Pain at the Pump: The Effect of Gasoline Prices on

New and Used Automobile Markets∗

Meghan R. Busse

Northwestern University and NBER

Christopher R. Knittel

MIT Sloan and NBER

Florian Zettelmeyer

Northwestern University and NBER

September 2011

∗We are grateful for helpful comments from Hunt Allcott, Eric Anderson, John Asker, Max Auffhammer, Severin

Borenstein, Tim Bresnahan, Igal Hendel, Ryan Kellogg, Aviv Nevo, Jorge Silva-Risso, Scott Stern and seminar

participants at Brigham Young University, the Chicago Federal Reserve Bank, Cornell, Harvard, Illinois Institute of

Technology, Iowa State, MIT, Northwestern, Ohio State, Purdue, Texas A&M, Triangle Resource and Environmental

Economics seminar, UC Berkeley, UC Irvine, University of British Columbia, University of Chicago, University of

Michigan, University of Rochester, University of Toronto, and Yale. We also thank participants at the ASSA, Milton

Friedman Institute Price Dynamics Conference, NBER IO, EEE, and Price Dynamics conferences, and the National

Tax Association. We thank the University of California Energy Institute (UCEI) for financial help in acquiring data.

Busse and Zettelmeyer gratefully acknowledge the support of NSF grants SES-0550508 and SES-0550911. Knittel

thanks the Institute of Transportation Studies at UC Davis for support. Addresses for correspondence: E-mail:

m-busse@kellogg.northwestern.edu, knittel@mit.edu, f-zettelmeyer@kellogg.northwestern.edu
Pain at the Pump: The Effect of Gasoline Prices on

New and Used Automobile Markets

Abstract

We estimate the effect of gasoline prices on short-run equilibrium prices, market shares,

and sales of new and used cars of different fuel economies. We find that gasoline prices

have larger effects on the prices of used cars than of new cars, but that they have

large effects on market shares and sales of new cars. We use our findings to investigate

whether consumers are myopic about future gasoline costs when they buy cars, and to

investigate how market structure can influence the effect of environmental policies.
1 Introduction

According to EPA estimates, gasoline combustion by passenger cars and light-duty trucks is the

source of about fifteen percent of U.S. greenhouse gas emissions, “the largest share of any end-use

economic sector.”1 As public concerns about climate change grow, so does interest in designing

policy instruments that will reduce carbon emissions from this source. In order to be effective, any

such policy must reduce gasoline consumption, since carbon emissions are essentially proportional

to the amount of gasoline used. The major policy instrument that has been used so far to influence gasoline consumption in the U.S. has been the Corporate Average Fuel Efficiency (CAFE)

standards (Goldberg (1998), Jacobsen (2010)). Some economists, however, contend that changing

the incentives to use gasoline—by increasing its price—would be a preferable approach. This is

because changing the price of gasoline has the potential to influence both what cars people buy

and how much people drive.

This paper addresses two questions that are crucial for assessing whether a gasoline price related

policy instrument (such as an increased gasoline tax or a carbon tax) could influence what cars

people buy. The first key question is how sensitive consumers are to expected future gasoline costs

when they make new car purchases. More precisely, how much does an increase in the price of

gasoline affect the willingness-to-pay of consumers for cars of different fuel economies? If consumers

are very myopic, meaning that their willingness-to-pay for a car is little affected by changes in the

expected future fuel costs of using that car, then a gasoline price instrument will not influence their

choices very much and will not be sufficient to achieve the first-best outcome in the presence of an

externality. One might think of this loosely as the “demand side” determinant of the effectiveness

of such a policy.2

There is also a “supply side” determinant. Consumer myopia determines how much a change

in the price of gasoline will change the demand for different kinds of cars. Whether the resulting

change in demand will change which cars consumers buy in equilibrium depends on the market

structure of the car market itself. In a competitive car market with very inelastic supply, a change

in the demand for a particular type of car would affect the equilibrium price of that type of car

by a lot, but the equilibrium quantity very little. Alternatively, a car manufacturer with market

power could adjust the prices of different types of cars in order to counteract the effect of gasoline

price changes.3In both these scenarios, a policy-induced change in gasoline prices would have little

1EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2006, p. 3-8.

2This condition is not unique to the case of gasoline consumption. Hausman (1979) was the first to investigate

whether consumers are myopic when purchasing durable goods that vary in energy costs. More generally, this is an

example of the quite obvious point that a policy must influence something that consumers pay attention to in order

to actually affect the choices consumers make.

3

In 2006, GM directly counteracted the effect of gasoline prices for buyers of its largest cars and SUVs. GM

1
effect on what cars were purchased in equilibrium. Conversely, in a competitive market with an

elastic supply curve, or in a market in which oligopolist manufacturers offered little price response,

a gasoline price change could have a larger effect on what cars are purchased. Understanding the

market structure of the target market is therefore also key to understanding the effects of a policy

intervention.

Our analysis proceeds in three steps. First, we estimate how the price of gasoline affects market

outcomes in both new and used car markets. Specifically, we use data on individual transactions

for new and used cars to estimate the effect of gasoline prices on equilibrium transaction prices,

market shares, and sales for new and used cars of different fuel economies. These estimates become

the building blocks for our next two steps.

In our second step, we use the estimated effect of gasoline prices on prices and quantities in

new and used car markets to learn about how consumers trade off the up-front capital cost of a

car and the ongoing usage cost of the car. We estimate a range of implied discount rates under

a range of assumptions about demand elasticities, vehicle miles travelled, and vehicle survival

probabilities. We find little evidence that consumers “undervalue” future gasoline costs when

purchasing cars. The implied discount rates we calculate correspond reasonably closely to interest

rates that customers pay when they finance their car purchases.

In our third step, we compare our price and quantity results for new car and used car markets

and discuss the reasons for the differences. The effects of gasoline prices on car prices and quantities

are very different in new and used markets. We find that a $1 change in the gasoline price is

associated with a very large change in relative prices of used cars of different fuel economies—a

difference of $1,945 in the relative price of the highest fuel economy and lowest fuel economy quartile

of cars. For new cars, the predicted relative price difference is much smaller—a $354 difference

between the highest and lowest fuel economy quartiles of cars. However, we find a large change

in the market shares of new cars when gasoline prices change. A $1 increase in the gasoline price

leads to a 21.1% increase in the market share of the highest fuel economy quartile of cars and a

27.1% decrease in the market share of the lowest fuel economy quartile of cars. The results suggest

that the industrial organization of markets does play a role in determining the effect that policy

interventions which mimic price changes would have on market outcomes.

This paper proceeds as follows. In the next section, we position this paper within the related

literature. In Section 3 we describe the data we use for the analysis in this paper. In Section 4 we

estimate the effect of gasoline prices on equilibrium market shares, unit sales, and prices in new

issued these customers pre-paid debit cards that credited them with the difference between the average retail price

of gasoline and $1.99 for each gallon of gasoline they purchased, effectively capping the price of gasoline at $1.99 for

these customers. (“GM offers to pay part of gasoline bill for California, Florida consumers,” Automotive News, May

23, 2006)

2
and used car markets. In Section 5 we use the results estimated in Section 4 to investigate whether

consumers are myopic, meaning whether they undervalue expected future fuel costs relative to the

up-front prices of cars of different fuel economics. In Section 6 we compare how gasoline prices affect

outcomes in new and used markets differently. Section 7 checks the robustness of our estimated

results. Section 8 offers some concluding remarks.

2 Related literature

There is no single, simple answer to the question “How do gasoline prices affect gasoline usage?,”

and, consequently, no single, omnibus paper that answers the entire question. This is because there

are many margins over which drivers, car buyers, and automobile manufacturers can adjust, each

of which will ultimately affect gasoline usage. Some of these adjustments can be made quickly;

others are much longer run adjustments.

For example, in the very short run, when gasoline prices change, drivers can very quickly

begin to alter how much they drive. Donna (2010), Goldberg (1998), and Hughes, Knittel, and

Sperling (2008) investigate three different measures of driving responses to gasoline prices. Donna

investigates how public transportation utilization is affected by gasoline prices, Goldberg estimates

the effect of gasoline prices on vehicle miles travelled, and Hughes et al. investigate monthly

gasoline consumption.

At the other extreme, in the long run, automobile manufacturers can change the fuel economy

of automobiles by changing the underlying characteristics—such as weight, power, and combustion

technology—of the cars they sell or by changing fuel technologies to hybrid or electric vehicles.

Gramlich (2009) investigates such manufacturer responses by relating year-to-year changes in the

MPG of individual car models to gasoline prices.

This paper belongs to a set of papers that examine the question: How do gasoline prices affect

the prices or sales of car models of different fuel economies? What this set of papers have in

common is that they investigate the effect of gasoline prices on car prices, sales, or market shares,

taking as given the set of cars currently available from manufacturers. This is a short-run effect,

although a longer run response than the immediate changes in driving behavior. Within this set

of papers there are some papers that study the effect of gasoline prices on car quantities and some

that study the effect of gasoline prices on car prices.4

4There is a very large literature (reaching back almost half a century) that has investigated the effect of gasoline

prices on car choices, the car industry, vehicles miles travelled, and that has estimated the elasticity of demand for

gasoline. In addition to the papers described in detail in the next section, other related papers include Blomqvist

and Haessel (1978), Carlson (1978), Ohta and Griliches (1986), Greenlees (1980), Sawhill (2008), Tishler (1982), and

West (2007).

3
2.1 Gasoline prices and car quantities

Two noteworthy papers that address the effect of gasoline prices on car quantities are Klier and

Linn (forthcoming) and Li, Timmins, and von Haefen (2009). Although the two papers address

similar questions, they use different data. Klier and Linn estimate the effect of national average

gasoline prices on national sales of new cars by detailed car model. They find that increases in the

price of gasoline reduce sales of low-MPG cars relative to high-MPG cars. Li, Timmins, and von

Haefen also use data on new car sales, but to this they add data on vehicle registrations, which

allows them to estimate the effect of gasoline price on the outflow from, as well as inflow to, the

vehicle fleet. They find differential effects for cars of different fuel economies: a gasoline price

increase increases the sales of high fuel economy new cars and the survival probabilities of high

fuel economy used cars, while decreasing the sales of low fuel economy new cars and the survival

probabilities of low fuel economy used cars.

2.2 Gasoline prices and car prices

There are several papers that investigate whether the relationship between car prices and gasoline

prices indicates that car buyers are myopic about future usage costs when they make car buying

decisions.

Kahn (1986) uses data from the 1970s to relate a used car’s price to the discounted value

of the expected future fuel costs of that car. He finds that used car prices do adjust to gasoline

prices, by about one-third to one-half the amount that would fully reflect the change in the gasoline

cost. This, he concludes, indicates some degree of myopia. Kilian and Sims (2006) repeat Kahn’s

exercise, with a longer time series, more granular data, and a number of extensions. They conclude

that buyers have asymmetric responses to gasoline price changes, responding nearly completely to

gasoline price increases, but very little to gasoline price decreases.

Allcott and Wozny (2011) address this question using pooled data on both new and used cars.

They also find that car buyers undervalue fuel costs. According to their estimates, consumers

equally value a $1 change in the purchase price of a vehicle and a 72-cent change in the discounted

expected future gasoline costs for the car. These estimates imply less myopia than do those of

Kahn (1986), although still not full adjustment.

Sallee, West, and Fan (2009) carry out a similar exercise as the papers above, also relating the

price of used cars to a measure of discounted expected future gasoline costs. Their paper differs

from others in that it controls very flexibly for odometer readings. This means that the identifying

variation they use is differences between cars of the same make, model, model year, trim, and

engine characteristics, but of different odometer readings. They find that car buyers adjust to

4
80-100% of the change in fuel costs, depending on the discount rate used.

Verboven (1999) implements a similar approach to the papers described above but using data

on European consumers’ choices to buy either a gasoline- or a diesel-powered car. This choice

also involves a trade-off between the upfront price for a car and the car’s future fuel cost, but

with variation over different fuels rather than over time in the price of a single fuel. He estimates

implied discount rates between 5 and 13 percent, a range that is comparable to contemporaneous

interest rates.

Goldberg (1998) approaches the question of consumer myopia in a completely different way.

She calculates the elasticity of demand for a car with respect to its purchase price and with respect

to its fuel cost. After adjusting the terms to be comparable, she finds that the two semi-elasticities

are very similar, leading her to conclude that car buyers are not myopic.

2.3 Differences from the previous literature

Our paper differs from the papers described above in three ways. First, our paper uses data on

individual new and used car transactions, rather than data from aggregate sales figures, from

registrations, or from surveys. Second, our data allow us to compare the effects of gasoline prices

on both prices and quantities of cars, and in both used and new markets, in data from a single

data source. Third, we choose to use more flexible functional forms than many (although not all)

of the papers above.

Transactions data: As described in more detail in Section 3, we observe individual transactions,

and observe a variety of characteristics about each transaction, such as location, purchase timing,

detailed car characteristics, and demographic characteristics of buyers. This allows us to use

extensive controls in our regressions, reducing the chances that our results arise from selection

issues or aggregation over heterogeneous regions, time periods, or car models.

Single data source: Using transactions-based data means that we observe prices and quantities

for new and used cars in a single data set. This enables us to do several things. First, we can

investigate whether the finding of no myopia by Goldberg (1998) in new cars differs from the

finding of at least some myopia in used cars by Kahn (1986), Kilian and Sims (2006), and Allcott

and Wozny (2011) because the effect is actually different for new and used cars, or for some other

reason. Second, this enables us to learn about the industrial organization of new and used car

markets, and specifically about how the differences in market structure might lead to differences

in how a policy intervention would play out in the two markets.

5
Flexible specification: In addressing the question of myopia, researchers face a choice. The

theoretical object to which customers should be responding is the present discounted value of

the expected future gasoline cost for the particular car at hand. Creating this variable means

having data on (or making assumptions about) how many miles the owner will drive in the future,

the miles per gallon of the particular car, the driver’s expectation about future gasoline prices,

and the discount rate. Having constructed this variable, a researcher can then estimate a single

parameter that measures the extent of consumer myopia. The advantage of estimating a structural

parameter such as this is that it can be used in policy simulations or counterfactual simulations

(as Li, Timmins, and von Haefen (2009), Allcott and Wozny (2011), and Goldberg (1998) do).

The disadvantage is that the specific assumptions the researcher has made are “baked into” the

data, and thereby into the results. It is not possible to ask how the researcher’s conclusions would

differ if an assumption were different without having access to the researcher’s data and redoing

the analysis. Furthermore, insofar as these assumptions are not correct, attenuation bias will bias

the coefficients towards myopia.

We choose to estimate more flexible, non-structural parameters. These cannot be used as structural parameters in policy simulations or counterfactuals. We argue that the results are nevertheless

inputs into very informative back-of-the-envelope calculations, and furthermore, calculations that

a reader of this paper can replicate for him- or herself with alternative assumptions about driving

behavior, discount rates, or vehicle life.

3 Data

We combine several types of data for the analysis. Our main data contain information on automobile transactions from a sample of about 20% of all new car dealerships in the U.S. from January

1, 1999 to June 30, 2008. The data were collected by a major market research firm, and include

every new car and used car transaction within the time period that occurred at the dealers in the

sample. For each transaction we observe the exact vehicle purchased, the price paid for the car,

information on any vehicle that was traded in, and (Census-based) demographic information on

the customer. We discuss the variables used in each specification later in the paper.

