An Economic Estimation of the Production Costs of Improving Automobile Fuel Efficiency

1. An Economic Estimation of the Production Costs of Improving Automobile Fuel Efficiency Takahiko Kiso August 8, 2011 Camp Resources XVIII

2. Introduction • Automobile fuel economy is an important policy issue • Current goal: improve average fuel economy by 40% between 2009 and 2016 • $1300 increase in new vehicle prices on average • Economics papers on policies to improve fuel economy • Austin and Dinan (2005) • Bento, Goulder, Jacobsen and von Haefen (2009) • Klier and Linn (2010) • Coleman and Harrington (2010) • etc

3. Introduction • Production cost estimates for fuel efficiency improvement are a crucial factor • National Research Council (2002, 2010) provides engineering-based estimates of incremental costs of fuel efficiency improvement.

4. Introduction • Estimated costs ($) of 0.1 gallon per 100 miles reduction in fuel consumption (NRC, 2002):

5. Introduction • Potential shortcomings of NRC estimates • “Free lunch” for some inexpensive technologies • Estimates available only at vehicle class level • Estimates have wide ranges

6. Goals • Provide alternative/complementary cost estimates based on economics • Estimate “hedonic” cost function for improving fuel efficiency through economic models • Incremental cost as function of vehicle attributes (fuel efficiency, weight, etc) • Provide cost estimates for each vehicle (NRC: vehicle class level) • Compare economics-based and engineering-based estimates • Simulate and compare effectiveness of different policies for improving fleet-wide fuel economy

7. Overview of results • Cost of reducing fuel consumption per 100 miles by 0.1 gallon is around $50-$80 • Overall, comparable to NRC’s estimates • Marginal costs vary within and across vehicle classes. • Higher cost of improvement if a vehicle is • More fuel efficient • Heavier

8. Model Framework • Estimate discrete choice model of consumers’ new vehicle purchases. • Express each vehicle model’s market share as function of parameters of discrete choice model. • Consider automaker’s optimization problem under oligopoly, using the market share function. • FOCs imply marginal cost of fuel efficiency improvement for each model. • Estimate hedonic (marginal) cost function by regressing implied marginal cost on vehicle attributes. • Analogous to 2nd stage of standard hedonic pricing model

9. Data • 2001 National Household Travel Survey • Each surveyed vehicle’s make, model, year & annual VMT estimate, owner’s individual & household characteristics • EPA fuel economy test data • Vehicle attributes (fuel economy, weight, horsepower, etc) • Use model year 2001 vehicles • Gas prices were stable back then • # of vehicles in the sample: 5914 • # of vehicle models: 492

10. Demand side • Similar to Bento et al. (2009) • Simultaneous estimation of discrete and continuous choices: • random parameters logit model of vehicle choice • continuous choice of vehicle miles traveled (VMT) • Discrete and continuous choices are connected by Roy’s identity

11. Demand side Type I extreme value error • Household i’s indirect utility function from vehicle j: • ni: random parameter varying over i • pj/D : annualized vehicle price. • T=4 (average length of new vehicle ownership) • d=0.9 (annual discount factor)ni vehicle fixed effect vehicle price $/100 miles= $/gal × gal/100 miles income

12. Demand side • By Roy’s identity, conditional on i choosing j, • niinduces correlation between vehicle and VMT choices • Can form likelihood that i chooses j and drive mij, as observed in data • Due to random parameters ni, use maximum simulated likelihood estimator VMT error

13. Supply side • Vehicle j’s unit production costs depend on its attributes: fuel consumption other attributes

14. Profit maximization • Nash equilibrium: Automaker a sets prices and fuel consumption rates (as well as other attributes) of its own vehicles, given prices and all attributes of other firms' vehicles: • Market sales is given by vehicle price sales set of a’s products production cost

15. First order conditions • Focus on FOCs with respect to vehicle prices and fuel consumption rates:

16. Marginal costs of fuel efficiency improvement • From FOCs, J: Total number of vehicle models in the market J×J J×J J×1

17. Intuitive explanation of Eq. (1) • Suppose each vehicle model is produced by a separate firm, then Eq. (1) simplifies to • Dmijgijeij is anticipated total fuel spending over consumer’s planning horizon. • ∂cj/∂ej (<0) above equals “average” anticipated total fuel cost savings due to marginal fuel efficiency improvement. • marginal production cost = marginal fuel cost savings gas price per gallon −1 × “average” VMT

18. Results: Marginal cost of fuel efficiency improvement • Plot (cost ($) for improving fuel efficiency by 0.1 gal/100miles) against vehicle size (Domestically produced vehicles only)

19. Comparing engineering and economic estimates • Engineering estimates of incremental costs of 0.1 gal/100 miles improvement (derived from National Research Council, 2002)

20. Estimating the cost function for fuel efficiency improvement other attributes • Unit production cost function: • From automaker’s FOCs, • Estimate hedonic marginal cost function for fuel efficiency improvement • Analogous to 2nd stage of standard hedonic pricing model, where demand or cost parameters are estimated • Endogenous attributes (simultaneity) • Instruments for model j of firm a: attributes of model k5 years before, where model k is produced by another firm and has very similar attributes to j’s this year weight acceleration (horsepower/weight)

21. Results: Cost function for fuel Efficiency improvement • Imply reasonable properties: • Higher marginal costs of fuel efficiency improvement if a vehicle is • More fuel efficient • Heavier • RWD or AWD

22. $ MC(e; q), q fixed Fuel consumption (gallons/100 mile)

23. Summary • Provide economics-based estimates of marginal costs of improving fuel efficiency for each vehicle model. • 0.1 gal/100 miles improvement costs between $50-$80. • Estimates are overall comparable to engineering estimates by NRC (2002). • Estimate hedonic cost function for fuel efficiency improvement. • Higher cost of improvement if a vehicle is • More fuel efficient • Heavier

24. Ongoing work • Policy simulations using demand and supply-side estimates. • Focus especially on comparing new footprint based fuel economy regulations with older “flat” regulations.

25. Thank you!

27. Results: Cost function for fuel Efficiency improvement • Cost increase for e0→e1 (e0>e1)

28. Demand side (3) • With iid type I extreme value distributed errors, probability that i chooses j, conditional on αi, is • With normally distributed errors, probability that Rij is realized, conditional on αi and j, is • Conditional probability of i choosing j and observing Rij is

29. Demand side (4) • Unconditional likelihood for i , given αi’s pdf f, is • Estimation by maximum simulated likelihood, assuming αi is normally distributed • Vehicle j’s predicted share as a function of demand side parameters: