Peer Review for the Report, 

“How Consumers Value Fuel Economy” 

A Literature Review
EPA-420-S-10-001

April 2010

Assessment and Standards Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

Peer Review for the Report, 

“How Consumers Value Fuel Economy”

A Literature Review
March 30, 2010 

MEMORANDUM 

SUBJECT: Peer Review for David Greene paper, “How Consumers Value Fuel Economy: A 

Literature Review” 

FROM: Gloria Helfand, Assessment and Standards Division 

Office of Transportation and Air Quality, U.S. Environmental Protection Agency 

In December 2009, EPA contracted with RTI International (RTI) to conduct a peer review of a 

literature survey conducted by David Greene of Oak Ridge National laboratory. The draft study, 

titled “How Consumers Value Fuel Economy: A Literature Review,” looked at 22 papers that, in 

some fashion, provided quantitative estimates of the role of fuel economy in consumer vehicle 

purchase decisions. 

The three peer reviewers selected by RTI were Drs. Carolyn Fischer of Resources for the Future, 

Christopher Knittel of the University of California at Davis, and Walter McManus of the 

University of Michigan. EPA would like to extend its appreciation to all three reviewers for their 

efforts in evaluating this survey. The three reviewers brought useful and distinctive views in 

response to the charge questions. 

The first section of this document contains the final RTI report summarizing the peer review of 

David Greene’s literature survey, including the detailed comments of each peer reviewer and an 

overview of the most significant comments compiled by RTI. The RTI report also contains the 

peer reviewers’ resumes, the charge letter, and cover letters from each reviewer explaining any 

real or perceived conflicts of interest. The second major section contains our responses to the 

peer reviewers’ comments. In this section, we repeat the summarized comments provided by 

RTI and, after each section of comments, provide our response. We have retained the 

organization reflected in RTI’s summary of the comments to aid the reader in moving from the 

RTI report to our responses. 

CONTENTS 

I. Peer Review of EPA’s Study of Vehicle Choice Models for Estimating Impacts of Fuel 

Economy, conducted by RTI International 

1. Background 

2. Description of Review Process 

3. Summary of Review Comments 

4. References 

Appendices 

A. Resumes of Selected Reviewers 

B. Charge Questions 

C. Cover Letters 

D. Reviews 

II.. EPA’s Response to Peer Review Comments 
 

TO: Kent Helmer, Gloria Helfand, U.S. Environmental Protection Agency, Office of 

Transportation and Air Quality (OTAQ) 

FROM: Paramita Sinha, RTI 

DATE: February 26, 2010 

SUBJECT: Peer Review of EPA’s Study of Vehicle Choice Models for Estimating Impacts of 

Fuel Economy 

1. Background 

The U.S. Environmental Protection Agency’s (EPA’s) Office of Transportation and Air Quality 

(OTAQ) is involved in exploring the regulation of CO2 and other greenhouse gas (GHG) 

emission control measures in motor vehicles and equipment. An important component of the 

costs of such regulations is the cost of improved technology, while the benefits include, among 

others, the value of reduced fuel consumption. Understanding the role of fuel economy in 

consumer vehicle purchases will contribute to EPA’s assessment of the impacts of its rules on 

vehicle sales. 

EPA is assessing the use of consumer vehicle choice models to analyze the impacts of regulatory 

programs that affect fuel economy. Coefficients from discrete choice models of consumer 

vehicle choice are typically used to estimate the value of improving fuel economy. Other studies 

use hedonic price methods. These vehicle choice models could help EPA anticipate market 

responses to vehicle regulations and thus judge the effectiveness of regulations. 

EPA is analyzing a set of existing consumer vehicle choice models to see whether there is a 

robust estimate of the value of additional fuel economy to consumers. EPA is seeking a peer 

review of its comparative study of consumer vehicle choice models and what each model may 

predict for the value of improving vehicle fuel economy at the time of vehicle purchase. This 

report contains documentation of the peer review process for the vehicle choice study. 

Section 2 of this memorandum provides a description of the process for choosing reviewers, 

administering the review process, and closing the peer review. Section 3 contains an overview of 

the comments made by the panel of reviewers. The appendices to this memorandum provide the 

resumes of the reviewers; charge letter to the peer reviewers, which describes their task and what was 

RTI International is a trade name of Research Triangle Institute 
 

 

 

Kent Helmer, Gloria Helfand 

February 26, 2010 

Page 2 

requested from them in terms of deliverables; and the cover letters and review comments from the 

panel. 

2. Description of Review Process 

In December 2009, EPA’s OTAQ contacted RTI International to facilitate the peer review of 

EPA’s study of vehicle choice models for estimating impacts of fuel economy regulations. This 

study, authored by David L. Greene, is entitled “How Consumers Value Fuel Economy: A 

Literature Review.” 

EPA provided a short list of subject matter experts from academia and industry (Appendix A of 

the performance work statement) to RTI, and this served as a “starting point” from which we 

assembled the list of peer reviewers. RTI selected three independent (as defined in Sections 1.2.6 

and 1.2.7 of EPA’s Peer Review Handbook) subject matter experts to conduct the requested 

reviews. Subject matter experts familiar with economic valuation, discrete choice models and the 

use of these models for valuation, and the use of these models for predicting automobile 

purchases were selected. 

To ensure that the review process was conducted in a timely manner, RTI contacted potential 

reviewers within a week of submitting the work plan and determined that each reviewer would 

be able to perform work during the period of performance. To make the review process as 

credible as possible, RTI did not consult the Agency in the final determination of reviewers. RTI 

obtained the resumes of the selected reviewers, and these are included in Appendix A. 

RTI provided the panel reviewers with the final edited version of the analysis that each subject 

matter expert was expected to review along with a set of charge questions (both provided by 

EPA) in the first week of January. 1 The memo from RTI to the reviewers with the charge 

questions is included in Appendix B of this report. 

A teleconference between EPA, the reviewers, and RTI was organized to provide an opportunity 

to the panel to discuss any questions or concerns they may have regarding the material provided 

and expected deliverables. Completed reviews from the panel were sent to EPA by the requested 

date. These reviews included the response to the charge questions and any additional comments 

the reviewer may have had (e.g., margin notes on review materials). From each reviewer, RTI 

1 Upon request from one of the reviewers, EPA provided RTI with two examples of previous peer reviews, and these were 

subsequently forwarded to them. These are entitled “Peer Review for the RTI Report, Automobile Industry Retail Price 

Equivalent and Indirect Cost Multipliers (Assessment and Standards Division, Office of Transportation and Air Quality, U.S. 

Environmental Protection Agency, June 2009, EPA-420-R-09-004 ) and “Review: Vehicle Greenhouse Gas Emission 

(VGHG) Emissions Cost and Compliance Model (Jonathan Rubin, January 2010). 
Kent Helmer, Gloria Helfand 

February 26, 2010 

Page 3 

obtained a cover letter stating the reviewer’s name; the name and address of their organization if 

applicable; a list of review documents/media received by the reviewer and which were actually 

reviewed; and a statement of any real or perceived conflict(s) of interest. The cover letters and 

reviews are included in Appendices C and D, respectively. 

3. Summary of Review Comments 

Carolyn Fischer (Resources for the Future), Christopher Knittel (Department of Economics, 

University of California at Davis), and Walter McManus (University of Michigan, 

Transportation Research Institute) reviewed “How Consumers Value Fuel Economy: A 

Literature Review.” This section provides a summary of the comments received from them. 

3.1 OVERALL APPROACH AND METHODOLOGY OF THE STUDY 

The reviewers are supportive of the overall approach and methodology. 

Walter: “The overall approach and methodology are within the mainstream of critical review 

practice in economics, and the execution in unexceptionable.” He does comment on the fact that 

“Greene (2009) adopts a conventional ‘literature review’ approach” and “both the conventional 

approach to reviewing economic research and the conventional approach to economic research 

itself do not give much help to decision makers who are understandably made uncomfortable by 

the widely differing expert opinions about parameters that are crucial in predicting the impacts of 

regulations and in choosing between alternatives. An overall critique of conventional practice is 

beyond the scope of this review. However, I do have some suggestions for improvements in the 

methodology for comparing studies and for dealing with the lack of consensus in expert 

opinions. (The References section below lists some papers by Edward E. Leamer, a professor at 

UCLA, in which a critique of conventional practice is developed along with alternative practices 

that would enhance the usefulness of economic research to policy makers.)” 

Chris comments that the study does not discuss “why consumers undervalue gasoline, relative 

society (if they do indeed undervalue fuel economy). This has important implications for which 

policies are most effective at aligning discount rates.” However, he recognizes that papers 

dealing with such issues are lacking. 
Kent Helmer, Gloria Helfand 

February 26, 2010 

Page 4 

3.2 APPROPRIATENESS OF THE DATASETS AND ANY OTHER 

INPUTS 

One reviewer commented that the choice of the papers was comprehensive. Another reviewer 

recommended including some explanation of how the studies were selected for inclusion in 

Greene (2009). 

3.3 DATA ANALYSIS CONDUCTED FOR THE STUDY 

Two reviewers comment on the fact that the type of analysis done for the different papers varies 

greatly. 

Chris: “It strikes me that there are two ways to conduct a review such as this. One is to report the 

results of papers with little, or no, discussion of the quality each paper. The other is to both 

review and critique each paper. I found a lack of uniformity in this regard. For many of the 

papers, the latter strategy is taken. The report discusses key weakness of these papers. Still many 

papers are simply summarized, with little or no discussion of their strengths or weaknesses. This 

gives the impression that the results in these papers are more accurate. This may actually be the 

case, but if it is, this should be stated more explicitly. 

For example, the first paper (Alcott and Wozny) is simply summarized with no editorializing. 

One possibility is that there are no weaknesses in this paper. But, many of the issues raised later 

for other papers also relate to this paper. For example, the report correctly states, when 

discussing the Sawhill paper, that in order to get a consistent estimate of how consumers value 

fuel economy, the author is required to correctly specify how consumers form expectations about 

gas prices and drive their vehicles. If any of these are incorrectly specified, the estimate will be 

biased. The same is true for the Alcott and Wozny paper. While I find Alcott and Wozny’s 

specification for how expectations are formed to likely be more accurate, ironically, they assume 

consumers are hyper-rational in the sense that they form their gas price expectations based on 

NYMEX futures prices. In some ways the myopia result is inconsistent with this key 

assumption.” 

Walter: “It is difficult for the reader to verify that the same standards are being used to assess 

each paper, especially since the amount of discussion by paper varies greatly. This leads the 

reader to have less confidence in the reviewer’s opinions of the individual studies (that this or 

that one had plausible or implausible assumption, methodologies, or results).” 
Kent Helmer, Gloria Helfand 

February 26, 2010 

Page 5 

3.4 APPROPRIATENESS OF THE CONCLUSIONS DRAWN 

There was general consensus among the reviewers on the major conclusion of the document: that 

the literature remains mixed as to whether consumers undervalue, overvalue, or approximately 

value fuel economy. 

One reviewer raised doubts that differences in assumptions, opinions, and methods have been 

sufficiently examined to support even the weak proposition that “there do not appear to be clear 

associations among methods or data sources and the resulting inferences.” 

One reviewer expressed doubts about what Greene means by “incorrect models” of consumer 

decision making about fuel economy but suggested efficient markets hypothesis and economic 

rationality to be a possible interpretation of this phrase. 

3.5 RECOMMENDATIONS FOR ALTERNATE DATA AND/OR 

ANALYSES 

3.5.1 Additional Data 

Two of the reviewers suggested several other studies that are relevant for this work. A list of 

these studies is provided below. 

Dreyfus, Mark K. and W. Kip Viscusi (1995). “Rates of Time Preference and Consumer 

Valuations of Automobile Safety and Fuel Efficiency.” Journal of Law and Economics 

38: 79-98. 

Hughes, Jonathan E., Christopher R. Knittel and Daniel Sperling (2008). “Evidence of a Shift in 

the Short-Run Price Elasticity of Gasoline Demand.” Energy Journal. 

Kilian, Lutz and Eric Sims (2006). The Effects of Real Gasoline Prices on Automobile Demand: 

A Structural Analysis Using Micro Data, Working Paper, University of Michigan (April). 

Klier and Linn (2010 working paper) “The Price of Gasoline and New Vehicle Fuel Economy: 

Evidence from Monthly Sales Data” 

Klier, Thomas, and Joshua Linn (2008). “The Price of Gasoline and the Demand for Fuel 

Efficiency: Evidence from Monthly New Vehicles Sales Data.” Working Paper, 

University of Illinois at Chicago (September). 

Sallee and West (2009 working paper) 
 

Kent Helmer, Gloria Helfand 

February 26, 2010 

Page 6 

Sallee, James, and Sarah West (2008). “Testing for Consumer Myopia: The Effect of Gasoline 

Price Changes on the Demand for Fuel Economy in Used Vehicles.” Working Paper, 

Macalester College (December). 

West, S. (2008). “The Effect of Gasoline Prices on the Demand for Sport Utility Vehicles.” 

Working Paper. Macalester College. 

3.5.2 Additional Analysis 

The reviewers suggested several additional analyses to strengthen the study. 

3.5.ii a. The author should take advantage of the summary section to indicate whether 

some approaches reviewed in the study are likely to be better than others. 

3.5.ii b. The analysis to explain the difference in results could be improved. 

Carolyn: “First, the discussion interweaves reasons that could explain why we can plausibly 

believe any of a range of results (e.g., heterogeneous decision rules, uncertainty) with reasons for 

why different studies get different results. It would be helpful to separate these discussions, 

reminding readers of the former, and then focusing on the latter, which is the most important 

contribution. This kind of analysis should help indicate best practices and directions for future 

research. Can we suggest some experiments (e.g., new techniques on old data sets, or old 

techniques on new data sets) that can help tease out what differences arise from alternative 

econometric approaches? If most studies have focused on other aspects of vehicle demand, are 

there better specifications for looking directly at the valuation of fuel economy?” 

A summary of key differences in approaches and data and an analysis of their consequences 

would be useful. These would include 

• levels of aggregation; 

• time horizons (how useful is information from the 1970s in estimating current 

demand?); 

• incorporation of the supply side; 

• details of other attributes and variable definitions; 

• allowing for separate responses to MPG and fuel costs (this was raised as lacking in 

the discussion of Gramlich, but it was not apparent which other studies had done 

this); 

• assumptions of vehicle lifetime, VMT, discount rates, etc.; and 

• econometric approach. 
 

Kent Helmer, Gloria Helfand 

February 26, 2010 

Page 7 

Although Table 10 provides some of these summary indicators (model type, data, and time), 

additional analysis such as the following would be useful: “Do we observe any trends across 

classes or time? Within a class of models, what drives the differences?” 

