of high consumer value of fuel savings, but perfect competition in t he case of low consumer value. Interagency commenters suggest that it might be easier to use one market power assumption, and in that case perfect competition might be more intuitive. However, interagency commenters recommend that agencies drop this example.Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-2 corresponding fuel savings at moire tha$1,500. In this case, one would expect ordinary market forces to result in these vehicles being pro duced in the absence of regulation, since consumers would pay the price of increase of $1,000 and still be better off than before.In this case, the regulation would have no effect on fuel e conomy or on vehicle sales. In a second scenario, if consumers valued the fuel savings at e xactly $1,000, the regulation might induce manufacturers to build more fuel-efficient cars, bu t it would would have no impact on sales. Because consumer willingness to pay would increase by exactly the amount of the cost, producers could raise prices to just cover their co sts, and the quantity demanded would be the same as before the improvement in fuel economy. Fi nally, if consumers valued the fuel savings at $750, it would not be in manufacturers’ interest to provide them. A requirement that they be produced would lead to cars being prod uced with higher fuel economy. However, while their price would rise by $1,000, th ey would be worth only an additional $750. Consumers would perceive the $250 difference as a pure price increase and sales would decline, with the magnitude of the decline de pending on the price elasticity of vehicle demand. In reality, consumers will vary in their v aluation of fuel savings, for a variety of reasons, including miles driven, local gasoline pri ces, willingness to trade off future savings against initial costs (measured by the rate at whic h consumers discount future fuel savings),
and so on. Producer decisions should take such con sumer heterogeneity into account. Nonetheless, the effect of fuel economy standards o n sales will depend on how consumers value future fuel savings relative to the increase in cost.

As noted above and in Preamble III.H, there is cons iderable evidence that consumers undervalue the fuel savings from increased fuel eco nomy. A simple way to capture this is by viewing consumers as applying a high discount rate to future fuel savings [cite literature]. For a consumer with an effective discount rate of 15%, the value of fuel savings – corresponding to the consumer’s willingness to pay for increased fuel economy – would therefore be calculated by discounting the first five years of f uel savings at 15% and summing them. For instance, the 2017 fuel savings per vehicle are the present value in year 2017 of fuel savings estimated for 2017 through 2021, using the assumed discount rate that reflects consumer
behavior.

To better understand the factors that can determine the effect of fuel economy standards on vehicle sales, we conduct a simple sal es impact analysis. As a benchmark for our analysis, we maintain the assumption used in th e compliance modeling (discussed in the Preamble XX and in Chapter YY of this DRIA) that ve hicle sales are unaffected by the proposed rule. This approach allows us to ask: What is the effective consumer discount rate for which the increased cost of greater fuel econom y just equals the consumer’s value for fuel savings, so that total vehicle sales are constant?

For the cost of improved fuel economy, we use the i ncrease in cost estimated by the
OMEGA model. A

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

A The higher vehicle price is likely to lead to an i ncrease in sales tax, insurance, and vehicle financ ing costs, as
well as increases in the resale value of the vehicl e. However, for simplicity we ignore those in this analysis. We point out that to the extent they are included, the y should be discounted using the same effective dis count rateSummary of interagency working comments received on draft rule under EO 12866 and EO

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

2-3 onto this vehicle for 5 years and then sells it. T his period is consistent with the length of a typical new light-duty vehicle loan, and is similar to the average time that a new vehicle purchaser holds onto the vehicle; it has also been used by NHTSA in previous CAFE rulemakings. B We request comment on whether there are more a ppropriate time periods to
consider for this calculation.

We can perform this analysis for an average vehicle in any given model-year. Chapter XX of this DRIA discusses the source of aggregate f uel savings, in dollars, for cars and trucks for each model year by year. These values are divi ded by the total number of the vehicles produced to get per-vehicle savings per year for th e first five years of the vehicle’s life. This method ignores the few vehicles of the new model ye ar that are scrapped. Because incorporating scrappage would reduce the denominato r, and thus increase per-vehicle fuel savings, it underestimates per-vehicle fuel savings by a small amount.

For example, consider model year 2025. In that yea r, the average increase in vehicle cost associated with improved fuel economy is $2100 , while average fuel savings are about $600 per year (Preamble XXX and DRIA Chapter YYY). Under the assumption that consumer consider five years of fuel savings, the “ breakeven discount rate” is 15.5% . In other words, if the entire increased cost of $2100 is passed onto consumer in the form of a higher initial purchase price, consumer willingness to pay for the resulting fuel savings would just balance out the initial cost if they apply a d iscount rate of 15.5% to the annual fuel savings for the first five years. Put yet another way, a discount rate of 15.5% is consistent with the following assumptions: (i) consumers do no t fully value fuel savings; (ii) producers seek to maximize profits; and (iii) fuel economy st andards will change the vehicle mix without affecting total vehicle sales.

We request comment on whether those parameters abov e are representative of current market conditions. For example, what discount rate is appropriate for consumer decision making on durable goods? The agency requests that commenters point to additional studies or data sources that can be used in updated those a ssumptions.

