Demographic Preferences and Price Discrimination inNew Vehicle Sales∗

Ashley LangerUniversity of Michigan†

August 2012

Abstract

Understanding why demographic groups may pay different prices for the same goodsis central to questions of consumer equity. While sellers may charge some groupshigher prices out of bias, they also have an incentive to charge price sensitive groupslower prices. This paper investigates how accounting for profit-maximizing statisticalprice discrimination changes our understanding of demographic groups’ treatment inthe U.S. new vehicle market. I estimate separate random-coefficient discrete choicemodels by marital status and gender and calculate optimal group markups. Controllingfor markups changes the correlation between prices and demographics in a way thatsuggests that while preferences matter in pricing, demographic groups also differ intheir ability to extract low prices from the market.

∗This paper is a revision of a chapter of my dissertation at the University of California, Berkeley. I wouldlike to thank David Card, Patrick Kline, Enrico Moretti, Kenneth Train, Clifford Winston, and CatherineWolfram for their guidance throughout this project. Seminar participants at UC Berkeley, the San FranciscoFederal Reserve, Collegio Carlo Alberto, the University of British Columbia, Arizona State University, TuftsUniversity, the University of Toronto, Columbia University, the Ohio State Univsersity, the University ofWisconsin, Stanford University, the University of Maryland, the University of Chicago, and the Universityof Michigan provided valuable comments and discussion. Andrew Lumsden and Zachary Anderson wereextraordinarily helpful with procuring the data.†Visiting the University of Arizona: 1130 E. Helen St, McClelland Hall 401, Tucson, AZ 85721;

alanger@umich.edu

1 Introduction

Even before Becker’s (1957) model of discrimination in the labor market, economists won-dered whether a retailer’s distaste for interacting with certain groups was leading to negativeoutcomes for disadvantaged groups and whether social policy should be called upon to inter-vene in markets with “taste-based” discrimination. Since then, researchers have employed avariety of datasets and econometric tools to attempt to empirically understand the role oftaste-based discrimination in many different markets. However, the fundamental problemwith empirical attempts to understand discrimination has been that demographic groupscan experience different market outcomes for a variety of reasons unrelated to taste-baseddiscrimination. While some of the reasons for differential treatment may be considered be-nign or even beneficial to disadvantaged groups (for instance statistical or third-degree pricediscrimination may decrease prices to particularly poor and price-sensitive groups), there arealso non-taste-based reasons for differential treatment that may warrant market interven-tion. For instance, if demographic groups have differential market access, whether differentsearch costs, information levels, or bargaining ability, then groups may be substantiallydisadvantaged in the market even if firms are equally happy to interact with all groups.

This paper begins to disentangle these varied explanations for why groups may experiencedifferent outcomes in the new vehicle market. Previously, Ayers and Siegelman (1995),Goldberg (1996), Harless and Hoffer (2002), and Scott Morton, Zettelmeyer, and Silva-Risso (2003) found mixed evidence as to whether different demographic groups pay identicalprices on average for new vehicles. However, if we wish to understand whether policy shouldbe called on to intervene in the market, then the cause of the disparity becomes critical,and these papers are generally unable to provide strong evidence about the mechanismsbehind any price differentials that do arise. If discrimination on the part of dealers iscausing one group to pay higher prices, then a non-discrimination policy may be necessary.However, if the group is paying high prices because they do not have access to informationabout where to find the least expensive products, then requiring that each dealer treat allgroups equally is unlikely to eliminate the disparity. In fact, if the information disparity iscombined with profit-maximizing statistical discrimination, then requiring that each dealertreat all consumers identically could exacerbate the disparity by reducing price-sensitivegroups’ ability to get a good deal at whichever dealer they end up choosing.

Since the correct policy response to differential treatment requires an understanding ofthe reasons why demographic groups are being treated differently in the market, then empir-ically disentangling these various reasons in different markets becomes critical. While therehave been numerous attempts to disentangle reasons for differential treatment empirically,

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this work has generally either been experimental (e.g. List (2004)) or focused on relativelyunusual market situations (e.g. Graddy (1995)). In this paper, I use the differentiated prod-ucts nature of a very standard market, the market for new vehicles in the United States,to begin to disentangle the reasons that demographic groups may pay different prices. Inparticular, I explicitly model firms’ incentives to charge demographic groups different pricesbased on third-degree price discrimination and then look at how actual market prices devi-ate from these predicted prices across demographic groups. To do this, I estimate vehiclepreferences separately for married and unmarried men and women using random coefficientsdiscrete choice methods similar to Berry, Levinsohn, and Pakes (1995 (henceforth BLP),2004 (henceforth MicroBLP)). I use disaggregate survey data of new vehicle purchaserscombined with maximum likelihood estimation in an approach that combines the economet-ric approachs of MicroBLP and Train and Winston (2007). I find that single consumers aremore price sensitive than married consumers, and that this difference in price sensitivityis reinforced by differences in income. Generally, I find that preference differences acrossgroups are consistent with intuition, for instance with married consumers having a strongerpreference for SUVs and vans over pickup trucks and sporty cars than single consumers.

Using the preference estimates, I predict the optimal markup for each vehicle sold to eachdemographic group. While these markups clearly vary over both vehicles and demographicgroups, they are generally in line with markups estimated in BLP and other discrete choicemodels as well as with the predicted markups used by the US Environmental ProtectionAgency to estimate the cost-impact of increased technology requirements in new vehicles(RTI International, 2009).

Finally, I re-run the price correlations that have previously appeared in the literatureand control for the predicted optimal markups. This allows me to understand whethertransactions prices are correlated with demographic group preferences across vehicles andwhether there are systematic deviations from optimal prices across demographic groups. Ifind that transactions prices do track the optimal markups, even controlling for vehicle fixedeffects, which suggests that dealers are engaging in statistical price discrimination acrossdemographic groups in their vehicle pricing. Additionally, I show that after controlling forthe optimal markups, single women pay a premium over single men and married men paya premium over married women. Furthermore, single consumers pay more than marriedconsumers.

While I cannot firmly conclude why these price differences exist, there is suggestiveevidence that differential market access, in particular information and bargaining ability,may be more important than taste-based discrimination. First, the fact that the male-female price differential changes sign for single and married consumers suggests that taste-

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based gender discrimination is not the primary driver of price differences. In fact, the genderdifferential appears more likely to be driven by the selection of the more skilled negotiator(or more informed consumer) by married households. Additional questions in my surveysuggest that while single people generally purchase the vehicle for themselves, married menare often purchasing the vehicle for someone else, likely a wife or other family member. Thismeans that the married women who do purchase vehicles for themselves likely think thatthey are in a better position to receive a low price for the vehicle than their husbands wouldbe.1 This change in the gender price gap across marital status may also help to explain whyearlier work on gender discrimination in car sales found mixed results. Finally, the abilityof married couples to send the stronger negotiator to purchase a new vehicle also explainswhy single consumers are paying higher prices than married consumers.2

The remainder of the paper is organized as follows: section 2 lays out some correlationsbetween vehicle prices and consumer demographics in the spirit of the earlier literatureto motivate a more preference-oriented approach to the data. In section 3 I describe myempirical specification. The data used is explained in detail in section 4. I then presentresults of the demand estimation and the correlations between price in excess of markup andconsumer demographics in section 5. Section 6 concludes.

2 Price Correlations

Before moving to the model of why prices may differ across demographic groups, it is usefulto understand how far cross-sectional correlations can take us in understanding differentialtreatment of demographic groups in the new vehicle market. Previous work has generallyused these correlations between price and demographics, controling for vehicle characteris-tics, to identify whether demographic groups are experiencing different treatment in the newvehicle market on average.3 However, there are many distinct reasons that groups are treateddifferently by the market, and these reasons can have offsetting effects on the average pricedifference in the market. As discussed above, if taste-based discrimination favors wealth-ier demographic groups, then when it is combined with statistical discrimination, whichgenerally favors more price-sensitive consumers, the average results are ambiguous. Thiscomparison doesn’t even take into account differences in information, search costs, or nego-

1The impacts of women having a distaste for negotiation is a long-discussed topic, as in Babcock andLaschever (2003).

2For a more in-depth analysis of the potential for information to change price outcomes, see Scott Morton,Silva-Risso, and Zettelmeyer (2011).

3See, for example, Ayers and Siegelman (1995), Goldberg (1996), Harless and Hoffer (2002), or ScottMorton, Zettelmeyer, and Silva-Risso (2003).

