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

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

<rebecca.yoon@dot.gov>, Ed Nam/AA/USEPA/US@EPA, Rob in Moran/AA/USEPA/US@EPA, "'Timothy.Mullins@dot.gov'" <Timothy.Mullins@dot.go v>, William Charmley/AA/USEPA/US@EPA, "'Steve.Wood@dot.gov'" <Steve.Wood@dot.gov> Date: 08/21/2012 07:11 PM Subject: Summary of interagency working comments received on draft final rule under EO 12866 review



## Please find attached additional interagency working comments received on draft final rule “Joint Rulem aking to Establish Light-Duty Vehicle Greenhouse Gas Emissio n Standards and Corporate Average Fuel Economy Standards” under EO 12866 and EO 13563 review. They are for your consideration and are subject to furt her policy review.

Thanks.

Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review.pdfFrom: Robin Moran/AA/USEPA/US

To: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop .gov> Cc: Ed Nam/AA/USEPA/US@EPA, "''james.tamm@dot.gov' (jam es.tamm@dot.gov)'" <james.tamm@dot.gov>,

"'rebecca.yoon@dot.gov'" <rebecca.yoon@dot.gov>, Wi lliam Charmley/AA/USEPA/US@EPA Date: 08/22/2012 03:17 PM Subject: Re: Summary of interagency working comments receive d on draft final rule under EO 12866 review



### Chandana,

Attached is EPA's response to the interagency comme nts on vehicle sales and employment received last n ight.

Robin Moran Senior Policy Advisor Assessment and Standards Division U.S. EPA, Office of Transportation and Air Quality 2000 Traverwood Dr. Ann Arbor, MI 48105 (734) 214-4781 (734) 214-4821 (fax)

Response to Interagency Comments received 081912.do cx

Preamble III.H.11_12 8_22_12.docx

RIA Chp 8 08 22 2012.docxSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

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

Interagency commenters provided the following suggested edits and comments on EPA’s Preamble discussion of Sales as well as EPA RIA’s chapter on sales and employment effects.

***

EPA Preamble Language:

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

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

a. Vehicle Sales Impacts

Predicting the effects of this rule on vehicle sales entails comparing two effects. On the one hand, the vehicles designed to meet the standards will become more expensive, which would, by itself, discourage sales. On the other hand, th e vehicles will have improved fuel economy and thus lower operating costs due to significant fuel savings, which could encourage sales. Which of these effects dominates for potential vehicle b uyers when they are considering a purchase will determine the effect on sales. Assessing the net effect of these two competing effects is uncertain, as it rests on how consumers value fuel savings at the time of purchase and the extent to which manufacturers and dealers reflect technol ogy costs in the purchase price. The empirical literature does not provide clear evidence on how much of the value of fuel savings consumers consider at the time of purchase. It also generally does not speak to the efficiency of manufacturing and dealer pricing decisions. Thus, we do not provide quantified estimates of potential sales impacts in this final rule. However, as discussed later in this section, there are several reasons why EPA believes this rulemaking will lead to an increase in vehicle sales, though we do not quantify a specific impact. An additional source of uncertainty in the analysis is understanding what would happen in the absence of this rule. Standard economic theory would suggest that, if automakers could profitably increase sales by adding more fuel-saving technologies to their vehicles, then manufacturers’ profit motives would lead them to voluntarily add those technologies in the absence of this rule. As discussed in Preamble Section III.D.1, we project, based on historical patterns, that auto makers would not go beyond the MY 2016 standards in the absence of this rule. Yet, if consumers consider just over thr ee years’ worth of fuel savings in their vehicle purchase decisions and our assumptions about technology costs and future gas price s are correct

,Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

2 the payback period analysis in Section III.H.5 suggests that sales would increase in response to this rule. Are these analyses in conflict?

Although it is possible we assume that manufacturers could would not find it profitable add at least some of the vehicleadditional fuel-saving technologies in the absence of the rule (see Section III.H.1.a for a discussion), there are also a number of reasonsmay be the potential for potential increases in vehicle sales as a result of the rule rule notwithstanding our assumption of a flat baseline . These explanations focus on conditions where the rule stimulates forces investments that would not happen in the rule’s absence. The explanations posed below raise possibilities that the rule, by requiring all automakers to meet the standards, may lead to mutually beneficial outcomes that might not happen in the absence of the rule. Consumers would then have the opportunity to purchase vehicles that would not be available in the absence of the rule; if consumers consider at least as many years of fuel savings when buying new vehicles as the payback period for the new technologies, and if manufacturers nonetheless would not have produced these vehicles in the absence of the rule, positive sales impacts could occur as a result of these final standards. The three possibilities we suggest for such outcomes are promotion of social learning, re duction of risk and uncertainty for manufacturers, and promotion of innovation.

i. Social Learning For many years, fuel economy standards did not change (see Preamble III.D.1). 1 As discussed in Preamble III.H.1.a, consumers may not have focused on fuel economy, or may have found it difficult to do calculations involving th e tradeoffs between fuel economy and increased vehicle costs, or may not have found vehicles with their preferred combination of fuel economy and other features. In recent years, though, fuel economy standards have started to increase. 2 In addition, high fuel prices have helped to focus consumer attention toward vehicle fuel economy. Finally, the recently revised fuel economy label, with prominent information on fuel savings, are starting to appear on new vehicles. These factors may contribute to consumers gaining experience with the benefits that accrue to them from owning and operating vehicles with greater fuel efficiency. Consumer households that include vehicles with a fairly wide range of fuel economy have an opportunity to learn about the value of fuel economy on their own. Consumer demand may be shifting towards such vehicles, not only because of higher fuel prices but also if many consumers are learning about the value of purchases based not only on initial costs but also on the total cost of owning and operating a vehicle over its lifetime. This type of learning should continue before and during the model years affected by this rule.

Today’s rule, combined with the new and easier-to-understand fuel economy labels required to be on all new vehicles in MY 2013, may increase sales by hastening this very type of

1 Car CAFE standards did not change from MYs 1990 thr ough 2010. Truck CAFE standards did not change from MYs 1996 through 2004, and changed only 0.5 mpg cumulatively from MYs 1991 through 2004. See "Summary of Fuel Economy Performance," March 12, 2012, DOT/NHTSA, http://www.nhtsa.gov/fuel-economy.

2 Truck CAFE standards began to rise in MY 2005 and have risen every year since. Car CAFE standards began to rise in MY 2011. Ibid.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

3 consumer learning. As more consumers experience the savings in time and expense from owning more fuel efficient vehicles, demand may shift yet further in the direction of the vehicles with improved fuel economy and reduced GHG emissions mandated under the rule. This social learning can take place both within and across households, as consumers learn from one another. First and most directly, the time and fuel saving s associated with operating more fuel efficient vehicles may be more salient to individuals who own them, which might cause their subsequent purchase decisions to shift closer to minimizing the total cost of ownership over the lifetime of the vehicle. Second, this appreciation may spre ad across households through word of mouth, marketing and advertising, and other forms of communications. Third, as more motorists experience the time and fuel savings associated wi th greater fuel efficiency, the price of used cars may better reflect such efficiency, further reducing the cost of owning more efficient vehicles for the buyers of new vehicles (since the resale price may increase). If these induced learning effects are strong, the rule could potentia lly increase total vehicle sales over time. This effect may be speeded or slowed by other factors that enter into a consumer’s valuation of fuel efficiency in selecting vehicles.

The possibility that the rule could (after a lag for consumer learning) increase sales need not rest on the assumption that automobile manufacturers are failing to pursue profitable opportunities to supply the vehicles that cons umers demand. In the absence of the rule, no individual automobile manufacturer would find it profitable to move toward more efficient vehicles to increase consumer learning because no individual company can fully internalize the potential future boost to demand. If one company were to make more efficient vehicles, counting on consumer learning to enhance demand in the future, that company would capture only a fraction of the extra sales so generated, because the learning at issue is not specific to any one company's fleet. Many of the extra sales could accrue to that company's competitors.

In other words, consumer learning about the be nefits of fuel efficient vehicles involves positive externalities (spillovers) fr om one company to the others. 3 These positive externalities may lead to benefits for manufacturers as a whole if they increase the demand for vehicles. We emphasize that this discussion has been tentative a nd qualified. It is not possible to quantify these learning effects years in advance

, and that these effect s may be speeded or slowed by other factors that enter into a consumer’s valuation of fuel efficiency in selecting vehicles. To be sure, social learning of related kinds has been identified in a number of contexts. 4 We asked for comments on the discussion offered here, with pa rticular reference to any relevant empirical findings. 76 FR 75150. We continue to explor e this issue but did not receive any comments on

3 Industrywide positive spillovers of this type are hardly unique to this situation. In many industries, companies form trade associations to promote industry-wide public go ods. For example, merchants in a given locale may band together to promote tourism in that locale. Antitrust law recognizes that this type of coordination can increase output.
4 See Hunt Alcott, Social Norms and Energy Conserva tion, Journal of Public Economics (forthcoming 2011), available at http://web.mit.edu/al lcott/www/Allcott%202 011%20JPubEc%20- %20Social%20Norms%20and%20Energy%20Conservation.p df (Docket EPA-HQ-OAR-0799-0825); Christophe Chamley, Rational Herds: Economic Models of Soci al Learning (Cambridge, 2003) (Docket EPA-HQ-OAR-0799- 1110).Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

4 the role of social learning, except in the context (discussed in Section III.H.1.a) of the effect of the rule on the visibility of and status associ ated with owning vehicles with improved fuel

economy.

ii. Reduction in Risk and Uncertainty for Manufacturers As discussed in Preamble III.H.1.a, there appear s to be a great deal of uncertainty about how consumers will respond to increases in fuel economy. Automakers may be cautious about adding more fuel-saving technology to vehicles if they are uncertain ho w buyers will respond. Even if they believe that buyers will respond positively, if a company is risk-averse, it may nevertheless hesitate to make the substantial major investments in new technologies and in research that would lead to increases in fuel economy across its fleet. 5 If a manufacturer invests substantially in fuel efficient technologies expecting higher consumer demand than realized, then the manufacturer has incurred the costs of inve stment but not reaped the benefits of those investments. On the other hand, if a manufacturer does not invest in fuel-efficient technologies, then the manufacturers may lose some sales in the short run if demand for fuel economy is higher than expected, but it still retains the opti on of investing in fuel-efficient technologies in the longer run. If its investments proved unsuccessful, the company might face substantial losses. Even if the probability of being unsuccessful is low, the manufacturer may nevertheless perceive the losses in that scenario as a substantial risk. If the investment proved successful, the company would, of course, take market share from other companies – but , assuming that there are not brand-loyalty or other advantages to be ing first to the market with new fuel-saving technologies, only until the other auto companies caught up. In other words, for a risk-averse company, being a first mover may appear to have a greater downside risk than upside risk, even if the investment, on an expected-value basis, would pay off. If all companies are risk-averse, then they may all seek a strategy of waiting for some other co mpany to be the first mover. In this case, caution about these major investments may lead to slow lack of adoption of new technologies, in the absence of the rulemaking , consistent with the flat baseline assumption

. This rulemaking, by requiring that all companies act at the same time, removes the scenario of one company bearing all the risk.

In addition, there may be risk aversion on the consumer side. The simultaneous investment by all companies may also encourage consumer confidence in the new technologies. If only one company adopted new technologies, early adopters might gravitate toward that company, but early adopters tend to be a relativ ely small portion of the public. More cautious buyers, who are likely to be more numerous, might wait for greater information before moving away from well-known technologies. If all compan ies adopt advanced technologies at the same time, though, potential buyers may perceive the new technologies as the new norm rather than as a risky innovation. They will then be more will ing to move to the new technologies. As some

5 Sunding, David, and David Zilberman, “The Agricultur al Innovation Process: Research and Technology Adoption in a Changing Agricultural Sector,” Chapter 4 in Handbo ok of Agricultural Economics, Volume 1, edited by B. Gardner and G. Rausser (Elsevier, 2001) show how delayi ng adoption of a new technology in order to gain more information may be a more profitable activity than adopting a technology, even if it has positive net benefits, when a potential adopter is risk-averse.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

5 commenters have pointed out, simultaneous action required by the rule may change buyers’ expectations (their reference points) for fuel economy, and investing in more fuel economy may seem less risky than in the absence of the rule.

The rule, then, may reduce manufacturers’ risk of making significant investments in fuelsaving technologies by providing a level playing field forrequiring that all companies produce more fuel vehicles . Under this outcome, it is possible for the rule to facilitate investment that would not happen in the absence of the rule, and vehicle sales could increase as a result of the rule.

iii. Promotion of Innovation Research among multiple parties can be a synergistic process: ideas by one researcher may stimulate new ideas by others, and more and better results occur than if the one researcher operated in isolation. One function that standards can serve is to promote research into low-CO 2 technologies that would not take place in the ab sence of the standards. Because all companies (both auto firms and auto suppliers) will have in centives to find better, less expensive ways of meeting the standards in this ru le, the possibilities for synergistic interactions may increase. Thus, the rule, by focusing all companies on finding more efficient ways of achieving the standards, may lead to better outcomes than if any one company operated on its own.

An additional aspect of the standards is the possibility of greater standardization. As more companies adopt new technologies, the incentives increase for additional suppliers and more availability of after-market replacement parts; these suppliers would be likely to find ways to increase compatibility across vehicle types. For example, though electric vehicles (EVs) are not expected to be more than a few percent of the vehicles produced in response to this rule, their adoption depends on such factors as batteries and charging methods that are compatible across different companies. In the absence of the rule, fewer companies would be pursuing this technology, and it would be considered a specialty product; the incentives to coordinate might be low. If EVs become more common, though, comp atible infrastructure and batteries may become more desirable, as potential buyers are likely to be encouraged toward this technology if they can easily find places to charge batteries.

Thus, the rule may direct and promote innovation and standardization that would not happen in the absence of this rule. Such changes could reduce the cost increase s associated with the rule and improve the qualities of the technologi es, which could result in an increase in vehicle sales. Further, the certainty of the regulations reduces the costs of meeting them, because there will be more economies of scale and mo re learning curve bene fits due to greater cumulative production of fuel-efficient technologies.

In these three plausible explanations for how the rule could increase vehicle sales, we have focused on ways that the rule could promote coordinated actions on the part of automakers and suppliers that lead to mutual benefit, but th at might not happen in the absence of the rule. These actions could lead to automakers developing technologies that they would not have Comment [A1]: Commenters note that this
discussion is confusing. Commenters believe that
manufacturers may not be collaborating on research

even due to this rule, and it is likely each
manufacturer will be conducting its own proprietary
research in an effort to lower their own costs of meeting the standards. They might not have any incentive to share these innovations with their competitors.

Comment [A2]: Commenters request clarity on why there would be incentives to coordinate?
Commenters note that unless the manufacturers

are colluding, they should each have an incentive to keep propriety technologies such as batteries for
themselves so that they can keep their own costs low relative to competitors.

Comment [A3]: Commenters note that absent
the rule, a manufacturer acting alone could achieve
economies of scale if consumers want to buy more fuel efficient cars. Commenters request clarity on whether this is just a reiteration of the risk aversion argument?Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

6 developed in the absence of the rule. Consumer s may respond positively to the vehicles resulting from this coordinated action.

Several commenters requested that we conduct a quantitative vehicle sales analysis. As discussed in the proposal, in previous rulemakings, EPA and NHTSA conducted vehicle sales analyses by comparing the up-front costs of the vehicles with the present value of five years’ worth of fuel savings; the direction of vehicl e sales would depend on whether up-front costs exceeded fuel savings (in which case sales would be expected to decline), or vice versa (in which case sales would be expected to increase). 6 Some commenters specifically requested that we use the method found in the MYs 2012-16 rule; some specifically supported the five-year payback period; others argued for the importance of conducting the analysis without recommending methods. Ceres estimates that the rule will incr ease vehicle sales by 4.7 percent; the Defour Group provided estimates that the rule will decrease vehicle sales by 6-10 percent. The differences in the results appear to depend on th e cost estimates used and on assumptions made about how vehicle buyers think about fuel savi ngs when deciding on vehicle purchases. The Defour Group, for instance, uses cost estimates of about $3000 per vehicle based on summing costs (but not benefits) across multiple rules (an estimate we consider to be unfounded for reasons explained in this section below and also in TSD Chapter 3.1.2) and the assumption that consumers consider only 25 percent of fuel savings in their vehicle purchase decisions (see discussion in Section III.H.1.a). Other commenters wanted specific information on the effect of the rule on vehicle costs and whether consumers will be willing to buy the new vehicles, while consumer and environmental organizations indicated that consumers want more fuel-efficient vehicles, even if the up-front costs of the vehicles increase. The costs of the rule are discussed in Preamble Section III.H.2. As discussed in Preambl e Section III.H.1.a, we do not at this point have sufficient confidence in the estimates of the role of fuel economy in consumers’ vehicle purchases to come to definitive conclusions about the impacts of the rule on vehicle sales. We do not, however, consider this uncertainty grounds for delaying the rule, as one comment suggested. The midterm evaluation provides an opportunity to revisit the impact of the rule on vehicle sales and consumer acceptance of the new technologies.

This rule takes effect for MY 2017-2025. In the intervening years, it is possible that the assumptions underlying a quantitative analysis, as well as market conditions, might change. As the United Auto Workers points out, the state of the economy is a major, if not the primary, determinant of total vehicle sales. The impact of the rule on sales may therefore depend, among other factors, on changes in the state of the economy. Other commenters discussed the importance of consumer confidence, fuel prices, and even of publicity over fuel prices, in consumers’ interest in additional fuel economy. Sales could be negatively affected if gasoline prices are lower than expected or technology cost s are higher than expected. In these cases, it is possible that the standards could require manufacturers to produce cars with higher levels of fuel economy than consumers would wish to buy. On the other hand, manufacturers’ marketing of

6 For instance, see U.S. Environmental Protection Agency (April 2010). “Final Rulemaking to Establish LightDuty Vehicle Greenhouse Gas Emission Standards and Corp orate Average Fuel Economy Standards: Regulatory Impact Analysis.” EPA-420-R-10-009, Chapter 8.1.1, pp. 8-1 to 8-4. Comment [A4]: Commenters request EPA to include citations.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

7 increased fuel economy levels is also likely to play a role in consumer response to these vehicles. EPA agrees that these factors ar e important, but we are not suffi ciently confident in quantitative estimates of the impacts of those factors to develo p numerical estimates. We instead provide this qualitative assessment to highlight the factors impor tant for understanding the effects of this rule on vehicle sales.

As several commenters point out, the effect of this rule on the use and scrappage of older vehicles will be related to its effects on new ve hicle prices, the fuel efficiency of new vehicle models, the fuel efficiency of used vehicles, and the total sales of new vehicles. If the value of fuel savings resulting from improved fuel efficiency to the typical potential buyer of a new vehicle outweighs the average increase in new m odels’ prices, sales of new vehicles could rise, the used vehicle market may increase in volume as new vehicle buyers sell their older vehicles, and scrappage rates of used vehi cles may increase slightly. This will cause both an influx of more efficient vehicles into the used vehicle market and an increase in the turnover of the vehicle fleet (i.e., the retirement of used vehicles and their replacement by new models), thus accentuating the anticipated effect of the rule on fleet-wide fuel consumption and CO 2 emissions. However, if potential buyers value future fuel savings resu lting from the increased fuel efficiency of new models at less than the increase in their average selling price, sales of new vehicles will decline, the used vehicle market may decrease in volume as people hold onto their vehicles longer, and there will be a reduction in th e rate at which used vehicles are retired from service. These effects will partly reduce the anticipated effects of this rule on fuel use and emissions. Because we do not have good estimates of the relationships between the new and used vehicle markets, we have not attempted to estimate explicitly the effects of the rule on the used vehicle market, scrappage of older vehicles, and the turnover of the vehicle fleet.

Consumer, environmental, and investor organizations, the United Auto Workers, as well as a citizens’ campaign suggested th at the rule will help the domestic auto industry, including the domestic supply base, compete in the global marketplace, through its encouragement of advanced technologies that may be useful in meeting emissions standards and consumer demands in foreign markets. We agree that this is likely, as generally the emission standards established in this rule are similar in stringenc y to emissions and fuel economy standards being considered by Japan, the European Union, South Korea, Canada, China and other international markets. Global manufacturers also design vehi cles using a common platform in order to reduce costs. Vehicles built on these common platforms are sold in many markets around the world. To the extent the domestic OEMs and suppliers can focus their limited research and product development efforts on the same technologies for the U.S. market as for international markets, this should enable the companies to compete more effectively outside the U.S. Comment [A5]: Commneters request EPA to delete this paragraph. Commenters note that this is speculative. The required standards may exceed
standards adopted by other countries in the future.
This could hurt manufacturers’ ability to compete there.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

8 

## 2 Vehicle Sales and Employment Impacts

1. Vehicle Sales Impacts

1. How Vehicle Sales Impacts were Estimated for this Rule

Predicting the effects of this rule on vehicle sales entails comparing two competing effects. On the one hand, as a result of this rule, the vehicl es will become more expensive, which would, by itself, discourage sales. On the other hand, th e vehicles will have improved fuel economy and thus lower operatin g costs, which makes them more attrac tive to consumers. As discussed in Preamble III.H.1.a, there are many competing hypotheses for why private markets are not providing what appear to be cost-effective energy -saving technologies, for vehicles as well as for other energy-conservation technologies. Ther e are few empirical studies testing these hypotheses, though. As discussed more fully in Preamble III.H.11.a, it is possible that enacting
this rule can contribute to increased vehicle sales by encouraging coordination among automakers to improve fuel economy that would not happen in the absence of the rule . The empirical literature does not provide clear evidence on how much of the value of fuel savings consumers consider at the time of vehicle purchase. It also generally does not speak to the efficiency of manufacturing and dealer prici ng decisions. Thus, we do not provide quantified estimates of potential sales impacts.

In previous rulemakings, EPA and NHTSA conducted vehicle sales analyses by comparing the up-front costs of the vehicles with the present valu e of five years’ worth of fuel savings. We assumed that the costs for the fuel-saving technologies would be passed along fully to vehicle buyers in the vehicle prices. The up-front vehicle co sts were adjusted to take into account
several factors that would affect consumer costs: the increased sales tax that consumers would pay, the increase in insurance premiums, the increase in loan payments that buyers would face, and a higher resale value, with all of these factor s due to the higher up-front cost of the vehicle. Those calculations resulted in an adjusted increase in costs to consumers. We then assumed that consumers considered the present value of five years of fuel savings in their vehicle purchase, which is consistent with the length of a typical new light-duty vehicle loan, and is similar to theSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

9 average time that a new vehicle purchaser holds onto the vehicle. 7 The present value of fuel savings was subtracted from technology costs to ge t a net effect on vehicle cost of ownership. We then used a short-run demand elasticity of -1 to convert a change in price into a change in quantity demanded of vehicles. 8 An elasticity of -1 means that a 1% increase in price leads to a 1% reduction in quantity sold.

We do not here present a vehicle sales analysis using this approach. This rule takes effect for MY 2017-2025. In the intervening years, it is possible that the assumptions underlying this analysis, as well as market conditions, might change. Instead, Chapter 5.5 includes a payback period analysis to estimate the number of years of fuel savings needed to recover the up-front costs of the new technologies. In other words, the payback period identifies the break-even point for new vehicle buyers. As discussed there, the payback period is 3.2 – 3.4 years for new vehicles, and even shorter for used vehicles (just over 1 year for a 5-year-old MY 2025 vehicle). That chapter also includes an assessment of th e lifetime costs and benefits that accrue to a
vehicle owner ; the net savings are about $5,000 with a 3 percent discount rate, and over $3,000 with a 7 percent discount rate

.

2. Consumer Vehicle Choice Modeling 9

An alternative to the vehicle sales analysis approach discussed above is the use of consumer
vehicle choice models. In this section we descri be some of the consumer vehicle choice models EPA has reviewed in the literature, and we describe the models’ results and limitations that we have identified. The evidence from consumer vehicle choice models indicates a huge range of estimates for consumers’ willingness to pay fo r additional fuel economy . Because consumer surplus estimates from consumer vehicle choice models depend critically on this value, we would consider any consumer surplus estimates of the effect of our rule from such models to be unreliable. In addition, the pred ictive ability of consumer vehicle choice models has not been tested. While vehicle choice models are based on sales of existing vehicles, vehicle models are likely to change, both independently and in respon se to this rule. The models may not predict

7 In this rule, the 5-year payback assumption corresponds to an assumption that vehicle buyers take into account between 30 and 50 percent of the present value of lifetime vehicle fuel savings (with the variation depending on discount rate and model year).

8 For a durable good such as an au to, the elasticity may be smaller in the long run: though people may be able to change the timing of their pur chase when price changes in the short run, they must eventually make the investment. We request comment on whether or when a long -run elasticity should be used for a rule that phases in over time, as well as how to find good estimates for the long –run elasticity.

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

10 well in response to these changes. Instead, we compare the value of the fuel savings associated with this rule with the increase in technology costs. EPA will continue its efforts to review the literature and (as described below) to explore the use of consumer choice modeling but, given the known limitations and uncertainties of vehicle choice models, EPA has not conducted an analysis using these models for this rule.

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

In response to these changes, the number and types of vehicles sold is likely to change. When consumers buy vehicles, they consider both their personal characteristics (such as age, family composition, income, and their vehicle needs) and the characteristics of vehicles (e.g., vehicle size, fuel economy, and price). In response to the changes in vehicle characteristics, consumers will reconsider their purchases. Increases in fuel economy are likely to be attractive to consumers, but increases in price, as well as any detrimental changes in other vehicle characteristics, may be deterrents to purchase. As a result, consumers may choose a different vehicle than they would have purchased in the absence of the rule. The changes in prices and vehicle characteristics are likely to influence co nsumers on multiple market scales: the total number of new vehicles sold; the mix of new vehicles sold; and the effects of the sales on the
used vehicle market.

Consumer vehicle choice modeling (CCM) is a method used to predict what vehicles consumers will purchase based on vehicle characteristics and prices. In principle, it should produce more accurate estimates of total compliance costs compared to models that hold fleet mix constant, since it predicts changes in the fleet mix that can affect total compliance costs. It can also be used to measure changes in consumer surplus, the benefit that consumers perceive from a good over and above the purchase price. (Consumer surplus is the difference between what consumers would be willing to pay for a good, represente d by the demand curve, and the amount they actually pay. For instance, if a consumer we re willing to pay $30,000 for a new vehicle, but ended up paying $25,000, the $5000 difference would be called consumer surplus.)

A number of consumer vehicle choice models have been developed. They vary in the methods used, the data sources, the factors included in the models, the research questions they are designed to answer, and the results of the models related to the effects of fuel economy on
consumer decisions. This sec tion will give some background on these differences among the
models.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

11 a. Methods

Consumer choice models (CCMs) of vehicle purc hases typically use a form of discrete choice modeling. Discrete choice models seek to explai n discrete rather than continuous decisions. An example of a continuous decision is how many pounds of food a farm might grow: the pounds of food can take any numerical value. Discrete decisions can take only a limited set of values. The decision to purchase a vehicle, for instance, can only take two values, yes or no. Vehicle purchases are typically modeled as discrete choices, where the choice is whether to purchase a specified vehicle. The result of these models is a prediction of the probability that a consumer will purchase a specified vehicle. A minor va riant on discrete choice models estimates the market share (a continuous variable between 0 and 1) for each vehicle. Because the market share is, essentially, the probability that consumers wi ll purchase a specific vehicle, these approaches are similar in process; they differ mostly in the kinds of data that they use.

The primary methods used to model vehicle choices are nested logit and mixed logit. 10 In a nested logit, the model is structured in layers. For instance, the first layer may be the choice of whether to buy a new or used vehicle. Given that the person chooses a new vehicle, the second layer may be whether to buy a car or a truck. Given that the person chooses a car, the third layer may be the choice among an economy, midsize, or luxury car. Examples of nested logit models include Goldberg, i Greene et al., ii and McManus. iii

In a mixed logit, personal characteristics of consum ers play a larger role than in nested logit. While nested logit can look at the effects of a ch ange in average consumer characteristics, mixed logit allows consideration of the effects of the distribution of consumer characteristics. As a result, mixed logit can be used to examine th e distributional effects on various socioeconomic groups, which nested logit is not designed to do. Examples of mixed logit models include Berry, Levinsohn, and Pakes, iv Bento et al., v and Train and Winston. vi

While discrete choice modeling appears to be the primary method for consumer choice modeling, others (such as Kleit vii and Austin and Dinan viii ) 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 we ll as Austin and Dinan used the elasticities from an internal GM
vehicle choice model.

b. Data Sources

The predictions of vehicle purchases from CCMs are based on consumer and vehicle characteristics. The CCMs identify the effects of changing the characteristics on the purchase

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

12 decisions. These effects are typically called the parameters or coefficients of the models. For instance, the model parameters mi ght predict that an increase in a person’s income of 10% would increase the probability of her purchasing vehicle A by 5%, and decrease the probability of her purchasing vehicle B by 10%.

The parameters in CCMs can be developed either from original data sources (estimated models), or using values taken from other studies (calibrated models).

Estimated models use datasets on consumer purchase patterns, consumer characteristics, and vehicle characteristics to develop their original sets of parameters. The datasets used in these studies sometimes come from surveys of individuals’ behaviors. ix Because they draw on the behavior of individuals, they provide what is sometimes called micro-level data. Other studies, that estimate market shares instead of discrete purchase decisions, use aggregated data that can cover long time periods. x

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

c. Factors Included in the Models

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

The models vary in their inclusion of both consumer and vehicle information. One model includes only vehicle price and the distributi on of income in the population influencing choice; xvii others include varying numbers and kind s of vehicle and consumer attributes.

Some models include only the consumer side of the vehicle market; xviii others seek to represent both consumer and producer decisions. xix Models that include only the consumer side are suitable for reflecting consumer choices, but they do not allow for revisions of vehicle characteristics in response to consumer preferen ces. Including producer behavior allows for vehicle characteristics such as price and fuel economy to be the result of market forces rather than characteristics of the existing fleet. For in stance, in the context of “feebates” (subsidizing fuel-efficient cars with revenue collected by ta xing fuel-inefficient vehicles), Greene et al.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

13 estimated that 95% of the increase in fuel economy was due to addition of technology rather than changes in vehicles sold. xx Including auto maker response is a complex exercise. Auto makers are commonly considered to have market power; they can influence the prices that consumers pay to increase their profits. As a result, the price increases that consumers face may reflect strategic factors that could make them higher or lower than the technology costs. In addition, auto makers may seek to influence consumer preferences through marketing and advertising. xxi
Even those vehicle choice models that include a producer model may not include much detail, due to computational limits: it is unusual fo r models to allow both buyers and producers to choose one vehicle characteristic, much less multiple characteristics. xxii

d. Research Questions for the Models

Consumer choice models have been developed to analyze many different research and policy questions. In part, these models have been developed to advance the state of economic modeling. The work of Berry, Levinsohn, and Pakes, xxiii for instance, is often cited outside the motor vehicle context for its incorporation of mul tiple new modeling issues into its framework. In addition, because the vehicle sector is a major part of the U.S. economy and involved in many public policy discussions, research questions cover a wide gamut. These topics have included the effects of voluntary export restraints on Japa nese vehicles compared to tariffs and quotas, xxiv
the market acceptability of alternative-fuel vehicles, xxv the effects of introduction and exit of vehicles from markets, xxvi causes of the decline in market shares of U.S. automakers, xxvii and the effects of gasoline taxes xxviii and “feebates” xxix (subsidizing fuel-effic ient cars with revenue collected by taxing fuel-inefficient vehicles).

e. The Effect of Fuel Economy on Consumer Decisions

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

Some models xxx incorporate fuel economy through its effects on the cost of owning a vehicle. With assumptions on the number of miles traveled per year and the cost of fuel, it is possible toestimate the fuel savings (and perhaps other operating costs) associated with a more fuelefficient vehicle. Those savings are considered to reduce the cost of owning a vehicle: effectively, they reduce the purchase price. This approach relies on the assumption that, when purchasing vehicles, consumers can estimate the fuel savings that they expect to receive from a more fuel-efficient vehicle and consider the savings equivalent to a reduction in purchase price. The vehicle sales method described in Chapter 8.1.1 uses a variant on this approach, in which it is assumed that consumers consider some fraction of future fuel savings. Turrentine and
Kurani xxxi question this assumption; they find, in fact, that consumers do not make thisSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

14 calculation when they purchase a vehicle. The question remains, then, how or whether consumers take fuel economy into account when they purchase their vehicles.