We supplement these transaction data with data on car models’ fuel consumption and data on

gasoline prices. We measure each car model’s fuel economy with the Environmental Protection

Agency (EPA)’s “Combined Fuel Economy” which is a weighted geometric average of the EPA

Highway (45%) and City (55%) Vehicle Mileage. As shown in Figure 1, the average MPG of

models available for sale in the United States declined slowly in the first part of our sample period,

6
Figure 1: Average MPG of available cars by model year

20

20.5

21

21.5

22

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Average MPG

199719981999200020012002200320042005200620072008

Model Year

then increased in the latter part.5 Overall, however, the average MPG of available models (not

sales-weighted) stays between about 21.5 and 23 miles per gallon for the entire decade.6

We also used gasoline price data from OPIS (Oil Price Information Service) which cover the

same time period. OPIS obtains gasoline price information from credit card and fleet fuel card

“swipes” at a station level. We purchased monthly station-level data for stations in 15,000 ZIP

codes. Ninety-eight percent of all new car purchases in our transaction data are made by buyers

who reside in one of these ZIP codes.

We aggregate the station-level data to obtain average prices for basic grade gasoline in each

local market, which we define as Nielsen Designated Market Areas, or “DMAs” for short. There

are 210 DMAs. Examples are “San Francisco-Oakland-San Jose, CA,” “Charlotte, NC,” and “Ft.

Myers-Naples, FL.” We aggregate station-level data to DMAs instead of to ZIP-codes for two

reasons. First, we only observe a small number of stations per ZIP-code, which may make a

ZIP-code average prone to measurement error.7 Second, consumers are likely to react not only

to the gasoline prices in their own ZIP-code but also to gasoline prices outside their immediate

neighborhood. This is especially true if price changes that are specific to individual ZIP-codes are

transitory in nature. Later we investigate the sensitivity of our results to different aggregations of

gasoline prices (see section 7.4).

Figure 2 gives a sense of the variation in the gasoline price data. The left panel graphs monthly

national average gasoline prices and shows substantial intertemporal variation within our sample

period; between 1999 and 2008, average national gasoline prices were as low as $1 and as high as

5

In 2008, the EPA changed how it calculates MPG. In this figure, the 2008 data point has been adjusted to be

consistent with the EPA’s previous MPG formula.

6While vehicles changed fairly little in terms of average fuel economy over this period, this does not mean that

there was no improvement in technology to make engines more fuel-efficient. The average horsepower of available

models increased substantially over the sample years, a trend that pushed toward higher fuel consumption, working

against any improvements in fuel efficiency technology. See Knittel (2009) for a discussion of these issues and

estimates of the rate of technological progress over this time period.

7

In our data, the median ZIP code reports data from 3 stations on average over the months of the year. More

than 25% of ZIP-codes have only one station reporting.

7
Figure 2: Monthly average gasoline prices (national and by DMA)

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Corpus Christi, TX Columbus, OH

San Francisco-Oakland-San Jose, CA

Average Gasoline Prices

$4. While gasoline prices were generally trending up during this period there are certainly months

where gasoline prices fall.

There is also substantial regional variation in gasoline prices. The right panel of Figure 2

illustrates this by comparing three DMAs: Corpus Christi, TX; Columbus, OH; and San FranciscoOakland-San Jose, CA. California gasoline prices are substantially higher than prices in Ohio (which

are close to the median) and Texas (which are low). While the three series generally track each

other, in some months the series are closer together and in other months they are farther apart,

reflecting the cross-sectional variation in the data.

To create our final dataset, we draw a 10% random sample of all transactions.8 After combining

the three datasets this leaves us with a new car dataset of 1,863,403 observations and a used car

dataset of 1,096,874 observations. Table 11 presents summary statistics for the two datasets.

4 Estimation and results

In this section we estimate the short-run equilibrium effects of changes in gasoline prices on the

transaction prices, market shares, and unit sales of cars of different fuel economics. We separate our

analysis by new and used markets. We will use the results estimated in this section to investigate,

in Section 5, whether car buyers “undervalue” future fuel costs and in Section 6 to discuss how

differences in market structure can influence the effects of environmental policies.

8The 10% sample is necessary to allow for estimation of specifications with multiple sets of high-dimensional fixed

effects, including fixed effect interactions, that we use later in the paper.

8
4.1 Specification and variables for car price results

At the most basic level, our approach is to model the effect of covariates on short-run equilibrium

price and (in the next subsection) quantity outcomes. For the car industry, the short-run horizon

is several months to a few years. During this time frame, a manufacturer can alter both price

and production quantities, but its offering of models is pre-determined, its model-specific capacity

is largely fixed, and a number of input arrangements are fixed (labor contracts, in particular).

While some of these aspects become more flexible over a year or two (models can be tweaked, some

capacity can be altered), only over a long-run horizon (four years or more), can a manufacturer

introduce fundamentally different models into its product offering.

We use a reduced form approach. In completely generic terms, this means regressing observed

car prices (P) on demand covariates (XD) and supply covariates (XS):

P = α0 + α1XD + α2XS + ν (1)

The estimated ˆα’s we obtain from this specification estimate neither parameters of the demand

curve nor of the supply curve, but instead estimate the effect of each covariate on the equilibrium

P, once demand and supply responses are both taken into account.

Our demand covariates are gasoline prices (the chief variable of interest), customer demographics, and variables describing the timing of the purchase, all described in greater detail below. We

also include region-specific year fixed effects, region-specific month-of-year fixed effects, and detailed

“car type” fixed effects. Supply covariates should presumably reflect costs of production of new

cars (raw materials, labor, energy, etc.). We suspect that these vary little within the region-specific

year and region-specific month-of-year fixed effects that are already included in the specification.

Furthermore, our interactions with executives responsible for short- to medium-run manufacturing

and pricing decisions for automobiles indicate that, in practice, these decisions are not made on

the basis of small changes to manufacturing costs.

We can write the specification we estimate more precisely as:

Pirjt = λ0 + λ1(GasolinePriceit · MPG Quartilej) + λ2Demogit+

λ3PurchaseTimingjt + δj + τrt + μrt + ijt.

(2)

The price variable recorded in our dataset is the pre-sales-tax price that the customer pays for

the vehicle, including factory installed accessories and options, and including any dealer-installed

accessories contracted for at the time of sale that contribute to the resale value of the car.9

9Dealer-installed accessories that contribute to the resale value include items such as upgraded tires or a sound

system, but would exclude options such as undercoating or waxing.

9
We make two adjustments in order to make Pirjt capture the customer’s total wealth outlay

for the car. First, we subtract off the manufacturer-supplied cash rebate to the customer if the car

is purchased under a such a rebate, since the manufacturer pays that amount on the customer’s

behalf. Second, we subtract from the purchase price any profit or add to the purchase price any

loss the customer made on his or her trade-in. Dealers are willing to trade off profits made on the

new vehicle transaction and profits made on the trade-in transaction, including being willing to

lose money on the trade-in.10 If a customer loses money on the trade-in transaction, part of his or

her payment for the new vehicle is an in-kind payment with the trade-in vehicle. By adding such

a loss to the negotiated (contract) price we adjust the price to include the value of this in-kind

payment. In Equation 2, Pirjt is the above-defined price for transaction i in region r on date t for

car j.

We estimate how gasoline prices affect the transaction prices paid for cars of different fuel

economies. One might think that higher gasoline prices, by making car ownership more expensive,

should lead to lower negotiated prices for all cars. Note, however, that cars do not increase

uniformly in fuel cost: a compact car has lower fuel costs than an SUV at every gasoline price,

but as gasoline price rises, its fuel cost advantage relative to the SUV actually rises. If enough

people continue to want to own cars, even when gasoline prices increase, then higher gasoline

prices may lead to increased demand for high fuel economy cars and decreased demand for low fuel

economy cars, and consequently to the transaction price rising for the highest fuel economy cars and

falling for the lowest fuel economy cars. To capture this, we estimate separate coefficients for the

GasolinePrice variable depending on the fuel economy quartile into which car j falls. Specifically,

we classify all transactions in our sample by the fuel economy quartile (based on the EPA Combined

Fuel Economy MPG rating for each model) into which the purchased car type falls.11 Quartiles are

re-defined each year based on the distribution of all models offered (as opposed to the distributions

of vehicles sold) in that year. Table A-1 reports the quartile cutoffs and mean MPG within quartile

for all years of the sample.

We use an extensive set of controls. First, we control for a wide range of demographic variables

(Demogit) using data from the 2000 Census: income, house value and ownership, household size,

vehicles per household, education, occupation, average travel time to work, English proficiency, and

race of buyers.12 We use data at the level of “block groups,” which, on average, contain about 1100

people. We also control for a series of variables that describe purchase timing (PurchaseTimingjt):

EndOfYear is a dummy variable that equals 1 if the car was sold within the last 5 days of the

10See Busse and Silva-Risso (2010) for further discussion of this.

11We obtain similar results if we estimate four separate regressions, thereby relaxing the constraint that the

parameters associated with the other covariates are equal across fuel economy quartiles.

12Demographic variables do not change over time in our data.

10
year; EndOfMonth is a dummy variable that equals 1 if the car was sold within the last 5 days

of the month; WeekEnd is a dummy variable that specifies whether the car was purchased on a

Saturday or Sunday. If there are volume targets or sales on weekends or near the end of the month

or the year, we will absorb their effects with these variables. For new cars, PurchaseTimingjt

includes fixed effects for the difference between the model year of the car and the year in which the

transaction occurs. This distinguishes between whether a car of the 2000 model year, for example,

was sold in calendar 2000 or in calendar 2001. For used cars, PurchaseTimingjt includes a flexible

function of the car’s odometer, described in more detail below, which controls for depreciation over

time.

We include year, τrt, and month-of-year, μrt, fixed effects corresponding to when the purchase

was made. Both year and month-of-year fixed effects are allowed to vary by the geographic region

(34 throughout the U.S.) in which the car was sold.13 The identifying variation we use is therefore

variation within a year and region that differs from the average pattern of seasonal variation within

that region.14 To examine the robustness of our results to which components of variation in the

data are used to identify the effect of gasoline prices, we repeat our estimation with a series of

different fixed effect specifications in Section 7.1. We also control for detailed characteristics of

the vehicle purchased by including “car type” fixed effects (δj ). A “car type” in our sample is the

interaction of make, model, model year, trim level, doors, body type, displacement, cylinders, and

transmission. (For example, one “car type” in our data is a 2003 Honda Accord EX 4-door sedan

with a 4-cylinder 2.4-liter engine and automatic transmission.)

Before describing the results, we note that our estimates should be interpreted as estimates of

the short-run effects of gasoline prices, meaning effects on prices, market shares, or sales over the

time horizon in which manufacturers would be unable to change the configurations of cars they

offer in response to gasoline price changes, a period of several months to a few years. Persistently

higher gasoline prices would presumably cause manufacturers to change the kinds of vehicles they

choose to produce, as U.S. manufacturers did in the 1970s at the time of the first oil price shock.15

The nature of our data, its time span, and our empirical approach are all unsuited to estimating

what the long-run effects of gasoline price would be on prices or sales. The short-run estimates

are nevertheless useful, we believe, for two reasons. First, the short run effect is indeed the effect

we want to estimate in order to investigate the question of consumer myopia. More generally,

13See Table A-19 for a list of regions and the DMAs within each region.

14The average price of gasoline in a DMA-month (our unit of observation) is $1.91; the standard deviation is 0.68.

The “within-region” standard deviation is 0.21, a value that is 11% of the mean. The “between-region” standard

deviation is 0.72. (The “within” standard deviation is the standard deviation of XDMA,month − X ̄region,year + X ̄

where X ̄region,year is the average for the region-year and X ̄ is the global mean. The between standard deviation is

the standard deviation of X ̄region,year.)

15As gasoline prices began to fall in the early 1980s, CAFE standards also affected manufacturer offerings.

11
short-run effects are important for auto manufacturers in the short-to-medium term (especially if

financial solvency is an issue) and because they yield some insight into the size of the pressures to

which manufacturers are responding as they move towards the long run.

4.2 New car price results

We first estimate Equation 2 using data on new car transactions. The full results from estimating

this specification are presented in Table A-2. The variable of primary interest is GasolinePrice in

month t in the DMA in which customer i resides.16 This variable is interacted with an indicator

variable which equals 1 if the observation is for cars in MPG quartile k. The coefficients of interest

are the four coefficients in the vector λ1 which represent the effect of gasoline prices on the prices

of cars in each of the four MPG quartiles; these coefficients and their standard errors are reported

in Table 1.17

Table 1: Gasoline price coefficients from new car price specification

Variable Coefficient SE

GasolinePrice*MPG Quart 1 (lowest fuel economy) -250** (72)

GasolinePrice*MPG Quart 2 -96** (37)

GasolinePrice*MPG Quart 3 -11 (26)

GasolinePrice*MPG Quart 4 (highest fuel economy) 104** (47)

These estimates indicate that a $1 increase in the price of gasoline is associated with a lower

negotiated price of cars in the lowest fuel economy quartile (by $250) but a higher price of cars

in the highest fuel economy quartile (by $104), a relative price difference of $354. Overall, the

change in negotiated prices appears to be monotonically related to fuel economy. Note that this

is an equilibrium price effect; it is the net effect of the manufacturer price response, any change in

consumers’ willingness-to-pay, and the change in the dealers’ reservation price for the car.18

4.3 Used car price results

In this section, we estimate the effect of gasoline prices on the transaction prices of used cars by

estimating Equation 2 (with some modifications) using the data on used car transactions. We

observe all the same car characteristics for used cars that we do for new cars, enabling us to use

all the covariates to estimate the used car price results that we used to estimate the results for

16Another approach would be to use a variable that represents gasoline price expectations, perhaps based on futures

prices for crude oil. In section 7.2 we explore such an approach.

17Two asterisks (**) signifies significance at the .01 level, * signifies significance at the .05 level and + at the .10

level.

18In previous versions, we estimated Equation 2 separately for different vehicle segments (such as compact, midsize,

SUV, etc.). These results are available from the authors.

12
new cars, including identical “car type” fixed effects. However, there is one important difference

between used cars and new cars. A new car of a given model-year can sell only during that modelyear; a used car of a given model-year can sell in many different years. Over that time period,

tastes may change, and individual vehicles will depreciate. To capture the effect of depreciation on

used car transaction prices, we include a spline in odometer (Odom) when we estimate Equation 2

using the data on used car transactions.19 The spline has knots at 10,000-mile increments, allowing

a different per mile rate of depreciation for each 10,000-mile range of mileage.20 We interact the

spline with segment indicator variables to allow different types of cars to have different depreciation

paths, and with indicators for five regions of the country defined by Petroleum Administration for

Defense Districts (PADDs) to allow these paths to vary regionally.21 In addition, in order to allow

for changes in tastes for different vehicles segments over time, we replace the year fixed effects in

Equation 2 with segment-specific year fixed effects.22 In the new car specification (Equation 2)

we allowed the year fixed effects to differ by region. We also allow the segment-specific year fixed

effects to vary by geography, however, to reduce the number of fixed effects we have to estimate,

we now interact the segment-specific year fixed effects with PADD instead of region.23 This three

way interaction controls for business cycle fluctuations that affect the entire car market, for yearto-year changes in tastes for different segments of cars (such as the increasing popularity of SUVs),

and allows both of these effects to vary across the five PADD regions of the country. Taking into

account these modifications, the specification we estimate for used cars is:

Pirjt = λ0 + λ1(GasolinePriceit · MPG Quartilej) + f10,000(Odomi, λ2rj) · Segmentj· PADDr

+ λ3Demogit + λ4PurchaseTimingjt + δj + τrjt + μrt + ijt,

(3)

where τrjt is the year-segment-PADD fixed effect.

