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MANAGEMENT SCIENCEVol. 59, No. 8, August 2013, pp. 1725–1742ISSN 0025-1909 (print) � ISSN 1526-5501 (online) http://dx.doi.org/10.1287/mnsc.1120.1680

© 2013 INFORMS

Customer-Driven Misconduct: How CompetitionCorrupts Business Practices

Victor Manuel BennettMarshall School of Business, University of Southern California, Los Angeles, California 90089,

[email protected]

Lamar PierceOlin School of Business, Washington University in St. Louis, St. Louis, Missouri 63130,

[email protected]

Jason A. SnyderAnderson School of Management, University of California, Los Angeles, Los Angeles, California 90095,

[email protected]

Michael W. ToffelHarvard Business School, Harvard University, Boston, Massachusetts 02163, [email protected]

Competition among firms yields many benefits but can also encourage firms to engage in corrupt or unethicalactivities. We argue that competition can lead organizations to provide services that customers demand but

that violate government regulations, especially when price competition is restricted. Using 28 million vehicleemissions tests from more than 11,000 facilities, we show that increased competition is associated with greaterinspection leniency, a service quality attribute that customers value but is illegal and socially costly. Firms withmore competitors pass customer vehicles at higher rates and are more likely to lose customers whom theyfail, suggesting that competition intensifies pressure on facilities to provide illegal leniency. We also show that,at least in markets in which pricing is restricted, firms use corrupt and unethical practices as an entry strategy.

Key words : environment; pollution; government; regulations; judicial; legal; crime prevention; organizationalstudies; strategy; microeconomics; market structure and pricing

History : Received April 21, 2011; accepted September 24, 2012, by Bruno Cassiman, business strategy.Published online in Articles in Advance February 15, 2013.

1. IntroductionCompetition among firms produces many positivesocietal outcomes, including lower prices, higher pro-ductivity, and greater consumer surplus (Bresnahanand Reiss 1991, Syverson 2004). In contrast, marketconcentration is typically viewed with suspicion foreliciting higher prices, sluggish innovation, corrup-tion, and discrimination (Ades and Di Tella 1999,Blundell et al. 1999, Aghion et al. 2005, Zitzewitz2012). Despite the primary focus in economics beingon competition’s impact on price (e.g., Bresnahanand Reiss 1991, Dafny et al. 2012), one of the majorbenefits of competition is increased quality of goodsand services (Spence 1975, Schmalensee 1979). Underintense competition, firms risk losing customers whenquality is low on dimensions such as retail inventory(Olivares and Cachon 2009, Matsa 2011), convenience(Buell et al. 2011), product features and attributes(Shaked and Sutton 1982), health outcomes (Gaynor2006), and timely delivery (Mazzeo 2003). The higherquality associated with competition is certainly betterfor the customers who receive it, but can competition

sometimes yield higher quality to customers in waysthat are socially costly or even illegal?

In this paper, we demonstrate that it can. We focuson circumstances in which customers encourage firmsto engage in corrupt practices that are socially harm-ful and often illegal, but nevertheless produce qual-ity improvements for the customer; that is, they canincrease utility, willingness to pay, and ultimatelydemand. This conceptualization of “quality” is basedon the value perceived by consumers. Although thisnotion of quality does not represent more accurateinformation or greater value to society, it is con-sistent with definitions of quality from economics(Griliches 1971, Leffler 1982), marketing (Parasura-man et al. 1985), and operations (Garvin 1984). In acompetitive market structure, firms may feel com-pelled to comply with illegal or unethical customerrequests if they anticipate rivals’ willingness to meetthis demand. When the government is unable to ade-quately monitor and enforce laws and regulations,competitive pressure can drive firms to match oneanother’s corrupt strategies in a “race to the bottom.”

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Bennett et al.: Customer-Driven Misconduct: How Competition Corrupts Business Practices1726 Management Science 59(8), pp. 1725–1742, © 2013 INFORMS

Recent theory by Shleifer (2004), Drugov (2010), andBolton et al. (2012) suggests that such customer pres-sure for corrupt practices may create a situation inwhich monopolies yield lower corruption and moresocially efficient outcomes than duopolies do.

