Adverse Selection in the Used-Car Market: Evidence from Purchase and Repair Patterns in the Consumer Expenditure Survey

Transcription

1 Cornell University From the SelectedWorks of Henry S Schneider 2014 Adverse Selection in the Used-Car Market: Evidence from Purchase and Repair Patterns in the Consumer Expenditure Survey Jonathan Peterson Henry S Schneider, Cornell University Available at:

2 ADVERSE SELECTION IN THE USED-CAR MARKET: EVIDENCE FROM PURCHASE AND REPAIR PATTERNS IN THE CONSUMER EXPENDITURE SURVEY 1 Jonathan R. Peterson Faculty of Management Higher School of Economics National Research University jpeterson@hse.ru Henry S. Schneider S. C. Johnson Graduate School of Management Cornell University henry.schneider@cornell.edu June 2013 Abstract We analyze adverse selection in the used-car market using a new approach that considers a car as an assemblage of parts, some with symmetric information and others with asymmetric information. Using data from the Consumer Expenditure Survey and Consumer Reports, we examine both turnover and repair patterns. We find evidence of adverse selection due to the conditions of the transmission, engine, and, during colder months, air conditioning; and sorting due to the conditions of the vehicle body and, during warmer months, air conditioning. Our quantification exercises indicate that adverse selection may have a meaningful effect on trade volume and quality in the secondhand market. 1 We thank Steve Berry, Justin Johnson, Fiona Scott Morton, Philip Schmidt-Dengler, Michael Waldman, two anonymous referees and the editor, and various seminar participants for helpful comments, and Romana Primus of Whaling City Ford, Paul Trembley of East Rock Auto Repair, George Iny of the Automobile Protection Association, and Eli Melnick of Start Auto Electric for background information about the used car and car repair markets.

3 1. INTRODUCTION Starting with the seminal work of Akerlof (1970), researchers have identified adverse selection as a potentially important inefficiency in secondhand markets for durable goods. We provide here new evidence about turnover and repair patterns for used cars. Our innovation is to consider a car as a collection of parts (e.g., transmission, engine) that can have different information properties. From this perspective, the conditions of different parts within a particular car may have heterogeneous effects on market outcomes. For example, adverse selection may arise due to engine condition but not to exterior vehicle-body condition since only the former may have asymmetric information. 2 First we examine turnover rates. Adverse selection would cause used cars with asymmetric information over their quality to have less turnover. The logic is from Akerlof (1970): Asymmetric information causes observably identical cars to sell for the same price regardless of their unobserved condition. As a result, owners of cars with good unobserved condition sell less often. In contrast, the efficient sorting of cars between drivers due to car condition would cause cars with a faster quality deterioration rate to have more turnover. The logic for this is also standard: cars with a faster deterioration rate are traded from drivers with a high valuation for quality, who purchased the car new and wish to trade up to another new car, to drivers with a low valuation for quality, who prefer the combination of lower price and lower quality. 3 Hendel and Lizzeri (1999a) model these processes formally, pointing out how these 2 Throughout, we will use the terms asymmetric information and unobserved to refer to aspects of condition that are observed by sellers but not by buyers. 3 Theoretical work on sorting of durable goods began with Swan (1970, 1971) on firm durability choice, and with Mussa and Rosen (1978) on trade due to heterogeneity over quality preferences. More recent work includes Anderson and Ginsburgh (1994), Waldman (1996a, 1996b), Hendel and Lizzeri (1999a, 1999b), and Porter and Sattler (1999). 1

4 differing turnover predictions can guide empirical work on adverse selection and sorting. 4 Using data from the Consumer Expenditure Survey (CES) and Consumer Reports magazine for 1991 to 2006, the left panel of figure 1 shows the relationship between the total defect rate for a model-vintage-age and its turnover rate. 5 A positive relationship is apparent, which is consistent with the presence of sorting due to car condition. Yet if we consider a car as a collection of parts with varying information properties, it becomes possible that significant adverse selection can be present despite the positive sorting relationship. In fact, reliability ratings from Consumer Reports indicate a positive correlation in defect rates across parts, which suggests that model-vintage-ages with the most adverse selection may also have the most sorting. Hence, a positive turnover effect does not rule out adverse selection, but indicates only that the (positive) sorting effect exceeds the (negative) adverse-selection effect. By conducting our analysis at the level of the individual car part, which can more narrowly be characterized as having either symmetric or asymmetric information than can the car as a whole, we can separately identify the effects of adverse selection and sorting more easily. A higher defect rate for observed parts, which represents faster deterioration, increases turnover. This is the sorting effect. In contrast, a higher defect rate for unobserved parts, which represents more asymmetric information, decreases turnover. This is the adverse-selection effect. 6 4 While the predictions in Hendel and Lizzeri (1999a) are based on comparing cars with differing information properties, our analysis compares car parts with differing information properties. The intuition, however, is similar. Also, Hendel and Lizzeri (1999a) focus on the relationship between price depreciation and trade volume. Our analysis directly relates asymmetric information and trade volume, and quality deterioration and trade volume, which make it unnecessary to control for prices. More broadly, we do not examine prices because an analysis of prices is complicated by the interaction of adverse selection and sorting, and is a topic of ongoing research. Price effects, however, are less interesting from an efficiency perspective compared with turnover and repair effects, since they represent simple transfers. 5 In this paper we refer to car brand (e.g., Ford) as make, car type (e.g., Focus) as model, and model year as vintage. 6 More precisely, asymmetric information increases as the defect range increases from 0 to.5 since uncertainty to buyers increases over this range (the defect rate is nearly always less than.5). Uncertainty decreases as the defect rate increases from.5 to 1. Note also that a higher defect rate for unobserved parts may generate an element of 2

