NERA Economic Consulting 

Evaluation of NHTSA’s 

Benefit-Cost Analysis of 

2011-2015 CAFE Standards 

Alliance of Automobile Manufacturers 
NERA Economic Consulting i 

Project Team 

David Harrison, Jr., Ph.D. 

Albert L. Nichols, Ph.D. 

James Johndrow 

Mark LeBel 

Bernard Reddy, Ph.D. 

NERA Economic Consulting 

 

www.nera.com
NERA Economic Consulting ii 

Contents 

List of Figures............................................................................................................................. v 

List of Tables.............................................................................................................................vi 

Acknowledgements..................................................................................................................viii 

Executive Summary................................................................................................................ S-1 

A. Overview of the NHTSA Benefit-Cost Analyses......................................................... S-1 

1. Volpe Model Used by NHTSA to Assess Costs and Benefits................................. S-1 

a. NHTSA’s Determination of “Optimized” CAFE Standards.............................. S-1 

2. NHTSA’s Proposed CAFE Standards.................................................................... S-2 

B. Detailed Evaluations of the NHTSA Benefit-Cost Analyses ........................................ S-3 

1. Summary of NHTSA and NERA Estimates of Private and External Benefits 

(Costs)................................................................................................................... S-3 

2. NHTSA Benefit-Cost Estimates............................................................................. S-3 

3. Alternative Estimates of Private Benefits and Costs............................................... S-5 

a. Private Benefits ............................................................................................... S-5 

b. Sierra Estimates of Private Costs...................................................................... S-6 

c. Implications for New-Vehicle Sales and Employment...................................... S-6 

4. NERA Estimates of External Benefits and Costs.................................................... S-7 

a. Revising the Rebound Effect to Reflect the Literature...................................... S-7 

b. Limiting CO2 Damages to Those in the United States ...................................... S-8 

c. Using More Realistic Upstream Emission Factors for Conventional Pollutants S-9 

d. Substituting OMB Estimates of the Value per Ton of Conventional Pollutants. S-9 

e. Revised Congestion Costs to Reflect Consistent Valuation of Savings in 

Vehicle Hours.................................................................................................. S-9 

5. Summary of Alternative Estimates of Social Net Benefits (Costs).......................... S-9 

6. Sensitivity Analysis of Price of Gasoline ............................................................... S-9 

C. Conclusions................................................................................................................S-10 

I. Introduction and Overview ......................................................................................................1 

A. Background.....................................................................................................................1 

B. NHTSA’s Proposed CAFE Standards..............................................................................2 

C. Overview of Report.........................................................................................................3 

II. Overview of the NHTSA Draft Benefit-Cost Analysis............................................................4 

A. Overview of NHTSA Benefit-Cost Methodology ............................................................4 

1. Baseline New Vehicle Fuel Economy ........................................................................4 

2. Costs of Increased New Vehicle Fuel Economy.........................................................4 

3. Benefits of Increased New Vehicle Fuel Economy.....................................................4 

B. The Volpe Model ............................................................................................................5 

C. NHTSA “Optimized” Proposed CAFE Standards............................................................6 
NERA Economic Consulting iii 

1. Alternative CAFE Standards Considered by NHTSA.................................................6 

2. NHTSA Benefit and Cost Estimates of Alternative CAFE Standards.........................7 

3. Benefits and Costs of NHTSA “Optimized” CAFE Standards....................................8 

D. Distribution of NHTSA Benefits and Costs across Categories .........................................9 

1. Private versus External Benefits ................................................................................9 

a. NHTSA Estimates Broken Down Into Private and Social Benefits/Costs........... 10 

2. NHTSA Private Benefit and Cost Estimates............................................................. 12 

a. Components of NHTSA Private Benefits and Costs Estimates........................... 12 

b. Implications of NHTSA Benefit and Cost Estimates.......................................... 12 

c. Implications of NHTSA Private Benefit and Cost Estimates .............................. 12 

3. NHTSA External Benefit and Cost Estimates .......................................................... 13 

a. Components of NHTSA External Benefits and Costs......................................... 13 

b. Implications of NHTSA External Benefits and Costs Estimates......................... 14 

E. Summary....................................................................................................................... 14 

III. Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs........................... 16 

A. Modeling and Projections.............................................................................................. 16 

1. Consumer Demand for New Motor Vehicles ........................................................... 16 

a. NHTSA Uses Two (Inconsistent) Methodologies for Valuing Improved Fuel 

Economy ........................................................................................................... 17 

b. Limitations of NHTSA’s Characterization of the New Vehicle Market .............. 18 

c. NERA New Vehicle Market Model ................................................................... 19 

2. Implications of New Vehicle Market Changes for Changes in the Overall Motor 

Vehicle Fleet ........................................................................................................... 19 

a. General Effects of Changes in New Vehicle Prices on the Overall Fleet............. 20 

b. NERA Modeling of Fleet Effects....................................................................... 20 

3. Rebound Effect........................................................................................................ 21 

B. Private Net Benefits and Market Failure ........................................................................ 22 

1. New Vehicle Markets and Absence of NHTSA Evidence of Market Failure ............ 22 

2. NERA Estimates of Private Benefits to New Vehicle Purchasers............................. 25 

3. Estimated Changes in New Vehicle Sales and Employment Based on NHTSA and 

NERA Estimates of Private Benefits and NHTSA Estimates of Private Costs.......... 26 

4. Sierra’s Estimates of Compliance Costs for New Vehicle Purchasers....................... 28 

5. Estimated Changes in New Vehicle Sales and Employment Based on NHTSA and 

NERA Estimates of Private Benefits and NHTSA and Sierra Estimates of Private 

Costs....................................................................................................................... 29 

C. External Benefits and Costs........................................................................................... 30 

1. Fuel Tax Revenues.................................................................................................. 30 

2. CO2 Emissions......................................................................................................... 31 

a. Global CO2 Damages per Tonne ........................................................................ 31 

b. Value for U.S. Damages per Tonne.................................................................... 31 

3. Conventional Air Pollutants..................................................................................... 32 

a. Stationary Source Emission Factors................................................................... 32 

b. Values per Ton of Conventional Pollutant.......................................................... 33 

c. Implications of Fleet Population Effects............................................................. 34 
NERA Economic Consulting iv 

4. Other VMT-related Externalities.............................................................................. 35 

5. Summary of NHTSA and NERA Estimates of External Costs and Benefits............. 36 

D. Alternative Estimates of Benefits and Costs of NHTSA Proposed CAFE Standards ...... 37 

E. Sensitivity Analyses of Gasoline Prices......................................................................... 40 

1. Qualitative Effects of Alternative Fuel Price Forecasts ............................................ 40 

2. Quantitative Effects of Alternative Gasoline Price Forecasts.................................... 40 

3. Implications of Sensitivity Analysis......................................................................... 42 

IV. Concluding Remarks........................................................................................................... 43 

References................................................................................................................................ 45 

Appendix A. New Vehicle Market Model ...............................................................................A-1 

A. Conceptual Approach: Nested Logit Model.................................................................A-1 

1. Basic Framework...................................................................................................A-1 

2. Nesting Assumptions.............................................................................................A-2 

B. Solving for Parameters................................................................................................A-4 

C. Estimating Consumer Valuation of Vehicle Attributes.................................................A-5 

D. Specific Implementation Parameters and Data .............................................................A-7 

1. Vehicle Sales.........................................................................................................A-7 

2. Vehicle Prices........................................................................................................A-8 

3. Vehicle Fuel Economy Data and Other Vehicle Characteristics.............................A-8 

4. Categorization of Vehicle Models into Nests.........................................................A-8 

5. Price Elasticity.......................................................................................................A-8 

Appendix B. Review of the Small and Van Dender Model for Estimating Rebound Effects....B-1 

A. Overview of Small and Van Dender Study ..................................................................B-1 

1. Small and Van Dender Study Variables and Data Set.............................................B-2 

2. Small and Van Dender’s Estimation Procedures....................................................B-2 

B. Modifications to Small and Van Dender Data and Estimation Procedure .....................B-3 

1. Income and Gasoline Prices...................................................................................B-4 

2. Trend Variable.......................................................................................................B-4 

3. Dummy Variables for 1974 and 1979 ....................................................................B-5 

4. Estimation .............................................................................................................B-5 

C. Estimated Rebound Effect ...........................................................................................B-5 
NERA Economic Consulting v 

List of Figures 

Figure S-1. Alternative Estimates of Net Social Benefits (Costs) for MY2015, Combined (Cars 

and Light Trucks)...................................................................................................... S-10 

Figure 1. Alternative Estimates of Net Social Benefits (Costs) for MY2015, Combined (Cars and 

Light Trucks) ................................................................................................................39 

Figure A-1. Nesting structure for NVMM ...............................................................................A-3 
NERA Economic Consulting vi 

List of Tables 

Table S-1. NHTSA Estimates of Total and Incremental Benefits and Costs of Alternative 

Standards for MY2015 (Cars and Light Trucks combined) .......................................... S-2 

Table S-2. Components of the Benefits and Costs of Proposed CAFE Standards for Cars and 

Light Trucks for MY2015 ........................................................................................... S-4 

Table S-3. Estimated Private Net Benefits per MY2015 Vehicle Based on Varying Combinations 

of Estimated Benefits and Costs.................................................................................. S-7 

Table 1. Summary of Average Fuel Economy Required by CAFE Standards Proposed by 

NHTSA...........................................................................................................................2 

Table 2. NHTSA’s Estimates of Benefits, Costs, and Net Benefits of Alternatives Standards for 

MY2015..........................................................................................................................8 

Table 3. NHTSA's Estimates of Benefits and Costs of "Optimized" CAFE Standards by Model 

Year ................................................................................................................................9 

Table 4. Components of NHTSA's Estimates of the Benefits of Proposed CAFE Standards for 

MY2015........................................................................................................................11 

Table 5. Summary of NHTSA’s Estimates of Private and External Costs and Benefits, Combined 

Cars and Light Trucks...................................................................................................15 

Table 6. Comparison of NHTSA and NERA Estimates of Private Benefits and Net Benefits per 

Vehicle for MY2015 .....................................................................................................26 

Table 7. Comparison of per-Vehicle Private Costs and Benefits for Cars and Light Trucks, 

Combined, All Model Years..........................................................................................27 

Table 8. Comparison of NHTSA and NERA Estimates of Impacts of "Optimized" Standards on 

Vehicle Sales and Employment .....................................................................................28 

Table 9. Comparison of NHTSA and Sierra's Estimated Costs per Vehicle of Meeting the 

MY2015 "optimized" standard ......................................................................................29 

Table 10. Estimated Private Net Benefits per MY2015 Vehicle Based on Varying Combinations 

of Estimated Benefits and Costs....................................................................................30 

Table 11. Comparison of 2002 Refinery Emissions Inventory with Emissions Based on NHTSA 

Emission Factors...........................................................................................................33 

Table 12. Estimated Values per Ton of Conventional Pollutant .................................................34 

Table 13. Impacts of NERA Adjustments to NHTSA External Benefits (Costs), MY2015 ........36 

Table 14. Summary of Adjustments to External Benefits (Costs), All Model Years...................37 

Table 15. Comparison of Alternative Benefit-Cost Estimates for All Model Years, Combined 

(Cars and Light Trucks).................................................................................................38 

Table 16. Impacts of Alternative 2015 Gasoline Prices on Estimated Net Benefits Using NERA 

Estimates of Private and External Benefits (Costs) and NHTSA Estimates of Private 

Compliance Costs..........................................................................................................41 

Table 17. Impacts of Alternative 2015 Gasoline Prices on Estimated Net Benefits Using NERA 

Estimates of Private and External Benefits (Costs) and Sierra Estimates of Private 

Compliance Costs..........................................................................................................42 

Table A-1. Estimation results for alternative-specific parameter model. ..................................A-7 

Table B-1. Variables Used in the Small and Van Dender Study...............................................B-3 

Table B-2. Estimation of the VMT Equation...........................................................................B-6 
NERA Economic Consulting vii 
NERA Economic Consulting viii 

Acknowledgements 

This study was prepared by NERA Economic Consulting, an international firm of more than 500 

consulting economists with 22 global offices that has extensive experience in the evaluation of 

energy and environmental policies and programs. The authors of the report are David Harrison, 

Albert Nichols, James Johndrow, Mark LeBel and Bernard Reddy. 

The report was prepared for the Alliance of Automobile Manufacturers. The views expressed in 

this report, however, are solely those of the authors and are not necessarily those of the Alliance 

of Automobile Manufacturers or any of its members. Responsibility for the report, and for any 

errors or omissions it may contain, rests solely with the authors.
NERA Economic Consulting S-1 

Executive Summary 

This report evaluates the benefit-cost analysis developed by the National Highway Traffic Safety 

Administration (“NHTSA”) in its Preliminary Regulatory Impact Analysis (“PRIA”) relating to 

proposed new Corporate Average Fuel Economy (“CAFE”) standards for passenger cars and 

light trucks under the Energy Independence and Security Act (“EISA”). EISA establishes CAFE 

requirements for model year 2020, leaving it to NHTSA to establish standards for intermediate 

years. The proposed standards evaluated in the PRIA cover new vehicles produced in model 

years (“MY”) 2011 to 2015, with separate car and truck limits set for each manufacturer in each 

model year. 

A. Overview of the NHTSA Benefit-Cost Analyses 

1. Volpe Model Used by NHTSA to Assess Costs and Benefits 

NHTSA developed its proposed CAFE standards using a model (referred to as the “Volpe 

Model”) that integrates information on the costs and benefits of increasingly stringent CAFE 

standards for each model year. NHTSA defines a model-by-model technology baseline based in 

part on product plans provided by vehicle manufacturers, assuming no increase in CAFE 

requirements, as well as technological options for increasing fuel economy, measured in miles 

per gallon (“MPG”). NHTSA uses this information to predict the order in which manufacturers 

would adopt additional technologies (based on cost-effectiveness, including technology learning 

effects) to meet increasingly stringent CAFE standards. This information allows NHTSA to 

develop estimates of the costs of the “least cost” technology options to meet increasingly 

stringent CAFE standards in each model year. 

On the benefits side, the Volpe Model quantifies benefits in a dozen or more categories, the 

largest of which is savings in fuel expenditures by vehicle owners as a result of reduced 

purchases of gasoline. These reductions in fuel consumption also are associated with reduced 

carbon dioxide (CO2) emissions and reduced emissions of conventional pollutants from fuel 

production and distribution. 

Higher CAFE standards tend to increase vehicle miles traveled (“VMT”) because improved 

MPG lowers the effective price per VMT. This “rebound effect” results in additional external 

effects associated with the additional VMT. The Volpe Model includes an estimate of the 

rebound effect and its effects on fuel use, CO2 emissions and the various negative externalities 

driven by VMT rather than fuel use (tailpipe emissions of conventional pollutants, accidents, 

congestion, and noise). 

a. NHTSA’s Determination of “Optimized” CAFE Standards 

NHTSA uses the Volpe Model to determine the “optimized” level of CAFE standards for cars 

and light trucks in each of the five model years. The “optimized” level is the one that maximizes 

the present value of net social benefits—i.e., benefits minus costs—as estimated by NHTSA 

(with future costs and benefits discounted at 7 percent per year). For each model year, separately 
Executive Summary 

NERA Economic Consulting S-2 

for cars and light trucks, NHTSA calculated social costs and benefits for six alternative CAFE 

levels, with one alternative less stringent than the “optimized” level and four alternatives that are 

more stringent than the “optimized” level.1

Table S-1 illustrates NHTSA’s calculations for finding the “optimized” standard, showing results 

for the combined (car and light truck) MY2015 alternative standards. The “optimized” standard 

has the highest social net benefits as estimated by NHTSA. The final three rows show 

incremental benefits, costs and net benefits. Thus, for example, the incremental benefits for the 

“optimized” standard are that standard’s benefits minus those of the next less stringent 

alternative. Net benefits rise when incremental benefits are greater than incremental costs, but 

begin to decline when incremental costs exceed incremental benefits.

2

2. NHTSA’s Proposed CAFE Standards 

The set of “optimized” standards proposed by NHTSA would tighten substantially the CAFE 

standards for new vehicles over the MY2011-MY2015 period. Under its “optimized” standards, 

NHTSA estimates that the combined industry-wide average fuel economy would increase from 

about 25.3 MPG in MY2010 to 31.6 MPG in MY2015. Under EISA, the average must be no less 

 

1 NHTSA’s inclusion of a range of possible standards—rather than evaluating the benefits and costs of a single set 

of standards—is commendable, although it would be appropriate to take a more balanced approach by including 

as many sets that are less stringent than are more stringent than the “optimized” standards. 

2 NHTSA’s general methodology for determining the “optimum” standard —to consider alternative levels of 

stringency and choose the alternative that maximizes net benefits—is sound as a general principle. This 

presumes, however, there is a “market failure” for which government action is justified and that the underlying 

benefit and cost information is accurate and complete. As discussed below, our results call into question both of 

these features of NHTSA’s assessment. 

Table S-1. NHTSA Estimates of Total and Incremental Benefits and Costs of Alternative Standards for 

MY2015 (Cars and Light Trucks combined) 

25% below

Optimized 

(7%) 25% above 50% above TC=TB

Technology 

Exhaustion

Total

Benefits $19,240 $25,612 $31,540 $35,387 $38,859 $42,820 

Costs $9,535 $13,623 $23,865 $30,950 $38,762 $44,589 

Net benefits $9,705 $11,989 $7,675 $4,437 $97 ($1,769)

Incremental

Benefits $19,240 $6,372 $5,928 $3,847 $3,472 $3,961 

Costs $9,535 $4,088 $10,242 $7,085 $7,812 $5,827 

Net benefits $9,705 $2,284 ($4,314) ($3,238) ($4,340) ($1,866)

Alternative Standards

Note: Entries are present values (discounted at 7 percent real to 2015) expressed in millions of 2006 dollars. 

Source: NHTSA PRIA. 
Executive Summary 

NERA Economic Consulting S-3 

than 35 MPG in MY2020. Thus, the proposed NHTSA MY2011-MY2015 standards would mean 

that the path to achieving the EISA 2020 requirement of 35 MPG would be “front loaded,” with 

the MY2015 proposed standards achieving about three-quarters3of the improvement required 

from MY2010 to MY2020. 

B. Detailed Evaluations of the NHTSA Benefit-Cost Analyses 

NERA has developed alternative estimates of the private benefits and external benefits and costs 

of the “optimized” CAFE standards, to the extent feasible given the information provided by 

NHTSA. In addition, we obtained alternative estimates of private compliance costs from Sierra 

Research Inc. (“Sierra”) that allow us to assess the sensitivity of the results to the estimated 

private costs as well. 

NHTSA did not provide breakdowns of its benefit estimates by category for alternatives other 

than the “optimized” CAFE standard in each model year. Thus, we cannot estimate the impacts 

of changes in individual benefit categories on the overall benefits or net benefits of alternative 

CAFE standards. 

1. Summary of NHTSA and NERA Estimates of Private and External 

Benefits (Costs) 

Table S-2 compares the NHTSA and NERA estimates of the benefits and costs of the MY2015 

“optimized” CAFE standards for cars and trucks combined. We have divided the estimates into 

two broad categories: (1) private benefits and costs that would accrue to owners of individual 

vehicles with higher MPG as a result of the tightened CAFE standards; and (2) external benefits 

and costs that would accrue to members of society as a whole. This distinction is important 

because regulations typically are justified by external costs, on the presumption that in making 

decisions individuals appropriately take into account their own private costs and benefits. As 

noted above and discussed in more detail below, we did not have sufficient information to 

evaluate certain elements of the benefits analysis. 

2. NHTSA Benefit-Cost Estimates 

Before considering the alternative NERA estimates, it is useful to reflect on the NHTSA results. 

Although NHTSA emphasizes that its proposed CAFE standards would lead to reductions in 

CO2 emissions and improve energy security, even NHTSA’s own assessments show that on 

balance the external effects of the CAFE standards would be negative. That is, even NHTSA 

estimates that its “optimized” CAFE standards would lead to greater negative than positive 

external effects. As noted in Table S-2, NHTSA results show that the proposed CAFE standards 

for MY2015 would result in net external costs of about $1.4 billion. This result is in dramatic 

contrast to the usual justification for government regulations—to reduce external effects (i.e., 

regulations would yield net external benefits). 