There is one difference between new and used cars in the definition of the dependent variable.

A customer who is buying a used car can use a trade-in in the transaction, just as a buyer of a new

car can, so the price definition subtracts any profits (or adds any losses) the customer makes on

the trade-in, just as it does for new cars. However, used cars never have customer rebates offered,

19In using odometer, our approach resembles Sallee, West, and Fan (2009). We differ from Allcott and Wozny

(2011), who use car age to measure depreciation. We use odometer for two reasons. First we find that adding car

age does very little (in an R2sense) to explain depreciation once odometer is accounted for. Second, since odometer

varies across individual vehicles, and does not move in lockstep with calendar time, odometer is less collinear with

gasoline price than car age is. Using odometer thus increases our ability to identify a gasoline price effect in the data,

if there is one.

20We drop the 0.97% of the sample with odometer readings of 150,000 miles or greater.

21There are seven segments: Compact, Midsize, Luxury, Sporty, SUV, Pickup, and Van. The five PADDs are East

Coast, Midwest, Gulf Coast, Rockies, and West Coast.

22In the new car specification, changes in tastes are captured by the car type fixed effects since any particular car

type sells as a new car only for one model-year.

23In unreported results we find that using year-times-segment-times-region fixed effects yields very similar results.

13
so there is no need to subtract that amount from the reported transaction price.

As we did for new cars, we estimate the effect of gasoline prices on used car prices separately

by the MPG quartile of the used car being purchased. The full results are reported in Table A-3.

The gasoline price coefficients are reported in Table 2.

Table 2: Gasoline price coefficients from used car price specification

Variable Coefficient SE

GasolinePrice*MPG Quart 1 (lowest fuel economy) -1182** (42)

GasolinePrice*MPG Quart 2 -101 (62)

GasolinePrice*MPG Quart 3 468** (36)

GasolinePrice*MPG Quart 4 (highest fuel economy) 763** (44)

These estimates show a much larger effect on the equilibrium prices of used cars than was

estimated for new cars. The estimates indicate that a $1 increase in gasoline price is associated

with a lower negotiated price of cars in the lowest fuel economy quartile (by $1,182) but a higher

price of cars in the highest fuel economy quartile (by $763), a relative price difference of $1,945,

compared to a difference of $354 for new cars. (We will consider the reasons for these differences

in Section 6.)

4.4 Specification and variables for car quantity results

In this section we estimate the reduced form effect of gasoline prices on the equilibrium market

shares and sales of new cars of different fuel economies. We can write an analog of Equation 1 that

gives a reduced form expression for new car quantity, or some function of quantity, as a function

of demand and supply covariates:

Q = β0 + β1XD + β2XS + η (4)

As with Equation 1, the estimated βˆs will measure neither parameters of the demand curve, nor

parameters of the supply curve, but instead the estimated short-run effects of the covariates on

equilibrium quantities.

We will estimate two variants of Equation 4. In the first variant, we will use the market shares of

vehicles of different types as an outcome variable, rather than unit sales. There are two advantages

to this approach. First, using market share controls for the substantial fluctuation in aggregate car

sales over the year. Second, this approach enables us to control for transaction- and buyer-specific

effects on car purchases. The disadvantage is that if changes in gasoline prices affect total unit

sales of new cars too much, changes in market share may not correspond to changes in unit sales.

In light of this, we will later estimate a second variant of Equation 4 using two different measures

of unit sales.

14
In our market share regression we estimate the effect of gasoline prices on market shares of cars

of different fuel economies using a set of linear probability models that can be written as:

Iirt(j ∈ K) = γ0 + γ1GasolinePriceit + γ2Demogit + γ3PurchaseTimingjt + τrt + μrt + ijt. (5)

Iirt(j ∈ K) is an indicator that equals 1 if transaction i in region r on date t for car type j was

for a car in class K.

24 We use quartiles of fuel economy to define the classes into which a car type

falls.25 As described in Section 4.1, quartiles are based on the distribution of fuel economies of car

models for sale in a given year (i.e., the model-weighted, not sales-weighted, distribution).

The variable of primary interest is GasolinePrice, which is specific to the month in which the

vehicle was purchased and to the DMA of the buyer. We use the same demographic and purchase

timing covariates and the same region-specific year and region-specific month-of-year fixed effects

that we used to estimate the effect of gasoline prices on new car prices in Equation 2, although in

estimating Equation 5 we cannot use the “car type” fixed effects that we used to estimate Equation 2

because “car type” would perfectly predict the fuel economy quartile of the transaction. We will

estimate Equation 5 four times, once for each fuel economy quartile.

In order to estimate the effect of gasoline prices on unit sales, we use two different measures of

unit sales. The first measure we use aggregates our individual transaction data into unit sales by

dealer, for each month, by MPG quartile.26 Using this measure, we estimate:

Qdkrt = γ0 + γ1(GasolinePricedt · MPG Quartilek) + γ2MPG Quartilek + δd + τrt + μrt + dkrt. (6)

Qdkrt is the unit sales at dealer d located in region r for vehicles in MPG quartile k that occur in

month t. The variable of primary interest is the GasolinePrice in month t in the DMA in which

dealer d is located. The coefficients of primary interest are γ1. These coefficients estimate the

average effect of gasoline prices on new car sales within a fuel economy quartile. We include fixed

effects for each of the MPG quartiles and for individual dealers (δd). Finally, as in Equation 5, we

include year, τrt, and month-of-year, μrt, fixed effects that are are allowed to vary by the geographic

region of the dealer.

While this measure enables us to look at effects on unit sales (instead of market share) while

still controlling for many local characteristics (via dealer fixed effects), the estimated coefficients

will represent the effects on sales at an average dealer. In our final specification, we measure sales at

24Our results do not depend on the linear probability specification; we obtain nearly identical results with a

multinomial logit model (see section 7.6).

25In previous versions we have used classes based on segments (e.g., compact, SUV, midsize). These results are

available from the authors.

26We aggregate from our full data set, not the 10% random sample that we use elsewhere in the paper.

15
the national level using information from Ward’s Auto Infobank.27 Using these data, we estimate:

Qkt = γ0 + γ1(GasolinePricet· MPG Quartilek) + γ2MPG Quartilek + τt + μt + kt. (7)

Qkt is the national unit sales for vehicles in MPG quartile k that occur in month t.

28 The variable

of primary interest is again GasolinePrice, which is now measured as the national average in month

t. The coefficients of interest are the four coefficients in the vector γ1 which represent the effects

of gasoline prices on the sales of cars in each of the four MPG quartiles. We include fixed effects

for each of the MPG quartiles, and for year, τt, and month-of-year, μt.

29

4.5 New car market share results

We first consider the effect of gasoline prices on the market shares of new cars in different quartiles

of fuel economy. Quartiles are re-defined each year based on the distribution of all models offered

(as opposed to the distributions of vehicles sold) in that year.

In order to estimate Equation 5, we define four different dependent variables. The dependent

variable in the first estimation is 1 if the purchased car is in fuel economy quartile 1, and 0

otherwise. The dependent variable in the second estimation is 1 if the purchased car is in fuel

economy quartile 2, and 0 otherwise, and so on. To account for correlation in the errors due to

either supply or demand factors, we cluster the standard errors at the DMA level.

The full estimation results are reported in Table A-4. The estimated gasoline price coefficients

(γ1) for each specification are presented in Table 3. We also report the standard errors of the

estimates, and the average market share of each MPG quartile in the sample period. (Since the

quartiles are based on the distribution of available models, market shares need not be 25% for each

quartile.) Combining information in the first and third column, we report in the last column the

percentage change in market share that the estimated coefficient implies would result from a $1

increase in gasoline prices.

These results suggest that a $1 increase in gasoline price decreases the market share of cars in

the lowest fuel economy quartile by 5.7 percentage points, or 27.1%. Conversely, we find that a $1

27Our transaction data are from a representative sample of dealers, according to our data source. So one approach

might be simply to use our data and multiply by the inverse of the sample percentage to get a national figure.

Unfortunately, the sample percentage changes slightly over time, and we don’t know the year-to-year scaling factor.

28Ward’s reports sales data for some cars by a more aggregate model designation than the EPA uses to report

MPGs. We use the sales fractions in our transaction data to allocate models to which this issue applies in the Ward’s

data into MPG quartiles.

29In results available from the authors, we use a third unit sales measure. That third measure uses the information

in our transaction data about the regional distribution of sales within an MPG quartile to divide the Ward’s national

sales into regional sales. Specifically, for each month in the sample, we calculate from the transaction data the

fraction of sales in each MPG quartile that occurred in each region. We then designate that fraction of the Ward’s

sales in the corresponding MPG quartile to have occurred in the corresponding region.

16
Table 3: Gasoline price coefficients from new car market share specification

Fuel Economy Coefficient SE Mean market share % Change in share

MPG Quartile 1 (lowest fuel economy) -0.057** (0.0048) 21.06% -27.1%

MPG Quartile 2 -0.014** (0.004) 20.95% -6.7%

MPG Quartile 3 0.0002 (0.0027) 24.28% 0.1%

MPG Quartile 4 (highest fuel economy) 0.071** (0.0058) 33.72% 21.1%

increase in gasoline price increases the market share of cars in the highest fuel economy quartile by

7.1 percentage points, or 21.1%. This provides evidence that higher gasoline prices are associated

with the purchase of cars with higher fuel economy.30 Notice that these estimates do not simply

reflect an overall trend of increasing gasoline prices and increasing fuel economy; since we control

for region-specific year fixed effects, all estimates rely on within-year, within-region variation in

gasoline prices and car purchases.31

4.6 New car sales results

While the market share results allow us to investigate the effect of gasoline prices on automobile

purchase choices while controlling for transaction- and buyer-specific characteristics, they do not

allow us to draw inferences directly about changes in unit sales. Changes in gasoline prices may be

correlated, for macroeconomic reasons, with changes in the total number of vehicles sold. A higher

market share of a smaller market could correspond to a unit decrease in sales, just as a smaller

market share of a bigger market could correspond to a unit increase in sales. In this subsection,

we report the results of our two unit sales specifications, Equation 6 and Equation 7.

The coefficient estimates for these two specifications are reported in Tables 4 and 5. The tables

report the estimated gasoline price coefficients for each of the four MPG quartiles, the average unit

sales, and the percentage change relative to the average implied by the coefficients for a $1 increase

in the price of gasoline. On average, dealers sell 11.2 cars per month in the lowest fuel economy

quartile of available cars; a $1 increase in gasoline prices is estimated to reduce that number by 3.1

cars, or 27.7%. On average, dealers sell 17.8 cars per month in the highest fuel economy quartile of

cars; a $1 increase in gasoline prices increases that number by 2.1 cars, or 11.8%. Adding up the

predicted effects across quartiles shows that an increase in gasoline prices is predicted to reduce

the total sales of new cars. Consistent with this, the percentage changes in unit sales are more

negative quartile-by-quartile than the percentage changes in market share reported in the previous

subsection.32

30While we do not restrict the γs to sum to zero, the sum of the coefficients (at seven decimal points) equals 0.

31Nor are the results due to seasonal correlations between gasoline prices and the types of cars purchased at

different times of year, since the regressions control for region-specific month-of-year fixed effects.

32This is consistent with Knittel and Sandler (2010) which finds that increases in gas prices reduces the scrappage

rates of used vehicles, in aggregate.

17
Table 4: Gasoline price coefficients from dealer-level unit sales specification

Fuel Economy Coefficient SE Average cars sold % Change in sales

per month in dealer

MPG Quartile 1 (lowest fuel economy) -3.1** (.091) 11.2 -27.7%

MPG Quartile 2 -0.83** (.087) 11.1 -7.5%

MPG Quartile 3 -0.71** (.088) 13.0 -5.5%

MPG Quartile 4 (highest fuel economy) 2.1** (0.11) 17.8 11.8%

According to the estimates using the Ward’s national sales data, reported in the next table,

when gasoline prices increase by $1, there are 79,169 fewer cars per month sold in the lowest fuel

economy quartile of cars. This is a 27.2% decrease relative to the 291,533 monthly average in this

quartile. In the highest fuel economy quartile, a $1 increase in gasoline prices is associated with

an increase in monthly sales of 40,116 cars, a 10.8% increase on the average monthly sales in this

quartile of 372,998.

Table 5: Gasoline price coefficients from national unit sales specification

Fuel Economy Coefficient SE Average cars sold % Change in sales

per month nationally

MPG Quartile 1(lowest fuel economy) -79,169** (9,421) 291,533 -27.2%

MPG Quartile 2 -14,761 (9,994) 262,453 -5.6%

MPG Quartile 3 -30,029** (9,609) 329,466 -9.1%

MPG Quartile 4 (highest fuel economy) 40,116** (11,800) 372,998 10.8%

Overall, the results we obtain using unit sales tell a very consistent story whether they are

measured at the dealer or national level. They are also broadly consistent with the market share

results estimated in the previous subsection, with the primary difference that the unit sales results

reveal a reduction in total car purchases when gasoline prices increase that is masked in the market

share results.

4.7 Used car transaction share results (an aside)

While we can easily estimate Equation 5 using our data on used car transactions, the estimates do

not have the same interpretation as the estimates for new cars. Changes in the market share of new

cars measure how the incremental additions to the U.S. vehicle fleet change when gasoline prices

change. The analogous estimates arising from the used car data would not measure changes in

market share in this sense, but instead changes in “transaction share;” namely how gasoline price

affects the share of used car transactions that are for cars in different quartiles. For completeness,

we present these results briefly.

We estimate Equation 5 using data from used car transactions at the same dealerships at which

we observe new car transactions. The full results of transaction share effects of gasoline prices by

18
MPG quartiles are reported in Table A-5. The gasoline price coefficients are as follows:

Table 6: Gasoline price coefficients from used car transaction share specification

Fuel Economy Coefficient SE Mean share % Change in share

MPG Quartile 1 (lowest fuel economy) 0.00018 (0.0069) 24.19% 0.07%

MPG Quartile 2 -0.007 (0.006) 20.89% -0.04%

MPG Quartile 3 0.017 (0.011) 27.32% 6.2%

MPG Quartile 4 (highest fuel economy) -0.009 (0.0074) 27.61% -0.03%

The results are both smaller in magnitude and weaker in statistical significance than the analogous results for new cars.

Summary of results

Overall, we see a modest effect of gasoline prices on new car transactions prices. The predicted

effect of a $1 gasoline price increase is to increase the price difference between the highest and

lowest fuel economy quartiles of new cars by $354. The estimated effects are much larger for used

cars; in this market, the predicted effect is to increase the price difference between the highest and

lowest fuel economy quartiles by $1,945.