Most estimated consumer choice models, instead of making assumptions about how consumers incorporate fuel economy into their decisions, us e data on consumer behavior to identify that effect. In some models, miles per gallon is one of the vehicle characteristics included to explain purchase decisions. Other models use fuel consumption per mile, the inverse of miles per gallon,

as a measure. xxxii Since consumers pay for gallons of fuel, then this measure can assess fuel savings relatively directly. xxxiii Yet other models multiply fuel consumption per mile by the cost of fuel to get the cost of driving a mile, xxxiv or they divide fuel economy by fuel cost to get miles per dollar. xxxv It is worth noting that these last two measures assume that consumers respond the same way to an increase in fuel economy as they do to a decrease in the price of fuel when each has the same effect on cost per mile driven. 11 On the one hand, while this assumption does not rely on as complex a calculation as the present value of fuel savings that Turrentine and Kurani examined, it suggests a calculating consumer. On the other hand, using a form of cost per mile is a way to recognize the role of fuel prices in consumers’ purchase of fuel economy: recent
research xxxvi presents results that higher fuel prices play a major role in that decision.

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

A newer literature review from David Greene xxxviii suggests continued lack of convergence on the value of increased fuel economy to consumers. Of 27 studies, willingness to pay for fuel economy as a percent of the exp ected value of fuel savings vari ed from highly positive to highly negative. Significant numbers of studies found that consumers overvalued fuel economy,

11 Likewise, these measures assume consumers respond th e same way to increases and decreases in cost per mile of driving, as well as if those increases and decreases are large shocks rather than small, gradual changes. The issue of potential asymmetric consumer response to increased fuel efficiency compared to other types of changes to the cost of driving also arises and is discussed in the c ontext of the VMT rebound effect (see Section III.H.4 of the Preamble and Chapter 4.2.5 of the TSD).Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

15 undervalued fuel economy, or roughly valued fuel economy correctly relative to fuel savings. Part of the difficulty may be, as these papers note, that fuel economy may be correlated (either positively or negatively) with other vehicle attribut es, such as size, power, or quality, not all of which may be included in the analyses; as a resu lt, “fuel economy” may in fact represent several characteristics at the same time. Indeed, Gramlich xxxix includes both fuel cost (dollars per mile) and miles per gallon in his analysis, with the argument that miles per gallon measures other undesirable quality attributes, while fuel cost picks up the consumer’s demand for improved fuel economy. Greene finds that, while some of the variation may be explainable due to issues in some of the studies, the variation shows up in st udies that appear to be well conducted. As a result, further work needs to be conducted before it is possible to identify the role of fuel economy in consumer purchase decisions.

Some studies xl argue that automakers could increase profits by increasing fuel economy because the amount that consumers are willing to pay for increased fuel economy ou tweighs the costs of that improvement. Other studies xli have found that increasing fuel economy standards imposes welfare losses on consumers and producers, because consumers should already be buying as much fuel economy as they want. In the course of reaching this result, though, at least one of
these studies xlii notes that its baseline model implies that consumers are willing to buy more fuel economy than producers have provided; they have to adjust their model to eliminate these “negative-cost” fuel economy improvements.

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

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

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

This disconnect between net present value estimates of energy-conserving cost savings and what consumers actually spend on energy conservation is often referred to as the Energy Paradox, xliii
since consumers appear to undervalue a wide range of investments in energy conservation. There are many possible explanations for the paradox discussed in the literature. xliv SomeSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

16 explanations point to costs or aspects of cons umer decision-making unaccounted for in a simple present value calculation, while others point to pot ential behavioral or market failures. There is little empirical literature to help the analyst determine which combinatio n of hypothesis offers the most credible explanation. Some possibilities include:

 Consumers might be “myopic” and hence unde rvalue future fuel savings in their purchasing decisions.

 Consumers might lack the information necessary to estimate the value of future fuel savings, or not have a full understanding of this information even when it is presented.

 Consumers may be accounting for uncertainty in future fuel savings when comparing upfront cost to future returns.

 Consumers may consider fuel economy after other vehicle attributes and, as such, not optimize the level of this attribute (instead “satisficing” – that is, selecting a vehicle that is acceptable rather than optimal -- or selecting vehicles that have some sufficient amount of fuel economy).

 Consumers might be especially averse to the short-term losses associated with the higher prices of energy efficient products relative to the future fuel savings (the behavioral phenomenon of “loss aversion”).

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

 When buying vehicles, consumers may focus on visible attributes that convey status, such as size, and pay less attention to attributes such as fuel economy that do not visibly convey status.

 Even if consumers have relevant knowledge, selecting a vehicle is a highly complex undertaking, involving many vehicle characteristics. In the face of such a complicated choice, consumers may use simplified decision rules.

 In the case of vehicle fuel efficiency, and perhaps as a result of one or more of the foregoing factors, consumers may have relatively few choices to purchase vehicles with greater fuel economy once other charact eristics, such as vehicle class, are chosen. 12

12 For instance, in MY 2010, the range of fuel economy (combined city and highway) available among all listed 6-cylinder minivans was 18 to 20 miles per gallon. With a manual-transmission 4-cylinder minivan, it isSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

17 The extent to which fuel economy is optimized rela tive to other, potentially more salient vehicle attributes (such as engine horsepower and seatin g capacity) in market outcomes for new vehicles remains an important area of uncertainty. There are significant challenges involved in effectively interpreting and anticip ating consumer preferences for various vehicle attributes and amenities. There are significant lead times to mark et, potential return to scale limits on the range of options provided for a given attribute or amen ity, market transaction frictional factors, and
other factors inherent to the nature of thes e costly durable goods which may contribute to imperfect satisfaction of market demand for fuel economy among a highly heterogeneous customer base. Both sides of the market would be expected to attempt to maximize the utility they gain from these transactions; they presumably rely heavily in their calculations on the uncertain benefits of savings from fuel economy improvements, and yet market outcomes may still appear to reflect potential foregone opportunities to increase utility. We remain interested in these market dynamics, their underlying causes, and their potential significance for assessing the potential incremental effects of pollution control standards.

g. Electric Vehicles and Other New Vehicles

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

As discussed above, some models rely on vehicle price as the primary or only explanatory variable. Even in these models, it is possible, with some additional inform ation, to consider the
effects of new vehicle introduction. The first step is to find a vehicle similar on as many dimensions as possible to the new vehicle. For instance, if the change is to create an electric vehicle (EV) version of an existing model, then the existing model serves as the base vehicle. Next, it is necessary to measure the changes in vehicle attributes of interest to potential vehicle buyers. For an EV, changes in vehicle driving range and cost of fueling may be two such attributes. The next requirement is information on the value to consumers of the attributes that change between the new and the base vehicle. Multiplying the value for that attribute by the

possible to get 24 mpg. See http://www.fueleconomy.gov, which is jointly maintained by the U.S. Department of Energy and the EPA.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

18 change in the attribute provides an estimate of the benefit or cost associated with changing that
characteristic. That amount can then be added to or subtracted from the vehicle purchase price to give an adjusted purchase price reflecting the ch anged characteristic. This procedure is just an extension of the approach, discussed above, used to incorporate fuel economy improvements into vehicle choice models, by calculating future fuel savings and subtracting them (either in whole or a fraction) from vehicle purchase price.

Incorporating new vehicles into a vehicle choice model, then, requires estimates of the changes in key attributes from conventional vehicles, and estimates of the value, also called the willingness to pay (WTP), that co nsumers put on those attributes.

Electric vehicles (EVs) will have a number of changes in vehicle characteristics from any baseline model. EVs are likely to have a smaller driving range between refuelings than conventional vehicles, due to the large battery capacity needed to increase range. The ability to recharge at home may be a convenient, desirable feature for people who have garages with electric hookups, but not for people who park on th e street. If an infrastructure develops for recharging vehicles with the convenience approaching that of buying gasoline, range or home recharging may become less of a barrier to purchase. The reduced tailpipe emissions and reduced noise may be attractive features to some consumers. 13 They may have different performance or storage capacity. If sufficient data were available, the changes in these attributes, combined with WTP for each of the attributes, could be used to adjust the purchase price of the baseline vehicle to estimate consumer s’ WTP for the electric version of a vehicle. Greene (2001), for instance, used this approach for a model that simulates choice, not only for EVs, but also for other alternative-fuel vehicles. xlv In that model, he considers only one base vehicle, a passenger car, but considers the effect on WTP of fuel cost per mile, range, acceleration, and several other vehicle attributes.

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

In several studies, researchers have used stated pr eference conjoint analysis to estimate the effect of vehicle range on consumer vehicle choice. In a conjoint analysis, consumers are given a
choice between several vehicles with different attributes. One choice might be, for instance,

13 For instance, Hidrue et al. (Hidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. “Willingness to Pay for Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 686-705) find that some consumers are willing to pay $5100 more for vehicles with 95% lower emissions than the vehicles they ot herwise aim to purchase.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

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

Table 2.1-1.1Table 8.1-1 summarizes results from several conjoint studies that include the effects of extending range (in the table, from 150 to 300 miles, to present standardized results).
Variation of results in the table is from in come or other demographic factors, not from confidence intervals. The results suggest that the value of additional range varies among consumers, and the amount of that variation is changing (perhaps shrinking) in more recent
studies.

Table 2.1-1.1 Willingness to Pay for Increasing Range Calculated from Various Studies

Study (Date) Value of extending range from 150 to 300 miles (dollar year) Value of additional range in 2010$ a

Bunch et al. (1993) b $7,600 (1991$) $11,300 Kavelek (1996) for California Energy
Commission c $2600 - $41,900 (1993$) $3700 - $59,400

Resource Systems Group (2009) for California Energy Commission d $2900 - $7500 (2009$) $2900 - $7600

Hess et al. (2009), using the same data as Resource Systems Group (2009) e $2400 - $8500 (2009$) $2400 - $8600

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

a Values adjusted to 2010$ using the Bur eau of Economic Analysis GDP deflator.

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

c Kavelek, Chris. “CALCARS: The California Conventional and Alternative Fuel Response Simulator.” Demand Analysis Office, California Energy Commission, April 1996. The variation in values is due to willingness to pay (WTP) va rying by income levels and for one-car and twoFormatted: Font: Times New Roman, 12 ptSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

20 car households. The coefficient on range for one-car households was not statistically significantly different from zero (t-statistic = 1.5), but it was for 2-car households (t-statistic = 3.02). The minima and maxima presented here represent the values across both ownership and income categories.

d Resource Systems Groups, Inc. “Transportation Fuel Demand Forecast Household and Commercial Fleet Survey Task 8 Report: Logistic Regression Analysis and Results.” Prepared for California Energy Commission, June 2009.

e Hess, S., T. Adler, M. Fowler and A. Bahreinian “The Use of Cross-nested Logit Models for Multi-Dimensional Choice Processes: The Case of the Demand for Alternative Fuel Vehicles,” Proceedings of the 2009 European Transport Conference , Leiden, Netherlands, 2009. This study uses the same data as the Resource Systems Group study. The coefficient on range was not statistically significantly different from zero in these regressions: t-statistics varied from 1.29 to 1.52. The variation in values is due to willingness to pay (WTP) varying by income levels and statistical specification. The minima and maxima presented here represent the values across both income categories and specifications.

f Hidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. “Willingness to Pay for Electric Vehicles and their Attri butes.” Resource and Energy Economics 33(3) (2011): 686-705. The range of values is due to the model separating consumers into “gasoline vehicle-oriented” and “electric vehicle-orient ed” groups. The EV-oriented group has higher WTP for additional range. Driving range may be a major factor in consumers’ decisions on EVs, but it is not the only attribute that may be important to potential buyers (e.g., as noted, Hidrue et al. find that some consumers appear willing to pay substantially for reduced tailpipe emissions). A model that does not incorporate the other factors important to consumers’ decisions may not perform well in predicting vehicle purchases. In addition, as mentioned above, and as seen in Table 2.1-1.1Table
8.1-1 , it is likely that the WTP values for attributes of EVs will change over time, particularly if EVs are used more widely, the infrastructure to fuel the vehicles becomes more accessible, and consumers develop more familiarity and understanding of the vehicles. Thus, challenges associated with predicting market shares for EVs are even more serious than those already serious challenges associated with predicti ng market shares for conventional vehicles.

h. EPA Exploration of Vehicle Choice Modeling

In order to develop greater understanding of these models, EPA is developing a vehicle choice model, although not for this rulemaking. In its current form, the model assumes that the vehicle fleet and all characteristics of each vehicle, ex cept vehicle prices and fuel economy, stay the same. The model will predict changes in the vehi cle fleet, at the individual-configuration level and at more aggregated levels, in response to changes in vehicle fuel economy and price.

The EPA model uses a nested logit structure common in the vehicle choice modeling literature, as discussed above in Chapter a8.1.2.1 . “Nesting” refers to the decision-tree structure of the Formatted: Font: Times New Roman, 12 ptSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

21 model, and “logit” refers to the fact that the choices are discrete (i.e., yes/no decisions about which vehicles to purchase, instead of continuous values).

The nesting involves a hierarchy of choices. In its current form, at the initial decision node, consumers choose between buying a new vehicle or not. Conditional on choosing a new vehicle, consumers then choose between passenger vehicl es, cargo vehicles, and ultra-luxury vehicles. The next set of choices subdivides each of these categories into vehicle type (e.g., standard car, minivan, SUV, etc.). Next, the vehicle types ar e 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 “nes t.” Substitution within a nest is considered much more likely than substitution acro ss nests, because the vehicles with in a nest are more similar to each other than vehicles in different nests. For instance, a person is more likely to substitute between a Chevrolet Aveo and a Toyota Yaris than between an Aveo and a pickup truck. In addition, substitution is greater at low decision nodes (such as individual configurations) than at higher decision nodes (such as the buy/no buy deci sion), because there are more choices at lower levels than at higher levels.

Parameters for the model (incl uding demand elasticities and th e value of fuel economy in purchase decisions) were selected based on a revi ew of values f ound in the literature on vehicle choice modeling. As discussed above, a number of studies have estimated these parameters. Those estimates, combined with some theoretical requirements, 14 assist in assigning values for the parameters. The model will allow indivi dual users to change those parameters.

The fuel economy of a vehicle is used to adjust the price of the vehicle, using a version of the procedure discussed in Chapter 21.2.g8.1.2.7 : the value that the consumer places on fuel economy is multiplied by the change in fuel econo my and incorporated into the “effective price” of the vehicle. In practice, implementing this calculation involves calculating the change in expenditures on fuel based on schedules of VMT, vehicle survival, and fuel prices in the future
consistent with those in OMEG A. As discussed in Chapter 21.2.e8.1.2.5 , there is no consensus value for consumers’ willingness to pay for improved fuel economy: estimates vary tremendously. The model assumes that consumers will use some years of discounted fuel savings, with the modeler able to input both the number of years and the discount rate to be used in the analysis.

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

ones.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

22 The vehicle choice model takes as inputs an initial fleet of vehicles (including the initial sales and fuel economy) in the absence of standards, the cost of technologies added to each vehicle to comply with standards, and the change in fuel economy. With the initial sales mix, for each vehicle, the model calculates a vehicle-specifi c constant that summarizes the value of all attributes of the vehicle other than price and fuel economy. This constant ensures that the model will predict changes in consumer response that wo uld result only from changes in price and fuel economy. This constant substitutes for estimatin g the effects of changes in all other vehicle characteristics; the underlying assumption is that these other vehicle characteristics do not change. 15 For instance, it assumes that a Ford Escape will not change in size, power, or accessories; the only changes will be to its cost and its fuel economy.

The model assumes that the increase in vehicle cost associated with increased technology is fully passed through as an increase in vehicle price, and some years of fuel savings (which the modeler may select) offset this price increase. It then calculates changes in total fleet size and in sales mix, at the individual-configuration level and at the level of vehicle class, due to the changes in fuel economy and vehicle prices. It also calculates changes in consumer surplus associated with the changes in fuel economy and vehicle prices.

The model has undergone peer review. xlvi The reviewers were generally supportive of the model structure and parameters, with two major qualifications.

First, peer reviewers recommended that the mode l should interact closely with OMEGA, EPA’s technology cost and effectiveness model, and its appropriate use may depend on that interaction. For instance, it is possible that the predicted ch anges in fleet mix will lead to predictions of vehicle sales for auto makers that do not meet the standards, because the mix and volume of vehicles sold changed from the initial levels. To co rrect this problem, it is necessary to feed the new fleet mix back into OMEGA (which calculates costs and compliance) and get a new set of output, which is then fed back into the vehicle choice model. OMEGA increases technologies, and thus costs, to improve compliance; those adjustments would then again affect vehicle demand. We expect that this iterative process would converge to a fleet mix that would meet standards. Performing this iteration requires development of an interface between the vehicle
choice model and OMEGA to ensure accurate transmission of data between the models. At this time, the vehicle choice model takes output from OMEGA, but the results of the modeling do not feed easily back into OMEGA. Building this interface is an expected part of our future modeling
work.

15 As explained in Section III.D of the preamble, as part of the technology cost analysis for the rule, the agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

23 Second, the peer review raised the issue of th e uncertainties surrounding the model parameters and suggested the development of capacity to conduct uncertainty analysis. As discussed in
Section e8.1.2.5 , the role of fuel economy in consumer decisions is one source of uncertainty; the price slopes used for the different nests are also not known with certainty. EPA agrees that use of the model should involve, at the least, sensitivity analysis over key parameters, and we plan to investigate greater incorporation of uncertainty analysis in the model.

We note as well that the current model does not take EVs or other alternatively fueled vehicles
into account. As discussed in Section g8.1.2.7 , the values for willingness to pay for features such as range and different refueling infrastructures appear to be subject to great uncertainty. EPA’s current model does not include these vehicles, because we are seeking to gain experience and confidence with the modeling where it is likely to work best before we investigate modeling where more uncertainty is involved. The incorporation of new vehicles of any kind in the modeling is another area for future work.

As discussed in Preamble Section III.H.1, EPA is not using its preliminary consumer choice model in this rulemaking because we believe it n eeds further development and testing before we have confidence in its use. As the peer revi ewers noted, it has not yet been integrated with OMEGA, an important step for ensuring that changes in the vehicle fleet estimated by the model will result in a fleet compliant with the standards. In addition, concerns remain that vehicle choice models have rarely been validated against real-world data. In response to these concerns, we would expect any use of the model to involve, at the least, a number of sensitivity analyses to
examine the robustness of results to key parame ters. We will continue model development and testing to understand better the results and limitations of using the model.

i. Summary and Additional Considerations

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

First, consumer vehicle choice modeling has the potential to describe more accurately the impact of a policy, by identifying market shifts. More accurate description of the market resulting from a policy can improve other estimates of policy impacts, such as the change in total vehicle emissions or vehicle miles traveled. The predicti ve ability of models, though, is not proven.

Vehicle choice models can incorporate the effects on consumer decisions of changes in vehicle characteristics, if there are estimates of the value that consumers put on changes in those characteristics. These willingness-to-pay values ma y, however, be sensitive to the ways they areSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

24 estimated, as indicated in the discussion of th e value that consumers place on fuel economy in their purchase decisions. Especially for charact eristics associated with advanced technology vehicles, such as EVs, the willingness-to-pay values may change over time as consumers develop more experience with the vehicles and these characteristics. Models based on current estimates may not predict well for the future.

Consumer choice modeling has the potential to improve estimates of the compliance costs of a rule. Consumers can either accept the new costs an d vehicle characteristics, or they can change which vehicles they buy. Using a vehicle choice model is likely to reduce estimated compliance costs: because the model allows consumers to choose among accepting the new vehicle, buying a different vehicle, or not buying a vehicle, th e model assumes that consumers have additional options, which improves their welfare relative to the assumption that consumers will not change their buying behavior.

An additional complication associated with cons umer choice modeling is accurate prediction of producers’ responses to the rule. While it is possible to include auto makers’ decisions (for instance, on setting prices) into vehicle choices, computational limits affect the richness of these models. In addition, the pricing paths predic ted by these models have not, to our knowledge, been tested against actual behavior; and auto makers may not pass along vehicle costs in the same way in the future as they have in the past. Technology costs, while an accurate measure of the opportunity cost of resources to society, may overestimate or underestimate the effect on the prices that consumers face.

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

Principles of welfare analysis can be useful for understanding the role of consumer vehicle choice models in benefit-cost analysis. In particular, except for EVs, the technology cost estimates developed in this rule take into account the costs to hold other vehicle attributes, such as size and performance, constant. In addition, the analysis assumes that the full technology costs are passed along to vehicle buyers. With these assumptions, because welfare losses are monetary estimates of how much buyers would have to be compensated to be made as well off as in the absence of the change, 16 the price increase measures the loss to the buyer. 17 Assuming that

16 This approach describes the economic concept of compen sating variation, a payment of money after a change that would make a consumer as well off after the change as before it. A related c oncept, equivalent variation, estimates the income change that would be an a lternative to the change taking place. The difference between them is whether the consumer’s point of reference is her welfare before the change (compensating variation) or after the change (equivalent variation). In practice, these two measures are typically very close together.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

25 the full technology cost gets passed along to the buyer as an increase in price, the technology cost thus measures the welfare loss to the buyer. Increasing fuel economy would have to lead to other changes in the vehicles that buyers find undesirable for there to be additional losses not included in the technology costs.

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

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

Because this rule is expected to increase the up-fr ont costs of vehicles, with the fuel savings that recover those costs coming in over time, questions have arisen about the effects of this rule on whether access to credit may limit consumers’ ability to purchase new vehicles, on low-income households, and on the availability of low-priced vehicles. Section III.H.11.b. of the Preamble discusses these issues in the context of public comments received on the rule; here we provide some background and information on sources of data in that discussion.

When a lender is deciding whether to issue a loan to a prospective vehicle buyer, the amount of the vehicle loan and the person’s income are two major factors in the loan application. If lenders in fact restrict themselves to consideration of only those two factors, then the higher up-front costs of the new vehicles subject to this rule woul d reduce buyers’ abilities to get loans. The fuel savings would not come into play to counter-balance this cost, even though, as shown in the payback period analysis (RIA Chapter 5.5), the fuel savings exceed the increased loan payments
from the first month of the loan . Thus, if lenders do not take fuel savings into account in providing loans, people who are borrowing near the limit of their abilities to borrow will either have to change what vehicles they buy, or not buy vehicles at all.

17 Indeed, it is likely to be an overestim ate of the loss to the buyer, because the buyer has choices other than buying the same vehicle with a higher price; she could choose a diffe rent vehicle, or decide not to buy a new vehicle. The buyer would choose one of those options only if the altern ative involves less loss than paying the higher price. Thus, the increase in price that the buyer faces would be the upper bound of loss of consumer welfare, unless there are other changes to the vehicle due to the fuel economy improvements that make the vehicle less desirable to buyers.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

26 On the other hand, some evidence suggests that the loan market may evolve to take fuel savings into greater account in the lending decision. Some lenders currently give discounts for loans to purchase more fuel-efficient vehicles. xlvii An internet search on the term “green auto loan” produced more than 50 lending institutions that provide reduced loan rates for more fuel-efficient
vehicles. xlviii There is no reason to expect that the nu mber of lenders instituting this policy will decrease in the future as fuel efficiency increas es beyond the levels seen today. Indeed, Ii t is possible (though unknown at this time) that the auto loan market may evolve to include further consideration of fuel savings, as those savings play a significant factor in offsetting the increase in up-front costs of vehicles.

It is possible that future trends in the auto loan market may affect future vehicle sales. It is also possible that some people who have significant debt loads may not be able to get financing for some of these new vehicles; they may have to buy different vehicles (including used vehicles) or delay purchase. For others who borrow on credit, though, as discussed in RIA Chapter 5.5, the fuel savings are expected to outweigh the increased loan costs from the time of vehicle purchase. The rule thus may make vehicles more affordable to the public, by reducing consumers’ vulnerability to fuel price jumps. EPA does not deny the possibility that the market for new vehicle loans may be affected by this rule, but we do not at this time have sufficient evidence to project whether that market will evolve to help or hinder sales of new, more fuel-efficient
vehicles.

The effects of this rule on low-income households depends on its impacts, not only in the new vehicle market, but also in the used vehicle market. Two sources of information on vehicle ownership by income are the 2010 Consumer Expenditure Survey (CES) conducted by the Bureau of Labor Statistics, xlix and the 2007 Survey of Consumer Finances (SCF) conducted by the Federal Reserve System. l The Consumer Expenditure Survey data indicate that, though the average household spent more on vehicle purchases ($2,588) than on gasoline and oil ($2,132), households in the bottom 40 percent of income spent more on fuel ($1,304) than on vehicles ($1,106); in addition, they spen t more on used vehicles ($756) than on new vehicles ($330). Households in the lowest 20 percent of income spent only $127 on new vehicles, $497 on used vehicles, and $1,009 on fuel. These data suggest that the used-vehicle market is more important for low-income households than the new-vehicle market, and that they are more vulnerable to changes in fuel prices than they are to changes in vehicle prices. The Survey of Consumer Finances asks households about purchase info rmation in a number of categories, including vehicles. For the 2007 survey, we identified the households in the survey who had bought MY 2007 or 2008 vehicles, and further looked at the income categories for those consumers. Those with income less than $35,200 (the maximum income for those in the bottom 40 percent of income in the CES) bought about 17 percent of new vehicles; those with income below $18,400 (the bottom quintile) bought fewer than 2 percent of new vehicles. These data further supportSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

27the idea that low-income households are more affected by the impact of the rule on the usedvehicle market than on the new-vehicle market.

The effect of this rule on the used vehicle market will be related to its effects on new vehicle prices, the fuel efficiency of new vehicle models , the fuel efficiency of used vehicles, and the total sales of new vehicles. If the value of fuel savings resulting from improved fuel efficiency to the typical potential buyer of a new vehicle outweighs the average increase in new models’ prices, sales of new vehicles could rise, and th e used vehicle market may increase in volume as new vehicle buyers sell their older vehicles. In this case, low-income households are likely to benefit from the increased availability of used vehicles. However, if potential buyers value future fuel savings resulting from the increased fu el efficiency of new models at less than the increase in their average selling price, sales of new vehicles will decline, and the used vehiclemarket may decrease in volume as people hold onto their vehicles longer. In this case, lowincome households are likely to face increased cost s due to reduced availability of used vehicles. Because, as discussed in 8.1.1, we have not estimated the effects of the rule on the new vehicle market, and because we do not have a good model of the relationship between the new and used vehicle markets, we do not have estimates of the impact of this rule on the used vehicle
market. However, due to the significant effect of th e rule on fuel savings, especially for used vehicles (see RIA Chapter 5.5), we expect low-income households to benefit from the more rapid payback period for used vehicles, though some of this benefit may be affected by the net effect of this rule on the prices and availability of used vehicles.

The low-priced vehicle segment of the market may also deserve consideration, because it may be an entry point for first-car buyers. Vehicles in the low-priced (economy-class) segment will bear technology costs needed to meet the new standards, but it is not known how manufacturers will decide to pass on these costs across their vehi cle fleets, including in the low-priced vehicle segment. If manufacturers decide to pass on the full cost of compliance in this segment, then it is possible that consumers who might barely afford new vehicles may be priced out of the newvehicle market or may not have access to loans. As discussed above, the rule’s impacts on availability of loans is unclear, because some lenders do factor fuel economy into their loans, and it is possible that this trend may expand. In addition, auto makers have some flexibility in how both technologies and price changes are applied to these vehicles; auto makers have ways to keep some vehicles in the low-priced vehicle segment if they so choose. Though the rule is expected to increase the prices of these vehicles , the degrees of price increase and the impacts of the price increases, especially when combined with the fuel savings that will accompany these changes, are much less clear.

In summary, we recognize that this rule may have impacts on consumers’ access to loans for new vehicles, on low-income households, and on the mark et for low-priced vehicles; less clear are theSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

28 directions of these effects. Lenders who only consider consumers’ debt-to-income ratios may reduce consumers’ abilities to purchase more expensive vehicles, but some lenders already take the fuel efficiency of vehicles into account. Low-income households will benefit from reduced fuel costs; Ww e do not estimate the direction of effects of this rule on used vehicle prices, which
are more relevant for low-income households than effects on new vehicles. The effects of this rule on low-priced vehicles depends on how manu facturers add technologies and price vehicles in this segment; they have flexibility to keep some vehicles in this segment if they so wish.

2. Employment Impacts

1. Introduction

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

This rule is expected to affect employment in the United States through the regulated sector – the auto manufacturing industry – and through several related sectors, specifically, industries that supply the auto manufacturing industry (e.g., vehicle parts), auto dealers, the fuel refining and supply sectors , and the general retail sector . According to the U.S. Bure au of Labor Statistics, in 2010, about 677,000 people in the U.S. were employed in Motor Vehicle and Parts Manufacturing Sector (NAICS 3361, 3362, and 3363). About 129,000 people in the U.S. were employed in the Automobile and Light Truck Manufacturing Sector (NAICS 33611) in December 2010; this is the directly regulated sector, since it encompasses the auto manufacturers that are responsible for complying with the standards. lii Changes in light duty vehicle sales, discussed in Chapter 21.18.1.1 , could affect employment for auto dealers. The employment effects of this rule are expected to expand to relatedbeyond the regulated sector s . Though some

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

29 of the parts used to achieve th e standards are likely to be built by auto manufacturers themselves, the auto parts manufacturing sector also plays a si gnificant role in providing those parts, and will also be affected by changes in vehicle sales. As discussed in Chapter 5.4 of this RIA, this rule is expected to reduce the amount of fuel these vehicl es use, and thus affect the petroleum refinery and supply industries. Finally, since the net reduction in co st associated with this rule is expected to lead to lower household expenditures on fuel net of vehicle costs, consumers then will have additional discretionary income that can be spent on other goods and services.

When the economy is at full employment, an envi ronmental regulation is unlikely to have much impact on net overall U.S. employment; instead, labor would primarily be shifted from one sector to another. These shifts in employ ment impose an opportunity cost on society, approximated by the wages of the employees, as regulation diverts workers from other activities in the economy. In this situation, any effects on net employment are likely to be transitory as workers change jobs (e.g., some workers may need to be retrained or require time to search for new jobs, while shortages in some sectors or regi ons could bid up wages to attract workers).

