Agency Problems and Reputation in Expert Services:

Evidence From Auto Repair∗

Henry Schneider†

October 16, 2007

Abstract

I investigate the nature of agency problems in the auto repair market and the ability of rep-

utation to limit them by examining data on 40 undercover garage visits I collected during a

field experiment and 51 undercover garages visits provided by a public-interest group. I docu-

ment clear patterns of agency problems and estimate that the resulting welfare loss represents

a substantial fraction of industry revenue. I find no evidence, however, that a mechanic’s con-

cern for her reputation improves service quality or limits inefficiencies. I conclude by drawing

inferences to expert services more generally and discussing possible remedies.

∗I thank Steve Berry, Justin Fox, Don Green, Justin Johnson, Dean Karlan, Josh Lustig, Steve Nafziger, PhilippSchmidt-Dengler, Fiona Scott Morton, Harsha Thirumurthy, Michael Waldman, and various seminar participants forcomments, and the Yale Institution for Social and Policy Studies for financial support. Michael Coughlin at PremierSubaru, Romana Primus at Whaling City Ford, and Paul Trembley at East Rock Auto Repair provided useful back-ground information about the auto repair industry. I am especially grateful to George Iny and his colleagues at theAutomobile Protection Association for invaluable assistance.

†henry.schneider@cornell.edu; Johnson School of Management, Cornell University

1 Introduction

In many service markets, such as automobile, bicycle and boat repair, home heating, plumbing

and roofing work, and many medical specialties, the seller of the service is also the expert who

diagnoses how much service is needed. This dual relationship creates incentives for experts to

provide a level of service that may not be optimal for customers, and generates agency problems

that have been analyzed in theoretical work by Darby and Karni (1973), Wolinsky (1993), Taylor

(1995), Emons (1998), Fong (2005), Alger and Salanie (2006), Dulleck and Kerschbamer (2006),

and others.

During the 1980s, however, a theoretical literature emerged predicting that a seller’s concern for

her reputation may limit these problems. Klein and Leffler (1981) showed that in an infinite-period

game with pure moral hazard, a seller’s concern for her reputation may induce her to provide high

quality. Milgrom and Roberts (1982) and Kreps and Wilson (1982) findings have been applied to

achieve a similar result in a finite number of periods by allowing for adverse selection over seller

type. In both cases, the possibility that buyers represent repeat business (alternatively, that sellers’

histories of actions are observable) incentivizes sellers to provide high quality.

Recent empirical work on reputation confirms some of these predictions: List (2006a) finds that

reputational concerns lead sports-card sellers to provide higher-quality products, but only when an

explicit quality-evaluation mechanism is present. This result indicates, very intuitively, that the

effectiveness of reputation is contingent upon the ability of buyers to evaluate quality. Work by

Hubbard (1998) and Hubbard (2002) on vehicle-emissions inspection service, Banerjee and Duflo

(2000) on the Indian customized software industry, and Jin and Leslie (2007) on the provision of

restaurant hygiene all occur in settings where the favorability of outcomes is often observable (e.g.,

a passing emissions inspection is favorable), and all find important reputation effects as well.

Unlike these industries, however, many expert services represent an in-between class of mar-

kets in which buyers are unable to evaluate seller behavior directly at reasonable cost (in particular,

1

whether the most parsimonious service was provided), yet still receive potentially-valuable infor-

mation from sellers’ choices of prices and service. Using a standard game-theoretic framework,

however, it is straightforward to show theoretically that this price and quantity information can still

be entirely sufficient to facilitate an effective reputation mechanism. The model in section 2 illus-

trates this process more formally, but the basic intuition is simple: Since opportunistic behavior

is most likely to appear as high-price service, buyers are less likely to return for repeat business

under this action; hence sellers provide more parsimonious service today in hopes of winning more

repeat business in the future. Ely and Valimaki (2003) and Ely, Fudenberg and Levine (2005) apply

a related mechanism to show that experts may even underprovide service in attempts to win good

reputations.

In this study, I attempt to characterize the nature of agency problems in auto repair, which is of-

ten described as a paradigm of a market with agency problems, and to quantify the extent to which

a mechanic’s pursuit of a good reputation limits them. I pursue these objectives by examining data

from 40 undercover garage visits I collected during a field experiment and 51 undercover garage

visits that were provided to me by the Canadian public-interest group, Automobile Protection As-

sociation (APA).1

During the field experiment, I posed as an ordinary motorist and submitted a test vehicle with

a prearranged set of defects to garages for repair recommendations. These defects were chosen for

their simplicity in order to test for intentional overtreatment and neglect instead of competency.

During each visit, I asked the mechanic to thoroughly inspect the vehicle, diagnose its condition,

make repair recommendations, and provide estimates of the costs of these repairs. Mechanics

were (unknowingly) randomly assigned to receive treatments in which reputation was either more

or less important (high and low-reputation treatments, respectively). During the high-reputation

treatment, I presented myself as a possible repeat customer by appearing to be moving in nearby.

1Field experiments is an emerging methodology in economics with a number of appealing features. See List(2006b) for a discussion.

2

During the low-reputation treatment, I presented myself as one-time business by appearing to be

moving away. I test whether mechanics receiving the high-reputation treatment discovered a larger

number of legitimate defects, charged lower diagnostic inspection fees, and recommended fewer

or less costly repairs.

I find that completely unnecessary repairs were present in 27 per cent of visits and represented

61 per cent of all charges. I also find that serious undertreatment occurred in 77 per cent of visits,

and that defects that could generate much larger problems in the future were often overlooked.

Meaningful charging for labor or parts that were not actually provided, however, appeared to be

virtually absent.

When I presented myself as possible repeat business, the average upfront diagnosis fee was

$37.70. When I appeared as one-time business, this fee was $59.75. The difference between

these amounts, after controlling for other factors, is equal to three-quarters of a standard deviation

of the inspection fee, and is statistically distinguishable from zero (p=0.03). I find no evidence,

however, that a mechanic’s pursuit of a good reputation affects repair recommendations, improves

service quality, or limits inefficiencies in a meaningful way: During both high and low-reputation

visits, the quality of diagnoses was often poor, and the type and amount of repairs were highly

inconsistent.

Supplementing these results with national-level data on individual households’ auto repair ex-

penditures from the Consumer Expenditure Survey, I provide a back-of-the-envelope calculation

showing that agency problems in the U.S. auto-repair market generate a welfare loss of approxi-

mately $8.2 billion, or 22 per cent of industry revenue.

An important characteristic of the upfront diagnosis fee is that motorists can evaluate whether

the price is favorable (e.g., free is favorable), which likely contributes to the large reputation effect

on this fee. In contrast, motorists are often unable to directly observe the favorability of service they

receive since the vehicle’s most visible defects are usually addressed regardless of whether over

or undertreatment occurred. This persistence in information asymmetry appears to prevent buyers

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from effectively employing reputational incentives, such as repeat business, to induce mechanics

to provide good service.

For reasons already mentioned, this failure of reputation was far from assured. The model in

section 2, however, offers several possible explanations for this outcome. For example, if a mod-

erate fraction of mechanics are a behavioral type who always act in the best interest of customers,

if the range of possible repair prices is sufficiently large, or if the mechanic believes the motorist

is unlikely to return for repeat business even if he lives locally, then the reputation result may no

longer hold. The discussion in section 2 provides intuition for these predictions. It is also possible

that individuals are just bad at Bayesian inference, which is required for the reputation result, and

previous research suggests this is the case (e.g., El-Gamal and Grether (1995)).

This paper makes several contributions to the literatures on agency problems and reputation.

First, empirical work on agency problems in expert services has mostly been limited to health care

markets and clear conclusions have yet to emerge (see the surveys in McGuire (2000) and Gaynor

and Vogt (2000) on induced-demand for medical services):2 The results presented here provide one

of the clearest pictures to date about the nature of agency problems in an expert service market,

and it is my hope that industry participants, policy makers, and researchers will find it informative.

Second, this analysis adds to the limited but growing body of empirical research on the ability

of reputation to limit information problems in markets for experience and credence goods, and is

particularly applicable to expert services. A practical understanding of the role of reputation in

such markets sheds light on the issue of when these markets can address information problems on

their own versus when public or private-sector interventions may be beneficial. These issues are

discussed further in the conclusion.