We find both statistically and economically significant effects of gasoline prices on new car

sales, measured either as market shares or by unit sales. This is particularly true for the highest

fuel economy and lowest fuel economy quartiles, where market share shifts by more than 20% in

response to a $1 increase in gasoline prices, and where unit sales decrease by more than 25% for

the lowest fuel economy quartile and rise by more than 10% for the highest fuel economy quartile.

5 Consumer valuation of future fuel costs

In this section, we draw upon the estimates in the previous section to investigate whether consumers

exhibit “myopia” about future fuel costs of different cars when they are considering the up-front

purchase decision. We will begin by describing our empirical approach.

5.1 Empirical approach

The basic starting point for the consumer myopia literature is a simple idea: if the expected future

usage cost of a durable good increases, all else equal, then the price of that durable good should

fall by the same amount. In other words, consumers’ total willingness-to-pay for the good should

be unchanged by a change in one of the cost components, all else equal. This means that if the

usage cost component of the total cost rises, the up-front cost must fall if consumers are to keep

purchasing the good. A direct approach to testing whether consumers “correctly” value future fuel

19
costs would be to estimate a demand relationship in which expected future fuel costs were included

as a covariate, and test whether the relevant coefficient has the value that would be implied by

consumers correctly valuing fuel costs.

In the automotive setting, there are two difficulties to actually estimating this relationship. One

is that, in the cross-section, differences between cars in fuel costs are often related to differences

between those cars in other attributes that are valued by consumers as goods; for example, size,

weight, power, or other, unobservable attributes. This can make the empirical cross-sectional

relationship between price and fuel cost positive. Of course, adequate controls for characteristics,

or detailed car fixed effects, could remedy this.33

A second problem is that if intertemporal variation in gasoline prices is used to identify the

relationship between a car’s price and its future fuel cost, the “all else equal” condition is violated:

a rise in the price of gasoline which increases the cost of operating one car will increase the cost

of operating all gasoline-powered cars. This means that if consumers are sufficiently unwilling to

substitute away from cars as a whole, a rise in the price of gasoline might well increase the price of

cars with relatively high fuel economy even if their operating costs have actually gone up, because

the operating cost would have decreased relative to that of a low fuel economy car.

To see how this latter point affects the estimation of the relationship between future fuel costs

and car prices, consider a market with two vehicles, 1 and 2. Suppose that the price of vehicle i is

given by pi and that the present discounted value of the expected future gasoline cost for operating

vehicle i over its lifetime is given by Gi. For simplicity, suppose that demand is linear, implying

the demand for vehicle 1 can be written as:

q1 = α1 + β11(p1 + G1) + β12(p2 + G2) (8)

Solving this for price implies the following relationship:

p1 = −γG1 +

1

β11

q1 −

α1

β11

−

β12

β11

(p2 + G2) (9)

where γ = −1 is implied by consumers who correctly value future fuel costs. One could test whether

consumers really do behave this way by estimating γ as a free parameter.

There are three difficulties in estimating this relationship in practice. First, a general model

would have to specify the price of vehicle i as a function of the fuel cost of vehicle i and of the fuel

costs of all other vehicles separately. Given the large number of vehicles offered in the U.S. market,

33A recent example of a paper that takes this approach is Espey and Nair (2005), who estimate a hedonic regression

of list prices on a variety of attributes for a cross-sectional sample of 2001 model year cars. They conclude that

consumers use fairly low discount rates when valuing future fuel cost savings.

20
this would be difficult to implement.34 A second difficulty is that there may be endogeneity between

qi and pi, arising from a supply relationship between the two variables. A third difficulty is that

the econometrician must have a good model of how the marginal consumer constructs the present

discounted value of the expected future gasoline cost. The inputs necessary for constructing this

variable are expectations of future gasoline prices, expected future driving behavior (vehicle miles

travelled), the discount rate, and the miles-per-gallon of the car. Of these, only the last is likely

to be observable as data. If the present discounted value of the expected future gasoline cost is

mismeasured, then attenuation bias is likely to exist, biasing the researcher toward concluding that

consumers are myopic.

In this paper, we will take an alternative approach. Our approach is to combine our reducedform estimates of price and quantity effects with estimates of the elasticity of demand for new

cars, and estimates of future gasoline prices, vehicle miles travelled, and discount rates in order to

address the question of whether consumers are myopic with respect to future fuel costs. While our

approach will lack the elegance of addressing the question in a single estimated parameter, it will

be more amenable to examining the effect of a variety of assumptions about vehicle miles travelled,

future gasoline prices, and discount rates. We will present a range of estimates; it will be fairly

straightforward for readers to substitute their own assumptions as well.

5.2 Consumer myopia results

In this subsection we address the question of whether consumers are myopic about future gasoline

prices when they make car purchase decisions. Analyzing this means, in simple terms, comparing

the effects of gasoline price changes on buyers’ willingness-to-pay for cars of different fuel economies

to the changes in the discounted value of future gasoline costs that is implied by the gasoline price

change and the fuel economy of the car. In practice, there are a few wrinkles.

First, to calculate the discounted value of expected future gasoline costs we need to know how

many miles car owners drive in a given year, conditional on the car surviving through that year,

and also annual survival rates. We calculate miles driven, conditional on survival, three ways. We

use NHTSA-assumed values for annual miles driven, separately for cars and light duty trucks, by

vintage. These data are used in a number of modeling efforts for both the NHTSA and DOT (Lu

(2006)). Our other two measures come from within our data: we compute the average annual miles

driven, by vintage, separately for cars and trucks, for vehicles in our used car transaction data and

34An alternative approach, used by Allcott and Wozny (2011), is to specify a nested logit demand system and then

to solve for equilibrium prices. The benefit of this approach is that in the logit model the usage cost of all other

vehicles drops out of the estimating equation once the market share of each car is divided by the share of the outside

good. The cost is that it imposes a specific functional form assumption on the data. If the model is not a good

match for the data, the estimates could lead to erroneous inferences.

21
for all trade-ins we observe being used to purchase either new or used cars in our transaction data.

If the typical new or used car purchased at our dealers is replacing the trade-in, one could argue that

the calculations based on the miles driven of trade-ins most accurately reflect the driving patterns

of those consumers in our data. We also use vehicle survival rates from NHTSA to calculate the

expected miles driven for each year of the vehicle’s life. Because the median used car is four years

old at the time of purchase, we calculate miles driven beginning at the fourth year of life for used

cars.

Second, we model consumers’ expectations of future gasoline prices as following a random walk

for real gasoline prices. This has the convenient implication that the current gasoline price is

the expected future real gasoline price. (Anderson, Kellogg, and Sallee (2011), discussed in more

detail in Section 7.2, show empirical evidence that this is indeed the gasoline price expectation that

consumers have on average.) One alternative is to assume that consumers are more sophisticated

and use information on crude oil futures markets to make projections into the future.35 It turns

out that during our sample period, the random walk is the more conservative assumption. This is

because for the vast majority of time during our sample, the crude market was in backwardation;

that is, the market expected crude prices to fall (See Figure A-1 for a plot of both the spot crude

price and the stream of expected prices in subsequent years for May of each year—the “forward

curve.”) This means that if consumers actually use crude futures prices to form expectations, and

we assume instead that they use a random walk, then for any observed set of changes in willingnessto-pay for cars of different fuel economies, consumers would be more patient than our estimates

would show.36 In other words, our approach biases us to finding myopia.

Third, we need to know what discount rate customers use to discount future gasoline costs.

We reserve this to be our free parameter. In other words, we use estimates and make assumptions

about the various other components of the calculation, and see what they imply for the discount

rate.

Fourth, in order to address the question of myopia, we need to observe the effects of gasoline

prices on consumers’ willingness-to-pay for cars of different fuel economy; what we have estimated

so far is the effect of gasoline prices on equilibrium transaction prices. In order to translate a change

in equilibrium price to a change in willingness-to-pay, we need to consider supply and demand in the

new and used car markets. In the used car market, one might argue that a fixed supply curve is a

reasonable assumption for used car supply. (See the discussion in Section 6 for the argument.) This

means that the equilibrium price effect will be equal to the change in willingness-to-pay. (Figure 3

35See section 7.2 for the results of such an approach.

36A third justification for using current gasoline prices is that consumers may not be sophisticated in forming

expectations, and may base their decisions on the most salient gasoline price they see—the one currently posted at

gas stations nearby.

22
Figure 3: Effects of gasoline price change on hypothetical used and new cars

D1 D2

P

Q

S

P1

P2

Change in 

willingness-to-pay

Observed 

price effect 

D1 D2

P

Q

P1

P2

S

Observed price effect =

Change in willingness-to-pay

Used Car New Car

shows a representation of this for a hypothetical used car model.) However, in the new car market,

one might well think that the supply relationship is more flexible and that auto manufacturers and

car dealers likely have some scope to respond to changes in demand by altering prices, quantities,

or both. (Again, see the discussion in Section 6.) This means that the equilibrium price effect

will be less than the change in the willingness-to-pay, and that the difference between the two will

be greater the more inelastic the demand curve is. (Figure 3 shows a representation of this for a

hypothetical new car model.)

Since we estimate the equilibrium effects on prices and quantities, we could recover the implied

effects of gasoline price changes on willingness-to-pay if we had an estimate of the elasticity of

demand, as well as an assumed functional form for demand. While estimating an elasticity of

demand is beyond the scope of this paper, there are a number of existing papers that have done

just this. Goldberg (1995) estimates residual demand elasticities of demand for specific vehicles that

are in the neighborhood of −2 to −4, while Berry, Levinsohn, and Pakes (1995) estimate elasticities

in the −3 to −6 range.37 We note that these estimates should be strong upper bounds (in absolute

value) to the relevant demand elasticity for our purposes. The estimates in Goldberg (1995) and

Berry, Levinsohn, and Pakes (1995) are residual demand elasticity estimates for a specific vehicle,

which are likely to be higher than the demand elasticity for a vehicle in a particular fuel economy

quartile, which is the relevant elasticity for us. Finally, we assume that demand has a constant

elasticity functional form.38

In the table below, we present the results of our investigation into the question of whether consumers are myopic. The entries in the last three columns are the implied discount rates necessary

37Goldberg (1995) reports average elasticites by vehicle segment and origin. The average elasticity across segments

is −3.4, while the median is −3.5. Berry, Levinsohn, and Pakes (1995) report elasticity estimates for 13 specific

vehicles. Assuming these are representative of the sample, the average elasticity is −5, while the median is −4.8.

38The assumption of a constant elasticity demand function has the benefit that, in order to make our calculations,

it requires only percentage changes in equilibrium quantities. The calculations assuming a linear demand model

where the slope and intercept are chosen such that the elasticity equals that of the constant elasticity demand curve

at the average price and quantity are very similar to those reported here.

23
to equate the relative price differences between vehicles of different fuel economies to the relative

differences in discounted expected future fuel costs between those vehicles. The relative price differences we use are the estimates from Table 1 for new cars and from Table 2 for used cars. Because

the expectation of future gasoline prices we have used is an expectation about real, not nominal,

gasoline prices, the implied discount rates we calculate do not contain inflation expectations. Note

that the table presents a range of possible estimates of the implied discount rate that hold for a

particular set of assumptions.39 Changing those assumptions would, of course, change the implied

discount rates obtained.40

The top panel of Table 7 reports the implied discount rates when comparing the estimated price

effects for the lowest fuel economy quartile of cars relative to the highest fuel economy quartile.

The middle panel reports for the lowest fuel economy quartile relative to the quartile with second

highest fuel economy; the bottom panel for the quartile with second lowest fuel economy relative

to the highest fuel economy quartile. The top row of each panel reports the implied discount

rates based on the relative price effects estimated for used cars. The next four rows report the

implied discount rates based on the relative price effects estimated for new cars, adjusted to implied

willingness-to-pay effects using elasticities of demand ranging from −2 to −5. Finally, the three

columns use estimates of vehicle miles travelled from NHTSA, from the used car transactions in

our data, and from the trade-ins in our data, respectively.

Overall, the implied discount rates range from moderate to quite small. In most cases, the

estimates are in the single digits, with some combinations of assumptions actually implying negative

discount rates. The more elastic new vehicle demand is assumed to be, the smaller the implied

change in willingness-to-pay is for a given relative price difference, and the higher is the implied

discount rate necessary to rationalize the willingness-to-pay change with a given change in expected

future gasoline costs. Since the largest elasticity for new vehicles we use in Table 7 was calculated

for individual vehicles rather than for quartiles, whose demand is presumably less elastic, the

estimates that use the lower values for elasticities may be the most relevant. The implied discount

rates arising from used car prices are generally higher than those implied by new car prices, although

the ranges overlap. Most of the estimates of implied discount rates are near or below typical rates

for car loans. In our sample, the 10th to 90th percentile range of APRs for consumers financing

their car purchase through the dealer is [1.9%, 11.6%] for new car buyers and [5.5%, 19.7%] for

39The spreadsheet that makes this calculations—and could be used to show the influence of different assumptions

from those presented here—is available from the authors.

40One plausible effect of gasoline prices that is not included in the assumptions underlying Table 7 is that vehicle

miles travelled fall when gasoline prices increase. If this is the case, then the expected future fuel costs of cars

of different fuel economies will be more similar than what we have assumed for the table, meaning that implied

discount rates will have to be smaller in order to reconcile the estimated change in willingness-to-pay with the change

in expected future fuel costs.

24
Table 7: New and Used Cars: Implied Discount Rates

NHTSA VMT from Used VMT from

VMT, Car Transactions, Tradeins,

Market Assumed Demand NHTSA NHTSA NHTSA

Elasticity Survival Rates Survival Rates Survival Rates

Used NA 11.6% 4.3% 5.9%

Q1 vs. Q4 New -2 -3.5% -6.6% -6.1%

New -3 1.6% -2.6% -1.9%

New -4 6.3% 0.1% 2.0%

New -5 10.7% 4.1% 5.6%

Used NA 7.0% 0.4% 2.0%

Q1 vs. Q3 New -2 -3.5% -6.6% -6.1%

New -3 1.7% -2.6% -1.8%

New -4 6.4% 0.1% 2.0%

New -5 10.8% 4.2% 5.5%

Used NA 20.0% 11.2% 13.0%

Q2 vs. Q4 New -2 1.8% -2.5% -1.7%

New -3 8.9% 2.8% 4.1%

New -4 15.5% 7.7% 9.4%

New -5 22.0% 12.2% 14.4%

used car buyers. These APRs are nominal interest rates. During our sample period, inflation rates

were between 1.1 and 5%. We calculate “real APRs” by subtracting from each APR observation

the inflation rate in the same month. The 10th to 90th percentile range for “real APRs” is [-1.2%,

8.8%] for new car purchases and [2.5%,16.8%] for used cars.41 While some of the implied discount

rates fall outside this range, the evidence in Table 7 suggests that the discount rates people use to

evaluate future fuel costs are generally comparable to interest rates they pay when they buy a car.

We conclude that there is little evidence that consumers dramatically undervalue changes in expected future fuel costs, and that the evidence from new and from used cars yield similar messages.