On the other hand, if a regulation comes into effect during a period of high unemployment, a change in labor demand due to regulation may affect net overall U.S. employment because the labor market is not in equilibrium. In such a period, both positive and negative employment effects are possible. 19 Schmalansee and Stavins point out that net positive employment effects are possible in the near term when the economy is at less than full employment due to the potential hiring of idle labor resources by the regulated sector to meet new requirements (e.g., to install new equipment) and new economic activity in sectors related to the regulated sector. liii In the longer run, the net effect on employment is more difficult to predict and will depend on the way in which the related industries respond to th e regulatory requirements. As Schmalansee and Stavins note, it is possible that the magnitude of the effect on employment could vary over time, region, and sector, and positive effects on employment in some regions or sectors could be offset by negative effects in other regions or sectors. For this reason, they urge caution in reporting partial employment effects since it can “paint an inaccurate picture of net employment impacts if not placed in the broader economic context.”

It is assumed that the official unemployment rate will have declined to 5.3 percent by the time by
the time this rule takes effect and so the effect of the regulation on labor will be to shift workers
from one sector to another. 20 Those shifts in employment impose an opportunity cost on society, approximated by the wages of the employees, as regulation diverts workers from other activities in the economy. In this situation, as discussed above, any effects on net employment are likely to

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

20 Office of Management and Budget, “Fiscal Year 2012 Mid-Session Review: Budget of the U.S. Government.” http://www.whitehouse.gov/sites/defau lt/files/omb/budget/fy2012/assets/12msr.pdf , p. 10.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

30 be transitory. It is also possible that the st ate of the economy will be such that positive or negative employment effects will occur.

A number of different approaches have been used in published literature to conduct employment analysis. This section describes some of the common methods, as well as some of their
limitations.

2. Approaches to Quantitative Employment Analysis

Measuring the employment impacts of a policy de pend on a number of inputs and assumptions. For instance, as discussed, assumptions about the overall state of unemployment in the economy play a major role in measured job impacts. The inputs to the models commonly are the changes in quantities or expenditures in the affected sectors; model results may vary in different studies depending on the assumptions about the levels of those inputs, and which sectors receive those changes. Which sectors are included in the study can also affect the results. For instance, a study of this program that looks only at employment impacts in the refinery sector may find negative effects, because consumers will purchase less gasoline; a study that looks only at the auto parts sector, on the other hand, may find positive impacts, because the program will require redesigned or additional parts for vehicles. In both instances, these would only be partial perspectives on the overall change in natio nal employment due to Federal regulation.

a. Conceptual Framework for Employment Impacts in the Regulated Sector

One study by Morgenstern, Pizer, and Shih liv provides a retrospective look at the impacts of regulation in employment in the regulated sect ors by estimating the effects on employment of spending on pollution abatement for four highly polluting/regulated U.S. industries (pulp and paper, plastics, steel, and petroleum refining) using data for six years between 1979 and 1991. The paper provides a theoretical framework that can be useful for examining the impacts of a regulatory change on the regulated sector in the medium to longer term. In particular, it identifies three separate ways that employment levels may change in th e regulated industry in response to a new (or more stringent) regulation.

Demand effect : higher production costs due to the regulation will lead to higher market prices; higher prices in turn reduce demand for the good, reducing the demand for labor to make that good. In the authors’ words, the “extent of this effect depends on the cost increase passed on to consumers as well as the demand elasticity of industry output.”

Cost effect : as costs go up, plants add more capital and labor (holding other factors constant), with potentially positive effects on employment. In the authors’ words, asSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

31 “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 more or less laborintensive (i.e., more/less labor is required per dollar of output). In the authors’ words, “environmental activities may be more labo r intensive than conventional production,” meaning that “the amount of labor per dollar of output will rise,” though it is also possible that “cleaner operations could involve automation and less employment, for example.”

According to the authors, the “demand effect” is expected to have a negative effect on employment, 21 the “cost effect” to have a positive effect on employment, and the “factor shift effect” to have an ambiguous effect on employment. Without more information with respect to the magnitudes of these competing effects, it is not possible to predict the total effect environmental regulation will have on em ployment levels in a regulated sector.

The authors conclude that increased abatement expenditures generally have not caused a significant change in employment in those sectors. More specifically, their results show that, on average across the industries studied, each ad ditional $1 million spent on pollution abatement results in a (statistically insignificant) net increase of 1.5 jobs.

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

The following discussion of additional methodol ogies draws from Berck and Hoffmann’s review of employment models. lv

21 As will be discussed below, the de mand effect in this rule is potentia lly an exception to this rule. While the vehicles become more expensive, they also produce re duced fuel expenditures; the reduced fuel costs provide a countervailing impact on vehicle sales. As discussed in Pr eamble Section III.H.1, this possibility that vehicles may become more attractive to consumers after the program pos es a conundrum: why have interactions between vehicle buyers and producers not provided these benefits without government intervention?Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

32 b. Computable General Equilibrium (CGE) Models

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

c. Input-Output (IO) Models

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

IO models are commonly used for regional analysis of projects. In a regional analysis, the markets are commonly considered small enough that wages and prices are determined outside the region, and any excess supply or demand is due to exports and imports (or, in the case oflabor, emigration or immigration). For national-level employment analysis, the use of inputoutput models requires the assumption that workers fl ow into or out of the labor market perfectlySummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

33 freely. Wages do not adjust; instead, people join into or depart from the labor pool as production requires them. For other markets as well, ther e is no substitution of less expensive inputs for more expensive ones. As a result, IO models provide an upper bound on employment impacts. As Berck and Hoffmann note, “For the same reason, they can be thought of as simulating very short-run adjustment,” in contrast to the CGE’s implicit assumption of long-run adjustment. Changes in production processes, introductions of new technologies, or learning over time due to new regulatory requirements are also generally not captured by IO models, as they are calibrated to already established relations hips between inputs and outputs.

d. Hybrid Models

As Berck and Hoffmann note, input-output models and CGE models “represent a continuum of closely related models.” Though not separately discussed by Berck and Hoffmann, some hybridmodels combine some of the features of CGE models (e.g., prices that can change) with inputoutput relationships. For instance, a hybrid model may include the ability to examine disequilibrium phenomena, such as labor being at less than full employment. Hybrid models depend on assumptions about how adjustments in the economy occur. CGE models characterize equilibria but say little about the pathway between them, while IO models assume that adjustments are largely constrained by previously defined relationships; the effectiveness of hybrid models depends on their success in overcoming the limitations of each of these approaches. Hybrid models could potentially be used to model labor market impacts of various vehicle policy options although a number of judgments need to be made about the appropriate assumptions underlying the model as well the empirical basis for the modeling results.

e. Single Sectors

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

f. Ex-Post Econometric Studies

A number of ex-post econometric analyses examine the net effect of regulation on employment in regulated sectors. Morgenstern, Pizer, and Shih (2002), discussed above, and Berman and Bui (2001) are two notable examples that rely on highly disaggregated establishment-level time series data to estimate longer-run employment effects. lvi While often a sophisticated treatment ofSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

34 the issues analyzed, these studies commonly analyze specific scenarios or sectors in the past; care needs to be taken in extrapolating their resu lts to other scenarios and to the future. For instance, neither of these two studies examines the auto industry and are therefore of limited applicability in this context.

g. Summary

All methods of estimating employment impacts of a regulation have advantages and limitations. CGE models may be most appropriate for long -term impacts, but the usual assumption of equilibrium in the employment market means that it is not useful for looking at changes in overall employment: overall levels are likely to be premised on full employment. IO models, on the other hand, may be most appropriate for small-scale, short-term effects, because they assume fixed relationships across sector s and do not require market equilibria. Hybrid models, which combine some features of CGEs with IO models, depend upon key assumptions and economic relationships that are built into them. Single-s ector models are simple and straightforward, but they are often based on the assumptions that la bor demand is proportional to output, and that other sectors are not affected. Finally, econometric models have been developed to evaluate the longer-run net effects of regulation on sector employment, though these are ex-post analyses commonly of specific sectors or situations, and the results may not have direct bearing for the regulation being reviewed.

3. Employment Analysis of This Rule

As mentioned above, this program is expected to affect employment in the regulated sector (auto manufacturing) and in other sectors directly aff ected by the rule: auto parts suppliers, auto dealers, the fuel supply market (which will face reduced petroleum production due to reduced fuel demand but which may see additional demand for electricity or other fuels) , and consumers (who will face higher vehicle costs and lower fuel e xpenditures). In addition, as the discussion above suggests, each of these sectors could potentia lly have ripple effects in the rest of the economy. These ripple effects depend much more heavily on the state of the macroeconomy than do the direct effects. At the national level, employment may increase in one industry or region and decrease in another, with the net eff ect being smaller than either individual-sector
effect . EPA does not attempt to quantify the net effects of the regulation on overall national employment.

The discussion that follows provides a partial, bottom-up quantitative estimate of the effects of this rule on the regulated sector (i.e., the auto industry; for reasons discussed below, we include some quantitative assessment of effects on supplier s to the auto industry although suppliers are not regulated directly). It also includes qualitati ve discussion of the effects of the rule on otherSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

35 sectors. Focusing quantification of employme nt impacts on the regulated sector has some advantages over quantifying all impacts. First, tT he analysis relies on data generated as part of the rulemaking process, which focuses on the regulated sector; as a result, what is presented here is based on internally consistent assump tions and estimates made in this rule. Second, as discussed above, net effects on employment in the economy as a whole depend heavily on the overall state of the economy when this rule has its effects. Focusing on the regulated sector provides insight into employment effects in that sector without having to make assumptions about the state of the economy when this rule has its impacts. We include a qualitative discussion of employment effects on other s ectors to provide a broader perspective on the impacts of this rule.

As noted above, in a full-e mployment economy, any changes in employment will result from people changing jobs or voluntarily entering or exiting the workforce. In a full-employment economy, employment impacts of this rule will change employment in specific sectors, but it will have small, if any, effect on aggregate em ployment. This rule would take effect in model years 2017 through 2025; by then, the current high unemployment may be moderated or ended. For that reason, this analysis does not includ e multiplier effects, but instead focuses on employment impacts in the most directly affected industries. Those sectors are likely to face the most concentrated employment impacts.

a. Employment Impacts in the Auto Industry

Following the Morgenstern et al. conceptual framework for the impacts of regulation on employment in the regulated sector, we consider three effects for the auto sector: the demand effect, the cost eff ect, and the factor shift effect. However, we are only able to offer quantitative
estimates for the cost effect. We note that thes e estimates, based on extrapolations from current data, become more uncertain as time goes on.

i. The Demand Effect

The demand effect depends on the effects of this rule on vehicle sales. If vehicle sales increase, then more people will be required to assemble vehicles and their components. If vehicle sales decrease, employment associated with these activities will unambiguously decrease. Unlike in Morgenstern et al.’s study, where the demand effect decreased employment, there are countervailing effects in the vehicle market due to the fuel savings resulting from this program. On one hand, this rule will increase vehicle costs; by itself, this effect would reduce vehicle sales. On the other hand, this rule will reduce th e fuel costs of operating the vehicle; by itself, this effect would increase vehicle sales, especially if potential buyers have an expectation of higher fuel prices. The sign of demand effect will depend on which of these effects dominates.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

36 Because, as described in Chapter 8.1, we have not quantified the impact on sales for this rule, we do not quantify the demand effect.

b. The Cost Effect

The demand effect, discussed above, measures employment changes due to new vehicle sales only. The cost effect measures employment impacts due to the auto manufacturers installing new or additional technologies needed for vehicles to comply with the standards.

One way to estimate the cost effect, given the co st estimates for complying with the rule, is to use the ratio of workers to each $1 million of expenditures in that sector. The use of these ratios has both advantages and limitations. It is ofte n possible to estimate these ratios for quite specific sectors of the economy: for instance, it is possible to estimate the average number of workers in the light-duty vehicle manufacturing sector per $1 million spent in the sector, rather than use the ratio from another, more aggregated sector, such as motor vehicle manufacturing. As a result, it is not necessary to extrapolate employment ratios from possibly unrelated sectors. On the other hand, these estimates are averages for the sectors, covering all the activities in those sectors; they may not be representative of the labor required when expenditures are required on specific activities, as the factor shift effect (discussed below) indicates. For instance, the ratio for the motor vehicle manufacturing sector represents the ratio for all vehicle manufacturing, not just for fuel efficiency improvements. In addition, these estimates do not include changes in sectors that supply these sectors, such as steel or electronics producers. They thus may best be viewed as the effects on employment in the auto sector due to the changes in expenditures in that sector, rather than as an assessment of all employment changes due to these changes in expenditures.

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

We use several sources for estimates of empl oyment per $1 million expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment Requirements Matrix (ERM), lvii which provides direct estimates of the employment per $1 million in sales of goods in 202 sectors. The estimates used here, updated from the NPRM, are from 2010 (adjusted to 2010$). Not all expenditures are for domestically produced vehicles, however. To estimate the proportion of domestic expenditures affected by the rule, we use data from Ward’s Automotive Group for total car and truck production in the U.S. compared to total car and truck sales in theSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

37 U.S. lviii For the period 2001-2010, the proportion is 66.7%. We thus weight sales by this factor to get an estimate of the effect on employment in the motor vehicle manufacturing sector due to
this rule.

The Annual Survey of Manufactures lix (ASM) provides another source of estimates based on a sample of 50,000 establishments out of a universe of 346,000 manufacturing establishments. It includes more sectoral detail than the BLS ERM: for instance, while the ERM includes the Motor Vehicle Manufacturing sector, the ASM has detail at the 6-digit NAICS code level (e.g., automobile manufacturing vs. light truck and utility vehicle manufacturing). While the ERM provides direct estimates of employees/$1 million in expenditures, the ASM separately provides number of employees and value of shipments; the direct employment estimates here are the ratio of those values. The data in the ASM are updated annually, except for years when the full Economic Census occurs. The tables presented here use data from 2010 (also updated from the NPRM). As with the ERM, we adjust for the ra tio of domestic production to domestic sales. The values reported are for Motor Vehicle Ma nufacturing (NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 336 11), and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Economic Census includes all large companies and a sample of smaller ones. The ASM is a subset of the Economic Census; though the Census itself is more complete, it is conducted only every 5 years, while the ASM is annual. The values presented here use data from 2007 (adjusted to 2010$), with the domestic production-to-sales adjustment. The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Auto mobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehi cle Parts Manufacturing (NAICS 3363).

Table 2.2-2.1Table 8.2-2 provides the values, either given (BLS) or calculated (ASM, Economic Census) for employment per $1 million of expenditures, all based on 2010 dollars, though the underlying data come from different years (which may account for some of the differences). The different data sources provide similar magnitud es for the estimates for the sectors. Parts manufacturing appears to be more labor-intensive than vehicle manufacturing; light-duty vehicle manufacturing appears to be slightly less labor-i ntensive than motor vehicle manufacturing as a
whole.

Table 2.2-2.1 Employment per $1 Million Expenditures (201 0$) 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 Formatted: Font: (Default) Times New Roman, 12 ptSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

38 BLS ERM Motor Vehicle Mfg 0.770 0.514

ASM Motor Vehicle Mfg 0.655 0.437

ASM Light Duty Vehicle Mfg 0.609 0.406

Economic
Census Motor Vehicle Mfg 0.665 0.443

Economic
Census Light Duty Vehicle Mfg 0.602 0.402

BLS ERM Motor Vehicle Parts Mfg 2.614 1.743

ASM Motor Vehicle Parts Mfg 2.309 1.540

Economic
Census Motor Vehicle Parts Mfg 2.712 1.809

BLS ERM refers to the U.S. Bureau of Labor Statistics’ Employment Requirement Matrix. ASM refers to the U.S. Census Bureau’s Annual Survey of Manufactures. Economic Census refers to the U.S. Census Bureau’s Economic Census. Over time, the amount of labor needed in the auto industry has changed: automation and improved production methods have led to significant productivity increases. The BLS ERM, for instance, provided estimates that, in 1993, 1.64 workers in the Motor Vehicle Manufacturing sector were needed per $1 million of 2005$, but only 0.86 workers by 2010 (in 2005$). Because the ERM is available annually for 1993-2010, we used these data to estimate productivity improvements over time. We regressed logged ERM values on year (to estimate percent change per year) for both the Motor Vehicle Manufact uring and Motor Vehicle Parts Manufacturing sectors. The results suggest a 3.9 percent per year productivity improvement in the Motor Vehicle Manufacturing Sector, and a 3.8 percent per year improvement in the Motor Vehicle Parts Manufacturing Sector. We then used the regression relationship to project the ERM through 2025. In the results presented below, these projected values (adjusted to 2010$) were used directly for the BLS ERM estimates. For the ASM, we used the ratio of the projected value in the future to the projected value in 2010 (the base year for the ASM); for the Economic Census estimates, we used the ratio of the projec ted value in the future to the projected value in 2007 (the base year for that estimate). This is a simple way to examine the relationship between labor required and expenditure.

Table 2.2-2.2Table 8.2-3 shows the cost estimates developed for this rule, discussed in Chapter
5. The maximum value in Table 2.2-2.1Table 8.2-2 for employment impacts per $1 million Comment [A6]: Commenters request EPA to clarify with additional information. Commenters do

not find it very clear.

Formatted: Font: Times New Roman, 12 pt

Formatted: Font: (Default) Times New Roman, 12 ptSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

39 expenditures (after accounting for the share of domestic production) is 1.809 in 2010 if all the additional costs are in the parts sector; the minimum value is 0.402 in 2010, if all the additional costs are in the light-duty vehicle manufacturing sector: that is, the range of employment impacts is between 0.4 and 2 additional jobs per $1 million expenditures in the sector in 2010.

The results in Table 2.2-2.2Table 8.2-3 include the productivity adjustment described above.

While we estimate employment impacts, measured in job-years, beginning with the first year of the standard (2017), some of these employment gains may occur earlier as auto manufacturers and parts suppliers hire staff in anticipation of compliance with the standard. A job-year is a way to calculate the amount of work needed to complete a specific task. For example, a job-year is one year of work for one person.

Table 2.2-2.2 Employment per $1 Million in the Motor Vehicle Manufacturing Sector, in job-years

Year Costs (before adjustment for domestic proportion of production) ($Millions) Minimum employment effect (if all expenditures

are in the parts sector) Maximum employment effect (if all expenditures are in the light duty vehicle mfg sector)

2017 $ 2,435 700 3,200

2018 $ 4,848 1,300 6,200

2019 $ 6,818 1,700 8,400

2020 $ 8,858 2,100 10,500

2021 $ 12,400 2,900 14,200

2022 $ 18,323 4,100 20,200

2023 $ 23,734 5,100 25,200

2024 $ 29,101 6,000 29,700

2025 $ 31,678 6,300 31,100 We note that the cost effect depends only on techno logy costs, not vehicle sales. It is therefore not sensitive to assumptions about how consumers consider fuel savings at the time of vehicle purchase.

i. The Factor Shift Effect

The factor shift effect looks at the effects on employment due to changes in labor intensity associated with a regulation. As noted above, th e estimates of the cost effect assume constant labor per $1 million in expenditures, though the new technologies may be either more or less Formatted: Font: Times New Roman, 12 ptSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

40 labor-intensive than the existing ones. An estimate of the factor shift effect would either increase
or decrease the estimate used for the cost effect.

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

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

Because labor cost was one of the sources of diffe rences between the technologies, it is possible, for those technologies, to see whether labor needs increase or decrease with the switch to technologies that might contribute to compliance with the standards. An incr ease in labor cost for the new technology indicates an increase in the labor needed for the new technology compared to the baseline technology. For instance, an 8-speed transmission requires $15.11 more in labor costs than a 6-speed transmission (as accounted for in EPA’s cost estimates for the rule). Dividing the labor cost by a wage per hour estimate provides an estimate of the additional hours (and thus the additional labor) needed for the new technology compared to the baseline technology. As with labor cost, an increase in labor hours per technology indicates greater employment needs for the new technologies. For this conversion, a weighted average wage rate (90 percent of the average wage in the Motor Vehicle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle Manufacturing Sector) of $46.36/hour in 2015, using 2008 dollars (the unit of analysis for the FEV study). For the change from a 6-speed to an 8- speed transmission, we thus estimate an additional 0.33 hours of labor per transmission.

Table 2.2-2.3Table 8.2-4 shows the changes in labor hours in moving from baseline to new fuelsaving technologies for technologies in the FEV study. It indicates that, in switching from the baseline to the new technologies, labor use per technology increased: the fuel-saving technologies use more labor than the baseline t echnologies. For a subset of the technologies likely to be used to meet the standards in this rule, then, the factor shift effect increases labor demand, at least in the short run; in the long run, as with all technologies, the cost structure is likely to change due to learning, economies of scale, etc. The technologies examined in this research are, however, only a subset of the technologies that auto makers may use to comply with the standards in this program. As a resu lt, these results cannot be considered definitive Formatted: Font: Times New Roman, 12 pt

Comment [A7]: Commenters note that this is confusing. Commenters request clarity on whether
these are all increases in labor relative to capital by deploying more fuel efficient technologies? Are
there any technologies that have the opposite
effect?Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

41 evidence that the factor shift effect increases employment for this rule. We therefore do not quantify the factor shift effect for this rule.

Table 2.2-2.3 Estimated Change in Labor for Selected Compliance Technologies

Technology FEV
Case Study Vehicle Class Labor
Costs Total
Costs Hours/ Technology

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

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

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

Electric A/C

compressor 0602 $4.68 $167.54 0.10

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

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

ii. Summary of Employment Effects in the Auto Sector

While we are not able to quantify the demand or factor shift effects, the cost effect results show that the employment effects of the increased spending in the regulated sector (and, possibly, the parts sector) are expected to be positive and on the order of a few thousand in the initial years of the program. As noted above, motor vehicle an d parts manufacturing sectors employed about 677,000 people in 2010, with automobile and light truck manufacturing accounting for about 129,000 of that total.

4. Effects on Employment for Auto Dealers

The effects of the standards on employment for auto dealers depend principally on the effects of the standards on light duty vehicle sales: increases in sales are likely to contribute to employment at dealerships, while reductions in sales are likely to have the opposite effect. In addition, auto dealers may be affected by changes in maintenance and service costs. Increases in those costs are likely to increase labor demand in dealerships, and reductions are likely to
decrease labor demand.Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

42 Although this rule predicts very small penetration of plug-in hybrid and electric vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. As discussed in Chapter 21.2.g8.1.2.7 , consumers may find some characteristics of electric vehicles and plug-in hybrid electric vehicles, such as the ability to fuel with electricity rather th an gasoline, attractive; they may find other characteristics, such as the limited range for electric vehicles, undesirable. As a result, some consumers will find that EV s will meet their needs, but other buyers will choose more conventional vehicles. Auto dealers may play a major role in explaining the merits and disadvantages of these new technologies to vehicle buyers. There may be a temporary need for increased employment to train sales staff in the new technologies as the new technologies
become available.

5. Effects on Employment in the Auto Parts Sector

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

6. Effects on Employment for Fuel Suppliers

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

Expected petroleum fuel consumption reductions can be found in RIA Chapter 5.4. While this reduced consumption represents fuel savings for pur chasers of fuel, it represents a loss in value of output for the petroleum refinery industry, fuel distributors, and gasoline stations. The loss of expenditures to petroleum fuel suppliers throughout the petroleum fuel supply chain, from the petroleum refiners to the gasoline stations, is likely to result in reduced employment in these

sectors.

This rule is also expected to lead to increases in electricity consumption by vehicles, as discussed in RIA Chapter 5.4. This new fuel may require additional infrastructure, such as electricitySummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

43 charging locations. Providing this infrastructure, as well as infrastructure for other alternative fuels (such as CNG), will require some increased employment. In addition, the generation of electricity is likely to require some additional la bor. We have insufficient information at this time to predict whether the increases in labor associated with increased infrastructure provision and generation for electricity will be greater or less than the employment reductions associated with reduced demand for petroleum fuels.

7. Effects on Employment due to Impacts on Consumer Expenditures

As a result of these standards, consumers will pay a higher up-front cost for the vehicles, but they will recover those costs in a fairly short payback period (see Preamble Section III.H.5 and Chapter 5.5 of this RIA); indeed, people who finance their vehicles are expected to find that their fuel savings per month exceed the increase in the loan cost (though this depends on the particular loan rate a consumer receives). As a result, consumers will have additi onal money to spend on other goods and services, though, for those who do not finance their vehicles, it will occur after the initial payback period. These increased expenditures will support employment in those sectors where consumers spend their savings.

These increased expenditures will occur in 2017 and beyond. If the economy returns to full employment by that time, any change in consumer expenditures would primarily represent a shift in employment among sectors. If, on the other hand, the economy still has substantial unemployment, these expenditures would contribute to employment through increased consumer
demand.

8.7. Summary

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

For the regulated sector, the cost effect is expected to increase employment by 700 – 3,200 jobyears in 2017, depending on the share of that employment that is in the auto manufacturingSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

44 sector compared to the auto parts manufacturing sector. As mentioned above, some of these job gains may occur earlier as auto manufacturers and parts suppliers hire staff to prepare to comply with the standard. The demand effect depends on changes in vehicle sales, which are not quantified for this rule. Though we do not have estimates of the factor shift effect for all potential compliance technologies, the evidence which we do have for some technologies suggests that many of the technologies will have increased labor needs.

Effects in other sectors that are predicated on vehicle sales are also ambiguous. Changes in vehicle sales are expected to affect labor needs in auto dealerships and in parts manufacturing. Increased expenditures for auto parts are expected to require increased labor to build parts, though this effect also depends on any changes in the labor intensity of production; as noted, the subset of potential compliance technologies for which data are available show increased labor requirements. Reduced petroleum fuel production implies less employment in the petroleum sectors, although there could be increases in employment related to providing infrastructure for alternative fuels such as electricity and CNG. Fi nally, consumer spending is expected to affect employment through changes in expenditures in general retail sectors; net fuel savings by consumers are expected to increase demand (a nd therefore employment) in other sectors.

i Goldberg, Pinelopi Koujianou, “Product Differentia tion and Oligopoly in In ternational Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-951 (Docket EPA-HQ-OAR-2010-0799-0665); Goldberg, Pinelopi Koujianou, “The Effects of the Corporate Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics 46(1) (March 1998): 1-33 (Docket EPA-HQ-OAR-2010-0799-0666).

ii Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared bythe Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. DEAC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-0799-0715); Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study ofIncentives for Increased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQOAR-2010-0799-0686).

iii McManus, Walter M., “Can Proactive Fuel Economy Strategies Help Automakers Mitigate Fuel-Price Risks?” University of Michigan Tran sportation Research Institute, September 14, 2006 (Docket EPA-HQ-OAR-2010-0799-0687).Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

45 iv Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799-0688); Berry, Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The Ne w Car Market,” Journal of Political Economy 112(1) (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799-0689).

v Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690).

vi Train, Kenneth E., and Clifford Winston, “Veh icle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469- 1496 (Docket EPA-HQ-OAR-2010-0799-0691).

vii Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-2010-0799-0826).

viii Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799-0692).

ix E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic Review 99(3) 2009): 667-699 (Docket EPA- HQ-OAR-2010-0799-0690); Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799-0691).

x E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799- 0688).

xi Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy Information Administration,” prepared bythe Oak Ridge National Laboratory for the U.S. Department of Energy under contract No. DEAC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-0799-0715).

xii NERA Economic Consulting, “Evaluation of NHTSA’s Benefit-Cost Analysis of 2011-2015 CAFE Standards,” 2008, available at http://www.heartland.org/policybot/results/234 95/Evaluation_of_NHTSAs_BenefitCost_Analysi s_Of_20112015_CAFE_Standards.html (Docket EPA-HQ-OAR-2010-0799-0693).Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

46 xiii E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799-0691).

xiv E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799- 0688).

xv E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690).

xvi Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Dr iven and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010- 0799-0694).

xvii NERA Economic Consulting, “Appendix B: New Vehicle Market Model,” “Impacts of the California Greenhouse Gas Emission Standards on Motor Vehicle Sales,” comments submittedto the U.S. Environmental Protection Agency at Regulations.gov, document number EPA-HQOAR-2006-0173-9053.1 (Docket EPA-HQ-OAR-2010-0799-0716).

xviii E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799-0691)

xix E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799- 0688).

xx Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799-0686).

xxi Rex, Emma, and Henrikke Baumann (2007). “Beyond ecolabels: what green marketing canlearn from conventional marketing.” Journal of Cleaner Production 15: 567-576 (Docket EPAHQ-OAR-2010-0799-0695).

xxii Whitefoot, Kate, Meredith Fowlie, and Stev en Skerlos, “Product Design Responses to Industrial Policy: Evaluating Fuel Economy Standards Using an Engineering Model ofSummary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

47 Endogenous Product Design,” Energy Institute at Haas Working Paper 214, University of California Energy Institute, February 2011 (Docket EPA-HQ-OAR-2010-0799).

xxiii Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799- 0688).

xxiv Goldberg, Pinelopi Koujianou, “Product Diff erentiation and Oligopoly in International Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-951 (Docket EPA-HQ-OAR-2010-0799-0665).

xxv Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with Flexible Substitution Patterns,” Journal of Econometrics 89 (1999): 109-129 (Docket EPA-HQ-OAR2010-0799-0697); Brownstone, David, David S. Bu nch, and Kenneth Train, “Joint Mixed Logit Models of Stated and Revealed Preferences fo r Alternative-Fuel Vehicles,” Transportation Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2010-0799-0698); Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” prepared by the OakRidge National Laboratory for the U.S. Department of Energy, July 2001 (Docket EPA-HQOAR-2010-0799-0717); Greene, David L., K. G. Duleep, and Walter McManus, “Future Potential of Hybrid and Diesel Powertrains in th e U.S. Light-Duty Vehicle Market,” prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, August 2004 (Docket EPA-HQ-OAR-2010-0799-0718).

xxvi Petrin, Amil, “Quantifying the Benefits of New Products: The Case of the Minivan,” Journal of Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799-0699); Berry, Steven, James Levinsohn, and Ariel Pakes, “Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The Ne w Car Market,” Journal of Political Economy 112 (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799-0689).

xxvii Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48 (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799-0691).

xxviii Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799-0690); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Dr iven and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010- 0799-0694).

xxix Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

48 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799-0686); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010-0799-0694); Greene, David L., “Feebates, Footprints andHighway Safety,” Transportation Research Part D 14 (2009): 375-384 (Docket EPA-HQ-OAR2010-0799-0685).

xxx E.g., Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799-0692).

xxxi Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2010-0799-0661).

xxxii E.g., Espey, Molly, and Santosh Nair, 2005. “Automobile Fuel Economy: What Is It Worth?” Contemporary Economic Policy 23: 317-323 (Docket EPA-HQ-OAR-2010-0799- 0700).

xxxiii Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 1593-1594 (Docket EPA-HQ-OAR-2010-0799-0662).

xxxiv E.g., Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International Markets: The Case of the U.S. Automobile Industry,” Econometrica 63(4) (July 1995): 891-951 (Docket EPA-HQ-OAR-2010-0799-0665).

xxxv E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-0799- 0688).

xxxvi Busse, Meghan, Christopher R. Knittel, and Flor ian Zettelmeyer, 2009. “Pain at the Pump: How Gasoline Prices Affect Automobile Purchasing in New and Used Markets,” working paper, http://web.mit.edu/knitte l/www/papers/gaspaper_latest.pdf (accessed 11/1/11) (Docket EPAHQ-OAR-2010-0799-0667); Li, Shanjun, Christopher Timmins, and Roger H. von Haefen, 2009. “How Do Gasoline Prices Affect Fleet Fuel Economy?” American Economic Journal: Economic Policy 1(2): 113-137 (Docket EP A-HQ-OAR-2010-0799-0701 ); Congressional Budget Office (2008) (Docket EPA-HQ-OAR -2010-0799); Congressional Budget Office (2008). Effects of Gasoline Prices on Driving Be havior and Vehicle Markets. The Congress of the United States, Pub. No. 2883 (Docket EPA-HQ-OAR-2010-0799-0719); West. Sarah (2007). “The Effect of Gasoline Prices on the Demand for Sport Utility Vehicles.” Working Paper. Macalester College, http://www.macalester.edu/~wests/SarahWestMEA2007.pdf, accessed 1/25/10 (Docket EPA-HQ-OAR-2010-0799-0702).Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

49 xxxvii Greene, David L., and Jin-Tan Liu, 1988. “Automotive Fuel Economy Improvements andConsumers’ Surplus,” Transportation Research Part A 22A (3): 203-218 (Docket EPA-HQOAR-2010-0799-0703).