In the next section, I provide a model to illustrate how a reputation mechanism could operate in

a common auto-repair setting. In section 3, I provide preliminary evidence about agency problems

and reputation effects based on data from undercover garage visits conducted by the Canadian

2Levitt and Syverson (2005) study of real-estate-agent behavior is a nice example outside of health care.

4

public-interest group. In section 4, I describe the experimental design. Sections 5 and 6 describe

the experimental outcomes and their implications. In the conclusion, I draw inferences to other

expert service markets and discuss possible remedies.

2 Equilibrium model of a market for auto repair

Many researchers have constructed models of the auto repair market and numerous predictions

about mechanic behavior exist. As Dulleck and Kerschbamer (2006) show, however, these pre-

dictions are sensitive to a small number of key modeling assumptions. My objective for including

a model here is to illustrate how a reputation mechanism could operate in a typical auto-repair

setting, and to show how the randomized treatments I apply to mechanics during my experiment

allow me to test for reputation effects.

I add reputation to a basic model of expert-service provision along the lines of Dulleck and

Kerschbamer (2006). I also allow for heterogeneity in mechanic type: The real-world observa-

tion that some mechanics provide good service in situations where opportunistic behavior is more

profitable, and anecdotal evidence that some mechanics are more honest than others, points to a

reputation game with both adverse selection over type and moral hazard.

Model

I consider a two-period model with two mechanics and a positive number of motorists. Including

two periods allows me to examine mechanic and motorist behavior in a situation in which motorists

represent possible repeat business, as occurs during the first period, and when they represent one-

time business, as occurs during the second period. The existence of a second mechanic permits the

motorist to reject the first mechanic’s repair recommendations and visit another mechanic. Each

mechanic has a sufficient capacity to service all motorists.

In both periods, each motorist’s car has a defect that is major, M, with probability α, or minor,

5

m, with probability (1−α). α is common knowledge. The motorist considers hiring a mechanic

to diagnose the problem, make repair recommendations, and possibly correct the defect. If hired,

the mechanic conducts an inspection and privately observes whether the defect is m or M, and then

recommends either the minor or major repair.

The motorist incurs a search cost of s for each mechanic visited. Mechanics must charge

industry-standardized prices for the minor and major repairs, p0 > 0 and p1 > p0, respectively.3

The minor repair corrects only the minor defect, whereas the major repair corrects both the minor

and major defects. The cost to the mechanic of conducting repairs is zero. (The assumption of zero

costs is not essential, but simplifies exposition.)

Upon receiving a repair recommendation, the motorist accepts or declines repairs. If he de-

clines repairs, he can visit the second mechanic during the same period for cost s and receive

another repair recommendation.4 After any repairs are conducted, the motorist observes whether

the defect is corrected, but, in the case of a major repair, not whether the minor repair would have

sufficed. The motorist knows the history of any previous actions the mechanic conducted for him,

but not for other motorists.

With probability (1−µ), the mechanic is a good type who always recommends the lowest-price

repair that corrects the defect. With probability µ, the mechanic is a bad type who chooses repairs

to maximize her expected two-period payoff. The bad mechanic’s single-period payoff is p if the

motorist visits and consents to repairs, and 0 otherwise. The motorist cannot observe mechanic

type directly, but µ is common knowledge. For simplicity, the mechanic does not time discount.

The motorists receives utility v > p1 +s from a functioning car and 0 otherwise, giving him the

payoff (v− p− s) when he consents to repairs and the defect is corrected, and (−p− s) when he

3In practice, industry-standardized labor times are listed in shop manuals owned by virtually all garages, thoughthese times are not legally binding. Under reasonable assumptions, qualitatively similar results can be achieved whenmechanics choose arbitrary p.

4With minor modifications, this search costs can be interpreted as an exogenously-determined diagnosis fee, as iscommon in the theoretical literature, and under reasonable conditions, a reputation result can even be achieved withendogenous s. A version of the model with endogenous prices and diagnosis fees is available from the author uponrequest.

6

consents to repairs and the defect is not corrected. The motorist can also choose the outside option

of visiting no mechanics and receiving payoff 0.

A bad mechanic’s pure strategies consist of four values of p corresponding to the two possible

realizations of defect type in both the first and second periods.5 A motorist’s pure strategies consist

of his decision of whether to visit a mechanic, and whether to accept or decline repair recommen-

dations of p0 and p1, from the first and second mechanics in the first and second periods. The set

of second-period actions are specified for all possible first-period outcomes.

Finally, I state the following condition on the search cost,

Condition 1 The search cost, s, incurred by motorists for each mechanic visit satisfies the follow-

ing condition,

s ≥(

(1−µ)(1−α)µ(1−α)µ+α

)(p1 − p0)

Condition 1 guarantees that once the motorist has sunk the search cost, he receives higher expected

utility from consenting to repairs than declining repairs and paying the search cost again to visit

the second mechanic.6

Result Under Condition 1, the following perfect Bayesian equilibrium exists,

1. Bad mechanics play the following strategy,

(a) In period one, play p0 for m and p1 for M.

(b) In period two, play p1 for m and M.

2. Motorists play the following strategy,

(a) In both periods, visit exactly one mechanic and consent to repairs.

5Whether or not the mechanic recognizes a motorist as a repeat customer in the second period is inconsequential,and is omitted from this discussion.

6Note also that motorist must receive non-negative expected utility from visiting a mechanic in the first place. Theearlier assumption that v > p1 + s guarantees this.

7

(b) If the mechanic played p0 in period one and the defect was corrected, return to the

same mechanic in period two with any probability r0 ≥ p1−p0p1

.

(c) If the mechanic played p0 in period one and the defect was not corrected, switch to the

other mechanic in period two.

(d) If the mechanic played p1 in period one, return to the same mechanic in period two

with probability r1 ≤ r0 − p1−p0p1

.

3. Motorists’ beliefs about mechanics’ types conform to Bayes’ Theorem, and are correct in

expectation.

Proof See the appendix.

Discussion of result

In this equilibrium, bad mechanics play the same strategy as good mechanics when facing repeat

motorists (first-period visits), always choosing the lowest-price repair that corrects the defect, but

always choose the expensive repair when facing one-time motorists (second-period visits).

The reason why bad mechanics recommend the lowest-price effective repair to possible repeat

customers is intuitive: Mechanics are more likely to win back motorists as repeat customers un-

der this action (i.e., r0 > r1). These probabilities are such that bad mechanics receive a higher

two-period payoff from recommending p0 under m in period one and winning more repeat cus-

tomers than recommending p1 for m and winning fewer repeat customers. Motorists can play

mixed strategies about whether to return for repeat business because mechanics’ first period ac-

tions reveal nothing about their type (good and bad mechanics play the same strategy in the first

period), and hence motorists receive the same expected utility from returning to the same mechanic

or switching.

Also note that when all mechanics are opportunistic (µ = 1), the reputation equilibrium result

8

still holds, but more in the spirit of a trust reputation game.7

It is important to point out that the validity of Condition 1, and hence the existence of the

equilibrium, depends on the values of the parameters. If Condition 1 fails, motorists will search

for second opinions in response to expensive repair recommendations, which limits mechanics’

incentives to pursue repeat business and makes a reputation outcome more difficult to achieve.

The validity of Condition 1 depends in practice on the particular characteristics of the market

(µ and s) and the motorist’s assessment about his vehicle’s possible defects (α and (p1− p0)): If the

fraction of mechanics who are bad types, µ, is low, the motorist infers that the mechanic is likely

a good type even when p1 is recommended, will consent to repairs, and the equilibrium holds.

Similarly, if the fraction of mechanics who are bad types is high, the motorist recognizes that he

likely to encounter another bad mechanic during a second search, and also consents to p1, and again

the equilibrium holds. However, when this fraction is intermediate, searching for a second opinion

becomes attractive and can cause the inequality to fail. Similarly, as the probability of requiring a

complex repair, α, decreases, as the potential price savings of locating a good mechanic, (p1− p0),

increases, and as the search cost decreases, the likelihood that Condition 1 fails increases.

Nevertheless, the model illustrates that bad mechanics will only take actions in pursuit of a good

reputation when motorists represent the possibility of repeat business. During my experiment, I

exploit this repeat business condition to test for reputation effects by exogenously varying whether

the motorist appears as one-time or possible repeat business.