Our findings on this are similar to Allcott and Wozny (2011) who calculate that their results correspond to a 16% implied discount rate, and to Sallee, West, and Fan (2009) who find somewhat less

undervaluation of future fuel costs than do Allcott and Wozny (2011). It bolsters our confidence in

the results of this entire set of papers that different configurations of identifying assumptions yield

similar results. In our view, this lessens the worry readers should have that the results in any of

these papers arise directly from a particular set of assumptions.

6 Market structure and the effect of policy intervention

Two of the policy interventions that have been advocated by economists in the public discussion

of climate change policy are an increased gasoline tax and a carbon tax. Both would reduce the

incentive for drivers to use gasoline. Drivers could respond to this changed incentive by altering

41The new car APRs are negative when manufacturers subsidize interest rates to fall below market rates.

25
their driving habits, changing their vehicles, or both. In this section we argue that in order to

predict the effect of such a policy intervention on vehicle markets, one has to consider the market

structure of the vehicle market itself.42

We begin by noting that the new and used car markets which we observe are similar in many

ways. All the transactions in our data occur at new car dealerships; at many of these dealerships,

the same salesperson sells both new and used cars. The used cars sold at new car dealerships

are also a positive selection of used cars; the average price of a used car in our sample is $15,637

compared to $25,515 for a new car. The consumers in our sample who buy used cars are also not

dissimilar to those who buy new cars. For example, on average, a used car buyer comes from a

Census block group with a median household income of $50,684 vs. $58,130 for new car buyers.

Where the two markets differ is in the supply of new and used cars. New cars are supplied

by the combined activities of auto manufacturers and dealers, while the supply of used cars arises

ultimately from the cumulated new car purchases of past years. For new cars, auto manufacturers

decide on the prices and quantities of each model they wish to sell. Prices can be adjusted quickly

by using promotions (Busse, Silva-Risso, and Zettelmeyer 2006). Production quantities can be

adjusted by adding or reducing shifts on assembly lines (Bresnahan and Ramey 1994), or for

some modern manufacturing plants by adjusting which kinds of vehicles are produced on a given

line.43 Car dealers can easily adjust the prices they negotiate with individual customers, and can

adjust quantities by changing inventory holdings and orders to manufacturers. Manufacturers are

likely to have market power in the supply of a particular model because of the differentiation of

individual car models, and dealers because of local market power. So both have some discretion,

when faced with a change in demand, to absorb the change in the model’s price, in its quantity,

or in a combination of the two. Profit maximization will determine what the optimal response is

depending on the shape of each model’s demand and marginal cost curves.

The stock of used cars is predetermined by the cumulation of past new car purchases, and

is likely to respond very little to gasoline prices.44 Many cars sold on the used market are fleet

turnovers and lease returns whose entry into the used car market will not be determined primarily

by gasoline prices. If consumers are also driven to replace their existing cars by factors unrelated

to gasoline prices, the supply of a particular used car model at any point in time could be thought

42Similar points are made by Busse and Keohane (2007) about sulfur dioxide regulation and low-sulfur coal markets

and by Mansur (2007) and Fowlie (2010) about nitrogen oxide regulation and electricity markets.

43For example, Honda can build the compact Civic on the same assembly line that builds the Ridgeline pickup

and the Acura MDX SUV (“Adaptability helps Honda weather industry changes,” Automotive News, June 8, 2009).

In 2008, the last year in our sample, the Civic was in highest fuel-economy quartile of cars while the Acura MDX

was in the lowest fuel-economy quartile.

44Davis and Kahn (2010) suggest that some low-MPG vehicles may be more likely to be traded to Mexico when

the U.S. price of gasoline deviates greatly from the prices set by PEMEX, the national petroleum company.

26
of as essentially fixed. If this is the case, then the effect of a change in demand for that model

ought to show up almost entirely in the equilibrium prices of used cars of different types, and could

have very little effect on equilibrium unit sales or transactions shares.45

Used car auctions also help facilitate rapid adjustment of prices. A large fraction of wholesale

transactions for used cars go through independent auctions, unaffiliated with car manufacturers.

Auction sites are ubiquitously available throughout the country, each transacting 1,000-3,000 cars

in a typical week. The auction price can adjust to changes in equilibrium market conditions quite

quickly. Since the auction price represents the outside option for any car on a dealer’s lot, the

existence of auctions will lead prices of used cars sold at car dealerships to adjust fairly quickly to

market-clearing equilibrium prices that reflect changes in gasoline prices.

The results that we have estimated in the paper so far are consistent with these differences

between new and used car supply. We find that equilibrium prices for used cars change in response

to changes in gasoline prices by roughly an order of magnitude more than do prices for new cars. In

the new car market, however, we estimate substantial effects of gasoline prices on the market shares

and sales of new cars. This suggests that the new car supply chain—manufacturers and dealers

combined—is choosing to respond to changes in demand by changing prices relatively little, and

absorbing the effect of the demand change in quantities—inventories or production levels—instead.

In this section, we present two pieces of supplementary evidence. First, we show that changes in

inventory are consistent with the changes in sales we have presented already. Second, we show that

the large effects we estimate for used car transaction prices are reflected also in dealers’ internal

valuations of used cars.

6.1 Inventories

In our data, we observe a variable called “days to turn” which counts the number of days that a

specific vehicle was on a dealer’s lot before it sold. Higher average days to turn for vehicles in a

particular class of cars indicates higher dealer inventories for that class of car.

45One might argue instead that a car owner’s decision to sell a used car should be influenced by gasoline prices;

for example, a commuter with a large SUV might be encouraged by high gasoline prices to sell or trade-in that car

in order to replace it with a higher-MPG car. We would argue that even if this is true, the effect of gasoline price

changes is likely to show up primarily in used car prices. To see this, consider a potential seller and a potential

buyer of a particular low-MPG used car. If the gasoline price increases by some amount, then the per-mile cost of

driving that particular car increases by the same amount for both drivers. If the two drivers have approximately the

same driving habits, then one might expect the effect of the gasoline price increase on the buyer and the seller to be

symmetric: for both the current owner and the potential buyer, the increased cost of usage for the current owner of

that car will exactly equal the increased cost of usage for the potential buyer if she buys the car. Taking this logic

one step further, if most drivers have similar driving habits (or if a large enough number of marginal buyers and

sellers have similar driving habits), then the demand curve for a used car should shift inward by roughly the same

amount that the supply curve for that car shifts outward (at least in the area of the demand and supply curves where

equilibrium occurs).If this were the case, then the prices of a particular used car should adjust to reflect the value of

the fuel expenditure disadvantage (or advantage) that car has, given the new gasoline prices.

27
In order to investigate inventory effects, we estimate the effect of gasoline prices on days to turn

by MPG quartiles. (The specification and results are reported in Table A-6.) We find that gasoline

prices have a larger effect on days to turn for new than for used cars. For new cars, the estimated

coefficients imply that a $1 increase in gasoline price is associated with a 12-day increase in days

to turn for cars in the lowest fuel economy quartile, a 17.7% increase from the sample mean of

67.7 days. The same gasoline price increase reduces by 5.5 days the time that a car in the highest

fuel economy quartile remains on the lot, an 11.5% decrease relative to an average of 48.9 days.

In contrast, for used cars, higher gasoline prices have a much smaller effect on days to turn. The

estimated effect for the lowest fuel economy quartile is a (statistically insignificant) 0.95-day (or

2.0%) increase and for the highest fuel economy quartile is a 1.9-day (or 4.3%) decrease.

6.2 Actual cash value of trade-ins

In our data, we also observe what dealers book as the “actual cash value” of trade-ins they receive.

While a dealer might wish to manipulate the price paid to the customer for his or her trade-in, the

“actual cash value” is the dealer’s internal assessment of the value of the vehicle. In this number,

the dealer is trying to approximate the price for which it could have purchased—or could sell—the

car at auction. We are interested in how the actual cash value of cars of different fuel economies

varies with gasoline prices.

In regressions described and reported in Table A-7, we estimate the effect of gasoline prices on

the actual cash value of trade-ins of different fuel economies and find results similar to the those

obtained for the gasoline price effect on used car prices. The results in Table 2 imply that when

gasoline prices increase by $1, the price of a used car in the highest fuel economy quartile rises by

$1,945 relative to that of a used car in the lowest fuel economy quartile. The results in Table A-7

imply that when gasoline prices rise by $1, the actual cash value of a trade-in car in the highest

quartile of fuel economy rises by $1,556 relative to that of a trade-in car in the lowest fuel economy

for trade-ins used to by new cars, and by $775 for trade-ins used to buy used cars. This result is

consistent with our argument that most of the adjustment to changes in demand in the used car

market occur in prices, and that prices are aided in their rapid adjustment by a well-functioning

wholesale market.46

46Trade-in cars are not entirely comparable to the used cars in our sample. Used cars sold in our sample sell for a

median price of $14,461, compared to $6,794 for new car trade-ins and $3,000 for used car trade-ins. The compression

in the range of actual cash values compared to used car prices may explain why the gasoline price effects on actual

cash values are smaller than the gasoline price effects on used car prices.

28
Summary

One of the chief arguments for an increased gasoline tax or a carbon tax in car markets is that it

would cause buyers to change the type of vehicle they buy, and thereby future gasoline consumption.

The results we have estimated for the effect of gasoline prices in new car markets suggests that this

would indeed be the effect of such interventions. This section has pointed out, however, that it is

important to be mindful of the market structure of the target market when such an intervention

is contemplated. If the new car market were structured like the used car market (very inflexible

supply and mechanisms to facilitate rapid adjustment to competitive market prices), then the effect

on vehicle market shares or volumes likely would be much smaller.

7 Robustness

In this section we explore the robustness of our results. First, we analyze whether our results

are robust to changing the component of variation in the data that is used to identify the effect

of gasoline prices. Second, we investigate the effect of using an estimate of future gasoline prices

(based on oil price futures) instead of current gasoline prices as our explanatory variable of interest.

Third, we allow the response to gasoline price to differ by the level and prior direction of gasoline

prices to see whether we are ignoring important response heterogeneity in our estimates. Fourth,

we analyze the robustness of our findings to the aggregation of gasoline prices. Fifth, we analyze

whether we should treat gasoline prices as being endogenous. Sixth, we examine whether our results

depend on our use of a linear probability model to estimate market share changes in response to

gasoline prices.

7.1 Source of variation

We now re-estimate the original specifications in this paper with a series of different fixed effect

combinations. So far, all specifications have controlled for region-specific year and region-specific

month-of-year fixed effects. This means that the estimated gasoline price effects have been identified

by within-year, region-specific variation in price, market share, or sales which deviates from regionspecific seasonal effects.

In order to investigate the robustness of theses estimates, we estimate eight additional specifications with different combinations of fixed effects. Five specifications are more parsimonious

than our base specifications reported in Tables 1, 2, and 3, meaning that less of the variation in

the left-hand-side variable is absorbed by fixed effects, and three specifications are richer, meaning

that more of the variation is absorbed into fixed effects. Table 8 reports the results for new car

prices, Table 9 for used car prices, and Table 10 for new car market shares. The most parsimo29
Table 8: Effect of time and seasonal fixed effects in new car price specification†

MPG MPG MPG MPG Price change

Region FE Time FE Seasonal FE Quartile 1 Quartile 2 Quartile 3 Quartile 4 Quar 1 to 4

1 Region – – -520** -373** -286** -165** $355

(82) (45) (31) (42)

2 Region – Month-of-year -251** -98* -19 87+ $338

(73) (40) (33) (49)

3 Region Year Month-of-year -245** -102** -17 82+ $327

(73) (37) (31) (49)

4 – – Month-of-year × -255** -104* -26 84+ $339

Region (73) (40) (34) (50)

5 – Year Month-of-year × -250** -108** -25 78 $328

Region (73) (38) (32) (50)

6 – Year × Region Month-of-year × -250** -96** -11 104* $354

(Base) Region (72) (37) (26) (47)

7 – Year × Region Month-of-year × -205** -62+ 18 127* $332

Year × Trend Region (68) (35) (29) (51)

8 – Quarter × Month-of-year × -161* -16 66+ 171** $332

Region Region (68) (41) (35) (58)

9 – Month × Region -313* -177 -90 14 $327

(157) (152) (155) (173)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the DMA level) in parentheses.

nious specification includes only region fixed effects (no year or month-of-year fixed effects) and

the richest specification uses month (not month-of-year) times region or PADD fixed effects. For

ease of comparison, in all the tables, row 6 reports the results of our base specification.

In Tables 8 and Table 9, the most direct way to compare the price estimates across rows is

to compare the implied change in the price of a car in quartile 1 relative to a car in quartile 4.

This number is reported in the last column of both tables. For new cars (Table 8), the implied

change in relative prices ranges from $327 to $355. This includes the results in row 9, which use

monthly fixed effects. For used cars (Table 9), it ranges from $1,917 to $2,563, although the first

seven rows vary only from $1,917 to $2,196. The fact that our estimates fall within a $28 range for

new cars and a $279 range for most of the used car results seem to us to be quite stable results,

especially considering the differences in the components of variation used to identify the effects in

different rows. (Increasing the frequency of the time fixed effect, in rows 8 and 9, has bigger effect

on the used car results than on the new car results. Using these estimates would reduce the implied

discount rates relative to what is reported in Table 7.)

Table 10 repeats the exercise for the market share specification. We find that the estimated

effect of a $1 increase in gasoline price on the market share of new cars in the lowest fuel economy

quartile ranges from -4.8 percentage points to -7 percentage points. The effect on the market share

of the highest fuel economy quartile ranges from 5.8 to 8.7 percentage points. The effect in quartile

2 ranges from 2.9 percentage points to statistically zero, and the effects for quartile 3 are almost

all statistically zero. The results seem to us to be again quite stable. The one exception to this

30
Table 9: Effect of time and seasonal fixed effects in used car price specification†

Region MPG MPG MPG MPG Price change

FE Time FE Seasonal FE Quartile 1 Quartile 2 Quartile 3 Quartile 4 Quar 1 to 4

1 Region – – -3454** -2923** -2105** -1258** $2196

(70) (98) (68) (43)

2 Region – Month-of-year -3773** -3240** -2428** -1589** $2184

(80) (108) (77) (55)

3 Region Year × Segment Month-of-year -1197** -118+ 436** 720** $1917

(42) (63) (39) (49)

4 – – Month-of-year × -3796** -3263** -2454** -1610** $2186

Region (82) (111) (79) (58)

5 – Year × Segment Month-of-year × -1185** -106+ 450** 734** $1919

Region (41) (59) (36) (45)

6 – Year × Segment × Month-of-year × -1182** -101 468** 763** $1945

(Base) PADD Region (42) (62) (36) (44)

7 – Year × Segment × Month-of-year × -1264** -181** 388** 693** $1957

PADD, Year × Trend Region (41) (59) (33) (41)

8 – Quarter × Segment × Month-of-year × -1552** -355** 377** 781** $2333

PADD Region (59) (75) (47) (55)

9 – Month × Segment × Month-of-year × -1759** -513** 321+ 804** $2563

PADD Region (178) (194) (182) (188)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the DMA level) in parentheses.

is row 9, which estimates a fixed effect for each month of the sample separately for each region of

the country; this approach taxes the data quite heavily, and the estimated effects, while not wildly

different in magnitude from those in the other rows, are no longer statistically significant.