5 In our study we focus on the four car parts that have the highest repair expenditures, and hence are the leading candidates for generating adverse selection and sorting. These parts are transmission, engine, vehicle body, and air conditioning. We expect the conditions of transmission and engine to be less visible to buyers and consequently to have more asymmetric information. We expect the conditions of vehicle body and air conditioning (in particular during warmer months) to be more visible to buyers and consequently to have less asymmetric information. A preliminary view of the data supports this hypothesis. The right panel of figure 1 shows that cars from model-vintage-ages with the most transmission and engine defects have less turnover than those with the most vehicle body and air conditioning defects. We formally test these predictions by estimating the relationship between the defect rates of individual parts and the probability that a car has turned over by a given age. Defect rates are measured at the level of the model-vintage-age from data in Consumer Reports. Turnover is measured at the level of the car from data in the CES. We find that ten-percentage-point increases in transmission and engine defect rates correspond to 6.4 percent (p=.06) and 7.7 percent (p=.10) less turnover, respectively, which is consistent with adverse selection for these parts. Conversely, we find that ten-percentage-point increases in vehicle body and air conditioning defect rates correspond to 7.3 percent (p<.01) and 6.3 percent (p=.10) more turnover, respectively, which is consistent with sorting for these parts. For a car as a whole, a ten-percentage-point increase in the defect rate corresponds to 4.0 percent (p<.01) more turnover, which indicates that sorting dominates adverse selection overall. 7 Our next tests involve comparing the repair rates of recently traded used cars to similar but non-traded used cars. We expect that adverse selection would increase the repair rate of sorting because a higher unobserved defect rate corresponds to more expected deterioration. This somewhat attenuates the negative effect of adverse selection on turnover. 7 Hendel and Lizzeri (1999a) and Porter and Sattler (1999) also found evidence of sorting in the aggregate. 3

6 traded cars, while sorting would not. The intuition is that sorting due to car condition arises primarily from a heterogeneous distaste for defects across drivers, such that used cars trade from sellers with a high distaste for defects to buyers who have a higher tolerance level for a car with defects. In this case, an inefficient repair is avoided. Such avoidance, however, is not the motive for trade under adverse selection because buyers do not observe the defect before buying the car, and hence buyers are more likely to repair the defect. As before, we test these predictions at the level of the individual car part to better identify adverse selection from sorting. Using CES data, the dependent variable is an indicator for whether a car had a repair for a particular part in the survey quarter. The explanatory variable of interest is an indicator for whether the car was purchased used within the prior year. We find that the traded cars have 15.2 percent (p=.05) and 13.4 percent (p=.03) more transmission and engine repairs, respectively, but 5.1 percent (p=.55) and 1.1 percent (p=.91) fewer vehicle body and air conditioning repairs, respectively, than non-traded cars. This is consistent with adverse selection due to transmission and engine, and sorting due to vehicle body and air conditioning. We then estimate the model for air conditioning repairs separately for cars traded during colder months of the year, when defects are presumably less visible to buyers, versus warmer months of the year, when defects are more visible. Cars traded during warmer months had 22.2 percent fewer repairs than non-traded cars (p=.11), while cars traded during colder months had 43 percent more repairs than cars traded during warmer months (p=.02). These results are consistent with adverse selection during colder months and sorting during warmer months. Finally, we conduct a quantification exercise that provides a sense of the market-wide effects of adverse selection. We use the estimated model to predict aggregate turnover rates in the hypothetical case of low transmission and engine defect rates for all cars, which provide a 4