 

3 This calculation is based on the changes in fuel consumption per mile (the inverse of MPG). 
Executive Summary 

NERA Economic Consulting S-4 

NHTSA’s benefit-cost assessment shows large net benefits for CAFE standards only because 

NHTSA estimates that private benefits to new vehicle purchasers would be roughly double the 

private costs they would incur—that is, for every extra dollar that car buyers spent on vehicles 

Table S-2. Components of the Benefits and Costs of Proposed CAFE Standards for Cars and Light Trucks 

for MY2015 

 

Item

NHTSA 

Combined

NERA 

Estimate

Private Benefits (costs)

Fuel expenditures (exclusive of taxes) $20,204

Reduced fuel tax payments $3,658

Consumer value of additional VMT $2,484

Time savings from greater range $1,077

Increased cost of new vehicles ($13,623) ($13,623) 1

CAFE Fines ($460) ($460)

 Subtotal, private benefits $13,340 ($2,637)

External benefits (costs)

Reduced fuel tax revenues ($3,658) ($3,476) 2

CAFE Fines $460 $460

Reduced CO2 emissions $869 $300 3

Reduced NOx emissions $89 $5 4

Reduced PM emissions $419 $164 4

Reduced SO2 emissions $244 $143 4

Reduced VOC emissions $9 $3 4

Accidents ($885) ($1,180) 5

Congestion ($1,802) ($4,036) 6

Noise ($26) ($34) 5

Petroleum market externalities $2,931 $2,785 5

 Subtotal, external benefits ($1,351) ($4,866)

Total net benefits $11,989 ($7,503)

$11,446

Note: Entries are present values (discounted at 7 percent real to 2015) expressed in millions of 2006 dollars. 

1 NERA did not assess the accuracy of the NHTSA cost estimate. Alternative cost estimates developed by 

Sierra are presented below. 

2 Smaller decrease in fuel tax revenues is due to larger rebound effect. 

3 Change reflects larger rebound effect and revised estimated value per ton based upon revised estimates 

of the value to U.S. rather than the rest of the world. 

4 Change reflects larger rebound effect, corrected upstream emission factors for NOx, PM, and SO2, use of 

OMB values per ton for all four pollutants valuation of the pollutants. 

5 Change reflects larger rebound effect. 

6 Change reflects larger rebound effect and revised value of vehicle time to be consistent with NHTSA 

estimate of value of reduced time spent refueling vehicles. 

Source: NHTSA PRIA and NERA calculations. 
Executive Summary 

NERA Economic Consulting S-5 

that would meet NHTSA’s “optimized” CAFE standard, they would reap, on average, about $2 

in benefits—although NHTSA’s baseline assumes that market forces would not lead to that level 

of fuel economy. Using a largely ad hoc assessment of the gains to motor-vehicle buyers from 

fuel economy improvements, NHTSA estimates that the CAFE standards would lower the 

effective price of new vehicles and lead to increased new vehicle sales. For example, they 

estimate an additional 79,000 sales of cars and light trucks in model year 2013. (We note that 

NHTSA acknowledges the inadequacy of its modeling of new vehicle markets in the PRIA.) 

NHTSA’s prediction that its proposed “optimized” CAFE standards would lead to increased new 

vehicle sales does not seem to make economic sense. If imposing the CAFE standard would 

increase new vehicle sales while covering manufacturers’ additional costs (including a markup), 

as NHTSA assumes, NHTSA has provided no economic reason why manufacturers would not 

make the changes on their own, without new CAFE standards. If manufacturers did make such 

changes, NHTSA’s baseline would be wrong. NHTSA does not provide a coherent explanation 

for why the new vehicle market would perform so poorly absent the CAFE standards, or provide 

any evidence that “market failures” are present to justify its proposed CAFE standards. As 

discussed below, more plausible explanations are that NHTSA’s estimates overstate the private 

benefits reaped by new-vehicle buyers, understate the private costs they would pay, or are some 

combination of the two. 

3. Alternative Estimates of Private Benefits and Costs 

a. Private Benefits 

Our analysis provides an explanation for the large gap between NHTSA’s estimate of private net 

benefits and vehicle owners’ own assessments implicit in their decision not to voluntarily choose 

vehicles that meet NHTSA’s “optimized” MPG levels. We conclude that NHTSA has 

substantially overestimated the private benefits reaped by vehicle owners from the “optimized” 

CAFE standards. NERA has developed a detailed market model that uses historical data at the 

level of individual vehicle models (e.g., Chevrolet Malibu, Toyota Corolla) to estimate the 

tradeoffs that new-vehicle purchasers make between vehicle price and various vehicle attributes, 

including the estimated cost of gasoline per mile (which is the price of gasoline divided by a 

vehicle’s average MPG).4 This model thus estimates the value consumers place on fuel economy 

based on their actual behavior. Based on that empirically calibrated model, we estimated the 

private benefits that vehicle buyers would reap from higher CAFE standards (using NHTSA’s 

forecast of the 2015 gasoline price) based on how much more they would be willing to pay for 

vehicles with greater fuel economy. These estimated private benefits are substantially less than 

NHTSA’s estimates of the private costs. 

As shown in Table S-2, our estimated private benefits for the MY2015 “optimized” standard 

($11.4 billion) are about $2.6 billion dollars lower than NHTSA’s estimates of the private costs 

($14.1 billion, equal to the increased cost of new vehicles and the CAFE fines) . Note that our 

 

4 Appendix A includes additional information on our model of our model of the new-vehicle market. 
Executive Summary 

NERA Economic Consulting S-6 

estimate of private benefits implicitly takes into account all of NHTSA’s categories of private 

benefits. 

In contrast to NHTSA’s estimates, ours imply that the “optimized” CAFE standards would 

increase “quality adjusted” new vehicle prices (i.e., the improvements in MPG would not be 

valued highly enough to overcome the large increases in new vehicle prices). Higher qualityadjusted prices would result in reduced sales of new vehicles. 

b. Sierra Estimates of Private Costs 

Another explanation for NHTSA’s conclusion that CAFE standards would lead to net private 

benefits for vehicle purchasers is that NHTSA has underestimated the increase in new vehicle 

prices needed to recover the costs of meeting the standard. As noted earlier, Sierra has developed 

alternative estimates of the costs of individual fuel economy improvements and the implications 

for alternative CAFE standards. Sierra’s estimates of compliance costs per vehicle imply that the 

cost of complying with NHTSA’s “optimized” CAFE standards for MY2015 would be about $35 

billion, more than double NHTSA’s estimate. 

c. Implications for New-Vehicle Sales and Employment 

Because NHTSA estimates that new-vehicle buyers would value improved fuel economy enough 

to more than offset the higher cost of new vehicles, it predicts that its proposed CAFE standards 

would increase sales of new vehicles and hence employment by auto manufacturers and their 

suppliers. Using NERA’s estimates of private benefits or Sierra’s estimates of private costs, 

however, buyers would find the private benefit-cost balance unfavorable, and hence would see 

an increase in quality adjusted prices, which would reduce motor-vehicle sales and reduce motorvehicle employment. 

We evaluated the impacts on vehicle sales and employment of four different combinations of 

alternative estimates of private benefits (NHTSA vs. NERA) and private costs (NHTSA vs. 

Sierra), with the results shown in Table S-3 for MY2015.5 The top half of the table shows 

estimated private costs and benefits on a per-vehicle basis and the bottom half shows estimated 

impacts on vehicle sales and employment. 

The average private net benefits per vehicle are positive if NHTSA’s estimates are used for both 

private costs and private benefits, resulting in NHTSA’s estimate that vehicle sales for that 

model year would increase by 86,000 units and that employment would rise by about 8,000 jobs. 

For the three other cases, the estimated private net benefits per vehicle are negative. Using 

NHTSA’s assumption about the price elasticity of demand for new vehicles (-1.0), those 

negative private net benefits result in lost sales of new vehicles of between 98,000 (NHTSA 

estimates of costs but NERA estimates of private benefits) to 856,000 (NERA estimates of 

 

5

In predicting changes in vehicle sales and employment, NHTSA estimated vehicle buyers’ perceived benefits in a 

different manner than it did in its social benefit-cost analysis. This ad hoc approach depends on several arbitrary 

assumptions, but we report those values here for consistency with NHTSA’s estimates of impacts on sales and 

employment. 
Executive Summary 

NERA Economic Consulting S-7 

benefits and Sierra estimates of costs). Using NHTSA’s estimate that a change in new-vehicle 

sales of 10.5 vehicles is associated with a change of one job in U.S. employment by auto 

manufacturers and their suppliers, those losses in sales imply job losses of 9,000 to 82,000. 

4. NERA Estimates of External Benefits and Costs 

NERA was able to assess some—but not all—of the NHTSA external cost and benefit 

categories. In particular, we made three major changes in the NHTSA estimates: 

§ Revised values to reflect a larger rebound effect more consistent with the literature; 

§ Revised the value for CO2 emissions; and 

§ Revised the values for other (conventional) emissions to reflect more realistic upstream 

emission factors. 

The following are summaries of these estimates. 

a. Revising the Rebound Effect to Reflect the Literature 

NHTSA estimates that the rebound effect—as measured by the elasticity of demand for VMT 

with respect to the cost per VMT—is equal to -0.15. We believe, however, that this value gives 

Table S-3. Estimated Private Net Benefits per MY2015 Vehicle Based on Varying Combinations of 

Estimated Benefits and Costs 

Benefits: NHTSA NERA NHTSA NERA 

Costs: NHTSA NHTSA Sierra Sierra

$968 $667 $968 $667 

$821 $821 $2,068 $2,068 

$147 ($154) ($1,100) ($1,401)

86,000 -98,000 -679,000 -856,000

8,000 -9,000 -65,000 -82,000

Per-Vehicle Benefits and Costs

Private benefits

Increased cost of new vehicles

Private net benefits

Car and light truck combined sales

Change in U.S. employment by 

vehicle manufacturers and suppliers

Notes: First row of column headings shows source of estimated private benefits. “NHTSA” benefit estimates are 

based on its analysis from buyer’s perspective. 

All dollar entries are retail price equivalents per vehicle (2006 dollars). 

Estimated changes in sales based on changes in quality-adjusted prices and an own-price elasticity of 

demand of -1.0. 

Estimated changes in employment are the estimated changes in sales divided by NHTSA’s estimate that 

for every change in sales of 10.5 vehicles, there is a corresponding change in employment of one job 

(NHTSA PRIA VII-53). NHTSA’s employment estimate applies to employment in original automobile 

manufacturers and suppliers. 

Source: NHTSA PRIA, Sierra Research (2008), and NERA calculations as explained in text. 
Executive Summary 

NERA Economic Consulting S-8 

disproportionate weight to a single study with empirical limitations.6 NHTSA’s own summary of 

estimates from the literature suggests that a value of -0.2, or even larger, is more appropriate. A 

larger rebound effect affects external benefits (costs) in several categories, in all but one case 

(tax revenue changes) leading to increased net external costs:7

§ Increased VMT increases fuel consumption, offsetting a portion of the lost tax revenues and 

thus reduces external costs. 

§ Increased fuel consumption increases CO2 emissions and thus increases external costs. 

§ Increased fuel consumption offsets some of the saving in petroleum-market externalities and 

thus increases net external costs. 

§ Increased VMT increases tailpipe emissions with associated increases in external costs. 

§ Increased VMT increases non-environmental external costs associated with congestion, 

accidents, and noise and thus increases external costs. 

The results shown in Table S-2 reflect adjustments to NHTSA’s estimates based on using a 

rebound elasticity of -0.2, as well as the additional factors discussed below. 

b. Limiting CO2 Damages to Those in the United States 

NHTSA uses a value per metric tonne of CO2 of $7, the midpoint of range for $0 to $14, where 

the latter is based on estimates of global damages developed by the Intergovernmental Panel on 

Climate Change. This approach overstates the U.S. share of potential damages due to global 

climate change because it implies that fully one-half of all global benefits from reducing 

greenhouse gas emissions would accrue to the United States, an assumption that NHTSA does 

not justify. A more reasonable assumption is that global damages would be proportional to 

national income. At present the United States accounts for about 21 percent of world gross 

product, and that share will fall over time. We assume that 20 percent of global damages occur in 

the United States, although many observers believe that the United States and other developed 

countries will be better able to adapt than developing countries and thus are likely to suffer 

smaller damages proportionally. Using a value for United States equal to 20 percent of global 

damages reduces NHTSA’s estimate of CO2 benefits with the “optimized standard” for MY2015 

from $869 million to $348 million. 

 

6 Appendix B describes the limitations of this study. 

7

Private costs and benefits from the rebound effect are incorporated into our market model independent of 

NHTSA’s estimates of the those effects, and hence do no need to be adjusted to reflect adjustments to NHTSA’s 

estimates of increased VMT due to the rebound effect. 
Executive Summary 

NERA Economic Consulting S-9 

c. Using More Realistic Upstream Emission Factors for Conventional 

Pollutants 

NHTSA’s estimates of “upstream” emissions associated with the production and distribution of 

fuel appear to be too high. NHTSA’s emission rates for diesel and gasoline production at 

refineries appear to imply substantially larger emissions of NOX and PM2.5 than are contained in 

the National Emission Inventory for production of all products. We adjusted NHTSA’s estimates 

downward to account for this apparent discrepancy. 

d. Substituting OMB Estimates of the Value per Ton of Conventional 

Pollutants 

NHTSA values changes in emissions of conventional pollutants using values recommended 

informally by EPA for which no documentation was provided as to their source or applicability. 

In the absence of information on the basis for EPA’s informal recommendations, we substitute 

estimates used by the Office of Management and Budget (“OMB”) in a recent report to 

Congress. 

e. Revised Congestion Costs to Reflect Consistent Valuation of 

Savings in Vehicle Hours 

In updating an earlier estimate of the value per mile of external congestion costs, NHTSA 

appears not to have made it consistent with NHTSA’s valuation of time savings for vehicle 

occupants in another part of its benefits analysis. Making the value consistent increases 

congestion costs per mile by about 68 percent. 

5. Summary of Alternative Estimates of Social Net Benefits (Costs) 

Figure S-1 summarizes the four different estimates of social net benefits for MY2015 that result 

from combinations of alternative sets of cost and benefit estimates. Overall net benefits are about 

$12 billion with NHTSA’s estimates of benefits and costs. In contrast, all of the three other cases 

yield substantial net costs. Using the two “intermediate” cases—NERA benefits and NHTSA 

costs and NHTSA benefits and Sierra costs—yields net costs of about $7.5 billion and about $9.4 

billion, respectively. The largest net costs result from using NERA benefits and Sierra costs 

estimates; that combination results in net costs of about $29 billion for NHTSA’s “optimized” 

car and light-truck CAFE standards for MY2015. 

6. Sensitivity Analysis of Price of Gasoline 

The estimated costs and benefits of new CAFE standards depend on future fuel prices. NHTSA 

conducted its analyses using the “Reference” gasoline price projections from the preliminary 

version of the Energy Information Administration (“EIA”) AEO 2008, which was released in 

December 2007. In March 2008, EIA updated its Reference gasoline price forecast. To test the 

sensitivity of our results to the future price of gasoline, we used this latest EIA Reference 

forecast and developed “low” and “high” estimates based on that forecast and EIA’s AEO 2007 

range of forecasts. For each price forecast, we recomputed private benefits based on NERA’s 
Executive Summary 

NERA Economic Consulting S-10 

new vehicle market model (in which the value per unit of improvement in fuel economy is 

proportional to the price of gasoline). Using the “high” gasoline price forecast, we found that the 

private benefits of NHTSA’s “optimized” CAFE standard exceeded NHTSA’s estimates of 

compliance costs, but not Sierra’s. However, even with NHTSA’s cost estimate, the sizable 

external costs mean that estimated social net benefits are negative even with the “high” gasoline 

price forecast. 

C. Conclusions 

The following are our principal conclusions regarding the NHTSA benefit-cost assessment of the 

MY2011-MY2015 CAFE standards contained in the PRIA. 

§ Although NHTSA emphasizes that its proposed CAFE standards would lead to reductions in 

CO2 emissions and improvements in energy security, even NHTSA’s own assessments show 

that on balance the external effects of the CAFE standards would be negative. That is, even 

NHTSA estimates that its “optimized” CAFE standards would lead to greater negative than 

positive external effects. This result is in dramatic contrast to the traditional justification for 

government regulations—to deal with negative external effects when economic actors make 

choices based on their private costs and benefits. 

($9.4)

($28.9)

($7.5)

$12.0

($35)

($30)

($25)

($20)

($15)

($10)

($5)

$0

$5

$10

$15

NHTSA/NHSTA NERA/NHTSA NHTSA/Sierra NERA/Sierra

Source of Benefits/Costs Estimates

S

ocial Net Benefits (billio

ns of 2006

d

ollars) 

Source: See Table 15. 

Figure S-1. Alternative Estimates of Net Social Benefits (Costs) for MY2015, Combined (Cars and Light 

Trucks) 
Executive Summary 

NERA Economic Consulting S-11 

§ NHTSA’s benefit-cost assessment shows large net benefits for CAFE standards because it 

estimates that the private benefits to new vehicle purchasers would be almost double the 

private costs they would incur. NHTSA’s baseline assumes that market forces would not lead 

to that level of fuel economy. 

§ NHTSA does not provide an explanation of why the new vehicle market would perform so 

poorly in response to the potential gains to new car buyers and motor vehicle manufacturers 

of greater fuel economy. Indeed, NHTSA does not discuss what market failures might be 

present to justify its proposed CAFE standards or to explain the basis for its assessment of 

the private benefits. 

§ Our benefit and cost estimates provide explanations for the apparently counter-intuitive 

NHTSA result. Based upon a detailed NERA model of the U.S. new vehicle market, we 

estimate that the private benefits received by new vehicle purchasers would be substantially 

smaller than NHTSA’s estimates. (We note that NHTSA acknowledges the inadequacy of its 

modeling of new vehicle markets.) In contrast to NHTSA, we estimate that the proposed 

CAFE standards would lead to increases in “quality-adjusted” new-vehicle prices and 

reductions in new vehicle sales (and automobile sector employment). 

§ We were able to examine in detail and, where appropriate, modify some of NHTSA’s 

estimates of external benefits and costs. These changes resulted in a large net increase in the 

external costs of the “optimized” CAFE standards. 

§ Combining our estimates of private benefits and external benefits and costs, but using 

NHTSA’s estimates of increases in new-vehicle costs, changes the conclusions regarding the 

social net benefits for NHTSA’s “optimized” standard. While NHTSA estimates large 

positive net benefits, our estimates show substantial negative net benefits (i.e., net costs) for 

NHTSA’s “optimized” standard for each of the proposed MY2011-MY2015 CAFE 

standards. 

§ The estimated net costs of NHTSA’s recommended CAFE standards—and the adverse 

effects on new vehicle sales and employment—are substantially greater using an alternative 

set of cost estimates developed by Sierra Research. 

§ The information that NHTSA has provided in the PRIA does not allow us to develop similar 

estimates of the costs and benefits of standards other than the “optimized” standards. Thus, 

we cannot at this time assess the net benefits or net costs of CAFE standards less stringent 

than the “optimized” standards identified by NHTSA. Note, however, that the lack of a clear 

market failure that would justify CAFE standards—and the empirical assessment in 

NHTSA’s own estimates that external effects are negative—strongly suggests that less 

stringent CAFE standards also would fail a benefit-cost test using NERA estimates of 

benefits. 
NERA Economic Consulting 1 

I. Introduction and Overview 

The National Highway Traffic Safety Administration (“NHTSA”) has proposed new 

Corporate Average Fuel Economy (“CAFE”) standards for passenger cars (“cars”) and light 

trucks to meet requirements under the Energy Independence and Security Act (“EISA”). The 

proposed standards cover new vehicles produced in model years 2011 to 2015. This report 

focuses on the benefit-cost analysis contained in the Preliminary Regulatory Impact statement 

(“PRIA”) published by NHTSA as part of the rule making. 

A. Background 

The CAFE standards specify the minimum average miles per gallon (“MPG”) that new 

vehicles must achieve. EISA requires setting separate CAFE standards for passenger cars and 

light trucks at levels sufficient to ensure that the average fuel economy of all passenger cars and 

light trucks sold by all manufacturers in the United States in model year (“MY”) 2020 equals or 

exceeds 35 MPG. The 2020 requirement represents a 40 percent increase above the average of 

about 25 miles per gallon for the current combined fleet. 

EISA also made other changes to the Energy Policy and Conservation Act (“EPCA”), the 

1975 statute that governs the CAFE program, in addition to the 2020 requirement. The following 

are among these changes. 

§ Limitation of five years on the number of model years that can be covered by a single 

rulemaking. 

§ Reforming of CAFE standards for passenger cars by requiring that standards be based 

upon one or more vehicle attributes (a requirement designed to deal with potential safety 

problems from CAFE standards). 

§ Requirement that domestic passenger cars of each manufacturer achieve an average fuel 

economy that is not less than 92 percent of the average of the combined domestic/nondomestic passenger cars sold in that model year. 

On May 2, 2008, NHTSA issued a notice of proposed rulemaking proposing specific CAFE 

standards for MY2011-2015 for both passenger cars and light trucks. At the same time, NHTSA 

issued the PRIA that describes the regulatory alternatives the agency considered and how the 

proposed standards were selected on the basis of maximizing the net benefits (i.e., benefits minus 

costs). 
Introduction and Overview 

NERA Economic Consulting 2 

B. NHTSA’s Proposed CAFE Standards 

The NHTSA proposal covers five model years (2011-2015) and sets separate limits for 

passenger cars and light trucks. Moreover, within each of those categories, the proposed rules 

specify a formula that varies the limit applicable to a vehicle based on its “footprint,” the product 

of its wheelbase and track width. 