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tiating ability across demographic groups, which may further complicate our understandingof mean price differences.

Nonetheless, average price differences are often the most obvious place to begin lookingfor differential treatment of demographic groups. Table 1 shows these correlations with mytransaction price data (which will be described in more detail in Section 4), controlling forvehicle model fixed effects. As in some of the earlier studies, I do not find a significantdifference in the prices paid by men and women regardless of the other demographics thatare included. Income significantly increases with the transaction price, and, when they areincluded together, being single or high income is correlated with a higher price, and beingmore educated is correlated with a lower price paid.4

The first response to the lack of significant differences between men and women in pricepaid for new vehicle might be to suspect that men and women are being treated similarlyin this market. Yet the fact that marital status, income, and education do seem to affecttransactions prices when included together does suggest that prices are not set uniformlyacross consumers.5 One way to ask whether prices differ between men and women is toconduct an F-test of whether there is a difference between the prices men and women payfor each individual vehicle model. This test is overwhelmingly rejected (F(210,10607) statistic= 1.80, Pr>F = 0.0000), suggesting that the average prices men and women pay for eachmodel are quite different, even if these differences are not statistically different from zerowhen averaged across all models. Thus it appears that looking at only the mean pricedifference between demographic groups may not be sufficient to reject that the groups arebeing treated identically in the market.

Just because men and women appear to be paying different prices for the same vehiclesdoes not mean that there is some sort of systematic discrimination (statistical or otherwise)leading to these differences. Goldberg (1996) includes vehicle segment indicator variablesas well as those segment indicators interacted with a minority indicator variable, and findsinsignificant results on the interaction for most segments. In Table 2 I present the coefficientson these interactions between segment indicator variables and indicators for male and maritalstatus for my data. The interaction with marital status is always insignificant, but the resultsby gender are particularly interesting. Men pay significantly less than women for compactcars, while men pay significantly more than women for luxury cars, sport utility vehicles,and sporty cars. This pattern of women paying more for smaller, less expensive vehicles andmen paying more for luxury and performance vehicles might start to suggest that differences

4The results presented in Table 1 are given in levels, but the results are fundamentally unchanged whenrun with the natural log of price paid.

5These correlations could also potentially result from differential pricing across markets.

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in preferences between vehicles allow dealers to statistically discriminate based on gender.Clearly these differences are merely suggestive, but they tend to contradict the idea that alack of correlation between price paid and gender on average is evidence of a lack of pricediscrimination based on gender. In order to better understand whether demographic grouppreferences are playing a role in dealers’ pricing decisions, I will have to directly estimatethe preferences of different demographic groups.

3 Empirical Specification

The empirical model aims to compare the firms’ optimal price for each demographic groupunder statistical price discrimination to the observed price for that group in order to identifythe extent of price discrimination in the market. This requires estimating the vehicle demandfunctions of each demographic group for each vehicle and pairing these estimates with amodel of manufacturer and dealer pricing behavior. In this sense, the model is similar towork done on international price discrimination in the European market by Verboven (1996)and Goldberg and Verboven (2001).

The data I use will be described in detail in section 4, but it consists of individual-level data on vehicle purchasers’ demographics and the price paid for the vehicle. I alsoobserve other vehicles considered by most consumers, allowing for a ranking of both thefirst and second choice vehicle for those consumers. This will allow me to directly tie thedemographics of each consumer to the vehicle choice, thus allowing preferences to vary basedon both observable (to the econometrician) and unobservable factors. Here I will describethe demand and supply approaches before explaining how I incorporate these preferenceestimates into the standard approach to measuring price differentials across demographicgroups.

3.1 Demand Functions

The demand function follows directly from MicroBLP but is estimated using maximumlikelihood as in Train and Winston (2007). I use the Berry (1994) market share inversion toreduce the dimensionality of the coefficient space.

Consumers are each assumed to belong to a single demographic group, d = 1, ..., D.Within these demographic groups, consumers are heterogeneous along both observable andunobservable individual characteristics. Consumer i’s utility for vehicle j = 0, 1, ..., J isassumed to be:

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Uidj = pjdαid +∑k

xjkβidk + ξdj + εidj (1)

where pjd is the price charged to i’s demographic group d; xj1, ..., xjK are the non-priceattributes of vehicle j; ξdj is the preference of demographic group d for the unobservableattributes of vehicle j; and εidj is an extreme value type 1 residual preference parameter.The αid and βidk are the individual’s preference for vehicle attributes pjd and xk respectively,which are assumed to have the form:

αid = αd +∑r

zidrαodr + νidpα

ud (2)

βidk = βdk +∑r

zidrβodkr + νidkβ

udk

Thus the individual’s preference for vehicle attribute xk is decomposed into a component(βdk) that is constant within that individual’s demographic group, a component (βodkr) thatvaries with consumer characteristics zidr that are observed by the econometrician, and acomponent (βudk) that varies with consumer characteristics νidk that are unobserved by theeconometrician, but are assumed to have a known distribution.6 These unobserved consumercharacteristics capture the fact that there is heterogeneity in preferences for different vehicleattributes in every demographic group, although we may not have variables that allow usto identify those consumers who get particular utility from horsepower, for instance, ratherthan fuel economy.

As noted, consumer demographics enter the model through both the d term that definesdemographic groups and the zidr term. The difference between these two components ofpreference is whether the demographic characteristic is observed by the dealer and thereforea potential basis for price discrimination. Since the objective of the model is to understandprice discrimination based on demographics that are observable to the dealer, estimatedpreferences are allowed to be substantially more flexible in d than in zidr (all coefficients mayvary in d while only the preferences for price and certain vehicle characteristics may vary inzidr). In particular, the unobservable quality of the vehicle, ξdj, varies in d but not in zidr.As discussed later, gender and marital status will be considered observable to the dealer,and thus enter as a part of d, while income will be considered unobservable to the dealerand enter as a part of zidr.7 Variations of these assumptions are tested in the appendix.

6I will generally assume that unobserved consumer characteristics have normal distributions.7Demographics that are not directly observable to the dealer, zidr, enter into price discrimination in

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Combining equations (1) and (2) leads to the consumer’s choice model:

Uidj = δdj +∑r

pjdzidrαodr +

∑k,r

xjkzidrβodkr + pjdνidpα

ud +

∑k

xjkνidkβudk + εidj (3)

where δdj = pjdαd +∑k

xjkβdk + ξdj for each j = 1, ..., J (4)

The consumer chooses the vehicle j = 1, ..., J or the outside option (j = 0, not purchasinga new vehicle) that maximizes this utility function.8 As this notation makes clear, thereis a component (δdj) to each individual’s utility for each vehicle that is common acrossall members of his or her demographic group d. Additionally, the term ∑

r pjdzidrαodr +∑

kr xjkzidrβodkr allows consumers with different observable characteristics to have different

tastes for certain vehicle attributes, and thus specifies the extent to which vehicle substitutionvaries with observable consumer demographics. Finally, there is a component of consumerpreference (pjdνidpαudk + ∑

k xjkνidkβudk) that is unobserved by the econometrician but helps

explain why certain consumers have stronger preferences for some vehicle attributes thanother consumers, and helps to explain why individuals may substitute more strongly betweencertain vehicles. The βudk and αud coefficients can be thought of as representing the standarddeviation in the unobserved preference within demographic group d for the vehicle attributeconditional on the consumer’s observed attributes. For notational ease, I define the vectorof distributional coefficients θd ≡ [αodr, αudr, βodkr, βudkr]′.

I estimate the θd and δd coefficients via maximum-likelihood. The extreme-value errorterm guarantees that the probability of vehicle j maximizing consumer i’s utility conditionalon the observable attributes of the vehicle (pjd, xjk) and the consumer’s observable (zidr)and unobservable (νid = [νidp, ν ′idk]′) characteristics has a closed form such that the expectedprobability of a consumer i in demographic group d choosing vehicle j can be expressed asan integral over the distribution of νid. Because the θd coefficients determine how consumerssubstitute between vehicles as attributes change, information on consumers’ first and secondchoice vehicles aids identification of θd. Thus, the joint probability that consumer i choosesvehicle j = 1 out of the full choice set, and j = 2 out of the choice set with j = 1 and theoutside good removed is:9

expectation. Therefore, if married men are generally for higher income households than single women, theexpected household income will enter into price discrimination but the variation in household income acrossmarried men will not.