Walter: “It would be useful to expand Table 10 (or to create a set of tables) to show other 

differences that could be important sources of differences in estimates. The items that could be 

included would show differences in: 

1. Maintained assumptions reported (assumptions that are maintained across any alternative 

specifications or sensitivities), 

2. Reported sensitivity analyses (is one done? which assumptions are varied? how widely?), 

3. Econometric challenges and approaches used to address them (Greene (2009) appears to 

discuss these comprehensively in the text.), and 

4. Out-of-sample predictive performance (are predictions made? about what? are the 

predictions compared to actual outcomes? how accurate are the predictions?). 

Adding these additional side-by-side comparisons would increase the reader’s confidence that 

the same standards are being used to form the reviewer’s opinions about the relative usefulness 

of each paper to inform decision making by EPA. 

My opinion: The additional elements are responsible for more of the variation in the estimates of 

consumer value for fuel economy than are the elements already included in Table 10.” 

He also suggested a measure of researcher/specification uncertainty and disagreement to 

quantitatively assess whether we have made progress toward a consensus. He also provides 

illustrative suggestions on ways to use and interpret this measure. “The idea is to compare the 

range of parameter estimates to the parameter value predicted by mainstream theory.” 

The metric is called Multiple Extreme Estimates Test (MEET) and is defined as the ratio of the 

value predicted by theory to the difference between the two extreme estimates. 

Value Predicted by Theory V MEET = = Largest Estimate - Smallest Estimate R 

lim MEET = 0 and lim MEET = ∞

R→∞	 R→0 

“Numerically, MEET measures the predicted value as a share of the range of expert opinion. The 

range of expert opinion, R, is a crude and partial measure of the uncertainty or fragility of the 

estimates due to differences in assumptions, specification, and opinions between researchers. The 

metric MEET views this fragility compared to the theoretical value of the parameter, V.” 
 

Kent Helmer, Gloria Helfand 

February 26, 2010 

Page 8 

3.5.ii.c. Though the issue of consumer heterogeneity has been mentioned in several 

places, this may be important area for additional analysis, both to explain differences and 

to suggest future research directions. 

3.6 OVERALL CLARITY OF THE PRESENTATION 

The reviewers agreed that the overall clarity of the document was high. 

Reviewers had several suggestions about organizing the paper. 

i. One reviewer suggested that since the articles are organized logically by class of data and 

methods, it would be helpful to review those methods at the beginning of each section 

“including the advantages and drawbacks, as well as key factors or assumptions that can 

affect the results.” 

ii. It has also been suggested that it would be helpful to begin with the seminal papers in 

each category, “so as to understand the evolution and improvements made subsequently.” 

iii. Two reviewers agreed that the publication status of papers should be recognized and one 

reviewer suggested that the review should “focus first on published papers and then 

second on unpublished papers.” 

iv. Chris Knittel: “Many of the papers reviewed did not focus on measuring the implicit 

discount rate, or even on the issue of how gas prices affect vehicle demand. If a paper did 

not focus on either of these two questions, it is difficult to gauge the robustness of their 

results with respect to these questions, the quality of the variation in gas prices in the 

data, etc. Perhaps a better method would be to first focus on those papers where gas 

prices and vehicle choice are the central research question, and those papers where this is 

more a tangential part of the analysis.” 

On a related note, another reviewer commented on studies that do not explicitly estimate the 

value of fuel economy: “Since few econometric studies have explicitly estimated the value of 

fuel economy, a significant contribution of the paper is to translate a wide variety of results into 

more consistent indicators. Yet, they could still be more consistent. In Table 10, can all of the 

results be converted into the same WTP metric, preferably using the same assumptions about 

vehicle lifespan, VMT, discounting, etc.?” 

Another comment on the presentation of the paper was that “the author’s baseline assumptions 

need to be stated clearly early in the paper, so careful attention can be given throughout to 

assumptions that deviate from that baseline. Indeed, the statement made in the Allcott and 

Wozny analysis (‘Calculating the discounted present value of fuel costs requires a number of 

assumptions...’) should be made earlier in a general context, so that the variety and range of 

assumptions can be understood and explored.” 
Kent Helmer, Gloria Helfand 

February 26, 2010 

Page 9 

3.7 OTHER CLARIFICATION COMMENTS 

One reviewer provided the following comments to clarify or rephrase certain statements. 

1. The authors should “use the phrase ‘at society’s discounted expected value...’ (top of 

page 5) instead of ‘does the market value fuel economy improvements at the discounted 

expected value of future fuel savings over the lifetime of the vehicle, or less, or more?’” 

2. The statement (on page 5) that “On the other hand, if consumers are myopic and consider 

only the first three years of fuel savings, for example, fuel economy standards can 

increase welfare even based solely on private costs and benefits” should be made clearer. 

3. One of the reviewers comment on the statement on page 5 that “it is surprising that there 

is no basic research on how consumers consider fuel economy.” “This is a strange use of 

the term basic research, which I take to mean research on pure science. Does the author 

equate basic science with interviews? I have never heard basic science used in this 

manner.” 

Two of the reviewers also provided several detailed comments on specific paper reviews, Section 

2 of the document, and editorial comments and corrections to typographical errors. 

4. References 

Rubin, J. January 2010. “Review: Vehicle Greenhouse Gas Emission (VGHG) Emissions Cost 

and Compliance Model.” 

U.S. Environmental Protection Agency, Office of Transportation and Air Quality, Assessment 

and Standards Division. June 2009. “Peer Review for the RTI Report, Automobile 

Industry Retail Price Equivalent and Indirect Cost Multipliers. EPA-420-R-09-004. 
Appendix A: 

Resumes of Selected Reviewers 
 

 

 

 

 

 

 

 

 

 

 

CAROLYN FISCHER 

Resources for the Future Phone: (202) 328-5012 

1616 P St., NW Fax: (202) 939-3460 

Washington, D.C. 20036 E-mail: fischer@rff.org 

EDUCATION 

University of Michigan, Ann Arbor, MI. 1991-97. 

Ph.D. in Economics with fields in public finance and natural resource economics. 

M.A. in Economics, 1993. 

University of Pennsylvania, Philadelphia, PA. 1986-90. 

B.A. in International Relations with honors (major Valedictorian) and Economics, 

minor in French, magna cum laude. 

PROFESSIONAL EXPERIENCE AND AFFILIATIONS 

Resources For the Future, Washington, DC. 1997-present. 

Senior Fellow. Indefinite appointment (tenure), 2004. 

Fellow, CESifo Research Network, Energy and Climate Economics Area, 2009-present. 

Board of Directors, Association of Environmental and Resource Economists, 2008-2010. 

Editorial Board, Resource and Energy Economics, 2006-2009. 

Leadership Committee for the Energy Collaborative Analysis Initiative, NREL, 2007-present. 

Fellow, Center for Advanced Study at the Norwegian Academy of Science and Letters, with the 

group “Environmental Economics: Policy Instruments, Technology Development, and 

International Cooperation,” 2005. 

Johns Hopkins University, 1999. Instructor, Natural Resource and Environmental Economics.
 

Council of Economic Advisors, Washington, DC. 1994-95. Staff economist.
 

The WEFA Group, Philadelphia, PA. 1990-91. Economic analyst in World Service division.
 

SELECTED PUBLICATIONS 

Current Work 

Fischer, C. and T.P. Lyon (2009) “Competing Environmental Labels.” RFF Working Paper. 

Fischer, C. and C. Coleman (2009) “Imperfect Competition, Consumer Behavior, and the 

Provision of Fuel Efficiency in Light-Duty Vehicles” 

Fischer, C. and A. K. Fox (2009) “Comparing Policies to Combat Emissions Leakage: Border 

Tax Adjustments versus Rebates” RFF DP 09-02. (under review at JEEM) 

Fischer, C. and A. K. Fox (2009) “Combining Rebates with Carbon Taxes: Optimal Strategies 

for Coping with Emissions Leakage and Tax Interactions” (under review at JPubE) 

Fischer, C. and R. Morgenstern (2008) “Metrics for Evaluating Policy Commitments in a 

Fragmented World: The Challenges of Equity and Integrity” Discussion Paper 08-17, 

Harvard Project on International Climate Agreements. 

Fischer, C. and T. Sterner (2008) “Climate Policy, Prudence, and the Role of Technological 

Innovation.” (under review at JEEM) 
 

 

 

 

 

 

 

 

 

 

 

2 

Journal Articles 

Fischer, C. (forthcoming) “Does trade help or hinder the conservation of natural resources?” 

Review of Environmental Economics and Policy. 

Fischer, C. (forthcoming) “When Do Renewable Portfolio Standards Lower Electricity Prices?” 

The Energy Journal 30 (4). 

Fischer, C., R. Morgenstern, and E. Herrnstadt (forthcoming) “Understanding Bias in EIA 

Forecasts of Energy Demand.” The Energy Journal. 

Fischer, C. and R.G. Newell (2008) “Environmental and Technology Policies for Climate 

Mitigation,” Journal of Environmental Economics and Management. 55 (2): 142-162. 

Fischer, C. (2008) “Emissions Pricing, Spillovers, and Public Investment in Environmentally 

Friendly Technologies,” Energy Economics. 30 (2): 487-502. 

Fischer, C. and A.K. Fox (2007) “Output-Based Allocation of Emissions Permits for Mitigating 

Tax and Trade Interactions,” Land Economics. 83: 575-599. 

Fischer, C., W. Harrington, and I.W.H. Parry (2007) “Do Market Failures Justify Tightening 

Corporate Average Fuel Economy (CAFE) Standards?” The Energy Journal 28 (4): 1-30. 

Bernard, A.L., C. Fischer, and A.K. Fox (2007) “Is There a Rationale for Output-Based Rebating 

of Environmental Levies?” Resource & Energy Economics 29 (2): 83-101. 

Fischer, C. (2005) “On the Importance of the Supply Side in Demand-Side Management,” 

Energy Economics, 27 (1): 165-180. 

Margolis, M., J.F. Shogren and Carolyn Fischer (2005) “How trade politics affect invasive 

species control,” Ecological Economics, 52 (3): 305-313. 

Fischer, C. and R. Laxaminarayan (2005) “Sequential Development and Exploitation of an 

Exhaustible Resource: Do Monopoly Rights Promote Conservation?” Journal of 

Environmental Economics and Management. 49 (3): 500-515. 

Fischer, C. and R. Laxaminarayan (2004) “Monopoly Extraction of an Exhaustible Resource 

with Two Markets,” Canadian Journal of Economics, 37 (1): 178-188. 

Fischer, C. (2004) “The Complex Interactions of Markets for Endangered Species Products,” 

Journal of Environmental Economics and Management, 48 (2): 926-953. 

Fischer, C., I.W.H. Parry and W.A. Pizer (2003) “Instrument Choice for Environmental 

Protection when Technological Innovation is Endogenous,” Journal of Environmental 

Economics and Management 45(3): 523-545. 

Fischer, C. (2001) “Read This Paper Later: Procrastination with Time-Consistent Preferences.” 

Journal of Economic Behavior and Organization, (46)3 pp. 249-269. 

Recent Policy Briefs: 

Fischer, C. (2009) “Are Consumers or Fuel Economy Policies Efficient?” in Sperling, Daniel and 

James S. Cannon, Eds., Reducing Climate Impacts in the Transportation Sector. Springer. 

Fischer, C. and R.G. Newell (2008) “What’s the Best Way to Promote Green Power? Don’t 

Forget the Emissions Price.” Resources, Summer (169): 10-13. 

Fischer, C. and C. Egenhofer. (2008). “The Critical Role of Technology for International Climate 

Change Policy,” in Beyond Bali: Strategic Issues for the international climate change 

negotiations, Brussels, Belgium: CEPS; and Washington, DC: Brookings Institution. 

2
 
 

 

 

 

 

 

 

 

CHRISTOPHER ROLAND KNITTEL 

University of California, Davis
 

Department of Economics
 

One Shields Ave
 

Davis, CA 95616
 

Office: 530.302.1032
 

Fax: 530.752.9382
 

crknittel@ucdavis.edu 

http://www.econ.ucdavis.edu/faculty/knittel 

CURRENT APPOINTMENTS: 

2006-present, Associate Professor of Economics, University of California, Davis 

2008-present, Chancellor’s Fellow, University of California, Davis
 

2007-present, Research Associate, National Bureau of Economic Research. Groups: 

Environmental Economics and Energy, Industrial Organization, and Productivity 

2003-present, Visiting Research Fellow, University of California Energy Institute 

2005-present, Faculty Affiliate, Institute of Transportation Studies, UC Davis 

2006-present, Strategy and Policy Thread Leader for STEPS 

2006-present, Associate Editor, The Journal of Industrial Economics 

2007-present, Associate Editor, American Economic Journal – Economic Policy 

2007-present, Associate Editor, The Journal of Energy Markets 

2008-present, Member, Economic and Allocation Advisory Committee for AB32’s cap-andtrade program, State of California 

PREVIOUS APPOINTMENTS: 

2002-2006, Assistant Professor of Economics, University of California, Davis
 

2004-2007, Faculty Research Fellow, National Bureau of Economic Research. Groups:
 

Environmental Economics and Energy, Industrial Organization, and Productivity
 

1999-2002, Assistant Professor of Finance and Economics, School of Management, Boston 

University
 

1996-1999, Research Assistant, University of California Energy Institute
 

1994-1996, Teaching Assistant, University of California, Davis 

EDUCATION: 

Ph.D., University of California, Berkeley, 1999 (Economics)
 

M.A., University of California, Davis, 1996 (Economics)
 

B.A., California State University, Stanislaus, summa cum laude, 1994 (Economics and Political 

Science)
 

PUBLICATIONS: 

 Knittel, Christopher R. and Jason J. Lepore. “Tacit Collusion in the Presence of Cyclical 

Demand and Endogenous Capacity Levels.” Forthcoming in The International Journal of 

Industrial Organization. 

Version:11/1/09 
 

 

 

CHRISTOPHER R. KNITTEL 

 Stewart, Scott, John J. Neumann, Christopher R. Knittel, and Jeffrey Heisler. “Absence of 

Value: An Analysis of Investment Allocation Decisions by Institutional Plan Sponsors,” 

Financial Analyst Journal, 65(6), November/December 2009. 

 Knittel, Christopher R. and Victor Stango. “How Does Incompatibility Affect Prices?: 

Evidence from ATMs,” The Journal of Industrial Economics, LVII(3), September 2009, pp. 

557-582. 

 Holland, Stephen P., Jonathan E. Hughes and Christopher R. Knittel. “Greenhouse Gas 

Reductions under Low Carbon Fuel Standards?,” The American Economic Journal – 

Economic Policy, 1(1), February 2009, pp. 106-146. 

 Borenstein, Severin, James Bushnell, Christopher R. Knittel and Catherine Wolfram. “Trading 

Inefficiencies in California's Electricity Markets,” The Journal of Industrial Economics, 

LVI(2), June 2008, pp. 347-378. 