7 sense of turnover rates under very limited adverse selection. We then compare this with the actual aggregate turnover rates. We find that for eight-year-old cars, adverse selection reduces trade volume by at least seven 7 percent and delays turnover by at least four months on average. Previous evidence about adverse selection in durable-goods markets is mixed. Bond (1982) found no difference in repair rates between traded and non-traded younger trucks, while Pratt and Hoffer (1984) and Bond (1984) found more repairs among older traded trucks. Lacko (1986) found more repairs among used cars purchased through newspaper ads than from friends and relatives, where the former presumably have more asymmetric information. Adams, Hosken, and Newberry (2011) found no evidence of adverse selection among younger used cars based on price-depreciation patterns. Emons and Sheldon (2009) and Engers, Hartmann, and Stern (2009) found evidence of adverse selection from turnover patterns, while Porter and Sattler (1999) did not. Gilligan (2004) found evidence of adverse selection among used airplanes based on the relationship between price depreciation and trade volume, while Lewis (2011) found that information disclosures can limit adverse selection online. 8 This article contributes to the literature on adverse selection. First, we provide detailed evidence that adverse selection significantly affects outcomes in an important secondhand market for durable goods. Our findings are corroborated through tests on two distinct outcomes: turnover rates and repair rates. Second, we demonstrate the value of allowing for varying asymmetric information within the good. Our aggregate-level analysis provides little evidence of adverse selection, while our part-level analysis reveals significant effects. These findings should apply to other durable-goods markets as well. Finally, we provide a sense of the market-level 8 In related literature, Rosenman and Wilson (1991), Genesove (1993), and Chezum and Wimmer (1997) examined used durable-goods markets and generally found that buyers could infer unobserved quality from observed seller characteristics. Theoretical work in Wilson (1980), Kim (1985), and Hendel, Lizzeri, and Siniscalchi (2005) examined mechanisms that may overcome the market failure identified in Akerlof (1970). 5

8 effects of adverse selection, which appear to be quite meaningful. In section 2 of this article we describe the data; in sections 3 and 4, we discuss our turnover and repair analyses; section 5 includes our quantification exercises; and section 6 contains our concluding remarks. 2. DATA We examine the interview portion of the CES, which is a rolling-panel data set with households that enter and exit every five quarters, from 1991 to In the turnover analysis the unit of observation is the car, and in the repair analysis, a car in a specific quarter. Up to four quarters of car expenditure data are available per household, with an average of 2.6 quarters of data per household due to non-responses in some of the quarters. The CES contains detailed characteristics about each car as well as car repair expenditures by part. We adjust all expenditures to 2008 dollars. Note that we use the term car to refer to both cars and trucks. 9 Table 1 describes the repair-analysis sample. The left panel of figure 2 shows the number of cars by turnover status in the turnover-analysis sample. The right panel of figure 2 shows repair rates by car age for the four main car parts in the repair-analysis sample. We also use data on car reliability from the 1996 to 2004 issues of Consumer Reports. 10 These data are from a survey of the magazine s readers concerning their experiences with their cars, and describe defect rates across 13 parts (e.g., engine) by model, vintage, and age for cars up to age eight. Through 2004, Consumer Reports reported defect rates for a model-vintage-age and part as being in one of five specified probability ranges. We use the midpoints of these 9 Trucks consists of sports-utility vehicles, pickup trucks, and vans, but not larger trucks such as semi-trailer trucks. We exclude cars used for business purposes and cars in households that reported car service-policy expenditures, because respondents may not pay for expenses in these cases. 10 Starting in 2005, Consumer Reports changed its reliability ratings such that the data in each category no longer corresponds to a pre-defined probability range and hence is not directly comparable to earlier years of data. 6

9 ranges as our measure of the defect rate. The mean (standard deviation) of the Consumer Reports defect rates for cars in the CES sample for all parts together, transmission, engine, body, and air conditioning are.382 (.149),.032 (.025),.024 (.019),.151 (.069), and.029 (.025), respectively. 11 While the defect rates across parts are positively correlated, there remains considerable variation with which to identify the effects of individual parts. 12 The online appendix to this article provides more details about the data, including the CES and Consumer Reports survey questions, precise definitions of the car parts, and correlations in defect rates across parts. 3. ANALYSIS OF TURNOVER RATES Empirical strategy We now estimate the relationship between the defect rates of individual car parts and turnover. We focus on the four parts with the largest repairs expenditures transmission, engine, vehicle body, and air conditioning because more expensive repairs are presumably more likely to determine sorting and adverse selection than less expensive repairs. The mean expenditures per repair for these parts are $562, $525, $470, and $307, respectively. The 90 th percentile of expenditures, which may be the repairs most likely to affect the turnover decision, are $1,436, $1,171, $935, and $747, respectively. 13 Table 2 shows repair expenditures by part. We expect transmission and engine defects to be relative less visible to buyers, and consequently to have more asymmetric information. We expect vehicle body defects to be relatively more visible to 11 For consistency between the turnover and repair analyses, we aggregate the Consumer Reports categories, body integrity, paint/trim/rust, and body hardware, into a single vehicle-body category to match the CES category body work and painting. In the online appendix, we show the disaggregated results, which are similar. 12 The survey receives responses on over a half million cars per year and the typical model-vintage-age defect rate for a particular part is based on several hundred responses. While the survey response rate is relatively low, response bias does not appear to be a problem. We provide more information about this issue in the online appendix. 13 Table 2 shows that expenditures for steering and rear end are similar to expenditures for air conditioning but substantially lower than for transmission, engine, and vehicle body. We include air conditioning for the interesting seasonal component. 7