We study the impact of competition on the pro-vision of an illegal form of quality in the vehicleemissions testing market. Although some state gov-ernments operate facilities to test passenger vehiclesto enforce emissions standards, most outsource thispractice to private firms under the justification of bet-ter service, lower costs, and more consumer choice.Although the benefits of this outsourced system maybe real, it creates a design similar to three-tieredagency models in economics (Tirole 1986), in whichthe principal (state) hires a supervisor (facility) tomonitor the behavior of the agent (vehicle owner).The implication of these models is that the agent mayengage in a side contract with the supervisor chargedwith monitoring him to receive lenient supervision,a collusive arrangement known to increase fraud infinancial auditing (Khalil and Lawaree 2006). In oursetting, the government-licensed facilities trade the“high-quality” service of a passing result (regardlessof actual emissions) for the side payment of a valuablefuture stream of service and repair business worththousands of dollars per year.1 Thus, even in marketsin which prices are fixed by the government, as isthe case with vehicle inspection in many states, pro-viding illegal quality in the form of leniency yieldslong-term returns. The California Bureau of Automo-tive Repair (BAR) noted that “it appears, based onBAR enforcement cases, that some stations improp-erly pass vehicles to garner more consumer loyalty fordelivering to consumers what they want: a passingSmog Check result” (California Bureau of Automo-tive Repair 2011, p. 22). When competition providesmultiple options, customers may seek the facility thatwill provide the most leniency, a process referred toin the accounting literature as “audit shopping” (e.g.,Davidson et al. 2006).

Using 28,001,355 emissions tests from 11,423 facili-ties in New York State, we show that increased facilitydensity, defined as the number of facilities operatingin the same market, is associated with higher passrates. We demonstrate that these higher pass rates arevery unlikely to be explained by vehicle differencesand show that facilities significantly increase leniencyonly in the presence of a greater number of proximatefacilities that are truly competitors capable of stealingcustomers. We exploit a discontinuity in testing pol-icy to show that competition produces significantly

1 Although the vast majority of testing facilities are service/repairshops, others include dealers and gas stations, many of which alsoprovide service and repairs. See Hubbard (2002) and Pierce andToffel (2013) for a description of these organizational types.

more leniency for those cars for which test results areeasiest to manipulate.

We also explore how firm heterogeneity moder-ates the effect of competition on fraud. Our paperfinds two important sources of heterogeneity. First,we show that new entrants, facing limited customerbases and low survival odds, are more likely thanincumbents to be lenient in the face of competition.The entrants lead the “race to the bottom” rather thanfollowing the lead of the incumbents. These results,consistent with recent theory by Branco and Villas-Boas (2012), suggest that entry, much like competitionamong incumbents, may produce deleterious resultswhen firms can skirt rules and regulations. Our find-ings also suggest that free entry may sometimes havenegative consequences for market and social out-comes. We show that violating laws or regulationsmay be a viable entry strategy, particularly when tra-ditional strategies such as introductory pricing arerestricted. Second, we find that client composition sig-nificantly affects a firm’s response to intensified com-petition. Facilities that serve customers who are lesslikely to leave are less likely to increase leniency inresponse to competition.

New York State’s substantial variation in popula-tion density also allows us to explore two criticalissues in the literature on competition and marketoutcomes. Like Bresnahan and Reiss (1991), we esti-mate the marginal effect of additional competitorson leniency, finding a decreasing marginal impactof additional competitors. Unlike those authors’work, however, our paper demonstrates the marginalimpact of competition on an illegal form of qual-ity when prices are fixed. Consistent with Olivaresand Cachon’s (2009) study of car dealer inventory,leniency in our market is impacted beyond the thirdcompetitor, with the marginal impact being posi-tive up to the sixth competitor. We also complementSyverson’s (2004) study of competition and produc-tivity by exploring how competition changes the dis-tribution of leniency in the market. Like him, we findthat competition has the biggest impact at the tails ofthe distribution, reducing the number of firms pro-viding stringent testing to customers.