 Feenstra, Robert and Christopher R. Knittel. “Re-Assessing the Quality Adjustment to 

Computer Prices: Do U.S. Procedures Overstate the Gains?,” forthcoming Price Index 

Concepts and Measurement, NBER and the Chicago Press. 

 Knittel, Christopher R. and Konstantinos Metaxoglou. “Diagnosing Unilateral Market Power 

in Electricity Reserves Market,” The Journal of Energy Markets, 1(1), Spring 2008. 

 Knittel, Christopher R. and Victor Stango. “Incompatibility, Product Attributes and Consumer 

Welfare: Evidence from ATMs,” The BE Journal of Economic Analysis & Policy, Advances, 

8(1), January 2008. Available at: http://www.bepress.com/bejeap/vol8/iss1/art1. 

 Hughes, Jonathan E., Christopher R. Knittel and Daniel Sperling. “Evidence of a Shift in the 

Short-Run Price Elasticity of Gasoline.” The Energy Journal, 29(1), January 2008. 

 Heisler, Jeffrey, Christopher R. Knittel, John J. Neumann and Scott Stewart. “Why Do 

Institutional Plan Sponsors Hire and Fire their Investment Managers?” Best Paper Award for 

the 31st NBEA Conference. The Journal of Business and Economics Studies, 13(1), Spring 

2007, pp. 88-116. 

 Kim, Dae-Wook and Christopher R. Knittel “Biases in Static Oligopoly Models? Evidence 

from the California Electricity Market,” The Journal of Industrial Economics, LIV(4), 

December 2006, pp. 451-470. 

 Knittel, Christopher R. “The Adoption of State Electricity Regulation: The Role of Interest 

Groups,” The Journal of Industrial Economics, LIV(2), June 2006. 

 Knittel, Christopher R. and Michael R. Roberts. “Financial Models of Deregulated Electricity 

Prices: An Application to the California Market,” Energy Economics, 27(5), September 2005, 

pp. 791-817. 

 Knittel, Christopher R. “Regulatory Restructuring and Incumbent Price Dynamics: The Case 

of Local Telephone Markets,” Review of Economics and Statistics, 86(2), May 2004, pp. 614-

625. 

 Knittel, Christopher R. and Victor Stango. “Price Ceilings as Focal Points for Tacit Collusion: 

Evidence from the Credit Card Market,” The American Economic Review, 93(5), December 

2003, pp. 1703-1729. 

 Knittel, Christopher R. “Market Structure and the Pricing of Electricity and Natural Gas,” The 

Journal of Industrial Economics, LI(2), June 2003, pp. 167-191. 

 Knittel, Christopher R. “Alternative Regulatory Methods and Firm Efficiency: Stochastic 

Frontier Evidence the US Electricity Industry,” Review of Economics and Statistics, 84(3), 

August 2002, pp. 530-540. 
 

 

 

 

 

CHRISTOPHER R. KNITTEL 

 Borenstein, Severin, James Bushnell, and Christopher R. Knittel. “Market Power in Electricity 

Markets: Beyond Concentration Measures,” The Energy Journal, 20(4), October 1999, pp. 

65-88. 

 Knittel, Christopher R. “Long Distance Rates: Search Costs, Switching Costs, and Market 

Power,” Review of Industrial Organization, 12(4), August 1997, pp. 519-536.
 

WORKING PAPERS: 

 “Automobiles on Steroids: Product Attribute Trade-offs and Technological Progress in the 

Automobile Sector” Revisions requested from The American Economic Review. 

 Knittel, Christopher R. and Victor Stango. “Strategic Incompatibility in ATMs.” Revisions 

requested from The Journal of Banking and Finance.
 

 “The Implied Cost of Carbon Dioxide under the Cash for Clunkers Program” Revisions 

requested from The BE Journal of Economic Analysis & Policy,.
 

 Busse, Meghan, Christopher R. Knittel and Florian Zettelmeyer. “Pain at the Pump: How 

Gasoline Prices Affect Automobile Purchasing.” In submission.
 

 Knittel, Christopher R. and Konstantinos Metaxoglou. “Estimation of Random Coefficient 

Demand Models: Challenges, Difficulties and Warnings.” In submission.
 

 Fowlie, Meredith, Christopher R. Knittel and Catherine Wolfram. “Sacred Cars: Optimal 

Regulation of Stationary and Non-stationary Pollution Sources.” In submission.
 

 Knittel, Christopher R. and Victor Stango. “The Productivity Benefits of IT Outsourcing.” 

 Knittel, Christopher R., Douglas Miller, and Nick Sanders. “Caution, Drivers! Children 

Present. Traffic, Pollution and Infant Health”
 

 Huckfeldt, Peter, and Christopher R. Knittel. “Patents, Pharmaceutical Use and Pharmaceutical 

Prices” 

AWARDS, HONORS, AND GRANTS: 

 Chancellor’s Fellowship, UC Davis (one of five faculty members), 2008 

 Barry D. McNutt Award for Excellence in Automotive Policy Analysis (with Jonathan 

Hughes and Dan Sperling), 2008
 

 National Science Foundation Grant (with Victor Stango), 2008-2010, $240,000 

 Chevron Bio-Fuel Research Grant, 2007-2008, $127,000 

 Chevron Bio-Fuel Research Grant, 2007-2008, $77,000 

 Chevron Bio-Fuel Research Grant (Co-PI), 2007-2009, $370,000 

 Woods Institute for the Environment Leadership Scholar Training, 2007 

 Distinguished Paper, 2006 Academy of Finance 

 University of California Energy Institute Research Grant, 2005-2006, $50,000 

 Best Paper Award for the 31st NBEA Conference 

 ASUCD Excellence in Teaching Award, 2004 

 University of California Energy Institute Research Grant, 2003 

 Faculty Research Grant, UC Davis, 2002, 2003, 20004, 2005, 2006 

 Institute of Governmental Affairs Junior Faculty Grant, 2002, 2003, 2004, 2005 

 Junior Faculty Research Grant, Boston University, 2001 

 Graduate Fellowship, University of California, Berkeley, 1997–1999 

 Graduate Fellowship, University of California, Davis, 1994–1996 

 Institute of Transportation Fellow, University of California, Davis, 1995–1996 

 Student Commencement Speaker, California State University, Stanislaus, 1994 
 

 

 

CHRISTOPHER R. KNITTEL 

REFEREE SERVICES: 

Agricultural Economics, American Economic Review, Bulletin of Economic Research, Census 

Bureau, Econometrica, Economic Inquiry, The Economic Journal, Economics Letters, Energy 

Economics, The Energy Journal, Energy Studies Review, European Economic Review, 

International Journal of Industrial Organization, International Journal of Power and Energy 

Systems, Journal of Banking and Finance, The Journal of Business, Journal of Business and 

Economic Statistics, Journal of Economic Behavior and Organization, Journal of Economic 

Education, Journal of Economics and Management Strategy, Journal of Futures Markets, Journal 

of Industrial Economics, Journal Institutional and Theoretical Economics, Journal of Law and 

Economics, Politics and Economics, Quarterly Journal of Economics, Rand Journal of 

Economics, Resource and Energy Economics, Review of Economic Studies, Review of Economics 

and Statistics, Review of Industrial Organization, Review of Network Economics, Southern 

Economic Journal, Socio-Economic Planning Sciences, Utilities Policy, University of California 

Energy Institute Grant Program, NSF Grant Program 

RECENT INVITED PRESENTATIONS: 

 Energy Institute @ Haas, Policy Conference, Sacramento, October 2009 

 Institute of Transportation Studies, UC Davis, October 2009 

 TREE Seminar, Raleigh/Durham/Chapel Hill, North Carolina, October 2009 

 UC Berkeley, ARE, February 2009 

 NBER Winter IO Meeting, February 2009 

 Iowa State, Economics, January 2009 

 NBER Summer EEE Meeting, July 2009 

 UC Berkeley IO/Innovation Seminar, November 2008 

 UC Berkeley IO/Innovation Seminar, October 2008 

 Department of Justice, March 2008 

 University Retirement Community, February 2008 

 New American Foundation, February 2008 

 Tainjin Chinese Delegation at UC Davis 

 UCEI Policy Conference, December 2007 

 University of California Energy Institute, July 2007 

 Institute of Transportation Studies, UC Davis, October 2007 

 NBER EEE, Summer Meeting, July 2007 

 NBER Winter IO Meetings, January 2008 

 UC Berkeley, Department of Economics, November 2007 

 University of Alberta and Calgary University Industrial Organization Conference, October 

2007
 

 Washington University, Olin School of Business, November 2007 

REGULATORY FILINGS: 

 Arons, S.M., A.R. Brandt, M.A. Delucchi, A. Eggert, A.E. Farrell, B.K. Haya, J. Hughes, 

B.M. Jenkins, A.D. Jones, D.M. Kammen, S.R. Kaffka, C.R. Knittel, D.M. Lemoine, E.W. 

Martin, M.W. Melaina, J.M. Ogden, R.J. Plevin, D. Sperling, B.T. Turner, R.B. Williams, C. 

Yang, 2007. “A Low-Carbon Fuel Standard for California, Part 1: Technical Analysis.” 

Available Online: http://www.lcfs.ucdavis.edu. 

 Brandt, A.R., A.E. Farrell, B.K. Haya, J. Hughes, B.M. Jenkins, A.D. Jones, D.M. Kammen, 

C.R. Knittel, M.W. Melaina, M. O’Hare, R.J. Plevin, D. Sperling, 2007. “A Low-Carbon Fuel 
 

 

 

 

CHRISTOPHER R. KNITTEL 

Standard for California, Part 2: Policy Analysis.” Available Online: 

http://www.lcfs.ucdavis.edu. 

 Peer Review Comments on AB 1493, California Environmental Protection Agency Air 

Resource Board, September 2004.
 

 “Comments on the Use of Computer Models for Merger Analysis in the Electricity Industry,” (Joint 

with Severin Borenstein and James Bushnell), Federal Energy Regulatory Commission. Docket No. 

PL98-6-000, June 1998. 

 “A Cournot-Nash Equilibrium Analysis of the New Jersey Electricity Market,” December 

1997. (Joint with Severin Borenstein and James Bushnell). Filed with the New Jersey Public 

Utility Commission as testimony on the potential for market power in a deregulated 

Pennsylvania-Jersey-Maryland Power Pool.
 

CONSULTING: 

Customers First! Coalition, Energy Information Agency, Korean Electric Power Company, 

California Air Resource Board, City of West Sacramento
 

PH.D. COMMITTEES (FIRST JOB): 

UC Davis: 

Anson Soderbery (on-going)
 

Nick Sanders (chair, on-going)
 

Chai-Wen Chen (chair, on-going)
 

Jonathan Hughes (chair, University of Colorado, Boulder)
 

Adib Bagh (University of Kentucky, Math and Economics)
 

Seungjoon Lee (Korean Insurance Research Institute) 

Jason Lepore (chair, Cal Poly)
 

Wei-Min Hu (Peking University)
 

Byeongil Ahn (Gyeongsang University)
 

Konstantinos Metaxoglou (chair, Bates and White LLC)
 

Lan Li (University of Melbourne)
 

Neil Norman (Cornerstone Research)
 

Dae-Wook Kim (chair, Korean Institute for Industrial Economics and Trade)
 

Boston University: 

Gustavo Genoni (2002, Finance, IAE, School of Business, Universidad Austral)
 

John Neumann (2003, Finance, St. John’s University)
 

TEACHING: 

•	 UC Davis 

o	 Graduate Empirical Industrial Organization (6 times) 

 Ratings: Mean 4.9 (out of 5) 

o	 Transportation Economics (4 times) 

 Ratings: Mean 4.7 

o	 Intermediate Microeconomics (1 time), 

 Ratings: Mean 4.8 

o	 Undergraduate Industrial Organization (9 times) 

 Ratings: Mean 4.8 

•	 Boston University 

o	 Modeling Business Decision Making, 

 Spring 2000, Spring 2001 and Spring 2002 
 

 

 

 

 

CHRISTOPHER R. KNITTEL 

 Ratings: 4.53 (out of 5), 4.77, 4.70 

o Modeling Business Decision Making (honors), 

 Spring 2001 and Spring 2002 

 Ratings: 4.88, 4.70 

UNIVERSITY SERVICE: 

UC Davis: 

2007-2008, Co-writer (with Jean Vandergehst) of a proposal for a Graduate Program in "Energy 

Science and Technology" and "Energy Policy and Management" 

2006-Present, Member, Energy Institute Steering Committee 

2008, Founding Faculty Member, UC Davis Energy Institute 

2005-2006, Hiring Committee and Interviewing Committee 

2004-2005, Hiring Committee and Interviewing Committee 

2002-2003, Hiring Committee and Interviewing Committee 

2002-2007, Graduate Advisor 

Oral committees: Dae-Wook Kim, Konstantinos Metaxoglou, Neil Norman (chair), Seungjoon 

Lee, Wei-Min Hu, Lan Li (ARE), Sunhwa Lee, Byeongil Ahn (ARE), Michele Amaral, David 

Ong, Adib Bagh, Jason Lepore, Bei Li, Chenguang Li (ARE), Tina Saitone (ARE), Carlo Russo 

(ARE), Sandhya Patlolla (ARE), Jon Hughes (TTP), Peter Huckfeldt, Kyungwon Rho, Nick 

Sanders, Chia-Wen Chen, Joeri de Witt (ARE), In-Sung Lee (TTP), Anson Soderbery, Nils 

Johnson (TTP), David McCollum (TTP) 

Boston University: 

2000-2001, Finance Hiring Committee and Interviewing Committee
 

1999-2000, Finance Hiring Committee
 

RECENT MEDIA CITATIONS: 

Print: Alameda Times-Star, Arizona Daily Star, Argus, ATMmarketplace.com, Austin-American 

Statesman, Boston Globe, Buffalo News, California Aggie, Contra Costa Times, PE.com, 

bankrate.com, marketwatch.com, Crain’s Business Report (New York), Credit Card Magazine, 

Kiosk Marketplace News, LA Observed, LA Times, International Herald Tribune, Northwestern 

Herald, Oakland Tribune, Oregonian, Philadelphia Inquirer, Providence Journal, New York 

Times, Sacramento Bee, St. Petersburg Times, Salon.com, San Diego Union Tribune, Salt Lake 

Tribune, San Diego Union Tribune, SF Chronicle, San Mateo County Times, Santa Rosa Press 

Democrat, Sarasota Herald-Tribune, Scripps News (DC), Tuscaloosa News Sun Herald, Quad 

City News (Iowa), Winston-Salem Journal, Worcester Telegram 

Radio: KQED’s “Forum”, KXJZ, KFBK, KUOP, KCBS, KNX, WHYY with Marty MossCoane, WPR with Kathleen Dunn, Bloomberg Radio, Lambasted by Rush Limbaugh 

Television: KCRA-3, CBS-13 Sacramento, NBC Nightly News, ABC World News, CBS 

Evening News, ABC Good Morning America 

REFERENCES: 

Severin Borenstein 

E.T. Grether Professor of Business Administration and Public Policy 

Haas School of Business
 

University of California at Berkeley
 

borenste@haas.berkeley.edu 
 

 

 

 

CHRISTOPHER R. KNITTEL 

Michael Greenstone 

3M Professor of Environmental Economics 

Department of Economics 

Massachusetts Institute of Technology 

mgreenst@mit.edu 

Frank Wolak 

Professor of Economics 

Stanford University 

wolak@zia.stanford.edu 

Catherine Wolfram 

Associate Professor 

Haas School of Business 

University of California at Berkeley 

wolfram@haas.berkeley.edu 
 

 

 

 

 

 

 

Résumé

Dr. Walter McManus 

Research Scientist and Director 

Economics, Energy, and Environment Research Group 

University of Michigan Transportation Research Institute 

2901 Baxter Rd. 

Ann Arbor, MI 

(248) 821-0493 

watsmcm@umich.edu 

Biography 

Dr. Walter McManus is a Research Scientist at the University of Michigan Transportation Research 

Institute (UMTRI) and Director of the Economics, Energy, and Environment Research Group. Dr. 