The breakeven discount rate calculated above can se rve as a benchmark in evaluating the effect of the regulation on sales under other a ssumptions about consumer behavior. In theory, if consumers apply a lower effective discou nt rate, so that they are willing to pay more for fuel economy, sales would increase following an improvement in fuel economy.

used to discount fuel savings. That significantly complicates the analysis. These factors weigh agai nst each other: the higher sales tax, insurance, and financ ing costs increase costs to consumers; the higher r esale value allows consumers to recover a portion of these cost s.
B As discussed further in Chapter 8.1.2.5, there is not a consensus in the literature on how consumers consider fuel economy in their vehicle purchases. Results a re inconsistent, possibly due to fuel economy not b eing a major focus of many of the studies, and possibly du e to sensitivity of results to modeling and data us ed. A survey by Greene (Greene, David L. “How Consumers V alue Fuel Economy: A Literature Review.” EPA Report EPA-420-R-10-008, March 2010 (Docket EPA-HQ- OAR-2009-0472-11575)) finds that estimates in the literature of the value that consumers place on fue l economy when buying a vehicle range from negative

– consumers would pay to reduce fuel economy – to more than 1000 times the value o f fuel savings. Comment [A2]: Interagency commenters note that it may be preferable to consider another year, e.g., 2017.Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-4 However, such an effect should already be reflected in the baseline without regulation (i.e., producers should already be producing the vehicles that consumers desire to purchase); otherwise, such an outcome would be inconsistent wi th the standard assumption that producers maximize profits.

If consumers apply an even higher effective discoun t rate in valuing fuel savings [as some studies of consumer durables suggest], then co nsumer valuation of potential fuel savings
would be less than the cost of the increased techno logy, and sales would be expected to decline. The magnitude of this impact would depend on the net cost of improved fuel economy, after accounting for fuel savings, as well as on the elasticity of demand. A standard value for the elasticity of demand for new vehicles of -1: 1 that is, an increase of 1% in the price of a new vehicle will lead to a 1% reduction in new vehicle sales. This elasticity is generally considered to be a short-run elasticity, reflecting the immediate impacts of an immediate price change on vehicle sales. For a dur able good such as an auto, the elasticity may be smaller in the long run: though people may be able to change the timing of their purchase when price changes in the short run, they must eventually make the investment. Using a smaller elasticity would reduce the magnitu de of the estimates presented here for vehicle sales, but it would not change the directio n. A short-run elasticity is more valid for initial responses to changes in price, but, over ti me, a long-run elasticity may better reflect behavior; thus, the results presented for the initi al years of the program may be more appropriate for modeling with the short-run elastic ity than the later years of the program. A

search of the literature has not found studies more recent than the 1970s that specifically investigate long-run elasticities. C This change in consumer expenditure as a percent of the average price of a new vehicle, with the elasticity of demand of -1, is the negative of the percent change in vehicle purchases.

Evaluating the impact of the fuel economy rule on v ehicle sales also depends on a number of assumptions that become less certain the further into the future they are applied. For example, consumers are heterogeneous in their b ehavior: some types of consumers may value fuel economy highly, while others give it les s weight. Thus the effect of fuel economy on sales will depend on the distribution of consume r types in the population. For vehicle sales, these include the future path of gasoline pr ices, changes in future cost of fuel-saving technologies, changing market conditions, and other shifts in the U.S. economy that could affect vehicle demand and national driving behavior

.

Adding to the uncertainty is the dynamic effect tha t the proposed rule may have on other relevant aspects of the vehicle sales market aside from purchase price. For example, the proposed rule may induce technological change that would not have occurred otherwise, and
introduce consumers to new vehicle attributes that impact vehicle demand in a positive way.

C E.g., Hymans, Saul H. “Consumer Durable Spending: Explanation and Prediction.” Brookings Papers on Economic Activity 1 (1970): 173-206. http://www.brookings.edu/~/media/Files/Programs/ES/ BPEA/1970_2_bpea_papers/1970b_bpea_hymans_ackley _juster.pdf finds a short-run elasticity of auto ex penditures (not sales) with respect to price of 0.7 8 50 1.17, and a long-run elasticity of 0.3 to 0.46.Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-5 Similarly, improvements in vehicle economy might ac company other changes in vehicle characteristics that are relevant for purchase deci sions.

The agency requests comment on how future uncertai nty about economic conditions my present unique difficulties in conducting a sale s impact analysis for this rulemaking.

While these factors are always present, they are pa rticularly acute in the current context, in which the rule will not take effect unt il 2017. As a result, we are not seeking to quantify the impacts of this proposed rule on vehic le sales. We welcome comments on all aspects of this discussion, including assumptions o n consumer behavior; how consumers value future fuel savings at the time of purchase; factors that should be taken into account when assessing the impact of fuel economy standard on aggregate vehicle sales; sources of uncertainty in estimating vehicle sales impacts. W e also welcome quantitative estimates of the effects on sales, accompanied by detailed descr iptions of the methodologies used.

1.1.2 Consumer Vehicle Choice Modeling D

The vehicle sales analysis outlined above focuses o n the comparison between the increased purchase price associated with improved f uel economy and the value of the resulting fuel savings. An alternative approach is the use of consumer vehicle choice models.
In this section we describe some of the consumer ve hicle 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 indica tes a huge range of estimates for consumers’ willingness to pay for additional fuel e conomy. Because consumer surplus estimates from consumer vehicle choice models depen d critically on this value, we would consider any consumer surplus estimates of the effe ct of our rule from such models to be unreliable. In addition, the predictive ability of consumer vehicle choice models may be
limited. While vehicle choice models are based on sales of existing vehicles, vehicle models are likely to change, both independently and in res ponse 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 tech nology costs. EPA will continue its efforts to review the literature, but, given the known limitat ions and uncertainties of vehicle choice models, EPA has not conducted an analysis using the se 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.,

D 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 rule under EO 12866 and EO

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

2-6 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.

1.1.2.1 Methods

Consumer choice models (CCMs) of vehicle purchases typically use a form of discrete choice modeling. Discrete choice models seek to 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. E
In a nested logit, the model is structured in layer s. For instance, the first layer may be the

E 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 rule under EO 12866 and EO

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

2-7 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, 2 Greene et al., 3 and McManus. 4

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

While discrete choice modeling appears to be the pr imary method for consumer choice modeling, others (such as Kleit 8 and Austin and Dinan 9 ) 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.