8The outside option of not purchasing a new vehicle is assumed to have utility equal to Uid0 =βo

d( 1Income i

) + βud0νid0 + εid0, where νi is a draw from a standard normal distribution and εid0 is a draw

from an EV1 distribution.9I remove the outside good from the second-choice choice set because the second choice information is

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Pri1(θd, δd)Pri2(θd, δd|1) =ˆ exp(Vid1(νid; θd, δd))∑J

l=0 exp(Vidl(νid; θd, δd))

(exp(Vid2(νid; θd, δd))∑Jl=2 exp(Vidl(νid; θd, δd))

)f(ν)dν

where Vidj(νid; θd, δd) is the non-stochastic component of consumer i’s utility for vehicle jfrom equation (3). To condense notation, I write Vidj(νid; θd, δd) and Pridj(νid; θd, δd) withthe understanding that the non-stochastic utility and probability are also a function of theobservable data. Since the probability of observing a particular first and second choice for anindividual is conditional upon the individual’s νid vector, the integration over the distributionof ν must be over the joint probability of the first and second vehicle choices. I approximatethis integral using simulation, and then sum the log of this probability over consumers i indemographic group d to calculate the log-likelihood function.10

The log-likelihood function is maximized over θd. For each value of θd, I choose δd toset the predicted market shares for each demographic group equal to the observed marketshares for that group as in Berry (1994):

Sdj =ˆzidr

ˆν

Pridj(θd, δ(θd))f(ν)f(zidr)dνdzidr (5)

= Prdj(θd, δ(θd))

where f(zidr) is the pdf of the consumer characteristics zidr in the demographic group d.Therefore it should be understood that the δd vector is estimated conditional on θd andis thus formally δ(θd). The maximum-likelihood procedure solves for the value of θd thatmaximizes the likelihood function subject to a market-share constraint that is a function ofboth θd and δ(θd).

This model differs from previous random-coefficient demand models in an importantway: the model interacts demographic group membership, d, with the full distribution ofpreferences rather than limiting the influence of demographic group membership to onlyone or two vehicle characteristics (the demographics zidr are still only interacted with a fewvehicle characteristics). This dramatically increases the flexibility of the model to capturepreference differences across the demographic groups that are observable to the dealer.11

based on the vehicle the consumer said she considered but did not purchase. It is not clear whether she wouldhave purchased the second choice vehicle if the first choice were not available (she may not have purchasedany vehicle), but it is her preferred alternative out of the set of vehicles once her first choice is removed.

10Simulation generally uses 100 scrambled Halton draws to approximate the integral for each consumer.11This also means that the preferences in the population (as estimated in the previous discrete choice

literature) are a combination of the preferences in each demographic group.

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While this means that demographic groups may value the observable (to the econometrician)attributes of the vehicles differently, it is particularly important that the unobservable (tothe econometrician) characteristics of a vehicle (ξdj) are also allowed to be valued differentlyby members of different demographic groups. A prime example of such varying preferencefor vehicle unobservables would be the vehicles that are commonly referred to as “chickcars” or “guy cars”12 such that the opposite gender might be interested in the vehicle forits physical attributes, but dissuaded from buying the car because of its social connotation.Additionally, options packages that appeal to one group rather than another (for instancespoilers or wheel rims) would potentially change the unobservable quality of the car fordifferent groups differently.

Once I have estimated θd and calculated δ(θd), I can use the δd vector to extract infor-mation about the αd and βdk coefficients rather than just the θd coefficients. Recall fromequation 4 that unobservable vehicle quality, ξdj may include vehicle attributes that allowfirms to charge more for the vehicle. Therefore, an OLS regression of the δdj vector on vehicleprice and attributes will estimate that consumers are less price sensitive than they actuallyare. In order to correct for this bias, I run a weighted IV regression of δdj on the vehicle priceand attributes.13 I use the standard Bresnahan (1987)/BLP instruments plus a distanceinstrument used in Train and Winston (2007):

∑l∈fj ,l 6=j

xlk,∑

l∈fj ,l 6=j(xjk − xlk)2,

∑l /∈fj

xlk, and∑l /∈fj

(xjk − xlk)2 (6)

which are the sum of each vehicle attribute for competing vehicles produced by the samefirm as vehicle j, fj, the sum of each vehicle attribute for competing vehicles produced byother firms, and the sum of the distance in attribute space between the vehicle and all otherssold by the same firm and all others sold by other firms. These instruments are intendedto capture the extent of price competition faced by vehicle j in the market. For instance,if a vehicle is competing with a set of vehicles that have particularly high horsepower, thencompetitive pressure will keep the vehicle’s price fairly low conditional on its attributes. Ifthe observed price is actually high conditional on attributes, it must be that the vehicle hasa high level of of unobservable quality that is increasing its demand.

Because demographic groups face different prices and value vehicle attributes differently,the competitive pressure on price created by competing vehicles’ attributes should vary overdemographic groups. Therefore the instrumental variables regression is run separately for

12See, for instance: http://www.cartalk.com/content/features/Guy-Chick-Cars/index.html13The weights are equal to the number of consumers of demographic group d who chose vehicle j.

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each demographic group.The estimated demand coefficients allow me to calculate demand elasticities. Because

the predicted demand of demographic group d for vehicle j is just the number of people ingroup d times the predicted market share of group d for vehicle j, the own-price elasticity ofdemand is just:

∂Prdj(θd)∂pdj

(pdj

Prdj(θd)

)(7)

which is straightforward to calculate given θd. This is the key formulation of demand forfirms that are choosing prices to maximize profits.

3.2 Supply

In order to understand how this demand specification should be paired with the standardanalysis of demographic price differences, I employ a simple model of vehicle supply thatclosely follows BLP, MicroBLP and Bresnahan (1981). Firms maximize profits over the setof vehicles they offer, which are assumed to have constant marginal cost. The equilibrium isNash in prices. The complication from the BLP supply model is that firms choose an optimalprice for each vehicle to offer to each demographic group. I explicitly assume that “firms,”which include both the manufacturer and its dealer network, are able to charge optimalprices to each demographic group. This means that there is perfect contracting between themanufacturer and its dealers; dealers are perfectly able to identify the demographic groupof each consumer, and consumers cannot engage in across-demographic-group arbitrage.This final assumption is stronger than the typical no-arbitrage assumption where consumersare assumed to not participate in a secondary resale market, since consumers often live inhouseholds with members of another demographic group.14 In this case consumers are alsoassumed not to obscure their demographic characteristics by sending someone of a differentdemographic group to purchase the new vehicle for them.15

Thus firms f = 1, ..., F set prices to maximize profits over the vehicles they sell:14Note that the used car market would not function as a means of arbitrage in this case because the

price difference between a new and a barely used vehicle is substantial, likely stemming from consumers fearthat a brand new vehicle that is on the used car market has shown itself to be a lemon (Akerlof, 1970).In particular, the gain from reselling a car to a different demographic group is small relative to the loss ofselling a “used” car rather than a new one.

15Recall that the in the estimation I will use data on those survey respondents who said that they areboth the principle buyer and driver of the new vehicle. I will also consider the selection that this restrictioninduces in the sample.

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πf =D∑d=1

∑j∈f

Qdj(pd)(pdj − cj −Dd) (8)

where the demand function of demographic group d for vehicle j, Qdj(pd), is a function ofthe vector of the demographic group’s prices for all vehicles, pd. I allow for the possibilityof animus or differences in average bargaining abilities by including a fixed cost of selling todemographic group d, Dd. The maximization of this set of profit functions for all firms leadsto the vector of optimal prices given the vector of marginal costs, c:

P ∗d = c+Dd − Ω−1d Qdj (9)

≡ c+Dd +Md (10)

where P ∗d is the optimal price vector for group d, Md is the vector of optimal markups, andΩd is the matrix of own and cross-price derivatives of demand:

[Ωdjk] =

∂Qdk(θd,pd)

∂pdjif j and k ∈ F

0 otherwise

From the demand estimation, I have estimates of θd and αd, and I can therefore con-struct estimates of the demographic group’s demand and price derivative matrix, Qdj(θd)and Ωd(θd, αd).16 Thus I have enough information to construct estimates of the optimalmarkups for each vehicle j sold to demographic group d, Mdj. While I do not have informa-tion on the costs of vehicle j, I do assume that the marginal vehicle costs are the same forall demographic groups, and therefore that they may be estimated with vehicle fixed effects.