This paper is, to our knowledge, the first to em-pirically demonstrate that increased competition canmotivate firms to provide an illegal form of qual-ity to avoid losing business. Although Snyder (2010)showed a relationship between competition and dis-honesty, the actions of the nonprofit liver-transplantcenters he studied are not illegal and have littleimpact on social welfare. Similarly, Becker andMilbourn (2011) identified inflated credit ratings that,although socially costly, are not explicitly illegal. Fur-thermore, our research is the first to empirically vali-date recent models by Drugov (2010) and Bolton et al.

Bennett et al.: Customer-Driven Misconduct: How Competition Corrupts Business PracticesManagement Science 59(8), pp. 1725–1742, © 2013 INFORMS 1727

(2012) that explain how markets with informationmanipulation by intermediaries might enjoy greaterefficiency under monopoly structures. These resultssuggest that firms seeking to enforce legal and eth-ical conduct among managers and employees mustbe especially vigilant when there is tough compe-tition. Similarly, creating internal competition withhigh-powered incentives could increase employees’tendency to use illicit means. Our results also sug-gest that increased competition within markets mayencourage competitors to cross legal boundaries inways that threaten the profits of the legally com-pliant firm. In the absence of effective monitoringby government institutions, firms may benefit fromprivately monitoring their competitors’ behavior toensure that rivals do not maintain a competitiveadvantage through corrupt business practices. Ourresults also suggest that policy makers must carefullyconsider the optimal market structure for industriesin which illegal or unethical actions yield cost reduc-tions or are demanded by customers. Although com-petition may yield lower prices and better choices forcustomers, it may also bring the increased social costsof illegal behavior by firms.

2. Literature and TheoryAlthough a large theoretical literature in economicshas examined how market structure and entry mightimpact quality choices by firms (e.g., Spence 1975,Gans 2002), empirical work has yielded mixed results.Berry and Waldfogel (2010) found that larger marketsfeature higher-quality newspapers and more high-quality restaurants per capita. Research results on therelationship between competition and service qual-ity (when measured by greater inventory) have alsobeen mixed. Whereas greater competition is associ-ated with higher inventory levels among new cardealers and supermarkets (Olivares and Cachon 2009,Matsa 2011), the opposite effect is found among fac-tories and retailers (Amihud and Mendelson 1989,Guar et al. 2005).

A few studies have explored why competitionmight increase illegal or unethical behavior by firms.This literature explains that although there are coststo unethical behavior from potential sanctions andreputation losses (Weigelt and Camerer 1988), illicitstrategies may yield real gains. Shleifer (2004) sug-gested that competition in developing countries forcesfirms to compete by bribing government officials.Staw and Szwajkoski (1975) similarly argued thatscarcity in market environments leads firms towardillegal activities, a correlation they found across abroad sample of corporations. In a more targetedstudy, Cai and Liu (2009) linked competition with taxavoidance by Chinese manufacturers. Edelman andLarkin (2009) found similar results among university

professors who fraudulently download their ownpapers when faced with internal competition andcomparison with colleagues. Similar results linkingcompetition to unethical behavior have been gener-ated in laboratory experiments (Hegarty and Sims1978, Schwieren and Weichselbaumer 2010, Kilduffet al. 2012) and examined in case studies (Kulik et al.2008). More recently, Branco and Villas-Boas (2012)formally modeled this link under conditions of quan-tity competition.

We bridge these literatures by examining whethermarket competition increases quality provision onexplicitly illegal dimensions. We define quality asattributes that increase customer utility or willingnessto pay, similar to the definition used in hedonic pricemodels (Griliches 1971, Rosen 1974). Although noexisting research has identified the link between com-petition and quality on explicitly illegal dimensions,recent work suggests that competition may motivatesuch behavior. Snyder (2010) showed that competitionled healthcare organizations to exaggerate the seri-ousness of their patients’ conditions to increase liver-transplant priority. Although these actions, whichinvolved unnecessarily admitting patients to intensivecare units, were dishonest, they were within the legaldiscretion of the medical facilities. Furthermore, thenet impact of this deception on patient health wasnegligible because competitors’ responses led to thesame outcome that would have arisen with universalhonesty. Similarly, Becker and Milbourn (2011) foundthat the entry of a third bond-rating agency increasedratings, which could be viewed as providing qual-ity to the issuing firms that paid for these ratings.2

Although not explicitly illegal, this rating inflationwas socially costly because it provided less accuratemarket information.

