Agency Problems and Reputation in Expert Services - Evidence From Auto Repair - Henry Schneider

1. 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, Philipp
Schmidt-Dengler, Fiona Scott Morton, Harsha Thirumurthy, Michael Waldman, and various seminar participants for
comments, and the Yale Institution for Social and Policy Studies for financial support. Michael Coughlin at Premier
Subaru, 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 the
Automobile Protection Association for invaluable assistance.
†henry.schneider@cornell.edu; Johnson School of Management, Cornell University

2. 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,
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3. 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.
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4. 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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5. 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

6. 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

7. 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, though
these times are not legally binding. Under reasonable assumptions, qualitatively similar results can be achieved when
mechanics choose arbitrary p.
4With minor modifications, this search costs can be interpreted as an exogenously-determined diagnosis fee, as is
common in the theoretical literature, and under reasonable conditions, a reputation result can even be achieved with
endogenous s. A version of the model with endogenous prices and diagnosis fees is available from the author upon
request.
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8. 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. The
earlier assumption that v p1 +s guarantees this.
7

9. (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−p0
p1
.
(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−p0
p1
.
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

10. 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.
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11. 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 an
industry standard amperage, voltage, resistance (AVR) test of the charging and starting system. APA documentation
notes that “the APA chose this test because it seemed foolproof. The instrumentation required for the AVR test is
installed 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 the
initiation of their study, including the battery, fuel filter, spark plugs and wires, brake rotors, and tires, and the vehicle
was transported between cities by railroad.
10The outcomes of the four female-couple visits were unexceptional.
10

12. 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 (the
product of the hourly labor rate and the industry-standardized labor time for that particular repair) as overcharging.
11

13. 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, yij, charged by
mechanic i to customer j with the following regression model,
yij = β0 +β1wj +∑
k
βkgik +εij (1)
where wj = 0 when the customer represents one-time business, and wj = 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 true
cities of residence.
13I also estimated variations of the model with garage type interacted with the repeat-business dummy, whether the
defect was corrected, and cost-of-living in the city in which the garage is located. No meaningful additional effects
were apparent.
12

14. 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 mechanics
recommended no repairs: This difference is -$17.56 after controlling for garage type (p=0.02). The mean inspection
price for company-owned chains is $37.93 and for franchises is $31.06. The difference is not statistical different than
zero (p=0.33). The APA visited only five independent and dealer shops, which precludes a test for differences between
these garage types.
15A probit regression containing a binary dependent variable indicating whether repairs were conducted, and the
same regressors as the model in equation 1, provides a similar result.
13

15. 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

16. 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 be
binding, and the mechanic could easily increase this price after the inspection was conducted by claiming small repairs
or 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 the
control group.
15

17. 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

18. 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 shops
in the two counties visited were AAA-certified for auto repair at the time. In the cities I chose, only 8 independent
shops were AAA-certified. Other historical data from the APA provide no evidence that AAA-certified are better than
non-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

19. 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 of
winning a large engine repair in the future. This possibility seems unlikely given the existence of legitimate defects
that could have been repaired immediately.
18

20. 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

21. 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,
yij = β0 +β1wj +xiβ2 +zjβ3 +εi (2)
where yij is the inspection cost charged by mechanic i during visit j; wj = 1 indicates that the
motorist represents the possibility of repeat business during visit j, and wj = 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; zj 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 εij 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 (20
in all), but in both cases an F-test fails to reject the null that the fixed effects are jointly statistically indistinguishable
from zero.
20

22. ceeding $50,
Pr[rij = 1] = Φ[α0 +α1wj +xiα2 +zjα3] (3)
where Pr is probability, rij 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[nij|·] = exp{γ0 +γ1wj +xiγ2 +zjγ3} (4)
where nij 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 percentage
points for each hour later in the day the car is dropped-off at the garage (p=0.07), and that mechanics at ASE garages
are 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

23. 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 than
nonlocal visits conditional on the same repairs being recommended, no obvious differences are apparent.
22

24. 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

25. 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

26. 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

27. 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

28. 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, especially
from 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 Table
4, and the average hygiene score of 76.77 (or 23.23 penalty) reported in their Table 1.
27

29. 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

30. 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−α0
)p0 −α0
p1 −s
(7)
where α0 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,
α0
=
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

31. 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

32. 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

33. 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

34. 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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34

36. Table 1: Characteristics of garages in Canadian data
Total Local Nonlocal
Total 51 29 22
Chains 23 13 10
Franchises 23 13 10
Independents 4 3 1
Dealers 1 0 1
Montreal 13 13 0
Toronto 16 16 0
Calgary 10 0 10
Vancouver 12 0 12
Note: The table above provides characteristics of the garages visited during the Canadian study. The
characteristics are split out by visits in which the undercover researchers appeared as potential repeat
customers (Local) and one-time customers (Nonlocal).
35

37. Table 2: Characteristics of garages in field experiment
Total Nonlocal Local
Total 40 20 20
Busyness 1.8 2.0 1.7
Gas Station 10 4 6
ASE-Certified 17 8 9
Dist. Assoc. 13 8 5
Bays in Use 2.6 2.8 2.4
HI90F 13 4 9
Drop-Off Time 10:52 AM 11:10 AM 10:35 AM
City 1 14 7 7
City 2 10 6 4
City 3 7 4 3
City 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 available
appointment. Dist. Assoc. indicates that the garage displays a sign for a parts distributors (either AC
Delco or NAPA). Bays in Use is my measure of garage size. ASE-Certified indicates that the garage
displays a sign indicating it employ ASE-certified mechanics. HI90F is an indicator for whether the
Heat Index exceeded 90 degrees Fahrenheit on the day of the visit. Drop-Off Time indicates the time
the test vehicle was brought to the garage. Cities 1 through 4 indicate the cities in which the garages
are located.
36