3 Preliminary evidence from Canadian data

The Canadian public-interest group, APA, conducted 51 undercover visits to garages in Montreal,

Toronto, Calgary, and Vancouver during 2003. 23 of these garages were company-owned chains,

23 were franchises, 4 were independent shops, and 1 was associated with a car dealership. During

7Condition 1 under µ = 1 is simply s ≥ 0.

9

each garage visit, the undercover researchers presented a vehicle with a loose battery cable, a defect

that causes intermittent starting failure, and was plainly visible in the engine compartment, easy

to diagnose and fix with equipment that is standard at all garages, and because of its simplicity,

designed to test for overtreatment and overcharging and not competency.8 At the beginning of each

visit, the mechanic was also told that the vehicle was purchased recently, and requested a general

inspection of the vehicle to diagnose any problems and make appropriate repairs.

The test vehicle was an off-warranty five-year-old Dodge Caravan with approximately 50,000

miles. All of the vehicle’s servicable parts were new or in excellent condition, and prior to each

visit, APA mechanics inspected the vehicle to ensure that quality remained constant across visits.9

The undercover researchers were a male-female couple in all cases except for four visits in Van-

couver, when the couple was two females.10 All of the researchers were Caucasian and varied in

age from late thirties to early fifties. They consented to any repairs that were recommended by

the mechanics, and requested back any parts that the mechanic removed from the car, which APA

mechanics re-installed into the vehicle before the next visit. I itemized inspection costs and repair

expenditures for these visits using service receipts provided to me by the APA, adjusting all prices

to 2005 US dollars.

Evidence about agency problems

Outcomes from APA’s visits are ideal for calculating overtreatment and overcharging because the

defect on their test vehicle was so straight-forward to diagnose, and the rest of the vehicle was

clearly in excellent condition: Unnecessary treatments strongly suggests opportunistic behavior.

8The solution is to check the electrical connections at the battery, starter, and perhaps alternator, and perform anindustry standard amperage, voltage, resistance (AVR) test of the charging and starting system. APA documentationnotes that “the APA chose this test because it seemed foolproof. The instrumentation required for the AVR test isinstalled at the battery terminal, which means the loose cable would of necessity be identified quickly.”

9Any parts that were not in excellent condition (and even some that were) were replaced with new parts prior to theinitiation of their study, including the battery, fuel filter, spark plugs and wires, brake rotors, and tires, and the vehiclewas transported between cities by railroad.

10The outcomes of the four female-couple visits were unexceptional.

10

APA mechanics estimated that a mechanic could easily diagnose and correct the loose battery

cable in twenty minutes, and that the correction and general vehicle inspection should take no

longer than sixty minutes. I allow an extra fifteen minutes for variation in ability and labor rates,

and assume an hourly rate of $70, for an upper bound on a reasonable price for the visit of $88.

Charges in excess of this amount when the battery cable is corrected but without additional work

performed are counted as overcharging. Charges in excess of this amount for additional work

being performed is counted as overtreatment.11 While these cutoff levels are somewhat ad hoc, the

results are robust to modifications in these levels. Note the importance of the distinction between

these quantities: Overtreating is clearly wasteful, while overcharging represents a simple transfer

from motorist to mechanic and generates smaller welfare losses.

In 40 of 51 visits (78 per cent), the defect was corrected, while in 11 of 51 of visits (22 per cent),

the defect was missed. Furthermore, in 14 of 51 visits (27 per cent), overtreating occurred, and by

an average amount of $244. However, in only 3 of 51 visits (6 per cent), overcharging occurred,

and by an average amount of only $32 per incident. There were two instances of sabotage of a

vehicle part to justify a repair, and in each case, the repair price was modest.

Dividing the sum of overcharges across all 51 visits by the sum of total charges for the 51

visits reveals that only 2 per cent of total charges were for overcharging. The same calculation for

overtreating reveals that a much larger 61 per cent of all charges represented completely unneces-

sary repairs.

Note that it is possible that these estimates may even understate a more typical rate of agency

problems since the undercover researchers requested the return of their replaced parts, which may

deter some forms of opportunistic behavior. However, it is unclear how important this action is:

Mechanics can sabotage a replaced part to cover-up overtreatment or can return a similar part that

had previously been removed from another car.

11I count any portion of charges for unnecessary treatment that exceeds the implicitly stated price for that repair (theproduct of the hourly labor rate and the industry-standardized labor time for that particular repair) as overcharging.

11

Evidence about reputation effects

During the 29 APA visits to garages in Montreal and Toronto, APA researchers provided an address

that was in the same city and had license plates that corresponded to the Canadian province of the

visit. During the 22 visits to garages in Calgary and Vancouver, the researchers gave an out-of-

province address, had out-of-province license plates, and stated that they were traveling through

the area on vacation.12 Table 1 provides characteristics of the garages visited by APA researchers,

and shows that the garage characteristics are balanced between local and nonlocal visits.

While I attribute differences in outcomes between cities to researchers’ appearances of being

local versus nonlocal, I cannot rule out the possibility that mechanic behavior varies systematically

across cities. For example, the vigorousness of consumer protection by regulators has historically

been strongest in Quebec, and average labor rates in Montreal and Toronto are modestly lower than

in Calgary and Vancouver. Any bias introduced by these factors, however, would likely amplify the

estimates of the reputation effects, and since minimal effects are apparent, city-effects are likely

unimportant. Nevertheless, this question of identification provides additional motivation for my

field experiment and is discussed in the next section.

I first test whether the possibility of repeat business affects the inspection fee, yi j, charged by

mechanic i to customer j with the following regression model,

yi j = β0 +β1w j +∑k

βkgik + εi j (1)

where w j = 0 when the customer represents one-time business, and w j = 1 when the customer

represents the possibility of repeat business; and gik is an indicator for a visit to garage type k,

which includes centrally-owned, franchised, independent, and dealer.13

12These local and nonlocal patterns were not deliberate choices of the researchers, but merely reflected their truecities of residence.

13I also estimated variations of the model with garage type interacted with the repeat-business dummy, whether thedefect was corrected, and cost-of-living in the city in which the garage is located. No meaningful additional effectswere apparent.

12

For garage visits in which the APA researcher had a local address and license plate, the average

inspection cost was $28.76. For visits in which the APA researcher had an out-of-province address

and license plate, the average inspection price was $45.67. The estimate of the coefficient β1 in

equation 1, which represents this difference after controlling for garage type, is -$15.30 (p=0.03),

and is equivalent to 0.60 standard deviations of the inspection price. Reputation appears to have a

first-order effect on the inspection fee.14

This reputation effect does not carry over to repairs. In 9 of the 29 local visits (31%), the

mechanic conducted repairs with a price exceeding $50, while in 4 of the 22 nonlocal visits (18%),

the mechanic conducted such repairs. Since the distribution over repair prices is highly skewed,

I test for differences in repair prices between local and nonlocal visits using the nonparametric

Mann-Whitney rank-sum test. The test fails to reject the null hypothesis that repair costs for the

two groups are the same (p=0.30).15

4 Experimental design

Motivation for field experiment

For the field experiment, I adapt the APA procedures to test more carefully for the effects of rep-

utation on mechanic behavior. Instead of relying on differences in garage location for variation in

the importance of reputation, I generate true exogenous variation by randomly assigning mechan-

ics to receive treatments in which reputation is either less or more important. I also control more

carefully for differences in the characteristics of garages and visits, such as garage size, mechanic

certifications, arrival time, weather, and researcher appearance, to ensure the absence of system-

14I also tested for differences in the inspection fee between the 20 local and 13 nonlocal visits in which mechanicsrecommended no repairs: This difference is -$17.56 after controlling for garage type (p=0.02). The mean inspectionprice for company-owned chains is $37.93 and for franchises is $31.06. The difference is not statistical different thanzero (p=0.33). The APA visited only five independent and dealer shops, which precludes a test for differences betweenthese garage types.

15A probit regression containing a binary dependent variable indicating whether repairs were conducted, and thesame regressors as the model in equation 1, provides a similar result.

13

atic differences in unmeasured customer and mechanic characteristics between visits with high and

low-reputation treatments.