Like these ethically questionable behaviors, explic-itly illegal behavior can be a viable strategy for orga-nizations when it increases the value of products orservices to customers. Customers might be complicitin the illegal behavior or be oblivious to its bene-fits; that is, they might seek out products and ser-vices from firms willing to break rules or laws, orthey might be unaware that a favored product con-tains an illegal chemical or was produced by childlabor or is based on stolen intellectual property. Whena firm’s adherence to organizational policies or gov-ernment laws and regulations results in lower qualityfor customers, some of these customers may rewardfirms willing to cross legal lines with increased busi-ness and loyalty. For example, customers would cer-tainly know that laws would be broken when asking

2 Becker and Milbourn (2011) defined quality in terms of ratingsaccuracy, which emphasizes that quality can have different mean-ings for different stakeholders.


a taxi driver to speed to the airport or when asking abartender to serve alcohol to a minor.

Firms that will not provide quality through corruptpractices risk losing business to competitors that will,which provides a strong incentive for firms to cheat,particularly when repeat business and long-term rela-tionships are important. Under such pressure, firmsthat strictly obey the law may lose considerable mar-ket share as customers flee to more lax firms. Whencompetition increases the threat of customer loss,firms are more likely to respond by matching theirrivals’ behavior and crossing legal boundaries. Thus,we predict that markets with high competition will,on average, see higher levels of illegal quality thanwill those with greater concentration.

The pressure to provide illegal quality may beparticularly intense for new firms entering marketswith established incumbents. Although studies ofentry and local competition typically focus on priceas the competitive dimension (e.g., Bresnahan andReiss 1991, Berry 1992, Gowrisankaran and Krainer2011), recent work highlights the importance ofquality choice (Mazzeo 2003) as an entry strategy.If new entrants cannot implement introductory pric-ing strategies (Klemperer 1995) due, for example, tocost disadvantages from economies of learning in themarket (Lieberman 1984), they may choose to com-pete instead on dimensions of quality, some of whichmay be illegal. These quality dimensions would beextremely important entry strategies in markets inwhich pricing is restricted or fixed (White 1972).Recent theory by Branco and Villas-Boas (2012) pre-dicts that entrants are indeed more likely to breakmarket rules, but once established as incumbents,they adopt more compliant practices. We thereforeexpect the existing competition among incumbents tohave a particularly strong impact on illegal qualityprovision by new entrants in the emissions testingmarket.

3. Empirical SettingThe vehicle emissions testing market in the UnitedStates has considerable potential for illegal behav-ior. The federal Environmental Protection Agency(EPA) requires many states to institute vehicle emis-sions programs, but allows state governments todecide how to implement them. Although some stategovernments directly test vehicles at state-ownedfacilities, most outsource testing to licensed private-sector firms. Concerns about corruption and frauddate back to the 1970s, when the federal and stategovernments first debated whether or not to priva-tize emissions testing (Rule 1978, Lazare 1980). Envi-ronmental advocates immediately recognized thatprivate facilities, with incentives to attract and retain

long-term customers, were unlikely to fully enforcethe regulations (Voas and Shelley 1995, Harringtonand McConnell 1999). Concerns about the ubiquity offraudulently passing vehicles continue today. Private-sector facilities conducting emissions tests “pass farmore vehicles than would be the case if all [such]inspections were performed properly” (CaliforniaBureau of Automotive Repair 2011, p. 22).

In New York, private testing facilities are legallyrequired to follow strict testing procedures. Carsbuilt before 1996 must undergo dynamometer-basedtailpipe testing, a procedure widely understood to bemanipulable. Mechanics can make temporary alter-ations that allow vehicles to pass emissions tests with-out ever addressing the underlying causes of theexcess pollution. Even the most polluting cars canpass if inspectors substitute other cars during testing,a trick commonly referred to as “clean piping” (Oliva2012). Studies by Hubbard (1998), Oliva (2012), andPierce and Snyder (2012) used data from California,Mexico, and New York, respectively, to estimate thatbetween 20% and 50% of the cars that should fail arefraudulently passed. These results are consistent witha 2001 covert audit program in Salt Lake City, Utah,that found nearly 10% of facilities overtly testing onecar in place of another (Groark 2002).