10 buyers, and consequently to have less asymmetric information. We expect air conditioning to have elements of both since defects may be more visible during warmer months and less visible during colder months. 14 We estimate the following index model, where the unit of observation is the car (each car appears once in the estimation sample), and the sample includes cars up to age eight (the oldest age in the Consumer Reports data), P(U = 1 X, D) = Φ(Dβ! + Xβ! ). (1) U = 1 if the car was purchased used, and U = 0 if the car is still in the possession of the original driver at the end of the sample period. 15 D is a row vector of the population defect rates for that model-vintage-age for parts i = 1,, I (i.e., D!,, D! and β!!,, β!! ). X is a row vector of other car characteristics (described below) that may also determine turnover rates. Φ is the evaluation of the standard normal distribution, which implies the usual probit model. With respect to a higher defect rate, we expect parts with asymmetric information to have relatively less turnover and those with symmetric information to have relatively more. Hence, we interpret β!! 0 as evidence of adverse selection and β!! > 0 as evidence of sorting. Note that β!! > 0 does not rule out adverse selection, but only shows that the sorting effect is stronger than the adverse-selection effect within the part, since there may be a mix of adverse selection and sorting within the part. We construct our turnover measure, U, from the CES data. The CES does not report whether a previous driver of a car owned or leased the car, so we cannot estimate this model separately for owned cars and leased cars. Our sample therefore includes both owned and leased 14 The seasonality of air conditioning observability could be due in part to saliency, which is not visibility per se, but would similarly determine the degree of asymmetric information. 15 We consider a car that was leased and then bought back as still being in the possession of the original owner. 8

11 cars and the results should be interpreted as applying to both. 16 We construct our defect-rate measure, D, from the Consumer Reports data as the mean of the model-vintage s defect rates from age one up to the car s age in the observation. For example, our engine defect-rate measure for a three-year-old 1996 Ford Taurus is the average of its engine defect rates over ages one, two, and three. While there may be other ways to construct a defect-rate measure, we believe this construction represents a sensible and parsimonious approach to measuring how a car s past defect rate affects whether the car has turned over by a given age. Results Table 3 presents estimates from the model in equation 1, which are reported as marginal effects calculated as the percentage change in the probability that a car has turned over due to a ten-percentage-point increase in the defect rate. 17 All specifications include dummies for car mileage in 10,000-mile intervals and age in one-year intervals for a flexible functional form. All specifications also include dummies for six car segments U.S. standard and luxury makes, Asian standard and luxury makes, and European standard and luxury makes because drivers of cars in these different categories may be quite different (including in their propensities for turnover). For example, the average Infiniti driver in the data is 41 years old and has 13 years of education, while the average Lincoln driver is 66 years old and has 9 years of education. We also include car segment interacted with car age since turnover-rate differences across driver types may vary by car age. Specifications with additional controls are also reported, including car vintage (column 4), car vintage interacted with segment and age (column 5), car vintage and make (column 6), and 16 Hendel and Lizzeri (2002) and Johnson and Waldman (2003, 2010) predict that leasing may limit adverse selection since the lease buyback price can be adjusted to achieve a more efficient level of trade. Johnson, Schneider, and Waldman (2012) provide empirical support for this prediction. Thus, we expect the adverse selection effect for owned cars alone to be larger (i.e., more negative). 17 Marginal effects are evaluated at the means of the explanatory variables. 9

12 car vintage and mean income, age, and education of drivers of that make (column 7). Including vintage helps to control for changes in turnover patterns during the 1990s due to the sharp increase in leasing rates. Including make instead of segment dummies provides a finer control for driver characteristics associated with particular cars. Note that including make dummies significantly limits the available defect-rate variation, and so the specification of interest has segment and vintage dummies alone (column 4), which represents a balance between having enough variation and controlling for unobserved driver differences. 18 Nine additional specifications are shown in table A4 of the online appendix, and results are similar. The model in column 2 of table 3 includes the defect rate for all parts together and shows that a ten-percentage-point increase in the defect rate increases turnover by 4.00 percent (p<.01). This indicates that sorting outweighs adverse selection overall. The model in column 4 includes the defect rates for transmission, engine, vehicle body, and air conditioning separately. Tenpercentage-point increases in transmission and engine defect rates correspond to 6.42 percent (p=.06) and 7.71 percent (p=.10) less turnover, respectively, which is consistent with adverse selection for these parts. In contrast, ten-percentage-point increases in vehicle body and air conditioning defect rates correspond to 7.32 percent (p<.01) and 6.26 percent (p=.10) more turnover, respectively, which is consistent with sorting for these parts. Finally, it should be noted that our test is not the result of an experimental or quasiexperimental variation, and our data set does not comprehensively contain all car characteristics nor does it contain the characteristics of the previous drivers of used cars. Consequently, we cannot rule out the possibility of an omitted-variable bias that would generate the differential transmission and engine versus body and air conditioning effects that we observe. This would 18 Recall that defect rates are reported at the model-vintage-age level. Furthermore, much of this variation occurs between makes; e.g., in general, Hondas are reliable while Dodges are not. 10