1.1.2.2 Data Sources

The predictions of vehicle purchases from CCMs are based on consumer and vehicle characteristics. The CCMs identify the effects of changing the characteristics on the purchase decisions. These effects are typically called the parameters or coefficients of the models. For instance, the model parameters might predict that 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. 10
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. 11

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 12 and the New Vehicle Market Model developed by NERA Economic Consulting 13 are examples of calibrated models.Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-8 1.1.2.3 Factors Included in the Models

Consumer choice models vary in their complexity and levels of analysis. Some focus only on the new vehicle market; 14 others consider the choice between new vehicles an d an outside good (possibly including a used vehicle); 15 others explicitly consider the relationship
between the new and used vehicle markets. 16 Some models include consideration of vehicle miles traveled, 17 though most do not.

The models vary in their inclusion of both consumer and vehicle information. One model includes only vehicle price and the distribut ion of income in the population influencing choice; 18 others include varying numbers and kinds of vehicl e and consumer attributes.

Some models include only the consumer side of the v ehicle market; 19 others seek to represent both consumer and producer decisions. 20 Models that include only the consumer side are suitable for reflecting consumer choices, but they do not allow for revisions of vehicle characteristics in response to consumer 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. 21 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. 22 Even those vehicle choice models that include a producer model may not include much detai l, due to computational limits: allowing market forces to determine multiple vehicle charact eristics is still under development. 23

1.1.2.4 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, 24 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 a stakeholder in many public policy discussions, rese arch questions cover a wide gamut. These topics have included the effects of voluntary expor t restraints on Japanese vehicles compared to tariffs and quotas, 25 the market acceptability of alternative-fuel vehic les, 26 the effects of introduction and exit of vehicles from markets, 27 causes of the decline in market shares of U.S. automakers, 28 and the effects of gasoline taxes 29 and “feebates” 30 (subsidizing fuelefficient cars with revenue collected by taxing fue l-inefficient vehicles).Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-9 1.1.2.5 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 31 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 32 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: 33 since consumers pay for gallons of fuel, then thi s measure can assess fuel savings relatively directly

. 34 Yet other models multiply fuel consumption per mile by the cost of fuel to get the cost of driving a mile, 35 or they divide fuel economy by fuel cost to get miles per dollar. 36 It is worth noting that these last two measures assume that consumers respond the same way to an 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. F On the one hand, while this assumption does not rely on as complex a calculation as the present value of fuel savings that Turrentine and Kurani examined , it suggests a calculating consumer. On the other hand, using a form of cost per mile is a way to recognize the role of fuel prices in consumers’ purchase of fuel economy: recent resear ch 37 presents results that higher fuel prices play a major role in that decision.

Greene and Liu, 38 in a paper published in 1988, reviewed 10 papers 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

F Likewise, these measures assume consumers respond the same way to increases and decreases in cost per mile of driving, as well as if those increases and 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 rule under EO 12866 and EO

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

2-10 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 review from David Greene 39 suggests continued lack of convergence on the value of increased fuel economy to consumers. Of 2 7 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 fo und that consumers overvalued fuel economy, undervalued fuel economy, or roughly value d fuel economy correctly relative to fuel savings. Part of the difficulty may be, as th ese 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 40 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 one value that re presents the role of fuel economy in consumer purchase decisions.

Some studies 41 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 42 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 43 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.

1.1.2.6 Modeling Electric Vehicles and Other New Vehicles

Modeling the introduction of new vehicles can be a greater challenge than modeling the existing vehicle market, because the modeler 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,Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-11 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. G 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. 44 In that model, he considers only one base vehicle, a passenger car, but considers the effect on WTP of fuel cost per mile, range, acceleration, and several other vehicle attributes.

G For instance, Hidrue et al. (Hidrue, Michael K., 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 rule under EO 12866 and EO

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

2-12 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 1.1-1 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 1.1-1Willingness to Pay for Increasing Range Calculated from Various Studies

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

year) Value of additional

range in 2009$ a

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

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

Resource Systems Group (2009) for California Energy Commission d $2900 - $7500 (2009$) $2900 - $7500Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-13 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 1.1-1, 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.

1.1.2.7 EPA Exploration of Vehicle Choice Modeling

In order to develop greater understanding of these models, EPA is in the process of developing a vehicle choice model. In its current form, the model assumes that the vehicleSummary of interagency working comments received on draft rule under EO 12866 and EO

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

2-14 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 1.1.2.1. “Nesting” refers to the decisiontree structure of the model, and “logit” refers to the fact that the choices are discrete (i.e., yes/no decisions about which vehicles to purchase, instead of continuous values).

The nesting involves a hierarchy of choices. In 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, H 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 1.1.2 .6: 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 1.1.2.5, there is no consensus value for consumers’ willingness to pay f or improved fuel economy: estimates

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

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

2-15 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 from O MEGA the initial fleet of vehicles (including the initial sales and fuel economy), the cost of technologies added to each vehicle, and the change in fuel economy. With the initial s ales mix, for each vehicle, the model calculates a vehicle-specific constant that summari zes the value of all attributes of the vehicle other than price and fuel economy. This constant e nsures that the model will predict the
baseline vehicle fleet when the baseline fleet char acteristics are used as inputs. This constant substitutes for estimating the effects of changes i n all other vehicle characteristics; the underlying assumption is that these other vehicle c haracteristics do not change. For instance, it assumes that a Ford Escape will not change in si ze, power, or accessories; the only changes will be to its cost and its fuel economy.