I also visit independent shops instead of chains and franchises since independent shop owners

are involved in most repair decisions and depend most directly on individual relationships with

customers for repeat business and referrals, as opposed to chains and franchises, which face a

more complicated set of incentives from factors such as brand names, multi-store advertising, and

revenue targets. The test vehicle is also chosen to have defects that give mechanics more discretion

during the diagnosis process, and is designed to elicit a wider range of outcomes with which to

measure agency problems and reputation effects. Note also that an experiment conducted in the

field involving uninformed subjects is well-suited to learning about behavior that may be oppor-

tunistic and illegal. Obtaining representative observations about the natural behavior of mechanics

without the use of deception would be much more challenging.

Test procedures

APA mechanics and researchers provided guidance in preparing the test vehicle and implementing

the experiment. As in the APA visits, the test vehicle was rigged with a loose battery cable designed

to cause intermittent starting failure. This was the ostensible reason for visiting the garage. Also

as in the APA visits, during each visit, the mechanic was told that the vehicle was purchased

recently and a thorough inspection to uncover any additional problems was requested. Unlike the

APA visits, however, the test vehicle had a number of additional defects that required immediate

attention or monitoring. These additional defects could legitimately be addressed with a range of

repair options and provided mechanics with a richer set of opportunities for investing in reputation.

They also allow me to test for undertreatment.

I scheduled an appointment by phone in advance. During this call, I asked for the date of the

first available appointment, and used the number of business days until this date as my measure of

garage busyness. I was usually asked for a description of the symptoms of the defect, the make

14

and model of the vehicle, a name and phone number, and on three occasions, a home address. The

script I used for this phone call is in the appendix.

Upon arrival to the garage, the mechanic usually asked for the car’s symptoms and service

history, a telephone number, and sometimes a home address. In all but one of the visits, the

inspection fee was unspecified at drop-off, and was presented after the diagnosis was made.16

During the low-reputation treatment, I said I was moving to Chicago (from Connecticut) in two

weeks and wanted the car examined for problems before the trip. During the high-reputation treat-

ment, I said I was moving in nearby and wanted the car examined for problems before traveling to

Montreal in two weeks. Chicago and Montreal were chosen as destinations because the round-trip

distance from Connecticut to Montreal is approximately equal to the one-way distance from Con-

necticut to Chicago. The exact scripts are provided in the appendix. The low-reputation treatment

was reinforced by placing two bags of UHaul foam moving peanuts, 10 flattened UHaul boxes, a

push cart, an air conditioner box, a DVD-player box, a Dell computer box, and a microwave box

in the car. During high-reputation treatment visits, the inside of the vehicle was bare.

Garages were paid for the diagnostic inspection. If repairs were recommended, I told the

mechanic I would consider them and call the next day if I desired to have them conducted.

Test vehicle and undercover researcher

The test vehicle is a 1992 Subaru Legacy L Wagon. The vehicle had 141,000 miles at the beginning

of the experiment, accumulated 4,000 additional miles during the period, had an appearance one

would expect of a well-maintained thirteen-year-old car, and was free of decals and stickers. New

license plates with a tag number corresponding to a June 2005 registration were installed prior to

data collection to add credibility to the script that I had just moved to the area.17 The registration

16I did not request to know the inspection fee prior to leaving the car for inspection. Such estimates would not bebinding, and the mechanic could easily increase this price after the inspection was conducted by claiming small repairsor extra tests. Indeed, this practice appears to occur with some frequency.

17There are plenty of reasons to have new license plates if the researcher were moving away, as is the case for thecontrol group.

15

sticker was absent to avoid revealing that the car was registered in CT in November 2003.

Prior to data collection, the vehicle received thorough inspections from two APA mechanics

who documented the condition of all of the car’s parts, noted defects, and made judgments about

whether these defects required immediate repair or just active monitoring. Table 3 lists the defects

and their judgments about the urgency of repairing them.

Five of these defects required immediate attention: the loose battery cable, the low coolant,

the missing back-up taillight, the misfit and worn spark plug wires, and the exhaust pipe leak. To

maintain the appearance of low coolant throughout the experiment period, I emptied the coolant

overflow tank before each garage visit. Low coolant, especially during the hot summer months

during which the experiment was conducted, makes possible engine overheating, and risks the

life of the vehicle. One spark plug wire was fitted with a boot that fit improperly into the engine

block. This allowed debris and rain water to enter on top of cylinder head, which could cause

engine misfiring and corrosion. The exhaust pipe leak was located near the front of the center

pipe beneath the driver’s seat. Six other defects were present that required monitoring but not

immediate attention: a slightly weak alternator that still reached sufficient voltage to effectively

charge the battery; an exhaust system with rust along the center pipe and muffler; an unknown

condition of the timing belt; moderately-worn shock absorbers; two moderate oil leaks from the

engine; and a rattling noise that occasionally emanated from the right-front brake that did not

compromise braking ability. The vehicle’s remaining parts were judged to be in good condition.

This author, a thirty-one year old Caucasian male at the time, conducted all of the undercover

garage visits wearing khaki pants and a polo shirt.

Subject population

The study involves independent auto repair shops located in two Connecticut counties. Attributes

that characterize independent shops include American Automobile Association (AAA) certifica-

tion, Better Business Bureau (BBB) certification, whether the garage employs Automotive Service

16

Excellence (ASE) certified mechanics, garage size, association with a gas station, on-site used car

sales, busyness, visible associations with the parts distributors NAPA or AC Delco, and geographic

location. To make the subject sample as homogeneous as possible to preserve test power, AAA

and BBB-approved garages, auto body and oil change shops, and garages that sold more than a

handful of cars were excluded.18

Garage selection involved choosing towns in southern and central Connecticut, selecting garages

that appeared in a Google maps search of that town, choosing a home address within 0.7 miles of

the garage to provide to the mechanic during the visit, and matching garages in pairs based on a

similarity of characteristics. Appointments were scheduled, and then a coin was flipped for the

assignment of high or low-reputation treatments to garages. While some garage heterogeneity re-

mained, Table 2 shows that garage and visit characteristics are reasonably-well balanced between

treatment groups.

5 Experiment results

The discretion designed into the field experiment provides mechanics with a rich set of opportuni-

ties for investing in reputation (i.e., wider latitude in providing lower or higher levels of service),

but this range of reasonable actions makes them more suitable for quantifying reputation effects

than explicit rates of overcharging and overtreating. Nevertheless, it is still possible to document

general patterns of agency problems here.

18Excluding AAA-approved shops was a natural choice since less than 10% (about 25) of independent repair shopsin the two counties visited were AAA-certified for auto repair at the time. In the cities I chose, only 8 independentshops were AAA-certified. Other historical data from the APA provide no evidence that AAA-certified are better thannon-certified garages: Twelve garages they have visited were certified by the CAA (the Canadian equivalent of AAA),and their rate of overcharging and overtreating was 4% higher than at non-approved garages.

17

Evidence about agency problems

Table 4 shows how many times each legitimate defect was discovered by mechanics during the

40 garage visits. It shows that all problems except the right-front-brake rattle were discovered at

least once, indicating that nearly all were apparent to a careful eye. However, the mode number

of defects discovered was one, and in 21 of 40 visits (55 per cent), two or fewer defects were

discovered. In only 4 visits (10 per cent) were a majority of the defects discovered. The blown

taillight was discovered in only 5 of 40 visits (13 per cent), showing that even trivial-to-discover

problems were usually overlooked. The loose battery cable was corrected in 27 of 40 visits (68

per cent), though when visits involving starter or battery replacement are included, which would

have led to the incidental correction of the loose battery cable, the intermittent starting problem is

corrected in 31 of 40 visits (78 per cent). This rate is identical to the rate from the Canadian data.

The defect requiring the most urgent attention was the low coolant level, which was easily

visible in the engine compartment, and insufficient coolant risks the life of the engine, especially

during the hot summer months in which the visits occurred. This defect was discovered in only 11

of 40 visits (28 per cent), indicating serious undertreatment during the majority of visits.19

Figure 1 and table 4 provide an overview of the outcomes of the field experiment visits. In

addition to the low quality of most inspections, the repair recommendations were highly inconsis-

tent across visits. In 22 of 40 visits, less than $50 in repairs were recommended. However, in 12

visits, over $400 repairs were recommended, and in two visits, $1,398 and $1,849 in repairs were

recommended. Three of 8 mechanics who discovered the weak alternator recommended replacing

it, while 5 recommended waiting; 6 of 16 mechanics who noticed the rusted muffler recommended

replacing it, while 10 of 16 advocated waiting; 9 of 21 mechanics who asked about the timing

belt recommended replacing it, while 12 recommended waiting or refused to make a recommen-

dation. Note that recommending repairs or advocating waiting are both reasonable actions, but the

19An alternative explanation for the poor coolant-level detection rate was intentional neglected in the hopes ofwinning a large engine repair in the future. This possibility seems unlikely given the existence of legitimate defectsthat could have been repaired immediately.