McManus has been a member of the UMTRI faculty since March 2005. Immediately prior to that, he was 

Executive Director of Forecasting and Analytics at the global marketing information company, J.D. Power 

and Associates. His business experience also includes nine years with General Motors in forecasting, 

marketing analysis and strategy, and new-product development. (He also spent a year as a production 

supervisor in a GM manufacturing plant.) 

Dr. McManus graduated from Louisiana State University (BA 1977) and earned a doctorate in economics 

from the University of California, Los Angeles (PhD 1983). Dr. McManus pursues a research program that 

is focused on issues arising from the interaction of transportation, society, and the environment. The 

research program generates knowledge through excellent, creative research on the social, economic, and 

environmental dynamics that are producing change in the automotive transportation energy sphere; with the 

goal of developing and delivering useful applications of findings to assist policy makers, industry, and 

stakeholders in anticipating future societal needs. 

Research Interests 

Transportation economics, energy, and environment; the automotive industry; adoption and diffusion of 

clean transportation technologies 

Tools 

Economic analysis (market demand and supply models, strategic behavior of firms, economic history), 

econometrics, forecasting and simulation, finance, public speaking 

Education 

PhD, Economics, University of California, Los Angeles, 1983 

BA, Economics, Louisiana State University, 1977 

Awards 

2008 UMTRI Research Excellence Award for the article in Business Economics 2007 

NABE Abramson Award for the best article published in Business Economics 2007 

GM Chairman’s Honors for innovations enhancing performance in new-product development 1991 & 98 

Sidney Stern Fellow; University of California, Los Angeles; 1979 – 82 

Affiliations 

Automotive Industry Expert Panel, U.S. Government Accountability Office, 2009 – present 

Ceres Stakeholder Committee on Sustainability, Ford Motor Company, 2009 – present 

Fellow, Michigan Memorial Phoenix Energy Research Institute, 2007 – present 

Executive Committee, Michigan Center for Advancing Safe Transportation throughout the Lifespan, 2007 

– present 

Transportation Energy Committee, Transportation Research Board 

Transportation Working Group, Energy Futures Coalition, 2003 – 04 

American Economic Association 

National Association for Business Economics 

Society of Automotive Engineers 
 

 

 

 

 

 

 

 

 

 

 

Walter McManus 

Professional History 

Research Scientist and Director, Economics, Energy, and Environment Research Group, University of 

Michigan Transportation Research Institute, Mar 2005 - Present 

Visiting Scholar and Research Engineer, Transportation Sustainability Research Center, Institute of 

Transportation Studies, University of California, Berkeley, Mar 2009 – Oct 2009 

Executive Director of Forecasting and Analytics, J.D. Power and Associates, Oct 1999 – Jan 2005 

Director of Marketing, Textron Automotive Company, Dec 1998 – Sept 1999 

Leader, Industry Analysis Group, General Motors Corporation, July 1996 – Nov 1998 

Manager, North American Market Analysis, General Motors Corporation, Jan 1994 – June 1996 

Economist, Delco Remy Division, General Motors Corporation, Anderson, IN, Aug 1991 – Dec 1993 

(Memo: included development assignment as Manufacturing Supervisor, Jan 1993 – Dec 1993) 

Economist, General Motors Corporation, Detroit, MI, June 1989 – Aug 1991 

Associate Professor of Economics and Fellow, Center for the Study of Business and Government, Baruch 

College, New York, NY, July 1988– May 1989 

Assistant Professor of Economics, University of Florida, Gainesville, FL, July 1983 – June 1988 

Testimony and Briefings 

U.S. EPA and NHTSA Public Hearing, Proposed Rulemaking to Establish Light-Duty Vehicle Greenhouse 

Gas Emission Standards and Corporate Average Fuel Economy Standards, October 21, 2009 

Investor Briefing, Citigroup Investment Research, CAFE Panel Conference Call & Briefing, April 2009. 

Testimony, Committee on Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy, 

National Research Council, March 16, 2009 

Testimony, US EPA, Hearing on California Greenhouse Gas Waiver, March 3, 2009 

Testimony, Environmental Regulation Commission Hearing, Greenhouse Gas Emissions Reduction-Florida 

Clean Car Emission Rule, Florida Department of Environmental Protection, October 29, 2008 

US Congressional Briefing, Environmental and Energy Study Institute & Investor Network on Climate 

Risk, December 4, 2007 

Investor Briefing, Citigroup Investment Research, CAFE and the U.S. Auto Industry: A Growing Auto 

Investor Issue, 2012-2020, October 31, 2007 

Public Briefing, National Commission on Energy Policy and the International Council on Clean 

Transportation, Fuel Economy: Technology Trends and Policy Options, Washington, DC. October 1, 2007. 

Congressional Testimony, U.S. Senate Finance Subcommittee on Energy, “Advanced Technology 

Vehicles: The Road Ahead”, May 1, 2007 

Congressional Testimony, U.S. Senate Energy and Natural Resources Committee, “the Consumer Market 

for Fuel Economy”, January 30, 2007 

Publications 

2 
 

 

 

 

 

 

 

Walter McManus 

Rogozhin, A., Gallaher, M., Helfand, G., and McManus, W. (2010 forthcoming), Using indirect cost 

multipliers to estimate the total cost of adding new technology in the automobile industry, International 

Journal of Production Economics. 

McManus, W., Senter, R., Curtin, R., and Garver, S. (2009), The demographic threat to Detroit’s 

automakers, Targeting, Measurement and Analysis for Marketing 17:81-92 

Senter, R. and McManus, W. (2009), General Motors in an age of corporate restructuring, in the second 

automobile revolution: the automobile firms' trajectories at the beginning of the 21st century (Chapter 9), 

Edited by M. Freyssenet, New York: Palgrave Macmillan 

McManus, W. (2007), The link between gasoline prices and vehicle sales: economic theory trumps 

conventional Detroit wisdom. Business Economics 1.42:54-60 

McManus, W. and Griffor, E. (2006), Toward a science of driving: Safety in rules-based versus adaptive 

self-regulating traffic systems, SAE Convergence, 2006-21-0064 

McManus, W. (1985), Estimates of the deterrent effect of capital punishment: the importance of the 

researcher's prior beliefs, Journal of Political Economy 93:417-25 

McManus, W. (1985), Labor market assimilation of immigrants: the importance of language skills, 

Contemporary Economic Policy 3:77-89 

McManus, W. (1985), Labor market costs of language disparity: an interpretation of Hispanic earnings 

differences, American Economic Review 75:818-27 

Theil, H., Rosalsky, M., and McManus, W. (1985), Lp-norm estimation of non-linear systems 

Economics Letters 17(1-2):123-125 

McManus, W. and Rosalsky, M. (1985), Are all asymptotic standard errors awful? Economics Letters 

17(3):243-245 

McManus, W., Gould, W., and Welch, F. (1983), Earnings of Hispanic men: the role of English language 

proficiency, Journal of Labor Economics 1:101-30 

Technical Reports and Working Papers 

McManus, W. and Senter, R., Market Models for Predicting PHEV Adoption and Diffusion, UMTRI-2009-

37, August 2009. 

McManus, W. and Kleinbaum, R., Fixing Detroit, How Far, How Fast, How Fuel-Efficient, UMTRI-2009-

26, June 2009 

Senter, R. and McManus, W., Reshaping the Big Three, GERPISA, June 2009 

Rogozhin, A., Gallaher, M., Helfand, G., and McManus, W., Automobile Industry Retail Price Equivalent 

and Indirect Cost Multipliers, EPA-420-R-09-003, Feb 2009 

McManus, W., The Impact of Attribute-Based Corporate Average Fuel Economy (CAFE) Standards: 

Preliminary Findings, Automotive Analysis Division, University of Michigan Transportation Research 

Institute (UMTRI), July 2007 

McManus, W., Economic Analysis of Feebates to Reduce Greenhouse Gas Emissions from Light Vehicles 

for California, Automotive Analysis Division, University of Michigan Transportation Research Institute 

(UMTRI), May 2007 

3 
 

 

 

 

 

 

Walter McManus 

McManus, W., Can Proactive Fuel Economy Strategies Help Automakers Mitigate Fuel-Price Risks? 

Automotive Analysis Division, University of Michigan Transportation Research Institute (UMTRI), 

September 2006 

McManus, W., Baum, A., Hwang, R., Luria, D., and Baura, G., In The Tank – How Oil Prices Threaten 

Automakers’ Profits and Jobs, Office for the Study of Automotive Transportation, July 2005 

McManus, W. and Berman, B., The 2005 OSAT – HybridCars.com Survey of Owners and Shoppers, 

Office for the Study of Automotive Transportation (OSAT), University of Michigan Transportation 

Research Institute (UMTRI), 2005. 

McManus, W., The Effects of Higher Gasoline Prices on U.S. Light Vehicle Sales, Prices, and Variable 

Profit by Segment and Manufacturer Group, 2001 and 2004. Office for the Study of Automotive 

Transportation (OSAT), University of Michigan Transportation Research Institute (UMTRI), June 2005. 

Greene, D., Duleep, K., and McManus, W., Future Potential of Hybrid and Diesel Powertrains in the US 

Light-Duty Vehicle Market, Report to Department of Energy, July 2004. 

McManus, W., Consumer Acceptance of Alternative Powertrains, OE Industry Review. Troy, MI: Original 

Equipment Suppliers Association, 2004. 

McManus, W., “Diesel vs. Hybrid-Electric Powertrains: Assessing Dependability,” Power Report (July 

2004) 

McManus, W., “Interest in Diesel Grows—Quietly,” Power Report (June 2004) 

McManus, W., Consumer Acceptance of Alternative Powertrains Study. Westlake Village, CA: J.D. Power 

and Associates, 2004. 

Malesh, T. and McManus, W., Clean Diesel Market Acceptance Study. Westlake Village, CA: J.D. Power 

and Associates, 2003. 

McManus, W., Analysis of Tax Credits to Stimulate Consumer Demand for Advanced-Technology FuelEfficient Vehicles: Final Report to Energy Future Coalition Transportation Working Group. Westlake 

Village, CA: J.D. Power and Associates, 2003. 

McManus, W., Generation Y Automotive Market Assessment. Westlake Village, CA: J.D. Power and 

Associates, 2002. 

McManus, W., Interaction Between New and Used Vehicle Sales in the U.S. Market. Westlake Village, 

CA: J.D. Power and Associates, 2002. 

McManus, W., Telematics Forecast. Westlake Village, CA: J.D. Power and Associates, 2002. 

McManus, W., Satellite Radio Forecast. Westlake Village, CA: J.D. Power and Associates, 2002. 

McManus, W. and Bussmann, W., Isuzu in the U.S., Westlake Village, CA: J.D. Power and Associates, 

McManus, W., Adaptive Cruise Control Forecast, Westlake Village, CA: J.D. Power and Associates, 2001 

4 

2001 
Appendix B: 

Charge Questions 
 

TO: Carolyn Fischer 

 Walter McManus 

 Christopher Knittel 

FROM: Paramita Sinha 

CC: Michael Gallaher 

DATE: December 31, 2009 

SUBJECT: Charge Questions for Peer Review of EPA’s Study of Vehicle Choice Models 

The US EPA’s Office of Transportation and Air Quality is currently analyzing a set of existing 

consumer vehicle choice models to see whether there is a robust estimate of the value of 

additional fuel economy to consumers. EPA is seeking a peer review of its comparative study of 

consumer vehicle choice models and what each model may predict for the value of improving 

vehicle fuel economy at the time of vehicle purchase. 

EPA has provided direction and charge questions for this review and these are included below. A 

teleconference call will also be arranged so that EPA can provide technical and/or background 

information on the analysis. Any future questions you may have can be directed back through 

RTI for resolution with EPA. 

The review will involve a written report that includes the response to the charge questions and 

any additional comments you may have, e.g., margin notes on review materials. Comments 

should be provided in an enclosure to a cover letter that clearly states the reviewer’s name, the 

name and address of their organization if applicable, which model review documents/media were 

received by the reviewer and which were actually reviewed and a statement of any real or 

perceived conflict(s) of interest. 

ELEMENTS TO BE ADDRESSED IN THE CHARGE TO THE 

REVIEWERS OF EPA’S ANALYSIS OF THE VALUE OF ADDITIONAL 

FUEL ECONOMY TO CONSUMERS 

EPA’s report on using vehicle choice models to estimate the impacts of fuel economy regulations 

will inform its analyses of the effects of regulatory programs that affect vehicle fuel economy. 

This report details an analysis of the value of additional fuel economy to consumers, estimated 

RTI International is a trade name of Research Triangle Institute 
Carolyn Fischer 

Walter McManus 

Christopher Knittel 

Page 2 

December 31, 2009 

from consumer vehicle choice models. No independent data analysis will be required for this 

review. 

Specifically, EPA is seeking the reviewers’ expert opinions on the data, concepts, and 

methodologies upon which the analysis relies, whether or not the analysis was conducted 

correctly, and whether the analysis draws appropriate conclusions. Toward this end, we ask that 

each subject matter expert review and comment on the following items: 

1. overall approach and methodology of the study; 

2. appropriateness of the datasets and any other inputs; 

3. data analysis conducted for the study; 

4. appropriateness of the conclusions drawn; 

5. recommendations for alternate data and/or analyses; and 

6. overall clarity of the presentation. 

In making their comments, the reviewers should distinguish between recommendations for 

clearly defined improvements that can be readily made based on data or literature reasonably 

available to EPA, and improvements that are more exploratory or dependent on information not 

readily available to EPA. Any comment should be sufficiently clear and detailed to allow a 

thorough understanding by EPA or other parties familiar with the model. EPA requests that the 

reviewers not release the peer review materials or their comments to anyone else until the 

Agency makes its report and supporting documentation public. 