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

It is possible that the predicted changes in fleet mix will lead to vehicle sales for auto makers that do not meet the proposed standards. To correct this problem, it would be necessary to feed the new fleet mix into OMEGA (whi ch calculates costs and compliance) and get a new set of output, which could then be fe d back into the vehicle choice model. OMEGA would increase technologies, and thus costs, to improve compliance; those adjustments would then again affect vehicle demand. We expect that this iteration process would converge to a fleet mix that would meet stand ards. Performing this iteration requires development of an interface between the vehicle cho ice 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 mod eling 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.

1.1.2.8 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 aboutSummary of interagency working comments received on draft rule under EO 12866 and EO

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

2-16 relevant effects on consumer welfare. In practice, the advantages depend on the success of models in predicting changes in fleet size and mix.

First, consumer vehicle choice modeling has the 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. The fact that consumers have additional options suggests that compliance costs are likely to be lower through incorporation of a consu mer choices than through use of a technology-cost model alone.

An additional complication associated with consumer choice modeling is accurate prediction of producers’ responses to the rule. 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 toSummary of interagency working comments received on draft rule under EO 12866 and EO

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

2-17 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 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.

1.2 Employment Impacts

1.2.1 Introduction

Although analysis of employment impacts is not part of a cost-benefit analysis (except to the extent that labor costs contribute to costs) , employment impacts of federal rules are of particular concern in the current economic climate of sizeable unemployment. When President Obama requested that the agencies develop this program, he sought a program that would “strenghten the [auto] industry and enhance j ob creation in the United States.” 45,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 e stimates of the effects of this proposal on domestic employment.

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.

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 rule under EO 12866 and EO

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

2-18 This proposal could affect employment in the United States through the regulated sector – the auto manufacturing industry – and thro ugh several related sectors, specifically, industries that supply the auto manufacturing indus try (e.g., vehicle parts), auto dealers, the fuel refining and supply sectors, and the general r etail 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 directl y regulated sector, since it encompasses the auto manufacturers that are responsible for com plying with the proposed standards. 46
Changes in light duty vehicle sales, discussed in C hapter Error! Reference source not
found. , could affect employment for auto dealers. The em ployment effects of this rule could also expand beyond the regulated sector. Though s ome of the parts used to achieve the proposed standards are likely to be built by auto m anufacturers themselves, the auto parts manufacturing sector also plays a significant role in providing those parts. As discussed in Chapter 5.3 of the RIA, this proposal is expected t o reduce the amount of fuel these vehicles use, and thus affect the petroleum refinery and sup ply industries. Finally, since the net reduction in cost associated with this proposal is expected to lead to lower household expenditures on fuel net of vehicle costs, consumer s then will have additional discretionary income that can be spent on other goods and service s.

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 employm ent 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 situatio n, 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. employment because the labor market is not in equil ibrium. 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. 47 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 Sta vins 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.”Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-19 Given the time frame of this proposed rule, we adop t the default presumption that the unemployment rate will have declined to 5.3 percent by the time by the time this rule takes effect (using official government forecasts), so th at the effect of the regulation on labor will
be to shift workers from one sector to another. 48 For example, to the extent that regulation increases the number of workers in the regulated se ctor (e.g., because of higher costs of producing vehicles with higher fuel economy), the o pportunity cost associated with those shifts in employment will be captured in labor cost s, 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 worke rs 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).

Nonetheless, it may be of interest to consider the qualitative impacts of the proposed rule on employment in various sectors. A number of different approaches have been used in published literature to conduct employment analysis . This section describes some of the common methods, as well as some of their limitation s.

1.2.2 Approaches to Quantitative Employment Analysis

Measuring the employment impacts of a policy depend on a number of inputs and assumptions. For instance, as discussed, 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 instances, these would only be partial perspectives on the overall change in national employment due to Federal regulation.

1.2.2.1 Conceptual Framework for Employment Impacts in the Regulated Sector

One study by Morgenstern, Pizer, and Shih 49 provides a retrospective look at the impacts of regulation in employment in the regulate d sectors. The paper provides a
theoretical framework that can be useful for examin ing 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 regul ated industry in response to a new (or more stringent) 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 thisSummary of interagency working comments received on draft rule under EO 12866 and EO

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

2-20 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 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. estimated the effects on employm ent 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 be tween 1979 and 1991. The authors conclude that increased abatement expenditures gene rally have not caused a significant change in employment in those sectors. More specif ically, their results show that, on average across the industries studied, each additional $1 m illion 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. In a ddition, it does not examine the effects of regulation on employment in sectors related to but outside of the regulated sector.

In addition, the empirical estimates provided by Mo rgenstern et al. are not relevant to the case of fuel economy standards, which are very different from the pollution control

L As will be discussed below, the demand effect in 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?Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-21 standards on industrial facilities that were consid ered in that study. Nevertheless, the theory that Morgenstern et al. developed continues to be u seful in this context.

The following discussion of additional methodologie s draws from Berck and Hoffmann’s review of employment models. 50

1.2.2.2 Computable General Equilibrium (CGE) Models

Computable general equilibrium (CGE) models are 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.” Thu s, 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.

1.2.2.3 Input-Output (IO) Models

Input-output models represent the economy through a matrix of coefficients that describe the connections between supplying and 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 ofSummary of interagency working comments received on draft rule under EO 12866 and EO

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

2-22 input-output models requires the assumption that 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.

1.2.2.4 Hybrid Models

As Berck and Hoffmann note, input-output models and CGE models “represent a continuum of closely related models.” Though not 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.

1.2.2.5 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.