This supply model assumes that the firms are able to observe the demographic groups,d, perfectly, but that they don’t observe any other consumer characteristics, such as thoseincluded in the z vector in the demand specification. I will discuss this assumption, alongwith the potential for consumers to obscure their demographic group, in the context of myspecific choice of d and z characteristics.

16In BLP and MicroBLP, the authors use a moment similar to equation 9 to estimate their model, allowingthe cost of each vehicle to be a linear combination of the vehicle’s observed attributes. I do not exploit thismoment, and therefore do not assume that the observed prices, pdj , are optimal. This leaves open thepossibility of animus or differences in average bargaining ability across groups in the observed data.

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3.3 Incorporating Preferences into Demographic Price Correla-tions

With the optimal markup for each demographic group for each vehicle in hand, I can returnto the correlations between transaction prices and consumer demographics from the literatureto better understand how prices vary across demographic groups once we consider groups’preferences. Equation 10 shows that the transaction price should be the combination of threeterms: the marginal cost of vehicle production and sales, the “cost” of selling vehicles to aparticular demographic group (which is constant over vehicles), and the optimal markup.17

By subtracting the optimal markup from the transaction price, I get a simple functionalform that can be used to better understand how transactions prices vary across demographicgroups, controlling for the optimal markup:

Pidj − Mdj = cj +Dd + εidj (11)

Here the price paid by individual i from demographic group d for vehicle j minus the optimalmarkup that the manufacturer would charge demographic group d for vehicle j should equalthe cost of selling the vehicle, which includes the marginal cost of production and sales, cj, aswell as the cost of selling a vehicle to demographic group d in particular. There is a residual,εidj, which includes the measurement error in the individual’s transaction price relative tothe optimal price that the manufacturer would like to charge, as well as any estimation errorin the Mdj.

This specification has the appeal of following directly from the earlier price discrimi-nation in new vehicle sales literature while incorporating the price that the manufacturerswould like to charge consumers of different demographic groups for each vehicle. However,there are a few complications with this functional form that focusing on demographic grouppreferences highlights. First, I am including the optimal markup as viewed by the manu-facturer rather than the dealer while the dealer is actually interacting with the consumersand potentially enforcing the discriminatory pricing schedule. This approach is appealingbecause, if the manufacturer has control over the extent and location of its dealerships, thenthe objective should be to charge the optimal price as viewed by the manufacturer. In thatcontext, any deviations from this optimal price schedule might be considered random andenter the residual.18 Additionally, by allowing costs to vary over demographic groups, this

17“Cost” is in quotations because it not only includes a taste-based cost component but also any excessprofit that can be extracted from a group via negotiation.

18This assumes perfect contracting between the manufacturer and the dealer. Alternatively, one couldstructure the model as a double-marginalization problem between the manfuacturer and the dealer. Whilethis might affect the magnitude of the estimates of discrimination, it is unlikely to affect the result that

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specification makes clear that Dd should be thought of as both any differences across groupsin “taste-based” discrimination that affects the dealers’ cost of interacting with a particulardemographic group as well as any differences in the benefit from negotiating with differentdemographic groups. Since this specification provides no way to distinguish between dealeranimus against a demographic group and a high cost of negotiating with that group, I willbe unable to make a strong statement about the reason that Dd varies over groups, otherthan to have excluded statistical price discrimination from consideration.19

4 Data

The primary data for this analysis is a survey of new vehicle buyers conducted by a majormarket research firm. This data is augmented with data from the Current Population Survey,the Automotive News Market Data book, and Autodata Solutions.

The survey of new vehicle buyers includes 25,875 respondents who purchased new vehiclesin the second quarter of 2005.20 The survey includes information on the model of vehiclepurchased and the other models considered,21 but does not include information on the trimlevel or the options packages of the vehicle. The survey asks respondents a series of questionsabout their purchase, including the price they paid for the vehicle, and whether they paidcash for the vehicle, leased it, or secured a loan. Additionally, respondents indicated theirage, gender, marital status, education, household income, and race on the survey.22 In aparticularly relevant question, the survey asks whether the respondent is both the “principle

certain demographic groups pay more than others, controlling for optimal markups.19While the above specification follows directly from the model, I will also run a more data-driven spec-

ification which allows for the possibility that dealers are unable to fully extract the variation in optimalmarkups across demographic groups. This could be because of consumer arbitrage (in this context, where aconsumer of one demographic group sends a friend or relative to purchase a new vehicle for her) or becauseof localized competition between dealers of the same manufacturer’s vehicles. This specification will merelyallow for a coefficient on the estimated optimal markup from equation 10:

Pidj = cj +Dd + γMdj + ε∗idj

In this specification, I will interpret γ a measure of firms ability to price to demographic groups accordingto their preferences without assuming perfect contracting between manufacturers and dealers.

20Because the survey is limited to consumers who purchased new vehicles in the second quarter of 2005 Iabstract from concerns about the changing demographic sales patterns over the calendar year that are raisedin Aizcorbe, Bridgman, and Nalewaik (2009).

21I will follow the standard practice of assuming that the other models considered are listed in the order inwhich they were considered in order to identify the consumer’s second choice vehicle. I only use the secondchoice information rather than the third and fourth because the number of respondents who entered a thirdor fourth choice is low. The results using only the first choice information are similar to those presentedhere.

22Consumers were asked to indicate the range in which their education and household income fell, ratherthan the exact amount.

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buyer and driver” of the vehicle. 21,085 respondents indicated that he or she was both theprinciple buyer and driver, and I will limit my analysis to these respondents in order toassure that the demographic information matches the driver of the vehicle and the personwho physically purchased the vehicle.23

My analysis will focus on four demographic groups: married and unmarried men andwomen. These groups are large enough to estimate demand functions for each. Gender andmarital status are attractive groups to use for this analysis because they are fairly evenlydistributed geographically, so it is likely that all dealerships have experience with consumersof all demographic groups. Additionally, gender is a readily observable variable to dealers andis often thought of as a dimension along which vehicle preferences may vary. Marital statusmay be less observable to dealers, so any differences in the amount of price discriminationbased on marital status relative to gender might be related to consumers’ ability to obscuretheir demographic group. Additionally, to the extent that married consumers are morelikely than single consumers to be older and have larger households that potentially includechildren, I would expect married consumers’ preferences to differ from single consumers ofthe same gender.24 Additionally, as discussed in section 2 and shown in table 1, these groupsseem to pay similar amounts for new vehicles on average, yet men and women pay differentamounts for different vehicle classes as shown in table 2 while married and single peopledo not. Thus if preferences are playing a role in pricing, gender and marital status areparticularly interesting places to examine it.25

In order to understand preference heterogeneity within these demographic groups, I allowpreferences for price and the outside good to vary with income.26 As noted earlier, I assumethat dealers are not able to price discriminate on income, although I report a complete set

23Of course, many people may take a friend or family member with them to purchase a vehicle, in whichcase the dealer may not be completely sure who the primary driver of the vehicle will be.

24These groups have the advantage of being fairly observable to dealers, but gender and marital statusare clearly only a subset of the demographics that a dealer may observe or infer. In this analysis, differencesin average income, age, education, and race across these four demographic groups will enter into the meanpreference coefficients, α and β. I use household income as an observed (to the econometrician but not to thedealer) determinant of consumer heterogeneity within demographic groups, but assume that prices are setfor the demographic group as a whole rather than for different income classes within the demographic group.Age, education, and race differences within demographic groups will contribute to unobserved consumerheterogeneity while differences across demographic groups will enter into the mean preference coefficients.These assumptions are further explored in the appendix.

25Price discrimination based on race would be of particular interest to policy makers. Unfortunately,there are too few African American new vehicle purchasers in my sample to accurately estimate preferences.Additionally, if we are concerned that African Americans face discrimination in the used vehicle marketas well as the new vehicle market (e.g. Charles, Hurst, and Stephens (2008)), then the assumption of theoutside good as being the same for both African American and white consumers would not be valid.

26Income categories include <$25k, $25-30k, $30-40k, $40-50k, $50-60k, $60-100k, $100-150k, and >150k.The income of the lowest category is assumed to be $25k and the income of the highest category is assumedto be $150k. Otherwise, the midpoints of the range are used as the consumer’s household income.

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of results with price discrimination based on gender and broad income group (householdincome above or below $100k) in the appendix.