13 occur if there were an unobserved factor that predicts turnover that is: (a) negatively correlated with the conditions of transmission and engine and (b) not negatively correlated with the conditions of vehicle body and air conditioning. We have no reason to suspect this type of bias. Nevertheless, we have reported a range of specifications here and in the online appendix that attempt to control for this possibility. In short, we find evidence of (a) adverse selection for transmission and engine, reflected in the negative relationship between their defect rate and turnover, and of (b) sorting for vehicle body and air conditioning, reflected in the positive relationship between their defect rate and turnover. The aggregate effect is a net positive relationship, indicating that sorting dominates adverse selection overall ANALYSIS OF REPAIR RATES Empirical strategy For additional evidence about sorting and adverse selection, we compare the repair rates of traded and non-traded cars. Sorting due to car defects would arise from the gains from trade between sellers with a high distaste for defects to buyers with a low distaste for defects. The lowvaluation buyers have less of an objection to operating a car with a defect, so the primary motive for sellers and buyers to trade is to avoid an inefficient repair. 20 Under sorting, then, traded cars 19 In the online appendix, we report results with defect rates for all 13 parts, as well as results from a duration model, which estimates the probability that a car still driven by the original owner on entering the sample period is disposed of during the sample period. This latter approach uses the intuitive duration-model functional form for disposal, and allows us to exclude leased cars. Both approaches have significant drawbacks (discussed in the appendix), but provide results that generally support the turnover findings of this section. For our measure of defect rates, we also tried using the mean CES repair rates instead of the Consumer Reports defect rates but found this to be infeasible due to insufficient CES sample size. In addition, the probability of repairing a defect may depend on the defect s observability, which would make repairs a biased measure of defects. 20 For example, suppose a driver with a high valuation for car appearance has a car with faded paint. Instead of conducting the repair, this driver might trade the car to a driver with a lower valuation for appearance. In this case 11

14 have more defects than non-traded cars but fewer repairs conditional on a defect. If this sorting effect is sufficiently large, traded cars may even have fewer repairs than non-traded cars unconditional on the higher defect rate. 21 Under adverse selection, because buyers do not discover any defects until after the trade, they would repair these inherited defects at the same rate as for any other defect encountered. The predictions, then, are as follows: Asymmetric information over the condition of a particular part increases the repair rate of traded cars relative to similar non-traded cars. This is the adverse selection effect. Symmetric information over the condition of a particular part may decrease the repair rate of traded cars relative to similar non-traded cars, and at a minimum would increase the repair rate by less than under adverse selection. This is the sorting effect. To test for adverse selection and sorting, then, we compare the repair rates of cars that were recently purchased used with similar cars that were purchased new and continuously held. Finally, since used-car buyers may have a lower overall valuation for car quality than new-car buyers (reflected in their decision to buy used cars rather than new), used-car buyers may have a lower general propensity to repair defects than new-car buyers unrelated to any particular part or defect even under adverse selection (Kim 1985 first made this point). 22 We account for this possibility by controlling for the repair rate of similar cars that were purchased used but not purchased recently. This test is captured in the following empirical model. We estimate the model for each part separately, where the unit of observation is a car in a particular quarter, and the sample there is an efficiency gain due to the possibility that the trade price will be lowered by more than the degree to which this buyer dislikes faded paint, but less than the repair cost. 21 Note that it is also possible for sorting to occur over repair costs, such that sellers with high repair costs trade to buyers with low repair costs. In this case, sorting would cause traded cars to have more defects and more repairs than non-traded cars. We discuss this possibility later. 22 Thus, while adverse selection may cause traded cars to have more defects than similar non-traded cars, adverse selection implies only that traded cars have more repairs than non-traded cars if we have controlled for differences between new and used-car buyers in repair propensities. 12

15 includes cars up to age 15: P R! = 1 X, W, W! = Φ(Xα!! + α!! W + α!! W! ). (2) X is a row vector of car and owner characteristics that affect the repair rate, such as car mileage and driver income; W = 1 if the car was purchased used any time in the past and W = 0 if the car was obtained new and continuously held; W! = 1 if the car was purchased used in the previous year and W! = 0 otherwise; and Φ is the evaluation of the standard normal distribution, which gives the usual probit model. We interpret α!! > 0 as evidence of adverse selection related to part i. We interpret α!! 0 as evidence of sorting related to part i. Since an individual part may contain a mix of adverse selection and sorting, we more generally interpret α!! > α!! as being consistent with more adverse selection for part i compared with part j. Results Table 4 shows estimates from the model in equation 2. The dependent variable is an indicator for positive repair expenditures for that part in the car-quarter. X includes owner income percentile (since income may affect the propensity for making repairs) and dummy variables for car make, vintage, and age in one-year intervals, and mileage in 10,000-mile intervals. 23 The table shows the marginal effects of purchasing used last year and purchasing used any time, calculated as the percent change in the repair rate due to each of these two variables. 24 Estimates in the top panel of the table are from the entire sample and are our primary 23 Including dummies for narrower groups (e.g., model) leaves insufficient variation for precise estimates since repairs of individual parts in a quarter are relatively rare events. 24 The marginal effect for purchased used last year is evaluated at the dummies for purchased used last year equal to 0 and purchased used any time equal to 1, and at the means of the other variables. The marginal effect for purchased used any time is evaluated at the dummies for both purchased used last year and purchased used any time equal to 0. The marginal effect for purchased used last year in winter is evaluated at the dummies for purchased used last year equal to 1, purchased used last year during winter equal to 0, and purchased used any time equal to 1. 13