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

xxxix Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S. Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket EPA-HQ-OAR-2010-0799-0711).

xl Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket EPA-HQ-OAR-2010-0799-0704); McManus, Walter, 2007. “The Impact of Attribute-Based Corporate Average Fuel Economy (CAFE) Standard s: Preliminary Results.” Ann Arbor, MI: University of Michigan Transportation Research Institute, Report No. UMTRI-2007-31 (Docket EPA-HQ-OAR-2010-0799-0705).

xli For instance, Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy(CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2010-0799-0672); Austin, David, and Terry Di nan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and Management 50 (2005): 562- (Docket EPA-HQ-OAR-2010- 0799-0692); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of the Corporate Average Fuel Economy Standard,” Resources for the Future Paper RFF DP 10- 50 (December 2010), http://www.rff.org/RFF/Do cuments/RFF-DP-10-50.pdf) (Docket EPAHQ-OAR-2010-0799-0706); Jacobsen, Mark, 2010. “Evaluating U.S. Fuel Economy Standards in a Model with Producer and Household Heterogeneity,” working paper, at http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAF E.pdf (accessed 11/1/11) (Docket EPA-HQOAR-2010-0799-0829) .

xlii Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799-0692).

xliii IEA. 2007. “Mind the Gap: Quantifying Prin cipal-Agent Problems in Energy Efficiency

. ” Paris, France: International Energy Agency (Docket EPA-HQ-OAR-2010-0799-0720); Jaffe, Adam B., Richard G. Newell, and Robert N. Stavins (2001). "Energy Efficient Technologies and Climate Change Policies: Issues and Evidence." In Climate Change Economics and Policy,Toman, Michael A., ed., Washington, D.C.: Resources for the Future. p.171-181 (Docket EPAHQ-OAR-2010-0799-0828); Metcalf, Gilbert E., and Kevin A. Hassett (1999). "Measuring the Energy Savings From Home Im provement Investments: Evidence From Monthly Billing Data." The Review of Economics and Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010-0799-Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

50 0659); Tietenberg, T. (2009). “Reflections – En ergy Efficiency Policy: Pipe Dream or Pipelineto the Future?” Review of Environmental Economics and Policy 3(2): 304-320 (Docket EPAHQ-OAR-2010-0799-0707).

xliv Helfand, Gloria, and Ann Wolverton (2011). “Evaluating the Consumer Response to Fuel Economy: A Review of the Literature.” Inte rnational Review of Environmental and Resource Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799-0652).

xlv Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799-0717).

xlvi U.S. Environmental Protection Agency. “Peer Review for the Consumer Vehicle Choice Model and Documentation.” Office of Transportation and Air Quality, Assessment and Standards Division, EPA-420-R-12-013, April 2012 (Docket EPA-HQ-OAR-2010-0799).

xlvii See, for instance, Ladika, Susan (2009). “’Green’ auto loans offer lower rates,” Bankrate.com, http://www.bankrate.com/finance/au to/green-auto-loans-offer-lower-rates-1.aspx , accessed 2/28/12 (Docket EPA-HQ-OAR-2010-0799).

xlviii Helfand, Gloria (2012). “Memorandum: Lending institutions that provide discounts for more fuel-efficient vehicles.” Assessment and Standards Division, Office of Transportation andAir Quality, U.S. Environmental Protection Agency, Docket EPA-HQ-OAR-0799 (Docket EPAHQ-OAR-2010-0799).

xlix U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey 2010. http://www.bls.gov/cex/#tables. This analysis us es the table Quintiles of Income before Taxes, accessed 6/14/12 (Docket EPA-HQ-OAR-2010-0799).

l Federal Reserve System, Survey of Consumer Finances 2007. http://www.federalreserve.gov/econresdata/scf/scfindex.htm. We applied the weighting scheme for the data, to make the data representative of the population as a whole, provided in the documentation for the results presented here (Docket EPA-HQ-OAR-2010-0799).

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

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

liii Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011 (Docket EPA-HQ-OAR-2010-0799-0676).Summary of interagency working comments received on draft rule under EO 12866 and EO 13563 review. They are for your consideration and are subject to further policy review.

51 liv Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. “Jobs Versus the Environment: An Industry-Level Perspective.” Journal of Environmental Economics and Management 43 (2002): 412-436 (Docket EPA-HQ-OAR-2010-0799-0677).

lv Berck, Peter, and Sandra Hoffmann. “Assessing the Employment Impacts of Environmental and Natural Resource Policy.” Environmental and Resource Economics 22 (2002): 133-156 (Docket EPA-HQ-OAR-2010-0799-0678).

lvi Berman, Eli, and Linda T. Bui, (2001) “Environmental Regulation and Labor Demand: Evidence from the

South Coast Air Basin,” Journal of Public Economics

, 79, 265

– 295 (Docket EPA

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lvii http://www.bls.gov/emp/ep_d ata_emp_requirements.htm

lviii U.S. Vehicle Production by Manufacturer, UsaPr 05.xls, and U.S. Vehicle Sales, UsaSa01.xls, accessed July 25, 2011 at http://subscribers.wardsauto.com/refcenter/ , ©Copyright 2011, Ward's Automotive Group, a division of Pent on Media Inc. Redistribution prohibited.

lix http://www.census.gov/manufacturing/asm/index.html**Deliberative Material - EPA Staff Draft**

Page numbers here refer to the pages on the Summary of interagency working comments. If we have not commented on a proposed edit, we have adopted it

.

**Preamble:** p. 1: Rather than delete the sentence at the end o f the first paragraph, we have revised it to corres pond to other statements that raise the possibility of i ncreased vehicle sales.

p. 2: We have done some edits on the comments to m ake them more accurate (provided that we understand the intended meanings behind the suggest ed edits). E.g., we do not assume that it is unprofitable to add additional fuel-saving technologies. As discussed below, for instance, a risk-averse firm may choose not to adopt technologies that are expected to be profitable because they are uncertain. The rule does not “force” investments by all manufa cturers. Though we expect all manufacturers to make some investments, they may als o buy credits as an alternative compliance path.

pp. 2-3: We have kept the footnotes. They are fact ual support for the statements in the text.

**Comment [A1]:** Commenters note that this discussion is confusing. C ommenters believe that manufacturers may not be collaborating on research eve n due to this rule, and it is likely each manufacturer will be conducting its own proprietary re search in an effort to lower their own costs of meeting the standards. They might not have any ince ntive to share these innovations with their competitors.

**EPA response.** EPA has added additional text to clarify this iss ue, and we will provide specific examples of collaboration among auto firms. The comment from the interagency is not technically correct. It is
common for auto firms to collaborate on research activi ties. GM/Ford co-developed the current generation of 6-speed transmissions used by both com panies. GM/Chrysler/BMW/Daimler had a multiyear development program to perform the research and initial development of the 2-mode hybrid system. Last year Ford and Toyota announced a new p artnership to develop hybrids for pickup trucks.
EPA will not delete this text as the basis

EPA text edits "Research among multiple parties can be a synergistic process: ideas by one researcher may stimulate new ideas by others, and more and better results oc cur than if the one researcher operated in isolatio n. 1 Collaboration between automotive companies or automo tive suppliers does occur ; for example

, in 2011 Toyota and Ford announced a new effort to collaborate on the development of hybrid technology for

1 Powell, Walter W., and Eric Giannella, “Collective Invention and Inventor Networks,” Chapter 13 in Ha ndbook of the Economics of Innovation, Volume 1, edited by B. Hall and N. Rosenberg (Elsevier, 2010) discuss how a “collective momentum” has led uncoordinated researc h efforts among a diverse set of players to develop advances in a number of technologies (such as elect ricity and telephones). They contrast this view of technological innovation with that of proprietary research in cor porate laboratories, where the research is part of a corporate strategy. Such momentum may result in part from al ignment of economic, social, political, and other g oals. Deleted:

,pickup tr u cks. 2 Another example was the four year joint development effort between General Motors

and Ford for from 2002-2006 for the development of a new six-speed automatic transmission. 3 "

**Comment [A2]:** Commenters request clarity on why there would be ince ntives to coordinate?
Commenters note that unless the manufacturers are co lluding, they should each have an incentive to keep propriety technologies such as batteries for the mselves so that they can keep their own costs low relative to competitors.

**EPA response.** EPA has added additional text to clarify this iss ue, pointing out the “network externalities” associated with development of infras tructure for electric vehicles.

EPA text edits: “An additional aspect of the standards is the possib ility of greater standardization. As more companies adopt new technologies, the incentives increase for ad ditional suppliers and more availability of aftermarket replacement parts; these suppliers would be l ikely to find ways to increase compatibility across vehicle types. For example, though electric vehicles (EVs) are not expected to be more than a few percent of the vehicles produced in response to this rule, their adoption depends on such factors as batteries and charging methods that are compatible acr oss different companies. These are examples of “network externalities,” where use of a technology by one party has greater benefits if more people ar e also using the technology. In this case, just as th e ability to buy gasoline from any station facilitat es owning a gasoline-based vehicle, the ability to rech arge an EV or get replacement parts easily facilitat es ownership of an EV. In the absence of the rule, fewer companies would be pursuing this technology, and it would be considered a specialty product; the i ncentives to coordinate might be low.”

**Comment [A3]:** Commenters note that absent the rule, a manufacturer acting alone could achieve economies of scale if consumers want to buy more fuel efficient cars. Commenters request clarity on whether this is just a reiteration of the risk aver sion argument?

**EPA Response.** The deleted sentence “Further, the certainty. . .” was taken from interagency comments provided on NHTSA’s analysis on 8/18/12. W e adopted it (as well as other statements) in response to the request to make conforming changes. EPA agrees with this statement. Manufacturer learning-by-doing is a function of cumulative product ion. To the extent that auto suppliers, who will manufacture much of the technology that will be used b y auto OEMS, see higher volumes due to multiple auto companies using a given technology, cost s will decrease as a result of those higher
volumes. The same can be said for economies of scale. It is not automatic that an auto company on its own will achieve economies of scale for a given techno logy. Hybrids is an excellent example. The majority of the components in a hybrid system are of ten not manufactured by the OEM, but by suppliers. It is unlikely that any given manufactur er will see very large %’s of hybrids, but collective ly the industry could produce volumes on the order of 1 mill ion per year or more – and many of the components will be manufactured by suppliers, who can then achieve economies of scale, that would not occur if the technology was only being pursued by a single firm.

p. 5-6: We are maintaining the paragraph as a summ ary of the points made immediately above, which
we believe is useful to the reader.

2 "Ford and Toyota to Work Together on Hybrid System for Trucks", New York Times, August 22, 2011

. 3 "Ford, GM Launch Joint 6-speed Automatic," Wards A utomotive, August 31, 2006. Deleted: u**Comment [A4]:** Commenters request EPA to include citations.

**EPA Response.** We have added the references.

**Comment [A5]:** Commneters request EPA to delete this paragraph. Co mmenters note that this is speculative. The required standards may exceed standa rds adopted by other countries in the future. This could hurt manufacturers’ ability to compete ther e.

**EPA Response.** This paragraph is not speculative. The standards contained in this final rule are of similar stringency to the standards in Europe and J apan for this time frame. 4 Canada is in the process of proposing the EPA/NHTSA standards. U.S. firms hav e limited R&D budgets, and many of those firms compete in international markets (Delphi, BorgWarner , Honeywell, Goodyear, etc., as well as Ford, GM, and Chrysler). These standards can enable those fir ms to compete more effectively, compared to a scenario where the U.S. GHG/CAFE standards were sign ificantly less stringent than other major market standards, which would be the case if these standard s are not in place. We have amended the paragraph to note that this effect should be true fo r “all global automakers.”

**RIA:** p. 8: We have maintained the sentence because it is an accurate reflection of the discussion in the Preamble, though we have modified the word “would” to “might.”

p. 26: We have maintained most of the deleted para graph. In the Preamble, it is the summary of the impacts we expect on this rule. (Most of the rest o f the Preamble discussion is response to comments.)
We therefore maintain it in the RIA as well. We ha ve deleted the last sentence.

p. 27: We disagree with the proposed deletion. We think it is an accurate and appropriately qualified
statement of the effect of the rule on low-income ho useholds. This issue received public comment, and it is important that we provide a clear response on this issue.

p. 28: We disagree with the proposed deletion. It is an accurate statement: fuel savings will benefit low-income households. In the previous sentence, we acknowledge uncertainty over the effects of the rule on consumers’ ability to purchase vehicles.

p. 28 and following: we disagree with the request to remove discussion of the impact of consumer fuel savings on employment. The language was included i n the proposal, and we received a number of public comments on it. We consider the discussion to be factual and balanced. We note that some of the deleted language (e.g., p. 34: “each of these s ectors could potentially have ripple effects in the re st of the economy. These ripple effects depend much more heavily on the state of the macroeconomy than do the direct effects”) is the same as language included in NHTSA’s discussion since OMB submission and received no comments in interagency comm ents to NHTSA received on 8/18/12.

**Comment [A6]:** Commenters request EPA to clarify with additional in formation. Commenters do not find it very clear.

4 See the International council for Clean Transporta tion, “Global Comparison of Light-Duty Vehicle Fuel Economy/GHG Emissions Standards,” Update June 2012. http://theicct.org/sites/default/files/ICCT_PVStd_J une2012_v0.pdf**EPA Response.** This is the same procedure used in the NPRM (with the spreadsheet included in the

docket), though with some additional data. We ran a “semilog” regression: that is, we took the natur al logarithm of the ERM values and ran an ordinary lea st squares regression with logged ERM as the dependent variable, and an intercept and year (as a time trend) as the independent variables. We used the semilog functional form because the coefficient on year can be interpreted directly as the percent change per year. It also had excellent fit and resu lts that are highly statistically significant. Belo w are results for the two sectors:

**Motor Vehicle Manufacturing**

SUMMARY OUTPUT Regression Statistics Multiple R 0.969586 R Square 0.940098 Adjusted R Square 0.936354 Standard Error 0.053978
Observations 18

ANOVA

df SS MS F Significance

F Regression 1 0.731617 0.731617 251.1025 3.35E-11 Residual 16 0.046618 0.002914
Total 17 0.778235

Coefficients Standard
Error t Stat P-value Lower 95% Upper
95% Intercept 77.94398 4.908248 15.8802 3.24E-11 67.53896 88.34901
Year -0.03886 0.002452 -15.8462 3.35E-11 -0.04406 -0.03366

Motor Vehicle Parts Manufacturing SUMMARY OUTPUT Regression Statistics Multiple R 0.969434 R Square 0.939802 Adjusted R Square 0.93604 Standard Error 0.053274
Observations 18

ANOVA

df SS MS F Significance

F Regression 1 0.708938 0.708938 249.7916 3.48E-11 Residual 16 0.04541 0.002838
Total 17 0.754347Coefficients Standard
Error t Stat P-value Lower 95% Upper
95% Intercept 77.98493 4.844236 16.0985 2.64E-11 67.71561 88.25425
Year -0.03825 0.00242 -15.8048 3.48E-11 -0.04338 -0.03312

We have revised the text to read: “We regressed lo gged ERM values on year for both the Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing se ctors. We used this approach because the coefficient describing the relationship between time a nd productivity is a direct measure of the percent change in productivity per year. The results suggest a 3.9 percent per year productiv ity improvement in the Motor Vehicle Manufacturing Sector, and a 3.8 per cent per year improvement in the Motor Vehicle Parts Manufacturing Sector. We then used the equation resulting from the regression to project the ERM through 2025. ”

**Comment [A7]:** Commenters note that this is confusing. Commenters r equest clarity on whether these are all increases in labor relative to capital by d eploying more fuel efficient technologies? Are there any technologies that have the opposite effect?

**EPA Response.** These values are not increases in labor relative t o capital, but rather a direct measure of labor, as stated in the text. As discussed in the t ext, the FEV analysis provides the change in costs on an extremely detailed basis for the change from a basel ine technology to a new technology. Those costs include labor costs. We then used the hourly wage i n the FEV analysis to convert these to hours per part. Thus, they are a direct measure of the change in labor associated with the change in the technology. This material is identical to what was p resented in the NPRM; the UAW provided supportive comments stating this was useful informat ion. We note in the text that these are “a subset of the technologies; ” “As a result, these results ca nnot be considered definitive evidence that the fact or shift effect increases employment for this rule. We therefore do not quantify the factor shift effect f or this rule.” It is possible that other technologies will have the opposite effect; as noted, we do not have data for all the technologies that might be used for compliance with this rule, and we therefore do not quantify this effect. Deleted: (to estimate percent change per year)

Deleted: relationshipIII. EPA MYs 2017-2025 Light-Duty Vehicle Greenhouse Gas Emissions Standards

H. What are the Estimated Cost, Economic, and Other Im pacts of the Rule?

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

a. Vehicle Sales Impacts

Predicting the effects of this rule on vehicle sale s entails comparing two effects. On the one hand, the vehicles designed to meet the standar ds will become more expensive, which would, by itself, discourage sales. On the other h and, the vehicles will have improved fuel economy and thus lower operating costs due to signi ficant fuel savings, which could encourage sales. Which of these effects dominates for potent ial vehicle buyers when they are considering a purchase will determine the effect on sales. Assess ing the net effect of these two competing effects is uncertain, as it rests on how consumers value fuel savings at the time of purchase and
the extent to which manufacturers and dealers refle ct technology costs in the purchase price. The empirical literature does not provide clear evidenc e on how much of the value of fuel savings consumers consider at the time of purchase. It als o generally does not speak to the efficiency of manufacturing and dealer pricing decisions. Thus, we do not provide quantified estimates of potential sales impacts in this final rule. Howeve r, as discussed later in this section, there are several reasons why EPA believes this rulemaking could lead to an increase in vehicle sales, though we do not quantify a specific impact.

An additional source of uncertainty in the analysis is understanding what would happen
in the absence of this rule. Standard economic the ory would suggest that, if automakers could profitably increase sales by adding more fuel-savin g technologies to their vehicles, then manufacturers’ profit motives would lead them to vo luntarily add those technologies in the
absence of this rule. As discussed in Preamble Sec tion III.D.1, we project, based on historical patterns, that auto makers would not go beyond the MY 2016 standards in the absence of this rule. Yet, if consumers consider just over three y ears’ worth of fuel savings in their vehicle purchase decisions and our assumptions about technology costs and fut ure gas prices are correct

, the payback period analysis in Section III.H.5 sugg ests that sales would increase in response to
this rule.

Although it is possible that manufacturers would not find it profitable to add at least some of the vehicle technologies in the absence of the r ule (see Section III.H.1.a for a discussion),
there may be the potential for increases in vehicle sales as a result of the rule. These explanations focus on conditions where the rule sti mulates investments that would not happen in the rule’s absence. The explanations posed below r aise possibilities that the rule, by requiring all automakers to meet the standards, may lead to mutua lly beneficial outcomes that might not happen in the absence of the rule. Consumers would then have the opportunity to purchase
vehicles that would not be available in the absence of the rule; if consumers consider at least as Deleted: will

Deleted: Are these analyses in conflict?

Deleted: could

Deleted: are also a number of reasons for

Deleted: notwithstanding our assumption of a flat baselinePage 2 of 26

many years of fuel savings when buying new vehicles as the payback period for the new technologies, and if manufacturers nonetheless would not have pro duced these vehicles in the absence of the rule, positive sales impacts could occur as a result of t hese final standards. The three possibilities we suggest for such outcomes ar e promotion of social learning, reduction of risk and uncertainty for manufacturers, and promoti on of innovation.

i. Social Learning

For many years, fuel economy standards did not chan ge (see Preamble III.D.1). 1 As discussed in Preamble III.H.1.a, consumers may not have focused on fuel economy, or may have found it difficult to do calculations involving the tradeoffs between fuel economy and increased vehicle costs, or may not have found vehicles with their preferred combination of fuel economy and other features. In recent years, though, fuel economy standards have started to increase. 2 In addition, high fuel prices have helped to focus con sumer attention toward vehicle fuel economy. Finally, the recently revised fuel economy label, w ith prominent information on fuel savings, are starting to appear on new vehicles. These factors may contribute to consumers gaining experience with the benefits that accrue to them fr om owning and operating vehicles with greater fuel efficiency. Consumer households that include v ehicles with a fairly wide range of fuel economy have an opportunity to learn about the valu e of fuel economy on their own. Consumer demand may be shifting towards such vehicles, not o nly because of higher fuel prices but also if many consumers are learning about the value of purc hases based not only on initial costs but also on the total cost of owning and operating a vehicle over its lifetime. This type of learning should continue before and during the model years affected by this rule.

Today’s rule, combined with the new and easier-to-u nderstand fuel economy labels required to be on all new vehicles in MY 2013, may increase sales by hastening this very type of consumer learning. As more consumers experience the savings in time and expense from owning more fuel efficient vehicles, demand may shift yet further in the direction of the vehicles with improved fuel economy and reduced GHG emissions man dated under the rule. This social learning can take place both within and across hous eholds, as consumers learn from one another. First and most directly, the time and fuel savings associated with operating more fuel efficient vehicles may be more salient to individuals who own them, which might cause their subsequent purchase decisions to shift closer to minimizing th e total cost of ownership over the lifetime of the vehicle. Second, this appreciation may spread across households through word of mouth, marketing and advertising, and other forms of commu nications. Third, as more motorists experience the time and fuel savings associated wit h greater fuel efficiency, the price of used cars may better reflect such efficiency, further re ducing the cost of owning more efficient

1 Car CAFE standards did not change from MYs 1990 th rough 2010. Truck CAFE standards did not change fr om MYs 1996 through 2004, and changed only 0.5 mpg cum ulatively from MYs 1991 through 2004. See "Summary of Fuel Economy Performance," March 12, 2012, DOT/NHTS A, http://www.nhtsa.gov/fuel-economy.

2 Truck CAFE standards began to rise in MY 2005 and have risen every year since. Car CAFE standards be gan to rise in MY 2011. Ibid.Page 3 of 26

vehicles for the buyers of new vehicles (since the resale price may increase). If these induced learning effects are strong, the rule could potenti ally increase total vehicle sales over time. This effect may be speeded or slowed by other factors th at enter into a consumer’s valuation of fuel efficiency in selecting vehicles.

The possibility that the rule could (after a lag fo r consumer learning) increase sales need not rest on the assumption that automobile manufact urers are failing to pursue profitable opportunities to supply the vehicles that consumers demand. In the absence of the rule, no individual automobile manufacturer would find it pr ofitable to move toward more efficient vehicles to increase consumer learning because no i ndividual company can fully internalize the potential future boost to demand. If one company we re to make more efficient vehicles, counting on consumer learning to enhance demand in the futur e, that company would capture only a fraction of the extra sales so generated, because t he learning at issue is not specific to any one company's fleet. Many of the extra sales could accr ue to that company's competitors.

In other words, consumer learning about the benefit s of fuel efficient vehicles involves positive externalities (spillovers) from one compan y to the others. 3 These positive externalities may lead to benefits for manufacturers as a whole i f they increase the demand for vehicles. We emphasize that this discussion has been tentative a nd qualified. It is not possible to quantify these learning effects years in advance, and these effect s may be speeded or slowed by other factors
that enter into a consumer’s valuation of fuel effi ciency in selecting vehicles. To be sure, social learning of related kinds has been identified in a number of contexts. 4 We asked for comments on the discussion offered here, with particular ref erence to any relevant empirical findings. 76 FR 75150. We continue to explore this issue but di d not receive any comments on the role of social learning, except in the context (discussed i n Section III.H.1.a) of the effect of the rule on the visibility of and status associated with owning vehicles with improved fuel economy.

ii. Reduction in Risk and Uncertainty for Manufacturers

As discussed in Preamble III.H.1.a, there appears t o be a great deal of uncertainty about how consumers will respond to increases in fuel eco nomy. Automakers may be cautious about adding more fuel-saving technology to vehicles if t hey are uncertain how buyers will respond. Even if they believe that buyers will respond posit ively, if a company is risk-averse, it may nevertheless hesitate to make the substantial major investments in new technologies and in

3 Industrywide positive spillovers of this type are hardly unique to this situation. In many industries , companies form trade associations to promote industry-wide public goods. For example, merchants in a given locale may band together to promote tourism in that locale. Antitru st law recognizes that this type of coordination ca n increase output.
4 See Hunt Alcott, Social Norms and Energy Conservat ion, Journal of Public Economics (forthcoming 2011)

, available at http://web.mit.edu/allcott/www/Allcott %202011%20JPubEc%20- %20Social%20Norms%20and%20Energy%20Conservation.pdf (Docket EPA-HQ-OAR-0799-0825); Christophe Chamley, Rational Herds: Economic Models of Social Learning (Cambridge, 2003) (Docket EPA-HQ-OAR-0799- 1110).Page 4 of 26

research that would lead to increases in fuel econo my across its fleet. 5 If a manufacturer invests substantially in fuel efficient technologies expect ing higher consumer demand than realized, then
the manufacturer has incurred the costs of investme nt but not reaped the benefits of those investments. On the other hand, if a manufacturer d oes not invest in fuel-efficient technologies, then the manufacturers may lose some sales in the s hort run if demand for fuel economy is higher than expected, but it still retains the opti on of investing in fuel-efficient technologies in the longer run . If its investments proved unsuccessful, the comp any might face substantial losses. Even if the probability of being unsuccess ful is low, the manufacturer may nevertheless perceive the losses in that scenario as a substanti al risk. If the investment proved successful, the company would, of course, take market share from ot her companies – but , assuming that there are not brand-loyalty or other advantages to being first in the market with new fuel-saving technologies, only until the other auto companies caught up. In other words, for a risk-averse company, being a first mover may appear to have a g reater downside risk than upside risk, even if the investment, on an expected-value basis, woul d pay off. If all companies are risk-averse, then they may all seek a strategy of waiting for so me other company to be the first mover. In this case, caution about these major investments ma y lead to lack of adoption of new technologies in the absence of the rulemaking , consistent with the flat baseline assumption . This rulemaking, by requiring that all companies act at the same time, removes the scenario of one company bearing all the risk.

In addition, there may be risk aversion on the cons umer side. The simultaneous investment by all companies may also encourage cons umer confidence in the new technologies. If only one company adopted new technologies, early adopters might gravitate toward that company, but early adopters tend to be a relatively small portion of the public. More cautious buyers, who are likely to be more numerous, might w ait for greater information before moving away from well-known technologies. If all companie s adopt advanced technologies at the same time, though, potential buyers may perceive the new technologies as the new norm rather than as a risky innovation. They will then be more willing to move to the new technologies. As some commenters have pointed out, simultaneous action re quired by the rule may change buyers’ expectations (their reference points) for fuel econ omy, and investing in more fuel economy may seem less risky than in the absence of the rule.

The rule, then, may reduce manufacturers’ risk of m aking significant investments in fuelsaving technologies by requiring that all companies produce more fuel-efficient vehicles . Under this outcome, it is possible for the rule to facili tate investment that would not happen in the absence of the rule, and vehicle sales could increa se as a result of the rule.

5 Sunding, David, and David Zilberman, “The Agricult ural Innovation Process: Research and Technology A doption in a Changing Agricultural Sector,” Chapter 4 in Ha ndbook of Agricultural Economics , Volume 1, edited by B. Gardner and G. Rausser (Elsevier, 2001) show how de laying adoption of a new technology in order to gai n more information may be a more profitable activity than adopting a technology, even if it has positive net benefits, when a potential adopter is risk-averse. Deleted: slow

Deleted:

,

Deleted: providing a level playing field forPage 5 of 26 iii. Promotion of Innovation

Research among multiple parties can be a synergisti c process: ideas by one researcher may stimulate new ideas by others, and more and bet ter results occur than if the one researcher operated in isolation. 6 Collaboration between automotive companies or aut omotive suppliers does occur; for example, in 2011 Toyota and Ford an nounced a new effort to collaborate on the development of hybrid technology for pickup trucks. 7 Another example was the four year joint development effort between General Motors and Ford for from 2002-2006 for the development of a new six-speed automatic transmission. 8 One function that standards can serve is to promote
research into low-CO 2 technologies that would not take place in the abse nce of the standards. Because all companies (both auto firms and auto sup pliers) will have incentives to find better, less expensive ways of meeting the standards in thi s rule, the possibilities for synergistic interactions may increase. Thus, the rule, by focu sing all companies on finding more efficient ways of achieving the standards, may lead to better outcomes than if any one company operated
on its own.

An additional aspect of the standards is the possib ility of greater standardization. As more companies adopt new technologies, the incentiv es increase for additional suppliers and more availability of after-market replacement parts ; these suppliers would be likely to find ways to increase compatibility across vehicle types. Fo r example, though electric vehicles (EVs) are not expected to be more than a few percent of the v ehicles produced in response to this rule, their adoption depends on such factors as batteries and c harging methods that are compatible across different companies. These are examples of “network externalities,” wher e use of a technology by one party has greater benefits if more people ar e also using the technology. In this case, just as the ability to buy gasoline from any station fac ilitates owning a gasoline-based vehicle, the ability to recharge an EV or get replacement parts easily facilitates ownership of an EV. In the absence of the rule, fewer companies would be pursu ing this technology, and it would be considered a specialty product; the incentives to c oordinate might be low. If EVs become more common, though, compatible infrastructure and batte ries may become more desirable, as potential buyers are likely to be encouraged toward this technology if they can easily find places to charge batteries.

Thus, the rule may direct and promote innovation an d standardization that would not happen in the absence of this rule. Such changes c ould reduce the cost increase s associated with

6 Powell, Walter W., and Eric Giannella, “Collective Invention and Inventor Networks,” Chapter 13 in Ha ndbook of the Economics of Innovation, Volume 1, edited by B. Hall and N. Rosenberg (Elsevier, 2010) (EPA DocketEPAHQ-OAR-2010-0799) discuss how a “collective momentu m” has led uncoordinated research efforts among a d iverse set of players to develop advances in a number of t echnologies (such as electricity and telephones). They contrast this view of technological innovation with that of proprietary research in corporate laboratories, whe re the research is part of a corporate strategy. Such momentum may result in part from alignment of economic, social, political, and other goals. 7 "Ford and Toyota to Work Together on Hybrid System for Trucks", New York Times, August 22, 2011. (EPA Docket EPA-HQ-OAR-2010-0799).