Table 1 summarizes NHTSA’s estimates of the average fuel economy that would be 

required for passenger cars and light trucks under its proposed CAFE standards.

1

If adopted, the proposed rule would increase substantially the required average fuel 

economy for new vehicles. For cars and light trucks combined, the rule would increase the salesweighted limit from about 25.3 MPG in MY2010 to about 31.6 MPG in MY2015. Note that 

these proposed standards mean that about three-quarters of the improvement in fuel economy 

required by EISA to be achieved by MY2020 would occur in the first five years.

2

 Thus, the 

proposed standards mean that the path to achieving the EISA 2020 requirement of 35 MPG 

would be “front loaded,” with most of the required fuel economy improvement achieved in the 

earlier years. 

 

1 Throughout this report, the MPG figures we report reflect the EPA testing procedure specified by Congress. In 

calculating benefits and costs, however, we follow NHTSA’s lead in reducing these MPG figures by 20 percent 

to reflect the lower fuel economy achieved in practice. 

2

For purposes of measuring the impact of greater fuel efficiency on fuel consumption, the relevant measure is 

gallons per mile (GPM), the inverse of MPG. The use of the harmonic mean in computing average MPG reflects 

this fact. The harmonic mean averages the inverse of each individual vehicle’s MPG (equivalent to the mean of 

GPM) and then takes the inverse of that average to put the results back in MPG. Taking the inverse of 25.3 MPG 

in MY2010 translates to 0.0395 GPM. Similarly, 31.6 MPG in MY2015 is equivalent to 0.0316 GPM and the 35 

MPG in MY2020 is equivalent to 0.0286 GPM. The total change from MY2010 to MY2020 is 0.109 GPM. The 

proposed change from 2010 to 2015 is 0.079 GPM, or 72 percent of the total. 

Table 1. Summary of Average Fuel Economy Required by CAFE Standards Proposed by NHTSA 

MY2011 MY2012 MY2013 MY2014 MY2015

Cars 31.2 32.8 34.0 34.8 35.7

Light trucks 25.0 26.4 27.8 28.2 28.6

Combined 27.8 29.3 30.6 31.0 31.6

Note: The figures shown are NHTSA’s estimates of the average MPG required by its proposed standards. They 

reflect NHTSA’s proposed formulas for setting limits based on footprints and NHTSA’s estimates of the 

distributions of vehicles by footprint and the split between cars and light trucks. These values refer to the 

EPA test MPG ratings. 

Source: NHTSA PRIA, tables VI-1b, VI-1e, VI-2b, and VI-2e. 
Introduction and Overview 

NERA Economic Consulting 3 

NHTSA estimates that the proposed rules would increase the average cost—and thus the 

average price—of a new car by $276 in MY2011, rising to $649 in MY2015 (NHTSA PRIA 

Table VII-4c).3 NHTSA estimates that the average price of a new light truck would rise by 

between $224 in MY2011 and $979 in MY2015 (NHTSA PRIA Table VII-5c). NHTSA 

estimates, however, that these increased costs to consumers would be more than balanced by 

various benefits, including private benefits to vehicle owners due to reduced fuel use —which 

NHTSA claims yield private benefits greater than the compliance costs—and the external 

benefits of reduced fuel use, including reduced carbon dioxide and conventional air emissions 

and reduced oil payments to foreign suppliers. NHTSA acknowledges that improved fuel 

economy will in turn lead to increases in some adverse external effects due to the well-known 

“rebound effect” (the additional vehicle miles of travel (“VMT”) that result when the price per 

mile for driving is reduced by improved fuel economy). NHTSA’s PRIA concludes that on 

balance its proposed MY2011-2015 CAFE standards would yield substantial net benefits (i.e., 

private and external benefits exceed private and external costs). 

C. Overview of Report 

The remainder of the report is organized as follows. Chapter II provides an overview of 

the NHTSA benefit-cost analysis, including a description of the empirical modeling framework 

that NHTSA developed to assess the costs and benefits of alternative CAFE standards. This 

chapter concludes with some general assessments of the NHTSA benefit-cost analysis. Chapter 

III provides detailed assessments of the NHTSA benefit-cost analyses. The assessments include 

evaluations of the models that NHTSA has developed (and in some cases not developed) in order 

to determine the effects of CAFE standards on the behavior of new vehicle purchasers, used 

vehicle owners, and vehicle drivers. Chapter III also assesses NHTSA’s values for various 

private and external benefits and costs, although, as we note, inadequacies of the documentation 

provided by NHTSA have limited somewhat our ability to reproduce their results (including the 

lack of any detailed information on CAFE standards other than NHTSA’s “optimized” standard 

for each model year). Chapter III ends with a set of alternative estimates of the benefits and costs 

of NHTSA’s proposed standards based upon alternative assessments. Chapter IV provides brief 

concluding remarks including a summary of our principal conclusions. 

 

3 NHTSA’s estimates of the retail-price equivalent of a technology’s costs include a “markup” intended to include 

a “normal” return on capital (NHTSA PRIA VII-1). These estimates also include projected fines paid by 

manufacturers that NHTSA predicts will not attain the proposed standards. 
NERA Economic Consulting 4 

II. Overview of the NHTSA Draft Benefit-Cost Analysis 

This chapter provides an overview of NHTSA’s benefit-cost methodology and results, 

including how NHTSA used its benefit and cost estimates to determine the “optimized” 

standards that it proposed in this rulemaking. 

A. Overview of NHTSA Benefit-Cost Methodology 

NHTSA developed estimates of social benefits and costs, i.e., the benefits and costs that 

would accrue to society if different levels of CAFE standards were in place. The estimates are 

based upon detailed information for various fuel economy technology options as well as 

modeling and other empirical information contained in an overall modeling structure referred to 

as the “Volpe Model.” 

1. Baseline New Vehicle Fuel Economy 

NHTSA’s benefit-cost analysis considers several different levels of stringency for CAFE 

standards for each of the five model years, from MY2011 to MY2015. For each model year, 

NHTSA defined a technology baseline based in part on confidential information from seven 

motor vehicle manufacturers on the mix of vehicles they expect to sell in those model years. This 

baseline includes estimates of what fuel-saving technologies would be used in the absence of 

more stringent CAFE standards and the resulting fuel economy on a model-by-model basis. 

2. Costs of Increased New Vehicle Fuel Economy 

NHTSA also identified technological options for increasing fuel economy and developed 

estimates of the costs of those technologies. NHTSA developed decision trees that show the 

order in which it assumes (based on estimated cost-effectiveness) manufacturers would adopt 

technologies as CAFE standards tightened. In addition, NHTSA assumed that the costs of many 

technologies would fall with production levels due to learning effects. It also limited the rate at 

which some of the technologies could be phased in. We have not reviewed the NHTSA cost 

estimates, but we have included a set of alternative cost estimates developed by Sierra Research, 

Inc. (“Sierra”). 

3. Benefits of Increased New Vehicle Fuel Economy 

On the benefits side, NHTSA quantified benefits in a dozen or more categories, the 

largest of which is savings in fuel expenditures by car owners as a result of reduced consumption 
Overview of the NHTSA Draft Benefit-Cost Analysis 

NERA Economic Consulting 5 

of gasoline.

4

 Those reductions in fuel consumption also are associated with lower CO2 emissions 

and reduced emissions of conventional pollutants from fuel production, distribution and storage. 

As noted above and discussed in detail in Chapter III, higher CAFE standards also tend to 

increase VMT per vehicle, which offsets some of the fuel economy benefits and also increases 

tailpipe emissions and other externalities associated with additional VMT. 

B. The Volpe Model 

NHTSA used a model developed by the Department of Transportation’s Volpe National 

Transportation Systems Center to integrate its cost and benefit estimates and to project them over 

time. The Volpe Model, as NHTSA calls it, includes model-by-model information in addition to 

the more general cost and benefit estimates described above. Its inputs also include additional 

general projections, including gasoline prices, overall vehicle sales, baseline VMT per vehicle by 

vehicle type by age, and survival rates for vehicles of different ages and types. For a given model 

year and CAFE limit, the Volpe model estimates the least-cost way of meeting the limit, projects 

the numbers of vehicles of each type that will remain in operation over time (up to 36 years in 

the case of light trucks), their VMT, and their fuel consumption. 

The Volpe Model includes an estimate of the rebound effect. When the cost per mile of 

operation falls, as it does if a vehicle’s MPG increases, owners of such vehicles tend to drive 

more; this effect is referred to as the rebound effect because these increases in VMT reduce the 

fuel savings estimated for CAFE (although the added VMT also provide value to consumers). 

Higher fuel consumption associated with the rebound effect also increases CO2 emissions and 

thus reduces the potential reductions estimated for CAFE. The rebound effect also increases 

negative externalities that are due to VMT rather than fuel use, including tailpipe emissions of 

conventional pollutants, congestion, accidents, and noise. 

The Volpe Model calculates estimated costs and benefits over the lifetimes of the 

vehicles produced in a particular model year, which it then discounts back to the model year in 

which the vehicles would be sold. NHTSA reports present values based on real (inflation 

adjusted) discount rates of both 7 and 3 percent, focusing for the most part on the higher rate. 

The model then calculates net benefits, i.e., the present value of benefits minus the higher costs 

incurred by new car buyers. 

 

4 NHTSA also considered diesel, and predicts that one strategy for compliance with tighter CAFE limits will be to 

convert some models from gasoline to diesel engines. However, NHTSA does not report separate estimates for 

gasoline and diesel. Thus, unless otherwise specified, “gasoline” in this report refers to diesel as well. Moreover, 

our understanding is that NHTSA has not distinguished among fuels with varying percentages of ethanol. 
Overview of the NHTSA Draft Benefit-Cost Analysis 

NERA Economic Consulting 6 

C. NHTSA “Optimized” Proposed CAFE Standards 

NHTSA used the Volpe Model to find the “optimized” level of CAFE standards for each 

of the five model years. The “optimized” level is the one that maximizes net benefits as 

estimated by NHTSA. From this “optimized” set of model-specific emission rates, NHTSA then 

estimated the relationship between vehicles’ footprints and MPG. NHTSA used that function to 

translate overall CAFE levels into model-specific MPG requirements, which it in turn translated 

into average MPG targets by manufacturer. It assumed that each manufacturer will choose the 

least-cost mix of compliance methods to meet its target. 

1. Alternative CAFE Standards Considered by NHTSA 

For each model year, separately for cars and light trucks, NHTSA calculated costs and 

benefits for six alternative CAFE levels:5

1. the “optimized” MPG level at a discount rate of 7 percent; 

2. a level such that total costs equal total benefits (“TC=TB”); 

3. a level that exceeds the “optimized” scenario by 25 percent of the interval between the 

“optimized” scenario and the “TC=TB” alternative (“25% above”); 

4. a level that exceeds the “optimized” scenario by 50 percent of the interval between the 

“optimized” scenario and the “TC=TB” alternative (“50% above”); 

5. a level that falls below the “optimized” scenario by the same amount that the “25% 

above” scenario exceeds it (“25% below”); and 

6. the limit that results from exhausting the available technologies. 

This set of alternatives allows NHTSA to estimate costs and benefits for a range of possible 

CAFE standards for each of the five model years. Note that one of these alternatives is less 

stringent than the “optimized” standard and four are more stringent. We commend NHTSA for 

its inclusion of a range of different possible standards—rather than evaluating the benefits and 

costs of a single set of standards—although given uncertainty in its estimates of costs and 

benefits, we believe NHTSA should have taken a more balanced approach in selecting 

alternatives, evaluating as many standards that were less stringent than its estimates suggested 

were “optimized” as it evaluated more-stringent standards. 

 

5 NHTSA also reports results for a standard “optimized” using a discount rate of 3 percent. We do not include that 

alternative here because it makes little sense to optimize for one discount rate (3 percent) and then evaluate it 

using another (7 percent). 
Overview of the NHTSA Draft Benefit-Cost Analysis 

NERA Economic Consulting 7 

2. NHTSA Benefit and Cost Estimates of Alternative CAFE Standards 

Table 2 shows NHTSA’s estimates for these six alternatives for MY2015 (from least 

stringent to most stringent). The first three horizontal sections show estimated benefits, costs, 

and net benefits for cars, light trucks, and the sum of cars and light trucks (combined vehicles). 

NHTSA’s estimates of net benefits are positive for the lowest CAFE limit evaluated and are 

larger at the “optimized” level. Further tightening of the standard beyond that level, however, 

reduces estimated net benefits, although in most cases net benefits remain positive. That pattern 

simply reflects NHTSA’s definition of the “optimized” standard as the one that maximizes net 

benefits. 

The bottom section of the table, the last three rows, shows incremental benefits, costs, 

and net benefits. The increment is defined as the difference between the level in question and the 

next less stringent standard. Thus, for example, the incremental benefits for the optimized 

standard are that standard’s benefits minus those of the “25% below” alternative. Net benefits 

rise when incremental benefits are greater than incremental costs, but begin to decline when 

incremental costs exceed incremental benefits. 

We commend NHTSA for its attempt to choose the standard that maximizes estimated 

net benefits, rather than simply testing to see if its proposed rule has positive net benefits. The 

“TC=TB” alternative NHTSA includes in its analysis illustrates the pitfalls of this more common 

approach. By design, it yields net benefits that are roughly zero. Had it been the only alternative 

evaluated, it might have been chosen. However, as shown in Table 2, that alternative would 

impose costs roughly $12.5 billion higher than NHTSA’s “optimized” alternative but yield 

additional benefits estimated by NHTSA to be only $7.0 billion, for an incremental loss of about 

$5.5 billion. As we discuss in Chapter III, however, although NHTSA’s criterion for choosing its 

proposed standards is commendable, the standards proposed are not appropriate if the underlying 

estimates of costs and benefits are incorrect. In that chapter, we present alternative estimates of 

private benefits and costs as well as external benefits and costs. 
Overview of the NHTSA Draft Benefit-Cost Analysis 

NERA Economic Consulting 8 

3. Benefits and Costs of NHTSA “Optimized” CAFE Standards 

For each model year, NHTSA has identified the “optimized” passenger car and light 

truck limits as the proposed standards. Table 3 reports NHTSA’s benefit and cost estimates for 

those “optimized” standards for each of the five model years covered by the proposed rule. For 

all model years, NHTSA’s estimates of the benefits are substantially greater than its estimates of 

the costs, yielding estimated net benefits ranging from $3 billion for MY2011 to $12 billion for 

MY2015. 

Table 2. NHTSA’s Estimates of Benefits, Costs, and Net Benefits of Alternatives Standards for MY2015 

25% below

Optimized 

(7%) 25% above 50% above TC=TB

Technology 

Exhaustion

Cars

Benefits $6,799 $9,420 $12,083 $14,495 $16,492 $18,061

Costs $3,209 $4,862 $9,084 $12,981 $17,398 $21,110

Net benefits $3,590 $4,558 $2,999 $1,514 -$906 -$3,049

Trucks

Benefits $12,441 $16,192 $19,457 $20,892 $22,367 $24,759

Costs $6,326 $8,761 $14,781 $17,969 $21,364 $23,479

Net benefits $6,115 $7,431 $4,676 $2,923 $1,003 $1,280

Combined

Benefits $19,240 $25,612 $31,540 $35,387 $38,859 $42,820 

Costs $9,535 $13,623 $23,865 $30,950 $38,762 $44,589 

Net benefits $9,705 $11,989 $7,675 $4,437 $97 ($1,769)

Combined incremental

Benefits $19,240 $6,372 $5,928 $3,847 $3,472 $3,961 

Costs $9,535 $4,088 $10,242 $7,085 $7,812 $5,827 

Net benefits $9,705 $2,284 ($4,314) ($3,238) ($4,340) ($1,866)

Alternative Standards

Note: Entries are in millions of 2006 dollars, discounted to 2015 at a real rate of 7 percent. 

Source: NHTSA PRIA table IX-1, table IX-2b 
Overview of the NHTSA Draft Benefit-Cost Analysis 

NERA Economic Consulting 9 

D. Distribution of NHTSA Benefits and Costs across Categories 

The estimated costs shown in Table 3 are comprised entirely of higher expenditures on 

new vehicles. NHTSA assumes that vehicle manufacturers will be able to pass on all of the costs 

of the standards to new-vehicle buyers. The benefits, as indicated generally above, are composed 

of a larger number of elements, some positive and some negative. This section decomposes the 

NHTSA cost and benefit categories, beginning with a distinction between private and external 

effects. 

1. Private versus External Benefits 

It is useful to break down the NHTSA benefit and cost estimates for its “optimized” 

standards into two major categories. 

1. Private benefits and costs that would accrue to the owners of individual vehicles with 

higher MPG as a result of tightened CAFE standards. 

Table 3. NHTSA's Estimates of Benefits and Costs of "Optimized" CAFE Standards by Model Year 

MY2011 MY2012 MY2013 MY2014 MY2015

Cars

Benefits $2,596 $4,933 $6,148 $7,889 $9,420

Costs $1,884 $2,373 $2,879 $3,798 $4,862

Net benefits $712 $2,560 $3,269 $4,091 $4,558

Trucks

Benefits $3,909 $8,779 $13,560 $14,915 $16,192

Costs $1,649 $4,986 $7,394 $8,160 $8,761

Net benefits $2,260 $3,793 $6,166 $6,755 $7,431

Combined

Benefits $6,505 $13,712 $19,708 $22,804 $25,612

Costs $3,533 $7,359 $10,273 $11,958 $13,623

Net benefits $2,972 $6,353 $9,435 $10,846 $11,989

Note: Entries are NHTSA estimates (present values, discounted at 7 percent real to start of each model year, in 

millions of 2006 dollars) for the car and light truck standards in each model year that produce the largest 

net benefits based on NHTSA’s estimates. 

Costs are as reported by NHTSA and do not include fines paid by manufacturers that do not comply. 

Correspondingly, benefits do not include revenues from fines. 

Source: NHTSA PRIA table IX-1, table IX-2b. 
Overview of the NHTSA Draft Benefit-Cost Analysis 

NERA Economic Consulting 10 

2. External benefits and costs that would accrue to members of society as a whole 

independent of the MPG achieved by an individual’s own vehicle(s) (if any). 

This distinction is important because economic theory and general experience indicates that in 

making personal decisions, customers consider private benefits and costs—benefits they receive 

and costs they bear—but not external benefits or costs. Thus, it is important to evaluate estimates 

of private costs and benefits in relation to observed behavior. Individual vehicle buyers, 

however, are assumed to ignore the external effects associated with their decisions because they 

bear an infinitesimal fraction of those external effects. Indeed, externalities are one of the most 

important sources of “market failure” invoked to justify government intervention in the market. 

The rationale for virtually all environmental regulation, for example, rests on the expectation that 

individual economic actors do not have the appropriate incentive to reduce the pollution (or other 

forms of environmental harm) associated with their activities because they are likely to pay the 

full costs of achieving the reductions but receive virtually none of the benefits (see, e.g., 

Tietenberg 2003). Indeed, NHTSA justifies its CAFE standards in part by reference to the 

negative externalities associated with fuel consumption, such as damages from emissions of CO2

and petroleum-related costs of import dependence and reduced security.

6 However, as the 

NHTSA analysis acknowledges, it is important to have a complete accounting of the proposed 

rules’ external effects, negative as well as positive. 

a. NHTSA Estimates Broken Down Into Private and Social 

Benefits/Costs 

Table 4 summarizes the estimated benefits (costs) of the proposed rule for MY2015, 

breaking them down first by whether they are private or external benefits (costs) and then by 

subcategories within those two categories. Note that negative values in the table (in parentheses) 

indicate that the component in question is a cost. The final column shows the share of total 

benefits for the combined standards for which the individual components account. Negative 

numbers indicate net costs. Note that the subtotal for private benefits is 105 percent of social net 

benefits and the subtotal for external benefits is -5 percent, indicating that on balance the external 

effects are negative (i.e., net costs). We discuss the implication of this result below. 

The results shown in Table 4 for MY2015 suggest two major conclusions based on the 

NHTSA estimates. First, according to NHTSA’s estimates, private benefits to vehicle purchasers 

exceed the private costs they would bear from increased new-vehicle prices. Second, according 

 

6 NHTSA also discusses how its proposed standards are consistent with the four statutory factors the EPCA 

requires NHTSA to consider: (1) technological feasibility; (2) economic practicability; (3) effect of other 

Government motor vehicle standards on fuel economy; and (4) need of the U.S. to conserve energy. 
Overview of the NHTSA Draft Benefit-Cost Analysis 

NERA Economic Consulting 11 

to NHTSA, external costs exceed external benefits. We examine the details behind these 

assessments below. 