Starting in 2004, New York State began testing allmodel year 1996 and newer cars using a new technol-ogy referred to as “Onboard Diagnostics II” (OBD-II),a computer-based system that monitors emissionscontrol and power-train (engine and transmission)systems for malfunctions that might lead to elevatedemissions levels. If the OBD-II system detects a mal-function, it illuminates a malfunction indicator light(also known as the check engine light) on the dash-board and stores trouble codes that can identify thesource of the problem during an emissions test. OBD-II can identify problems before they become severe,takes less time than tailpipe testing, and costs signifi-cantly less to implement (Lyons and McCarthy 2009).

Cars failing an OBD-II test must be repaired andthen operated until the readiness indicators allowthe car to be retested (approximately 50 miles). Cus-tomers can then retest the vehicle at the facility oftheir choice. The repairs may involve simple (buttime-intensive) replacements of engine seals or moreexpensive replacements of emission sensors or thecatalytic converter. Repairs often result in a vehiclepassing its retest after the fundamental mechanicalproblem has been corrected. In some cases, how-ever, the car will continue to fail, as the mechanicalproblems are either too extensive to be fixed or tooexpensive for the customer. In this case, if the vehi-cle has received over $450 in repairs, the consumercan receive a one-year exemption from the Depart-ment of Motor Vehicles (DMV). The facility must keep


receipts of these repairs on-site in case of a DMVaudit.3 Although the facility could write fraudulentrepair receipts for customers, this would increase thecost to the facility of getting caught by involvingmuch more serious tax fraud implications. If the vehi-cle has already received its one-year exemption, thecustomer can either junk the car or resell it to a regionwith less stringent emissions requirements.

Whereas prior work (Hubbard 1998, Pierce andSnyder 2008) has exclusively focused on tailpipetesting technology, no research has examined thenewer OBD-II approach. This distinction is importantfor two reasons. First, OBD-II has widely replacedtailpipe testing across the United States and in mostdeveloped countries (although not in the devel-oping world). Second, OBD-II is considered moredifficult to manipulate, because the inspectors haveconsiderably less discretion in testing.4 Even so,facilities have found ways to fraudulently pass carswith OBD-II systems (Sosnowski and Gardetto 2001).“Clean scanning”—the practice of collecting emis-sions data from a clean surrogate vehicle—is a com-mon concern among regulators (Lyons and McCarthy2009) and was recently used to fraudulently pass1,400 deficient vehicles in Atlanta over a five-monthperiod (Crosby 2011). While clean scanning is notablyharder to accomplish than clean piping because itrequires a surrogate car of the identical make andmodel, it is still considered a significant issue thatmust be addressed in emissions testing programs.

OBD-II testing can also be manipulated through“code clearing,” which involves turning off the mal-function indicator light and erasing stored malfunc-tion data prior to inspection. Mechanics can clearcodes by using professional diagnostic tools or bydisconnecting the battery, which may legitimatelyoccur during repairs or due to a dead battery. OBD-II systems are also equipped with a total of threeto six readiness indicators that are only rendered“ready” after substantial driving following code clear-ing. When these indicators are “unset,” the vehicle’sOBD-II system is believed to have had insufficientoperational time to sense any emissions malfunctions.For model years 1996–2000, a vehicle can have up

3 The New York Department of Motor Vehicles, the state agencyresponsible for ensuring the regulatory compliance of inspectionstations, conducted nearly 7,000 overt and covert audits in 2010,including at least one audit per inspection facility (New YorkDepartment of Environmental Conservation 2011). These audits,along with consumer complaints, led to inspection license revoca-tions for 66 facilities and license suspensions for 109 facilities thatyear. For annual reports describing the state’s enforcement efforts,see New York Department of Environmental Conservation (2012).4 Interviews with regulators revealed that the adoption of OBD-IItechnology was one of the principal conditions for the EPA allow-ing Missouri to decentralize its emissions testing system.

to two unset indicators. Cars manufactured in 2001or later are limited to one. The importance of readi-ness codes for test manipulation was demonstratedin a study conducted by the University of California,Riverside’s Center for Environmental Research andTechnology. They found that with an allowance oftwo unset indicators, half of the failing vehicles theyreset could be fraudulently passed because the carwas deemed ready before the malfunction indica-tor light illuminated. Reducing this allowance to oneindicator cut the possibility of fraud by one-half(Lyons and McCarthy 2009).