7.2 Future vs. current gasoline prices

In the results we have presented so far, we have estimated the effect of current gasoline prices on

the market outcomes from new and used cars. One might argue that since cars are durable goods,

buyers should make decisions in response to their expectations of future gasoline prices, rather

than current gasoline prices. There are several justifications for using current gasoline prices as

our explanatory variable of interest. First, it may be the case that car buyers are not sophisticated

in thinking about expectations, and that they instead respond to the price that they see posted

prominently at gas stations and hear discussed in the news media. Second, if gasoline prices

are a random walk, then the expected future gasoline price is the current gasoline price. With

respect to this point, Anderson, Kellogg, and Sallee (2011) use a set of questions on the Michigan

Survey of Consumers that ask explicitly about consumers’ gasoline price expectations to show that

consumers’ average forecast is for “no change in real gasoline prices.” This would suggest that

our empirical approach, by using current prices, may indeed estimate the response to consumers’

expectations of real gasoline prices. This also supports our interpretation of the consumer myopia

results as measuring the implied discount rate that rationalizes the current change in car prices

with the change in future fuel costs implied by a change in the future real price of gasoline.

31
Table 10: Effect of time and seasonal fixed effects in market share specification†

MPG MPG MPG MPG

Region FE Time FE Seasonal FE Quartile 1 Quartile 2 Quartile 3 Quartile 4

New Cars

1 Region – – -.05** -.0062 -.0055+ .061**

(.0044) (.0047) (.0031) (.0058)

2 Region – Month-of-year -.049** -.0056 -.005+ .059**

(.0043) (.0046) (.003) (.0056)

3 Region Year Month-of-year -.052** -.016** -.003 .071**

(.0051) (.0058) (.0034) (.0077)

4 – – Month-of-year × -.048** -.0053 -.0041 .058**

Region (.0042) (.0043) (.003) (.0051)

5 – Year Month-of-year × -.053** -.016** -.00038 .07**

Region (.0046) (.0051) (.003) (.006)

6 (Base) – Year × Region Month-of-year × -.057** -.014** .0002 .071**

Region (.0048) (.004) (.0027) (.0058)

7 – Year × Region Month-of-year × -.049** -.025** -.00084 .075**

Year × Trend Region (.005) (.0034) (.0034) (.0043)

8 – Quarter × Month-of-year × -.07** -.029** .012** .087**

Region Region (.0069) (.0056) (.0047) (.0065)

9 – Month × Region -.12+ -.013 .036 .097

(.068) (.04) (.03) (.078)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the DMA level)

in parentheses.

In this section, we take an alternative approach, which is to make use of the active futures

market for crude oil to create a measure of consumers’ expectations of future gasoline prices.

While futures contracts for gasoline are listed on the NYMEX, futures in oil are actively traded

in much larger volumes. Furthermore, gasoline prices are sufficiently closely correlated with oil

prices that we suspect that a gasoline price forecast based on futures prices for oil will be a better

measure of expectations of gasoline prices than using gasoline futures prices directly.

To be precise, we regress monthly, DMA-level gasoline prices on current, world prices for crude

oil, and on DMA times month-of-year fixed effects. This allows us to translate the price of a barrel

of oil into the price for a gallon of gasoline, allowing the “markup” between these two to vary by

region, and by season differentially for each region. (This allows, for example, for refinery margins

that vary geographically and seasonally.) We use the estimated coefficients from this regression to

predict an expected future price for gasoline by using alternately the 6-month-ahead, 12-monthahead, and 24-month-ahead futures price for crude oil in place of the current price of oil. We then

use our prediction of the expected future price of gasoline in place of the current price of gasoline in

our benchmark specifications for new and used car prices and new car market shares. The results

using our prediction of the expected future price of gasoline are reported in Tables A-8 and A-9.

The estimated results for the new car market share are very similar whether we use current

gasoline prices or our prediction of expected future gasoline prices. For the estimate of the effect

of gasoline prices on car prices, using our prediction of the expected future gasoline prices yields

32
relative price effects that are 61% - 87% larger in magnitude for new cars and 17% - 19% larger in

magnitude for used cars than using the current gasoline price. These results imply that consumers

adjust their willingness-to-pay more in response to changes in future fuel costs than is reflected

in the estimates used to produce Table 7. Using the estimates from Tables A-8 and A-9 would

generate lower implied discount rates than what is reported in Table 7, corresponding to less myopia

among car buyers.

7.3 Heterogeneity in gasoline price response

Our base specifications estimated a single coefficient on GasolinePrice for each quartile, disallowing

other types of response heterogeneity to gasoline prices. In this subsection, we investigate two

types of heterogeneity that might exist. First, we investigate whether, for example, a $1 increase in

gasoline prices has a different effect on car prices and market shares when gasoline currently costs

$1.50 per gallon from when it costs $3.50 per gallon. Second, we investigate whether the response

to gasoline price differs by whether gasoline prices have been consistently rising or falling in prior

periods.

To answer the first question we repeat the main results in Section 4 using GasolinePrice interacted with indicators for whether the gasoline price lies in the range <$1.50, $1.50-$2.50, $2.50-

$3.50, or >$3.50. The purpose is to see whether there is an inflection point of gasoline prices at

which the effects suddenly kick in, or at which they grow much larger. News reports have posited

that there is a gasoline price “threshold” above which consumers change their behavior more dramatically.47 Our market share results are fairly similar across different ranges of gasoline prices,

except that when gasoline prices are above $3.50, new car buyers shift into quartile 4 cars at a

slightly higher rate, and into quartile 3 cars at a much lower rate, than when gasoline prices are

lower. The effect of gasoline prices on the relative prices of cars of different fuel economies, both

new and used, is also pretty similar across the range of gasoline prices; the effect is bigger for the

relative prices of new cars when gasoline is above $3.50 per gallon. See Tables A-10 and A-11 for

a summary of these results; full results are available from the authors.

To answer the second question we also repeat the main results of this section interacting GasolinePrice with an indicator variable that records whether gasoline prices went up monotonically in

the previous three months, went down monotonically in the previous three months, or had a nonmonotonic pattern. These results show some heterogeneity in the gasoline price response, although

not enough to change the conclusions of the paper. The greatest heterogeneity is in the effect of

47For example, an article in Automotive News on 5/22/08 entitled “Ford: $3.50 gasoline was tipping point for

sales shift” states: “The segment shifts [away from SUVs and Pickups] ‘really started to move’ when gasoline prices

hit $3.50 a gallon, [Ford CEO Alan] Mulally said. ‘It seemed to us that we reached a tipping point where customers

began shifting away from these vehicles at an accelerated rate,’....”

33
gasoline price changes on used car prices. The results imply that a change in the price of gasoline

has a larger effect on the relative prices of high vs. low fuel economy cars when gasoline prices

have been trending down than when they have been trending up or have been neither trending up

nor down. See Tables A-12 and A-13 for a summary of these results; full results are available from

the authors.

7.4 Gasoline price aggregation

Next, we investigate the robustness of our findings to the aggregation of gasoline prices to local

markets (DMAs) rather than to ZIP-codes, which would be possible in our data. The advantage

of using the higher level of aggregation is that we reduce the possibility of measurement error that

could arise from our observing only a small number of stations per ZIP-code. The higher level of

aggregation also allows for consumers to react not only to the gasoline prices in their local ZIP-code

but also to gasoline prices in a broader area. At the same time, however, we eliminate some of the

cross-sectional variation that less aggregate data would allow us to use.

One could also make the argument that we should use a more aggregate measure of gasoline

prices than DMA-level prices because consumers may notice gasoline price changes only once they

have affected a large enough area to be reported in the media, or because local price variation

contains transitory price shocks that do not enter into long-run forecasts of gasoline prices.

To investigate whether our conclusions depend of the level of aggregation of gasoline prices, we

re-estimate our original car price and market share specifications (Equations 2, 3, and 5) using a

less aggregated and a more aggregated measure of gasoline prices. We use 4-digit ZIP-code level

gasoline price as our less aggregated measure.48 For our more aggregated measure, we average the

prices for basic grade over all stations in each “Petroleum Administration for Defense District”

(PADD). PADDs are the standard geographical classification used by the Energy Information

Administration, defined such that they delineate a region in which gasoline supply is homogenous.

There are five PADDs: East Coast, Midwest, Gulf Coast, Rockies, and West Coast. There remains

substantial variation in gasoline prices between PADDs and across PADDs over time.

The full results are reported in Tables A-14 and A-15. We find that the coefficients on gasoline

prices in the 4-digit ZIP code aggregation are similar to those in our (original) DMA aggregation

but somewhat smaller in magnitude. This is consistent with some measurement error occurring in

the 4-digit ZIP code aggregation. If we aggregate gasoline prices at the PADD level, most coefficient

estimates in the market share regression are unchanged. In the price regression, the PADD-level

48We use this instead of 5-digit ZIP-code level price because too many 5-digit ZIP-codes have too few gas stations

to calculate a reliable average. In our data, the median 4-digit ZIP code reports data from 11.5 stations on average

over the months of the year, up from 3 for 5-digit ZIP codes.

34
estimates are distinctly larger in magnitude than the estimates using DMA-level prices.

Overall, we would reach many of the same conclusions about the effects of gasoline price changes

if we aggregated gasoline prices within 4-digit ZIP code or within PADD instead of within DMAs.

7.5 Endogeneity

So far we have assumed that gasoline prices are uncorrelated with the error term in the market

share and price specifications. In this subsection, we relax that assumption.

It seems unlikely that such a correlation would arise due to reverse causality; U.S. gasoline prices

are determined by world oil prices and refinery margins, and these are unlikely to be influenced

by car transactions in the U.S. However, there are other potential sources of endogeneity which

could taint our coefficient estimates. First, there could be local variations in economic conditions

that are correlated with local variations in gasoline prices. If the changes in economic conditions

change what cars people buy or how much they are willing to spend on them, then our gasoline

price coefficients will capture (in part) cyclical effects on car sales and prices. Second, gasoline tax

changes might be endogenous to economic conditions which also affect car sales and prices. Third,

changes in gasoline prices could cause income shocks in local areas (say, areas with refineries or

with car plants) and these income shocks may drive car sales and prices.

One way to address the potential endogeneity of gasoline prices would be to use a more aggregate

measure of gasoline price; this would make it less likely that local shocks lead to correlation between

gasoline prices and the error term in the market share and price specifications. The specification

using PADD-level gasoline prices (described in the previous section and reported in Tables A-14

and A-15) does exactly this.

A second approach we take is to use world oil price as an instrument for gasoline prices at the

PADD level. Clearly, world oil prices are correlated with regional fuel prices. At the same time, it

seems highly unlikely that local or regional variation in economic conditions, gasoline tax changes,

or income shocks would have a meaningful effect on world oil prices. To allow for some variation

by PADD in the correlation with world oil prices, we use as instruments world oil prices interacted

with PADD dummies. The results of these two approaches are reported in Tables A-16 and A-17.

We have already concluded that the OLS regression with PADD-level gasoline prices estimates

similar market share effects but somewhat larger price effects compared to the original OLS regression with DMA-level gasoline prices. The PADD-level IV estimates of the effect of gasoline

prices on market share are about 10% larger than the PADD-level OLS estimates for the lowest

fuel economy quartile, and about 15% smaller for the highest fuel economy quartile. We find that

the estimates of the effect of gasoline prices on car prices are generally larger in the PADD-level

IV specification than in the PADD-level OLS specification. This can be seen in Table A-17.

35
In summary, controlling for endogeneity suggests that our original specification may have underestimated the magnitude of the gasoline price effect on car prices. Using the PADD-level IV

estimates in our myopia calculations would lead to smaller implied discount rates—implying consumers who value the future more—than what is reported in Table 7.

7.6 Alternative market share specification

As our last robustness check we address potential limitations of the linear probability model we

have used to estimate the effect of gasoline prices on markets shares. One might be concerned that

the linear probability model does not constrain the estimates in the market share regressions to

add up to 1.49 To address this we reestimate our basic market share specification (Equation 5)

with a multinomial logit (“mlogit” in Stata) which estimates the probability that, conditional on

purchase, a car falls into MPG Quartile 1, 2, 3, or 4. (All variables and controls are the same as

those specified in Equation 5.) Full estimation results are reported in Table A-18.

The marginal effects in probability associated with a $1 increase in gasoline prices as estimated

by the multinomial logit are slightly larger than in the linear probability model (-0.064** vs. -

0.057** for MPG quartile 1, -0.014 vs. -0.014 for MPG quartile 2, -0.004 vs. 0.0002 for MPG

quartile 3, and 0.075** vs. 0.071** for MPG quartile 4, where ** indicates estimates that are

statistically significiant at the 1% level). We conclude that our market share results do not depend

on our use of the linear probability model.

8 Concluding remarks

In this paper we have estimated the effect of gasoline prices on the short-run equilibrium prices,

market shares, and sales of new and used cars of different fuel economies. We have used these

estimates to address two questions that are important for understanding the ability of a policy

intervention such as a gasoline tax or a carbon tax to influence what cars people buy, which is one

avenue through which such an intervention can affect greenhouse gas emissions.50

We estimated that a $1 increase in the price of gasoline increases the market share of cars in

the highest fuel economy quartile by 21.1% and decreases the market share of cars in the lowest

fuel economy quartile by 27.1%. We also estimated the effect of a $1 increase in gasoline prices

on unit sales of new cars and found that sales in the highest fuel economy quartile increased by

10–12%, while sales in the lowest fuel economy quartile fell by 27–28%. We estimated the effect

49In fact, the market shares predicted by the linear probability results in Section 4.5 come very close to summing

to 1, despite no constraint to do so.

50Other potential avenues including changing vehicle miles travelled, car designs, fuel technologies, or urban design.

36
of gasoline prices on the equilibrium prices of new cars and found that a $1 increase in the price

of gasoline is associated with an increase of $354 in the average price of the highest fuel economy

quartile of cars relative to that of the lowest fuel economy quartile. For used cars, the estimated

relative price difference is $1,945.

The first question we addressed is whether the changes in equilibrium prices for new and used

cars associated with changes in gasoline prices show evidence that consumers undervalue future

gasoline costs of cars with different fuel economies relative to the prices of those cars. This could be

thought of as a necessary “demand side” condition for effective policy: the more car buyers discount

future fuel costs, the less effective a gasoline tax or carbon tax will be in influencing vehicle choice.

Using several different assumptions about vehicle miles travelled, a range of assumptions about the

elasticity of demand, and comparing the relative price differences between different quartiles, we

find little evidence of consumer myopia. Many of our implied discount rates are near zero, most

are less than 20%.51

The second question we addressed is whether gasoline prices have similar effects in new and

used markets. This could be thought of as a “supply side” necessary condition for effective policy:

actions that car manufacturers take that counteract the effect of gasoline prices will reduce the

effectiveness of a gasoline tax or carbon tax in influencing vehicle choice. We find very different

equilibrium effects in new and used markets; a gasoline price increase has a much larger effect on

prices in the used car market than in the new car market, but has a substantial effect on sales in

the new car market. We argued that these differences can be explained by differences in supply

between new and used markets. A general implication of this point is that when designing policy,

policymakers need to take into account the market structure of the markets whose outcome they

wish to influence.

Forecasting the effect of policy interventions such as a carbon tax or a gasoline tax increase

on greenhouse gas emissions from non-commercial vehicles is challenging because there are many

possible margins of adjustment. We believe that our investigation of the effect of gasoline price on

market outcomes in new and used car markets is useful for understanding some of these margins.