18

lack of a systematic protocol appears to generate highly inconsistent outcomes. Inspection prices

displayed a similarly dispersed pattern, and were essentially uncorrelated with repair estimates.

Despite the existence of legitimate defects requiring attention, unnecessary repairs were rec-

ommended in many visits. Replacing a well-functioning starter motor, for example, was a common

prescription for the intermittent starting problem, occurring in 7 of 40 visits (18 per cent), despite

the presence of a visibly-loose battery cable, and mechanics recommended replacing a perfectly

healthy battery in 3 of 40 visits (8 per cent).

There were no egregious cases of overcharging for recommended repairs. In fact, conditional

on repair type, prices were fairly uniform. For example, for the five visits in which starter-motor

replacement was recommended and the price was listed separately, the prices are $190, $206,

$235, $254, and $240, and some of this variation is simply the result of differences in the posted

hourly-labor rates. Variation in the prices for exhaust work and belt service was quite large, but

mostly because different degrees of work were recommended. For example, some mechanics

recommended replacing only the timing belt, while others recommended replacing all three drive

belts, while still others recommended replacing the water pump concomitantly.

Since repair recommendations were never consented to, I cannot verify directly whether the

mechanics would have actually conducted the repairs being charged for. Nevertheless, based on

the types of repairs that were recommended, I can infer that the repairs would have almost surely

been conducted. The most frequently recommended repairs were for the exhaust system, alternator,

starter motor, battery, spark plug wires, and timing belt. In all of these cases, it would be clear to

an informed motorist and future mechanics whether the repair was conducted. For example, the

exhaust system had conspicuous rust along its entire length, which would be difficult to conceal.

Evidence about reputation effects

The average inspection price for visits in which I appeared to be moving away is $59.75. The

average price for visits in which I appeared to be local is $37.70. The difference is $22.05 and a

19

simple t-test shows that amount is statistically different than zero (p=0.05).

I then estimate the following regression model of being local on the inspection price, control-

ling for a number of additional factors,

yi j = β0 +β1w j + xiβ2 + z jβ3 + εi (2)

where yi j is the inspection cost charged by mechanic i during visit j; w j = 1 indicates that the

motorist represents the possibility of repeat business during visit j, and w j = 0 when the motorist

represents one-time business; xi is a vector of attributes describing garage i, and includes ASE-

certification, garage busyness, and garage size; z j is a vector of attributes describing visit j, and

includes the time-of-day when the vehicle was brought to the garage, and a dummy variable for

whether the Heat Index exceeded 90 degrees Fahrenheit that day; and εi j is a mean-zero, indepen-

dent random error.20,21

The estimate from the most basic regression model, in column (1) of table 5, shows that the

inspection cost is $22.01 (p=0.04) lower when the motorist represents the possibility of repeat

business. The estimate from the full specification is $25.48 (p=0.03). These estimate are very

similar to the estimates from the Canadian data in section 3.

As figure 1 shows, the distribution over repair costs is highly skewed. For this reason, I test for

differences in the repair prices between local and nonlocal visits using the non-parametric Mann-

Whitney rank-sum test. This test fails by a wide margin to reject the hypothesis that mean repair

costs for the two groups are the same (p=0.76).

I also estimate the following index model for whether the mechanic recommends repairs ex-

20The Heat Index combines air temperature and relative humidity to generate an index that reflects how hot it actu-ally feels. Since garages typically lack air conditioning, the level of the Heat Index may affect mechanics’ decisions.

21I also estimated versions of the models in equation 2 and 4 with a fixed effect for each matched garage pair (20in all), but in both cases an F-test fails to reject the null that the fixed effects are jointly statistically indistinguishablefrom zero.

20

ceeding $50,

Pr[ri j = 1] = Φ[α0 +α1w j + xiα2 + z jα3] (3)

where Pr is probability, ri j is an indicator variable for whether repairs were recommended, Φ is the

evaluation of the standard normal CDF, which implies the usual probit specification, and the re-

gressors are defined as before. Column (3) in table 6 contains estimates from the full specification.

The estimate of the effect of being local is small in magnitude and not statistically distinguish-

able from zero (p=0.69), providing no evidence that reputation affects a mechanics’ propensity to

recommend repairs.22

Next, I test whether the quality of the inspection, measured as the number of legitimate defects

discovered, is higher for visits in which I represented the possibility of repeat business. I estimate

a Poisson maximum likelihood regression model with the following conditional mean function,

E[ni j|·] = exp{γ0 + γ1w j + xiγ2 + z jγ3} (4)

where ni j is the number of legitimate defects discovered during the inspection, including the loose

battery cable, and the regressors are defined as before. Columns (6) in table 6 provides estimates

from the full specification and shows that being local increases the number of legitimate defects

discovered by only 0.06, which represents a negligible fraction of the mean number of discovered

defects of 2.58.23

Using a similar model as in equation 3, I also estimate whether the possibility of repeat business

increases the probability that the loose battery cable and the low coolant level are corrected. The es-

timates of the effect of being local on loose battery-cable correction, given in columns (1) through

(3) in table 7, are small in magnitude and not statistically distinguishable from zero. The estimates

22Interestingly, estimates in column (3) of table 6 show that the probability of repairs decreases by 11 percentagepoints for each hour later in the day the car is dropped-off at the garage (p=0.07), and that mechanics at ASE garagesare 41 percentage points less likely to recommend repairs than mechanics at non-ASE garages (p=0.02).

23A similar model estimated using the ordinary least squares method yields similar results.

21

of the effect of living locally on low coolant-level correction, given in columns (4) through (6) of

table 7, are also approximately zero.24

Evidence from field experiment and Canadian data combined

Table 8 contains estimates based on the pooled outcomes of the field experiment and Canadian

visits. The estimate of the effect of being local on inspection fees in column (1) is -19.22 (p=0.00),

mirroring the estimates from the field experiment and Canadian visits individually. The model in

column (2) includes an interaction term between being local and whether the observation was a

Canadian visit to test for a difference in the magnitude of the reputation effect between the field

experiment and Canadian visits. No meaningful difference is apparent.

Column (3) contains an estimate of the effect of being local on the probability that repairs are

recommended. Again, the estimate mirrors those from the field experiment and Canadian visits

individually. The effect of being local on the probability of repairs is -0.052 and is not distinguish-

able from zero (p=0.61). An interaction term between being local and whether the observation

was a Canadian visit, in the model in column (4), again shows no difference in reputation effects

between field experiment and Canadian visits. Finally, the estimates in columns (5) and (6) ex-

amine the effects of being local on the probability that the loose battery cable is corrected using

the pooled data. Again, reputation appears to have little effect, and no difference between the field

experiment and Canadian visits is apparent.

24While there is an insufficient amount of data to test whether the repair prices for local visits were lower thannonlocal visits conditional on the same repairs being recommended, no obvious differences are apparent.

22

6 Discussion of results

Summary and analysis of outcomes

Overcharging can appear either as charging for labor that was not provided, or for entire repairs

that were not provided. Meaningful overcharging for labor was absent in both the field experiment

and Canadian visits. While the methodology used during data collection prevents me from directly

observing whether mechanics charged for entire repairs they failed to conduct, the data suggest

this is uncommon. In fact, prices conditional on specific repair type were reasonably uniform.

Under and overtreatment, on the other hand, was common, and repair recommendations across

visits were highly inconsistent. Since thorough inspections were explicitly requested, and standard

inspection procedures cover the test vehicle’s most pressing defects, the poor average quality of

inspections reveals widespread undertreatment.

During the field experiment and Canadian visits, reputation was either less or more important

depending on whether the undercover researcher represented one-time or possible repeat business.

When reputation was important, mechanics charged substantially lower upfront diagnosis fees,

which is consistent with previous findings that reputation affects seller behavior when buyers can

directly evaluate the favorability of outcomes (e.g., free is favorable). I find no evidence, however,

that reputation affects repair recommendations, repair prices, or the number of legitimate defects

discovered during the inspection, including the loose battery cable, which was the ostensible reason

for visiting the garage and for which symptoms were explicitly described, and the low coolant level,

which was trivial to discover and risked the life of the engine.