If a reviewer has questions about what is required in order to complete this review or need 

additional background material, please direct the reviewer to contact (RTI project manager/PI). If 

a reviewer has any questions about the EPA peer review process itself, please have the reviewer 

contact Ms. Ruth Schenk in EPA’s Quality Office, National Vehicle and Fuel Emissions 

Laboratory by phone (734-214-4017) or through e-mail (schenk.ruth@epa.gov. 
Appendix C: 

Cover Letters 
To: 

Gloria Helfand 

US EPA, Assessment and Standards Division (OTAQ)
 

2000 Traverwood Drive 

Ann Arbor, Michigan 48105 

From:
 

Dr. Carolyn Fischer 

3215 Pauline Dr. 

Chevy Chase, MD 20815 

January 27, 2010 

Cover Letter to Accompany “Review of Greene ‘How Consumers Value Fuel Economy’” 

Greetings: 

The documents that I received from EPA (or RTI International) were a memo containing the 

charge questions and the draft report by Greene (2009). I also furnished versions of some
 

unpublished papers relevant to the report, including Alcott and Wozny (2009); Busse, Knittel, 

and Zettelmeyer (2009); and Sallee, West, and Fan (2009). 

I reviewed all of the documents that I received in developing my expert opinion as contained in 

the “Review of Greene ‘How Consumers Value Fuel Economy’,” submitted on January 25. 

I declare that there are no real or perceived conflicts of interest concerning my involvement in 

this review for the EPA. 

Best regards,
 

Carolyn Fischer 

C-1 
To: 

Gloria Helfand 

US EPA, Assessment and Standards Division (OTAQ) 

2000 Traverwood Drive 

Ann Arbor, Michigan 48105 

From: 

Christopher R. Knittel 

Department of Economics 

University of California, Davis 

Davis, CA 95616 

January 27, 2010 

Cover Letter to Accompany Review How Consumers Value Fuel Economy: A Literature 

Review, by David Greene. 

Greetings: 

The documents that I received from EPA (or RTI International) were a memo containing the 

charge questions, the draft report by Greene (2009), and two unpublished papers that are covered 

in the draft report. The unpublished papers are Alcott and Wozny (2009) and Sallee, West, and 

Fan (2009). 

I reviewed all of the documents that I received in developing my expert opinion as contained in 

the enclosed Review of How Consumers Value Fuel Economy: A Literature Review, by David 

Greene. 

I declare that there are no real or perceived conflicts of interest concerning my involvement in 

this review for the EPA. 

Sincerely, 

Christopher R. Knittel 

C-2 
To: 

Gloria Helfand 

US EPA, Assessment and Standards Division (OTAQ) 

2000 Traverwood Drive 

Ann Arbor, Michigan 48105 

From: 

Walter McManus 

University of Michigan Transportation Research Institute 

2901 Baxter Rd. 

Ann Arbor, MI 48109-2150 

January 25, 2010 

Cover Letter to Accompany Review of Greene, D.L. (2009) How Consumers Value Fuel 

Economy: A Literature Review 

Greetings: 

The documents that I received from EPA (or RTI International) were a memo containing the 

charge questions, the draft report by Greene (2009), and three unpublished papers that are 

covered in the draft report. The unpublished papers are Alcott and Wozny (2009); Busse, Knittel, 

and Zettelmeyer (2009); and Sallee, West, and Fan (2009). 

I reviewed all of the documents that I received in developing my expert opinion as contained in 

the enclosed Review of Greene, D.L. (2009) How Consumers Value Fuel Economy: A Literature 

Review. 

I declare that there are no real or perceived conflicts of interest concerning my involvement in 

this review for the EPA. 

Best regards, 

Walter McManus 

C-3 
Appendix D: 

Reviews
 
 

 

Review of Greene 

“How Consumers Value Fuel Economy” 

by Carolyn Fischer 

David Greene is contributing an in-depth review of the recent literature on the demand for 

vehicle fuel economy that will be extremely valuable to researchers and policymakers alike. 

Although ultimately he is unable answer the key question—how do consumers value fuel 

economy?—he does provide considerable insight into how researchers have attempted to answer 

this question and why we see so much variation in the results. 

This review is structured to provide feedback and suggestions aimed at improving this work and 

ultimately enhancing its impact. 

Presentation 

Overall, the writing is excellent. Some modest changes could help the reader. 

The reviewed articles are organized logically by class of data and methods. At the beginning of 

each section, it would be helpful to review those methods in general fashion, including the 

advantages and drawbacks, as well as key factors or assumptions that can affect the results. Also, 

it might be easier to begin with the seminal papers in each category, so as to understand the 

evolution and improvements made subsequently. 

The status of the papers should also be recognized, since many of the important new studies are 

still unpublished working papers and subject to change. 

Since few econometric studies have explicitly estimated the value of fuel economy, a significant 

contribution of the paper is to translate a wide variety of results into more consistent indicators. 

Yet, they could still be more consistent. In Table 10, can all of the results be converted into the 

same WTP metric, preferably using the same assumptions about vehicle lifespan, VMT, 

discounting, etc.? 

Also, the author’s baseline assumptions need to be stated clearly early in the paper, so careful 

attention can be given throughout to assumptions that deviate from that baseline. Indeed, the 

statement made in the Allcott and Wozny analysis (“Calculating the discounted present value of 

fuel costs requires a number of assumptions...”) should be made earlier in a general context, so 

that the variety and range of assumptions can be understood and explored. 

Additional Analysis 

The paper reviews many approaches, and the author should take advantage of the summary 

section to indicate whether some approaches are likely to be better than others. 

That section does point out some factors that may explain such different results, but this analysis 

could be improved. First, the discussion interweaves reasons that could explain why we can 

plausibly believe any of a range of results (e.g., heterogeneous decision rules, uncertainty) with 

D-1 
 

reasons for why different studies get different results. It would be helpful to separate these 

discussions, reminding readers of the former, and then focusing on the latter, which is the most 

important contribution. This kind of analysis should help indicate best practices and directions 

for future research. Can we suggest some experiments (e.g., new techniques on old data sets, or 

old techniques on new data sets) that can help tease out what differences arise from alternative 

econometric approaches? If most studies have focused on other aspects of vehicle demand, are 

there better specifications for looking directly at the valuation of fuel economy? 

It would be useful to summarize key differences in approaches and data and analyze their 

consequences. Some that were gleaned along the way: 

 levels of aggregation, 

 time horizons (how useful is info from the 1970s in estimating current demand?), 

 incorporation of the supply side, 

 details of other attributes and variable definitions, 

 allowing for separate responses to MPG and fuel costs (this was raised as lacking in the 

discussion of Gramlich, but it wasn’t apparent what other studies had done this), 

 assumptions of vehicle lifetime, VMT, discount rates, etc. 

 econometric approach 

Some of these summary indicators (model type, data, and time) are given in Table 10, but some 

additional analysis would be useful. Do we observe any trends across classes or time? Within a 

class of models, what drives the differences? 

The paper raises the issue of consumer heterogeneity in several places, but the importance of this 

fact should be pushed further. Fischer (2009) points out that, facing consumers with 

heterogeneous preferences over fuel economy, manufacturers with market power have distorted 

incentives for providing fuel economy. More generally, failure to capture such consumer 

heterogeneity can lead to significant errors in predicting the distribution of effort in complying 

with regulation, as well as the calculation and distribution of the benefits.2 Portions of this 

review highlight certain studies that captured differences in tastes for fuel economy (BLP, 

Goldberg, Brownstone et al.), while most studies ignored such heterogeneity. This seems like an 

important area for additional analysis, both to explain differences and to suggest future research 

directions. 

Additional Empirical Literature 

I am aware of a few other studies that likely deserve attention as well. 

Sallee and West (2009 working paper) “The Effect of Gasoline Prices on the Demand for Fuel 

Economy in Used Vehicles: Empirical Evidence and Policy Implications” 

Abstract: Fossil fuel consumption and greenhouse gas emissions from the personal transportation 

sector pose serious challenges to today’s policy-makers. If consumers acknowledge the full value 

of fuel economy, a tax on gasoline could be as efficient as a tax on greenhouse gas emissions, 

since the amount of carbon released by a gallon of gasoline is independent of the manner in 

which it is combusted. If, however, consumers do not sufficiently value fuel economy when 

2 Fischer, Carolyn (2009) “Imperfect Competition, Consumer Behavior, and the Provision of. Fuel Efficiency in Vehicles.” 

Working Paper, Washington, DC: Resources for the Future. 

D-2 
 

making the decision about what car to buy, an additional complementary policy, such as a fuel 

economy standard or a feebate, can be welfare improving. We use microdata on used vehicle 

prices and a unique identification strategy based on micro-level variation in vehicle odometers to 

test whether used car prices change by the amount predicted by a fully rational asset pricing 

model. Our baseline results indicate that used car prices adjust by about 80% of the amount 

predicted by theory, and sensitivity analysis suggests that for a reasonable discount rate, they 

adjust by the full amount. These results contrast with recent literature, which finds adjustment 

closer to 25%, and suggest a limited role for complementary policy tools. 

Klier and Linn (2010 working paper) “The Price of Gasoline and New Vehicle Fuel Economy: 

Evidence from Monthly Sales Data” 

This paper uses a unique data set of monthly new vehicle sales by detailed model from 1978- 

2007, and implements a new identification strategy to estimate the effect of the price of gasoline 

on individual vehicle model sales. We control for unobserved vehicle and consumer 

characteristics by using within model-year changes in the price of gasoline and sales. We find a 

significant sales response, suggesting that the gasoline price increase from 2002-2007 explains 

nearly half of the decline in market share of U.S. manufacturers. On the other hand, an increase 

in the gasoline tax would only modestly raise average fuel economy. 

(Their estimate implies that a one dollar increase in the price of gasoline raises average fuel 

economy by 0.5-1 MPG.) 

Dreyfus, Mark K. and W. Kip Viscusi (1995). “Rates of Time Preference and Consumer 

Valuations of Automobile Safety and Fuel Efficiency.” Journal of Law and Economics 38: 79-

98. 

This seems like an earlier, influential study, of the same era as BLP, so worth some comparison, 

although fuel economy demand was a secondary target of interest. 

Description from Howarth (2004): “Dreyfus and Viscusi (1995) undertook a hedonic price 

analysis of the U.S. automobile market to assess consumers’ willingness to pay for improved 

safety and energy efficiency. On the assumption that consumers use a common discount rate in 

evaluating both safety and fuel economy, the study calculates an implicit discount rate that 

ranges from 11 to 17 percent in alternative specifications. More tellingly, Dreyfus and Viscusi 

conclude that only 35 percent of the present-value cost savings provided by improved energy 

efficiency is capitalized in the purchase price of vehicles.” (derived from coefficients from 

operating cost and that interacted with weight) 

Hughes, Jonathan E., Christopher R. Knittel and Daniel Sperling (2008). “Evidence of a 

Shift in the Short-Run Price Elasticity of Gasoline Demand.” Energy Journal. 

West, S. (2008). “The Effect of Gasoline Prices on the Demand for Sport Utility Vehicles.” 

Working Paper. Macalester College. 

(mentioned in Helfand and Wolverton) 

D-3 
Other Points 

2.0	 in the discussion of Turrentine and Kurani, recent research on “The MPG Illusion” may 

be relevant to mention, supporting the difficulty of consumers in calculating fuel cost 

savings. 

Larrick, R. P., & Soll, J. B. (2008). The MPG illusion. Science, 320, 1593-1594. 

http://faculty.fuqua.duke.edu/~larrick/bio/Reshighlights.htm 

2.1	 The author talks about the global vehicle market; it seems relevant to give the statistics 

for the U.S. market. 

2.2	 The review of the literature on the demand for energy efficiency should include 

Gillinham et al., who “review economic concepts underlying consumer decision making 

in energy efficiency and conservation and examine related empirical literature. In 

particular, we provide an economic perspective on the range of market barriers, market 

failures, and behavioral failures that have been cited in the energy efficiency context.” 

Gillingham, K.T., R.G. Newell and K.L. Palmer (2009). “Energy Efficiency Economics 

and Policy.” RFF Discussion Paper 09-13, April 2009. 

The statement “With the exception of externalities, there is little quantitative evidence of 

the impact of these failures on consumers’ choices of energy using durable goods” seems 

hard to reconcile with the preceding discussion. Is there any better (peer-reviewed) 

support than ACEEE (2007)? 

This section also offers the opportunity to introduce other studies outside the economics 

literature—are there any documented industry studies on their approach to consumer 

payback expectations? 

Section 3 

BLP (1995) is a seminal paper, and should probably be presented first. Is the same critical eye 

being given to them with respect to omitted variables. They used HP/weight to represent power, 

but what if weight is valued separately, such as for safety concerns in that era? 

Dasgupta et al. (2007): this description seems too curt. I had a hard time following the structure 

of the model and the conversion of the results. (Also later in the discussion of McManus, where 

the coefficient units are interpreted as discounted lifetime miles). 

Bento et al. (2005): I believe the parameter estimates are provided in the Appendix. Are they in a 

form that can be useful for comparison? 

Feng et al. (2005): given the important question raised of the units, it seems worth asking the 

authors directly. 

Brownstone et al. (1996): did any of the identification for WTP for fuel economy come from the 

demand for alternative fuel options? It wasn’t clear in the discussion what was done with the 

D-4 
 

information about alternatives, and whether that is reliable, given the lack of availability of these 

technologies. 

Cambridge Econometrics: if they included both fuel costs and fuel consumption rates, was there 

some other heterogeneity to distinguish between the two? 

Espey and Nair (2005) is referred to before it is discussed; perhaps the order should be reversed. 

Also, since the gas guzzler tax is mentioned, one should perhaps point out that they 

mischaracterized the policy in such a way as to affect their consumer price calculations (it is 

actually paid by producers, not consumers), although this is unlikely to affect their results. The 

lack of controlling for manufacturer may, however. 

In discussing the differences with the Rosen method, the implications for how their results might 

be biased were unclear. 

Bhat and Sen (2006): Table 6 is difficult to interpret. 

Busse et al.: p. 32 “These results do no necessarily imply that new car buyers undervalue fuel 

economy, since the change in price also depends on the elasticities of supply and demand.” It 

seems they also depend on the formulation of price expectations. 

Minutiae 

p. 8 “Two recent analysis have quantified the [potential] impacts of uncertainty...” 

p. 10 2nd para: market imperfect[ion] 

sales[-]mix (might be preferred) 

p. 12 end of the Gramlich discussion is a bit confusing; it was not clear what his model 

predicted for fuel economy in comparison to the EPA numbers. May just need to be clarified 

which numbers are being discussed. 

p. 12 [reverse order so that citation always begins the paragraph] BLP (1995), in a seminal 

paper,... 