Table 4. Components of NHTSA's Estimates of the Benefits of Proposed CAFE Standards for MY2015 

Item Cars

Light 

trucks Combined

Share of total 

benefits

Private Benefits

Fuel expenditures (exclusive of taxes) $7,543 $12,661 $20,204 77%

Reduced fuel tax payments $1,274 $2,384 $3,658 14%

Consumer value of additional VMT $923 $1,561 $2,484 10%

Time savings from greater range $474 $602 $1,077 4%

 Subtotal, private benefits $10,214 $17,208 $27,423 105%

External benefits (costs)

Reduced fuel tax revenues ($1,274) ($2,384) ($3,658) -14%

CAFE Fines $459 $1 $460 2%

Reduced CO2 emissions $322 $546 $869 3%

Reduced NOX, PM, SO2 and VOC Emissions $239 $521 $760 3%

Accidents, congestion, noise ($1,175) ($1,537) ($2,712) -10%

Petroleum market externalities $1,093 $1,838 $2,931 11%

 Subtotal, external benefits ($336) ($1,015) ($1,351) -5%

Total benefits $9,879 $16,194 $26,072 100%

Increased cost of new vehicles

Compliance Costs $4,862 $8,761 $13,623 

CAFE Fines $459 $1 $460 

 Subtotal, private costs $5,321 $8,762 $14,083 

Net benefits

Private $4,894 $8,446 $13,340 

External ($336) ($1,015) ($1,351)

Total $4,558 $7,431 $11,989 

Note: Entries are in millions of 2006 dollars, discounted to 2015 at a real rate of 7 percent. 

NHTSA does not provide separate estimates of reductions in fuel-tax collections and CAFE fines because 

the benefits to vehicle owners are matched by the costs to the government/taxpayers of reductions in fueltax receipts. We have estimated them to highlight the fact that the change in tax revenues is a private 

benefit but an external cost. Similarly, fines are a private cost but an external benefit. 

Source: NHTSA PRIA Tables IX-1, VIII-9, and VIII-15 and NERA calculations of fuel taxes based on NHTSA 

inputs to Volpe model. 
Overview of the NHTSA Draft Benefit-Cost Analysis 

NERA Economic Consulting 12 

2. NHTSA Private Benefit and Cost Estimates 

a. Components of NHTSA Private Benefits and Costs Estimates 

The NHTSA estimates of private costs and benefits (i.e., benefits and costs accruing to 

new vehicle purchasers) include several categories. The major private benefit category is cost 

savings from purchasing less fuel as a result of the improved fuel economy assumed to be due to 

CAFE standards. We show the fuel-related benefits in two lines, one for fuel expenditures net of 

taxes (which NHTSA reports) and one for the reduced tax payments related to the fuel 

expenditures. From a larger societal perspective, those private savings in fuel taxes are offset by 

reduced tax revenues for governments at local, state, and national levels, but those reductions are 

an externality from the perspective of the individual car owner. We include those tax-related 

external effects as negative external benefits below. 

The private costs for vehicle purchasers have two elements: (1) the retail price 

equivalents of the costs of increasing fuel economy to meet the CAFE requirements and (2) the 

fines due on vehicles sold by manufacturers that fail to meet the CAFE requirements. NHTSA 

includes the second item in its reported changes in the per vehicle cost of new vehicles but does 

not break it out as a cost in its social benefit-cost analysis because the cost to vehicle buyers of 

the fines is offset by additional revenues for the government. We show the two offsetting items 

separately, because they represent a private cost and an external benefit, analogous to (though 

opposite in sign of) fuel tax payments/revenues. 

b. Implications of NHTSA Benefit and Cost Estimates 

In addition to fuel-cost savings and vehicle costs, NHTSA considered two additional 

private benefits: (1) consumer’s surplus associated with additional driving resulting from the 

rebound effect7and (2) time savings from longer intervals between refueling as a result of higher 

MPG and an associated increase in driving range. 

c. Implications of NHTSA Private Benefit and Cost Estimates 

With regard to vehicle owners, NHTSA’s private benefits are almost double NHTSA’s 

estimate of the costs borne by car owners ($27.4 billion in private benefits vs. $14.1 billion in 

 

7 NHTSA refers to this item as “consumer surplus” and describes its calculation in the terms of the conventional 

triangle under the demand curve but above the price paid. However, it appears that instead NHTSA estimated the 

total area under the demand curve for the extra VMT traveled. That is appropriate if NHTSA’s estimates of net 

savings in fuel expenditures include additional expenditures on the additional fuel consumed as a result of the 

rebound effect. 
Overview of the NHTSA Draft Benefit-Cost Analysis 

NERA Economic Consulting 13 

costs). In other words, NHTSA’s estimates mean that it believes that for every dollar spent by car 

buyers to meet NHTSA’s “optimized” CAFE standard, they would reap, on average, almost $2 

in benefits. 

NHTSA’s baseline assumes that market forces alone would not lead to that level of fuel 

economy. This fact raises the obvious question: if greater fuel economy is so beneficial from a 

private perspective, why is government regulation needed? Why does the market not yield 

greater fuel economy than NHTSA’s baseline predicts? NHTSA’s analysis implicitly assumes 

that new vehicle consumers make very poor decisions for themselves and/or that motor vehicle 

manufacturers do not respond to demand for more vehicles with better fuel economy. As we 

discuss later, NHTSA does not provide an explicit analysis of why the market would perform so 

poorly or provide evidence for such market failures. The alternative explanations, as we discuss 

in Chapter III, are that NHTSA has underestimated the real costs of increasing vehicles’ fuel 

economy or overestimated the benefits reaped by vehicle owners, or some combination of the 

two. 

3. NHTSA External Benefit and Cost Estimates 

a. Components of NHTSA External Benefits and Costs 

Under NHTSA’s accounting, the external costs and benefits are a mix of positive and 

negative impacts. We classify them into several categories: 

1. Lost tax revenues result from reduced sales of gasoline. These are the mirror image of the 

portion of private benefits that result from reduced tax payments by vehicle owners who 

reduce fuel purchases. 

2. Revenues from CAFE fines are the mirror image of the private cost of paying the fines. 

They represent higher federal revenues that partly offset the loss in tax revenues. 

3. Reduced externalities from CO2 emissions reflect the fact that reduced consumption of 

gasoline results in lower emissions of CO2 from vehicles and from upstream sources, 

leading to reduced impacts from climate change. 

4. Changes in externalities from conventional air pollutants, in particular volatile organic 

compound (VOCs, often referred to as “hydrocarbons” in the context of vehicle 

emissions), nitrogen oxides (NOX), particulate matter (PM), and sulfur dioxide (SO2). 

NHTSA’s estimates of changes in emissions of these four pollutants have three 

components (which NHTSA does not report separately). 

a. Reduced “upstream” emissions from reduced consumption of fuel, which leads to 

reduced emissions of various pollutants. Transportation and extraction of crude oil, 
Overview of the NHTSA Draft Benefit-Cost Analysis 

NERA Economic Consulting 14 

petroleum refining, and the distribution and storage of gasoline are the sources of 

these emissions. 

b. Reduced emissions from substitution of diesel for gasoline as a result of NHTSA’s 

prediction that vehicle manufacturer’s will meet tighter CAFE limits in part by 

switching some models from gasoline to diesel engines. 

c. Increased emissions from vehicles associated with the rebound effect. With the 

increase in VMT due to the rebound effect, “tailpipe” emissions will rise. 

5. Increased other externalities resulting from rebound effect. Increased VMT also 

increases non-pollution externalities from driving, including the following: 

a. congestion, with greater VMT leading to greater time losses from congestion; 

b. accidents, with greater VMT leading to more accidents and their associated external 

damages; and 

c. noise, with greater VMT leading to more damages from traffic noise. 

6. Reduced petroleum-market externalities are associated with reductions in oil imports as a 

result of lower fuel consumption. NHTSA’s estimates have two subcomponents: 

(1) “monopsony” effects result from the fact that reduced fuel consumption in the United 

States may reduce world oil prices, benefiting U.S. consumers, and (2) “disruption” 

effects that reflect impacts on the economy when world oil prices rise sharply due to 

disruptions in supply may fall with lower oil consumption. 

b. Implications of NHTSA External Benefits and Costs Estimates 

As shown in Table 4 for MY2015, using NHTSA’s estimates, the sum of these positive 

and negative external benefits is negative; i.e., NHTSA’s “optimized” 2015 CAFE standards 

increase adverse external effects rather than reducing them. As shown in Table 5, the same result 

holds for four of the five model years. According to NHTSA’s own estimates, with the exception 

of MY2011, the proposed standards would increase external costs. 

E. Summary 

NHTSA emphasizes that its proposed CAFE standards would lead to reductions in CO2

emissions and petroleum market externalities. But NHTSA’s own assessments show that on 

balance the external effects would be negative for most model years. That is, even NHTSA’s 

estimates indicate that its “optimized” CAFE standard would lead to greater negative than 

positive external effects. As noted above, for example, NHTSA estimates that the proposed 

CAFE standards for MY2015 would result in net external costs of about $1.4 billion. 
Overview of the NHTSA Draft Benefit-Cost Analysis 

NERA Economic Consulting 15 

This conclusion is the opposite of what one generally expects from regulation. Normally 

one thinks of regulation as designed to reduce externalities at some cost to the individuals or 

firms engaged in the activity. For example, regulations to control tailpipe emissions raise the cost 

of owning and operating a vehicle and provide no substantial benefits to the vehicle’s owner, but 

they reduce negative externalities from air pollution that are borne by all members of society.

8

From an economic perspective, such regulations are justified when the external benefits exceed 

the private net costs. In the case of NHTSA’s proposed CAFE standards, however, the implicit 

argument is that larger adverse externalities should be borne by society as a whole in order to 

provide private benefits to vehicle owners. As noted, NHTSA has not provided any rationale for 

a major market failure that would justify such regulation. 

In Chapter III, we develop alternative estimates of private benefits and private costs that 

suggest that NHTSA’s estimates overstate private benefits and understate private costs. In 

addition, we develop alternative estimates of external benefits and costs that lead to greater net 

costs than NHTSA has estimated. 

 

8 As discussed in Chapter III, overall vehicle fleet emissions may not be reduced—or at least not reduced as much 

as implied by the new vehicle standards—because higher new vehicle prices lead to decreased scrappage rates 

for existing vehicles and thus greater emissions from the existing vehicle fleet. The net effect on overall motor 

vehicle emissions due to new vehicle standards is thus an empirical question. 

Table 5. Summary of NHTSA’s Estimates of Private and External Costs and Benefits, Combined Cars and 

Light Trucks 

MY2011 MY2012 MY2013 MY2014 MY2015

Private

Benefits $7,033 $14,836 $21,170 $24,445 $27,423

Costs $4,208 $8,013 $10,819 $12,416 $14,083

Net benefits $2,825 $6,823 $10,351 $12,029 $13,340

External net benefits (costs) $146 ($470) ($916) ($1,182) ($1,351)

Social Net Benefits $2,972 $6,353 $9,435 $10,846 $11,989

Note: Entries are in millions of 2006 dollars, discounted to the beginning of the respective model years at a real 

rate of 7 percent. 

Source: NHTSA PRIA Tables IX-1, VIII-9, and VIII-15 and NERA calculations of fuel taxes based on NHTSA 

inputs to Volpe model. 
NERA Economic Consulting 16 

III. Assessments of NHTSA’s Modeling and Estimates of 

Benefits and Costs 

This chapter summarizes our assessments of the various components of NHTSA’s 

benefit-cost analyses. We begin by considering the major modeling efforts that are involved in 

assessing the likely effects of CAFE standards on new vehicle markets, on motor vehicle fleet 

populations (including effects on used vehicles), and on VMT. We then consider NHTSA’s 

private benefit and cost estimates and after that NHTSA’s external benefit and cost estimates. In 

each of these sections, we develop alternative estimates and, in the case of private costs, report 

estimates developed by Sierra. The next section summarizes the alternative estimates and their 

implications for the net benefits/costs of the NHTSA “optimized” CAFE standards. The final 

section provides information on the sensitivity of the results to uncertainties regarding future 

gasoline prices. 

A. Modeling and Projections 

Our assessments of NHTSA’s modeling of consumers’ decisions indicate some major 

areas of concern. In particular, we address three major areas. 

1. NHTSA’s acknowledged failure to model consumer demand for new motor vehicles, 

calling into question its prediction that consumers will purchase more new vehicles if 

NHTSA promulgates the proposed CAFE standards; 

2. NHTSA’s acknowledged failure to evaluate the likely effects of changes in new vehicle 

prices and sales on the scrappage rates for existing vehicles and thus the effects of the 

proposed CAFE standards on the overall vehicle fleet; and 

3. NHTSA’s underestimate of the rebound effect. 

1. Consumer Demand for New Motor Vehicles 

The impact of the proposed CAFE standards on consumer demand for new motor 

vehicles depends on how consumers value the resulting increases in fuel economy relative to the 

increases in the prices of new vehicles. We summarize NHTSA’s approach, which includes 

separate and apparently inconsistent methodologies used to estimate social benefits and to 

estimate effects on new motor vehicle sales. We then summarize the model of the new vehicle 

market developed by NERA and use it to develop alternative estimates of the values that new 

vehicle purchasers place on the net effects of changes in new vehicle’s costs and in fuel economy 

attributed to the “optimized” CAFE standards. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 17 

a. NHTSA Uses Two (Inconsistent) Methodologies for Valuing Improved 

Fuel Economy 

NHTSA estimates the value of greater fuel economy to vehicle owners in two different 

ways. The first set of calculations relates to the NHTSA calculation of social benefits. In 

calculating social benefits that accrue to vehicle owners, NHTSA computes the sum of annual 

reductions in fuel expenditures, an increase in consumer value due to additional driving resulting 

from the rebound effect, and time savings from a reduction in the frequency of refueling. It then 

discounts those annual benefits at a rate of 7 percent (in the base case). This calculation results in 

estimated private benefits to vehicle owners that, as noted above, are more than double 

NHTSA’s estimates of the corresponding increases in new vehicle prices. These results imply 

that the proposed CAFE standards would increase vehicle purchases substantially, because in 

effect the quality-adjusted price of vehicles would decline substantially. Of course, that 

prediction begs the question: If higher MPG vehicles are so desirable, why do market forces not 

lead to the same outcome without new CAFE standards? 

NHTSA uses a second set of calculations to estimate the change in new vehicle sales (and 

related vehicle-manufacture employment). NHTSA recognizes that consumers do not attach the 

same value to fuel economy improvements that its benefit-cost analysis assumes. In estimating 

the impact of the standards on new-car purchases, NHTSA uses a completely different approach 

to estimating the value vehicle buyers place on greater fuel economy. The NHTSA calculations 

assume the following. 

1. Vehicle buyers value reductions in fuel use only over the first five years, but they do not 

discount those savings. 

2. Vehicle buyers bear additional costs in terms of higher sales taxes and insurance charges 

on more expensive vehicles. 

3. Vehicle buyers presume that the higher cost of the new vehicle will translate into a higher 

resale value in five years, but they discount that higher resale value at 3 percent. 

NHTSA does not provide an empirical foundation for these assumptions. Other than speculation 

about the lifetime of new-car loans (which may or may not be relevant), it offers no rationale for 

the five-year horizon for computing fuel savings. Nor does it explain why vehicle buyers would 

not discount those cost savings, or why they would discount resale value at 3 percent per year. It 

is not clear why higher insurance costs (presumably reflecting higher costs of repairing more 

costly vehicles) should not also be included in the social-cost calculation, or why consumers 

consider those costs but not the net benefits of driving more because of lower operating costs or 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 18 

the time savings NHTSA includes in its social benefit-cost analysis. In sum, NHTSA’s ad hoc

method for estimating how new-car buyers will value improved fuel economy appears to be 

largely arbitrary without a clear empirical or behavioral foundation. 

This second approach yields estimates of benefits to new-car buyers that are only about 

60 percent of NHTSA’s estimates of private benefits to car owners in its benefit-cost 

calculations.9 Nonetheless, it still generates estimated benefits to new-vehicle buyers that exceed 

NHTSA’s estimates of the compliance costs for the “optimized” CAFE standards for all years. 

As a result, NHTSA predicts that imposing the “optimized” standards would increase newvehicle sales (NHTSA PRIA, p. VII-43) and as a result that the standards will lead to increases in 

automobile sector employment. 

b. Limitations of NHTSA’s Characterization of the New Vehicle Market 

NHTSA’s prediction that its proposed “optimized” CAFE standards would lead to 

increased new vehicle sales does not make economic sense in terms of standard economic 

predictions about how markets operate. If imposing the standard would increase new car sales 

while covering manufacturers’ additional costs (including a markup), as NHTSA assumes, there 

is no economic reason why manufacturers would not make the changes on their own, without the 

CAFE standards, in which case NHTSA’s baseline for calculating CAFE effects would be too 

low. 

NHTSA recognizes the discrepancy between its prediction and actual market outcomes 

and requests comment on potential market failures that would lead auto manufacturers not to 

meet consumer demand (NHTSA PRIA pp. VII-43, VII-44). The only possible explanation 

(other than limitations in NHTSA’s estimates) is that vehicle manufacturers know less about 

their customers’ preferences or their own costs than NHTSA does, and thus pass up opportunities 

to improve the quality (e.g., fuel economy) of their vehicles in ways that will lead to higher sales 

while fully recovering the costs of those quality improvements. This explanation does not seem 

plausible in light of the economics of markets, leaving an alternative and more plausible 

explanation that NHTSA has underestimated costs or overestimated the benefits that vehicle 

buyers would receive, as evaluated by buyers of new vehicles. 

NHTSA recognizes that its current approach to estimating new-vehicle market responses 

to CAFE is inadequate, and requests comment on the desirability of developing a market model 

(NHTSA PRIA pp. V-59). We believe that such a model is essential to accurately estimating 

 

9

See Table 6 below. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 19 

how vehicle owners value the cost-MPG tradeoffs and how they would respond to new CAFE 

standards that increase both vehicle costs and vehicle fuel economy. 

c. NERA New Vehicle Market Model 

NERA has developed a model of the U.S. market for new cars and light-duty trucks. 

NERA used this model to analyze vehicle emissions regulations at the state-level, including 

California’s AB 1493 rule regulating CO2 emissions from motor vehicles. We have updated and 

reestimated the model with more recent data.10 Our model operates at the level of individual 

vehicle models (e.g. Chevrolet Malibu, Toyota Corolla) and their characteristics. The model 

utilizes data on transaction prices (as distinct from manufacturers’ suggested retail prices), sales 

volumes, and detailed vehicle characteristics for model years 2001 through 2007. Using this 

model, we are able to estimate the value that consumers place on reductions in fuel operating 

cost while controlling for other factors. Our model provides a more appropriate method for 

assessing private benefits than the ad hoc process developed by NHTSA because it is based on 

revealed consumer preferences and behavior rather than on a calculation of the operating cost 

savings that the average consumer would realize over some arbitrarily chosen time period.11 Put 

another way, our model is able to capture factors that matter to consumers but that are not 

included in the calculations performed by NHTSA in support of this rule making. In section B.2, 

below, we use our model to assess the value that new vehicle purchasers place on improved fuel 

economy as revealed by their market behavior. 

2. Implications of New Vehicle Market Changes for Changes in the Overall 

Motor Vehicle Fleet 

Changes in new vehicle prices and sales in turn will affect the scrappage of used vehicles 

and thus the changes over time in the age profile of the vehicle fleet. These changes are 

important because some important external impacts of CAFE—notably effects on criteria 

pollutant emissions and CO2 emissions—depend upon these overall fleet effects. 

 

10 Appendix B provides a more extensive description of the model and the data used to estimate the model 

parameters. 

11 The NERA model also avoids the need to use a discount rate to determine the value that new vehicle purchasers 

place on fuel economy improvements, which is particularly important given the difficulty of determining an 

appropriate discount rate for predicting consumer choices (see, e.g., Hausman 1979; Dreyfus and Viscusi 1995). 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 20 

a. General Effects of Changes in New Vehicle Prices on the Overall 

Fleet 

As discussed above and shown empirically below, the quantity of new vehicles sold 

would decline as a result of tighter CAFE standards because quality-adjusted prices would rise. 

In some cases those lost sales would translate into a smaller fleet of vehicles, but in many cases 

lost sales of new vehicles would represent owners of existing vehicles holding on to them longer; 

if a new vehicle costs more, it is worth spending more to maintain an older vehicle. As a result, 

the average age of the fleet would rise. 

Increasing the average age of the fleet would have implications for the external effects of 

the CAFE standards. In particular, increases in the average age of the fleet would lead to 

increases in the emissions of both CO2 emissions and conventional (criteria) pollutants. Criteria 

pollutants would increase for two reasons: (1) vehicles sold years ago were subject to less 

stringent standards; and (2) as vehicles age, their emission rates increase, whatever the emission 

rates were when the vehicles were new. For both reasons, additional older vehicles on the road 

would mean increased emissions. In addition, to the extent that older vehicles are less fuel 

efficient than new ones, aging the fleet also would reduce its average fuel economy and hence 

also raise CO2 emissions per mile. 

b. NERA Modeling of Fleet Effects 

Estimates of the quantitative impact of the scrappage effect on the composition of the 

vehicle fleet have been published in the literature for more than 25 years (e.g., see Gruenspecht 

1982; Berkovec 1985). In connection with regulation of CO2 emissions from motor vehicles in 

California, which are roughly equivalent in effect to tightening CAFE standards, NERA 

developed a modeling framework to assess the effects of changes in new vehicle costs and MPG 

on used vehicle scrappage rates and thus on the overall vehicle fleet over time. The changes in 

vehicle fleet were used to estimate effects on criteria pollutant emissions. 