8 "Ford, GM Launch Joint 6-speed Automatic," Wards A utomotive, August 31, 2006.Page 6 of 26

the rule and improve the qualities of the technologies, whic h could result in an increase in vehicle sales. Further, the certainty of the regul ations reduces the costs of meeting them, because
there will be more economies of scale and more lear ning curve benefits due to greater cumulative production of fuel-efficient technologie s.

In summary, in these three plausible explanations for how the rule could increase vehicle sales, we have focused on ways that the rule could promote coordinated actions on the part of automakers and suppliers that lead to mutual benefi t, but that might not happen in the absence of
the rule. These actions could lead to automakers developing technologies that they would not have developed in the absence of the rule. Consume rs may respond positively to the vehicles resulting from this coordinated action.

Several commenters requested that we conduct a quan titative vehicle sales analysis. As discussed in the proposal, in previous rulemakings, EPA and NHTSA conducted vehicle sales analyses by comparing the up-front costs of the veh icles with the present value of five years’ worth of fuel savings; the direction of vehicle sal es would depend on whether up-front costs exceeded fuel savings (in which case sales would be expected to decline), or vice versa (in which case sales would be expected to increase). 9 Some commenters specifically requested that we us e the method found in the MYs 2012-16 rule; some spec ifically supported the five-year payback period; others argued for the importance of conduct ing the analysis without recommending
methods. Ceres estimates that the rule will increa se vehicle sales by 4.7 percent; 10 the Defour Group provided estimates that the rule will decreas e vehicle sales by 6-10 percent. 11 The differences in the results appear to depend on the cost estimates used and on assumptions made about how vehicle buyers think about fuel savings w hen deciding on vehicle purchases. The Defour Group, for instance, uses cost estimates of about $3000 per vehicle based on summing costs (but not benefits) across multiple rules (an estimate we consider to be unfounded for reasons explained in this section below and also in TSD Chapter 3.1.2) and the assumption that consumers consider only 25 percent of fuel savings in their vehicle purchase decisions (see discussion in Section III.H.1.a). Other commenters wanted specific information on the effect of
the rule on vehicle costs and whether consumers wil l be willing to buy the new vehicles, while consumer and environmental organizations indicated that consumers want more fuel-efficient vehicles, even if the up-front costs of the vehicle s increase. The costs of the rule are discussed in
Preamble Section III.H.2. As discussed in Preamble Section III.H.1.a, we do not at this point
have sufficient confidence in the estimates of the role of fuel economy in consumers’ vehicle

9 For instance, see U.S. Environmental Protection Ag ency (April 2010). “Final Rulemaking to Establish LightDuty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards: Regulato ry Impact Analysis.” EPA-420-R-10-009, Chapter 8.1.1, pp. 8-1 to 8-4.

10 Comments on this rule from Ceres, Docket EPA-HQ-OA R-2010-0799-9475, referring to a forthcoming report

, Citi Investment Research and Analysis, “U.S. Autos and Auto Parts: Fuel Economy Focus: Industry Pers pectives on 2020,” April 3, 2012, Docket EPA-HQ-OAR-2010-079 9.

11 Walton, Thomas F., and Dean Drake, Defour Group, L LC. “Comments on the Notice of Proposed Rulemaking and Preliminary Regulatory Impact Analysis for MY 2 017 to 2025 Fuel Economy Standards.” February 13, 2012. Docket EPA-HQ-OAR-2010-0799-9319-A1.Page 7 of 26

purchases to come to definitive conclusions about t he impacts of the rule on vehicle sales. We do not, however, consider this uncertainty grounds for delaying the rule, as one comment suggested. The midterm evaluation provides an oppo rtunity to revisit the impact of the rule on vehicle sales and consumer acceptance of the new te chnologies.

This rule takes effect for MY 2017-2025. In the in tervening years, it is possible that the assumptions underlying a quantitative analysis, as well as market conditions, might change. As the United Auto Workers points out, the state of th e economy is a major, if not the primary, determinant of total vehicle sales. The impact of the rule on sales may therefore depend, among other factors, on changes in the state of the econo my. Other commenters discussed the importance of consumer confidence, fuel prices, and even of publicity over fuel prices, in consumers’ interest in additional fuel economy. Sa les could be negatively affected if gasoline prices are lower than expected or technology costs are higher than expected. In these cases, it is possible that the standards could require manufactu rers to produce cars with higher levels of fuel economy than consumers would wish to buy. On the o ther hand, manufacturers’ marketing of increased fuel economy levels is also likely to pla y a role in consumer response to these vehicles. EPA agrees that these factors are important, but we are not sufficiently confident in quantitative estimates of the impacts of those factors to develo p numerical estimates. We instead provide this qualitative assessment to highlight the factors imp ortant for understanding the effects of this rule
on vehicle sales.

As several commenters point out, the effect of this rule on the use and scrappage of older
vehicles will be related to its effects on new vehi cle prices, the fuel efficiency of new vehicle models, the fuel efficiency of used vehicles, and t he total sales of new vehicles. If the value of fuel savings resulting from improved fuel efficienc y to the typical potential buyer of a new vehicle outweighs the average increase in new model s’ prices, sales of new vehicles could rise, the used vehicle market may increase in volume as n ew vehicle buyers sell their older vehicles, and scrappage rates of used vehicles may increase s lightly. This will cause both an influx of
more efficient vehicles into the used vehicle marke t and an increase in the turnover of the vehicle fleet (i.e., the retirement of used vehicles and th eir replacement by new models), thus accentuating the anticipated effect of the rule on fleet-wide fuel consumption and CO 2 emissions. However, if potential buyers value futu re fuel savings resulting from the increased fuel efficiency of new models at less than the incr ease in their average selling price, sales of new vehicles will decline, the used vehicle market may decrease in volume as people hold onto their vehicles longer, and there will be a reduction in t he rate at which used vehicles are retired from service. These effects will partly reduce the anti cipated effects of this rule on fuel use and emissions. Because we do not have good estimates of the relationships between the new and used vehicle markets, we have not attempted to estimate explicitly the effects of the rule on the used vehicle market, scrappage of older vehicles, and th e turnover of the vehicle fleet.

Consumer, environmental, and investor organizations , the United Auto Workers, as well as a citizens’ campaign suggested that the rule wil l help the domestic auto industry, including thePage 8 of 26

domestic supply base, compete in the global marketp lace, through its encouragement of advanced technologies that may be useful in meeting emissions standards and consumer demands in foreign markets. We agree that this is likely for all global automakers , as generally
the emission standards established in this rule are similar in stringency to emissions and fuel economy standards being considered by Japan, the Eu ropean Union, South Korea, Canada, China
and other international markets. Global manufactur ers also design vehicles using a common platform in order to reduce costs. Vehicles built on these common platforms are sold in many
markets around the world. To the extent the domest ic OEMs and suppliers can focus their limited research and product development efforts on the same technologies for the U.S. market as for international markets, this should enable th e companies to compete more effectively
outside the U.S.

Chapter 8 of EPA’s RIA has further discussion of me thods for examining the effects of
this rule on vehicle sales.

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

Several organizations provided comments about the e ffect of the rule on the affordability of new vehicles, as well as the impacts of the rule specifically on low-income households.

Comments from Consumer Federation of America (CFA) and 23 other consumer groups, as well as Consumers Union (CU) and several environ mental organizations, argued that lowincome households will benefit from the rule. Thes e commenters cite Bureau of Labor Statistics data that low-income households spend more on fuel than they do on new vehicles each year and
are thus more vulnerable to fuel costs. CU comment s that low-income households pay a disproportionately large portion of their income on fuel and are thus most vulnerable to price spikes in gasoline. CFA reported that in 2010, hou seholds with incomes below $20,000 spent 7.3 times as much on gasoline as on new car payment s, compared with 1.2 times as much for households with incomes above $70,000. This commen ter believes that consumers will benefit greatly from the fuel savings that come with improv ed fuel economy. These organizations note that low-income households account for a very small portion of new car buyers, since they primarily purchase used cars, and are therefore les s affected by the up-front costs of the more efficient vehicles than those who buy new vehicles. CU further comments that Consumers Reports survey data show that low-income households support improved fuel economy. In a recent survey, 71% of low-income households respond ed that they expect to choose a model with better fuel economy, compared to 59% of modera te and high-income respondents. In addition, 79% of low-income respondents to the surv ey reported that they were willing to pay extra for a more fuel efficient vehicle if they can recover the additional cost through lower fuel costs within five years, compared to 86% of moderat e and high-income respondents.

In addition, these commenters agreed with EPA’s ass essment in the NPRM that consumers who buy their vehicles with loans save mo re in fuel each month than they do inPage 9 of 26

increased loan payments. CU points out that this i s especially true for buyers of future, more fuel-efficient used vehicles: the increase in up-f ront cost is much lower on a used vehicle, due to depreciation, while the fuel economy of the vehicle is unlikely to change over time. Because low-income households disproportionately buy used v ehicles, they will benefit from this more rapid cost recovery. Because most of the increased vehicle cost depreciates after five years, the payback period for improved fuel economy in used MY 2017 and later vehicles will be shorter than the payback period for these vehicles when new ly purchased (under two years for some examples). EPA agrees that more efficient vehicles will reduce operating costs for buyers of used vehicles as well as new vehicles, because the fuel-saving technologies maintain their effectiveness over time; indeed, GHG standards cont inue to apply in-use. As shown in RIA Chapter 5.5, our estimate of the payback period for five-year-old MY 2025 vehicles is approximately 1.1 years, less than the payback peri od of about 3.2-3.4 years for new MY 2025 vehicles. We also note that depreciation rates may be affected by the rule: increases in reliability would decrease depreciation, and decrea ses in reliability would increase depreciation. Finally, CU points out that some auto lenders take into consideration the fuel economy of new vehicles, and offer discounted rates for more effic ient vehicles. 12 As discussed further below,
EPA also finds that a number of financial instituti ons give a discount on loans for more fuelefficient vehicles.

The National Automobile Dealers Association (NADA) and the Institute for Energy Research emphasized that the increase in the up-fro nt vehicle costs would be a factor in consumers’ abilities to purchase. In particular, t hey stated that, if vehicle buyers are not able to get loans for vehicles that have become more expens ive as a result of new standards, because they cannot get access to credit for the additional cost, then they will be unable to participate in
the new vehicle market even if the new vehicles off er significant fuel savings. This argument is
based on the statement from NADA that auto lenders do not take into account the fuel economy of the vehicles when they are deciding on providing loans; the lenders consider only consumers’ debt-to-income ratios. NADA provided an analysis t hat concludes that 6.8 million licensed drivers may no longer have access to new vehicles. According to NADA’s analysis, this
estimate is the number of licensed drivers who live in the 3.1 – 4.2 million households that could borrow $11,750, the loan amount for the least expen sive new vehicle in 2011 after a $1000 down payment, but could not borrow $14,750. 13 This difference of $3,000 is meant to represent w hat NADA views as the cost increase of new fuel economy standards, which EPA believes is incorrect and responds to further below.

In assessing these comments, EPA finds that the NA DA study does not provide a usable
estimate of those consumers in the market for new v ehicles who might have trouble getting

12 See, for instance, Ladika, Susan (2009). “’Green’ auto loans offer lower rates,” Bankrate.com, http://www.bankrate.com/finance/auto/green-auto-loa ns-offer-lower-rates-1.aspx , accessed 2/28/12.

13 Wagner, D., P. Nusinovich, and E. Plaza-Jennings, National Automobile Dealers Association (February 1 3, 2012). “The Effect of Proposed MY 2017-2025 Corporate Aver age Fuel Economy (CAFE) Standards on the New Vehicl e Market Population.” Docket EPA-HQ-OAR-0799.Page 10 of 26

loans, and is not a usable estimate of the impacts of the rule on the new vehicle market. Because the NADA study does not separate consumers who migh t consider new vehicles from consumers who are not in the market for new vehicles, the 6.8 million licensed driver figure significantly overestimates any impact of this rule on the new ve hicle market.

The NADA study suffers from a number of inaccuracie s and weaknesses. First, it is important to understand what NADA’s 6.8 million est imate actually represents. NADA simply
looked at the 113 million households in the U.S. wh o could afford to borrow $11,750 and

estimated which ones of those could not afford to t ake out a loan of $14,750. 14 NADA’s analysis unfortunately neglects a fundamental factor that co uld make this analysis relevant to this rulemaking – how many of those households would in fact even be in the market for a new vehicle. EPA believes that the vast majority of th ese households would not be in the market for new vehicles (for context, the total new vehicle ma rket is estimated to be 17.2 million vehicles in 2025; see TSD Chapter 1.3.2.1). As documented by m any other commenters and as can be found in the Federal Reserve Board’s Survey of Cons umer Finances, 15 low-income households account for a very small portion of new car buyers, since they primarily purchase used cars. Thus, the NADA estimate is severely flawed and does not contribute usable information to identify the impacts of this rule on the vehicle ma rket or on low-income households.

Second, the NADA estimate is based, not on people w ho are considering purchasing new vehicles, but on the number of licensed drivers in households in the U.S. who could theoretically qualify to borrow $11,750 but not $14,750, based pu rely on debt-to-income ratio. 16 Even accepting NADA’s study at face value, the relevant unit for the financial decision would be the number of households – not every licensed driver in a low income household would purchase a separate vehicle. The number of households in the NADA study is 3.1 to 4.2 million, already
far lower than the estimate of 6.8 million drivers.

Third, NADA’s assumption of a $3,000 cost increase per vehicle is based on summing the costs of MY 2011, MY 2012-16, and MY 2017-25 ru les. This estimate does not correspond to EPA’s estimate, an average cost of about $1,800 per vehicle by MY2025, in several ways. For analyzing the effects of this rulemaking, it is appropriate to focus on the costs and benefits associated with this rulemaking, not those of previ ous rulemakings. The impacts of the other

14 The Bureau of Labor Statistics’ Consumer Expenditu re Survey, on which the Wagner et al. paper is base d, measures 121,000 households in the U.S. in 2010. W agner et al. find that “an estimated 93% of all con sumer units have a financial profile that would allow them to m eet the 40% maximum debt to income ratio after purc hasing the current minimum cost new vehicle ($12,750).” (See f ootnote 13 , p. 4.) Ninety-three percent of 121 million households is about 113 million households; Wagner et al.’s estimate of 3.1 to 4.2 million of those wh o can borrow $11,750 but not $14,750 is 2.8 to 3.7 percent of th at total.

15 In the Federal Reserve Board’s 2007 Survey of Cons umer Finances, households with income below $35,200 (about the lower 40% of population by income) bough t about 17% of new vehicles; those in the bottom qu intile of income bought fewer than 2% of new vehicles. See F ederal Reserve Board, 2007 Survey of Consumer Finan ces, http://www.federalreserve.gov/econresdata/scf/scf_2 007.htm.

16 As noted, these amounts are based on the cost of t he least expensive vehicle in 2011, with $1000 down payment, with the assumption that it will become $3000 more expensive as the result of three rulemakings, for M Ys 2011, 2012-16, and 2017-25 (see Wagner et al., footnote 13 ). Deleted: 507

Deleted: 507Page 11 of 26

rules are included in the reference case for this r ule. The NADA cost estimate, based on a MY 2011 vehicle, appears to double-count MY 2011 costs , because those should already be included in the price of the MY 2011 vehicle used in its stu dy. Further, the costs of meeting MY 2016 standards in 2025 are expected to be lower than the costs of meeting those standards in 2016, the value used by NADA, due to manufacturer learning. Moreover, EPA’s costs estimates are based on industry-wide averages, not applicable to specif ic vehicle models. As discussed further below, impacts of the rule on the prices of low-pri ce vehicles may well be less than these

averages.

Fourth, the estimate does not take into account, as pointed out by CU and as EPA has documented, that some lenders currently give discou nts for loans to purchase more fuel-efficient
vehicles. 17 It is possible (though unknown at this time) that the auto loan market may evolve to include further consideration of fuel savings, as t hose savings play a significant factor in offsetting the increase in up-front costs of vehicl es.

Fifth, the NADA analysis is based on the cost of th e least expensive vehicle in the MY 2011 market, but the market size for low-priced veh icles is only about one-tenth the size of NADA’s estimate of 6.8 million affected people. Th e agencies’ baseline estimates of the vehicle
fleet in 2025 finds that total sales of vehicles co sting less than $15,000 (a price point that low

income consumers in the new car market would most l ikely be pursuing) in the absence of the
rule are estimated to be well below 1 million in MY 2025; there is also no relationship between the NADA estimate and the potential impact of this rule on sales of low-priced vehicles.

Sixth, if NADA’s estimate reflected a measurable ef fect of the rule, that effect would be
reflected in a commensurate reduction in vehicle sa les. Yet there is no connection between any vehicle sales estimates provided in comments on thi s rule and the NADA estimate. As discussed in section III.H.11.a, many commenters predict an i ncrease in vehicle sales as a result of the rule, though others predict decreases. 18 However, even the most negative estimate provided in public comments of the GHG rule’s impact on vehicle sales, from the Defour Group (which we address in detail in Section III.H.11.a), is a reduction of 1.8 million vehicles. The NADA estimate appears significantly overstated even compared to t his commenter’s most negative estimate of vehicle sales impacts.

For these reasons, we find the NADA study does not provide a usable estimate of consumers in the market for new vehicles who might have trouble getting new vehicle loans, nor do we find it a usable estimate of the impacts of t he rule on the new vehicle market.

17 See footnote 12 , above. An internet search on the term “green aut o loan” produced more than 50 lending institutions that provide reduced rates for more ef ficient vehicles. See Helfand, Gloria (2012). “Me morandum: Lending institutions that provide discounts for mor e fuel-efficient vehicles.” Assessment and Standar ds Division, Office of Transportation and Air Quality, U.S. Envi ronmental Protection Agency, Docket EPA-HQ-OAR-0799

. 18 We note that the role of vehicle financing in vehi cle purchase decisions is not a separate factor in typical studies of the determinants of vehicle sales. Estimates of vehicle sales in the literature, which commonly ar e dependent on both up-front vehicle costs and fuel costs, implici tly account for effects of the loan market. Deleted: There is no reason to expect that the number of lenders instituting this policy will decrease in the future as fuel efficiency increases beyond the levels seen today.

Deleted: ndeed, i

Deleted: 506Page 12 of 26

It is possible that future trends in the auto loan market may affect future vehicle sales. It is also possible that some people who have signific ant debt loads may not be able to get financing for some of these new vehicles; they may have to buy different vehicles (including used vehicles) or delay purchase. For others who b orrow on credit, though, as discussed in Section III.H.5, the fuel savings are expected to o utweigh the increased loan costs from the time of vehicle purchase. As some comments suggest, the rule thus may make vehicles more affordable to the public, by reducing consumers’ vu lnerability to fuel price jumps.

Some comments raised concerns about the impacts of the rule specifically on low-priced vehicles. EPA agrees that vehicles in the low-pric ed (economy-class) segment will bear technology costs needed to meet the new standards, but it is not known how manufacturers will decide to pass on these costs across their vehicle fleets, including in the low-priced vehicle segment. If manufacturers decide to pass on the fu ll cost of compliance in this segment, then it is possible that consumers who might barely afford new vehicles may be priced out of the newvehicle market or may not have access to loans. As just discussed, the rule’s impacts on availability of loans are unclear, because some len ders do factor fuel economy into their loans, and it is possible that this trend may expand. In addition, as the Union of Concerned Scientists comments, auto makers have some flexibility in how both technologies and price changes are applied to these vehicles; auto makers have ways to keep some vehicles in the low-priced vehicle segment if they so choose. Though the rule is expe cted to increase the prices of these vehicles, the degrees of price increase and the impacts of th e price increases, especially when combined with the fuel savings that will accompany these cha nges, are much less clear.

The Defour Group suggests that the standards are re gressive, with adverse impacts falling disproportionately on low-income households, and po ssibly limiting their ability to obtain employment because of limited mobility. The commen ter’s regressivity assessment is based on a study of a non-footprint-based fuel economy program ; 19 the disproportionate impact on lowincome households is based on the increased prices of used vehicles and the shift toward smaller
vehicles. As discussed above in Section III.H.11.a , EPA finds that the impact on the used vehicle market depends on the impact of the rule on new vehicle sales, which we have not quantified. Because the footprint-based standard r educes incentives to downsize vehicles, we do not accept the conclusion that the rule will result in buyers of used vehicles getting smaller ones with a consequent welfare loss. For these reasons, the regressivity finding from Jacobsen’s paper is not applicable to the effects of this rule.

In summary, the net effect of the rule on low-incom e households depends on several factors: the way that manufacturers choose to tran slate cost increases into price increases; the effects on sales of used vehicles, which depend on the effects on sales of new vehicles; the fuel savings that the new (and used) vehicles will provi de; and any effects on access to credit for new

19 Jacobsen, Mark. “Evaluating U.S. Fuel Economy Sta ndards In a Model with Producer and Household Heterogeneity.” Working paper, University of Calif ornia, San Diego, September 2010. Docket EPA-HQ-OAR

0799-0829. Deleted: EPA does not deny the possibility that the market for new vehicle loans may be affected by thi s rule, but we do not at this time have sufficient evidence to project whether that market will evolve to help or hinder sales of new, more fuel-efficient vehicles.Page 13 of 26

and used vehicles. For reasons outlined above, we do not at this time have quantitative
assessments of how these effects interact and affec t low-income households. However, due to the significant effect of the rule on fuel savings, especially for used vehicles (see RIA Chapter 5.5), we expect low-income households to benefit fr om the more rapid payback period for used vehicles, though some of this benefit may be affect ed by the net effect of this rule on the prices and availability of used vehicles, which we have no t estimated.

In addition, the net effect of the rule on low-pric ed vehicles is difficult to assess; though we expect the prices of these vehicles to increase, it is also possible that auto makers may find ways to preserve the entry-vehicle segment, by addi ng less additional technology to these vehicles or through pricing strategies. The net ef fect of the rule on access to credit is also difficult to assess: though some consumers may fin d themselves credit-constrained, some auto lenders are already giving interest rate discounts for more fuel-efficient vehicles, and the loan market may continue to evolve.

12. Employment Impacts

a. Introduction

Although analysis of employment impacts is not part of a cost-benefit analysis (except to the extent that labor costs contribute to costs), e mployment 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 prog ram, he sought a program that would “strengthen the [auto] industry and enhance job cre ation in the United States.” 20 The recently issued Executive Order 13563, “Improving Regulation and Regulatory Review” (January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety, and our
environment

while promoting economic growth, innovation, compe titiveness, and job creation ” (emphasis added). EPA is accordingly providing par tial estimates of the effects of this rule on domestic employment in the auto manufacturing and p arts sectors, while qualitatively discussing how it may affect employment in other sectors more generally. Several commenters specifically pointed to the desirability of our conducting emplo yment analyses, to provide insights into the effects of the rule on economic recovery and the he alth of the auto industry; we did not receive comments opposed to the inclusion of employment imp acts.

This rule is expected to affect employment in the U nited 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 retail sector. According to the U.S. Bureau of Labor Statistics, in 2010, about 677,000 people in the U. S. were employed in Motor Vehicle and Parts

20 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency Standards. The White Hou se, Office of the Press Secretary, May 21, 2010. http: //www.whitehouse.gov/the-press-office/presidentialmemorandum-regarding-fuel-efficiency-standardsPage 14 of 26

Manufacturing Sector (NAICS 3361, 3362, and 3363). About 129,000 people in the U.S. were employed specifically in the Automobile and Light T ruck Manufacturing Sector (NAICS 33611), the directly regulated sector, since it encompasses the auto manufacturers that are responsible for complying with the standards. 21 The employment effects of this rule are expected to expand beyond the regulated sector. Though some of the pa rts used to achieve the standards are likely to be built by auto manufacturers themselves, the auto parts manufacturing sector also plays a significant role in providing those parts, and will also be affected by changes in vehicle sales. Changes in light duty vehicle sales, discussed in S ection III.H.11, could affect employment for auto dealers. As discussed in Section III.H.4, thi s rule is expected to reduce the amount of fuel these vehicles use, and thus affect the petroleum r efinery and supply industries. Finally, since
the net reduction in cost associated with this rule is expected to lead to lower household expenditures on fuel net of vehicle costs, 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; instead , labor would primarily be shifted from one sector to another. These shifts in employment impo se an opportunity cost on society, approximated by the wages of the employees, as regu lation diverts workers from other activities in the economy. In this situation, any effects on net employment are likely to be transitory as workers change jobs (e.g., some workers may need to be retrained or require time to search for new jobs, while shortages in some sectors or region s could bid up wages to attract workers).

On the other hand, if a regulation comes into effec t during a period of high unemployment, a change in labor demand due to regul ation may affect net overall U.S. employment because the labor market is not in equil ibrium. In such a period, both positive and negative employment effects are possible. 22 Schmalansee and Stavins point out that net positi ve employment effects are possible in the near term wh en the economy is at less than full employment due to the potential hiring of idle labo r resources by the regulated sector to meet new requirements (e.g., to install new equipment) a nd new economic activity in sectors related to the regulated sector. 23 In the longer run, the net effect on employment i s more difficult to predict and will depend on the way in which the related ind ustries respond to the regulatory requirements. As Schmalansee and Stavins note, it is possible that the magnitude of the effect on employment could vary over time, region, and sector , and positive effects on employment in some regions or sectors could be offset by negative effects in other regions or sectors. For this reason, they urge caution in reporting partial empl oyment effects since it can “paint an inaccurate picture of net employment impacts if not placed in the broader economic context.”

21 U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed on August 9, 2 011.

22 Masur and Posner, 2011. “Regulation, Unemployment , and Cost-Benefit Analysis.” http://papers.ssrn.com/sol3/papers.cfm?abstract_id= 1920441 (Docket EPA-HQ-OAR-2010-0799-1222).

23 Schmalensee, Richard, and Robert N. Stavins. “A G uide to Economic and Policy Analysis of EPA’s Trans port Rule.” White paper commissioned by Excelon Corpora tion, March 2011 (Docket EPA-HQ-OAR-2010-0799-0676)

.Page 15 of 26

It is assumed that the official unemployment rate w ill have declined to 5.3 percent by the time by the time this rule takes effect and so the effect of the regulation on labor will be to shift
workers from one sector to another. 24 Those shifts in employment impose an opportunity cost on society, approximated by the wages of the employees , as regulation diverts workers from other activities in the economy. In this situation, any e ffects 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 some sector s or regions could bid up wages to attract workers). It is also possible that the state of th e economy will be such that positive or negative employment effects will occur.

Measuring the employment impacts of a policy depend on a number of inputs and assumptions. For instance, as discussed, assumptio ns about the overall state of unemployment in the economy play a major role in measured job impac ts. The inputs to the models commonly are the changes in quantities or expenditures in the af fected sectors; model results may vary in different studies depending on the assumptions abou t the levels of those inputs, and which sectors receive those changes. Which sectors are i ncluded in the study can also affect the results. For instance, a study of this program that looks on ly at employment impacts in the refinery sector may find negative effects, because consumers will purchase less gasoline; a study that looks only at the auto parts sector, on the other h and, may find positive impacts, because the program will require redesigned or additional parts for vehicles. In both instances, these would only be partial perspectives on the overall change in national employment due to Federal regulation.

The NPRM included a discussion of different methods for conducting employment analysis (see the discussion in RIA Chapter 8.2.2), including computable general equilibrum models, input-output models, hybrid models, and sin gle-sector models, and requested comment
on those methods. See 76 FR 75155-156. That discu ssion noted that all potential methods of estimating employment impacts of a rule have advant ages and limitations. We did not receive comments about methods, except for some support for EPA’s approach in the NPRM, and some support (discussed further below) for including mul tiplier impacts.

We received a number of comments (from the Defour G roup and from some private individuals) asserting that there will be decreases in employment as a result of the costs of the rule, and a number of comments (from the United Aut o Workers, environmental organizations, sustainable business groups, some private individua ls, and others) asserting increases in employment, based on the development of advanced te chnologies and the reduction in net costs due to fuel savings. An assessment by the Defour G roup predicts a loss of 155,000 jobs in

24 Office of Management and Budget, “Fiscal Year 2012 Mid-Session Review: Budget of the U.S. Government .” http://www.whitehouse.gov/sites/default/files/omb/b udget/fy2012/assets/12msr.pdf , p. 10.Page 16 of 26

manufacturing and supply, plus another 50,000 in di stribution. 25 A study by Ceres predicts job gains of 43,000 in the auto industry and 484,000 ec onomy-wide. 26 Some comments cite a study by the Natural Resources Defense Council, National Wildlife Federation, and United Auto Workers that 150,000 auto workers already are worki ng to supply clean, fuel-efficient technologies. 27 The differences in results for quantitative emplo yment impacts are due to factors
such as those discussed above. Estimates of decrea ses in employment commonly come from studies that use cost estimates higher than those o f EPA, and sometimes lower benefits estimates, resulting in reductions in vehicle sales. For inst ance, some comments from individuals cite the
National Automobile Dealers Association and Center for Automotive Research for cost estimates of $5000 to $6000 per vehicle, much higher than tho se estimated in Section III.H.2; EPA does not endorse those alternative cost estimates, as di scussed in Section 18.2 of the Response to Comments. The NADA estimates inappropriately inclu de the costs of other rulemakings and use indirect cost estimates which we consider inappropr iate (see TSD Chapter 3.1.2.2). The Center
for Automotive Research estimates do not take into account expected technological advances, do not reflect the use of air conditioning credits in this rule, and calculate costs from a baseline of
2008 instead of MY 2016 standards. Those studies c ommonly look at the employment associated with vehicle sales, but not the employme nt associated with producing the technologies needed to comply with the standards, or changes in labor intensity of production. Analyses that find increases in employment commonly start with in creased vehicle sales as a result of the rule, and take into consideration the employment effects associated with additional technologies. In both cases, “multiplier” effects, which extend empl oyment impacts beyond the auto sector to impacts on suppliers, other sectors, and expenditur e changes by workers, lead to large estimates, either positive or negative, of the employment effe cts of the rule. We received the suggestion to include in our analysis an alternative scenario whe re there is less than full employment; the implication of less than full employment is that mu ltiplier effects are more likely. We also requested comment on other sectors that warranted c onsideration in this rule, 76 FR 75157, but we did not receive suggestions.

After considering these comments, EPA is continuing with the employment approach in the NPRM, though with some updating of quantitative impacts in the auto sector. For impacts in the auto sector, EPA uses a conceptual framework th at identifies employment impacts due to changes in vehicle sales, changes in costs, and cha nges in the labor intensity of production. For impacts in related sectors, EPA presents qualitativ e discussions. We do not quantify multiplier

25 Walton, Thomas F., and Dean Drake, Defour Group LL C (February 13, 2012). “Comments on the Notice of Proposed Rulemaking and Preliminary Regulatory Impa ct Analysis for MY 2017 to 2025 Fuel Economy Standa rds.” Docket EPA-HQ-OAR-2010-0799-9319.

26 Management Information Services, Inc. (July 2011). “More Jobs per Gallon: How Strong Fuel Economy/G HG Standards Will Fuel American Jobs.” Boston, MA: C eres. Docket EPA-HQ-OAR-2010-0799-0709.