16 results. Columns 2 and 3 show that cars purchased used last year received percent more transmission repairs (p=.05) and percent more engine repairs (p=.03) than non-traded cars, which is consistent with adverse selection for these parts. Columns 4 and 5 show that cars purchased used last year receive 5.10 percent fewer vehicle body repairs (p=.55) and 1.09 percent fewer air conditioning repairs (p=.91) than non-traded cars. This is consistent with sorting for these parts. Finally, in column 6, we separate the air conditioning effect by season of purchase, showing the effect for cars purchased used last year in warmer months, captured by used last year, and cars purchased used last year in colder months, which is the sum of used last year and used last year winter. We expect sorting during warmer months and adverse selection during colder months. Indeed, cars purchased used last year in warmer months had 22 percent fewer repairs (p=.11) than non-traded cars after controlling for used-buyers general lower propensity to conduct repairs, while cars purchased used last year in colder months had 43 percent more repairs (p=.02) than those in warmer months. 25 The results in the middle panel of table 4 are for samples that limit the cars purchased used to those purchased privately, and the bottom panel to those purchased from a dealer. We include these results for completeness, but they are more difficult to interpret. First, the sample 25 Given that air conditioning repairs are more likely to be made during warmer months regardless of when a car was purchased, there is a risk of inadvertently attributing seasonal repair effects to seasonal purchase effects. We are careful to track repairs for just the twelve months after used purchase so that the full twelve months of repairs i.e., the full set of both summer and winter repairs are included regardless of the season in which the car was purchased. Note that including less or more than twelve months would be problematic given that this would give a differentially longer summer repair period or winter repair period depending on whether the car was purchased during the summer or winter. As mentioned in section 2, we exclude the car-quarter in which a car was purchased. Since respondents are asked about repairs over the prior three months (to be precise, since the 1 st of month, 3 months ago ), this avoids attributing repairs for a prior car to the newly purchased used car. Thus, for a car purchased in, e.g., January 2003, we consider repairs that were reported in interviews between April 2003 and January 2004, hence capturing repairs from January 2003 to January

17 sizes are significantly smaller compared to the whole sample, and so the estimates are less precise. Second, the selection process by which drivers choose to dispose of their used car privately versus through a dealer (as a trade-in) may be endogenous to the presence and observability of defects. If so, then aggregating private and dealer trades will capture the overall adverse-selection effect more accurately. Nevertheless, the results suggest that adverse selection is more prevalent in private trades than in dealer trades, which is consistent with dealers having reputational incentives to not sell lemons. There are several alternative explanations for the higher transmission and engine repair rates. One is sorting over repair costs, mentioned in footnote 21. In the online appendix, we provide suggestive evidence that transmission and engine repair expenditures conditional on repair are about the same for traded cars and non-traded cars. Another explanation deals with a pre-purchase moral hazard over maintenance: drivers who plan to sell their car may do less preventative maintenance if it will not be observed by buyers and reward for the maintenance would not be reflected in the trade price. In the online appendix, we provide evidence that maintenance expenditures prior to trade are not lower than maintenance expenditures for nontraded cars. 26 Finally, note that the repair analysis may understate the adverse-selection effect for the following reasons: (a) the analysis only identifies adverse selection due to aspects of quality that can be repaired, and (b) adverse selection and sorting could both occur for the same part (i.e., only some defects for a part may be observed). 5. QUANTIFICATION EXERCISES 26 A third explanation is that drivers have a higher propensity to repair defects soon after purchase. Perhaps, for example, it is more efficient to repair defects around the time of an inspection associated with the car purchase than later on. However, this is unlikely to generate disproportionally more transmission and engine repairs than vehicle body and air conditioning repairs if adverse selection were not the cause. Furthermore, the turnover results in section 3 are not subject to this factor. 15

18 The following quantification exercises provide a rough sense of the market-level effects of adverse selection and how they may vary by car age and make. To estimate the aggregate effect of adverse selection on trade volume, we use our estimated turnover model (equation 3) to predict how much turnover would occur in our estimation sample if the transmission and engine defect rates were 1 percent instead of the true defect rates, which have a mean of 3.2 percent for transmission and 2.4 percent for engine. Our empirical approach throughout has been reduced-form in nature, including the model in equation 1, and so we have chosen 1 percent to avoid making out-of-sample predictions. In our estimation sample, 19 and 40 percent of cars have defect rates of 1 percent for both transmission and engine, respectively. We view these results as a lower bound on the marketlevel effect of adverse selection because (a) an unobserved defect rate of 1 percent still represents some adverse selection, and (b) the only parts over which we are accounting for adverse selection are transmission and engine. Let λ! = N!! N! be the hazard rate for turnover for a car of age a, where N!! is the number of cars that were purchased new that trade at age a and N! is the number of cars that were purchased new that were continuously held until at least the beginning of age a. The left panel of figure 3 shows the ratio of the actual hazard rate in the data to the predicted hazard rate with limited adverse selection, λ! λ!. Adverse selection increases with age, reducing turnover by about 7 percent for eight-year-old cars. Of the most common makes in our data set, the effects of adverse selection are larger for American makes of cars and smaller for Japanese makes. We also estimate the mean delay in the age at which new cars first turn over due to adverse selection, a a, where a is the mean age of turnover among cars that have not turned over by age a, and a is this mean age but for the hypothetical case of 1 percent transmission and 16