Prior research indicates that firms have a strongincentive to exploit this opportunity to cheat: Cus-tomers are more likely to return to facilities thathave previously passed them (Hubbard 2002). Weconfirmed that the loyalty incentives Hubbard (2002)found in California were also present in New Yorkand found that failing an emissions test made vehi-cle owners 11% less likely to return to that facilitythe following year.5 Firms in the emissions testingmarket profit from fraudulently passing older carsbecause it extends the time these cars can remain onthe road and older cars are far more likely to requirerepairs than the newer cars that would replace them.Although failing a vehicle may bring the short-termbenefit of repair work needed to pass the inspection,this incentive is limited by two factors. First, car own-ers are free to retest a failing car at another facil-ity. As the California Bureau of Automotive Repair(2011, p. 22) recently noted, “Consumers can and doseek second opinions when their vehicle improperlyfails,” and they “may file a complaint that could resultin disciplinary action of the station and or techni-cian and monetary reimbursement.” Second, ownersreceive a one-year waiver if they spend $450 and thevehicle continues to fail. With these limitations, theshort-term benefit of failing a vehicle pales in com-parison to the long-term benefit of retaining the cus-tomer’s service and repair business. Annual serviceand repair expenses on a 2006 Jeep Grand Cherokee,for example, are estimated at over $2,200/year byEdmunds.com,6 with gross margins on such repairstypically at 50% (First Research 2007).

Although individual inspectors may have somepersonal or career interest in providing leniency,because of managerial or peer pressure to ensurethe financial health of the organization, direct finan-cial incentives are unlikely to motivate this behavior.Inspectors are paid almost exclusively through hourlywages, and undercover operations have found bribes

5 This analysis is presented in the online appendix (available athttp://nrs.harvard.edu/urn-3:HUL.InstRepos:9667364).6 Annual service and repair costs for most recent cars can be viewedat http://www.edmunds.com/tco.html.


Figure 1 Facilities in New York State, January 2010

to individual inspectors to be relatively rare (Hubbard1998). This, along with evidence that inspectors con-form to facility-level pass rates when changing jobs(Pierce and Snyder 2008), suggests that leniency is pri-marily a facility-level decision.

New York State, like many states, mandates afixed price of approximately $27 for tests withinthe New York metropolitan area and $11 elsewhere.7

Because this eliminates price as a dimension on whichfirms can compete for customers, facilities must com-pete on quality. In general, quality competition mightinvolve timeliness or other dimensions of customerservice, but in emissions testing, the critical dimen-sion of quality is the test outcome.

Allowing polluting cars to pass emissions tests hasclear costs for society, increasing air pollution in urbanareas (Currie et al. 2009). There are proven healthconsequences from each of the three tested pollu-tants: carbon monoxide, hydrocarbons, and nitrogenoxides. Carbon monoxide, an odorless poisonous gas,inhibits the transport of oxygen from blood into tis-sues and can cause general difficulties in the car-diovascular and neural systems. Hydrocarbons andnitrogen oxides, when combined in the presence ofsunlight, form ground-level ozone that aggravatesrespiratory problems, especially in children, and maycause permanent lung damage (Utell et al. 1994). Thehealth cost of vehicle emissions has been estimatedat between $29 billion and $530 billion (National

7 In §5.5.3, we describe an analysis that finds no impact of this pricedifferential on pass rates.

Research Council 2001). A 10-year study of childrenfound evidence linking air pollution to reduced lungfunction growth, asthma exacerbation and develop-ment, and higher school absenteeism due to respira-tory problems (Gauderman et al. 2002).

