Debate about the appropriate design of energy policy hinges critically on whether consumers might undervalue energy efficiency, due to myopia or some other manifestation of limited rationality. We contribute to this debate by measuring consumers' willingness to pay for fuel economy using a novel identification strategy and high quality microdata from wholesale used car auctions. We leverage differences in future fuel costs across otherwise identical vehicles that have different current mileage, and therefore different remaining lifetimes. By seeing how price differences across high and low mileage vehicles of different fuel economies change in response to shocks to the price of gasoline, we estimate the relationship between vehicle prices and future fuel costs. Our data suggest that used automobile prices move one for one with changes in present discounted future fuel costs, which implies that consumers fully value fuel economy.
The most well-known market failure in the market for carbon-producing goods is the negative externality of pollution: consumers rationally do not internalize the harm that their carbon emissions will place on others. The Econ 101 solution for this is a Pigouvian carbon tax or, equivalently (in terms of efficiency) a cap-and-trade system.
But some argue that consumer inattention causes a second market failure: consumers undervalue their own savings from energy efficiency. This failure would cause the level of carbon emissions to be too high even under the optimal Econ 101 Pigouvian tax. As a result, there is a long literature (with which I'm not too familiar) that tries to estimate consumers' valuation of fuel efficiency.
At a first glance, the simplest way of answering this question in the context of automobiles would be cross-sectional: compare the sales prices of cars with varying fuel efficiency, while richly controlling for observable characteristics. Of course, the price of a given car is substantially determined by unobservable characteristics which are correlated with fuel economy, so this strategy is not credible.
A slightly more sophisticated strategy would exploit changes in the price of gasoline, and compare the change in price for high-efficiency and low-efficiency vehicles. An increase in the price of gasoline should cause the price for a Hummer to fall by more than the price of a Camry. For this to measure consumer valuation of energy efficiency, there can't be anything else differentially occurring for high- and low-efficiency vehicles correlated with energy prices. But if more fuel-efficient models are introduced in response to a fuel price increase---increasing competition in that segment---we could see a fall in the price of high-efficiency vehicles that isn't caused by consumer valuation (see Langer and Miller (2013)).
Enter Sallee, West, and Fan (2015). Their strategy makes use of more subtle variation. They use variation in the odometer readings, interacted with variation in fuel prices. Intuitively, fuel prices should matter less for car prices if the car has a shorter expected life---i.e., the change in the present discounted cost of fuel will be smaller if the life is shorter. The beauty of this strategy is that you can look within vehicle-month cells.
This is, in some sense, analogous to a triple difference.
The first difference is within a single vehicle type sold in a given month; say, two 2007 Honda Civics sold in May 2013, where the only variation is the odometer reading. We can essentially estimate the slope of the mileage-price curve, which will presumably be negative (lower mileage cars are more valuable).
The second difference is across vehicle types: between 2007 Honda Civics and 2007 Ford F-150s. We compare the mileage-price curve of both types of vehicles. Holding all else constant, we'd expect the 2007 Ford F-150 to have a flatter mileage-price curve, since higher mileage means the expected life of the vehicle --- and thus the life during which its driver will "suffer" from its relative fuel inefficiency --- is smaller.
Of course, not all else is constant: it's possible that trucks have better longevity, or vice versa, which would contaminate the mileage-price curve. So, enter the third difference: when fuel prices are higher and lower. Intuitively, the extent to which the F-150 mileage-price curve is flatter than that of the Honda Civic mileage-price curve should be increasing in the fuel price. In practice, this triple difference is estimated by using vehicle type X month fixed effects.
What do they find? Putting aside the caveats that their fuel-cost variables are constructed with many assumptions (to which the results may not be fully robust), they find that an increase in the PDV of fuel costs---variation in which comes solely from variation in mileage---is passed through as a 1-to-1 reduction in the wholesale purchase price.
This, to me at least, was a somewhat surprising result. Their calculation of the PDV of the fuel cost is non-trivial, and I highly doubt that consumers are literally making that calculation. Instead, some combination of rules-of-thumb and other market forces are combining to set the market price of these cars "correctly." It is a very interesting question why the rules-of-thumb and market forces provide the "correct" price in this market, but not in other markets that suffer from similar complexities, e.g., health care, retirement savings, etc. This, to me, seems a central question at the intersection of neo-classical and behavioral economics, to which I don't think a satisfying answer has been provided.