As a result of the proposed CO2 regulation, our model projected that five years after the 

regulation was to be fully implemented, the total population of pre-regulation (older than 

MY2009) vehicles would increase by 250,000 vehicles (out of a projected fleet of roughly 25 

million vehicles in California in 2020). This projected increase in the population of older 

vehicles led to an estimated increase of 13,000 tons per year in combined emissions of NOx and 

VOCs from the vehicle fleet in California because these older vehicles are substantially higheremitting than newer vehicles. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 21 

In the time available to prepare these comments, we have not been able to develop the 

detailed data necessary to estimate the impact of higher prices on the age distribution of the 

national vehicle fleet starting in 2011 and the subsequent impact on emissions. However, such 

effects could be substantial, especially for NOX because as a vehicle ages, the effectiveness of 

NOX controls declines substantially. Compared to its own initial emission rate, a 10-yeAR-old 

MY2015 vehicle would emit about 14 times as much NOX per mile as it did when it was new, 

based on emission rates provided with the Volpe model. Although we cannot estimate the 

magnitude of the effects of NHTSA’s proposed CAFE standards on the age distribution of the 

fleet, the direction of the impact is clear—higher quality-adjusted prices for new vehicles would 

increase the average age of the fleet and further increase the external net costs generated by the 

proposed CAFE limits. 

3. Rebound Effect 

NHTSA assumes that the rebound effect, (i.e., the elasticity of VMT per vehicle with 

respect to fuel cost per mile) is -0.15. For example, if CAFE standards were to reduce fuel 

consumption per mile by 20 percent, NHTSA assumes that VMT will rise on average by 3 

percent for vehicles sold that are subject to the tighter limit. NHTSA’s current estimate is lower 

than the -0.2 value it used in 2006 for the MY2008-MY2011 Light Truck CAFE proceedings. 

The value of -0.2 appears more consistent with the literature summarized by NHTSA in Table 

VIII-1c (though still lower than the median estimates from most studies). 

NHTSA apparently has switched to the lower value based primarily on a single study 

(Small and Van Dender 2007) that was an unpublished working paper at the time of the earlier 

rule making but that since has been published (NHTSA PRIA, VIII-8). Our own detailed review 

of Small and Van Dender, however, shows that it has several weaknesses, including the failure of 

the model to properly account for differences in the cost of living across states.

12 As a result, we 

believe it is inappropriate for NHTSA to give that one paper disproportionate weight. We 

recommend that NHTSA rely on its earlier estimate of -0.2, which is more representative of the 

literature. 

Increasing the rebound elasticity from -0.15 to -0.2 increases the rebound-related change 

in VMT by about one-third and has the same effect on the various VMT-related benefits (costs). 

These effects include: 

 

12 Appendix B summarizes our review of the Small and Van Dender analysis. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 22 

1. Private benefits related to fuel-cost savings fall but consumer values from increased 

VMT increase, resulting in a small net gain in estimated private benefits using NHTSA’s 

approach to benefit estimation. Our market-based approach already incorporates 

consumers’ valuation of the fact that higher MPG lowers the marginal cost of driving and 

may lead to additional travel. 

2. External benefits related to reduced fuel use fall—and thus net external costs rise—

because of a reduction in: (1) petroleum-market effects; (2) CO2 emissions; and 

(3) upstream emissions of conventional pollutants from fuel production, distribution; and 

storage. 

3. External costs related to lost fuel-tax revenues decline because of the smaller reductions 

in fuel use. 

4. External costs related to VMT increase as a result of increases in (1) vehicle emissions of 

conventional pollutants; (2) congestion; (3) accidents; and (4) noise. 

We provide quantitative estimates of these effects below and their implications for the benefits 

and costs of the “optimized” CAFE standards. 

B. Private Net Benefits and Market Failure 

As discussed above, NHTSA’s estimates of vehicle owners’ benefits and costs appear to 

be inconsistent with market outcomes and with NHTSA’s projected baseline. There are three 

major potential explanations of these apparent inconsistencies: 

1. There are major market failures that result in vehicle owners and/or vehicle 

manufacturers failing to act in their own best interests. 

2. NHTSA’s estimates of fuel economy benefits to new-vehicle purchasers are too high. 

3. NHTSA’s cost estimates of fuel economy changes are too low. 

The estimates in this section provide alternative estimates of fuel economy benefits and costs that 

suggest that the second and third explanations are plausible explanations for the NHTSA results. 

We first consider the market failure issue. 

1. New Vehicle Markets and Absence of NHTSA Evidence of Market Failure 

In estimating private benefits to consumers, economists generally rely on consumers’ 

own preferences as revealed by their choices. If consumers choose not to buy a product at a price 

of $100, that choice reveals that the value they place on the product is less than $100. NHTSA 

takes this approach to a limited extent, for example, in estimating the value (consumer’s surplus) 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 23 

consumers place on additional VMT that result from the rebound effect; in that case, NHTSA 

computes the change in area under an empirically estimated demand curve, which values the 

additional VMT at the prices consumers have revealed they are willing to pay. Similarly, 

estimates of the damages of emissions of fine particles include estimates of the value of reducing 

risks to life derived from empirical studies of the tradeoffs workers make between those risks 

and higher wages, as revealed by the choices they make. For the potentially much larger benefits 

associated with reduced fuel consumption, however, NHTSA appears to ignore consumers’ 

revealed preferences and to substitute its own judgments. 

In ignoring consumers’ revealed preferences in calculating private benefits to those same 

consumers, NHTSA implicitly assumes that some (unidentified) market failures cause vehicle 

owners to underestimate substantially the value to themselves of more fuel-efficient vehicles. 

NHTSA does not explain what it believes those market failures to be or provide evidence to 

support the existence of such market failures. In responding to the requirement of Executive 

Order 12866 that all new federal regulations identify and evaluate the relevant market failure, 

NHTSA points to EPCA and a judgment that in passing EPCA Congress had made the decision 

that the market would not provide the appropriate balancing (NHTSA PRIA, p. XI-3). 

Nevertheless, it would be useful for NHTSA to clarify the assumed market failure if for no other 

reason than to justify the basis for its assessment of the private benefits of the proposed CAFE 

standards.13

One “potential” market failure is that car buyers grossly underestimate the savings from 

vehicles with higher MPG. In a society in which consumers are generally considered competent 

to choose the products they consume, absent significant externalities, such an “explanation” 

requires showing that consumers are systematically underestimating their own value of fuel 

economy improvements. The mere fact that consumers’ implicit judgments about their private 

value of fuel economy benefits differ from NHTSA’s estimates is not sufficient to justify 

NHTSA’s estimates. New car buyers may consider other factors that matter to them and may 

 

13 EO 12866 also requires that agencies identify and assess alternative forms of regulation. As numerous observers 

have noted (e.g., Austin and Dinan 2005; Congressional Budget Office 2003; Fischer et al. 2007), a gasoline tax 

would be a much more economically efficient tool for reducing gasoline consumption than CAFE standards. 

Compared to CAFE, a gasoline tax would provide balanced incentives to reduce fuel use in several ways in 

addition to purchasing more fuel efficient vehicles. In particular, rather than creating a rebound effect, a gasoline 

tax would provide incentives for vehicle owners to drive less, and those incentives would apply to the owners of 

existing vehicles as well. Morover, whereas CAFE provides a disincentive to retire older vehicles, a gasoline tax 

would provide incentives for owners of less fuel-efficient vehicles to retire them. In terms of the objective of 

reducing CO2 emissions, as many observers have noted, a carbon tax or a cap-and-trade program would provide 

more efficient incentives than technology standards for individual sectors (see, e.g., Portney et al. 2003). 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 24 

evaluate costs and benefits over time using different implicit discount rates that reflect their own 

situations. Consumers make many choices that involve tradeoffs between upfront costs and 

operating costs, and most of those tradeoffs involve substantially less information than vehicle 

buyers have regarding fuel economy. EPA tests vehicles for fuel economy and manufacturers 

post the results in the windows of new cars, providing vehicle buyers with clear information on 

fuel consumption. In contrast no government agency tests and rates vehicles for their reliability 

and durability or provides information on routine service costs. As a result, consumers make 

tradeoffs between vehicle purchase prices and those operating costs based on much less 

information than they have with respect to fuel economy. Moreover, consumers make similar 

tradeoffs between initial cost and durability for a host of products, from air conditioners to 

carpets, yet no one suggests that there are major market failures warranting government 

regulation of product durability. 

The issue of market failure is brought into particularly sharp focus here because the net 

external effects of the proposed standards—which all agree are a result of market failure—are 

negative; i.e., NHTSA’s conclusion that the rule provides positive net benefits rests on its 

estimate that the rule would be net beneficial to vehicle owners by a large enough margin to 

offset the external costs imposed by the CAFE regulation on society at large. However, the fact 

that NHTSA’s baseline assumes that consumers would not voluntarily choose the higher MPG 

levels required under the standard implies that the costs exceed the private benefits; this would 

indicate that at best, the private net benefits would be zero. Normally the question then would be 

whether the external net benefits were large enough to tip the balance in favor of regulation. In 

this case, however, the externalities are negative, so zero net benefits from the perspective of 

vehicle owners means negative net benefits from a societal perspective. 

We urge NHTSA to continue to pursue development of a market model that would 

provide both more realistic predictions of market responses and the basis for using car owners’ 

own preferences, as revealed in their choices, to value the net costs to them of higher CAFE 

standards. Such an approach would be consistent with how benefit-cost analyses (including other 

parts of NHTSA’s own analysis) routinely value benefits by using market demand curves to infer 

consumer values. In the absence of such a model and a compelling argument that clearly 

identified market failures have cause vehicle buyer not to act in their own private best interests, 

we see no reason for NHTSA to conclude that the private net benefits (private benefits minus 

higher new-vehicle purchase prices) from its “optimized” CAFE standard would be greater than 

zero. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 25 

2. NERA Estimates of Private Benefits to New Vehicle Purchasers 

We used our new vehicle market model, described above, to estimate the value that new 

vehicle purchasers place on NHTSA’s estimates of fuel economy improvements for cars and 

light trucks. These estimates are used in this section to estimate per vehicle benefits and costs 

and, in conjunction with NHTSA per vehicle cost estimates, to estimate effects on new vehicle 

sales and sector employment. A subsequent section uses the NERA consumer valuations of fuel 

economy to assess the private benefits of the “optimized” standard. 

Using our model, we estimate that new vehicle buyers on average place a value of $1,111 

on a reduction in fuel consumption of 1 gallon/100 miles at a retail price, inclusive of taxes, of 

$2.23/gallon (2006 dollars), NHTSA’s projected price in 2015 based on the “reference” gasoline 

price forecast made by the Energy Information Administration (“EIA”).14 (In section E, below, 

we test the effects of alternative fuel prices, including EIA’s “high” gasoline price forecast.) 

We applied the estimated value from the model to the 2015 “optimized” standard, which 

NHTSA estimates would improve the average passenger car on-road MPG from 24.6 to 27.8 

MPG, or a reduction in fuel consumption of 0.469 gallons/100 miles.15 The estimated value of 

this improvement to consumers is 0.469 x $1,111 = $521 using NHTSA’s projected MY2015 gas 

price. NHTSA estimated the average cost per car to be $649. Thus, our estimate indicates that 

car buyers would see the proposed CAFE standard as raising the quality-adjusted price of an 

average new car by $649 - $521 = $128. This increase in quality-adjusted price would result in 

lower car sales, the opposite of the conclusion NHTSA reaches. Note that our prediction—that 

vehicle sales would decline—is consistent with NHTSA’s baseline that implicitly assumes that 

the standard would not be met absent the compulsion of higher CAFE standards. 

Table 6 summarizes the results for MY2015, comparing the two NHTSA estimates of 

benefits and those based on NERA’s market model of revealed preferences for passenger cars 

and light trucks. In all three cases, we use NHTSA’s estimated costs to compute private net 

benefits. For cars and light trucks combined, NHTSA’s social benefit-cost analysis estimates that 

vehicle owners will reap an average of $778 per vehicle in net benefits. NHTSA’s ad hoc

approach to estimating the value that vehicle buyers actually place on improved fuel economy 

 

14 An improvement of 1 gallon/100 miles is equivalent to an improvement of about 12.9 MPG from a base of 30 

MPG. 30 MPG = 3.33 gallons per hundred miles. 42.9 MPG = 2.33 gallons per hundred miles. Note that our 

regression uses data on individual model’s MPG as rated by the same EPA test used to evaluate compliance with 

the CAFE limits. The EPA test MPGs are multiplied by 0.8 to obtain the “on-road” vehicle MPG for use in the 

regression. 

15 1/(27.8 MPG) – 1/(24.6 MPG) = 0.00469 gallons per mile. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 26 

yields an estimate of private net benefits of $147, which is smaller but still positive. Finally, 

based on the NERA model of consumer choice of new vehicles, we estimate negative net private 

benefits with an average private net cost of $154 per vehicle. 

Table 7 provides a summary comparison of the cAR-truck combined estimates for all 

model years. As with MY2015, NHTSA’s social benefit-cost analysis implies large private net 

benefits per vehicle. NHTSA’s analysis from the perspective of vehicle buyers yields positive net 

benefits for every model year. In contrast, NERA’s market model estimates that the value to 

consumers of the proposed standard is substantially less than NHTSA’s estimated costs. 

3. Estimated Changes in New Vehicle Sales and Employment Based on 

NHTSA and NERA Estimates of Private Benefits and NHTSA Estimates 

of Private Costs 

Based on its estimate that the “optimized” CAFE standard for MY2015 reduces the net 

cost of new vehicles from the perspective of new-vehicle buyers by an average of $147 vehicle 

and an assumed demand elasticity of -1.0, NHTSA estimates that new-vehicle sales in MY2015 

would increase by 86,000 units. Furthermore, based upon the average number of new vehicles 

produced per worker (10.5 vehicles per job), NHTSA estimates that such an increase in 

Table 6. Comparison of NHTSA and NERA Estimates of Private Benefits and Net Benefits per Vehicle for 

MY2015 

Estimate Cars Light Trucks Combined

Private Benefits

 NHTSA social B-C analysis $1,246 $1,923 $1,599 

 NHTSA analysis vehicle-buyer perspective $713 $1,202 $968 

 NERA estimate of revealed preference $521 $801 $667 

Private Costs (NHTSA) $649 $979 $821 

Private Net Benefits

 NHTSA social B-C analysis $597 $944 $778 

 NHTSA analysis from vehicle-buyer’s perspective $64 $223 $147 

 NERA estimate based on revealed preferences ($128) ($178) ($154)

Note: All entries are per vehicle (2006 dollars). 

The NHTSA analysis of benefits from the buyer’s perspective includes the amount [(1-0.911) x cost] to 

account for NHTSA’s adjustment to purchase price that accounts for the residual value of more costly 

vehicles after five years and the cost of insurance. This is not necessary using the NERA method because 

our coefficient on $/mile incorporates any change in vehicle resale value and the cost of insurance. 

Source: NHTSA PRIA table VII-5c and VII-4c and NERA calculations as explained in text. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 27 

production would increase U.S. employment in the vehicle manufacturing industry by about 

8,200 jobs. 

In contrast, our estimates of net benefits to vehicle buyers are negative, meaning that 

quality-adjusted prices would rise and sales would fall, as would employment in the industry. 

Using NHTSA’s estimate of the own-price elasticity of demand for new vehicles, we estimate 

that sales of new vehicles would fall in MY2015 by 98,000 units. Applying NHTSA’s estimate 

of employment effects per vehicle, that reduction in MY2015 new vehicle sales would translate 

to a loss of 9,000 jobs. As shown in Table 8, our results lead to reductions in new vehicle sales 

and automotive sector jobs for all model years. 

In sum, NERA’s estimates of private benefits imply very different conclusions regarding 

the effects of the “optimized” CAFE standards on new vehicle sales and automobile sector 

employment. NHTSA predicts that the “optimized” standards would increase sales and 

employment, and NERA’s estimates of vehicle-buyers’ benefits indicate that sales and 

employment would fall. Note that both sets of estimates in Table 8 assume NHTSA’s 

compliance cost estimates; as discussed below, the negative impacts on new vehicle sales and 

employment would be much greater if Sierra’s estimates of costs are used. Note also that our 

estimate of employment impacts applies NHTSA’s estimate of jobs per vehicle sold to our 

estimate of changes in sales. 

Table 7. Comparison of per-Vehicle Private Costs and Benefits for Cars and Light Trucks, Combined, All 

Model Years 

Estimate MY2011 MY2012 MY2013 MY2014 MY2015

Private Benefits

NHTSA social B-C analysis $419 $879 $1,242 $1,427 $1,599 

NHTSA analysis vehicle-buyer perspective $295 $550 $763 $869 $968 

NERA estimate of revealed preference $187 $389 $527 $606 $667 

Private Costs (NHTSA) $251 $475 $635 $725 $821 

Private Net Benefits

NHTSA social B-C analysis $168 $404 $607 $702 $778 

NHTSA analysis from vehicle-buyer’s perspective $44 $75 $128 $144 $147 

NERA estimate based on revealed preferences ($64) ($86) ($108) ($119) ($154)

Note: All entries are per vehicle (2006 dollars). 

NHTSA cost estimate includes fines for manufacturers that do not meet CAFE standards. 

Source: NHTSA PRIA table VII-5c and VII-4c and NERA calculations based on NHTSA PRIA text and data. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 28 

4. Sierra’s Estimates of Compliance Costs for New Vehicle Purchasers 

As noted above, we identify two likely explanations for the inconsistency between 

NHTSA’s estimate of large net benefits to vehicle buyers from greater fuel economy and 

NHTSA’s implicit judgment that market forces would not lead to the contemplated MPG levels 

without tighter CAFE standards that would require them. The first explanation is that NHTSA 

has overestimated the private benefits of higher fuel economy. The second explanation is that 

NHTSA has underestimated the costs of making vehicles that would meet its proposed standards. 

These explanations are not mutually exclusive; NHTSA’s estimates of private benefits could be 

too high and its cost estimates also could be too low. 

We have not reviewed NHTSA’s estimates of the costs of implementing the technologies 

needed to meet tighter CAFE standards. Sierra, however, has developed its own independent 

estimates of the costs to motor vehicle manufacturers to meet the “optimized” MY2015 

standards (Sierra 2008). Table 9 compares Sierra’s estimated costs per vehicle to NHTSA’s. The 

last column shows the ratios of the two estimates. 

16For cars, Sierra’s estimates average 18 

 

16 We use Sierra’s estimates of compliance costs measured incrementally to the 2015 baseline in order to be 

consistent with NHTSA’s and our benefit estimates, which also are measured relative to the 2015 baseline. 

Sierra’s summary Table 1 shows higher values because they are measured relative to a 2006 baseline. The values 

we report are in Table 6-9 of the attachment to Sierra’s report. 

Table 8. Comparison of NHTSA and NERA Estimates of Impacts of "Optimized" Standards on Vehicle 

Sales and Employment 

Estimate MY2011 MY2012 MY2013 MY2014 MY2015

Car and light truck combined sales

NHTSA analysis from vehicle-buyer’s perspective 26,839 47,716 78,935 86,698 85,769

NERA estimate of revealed preference -40,000 -54,000 -69,000 -76,000 -98,000

Change in U.S. employment by 

vehicle manufacturers and suppliers

NHTSA analysis from vehicle-buyer’s perspective 2,556 4,544 7,518 8,257 8,169

NERA estimate of revealed preference -4,000 -5,000 -7,000 -7,000 -9,000

Note: Estimated changes in sales based on changes in quality-adjusted prices (Table 7) and an own-price 

elasticity of demand of -1.0. 

Estimated changes in employment are the estimated changes in sales divided by NHTSA’s estimate that 

for every change in sales of 10.5 vehicles, there is a corresponding change in employment of one job 

(NHTSA PRIA VII-53). NHTSA’s employment estimate applies to employment in original automobile 

manufacturers and suppliers. 

Source: NERA calculations and NHTSA PRIA Tables VII-6b and VII-8. Note that we have corrected an apparent 

typographical error in the value from table VII-8 for MY2014 – the jobs impact was reported as negative 

despite the sales impact being positive in that year. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 29 

percent higher than NHTSA’s. For light trucks, Sierra’s estimates are more than three times 

NHTSA’s. 

5. Estimated Changes in New Vehicle Sales and Employment Based on 

NHTSA and NERA Estimates of Private Benefits and NHTSA and Sierra 

Estimates of Private Costs 

We used Sierra’s cost estimates to estimate the private net benefits per vehicle of the 

“optimized” MY2015 standards, using both NHTSA’s estimates of private benefits and our own. 

We then used these sets of estimated net private benefits to estimate changes in vehicle sales and 

employment. Table 10 shows the results for all four possible combinations of private benefit and 

cost estimates: 

1. NHTSA benefits and NHTSA costs; 

2. NERA benefits and NHTSA costs; 

3. NHTSA benefits and Sierra costs; and 

4. NERA benefits and Sierra costs. 

The first two columns repeat results from Table 7 and Table 8 for ease of comparison. 