I remove from consideration any consumers who purchased a vehicle with an averagesales price of over 75 thousand dollars in order to limit the analysis to commonly purchasedvehicles. Finally, I only consider respondents who reported their gender, marital status,education level, household income level, and the price paid for their vehicle, which bringsmy dataset down to 12,014 consumers. 60.28% of my sample is male and 68.40% is married.27

New car buyers tend to be wealthier than the average American, with 38.39% of respondentscoming from households making over $100,000 and only 32.23% coming from householdsmaking less than $60,000. 31.00% of respondents in my sample have a college degree and21.18% have more education than a college degree. In order to calculate prices for eachdemographic group for every vehicle, I only include vehicles purchased by at least one memberof each sex and marital status group. If a consumer purchases a vehicle that is not includedin the choice set, then he or she is assumed to have chosen the outside good.

By limiting the data to those consumers who are both principle buyers and drivers oftheir new vehicles, I do introduce some selection into my analysis. This selection is mostlikely strongest for married consumers, who have another adult in the household who mightnegotiate for the new vehicle in the consumer’s place, while single consumers may not haveanother adult who could easily replace him or her in purchasing the vehicle. In fact, of therespondents with complete data, 22% of married men report not being the principle buyerand driver and 11% of married women report not being the principle buyer and driver. Forsingle men, only 5% of respondents are not the principle buyer and driver, and for singlewomen 4% are not the principle buyer and driver. If a survey respondent reports that sheis not the principle buyer and driver of the new vehicle, it is impossible to tell whethershe is the principle buyer but someone else is driving the vehicle or whether someone elsepurchased the vehicle for her to drive. Research on women’s propensity to avoid negotiation(Babcock and Laschever, 2003) would indicate that married men may be purchasing vehiclesfor their wives and then sometimes filling out the accompanying survey. This might meanthat the women who purchase vehicles for themselves are particularly strong negotiators,leading selection to bias my results towards finding that married men pay more for all newvehicles. Regardless of the predicted sign of the selection bias, the fact that single peoplegenerally are both the principle buyer and driver of the new vehicle would indicate thatselection will be low for single consumers. Similarly, if married couples choose to send thebetter negotiator to purchase all of their vehicles, then I would expect them to pay less than

27Of the 12,014 observations in my sample of purchasers, 2,852 are married women, 5,366 are marriedmen, 1,876 are single women, and 1,920 are single men.

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single consumers for new vehicles, although this may be countered by married couples havinga higher value of time (either due to higher incomes or child-rearing) than single consumersand therefore being willing to spend less time negotiating.

The other type of selection that may enter my analysis is the selection incurred by limitingthe dataset to only those consumers who provide full price and demographic information onthe survey. If a consumer feels that she got a particularly bad deal on a car she might chooseto “forget” how much she paid when it comes time to complete the survey. As long as thisselective omission is similar for different demographic groups and similar over vehicles forwhich a group has different preference intensities, I would not expect missing data to impactmy conclusions. However, future work might benefit from using a dataset that is matchedto actual transaction data both to remove the possibility of missing values and to confirmthat consumers’ recollections of the price they paid for their new vehicles matches the actualtransaction price.28

In order to account for customers who decided not to purchase a new vehicle in thesecond quarter of 2005, I append observations to my sample with consumers from eachdemographic group who purchased the outside good. This approach is valid if the populationdistributions of the observed consumer characteristics, d (demographic group) and z (otherconsumer characteristics), are known, the consumer characteristics, d and z, have discretedistributions, and the fraction of non-buyers is known for each d, z cell.

Working through the requirements, I begin by using information from GfK AutomotiveResearch which says that approximately 20% of Americans considered buying a new vehiclein the previous year. I therefore assume that 10% of Americans considered buying a newvehicle in the second quarter of 2005.29 I assume that that consumer characteristics of this10% of Americans mirror the attributes of the American population as a whole, as measuredby the Current Population Survey for May 2005.30 These assumptions give me the populationdistributions of d and z. Since d is by definition a discrete demographic group and z is theconsumer’s response to discretely-valued survey questions, the consumer characteristics havediscrete distributions.

In order to know the fraction of non-buyers in each d, z cell, I start with information28The Scott Morton, Zettelmeyer, and Silva-Risso (2003) dataset would avoid the issues of self-reported

prices and has substantially more observations, but does not have information directly from the consumeron demographics, consumers’ second choices, or whether a consumer is both the principle buyer and driver.

29Assuming 5% or 15% of Americans considered buying a new vehicle in the second quarter of 2005 doesnot change the results substantially.

30This assumption mirrors assumptions in BLP and MicroBLP that all Americans consider buying a newvehicle each year, but limits the population to a more relevant group. The other extreme assumption wouldbe that consumers who consider buying a new vehicle have the characteristics of the consumers who do buynew vehicles, although this is not standard in the literature.

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from the Automotive News Market Data Book on the total number of vehicles of each modelsold in the second quarter of 2005.31 I assume that the distribution of consumer characteris-tics for purchasers of each vehicle is the same as the distribution of consumer characteristicsfor purchasers of that vehicle in my survey data. By summing the characteristics of con-sumers over the total number of vehicles sold in the quarter, I then know the number of newvehicle purchasers in each d, z cell. Thus I have both the total number of consumers ineach cell who considered purchasing a new vehicle and the total number who did purchasea new vehicle. The difference is the weight I place on the non-purchase observation for thatconsumer characteristic cell, thus satisfying requirement 3 above. Thus my data satisfies thefour requirements above and the data augmentation procedure gives me a sample of vehiclepurchasers and non-purchasers.

I pair this data with data from AutoData Solutions on the attributes of model year 2005vehicles. This data includes extensive information on the vehicle, including the manufac-turer’s suggested retail price (MSRP), horsepower, curb weight, wheel base, fuel economy,turning radius, and whether the vehicle has stability control, traction control, or side airbags.This data is at the vehicle trim level, which allows it to differ for the same vehicle modelbased on differences such as engine type (e.g. V6 vs V8) or body style (e.g. hatchbackvs sedan). Since my consumer choice data only specifies a consumer’s purchase decisionat the model level, I use the vehicle attributes of the trim with the lowest MSRP as themodel attributes and consider any deviations from this unobserved quality. This reinforcesthe idea that consumers of different demographic groups might have different valuations ofunobserved quality, since not only the vehicle’s styling may be valued differently but alsothe average trim level chosen may vary by demographic group. To the extent that manyoptions such as leather seats, rear spoilers, or sunroofs may be fairly inexpensive to producebut command a large markup, these options packages may be a way for firms to encourageconsumers to self-select into options packages that are priced to further price discriminate.32

4.1 Posted Prices

While my data includes the actual transaction price for the vehicle purchased, I clearly cannotobserve the price that each consumer would have negotiated for every vehicle that she didnot purchase. This is a long-standing problem in the vehicle-choice literature, to the extent

31Note that in the second quarter of the year almost every vehicle has the model year equal to the calendaryear, which alleviates the issue of the mix of model years of vehicles sold.

32Although this is technically second-degree price discrimination, where firms configure product offeringssuch that consumers will sort by willingness to pay, I will estimate it as a part of what I call statisticalprice discrimination. Ireland (1992) provides an interesting discussion of the identification of this type ofsecond-degree price discrimination using the assumption that costs are linear in vehicle attributes.

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that the nearly every discrete-choice vehicle demand paper uses the manufacturers’ suggestedretail price (e.g. BLP) or an average transaction price (e.g. Alcott and Wozny (2012)) for allvehicles as the price the consumer actually pays. When studying the variation in prices overconsumers, this is clearly not a reasonable approach. Instead, as mentioned in section 3.3,I will use the average price paid by a consumer’s demographic group for each vehicle in thechoice set as the price the consumer pays for that vehicle. This assumes that the consumeris aware of the price paid for each vehicle by other consumers in her demographic group, andthat she choses which vehicle to purchase based on those commonly known prices. Thus anyactual variation in the price that a specific consumer would pay relative to other consumersin her demographic group must be random. While this is clearly a strong assumption, itavoids issues of heterogeneity in negotiation across individuals within a demographic groupthat are beyond the scope of this paper, although potentially interesting for future research.