The prevalence of under and overtreating, even when repeat business is possible, suggests that

motorists are typically unable to detect its presence, and indicates that diagnoses may be highly

unreliable in such circumstances. In contrast, the absence of overcharging suggests that motorists

can sometimes verify whether a repair price is favorable compared to what other mechanics may

charge. This result is intuitive: Motorists can easily call other garages for additional price esti-

23

mates, but would incur much larger costs from visiting other garages for second-opinions about

diagnoses. Furthermore, the inconsistency of diagnoses itself raises questions about how informa-

tive second-opinions would be at all.

This inability to directly assess mechanic behavior appears to prevent motorists from employ-

ing reputation incentives, such as possible repeat business, to discipline mechanics. As the model

in section 2 illustrates, this results was not at all a foregone conclusion: Predictions from a standard

game-theoretic framework indicate that price and quantity information alone can be entirely suffi-

cient to facilitate a reputation mechanism. The discussion in section 2 offers possible explanations

for this failure.

Note that the outcomes of my and the Canadian visits correspond closely, despite occurring in

different countries, with different undercover researchers, test vehicles, and garage types, lending

confidence to the representativeness of both sets of outcomes. Also note that some mechanics

provided favorable service even to one-time customers when it was clearly not in their financial

interest to do so, which suggests that heterogeneity in seller types exists.

Discussion of welfare

Obtaining a precise estimate of the welfare loss from agency problems in auto repair requires

significantly more information than is currently available. However, a rough approximation is

entirely sufficient for gauging its importance and assessing the benefit of possible remedies.

Of the 256,756 usable observations of individual households’ repair expenditures by vehicle

and month from the Consumer Expenditure Survey (CES) for survey years 1995 to 2004, 18,295

(7 per cent) had positive values. Figure 2 is a histogram of these positive repair expenditures

per vehicle and month.25 To obtain a back-of-the-envelope estimate of the welfare loss, I assume

repair expenditures per vehicle and month are representative of repair expenditures per garage visit

(possibly occurring over multiple but contiguous trips to the same mechanic). I also assume the

25Expenditures on routine maintenance, such as oil changes, are excluded.

24

following conditions about the U.S. auto-repair market,

1. No overcharging occurs. Results from the current analysis indicate that overcharging is

limited. Opportunistic behavior by mechanics thus appears as overtreating.

2. Repairs under $200 are minor (m). All minor repairs correct what I will call minor defects

and are efficient.

3. Repairs of at least $200 are major (M). All major repairs correct what I will call major

defects and are efficient, except for a fraction that are used to treat minor defects.

4. Twenty-seven per cent of cars that are brought to a garage with a minor defect are oppor-

tunistically overtreated with a major repair. This figure is the fraction of Canadian visits in

which overtreatment clearly occurred.

5. Fifty-three per cent of repairs are minor, while the remaining repairs are major. Also, the

average minor and major repair charges are $91 and $598, respectively. These figures are

obtained from the national-level CES data on repair expenditures.

Under these assumptions, the probability of the defect being minor when the repair is major (i.e.,

the fraction of major repairs that represent overtreatment), Pr(m|p1), is given by Bayes’ Theorem,

Pr(m|p1) =Pr(p1|m)Pr(m)

Pr(p1)(5)

where p1 indicates the major repair was conducted, and m indicates that the true defect is minor.

From the assumptions above, Pr(p1|m) = 0.27, Pr(p0) = 0.53, Pr(p1) = 0.47, Pr(m|p0) = 1, and

Pr(m) = Pr(p0)Pr(m|p0)+Pr(p1)Pr(m|p1) (6)

Solving equations 5 and 6 simultaneously gives Pr(m|p1) = 0.42, or 42 per cent of major repairs

represent overtreatment.

25

Using the average expenditures for minor and major repairs of $91 and $598 from above, the

average amount of overtreatment per incident is $507. Since overtreatment may sometimes extend

the life of replaced parts, the price of parts may be marked-up from the costs of producing and

distributing them, and mechanics’ labor rates may exceed the shadow value of the time they used

to conduct the unnecessary repairs, the full amount of overtreatment does not represent waste.26

However, if half of the amount of overtreatment represents waste, then a simple calculation based

on pr(m), pr(M), and pr(m|p1) shows that 22 per cent of all auto repairs conducted by mechan-

ics in the U.S. represent a welfare loss. Note that this estimate excludes several additional causes

of inefficiency, such as welfare losses from undertreatment, which appears to be widespread, and

from motorists delaying garage visits or scrapping vehicles early because of higher expected ex-

penditures from overtreatment.

According to the Economic Census of the United States, $38 Billion in repairs were conducted

in the United States in 2002. Twenty-two per cent of this amount is $8.4 billion, which indicates

that agency problems have a first-order effect on efficiency in this market.

7 Concluding remarks

Car owners are often unable to evaluate the necessity of the service they receive, yet can sometimes

verify that the stated service was actually provided and easily obtain competing price estimates. As

the results of this study indicate, in these settings, the risk of overtreatment is high, but meaningful

overcharging may be rare. Other expert services, such as boat and bicycle repair, dentistry and

optometry, and home heating, plumbing, and roofing work, sometimes share these features, which

raises concerns about similar patterns of agency problems in these markets. Despite institutional

differences, auto repair and many medical specialties are also alike, which raises concerns about

26Note however that mechanics rarely reuse or resell parts that were removed from the vehicle.

26

the prevalence of overtreatment in many pay-for-procedure medical settings.27

I find that reputation does appear to discipline sellers in a setting where repeat business is

possible and buyers can explicitly evaluate seller behavior. Thus reputation may reduce informa-

tion problems in markets for services with these features, such as tailoring, animal grooming, hair

styling, film developing, and housecleaning. These results, however, are less informative about

markets where buyers can evaluate seller behavior but repeat business and referrals is less immedi-

ate. Piano movers, deck and porch builders, funeral service providers, and pest exterminators, for

example, all fall into this category.

An examination of the outcomes of the current study in the context of existing work also per-

mits an opportunity for a broader assessment of the effectiveness of reputation at limiting informa-

tion problems. While reputation does seem to influence seller behavior under some conditions, it

does not appear to be a panacea, even in settings with seemingly-strong reputational incentives. For

example, in the current study, many mechanics do not charge a low diagnosis fee to possible repeat

customers, and in List (2006a), sports-card sellers in the high-reputation setting provide only 31

to 35 cents in added value for every additional dollar offered by the seller (a fully-effective repu-

tation mechanism would return closer to unity). Similarly, in Jin and Leslie (2007), reputational

incentives associated with chain affiliation reduce hygiene infractions by a moderate 23 per cent,

and low hygiene scores are not uncommon when reputational incentives are strongest.28 More pes-

simistic still, the current results indicate that reputation can be completely ineffective even when

sellers’ choices of price and quantity are observable. This result compliments the finding in List

(2006a), that the effectiveness of reputation and the ability of buyers to evaluate seller behavior

are compliments, by shedding light on an important in-between class of markets in which buyers

cannot evaluate quality directly but still observe sellers’ choices of prices and quantities.

27For example, patients are sometimes unable to evaluate the necessity of surgery for lower-back pain, especiallyfrom an orthopedist who receives higher profits from surgery than advising physical therapy and medication. Further-more, as Hubbard (1998) notes, patients’ tendency to trust white-collar professionals may exacerbate these problems.

28This percentage is my own calculation based on the authors’ estimates of a 5.39 point effect reported in their Table4, and the average hygiene score of 76.77 (or 23.23 penalty) reported in their Table 1.

27

While it is beyond the scope of this study to analyze specific remedies, one that involves the

public posting of outcomes from third-party evaluations may capture some of these losses. Such

programs have been moderately effective in other markets. The Los Angeles County Department

of Health, for example, conducts hygiene inspections of all 22,000 restaurants in the county ap-

proximately four times per year. In 1998, the county began requiring these restaurants to post their

hygiene quality scores in their front windows. Jin and Leslie (2003) show that this policy caused

restaurant health inspection scores to increase by approximately 5 per cent (0.4 standard deviations

in hygiene score) and the number of foodborne-illness hospitalizations to fall by 20 per cent.