The last two columns show the new results incorporating Sierra’s cost estimates. The results 

using Sierra’s costs would mean much larger reductions in new vehicle sales and automotive 

sector employment than using NHTSA’s cost estimates. With the Sierra cost estimates, the 

estimated reductions in new vehicle sales for MY2015 range from about 680,000 to almost 

860,000, depending upon whether NHTSA or NERA benefit are used. These reductions in sales 

would lead to reductions in estimated automotive sector employment of between 65,000 and 

82,000 using the same NHTSA assumptions as noted above regarding the average number of 

vehicles per worker. 

Table 9. Comparison of NHTSA and Sierra's Estimated Costs per Vehicle of Meeting the MY2015 

"optimized" standard 

NHTSA Sierra Ratio

Cars $649 $766 1.18

Light Trucks $979 $3,262 3.33

Note: All entries are per vehicle (2006 dollars). 

Source: NHTSA PRIA and Sierra Research (2008, Table 6-9 of Attachment) 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 30 

After considering NHTSA’s external benefits and costs in the next section—including 

developing a set of NERA estimates—we develop private and external benefit estimates for the 

“optimized” CAFE standard under these same four set of alternatives. 

C. External Benefits and Costs 

As summarized in the previous chapter, NHTSA estimates external benefits and costs in 

several categories. Virtually all of these estimates are affected by the rebound effect, as discussed 

above. This section develops alternative estimates of external costs and benefits based upon a 

revised rebound effect and other changes. 

1. Fuel Tax Revenues 

Reduced fuel use results in lower fuel tax payments by vehicle owners (a private benefit) 

and lower fuel tax receipts by government (an external cost). A larger rebound effect makes the 

change in tax payments smaller, thus reducing external costs.17

 

17 It also reduces the tax savings for vehicle owners, but the rebound effect on vehicle owners is reflected in the 

market model’s estimates of revealed preference. 

Table 10. Estimated Private Net Benefits per MY2015 Vehicle Based on Varying Combinations of 

Estimated Benefits and Costs 

Benefits: NHTSA NERA NHTSA NERA 

Costs: NHTSA NHTSA Sierra Sierra

$968 $667 $968 $667 

$821 $821 $2,069 $2,069 

$147 ($154) ($1,101) ($1,401)

86,000 -856,000 -680,000 -856,000

8,000 -82,000 -65,000 -82,000

Per-Vehicle Benefits and Costs

Private benefits

Increased cost of new vehicles

Private net benefits

Car and light truck combined sales

Change in U.S. employment by 

vehicle manufacturers and suppliers

Notes: First row of column headings shows source of estimated private benefits. “NHTSA” benefit estimates are 

based on its analysis from buyer’s perspective. 

All dollar entries are retail price equivalents per vehicle (2006 dollars). 

Estimated changes in sales based on changes in quality-adjusted prices and an own-price elasticity of 

demand of -1.0. 

Estimated changes in employment are the estimated changes in sales divided by NHTSA’s estimate that 

for every change in sales of 10.5 vehicles, there is a corresponding change in employment of one job 

(NHTSA PRIA VII-53). NHTSA’s employment estimate applies to employment in original automobile 

manufacturers and suppliers. 

Source: NHTSA PRIA, Sierra Research (2008), and NERA calculations as explained in text. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 31 

2. CO2 Emissions 

a. Global CO2 Damages per Tonne 

NHTSA’s estimate of the value of reductions in CO2 emissions is based upon an estimate 

made by the Intergovernmental Panel on Climate Change (IPCC 2007), which estimates 

damages to be about $12 per metric tonne currently (in 2006 dollars). The IPCC estimate in turn 

relies heavily on a review of the literature by Tol (2005). NHTSA assumes that damages per 

tonne are growing about 2.4 percent per year, yielding an estimated value of $14 per tonne in 

2011. 

b. Value for U.S. Damages per Tonne 

As NHTSA acknowledges, a fundamental problem with this estimate is that it is based on 

global damages rather than limited to damages in the United States, as NHTSA is required to 

consider. NHTSA states that it intends to revise its CO2 estimates to reflect damages only in the 

United States. As an interim measure, it uses a range of $0 to $14 per tonne for U.S. damages, 

with a central value of $ per tonne. The identification of $7 per ton as the “central value” implies 

that NHTSA’s “most likely” estimate is that fully half of all damages from global CO2 emissions 

would occur in the United States, an assumption that NHTSA does not justify. 

Unfortunately, the IPCC and others have focused primarily on global rather than countryspecific damages. In his review of the literature, for example, Tol does not report separate results 

by country. The impacts of climate change will vary from country to country, depending on a 

large number of factors. A more reasonable assumption than NHTSA’s assumption that the 

United States would obtain half of all benefits would be to assume that benefits from reduced 

CO2 emissions would be roughly proportional to national income.18 The International Monetary 

Fund estimates that, as of 2007, U.S. GDP comprised about 21 percent of the world total, based 

on exchange rates reflecting purchasing power parity (IMF 2007). That share was down from 

earlier years and is likely to continue to fall as the developing world’s economies expand faster 

then those of the United States and other developed countries. Using a value of 20 percent, the 

global value for CO2 damages of $14 per tonne translates to a “most likely” value of $2.80 for 

the U.S. alone in 2011. Correspondingly, NHTSA estimates of total carbon-related benefits fall 

 

18 The assumption that CO2 benefits are proportional to national income may overstate benefits in the United States 

for several reasons. For example, wealthier nations such as the United States have smaller fractions of their 

economy in sectors such as agriculture and forestry that may be most affected by climate change. Wealthier 

countries also may be better able to adapt to climate change (Schelling 1992). 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 32 

by 60 percent with the revised value. For example, the estimated carbon benefits for MY2015 

under the “optimized” CAFE limit would fall from $869 million to $348 million. 

3. Conventional Air Pollutants 

NHTSA’s estimates of reduced emissions of conventional air pollutants (NOX, VOCs, 

PM, and SO2) reflect the sum of changes in emissions in three broad categories: (1) reductions in 

upstream emissions from production, distribution, and storage of fuels; (2) reductions in 

upstream and vehicular emissions dues to NHTSA’s projection that manufacturers will respond 

to tighter CAFE limits by switching some models from gasoline to diesel engines19; and 

(3) increases in tailpipe emissions as a result of increased VMT associated with the rebound 

effect. Unfortunately NHTSA reports only the combined net effect of these three impacts, and it 

does not provide sufficient information to replicate its results. However, we have been able to do 

rough breakdowns of the individual components. 

a. Stationary Source Emission Factors 

We were able to investigate the plausibility of some of the emission factors used by 

NHTSA to estimate reductions in upstream emissions as the result of lower fuel consumption. 

We compared estimates of total emissions from refineries in the National Emission Inventory 

database for 2002 (the most recent inventory publicly available) to estimates of refining 

emissions based on NHTSA’s emission factors for refineries and U.S. production of gasoline and 

diesel fuels in that same year (EPA 2002). The estimates based on NHTSA’s emission factors 

should be smaller than the inventory figures, because refineries produce other products besides 

gasoline and diesel fuel. However, as shown in Table 11, the reverse is true; for all but one of the 

pollutants, the estimates based on NHTSA’s rates for only two refinery products (gasoline and 

diesel fuel) are larger than the NEI estimates for all refinery operations. The differences are 

particularly large for NOx and PM2.5, for which the NHTSA estimates are more than two and 

three times as high, respectively. These comparisons suggest that NHTSA’s emission factors are 

too high for NOx and PM2.5.

20 Lower emission rates would reduce the benefits from reductions in 

upstream emissions substantially. As a partial correction of the emission factors used by 

 

19 Diesel engines have significantly lower emission rates for several pollutants and diesel fuel has slightly lower 

upstream emissions per gallon than gasoline. 

20 Emissions of VOCs in the inventory are higher than those implied by NHTSA’s emission rates for gasoline and 

diesel. However, one cannot infer that NHTSA’s emission rate for VOC emissions from refineries is inconstant 

with the inventory, because refineries product many products besides gasoline and diesel fuel. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 33 

NHTSA, we scale them back by the amount by which the NHTSA estimates for the two products 

over-predict the NEI estimates for all products.

21

In addition to the adjustments related to the NEI, we note that it probably is not 

appropriate to use average refinery emissions from the present or recent past to predict 

incremental emissions from refineries from 2011 through 2051 (the year in which the last light 

truck produced in MY2015 is scrapped based on NHTSA’s survival estimates). There are two 

reasons: (1) over time, many existing refineries will be replaced by new refineries subject to 

stringent New Source Performance Standards or their emissions will be reduced by tighter rules 

for existing refineries in non-attainment areas and (2) at the margin, reductions in fuel demand in 

the long run are likely to be met by reduced construction of new refineries (with very low 

emissions) or reduced imports of fuel from outside the United States (which do not have any 

U.S. emissions). We have not attempted to quantify these effects, however. 

b. Values per Ton of Conventional Pollutant 

In valuing changes in emissions of conventional pollutants, NHTSA used the values 

shown in the first numerical column of Table 12. As its basis for these values, NHTSA cites 

“‘Mobile Source $ per Ton Estimates’ provided to NHTSA by staff in EPA’s Office of 

Transportation and Air Quality” (NHTSA PRIA 2008, footnote 203). This “document,” 

however, apparently is just a table with recommendations from EPA staff.22 It provides separate 

 

21 Emissions of VOCs in the inventory are higher than those implied by NHTSA’s emission rates for gasoline and 

diesel. However, one cannot infer that NHTSA’s emission rate for VOC emissions from refineries is too low 

compared to the inventory because refineries produce many products besides gasoline and diesel fuel. 

22 This table is actually titled “$/ton recommendation.” Another table is entitled “Mobile Source $ per Ton 

Estimates (2000$).” This table reports values for various years apparently from RIAs for two truck rules, but 

there is no obvious relationship between the values in this table and the values used by NHTSA. 

Table 11. Comparison of 2002 Refinery Emissions Inventory with Emissions Based on NHTSA Emission 

Factors 

Source CO NOx SO2 VOC PM2.5

2002 NEI (Petroleum Refining) 145,111 195,167 338,710 125,186 22,763

Projected using NHTSA Rates and 2002 

Refinery Output for Gasoline and Diesel Only 158,537 428,411 348,871 80,007 79,593

NHTSA as percent of NEI 109% 220% 103% 64% 350%

Note: All entries are in short tons per year. 

Source: Volpe model input data, NERA calculations, and EPA 2002 National Emissions Inventory 

(http://www.epa.gov/ttn/chief/net/2002inventory.html). 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 34 

recommended values for tailpipe and upstream emissions, but it appears that NHTSA simply 

averaged those estimates. 

For comparison, the second column of Table 12 shows the mid-ranges of values used by 

OMB in a 2006 report to Congress. As OMB emphasizes, the values it reports are not 

independent estimates, but rather OMB’s summary of values contained in EPA RIAs. OMB’s 

values are slightly higher than NHTSA’s for VOCs, but substantially lower for PM2.5 and SOX. 

For NOX, OMB reports separate values for emissions from mobile and stationary sources, with 

the stationary-source value less than half of the NHTSA value and the mobile-source value about 

50 percent larger. Recall, however, that NHTSA’s estimates of changes in emissions generally 

reflect increases in mobile-source emissions from the rebound effect and reductions in upstream 

stationary source emissions. As a result, the most appropriate approach would be to use 

stationary-source values for changes in emissions from stationary sources and mobile-source 

values for the changes increases in emissions from mobile sources. Unfortunately, NOX is the 

only pollutant for which OMB reports separate values for stationary and mobile sources. 

c. Implications of Fleet Population Effects 

As discussed earlier, increasing quality-adjusted new vehicle prices will lead to an 

increase in the average age of the vehicle fleet. That change in the age distribution of vehicles on 

the road will increase emissions both because older vehicles faced less stringent emission 

standards when sold and because the effectiveness of controls (especially those for NOX) 

declines as the vehicle ages. We have not been able to quantify this effect in the time available, 

however. 

Table 12. Estimated Values per Ton of Conventional Pollutant 

Pollutant NHTSA OMB

VOC $1,700 $1,900

NOX, stationary $1,600

NOX, mobile $5,800

PM2.5 $164,000 $62,600

SOX $16,000 $9,200

$3,900

Note: All entries are 2006 dollars/ton of emissions. 

Source: NHTSA: PRIA 2008, table VIII-B. 

OMB: OMB (2006, p. 64) updated from 2001 to 2006 dollars using GDP deflator. The table shows the 

average of OMB’s “low” and “high” estimates. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 35 

4. Other VMT-related Externalities 

NHTSA identified three types of non-pollution externalities associated with increased 

VMT from the rebound effect: congestion; accidents; and noise. For MY2015, these three 

categories account for a total of $2.7 billion in external costs. Of that total, 66 percent is 

associated with congestion, 33 percent with accidents, and only 1 percent with noise. Because 

these effects are proportional to the rebound effect, they all increase by about one third if we use 

a rebound elasticity of -0.2 instead of -0.15, as we recommended earlier. 

It also is likely that NHTSA has underestimated congestion costs per additional VMT 

because it failed to adjust the older FHA estimate on which it relied for consistency with the 

value of time it uses in its analysis of the benefits of reduced refueling as a result of higher MPG. 

NHTSA uses values for congestion of $0.0522/VMT for cars and $0.0466/VMT for full-size 

vans and pick-up trucks. It cites a 1997 study by the Federal Highway Administration (“FHA”) 

as the basis for these estimates. That study reports values of $0.0448/VMT for cars and 

$0.040/VMT for vans and pickups in 1994 dollars (FHA 1997, Table V-22). NHTSA apparently 

updated those values to 2006 dollars, though it is not clear how it did so. 

Further examination of the 1997 study shows that it was based on a value of $12.38 per 

vehicle hour (in 1994 dollars) (FHA 1997, Table III-11). In the PRIA, NHTSA uses a value of 

$24 per vehicle hour to value time savings it estimates would result from fewer fill-ups as a 

result of higher MPG and increased range for a tank of fuel. These values imply that NHTSA 

currently values each vehicle hour roughly 1.95 times as much (in 2006 dollars) as the FHA did 

in 1997 (in 1994 dollars).23 Applying that ratio to the estimates of congestion costs per vehicle 

mile in the FHA report yields estimates in 2006 dollars of $0.088/mile for cars and $0.078/mile 

for full-size vans and pickups. These per hour values are about 68 percent higher than the ones 

used by NHTSA, which would increase NHTSA’s estimated congestion costs by the same 

amount. 

In addition, it is likely that the 1997 congestion values should be adjusted upward even 

more to reflect increasing levels of congestion between then and now and the further increases 

likely between 2011 and 2051 (the range of years over which NHTSA computes congestion 

 

23 This ratio does not reflect changes in average wages only. Presumably it also reflects changes in the assumptions 

that NHTSA or the FHA made about the fraction of the wage rate that represents how people value their time in 

vehicles. Our adjustment reflects a judgment that time spent in traffic congestion is valued at least as highly as 

time spent in service stations while filling up, in part because stops at service stations often serve multiple 

purposes, not just refueling. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 36 

costs).24 With higher baseline congestion, the marginal impact of additional VMT will increase 

because congestion, like other queuing phenomena, increases at an increasing rate as capacity 

utilization grows. 

We have not reviewed NHTSA’s estimates of accident and noise costs per mile. We note 

on accident costs that a recent study by researchers at Resources for the Future (Parry et al. 2007) 

estimated a value per mile roughly 20 percent higher ($0.030 vs. $0.025) than NHTSA’s. 

5. Summary of NHTSA and NERA Estimates of External Costs and Benefits 

Table 13 summarizes the effects of our estimated external benefits and costs, based upon 

the revisions discussed above. The first column shows NHTSA’s estimates. The next three 

columns show the effects of our revisions, with separate results for rebound-related and other 

changes, along with the total impact. The last column shows the revised estimate accounting for 

all of the quantified changes. Each row reports a different category of external benefits (costs). 

 

24 NHTSA projects VMT for light trucks for 36 years from the point of sale. For MY2015 light trucks, the 

projection would go out to 2051. 

Table 13. Impacts of NERA Adjustments to NHTSA External Benefits (Costs), MY2015 

Item Rebound Other Total

Reduced fuel tax revenues ($3,658) $183 $0 $183 ($3,476)

CAFE Fines $460 $0 $0 $0 $460

Reduced CO2 emissions $869 ($47) ($522) 1

($569) $300

VOC Emissions $9 ($6) $0 2

($6) $3

NOX Emissions $89 ($14) ($70) 2,3 ($84) $5

PM Emissions $419 ($45) ($211) 2,3 ($255) $164

SO2 Emissions $244 ($6) ($95) 2,3 ($101) $143

Congestion ($1,802) ($601) ($1,634) 4

($2,234) ($4,036)

Noise ($26) ($9) $0 ($9) ($34)

Accidents ($885) ($295) $0 ($295) ($1,180)

Petroleum market externalities $2,931 ($146) $0 ($146) $2,785

External net benefits ($1,351) ($985) ($2,531) ($3,515) ($4,866)

NHTSA 

Total

NERA 

Total

Changes due to:

Notes: All entries are present values (discounted at 7 percent real to start of 2015), in millions of 2006 dollars. 

1Revised value per tonne to reflect U.S. damages only. 

2

Substitute OMB value(s) per ton. 

3 Adjust refinery emissions factor based on NEI. 

4Adjusted for consistency in value placed on vehicle-hours. 

Source: NHTSA PRIA and NERA calculations as explained in text. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 37 

Overall, as shown in the last row, the NERA estimates are about $4.9 billion, an increase 

in external costs of about $3.5 billion over NHTSA’s estimate of about $1.4 billion. Of the total 

change, about $1 billion reflects the impacts of using a higher rebound elasticity that is more 

representative of the literature. The remainder of the difference, about $2.5 billion, reflects the 

other adjustments, mostly due to the increase in congestion costs and the reduction in CO2

damages to reflect impacts in the United States only, as required by law and as consistent with 

NHTSA’s treatment of petroleum-market related external benefits. 

Table 14 summarizes these adjustments for the proposed CAFE standards for all model 

years. In all years, our adjustments increase external net costs substantially, with the revised net 

costs ranging from $0.7 billion for MY2011 to $4.9 billion for MY2015. 

These results reinforce the conclusion reached earlier using NHTSA’s own estimates. In 

contrast to the usual rationale for government regulation, these proposed regulations do not 

internalize externalities that private decision makers otherwise would ignore; rather the proposed 

CAFE “optimized” standard would increase external costs beyond what the market would yield 

on its own. 

D. Alternative Estimates of Benefits and Costs of NHTSA Proposed 

CAFE Standards 

As noted above, we develop results for the “optimized” standard using four combinations 

of benefit and cost estimates: 

1. NHTSA benefits and NHTSA private costs; 

2. NERA benefits and NHTSA private costs; 

Table 14. Summary of Adjustments to External Benefits (Costs), All Model Years 

Model Year Rebound Other Total

2011 $146 ($244) ($639) ($882) ($736)

2012 ($470) ($514) ($1,343) ($1,857) ($2,327)

2013 ($916) ($738) ($1,907) ($2,644) ($3,561)

2014 ($1,182) ($864) ($2,234) ($3,099) ($4,281)

2015 ($1,351) ($985) ($2,531) ($3,515) ($4,866)

NHTSA 

Total

NERA 

Total

Changes due to:

Note: Entries are present values (discounted at 7 percent real to the start of the model year in question) 

expressed in millions of 2006 dollars. 

“All Years” reflects the value for each year discounted to a common year (2011) and summed. 

Source: NHTSA PRIA tables VIII-9 and VIII-15 and NERA calculations. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 38 

3. NHTSA benefits and Sierra private costs; and 

4. NERA benefits and Sierra private costs. 

Table 15 summarizes these four sets of results. The NHTSA cost and benefit estimates and the 

NERA benefit estimates are available for all five model years. The Sierra cost estimates, 

however, cover only MY 2015, so net benefit calculations that rely on those costs estimates are 

also limited to that year. For all model years, the results in the table are for the NHTSA proposed 

(“optimized”) CAFE standards and the cost and benefits shown are for cars and light trucks 

combined. 

Table 15 shows substantial differences among the estimates of private benefits, private 

costs and external benefits/costs. With regard to private benefits, the NERA benefit estimates are 

about 40 percent of the NHTSA values. As a result, although NHTSA predicts that the private 

benefits to vehicle owners would far exceed the costs, we estimate that the proposed standards 

would impose substantial net costs on vehicle buyers, even assuming the NHTSA estimates of 

Table 15. Comparison of Alternative Benefit-Cost Estimates for All Model Years, Combined (Cars and 

Light Trucks) 

MY2011 MY2012 MY2013 MY2014 MY2015

Private Benefits

NHTSA $7,033 $14,836 $21,170 $24,445 $27,423

NERA $3,135 $6,567 $8,981 $10,376 $11,446

New vehicle costs

NHTSA $4,208 $8,013 $10,819 $12,416 $14,083

Sierra $35,480

External Benefits (Costs)

NHTSA $146 ($470) ($916) ($1,182) ($1,351)

NERA ($736) ($2,327) ($3,561) ($4,281) ($4,866)

Social Net Benefits (Costs) 

(source of benefits/costs)

NHTSA/NHSTA $2,972 $6,353 $9,435 $10,846 $11,989

NERA/NHTSA ($1,809) ($3,773) ($5,398) ($6,321) ($7,503)

NHTSA/Sierra ($9,407)

NERA/Sierra ($28,900)

Note: Entries are present values (discounted at 7 percent real to the start of the model year in question) 

expressed in millions of 2006 dollars. 