“consumer I” should be “consumer i” 

p. 16 last full sentence (“Different influences... in econometric analyses of VMT, for 

example”) could use some citations. 

p. 23 top para: “But this is not stated in the report[;] indeed,... 

p. 25 figure says “Qin and Rubin” instead of “Fan and Rubin” 

References please check working papers to update citations 

p. 36 2nd para: remove the “,” after “Allcott and Wozny” 

e.g., Fischer et al. (2007) is Energy Journal 28: 1-29. 

D-5 
Review of: 

“How Consumers Value Fuel Economy: A Literature Review” 

by David L. Greene 

Reviewed by Christopher R. Knittel 

January 22, 2010 

Dear Sir/Madam: 

This report summarizes the literature regarding the value that consumers place on fuel economy 

at the time of purchasing a vehicle. Understanding this is important for a variety of public policy 

issues. How consumers trade-off paying more for fuel economy with future fuel savings defines 

their discount rate. Therefore, statements such as “consumers undervalue fuel economy” 

necessarily imply that the consumers’ discount rate is below society’s discount rate. If consumers 

do not value fuel economy as much as society-at-large, then gasoline will be over-consumed and 

policies may improve social welfare. 

Equally important, though not analyzed in this report (it is discussed however), is why consumers 

undervalue gasoline, relative society (if they do indeed undervalue fuel economy). This has 

important implications for which policies are most effective at aligning discount rates. For 

example, do consumers undervalue fuel economy because capital markets are imperfect? 

Subsidized loans would likely be the most effective policy. Do they undervalue fuel economy 

because of a lack of cognitive ability? Education policies and/or information campaigns are 

likely to be most effective. Papers dealing with these issues are largely lacking, unfortunately. 

Bottom line: This is an excellent review. This review will be an asset to policy makers. I agree 

with the conclusion that the literature remains mixed as to whether consumers full value fuel 

economy at reasonable discount rates. I provide some comments that may improve the review. 

Is the goal to summarize, or analyze papers?: 

It strikes me that there are two ways to conduct a review such as this. One is to report the results 

of papers with little, or no, discussion of the quality each paper. The other is to both review and 

critique each paper. I found a lack of uniformity in this regard. For many of the papers, the latter 

D-6 
strategy is taken. The report discusses key weakness of these papers. Still many papers are 

simply summarized, with little or no discussion of their strengths or weaknesses. This gives the 

impression that the results in these papers are more accurate. This may actually be the case, but if 

it is, this should be stated more explicitly. 

For example, the first paper (Alcott and Wozny) is simply summarized with no editorializing. 

One possibility is that there are no weaknesses in this paper. But, many of the issues raised later 

for other papers also relate to this paper. For example, the report correctly states, when 

discussing the Sawhill paper, that in order to get a consistent estimate of how consumers value 

fuel economy, the author is required to correctly specify how consumers form expectations about 

gas prices and drive their vehicles. If any of these are incorrectly specified, the estimate will be 

biased. The same is true for the Alcott and Wozny paper. While I find Alcott and Wozny’s 

specification for how expectations are formed to likely be more accurate, ironically, they assume 

consumers are hyper-rational in the sense that they form their gas price expectations based on 

NYMEX futures prices. In some ways the myopia result is inconsistent with this key assumption. 

Differentiating papers by their focus: 

The author should be commended for reviewing such a diverse set of papers. The report 

categorizes papers by the data used in the analysis and empirical model (e.g., aggregate data v. 

micro-level data and discrete choice v. hedonic pricing model). I propose two alternatives. Many 

of the papers reviewed did not focus on measuring the implicit discount rate, or even on the issue 

of how gas prices affect vehicle demand. If a paper did not focus on either of these two 

questions, it is difficult to gauge the robustness of their results with respect to these questions, 

the quality of the variation in gas prices in the data, etc. Perhaps a better method would be to first 

focus on those papers where gas prices and vehicle choice are the central research question, and 

those papers where this is more a tangential part of the analysis. 

Differentiating papers by their publication status: 

Many of the papers reviewed have not undergone the peer-review process. While I do not claim 

that mistakes do not make it through the peer-review process, related to comment above, in some 

ways a review of an unpublished paper requires a more thorough review of the paper itself. A 

D-7 
second way to organize the review would be to focus first on published papers and then second 

on unpublished papers. At the very least the fact that a number of the papers are unpublished 

should be explicitly noted in the text. Many readers may never make it to the bibliography. 

Comments related to discussion of issues: 

The review has an excellent discussion of the issues related to how consumers value fuel 

economy and the market for fuel economy more generally. I enjoyed reading this section. I have 

a few comments related to it, however. 

At the top of page 5, the author poses: “does the market value fuel economy improvements at the 

discounted expected value of future fuel savings over the lifetime of the vehicle, or less, or 

more?” 

The author should use the phrase “at society’s discounted expected value...” 

In many ways questioning whether the market values fuel economy improvements at the 

discounted expected value of future fuel savings is simply a tautology. There is always a 

discount rate that equates how much consumers are willing to pay for fuel economy 

improvements given the expected value of the future fuel savings. Therefore, any statement 

about whether they are willing to pay too little, or too much, is actually a statement about how 

the consumers’ discount rates compare with society’s discount rate. 

On page 5, the author states, “On the other hand, if consumers are myopic and consider only the 

first three years of fuel savings, for example, fuel economy standards can increase welfare even 

based solely on private costs and benefits.” 

If the author is making a statement about the consumers’ welfare, and not society’s, then more 

should be added here. If consumers are myopic because their utility function is myopic, then I do 

not see how fuel economy standards make them better off. If my discount rate is actually 20%, 

because these are my preferences, then I am better off by investing less in fuel economy 

compared to someone whose discount rate is 10%. Forcing standards does not improve my 

D-8 
 

welfare. Someone with a 10% discount rate may disagree with my choice, but there is nothing 

here that makes my choice non-optimal for me. In contrast, if I behave myopically for reasons 

outside of my utility function, such as a lack of access to credit or a lack of cognitive ability, then 

fuel economy standards, as well as subsidized loans and/or information campaigns, can improve 

my welfare. Alternatively, the author can also point to more behavioral economics reasons why 

this market might fail, for example, because consumers have self-control issues. I find many of 

these reasons to be quite compelling. My point is that the author should be more clear here. As it 

currently reads, it implies that high discount rates, alone, imply fuel economy standards are 

welfare improving. This is simply not the case.3 

On page 5 the author states that it is surprising that there is no basic research on how consumers 

consider fuel economy. This is a strange use of the term basic research, which I take to mean 

research on pure science. Does the author equate basic science with interviews? I have never 

heard basic science used in this manner. 

Comments on specific paper reviews: 

The reviews of the papers are very well done and the choice of papers very comprehensive. A 

have a few comments that may improve this discussion, as well as a few papers to be added. 

Busse, Knittel and Zettelmeyer 

Unfortunately the discussion of Busse et al. on page 32 is incorrect. The report states that the 

paper does not control for vehicle attributes. In fact, it controls for an extremely wide set of 

vehicle attributes through the use of “fixed effects.” In the price equations there are fixed effects 

for vehicle type, defined as the interaction of make, model, model year, trim level, doors, body 

type, displacement, cylinders, and transmission. Put in more layman’s terms, the paper is 

controlling for all vehicle attributes within this vehicle type. That is, the only way something like 

horsepower would not be entirely controlled for is if there were two vehicles of the same make, 

model, model year, trim level, doors, body type, displacement, cylinders, and transmission that 

had different horsepower levels. So, for example, any attribute that does not vary within 2009 

3 I freely admit that the use of “can improve” helps. 

D-9 
Honda Accord four-door EX-L, with a 3.5 liter, 6-cylinder and an automatic transmission 

vehicles is completely controlled for. The only attributes that would not be entirely controlled for 

are attributes that vary within this definition, such as color or dealer installed options. Variables 

such as horsepower likely do not vary within the same make/model/engine size/number of 

cylinders. Viewed in this way, perhaps this paper is best included in the Hedonic Models section. 

In addition, the latest version (NBER Working Paper 15590) also controls for when in the 

lifecycle of vehicles (both new and used) the transaction took place. This has cleared up a lot of 

the issues that the author raises at the top of page 32. 

The newest version of Busse et al. also is clearer on what is required, theoretically, to account for 

the differences across new and used car markets. Market power in the new car market is not 

required. As the author of this report notes, the results simply imply that the supply elasticity in 

the new car market is more elastic than in the used car market (where a perfectly inelastic supply 

is likely a good first-order approximation). Therefore, I would recommend that the author edit or 

drop the sentence that states “The used car market was believed to function more efficiently.” 

Finally, Table 10 of the report should include the used car results for Busse et al. Without taking 

into account the results regarding market shares in the new car specifications, the used car results 

are a more accurate reflection of the shift in demand from increases in gas prices. So, if only one 

of the sets of results is to be reported, the used car results should be reported. Reporting only the 

new car results, without a discussion of the quantity effects, or the used car results, is misleading. 

Papers that solve for price from a discrete choice model 

A number of papers in the literature, and among those reviewed here, argue that if consumers 

value fuel economy “correctly,” the price of a vehicle should rise and fall one-for-one with the 

vehicle’s lifetime fuel costs. If prices move less than one-for-one, this is taken as evidence that 

consumers are myopic. These papers typically specify utility to be some variant of the logit 

model (e.g., simple logit, nested logit, etc.) and then solve for the equilibrium price for a vehicle, 

conditional on the assumed functional form for utility. The intuition behind this empirical 

D-10 
strategy is best summarized by Alcott and Wozny’s motivating example, where price is 

expressed as: 

1 

p = (−γG +α − q)

η

where G is the discounted fuel cost and q the quantity of vehicles available. They note that: “To 

keep this consumers indifferent between the vehicles and the most attractive outside option, the 

overall product price p+G must stay the same as G changes.” This statement requires that the 

utility from the outside option does not also change with G. For those consumers where the “no 

car ownership” option is not the most attractive outside option this restriction is unlikely to hold. 

For example, imagine using this framework to test whether consumers correctly value fuel 

economy using an ideal experiment and tracking Prius prices. In one universe gas prices are $2, 

in another alternate universe gas prices are $3. Given the condition above, in order to keep 

consumers from shifting demand from the Prius to the next best option, whose operating cost is 

assumed not to have increased, the price of the Prius must be lower in the $3-universe by the 

increase in fuel costs over the life of the vehicle. If the price of the Prius falls by less than this 

amount, estimating the above equation would force you to conclude that consumers undervalue 

fuel economy. 

Now instead imagine that the fuel cost of the next best option for many Prius owners has 

increased by more than the Prius, because the next best option is likely to be something like a 

Camry. In this case, the demand for the Prius actually increases, and prices increase. Thus, we 

have explained why we concluded that consumers are myopic in the above regression. Indeed, 

this is precisely the reason why Busse et al. argues, and shows empirical support for, that the 

demand, and therefore price, of more fuel efficient cars will increase when gas prices increase. 

And, is the reason why these authors focus on the difference in price changes between high and 

low fuel economy vehicles. 

D-11 
 

Of course across all vehicles, the price of some vehicles will decrease and the price of others will 

increase. Therefore, we should not expect the coefficient associated with discounted fuel costs to 

be negative one, since it will represent the average of these prices changes. 

In principle, if one were to specify the correct utility function and demand system, then the above 

empirical strategy would be a valid test of myopia. The most general demand system would 

imply that the price of a given vehicle is a function of its fuel costs, as well as the fuel costs of all 

competitors. This is often intractable. Instead, the profession has migrated to specific demand 

models that are more parsimonious, such as logit demand models, since the implication of these 

demand models is that the ratio of the demand for a particular vehicle to the demand for the 

outside good is independent of the fuel costs of other vehicles. However, this is simply a byproduct of the functional form assumptions, and as the Prius example illustrates, is not a general 

theoretical result, or one that is very intuitive. Insofar as these models are mis-specified, we will 

be biased towards finding myopic consumers. 

A conundrum that may be impossible to solve: 

As noted by the author in his review of the Sawhill paper, the correct statistical test of consumer 

myopia requires specifying the present discounted value of future fuel expenditures for each 

vehicle. This requires assumptions regarding how consumers form expectations regarding future 

gas prices. The available choices for this are limitless, ranging from assuming that consumers 

view gas prices as a random walk (so today’s price is sufficient to describe the expected value of 

future gas prices) to models that assume consumers use some number of recent prices within an 

autoregressive model, to a hyper-rational model where consumers use NYMEX futures on crude 

oil and a regression model of how gas prices move with crude oil. 

For ordinary least squares to be unbiased, the econometrician must have the correct model of 

how consumers form expectations. Otherwise, the key right hand side variable is measured with 

error and the estimated coefficient will be biased towards zero.4 This typically biases the 

statistical test towards concluding consumers are myopic. Unfortunately, in the end there may 

D-12 
 

not be a solution to this. Indeed, the irony is that some papers assume complex ways in which 

consumers form expectations, but then conclude they are myopic. In some way this strikes me as 

being inconsistent. If consumers are myopic, then they are also not likely to use NYMEX futures 

to form their expectations. Therefore, using NYMEX futures to generate the variable of interest 

is likely to bias a researcher towards concluding myopia. 

Other papers to consider in the review: 

The paper should consider reviewing the following papers: 

Kilian, Lutz and Eric Sims (2006). The Effects of Real Gasoline Prices on Automobile Demand: 

A Structural Analysis Using Micro Data, Working Paper, University of Michigan (April). 

Klier, Thomas, and Joshua Linn (2008). “The Price of Gasoline and the Demand for Fuel 

Efficiency: Evidence from Monthly New Vehicles Sales Data.” Working Paper, University of 

Illinois at Chicago (September). 

Sallee, James, and Sarah West (2008). “Testing for Consumer Myopia: The Effect of Gasoline 

Price Changes on the Demand for Fuel Economy in Used Vehicles.” Working Paper, Macalester 

College (December). 

4 This holds when the measurement error is uncorrelated with the regressor. When the measurement error is correlated with the 

regressor, the bias can go in either direction. 

D-13 
Review of Greene, D.L. (2009) How Consumers Value Fuel Economy: A 

Literature Review 

Walter McManus, University of Michigan Transportation Research Institute 

EPA intends to use vehicle choice models to inform the Agency’s analyses of the impacts of 

regulatory programs that affect vehicle fuel economy. To adequately predict the ultimate impacts 

and judge the effectiveness of particular regulatory actions, EPA needs to understand how 

consumers (and producers) are likely to change their market behavior in response to the 

regulatory actions. 

Market choices by producers and consumers of vehicles and fuels have direct negative impacts 

on the nation’s air quality and the stratospheric ozone layer. EPA, which is responsible for 

protecting and improving air quality and the ozone layer, can influence but cannot control the 

behavior of consumers and producers. Market models, including models of consumer vehicle 

choice and models of producer technology and product decisions, could help EPA anticipate 

market responses to regulatory actions. 

