The market valuation of new car quality

Trampn. Res:A, Vol. EA. Rimed in Great Bntam.

No. 5. pp 361-373.

1988

0191-1607~88 $3 w l .oo & 1988 PergamonPress plc

THE MARKET VALUATION CAR QUALITY-f

OF NEW

NOEL D. URI Resources and Technology Division, Economic Research Service, U.S. Department Agriculture, Washington, D.C. 20005, U.S.A.

of

(Received 17 March 1987; in revised form 20 March 1988)

Abstract-The study examines the question of whether the new car market values put a premium on superior quality cars while penalizing those cars that are of relatively inferior quality. The results suggest that the market does value cars that ex post have a much better than average or better than average reliability rating based on the criteria used in Consumer Reports. Moreover, cars that perform relatively poorly in terms of reliability ratings have a lower associated price, all other things equal. Finally, the results show that the market does value safety as a quality characteristic.

1.

INTRODUCTION

endeavors to determine if the new car market puts a premium on superior quality cars while penalizing those cars that are of relatively inferior quality.+ If the market does not value quality characteristics of new cars, one would expect that the average level of quality characteristics possessed by new cars would decline since producers would have no incentive to provide cars with higher levels of the quality characteristics. Manufacturers of new cars with relatively higher levels of the quality characteristics will not be able to recover the costs incurred in producing these characteristics and will either cease producing new cars with these characteristics or withdraw entirely from the market. Under these circumstances consumers would not get the mix of physical and performance characteristics they would be willing to pay for and hence market failure would occur. Consequently, it is important from a policy perspective to find out if the market is properly valuing new car characteristics. In what follows, an effort is directed at empirically examining whether the market for new cars in 1982 did value (i.e. put a premium on) cars that ex post had higher quality. Several measures of quality are used in the analysis. The results suggest that the market is functioning efficiently with regard to the valuation of these reliability characteristics. The This study

tThe views expressed are those of the author and do not necessarily represent the policies of the Department of Agriculture or the views of other Department of Agriculture staff members. *The theoretical economic literature as whether the market values the quality aspects of goods and services is quite extensive. The reader is referred to Akerlof (1970). Allen (1984), Archibald er al. (1983), Cowling and Rgyne; (1970). Gorman (1980). Jacobv and Olson (1985). Lvnch er al. (1986). Schmaiensee (i985). Shapiro‘(l98%), and Stiglitz (1987). These references provide an overview of the relevant issues and concerns.

study also examines the market valuation of the quality characteristics of overall car safety and the absence of safety defects. The market valuation of these characteristics is less definite but the results generally indicate that there is no market failure for quality with regard to these characteristics. 2. THE DETERMINANTS

OF NEW CAR

QUALITY

If the market did not value quality characteristics, the level of these characteristics would decline until only a single level of these characteristics was offered.0 Conversely, if the market is assimilating the presence of the quality characteristics of new cars into its valuation, then the price of a new car would be relatively higher the more of the desirable quality characteristics it possesses. There are three forces operating in the new car market that determine whether the market provides various levels of quality. One is that automobile manufacturers have an incentive to build and maintain reputations with regard to the various quality characteristics due to the importance of repeat purchases and future sales to others.11 The second force is the presence of signaling mechanisms, such as warranties, which enable consumers to ascertain variations in quality characteristics of a car. Third, the production of characteristics valued by the market and making consumers aware of them add to the cost of bringing a new car to the market. If the first two forces operate effectively in the new car market then one would see consumers purchasing products

§This is normally referred to as the “lemons” hypothesis. The term “lemon” was coined by Akerlof (1970) in reference to used cars. As a general notion, “lemons” refers to any relatively poor-quality product. As we will use the term in this investigation, the term will refer to market failure such that the quality characteristics are not valued by the market. /ISee, e.g. Heal (1976), Klein and Leffler (1981). Shapiro (1983), and Telser (1980).

N. D.

362

having the characteristics they desire and they would pay only for the amounts of the characteristics the product possessed and these payments would equal the cost of providing the characteristics. What can we conclude from the actual behavior of new car manufacturers with regard to signalling quality? Unfortunately, comprehensive data on advertising efforts across manufacturers are not available publicly to allow us to gain insights into the behavior of producers by making cross-company comparisons. That is, for example, based on Nelson’s (1974) argument, one could conclude that a firm advertising relatively more intensively has a relatively higher-quality product and this higher-quality level is reflected in the price of the product. Absence of the requisite data, however, precludes any such inference. A similar problem occurs with regard to warranty information. There is nothing available in generally accessible sources. The substance of these foregoing considerations is that on the demand side, one has a testable hypothesis that consumers with different preferences would be willing to pay different amounts for higher quality and on the supply side one has the testable hypothesis that higher quality is more costly to produce. To the extent these two hypotheses are correct, the market should produce cars of differing quality and the higher-quality cars should have higher prices and higher-quality cars would be purchased by consumers who valued this higher quality, and such cars would not be purchased by consumers who do not value quality. Due to data limitations, this study cannot address all of these aspects of new car quality. Instead, the focus is limited to measuring whether higher-quality cars sell for more than lower-quality cars. 3. QUALITY

CHARACTERISTICS

There are a variety of quality characteristics that are possessed by new cars. These include such factors as reliability, the safety of a car, and maintenance and repair costs. Each of these characteristics potentially affects the market valuation of a new car. Before examining whether this de facto is the case, it is useful to define what each quality characteristic entails and why it might be expected to affect the price of a new car. 3.1 Reliability One of the most obvious of the quality characteristics of a new car is reliability. Reliability, as the term is used in this investigation, relies on the inputs and processes used to create a new car and hence focuses on such aspects of a new car as durability and freedom from defects. A priori, one would expect consumers to be concerned about reliability. The purchase of a new car involves a substantial outlay. The explicit costs (e.g. costs of maintenance and repair) and implicit costs (e.g. the opportunity cost of taking a car in for repair

URI

or doing without a car while it is being serviced) associated with a relatively less reliable car can be quite significant. 3.2 Safety Another quality characteristic is the safety of a vehicle. There are a variety of ways in which one can consider the relative safety of a vehicle.+ For the purposes of this study, safety will be considered in the context of a car’s crashworthiness where crashworthiness is defined as how well the car protects occupants in accidents and how are collision losses affected. The more crashworthy a car is, all other things equal, the lower the level of personal injury and collision losses and hence the higher one would expect the market to value the car. Alternatively, it has been argued that most consumers do not value crashworthiness.$ There is one additional safety consideration that is relevant when discussing quality characteristics. Since 1966, more than 150 million cars have been recalled for inspection and correction of safety-related defects. These defects have been related to seat belts, the steering and suspension system, the fuel system. the transmission, and the electrical system. One might expect that the presence of a safetyrelated defect resulting in a recall to adversely impact the market valuation of the new car since a perception on the part of consumers seems to linger that if a product has been recalled due to a safety defect then there are other inherent problems with the product.9 Alternatively, it has been argued that the impact of safety-related recalls on the demand for cars is, at best, limited with brand loyalty being most important in new car purchase decisions.11 The empirical question concerning the impact of safety-related defects was considered in the preliminary empirical analyses. Unfortunately, multicollinearity problems precluded inclusion of this factor in the final analysis.#

tGraham and Garber (1984) discuss a number of these. Among them is the avoidability of an accident. (This is manifest in new cars, for example, in the form of antilock breaking systems.) $Peltzman (1975) and Graham and Garber (1984) suggest that to the extent a car’s crashworthiness is increased, the driver’s behavior is altered with the result that some of the benefits (in terms of expected reduced collision or personal injury losses) are negated. §See Jolly and Mowen (1985) and Hartman (1987) for a fairly extensive list of references that focus on this perception. ItSee, e.g. Crafton et al. (1981) and Reilly and Hoffer (1983) for arguments supporting this contentton. #That is, whether a car was recalled due to a manufacturing safetv defect and whether a car has a much better than&era& reliability rating were significantly correlated. The relevant correlations are -0.50, -0.69, and - 0.67 for subcompact, compact, and intermediate-size cars. (Too few full-size cars had a much better than average reliability rating.) These values are based on defining a dichotomous variable equal to one if a car was recalled and zero otherwise. Alternative definitions of the recall variable fol-