27 Natural Resources Defense Council, National Wildli fe Federation, and United Auto Workers (August 2011 ). “Supplying Ingenuity: U.S. Suppliers of Clean, Fue l-Efficient Vehicle Technologies.” http://www.nrdc.org/transportation/autosuppliers/fi les/SupplierMappingReport.pdf (Docket EPA-HQ-OAR-20 10- 0799-).Page 17 of 26

effects, due to uncertainty over the state of the e conomy at the time this rule takes effect as well as the market evolutions that are likely to occur b etween now and implementation.

b. Conceptual Framework for Employment Impacts in the Regulated Sector

A study by Morgenstern, Pizer, and Shih 28 provides a retrospective look at the impacts of regulation in employment in the regulated sectors b y estimating the effects on employment of spending on pollution abatement for four highly pol luting/regulated U.S. industries (pulp and paper, plastics, steel, and petroleum refining) usi ng data for six years between 1979 and 1991. The paper provides a theoretical framework that can be useful for examining the impacts of a regulatory change on the regulated sector in the me dium to longer term. In particular, it identifies three separate ways that employment leve ls may change in the regulated 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 this effect depends on the cost increase passed on to consumers as well as the demand elasticity of industry output.”

• Cost effect : as costs go up, plants add more capital and labo r (holding other factors constant), with potentially positive effects on emp loyment. In the authors’ words, as “production costs rise, more inputs, including labo r, 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 more labor intensive than conventional production,” meaning that “the amount of labor per dollar of output will rise,” though it is also possible that “cleaner operations could involve automation and less employment, for example.”

According to the authors, the “demand effect” is ex pected to have a negative effect on employment, 29 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 no t possible to predict the total effect environmental regulation will have on employment le vels in a regulated sector.

28 Morgenstern, Richard D., William A. Pizer, and Jhi h-Shyang Shih. “Jobs Versus the Environment: An I ndustryLevel Perspective.” Journal of Environmental Econo mics and Management43 (2002): 412-436 (Docket EPA-HQOAR-2010-0799-1011).

29 As will be discussed below, the demand effect is p otentially an exception to this rule. While the ve hicles become more expensive, they also produce reduced fuel expe nditures; the reduced fuel costs provide a counterv ailing impact on vehicle sales. As discussed in Preamble Section III.H.1, this possibility that vehicles may become more attractive to consumers after the program poses a conundrum: why have interactions between vehicle buyers and pr oducers not provided these benefits without government inte rvention?Page 18 of 26

The authors conclude that increased abatement expen ditures generally have not caused a significant change in employment in those sectors. More specifically, their results show that, on average across the industries studied, each additio nal $1 million spent on pollution abatement results in a (statistically insignificant) net incr ease 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 for only four sectors. It could also be very difficult to update the study for other sectors, because one of the databases on which it relies, the Pollution Abatement Cost and Expenditure survey, ha s been conducted infrequently since 1994, with the last survey conducted in 2005. The empiri cal estimates provided by Morgenstern et al. are not relevant to the case of fuel economy standa rds, which are very different from the pollution control standards on industrial facilitie s that were considered in that study. In addition, it does not examine the effects of regulation on em ployment in sectors related to but outside of the regulated sector. Nevertheless, the theory tha t Morgenstern et al. developed continues to be useful in this context for examining the impacts of the rule on the auto sector.

c. Employment Analysis of This Rule

As mentioned above, this program is expected to aff ect employment in the regulated sector (auto manufacturing) and other sectors direc tly affected by the rule: auto parts suppliers, auto dealers, and the fuel supply market (which will face reduced pet roleum production due to reduced fuel demand but which may see additional de mand for electricity or other fuels)

. Changes in consumer expenditure s due to higher vehicle costs and lower fuel expen ses will also affect employment . In addition, as the discussion above suggests, e ach of these sectors could potentially have ripple effects in the rest of the economy. These ripple effects depend much more heavily on the state of the macroeconomy than do the direct effects. At the national level, employment may increase in one industry or region a nd 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 emplo yment.

The discussion that follows provides a partial, bot tom-up quantitative estimate of the effects of this rule on the regulated sector (the a uto 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 qualitat ive discussion of the effects of the rule on other sectors. Focusing quantification of employment imp acts on the regulated sector has some advantages over quantifying all impacts. T he analysis relies on data generated as part of the rulemaking process, which focuses on the regulated sector; as a result, what is presented here is based on internally consistent assumptions and esti mates made in this rule. Focusing on the regulated sector provides insight into employment e ffects in that sector without having to make assumptions about the state of the economy when thi s rule has its impacts. We include a Deleted: , and

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Deleted: Secondly, as discussed above, net effects on employment in the economy as a whole depend heavily on the overall state of the economy when th is rule has its effects.Page 19 of 26

qualitative discussion of employment effects in oth er sectors to provide a broader perspective on the impacts of this rule.

As noted above, in a full-employment economy, any c hanges in employment will result from people changing jobs or voluntarily entering o r exiting the workforce. In a fullemployment economy, employment impacts of this rule will change employment in specific sectors, but it will have small, if any, effect on aggregate employment. This rule would take effect in 2017 through 2025; by then, the current h igh unemployment may be moderated or ended. For that reason, this analysis does not inc lude multiplier effects, but instead focuses on employment impacts in the most directly affected in dustries. Those sectors are likely to face the most concentrated employment impacts.

i. 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 thr ee effects for the auto sector: the demand effect, the cost effect, and the factor shift effec t. However, we are only able to offer quantitative
estimates for the cost effect. We note that these estimates, based on extrapolations from current data, become more uncertain as time goes on.

(1) The Demand Effect

The demand effect depends on the effects of this ru le on vehicle sales. If vehicle sales increase, then more people will be required to asse mble vehicles and their components. If vehicle sales decrease, employment associated with these activities will unambiguously decrease. Unlike in Morgenstern et al.’s study, where the dem and effect decreased employment, there are countervailing effects in the vehicle market due to the fuel savings resulting from this program. On one hand, this rule will increase vehicle costs; by itself, this effect would reduce vehicle sales. On the other hand, this rule will reduce th e fuel costs of operating the vehicle; by itself, this effect would increase vehicle sales, especiall y if potential buyers have an expectation of higher fuel prices. The sign of the demand effect will depend on which of these effects
dominates. This issue is discussed further in Sect ions III.H.1 and III.H.11. Some comments encouraged us to quantify this effect, once we quan tified estimates for vehicle sales. Because, as described in Section III.H.11, we have not quantifi ed the impact on sales for this rule, we do not quantify the demand effect.

(2) The Cost Effect

The demand effect measures employment changes due t o new vehicle sales only. The cost effect measures employment impacts due to the development, manufacturing, and installation by auto suppliers and manufacturers of the new or additional technologies needed for vehicles to comply with the standards. As RIA Chap ter 8.2.3.1.2 explains, we estimate the cost effect by multiplying the costs of rule compliance by ratios of workers to each $1 million of Deleted: ¶Page 20 of 26

expenditures in that sector. The magnitude and rel ative size of these ratios depends on the sectors’ labor intensity of the production process. Several commenters mentioned the importance of this rule in encouraging employment r elated to the technologies expected to be used to comply with this rule. We received no comm ents criticizing the approach used here; the
UAW commended EPA for it.

The use of these ratios has both advantages and lim itations. It is often possible to estimate these ratios for quite specific sectors of the economy; as a result, it is not necessary to extrapolate employment ratios from possibly unrelat ed sectors. On the other hand, these estimates are averages for the sectors, covering al l the activities in those sectors; they may not be representative of the labor required when expenditu res are required on specific activities, as the factor shift effect (discussed below) indicates. I n addition, these estimates do not include changes in sectors that supply these sectors, such as steel or electronics producers. They thus may best be viewed as the effects on employment in the auto sector due to the changes in expenditures in that sector, rather than as an asse ssment of all employment changes due to these changes in expenditures.

Some of the costs of this rule will be spent direct ly in the auto manufacturing sector, but some of the costs will be spent in the auto parts m anufacturing sector. Because we do not have information on the proportion of expenditures in ea ch sector, we separately present the ratios for both the auto manufacturing sector and the auto par ts manufacturing sector. These are not additive, but should instead be considered as a ran ge of estimates for the cost effect, depending on which sector adds technologies to the vehicles t o 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) Employme nt Requirements Matrix (ERM); 30 the Census Bureau’s Annual Survey of Manufactures 31 (ASM); and the Census Bureau’s Economic Census. RIA Chapter 8.2.3.1.2 provides details on all these sources. The ASM and the
Economic Census have more sectoral detail than the ERM; we provide estimates for both Motor Vehicle Manufacturing and Light Duty Vehicle Manufa cturing sectors for comparison purposes. For all of these, we adjust for the ratio of domest ic production to domestic sales (as supported by a commenter). The maximum value for employment imp acts per $1 million expenditures (after accounting for the share of domestic production) in 2010 was estimated to be 1.809 if all the additional costs are in the parts sector; the minim um value is 0.402, if all the additional costs are in the light-duty vehicle manufacturing sector: th at is, the range of employment impacts is between 0.4 and 2 additional jobs per $1 million ex penditures in the sector. The different data sources provide similar magnitudes for the estimate s for the sectors. Parts manufacturing appears to be more labor-intensive than vehicle man ufacturing; light-duty vehicle manufacturing appears to be slightly less labor-intensive than mo tor vehicle manufacturing as a whole. As discussed in the RIA, trends in the BLS ERM are use d to estimate productivity improvements

30 http://www.bls.gov/emp/ep_data_emp_requirements.ht m 31 http://www.census.gov/manufacturing/asm/index.htmlPage 21 of 26

over time that are used to adjust these ratios over time. Table III-1 shows the cost estimates developed for this rule, discussed in Section III.H .2. Multiplying those cost estimates by the maximum and minimum values for the cost effect (max imum using the Economic Census ratio if all additional costs are in the parts sector, and m inimum using the Economic Census ratio for the light-duty sector if all additional costs are borne by auto manufacturers) provides the cost effect employment estimates. This is a simple way to exami ne the relationship between labor required and expenditure.

While we estimate employment impacts, in job-years, beginning with th e first year of the standard (2017), some of these employment gains may occur earlier as auto manufacturers and parts suppliers hire staff in anticipation of compl iance with the standard

. A job-years is a way to calculate the amount of work needed to complete a s pecific task. For example, a job-year is one year of work for one person, or 6 months of work fo r 2 people.

Table III- 1 Employment Effects due to Increased Expenditures on Vehicles and Parts, in job-years

Year Costs (before

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

effect if all expenditures are in light duty vehicle mfg sector Maximum employment

effect if all expenditures are in the parts sector

2017 $ 2,435 700 3,200 2018 $ 4,848 1,300 6,200 2019 $ 6,818 1,700 8,400 2020 $ 8,858 2,100 10,500 2021 $ 12,400 2,900 14,200 2022 $ 18,323 4,100 20,200 2023 $ 23,734 5,100 25,200 2024 $ 29,101 6,000 29,700 2025 $ 31,678 6,300 31,100

(3) The Factor Shift Effect

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

.
We are not quantifying the factor shift effect here , for lack of data on the labor intensity of all the possible technologies that manufacturers could use to comply with the standards. As discussed in RIA Chapter 8.2.3.1.3, for a subset of the technologies, EPA-sponsored research (discussed in Chapter 3.1.1.1 of the Joint TSD), wh ich compared new technologies to existing Deleted: Table III-107

Deleted: 107Page 22 of 26

ones at the level of individual components, found t hat labor use for those new technologies increased: those new fuel-saving technologies use more labor than the baseline technologies. For instance, switching from a conventional mid-siz e vehicle to a hybrid version of that vehicle involves an additional $395.85 in labor costs, whic h we estimate to require an additional 8.6 hours per vehicle. 32 For a subset of the technologies likely to be use d to meet the standards in this rule, then, the factor shift effect increases labor demand, at least in the short run; in the lon g run, as with all technologies, the cost structure i s likely to change due to learning, economies of scale, etc. The technologies examined in this rese arch are, however, only a subset of the technologies that auto makers may use to comply wit h the standards. As a result, these results
cannot be considered definitive evidence that the f actor-shift effect increases employment for this rule. We therefore do not quantify the factor shift effect. Comments supported this approach and encouraged development of these estima tes for more technologies. Because of the complexity of the estimation process, we are not pr esenting additional estimates in the RIA.

(4) Summary of Employment Effects in the Auto Sector

While we are not able to quantify the demand or fac tor shift effects, the cost effect results show that the employment effects of the increased s pending in the regulated sector (and, possibly, the parts sector) are expected to be posi tive and on the order of a few thousand in the initial years of the program. As noted above, the motor vehicle and parts manufacturing sectors employed about 677,000 people in 2010, with automob ile and light truck manufacturing accounting for about 129,000 of that total.

ii. Effects on Employment for Auto Dealers

The effects of the standards on employment for auto dealers depend principally on the effects of the standards on light duty vehicle sale s: increases in sales are likely to contribute to employment at dealerships, while reductions in sale s are likely to have the opposite effect. In addition, auto dealers may be affected by changes i n maintenance and service costs. Increases in those costs are likely to increase labor demand in dealerships, and reductions are likely to
decrease labor demand.

The Defour Group as part of its employment estimate (discussed in III.H.12.a) expressed concern about employment in this sector, due to the potential impacts of the rule on vehicle sales; they provide an estimate of 35,000 jobs lost at auto dealers due to their predicted sales
reductions for MY 2025. 33 As discussed in III.H.11, we do not at this point provide a quantitative estimate of the effects of this rule o n vehicle sales. The National Automobile Dealers Association encouraged additional informati on to help consumers better understand the

32 FEV, Inc. “Light-Duty Technology Cost Analysis, P ower-Split and P2 HEV Case Studies.” EPA Report EP A420-R-11_015, November 2011 (Docket EPA-HQ-OAR-2010 -0799-1101).

33 See footnote 25

. Deleted: 519Page 23 of 26

benefits of investing in improved fuel economy, and noted the information provided by the new fuel economy label developed by the agencies. 34

Although this rule predicts very small penetration of plug-in hybrids and electric vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. As discussed in Section III.H.1.b, consumers may fi nd some characteristics of electric vehicles and plug-in hybrid electric vehicles, such as the a bility to fuel with electricity rather than gasoline, attractive; they may find other character istics, such as the limited range for electric vehicles, undesirable. As a result, some consumers will find that EVs will meet their needs, but other buyers will choose more conventional vehicles . Auto dealers may play a major role in explaining the merits and disadvantages of these ne w technologies to vehicle buyers. There may be a temporary need for increased employment to tra in sales staff in the new technologies as the new technologies become available. We agree with t he comment that consumer information has the potential to play an important role in consumer acceptance of vehicles subject to this rule.

iii. Effects on Employment in the Auto Parts Sector

As discussed in the context of employment in the 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 standards as well. The additional expenditures on technologies are expected to have a positive eff ect on employment in the parts sector as well as the manufacturing sector; the breakdown in emplo yment between the two sectors is difficult to predict. The effects on the parts sector also depe nd on the effects of the standards on vehicle sales and on the labor intensity of the new technol ogies, qualitatively in the same ways as for the auto manufacturing sector. The United Auto Workers , Blue-Green Alliance, environmental organizations, and various others specifically note d the employment gains associated with development and use of these advanced technologies.

iv. Effects on Employment for Fuel Suppliers

In addition to the effects on the auto 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 standards result in increased use of electricity, n atural gas, or other fuels, employment effects will result from providing these fuels and developi ng the infrastructure to supply them to

consumers.

Expected petroleum fuel consumption reductions can be found in Section III.H.4. While those figures represent fuel savings for purchasers of fuel, it represents a loss in value of output for the petroleum refinery industry, fuel distribut ion, and gasoline stations. The loss of

34 Information on the label may be found at http://ww w.epa.gov/otaq/carlabel/index.htm .Page 24 of 26

expenditures to petroleum fuel suppliers throughout the petroleum fuel supply chain, from the petroleum refiners to the gasoline stations, is lik ely to result in reduced employment in these sectors. Comments from the United Auto Workers (UA W), Blue-Green Alliance, environmental organizations, and Investor Network on Climate Risk suggested that, because other sectors are more labor-intensive than gasoline production and s ales, reducing expenditures on gasoline and making them available for other consumer goods may increase employment. EPA has not
estimated this effect.

This rule is also expected to lead to increases in electricity consumption by vehicles, as discussed in Section III.H.4. This new fuel may re quire additional infrastructure, such as electricity charging locations. Providing this inf rastructure will require some increased employment. In addition, the generation of electri city will also require some additional labor. We have insufficient information at this time to pr edict whether the increases in labor associated with increased infrastructure provision and fuel ge neration for these newer fuels will be greater or less than the employment reductions associated w ith reduced demand for petroleum fuels.

v. Effects on Employment due to Impacts on Consumer Ex penditures

As a result of these standards, consumers will pay a higher up-front cost for the vehicles, but they will recover those costs in a fairly short payback period (see Section III.H.5); indeed, people who finance their vehicles are expected to f ind that their fuel savings per month exceed the increase in the loan cost (except at very high interest rate levels). As a result, consumers will have additional money to spend on other goods and s ervices (for those consumers who pay cash for their vehicles, it will occur after the initial payback period). These increased expenditures will support employment in those sectors where cons umers 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 consume r expenditures would primarily represent a shift in employment among sectors. If, on the othe r hand, the economy still has substantial unemployment, these expenditures would contribute t o employment through increased consumer
demand.

Environmental organizations, CFA, the National Asso ciation of Clean Air Agencies, American Council for an Energy-Efficient Economy (A CEEE), UAW, Business for Innovative Climate & Energy Policy (BICEP), Ceres, and some pr ivate citizens suggested in written comments and in public hearings that this rule woul d increase economic growth in the U.S. The Center for Biological Diversity, International Coun cil for Clean Transportation, Natural Resources Defense Council, and Union of Concerned S cientists (UCS) recommended that EPA include an analysis of the economy-wide impacts of the rule, including impacts on U.S. gross domestic product (GDP) and consumption patterns. A CEEE, Ceres, BICEP, and UCS suggested that fuel savings from the rule would allow consume rs to increase their spending on other goods and services in more productive sectors of the econ omy, which would likely increase GDP and consumption in the U.S. CFA specifically recommend ed that EPA use a GDP multiplierPage 25 of 26

approach that recognizes that national output would increase from the rule as a result of reducing U.S. oil imports. Ceres, BICEP, UCS, and the Nation al Wildlife Federation cited a report for Ceres by Management Information Services, Inc. that found that a 4% annual improvement in fuel economy would increase U.S. gross economic out put by $21.3 billion, personal income by $14.2 billion, and revenue for federal, state, and local governments by $12.7 billion in 2030. 35
On the other hand, other private citizens suggested the economy could be harmed as a result of this rule, but did not offer any specific data to s upport the claim. Analyzing the economy-wide impacts from this rule is challenging due to the in herent uncertainty in projecting a myriad of economic parameters into the future (e.g., levels o f employment of labor and capital, the structure of the economy, prices of goods and servi ces) and determining an appropriate economic framework to model (e.g., supply equaling demand in all markets and specific forms of market interactions). EPA has not been able to identify a widely agreed u pon methodology and thus we continue to not quantify the impacts of the rule on overall economic patterns in the U.S.

d. Summary

The primary employment effects of this rule are exp ected to be found throughout several key sectors: auto manufacturers, auto dealers, auto parts manufacturing, fuel production and supply, and consumers. This rule initially takes e ffect in model year 2017, a time period sufficiently far in the future that the current sus tained high unemployment at the national level may be moderated or ended. In an economy with full employment, the primary employment effect of a rulemaking is likely to be to move empl oyment from one sector to another, rather than to increase or decrease employment. For that reaso n, we focus our partial quantitative analysis on employment in the regulated sector, to examine t he impacts on that sector directly. We discuss the likely direction of other impacts in th e regulated sector as well as in other directly related sectors, but we do not quantify those impac ts, because they are more difficult to quantify with reasonable accuracy, particularly so far into the future.

For the regulated sector, we have not quantified th e demand effect. The cost effect is expected to increase employment by 700 – 3,200 job- years in 2017 depending on the share of that employment that is in the auto manufacturing s ector compared to the auto parts manufacturing sector. As mentioned above, some of these job gains may occur earlier as auto manufacturers and parts suppliers hire staff to pre pare to comply with the standard. Though we
do not have estimates of the factor shift effect fo r all potential compliance technologies, the evidence which we do have for some technologies sug gests that many of the technologies will
have increased labor needs.

Changes in vehicle sales are expected to affect lab or needs in auto dealerships and in parts manufacturing. Increased expenditures for au to parts are expected to require increased labor to build parts, though this effect also depen ds on any changes in the labor intensity of

35 Management Information Services, Inc., July 2011, “More Jobs Per Gallon: How Strong Fuel Economy/GHG Standards Will Fuel American Jobs”, A Ceres Report, Washington DC.Page 26 of 26

production; as noted, the subset of potential compl iance technologies for which data are available show increased labor requirements. Reduced fuel pr oduction implies less employment in the petroleum sectors. Finally, consumer spending is e xpected to affect employment through changes in expenditures in general retail sectors; net fuel savings by consumers are expected to increase demand (and therefore employment) in other sectors.MY 2017 and Later - Regulatory Impact Analysis

8-1 

## Chapter 8: Vehicle Sales and Employment Impacts

8.1 Vehicle Sales Impacts

8.1.1 How Vehicle Sales Impacts were Estimated for this R ule

Predicting the effects of this rule on vehicle sale s entails comparing two competing effects. On the one hand, as a result of this rule , the vehicles will become more expensive, which would, by itself, discourage sales. On the o ther hand, the vehicles will have improved fuel economy and thus lower operating costs, which makes them more attractive to consumers. As discussed in Preamble III.H.1.a, the re are many competing hypotheses for why private markets are not providing what appear t o be cost-effective energy-saving technologies, for vehicles as well as for other ene rgy-conservation technologies. There are few empirical studies testing these hypotheses, tho ugh. As discussed more fully in Preamble III.H.11.a, it is possible that enacting this rule can contribute to increased vehicle sales by encouraging coordination among automakers to improv e fuel economy that might not happen in the absence of the rule. The empirical literat ure does not provide clear evidence on how much of the value of fuel savings consumers conside r at the time of vehicle purchase. It also generally does not speak to the efficiency of manuf acturing and dealer pricing decisions. Thus, we do not provide quantified estimates of pot ential sales impacts.

In previous rulemakings, EPA and NHTSA conducted ve hicle sales analyses by comparing the up-front costs of the vehicles with t he present value of five years’ worth of fuel savings. We assumed that the costs for the fuel-sa ving technologies would be passed along fully to vehicle buyers in the vehicle prices. The up-front vehicle costs were adjusted to take into account several factors that would affect cons umer costs: the increased sales tax that consumers would pay, the increase in insurance prem iums, the increase in loan payments that buyers would face, and a higher resale value, with all of these factors due to the higher upfront cost of the vehicle. Those calculations resu lted in an adjusted increase in costs to consumers. We then assumed that consumers consider ed the present value of five years of fuel savings in their vehicle purchase, which is co nsistent with the length of a typical new light-duty vehicle loan, and is similar to the aver age time that a new vehicle purchaser holds onto the vehicle. A

The present value of fuel savings was subtracted from technology costs to get a net effect on vehicle cost of ownership. We then used a short-run demand elasticity of - 1 to convert a change in price into a change in qua ntity demanded of vehicles. B An elasticity of -1 means that a 1% increase in price leads to a 1% reduction in quantity sold.

A In this rule, the 5-year payback assumption corres ponds to an assumption that vehicle buyers take int o account between 30 and 50 percent of the present value of l ifetime vehicle fuel savings (with the variation de pending on discount rate and model year).

B For a durable good such as an auto, the elasticity may be smaller in the long run: though people may be able to change the timing of their purchase when price chan ges in the short run, they must eventually make the investment. We request comment on whether or when a long-run elasticity should be used for a rule tha t phases in over time, as well as how to find good estimates for the long –run elasticity. Deleted: wouldChapter 8

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We do not here present a vehicle sales analysis usi ng this approach. This rule takes effect for MY 2017-2025. In the intervening years, it is possible that the assumptions underlying this analysis, as well as market conditi ons, might change. Instead, Chapter 5.5 includes a payback period analysis to estimate the number of years of fuel savings needed to recover the up-front costs of the new technologies. In other words, the payback period identifies the break-even point for new vehicle buy ers. As discussed there, the payback period is 3.2 – 3.4 years for new vehicles, and eve n shorter for used vehicles (just over 1 year for a 5-year-old MY 2025 vehicle). That chapter al so includes an assessment of the lifetime costs and benefits that accrue to a vehicle owner.

8.1.2 Consumer Vehicle Choice Modeling C

An alternative to the vehicle sales analysis approa ch discussed above is the use of consumer vehicle choice models. In this section we describe some of the consumer vehicle choice models EPA has reviewed in the literature, a nd we describe the models’ results and limitations that we have identified. The evidence from consumer vehicle choice models indicates a huge range of estimates for consumers’ willingness to pay for additional fuel economy. Because consumer surplus estimates from c onsumer vehicle choice models depend critically on this value, we would consider any con sumer surplus estimates of the effect of our rule from such models to be unreliable. In additio n, the predictive ability of consumer vehicle choice models has not been tested. While vehicle c hoice models are based on sales of existing vehicles, vehicle models are likely to cha nge, both independently and in response to this rule. The models may not predict well in resp onse to these changes. Instead, we compare the value of the fuel savings associated with this rule with the increase in technology costs. EPA will continue its efforts to review the literat ure and (as described below) to explore the use of consumer choice modeling but, given the know n limitations and uncertainties of vehicle choice models, EPA has not conducted an ana lysis using these models for this rule.

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

In response to these changes, the number and types of vehicles sold is likely to change. When consumers buy vehicles, they consider both the ir personal characteristics (such as age, family composition, income, and their vehicle needs ) and the characteristics of vehicles (e.g., vehicle size, fuel economy, and price). In respon se to the changes in vehicle characteristics, consumers will reconsider their purchases. Increas es in fuel economy are likely to be attractive to consumers, but increases in price, as well as any detrimental changes in other vehicle characteristics, may be deterrents to purch ase. As a result, consumers may choose a different vehicle than they would have purchased in the absence of the rule. The changes in prices and vehicle characteristics are likely to in fluence consumers on multiple market scales:

C This section is drawn heavily from Helfand, Gloria , and Ann Wolverton, “Evaluating the Consumer Respo nse to Fuel Economy: A Review of the Literature.” Int ernational Review of Environmental and Resource Economics 5 (2011): 103-146. Deleted: ; the net savings are about $5,000 with a 3 percent discount rate, and over $3,000 with a 7 percent discount rateMY 2017 and Later - Regulatory Impact Analysis

8-3 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 total compliance costs compared to models that hold fleet mix constant, since it predicts changes in the flee t mix that can affect total compliance costs. It can also be used to measure changes in consumer surplus, the benefit that consumers perceive from a good over and above the purchase pr ice. (Consumer surplus is the difference between what consumers would be willing to pay for a good, represented by the demand curve, and the amount they actually pay. For insta nce, if a consumer were willing to pay $30,000 for a new vehicle, but ended up paying $25, 000, the $5000 difference would be called consumer surplus.)

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

8.1.2.1 Methods

Consumer choice models (CCMs) of vehicle purchases typically use a form of discrete choice modeling. Discrete choice models seek to ex plain discrete rather than continuous decisions. An example of a continuous decision is how many pounds of food a farm might grow: the pounds of food can take any numerical va lue. Discrete decisions can take only 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. D In a nested logit, the model is structured in layer s. For instance, the first layer may be the choice of whether to buy a new or used vehicle. Gi ven that the person chooses a new vehicle, the second layer may be whether to buy a car or a t ruck. Given that the person chooses a car, the third layer may be the choice among an economy, midsize, or luxury car. Examples of nested logit models include Goldberg, 1 Greene et al., 2 and McManus. 3

D Logit refers to a statistical analysis method used for analyzing the factors that affect discrete cho ices (i.e., yes/no decisions or the choice among a countable nu mber of options).Chapter 8

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

While discrete choice modeling appears to be the pr imary method for consumer choice modeling, others (such as Kleit 7 and Austin and Dinan 8 ) have used a matrix of demand elasticities to estimate the effects of changes in cost. The discrete choice models can produce such elasticities. Kleit as well as Austin and Din an used the elasticities from an internal GM vehicle choice model.

8.1.2.2 Data Sources

The predictions of vehicle purchases from CCMs are based on consumer and vehicle characteristics. The CCMs identify the effects of changing the characteristics on the purchase decisions. These effects are typically called the parameters or coefficients of the models. For instance, the model parameters might predict that a n increase in a person’s income of 10% would increase the probability of her purchasing ve hicle A by 5%, and decrease the probability of her purchasing vehicle B by 10%.

The parameters in CCMs can be developed either from original data sources (estimated models), or using values taken from othe r studies (calibrated models).

Estimated models use datasets on consumer purchase patterns, consumer characteristics, and vehicle characteristics to dev elop their original sets of parameters. The datasets used in these studies sometimes come from surveys of individuals’ behaviors. 9 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. 10

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 11 and the New Vehicle Market Model developed by NERA Economic Consulting 12 are examples of calibrated models.

8.1.2.3 Factors Included in the Models

Consumer choice models vary in their complexity and levels of analysis. Some focus only on the new vehicle market; 13 others consider the choice between new vehicles an d an outside good (possibly including a used vehicle); 14 others explicitly consider the relationship between the new and used vehicle markets. 15 Some models include consideration of vehicle miles traveled, 16 though most do not.MY 2017 and Later - Regulatory Impact Analysis

8-5 The models vary in their inclusion of both consumer and vehicle information. One model includes only vehicle price and the distribut ion of income in the population influencing choice; 17 others include varying numbers and kinds of vehicl e and consumer attributes.

Some models include only the consumer side of the v ehicle market; 18 others seek to represent both consumer and producer decisions. 19 Models that include only the consumer side are suitable for reflecting consumer choices, but they do not allow for revisions of vehicle characteristics in response to consumer 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. 20 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. 21 Even those vehicle choice models that include a producer model may not include much detai l, due to computational limits: it is unusual for models to allow both buyers and produce rs to choose one vehicle characteristic, much less multiple characteristics. 22

8.1.2.4 Research Questions for the Models

Consumer choice models have been developed to analy ze many different research and policy questions. In part, these models have been developed to advance the state of economic modeling. The work of Berry, Levinsohn, and Pakes, 23 for instance, is often cited outside the motor vehicle context for its incorporation of mult iple new modeling issues into its framework. In addition, because the vehicle sector is a major part of the U.S. economy and involved in many public policy discussions, researc h questions cover a wide gamut. These topics have included the effects of voluntary expor t restraints on Japanese vehicles compared to tariffs and quotas, 24 the market acceptability of alternative-fuel vehic les, 25 the effects of introduction and exit of vehicles from markets, 26 causes of the decline in market shares of U.S. automakers, 27 and the effects of gasoline taxes 28 and “feebates” 29 (subsidizing fuelefficient cars with revenue collected by taxing fue l-inefficient vehicles).

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions

Consumer vehicle choice models typically consider t he effect of fuel economy on vehicle purchase decisions. Fuel economy can appea r in various forms in these models.