19 engine defects rates. This turnover delay is illustrated in the right panel of figure 3. Naturally, the delay is larger for older cars due to their higher transmission and engine defect rates: the delay is approximately one month for one-year-old cars and almost four months for eight-year-old cars. Finally, because secondhand trade often results from drivers trading up to new cars (as in Hendel and Lizzeri 1999a), it is apparent that fewer used-car sales imply fewer new-car sales. Suppose the driver of car c sells the car when it reaches age a! and trades up to a new car. In a steady state, this driver buys! cars per year. We can then approximate new-car sales associated!! with secondhand trade over all drivers as!!. The same calculation can be done using the!! predicted age of first turnover in the absence of adverse selection, a!!, from the calculation above. Thus, a rough sense of the reduction in new-car sales due to adverse selection is!!!!!!!!. For all makes together, this calculation indicates that adverse selection reduces new-car sales by 2.3 percent. Furthermore, if we assume that when used-car sellers trade up they do so to a new car of the same make, we can calculate this effect by make. The effects for Honda, Toyota, Chevrolet, Ford, and Dodge are 1.0, 1.2, 2.0, 2.4, and 4.3 percent, respectively. These results suggest that adverse selection not only has a significant effect on the used-car market but may have a tangible effect on the new-car market as well. Note that we report these statistics to provide a sense of the magnitudes of the effects and how they vary by car age and make. However, they are only rough estimates for several reasons. First, some used-car sellers may not trade up to a new car, either buying another used car or buying no car at all. Second, we have not accounted for general-equilibrium effects, which could 17

20 somewhat attenuate the reported effects of adverse selection on trade volume. 27 On the other hand, our estimates may understate the adverse-selection effect for the reasons mentioned above. 6. CONCLUDING REMARKS The approach of this study is to examine the effects of adverse selection in a way that accounts for variation in the information properties of different car parts within the same car. Our results indicate that adverse selection may affect market outcomes due to the conditions of some parts but not of others. We provide empirical support for this hypothesis through an analysis of both turnover and repair patterns and show that the aggregate effects of adverse selection may be quite meaningful. Our results also demonstrate the value of testing for adverse selection along narrower dimensions of quality, as opposed to a monolithic quality, and how this approach can provide a more robust view of the operations of durable-goods markets. REFERENCES Adams, C., L. Hosken, and P. Newberry. Vettes and Lemons on ebay. Quantitative Marketing and Economics, 9 (2011), pp Akerlof, G. The Market for Lemons: Qualitative Uncertainty and the Market Mechanism. Quarterly Journal of Economics, 84 (1970), pp Anderson, S. and V. Ginsburgh. Price Discrimination Via Second-Hand Markets. European Economic Review, 38 (1994), pp Bond, E. A Direct Test of the Lemons Model: The Market for Used Pickup Trucks. American Economics Review, 72 (1982), pp Bond, E. Test of the Lemons Model: Reply. American Economic Review, 74 (1984), pp For example, in the absence of adverse selection, the increase in the supply of used cars and increase in demand for new cars may reduce prices for used cars and perhaps increase prices for new cars, depending on assumptions about market competitiveness and loyalty to particular makes. 18

21 Chezum, B. and B. Wimmer. Roses or Lemons: Adverse Selection in the Market for Thoroughbred Yearlings. Review of Economics and Statistics, 79 (1997), pp Emons, W. and G. Sheldon. The Market for Used Cars: New Evidence of the Lemons Phenomenon. Applied Economics, 41 (2009), pp Engers, M., M. Hartmann, and S. Stern. Are Lemons Really Hot Potatoes? International Journal of Industrial Organization, 27 (2009), pp Genesove, D. Adverse Selection in the Wholesale Used Car Market. Journal of Political Economy, 101 (1993), pp Gilligan, T. Lemons and Leases in the Used Business Aircraft Market. Journal of Political Economy, 112 (2004), pp Hendel, I. and A. Lizzeri. Adverse Selection in Durable Goods Markets. American Economic Review, 89 (1999a), pp Hendel, I. and A. Lizzeri. Interfering with Secondary Markets. Rand Journal of Economics, 30 (1999b), pp Hendel, I. and A. Lizzeri. The Role of Leasing Under Adverse Selection. Journal of Political Economy, 110 (2002), pp Hendel, I., A. Lizzeri, and M. Siniscalchi. Efficient Sorting in a Dynamic Adverse-Selection Model. Review of Economic Studies, 72 (2005), pp Johnson, J. and M. Waldman. Leasing, Lemons, and Buybacks. Rand Journal of Economics, 34 (2003), pp Johnson, J. and M. Waldman. Leasing, Lemons, and Moral Hazard. Journal of Law and Economics, 53 (2010), pp Johnson, J., H. Schneider, and M. Waldman. Information Costs, Transaction Costs, and Leasing: Theory and Evidence from the Automobile Industry. Working paper. Kim, J. The Market for Lemons Reconsidered: A Model of the Used Car Market with Asymmetric Information. American Economic Review, 75 (1985), pp Lacko, J. Product Quality and Information in the Used Car Market. Bureau of Economics Staff Report to the Federal Trade Commission, (1986). Lewis, G. Asymmetric Information, Adverse Selection, and Online Disclosures: The Case of ebay Motors. American Economic Review, 101 (2011), pp