The market valuation of new car quality 3.3 Maintenance and repair costs Yet other quality characteristics

associated with the purchase of new car are the cost of preventive maintenance (e.g. the cost of periodically servicing a car as specified by the manufacturer to keep it running properly) and the cost of repairs (e.g. the cost of replacing a component). These characteristics were not considered further since they proved to be highly correlated with the reliability measures. It is simply the case, in general, that cars with good performance records in terms of reliability are not going to incur large maintenance and repair bills. 3.4 Insurance cost There is one final factor that should be addressed in the context of quality considerations. While it is not properly classified as a quality characteristic, it is very much dependent on quality. This factor is insurance cost and has the potential for affecting the market valuation of a new car. A car’s design and accident history may affect insurance rates. Some cars cost less to insure because they are damaged less or are less expensive to repair after a collision. For example, a car with a well-designed bumper may escape damage in a low-speed crash but be more costly to repair in a high-speed crash.i Some cars are easier to repair than others or may have less expensive parts. Cars with four doors tend to be damaged less than cars with two doors. Discounts and surcharges usually range from 10% to 30%.$ 3.5 Summary These enumerated quality characteristics, then, reflect the major considerations that will be examined in this study. There are obviously other dimensions of quality that one could focus on but their consideration would be eschewed here. The nature and extent to which the previously enumerated characteristics are valued by the market will be subjected to empirical examination. This is the subject of what follows. 1.

AN EMPIRICAL VALUATION

APPROACH OF NEW

TO THE CAR

MARKET

QUALITY

Background. There is a long history of studies looking at the market valuation of various characteristics of new cars. The majority of these studies have relied on the hedonic approach. The hedonic or characteristics approach is based

*See, e.g. the results of the Insurance Institute for Highway Safety low-speed crash tests as reported in the Wushingron Post (Csongos, 1987). $See Gillis (1986).

lowing Hartman (1987) were also used. the recall variable based on the severity collinearity problem persisted. Finally, are not considered. Comprehensive data not available.

Hartman defines of the recall. The voluntary recalls across models are

363

on the empirical hypothesis which asserts that the multiple models and varieties of a particular commodity can be comprehended in terms of its basic attributes. Thus, the overall price of a commodity can be viewed as an aggregation of the prices associated with its individual components and characteristics. The greater the number of desirable characteristics or the more valuable the individual components, the higher the price. In estimating a hedonic function, all of the characteristics potentially valued by the market should be considered in the functional specification. For new cars, these characteristics are of two typesphysical characteristics (e.g. engine size, interior volume, length, etc.) and performance characteristics (e.g. acceleration, fuel economy, ride comfort, etc.).9 It needs to be emphasized that the hedonic function is exogenous as far as both consumers and producers are concerned. Rosen (1974) shows how the hedonic function is determined by demand and supply forces in the market and that, by itself, does not identify consumer preferences or producer costs.# That is not problematic here since the focus of the study is on the overall market valuation of the various new car characteristics and does not specifically concern itself with consumers’ valuation nor producers’ costs associated with the purchase or production of a new car.tt Data. The majority of the data used in the hedonic estimation were obtained from the 1983 Customer Satisfaction with Dealer Service survey conducted by J. D. Power and Associates in April 1983. The survey queried individuals who had purchased new 1982 model cars in the spring (March and April) of 1982. That is, the new cars considered are 1982 makes and models. The survey was sent to a sample of new car buyers drawn randomly from R. L. Polk’s new car registrations. The names of more than 22,500 new car buyers were selected. One thousand owners each of 21 major automobile models were selected to provide national representation among each manufacturer. Random selection within each nameplate surveyed provided models and body styles representative of each manufacturer’s sales mix. From this original mailing, 7,109 responses were received.

$The studv. bv_ Ohta and Griliches (1976) examines this issue most intensively of all available hedonic new car investigations. Obviously, physical characteristics in general are not valued for their own sake but rather because they affect the overall performance of a new car. We will continue to delineate between the two different types of characteristics, however, since this is what one commonly finds in the literature on this subject. [[See, e.g. Ohta and Griliches (1986) for an elaboration of this. ttAn implicit assumption is that since the study looks only at new cars, the new car market and the used car market are presumed to be separable. That is, the new car market is assumed to be unaffected by changes in the used car market.

364

N. D.

Only 6,201 of these responses are usable in the analysis, however, either due to incomplete responses on the questionnaire or because the ancillary data on a particular model needed to estimate the hedonic relationship are not available. The survey data used in the present study are the transaction price (i.e. the actual price paid for the new car), the body style (e.g. two-door sedans, fourdoor hatchback, etc.), the type of engine (e.g. gasoline or diesel), the number of cylinders, the type of transmission (automatic, four-speed manual, etc.), and all of the options included on the new car (e.g. fuel injection, AM and FM radio, air conditioning, etc.). Table 1 details, among other things, the specific types and categories of data included on this survey. The survey did not include questions to determine whether the car possessed such options as power steering or power brakes nor did it not contain questions on most of the physical and performance characteristics that should be included in a hedonic specification. Consequently, data on other variables that were used in the estimation process were obtained from a variety of sources. The length of the new car. the weight, the engine size (in cubic inches), the interior volume, the trunk volume, and the horsepower of the engine are either taken from Consumer Reports or Ward’s Automotive Yearbook 1983. With regard to the measurement of the reliability characteristics, the most comprehensive source of information on automobile reliability is the annual survey published in the April issue of Consumer Reports. The results are based on reader experiences involving typically more than 100,000 automobiles. The reliability of a specific model is represented by a trouble index that can take on any one of five values: 1 = much better than average, 2 = better than average, 3 = average, 4 = worse than average, and 5 = much worse than average. The measure is based on the number of trouble spots for a specific model. The automobile components considered in the survey include air conditioning, body exterior (paint), body exterior (rust), body hardware, body integrity, brakes, clutch, driveline, electrical system, engine cooling, engine mechanical, exhaust system, fuel system, ignition system, suspension, and transmission (both manual and automatic). Moreover, since cars develop problems not only as a result of age but also as a result of the number of miles driven, an adjustment to the reliability measure is made to eliminate differences among models due solely to their different mileage. No account is taken of different body types.t The reliability measures em-

:The reliability measure employed in the present study is different from that used by Ohta and Griliches (1976). They use the ratings based on tests by Consumers Union in a controlled environment, while the reliability ratings employed in this study are based on actual owner experience over a three-year period.

URI

Table 1. Variables considered in the hedonic analysis mblc

Na

.

Variable

AC

- I if car had air condirioaing = 0 otherwise.

AMFMR

- 1 if car had both an AM and an FM radio - 0 otherwise.

AMR

I I if car had an AM only = 0 orhcrwisc.

BSM

= 1 if car had body side mouldinn = 0 otherwise.

CALDUM

= I if car is sold in California = 0 otherwise.

COST1

- 1 if car has much better than average cosf performance = 0 otherwise.