5 Results

The results are presented in three steps: the demand coefficients are presented first andinclude the δs (the mean preference of each demographic group for each vehicle), meanpreference coefficients (how different vehicle attributes contribute to the δ vector for eachdemographic group), and the coefficients governing observed and unobserved preference het-erogeneity within demographic groups. I then present the elasticities and optimal markupsthat are calculated from these demand coefficients, and finally I compare the predictedmarkup differences between pairs of demographic groups to the observed average price dif-ferences for those groups.

5.1 Demand Estimation Results

The δ vector contains the values of the mean preference for each vehicle for each demographicgroup that set the predicted market share for each vehicle equal to its observed marketshare, conditional upon the consumer heterogeneity coefficients. In this respect, the δ vectoracts as an adjusted market share, where the adjustment comes from the fact that someconsumers with extreme preferences will buy certain vehicles frequently enough to matcha substantial portion of the vehicle’s market share even when the mean consumer stronglydislikes the vehicle. A good example of such a scenario occurs in my results with luxuryvehicles like the Lexus SC 430 or the Buick Park Avenue. While the average consumer ofany demographic group might dislike the high price of these vehicles, there may be someconsumers in each group with a high demand for luxury and a low distaste for price who find

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these cars attractive. These mean preference coefficients order vehicles by the preferenceof the average consumer of each demographic group, making them a useful reality-checkbefore moving on to the coefficient estimates. The highest and lowest five vehicles for eachdemographic group are listed in Table 3. Across all demographic groups, small and mid-sized SUVs such as the Jeep Liberty and the Honda CR-V frequently appear among the top 5vehicles in terms of mean preference. Similarly, the Hummer H2 SUT 33 frequently appearsamong the bottom 5 vehicles, likely because of its unusual features and their extremelyhigh price. It is additionally interesting to note some of the differences between differentdemographic groups that might conform to our priors. Married women’s top 5 vehiclesincludes two minivans while other groups generally prefer SUVs. The Toyota Echo, a small,sub-compact car, appears among the bottom 5 vehicles for both married and single men,while married and single women’s bottom 5 are dominated by expensive luxury vehicles anda few sporty cars.

Regressing the δ vector on vehicle attributes using weighted instrumental variables gen-erates the mean preference coefficients for each demographic group.34 In these regressions,I include price (instrumented with the BLP instruments as discussed earlier), whether thevehicle is a car, and if so whether it is a “sporty” car (generally a small, high horsepower carlike the Acura RSX, the Mazda Miata, or the VW GTI), and whether the vehicle is a truck.I also include vehicle curb weight, number of passengers, turning radius, and whether thevehicle is manufactured by a domestic manufacturer in the mean preference specification.

Table 4 reports the mean preference coefficients for each demographic group. Marriedwomen are the least price sensitive on average while single women are the most price sensitive.All four groups dislike cars relative to the omitted groups (SUVs and vans), while onlymarried consumers dislike pickup trucks relative to SUVs and Vans. All groups dislikesporty cars even more than they dislike cars as a whole (the total preference for sporty carsrelative to SUVs and Vans is the sum of the car coefficient plus the sporty car coefficient),but this distaste is particularly intense for married consumers. The preferences for vehiclecharacteristics are generally what we might expect. All demographic groups like biggervehicles, as measured by curbweight, which are also generally safer. Conditional on vehicleweight and type, however, consumers dislike vehicles with higher seating capacity, a distastewhich is stronger for men than women. All consumers prefer vehicles with smaller turningradii, which likely captures other aspects of the vehicle’s performance as well. Finally, alldemographic groups prefer imported vehicles in domestic vehicles controlling for attributes,

33SUT stands for sport utility truck, which is effectively a large SUV with a pickup bed.34Weights are equal to the inverse of the number of consumers purchasing each vehicle in each demographic

group in my sample.

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but this preference is particularly strong for women.The specification of consumer demand heterogeneity includes price, price divided by

household income, the four vehicle types (car, truck, SUV, and van) as well as the fueluse (in gallons per mile), horsepower, and curbweight, as well as an indicator variable forthe outside good and the outside good divided by household income. I specify all vehiclecharacteristics that are not interacted with observable consumer characteristics as havingnormally distributed unobservable heterogeneity. The coefficients on all of the normallydistributed unobservable heterogeneity terms can be interpreted as the standard deviationin the demographic group’s preference for the vehicle attribute, while the coefficients onprice divided by income and the outside good divided by income are the extent to which thepreference for price and the outside good vary with income.

Table 5 presents the coefficient estimates for these consumer heterogeneity terms byincome group and gender. The first thing to notice is that there is very little variation inthe price coefficient once household income is taken into account, but household incomedramatically affects consumers’ sensitivity to a vehicle’s price. The sign of this effect iswhat would be expected: consumers in lower income households react more negatively to avehicle’s price than consumers in higher income households. There is substantial variationin preferences for vehicle types (SUV, truck, van, car) for all groups except single women,although single women do show variation in the strength of their preference for cars, and thevariation in their preference for vans is large but not statistically significant. Interestingly,the variation in preference for vehicle attributes is less generic across groups. Only marriedmen show large variation in their preference for horsepower, and only men (both single andmarried) have variation in their preference for fuel use. No group shows substantial variationin their preference for curb weight. Finally, groups generally do have large variation intheir preference for the outside good, and this variation is also correlated with householdincome. Women have particularly high variation in their preference for the outside good.Perhaps the most surprising coefficient in the demand estimation results is that consumersfrom households with lower income have a stronger distaste for the outside good (which,recall, includes purchasing a used vehicle or continuing to drive your current vehicle). Thisresult is less surprising in light of the fact that the price of the outside good has alreadybeen controlled for in the price interacted with household income coefficient, so this resultmust be picking up differences in preferences for the attributes of new cars relative to theoutside good or differences in the methods of financing new vehicle purchases. If low incomehouseholds are using leases to purchase new vehicles while high income households purchasenew vehicles with cash, then I would expect a stronger preference for the outside good amonghigh income households as I find here.

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5.2 Elasticities and Optimal Markups

When converting these coefficient estimates into estimated markups, a useful statistic withsome economic intuition is the aggregate own-price demand elasticity for each demographicgroup for each vehicle. Table 6 give some descriptive statistics on own-price elasticitiesand markups across demographic groups. Generally, the elasticities average from approx-imately 6 (in absolute value) for married men and education groups to over 17 for singleconsumers. While these elasticities are fairly high, the markups that result from them seemquite reasonable. The average predicted markup across all demographic groups is 30.2%of the transaction price, and the US Environmental Protection Agency uses manufacturermarkups of 31.5% to estimate the impact of emissions control regulations (RTI International(2009)). We see relatively high markups relative to transaction prices in the new vehiclemarkup because the high fixed costs for each model produced prevent firms from bringingvehicles to the market that aren’t expected to earn high markups over marginal costs. Whilethe range in the percent markups is fairly large, from 15 to 60%, it is not surprising that thesedifferent demographic groups, with substantially different household incomes, are willing topay substantially different prices for the same vehicles.

5.3 Understanding Variation in Prices

As explained in section 3.3, by subtracting the optimal markup from the price paid by eachindividual, I can look at how prices paid by consumers of different demographic groups vary,controlling for the optimal markup for the manufacturer to charge each demographic group.This variation will include the marginal cost of each vehicle (which is assumed to be constantover demographic groups) as well as any taste-based cost of interacting with consumers ofdifferent demographic groups. This estimate will also include any differences in dealers’ability to extract higher prices from some groups than others through negotiation, andtherefore we must be careful not to interpret differences across demographic groups purelyas dealer animus. In fact, many of the results are indicative of differences in bargainingability being a primary driver of price differences across demographic groups once I controllfor optimal markups.

