Harnessing Economics to Respond to New Fuel Economy Standards
A Q&A with Affiliate Martin Zimmerman and Managing Principals Brian Gorin and Edward Tuttle
New federal Corporate Average Fuel Economy (CAFE) rules raise fuel economy standards for cars and light trucks and change the way compliance is determined. Analysis Group Managing Principals Brian Gorin and Edward Tuttle sat down with Martin Zimmerman, Ford Motor Company Clinical Professor of Business Administration at the Ross School of Business, University of Michigan, former chief economist at Ford Motor Company, and Analysis Group affiliate, to discuss the impact of these rules, automakers’ potential responses, and the central role economic analysis can play in identifying optimal responses.
Brian Gorin: Why are the new fuel economy standards such a critical challenge for the auto industry?
Professor Zimmerman: First, the National Highway Traffic Safety Administration (NHTSA) estimates that, under the new rules, passenger cars will have to achieve almost 30 mpg in 2012 and over 34 mpg in 2016 – compared to only 27.5 mpg since 1990. Second, an auto manufacturer’s fuel economy target is no longer fixed but depends on the size and mix of the vehicles it sells. A manufacturer making smaller cars will have a higher fuel economy target. Third, manufacturers exceeding their targets now earn “credits” that they can use themselves or sell to other manufacturers.
These changes dramatically alter the incentives facing automakers. The industry spent 20 years optimizing their decision making under the old CAFE program, but now the rules have changed substantially. The new incentives introduce great potential for smart players to adapt and re-optimize their pricing, production, and marketing strategies.
Edward Tuttle: Won’t adapting to these new rules require not only technological know-how but also a solid understanding of the marketplace and underlying economic fundamentals?
Professor Zimmerman: Absolutely. To meet their fuel economy targets, automakers have technological options: for example, an automaker could boost the fuel economy of one model in its fleet above its target, thereby raising its overall fleet average and making it unnecessary to improve the fuel economy of other models in its fleet. Or an automaker could change its actual fuel economy requirement by modifying its vehicles’ footprints – a slightly wider or longer (but not necessarily heavier) vehicle means a lower fuel economy requirement.
But automakers also have options that rely on economic strategy, not technology, to achieve compliance. For example, an automaker could subsidize the purchase price of a vehicle model that substantially exceeds its target. Every vehicle it sells as a result of the subsidy raises its overall fleet average, making it easier to meet its fuel economy requirement. The automaker could even decide to alter the entire mix of vehicles it manufactures and sells – more of model X, less of model Y, and so on. This may be the cheapest way to meet the new fuel economy standards. I say “may,” because figuring that out is difficult.
Mr. Tuttle: And that’s where economic analysis comes in. Unlike most engineering models of compliance options, economic models explicitly consider how the behavior of consumers, suppliers, and competitors affects optimal decision making. Without an understanding of these market dynamics, can strategic decisions on compliance be made?
Professor Zimmerman: Not likely. Say an automaker decides to subsidize a particular fuel-efficient model. How can they tell how well the subsidy will work? Will consumers purchase enough vehicles to make the subsidy worthwhile? If an automaker decides to alter the mix of vehicles it makes, how will competitors respond? Will they make changes, too? What are the implications for prices and profit? Even simple technological solutions, such as better engine or tire performance, have an economic dimension, since their costs depend in part on suppliers and on the reactions of consumers.
Consider this: Later this year, Nissan will start selling its all-electric Leaf in the United States. The Leaf will have a very high fuel economy rating, far above the target for similarly sized vehicles. Thus, every Leaf that Nissan sells provides a CAFE “bump,” making it easier for Nissan to meet its fleetwide requirement.
So one way Nissan could meet its fuel economy standard in the future would be to lower the price of a Leaf to sell a lot of them. Will it work? How many Leafs can Nissan reasonably expect to sell at a given price? Would the foregone margin be worth it? Or would Nissan be better off pursuing a strategy similar to Ford’s – lower reliance on electrics while investing in turbochargers or other traditional technologies to raise fuel efficiency across its fleet? The answers depend, of course, on economic factors, such as elasticities of demand in the market, the behavior of competitors, and other variables that economists are adept at modeling.
Mr. Gorin: Aren’t there some key uncertainties in the new fuel economy standards and how automakers should respond to them?
Professor Zimmerman: That’s right. From a policy standpoint, the first is the path of future CAFE standards. NHTSA projects a nationwide average of 34.1 mpg in 2016. Will standards continue to rise after that, or stagnate as they did from 1990 until now? The second key uncertainty is how the EPA will rate new hybrid and particularly electric vehicles. These vehicles will certainly exceed fuel economy targets for their sizes, but the EPA has not yet determined precise ratings. From the automakers’ standpoint, another important uncertainty is the development and success of new technologies. For example, will the Nissan Leaf take off, and will electric vehicle technologies improve?
Clearly, automakers must make critical decisions in the face of uncertainty. But economists can help them to prioritize research and product development projects, even when the success of any project is uncertain at the start. Sophisticated economic modeling can take into account many complex relationships among variables and can help decision makers understand, reduce, and respond to uncertainty. ■