COST2

= I if car has bertcr than average cosf performance - 0 olherwisc.

COST3

= I if car has average performance = 0 otherwise.

COST4

- I if car has worse than average co*t performance = 0 otherwise.

COSTS

- 1 if car has much worse than average cost performance = 0 otherwise.

CRCON

= 1 if car had cruise control = 0 otherwise.

DID

= I if car had a digital instrument display = 0 otherwise.

DIGCLK

= 1 if car had digital clock = 0 otherwise.

ENGSIZE

Engine size measured cubic inches of displacmcnt.

ETYPEI

= 1 if engine was gasoline powered = 0 otherwise.

ETYPE2

= I if engine was turbo gasoline powered = 0 otherwise.

ETYPE3

- I if engine powered - 0 otherwise.

ETYPE4

= 1 if cnginc was turbo diesel powered - 0 otherwise.

EUROPE

= 1 if car is manufactured Europe = 0 otherwise.

Fl

- 1 if car had a fuel injection engine = 0 otherwise.

FMR

- I if car had ao FM radio only - 0 otherwise.

radio

cost

in

was diesel

in

The market valuation of new car quality Table Variable

Table

1 (Continued) Yariablc

.

Variable

365 1 (Continued) Yariablc

SD

= 1 if the car was recalled due to a safety defect = 0 otherwise.

SPECLUX

= I if a car is classified as a luxury or specialty car - 0 otherwise.

SPD

- 1 if car had special paint and/or decals - 0 otherwise.

STP

- I if car had a stereo tape player = 0 otherwise.

= 1 if discount is given on insurance rates for a car = 0 otherwise.

STWH

= 1 if car had styled wheels = 0 otherwise.

IRS

= 1 if surcharge is levied OD insurance rates for a car = 0 otherwise.

SUNR

= 1 if car had a sue roof = 0 otherwise.

JAPAN

- I if car is manufactured Japan = 0 otherwise.

TRANSl

= I if car had an automatic transmission. = 0 otherwise.

LENGTH

Length in inches of a car.

TRANSZ

LRACK

- 1 if car had a luggage rack = 0 otherwise.

= 1 if car had 4-speed manual transmission = 0 otherwise.

TRANS3

Miles driven of a cat per gallon of fuel used.

= 1 if car had S-speed manual transmission = 0 otherwise.

TRUNKVOL

Volume of the trunk compartment.

HLDI

The Highway Loss Data Institute measure of overall injury losses from 1982-1984 model cars.

HPWT

Horsepower of a car divided by its weight.

HORSEPOWER

The horsepower rating of the engine of the car measured in footpounds per minute.

INTERVOL

Interior volume of a car measured in cubic inches.

IRD

MPG

in

NOCYL4

- I if engine had four cylinders = 0 otherwise.

VTYPEI

- 1 if body style was 2-door sedan = 0 otherwise.

NOCYLS

- I if engine had five cylinders - 0 otherwise.

VTYPE2

= 1 if body style was 2-door hatchback - 0 otherwise.

NOCYL6

- I if engine had six cylinders - 0 otherwise.

VTYPE3

= 1 if body style was 4-door sedan - 0 otherwise.

NOCY LB

= 1 if engine had eight cylinders = 0 otherwise.

VTYPE4

= 1 if body style was 4-door hatchback = 0 otherwise.

OBCOMP

- I if car had an on board computer - 0 otherwise.

VTYPES

- I if body style was station wagon = 0 otherwise.

PRICE

Purchase (transaction) price of the car.

VTYPE6

RELI

= 1 if reliability rating is much better than average = 0 otherwise.

= 1 if body style was convertible - 0 otherwise.

WEIGHT

The gross weight of the car in pounds.

REL2

- 1 if reliability rating is better than average - 0 otherwise.

REL3

- I if reliability average - 0 otherwise.

REL4

- 1 if reliability rating is worse than average - 0 otherwise.

RELS

- 1 reliability rating is much worse than average = 0 otherwise.

rating

is

ployed in this study are based on actual owner experience over a three-year period. That is, we average the reliability ratings for a given make and model of 1982 car as reported in the April 1983, 1984, and 1985 issues of Consumer Reports. It is interesting to note that there is very little year-toyear variation in a car’s reliability rating. By employing this measure of reliability we are considering the ex post valuation of rehability by the market.

366

N. D.

URI

This will enable us to measure what consumers paid for a specific reliability level actually received. Thus, since the measure is focusing on actual or realized reliability, the estimates must be interpreted as what consumers in fact paid for a given level of reliability as opposed to what they perceived they were paying when they purchased the new car in 1982.t The second quality characteristic considered is safety. There are various measures available that reflect car safety. The one employed here is based on insurance losses. The Highway Loss Data Institute (HLDI, 1985) reports these data for different kinds of vehicles. Both insurance injury and collision loss experience of passenger cars are tabulated. Two measures of injury loss are computed-overall and severe. The overall injury loss for a specific make and model of car is the frequency of all medical claims. The severe injury loss represents the frequency of claims for paid medical losses exceeding $500. Collision losses are computed in terms of average loss payments per insured vehicle year. All losses are tabulated in relative terms. When considered across all types of new cars, the three HLDI measures are relatively highly correlated. Since all three measures cannot be used simultaneously in the estimation (due to multicollinearity), the overall injury loss experience measure is used rather than one of the other two. Clearly, there is no objective basis on which to prefer one over the others. Finally, the HLDI measure is based on 1982-1981 model years. Data for just 1982 models are not available. It is suggested by HLDI that this is not problematic since loss experience of particular makes and models generally is consistent from one year to another based on their own computations. For the insurance considerations, the data reported by Gillis (1986) are used to determine whether a surcharge is imposed on the insurance rate for a car or whether it is afforded a discount. For now, there is one final data consideration. A car sold in California must have emissions equipment in addition to that installed on cars sold in other

states in order to meet the relatively more stringent new car emissions standards in that state.$ This results in a higher price for cars sold in California, all other things equal. Consequently, a dummy variable is introduced to reflect the additional cost to consumers and is defined to equal one if the new car purchaser was a resident of California and zero otherwise. Estimation considerations. As most hedonic studies (whether they consider new cars or some other good or service) note, one of the most consequential estimation problems is that multicollinearity among characteristics of a commodity frequently results in imprecise and implausible estimates of the prices of characteristics, including estimates with theoretically incorrect signs. In the face of multicollinearity, the reliance one can place on the coefficient estimates among the multicollinear variables is small at best. This can present a real problem if it is believed that one or both of the variables in question ought to be in the model specification. It may be reasonable in such instances to drop one of the two variables from the equation and reestimate it. This, however, c,.. intraduce specification bias into the reestimated coefficients. Only when the omitted variable is uncorrelated with all the included independent variables does this bias not exist, and this is not very likely with economic data. As a practical matter, it is the extent of the specification bias (resulting from the specification error) that is important. This suggests that one should consider not only the question of missing variables but their possible correlation with variables actually ineluded in a specification as well. If one is not concerned about the resulting bias, then the omitted variable specification has some merit.5 The difficulties caused by multicollinearity have been readily apparent in the previous hedonic studies of new cars, especially those devoted to estimating the implicit price of fuel efficiency for new cars. These hedonic studies considering fuel efficiency typically attempt to estimate an equation in which the price of a car is a function of fuel efficiency and other physical and performance characteristics.

iThe definition of the reliability variables predetermine what aspect(s) of reliability will be measured. As defined here. the associated coefficient estimates indicate at what level the market de facto valued the various reliability levels. There are obviously other definitions of reliability than the one considered here. These include, but are not limited to, using expected (forecasted) measures of reliability as well as ex anfe (or previous years’) reliability ratings. A variety of these measures were considered in ancillary analyses, the results of which are available from the author upon request. The bottom line in using these other measures is that there are some variations in the magnitudes of the estimates (as one would a priori expect). However, it is not possible to conclude definitivelv that the market was wrong with regard to its expectations of reliability based on the apparent differences in the estimated coefficients. The qualitative inferences based on the differences in estimation results with regard to market valuation of reliability remain unaltered.