Table 7 shows correlations similar to those presented in Table 1, but this time withprice paid minus the optimal markup as the dependent variable. The omitted demographicgroup is single women, so table 7 indicates that single men pay slightly less than singlewomen, while married men and women pay substantially less than single women, controllingfor optimal markups. The fact that married people pay less than single people for the same

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vehicles indicates that households with more than one potential buyer likely send the strongernegotiator to purchase new vehicles. It may also indicate that married consumers, who aregenerally older, more educated, and from higher income households than single consumers,are stronger negotiators overall or have more information about where to get good dealsthan single consumers.35

Interestingly, while single men pay slightly less than single women, married men pay morethan married women for same vehicles. This pattern seems to indicate the strong role ofnegotiation and search in the variation in prices paid by consumers of different demographicgroups. If men are generally stronger negotiators or know more about cars than women,then I would expect men to pay less than women for new cars when the sample includesno selection. The closest I can come to a sample that is free of selection based on negoti-ating ability is with the single male and female purchasers, since they likely do not have ahousehold member of the opposite sex who could purchase a vehicle for them (as indicatedby their overwhelming response that they are both the pricinple buyers and drivers of thevehicle purchased). The result that single men pay less than single women once I controlfor optimal markups suggests that women may be in a weaker negotiating position thanmen, as suggested by Babcock and Laschever (2003).36 Married men and women do have afamily member who could purchase the vehicle for them, so we would expect the strongernegotiator or more informed consumer to be the likely purchaser of new vehicles for thehousehold. Since my data only contains consumers who are both the primary buyer anddriver of the new vehicle, it is likely that the married women in my dataset are particularlystrong negotiators or particularly well-informed relative to married women as a whole. Thusdifferences in negotiating strength, interacted with the availability of another family memberto purchase the vehicle, explains the how men might get better deals on new vehicles thanwomen when they are both single, but worse deals than women when both are married.

Of course, these results do not rule out the possibility that dealers are engaging in taste-based discrimination (animus) against single people, or single women in particular. The factthat women pay less than men when they are married but more than men when they aresingle seems to argue against gender discrimination in general, but if married women whopurchase their own cars are in substantially stronger negotiating positions than married menwho purchase their own cars, then this could obscure taste-based discrimination againstwomen in general. More likely is that there is a systematic increase in the price paid byyounger and less well-educated consumers, who are more likely to be single. While there

35The idea that marital status might be correlated with income and therefore affecting preferences andnegotiating ability via channels other than marriage is explored in greater detail in the appendix.

36Alternatively, a type of animus may explain this result if dealers negotiate more aggressively with singlewomen than with single men.

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may be some dealers who dislike interacting with younger consumers, the fact that consumerstend to purchase the same brand of vehicle repeatedly (Winston and Train (2007), Anderson,Kellogg, Langer, and Sallee (2012)) would seem to indicate that the long-run profits fromselling to a younger consumer are larger than the long-run profits from selling to an olderconsumer, which suggests that dealers would be inclined to give younger consumers discounts.Generally, although I find the evidence suggestive of differences in negotiating ability acrossdemographic groups, I cannot rule out that taste-based discrimination is also playing a role.

Finally, in order to confirm that dealers are adjusting transactions prices with the pre-dicted optimal markups, I run the price correlation regression again, but controlling for theoptimal markup as an independent variable. The results are shown in column 2 of table 7,and indicate that while prices do rise with optimal markups, the relationship is not one-for-one. This could be because the markups I calculate are optimal for manufacturers, whiledealers face slightly different incentives. In particular, dealers are spatially differentiatedfrom each other, but a consumer who is willing to travel can purchase the same vehicle frommultiple dealerships. This would decrease the dealers’ ability to price discriminate, and couldexplain why only 39.4% of the variation in the optimal markup is being internalized in thevariation in prices. Additionally, this less-than-complete price discrimination could resultfrom consumers ability to obscure their demographic group by sending a friend or familymember to negotiate for a vehicle in their place.37 However, controlling for the optimalmarkup in this way does not change the basic demographic group results. While single menare still estimated to pay less than single women, the difference is not statistically signifi-cant. Married men and women are still expected to pay less than single men and women,and that difference, while smaller than before, is still statistically significant.38 Finally, thepattern of single men paying less than single men but married men paying more than marriedwomen remains when I control for the optimal markup as an independent variable ratherthan subtracting it from the price paid.39

37Additionally, when the markup is interacted with marital status, markups are transferred into pricesat a much higher rate for single consumers than for married consumers, indicating that consumers’ abilityto obscure their type by sending a family member to purchase a vehicle limits dealers ability to pricediscriminate.

38Of course, all of these results could also be explained by consumers who purchase inexpensive vehiclesbeing stronger negotiators than consumers who purchase more expensive vehicles, but given that I havecontrolled for the expected price sensitivity of each group, these differences in negotiating ability would haveto stem from something other than pure price sensitivity, which seems the reverse of what we would actuallyexpect.

39If this regression is run with log price paid as the dependent varible and log markup as an independentvariable the relative price results are unchanged and all of the demographic group coefficients are significantat the 1% level.

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6 Conclusion

This paper explores the role of consumer preferences that vary with demographic group innew vehicle pricing. I find that dealers do appear to price discriminate between consumersbased on demographic group preferences, but that this discrimination may be mitigatedby consumers ability to obscure their preferences by sending a family member to purchasethe new vehicle for them. The results also suggest that differences in the prices paid fornew vehicles controling for demographic group preferences appear to stem from differencesin consumer knowledge or negotiating strength. While these results do not rule out dealeranimus against a particular demographic group, I do not find evidence of such behavioreither.

Ideally, future reseach could take this approach to understanding optimal statistical pricediscrimination to demographic distinctions where there is substantial concern about taste-based or animus discrimination, such as race. There are very few black new vehicle purchasersin my data, and the possibility of discrimination in the used car market makes preferenceestimation that assumes a uniform outside good potentially confounding. However, a modelthat includes both new and used vehicle purchases and transaction costs might be able toshed light on the role of preferences in price differences across races. Given that blacks havelower income on average than white, it is likely that controlling for statistical discriminationwill increase the size of black price premiums estimated in Ayers and Siegelman (1995).

Finally, this paper develops an approach to identifying the effect of preferences in marketoutcomes that could be taken to many other settings. Preferences could affect where con-sumers choose to live, work, and study, and could interact with home sellers, employers, andschool admissions committees in ways that obscure whether taste-based discrimination isoccurring in a market. Understanding the role of preferences in consumers’ decision-makingwould allow policymakers to better target markets where discrimination is leading to adversedistributional outcomes.

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[35] McFadden, Daniel. 1974 “Conditional logit analysis of qualitative choice behav-ior” in P. Zarembka (ed.) Frontiers in Econometrics: 105-142

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[36] Phelps, Edmund S. 1972 “The statistical theory of racism and sexism” AmericanEconomic Review 62: 659-661

[37] Revelt, D. and K. Train. 1998 “Mixed logit with repeated choices: House-holds’ choices of appliance efficiency level” Review of Economics and StatisticsLXXX(4): 647-657

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[39] Salop, Steven, and Joseph Stiglitz. 1977 “Bargains and ripoffs: A model of mo-nopolistically competitive price dispersion,” Review of Economic Studies XLIV:493-510

[40] Schmalensee, Richard 1981 “Output and welfare implications of monopolisticthird-degree price discrimination” American Economic Review 71: 242-247

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28

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29

Table1:

Correlatio

nsBe

tweenPr

icePa

idan

dDem

ograph

icGroup

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Male

32.01

-35.04

48.32

36.52

23.76

19.27

(116.95)

(117.41)

(118.87)

(117.70)

(116.72)

(118.03)

Hou

seho

ldIncome

106.30***

106.67***

141.93***

(tensof

thou

sand

sof

$)(17.93)

(18.10)

(20.33)

Marrie

d-103.94

-111.56

-462.97***

(133.73)

(135.92)

(147.00)

Yearsof

Education

-37.71

-37.95

-114.99***

(33.29)

(33.42)

(34.39)

Urban

219.14

218.19

129.40

(147.37)

(147.16)

(149.31)

Mod

elfix

edeff

ects

YY

YY

YY

YY

YY

F17.64

0.37

0.65

1.11

11.06

JointSign

ificance

0.0000

0.6900

0.5210

0.3292

0.0000

R2

0.8047

0.8061

0.8047

0.8047

0.8048

0.8062

0.8047

0.8047

0.8048

0.8070

Regressionof

pricepa

idon

consum

ercharacteris

ticsan

dvehiclefix

edeff

ects.Rob

uststan

dard

errors

inpa

rentheses.

Coefficients

markedwith

a*,

**,o

r***aresig

nifican

tat

the10%,5

%,o

r1%

levelr

espe

ctively.

Allregressio

nsinclud

e11029ob

servations

and210vehiclefix

edeff

ects.The

numbe

rof

observations

intheseregressio

nsis

smallerthan

thenu

mbe

rof

observations

inthediscrete

choice

mod

elbe

causeof

theexclusionof

consum

erswho

purcha

setheou

tsidego

odfrom

theseregressio

ns.