1.2.2.6 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. 51 While often a sophisticated treatment of the issues analy zed, these studies commonly analyze specific scenarios or sectors in the past; care nee ds to be taken in extrapolating their results toSummary of interagency working comments received on draft rule under EO 12866 and EO

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

2-23 other scenarios and to the future. For instance, ne ither of these two studies examines the auto industry and are therefore of limited applicability in this context.

1.2.2.7 Summary

All methods of estimating employment impacts of a 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.

1.2.3 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 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 quali tative 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 sectorSummary of interagency working comments received on draft rule under EO 12866 and EO

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

2-24 without having to make assumptions about the state of the economy when this rule has its impacts. We include a qualitative discussion of 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 2017 through 2025; by then, th e 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 d irectly affected industries. Those sectors are likely to face the most concentrated employment impacts. The agencies seek comment on other sectors that are likely to be significantly a ffected and thus warrant further analysis in the final rulemaking analysis.

1.2.3.1 Employment Impacts in the Auto Industry

Following the Morgenstern et al. conceptual framewo rk for the impacts of regulation on employment in the regulated sector, we consider three effects for the auto sector: the demand effect, the cost effect, and the factor shif t effect. However, we are only able to offer quantitative estimates for the demand and cost effe cts. We note that these estimates, based on extrapolations from current data, become more uncer tain as time goes on.

1.2.3.1.1 The Demand Effect

The demand effect depends on the effects of this pr oposal on vehicle sales. If vehicle sales increase, then more people will be required t o assemble vehicles and their components. If vehicle sales decrease, employment associated wi th these activities will unambiguously decrease. Unlike in Morgenstern et al.’s study, wh ere the demand effect decreased employment, there are countervailing effects in the vehicle market due to the fuel savings resulting from this program. On one hand, this pro posal will increase vehicle costs; by itself,
this effect would reduce vehicle sales. On the oth er hand, this proposal will reduce the fuel costs of operating the vehicle; by itself, this eff ect would increase vehicle sales, especially if potential buyers have an expectation of higher fuel prices. As noted earlier in this chapter, the assumption that consumer willingness-to-pay tend to undervalue improved fuel economy, along with standard assumptions about producer beha vior, would suggest that vehicle sales will decrease, but the magnitude of any such effect is highly uncertain. For that reason, we are also not seeking to quantify the demand effect.

1.2.3.1.2 The Cost Effect

The demand effect, discussed above, measures 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 on draft rule under EO 12866 and EO

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

2-25 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), 52 which provides direct estimates of the employment per $1 million in sales of goods in 202 sectors. The most recent estimates, used here, are from 2008 (adjusted to 2009$). The tables used here are 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 53 (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 domesticSummary of interagency working comments received on draft rule under EO 12866 and EO

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

2-26 production to domestic sales. The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty Motor 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 1.2-1 provides the values, either given (BLS) or calculate d (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 sim ilar magnitudes for the estimates for the sectors. Parts manufacturing appears to be more labor-intensive than vehicle manufacturing; light-duty vehicle manufacturing appear s to be slightly less labor-intensive than motor vehicle manufacturing as a whole.

Table 1.2-1 Employment per $1 Million Expenditures (2009$) in the Motor Vehicle Manufacturing
Sector*

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

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

ASM Motor Vehicle Mfg 0.824 0.549

ASM Light Duty Vehicle Mfg 0.757 0.505

Economic

Census Motor Vehicle Mfg 0.674 0.449

Economic

Census Light Duty Vehicle Mfg 0.610 0.407

BLS ERM Motor Vehicle Parts Mfg 3.073 2.049

ASM Motor Vehicle Parts Mfg 3.093 2.063Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-27 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 indust ry has changed: automation and improved methods have led to significant productivity increases. The BLS ERM, for instance, provided estimates that, in 1993, 1.52 workers were needed per $1 million of 2000$, but only 0.83 workers by 2008 (in 20 00$). 54 Because the ERM is available annually for 1993-2008, we used these data to estim ate productivity improvements over time. We regressed logged ERM values on year (to es timate percent change per year) for both the Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing sectors. The results suggest a 4.4 percent per year productiv ity improvement in the Motor Vehicle Manufacturing Sector, and a 3.6 percent per year imp rovement in the Motor Vehicle Parts Manufacturing Sector. We then used the regression re lationship to project the ERM through 2025. In the results presented below, these p rojected values (adjusted to 2009$) 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 2 009 (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 estim ate). As noted above, we adjusted the estimate of workers per vehicle for the demand effec t in Chapter 1.2.3.1.1, above, using the productivity improvement for the Motor Vehicle Manufa cturing sector; because the estimate of workers per vehicle for the demand effec t was based on data from 2001 to 2010, we used the projected value of the ERM in 2005 as the denominator of the ratio.

Table 1.2-2 shows the cost estimates developed for this rule, discussed in Chapter 5.
The maximum value in Table 1.2-2 for employment impacts per $1 million expenditures (after accounting for the share of domestic production ) is 2.049 in 2009 if all the additional costs are in the parts sector; the minimum v alue is 0.407 in 2009, if all the additional costs are in the light-duty vehicle manufact uring sector: that is, the range of employment impacts is between 0.4 and 2 additional jobs per $1 million expenditures in

the sector in 2009. The results in Table 1.2-2 include the productivity adjustment described

above.