Since

many

of

these

other

characteristics-e.g.

SCalifornia vehicle emissions standards have typically been more stringent than federal standards. This ckitainl; was the case in 1982 (see, e.g. Crandall er al. (1986. p. 88)). To meef the more demanding California standards, car manufacturers had to install catalysts and closed-loop control systems capable of monitoring and controlling the requisite fuel mixture in order to maintain the composition of the engine exhaust streams within the narrow range required for the engine’s effective operation. The bottom line, then, is that the installation of the equipment required to meet the more stringent California emission standards increased the price of a new car. §See Theil(1957) for a comprehensive exposition on this issue.

The market valuation of new car quality horsepower, weight, number of cylinders a car hasdetermine fuel efficiency, it is not surprising that the efforts to account for both the effects of fuel efficiency and, say, horsepower lead to misleading results due to multicollinearity. To get around the collinearity difficulties, the relationship between fuel efficiency and the other characteristics can be formalized by including in the regression model a technical transformation function as suggested by Atkinson and Halvorsen (1984). This permits one to specify the hedonic price function (i.e. the relationship between price and the physical and performance characteristics of a car) without including fuel efficiency variable [as measured by, say, the Environmental Protection Agency (1982)]. Hedonic model specification. The choice of functional form for the hedonic equation was based on the value of the logarithm of the likelihood function. The preliminary analyses suggested the semilogarithmic functional form was superior to the others available.? That is, the natural logarithm of the price is used as the dependent variable while the independent variables are untransformed. The implicit valuation of a particular characteristic is obtained by partial differentiation of the hedonic equation. Care must be exercised, however, in this computation process with regard to the qualitative (discontinuous) variables.$ There is a problem in determining an unbiased estimate of the impact that discontinuous variables have on the dependent variable. Because it is computationally tractable and introduces the least bias, we follow the suggestion of Kennedy to utilize the estimated coefficient and its associated standard error to determine the impact of a discontinuous independent variable on a continuous dependent variable.0 Another concern has to do with grouping the data across all makes and model types. Given that different sizes of cars appeal to different types of consumers, the market valuation of new car characteristics can be better understood by considering the different sizes of cars independently, in a fashion analogous to that used by Boyd and Mellman tThe decision criterion was to choose the specification with the largest likelihood function. The functional specifications considered included linear, linear in logarithms, and semilogarithmic (with a transformation of the dependent variable and transformations of the exogenous variable considered individually). $Halvorsen and Palmquist (1980), Kennedy (1981). and Derrick (1984) all discuss this interpretation. PSpecifically, the impact of a discontinuous variable on the dependent variable is measured as exp (b, - 0.5 V(b,)) - 1, where b, is the estimated coefficient and V(b,) is its associated standard error. In small samples, then, the estimate is a function of the variance. The estimate is consistent and consequently it is asymptotically unbiased. Moreover, given the relatively large sample sizes, any bias in the estimate will be minimal. Finally, for large computed r-statistics, little is lost by simply setting b, equal to In (1 -t g), where 100 g is the correct measure of the percentage impact of the dummy variable. TR(A1

22:5-D

367

(1980).11In the industry four size categories are typically considered-subcompact, compact, intermediate (or medium) size, and full size. While there are no universally accepted criteria as to what constitutes, for example, a subcompact vs. a compact car, Ward’s Automotive Yearbook is the generally accepted authoritative source in making this delineation. Consequently, its classifications are used here. Additionally, Ward’s Automotive Yearbook makes the distinction between a regular (standard) car and a specialty or luxury version of the same car. The specialty or luxury cars have bigger engines (e.g. muscle cars), better upholstery, slightly different body styling, styled,wheels, etc. An additional dummy variable is introduced into our analysis to take account of whether a car is a luxury/specialty car or not. It is defined to equal one if a car is classified as a specialty/luxury car and zero otherwise. There remain a few other considerations before reporting the results of the estimation. One of these has to do with heteroscedasticity. In our current analysis, this problem would occur if the regression results for higher-priced cars indicate a larger variation in the error terms than one observes for lower-priced cars. For the sample being used here, Bartlett’s test (1937) for heteroscedasticity was performed for each of the four car sizes. Subsamples were chosen based on (1) model (e.g. Chevrolet, Buick, Dodge, etc.) and (2) price-each $1,000 increment. The null hypothesis of homoscedasticity could not be rejected for any subsample at the 95% level. # As is customarily the case, a great deal of preliminary analyses must be undertaken in examining which variables are multicollinear. The objective is one of minimizing the impact of multicollinearity while at the same time maintaining the integrity of the functional specification. Consequently, variables I/Thus, for example, any incongruities arising in the estimates due to the skewness of the sample with regard to income will be lessened if, as seems intuitively reasonable but objectively unverifiable, relatively lower-income consumers in the population as a whole purchase relatively more subcompact cars. Subcompact cars, of course, are relatively less expensive. Note that in the sample, relatively more subcompacts are purchased by consumers with relatively lower income (below $25,000). The sample size for subcompacts was 2,260, while for compact, intermediatesize cars. and full-size cars the samole size was 1,587, 1,822, and 532; respectively. #Another concern has to do with the variable introduced by Dhrymes (1967) into his hedonic specification to account for the number of units of a model produced in a given year. The statistical significance of this variable led to the conclusion that one could not use the estimated coefficients to infer anything about market valuation of the characteristics of a car. In the current investigation, a similar variable was considered. As it turns out, neither the number of units of a specific model produced nor the number of units produced by a specific manufacturer proved to have a statistically identifiable impact on the price of a new car. Consequently, Dhrymes’ contention, at least for the J. D. Power and Associates sample, is rejected. [Note that Ohta and Griliches (1976) also reject this hypothesis.]

368

N. D.

measuring some of the physical characteristics were dropped because they were highly collinear (which is to be expected) and additional variable defined. The newly defined variable is just the horsepower divided by the weight of the car. This is an attempt to measure the “get up and go” of a car. The more horsepower a car possesses, the faster the acceleration, while the heavier the car, the slower the acceleration. This variable when compared to just weight or horsepower or engine size (separately and in combination) performed better (in the statistical sense) and hence was used rather than these other variables. Two qualitative variables were considered in preliminary analysis to account for where a car was manufactured. One variable was introduced to account for whether a car was manufactured in Japan and another for whether a car was manufactured in Europe. However, there is a very high positive correlation between cars manufactured in Japan and Europe and cars with much better than average and better than average reliability ratings. Given this, the decision was made to eliminate the two variables accounting for whether the car was manufactured in Japan or Europe from the final specification. This has the result of introducing some bias (via specification error) into the estimates of the other coefficients. Unfortunately, there is no objective way of determining the magnitude of this basis. Finally, in preliminary analyses the results suggested that the coefficient estimates on some of the reliability ratings variables might be equal. That is, for example, there was a suggestion that for full-size cars the coefficient estimate on the worse than average and the much worse than average reliability ratings variables were the same. These hypotheses were tested via a Chow test.t This is significant because it suggests that the market does not distinguish (in terms of implicit price) between, say, full-size cars with a worse than average reliability rating and full-size cars with a much worse than average reliability rating. Where the null hypothesis of equal coefficients was accepted, it was imposed in the final specification. Estimation resulfs. Given all of these comments on what was done and why, we now turn to the actual coefficient estimates.$ All of the estimates were ob-

iSee Chow (1960) and Fisher (1970). $What should be obvious but probably warrants comment anyway is that only four reliability variables are used in the estimation-RELl, REL2, REL4, and REL5. To introduce the REL3 variable would make the five reliability rating variables perfectly collinear with the constant term. In interpreting the coefficient estimates, then, we must remember that we are estimating values above or below the norm (which is given by REL3). Assuming that there is no premium placed on cars that are of average reliability (in the sense that we have been discussing), the estimated coefficient on RELl will indicate the premium that the market places on cars that are of much better than average reliability. Coefficient estimates on the other reliability ratings are analogously interpreted. See Suits (1984) and Kennedy (1986) for more on such coefficient interpretation.