Source: NHTSA PRIA, Sierra (2008), and NERA calculations. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 39 

private costs. If the Sierra private cost estimates are used, the estimated net private costs are 

substantially greater, particularly using the NERA benefit estimates. 

NHTSA and NERA both estimate that on net the proposed rules would generate negative 

external effects, i.e., net external costs (although NHTSA shows a small net external benefit for 

MY2011). But the NERA estimates of external costs are substantially greater than NHTSA’s. In 

MY2015, for example, the NERA estimates are about 3.6 times the NHTSA estimate of net 

external costs. 

Figure 1 summarizes the net benefits of the “optimized” CAFE standards under the four 

alternative sets of estimates for MY2015. Overall net benefits are about $12 billion with 

NHTSA’s estimates of benefits and costs. In contrast, all of the three other cases yield substantial 

net costs. Using the two “intermediate” cases—NERA benefits and NHTSA costs and NHTSA 

benefits and Sierra costs—yields net costs of about $7.5 billion and about $9.4 billion, 

Figure 1. Alternative Estimates of Net Social Benefits (Costs) for MY2015, Combined (Cars and Light 

Trucks) 

($9.4)

($28.9)

($7.5)

$12.0

($35)

($30)

($25)

($20)

($15)

($10)

($5)

$0

$5

$10

$15

NHTSA/NHSTA NERA/NHTSA NHTSA/Sierra NERA/Sierra

Source of Benefits/Costs Estimates

S

ocial Net Benefits (billio

ns of 2006

d

ollars) 

Note: Entries are present values (discounted at 7 percent real to the start of the model year 2015) expressed in 

millions of 2006 dollars. 

Source: NHTSA PRIA, Sierra (2008), and NERA calculations. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 40 

respectively. The largest net costs result from using NERA benefits and Sierra costs estimates; 

that combination results in net costs of about $29 billion for the “optimized” car and light-truck 

CAFE standards for MY2015. 

E. Sensitivity Analyses of Gasoline Prices 

This section provides estimates of the sensitivity of the results to alternative estimates of 

future gasoline prices. 

1. Qualitative Effects of Alternative Fuel Price Forecasts 

Changes in fuel prices have two major impacts on the costs and benefits of CAFE 

standards. 

1. Higher fuel prices increase the private benefits of vehicles with greater fuel economy, 

which intuitively suggests that any given CAFE standard will have larger benefits. 

2. By raising private benefits, however, higher fuel prices would increase the baseline from 

which costs and benefits of a given CAFE standard are measured. Even in the absence of 

new CAFE standards, one would expect consumers to choose vehicles with greater MPG 

and for manufacturers to provide them. A higher baseline means: 

a. Benefits would fall because the CAFE standard would yield a smaller improvement in 

fuel economy; and 

b. Costs also would fall, but likely by a smaller proportion, as the least expensive 

incremental controls would become part of the new baseline leaving the more 

expensive controls relevant for the CAFE standard. 

We do not have information that would allow us to estimate the second effect, the impact of 

higher fuel prices on baseline fuel economy. As a result, we provide information only on the first 

effect (greater consumer benefits) and simply note that this analysis is incomplete and overstates 

the impact of higher gasoline prices on net benefits from CAFE. 

2. Quantitative Effects of Alternative Gasoline Price Forecasts 

Table 16 compares the results for the MY2015 “optimized” CAFE standard for four 

different projections of the 2015 gasoline price.25 All of the entries reflect NERA’s estimated 

 

25 We note that the PRIA uses the preliminary forecast from the AEO 2008 Early Release. The most recent 

published update, the AEO 2008 Revised Early Release, forecasts prices that are about $0.05 higher per gallon in 

2020 but $0.05 lower in 2030. This is partially a result of the inclusion of the 2007 Energy Independence and 

Security Act in the AEO analysis. For the Final Regulatory Impact Analysis, we recommend that NHTSA use 

EIA’s then-current forecast of gasoline costs. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 41 

external benefits of negative $4.9 billion, but use NHTSA’s estimate of private compliance costs 

($14.1 billion). 

As shown in Table 16, if we use NHTSA’s estimate of compliance costs but our 

estimated benefits, NHTSA’s “optimized” CAFE would yield negative private and social net 

benefits—i.e., positive net costs—for the “Reference” and “Low” 2015 gasoline price 

forecasts.26 The “High” forecast of gasoline prices results in positive private net benefits but 

negative social net benefits. For this high-price case, however, it is essential to remember that the 

NHTSA baseline would not be valid for substantially higher fuel prices, because market forces 

would lead to greater fuel economy even in the absence of tighter CAFE limits. Such changes are 

occurring now, with current, historically high gasoline prices. 

Table 17 repeats Table 16, but substitutes Sierra’s estimate of the private costs of 

complying with the CAFE standard. With Sierra’s estimated costs, private and social net benefits 

are negative at all four of the gasoline prices. 

 

26 EIA has not yet released AEO 2008 “Low” or “High” gasoline price forecasts, only its “Reference” forecast. We 

constructed the “Low” forecast by multiplying the “Reference” forecast by the ratio of the “Low” and 

“Reference” forecasts in AEO 2007. Similarly, for the “High” forecast we multiplied by the corresponding AEO 

2007 ratio of “High” and “Reference” forecasts. 

Table 16. Impacts of Alternative 2015 Gasoline Prices on Estimated Net Benefits Using NERA Estimates of 

Private and External Benefits (Costs) and NHTSA Estimates of Private Compliance Costs 

Price case

2015 Price of 

Gasoline

Private 

Benefits

Private Net 

Benefits

Social Net 

Benefits

NHTSA Forecast $2.23 $11,446 ($2,637) ($7,503)

AEO 2008 Revised Reference Forecast $2.25 $11,549 ($2,534) ($7,400)

"Low" Forecast $1.93 $9,905 ($4,179) ($9,045)

"High" Forecast $3.11 $15,924 $1,841 ($3,025)

Notes: Gasoline prices are in 2006 dollars. Other entries are present values (discounted at 7 percent real to 2015) 

expressed in millions of 2006 dollars. 

EIA has not issued its “Low” or “High” AEO 2008 forecasts. “Low” and “High” estimates are 

constructed by taking the AEO 2008 “reference” forecast and multiplying it by corresponding ratio of the 

AEO 2007 “Low” or “High” forecast to the 2007 “Reference” forecast. 

In all cases, private costs are assumed to be $14.1 billion (NHTSA’s estimated retail-equivalent price 

increases) and external benefits are -$4.86 billion (NHTSA estimates as partially adjusted by NERA). 

Source: Gasoline prices: NHTSA PRIA, EIA (2007 and 2008), and NERA calculations. Benefits and costs 

computed by NERA as explained in text. 
Assessments of NHTSA’s Modeling and Estimates of Benefits and Costs 

NERA Economic Consulting 42 

3. Implications of Sensitivity Analysis 

This sensitivity analysis suggests that even if EIA’s “high” forecast of gasoline prices 

proves be correct, the “optimized” CAFE limits proposed for MY2015 will not yield social net 

benefits, whether one relies on NHTSA’s or Sierra’s estimates of compliance costs. This 

conclusion is reinforced if one considers the other changes likely to accompany higher gas 

prices. First, new-vehicle buyers would purchase more fuel-efficient vehicles than NTHSA 

assumes in its baseline, which could substantially reduce the effects of the standard. With a 

higher baseline, the costs and benefits would be lower because the proposed CAFE standards 

would represent a smaller change. However, the higher baseline would incorporate the relatively 

cost-effective methods of increasing fuel economy, so that the compliance costs of the proposed 

standards would not fall proportionally by as much the reduction in MPG required. 

Table 17. Impacts of Alternative 2015 Gasoline Prices on Estimated Net Benefits Using NERA Estimates of 

Private and External Benefits (Costs) and Sierra Estimates of Private Compliance Costs 

Price case

2015 Price of 

Gasoline

Private 

Benefits

Private Net 

Benefits

Social Net 

Benefits

NHTSA Forecast $2.23 $11,446 ($24,033) ($28,900)

AEO 2008 Revised Reference Forecast $2.25 $11,549 ($23,931) ($28,797)

"Low" Forecast $1.93 $9,905 ($25,575) ($30,441)

"High" Forecast $3.11 $15,924 ($19,556) ($24,422)

Notes: Gasoline prices are in real year 2006 dollars. Other entries are present values (discounted at 7 percent real 

to 2015) expressed in millions of 2006 dollars. 

In all cases, private costs are assumed to be $35.5 billion (Sierra’s estimated retail-equivalent price 

increases) and external benefits are -$4.86 billion (NHTSA estimates as partially adjusted by NERA). 

Source: NERA calculations as explained in text. 
NERA Economic Consulting 43 

IV. Concluding Remarks 

The following are our principal conclusions regarding the NHTSA benefit-cost 

assessment of the MY2011-MY2015 CAFE standards contained in the PRIA. 

§ Although NHTSA emphasizes that its proposed CAFE standards would lead to reductions in 

CO2 emissions and improvements in energy security, even NHTSA’s own assessments show 

that on balance the external effects of the CAFE standards would be negative. That is, even 

NHTSA estimates that its “optimized” CAFE standards would lead to greater negative than 

positive external effects. This result is in dramatic contrast to the traditional justification for 

government regulations—to deal with negative external effects when economic actors make 

choices based on their private costs and benefits. 

§ NHTSA’s benefit-cost assessment shows large net benefits for CAFE standards because it 

estimates that the private benefits to new vehicle purchasers would be almost double the 

private costs they would incur, but NHTSA’s baseline assumes that market forces would not 

lead to that level of fuel economy. 

§ NHTSA does not provide an explanation of why the new vehicle market would perform so 

poorly, or any evidence that major market failures are present to justify its proposed CAFE 

standards. 

§ Our benefit and cost estimates provide explanations for the apparent counter-intuitive result. 

Based upon a detailed NERA model of the U.S. new vehicle market, we estimate that the 

private benefits received by new vehicle purchasers would be substantially smaller than 

NHTSA’s estimates. (We note that NHTSA acknowledges the inadequacy of its modeling of 

new vehicle markets.) In contrast to NHTSA, we estimate that the proposed CAFE standards 

would lead to increases in “quality-adjusted” new-vehicle prices and reductions in new 

vehicle sales (and automobile sector employment). 

§ We were able to examine in detail and, where appropriate, modify some of NHTSA’s 

estimates of external benefits and costs. These changes resulted in a large net increase in the 

external costs of the “optimized” CAFE standards. 

§ Combining our estimates of private and external benefits (costs), but using NHTSA’s 

estimates of increases in new-vehicle costs, reverses the sign of social net benefits for 

NHTSA’s “optimized” standard, going from large positive net benefits to substantial 

negative net benefits for each of the proposed MY2011-MY2015 CAFE standards. 

§ The estimated net costs of NHTSA’s recommended CAFE standards—and the adverse 

effects on new vehicle sales—are substantially greater using an alternative set of cost 

estimates developed by Sierra Research. 
Concluding Remarks 

NERA Economic Consulting 44 

§ The information that NHTSA provided in the PRIA has not allowed us to develop reliable 

estimates of the costs and benefits of standards other than the “optimized” standards 

proposed. Thus, we cannot at this time assess the net benefits or net costs of CAFE standards 

less stringent than the “optimized” standards identified by NHTSA. Note, however, that the 

lack of a clear market failure that would justify CAFE standards—and the empirical 

assessment in NHTSA’s own estimates that external effects are negative—strongly suggests 

that less stringent CAFE standards also would fail a benefit-cost test using NERA revised 

estimates of benefits. 
NERA Economic Consulting 45 

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NERA Economic Consulting A-1 

Appendix A. New Vehicle Market Model 

This appendix provides information regarding the model we have developed to explain 

the market for new motor vehicles, which we refer to as the New Vehicle Market Model 

(“NVMM”). The NVMM is used in this study to develop estimates of the value that consumers 

place on fuel economy improvements. 

A. Conceptual Approach: Nested Logit Model 

Logit discrete choice analysis provides a method for predicting consumer choices, and 

therefore demand, based on previously observed consumer behavior and other assumptions about 

demand (see, e.g., Ben-Akiva and Lerman 1985). The most basic logit model, also referred to as 

the simple logit, groups all product alternatives together and therefore allows only limited 

variation in own-price and cross-price elasticity between different alternatives. This limitation is 

often referred to as the “Independence of Irrelevant Alternatives” (“IIA”) problem. The nested 

logit model builds on this simple framework, while allowing for a much richer pattern of crosssubstitution between different alternatives through the nesting structure. 

1. Basic Framework 

In our NVMM, consumers choose among a set of vehicle models, and may also choose 

not to purchase a vehicle at all. For alternative (vehicle model) i, the utility that a given 

consumer obtains from choosing that alternative can be written as a function of an alternativespecific parameter and the price for the alternative: 

Ui i Pi i = a − b + e (1) 

Alternative “0” is defined as the no-purchase alternative, and the remaining alternatives 

represent decisions to purchase individual vehicle models. The parameter ai measures the 

attractiveness of good i to consumers. We assume that the price for the outside good is zero. Pi is 

the price of alternative i, and b is a positive coefficient. The random error terms ei are assumed to 

be distributed as a multivariate generalization of the standard extreme value distribution. It is this 

assumption about the distribution of the ei that gives rise to the logit model. 

The potential purchaser is assumed to choose the alternative that yields the highest utility, 

taking into account both the deterministic and random components of utility. Given the logit 

demand assumptions, we determine the expected market share for each vehicle model. 

Conditional upon the consumer’s decision to purchase a vehicle model within a vehicle group (or 

“nest”) A (as described below), the expected share for vehicle model i can be written as: 
New Vehicle Market Model 

NERA Economic Consulting A-2 

∑∈

−

−

=

j A

j j A

i i A

i A

P

P

s

exp(( )/ )

exp(( ) / )

|

a b l

a b l

, (2) 

where lA is the “nesting parameter” for the appropriate vehicle group, or “nest” (as described 

below).1

2. Nesting Assumptions 

Our logit model assumes the nesting structure shown in 

Figure A-1. We divide the choice problem first into the decision of whether to buy a new 

vehicle. Conditional upon the choice to purchase a new vehicle, consumers choose the vehicle 

type—in this case, passenger cars, pickup trucks or full-size vans, and SUVs or minivans. 

Conditional on the choice of vehicle type, consumers choose the vehicle class—for example, 

small cars or mid-range cars (among others) in the passenger-car group. Conditional on the 

vehicle class (e.g., mid-range car, small SUV, etc.), consumers choose one of the individual 

vehicle models available. The bottom level of the nesting structure includes over 200 vehicle 

models from which consumers may choose. 

The market model allows the utility that consumers derive from the purchase of different 

models to depend on the vehicle category and class via the nesting parameters (lA), which take 

values between zero and one.

2 One nesting parameter applies to the purchase decision (buy or do 

not buy a vehicle); another nesting parameter applies to the choice of vehicle type; and a third 

applies to the choice of vehicle classes. The nesting parameter for the purchase decision must be 

at least as large as the nesting parameter for vehicle type. For each vehicle-type nest, the nesting 

parameter must be at least as large as the nesting parameter for all vehicle nests contained within 

the “parent” nest. The nesting structure implies that vehicles within one group are closer 

substitutes for each other than they are for vehicles in different groups. The cross-price 

elasticities between vehicles within the same group are therefore higher than the cross-price 

elasticities for vehicles in different groups. 

As noted above, one advantage of the nested logit model over the simple logit model is 

that it provides for a richer pattern of own- and cross-price elasticities. In the nested logit model, 

the IIA property need not hold across groups. That is, the ratio of the share for a particular car 

 

1 Because terms that are constant across all alternatives do not affect the choice of alternatives, it is common to 

normalize the alternative-specific term αi. As a normalization rule, we set αi equal to zero for one vehicle model. 

Adding or subtracting a common amount to all the αiterms leaves the choice of alternatives unchanged, so any 

other normalization rule would yield identical results. 

2 The nesting parameters are sometimes called “inclusive value coefficients.” 
New Vehicle Market Model 

NERA Economic Consulting A-3 

model in one bottom-level nest to the share of a vehicle in a different bottom-level nest, for 

example, depends not only on the characteristics of those two vehicle models, but also on the 

substitution patterns implied by the nesting structure and nesting parameters. The nesting 

parameters therefore enrich the simple logit model. If all nesting parameters equal one, then the 

nested logit model becomes a simple logit model. 

The inclusive value term IA for bottom-level group A (the vehicle class) is defined as: 













= ∑ −

j∈A

A j Pj A

I ln exp((a b ) / l ) (3) 

The inclusive value term IX for the top-level group X (the vehicle type) is defined as: 













= ∑

A∈X

X A A X

I ln exp(I l / l ) (4) 

Consumer Decisons

Sports Wagon SUV

Buy Don’t Buy

Car

Truck/Van

SUV/Minivan

Premium SUV

Minivan

Large SUV

Mid-Range SUV

Small SUV

Small Pickup

Full Pickup

Full Van

Small Car

Mid-Range Car

Upscale Car

Sporty Car

Traditional Car

Consumer Decisons

Sports Wagon SUV

Buy Don’t Buy

Car

Truck/Van

SUV/Minivan

Premium SUV

Minivan

Large SUV

Mid-Range SUV

Small SUV

Small Pickup

Full Pickup

Full Van

Small Car

Mid-Range Car

Upscale Car

Sporty Car

Traditional Car

Figure A-1. Nesting structure for NVMM 
New Vehicle Market Model 

NERA Economic Consulting A-4 

Finally, the inclusive value term IBuy for the purchase alternative is defined as: 













= ∑

X

Buy X X Buy I ln exp(I l / l ) (5) 

The share of the bottom-level group A in purchases within the top-level group X to which 

A belongs can be written as: 

∑∈

=

B X

B B X

A A X

A X

I

I

s

exp( / )

exp( / )

|

l l

l l

. (6) 

The share of top-level group X in total purchases can be written as: 

∑

=

Y

Y Y buy

X X buy

X buy I

I

s

exp( / )

exp( / )

|

l l

l l

. (7) 

The logit framework gives an expression for the share of potential buyers who choose to 

purchase a vehicle: 

exp( ) exp( )

exp( )

l a0

l

+

=

buy buy

buy buy

buy I

I

s , (8) 

where a0 is the value derived by the consumer from a no-purchase decision. 

The unconditional share for alternative i can be written as the product of the purchase 

probability and the conditional probabilities: 

i i i Ai

i buy X buy A X i

s s s s s = | | | , (9) 

where Ai is the bottom-level group to which i belongs and Xi is the top-level group to which Ai

belongs. 

B. Solving for Parameters 

As described above, the nested logit choice framework provides a method to estimate 

consumer demand for differentiated products, using as data the prices and parameters that 

measure the relative attractiveness of each product. Using the logit framework, we solve 
New Vehicle Market Model 

NERA Economic Consulting A-5 

simultaneously for the beta parameters and “alternative-specific” parameters that are consistent 

with the observed market shares and prices. If two products in the same group have the same 

price but different market shares, then the one with the higher share must be more attractive to 

consumers. Similarly, if two products in the same group have the same market share but different 

prices, then the one with the higher price must be more attractive to consumers, since consumers 

are observed to pay a premium for it. 

We use the logit framework to estimate alternative-specific parameters for each vehicle 

model. We make assumptions concerning the nesting parameters, the aggregate price elasticity of 

demand, and the price elasticity of demand for one specific alternative. Given the structure of our 

nested logit model, these assumptions, the observed prices, and the observed market shares are 

sufficient to derive estimates of the alternative-specific parameters (including those for the 

outside good). 

C. Estimating Consumer Valuation of Vehicle Attributes 

Once the alternative-specific parameters implied by the observed vehicle shares and 

prices are calculated, we estimate the extent to which consumers value each vehicle attribute 

through a “second-stage” regression for the alternative specific parameters. For each vehicle in 

the sample, the alternative specific parameters are regressed on vehicle characteristics such as 

horsepower/weight (a common measure of acceleration), size (length multiplied by width), and 

the cost of fuel per mile driven ($/mile, which is the product of mpg and the current fuel price). 