Table 1.2-2 Employment per $1 Million in the Motor Vehicle Manufact uring Sector

Year Costs (before

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

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

employment effect (if all expenditures are in the light duty vehicle mfg sector)Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-28 2017 $ 1,997 500 3,100

2018 $ 4,179 1,000 6,200

2019 $ 6,255 1,500 9,000

2020 $ 8,369 1,900 11,600

2021 $ 11,875 2,600 15,900

2022 $ 17,374 3,700 22,500

2023 $ 22,690 4,600 28,300

2024 $ 28,151 5,400 33,900

2025 $ 33,566 6,200 39,000

1.2.3.1.3 The Factor Shift Effect

The factor shift effect looks at the effects on empl oyment due to changes in labor intensity associated with a regulation. As noted abov e, the estimates of the cost effect assume constant labor per $1 million in expenditures, t hough 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 estimat e 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 manuf acturers could use to comply with the proposed standards. For a subset of the technologie s, though, EPA-sponsored research (discussed in Chapter 3.2.1.1 of the Joint TSD) whic h compared new technologies to existing ones at the level of individual components pr ovides some insights into the factor shift effect.

The comparison involved tearing down the selected tech nologies to their individual components and looking at the differences in materials and labor needs in moving from the conventional to the new technologies. 55 For instance, the analysis compared all the parts and labor associated with an 8-speed automatic trans mission 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 compl iance with the proposed standards. An increase in labor cost for the new technology indicate s an increase in the labor needed for the new technology compared to the baseline techn ology. For instance, an 8-speed transmission requires $15.11 more in labor costs tha n a 6-speed transmission. Dividing the labor cost by a wage per hour estimate provides an es timate of the additional hours (and thus the additional labor) needed for the new techn ology 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 c onversion, a weighted average wage rate (90 percent of the average wage in the Motor Veh icle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle Manufacturing Sector) of $46.36/hourSummary of interagency working comments received on draft rule under EO 12866 and EO

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

2-29 in 2015, using 2008 dollars (the unit of analysis for t he FEV study). For the change from a 6-speed to an 8-speed transmission, we thus estimate an additional 0.33 hours of labor per

transmission.

Table 1.2-3 shows the changes in labor hours in moving from basel ine to new fuelsaving technologies for technologies in the FEV study. It indicates that, in switching from the baseline to the new technologies, labor use per technology increased: the fuel-saving technologies use more labor than the baseline technol ogies. For a subset of the technologies likely to be used to meet the standards in this proposal, then, the factor shift effect increases labor demand, at least in the short run; in the long run, as with all technologies, the cost structure is likely to change due to learning, economies of scale, etc. The technologies examined in this research are, 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.

Table 1.2-3 Estimated Change in Labor for Selected Compliance Technologies

Technology FEV

Case Study Vehicle Class Labor
Costs Total

Costs Hours/

Technology

Downsized
Turbo GDI 4 0101 Compact C $72.58 $537.70 1.57

Downsized
Turbo GDI V6 0102 Mid/Large C $25.76 $87.38 0.56

Downsized
Turbo GDI V6 0104 SUV/Trucks $84.19 $789.53 1.82

Electric A/C

compressor 0602 $4.68 $167.54 0.10

Power split hybrid 0502 Mid/Large C $395.85 $3,435.01 8.54

6- to 8-speed
transmission 0803 Mid/Large C $15.11 $61.84 0.33

1.2.4 Effects on Employment for Auto Dealers

The effects of the proposed standards on employment for auto dealers depend principally on the effects of the standards on ligh t duty vehicle sales.

In addition, auto dealers may be affected by change s in maintenance and service costs. Increases in those costs are likely to increase lab or demand in dealerships.Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-30 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 1.1.2.6, 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. Auto dealers may play a maj or 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 th e new technologies as the new technologies
become available.

1.2.5 Effects on Employment in the Auto Parts Sector

As discussed in the context of employment in the 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.

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

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

2-31 1.2.7 Effects on Employment due to Impacts on Consumer Ex penditures

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 the ir vehicles are expected to find that their fuel savings per month exceed the increase in the l oan cost. As a result, consumers will have additional money to spend on other goods and servic es, though, for those who do not finance their vehicles, it will occur after the initial pay back period. These increased expenditures will support employment in those sectors where consumers spend their savings.

These increased expenditures will occur in 2017 and beyond. If the economy returns to full employment by that time, any change in cons umer expenditures (as discussed in the economy-wide impact analysis in section 8.3) would primarily represent a shift in employment among sectors. If, on the other hand, t he economy still has substantial unemployment, these expenditures would contribute t o employment through increased
consumer demand.

1.2.8 Summary

The primary employment effects of this proposal are expected to be found throughout several key sectors: auto manufacturers, auto deale rs, auto parts manufacturing, fuel production and supply, and consumers. We emphasize that the discussion here is a partial view of these effects. Whether the economy is in fu ll employment will determine whether the discussed changes in employment in these sectors is offset in other sectors of the economy. The extent to which employment in the sectors discu ssed above responds to this program will be influenced by how consumers consider fuel saving s in their vehicle purchase decisions, changes in vehicle manufacturing technologies and l ocations, and other factors.

This rule initially takes effect in model year 2017 , a time period sufficiently far in the future that the current sustained high unemployment at the national level may be moderated or ended. In an economy with full employment, the pri mary employment effect of a rulemaking is likely to be to move employment from one sector to another, rather than to increase or decrease employment. For that reason, we focus our limited quantitative analysis on employment in the regulated sector, to examine the impacts on that sector directly. We discuss the likely direction of impacts in directly related sectors, but we do not quantify those impacts, because employment impacts outside the reg ulated sector are more difficult to quantify with reasonable accuracy, particularly so far into the future.