URI

tained via ordinary least squares.0 The estimation results are given in Table 2. (Recall, the variable names are described in Table 1.) The standard errors of the estimates are given in parentheses below the estimates.11 The results are fairly revealing. As the horsepower/weight ratio (HPWT) increases, the price of the car increases.# A specialty/luxury (SPECLUX) car sells for a higher price than does a regular car. Cars with large interior volumes (INTVOL) command a higher price in only three out of the four size categories.?: Four-door sedans (V7YPE3) have a higher associated price than do two-door sedans (VTYPEl) or two-door hatchbacks (V2YPE2). Cars that use regular gasoline as a fuel (E7YPEl) (and are not turbo powered) have a lower price than cars using diesel fuel (ETYPE3). Cars with eight cylinders (NOCYL8) cost more than cars with only four or six cylinders (NOCYL4 or NOCYL6). Cars that have the various and sundry available options are priced higher than cars devoid of these options. Cars sold in California (CALDUM) cost more than those sold in other states, in part due to the additional emissions equipment required on cars registered in California.$$ How do the results obtained here compare with those obtained by others? There is but a single other study that uses data on actual new car purchases and hence has available actual transactions prices and §There is a large and growing literature on how properly a hedonic model in the soirit of Rosen (1974) should be estimated. Since we are ‘concerned here with only estimation of the price function (and not the bid curves for the various characteristics nor the offer curves for the various characteristics of new cars), ordinary least squares is acceptable (see, e.g. Epple, (1987). For more on these estimation considerations, the interested reader is referred to Bartik (1987). jlRecall that when a set of dichotomous variables are used to delineate a specific new car characteristic (e.g. vehicle type or number of cylinders) not all of the elements in this set can be used provided a constant term is also included in the specification. Consequently, at least one dichotomous variable from each set of dichotomous variables was omitted. It is this omitted variable that should properly be interpreted as constituting the norm. For each set of dichotomous variables considered in the estimation, the omitted variable is identified as follows: for engine type-E7YPE3 and ETYPE4 (for ETYPM there were only six observations in the entire sample): for number of cvlinders-NCJCYL8 (there were no kOkYL5 in the sample): for reliabilitvREL3: for tvoe of transmission--7RANSl; for vehicle typeiUYP’E4, V7YPE5, and V7YPE6 (there were only eight V7YPE5 in the sample and no VNPE6). #The average prices of cars in the sample are as follows: Subcompact Compact Intermediate-size Full-size

$ 7,769.80 10.488.11 14541.96 l&694.79

+tFor compact cars, the estimated coefficient is negative but statistically insignificant. This implies that the market does not value interior volume. SfImplicit in interpreting the coefficient estimate on the California dummy variable is the presumption that for each size of car, the costs of the additional emissions equipment plus whatever other factors are important are identical.

The market valuation of new car quality Table

2. Coefficient

estimate

for

equatiens’l1,“lJ3l

new car

hedonic

Table 2 (Conrinued) Equation

Equation /Variable

Subcompact

Compact

Full

Intermediate Size

TRANS3 10.7273 (0.1936)

0.0707 (0.0 100)

0.0669 (0.0 159)

0.06 I3 (0.0173)

0.0244 (0.0079)

0.0253 (0.0095)

0.0173* (0.0105)

-0.0 132+

0.0030+ (0.0075)

-0.0054+ (0.0082)

0.0145

-0.0270+

(0.0045)

(O.Ol52)

CALDUM

0.0358 (0.0104)

0.033 I (0.0098)

0.0310 (0.0115)

0.0652 (0.0178)

CRCON

0.0506 (0.01 16)

0.04 I3 (0.0093)

(0.0112)

0.0407 (0.0200)

0.0402 (0.0085)

0.0236 (0.0106)

0.0771 (0.0101)

-0.0188 (0.0179)

-0.08 I6 (0.0170)

-0.0855 (0.0388)

-0.1620 (0.0222)

-0.1656 (0.02 12)

8.4336 (0.2186)

AC

0.1032 (0.008 I)

AMFMR

BSM

11.040 (10.1178)

VTYPEI

ETYPEl

ETYPEZ

l

0.0109+ (0.1765)

FI

HPWT

INTERVOL

IRD

IRS

NOCYL4

NOCYL6

0.0195 (0.0l00)

0.0326 (0.0 174)

-0.0011

-0.0025

(0.0002)

(0.0007)

-0.0065 (0.0004)

-0.0042 (0.00 IS)

4.3446 (0.7030)

2.3 145 (0.8304)

(1.2349)

-0.02 14+ (0.0210)

0.0059 (0.0017)

0.007 I (0.0027)

0.0289 (0.0016)

-0.0328+

(0.0154)

(0.0191)

0.0885 (0.0428)

-0.0159 (0.0071)

0.1689+ (0.1237)

(0.0103)

-0.0763 (0.0369)

-0. I896 (0.0236)

-0.1220

-0.0781+

-0.1133 (0.0253)

-0.1143 (0.0299)

0.0061

0.0474

0.0343 (0.0112)

REL2

l.

-0.0398

-0.02 I6

0.0943 (0.0265) l.

-0.0 154 (0.0043) l

(0.0249)

.

.

l

RELI

REL4

2.7090

5.0521 (0.9938)

(0.0731) OBCOMP

l

0.0265

(0.0009)

-0.0353 (0.0132) 0.024 I (0.0103)

0.065 I (0.0211)

.

.I

. -0.0714

(0.0165)

-0.1568 (0.0697)

-0.08 10 (0.0159)

.*

-0.1972

l.

..

0.0 184 (0.0092)

0.0308 (0.0 106)

0.0 I69 (0.00 19)

0.5 I29 (0.02S4)

0.2703 (0.1 165)

0.3832 (0.0231)

0.0529 (0.0084)

0.0568 (0.0096)

0.0437 (0.0118)

0.0533 (0.0159)

0.0699 (0.0098)

0.0205 (0.0093)

0.0465 (0.0100)

0.0138 (0.0063)

SUNR

0.030 1 (0.0109)

0.0485 (0.0 134)

0.0265 (0.0 130)

0.0849 (0.0273)

TRANS2

-0.0668 (0.02 17)

-0.0310 (0.0089)

-0.0385 (0.0191)

REL5

(0.0296)

(0.0888) SPD

SPECLUX

STP

STWH

0.007 1+ (0.0089)

.