22 Mussa, M. and S. Rosen. Monopoly and Product Quality. Journal of Economic Theory, 18 (1978), pp Porter, R. and P. Sattler. Patterns of Trade in the Market for Used Durables: Theory and Evidence. NBER working paper, w7149, (1999). Pratt, M. and G. Hoffer. Test of the Lemons Model: Comment. American Economic Review, 74 (1984), pp Rosenman, Robert, and Wesley Wilson. Quality Differentials and Prices: Are Cherries Lemons? Journal of Industrial Economics, 39 (1991), pp Swan, P. Durability of Consumption Goods. American Economic Review, 60 (1970), pp Swan, P. The Durability of Goods and Regulation of Monopoly. Bell Journal of Economics and Management Science, 2 (1971), pp Waldman, M. Durable Goods Pricing When Quality Matters. Journal of Business, 69 (1996a), pp Waldman, M. Planned Obsolescence and the R&D Decision. Rand Journal of Economics, 27 (1996b), pp Wilson, C. The Nature of Equilibrium in Markets with Adverse Selection. Bell Journal of Economics, 11 (1980), pp

23 Figure 1: Turnover rate by defect rate and age Fraction of cars that have turned over at least once Cars with high defect rates Cars with moderate defect rates Cars with low defect rates Car age Cars with high body and A/C defect rates Cars with moderate transmission, engine, body, and A/C defect rates Cars with high transmission and engine defect rates Car age Notes: The left panel shows the fraction of cars that have turned over at least once by a given age for model-vintage-ages in the top quartile of the total defect rate ( cars with high defect rates ), the middle two quartiles of the total defect rate ( cars with moderate defect rates ), and the bottom quartile of the total defect rate ( cars with low defect rates ). The right panel shows the fraction of cars that have turned over at least once by a given age for model-vintage-ages in the top quartiles of body and air conditioning defect rates and not in the top quartiles of transmission and engine defect rates ( cars with high body and A/C defect rates ), the top quartiles of transmission and engine defect rates and not in the top quartiles of body and air conditioning defect rates ( cars with high transmission and engine defect rates ), and the middle two quartiles of body, air conditioning, engine, and transmission defect rates ( cars with moderate transmission, engine body, and A/C defect rates ). 21

24 Figure 2: Number of cars by trade status and age (left), and repair rate by part and age (right) 4,500 5% Obtained new Engine Obtained used any time Transmission Body Obtained used last year A/C 3,600 4% 2,700 3% 1,800 2% 900 1% Car age 0% Notes: The left panel shows the number of cars in the estimation sample by trade status and car age. Bought new are cars that were obtained new and continuously held. Obtained used any time and obtained used last year are cars that were obtained used any number of years ago and in the previous year, respectively (the second is a subset of the first). The right panel shows the percentage of car-quarters with repairs in the estimation sample by part and car age. Car age 22

25 Figure 3: Predicted market effects of adverse selection on turnover rate and turnover delay All makes Honda Toyota Chevrolet Ford Dodge Car age Notes: The left panel shows the ratio of the turnover rate in the presence of adverse selection to the predicted turnover rate in the (approximate) absence of adverse selection, by car age. The right panel shows the predicted mean delay in years of the age of first turnover due to adverse selection for cars that have reached the specified age without having turned over yet, by age Car age 23

26 Table 1: Summary statistics for the repair-analysis sample Mean SD Min/Max Vintage /2005 Age /15 Odometer (x10-4 ) /24.9 Household income percentile /100 Purchased used any year /1 Purchased used last year /1 Purchased used last year November-April /1 Notes: The unit of observation is the car-quarter with 141,026 car-quarters from the estimation sample. Table 2: Repair rates and expenditures by part in the repair-analysis sample Expenditures conditional on repair Repair rate (%) 90th percentile ($) Mean ($) SD ($) Transmission Engine Bodywork A/C Steering Rear end Brakes Shocks Cooling Electrical Exhaust Motor tune-up Oil change Notes: The unit of observation is the car-quarter with 141,026 car-quarters from the estimation sample. Repair rate is the percentage of car-quarters with repairs of that part. The right three columns are the 90 th percentile, mean, and standard deviation of repair expenditures conditional on repair. The last two rows show maintenance rates per car-quarter by maintenance type, and expenditures conditional on maintenance. 24