While our paper is not the only one to address these issues, we believe our paper’s particular

advantages are that it uses transaction data; that the data on prices and quantities and on new and

used markets are from the same source; and that the flexible specifications used allow us to estimate

parameters whose interpretation is not dependent on a particular model of the data generating

process and which can be combined with a range of assumptions about related parameters in order

to answer policy-relevant questions.

51The alternative specifications we investigated in Section 7 generally led to larger relative price effect estimates,

which would reduce the estimated implied discount rates compared to what is reported in Table 7.

37
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39
Table 11: Summary Statistics

New Cars Used Cars

Variable N Mean Median SD Min Max N Mean Median SD Min MaxGasolinePrice 1863403 2 1.8 .67 .82 4.6 1096874 2 1.8 .69 .82 4.6MPG 1863403 22 22 5.7 10 65 1096874 22 22 4.7 9.8 65Price 1863403 25515 23295 10874 2576 195935 1096874 15637 14495 8281 1 173000DaysToTurn 1798625 57 27 76 1 730 1058521 48 26 66 1 730ModelYear 1863403 2004 2004 2.5 1997 2008 1096874 2001 2001 3.5 1985 2008CarAge 1863403 .79 1 .46 0 3 1096874 3.9 4 2.4 0 24TradeValue 795457 8619 6794 8107 0 198000 435813 5233 3000 5992 0 150000PctWhite 1863403 .72 .82 .26 0 1 1096874 .7 .81 .28 0 1PctBlack 1863403 .082 .024 .16 0 1 1096874 .11 .028 .2 0 1PctAsian 1863403 .05 .02 .087 0 1 1096874 .038 .013 .07 0 1PctHispanic 1863403 .12 .053 .18 0 1 1096874 .13 .05 .19 0 1PctLessHighSchool 1863403 .15 .12 .13 0 1 1096874 .18 .14 .13 0 1PctCollege 1863403 .38 .36 .19 0 1 1096874 .33 .29 .18 0 1PctManagment 1863403 .16 .15 .082 0 1 1096874 .14 .13 .074 0 1PctProfessional 1863403 .22 .22 .097 0 1 1096874 .2 .19 .092 0 1PctHeath 1863403 .016 .012 .018 0 1 1096874 .019 .014 .02 0 1PctProtective 1863403 .02 .016 .021 0 1 1096874 .021 .017 .021 0 1PctFood 1863403 .041 .035 .031 0 1 1096874 .046 .04 .033 0 1PctMaintenance 1863403 .028 .021 .029 0 1 1096874 .032 .025 .031 0 1PctHousework 1863403 .027 .024 .021 0 1 1096874 .028 .025 .022 0 1PctSales 1863403 .12 .12 .046 0 1 1096874 .12 .11 .044 0 1PctAdmin 1863403 .15 .15 .054 0 1 1096874 .16 .16 .054 0 1PctConstruction 1863403 .049 .042 .039 0 1 1096874 .056 .049 .041 0 1PctRepair 1863403 .036 .033 .027 0 1 1096874 .04 .037 .027 0 1PctProduction 1863403 .063 .049 .053 0 1 1096874 .075 .061 .059 0 1PctTransportation 1863403 .05 .044 .037 0 1 1096874 .059 .053 .039 0 1Income 1863403 58110 53188 26274 0 200001 1096874 50684 46556 22031 0 200001MedianHHSize 1863403 2.7 2.7 .52 0 9 1096874 2.7 2.7 .51 0 8.5MedianHouseValue 1863403 178306 144700 131956 0 1000001 1096874 145545 121997 102923 0 1000001VehPerHousehold 1863403 1.8 1.9 .39 0 7 1096874 1.8 1.8 .39 0 7PctOwned 1863403 .72 .8 .23 0 1 1096874 .69 .76 .24 0 1PctVacant 1863403 .063 .042 .078 0 1 1096874 .067 .048 .076 0 1TravelTime 1863403 27 27 6.8 0 200 1096874 27 26 6.9 0 200PctUnemployed 1863403 .047 .037 .043 0 1 1096874 .053 .041 .046 0 1PctBadEnglish 1863403 .044 .016 .078 0 1 1096874 .045 .014 .079 0 1PctPoverty 1863403 .084 .057 .085 0 1 1096874 .1 .072 .095 0 1Weekend 1863403 .25 0 .44 0 1 1096874 .25 0 .44 0 1EndOfMonth 1863403 .25 0 .43 0 1 1096874 .21 0 .41 0 1EndOfYear 1863403 .022 0 .15 0 1 1096874 .017 0 .13 0 1

40
NOT FOR PUBLICATION

Appendix: Additional Tables

Table A-1: New car fuel economy quartile cutoffs

MPG Q1 25th MPG Q2 50th MPG Q3 75th MPG Q4

Modelyear Mean Pctile Mean Pctile Mean Pctile Mean

1997 16.3 18.6 20.1 21.3 23.0 24.7 27.7

1998 16.1 17.9 19.6 21.3 22.6 24.3 27.2

1999 15.8 17.9 19.6 21.6 22.7 24.3 27.2

2000 15.9 17.9 19.4 20.9 22.5 24.0 27.2

2001 15.7 17.2 18.9 20.6 22.1 23.7 27.2

2002 15.6 17.2 18.9 20.6 22.0 23.3 27.1

2003 15.5 17.2 19.0 20.6 22.2 23.5 27.5

2004 16.0 17.6 19.1 20.6 22.2 24.0 27.9

2005 15.9 17.9 19.3 20.9 22.2 24.0 27.8

2006 16.1 17.6 18.9 20.6 22.0 23.8 27.7

2007 16.8 18.3 19.9 21.3 22.8 25.0 28.8

2008 16.6 18.9 20.0 21.6 23.2 25.4 29.5

Appendix-1
Table A-2: New Cars: Price results,

fuel economy quartiles†

Variable Coefficient/SE

FuelPrice*MGP Quart 1 -250**

(72)

FuelPrice*MGP Quart 2 -96**

(37)

FuelPrice*MGP Quart 3 -11

(26)

FuelPrice*MGP Quart 4 104*

(47)

PctLessHighSchool 163+

(83)

PctCollege -51

(58)

Income .0015**

(.00044)

MedianHHSize 43**

(16)

MedianHouseValue .00012

(.000084)

VehiclePerHH 402**

(120)

TravelTime -.82

(1.1)

Weekend -13*

(6)

EndOfMonth -135**

(4.3)

EndOfYear -78**

(17)

Constant 27246**

(79)

Observations 1863403

R-squared 0.053

*

significant at 5%; ** significant at 1%; +

significant at 10% level. SEs (robust and

clustered at the DMA level) in parentheses.

Not reported: Region × year, region ×

month-of-year fixed effects, car type, and

car age fixed effects. We also don’t report

house ownership, occupation, english proficiency, and race of buyers.

Appendix-2
Table A-3: Used Cars: Price results,

fuel economy quartiles†

Variable Coefficient/SE

FuelPrice*MGP Quart 1 -1182**

(42)

FuelPrice*MGP Quart 2 -101

(62)

FuelPrice*MGP Quart 3 468**

(36)

FuelPrice*MGP Quart 4 763**

(44)

PctLessHighSchool 239**

(69)

PctCollege -4.5

(50)

Income .0017**

(.0005)

MedianHHSize 26

(22)

MedianHouseValue .000056

(.00014)

VehiclePerHH 162

(157)

TravelTime .27

(.72)

Weekend 120**

(10)

EndOfMonth -71**

(6.3)

EndOfYear -18

(23)

Constant 24010**

(252)

Observations 1096874

R-squared 0.718

*

significant at 5%; ** significant at 1%; +

significant at 10% level. SEs (robust and

clustered at the DMA level) in parentheses.

Not reported: Region × year, region ×

month-of-year fixed effects, car type, and

odometer spline. We also don’t report

house ownership, occupation, english proficiency, and race of buyers.

Appendix-3
Table A-4: New Cars: Market share results, fuel economy quartiles†

MPG Quartile 1 MPG Quartile 2 MPG Quartile 3 MPG Quartile 4

FuelPrice -.057** -.014** .0002 .071**

(.0048) (.004) (.0027) (.0058)

PctLessHighSchool .044** .031** -.027* -.047*

(.015) (.0089) (.013) (.02)

PctCollege -.061** .0079 .0033 .05**

(.013) (.011) (.013) (.018)

Income -5.9e-08 3.4e-07** 3.0e-07** -5.8e-07**

(8.7e-08) (9.4e-08) (1.1e-07) (1.2e-07)

MedianHHSize .01** .0048+ -.0067 -.0083+

(.0029) (.0025) (.0048) (.0047)

MedianHouseValue 7.2e-08* 3.3e-08+ 1.3e-08 -1.2e-07**

(2.9e-08) (1.7e-08) (8.7e-09) (3.7e-08)

VehiclePerHH .016 -.015 .042 -.043

(.022) (.022) (.026) (.033)

TravelTime -.0002 -.00037** -.00025* .00082**

(.00019) (.000098) (.00013) (.00022)

Weekend -.02** -.0046** -.0022 .027**

(.0019) (.0016) (.0016) (.0022)

EndOfMonth .0058** .0033** .0048** -.014**

(.00099) (.0011) (.001) (.0012)

EndOfYear -.0043 -.0058** -.0013 .011**

(.0027) (.0022) (.0028) (.004)

Constant .33** .23** .27** .18**

(.017) (.013) (.01) (.018)

Observations 1863403 1863403 1863403 1863403

R-squared 0.034 0.011 0.009 0.040

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at

the DMA level) in parentheses.

Not reported: Region × year, region × month-of-year, and car age fixed effects. We also don’t

report house ownership, occupation, english proficiency, and race of buyers.

Appendix-4
Table A-5: Used Cars: Transaction share results, fuel economy quartiles†

MPG Quartile 1 MPG Quartile 2 MPG Quartile 3 MPG Quartile 4

FuelPrice .00018 -.0077 .017 -.009

(.0069) (.0058) (.011) (.0074)

PctLessHighSchool .006 .023+ -.018 -.011

(.01) (.012) (.024) (.02)

PctCollege -.016 -.02 -.022 .058**

(.01) (.014) (.015) (.014)

Income -2.0e-07* 2.6e-07** 2.7e-07* -3.3e-07**

(8.7e-08) (8.2e-08) (1.1e-07) (9.7e-08)

MedianHHSize .0018 -.0042+ .0041 -.0018

(.0024) (.0024) (.0034) (.0027)

MedianHouseValue 2.0e-08 2.0e-08+ -7.6e-08** 3.6e-08**

(1.3e-08) (1.1e-08) (1.6e-08) (1.3e-08)

VehiclePerHH .0037 .03 -.11* .08

(.026) (.031) (.055) (.053)

TravelTime .000029 -.00016 -.00011 .00023

(.00013) (.00013) (.00018) (.00019)

Weekend -.0076** -.0042* .0056* .0062**

(.0014) (.0019) (.0022) (.0019)

EndOfMonth .0038+ -.0051** .0019 -.00061

(.0021) (.0018) (.0021) (.0019)

EndOfYear -.011** .0042 .00083 .0059*

(.0028) (.0036) (.0036) (.0029)

Constant .014 .027 -.031 .99**

(.02) (.018) (.026) (.017)

Observations 1096874 1096874 1096874 1096874

R-squared 0.485 0.224 0.235 0.531

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at

the DMA level) in parentheses.

Not reported: Region × year, region × month-of-year, and car age fixed effects. We also don’t

report house ownership, occupation, english proficiency, and race of buyers.

Appendix-5
Figure A-1: Crude spot and futures prices during our sample

0 20 40 60 80 100 120

Real May 2008 $/bbl

Jan 98 Jan 00 Jan 02 Jan 04 Jan 06 Jan 08 Jan 10 Jan 12 Jan 14 Jan 16

Forward curves inflation-adjusted according to their trade date, not their contract date.

Solid line is front month contract; forward curves taken every May

Table A-6: New and Used Cars: Inventory results†

New Cars Used Cars

Coefficient DTT sample % Change Coefficient DTT sample % Change

Variable (SE) mean in DTT (SE) mean in DTT

GasolinePrice * Quart. 1 12** 67.7 17.7% .95+ 46.9 2.0%

(lowest fuel economy) (1.9) (.56)

GasolinePrice * Quart. 2 2* 60.7 3.3% 1.2+ 46.2 2.6%

(.8) (.67)

GasolinePrice * Quart. 3 1.5* 57.7 2.6% .053 48.0 0.1%

(.7) (.65)

GasolinePrice * Quart. 4 -5.5** 48.9 -11.5% -1.9** 43.9 -4.3%

(highest fuel economy) (.7) (.59)

†

This table only reports the coefficients on gasoline prices. The full specification for both new and used cars is:

DT Tirdjt = ω0 +ω1(GasolinePriceit ·MPG Quartilej) +ω2Demogit +ω3PurchaseTimingjt +δdj +τrt +μrt +νijt

where DTTirdjt measures days to turn for transaction i in region r at dealer d on date t for car j. We use the

same extensive set of controls we have used in the market share specification (see page 10) with one addition. To

control for the fact that different dealerships may have different inventory policies we include car type × dealer

fixed effects (δdj ).

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the DMA level)

in parentheses.

Appendix-6
Table A-7: New and Used Cars: Actual cash value of trade-in†

New Car Trade-in Used Car Trade-in Used Car

Actual Cash Value Actual Cash Value Transaction Prices

GasolinePrice*MPG Quart 1 -1109** -683** -1182**

(40) (39) (42)

GasolinePrice*MPG Quart 2 -77+ -5.6 -101

(40) (43) (62)

GasolinePrice*MPG Quart 3 364** 158** 468**

(37) (34) (36)

GasolinePrice*MPG Quart 4 447** 92* 763**

(41) (39) (44)

†

This table only report the coefficients on gasoline prices. The full specification for both new and used cars in

columns 1 and 2 is:

ACVilrt = β0+β1GasolinePriceit·MPG Quartilel+β2Odometerilt+β3Demogit+β4PurchaseTimingjt+δl+τrt+μrt+ξilrt

where ACVilrt is the actual cash value booked in transaction i for trade-in car l in region r on date t. We add

a new control variable to this specification, which is the odometer reading of the trade-in car; cars with higher

odometer readings have experienced greater depreciation and should be booked at lower actual cash values,

all else equal. In the specification, we include the demographic characteristics of the buyer; these should not

have a direct effect on the average cash value, but may be correlated with unobservable quality characteristics

(“wear and tear”) of the trade-in car. We also include the purchase timing of the transaction, in case cars are

assigned different actual cash values on, for example, weekend days, when there is typically higher transaction

volume. Finally, we include detailed “car type” fixed effects for the trade-in, as well as our region-specific year

and region-specific month-of-year fixed effects.

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the DMA level)

in parentheses.