Full Size

size

0.0231+ (0.0216)

-0.0278+ (0.0239)

-0.0339+ (0.0243)

l

-0.0683

-0.1151

(0.0157)

(0.0192)

-0.0013+ (0.0269)

-0.1208

-0.0708

-0.1319

(0.0 124)

(0.0 185)

(0.0234)

0.1291+ (0.1487)

(0.0137)

0.0884 (0.0154)

0.1054 (0.0162)

0.0384 (0.0183)

R’(4)

0.7206

0.8959

0.8597

0.9189

SE.(S)

0.1720

0.1556

0.1932

0.1454

(0.0199)

(0.0106)

0.0132 (0.006 I)

HLDI

-0.1896 (0.0251)

Intermediate

-0.1089

VTYPE3

DIGCLK

Compact

(0.0 156) VTYPE2

0.04 I5

Subcompact

/Variable

Size

19.7531 (0.1783)

Constant

369

.

0.0599

(‘Standard errors of estimates in parentheses. (*‘Coefficients are statistically different than zero at the 95% level or better unless otherwise noted. (“The samole sizes are eiven in the footnote on oaee 367. (“Coefficient of determination (unadjusted.) . ?S.tandard error of regression. ‘Too few observations in the sample having this characteristic to compute a meaningful coefficient estimate. **The coefficients on RELlIREL2 or REL4IREL5 were found to equal each other. See text for discussion. ‘Not statistically significantly different than zero at the 95% level or better.

information on the various options and body styles. This is a study done by Agarwal and Ratchford (1980) that follows the suggestions of Ratchford (1975). The data set consists of information on actual new cars purchased by 255 consumers in Erie County, New York, in 1976. Most unfortunately, Agarwal and Ratchford make ad hoc adjustments to the transactions price data to reflect differences in options and body styles. Consequently, it is not possible to make a direct comparison between their results and what has been obtained here. What they do find is that cars with larger trunk volumes command a higher price. If we realize that the interior volume of a new car and its trunk volume are highly correlated (in excess of 0.80 across different car sizes for 1982 new cars), then their results are consistent with those obtained here. Ohta and Griliches (1976) hypothesize a hedonic specification where price is a function of acceleration, top speed, fuel economy, handling, frequency of repair records, engine power, ride comfort, probable trade-in value, etc. Their specification, while superficially similar to the one used in this study, differs in that list prices are used (rather than transaction prices) while data on the frequency-of-repair records are based on an ex ante assessment of the likely repairs rather than the expost evaluation used in the current study. Ohta and Griliches, however, do find that their performance characteristics do a very good job of explaining the variation in new car list prices (across various makes and models). There are a myriad of other hedonic studies looking at the market valuation of new car characteristics, dating back to Court (1939). All of these,

370

N. D. URI

with the exception noted above, use list prices. A number of those studies, including Griliches (1961,1964), Fisher eta/. (1962), Cagan (1965), Triplett (1969), Dhrymes (1967), Cowling and Cubbin (1971,1972), Hogarty (1975), Cubbin (1975), Goodman (1983), and Falvey et al. (1986), use both horsepower and the weight of the new car and interior volume (or some highly collinear variable such as trunk volume or overall length). To the extent that multicollinearity is not problematic in these studies, the variables have a statistically significant impact on the price of a new car. These results are consistent with what has been obtained here (realizing that this study is a new variable combining the horsepower and weight variable has been defined). What about the quality characteristics? Based on the estimation results, the market does value reliability, safety, and insurance rate differentials. Cars that have a much better than average (RELl) or better than average reliability (REf.2) rating command a higher price than those with an average reliability rating, while cars with a worse than average (REL4) or a much worse than average reliability rating (RELS) are sold for less than those cars with an average reliability rating. Next, cars that have a worse than average overall injury loss experience (as measured by HLDI) have a lower price, while those with a better than average overall injury loss experience have a higher price. Finally, the market in general makes an allowance in the price paid for a car for any insurance rate discount or surcharge (as measured by IRD or IRS). Beyond these descriptive results, is there anything quantitative that can be concluded? The answer is yes, but care must be taken in interpreting the quantitative results. Two types of variables have been used in the estimation-continuous and discrete (dichotomous) or discontinuous. For the continuous variables-e.g. the interior volume variable ([NTVOL)-the estimated coefficients x 100 are equal to the percent effect on price of a one percent change in the variables. (This is so since we have used a semilogarithmic specification.) Hence, for example, a one-unit (cubic inch) increase in interior volume would result, on average, in a 0.61% increase in the price of a subcompact car. The results suggest that a lo-point increase in safety (measured by a 10% fall in the HLDI overall injury loss experience index from, for example, 100 to 90) will lead, on average, to a 1.1% increase in the price of subcompact cars, a 2.5% increase in the price of compact cars, a 6.5% increase in the price of intermediate-size cars, and a 4.2% increase in the price of full-size cars. The magnitude of these increases in terms of changes in the price of a new car, together with the associated 95% confidence intervals for various sizes of cars, is given in Table 3. On average across all sizes of cars, we see that the market values a lo-point increase in safety (manifest in the form of reduced overall injury loss experience) by the amount $443.77.

Table 3. Market value of a lo-point increase in safety performance Increase in

Price of Car Subcompacts

a

as.47

95% Confidence Interval Lower Bound/Upper Bound

f 55.01

$115.93

Compacts

262.20

I la.30

344.43

Intermediates

945.42

831.39

1059.45

Full Size Cars

785.18

125.63

1444.73

s443.77

5333.73

5563.55

Weighted Avg.’ I

Bared

on

size representation

in the sample.

The interpretation of the coefficient estimates associated with the dichotomous variables is different than that for the continuous variables. That is, as noted in the subsection above on estimation considerations, the coefficient estimates associated with dichotomous variables cannot be immediately interpreted. Such coefficient estimates measure the discontinuous effects of the presence of the factors represented by these variables on the price of a new car. To enable one to interpret the effects of the presence of the relevant factors, the computation involving the coefficient estimates and their estimated variances must be undertaken. These resultant values x 100 will give point estimates of the effects on the price of a new car of the various factors.? What is of interest in the context of this study is the market valuation of reliability and the valuation the market puts on cars that receive an insurance rate discount or cars assessed surcharges on their insurance rates.+ First, consider the reliability valuation issue. Table 4 indicates the increase in the price of a new car in 1982 (1) because it had either a much better than average or better than average reliability rating or (2) because it had a worse than average or much worse than average reliability rating. Remember that the norm is a car with an average reliability rating. Cars that have a much better than average or better than average reliability rating have between a $226.10 and $885.20 price premium associated with them (depending on the. size of the car), while cars that have a worse than average or much worse than average reliability rating are priced between $364.40 and $2,039.94 lower than average reliability cars. How do these results compare with those obtained

iUnfortunately, given the nonlinear nature of the computation (i.e. involving exponentiation), there is no way to calculate an associated confidence interval. iSince the price of the various options on a new car are not of special interest to us (recall that they are measured as dichotomous variables), a determination of what they tell us about the price of a new car is not discussed here.