Such a policy applied to auto repair establishments may pay for itself. For example, the Eco-

nomic Census shows that $607 million in auto repairs were conducted in Connecticut in 2002. If

all 1,230 licensed auto repair establishments in Connecticut were tested by undercover inspectors

four times per year, and each inspection costs $1,000, the annual cost of this policy ($4.9 million)

would be dwarfed by the potential welfare gains.29 Businesses providing diagnosis-only inspec-

tions would also reduce mechanics’ incentives to recommend inefficient levels of service. While

appealing in some ways, this approach raises other sets of agency problems, and still requires that

costly inspections be conducted.

Finally, an intriguing remedy to the information problem is the public aggregation of buyers’

experiences so that a history of sellers’ actions is observable. Even in settings in which buyers are

unable to evaluate the quality of any single seller action, a history of actions made public can reveal

clear patterns about behavior. The publication Consumers’ Checkbook and the website Angie’s

List, for example, are early incarnations of what could become effective means for disseminating

such information.29Each undercover garage visit by the APA costs the organization approximately $1,000 to conduct.

28

Appendix A: Proof of equilibrium result

The proof of the equilibrium result in section 2 contains three steps. In the first step, I show that bad

mechanics always play p1 in period two, and motorists always visit and consent to these repairs.

A motorist consents to p1 in period two if the utility of consenting exceeds the expected utility

of declining and visiting the second mechanic. The following equation represents this condition,

(v− p1) ≥ µ(v− p1 − s)+(1−µ)(v− (1−α

′)p0 −α′p1 − s

)(7)

where α′ is a motorist’s assessment of the probability that his car requires a complex repair condi-

tional on the first mechanic recommending p1. Using Bayes’ Theorem, this probability is,

α′ =

α

µ(1−α)+α(8)

Combining equations 7 and 8, and rearranging, yields Condition 1,

s ≥(

µ(1−µ)(1−α)µ(1−µ)+α

)(p1 − p0) (9)

For a motorist to visit a mechanic in the second period at all, expected utility must be non-negative,

v−E[p]− s ≥ 0 (10)

Since I assume v≥ p1+s, this condition always holds. Hence, Condition 1 is sufficient to guarantee

that motorists always consent to p1 in period two. Since motorists always consent to p1, bad

mechanics always choose p1.

In the second step, I show that bad mechanics win back repeat customers in period two only if

they mimic good mechanics in the first period, playing p0 in state m and p1 in state M. Suppose

mechanics play p1 in state m and period one with probability q > 0. Then in equilibrium when the

29

motorist receives repair recommendation p1, the motorist will use Bayes’ Theorem to update his

prior that the mechanic is a bad type to,

pr(badtype|p = p1) =µ(α+(1−α)q)

(1−µ)α+µ(α+(1−α)q)(11)

Since the right-hand side of equation 11 is greater than µ, the motorist benefits from switching to

the other mechanic in period two, and the mechanic never wins repeat business under this action.

Since conducting the minor repair in state M reveals the mechanic as a bad type (recall that the

motorist observes whether the defect was fixed), the mechanic never wins repeat business under

this action either.

In the third step, I show that bad mechanics weakly prefer mimicking good mechanics in pe-

riod one and winning repeat customers (step 2 showed that mimicking is required to win repeat

customers), instead of playing another action and winning no repeat customers. Mechanics clearly

prefer to play p1 over p0 for M in period one, as p0 returns smaller current-period profits and no

repeat customers. For mechanics to play p0 for m in period one, mechanics’ two-period payoff

from p0 must larger than from p1,

p0 + r0 p1 ≥ p1 + r1 p1 (12)

Rearranging shows that following condition on r0 and r1 is required,

r1 ≤ r0 −p1 − p0

p1(13)

However, for condition 13 to be viable, it must be the case that,

r0 ≥p1 − p0

p1(14)

30

Finally, motorists are willing to play mixed strategies over whether to return to the same me-

chanic in period two for the following reason: Since mechanics always play honestly in the first

period, motorists learn nothing about mechanic type in the first period. Therefore, motorists are in-

different about returning to the same mechanic or switching mechanics in period two, and receive

the same expected utility for any choice of r0 and r1.

Appendix B: Field experiment script

Calling for appointment

Hi, I’m wondering if I can arrange a time when I can bring in my car for service?

We bought the car recently and it hasn’t started a few times - can you check that out?

We also want to get a thorough inspection to see if any other work needs to be done.

When’s the soonest I can bring it in?

If asked to keep the car overnight

My wife needs the car in the evenings. Can I just bring it in on a day when you’ll have

time to look at it? I can drop it off early.

Script for low-reputation treatment

My wife and I are moving to Chicago in two weeks and we’re taking the car with

us - we just wanted to have some things looked at before the trip. I scheduled an

appointment for this morning.

We bought the car recently and we should have had it looked at before we bought it,

but we didn’t. It hasn’t started a few times - can you check that out? We’d also like a

thorough inspection to let us know if any other work needs to be done.

31

Will you give me a call to let me know how things are going? Roughly how long do

you think that’ll take?

If asked for an address

Our new address in Chicago is 203 Water street. We’re staying with friends down the

street for now, on [local street].

Script for high-reputation treatment

I’m moving in just down the street so I figured I’d come check you guys out. I sched-

uled an appointment for this morning.

My wife and I are taking a trip to Montreal in two weeks - and we’re taking the car

with us. We just wanted to have some things looked at before the trip.

We bought the car recently and we should have had it looked at before we bought it,

but we didn’t. It hasn’t started a few times - can you check that out? We’d also like a

thorough inspection to let us know if any other work needs to be done.

I’m going to run home for a few minutes. Will you give me a call to let me know how

things are going? How long do you think that’ll take?

If asked about the car’s service history

We haven’t had anything done since we got the car a couple of months ago. Just an oil

change.

If asked about the timing belt

I’m not sure - I don’t know much about it.

If asked whether the previous owner could be contacted about the car’s service history

It’s someone my dad knew. It’d be hard to contact him.

32

If recommendations are ambiguous

What work do you think we should do?

Declining service

Let me talk to my wife - she drives the car most of the time.

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Table 1: Characteristics of garages in Canadian data

Total Local NonlocalTotal 51 29 22

Chains 23 13 10Franchises 23 13 10

Independents 4 3 1Dealers 1 0 1

Montreal 13 13 0Toronto 16 16 0Calgary 10 0 10

Vancouver 12 0 12

Note: The table above provides characteristics of the garages visited during the Canadian study. Thecharacteristics are split out by visits in which the undercover researchers appeared as potential repeatcustomers (Local) and one-time customers (Nonlocal).

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Table 2: Characteristics of garages in field experiment

Total Nonlocal LocalTotal 40 20 20

Busyness 1.8 2.0 1.7Gas Station 10 4 6

ASE-Certified 17 8 9Dist. Assoc. 13 8 5Bays in Use 2.6 2.8 2.4

HI>90F 13 4 9Drop-Off Time 10:52 AM 11:10 AM 10:35 AM

City 1 14 7 7City 2 10 6 4City 3 7 4 3City 4 9 3 6

Note: The table above provides descriptive characteristics of the garages visited in the field experiment.The characteristics are split out by visits in which I presented myself as a repeat customer (Local)and a one-time customer (Nonlocal). Busyness is the number of days wait until the first availableappointment. Dist. Assoc. indicates that the garage displays a sign for a parts distributors (either ACDelco or NAPA). Bays in Use is my measure of garage size. ASE-Certified indicates that the garagedisplays a sign indicating it employ ASE-certified mechanics. HI>90F is an indicator for whether theHeat Index exceeded 90 degrees Fahrenheit on the day of the visit. Drop-Off Time indicates the timethe test vehicle was brought to the garage. Cities 1 through 4 indicate the cities in which the garagesare located.

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Table 3: Defects documented during pre-trial design

RepairLow coolant Yes

Blown taillight YesWorn/misfit plug wires Yes

Exhaust pipe leak YesRust on muffler DiscretionWeak alternator Discretion

Timing belt service DiscretionWorn shocks Discretion

Oil leaks DiscretionRight front brake rattle Discretion

Note: Two APA mechanics inspected the test vehicle to document its condition prior to the initiation ofgarage visits. The table above lists the defects discovered by the APA mechanics, and their judgmentsabout the urgency of repairing them. Yes indicates that defect should be repaired immediately. Discre-tion indicates that recommending repairs is a reasonable action, but that a wait-and-watch approach isalso suitable.