Table A-8: Effect of predicted gasoline price expectations in price regression†

New Cars

Gas Price Variable MPG Quartile 1 MPG Quartile 2 MPG Quartile 3 MPG Quartile 4

Current Price -250** -96** -11 104*

(original specification) (72) (37) (26) (47)

Gasoline Price Future -456** -167** -56+ 113+

Delivery in 6 months (96) (48) (32) (63)

Gasoline Price Future -536** -222** -92* 86

Delivery in 12 months (102) (53) (36) (66)

Gasoline Price Future -583** -251** -108** 80

Delivery in 24 months (106) (56) (38) (68)

Used Cars

Gas Price Variable MPG Quartile 1 MPG Quartile 2 MPG Quartile 3 MPG Quartile 4

Current Price -1182** -101 468** 763**

(original specification) (42) (62) (36) (44)

Gasoline Price Future -1365** -168* 520** 905**

Delivery in 6 months (58) (66) (40) (47)

Gasoline Price Future -1414** -204** 505** 905**

Delivery in 12 months (60) (66) (42) (50)

Gasoline Price Future -1495** -269** 455** 867**

Delivery in 24 months (63) (66) (44) (52)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the

DMA level) in parentheses.

Appendix-7
Table A-9: Effect of predicted gasoline price expectations in market share regression†

New Cars

Gas Price Variable MPG Quartile 1 MPG Quartile 2 MPG Quartile 3 MPG Quartile 4

Current Price -.057** -.014** .0002 .071**

(original specification) (.0048) (.004) (.0027) (.0058)

Gasoline Price Future -.065** -.0024 .0014 .066**

Delivery in 6 months (.006) (.008) (.0041) (.009)

Gasoline Price Future -.067** -.0046 .00035 .071**

Delivery in 12 months (.0066) (.0096) (.0047) (.01)

Gasoline Price Future -.062** -.0061 -.003 .071**

Delivery in 24 months (.0071) (.011) (.0051) (.012)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the

DMA level) in parentheses.

Table A-10: New Cars: Market share (quartile) results by gasoline price levels†

New Cars

MPG Quartile 1 MPG Quartile 2 MPG Quartile 3 MPG Quartile 4

GasolinePrice (<1.5 dollar) -.061** -.013* .0072* .067**

(.0064) (.0054) (.0035) (.006)

GasolinePrice (1.5-2.5 dollars) -.065** -.0095* .0088** .065**

(.0059) (.0048) (.0031) (.0055)

GasolinePrice (2.5-3.5 dollars) -.061** -.011** .0075** .065**

(.0053) (.0042) (.0027) (.0047)

GasolinePrice (>3.5 dollars) -.062** -.013* -.0043 .079**

(.006) (.0059) (.0034) (.0084)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the DMA

level) in parentheses.

This table only reports the coefficients on gasoline prices.

Appendix-8
Table A-11: New and Used Cars: Price results by gasoline price levels†

New Cars, MPG Quartiles Used Cars, MPG Quartiles

GasolinePrice(< 1.5)*MPG Quart 1 -274** -1373**

(83) (72)

GasolinePrice(< 1.5)*MPG Quart 2 -186** -127

(59) (126)

GasolinePrice(< 1.5)*MPG Quart 3 -73+ 825**

(39) (71)

GasolinePrice(< 1.5)*MPG Quart 4 51 1120**

(65) (109)

GasolinePrice(1.5-2.5)*MPG Quart 1 -235* -1259**

(97) (57)

GasolinePrice(1.5-2.5)*MPG Quart 2 -155** -65

(56) (105)

GasolinePrice(1.5-2.5)*MPG Quart 3 -89* 774**

(36) (74)

GasolinePrice(1.5-2.5)*MPG Quart 4 -14 1065**

(72) (113)

GasolinePrice(2.5-3.5)*MPG Quart 1 -239** -1186**

(79) (51)

GasolinePrice(2.5-3.5)*MPG Quart 2 -150** -48

(46) (90)

GasolinePrice(2.5-3.5)*MPG Quart 3 -63* 682**

(29) (59)

GasolinePrice(2.5-3.5)*MPG Quart 4 23 969**

(58) (83)

GasolinePrice(> 3.5)*MPG Quart 1 -399** -1424**

(71) (62)

GasolinePrice(> 3.5)*MPG Quart 2 -118* -215*

(46) (90)

GasolinePrice(> 3.5)*MPG Quart 3 -29 500**

(25) (55)

GasolinePrice(> 3.5)*MPG Quart 4 110* 853**

(48) (75)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at

the DMA level) in parentheses.

This table only reports the coefficients on gasoline prices.

Table A-12: New Cars: Market share (quartile) results by gasoline price trends†

New Cars Results MPG Quartile 1 MPG Quartile 2 MPG Quartile 3 MPG Quartile 4

GasolinePrice (3 months up) -.061** -.015** .0043 .072**

(.0049) (.0036) (.0034) (.005)

GasolinePrice (3 months mixed) -.062** -.015** .0064+ .07**

(.0054) (.0039) (.0038) (.0049)

GasolinePrice (3 months down) -.065** -.017** .0091* .073**

(.0059) (.0042) (.0044) (.0053)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the DMA level)

in parentheses.

This table only reports the coefficients on gasoline prices.

Appendix-9
Table A-13: New and Used Cars: Price results by gasoline price trends†

New Cars, MPG Quartiles Used Cars, MPG Quartiles

GasolinePrice(3 mo up)*MPG Quart 1 -308** -1189**

(78) (46)

GasolinePrice(3 mo up)*MPG Quart 2 -141** -108

(42) (66)

GasolinePrice(3 mo up)*MPG Quart 3 -53+ 497**

(30) (42)

GasolinePrice(3 mo up)*MPG Quart 4 66 821**

(56) (47)

GasolinePrice(3 mo mixed)*MPG Quart 1 -330** -1276**

(88) (51)

GasolinePrice(3 mo mixed)*MPG Quart 2 -167** -105

(46) (75)

GasolinePrice(3 mo mixed)*MPG Quart 3 -69* 552**

(34) (46)

GasolinePrice(3 mo mixed)*MPG Quart 4 51 923**

(64) (52)

GasolinePrice(3 mo down)*MPG Quart 1 -384** -1401**

(97) (59)

GasolinePrice(3 mo down)*MPG Quart 2 -183** -158*

(52) (77)

GasolinePrice(3 mo down)*MPG Quart 3 -92* 585**

(38) (50)

GasolinePrice(3 mo down)*MPG Quart 4 42 1020**

(69) (55)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the

DMA level) in parentheses.

This table only reports the coefficients on gasoline prices.

Table A-14: Effect of gasoline price aggregation in market share regression†

New Cars

Gas Price Aggregation MPG Quartile 1 MPG Quartile 2 MPG Quartile 3 MPG Quartile 4

4-digit ZIP -.044** -.016** .00065 .06**

(.0076) (.0039) (.0026) (.0078)

DMA -.057** -.014** .0002 .071**

(original specification) (.0048) (.004) (.0027) (.0058)

PADD -.059** -.013** .00033 .072**

(.005) (.0049) (.0032) (.0058)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the

DMA level) in parentheses.

Appendix-10
Table A-15: Effect of gasoline price aggregation in price regression†

New Cars

Gas Price Aggregation MPG Quartile 1 MPG Quartile 2 MPG Quartile 3 MPG Quartile 4

4-digit ZIP -220** -84** -7 110*

(68) (31) (28) (49)

DMA -250** -96** -11 104*

(Base Case) (72) (37) (26) (47)

PADD -358** -88* 34 150**

(74) (38) (30) (37)

Used Cars

Gas Price Aggregation MPG Quartile 1 MPG Quartile 2 MPG Quartile 3 MPG Quartile 4

4-digit ZIP -1156** -95 454** 735**

(46) (65) (40) (46)

DMA -1182** -101 468** 763**

(Base Case) (42) (62) (36) (44)

PADD -1266** -121* 524** 859**

(49) (58) (32) (36)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the

DMA level) in parentheses.

Table A-16: OLS and IV results in market share specification†

MPG Quartile 1 MPG Quartile 2 MPG Quartile 3 MPG Quartile 4

New Cars

DMA-level gas prices -.057** -.014** .0002 .071**

OLS (original) (.0048) (.004) (.0027) (.0058)

PADD-level gas prices -.059** -.013** .00033 .072**

OLS (.005) (.0049) (.0032) (.0058)

PADD-level gas prices -.065** .0024 .0011 .061**

IV (.0067) (.0088) (.0045) (.011)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the

DMA level) in parentheses.

Table A-17: OLS and IV results in price specification†

MPG Quartile 1 MPG Quartile 2 MPG Quartile 3 MPG Quartile 4

New Cars

DMA-level gas prices -250** -96** -11 104*

OLS (original specif.) (72) (37) (26) (47)

PADD-level gas prices -358** -88* 34 150**

OLS (74) (38) (30) (37)

PADD-level gas prices -484** -85** 58** 226**

IV (24) (24) (22) (20)

Used Cars

DMA-level gas prices -1182** -101 468** 763**

OLS (original specif.) (42) (62) (36) (44)

PADD-level gas prices -1266** -121* 524** 859**

OLS (49) (58) (32) (36)

PADD-level gas prices -1751** -394** 519** 1055**

IV (23) (22) (21) (22)

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (robust and clustered at the

DMA level) in parentheses.

Appendix-11
Table A-18: mlogit estimates (baseline is MPG Quartile 4) †

New Cars

MPG Quartile 1 MPG Quartile 2 MPG Quartile 3

FuelPrice -.55** -.3** -.22**

(.038) (.033) (.023)

PctLessHighSchool .31* .27** -.034

(.12) (.089) (.093)

PctCollege -.52** -.15 -.19*

(.11) (.1) (.078)

Income 2.3e-06** 3.8e-06** 3.7e-06**

(7.9e-07) (6.7e-07) (5.9e-07)

MedianHHSize .086** .044* -.01

(.021) (.02) (.03)

MedianHouseValue 7.4e-07** 5.3e-07** 4.2e-07**

(2.8e-07) (1.9e-07) (1.4e-07)

VehiclePerHH .15 .05 .31

(.18) (.18) (.19)

TravelTime -.004** -.0042** -.0033**

(.0014) (.00099) (.0009)

Weekend -.18** -.1** -.088**

(.015) (.012) (.0092)

EndOfMonth .054** .045** .056**

(.0072) (.0083) (.0055)

EndOfYear -.058** -.061** -.039+

(.021) (.02) (.021)

Observations 1863403

Log pseudolikelihood -2483741.3

*

significant at 5%; ** significant at 1%; + significant at 10% level. SEs (clustered

at the DMA level) in parentheses.

Appendix-12
Table A-19: Regions and DMAs

Baltimore/Washington Gulf Nevada Seattle/Portland

Baltimore Alexandria, LA Boise Bend, OR

Harrisonburg Anniston Idaho Falls-Pocatello Eugene

Richmond-Petersburg Baton Rouge Las Vegas Medford-Klamath Falls

Salisbury Biloxi-Gulfport Reno Portland, OR

Washington, Dc, Hagerstown Birmingham Salt Lake City Seattle-Tacoma

Charlotte Dothan Twin Falls Spokane

Bluefield-Beckley-Oak Hill Hattiesburg-Laurel New England Yakima-Pasco-Rchlnd-Knnwck

Charleston, SC Jackson, MS Bangor South Texas

Charlotte Lafayette, LA Boston Austin

Columbia, SC Lake Charles Burlington-Plattsburgh Beaumont-Port Arthur

Florence-Myrtle Beach Meridian Portland-Auburn Corpus Christi

Greensboro-H.Point-W.Salem Mobile-Pensacola Presque Isle Houston

Greenville-N.Bern-Washngtn Monroe-El Dorado Providence-New Bedford Victoria

Greenvll-Spart-Ashevll-And Montgomery Springfield-Holyoke Southern California

Raleigh-Durham New Orleans New York Bakersfield

Tri-Cities, TN-VA Panama City Albany-Schenectady-Troy Los Angeles

Wilmington Tallahassee-Thomasville Binghamton Palm Springs

Cincinnati Tuscaloosa Elmira San Diego

Charleston-Huntington Hawaii Hartford & New Haven SantaBarbra-SanMar-SanLuOb

Cincinnati Honolulu New York Yuma-El Centro

Lexington Illinois/Indiana Rochester, NY Tampa

Cleveland Champaign&Sprngfld-Decatur Syracuse Ft. Myers-Naples

Cleveland Chicago Utica Tampa-St. Pete,Sarasota

Parkersburg Davenport-R.Island-Moline Watertown Tennessee

Wheeling-Steubenville Peoria-Bloomington Norfolk/Virginia Beach Bowling Green

Youngstown Rockford Charlottesville Chattanooga

Colorado Indianapolis Norfolk-Portsmth-Newpt Nws Columbus-Tupelo-West Point

Cheyenne-Scottsbluf-Strlng Evansville Roanoke-Lynchburg Greenwood-Greenville

Colorado Springs-Pueblo Ft. Wayne Northern California Huntsville-Decatur,Flor

Denver Indianapolis Chico-Redding Jackson, TN

Grand Junction-Montrose Lafayette, IN Eureka Jonesboro

Columbus Louisville Fresno-Visalia Knoxville

Columbus, OH South Bend-Elkhart Monterey-Salinas Memphis

Dayton Terre Haute Sacramnto-Stkton-Modesto Nashville

Lima Kansas City San Francisco-Oak-San Jose Texas

Toledo Kansas City Oklahoma Abilene-Sweetwater

Zanesville Lincoln & Hstngs-Krny Plus Amarillo Dallas-Ft. Worth

Dakotas North Platte Ft. Smith-Fay-Sprngdl-Rgrs El Paso

Billings Omaha Joplin-Pittsburg Little Rock-Pine Bluff

Butte-Bozeman Springfield, MO Oklahoma City Lubbock

Casper-Riverton St. Joseph Tulsa Odessa-Midland

Fargo-Valley City Topeka Wichita Falls & Lawton Sherman-Ada

Glendive Wichita-Hutchinson Plus Orlando Shreveport

Great Falls Miami Gainesville Tyler-Longview(Lfkn&Ncgd)

Helena Miami-Ft. Lauderdale Jacksonville, Brunswick Waco-Temple-Bryan

Minot-Bismarck-Dickinson West Palm Beach-Ft. Pierce Orlando-Daytona Bch-Melbrn

Missoula Minneapolis Pennsylvania

Rapid City Duluth-Superior Harrisburg-Lncstr-Leb-York

Sioux City Green Bay-Appleton Philadelphia

Sioux Falls(Mitchell) La Crosse-Eau Claire Wilkes Barre-Scranton

Detroit Madison Phoenix

Alpena Mankato Albuquerque-Santa Fe

Detroit Marquette Phoenix

Flint-Saginaw-Bay City Milwaukee Tucson(Nogales)

Grand Rapids-Kalmzoo-B.Crk Minneapolis-St. Paul Pittsburgh

Lansing Rochestr-Mason City-Austin Buffalo

Traverse City-Cadillac Wausau-Rhinelander Clarksburg-Weston

Georgia Missouri Erie

Albany, GA Cedar Rapids-Waterloo&Dubq Johnstown-Altoona

Atlanta Columbia-Jefferson City Pittsburgh

Augusta Des Moines-Ames San Antonio

Columbus, GA Ottumwa-Kirksville Harlingen-Wslco-Brnsvl-Mca

Macon Paducah-C.Gird-Harbg-Mt VN Laredo

Savannah Quincy-Hannibal-Keokuk San Angelo

St. Louis San Antonio

Appendix-13