The market valuation of new car quality

Table 4. Average price change due to a car’s relative reliability rating Much Avg. Than bility

Subcompacts

Better Than or Better Avg. RcliaRating

$226.10

-5364.40

885.20

-2039.94

Compacts Intermediate

Worse Than Avg. ot Much Worse Than Avg. Reliability Rating

815.47

Six

- 1237.78

-- I

Full Sire Cars 1 Insufficient compute this.

number

of

observations

-1544.19

in

the

sample

to

by others? Unfortunately, there are no other studies of new cars that can serve as a basis for comparison. With regard to used cars, Lacko (1986) finds that cars rated worse than average (a combination of our worse than average and much worse than average categories) sell for 11.4% less than cars with an average reliability rating. This is approximately the same as the values obtained here. For better than average rated cars (corresponding to the much better than average and better than average reliability ratings used in this study), Lacko finds that the market places no value on these cars over average reliability rated cars. The final estimates that we would like to discuss are those on the two insurance rate variables. For subcompact cars and compact cars, the market does reflect an insurance rate discount, while for subcompact cars, intermediate-size cars, and full-size cars, the presence of a surcharge is reflected in the price at which a new car sells. Table 5 reports the nominal value of the discount or surcharge based on the average transactions price of a new car in the sample. Are these numbers meaningful? First, it must be remembered that any estimated savings due to a discount or any estimated additional cost due to a surcharge must be capitalized over the length of time a consumer owns (or plans to own) the new car. In 1982, the average (mode) length of time purchasers of new cars intended to keep their cars was five

371

years.t This means that if we assume the savings or additional cost are spread out evenly over the five years (which they technically are not since the insurance premium declines with the value of the car and the resale price of the car would reflect any insurance rate discounts and or premiums expected to be realized after five years) and assuming that the appropriate discount rate is 6.75%,$ then in present value terms, a $314.68 higher price for a new car translates into $75.22 per year.0 That is, to conclude that purchasers of new cars are rational in that they fully capitalize any reduction in the insurance rate into a higher new car price over the time period for which they expect to own the car, it would be expected that the annual savings on the insurance expenditures attributable to the discount should be about $75.00./1 For compact cars, based on the estimates obtained here, the additional price paid for new cars afforded an insurance rate discount is equivalent to $71.45 per year of expected ownership. For the surcharge, the values for subcompact cars, intermediate-size cars, and full-size cars. respectively, are $35.84, $91.77, and $77.75 per year. How do these estimates compare with the actual discounts and surcharges for insurance rates? While the precise information we need to make such a determination is not available, some meaningful approximations can be made. In particular, the only data available on a comprehensive basis on car insurance rates come from the American Automobile Association (1982) and are for the average car. The data are not disaggregated by size of car. Moreover, the data are aggregated across all vintages of cars, not just cars manufactured in 1982. Based on this source, the average total premium in 1982 was $449. Discounts and surcharges on insurance rates usually range from 10% to 30%, as we noted above. If we take the mid-point of this range as being representative of the average insurance rate discount or surcharge, then a 20% discount or surcharge on a $449.00 insurance premium is $89.80. It would appear that, given the inherent in the computations, the market is slightly undervaluing a car based on the insurance rate discounts and surcharges that can be realized.# Once again, however, it is not possible ‘FAs reported in Motor Vehicle Manufacturers Association (1983).

Table 5. Average change in the price of a new car due to the allowance in the insurance rate of a discount or surcharge

Subcompacts Compacts Intermediate

Discount

Surcharge

5314.68

-S149.96

298.91 Size Car

Full Size Car

’ No statistically

identifiable

..t

..t

-383.99

..I

-325.29

impact.

$This is an actual real interest rate computed as the average of the three-month Treasury bill rate over the period 1982-1985, adjusted for inflation based on the change in the Gross National Product implicit price deflator (Council of Economic Advisors, 1986. §See Mixon and Uri (1985) for a discussion of the mechanics of making such a computation. l/Note that once again it is more appropriate to discuss these values in terms of confidence intervals but, as we discussed above, there is no way to compute such intervals. #It should be noted that upon resale of a car, the resale price of it should reflect the expected present value of any insurance savings after the sale to the new owner. Consequently, savings in insurance costs over only five years should underestimate the total value (over the entire useful life of the car) of insurance savings.

N. D.

372

to definitively conclude this to be the case since the estimates of the market valuation of a discount or surcharge for specific size cars are statistics and, as such, are subject to some uncertainty.

URI

existence of these characteristics undertakings.

are not vacuous

Acknowledgemenr-The author would like to thank two anonymous referees for very helpful comments.

5. CONCLUSION This study has focused on determining the nature and extent of market valuation of quality characteristics possessed by new cars. In particular the concern has been with whether the market puts a premium on superior-quality products while penalizing (relative to the norm) those products that are of relatively inferior quality. The study finds no evidence to support complete market inefficiency with regard to some quality characteristics possessed by new cars sold in 1982. Specifically with regard to reliability, the market does value (i.e. it puts a premium) on cars that e.r post have a much better than average or better than average rehability rating based on the criteria used in Consumer Reports. Additionally, cars that perform relatively poorly in terms of reliability ratings have a lower associated price, all other things equal. The higher quality of a car can be translated into a higher selling price. Concerning safety as a quality characteristic, it is shown that the market does value this both in terms of loss experience and in terms of recalls due to safety defects. Thus, given that complete market failure does not occur with regard to safety,? an obvious question is why has the federal government required car manufacturers to install so many safety-related features on new cars as opposed to simply letting the market dictate which safety-related feature should be included on a new car. Weidenbaum (1978) has estimated that the cost of manufacturing a car in 1978 was increased by an average of $349.58 ($484.18 in 1982 dollars) in order to meet all federal safety regulations. Unfortunately, the available studies on the benefits of safety regulations do not indicate what has happened in terms of, for example, overall injury losses as a result of the various government regulations. Consequently, it is not possible to compare the market value of safety as computed in this study with any realized benefits. This clearly would be a fruitful area for further investigation. In sum then, the substance of this study supports the notion that there is an incentive for producers of new cars to differentiate their products on the basis of such quality characteristics as reliability and safety and that attempts to convey to consumers the

;To be totally accurate, it should be noted that the positive relationship between prices and safety implies only that there is not complete market failure. The results do not tell us whether the market provides safety at the op timal level nor do they indicate whether consumers have the optimal level of information. Consequently. one cannot unequivocally conclude that there is no market failure, only that there is not complete market failure.

REFERENCES Agarwal M. K. and Ratchford B. T. (1980) Estimating demand functions for product characteristics: The case of automobiles. /. Consumer Res. 7, 249-262. Akerlof G. A. (1970) The market for “lemons”: Quality uncertainty and the market mechanism. Q. J. Econ. 84, 488-500. Allen F. (198-t) Reputation and product quality. Rand J. Econ. 15. 311-327. American Automobile Association (1982) Your Driving Costs. American Automobile Association, Fairfax. Virginia. Archibald R. B., Haulman C. A. and bfood C. E. (1983) Quality, price, advertising, and published quality ratings. 3. Consumer Res. 9, 347-356. Atkinson S. E. and Halvorsen R. (1984) A new hedonic technique for estimating attribute demand: an application to the demand for automobile fuel efficiency. Rev. Econ. Statistics 66, 417-426. Bartlett M. S. (1937) Properties of sufficiency and statistical tests. Proc. Royal Society, Series A 160.268-284. Bartik T. J. (1987) The estimation of demand oarameters in hedonic‘price models. J. Polit. Econ. 95, ‘sl-88. Boyd H. J. and Mellman R. E. (1980) The effect of fuel economy standards on the U.S. automotive market: An hedonic demand analysis. Transp. Res. Il.+. 367-378. Cagan P. (1965) Measuring quality changes and the purchasing power of money: An exploratory study of automobiles. Nat. Bank. Rev. 3, 217-236. Chow G. C. (1960) Tests of equality between sets of coefficients in two linear regressions. Econometrica 28, 591605. Council of Economic Advisors (1986) Economic Report of the President, 1986. U.S. Government Printing Office. Washington, D.C. Consume;Reports (April 1982). (April 1983). April (198-l). (April 1985) Consumers Union of the United States, Inc., Mount Vernon, New York, pp. 47, 48, 49, 50. Court A. T. (1939) Hedonic price indexes with automobile examples. In The Dynamics ofAutomobile Demand, 99117. General Motors Corporation, New York. Cowling K. and Cubbin J. 11972) Hedonic orice indexes for United Kingdom cars.‘Econ. I. 82, 965-978. Cowling K. and Cubbin J. (1971) Price, quality, and advertising competition: An econometric investigation of the United Kingdom car market. Economica 38, 378394. Cowling K. and Rayner A. J. (1970) Price. quality. and market share. 1. Polit. Econ. 78 (November), 12931309. Crafton S. M., Hoffer G. E. and Reilly R. J. (1981) Testing the impact of recalls on the demand for automobiles. Econ. Inquiry 19, 694-703. Crandall R.‘W.[ Gruenspecht H. K., Keeler T. E. and Lave L. B. (1986) Reaularinp the Automobile. The Brookings Instituiion, ‘Wasiingtoi, D.C. Csongos F. T. (1987) Chevy Corsica a smash hit in tests. Washington POSIFebruary 26, p. E-l. Cubbin Jr (1975) Quality change-and pricing behavior in the United Kingdom car industrv. 1956-1968. Economica 42, 43-58‘ Derrick F. W. (1984) Interpretation of dummy variables in semilogarithmic equations: small sample Implications. South. Econ. /. 50, 1185-1188.