Table 4: Defects discovered during field experiment

Total Nonlocal LocalDiscover Repair Discover Repair Discover Repair

Battery cable 27 27 14 14 13 13Low coolant 11 11 6 6 5 5

Blown taillight 5 5 3 3 2 2Plug wires 9 5 4 1 5 4

Exhaust leak 6 3 2 1 4 2Rusted muffler 16 6 8 4 8 2

Weak alternator 8 3 2 1 5 2Timing belt 21 9 10 4 11 5

Worn shocks 1 0 0 0 1 0Oil leaks 2 0 0 0 2 0

Brake rattle 0 0 0 0 0 0Total 63 26 26 11 36 15

Note: The table above lists the number of times each defect was discovered by mechanics during the 40 garage visitsconducted for the field experiment (Discover). It also lists the number of times mechanics recommended repairingthe defects (Repair). Repair can be less than Discover since mechanics often recommended a wait-and-see approach.Local indicates that I presented myself as possible repeat business. Nonlocal indicates that I presented myself asone-time business.

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Table 5: Predictors of inspection fees - field experiment

(1) (2) (3)Inspect. Inspect. Inspect.

Fee Fee FeeLocal -22.01** -27.39** -25.48**

(-2.14) (-2.62) (-2.34)ASE 13.83 13.33

(1.34) (1.24)Garage Size 4.06 3.88

(0.91) (0.85)HI>90F 24.43** 23.31*

(2.12) (1.98)Busyness 1.39

(0.68)Time of Day 1.97

(0.58)Constant 57.43** 35.65** 11.75

(7.88) (2.25) (0.28)

N 40 40 40R2 0.11 0.23 0.25

Note: The table above contains estimates of the effects of the various regressors on the inspection feescharged by mechanic. t-values are reported in parentheses. Local indicates that I presented myself aspossible repeat business. ASE indicates that the garage displayed an ASE sign. Busyness is a measureof how busy the garage is. Time of Day is the time I brought the car to the garage. Garage Size ismeasured as the number of bays in use. HI>90F is an indicator for whether the Heat Index exceeded 90degrees Fahrenheit on the day of the visit. ∗ and ∗∗ indicate that the estimated coefficient is statisticallydifferent than zero at the 90 and 95 per cent level, respectively.

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Table 6: Predictors of repairs, inspection quality - field experiment

(1) (2) (3) (4) (5) (6)Repairs Repairs Repairs N. Defects N. Defects N. DefectsProbit Probit Probit Poisson Poisson Poisson

Local -0.050 -0.000 -0.072 0.136 0.103 0.056(-0.32) (-0.00) (-0.40) (0.69) (0.50) (0.27)

ASE -0.356** -0.406** 0.254 0.203(-2.15) (-2.29) (1.24) (0.96)

Garage size 0.059 0.062 0.146* 0.152*(0.82) (0.84) (1.78) (1.83)

HI>90F -0.112 -0.108 0.269 0.289(-0.59) (-0.55) (1.19) (1.27)

Busyness -0.024 0.015(-0.60) (0.41)

Time of day -0.114* -0.077(-1.85) (-1.11)

Constant 0.875 0.291 1.12(6.07) (0.91) (1.36)

N 40 40 40 40 40 40Pseudo R2 0.00 0.11 0.18 0.00 0.03 0.04X-bar 0.43 0.43 0.43 2.58 2.58 2.58

Note: The first three columns of the table contain the estimated marginal effects of the listed explanatoryvariables on the probability of repairs being recommended from a Probit maximum likelihood model.The second three columns contain the estimated marginal effects from the conditional mean function ofa Poisson regression model. Local indicates that I presented myself as possible repeat business. ASEindicates that the garage displayed an ASE sign. Busyness is a measure of how busy the garage is. Timeof day is the time I brought the car to the garage. Garage Size is measured as the number of bays in use.HI>90F is an indicator for whether the Heat Index exceeded 90 degrees Fahrenheit on the day of thevisit. Z-values are reported in parentheses. X-bar is the observed mean value of the dependent variable.∗ and ∗∗ indicate that the estimated coefficient is statistically different than zero at the 90 and 95 percent level, respectively.

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Table 7: Predictors of battery cable, coolant correction - field experiment

(1) (2) (3) (4) (5) (6)Cable Cable Cable Coolant Coolant CoolantProbit Probit Probit Probit Probit Probit

Local 0.000 0.030 0.076 0.000 -0.022 -0.053(0.00) (0.19) (0.45) (0.00) (-0.14) (-0.34)

ASE -0.008 0.027 0.447** 0.418**(-0.05) (0.17) (2.79) (2.56)

Garage size 0.052 0.050 0.078 0.078(0.74) (0.70) (1.14) (1.13)

HI>90F -0.027 -0.048 0.171 0.173(-0.15) (-0.26) (0.95) (0.94)

Busyness -0.014 -0.047(-0.46) (-0.86)

Time of day 0.074 0.013(1.41) (0.47)

N 40 40 40 40 40 40Pseudo R2 0.00 0.01 0.06 0.00 0.19 0.21X-bar 0.65 0.65 0.65 0.30 0.30 0.30

Note: The first three columns of the table contain the estimated marginal effects of the listed explanatoryvariables on the probability that the loose battery cable was corrected. The second three columns containthe estimated marginal effects of the explanatory variables on the probability that the low coolant levelwas corrected. Both sets of coefficients are estimated using a Probit maximum likelihood model. Localindicates that I presented myself as possible repeat business. ASE indicates that the garage displayedan ASE sign. Busyness is a measure of how busy the garage is. Time of day is the time I brought thecar to the garage. Garage Size is measured as the number of bays in use. HI>90F is an indicator forwhether the Heat Index exceeded 90 degrees Fahrenheit on the day of the visit. Z-values are reportedin parentheses. X-bar is the observed mean value of the dependent variable. ∗ and ∗∗ indicate that theestimated coefficient is statistically different than zero at the 90 and 95 per cent level, respectively.

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Table 8: Combined field experiment and Canadian outcomes

(1) (2) (3) (4) (5) (6)Inspect. Inspect.

Fee Fee Repairs Repairs Cable CableOLS OLS Probit Probit Probit Probit

Local -19.22** -22.01** -0.052 -0.048 -0.011 -0.049(-3.22) (-2.46) (-0.51) (-0.32) (-0.12) (-0.38)

Canadian -9.09 -11.76 -0.088 -0.085 0.010 -0.028(-1.52) (-1.35) (-0.86) (-0.57) (0.11) (-0.22)

LocalXCanad. 5.09 -0.01 0.068(0.42) (-0.03) (0.40)

Constant 56.04** 57.43**(10.47) (9.10)

N 89 89 91 91 91 91(Pseudo) R2 0.13 0.13 0.01 0.01 0.00 0.00

X-bar 0.37 0.37 0.78 0.78

Note: The table above contains estimates from the models of inspection fees, whether repairs wererecommended/conducted, and whether the loose battery cable was corrected based on the pooled fieldexperiment and Canadian data. Battery cable connection is set to one for field experiment visits whenthe mechanic recommended starter replacement to make these data consistent with Canadian data. tand z-values are reported in parentheses in columns (1) and (2), and (3) through (6), respectively. Localindicates that I presented myself as possible repeat business. Canadian is an indicator variable for aCanadian visit. LocalXCanad. is the interaction of the two. X-bar is the observed mean value of thedependent variable. ∗∗ indicates that the estimated coefficient is statistically different than zero at the95 per cent level.

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Figure 1: Outcomes of field experiment visits in chronological order

Note: The figures above contain inspections costs, number of defects found, and repair estimates for the 40 garagevisits in the field experiment in chronological order. The shaded background areas indicate visits in which I presentedmyself as possible repeat business. The unshaded background areas indicate visits in which I presented myself asone-time business.

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Figure 2: Conditional repair expenditures per garage visit

Note: The figure is a histogram of individual households’ repair expenditures per vehicle and month in the U.S., con-ditional on positive expenditures, from the Consumer Expenditure Survey for years 1995 through 2004. Expendituresfor routine maintenance such as oil changes are excluded.

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