30

Table 2: Correlations Between Price Paid, Demographic Group, and Vehicle TypeDependent Variable: Price PaidIndependent Variable Interacted with: Male SingleCompact Car -338.73** 185.97

(171.93) (165.68)Fullsize Car 220.42 -236.07

(455.44) (595.94)Luxury Car 1424.65*** -17.50

(454.81) (506.44)Midsize Car 200.40 -6.07

(276.52) (270.11)Pickup -1272.62 17.70

(796.95) (494.26)Sport Utility Vehicle 682.69*** -377.01

(258.21) (277.58)Sporty Car 793.21* 192.54

(423.37) (398.34)Van 487.34 -368.23

(509.83) (552.82)F-test rejection level 0.0058 0.8725R2 0.8055 0.8041Regression of price paid on consumer characteristics and vehicle fixed effects.Robust standard errors in parentheses. Coefficients marked with *, **, and ***are significant at the 10%, 5%, and 1% level respectively. All regressions include11,029 observations and 210 vehicle fixed effects. The number of observations inthese regressions is smaller than the number of observations in the discrete choicemodel because of the exclusion of consumers who purchase the outside good fromthese regressions.

31

Table3:

MeanPr

eferencesby

Genderan

dMarita

lStatus

Dem

ograph

icGroup

Marrie

dSing

leWom

enMen

Wom

enMen

TopFive

Vehicles:

Toyo

taHighlan

der

Jeep

Grand

Cherokee

Hon

daCR-V

Dod

geCaravan

(inorde

r,high

estto

lowest)

Dod

geCaravan

Chevrolet

Silverad

oLD

Toyo

taRav

4Chevrolet

Express

Jeep

Libe

rty

Hon

daCR-V

Jeep

Libe

rty

Jeep

Grand

Cherokee

Hon

daCR-V

Toyo

taHighlan

der

Ford

Escape

Jeep

Wrang

ler

Toyo

taSienna

Jeep

Libe

rty

Jeep

Grand

Cherokee

Jeep

Libe

rty

Bottom

Five

Vehicles:

Chevrolet

SSR

Hum

mer

H2SU

TBu

ickPa

rkAv

enue

Buick

Park

Avenue

(inorde

r,lowestto

high

est)

Buick

Park

Avenue

Aud

iTT

Lexu

sLS

430

Lexu

sSC

430

Hum

mer

H2SU

TLe

xusSC

430

Lexu

sLX

470

Buick

Century

Buick

Century

Jagu

arS-Ty

peChevrolet

Corvette

Toyo

taEc

hoVo

lksw

agen

GTI

Toyo

taEc

hoLe

xusSC

430

Saab

9-5

Vehicles

areorderedby

theestim

ated

meanpreference

ofeach

demograph

icgrou

p,controlin

gforheterogeneity

ingrou

ppreference.The

mean

preference

isestim

ated

usingtheBerry

(1994)

inversionin

themax

imum

likelihoo

destim

ationof

thecoeffi

cients

that

describ

ewith

in-dem

ograph

icgrou

pheterogeneity

inpreferences.

32

Table 4: Mean Preference Coefficients by Gender and Marital StautsGender and Marital Status

Variable Married Females Married Males Single Females Single Males

Price -1.36*** -1.42*** -1.88*** -1.64***(tens of thousands of dollars) (0.25) (0.31) (0.32) (0.29)

Car -1.76*** -1.57*** -1.82*** -1.67**(0.31) (0.36) (0.36) (0.35)

Pickup -2.62*** -1.20*** -0.56 -0.28(0.38) (0.45) (0.41) (0.41)

Sporty Car -1.04** -1.54*** -0.87* -0.52(0.41) (0.48) (0.46) (0.47)

Curbweight 1.34*** 1.21*** 0.97** 1.32***(thousands of pounds) (0.33) (0.41) (0.44) (0.38)

Number of Passengers -0.23** -0.57*** -0.28** -0.49***(0.10) (0.12) (0.12) (0.11)

Turning Radius -4.60*** -3.83*** -3.76*** -4.38***(feet) (0.41) (0.47) (0.46) (0.46)

Domestic -1.28*** -0.59** -1.93*** -0.67***(0.21) (0.24) (0.24) (0.24)

Number of Observations 213 213 213 213Instrumental variables regression of the mean preference of each group for each vehicle on vehiclecharacteristics. Instruments are functions of the vehicle attributes of competing vehicles, as in BLP.Weighted instrumental variables standard errors in parentheses, where the weights are equal to the numberof observations for that demographic group that purchased that vehicle in the maximum likelihood stage.Significance level indicated by: *=10%, **=5%, ***=1%.

33

Table5:

Preference

Heterogeneity

Coefficients

byGenderan

dMarita

lStatus

Genderan

dMarita

lStatus

Varia

ble

Varia

bleTy

peMarrie

dFe

males

Marrie

dMales

Sing

leFe

males

Sing

leMales

Price

StdDev

0.07**

0.06**

0.004

0.009

(tensof

thou

sand

sof

dolla

rs)

(0.03)

(0.02)

(0.045)

(0.038)

Divided

by-7.15***

-5.86***

-5.90***

-4.62***

Income

(1.00)

(0.53)

(1.19)

(0.94)

SUV

StdDev

1.47***

2.42***

0.03

1.76***

(0.32)

(0.27)

(0.19)

(0.52)

Pickup

StdDev

2.55***

3.78***

0.16

2.31***

(0.56)

(0.37)

(0.36)

(0.65)

Van

StdDev

1.83***

2.69***

1.59

0.88

(0.48)

(0.40)

(1.06)

(1.72)

Car

StdDev

2.90***

4.08***

2.80***

3.26***

(0.41)

(0.35)

(0.61)

(0.75)

Horsepo

wer

StdDev

0.07

0.59***

0.37

0.39

(hun

dreds)

(0.20)

(0.11)

(0.31)

(0.24)

Fuel

Use

StdDev

0.10

0.26***

0.11

0.15**

(gallons

perhu

ndredmiles)

(0.09)

(0.06)

(0.16)

(0.07)

CurbWeigh

tStdDev

0.004

0.03

0.34

0.005

(tho

usan

dsof

poun

ds)

(0.091)

(0.09)

(0.21)

(0.031)

Outsid

eGoo

dStdDev

1.41***

0.46**

1.74***

0.82*

(0.28)

(0.24)

(0.46)

(0.46)

Divided

by-12.37***

-6.42***

-12.02***

-8.43***

Income

(2.69)

(1.55)

(3.09)

(2.68)

Num

berof

HaltonDraws

100

80100

100

Num

berof

Observatio

ns3092

5606

2400

2356

Stan

dard

errors

inpa

rentheses.

Sign

ificancelevelind

icated

by:*=

10%,*

*=5%

,***=1%

.Coefficients

estim

ated

with

max

imum

likelihoo

d.

34

Table 6: Estimated Elasticities and MarkupsGender and Marital Status

Variable Statistic Married Females Married Males Single Females Single MalesElasticity Mean -10.10 -6.39 -17.82 -17.32

$ Markup Mean 10,078 9,107 7,349 7,605Min 5,560 6,227 4,484 5,333Max 14,752 14,311 12,457 10,807

% Markup Mean 35.25 32.78 25.58 27.17Min 21.19 15.88 16.64 14.58Max 57.75 60.73 45.61 49.79

Number of Vehicles 213 213 213 213Descriptive statistics are over the 211 vehicles in the sample. All numbers are calculatedusing the demand coefficients presented in tables 4 and 5. Percent Markup is the markupdivided by the average price paid for the vehicle by that demographic group.

35

Table 7: Correlations Between Price Paid and Demographic Group Controlling for MarkupsDependent Variable

Price-markup PriceSingle Male -411.58** -209.09

(161.01) (170.94)Married Female -2955.33*** -1281.50***

(132.31) (332.97)Married Male -2135.81*** -903.92***

(203.72) (331.06)Markup 0.3941***

(0.1241)Model fixed effects Y YF 171.52 5.64Joint Significance 0.0000 0.0010R2 0.7548 0.8080Regression of price paid minus optimal markupon consumer characteristics and vehicle fixedeffects. Standard errors clustered by vehicle inparentheses. Coefficients marked with a *, **, or*** are significant at the 10%, 5%, or 1% levelrespectively. All regressions include 11115observations and 213 vehicle fixed effects. Thenumber of observations in these regressions issmaller than the number of observations in thediscrete choice model because of the exclusion ofconsumers who purchase the outside good fromthese regressions.

36