The market valuation of new car quality Dhrymes P. J. (1967) On the measurement of price and quality changes in some consumer capital goods. Am. Econ. Rev. 57, 501-518. Environmental Protection Agency (1982) Gas Mileage Guide. U.S. Government Printing Office, Washington, D.C. Epple D. (1987) Hedonic prices and implicit markets: Estimating demand and supplv functions for differentiated produ& 1. Polit. Econ: ‘95, 59-80. Falvey R. E., Fran J., Fried H. 0. and Babunovic M. (1986) Fuel economy standards and automobile prices. J. Transp. Econ. Policy 20, 31-45.

Fisher F. IM. (1970) Tests of eaualitv between sets of coefficients in two linear regresssions: An expository note. Econometrica

38, 361-366.

Fisher F., Griliches Z. and Kaysen C. (1962) The cost of automobile model changes since 1949. J. Polit. Econ. 70, 433-451. Gillis J. (1986) The Car Book. Harper and Row, New York. Goodman A. C. (1983) Willingness to pay for car efficiency: The hedonic price approach. J. Transp. Econ. Policy 17, 247-266.

German W. M. (1980) A possible procedure for analyzing aualitv differentials in the ege market. Rev. Econ. Stud. 87, 843-856. Graham J. D. and Garber S. (1984) Evaluating the effects of automobile safety regulations. J. Policy Anal. ManII

age. 3. 206-224.

Griliches Z. (1961) Hedonic price indexes for automobiles: An economic analysis of quality change. In The Price Statistics of the Federal Government, General Series. No. 73, 137-196. National Bureau of Economic Research, New York. Griliches Z. (1964) Notes on the measurement of price and quality changes. In Models of Income Determination, 301-304. National Bureau of Economic Research. New York. Halvorsen R. and Palmquist R. (1980) The interpretation of dummy variables in semiloaarithmic equations. Am. Econ. Rev. 70, 474-475.

-

Hartman R. S. (1987) Product quality and market efficiency: The effect of product recalls on resale prices and firm valuation. Rev. Econ. Statistics 69, 367-372. Heal G. M. (1976) Do bad oroducts drive out _ good? 0. J. Econ. 96, 499-503.

’

Highway Loss Data Institute (1985) Injury and Collision Loss Experience. Highway Loss Data Institute, Washington, D.C. Hogarty T. F. (1975) Price-quality relations for automobiles: A new approach. Appl. Econ. 7, 41-51. Jacoby J. and Olson J. (1985) Perceived Quality. Lexington Books, Lexington, Massachusetts. Jolly D. W. and Mowen J. C. (1985) Product recall communications: The effects of source, media, and social responsibility information. Adv. Consumer Res. l2,471475. Kennedy P. E. (1986) Interpreting dummy variables. Rev. Econ. Statistics 68, 174-175. Kennedy P. E. (1981) Estimation with correctly interpreted dummy variables in semilogarithmic equations. Am. Econ. Rev. 71, 801. Klein B. and Leffler K. B. (1981) The role of market forces in assuring contractual performance. J. Polit. Econ. 89, 615-641.

373

Lacko J. M. (1986) Product Quality and Information in the Used Car Market. Federal Trade Commission, Washington, D.C. Lynch M., Miller R. M., Piott C. R. and Porter R (1986) Product quality, consumer information and lemons in experimental markets. In Empirical Approaches to Consumer Protection Economics: Proceedings of a Bureau of Economics Conference at the Federal Trade Commission, 136-171 (Edited by Ippolito P. and Scheff-

man D.) U.S. Government Printing Office, Washington, D.C. Mixon J. W. and Uri N. D. (1985) Managerial Economics. Macmillan, New York. Motor Vehicle Manufacturers Association (1983) Motor Vehicle Facts and Figures ‘83. Motor Vehicle Manufacturers Association, Detroit, Michigan. Nelson P. (1974) Advertising as information. J. Polit. Econ. 82, 729-754.

Ohta M. (1971) Hedonic price index for boiler and turbogenerator: A cost function approach. Technical Report No. 40. Project for the Evaluation and Optimization of Economic Growth, University of California (Berkeley). Ohta M. and Griliches Z. (1976) Automobile prices revisited: Extensions of the hedonic hypothesis. In Household Production and Consumption, 32.5-390 (Edited by Terleckyj N.). Columbia University Press, New York. Ohta M. and Griliches Z. (1986) Automobile prices and quality: Did the gasoline price increase change consumer tastes in the U.S.? J. Bus. Econ. Statistics 4. 187-198. Peltzman S. (1975) The effects of automobile’safety regulation. J. Polit. Econ. 83, 677-725. Power J. D. (1983) 1983 Customer Satisfaction With Dealer Service. J. D. Power and Associates, Detroit, Michigan. Ratchford B. T. (1975) The new economic theory of consumer behavior: An interpretive essay. J. Consumer Res. 2, 65-75.

Reilly R. J. and Hoffer G. E. (1983) Will retarding the information flow on automobile recalls affect consumer demand? Econ. Inquiry 21, 444-447. Rosen S. (1974) Hedonic prices and implicit markets: product differentiation in pure competition. J. Polit. Econ. 82, W-55.

Schmalensee R. (1978) A model of advertising and product quality. J. Polit. Econ. 86, 485-503. Shapiro-C. (1983) Premiums for high-quality products as returns to reputations. 0. 1. Econ. 98, 659-679. Stiglitz J. E. (j987) The &uses and consequences of the dependency of quality on price. J. Econ. Lit. 25, l-48. Suits D. B. (1984) Dummv variables: Mechanics v. interpretation. Rev. &on.

Statistics 66, 177-180.

Telser L. G. (1980) A theory of self-enforcing agreements. J. Bus. 53, 27-44.

Theil H. (1957) Specification errors and the estimation of economic relationshios. Rev. Int. Statistical Inst. 25. 4151. Triplett J. E. (1969) Automobiles and hedonic quality measurement. J. Polit. Econ. 77, 408-417. Ward’s Communications (1978-1987). Ward’s Automotive Yearbook. Ward’s Communications, Inc., Detroit, Michigan. Weidenbaum M. (1978) The Impacts of Government Regulations. Center for the Studv of American Business, Washington University, St. Louis, Missouri.