We assume each vehicle’s alternative-specific parameter depends upon the vehicle’s 

model and attributes according to the following model: 

t mileX mile t hp weight X hp weight t size X size t yearDyear seasonDseason elD el t a = j +j +j + d + g +f + e $ / $ / , / / , , mod mod

(10) 

where: 

at is the alternative-specific coefficient at time t, 

Xhp/weight,t, and Xsize,t are vehicle characteristics at time t, 

X$/mile,t is a four-quarter moving average of the gasoline price divided by the vehicle’s 

combined MPG rating multiplied by 0.8 to account for the “on-road” MPG gap, 

Dyear are dummy variables corresponding to vehicle model years, 

Dseason are dummy variables corresponding to seasons, 
New Vehicle Market Model 

NERA Economic Consulting A-6 

Dmodel are dummy variables corresponding to the vehicle model, 

et is an error term capturing unobserved characteristics at time t, and 

j$/mile, jhp/weight, jsize, dyear, and fmodel are estimated parameters. 

Our model uses quarterly data on sales and prices and data on vehicle characteristics for 

each model year. Thus, the t subscript indexes quarters. We estimated this equation using 

ordinary least squares and tested for the presence of autocorrelation in the error terms. We found 

significant autocorrelation, as evidenced by a statistically significant coefficient on the lagged 

residuals in a regression of the residuals from the OLS estimation on the residuals lagged one 

period. To correct for autocorrelation in the error terms, we re-estimated the equation using 

feasible generalized least-squares (FGLS) with first-order autoregressive disturbances. In this 

model, the error terms are related to their lagged values by t t t ste = r e −1 + . 

Table A-1 reports the results of the FGLS estimation. The coefficients on size and 

horsepower/weight are positive and significant, as expected. The model-year, seasonal, and 

vehicle-model sets of categorical dummy variables are each jointly significant. The coefficient 

on $/mile is negative, as expected (the higher the $/mile, the higher the operating cost, and the 

less a consumer would value the vehicle, controlling for other factors) and highly significant. 

The estimated coefficient provides a means of valuing improvements in miles per gallon 

(decreases in $/mile) that can be used to estimate the benefits to new vehicle purchasers of 

changes in fuel economy. 
New Vehicle Market Model 

NERA Economic Consulting A-7 

The dollar value of a unit improvement in a vehicle attribute can be inferred from this 

model by taking the ratio of the coefficient on that attribute to the price coefficient multiplied by 

-1. The price coefficient is the β term in Equation 10. We express price in units of $20,000. The 

mean of the parameter estimate for β is 1.1702. Thus, the dollar value of a $1/mile reduction in 

operating costs can be calculated as: 

$20,000 $49,725

1.1712012

2.911918 $20,000

1

$ /

⋅ ≈

−

−

⋅ =

− ⋅ b

j mile (11) 

That is, improving fuel economy by an amount equivalent to lowering the cost per mile by $1 is 

worth an extra $49,725 in the price of a new vehicle. For example, suppose the mpg of an 

average vehicle increases from 30 mpg to 35 mpg at a gasoline price of $2.50. At 30 mpg, 

vehicle fuel costs $2.50/30 = $0.0833 per mile. At 35 mpg, the cost is $2.50/35 = $0.0714 per 

mile, for a reduction of $0.0833 - $0.0714 = $0.0119 per mile. This reduction in fuel cost per 

mile should be worth about 0.0119 * $49,725 = $592 based upon the information implied in 

vehicle purchasers’ behavior. 

D. Specific Implementation Parameters and Data 

1. Vehicle Sales 

We use quarterly vehicle sales data from J.D. Power and Associates to determine the 

market share for each vehicle model, aggregated across trim levels, for model years 2001-2007. 

We match model year vehicle characteristics to the period October of the previous calendar year 

Table A-1. Estimation results for alternative-specific parameter model. 

independent variable coefficient t-statistic std. error p>|t|

$/mile -2.912 -6.960 0.418 0.0000

hp / weight 7.284 7.477 0.974 0.0000

size 0.913 9.039 0.101 0.0000

Constant -2.030 -12.77 0.159 0.0000

categorical dummies

significance 

test χ

2

-statistic p>|χ

2

|

Model Year χ

2

168.5 0.0000

Season χ

2

26.2 0.0000

Vehicle Model χ

2

15277.4 0.0000

Autocorrelation 

parameter

estimated 

value

ρ 0.5429

σ 0.1799

Note: The weight variable is curb weight in units of pounds. The size variable is the product of vehicle length and 

width (both expressed in hundreds of inches). 

Source: NERA calculations as explained in text. 
New Vehicle Market Model 

NERA Economic Consulting A-8 

to September of the same calendar year to reflect as accurately as possible the timing of new 

model availability. For example, vehicle characteristics for model year 2005 vehicles are paired 

with sales data for the fourth quarter of 2004 through the third quarter of 2005. If a vehicle 

model sold less than 5,000 units in a given year, we eliminated it from the dataset for that year. 

We also eliminated observations where models sold substantially more or fewer units than the 

following or previous model year to avoid biasing our estimates of characteristics valuation due 

to introduction or discontinuation of vehicle models. 

2. Vehicle Prices 

We use quarterly data on transaction prices for each model from J.D. Power and 

Associates for the United States. The transaction prices are sales weighted and reflect the 

different prices charged for different trim levels. 

3. Vehicle Fuel Economy Data and Other Vehicle Characteristics 

We use vehicle characteristic data from Ward’s to determine the fuel economy of each 

model. The fuel economy used in this analysis is the EPA adjusted combined (city and highway 

combined) miles per gallon, multiplied by 0.8 to reflect the “on-road” fuel economy gap. We 

also rely on data from Ward’s for information on other vehicle attributes, including curb or test 

weight, horsepower, length, and width. 

4. Categorization of Vehicle Models into Nests 

We use vehicle categorizations from the 2007 Automotive News Market Data Book to 

define the vehicle nesting structure depicted in Figure A-1. Where the appropriate category for a 

particular model could not be determined based on the 2007 Automotive News Market Data 

Book, we used older Automotive News Market Data Books or Ward’s categorizations. 

5. Price Elasticity 

Consistent with various literature sources, we assume an aggregate elasticity for the new 

vehicle market of –1.0.3 We set the own-price elasticity of the “normalized” vehicle model 

(whose alternative-specific parameter is normalized to zero) to be –4.0, which is consistent with 

various other literature estimates of individual model own-price elasticities.

4

 

3

See, for example, Gruenspecht (2000). 

4

See, for example, Berry, Levinsohn, and Pakes (1995). 
New Vehicle Market Model 

NERA Economic Consulting A-9 

The nesting parameters for nested logit models represent the similarity between choices 

for vehicles falling within the same “nest.” The nesting parameters influence the relative 

substitutability within each nest, and also between different nests. Nesting parameters may take 

any value between zero and one, with lower values indicating greater similarity between the 

alternatives within the respective nest. For the “Buy” nest we use a nesting parameter equal to 

0.9, for vehicle types we use a nesting parameter equal to 0.6 and for vehicle classes we use a 

nesting parameter equal to 0.3. 

References 

Automotive News. 2001. 2001 Market Data Book. Crain Communications, Inc. May. 

Automotive News. 2002. 2002 Market Data Book. Crain Communications, Inc. May. 

Automotive News. 2003. 2003 Market Data Book. Crain Communications, Inc. May. 

Automotive News. 2005. 2005 Market Data Book. Crain Communications, Inc. May. 

Automotive News. 2006. 2006 Market Data Book. Crain Communications, Inc. May. 

Automotive News. 2007. 2007 Market Data Book. Crain Communications, Inc. May. 

Ben-Akiva, Moshe and Steven R. Lerman. 1985. Discrete Choice Analysis. MIT Press, 

Cambridge. 

Berry, Steven, James Levinsohn, and Ariel Pakes. 1995. “Automobile Prices in Market 

Equilibrium,” Econometrica, Vol. 63, No. 4, July 1995, pp. 841-890. 

Carlton, Dennis W. and Jeffrey M. Perloff. 1999. Modern Industrial Organization. Third 

Edition. Addison-Wesley: New York. 

Goldberg, Pinelopi K. 1995. “Product Differentiation and Oligopoly in International Markets: 

The Case of the U.S. Automobile Industry,” Econometrica, pp. 891-951. 

Gruenspecht, Howard K. 2000. Comments on 2000 Biennial Review of Zero Emission Vehicle 

Program before the California Air Resources Board. September 5. 

J.D. Power and Associates, Power Information Network. PIN Markets: National. Purchase of 

proprietary data, December 2007. 

United States Department of Labor, Bureau of Labor Statistics (BLS). Consumer Price Index—

All Urban Consumers, U.S. All Items, 1966-2007. http://www.bls.gov/cpi/home.htm. 
New Vehicle Market Model 

NERA Economic Consulting A-10 

Ward’s Communications. 2001. Ward’s ’01 Model U.S. Car and Light Truck Specifications and 

Prices. Subscription service. 

Ward’s Communications. 2002. Ward’s ’02 Model U.S. Car and Light Truck Specifications and 

Prices. Subscription service. 

Ward’s Communications. 2003. Ward’s ’03 Model U.S. Car and Light Truck Specifications and 

Prices. Subscription service. 

Ward’s Communications. 2004. Ward’s ’04 Model U.S. Car and Light Truck Specifications and 

Prices. Subscription service. 

Ward’s Communications. 2005. Ward’s ’05 Model U.S. Car and Light Truck Specifications and 

Prices. Subscription service. 

Ward’s Communications. 2006. Ward’s ’06 Model U.S. Car and Light Truck Specifications and 

Prices. Subscription service. 

Ward’s Communications. 2007. Ward’s ’07 Model U.S. Car and Light Truck Specifications and 

Prices. Subscription service. 
NERA Economic Consulting B-1 

Appendix B. Review of the Small and Van Dender Model for 

Estimating Rebound Effects 

This Appendix provides information on the “rebound effect” that is relevant for 

determining the effects of changes in the fuel economy of the vehicle fleet on vehicle miles 

traveled (“VMT”). The rebound effect is measured as the effect of changes in the cost per mile of 

travel (e.g., fuel costs) on VMT; the effect is traditionally measured as an elasticity—the 

percentage increase in VMT that would results from a one percent decrease in the cost per mile 

of travel. The model we develop to estimate the rebound effect is based upon the general 

methodology and data employed by Small and Van Dender (2006 and 2007). We conclude that 

the long-run rebound elasticity using a corrected version of the Small and Van Dender method is 

-0.24, in contrast to the value of -0.15 used by NHTSA. (Note that our revision of the NHTSA 

study uses a value of -0.2.) 

This Appendix consists of the following three parts: 

§ Overview of the Small and Van Dender study; 

§ Modifications to study data and estimation procedures; and 

§ Estimated rebound effect. 

A. Overview of Small and Van Dender Study 

The Small and Van Dender study develops a model that posits simultaneous 

determination of three variables (using three equations) related to vehicle use: fleetwide VMT; 

fleetwide vehicle stock (number of vehicles); and fleetwide vehicle fuel intensity (the inverse of 

fuel economy).5In the model, VMT and vehicle stock depend on lagged dependent variables, 

several exogenous variables, and cost per mile of travel (pm)—defined as the price of fuel 

(dollars per gallon) divided by fuel economy (miles per gallon). 6 The variable pm is endogenous 

because it depends on fuel economy. The VMT equation also includes the endogenous vehicle 

stock variable as well as interaction terms of pm with income, pm with urbanization, and pm with 

itself (i.e., pm

2

). 

 

5 The authors use the natural logarithm of VMT per adult as one of the three endogenously determined variables. For 

simplicity, we refer to this variable as VMT. 

6 Most of the variables in the Irvine study are expressed as natural logarithms for the estimation. Following the 

notation used in the Irvine study, we denote these variables in lower case and we denote non-logarithmic 

variables in upper case. 
Review of the Small and Van Dender Model for Estimating Rebound Effects 

NERA Economic Consulting B-2 

The fuel intensity equation includes a lagged dependent variable, several exogenous 

explanatory variables, a Corporate Average Fuel Economy (“CAFE”) variable, and the 

endogenous VMT variable. Small and Van Dender treat CAFE regulation as a factor that 

contributes to actual fuel economy rather than as a measure of observed fuel economy. Thus the 

intent of the CAFE variable is to measure the ratio of fuel economy under CAFE to what fuel 

economy would have been without CAFE. The Small and Van Dender study uses fuel economy 

data for years prior to CAFE to predict what fuel economy would have been without CAFE in 

later years. 

The estimated short-run national rebound effect in the Small and Van Dender study 

depends most importantly on the estimated elasticity of VMT with respect to pm. The estimated 

long-run national rebound effect in the Small and Van Dender study depends most importantly 

on the estimated short-run elasticity of VMT with respect to pm and the estimated coefficient of 

lagged VMT in the VMT equation. 

1. Small and Van Dender Study Variables and Data Set 

Small and Van Dender use a cross-sectional time-series dataset with data on all fifty U.S. 

states and the District of Columbia over the years 1966 through 2001. Table B-1 provides an 

overview of the variables used by Small and Van Dender. The description of each variable 

explains the data used to construct it. Though the study uses a cross-sectional time-series dataset, 

not every variable has state-specific values (e.g., only national data were obtained on new car 

loan interest rates, so the variable interest differs over time but not across states). In addition, 

some variables mix national and state-specific elements. For example, income (inc) and the price 

of fuel (pf) come state-specific data on nominal values, but are translated into real terms with 

national deflators. 

2. Small and Van Dender’s Estimation Procedures 

Small and Van Dender estimate the three equations described above using several 

different approaches: ordinary least squares (“OLS”); two-stage least squares (“2SLS”); threestage least squares (“3SLS”); and generalized method of moments (“GMM”). The authors 

conclude that the most appropriate estimator is 3SLS. 
Review of the Small and Van Dender Model for Estimating Rebound Effects 

NERA Economic Consulting B-3 

B. Modifications to Small and Van Dender Data and Estimation 

Procedure 

We develop an estimated rebound effect based upon modifications to several data series 

as well as the primary estimation procedure of the Small and Van Dender study. We received all 

of the underlying data and programs (Small and Van Dender 2006a) used in recent versions of 

the Small and Van Dender studies (Small and Van Dender 2006 and 2006a).7To develop more 

appropriate results for the rebound effect, we have made several modifications in the data and 

estimation methodology developed by Small and Van Dender. 

 

7 Our review of the reported rebound elasticities and description of methods in the published paper (Small and 

Van Dender 2007) indicates that the methodology and data used in the published study do not differ from that 

used in the earlier working paper on which our modifications are based. 

Table B-1. Variables Used in the Small and Van Dender Study 

Variable Name Description Detail Level

Endogenous

vma VMT per adult State

vehstock Registered vehicles per adult State

fint Fuel intensity (fuel use per mile) State

Exogenous

adrm Adults per mile of roadway State

Cafe The ratio of CAFE regulated fuel efficiency to desired fuel efficiency National

D7479 Dummy variable for years 1974 and 1979 National

inc Per capita personal income (deflated by national CPI) State/National

interest National new car loan interest rate National

licad Licensed drivers per adult State

pf Price of gasoline (deflated by national CPI) State/National

pm=pf+fint Cost of travel State

pm 2 Cost of travel squared State

pm*(inc-minc): Interaction of p m and inc variable, where minc is the mean of inc State

pm*(UrbanmUrban)Interaction of p m and Urban , where mUrba n is the mean of Urban State

popratio Population per adult State

pv Index of new car prices National

Trend, Trend66-73, 

Trend74-79, 

Trend80+

Linear time trends National

Railpop Fraction of state’s population living in MSAs with heavy rail transit State

Urban Fraction of state’s population living in metropolitan areas State

Z1, . . ., Z51 Dummy variable for each state State

Notes: Lower-case variables are in natural logarithms. 

Source: Small and Van Dender (2006). 
Review of the Small and Van Dender Model for Estimating Rebound Effects 

NERA Economic Consulting B-4 

§ State dummy variables. The code for the VMT equation restricted the coefficients of the 

dummy variables for the States of Nevada and Nebraska to be the same. We edited the 

estimation code so that the two states had separate coefficients. 

§ Income and gasoline prices. We adjusted the income and gasoline price data to reflect 

state differences in cost of living. With these data, we were able to update the estimation 

of the VMT equation and other equations 

§ Trend variable. We modified the Trend variable to provide a superior and more 

consistent specification. Rather than using a single Trend variable for the VMT and 

vehicle stock equations, we used three separate Trend variables, the first covering the 

years 1966 through 1973, the second covering the years 1974 through 1979, and the third 

covering the years 1980 through 2001. 

§ Dummy variables for 1974 and 1979. We split the joint indicator variable for 1974 and 

1979 into two separate dummy variables to allow the effects of these two independent 

shocks to differ from one another. 

§ Estimation. We estimated the VMT equation using 2SLS (rather than 3SLS). 

1. Income and Gasoline Prices 

Small and Van Dender use nominal state-level income data from the U.S. Bureau of 

Economic Analysis (“BEA”) and state population data from the U.S. Census Bureau to derive 

nominal income per capita estimates. For the gas price data, the authors use state-level nominal 

data from the U.S. Energy Information Administration (“EIA”). The authors deflated both of 

these data series to 1987 dollars using the national consumer price index for all urban consumers 

(“CPI”) from the U.S. Bureau of Labor Statistics (“BLS”). These values do not account for 

differences in the cost of living across states. To account for these differences, we developed 

state-specific cost of living indices based on city-level data from ACCRA (formerly “American 

Chamber of Commerce Researchers Association”) and the BLS. These indices are used to 

calculate real income and gas price data that reflect differences in purchasing power across states 

and over time. 

2. Trend Variable 

Small and Van Dender incorporate different trend variables in different equations. In the 

VMT and vehicle stock equations, Small and Van Dender use a linear time trend (Trend). In the 

fuel economy equation, Small and Van Dender uses three separate linear trends—one for the 

period from 1966 to 1973 (Trend66-73) to cover the years before the OPEC embargo in 1974, 

one from 1974 to 1979 (Trend74-79) to cover the years between the OPEC embargo and the 

Iranian revolution in 1979, and one from 1980 to 2001 (Trend80+) to cover the period after the 
Review of the Small and Van Dender Model for Estimating Rebound Effects 

NERA Economic Consulting B-5 

Iranian revolution. To test for the possibility that driving behavior was also affected differently 

in these three periods, we performed a Wald test for the inclusion of Trend66-73 and Trend74-79

in addition to Trend. (This specification is econometrically equivalent to including the three 

trends from the fuel economy equation since Trend80+ is constructed as a linear combination of 

the other trend variables.) The Wald test produced evidence that a nonlinear three-part trend in 

the VMT equation is superior to the simple linear trend used by the authors. Another Wald test 

produced similar evidence for the vehicle stock equation. So we modified the trend variable in 

the VMT and vehicle stock equations to correspond to the three-period trend variable used by the 

authors in their fuel economy equation. 

3. Dummy Variables for 1974 and 1979 

Small and Van Dender use a single dummy variable in the VMT and fuel economy 

equations for 1974 and 1979 (D7479) to “represent gasoline supply disruptions in 1974 and 

1979,” presumably referring to the 1974 oil embargo and the 1979 Iranian revolution. We used 

two separate dummy variables to allow the effects of these two “shock years” to differ from one 

another. There is no a priori reason to expect that the effects of these two events were identical. 

A Wald test produced evidence that separating the two effects provides a superior specification. 

4. Estimation 

We used 2SLS to estimate the VMT equation because it is a non-system estimator that 

prevents specification errors in one equation from affecting parameter estimates for other 

equations. We concluded that 3SLS was less appropriate for this model, due to concerns about 

the data used to construct some variables, and, in particular, concerns about the construction of 

the CAFE variable. 

C. Estimated Rebound Effect 

Table B-2 shows the results of our estimation of the VMT equation. Because the 

estimated coefficient of the interaction term of pm with income (i.e., pm*(inc-minc)) was 

statistically insignificant, that variable is not included in from our estimation of the VMT 

equation. The estimated coefficient on pm in the VMT equation gives the short-run national 

rebound effect at the sample mean of the dataset used in the analysis. The long-run effect is a 

function of the coefficient on lagged VMT. 
Review of the Small and Van Dender Model for Estimating Rebound Effects 

NERA Economic Consulting B-6 

Notes: t-statistics in parenthesis; rebound effects calculated using only coefficients from the VMT equation. 

References 

Small, Kenneth and Kurt Van Dender. 2006. “Fuel Efficiency and Motor Vehicle Travel: The 

Declining Rebound Effect,” Department of Economics, University of California Irvine, March 

21. 

Small, Kenneth and Kurt Van Dender. 2006a. “Fuel Efficiency and Motor Vehicle Travel: The 

Declining Rebound Effect,” Department of Economics, University of California, Irvine, April 10 

(corrected July 17). 

Small, Kenneth and Kurt Van Dender. 2007. “Fuel Efficiency and Motor Vehicle Travel: The 

Declining Rebound Effect.” Energy Journal. Vol. 28, No. 1. 

Table B-2. Estimation of the VMT Equation 

Variables Coefficients from 

2SLS Estimation

pm=pf+fint -0.0497 (-8.41)

pm 2

-0.0280 (-3.32)

pm*(inc-minc):

pm*(Urban-mUrban) 0.0523 (3.95)

vma (t-1) 0.7933 (53.79)

Sample Average 

Rebound Effect

Short-Run 4.97%

Long-Run 24.03%