For the regulated sector, the cost effect is expect ed to increase employment by 500 – 2,900 workers in 2017, depending on the share of th at employment that is in the auto manufacturing sector compared to the auto parts man ufacturing sector. Though we do not have estimates of the factor shift effect for all p otential compliance technologies, the evidence which we do have for some technologies suggests tha t many of the technologies will have
increased labor needs.Summary of interagency working comments received on draft rule under EO 12866 and EO

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

2-32 Increased vehicle sales are expected to lead to inc reased labor needs in auto dealerships. Increased vehicle sales and increased expenditures for auto parts are expected to require increased labor to build parts, though this effect also depends on any changes in the labor intensity of production; as noted, the subset of potential compliance technologies for which data are available show increased labor requi rements. Reduced fuel production implies less employment in the fuel provision sectors. Fin ally, net cost savings to consumers are expected to increase employment through their incre ased expenditures in general retail

sectors.

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

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

33 

## 2 Small Business Flexibility Analysis

1 Kleit A.N., 1990, “The Effect of Annual Changes in Automobile Fuel Economy Standards,” Journal of Regulatory Economics 2: 151-172 (Docket EPA-HQ-OAR-2009-0472-0015); McCarthy, Patrick S., 1996. “Market Price and Incom e Elasticities of New Vehicle Demands.” Review of Economics and Statistics 78: 543-547 (Doc ket EPA-HQ-OAR-2009-0472-0016); Goldberg, Pinelopi K., 1998. “The Effects of the Co rporate Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics 46(1): 1-33 (Docket EPA-HQ-OAR-2009-0472- 0017); Greene, David L., “Feebates, Footprints and Highway Safety,” Transportation Research

Part D 14 (2009): 375-384 (Docket EPA-HQ-OAR-2009-0472-0 019).

2 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-2009-0472-0021); 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-2009-0472-00 17).

3 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. Depa rtment of Energy under contract No. DEAC0500OR22725, 2005 (Docket EPA-HQ-OAR-2009-0472-00 22); Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, R ebates, and Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Poli cy33 (2005): 757-775 (Docket EPA-HQOAR-2009-0472-0023).

4 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-2009-0472-0024).

5 Berry, Steven, James Levinsohn, and Ariel Pakes, “ Automobile Prices in Market Equilibrium,”
Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-200 9-0472-0025); Berry, Steven, James Levinsohn, and Ariel Pakes, “Differen tiated Products Demand Systems from a
Combination of Micro and Macro Data: The New Car M arket,” Journal of Political Economy 112(1) (2004): 68-105 (Docket EPA-HQ-OAR-2009-0472 -0026).

6 Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of G asoline Taxes: An Econometrically Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 (Docket EPAHQ-OAR-2009-0472-0029).Summary of interagency working comments received on draft rule under EO 12866 and EO

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

34 7 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic R eview 48(4) (November 2007): 1469- 1496 (Docket EPA-HQ-OAR-2009-0472-0030).

8 Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry

42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-20 09-0472-0031).

9 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-2009-0472-0 032).

10 E.g., Bento, Antonio M., Lawrence H. Goulder, Emer ic Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impac ts of Gasoline Taxes: An Econometrically Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 (Docket EPA-HQ-OAR-2009-0472-0029); Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Aut omakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OA R-2009-0472-0030).

11 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-200 9-0472- 0025).

12 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. Depa rtment of Energy under contract No. DEAC0500OR22725, 2005 (Docket EPA-HQ-OAR-2009-0472-00 22).

13 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_Analysi s_Of_20112015_CAFE_Standards.html (Docket EPA-HQ-OA R-2009-0472-0033).

14 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-2009-0472-0030).

15 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-200 9-0472- 0025).

16 E.g., Bento, Antonio M., Lawrence H. Goulder, Emer ic Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impac ts of Gasoline Taxes: An Econometrically Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 (Docket EPA-HQ-OAR-2009-0472-0029).Summary of interagency working comments received on draft rule under EO 12866 and EO

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

35 17 Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes: An Econometrically Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 (Docket EPA-HQ-OAR-2009-0472-0029); Feng, Yi, Don Fullerton , and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf, accessed 5/12/09 (Docket EPA-HQOAR-2009-0472-0034).

18 NERA Economic Consulting, “Appendix B: New 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 Agency at Regu lations.gov, document number EPA-HQOAR-2006-0173-9053.1 (Docket EPA-HQ-OAR-2009-0472-0 035).

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-2009-0472-0030).

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-200 9-0472- 0025).

21 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-2009-0472-0023)

.

22 Rex, Emma, and Henrikke Baumann (2007). “Beyond e colabels: what green marketing can learn from conventional marketing.” Journal of Cle aner Production 15: 567-576.

23 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.

24 Berry, Steven, James Levinsohn, and Ariel Pakes, “ Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-200 9-0472- 0025).

25 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-2009-0472-0021).

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

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

36 Models of Stated and Revealed Preferences for Alter native-Fuel Vehicles,” Transportation
Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2009-0472-0 0357); Greene David L., “TAFV Alternative Fuels and Vehicles Choice Mod el Documentation,” prepared by the Oak Ridge National Laboratory for the U.S. Department o f Energy, July 2001 (Docket EPA-HQOAR-2009-0472-0038); Greene, David L., K. G. Duleep , and Walter McManus, “Future Potential of Hybrid and Diesel Powertrains in the U .S. Light-Duty Vehicle Market,” prepared by the Oak Ridge National Laboratory for the U.S. Depa rtment of Energy, August 2004 (Docket EPA-HQ-OAR-2009-0472-0039).

27 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-2009-0472- 0040); Berry, Steven, James Levinsohn, and Ariel Pakes, “Differen tiated Products Demand Systems from a
Combination of Micro and Macro Data: The New Car M arket,” Journal of Political Economy 112 (2004): 68-105 (Docket EPA-HQ-OAR-2009-0472-00 26).

28 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic R eview 48 (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2009-0472-0030).