This report is a peer review of EPA’s comparison of recent mainstream economic estimates of 

the value of additional fuel economy to consumers. The documents that I received from EPA (or 

RTI International) a memo containing the charge questions, the draft report by Greene (2009), 

and three unpublished papers that are covered in the draft report. The unpublished papers are 

Alcott and Wozny (2009); Busse, Knittel, and Zettelmeyer (2009); and Sallee, West, and Fan 

(2009). I reviewed all of these documents in developing my expert opinion as reported here. 

Overall approach and methodology of the study 

Greene (2009) adopts a conventional “literature review” approach. The question of how 

consumers value fuel economy in vehicle choice has been the subject of a lively conversation on 

and off for decades. One appealing feature of the paper is that the same author conducted a 

similar review in Greene (1983). Consumers of research face many temptations to undervalue the 

retrospective approach, and it is encouraging that EPA’s choice of authors reveals the Agency’s 

rationality as a consumer of research. 

The motivation, objectives, and structure of the paper are described in two paragraphs. 

“Recent historically high fuel prices, combined with renewed interest in fuel economy and 

greenhouse gas emissions standards for automobiles have engendered a number of new 

assessments, many specifically aimed at understanding the effects of fuel prices and fuel 

economy on consumers’ vehicle choices. This paper reviews those studies, published and 

unpublished, with the objectives of determining whether a consensus now exists on the value 

consumers place on fuel economy, and of gleaning insights into how consumers use fuel 

economy information in their car buying decisions. 

“This paper is organized as follows. Section 2 summarizes views of how the market for fuel 

economy functions, including both supply and demand. The body of the report is Section 3 which 

reviews recent empirical estimates of the value of fuel economy based on aggregate and 

disaggregate data, discrete choice models and hedonic demand analyses. Section 4 discusses the 

implication of those estimates and Section 5 contains concluding observations.” (page 4) 

D-14 
The overall approach and methodology are within the mainstream of critical review practice in 

economics, and the execution in unexceptionable. However, both the conventional approach to 

reviewing economic research and the conventional approach to economic research itself do not 

give much help to decision makers who are understandably made uncomfortable by the widely 

differing expert opinions about parameters that are crucial in predicting the impacts of 

regulations and in choosing between alternatives. An overall critique of conventional practice is 

beyond the scope of this review. However, I do have some suggestions for improvements in the 

methodology for comparing studies and for dealing with the lack of consensus in expert 

opinions. (The References section below lists some papers by Edward E. Leamer, a professor at 

UCLA, in which a critique of conventional practice is developed along with alternative practices 

that would enhance the usefulness of economic research to policy makers.) 

Data analysis conducted for the study 

The experts’ papers differ in several dimensions that could be responsible for some of the 

differences in opinions between experts. Greene (2009) helpfully provides a side-by-side 

comparison for econometric model type, data structure, and estimation period. In the text he 

covers other issues such as the econometric challenges each author faced and the strategies 

chosen to address the challenges. It is difficult for the reader to verify that the same standards are 

being used to assess each paper, especially since the amount of discussion by paper varies 

greatly. This leads the reader to have less confidence in the reviewer’s opinions of the individual 

studies (that this or that one had plausible or implausible assumption, methodologies, or results). 

Recommendations for alternate data and/or analyses 

It would be useful to expand Table 10 (or to create a set of tables) to show other differences that 

could be important sources of differences in estimates. The items that could be included would 

show differences in: 

1.	 Maintained assumptions reported (assumptions that are maintained across any alternative 

specifications or sensitivities), 

2. Reported sensitivity analyses (is one done? which assumptions are varied? how widely?), 

3.	 Econometric challenges and approaches used to address them (Greene (2009) appears to 

discuss these comprehensively in the text.), and 

4.	 Out-of-sample predictive performance (are predictions made? about what? are the predictions 

compared to actual outcomes? how accurate are the predictions?). 

Adding these additional side-by-side comparisons would increase the reader’s confidence that 

the same standards are being used to form the reviewer’s opinions about the relative usefulness 

of each paper to inform decision making by EPA. 

My opinion: The additional elements are responsible for more of the variation in the estimates of 

consumer value for fuel economy than are the elements already included in Table 10. 

To quantitatively assess whether we have made progress toward a consensus, we need a measure 

of researcher/specification uncertainty and disagreement. The 1983 and 2009 results are not 

compared quantitatively. Greene (1983) compared estimates of the consumer discount rate, while 

Greene (2009) compared estimates of consumer willingness to pay for improvements in fuel 

D-15 
economy. The idea is to compare the range of parameter estimates to the parameter value 

predicted by mainstream theory. For the consumer discount rate the theoretical value is the real 

discount rate, which is assumed to be 7% the following. For the willingness to pay version, the 

theoretical value is 100% (or willing to pay $1 for future fuel cost savings with a discounted 

present value of $1). 

The metric, which I call the Multiple Extreme Estimates Test (MEET), is defined as the ratio of 

the value predicted by theory to the difference between the two extreme estimates. 

Value Predicted by Theory V MEET = = Largest Estimate - Smallest Estimate R 

lim MEET = 0 and lim MEET = ∞

R→∞ R→0 

Numerically, MEET measures the predicted value as a share of the range of expert opinion. The 

range of expert opinion, R, is a crude and partial measure of the uncertainty or fragility of the 

estimates due to differences in assumptions, specification, and opinions between researchers. The 

metric MEET views this fragility compared to the theoretical value of the parameter, V. 

If the span of disagreement between experts is large, then MEET is small. The limiting case, 

infinite disagreement, produces a MEET of 0; there is not a meeting of the minds. If the span of 

disagreement is very small (compared to the theoretical value of the parameter), then MEET is 

large. Complete agreement implies an infinite MEET. 

Multiple Extreme Estimates Test (MEET) 

(The ratio of the parameter value predicted by theory to the range of 

extreme parameter estimates.)
 

Greene Greene Estimates used to compute MEET (1983) (2009) 

All studies* 0.002 0.055 

All but one "outlier" ** 0.096 0.080 

Greene (1983) "plausible" *** 0.194 

* 22/22 in 2009 & 8/8 in 1983 

** The 1983 range 0% to 73% is used, based on Table 1. In 

the text Greene (2009) reports the range for 1983 as 2% to 

73%. The MEET for the narrower range is 0.099. 

*** In my opinion, this represents Greene's opinion that the 

plausible range is 4% to 40%. 

Do the 1983-2009 changes in MEET provide any evidence that a meeting of the minds is closer 

today than it was in 1983? The table shows several alternative calculations of MEET for Greene 

(1983) and Greene (2009). The alternatives vary by which of the studies in each year are actually 

used in computing the metric for that year (based on my interpretation of comparisons made by 

Greene to which are added alternatives that include all studies in each year). 

The first thing to observe is that all of the MEET values are very low, indicating that the 

disagreement between experts is very large. Using all studies each year in the computation of 

MEET shows an increase from 0.002 to 0.055. This result is driven by the extreme outliers—the 

1983 outlier (-164% to 2840% range for consumer discount rate compared to 7% predicted by 

D-16 
theory) is more of an outlier than is the 2009 outlier (-360% to 1410% range for willingness to 

pay compared to the 100% predicted by theory). 

Dropping the outliers (not something I would be comfortable doing if the authors of the 30 

studies had reported more informative and thorough sensitivity analyses) would result in a drop 

in MEET from 0.096 in 1983 to 0.080 in 2009. We are getting farther away from a consensus, 

but the backtrack is small in absolute terms. If the meeting of the minds is in Ann Arbor, and 

there was only one holdout and he was located in Davis (CA) in 1983, instead of beginning the 

2,300-mile hike to Ann Arbor (according to Google), he wandered 35 miles in the opposite 

direction. 

Appropriateness of the datasets and any other inputs 

It is not clear how the studies were selected for inclusion in Greene (2009). There needs to be 

some explanation. 

Appropriateness of the conclusions drawn 

There are two fundamental conclusions made by Greene (2009). Firstly, expert opinion remains 

widely divided about the value of fuel economy to consumers. Despite the improvements in the 

quality of data about consumer behavior and in econometric tools, opinion is as widely divided 

today as it was three decades ago. I agree that this is the appropriate, discouraging conclusion to 

draw. 

The second fundamental conclusion drawn by Greene (2009) concerns the causes of the wide 

divisions in expert opinion and what should be done about it. 

“Although the methodologies or model formulations of a few of the studies are questionable, 

there do not appear to be clear associations among methods or data sources and the resulting 

inferences. It is suggested that such conflicting results may be attributable to incorrect models of 

consumer decision making about fuel economy, the statistical problems caused by omitted 

variables, errors in variables and correlated variables, and the complexity of consumers’ vehicle 

choice decisions. Additional, empirical behavioral research appears to be needed to resolve the 

issue.” (abstract) 

I am not convinced that the differences in assumptions, opinions, and methods have been 

sufficiently examined to support even the weak proposition that “there do not appear to be clear 

associations among methods or data sources and the resulting inferences.” See my 

recommendations on pp. 2-4 above. I am not sure exactly what Greene means by “incorrect 

models” of consumer decision making about fuel economy, but if he means the efficient markets 

hypothesis and economic rationality, then I agree. Assuming that it is this deficiency in our 

models of consumer behavior that would be the subject of “behavioral research,” I agree. 

Overall clarity of the presentation 

The overall clarity of the presentation was very high. 

D-17 
References 

Leamer, E. E. (1983), Let’s take the con out of econometrics, American Economic Review, 73, 

31-43. 

Leamer, E. E. (1985), Sensitivity analyses would help, American Economic Review, 75, 308-

313. 

Leamer, E. E. (1987), Econometric metaphors, in T. F. Bewley (ed.), Advances in Econometrics, 

Cambridge University Press, Cambridge, pp. 1-29. 

Leamer, E. E. (1989), Planning, Criticism, and Revision, Journal of Applied Econometrics, 4, 

Supplement, S5-S27. 

Leamer, E. E. (1992), Bayesian Elicitation Diagnostics, Econometrica, 60, 919-942. 

D-18 
______________________________________________________________________________ 

March 30, 2010 

MEMORANDUM 

SUBJECT: EPA Response to Comments on David Greene paper, “How Consumers Value 

Fuel Economy: A Literature Review,” by peer reviewers Drs. Carolyn Fischer, 

Christopher Knittel, and Walter McManus 

FROM: Dr. Gloria Helfand, Assessment and Standards Division 

Dr. Carolyn Fischer of Resources for the Future, Dr. Christopher Knittel of the Department of 

Economics of the University of California at Davis, and Dr. Walter McManus of the University 

of Michigan Transportation Research Institute reviewed David Greene’s paper, “How 

Consumers Value Fuel Economy: A Literature Review,” dated December 29, 2009. 

This memo includes a summary of comments prepared by RTI International, and responses and 

actions in response to those comments from Dr. David Greene of Oak Ridge National Laboratory 

and EPA. 

3.1 OVERALL APPROACH AND METHODOLOGY OF THE STUDY 

The reviewers are supportive of the overall approach and methodology. 

Walter: “The overall approach and methodology are within the mainstream of critical review 

practice in economics, and the execution in unexceptionable.” He does comment on the fact that 

“Greene (2009) adopts a conventional ‘literature review’ approach” and “both the conventional 

approach to reviewing economic research and the conventional approach to economic research 

itself do not give much help to decision makers who are understandably made uncomfortable by 

the widely differing expert opinions about parameters that are crucial in predicting the impacts of 

regulations and in choosing between alternatives. An overall critique of conventional practice is 

beyond the scope of this review. However, I do have some suggestions for improvements in the 

methodology for comparing studies and for dealing with the lack of consensus in expert 

opinions. (The References section below lists some papers by Edward E. Leamer, a professor at 

UCLA, in which a critique of conventional practice is developed along with alternative practices 

that would enhance the usefulness of economic research to policy makers.)” 

Chris comments that the study does not discuss “why consumers undervalue gasoline, relative 

society (if they do indeed undervalue fuel economy). This has important implications for which 

policies are most effective at aligning discount rates.” However, he recognizes that papers 

dealing with such issues are lacking. 

Response: 

EPA is satisfied with the “conventional” literature review approach that Dr. Greene has used. 

If there is a lack of consensus about model parameters, this is important information for EPA as 

it pursues its modeling activities. 
EPA did not request that Dr. Greene discuss why consumers may undervalue – or, more 

generally, misestimate the value of -- gasoline. EPA’s initial interest is learning how consumers 

consider fuel economy when they are purchasing vehicles. Greene includes some discussion of 

this topic on p. 8 as well as in the Executive Summary and the Conclusion. We agree that 

understanding why consumers may misestimate this value is an important topic. 

3.2 APPROPRIATENESS OF THE DATASETS AND ANY OTHER 

INPUTS 

One reviewer commented that the choice of the papers was comprehensive. Another reviewer 

recommended including some explanation of how the studies were selected for inclusion in 

Greene (2009). 

Response: 

The paper includes a discussion of its selection of papers on p. 2. In particular, it states Dr. 

Greene’s intent to be comprehensive. The literature review includes some of the most well 

known papers in the field (such as papers by Berry, Levinsohn, and Pakes and Goldberg) as well 

as other less well known works and papers in progress. Dr. Greene added additional papers to 

the review between the initial draft and the final report, in response to reviewers’ suggestions. 

Dr. Greene notes that he felt it important to include studies not (yet) published in peer reviewed 

literature because he considered them of publishable quality. EPA’s assessment is that, even if 

the review may have missed some papers, there does not appear to be any systematic bias in 

inclusion or exclusion. In addition, we support the inclusion of gray literature, because it is the 

most current research being conducted. 

3.3 DATA ANALYSIS CONDUCTED FOR THE STUDY 

Two reviewers comment on the fact that the type of analysis done for the different papers varies 

greatly. 

Chris: “It strikes me that there are two ways to conduct a review such as this. One is to report the 

results of papers with little, or no, discussion of the quality each paper. The other is to both 

review and critique each paper. I found a lack of uniformity in this regard. For many of the 

papers, the latter strategy is taken. The report discusses key weakness of these papers. Still many 

papers are simply summarized, with little or no discussion of their strengths or weaknesses. This 

gives the impression that the results in these papers are more accurate. This may actually be the 

case, but if it is, this should be stated more explicitly. 

For example, the first paper (Alcott and Wozny) is simply summarized with no editorializing. 

One possibility is that there are no weaknesses in this paper. But, many of the issues raised later 

for other papers also relate to this paper. For example, the report correctly states, when 

discussing the Sawhill paper, that in order to get a consistent estimate of how consumers value 

fuel economy, the author is required to correctly specify how consumers form expectations about 

gas prices and drive their vehicles. If any of these are incorrectly specified, the estimate will be 

biased. The same is true for the Alcott and Wozny paper. While I find Alcott and Wozny’s 

specification for how expectations are formed to likely be more accurate, ironically, they assume 
consumers are hyper-rational in the sense that they form their gas price expectations based on 

NYMEX futures prices. In some ways the myopia result is inconsistent with this key 

assumption.” 