38. Table 3: Defects documented during pre-trial design
Repair
Low coolant Yes
Blown taillight Yes
Worn/misfit plug wires Yes
Exhaust pipe leak Yes
Rust on muffler Discretion
Weak alternator Discretion
Timing belt service Discretion
Worn shocks Discretion
Oil leaks Discretion
Right front brake rattle Discretion
Note: Two APA mechanics inspected the test vehicle to document its condition prior to the initiation of
garage visits. The table above lists the defects discovered by the APA mechanics, and their judgments
about 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 is
also suitable.
Table 4: Defects discovered during field experiment
Total Nonlocal Local
Discover Repair Discover Repair Discover Repair
Battery cable 27 27 14 14 13 13
Low coolant 11 11 6 6 5 5
Blown taillight 5 5 3 3 2 2
Plug wires 9 5 4 1 5 4
Exhaust leak 6 3 2 1 4 2
Rusted muffler 16 6 8 4 8 2
Weak alternator 8 3 2 1 5 2
Timing belt 21 9 10 4 11 5
Worn shocks 1 0 0 0 1 0
Oil leaks 2 0 0 0 2 0
Brake rattle 0 0 0 0 0 0
Total 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 visits
conducted for the field experiment (Discover). It also lists the number of times mechanics recommended repairing
the 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 as
one-time business.
37

39. Table 5: Predictors of inspection fees - field experiment
(1) (2) (3)
Inspect. Inspect. Inspect.
Fee Fee Fee
Local -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)
HI90F 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 40
R2 0.11 0.23 0.25
Note: The table above contains estimates of the effects of the various regressors on the inspection fees
charged by mechanic. t-values are reported in parentheses. Local indicates that I presented myself as
possible repeat business. ASE indicates that the garage displayed an ASE sign. Busyness is a measure
of how busy the garage is. Time of Day is the time I brought the car to the garage. Garage Size is
measured as the number of bays in use. HI90F is an indicator for whether the Heat Index exceeded 90
degrees Fahrenheit on the day of the visit. ∗ and ∗∗ indicate that the estimated coefficient is statistically
different than zero at the 90 and 95 per cent level, respectively.
38

40. Table 6: Predictors of repairs, inspection quality - field experiment
(1) (2) (3) (4) (5) (6)
Repairs Repairs Repairs N. Defects N. Defects N. Defects
Probit 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)
HI90F -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 40
Pseudo R2 0.00 0.11 0.18 0.00 0.03 0.04
X-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 explanatory
variables 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 of
a Poisson regression model. Local indicates that I presented myself as possible repeat business. ASE
indicates that the garage displayed an ASE sign. Busyness is a measure of how busy the garage is. Time
of day is the time I brought the car to the garage. Garage Size is measured as the number of bays in use.
HI90F is an indicator for whether the Heat Index exceeded 90 degrees Fahrenheit on the day of the
visit. 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 per
cent level, respectively.
39

41. Table 7: Predictors of battery cable, coolant correction - field experiment
(1) (2) (3) (4) (5) (6)
Cable Cable Cable Coolant Coolant Coolant
Probit 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)
HI90F -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 40
Pseudo R2 0.00 0.01 0.06 0.00 0.19 0.21
X-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 explanatory
variables on the probability that the loose battery cable was corrected. The second three columns contain
the estimated marginal effects of the explanatory variables on the probability that the low coolant level
was corrected. Both sets of coefficients are estimated using a Probit maximum likelihood model. Local
indicates that I presented myself as possible repeat business. ASE indicates that the garage displayed
an ASE sign. Busyness is a measure of how busy the garage is. Time of day is the time I brought the
car to the garage. Garage Size is measured as the number of bays in use. HI90F is an indicator for
whether the Heat Index exceeded 90 degrees Fahrenheit on the day of the visit. 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 per cent level, respectively.
40

42. Table 8: Combined field experiment and Canadian outcomes
(1) (2) (3) (4) (5) (6)
Inspect. Inspect.
Fee Fee Repairs Repairs Cable Cable
OLS 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 were
recommended/conducted, and whether the loose battery cable was corrected based on the pooled field
experiment and Canadian data. Battery cable connection is set to one for field experiment visits when
the mechanic recommended starter replacement to make these data consistent with Canadian data. t
and z-values are reported in parentheses in columns (1) and (2), and (3) through (6), respectively. Local
indicates that I presented myself as possible repeat business. Canadian is an indicator variable for a
Canadian visit. LocalXCanad. is the interaction of the two. X-bar is the observed mean value of the
dependent variable. ∗∗ indicates that the estimated coefficient is statistically different than zero at the
95 per cent level.
41

43. 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 garage
visits in the field experiment in chronological order. The shaded background areas indicate visits in which I presented
myself as possible repeat business. The unshaded background areas indicate visits in which I presented myself as
one-time business.
42

44. 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. Expenditures
for routine maintenance such as oil changes are excluded.
43