29 Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes: An Econometrically Based Multi-Market Study,” American Economic Review 95(2) (May 2005): 282-287 (Docket EPA-HQ-OAR-2009-0472-0029); Feng, Yi, Don Fullerton , and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf, accessed 5/12/09 (Docket EPA-HQOAR-2009-0472-0034).

30 Greene, David L., Philip D. Patterson, Margaret 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-2009-0472-0023) ; Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Pollution P olicies,” 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-2009-0472-0034); Greene, David L., “Feebates, Footprints and Highway Safety,” Transportation Research Part D (2009) (in press) (Docket EPA-HQ-OAR2009-0472-0019).

31 E.g., Austin, David, and Terry Dinan, “Clearing 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-2009-0472-0 032).

32 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2009-0472 -0041).Summary of interagency working comments received on draft rule under EO 12866 and EO

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

37 33 E.g., Espey, Molly, and Santosh Nair, 2005. “Autom obile Fuel Economy: What Is It Worth?” Contemporary Economic Policy 23: 317-323 (Docket EP A-HQ-OAR-2009-0472-0042).

34 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 1593- 1594 (Docket EPA-HQ-OAR-2009-0472-0043).

35 E.g., Goldberg, Pinelopi Koujianou, “Product 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-2009-0472-0021).

36 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-200 9-0472- 0025).

37 Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. “Pain at the Pump: How Gasoline Prices Affect Automobile Purchasing in New and Used Markets,” working paper, http://www.econ.ucdavis.edu/faculty/knittel/papers/ gaspaper_latest.pdf (accessed 6/16/09) (Docket EPA-HQ-OAR-2009-0472-0044); Li, Shanjun, Ch ristopher Timmins, and Roger H. von Haefen, 2009. “How Do Gasoline Prices Affect Fleet Fuel Economy?” American Economic Journal: Economic Policy 1(2): 113-137; Congressional Budget Office (2008) . Effects of Gasoline Prices on Driving Behavior and Vehicle Mar kets . The Congress of the United States, Pub. No. 2883; West. Sarah (2007). “The Effect of Gasoline Prices on the Demand for Sport Utility Vehicles.” Working Paper. Macalester Coll ege, http://www.macalester.edu/~wests/SarahWestMEA2007.p df, accessed 1/25/10.

38 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-2009-0472-0045).

39 Greene, David L. (2010). “How Consumers Value Fue l Economy: A Literature Review.” EPA Report EPA-420-R-10-008. (Docket EPA-HQ-OAR-20 09-0472-11575).

40 Gramlich, Jacob, “Gas Prices and Endogenous Produc t Selection in the U.S. Automobile Industry,” http://www.econ.yale.edu/seminars/apmicr o/am08/gramlich-081216.pdf , accessed 5/11/09 (Docket EPA-HQ-OAR-2009-0472-0046).

41 Gramlich, Jacob, “Gas Prices and Endogenous Produc t Selection in the U.S. Automobile Industry,” http://www.econ.yale.edu/seminars/apmicr o/am08/gramlich-081216.pdf , accessed 5/11/09 (Docket EPA-HQ-OAR-2009-0472-0046); McManus , Walter, 2007. “The Impact of Attribute-Based Corporate Average Fuel Economy (CAF E) Standards: Preliminary Results.” Ann Arbor, MI: University of Michigan Transportati on Research Institute, Report No. UMTRI2007-31 (Docket EPA-HQ-OAR-2009-0472-0054).Summary of interagency working comments received on draft rule under EO 12866 and EO

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

38 42 For instance, Kleit, Andrew N., “Impacts of Long-R ange Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2009-0472-0031); Austin, David, and Terry Dinan, “C learing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2009- 0472-0032); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of the Corporate Average Fuel Economy Standard,” Fe deral Reserve Bank of Chicago WP 2008- 13, at http://www.chicagofed.org/publications/worki ngpapers/wp2008_13.pdf (accessed 5/6/09) (Docket EPA-HQ-OAR-2009-0472-0047); Jacobsen, Mark, 2008. “Evaluating U.S. Fuel Economy Standards in a Model with Producer and Hous ehold Heterogeneity,” working paper, at http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf (a ccessed 5/6/09) (Docket EPA-HQ-OAR2009-0472-0048).

43 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journ al of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2009-0472-0 032).

44 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-2009-0472-0038).

45 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency Standards. The White House, Office of the Press Secretary, May21, 2010. http://www.whitehouse.gov/thepress-office/presidential-memorandum-regarding-fuel -efficiency-standards

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

47 Schmalensee, Richard, and Robert N. Stavins. “A G uide to Economic and Policy Analysis of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011.

48 Fiscal Year 2012 Mid-Session Review, available at http://www.whitehouse.gov/sites/default/files/omb/b udget/fy2012/assets/12msr.pdf

49 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.

50 Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental and Natural Resource Policy.” Environmental and Re source Economics 22 (2002): 133-156.

51 Berman, Eli, and Linda T. Bui, (2001) “Environmenta l Regulation and Labor Demand: Evidence from the So uth
Coast Air Basin,” Journal of Public Economics , 79, 265 – 295.Summary of interagency working comments received on draft rule under EO 12866 and EO

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

39 52 http://www.bls.gov/emp/ep_data_emp_requirements.ht m

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

54 http://www.bls.gov/emp/ep_data_emp_requirements.ht m; this analysis used data for sectors 88 (Motor Vehicle Manufacturing) and 90 (Motor Vehicle Parts Manufacturing) from “Chainweighted (2000 dollars) real domestic employment re quirements table. . . adjusted to remove imports.”

55 FOOTNOTE FOR FEV TEARDOWN WORK