Walter: “It is difficult for the reader to verify that the same standards are being used to assess 

each paper, especially since the amount of discussion by paper varies greatly. This leads the 

reader to have less confidence in the reviewer’s opinions of the individual studies (that this or 

that one had plausible or implausible assumption, methodologies, or results).” 

Response: 

As Dr. Greene discusses on p. 2 of the paper, “This review attempts to compare the inferences of 

different studies on a consistent basis: a typical consumer’s willingness to pay for a reduction in 

the present value of fuel costs through improved fuel economy.” The comments on individual 

studies are valuable in identifying issues and difficulties that arise in conducting these studies. 

These critiques are, to EPA, nevertheless less important than a comparison of the estimates of 

the willingness to pay for fuel economy across studies. Table 12 of the paper (pp. 50-51) 

provides that comparison. 

3.4 APPROPRIATENESS OF THE CONCLUSIONS DRAWN 

There was general consensus among the reviewers on the major conclusion of the document: that 

the literature remains mixed as to whether consumers undervalue, overvalue, or approximately 

value fuel economy. 

One reviewer raised doubts that differences in assumptions, opinions, and methods have been 

sufficiently examined to support even the weak proposition that “there do not appear to be clear 

associations among methods or data sources and the resulting inferences.” 

One reviewer expressed doubts about what Greene means by “incorrect models” of consumer 

decision making about fuel economy but suggested efficient markets hypothesis and economic 

rationality to be a possible interpretation of this phrase. 

Response: 

EPA accepts the general conclusion that the literature remains mixed and unsettled on how 

consumers consider fuel economy when buying vehicles. 

The Executive Summary, the Conclusion, and Table 13 of the report provide new, further 

identification and assessment of the effects of different factors on modeling results. As Greene 

notes on p. 55, “There does not appear to be an obvious explanation for the widely divergent 

results. Neither model type, formulation of the variable representing fuel economy, data type, 

time period, nor any other readily identifiable factor shows a strong association with inferences 

about the values consumers place on fuel economy (Table 13).” 

About the comment that there has been insufficient examination to support the proposition that 

“there do not appear to be clear associations among methods or data sources and the resulting 

inferences:” it is possible that further examination may reveal associations between results and 
 

underlying characteristics of the studies, and Greene’s statement leaves open that possibility. In 

the meantime, Greene was not able to find such patterns. 

Greene has modified the “incorrect models” language to refer to “the likelihood that the 

rational economic consumer model does not adequately describe the decision-making of 

consumers in the real world” (p. xix, the Abstract; see p. 9 for similarly revised language). This 

new language appears to correspond to the reviewer’s interpretation. 

3.5 RECOMMENDATIONS FOR ALTERNATE DATA AND/OR 

ANALYSES 

3.5.1 Additional Data 

Two of the reviewers suggested several other studies that are relevant for this work. A list of 

these studies is provided below. 

Dreyfus, Mark K. and W. Kip Viscusi (1995). “Rates of Time Preference and Consumer 

Valuations of Automobile Safety and Fuel Efficiency.” Journal of Law and Economics 

38: 79-98. 

Hughes, Jonathan E., Christopher R. Knittel and Daniel Sperling (2008). “Evidence of a Shift in 

the Short-Run Price Elasticity of Gasoline Demand.” Energy Journal. 

Kilian, Lutz and Eric Sims (2006). The Effects of Real Gasoline Prices on Automobile Demand: 

A Structural Analysis Using Micro Data, Working Paper, University of Michigan (April). 

Klier and Linn (2010 working paper) “The Price of Gasoline and New Vehicle Fuel Economy: 

Evidence from Monthly Sales Data” 

Klier, Thomas, and Joshua Linn (2008). “The Price of Gasoline and the Demand for Fuel 

Efficiency: Evidence from Monthly New Vehicles Sales Data.” Working Paper, 

University of Illinois at Chicago (September). 

Sallee and West (2009 working paper) 

Sallee, James, and Sarah West (2008). “Testing for Consumer Myopia: The Effect of Gasoline 

Price Changes on the Demand for Fuel Economy in Used Vehicles.” Working Paper, 

Macalester College (December). 

West, S. (2008). “The Effect of Gasoline Prices on the Demand for Sport Utility Vehicles.” 

Working Paper. Macalester College. 

Response: 

Greene has added 5 papers to the review since the draft that the reviewers saw: Kilian and Sims 

(2006), Klier and Linn (2008), Sallee, West, and Fan (2010), Vance and Mehlin (2009), and 

Fifer and Bunn (2009). Time limitations affected his ability to incorporate additional papers. 

3.5.2 Additional Analysis 

The reviewers suggested several additional analyses to strengthen the study. 

3.5.ii a. The author should take advantage of the summary section to indicate whether 

some approaches reviewed in the study are likely to be better than others. 
 

 

 

Response: 

In the Executive Summary, Greene suggests convening researchers to investigate jointly why 

results differ so greatly. He also suggests investigation of alternatives to the rational economic 

model as a starting point for the analyses. 

The conclusion has some discussion of study characteristics that might be superior to others. As 

he notes, it is not clear whether studies that had superior characteristics produced more 

consistent results than studies with less ideal characteristics. He suggests several factors (pp. 

54-55) that might improve results: recognition that consumers do not appear to follow “the 

strict model of rational economic behavior;” consideration of consumer heterogeneity; and use 

of fixed effects. 

3.5.ii b. The analysis to explain the difference in results could be improved. 

Carolyn: “First, the discussion interweaves reasons that could explain why we can plausibly 

believe any of a range of results (e.g., heterogeneous decision rules, uncertainty) with reasons for 

why different studies get different results. It would be helpful to separate these discussions, 

reminding readers of the former, and then focusing on the latter, which is the most important 

contribution. This kind of analysis should help indicate best practices and directions for future 

research. Can we suggest some experiments (e.g., new techniques on old data sets, or old 

techniques on new data sets) that can help tease out what differences arise from alternative 

econometric approaches? If most studies have focused on other aspects of vehicle demand, are 

there better specifications for looking directly at the valuation of fuel economy?” 

A summary of key differences in approaches and data and an analysis of their consequences 

would be useful. These would include 

• levels of aggregation; 

• time horizons (how useful is information from the 1970s in estimating current 

demand?); 

• incorporation of the supply side; 

• details of other attributes and variable definitions; 

• allowing for separate responses to MPG and fuel costs (this was raised as lacking in 

the discussion of Gramlich, but it was not apparent which other studies had done 

this); 

• assumptions of vehicle lifetime, VMT, discount rates, etc.; and 

• econometric approach. 

Although Table 10 provides some of these summary indicators (model type, data, and time), 

additional analysis such as the following would be useful: “Do we observe any trends across 

classes or time? Within a class of models, what drives the differences?” 

Walter: “It would be useful to expand Table 10 (or to create a set of tables) to show other 

differences that could be important sources of differences in estimates. The items that could be 

included would show differences in: 
 

 

1. Maintained assumptions reported (assumptions that are maintained across any alternative 

specifications or sensitivities), 

2. Reported sensitivity analyses (is one done? which assumptions are varied? how widely?), 

3. Econometric challenges and approaches used to address them (Greene (2009) appears to 

discuss these comprehensively in the text.), and 

4. Out-of-sample predictive performance (are predictions made? about what? are the 

predictions compared to actual outcomes? how accurate are the predictions?). 

Adding these additional side-by-side comparisons would increase the reader’s confidence that 

the same standards are being used to form the reviewer’s opinions about the relative usefulness 

of each paper to inform decision making by EPA. 

My opinion: The additional elements are responsible for more of the variation in the estimates of 

consumer value for fuel economy than are the elements already included in Table 10.” 

He also suggested a measure of researcher/specification uncertainty and disagreement to 

quantitatively assess whether we have made progress toward a consensus. He also provides 

illustrative suggestions on ways to use and interpret this measure. “The idea is to compare the 

range of parameter estimates to the parameter value predicted by mainstream theory.” 

The metric is called Multiple Extreme Estimates Test (MEET) and is defined as the ratio of the 

value predicted by theory to the difference between the two extreme estimates. 

Value Predicted by Theory V MEET 

Largest Estimate - Smallest Estimate R 

lim MEET 0 and lim MEET 

R R 0 

“Numerically, MEET measures the predicted value as a share of the range of expert opinion. The 

range of expert opinion, R, is a crude and partial measure of the uncertainty or fragility of the 

estimates due to differences in assumptions, specification, and opinions between researchers. The 

metric MEET views this fragility compared to the theoretical value of the parameter, V.” 

Response: 

The Executive Summary, Conclusion, and Table 13 include further identification and assessment 

of the characteristics that might, in principle, be useful in a meta-analysis: the publication 

status of the study; the kind of model used; the dependent variable; the type of data; the time 

period covered; the fuel economy measure; the form of price expectations; whether actual 

transactions prices were used; whether the model allowed for heterogeneous tastes; whether the 

model included both the demand and the supply side; and whether fuel economy standards were 

included. This list does not include all the variables that the reviewers cite, but it includes many 

of them. The discussions of individual papers include some of the other factors, such as some of 

the assumptions of the models. As the Executive Summary and Conclusion indicate, there are no 

obvious connections among these factors and the model results. 
With so many variables and not very many studies, a statistical meta-analysis of these results is 

unlikely to produce statistically valid results. 

The MEET criterion is an interesting way to think about the variation in study results. EPA at 

this time is satisfied with a qualitative discussion of the variation of these results. 

3.5.ii.c. Though the issue of consumer heterogeneity has been mentioned in several 

places, this may be important area for additional analysis, both to explain differences and 

to suggest future research directions. 

Response: 

The issue of modeling consumer heterogeneity is likely to be important in these models. At this 

time it is a source of distinction among models: some approaches, especially mixed logit, 

incorporate this feature. The review at this time does not extend to include a discussion of the 

“dream” analysis – what would be the ideal data sets, methods, etc. Assessing the state of the 

literature is a first step to get to that discussion, and this review has focused on that step. 

3.6 OVERALL CLARITY OF THE PRESENTATION 

The reviewers agreed that the overall clarity of the document was high. 

Reviewers had several suggestions about organizing the paper. 

i. One reviewer suggested that since the articles are organized logically by class of data and 

methods, it would be helpful to review those methods at the beginning of each section 

“including the advantages and drawbacks, as well as key factors or assumptions that can 

affect the results.” 

Response: 

Greene has not included this review of methods. EPA agrees that it would be useful information, 

but such review information is available elsewhere. 

ii. It has also been suggested that it would be helpful to begin with the seminal papers in 

each category, “so as to understand the evolution and improvements made subsequently.” 

Response: 

Greene has reorganized the presentation of the papers. For instance, the section on discrete 

choice models with aggregate data now begins with the paper by Berry, Levinsohn, and Pakes 

(1995), a widely cited paper. 

iii. Two reviewers agreed that the publication status of papers should be recognized and one 

reviewer suggested that the review should “focus first on published papers and then 

second on unpublished papers.” 

Response: 
 

Greene discusses, on p. 2, his reasons for including unpublished papers – in particular, his 

opinion that “they are of publishable quality.” The new Table 13 has a column identifying the 

publication status of all the studies included. 

iv. Chris Knittel: “Many of the papers reviewed did not focus on measuring the implicit 

discount rate, or even on the issue of how gas prices affect vehicle demand. If a paper did 

not focus on either of these two questions, it is difficult to gauge the robustness of their 

results with respect to these questions, the quality of the variation in gas prices in the 

data, etc. Perhaps a better method would be to first focus on those papers where gas 

prices and vehicle choice are the central research question, and those papers where this is 

more a tangential part of the analysis.” 

Response: 

Perhaps the major concern with studies that do not focus on fuel economy decisions is that there 

may not have been adequate attention to specification of the variable, or problems with omitted 

or collinear variables. For those reasons, the studies may not produce robust estimates of the 

implicit value of fuel economy. 

On the other hand, studies that focus on these variables may suffer from the attention as well: if 

the results come out against expectations, there can be a temptation to revise the analysis until 

the results meet expectations. Only if results to all specifications are reported is it possible to 

see whether the reported results are robust. 

As Greene discusses in the conclusion (p. 55), omitted variables, errors in variables, and 

correlated variables are likely to be problems that may be difficult to overcome in the vehicle 

choice context. Fixed effects may help, but may not solve the problem. 

On a related note, another reviewer commented on studies that do not explicitly estimate the 

value of fuel economy: “Since few econometric studies have explicitly estimated the value of 

fuel economy, a significant contribution of the paper is to translate a wide variety of results into 

more consistent indicators. Yet, they could still be more consistent. In Table 10, can all of the 

results be converted into the same WTP metric, preferably using the same assumptions about 

vehicle lifespan, VMT, discounting, etc.?” 

Response: 

Table 12 seeks to translate estimates from all the papers into both a willingness to pay for fuel 

economy as a percent of the discounted present value of fuel savings, and an implied annual 

discount rate. As Greene discusses in the paper, it is not possible with some of the studies to find 

the common metric; with others, he has questions about some of the values reported that are 

necessary for the metric. EPA hopes that identifying these difficulties may stimulate future 

researchers to report these values more consistently. 

Another comment on the presentation of the paper was that “the author’s baseline assumptions 

need to be stated clearly early in the paper, so careful attention can be given throughout to 

assumptions that deviate from that baseline. Indeed, the statement made in the Allcott and 

Wozny analysis (‘Calculating the discounted present value of fuel costs requires a number of 
assumptions...’) should be made earlier in a general context, so that the variety and range of 

assumptions can be understood and explored.” 

Response: 

Greene includes his assumptions for a baseline analysis in the Appendix to the paper and 

discusses these assumptions on p. 2. 

3.7 OTHER CLARIFICATION COMMENTS 

One reviewer provided the following comments to clarify or rephrase certain statements. 

1. The authors should “use the phrase ‘at society’s discounted expected value...’ (top of 

page 5) instead of ‘does the market value fuel economy improvements at the discounted 

expected value of future fuel savings over the lifetime of the vehicle, or less, or more?’” 

Response: 

This change has been made. 

2. The statement (on page 5) that “On the other hand, if consumers are myopic and consider 

only the first three years of fuel savings, for example, fuel economy standards can 

increase welfare even based solely on private costs and benefits” should be made clearer. 

Response: 

This language continues in its original form. 

3. One of the reviewers comment on the statement on page 5 that “it is surprising that there 

is no basic research on how consumers consider fuel economy.” “This is a strange use of 

the term basic research, which I take to mean research on pure science. Does the author 

equate basic science with interviews? I have never heard basic science used in this 

manner.” 

Response: 

The word “basic” has been changed to “behavioral.” 

Two of the reviewers also provided several detailed comments on specific paper reviews, Section 

2 of the document, and editorial comments and corrections to typographical errors. 

Response: 

Greene has incorporated most of these comments into the revision.