Some models 30 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 toChapter 8

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receive from a more fuel-efficient vehicle and cons ider the savings equivalent to a reduction in purchase price. The vehicle sales method descri bed in Chapter 8.1.1 uses a variant on this approach, in which it is assumed that consumers con sider some fraction of future fuel savings. Turrentine and Kurani 31 question this assumption; they find, in fact, that consumers do not make this calculation when they purchase a vehicle. The question remains, then, how or whether consumers take fuel economy into account wh en 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. 32 Since consumers pay for gallons of fuel, then thi s measure can assess fuel savings relatively directly

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

Greene and Liu, 37 in a paper published in 1988, reviewed 10 papers u sing consumer vehicle choice models and estimated for each one ho w much consumers would be willing to pay at time of purchase to reduce vehicle operating costs by $1 per year. They found that people were willing to pay between $0.74 and $25.97 for a $1 decrease in annual operating costs for a vehicle. This is clearly a very wide r ange: while the lowest estimate suggests that people are not willing to pay $1 once to get $1 per year reduced costs of operating their vehicles, the maximum suggests a willingness to pay 35 times as high. For comparison, the present value of saving $1 per year for 15 years at a 3% discount rate is $11.94, while a 7% discount rate produces a present value of $8.78. W hile this study is quite old, it suggests that, at least as of that time, consumer vehicle choice m odels produced widely varying estimates of the value of reduced vehicle operating costs.

A newer literature review from David Greene 38 suggests continued lack of convergence on the value of increased fuel economy to consumers. Of 27 studies, willingness to pay for fuel economy as a percent of the expecte d value of fuel savings varied from highly positive to highly negative. Significant numbers o f studies found that consumers overvalued fuel economy, undervalued fuel economy, or roughly valued fuel economy correctly relative

E Likewise, these measures assume consumers respond the same way to increases and decreases in cost per mile of driving, as well as if those increases and decre ases are large shocks rather than small, gradual ch anges. The issue of potential asymmetric consumer response to increased fuel efficiency compared to other types o f changes to the cost of driving also arises and is discussed in the context of the VMT rebound effect (see Sect ion III.H.4 of the Preamble and Chapter 4.2.5 of the TSD).MY 2017 and Later - Regulatory Impact Analysis

8-7 to fuel savings. Part of the difficulty may be, as these papers note, that fuel economy may be correlated (either positively or negatively) with 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 39 includes both fuel cost (dollars per mile) and miles per gallon i n his analysis, with the argument that miles per gallon measures other undesirable quality attri butes, while fuel cost picks up the consumer’s demand for improved fuel economy. Green e finds that, while some of the variation may be explainable due to issues in some of the studies, the variation shows up in studies that appear to be well conducted. As a res ult, further work needs to be conducted before it is possible to identify the role of fuel economy in consumer purchase decisions.

Some studies 40 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 41 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 42 notes that its baseline model implies that consumers are willing to buy more fuel economy than producers have provided; they have to adjust their model to eliminate these “negative-cos t” fuel economy improvements.

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

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

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

This disconnect between net present value estimates of energy-conserving cost savings and what consumers actually spend on energy conserv ation is often referred to as the Energy Paradox, 43 since consumers appear to undervalue a wide range of investments in energy conservation. There are many possible explanations for the paradox discussed in the literature. 44 Some explanations point to costs or aspects of co nsumer decision-making unaccounted for in a simple present value calculati on, while others point to potential behavioral or market failures. There is little emp irical literature to help the analyst determine which combination of hypothesis offers the most cre dible explanation. Some possibilities include:

• Consumers might be “myopic” and hence undervalue fu ture fuel savings in their purchasing decisions.Chapter 8

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• Consumers might lack the information necessary to e stimate the value of future fuel savings, or not have a full understanding of t his information even when it is presented.

• Consumers may be accounting for uncertainty in futu re fuel savings when comparing upfront cost to future returns.

• Consumers may consider fuel economy after other veh icle attributes and, as such, not optimize the level of this attribute (instead “ satisficing” – that is, selecting a vehicle that is acceptable rather than optimal -- o r selecting vehicles that have some sufficient amount of fuel economy).

• Consumers might be especially averse to the short-t erm losses associated with the higher prices of energy efficient products relative to the future fuel savings (the behavioral phenomenon of “loss aversion”).

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

• When buying vehicles, consumers may focus on visibl e attributes that convey status, such as size, and pay less attention to att ributes such as fuel economy that do not visibly convey status.

• Even if consumers have relevant knowledge, selectin g a vehicle is a highly complex undertaking, involving many vehicle charact eristics. In the face of such a complicated choice, consumers may use simplified de cision rules.

• In the case of vehicle fuel efficiency, and perhaps as a result of one or more of the foregoing factors, consumers may have relatively fe w choices to purchase vehicles with greater fuel economy once other characteristic s, such as vehicle class, are chosen. F

The extent to which fuel economy is optimized relat ive to other, potentially more salient vehicle attributes (such as engine horsepow er and seating capacity) in market outcomes for new vehicles remains an important area of uncertainty. There are significant challenges involved in effectively interpreting and anticipating consumer preferences for various vehicle attributes and amenities. There ar e significant lead times to market, potential return to scale limits on the range of options prov ided for a given attribute or amenity, market transaction frictional factors, and other factors i nherent to the nature of these costly durable goods which may contribute to imperfect satisfactio n of market demand for fuel economy among a highly heterogeneous customer base. Both s ides of the market would be expected to

F For instance, in MY 2010, the range of fuel econom y (combined city and highway) available among all l isted 6-cylinder minivans was 18 to 20 miles per gallon. With a manual-transmission 4-cylinder minivan, it is possible to get 24 mpg. See http://www.fueleconomy .gov, which is jointly maintained by the U.S. Depar tment of Energy and the EPA.MY 2017 and Later - Regulatory Impact Analysis

8-9 attempt to maximize the utility they gain from thes e transactions; they presumably rely heavily in their calculations on the uncertain bene fits of savings from fuel economy improvements, and yet market outcomes may still app ear to reflect potential foregone opportunities to increase utility. We remain inter ested in these market dynamics, their underlying causes, and their potential significance for assessing the potential incremental effects of pollution control standards.

8.1.2.7 Electric Vehicles and Other New Vehicles

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

As discussed above, some models rely on vehicle pri ce as the primary or only explanatory variable. Even in these models, it is possible, with some additional information, to consider the effects of new vehicle introduction . The first step is to find a vehicle similar on as many dimensions as possible to the new vehicl e. For instance, if the change is to create an electric vehicle (EV) version of an existing mod el, then the existing model serves as the base vehicle. Next, it is necessary to measure the changes in vehicle attributes of interest to potential vehicle buyers. For an EV, changes in ve hicle driving range and cost of fueling may be two such attributes. The next requirement is in formation on the value to consumers of the attributes that change between the new and the base vehicle. Multiplying the value for that attribute by the change in the attribute provides a n estimate of the benefit or cost associated with changing that characteristic. That amount can then be added to or subtracted from the vehicle purchase price to give an adjusted purchase price reflecting the changed characteristic. This procedure is just an extension of the approach , discussed above, used to incorporate fuel economy improvements into vehicle choice models, by calculating future fuel savings and subtracting them (either in whole or a fraction) fr om vehicle purchase price.

Incorporating new vehicles into a vehicle choice mo del, then, requires estimates of the changes in key attributes from conventional vehicle s, and estimates of the value, also called the willingness to pay (WTP), that consumers put on those attributes.

Electric vehicles (EVs) will have a number of chang es in vehicle characteristics from any baseline model. EVs are likely to have a small er driving range between refuelings than conventional vehicles, due to the large battery cap acity needed to increase range. The 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 forChapter 8

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

Vehicle driving range has received attention 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 -- indeed, a factor in the development of plug-in hybrid-electri c vehicles is responding to this concern -- it is starting to draw attention in the research commu nity.

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

Table 8.1-1 summarizes results from several conjoint studies t hat 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.

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. Deleted: Table 8.1-1MY 2017 and Later - Regulatory Impact Analysis

8-11 Table 8.1-1 Willingness to Pay for Increasing Range Calculated from Various Studies

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

year) Value of additional

range in 2010$ a

Bunch et al. (1993) b $7,600 (1991$) $11, 3 00 Kavelek (1996) for California Energy Commission c $2600

- $41,900 (1993$) $3700

- $5 9

, 4 00

Resource Systems Group (2009) for California Energy Commission d $2900 - $7500 (2009$) $2900 - $7600

Hess et al. (2009), using the same data as Resource Systems Group (2009) e $2400 - $8500 (2009$) $2400 - $8600

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

a Values adjusted to 2010$ using the Bureau of Econom ic Analysis GDP deflator.

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

c Kavelek, Chris. “CALCARS: The California Conventi onal and Alternative Fuel Response Simulator.” Demand Analysis Office, California Energy Commissio n, April 1996. The variation in values is due to willingness to pay (WTP) varying by income levels a nd for one-car and two-car households. The coeffic ient on range for one-car households was not statistically significantly different from zero (t-statistic = 1. 5), but it was for 2-car households (t-statistic = 3.02). The min ima and maxima presented here represent the values across both ownership and income categories.

d Resource Systems Groups, Inc. “Transportation Fuel Demand Forecast Household and Commercial Fleet Survey Task 8 Report: Logistic Regression Analysis and Results.” Prepared for California Energy Comm ission, June 2009.

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

f Hidrue, Michael K., George R. Parsons, Willett Kemp ton, and Meryl P. Gardner. “Willingness to Pay for Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 686-705. The range of values is due to the model separating consumers int o “gasoline vehicle-oriented” and “electric vehicle -oriented” groups. The EV-oriented group has higher WTP for a dditional range.

Driving range may be a major factor in consumers’ d ecisions on EVs, but it is not the only attribute that may be important to potential b uyers (e.g., as noted, Hidrue et al. find that some consumers appear willing to pay substantially for reduced tailpipe emissions). A model that does not incorporate the other factors importa nt to consumers’ decisions may not perform well in predicting vehicle purchases. In addition, as mentioned above, and as seen in Table 8.1-1 , it is likely that the WTP values for attributes o f 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. Deleted: Table 8.1-1Chapter 8

8-12 8.1.2.8 EPA Exploration of Vehicle Choice Modeling

In order to develop greater understanding of these models, EPA is developing a vehicle choice model, although not for this rulemak ing. In its current form, the model assumes that the vehicle fleet and all characterist ics of each vehicle, except vehicle prices and fuel economy, stay the same. The model will predic t changes in the vehicle fleet, at the individual-configuration level and at more aggregat ed levels, in response to changes in vehicle fuel economy and price.

The EPA model uses a nested logit structure common in the vehicle choice modeling literature, as discussed above in Chapter 8.1.2.1. “Nesting” refers to the decision-tree structure of the model, and “logit” refers to the f act 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) were selected based on a rev iew of values found in the literature on vehicle choice modeling. As discussed above, a num ber 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 8.1.2 .7: the value that the consumer places on fuel economy is multiplied by the change in fuel ec onomy and incorporated into the “effective

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.MY 2017 and Later - Regulatory Impact Analysis

8-13 price” of the vehicle. In practice, implementing t his calculation involves calculating the change in expenditures on fuel based on schedules o f VMT, vehicle survival, and fuel prices in the future consistent with those in OMEGA. As d iscussed in Chapter 8.1.2.5, there is no consensus value for consumers’ willingness to pay f or improved fuel economy: estimates vary tremendously. The model assumes that consumer s will use some years of discounted fuel savings, with the modeler able to input both t he number of years and the discount rate to be used in the analysis.

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

The model assumes that the increase in vehicle cost associated with increased technology is fully passed through as an increase i n vehicle price, and some years of fuel savings (which the modeler may select) offset this price increase. It then calculates changes in total fleet size and in sales mix, at the indivi dual-configuration level and at the level of vehicle class, due to the changes in fuel economy a nd vehicle prices. It also calculates changes in consumer surplus associated with the cha nges in fuel economy and vehicle prices.

The model has undergone peer review. 46 The reviewers were generally supportive of the model structure and parameters, with two major qualifications.

First, peer reviewers recommended that the model sh ould interact closely with OMEGA, EPA’s technology cost and effectiveness mode l, and its appropriate use may depend on that interaction. For instance, it is po ssible that the predicted changes in fleet mix will lead to predictions of vehicle sales for auto makers that do not meet the standards, because the mix and volume of vehicles sold changed from the initial levels. To correct this problem, it is necessary to feed the new fleet mix back into OMEGA (which calculates costs and compliance) and get a new set of output, which is then fed back into the vehicle choice model. OMEGA increases technologies, and thus cost s, to improve compliance; those adjustments would then again affect vehicle demand. We expect that this iterative process would converge to a fleet mix that would meet 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.

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

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Second, the peer review raised the issue of the unc ertainties surrounding the model parameters and suggested the development of capacit y to conduct uncertainty analysis. As discussed in Section 8.1.2.5, the role of fuel econ omy in consumer decisions is one source of uncertainty; the price slopes used for the differen t nests are also not known with certainty. EPA agrees that use of the model should involve, at the least, sensitivity analysis over key parameters, and we plan to investigate greater inco rporation of uncertainty analysis in the model.

We note as well that the current model does not tak e EVs or other alternatively fueled vehicles into account. As discussed in Section 8.1 .2.7, the values for willingness to pay for features such as range and different refueling infr astructures appear to be subject to great uncertainty. EPA’s current model does not include these vehicles, because we are seeking to gain experience and confidence with the modeling wh ere it is likely to work best before we investigate modeling where more uncertainty is invo lved. The incorporation of new vehicles of any kind in the modeling is another area for fut ure work.

As discussed in Preamble Section III.H.1, EPA is no t using its preliminary consumer choice model in this rulemaking because we believe it needs further development and testing before we have confidence in its use. As the peer reviewers noted, it has not yet been integrated with OMEGA, an important step for ensuri ng that changes in the vehicle fleet estimated by the model will result in a fleet compl iant with the standards. In addition, concerns remain that vehicle choice models have rar ely been validated against real-world data. In response to these concerns, we would expe ct any use of the model to involve, at the least, a number of sensitivity analyses to examine the robustness of results to key parameters. We will continue model development and testing to u nderstand better the results and limitations of using the model.

8.1.2.9 Summary and Additional Considerations

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

First, consumer vehicle choice modeling has the pot ential to describe more accurately the impact of a policy, by identifying market shift s. More accurate description of the market resulting from a policy can improve other estimates of policy impacts, such as the change in total vehicle emissions or vehicle miles traveled. The predictive ability of models, though, is not proven.

Vehicle choice models can incorporate the effects o n consumer decisions of changes in vehicle characteristics, if there are estimates of the value that consumers put on changes in those characteristics. These willingness-to-pay va lues may, however, be sensitive to the ways they are estimated, as indicated in the discussion of the value that consumers place on fuel economy in their purchase decisions. Especially fo r characteristics associated with advanced technology vehicles, such as EVs, the willingness-t o-pay values may change over time asMY 2017 and Later - Regulatory Impact Analysis

8-15 consumers develop more experience with the vehicles and these characteristics. Models based on current estimates may not predict well for the future.

Consumer choice modeling has the potential to impro ve estimates of the compliance costs of a rule. Consumers can either accept the n ew costs and vehicle characteristics, or they can change which vehicles they buy. Using a vehicl e choice model is likely to reduce estimated compliance costs: because the model allows consume rs to choose among accepting the new vehicle, buying a different vehicle, or not buying a vehicle, the model assumes that consumers have additional options, which improves t heir welfare relative to the assumption that consumers will not change their buying behavio r.

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. In addition, the pricing paths predicted by these models have not, to our knowledge, been tested against actual behavior; and auto makers may not pass along vehicle costs in the same way in the future as they have in the past. Technology costs, while an accurate measure of the opportunity cost of reso urces to society, may overestimate or underestimate the effect on the prices that consume rs 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 rule take into acc ount the costs to hold other vehicle attributes, such as size and performance, constant. In addition, the analysis assumes that the full technology costs are passed along to vehicle b uyers. With these assumptions, because welfare losses are monetary estimates of how much b uyers would have to be compensated to be made as well off as in the absence of the change

, J the price increase measures the loss to the buyer. K 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

J 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.

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

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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 rule EPA holds constant the vehicle fleet size and mix i n 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 op tions for including consumer and producer choice in modeling the impacts of fuel economy-rela ted regulations. This effort includes further review of existing consumer vehicle choice models, the estimates of consumers’ willingness to pay for increased fuel economy, and overall effects on consumer welfare, as well as EPA’s further development of its vehicle ch oice model for use in the future.

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

Because this rule is expected to increase the up-fr ont costs of vehicles, with the fuel savings that recover those costs coming in over tim e, questions have arisen about the effects of this rule on whether access to credit may limit consumers’ ability to purchase new vehicles, on low-income households, and on the availability o f low-priced vehicles. Section III.H.11.b. of the Preamble discusses these issues in the conte xt of public comments received on the rule; here we provide some background and information on sources of data in that discussion.

When a lender is deciding whether to issue a loan t o a prospective vehicle buyer, the amount of the vehicle loan and the person’s income are two major factors in the loan application. If lenders in fact restrict themselve s to consideration of only those two factors, then the higher up-front costs of the new vehicles subject to this rule would reduce buyers’ abilities to get loans. The fuel savings would not come into play to counter-balance this cost, even though, as shown in the payback period analysi s (RIA Chapter 5.5), the fuel savings exceed the increased loan payments from the first m onth of the loan. Thus, if lenders do not take fuel savings into account in providing loans, people who are borrowing near the limit of their abilities to borrow will either have to chang e what vehicles they buy, or not buy vehicles at all.

On the other hand, some evidence suggests that the loan market may evolve to take fuel savings into greater account in the lending de cision. Some lenders currently give discounts for loans to purchase more fuel-efficient vehicles. 47 An internet search on the term “green auto loan” produced more than 50 lending ins titutions that provide reduced loan rates for more fuel-efficient vehicles. 48 It is possible (though unknown at this time) that the auto loan market may evolve to include further considera tion of fuel savings, as those savings play a significant factor in offsetting the increase in up-front costs of vehicles.

It is possible that future trends in the auto loan market may affect future vehicle sales. It is also possible that some people who have signi ficant debt loads may not be able to get financing for some of these new vehicles; they may have to buy different vehicles (including used vehicles) or delay purchase. For others who b orrow on credit, though, as discussed in RIA Chapter 5.5, the fuel savings are expected to o utweigh the increased loan costs from the Deleted: There is no reason to expect that the number of lenders instituting this policy will decrease in the future as fuel efficiency increases beyond the levels seen today.

Deleted: ndeed, iMY 2017 and Later - Regulatory Impact Analysis

8-17 time of vehicle purchase. The rule thus may make v ehicles more affordable to the public, by reducing consumers’ vulnerability to fuel price jum ps.

The effects of this rule on low-income households d epends on its impacts, not only in the new vehicle market, but also in the used vehicl e market. Two sources of information on vehicle ownership by income are the 2010 Consumer E xpenditure Survey (CES) conducted by the Bureau of Labor Statistics, 49 and the 2007 Survey of Consumer Finances (SCF) conducted by the Federal Reserve System. 50 The Consumer Expenditure Survey data indicate that, though the average household spent more on ve hicle purchases ($2,588) than on gasoline and oil ($2,132), households in the bottom 40 perce nt of income spent more on fuel ($1,304) than on vehicles ($1,106); in addition, they spent more on used vehicles ($756) than on new vehicles ($330). Households in the lowest 20 perce nt of income spent only $127 on new vehicles, $497 on used vehicles, and $1,009 on fuel . These data suggest that the used-vehicle market is more important for low-income households than the new-vehicle market, and that they are more vulnerable to changes in fuel prices than they are to changes in vehicle prices. The Survey of Consumer Finances asks households abo ut purchase information in a number of categories, including vehicles. For the 2007 su rvey, we identified the households in the survey who had bought MY 2007 or 2008 vehicles, and further looked at the income categories for those consumers. Those with income less than $35,200 (the maximum income for those in the bottom 40 percent of income in the CES) bought about 17 percent of new vehicles; those with income below $18,400 (the bott om quintile) bought fewer than 2 percent of new vehicles. These data further support the id ea that low-income households are more affected by the impact of the rule on the used-vehi cle market than on the new-vehicle market.

The effect of this rule on the used vehicle market will be related to its effects on new vehicle prices, the fuel efficiency of new vehicle models, the fuel efficiency of used vehicles, and the total sales of new vehicles. If the value of fuel savings resulting from improved fuel efficiency to the typical potential buyer of a new vehicle outweighs the average increase in new models’ prices, sales of new vehicles could ris e, and the used vehicle market may increase in volume as new vehicle buyers sell their older vehicles. In this case, low-income households are likely to benefit from the increased availability of used vehicles. However, if potential buyers value future fuel savings resultin g from the increased fuel efficiency of new models at less than the increase in their average s elling price, sales of new vehicles will decline, and the used vehicle market may decrease i n volume as people hold onto their vehicles longer. In this case, low-income househol ds are likely to face increased costs due to reduced availability of used vehicles. Because, as discussed in 8.1.1, we have not estimated the effects of the rule on the new vehicle market, and because we do not have a good model of the relationship between the new and used vehicle m arkets, we do not have estimates of the impact of this rule on the used vehicle market. How ever, due to the significant effect of the rule on fuel savings, especially for used vehicles (see RIA Chapter 5.5), we expect lowincome households to benefit from the more rapid pa yback period for used vehicles, though some of this benefit may be affected by the net eff ect of this rule on the prices and availability of used vehicles.

The low-priced vehicle segment of the market may al so deserve consideration, because it may be an entry point for first-car buye rs. Vehicles in the low-priced (economyclass) segment will bear technology costs needed to meet the new standards, but it is not Deleted: EPA does not deny the possibility that the market for new vehicle loans may be affected by thi s rule, but we do not at this time have sufficient evidence to project whether that market will evolve to help or hinder sales of new, more fuel-efficient vehicles.Chapter 8

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known how manufacturers will decide to pass on thes e costs across their vehicle fleets, including in the low-priced vehicle segment. If ma nufacturers decide to pass on the full cost of compliance in this segment, then it is possible that consumers who might barely afford new vehicles may be priced out of the new-vehicle marke t or may not have access to loans. As discussed above, the rule’s impacts on availability of loans is unclear, because some lenders do factor fuel economy into their loans, and it is possible that this trend may expand. In addition, auto makers have some flexibility in how both technologies and price changes are applied to these vehicles; auto makers have ways to keep some vehicles in the low-priced vehicle segment if they so choose. Though the rule is expected to increase the prices of these vehicles, the degrees of price increase and the imp acts of the price increases, especially when combined with the fuel savings that will accompany these changes, are much less clear.

In summary, we recognize that this rule may have im pacts on consumers’ access to loans for new vehicles, on low-income households, a nd on the market for low-priced vehicles; less clear are the directions of these effects. Le nders who only consider consumers’ debt-toincome ratios may reduce consumers’ abilities to pu rchase more expensive vehicles, but some lenders already take the fuel efficiency of vehicle s into account. Low-income households will benefit from reduced fuel costs; we do not estimate the direction of effects of this rule on used vehicle prices, which are more relevant for low-inc ome households than effects on new vehicles. The effects of this rule on low-priced vehicles dep ends on how manufacturers add technologies and price vehicles in this segment; th ey have flexibility to keep some vehicles in this segment if they so wish.

8.2 Employment Impacts

8.2.1 Introduction

Although analysis of employment impacts is not part of a cost-benefit analysis (except to the extent that labor costs contribute to costs) , employment impacts of federal rules are of particular concern in the current economic climate of sizeable unemployment. When President Obama requested that the agencies develop this program, he sought a program that would “strengthen the [auto] industry and enhance j ob creation in the United States.” 51,L The recently issued Executive Order 13563, “Improving R egulation and Regulatory Review” (January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety, and our environment while promoting economic growth, innovation, compe titiveness, and job creation ” (emphasis added). EPA is accordingly providing p artial estimates of the effects of this rule on domestic employment in the auto manufa cturing and parts sectors, while qualitatively discussing how it may affect employme nt in other sectors more generally.

This rule is expected to affect employment in the U nited States through the regulated sector – the auto manufacturing industry – and thro ugh several related sectors, specifically,

L The May 21, 2010 Presidential Memorandum also requ ested that EPA and NHTSA, in developing the technic al assessment to inform the rulemaking process (which was issued by the agencies and CARB on September 30

, 2010), include, among other things, the “impacts on jobs and the automotive manufacturing base in the United States.”MY 2017 and Later - Regulatory Impact Analysis

8-19 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 standards. 52 Changes in light duty vehicle sales, discussed in Chapter 8.1. 1, could affect employment for auto dealers. The employment effects of this rule are expected to expand beyond the regulated sector. Though some of the parts used to achieve the standa rds are likely to be built by auto manufacturers themselves, the auto parts manufactur ing sector also plays a significant role in providing those parts, and will also be affected by changes in vehicle sales. As discussed in Chapter 5.4 of this RIA, this rule is expected to r educe the amount of fuel these vehicles use, and thus affect the petroleum refinery and supply i ndustries. Finally, since the net reduction in cost associated with this rule is expected to le ad to lower household expenditures on fuel net of vehicle costs, consumers then will have addi tional discretionary income that can be spent on other goods and services.

When the economy is at full employment, an environm ental regulation is unlikely to have much impact on net overall U.S. employment; in stead, labor would primarily be shifted from one sector to another. These shifts in employ ment impose an opportunity cost on society, approximated by the wages of the employees , as regulation diverts workers from other activities in the economy. In this situation , any effects on net employment are likely to be transitory as workers change jobs (e.g., some wo rkers may need to be retrained or require time to search for new jobs, while shortages in som e sectors or regions could bid up wages to attract workers).

On the other hand, if a regulation comes into effec t during a period of high unemployment, a change in labor demand due to regul ation may affect net overall U.S. employment because the labor market is not in equil ibrium. In such a period, both positive and negative employment effects are possible. M Schmalansee and Stavins point out that net positive employment effects are possible in the nea r term when the economy is at less than full employment due to the potential hiring of idle labor resources by the regulated sector to meet new requirements (e.g., to install new equipme nt) and new economic activity in sectors related to the regulated sector. 53 In the longer run, the net effect on employment i s more difficult to predict and will depend on the way in which the related industries respond to the regulatory requirements. As Schmalansee and Stavin s note, it is possible that the magnitude of the effect on employment could vary over time, r egion, and sector, and positive effects on employment in some regions or sectors could be offs et by negative effects in other regions or sectors. For this reason, they urge caution in rep orting partial employment effects since it can “paint an inaccurate picture of net employment impa cts if not placed in the broader economic context.”

M Masur and Posner, available at http://papers.ssrn .com/sol3/papers.cfm?abstract_id=1920441Chapter 8

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It is assumed that the official unemployment rate w ill have declined to 5.3 percent by the time by the time this rule takes effect and so the effect of the regulation on labor will be to shift workers from one sector to another. N Those shifts in employment impose an opportunity cost on society, approximated by the wages of the e mployees, as regulation diverts workers from other activities in the economy. In this situa tion, as discussed above, any effects on net employment are likely to be transitory. It is also possible that the state of the economy will be such that positive or negative employment effects w ill occur.

A number of different approaches have been used in published literature to conduct employment analysis. This section describes some o f the common methods, as well as some of their limitations.

8.2.2 Approaches to Quantitative Employment Analysis

Measuring the employment impacts of a policy depend on a number of inputs and assumptions. For instance, as discussed, assumptio ns about the overall state of unemployment in the economy play a major role in me asured job impacts. The inputs to the models commonly are the changes in quantities or ex penditures in the affected sectors; model results may vary in different studies depending on the assumptions about the levels of those inputs, and which sectors receive those changes. W hich sectors are included in the study can also affect the results. For instance, a study of this program that looks only at employment impacts in the refinery sector may find negative ef fects, because consumers will purchase less gasoline; a study that looks only at the auto parts sector, on the other hand, may find positive impacts, because the program will require redesigne d or additional parts for vehicles. In both instances, these would only be partial perspectives on the overall change in national employment due to Federal regulation.

8.2.2.1 Conceptual Framework for Employment Impacts in the Regulated Sector

One study by Morgenstern, Pizer, and Shih 54 provides a retrospective look at the impacts of regulation in employment in the regulate d sectors by estimating the effects on employment of spending on pollution abatement for f our highly polluting/regulated U.S. industries (pulp and paper, plastics, steel, and pe troleum refining) using data for six years between 1979 and 1991. The paper provides a theore tical framework that can be useful for examining the impacts of a regulatory change on the regulated sector in the medium to longer term. In particular, it identifies three separate ways that employment levels may change in the regulated industry in response to a new (or more st ringent) regulation.

• Demand effect : higher production costs due to the regulation wi ll lead to higher market prices; higher prices in turn reduce demand for the good, reducing the demand for labor to make that good. In the authors ’ words, the “extent of this

N Office of Management and Budget, “Fiscal Year 2012 Mid-Session Review: Budget of the U.S. Government .” http://www.whitehouse.gov/sites/default/files/omb/b udget/fy2012/assets/12msr.pdf , p. 10.MY 2017 and Later - Regulatory Impact Analysis

8-21 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, O 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.

The authors conclude that increased abatement expen ditures generally have not caused a significant change in employment in those sectors . More specifically, their results show that, on average across the industries studied, eac h additional $1 million spent on pollution abatement results in a (statistically insignificant ) net increase of 1.5 jobs.

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

The following discussion of additional methodologie s draws from Berck and Hoffmann’s review of employment models. 55

O As will be discussed below, the demand effect in t his rule is potentially an exception to this rule. While the vehicles become more expensive, they also produce r educed fuel expenditures; the reduced fuel costs pr ovide a countervailing impact on vehicle sales. As discuss ed in Preamble Section III.H.1, this possibility th at 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?Chapter 8

8-22 8.2.2.2 Computable General Equilibrium (CGE) Models

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

8.2.2.3 Input-Output (IO) Models

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

IO models are commonly used for regional analysis o f projects. In a regional analysis, the markets are commonly considered small enough th at wages and prices are determined outside the region, and any excess supply or demand is due to exports and imports (or, in the case of labor, emigration or immigration). For nat ional-level employment analysis, the use of input-output models requires the assumption that wo rkers flow into or out of the labor market perfectly freely. Wages do not adjust; instead, pe ople join into or depart from the labor pool as production requires them. For other markets as well, there is no substitution of less expensive inputs for more expensive ones. As a res ult, IO models provide an upper bound on employment impacts. As Berck and Hoffmann note, “F or the same reason, they can be thought of as simulating very short-run adjustment, ” in contrast to the CGE’s implicit assumption of long-run adjustment. Changes in prod uction processes, introductions of new technologies, or learning over time due to new regu latory requirements are also generally notMY 2017 and Later - Regulatory Impact Analysis

8-23 captured by IO models, as they are calibrated to al ready established relationships between inputs and outputs.

8.2.2.4 Hybrid Models

As Berck and Hoffmann note, input-output models and CGE models “represent a continuum of closely related models.” Though not s eparately discussed by Berck and Hoffmann, some hybrid models combine some of the fe atures of CGE models (e.g., prices that can change) with input-output relationships. For instance, a hybrid model may include the ability to examine disequilibrium phenomena, su ch as labor being at less than full employment. Hybrid models depend on assumptions ab out how adjustments in the economy occur. CGE models characterize equilibria but say little about the pathway between them, while IO models assume that adjustments are largely constrained by previously defined relationships; the effectiveness of hybrid models d epends on their success in overcoming the limitations of each of these approaches. Hybrid mo dels could potentially be used to model labor market impacts of various vehicle policy opti ons although a number of judgments need to be made about the appropriate assumptions underl ying the model as well the empirical basis for the modeling results.

8.2.2.5 Single Sectors

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

8.2.2.6 Ex-Post Econometric Studies

A number of ex-post econometric analyses examine th e net effect of regulation on employment in regulated sectors. Morgenstern, Pizer , and Shih (2002), discussed above, and Berman and Bui (2001) are two notable examples that rely on highly disaggregated establishment-level time series data to estimate lo nger-run employment effects. 56 While often a sophisticated treatment of the issues analyzed, t hese studies commonly analyze specific scenarios or sectors in the past; care needs to be taken in extrapolating their results to other scenarios and to the future. For instance, neither of these two studies examines the auto industry and are therefore of limited applicability in this context.

8.2.2.7 Summary

All methods of estimating employment impacts of a r egulation have advantages and limitations. CGE models may be most appropriate fo r long-term impacts, but the usual assumption of equilibrium in the employment market means that it is not useful for looking at changes in overall employment: overall levels are likely to be premised on full employment. IO models, on the other hand, may be most appropria te for small-scale, short-term effects, because they assume fixed relationships across sect ors and do not require market equilibria.Chapter 8

8-24

Hybrid models, which combine some features of CGEs with IO models, depend upon key assumptions and economic relationships that are bui lt into them. Single-sector models are simple and straightforward, but they are often base d on the assumptions that labor demand is proportional to output, and that other sectors are not affected. Finally, econometric models have been developed to evaluate the longer-run net effects of regulation on sector employment, though these are ex-post analyses commo nly of specific sectors or situations, and the results may not have direct bearing for the regulation being reviewed.

8.2.3 Employment Analysis of This Rule

As mentioned above, this program is expected to aff ect employment in the regulated sector (auto manufacturing) and in other sectors di rectly affected by the rule: auto parts suppliers, auto dealers, and the fuel supply market (which will face reduced pet roleum production due to reduced fuel demand but which may see additional demand for electricity or other fuels)

. Changes in consumer expenditure s due to higher vehicle costs and lower fuel expenses will also affect employment . In addition, as the discussion above suggests, e ach of these sectors could potentially have ripple effects in the rest of the economy. These ripple effects depend much more heavily on the state of th e macroeconomy than do the direct effects. At the national level, employment may inc rease in one industry or region and decrease in another, with the net effect being smal ler 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 rule on the regulated sector (i.e., the auto industry; for reasons discussed below, we include some quantitative assessment of effects on suppliers to the auto industry although suppliers are not regulated directly). It also inc ludes qualitative discussion of the effects of the rule on other sectors. Focusing quantification of employment impacts on the regulated sector has some advantages over quantifying all imp acts. First, the analysis relies on data generated as part of the rulemaking process, which focuses on the regulated sector; as a result, what is presented here is based on internally consi stent assumptions and estimates made in this rule. Second, as discussed above, net effects on employment in the economy as a whole depend heavily on the overall state of the economy when this rule has its effects. Focusing on the regulated sector provides insight into employme nt effects in that sector without having to make assumptions about the state of the economy whe n this rule has its impacts. We include a qualitative discussion of employment effects on o ther sectors to provide a broader perspective on the impacts of this 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 rule will change employment in specific sectors, but it will have small, if any, effect on aggregate employment. This rule would take effect in model years 2017 through 2025; by then, t he 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. Deleted:

, Deleted: and

Deleted: (who will face

Deleted: expenditures)MY 2017 and Later - Regulatory Impact Analysis

8-25 8.2.3.1 Employment Impacts in the Auto Industry

Following the Morgenstern et al. conceptual 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 cost effect. We not e that these estimates, based on extrapolations from current data, become more uncer tain as time goes on.

8.2.3.1.1 The Demand Effect

The demand effect depends on the effects of this ru le on vehicle sales. If vehicle sales increase, then more people will be required to asse mble vehicles and their components. If vehicle sales decrease, employment associated with 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 rul e will increase vehicle costs; by itself, this effect would reduce vehicle sales. On the other ha nd, this rule will reduce the fuel costs of operating the vehicle; by itself, this effect would increase vehicle sales, especially if potential buyers have an expectation of higher fuel prices. The sign of demand effect will depend on which of these effects dominates. Because, as descr ibed in Chapter 8.1, we have not quantified the impact on sales for this rule, we do not quantify the demand effect.

8.2.3.2 The Cost Effect

The demand effect, discussed above, measures employ ment changes due to new vehicle sales only. The cost effect measures emplo yment impacts due to the development, manufacturing, and installation by auto suppliers a nd manufacturers of the new or additional technologies needed for vehicles to comply with the standards.

One way to estimate the cost effect, given the cost estimates for complying with the rule, is to use the ratio of workers to each $1 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.Chapter 8

8-26

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

We use several sources for estimates of employment per $1 million expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment Requirements Matrix (ERM), 57 which provides direct estimates of the employment per $1 million in sales of goods in 202 sectors. The estimates used here, updated f rom the NPRM, are from 2010 (adjusted to 2010$). Not all expenditures are for domestically produced vehicles, however. To estimate the proportion of domestic expenditures affected by the rule, we use data from Ward’s Automotive Group for total car and truck production in the U.S. compared to total car and truck sales in the U.S. 58 For the period 2001-2010, the proportion is 66.7% . We thus weight sales by this factor to get an estimate of the effe ct on employment in the motor vehicle manufacturing sector due to this rule.

The Annual Survey of Manufactures 59 (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 of 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 2010 (also updated fro m the NPRM). As with the ERM, we adjust for the ratio of domestic production to dome stic sales. The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobil e and Light Duty Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehicle Part s 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 2010$), with the domestic pr oduction-to-sales adjustment. The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), and Moto r Vehicle Parts Manufacturing (NAICS 3363).

Table 8.2-2 provides the values, either given (BLS) or calcula ted (ASM, Economic Census) for employment per $1 million of expenditur es, all based on 2010 dollars, though the underlying data come from different years (which ma y account for some of the differences). The different data sources provide similar magnitud es for the estimates for the sectors. Parts manufacturing appears to be more labor-intensive th an vehicle manufacturing; light-duty vehicle manufacturing appears to be slightly less l abor-intensive than motor vehicle manufacturing as a whole. Deleted: Table 8.2-2MY 2017 and Later - Regulatory Impact Analysis

8-27 Table 8.2-2 Employment per $1 Million Expenditures (2010$) in the Motor Vehicle Manufacturing Sector*

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

million expenditures, adjusted for domestic vs. foreign production BLS ERM Motor Vehicle Mfg 0.770 0.514

ASM Motor Vehicle Mfg 0.655 0.437

ASM Light Duty Vehicle Mfg 0.609 0.406

Economic Census Motor Vehicle Mfg 0.665 0.443

Economic Census Light Duty Vehicle Mfg 0.602 0.402

BLS ERM Motor Vehicle Parts Mfg 2.614 1.743

ASM Motor Vehicle Parts Mfg 2.309 1.540

Economic Census Motor Vehicle Parts Mfg 2.712 1.809

BLS ERM refers to the U.S. Bureau of Labor Statisti cs’ Employment Requirement Matrix. ASM refers to t he U.S. Census Bureau’s Annual Survey of Manufactures. Economic Census refers to the U.S. Census Bureau’ s Economic Census.

Over time, the amount of labor needed in the auto i ndustry has changed: automation and improved production methods have led to signifi cant productivity increases. The BLS ERM, for instance, provided estimates that, in 1993 , 1.64 workers in the Motor Vehicle Manufacturing sector were needed per $1 million of 2005$, but only 0.86 workers by 2010 (in 2005$). Because the ERM is available annually for 1993-2010, we used these data to estimate productivity improvements over time. We r egressed logged ERM values on year for both the Motor Vehicle Manufacturing and Motor Vehi cle Parts Manufacturing sectors. We used this approach because the coefficient describi ng the relationship between time and productivity is a direct measure of the percent cha nge in productivity per year. The results suggest a 3.9 percent per year productivity improve ment in the Motor Vehicle Manufacturing Sector, and a 3.8 percent per year improvement in t he Motor Vehicle Parts Manufacturing Sector. We then used the equation resulting from the regression to project the ERM through 2025. In the results presented below, these projec ted values (adjusted to 2010$) were used directly for the BLS ERM estimates. For the ASM, w e used the ratio of the projected value in the future to the projected value in 2010 (the base year for the ASM); for the Economic Census estimates, we used the ratio of the projecte d value in the future to the projected value in 2007 (the base year for that estimate). This is a simple way to examine the relationship between labor required and expenditure. Deleted: (to estimate percent change per year)

Deleted: relationshipChapter 8

8-28

Table 8.2-3 shows the cost estimates developed for this rule, discussed in Chapter 5. The maximum value in Table 8.2-2 for employment impacts per $1 million expenditures (after accounting for the share of domestic product ion) is 1.809 in 2010 if all the additional costs are in the parts sector; the minimum value is 0.402 in 2010, if all the additional costs are in the light-duty vehicle manufacturing sector: th at is, the range of employment impacts is between 0.4 and 2 additional jobs per $1 million ex penditures in the sector in 2010. The results in Table 8.2-3 include the productivity adj ustment described above.

While we estimate employment impacts, measured in j ob-years, beginning with the first year of the standard (2017), some of these em ployment gains may occur earlier as auto manufacturers and parts suppliers hire staff in ant icipation of compliance with the standard. A job-year is a way to calculate the amount of work needed to complete a specific task. For example, a job-year is one year of work for one per son.

Table 8.2-3 Employment per $1 Million in the Motor Vehicle Manufacturing Sector, in job-years

Year Costs (before

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

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

employment effect (if all expenditures are in the light duty vehicle mfg sector) 2017 $ 2,435 700 3,200 2018 $ 4,848 1,300 6,200 2019 $ 6,818 1,700 8,400 2020 $ 8,858 2,100 10,500
2021 $ 12,400 2,900 14,200 2022 $ 18,323 4,100 20,200 2023 $ 23,734 5,100 25,200
2024 $ 29,101 6,000 29,700 2025 $ 31,678 6,300 31,100

We note that the cost effect depends only on techno logy costs, not vehicle sales. It is therefore not sensitive to assumptions about how co nsumers consider fuel savings at the time of vehicle purchase.

8.2.3.2.1 The Factor Shift Effect

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

We are not quantifying the factor shift effect here , for lack of data on the labor intensity of all the possible technologies that man ufacturers could use to comply with the standards. For a subset of the technologies, thoug h, EPA-sponsored research (discussed in Deleted: Table 8.2-2MY 2017 and Later - Regulatory Impact Analysis

8-29 Chapter 3.2.1.1 of the Joint TSD) which compared ne w technologies to existing ones at the level of individual components provides some insigh ts into the factor shift effect.

The comparison involved tearing down the selected t echnologies to their individual components and looking at the differences in materi als and labor needs in moving from the conventional to the new technologies. For instanc e, the analysis compared all the parts and labor associated with an 8-speed automatic transmis sion to those needed for a 6-speed automatic transmission.

Because labor cost was one of the sources of differ ences between the technologies, it is possible, for those technologies, to see whether labor needs increase or decrease with the switch to technologies that might contribute to com pliance with the standards. An increase in labor cost for the new technology indicates an incr ease in the labor needed for the new technology compared to the baseline technology. Fo r instance, an 8-speed transmission requires $15.11 more in labor costs than a 6-speed transmission (as accounted for in EPA’s cost estimates for the rule). Dividing the labor c ost by a wage per hour estimate provides an estimate of the additional hours (and thus the addi tional labor) needed for the new technology compared to the baseline technology. As with labor cost, an increase in labor hours per technology indicates greater employment needs for t he new technologies. For this conversion, a weighted average wage rate (90 percen t of the average wage in the Motor Vehicle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle Manufacturing Sector) of $46.36/hour in 2015, using 2008 dollars (the unit of analysis for the FEV study). For the change from a 6-speed to an 8- speed transmission, we thus estimate an additional 0.33 hours of labor per transmission.

Table 8.2-4 shows the changes in labor hours in mov ing from baseline to new fuelsaving technologies for technologies in the FEV stu dy. It indicates that, in switching from the baseline to the new technologies, labor use per tec hnology increased: the fuel-saving technologies use more labor than the baseline techn ologies. For a subset of the technologies likely to be used to meet the standards in this rul e, 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 scal e, etc. The technologies examined in this research are, however, only a subset of the technol ogies that auto makers may use to comply with the standards in this program. As a result, t hese results cannot be considered definitive evidence that the factor shift effect increases emp loyment for this rule. We therefore do not quantify the factor shift effect for this rule.

Table 8.2-4 Estimated Change in Labor for Selected Compliance Technologies

Technology FEV

Case Study Vehicle Class Labor Costs Total

Costs Hours/

Technology

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

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

Downsized 0104 SUV/Trucks $84.19 $789.53 1.82Chapter 8

8-30

Turbo GDI V6 Electric A/C

compressor 0602 $4.68 $167.54 0.10

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

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

8.2.3.2.2 Summary of Employment Effects in the Auto Sector

While we are not able to quantify the demand or fac tor shift effects, the cost effect results show that the employment effects of the inc reased spending in the regulated sector (and, possibly, the parts sector) are expected to b e positive and on the order of a few thousand in the initial years of the program. As noted abov e, motor vehicle and parts manufacturing sectors employed about 677,000 people in 2010, with automobile and light truck manufacturing accounting for about 129,000 of that total.

8.2.4 Effects on Employment for Auto Dealers

The effects of the standards on employment for auto dealers depend principally on the effects of the standards on light duty vehicle sale s: increases in sales are likely to contribute to employment at dealerships, while reductions in s ales are likely to have the opposite effect. 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, and reductions are likely to decrease labor demand.

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

8.2.5 Effects on Employment in the Auto Parts Sector

As discussed in the context of employment in the au to industry, some vehicle parts are made in-house by auto manufacturers; others are mad e by independent suppliers who are not directly regulated, but who will be affected by the 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 standards on vehicle sales and on the labor int ensity of the new technologies, qualitatively in the same ways as for the auto manufacturing sect or.MY 2017 and Later - Regulatory Impact Analysis

8-31 8.2.6 Effects on Employment for Fuel Suppliers

In addition to the effects on the auto 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 standards result in increased use of electricit y or other new fuels, employment effects will result from providing these fuels and developing th e infrastructure to supply them to

consumers.

Expected petroleum fuel consumption reductions can be found in RIA Chapter 5.4. While this reduced consumption represents fuel savi ngs for purchasers of fuel, it represents a loss in value of output for the petroleum refinery industry, fuel distributors, and gasoline stations. The loss of expenditures to petroleum fu el suppliers throughout the petroleum fuel supply chain, from the petroleum refiners to the ga soline stations, is likely to result in reduced employment in these sectors.

This rule is also expected to lead to increases in electricity consumption by vehicles, as discussed in RIA Chapter 5.4. This new fuel may require additional infrastructure, such as electricity charging locations. Providing this inf rastructure, as well as infrastructure for other alternative fuels (such as CNG), will require some increased employment. In addition, the generation of electricity is likely to require some additional labor. We have insufficient information at this time to predict whether the inc reases in labor associated with increased infrastructure provision and generation for electri city will be greater or less than the employment reductions associated with reduced deman d for petroleum fuels.

8.2.7 Effects on Employment due to Impacts on Consumer Ex penditures

As a result of these standards, consumers will pay a higher up-front cost for the vehicles, but they will recover those costs in a fa irly short payback period (see Preamble Section III.H.5 and Chapter 5.5 of this RIA); indee d, people who finance their vehicles are expected to find that their fuel savings per month exceed the increase in the loan cost (though this depends on the particular loan rate a consumer receives). As a result, consumers will have additional money to spend on other goods and s ervices, though, for those who do not finance their vehicles, it will occur after the ini tial payback period. These increased expenditures will support employment in those secto rs 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 would primarily represent a shift in employment among sectors. If, on the other hand, the economy still has substantial unemployment, these expenditures would contribute to employment through increased consumer demand.

8.2.8 Summary

The primary employment effects of this rule are exp ected to be found throughout several key sectors: auto manufacturers, auto deale rs, auto parts manufacturing, fuel production and supply, and consumers. These standar ds initially take effect in model yearChapter 8

8-32

2017, a time period sufficiently far in the future that the current sustained high unemployment at the national level may be moderated or ended. I n an economy with full employment, the primary employment effect of a rulemaking is likely to be to move employment from one sector to another, rather than to increase or decre ase employment. For that reason, we focus our partial quantitative analysis on employment in the regulated sector, to examine the impacts on that sector directly. We discuss the li kely direction of other impacts in the regulated sector as well as in other directly relat ed sectors, but we do not quantify those impacts, because they are more difficult to quantif y with reasonable accuracy, particularly so far into the future.

For the regulated sector, the cost effect is expect ed to increase employment by 700 – 3,200 job-years in 2017, depending on the share of that employment that is in the auto manufacturing sector compared to the auto parts man ufacturing sector. As mentioned above, some of these job gains may occur earlier as auto m anufacturers and parts suppliers hire staff to prepare to comply with the standard. The demand effect depends on changes in vehicle sales, which are not quantified for this rule. Tho ugh we do not have estimates of the factor shift effect for all potential compliance technolog ies, the evidence which we do have for some technologies suggests that many of the technologies will have increased labor needs.

Effects in other sectors that are predicated on veh icle sales are also ambiguous. Changes in vehicle sales are expected to affect lab or needs in auto dealerships and in parts manufacturing. Increased expenditures for auto par ts 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 compl iance technologies for which data are available show increased labor requirements. Reduc ed petroleum fuel production implies less employment in the petroleum sectors, although there could be increases in employment related to providing infrastructure for alternative fuels such as electricity and CNG. Finally, consumer spending is expected to affect employment through changes in expenditures in general retail sectors; net fuel savings by consume rs are expected to increase demand (and therefore employment) in other sectors.MY 2017 and Later - Regulatory Impact Analysis

8-33 References

1 Goldberg, Pinelopi Koujianou, “Product Differentia tion and Oligopoly in International Markets: The Case of the U.S. Automobile Industry, ” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799-0665); Goldberg, Pinelopi Koujianou, “The Effects of the Corporate Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics 46(1) (March 1998): 1-33 (Docket EPA-HQ-OAR-201 0-0799-0666).

2 Greene, David L., K.G. Duleep, Doug Elliott, and S anjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy In formation Administration,” prepared by the Oak Ridge National Laboratory for the U.S. D epartment of Energy under contract No. DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-079 9-0715); Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “F eebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799-0686).

3 McManus, Walter M., “Can Proactive Fuel Economy St rategies Help Automakers Mitigate Fuel-Price Risks?” University of Michigan Transpor tation Research Institute, September 14, 2006 (Docket EPA-HQ-OAR-2010-0799-0687).

4 Berry, Steven, James Levinsohn, and Ariel Pakes, “ Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10-0799- 0688); Berry, Steven, James Levinsohn, and Ariel Pa kes, “Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market,” Journal of Political Economy

112(1) (2004): 68-105 (Docket EPA-HQ-OAR-2010-07 99-0689).

5 Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799 -0690).

6 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799-0691).

7 Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR-2 010- 0799-0826).

8 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journ al of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799- 0692).

9 E.g., Bento, Antonio M., Lawrence H. Goulder, Emer ic Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-07 99-0690); Train,Chapter 8

8-34

Kenneth E., and Clifford Winston, “Vehicle Choice B ehavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799-0691).

10 E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10-0799- 0688).

11 Greene, David L., K.G. Duleep, Doug Elliott, and S anjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy In formation Administration,” prepared by the Oak Ridge National Laboratory for the U.S. D epartment of Energy under contract No. DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-079 9-0715).

12 NERA Economic Consulting, “Evaluation of NHTSA’s B enefit-Cost Analysis of 2011- 2015 CAFE Standards,” 2008, available at http://www.heartland.org/policybot/results/23495/Ev aluation_of_NHTSAs_BenefitCost_Anal ysis_Of_20112015_CAFE_Standards.html (Docket EPA-H Q-OAR-2010-0799-0693).

13 E.g., Train, Kenneth E., and Clifford Winston, “Ve hicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799-0691).

14 E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10-0799- 0688).

15 E.g., Bento, Antonio M., Lawrence H. Goulder, Emer ic Henry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-07 99-0690).

16 Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-079 9-0690); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driv en and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, av ailable at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5 /12/09 (Docket EPA-HQ-OAR-2010- 0799-0694).

17 NERA Economic Consulting, “Appendix B: New Vehicl e Market Model,” “Impacts of the California Greenhouse Gas Emission Standards on Mot or Vehicle Sales,” comments submitted to the U.S. Environmental Protection Agen cy at Regulations.gov, document number EPA-HQ-OAR-2006-0173-9053.1 (Docket EPA-HQ- OAR-2010-0799-0716).

18 E.g., Train, Kenneth E., and Clifford Winston, “Ve hicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799-0691)MY 2017 and Later - Regulatory Impact Analysis

8-35 19 E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10-0799- 0688).

20 Greene, David L., Philip D. Patterson, Margaret Si ngh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Incre ased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799-0686 ).

21 Rex, Emma, and Henrikke Baumann (2007). “Beyond e colabels: what green marketing can learn from conventional marketing.” Journal of Cleaner Production 15: 567-576 (Docket EPA-HQ-OAR-2010-0799-0695).

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

23 Berry, Steven, James Levinsohn, and Ariel Pakes, “ Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10-0799- 0688).

24 Goldberg, Pinelopi Koujianou, “Product Differentia tion and Oligopoly in International Markets: The Case of the U.S. Automobile Industry, ” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799-0665).

25 Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with Flexible Substitution Patterns,” Journal of Econome trics 89 (1999): 109-129 (Docket EPAHQ-OAR-2010-0799-0697); Brownstone, David, David S. Bunch, and Kenneth Train, “Joint Mixed Logit Models of Stated and Revealed Preferenc es for Alternative-Fuel Vehicles,” Transportation Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2010-0799- 0698); Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799-071 7); Greene, David L., K. G. Duleep, and Walter McManus, “Future Potential of Hy brid and Diesel Powertrains in the U.S. Light-Duty Vehicle Market,” prepared by the Oak Rid ge National Laboratory for the U.S. Department of Energy, August 2004 (Docket EPA-HQ-O AR-2010-0799-0718).

26 Petrin, Amil, “Quantifying the Benefits of New Pro ducts: The Case of the Minivan,” Journal of Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799

- 0699); Berry, Steven, James Levinsohn, and Ariel Pa kes, “Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market,” Journal of Political Economy 112 (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799-0 689).

27 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48 (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799-0691).Chapter 8

8-36 28 Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, Mark R. Jacobsen, and Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-079 9-0690); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driv en and Pollution Policies,” National Bureau of Economic Analysis Working Paper 11553, av ailable at http://econweb.tamu.edu/gan/w11553.pdf , accessed 5 /12/09 (Docket EPA-HQ-OAR-2010- 0799-0694).

29 Greene, David L., Philip D. Patterson, Margaret Si ngh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Incre ased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799-0686 ); 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-2010-0799-0694 ); Greene, David L., “Feebates, Footprints and Highway Safety,” Transportation Rese arch Part D 14 (2009): 375-384 (Docket EPA-HQ-OAR-2010-0799-0685).

30 E.g., Austin, David, and Terry Dinan, “Clearing th e Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes, ” Journal of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799- 0692).

31 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2010-079 9-0661).

32 E.g., Espey, Molly, and Santosh Nair, 2005. “Autom obile Fuel Economy: What Is It Worth?” Contemporary Economic Policy 23: 317-323 ( Docket EPA-HQ-OAR-2010-0799- 0700).

33 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 1593-1594 (Docket EPA-HQ-OAR-2010-0799-0662).

34 E.g., Goldberg, Pinelopi Koujianou, “Product Diffe rentiation and Oligopoly in International Markets: The Case of the U.S. Automobile Industry, ” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799-0665).

35 E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10-0799- 0688).

36 Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. “Pain at the Pump: How Gasoline Prices Affect Automobile Purchasing in New and Used Markets,” working paper, http://web.mit.edu/knittel/www/papers/gaspap er_latest.pdf (accessed 11/1/11) (Docket EPA-HQ-OAR-2010-0799-0667); Li, Shanjun, Christophe r Timmins, and Roger H. von Haefen, 2009. “How Do Gasoline Prices Affect Fleet Fuel Economy?” American Economic Journal: Economic Policy 1(2): 113-137 (Docket EPA-HQ-OAR-2010-0799-0701); Congressional Budget Office (2008) (Docket EPA-HQ-O AR-2010-0799); Congressional Budget Office (2008). Effects of Gasoline Prices o n Driving Behavior and Vehicle Markets

.MY 2017 and Later - Regulatory Impact Analysis

8-37 The Congress of the United States, Pub. No. 2883 (D ocket EPA-HQ-OAR-2010-0799-0719); West. Sarah (2007). “The Effect of Gasoline Prices on the Demand for Sport Utility Vehicles.” Working Paper. Macalester College, http://www.macalester.edu/~wests/SarahWestMEA2007.p df, accessed 1/25/10 (Docket EPAHQ-OAR-2010-0799-0702).

37 Greene, David L., and Jin-Tan Liu, 1988. “Automot ive Fuel Economy Improvements and Consumers’ Surplus,” Transportation Research Part A 22A (3): 203-218 (Docket EPA-HQOAR-2010-0799-0703).

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

39 Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S. Automobile Industry,” http://faculty.msb.edu/jpg72/ Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket EPA-HQ-OAR-2010-0799-0711).

40 Gramlich, Jacob, “Gas Prices and Endogenous Produc t Selection in the U.S. Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_Graml ichJacob.pdf , accessed 11/1/11 (Docket EPA-HQ-OAR-2010-0799-0704); McManus, Walter, 2007. “The Impact of Attribute-Based Corporate Average Fuel Economy (CAFE) Standards: P reliminary Results.” Ann Arbor, MI: University of Michigan Transportation Research Inst itute, Report No. UMTRI-2007-31 (Docket EPA-HQ-OAR-2010-0799-0705).

41 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-OAR2010-0799-0672); 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- (Docket EPA-HQ-OAR-2010- 0799-0692); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of the Corporate Average Fuel Economy Standard ,” Resources for the Future Paper RFF DP 10-50 (December 2010), http://www.rff.org/RFF/Do cuments/RFF-DP-10-50.pdf) (Docket EPA-HQ-OAR-2010-0799-0706); Jacobsen, Mark, 2010. “Evaluating U.S. Fuel Economy Standards in a Model with Producer and Household He terogeneity,” working paper, at http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf (a ccessed 11/1/11) (Docket EPA-HQOAR-2010-0799-0829) .

42 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journ al of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799-0 692).

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

. ” Paris, France: International Energy Agency (Docket EPA-HQ-OAR-2010-0799-0720); Jaffe, Adam B., Richard G. Newell, and Robert N. Stavins ( 2001). "Energy Efficient Technologies and Climate Change Policies: Issues and Evidence." In Climate Change Economics and Policy , Toman, Michael A., ed., Washington, D.C.: Resourc es for the Future. p.171-181 (Docket EPA-HQ-OAR-2010-0799-0828); Metcalf, Gilber t E., and Kevin A. Hassett (1999).Chapter 8

8-38

"Measuring the Energy Savings From Home Improvement Investments: Evidence From Monthly Billing Data." The Review of Economics and Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010-0799-0659); Tietenberg, T. (2009). “Reflections – Energy Efficiency Policy: Pipe Dream or Pipeline to the Future?” Rev iew of Environmental Economics and Policy 3(2): 304-320 (Docket EPA-HQ-OAR-2010-0799-0707).

44 Helfand, Gloria, and Ann Wolverton (2011). “Evalu ating the Consumer Response to Fuel Economy: A Review of the Literature.” Internation al Review of Environmental and Resource Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799-06 52).

45 Greene David L., “TAFV Alternative Fuels and Vehic les Choice Model Documentation,” prepared by the Oak Ridge National Laboratory for t he U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799-0717).

46 U.S. Environmental Protection Agency. “Peer Revie w for the Consumer Vehicle Choice Model and Documentation.” Office of Transportation and Air Quality, Assessment and Standards Division, EPA-420-R-12-013, April 2012 (D ocket EPA-HQ-OAR-2010-0799).

47 See, for instance, Ladika, Susan (2009). “’Green’ auto loans offer lower rates,” Bankrate.com, http://www.bankrate.com/finance/auto/ green-auto-loans-offer-lower-rates1.aspx , accessed 2/28/12 (Docket EPA-HQ-OAR-2010-0 799).

48 Helfand, Gloria (2012). “Memorandum: Lending ins titutions that provide discounts for more fuel-efficient vehicles.” Assessment and Stan dards Division, Office of Transportation and Air Quality, U.S. Environmental Protection Agen cy, Docket EPA-HQ-OAR-0799 (Docket EPA-HQ-OAR-2010-0799).

49 U.S. Department of Labor, Bureau of Labor Statisti cs, Consumer Expenditure Survey 2010. http://www.bls.gov/cex/#tables. This analysis uses the table Quintiles of Income before Taxes, accessed 6/14/12 (Docket EPA-HQ-OAR-2010-079 9).

50 Federal Reserve System, Survey of Consumer Finance s 2007. http://www.federalreserve.gov/econresdata/scf/scfin dex.htm. We applied the weighting scheme for the data, to make the data representativ e of the population as a whole, provided in the documentation for the results presented here (D ocket EPA-HQ-OAR-2010-0799).

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

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

53 Schmalensee, Richard, and Robert N. Stavins. “A G uide to Economic and Policy Analysis of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011 (Docket EPA-HQ-OAR-2010-0799-0676).MY 2017 and Later - Regulatory Impact Analysis

8-39 54 Morgenstern, Richard D., William A. Pizer, and Jhi h-Shyang Shih. “Jobs Versus the Environment: An Industry-Level Perspective.” Jour nal of Environmental Economics and Management 43 (2002): 412-436 (Docket EPA-HQ-OAR-2010-0799-0 677).

55 Berck, Peter, and Sandra Hoffmann. “Assessing the Employment Impacts of Environmental and Natural Resource Policy.” Enviro nmental and Resource Economics 22 (2002): 133-156 (Docket EPA-HQ-OAR-2010-0799-0678)

.

56 Berman, Eli, and Linda T. Bui, (2001) “Environment al Regulation and Labor Demand: Evidence from the South Coast Air Basin,” Journal of Public Economics , 79, 265 – 295 (Docket EPA-HQ-OAR-2010-0799-0679)

.

57 http://www.bls.gov/emp/ep_data_emp_requirements.ht m; see the “Cost Effect Employment Impacts Final Rulemaking” spreadsheet, Docket EPA-H Q-OAR-2010-0799.

58 U.S. Vehicle Production by Manufacturer, UsaPr05.x ls, and U.S. Vehicle Sales, UsaSa01.xls, accessed July 25, 2011 at http://subs cribers.wardsauto.com/refcenter/ , ©Copyright 2011, Ward's Automotive Group, a divisio n of Penton Media Inc. Redistribution prohibited. Docket EPA-HQ-OAR-2010-0799.

59 http://www.census.gov/manufacturing/asm/index.html ; see the “Cost Effect Employment Impacts Final Rulemaking” spreadsheet, Docket EPA-H Q-OAR-2010-0799.