4. Data and MeasuresFrom the New York State Department of Environ-mental Conservation, we obtained all 28 milliononboard diagnostic (OBD-II) inspections conductedfor gasoline-powered vehicles under 8,500 pounds atthe 11,423 licensed testing facilities throughout thestate from 2007 through 2010. Emissions testing inNew York is conducted by licensed private firms suchas gasoline retailers, service and repair stations, andcar dealers. Figure 1 presents the substantial varia-tion in the density of these facilities, ranging from thehighly dense New York metropolitan area to the ruralareas and isolated markets upstate, which allows usto estimate the effects of competition when thereare few competitors. During this period, all vehiclesyounger than model year 1995 (the vast majority)were required to use OBD-II technology. An addi-tional value of using this sample is that markets usingOBD-II technology have yet to be studied, despite thefact that it is broadly replacing older tailpipe testing.

In our context, we look for evidence of facilitiesproviding fraudulent leniency by observing system-atically higher vehicle pass rates after controlling fornearly all vehicle and test characteristics that mightlegitimately influence emissions test results across afacility’s portfolio of cars (Pierce and Snyder 2008).


Although individual vehicles have idiosyncratic char-acteristics that influence their emissions, these idiosyn-crasies are unlikely to be consistent across a facility’sportfolio of vehicles. This risk-adjustment approach issimilar to that used in studies of worker productivity(Mas and Moretti 2009) and lawyer and surgeon skill(Huckman and Pisano 2006, Abrams and Yoon 2007).

As described below, our data include facility-,vehicle-, and inspection-level characteristics. The datainclude tests of vehicles owned by individuals, cor-porations, fleets, and government agencies, but con-fidentiality concerns prevented us from obtainingvehicle owner identity or characteristics.

Our dependent variable is whether or not a vehiclepassed an inspection. We measure this as a dichoto-mous variable (passed inspection), coded 1 if the vehiclepassed and 0 if it failed. A vehicle fails if (a) a mal-function indicator light is illuminated (with relatedfault code) or (b) the vehicle exceeds its allowablenumber of unset readiness indicators. As detailedearlier, vehicles with model years of 2000 or olderare allowed two unset readiness indicators, whereasnewer cars are allowed only one (Sosnowski andGardetto 2001).

We created several measures to capture character-istics of the market in which each inspection facil-ity operates. We measure the number of proximatefacilities using a standard technique in the healtheconomics literature (Garnick et al. 1987), obtaininglatitude and longitude measures of each facility’s loca-tion and counting the number of other facilities withina Euclidean radius. This method is based on consumerchoice models in which, under limited price variation,

Figure 2 Distribution of Proximate Facilities

5,265

2,682

1,498

824

470220 121 104 71 77 33 16 10 9 8 4 4 4 2 1

0

1,000

2,000

3,000

4,000

5,000

Num

ber

of f

acili

ties

0 5 10 15 20

Number of proximate facilities within 0.2 miles

geographic distance becomes the primary influence(e.g., Gowrisankaran and Krainer 2011). Because priceis fixed for emissions tests in New York State, thecustomer selects a facility based on this distance andon service quality, assessing the latter both on legaldimensions (e.g., cleanliness or timeliness) and illegaldimensions (anticipated leniency). When the costs ofvisiting two facilities are similar because they are nearone another, the customer’s decision is predicated onquality. As we previously noted, the primary qualitydimension is the test outcome. For the researcher, thetask is to select a radius that is large enough to includethe facilities to which the focal facility will respondcompetitively. We selected a radius of 0.2 miles, orapproximately two city blocks, within which facilitieshave an average of 1.6 competitors. This market def-inition is much smaller than that in previous work(Olivares and Cachon 2009), but still contains sub-stantial variation in firm density due to the frequentagglomeration by facilities. Figure 2 presents a his-togram depicting the number of proximate facilitiesfaced by each facility in our sample. Because any geo-graphic delineation of the market size is somewhatarbitrary, we later assess the robustness of our resultsto smaller and larger market definitions.

We also developed several dichotomous measuresto capture facility characteristics. Fleet facilities areinspection facilities that service their own fleet vehi-cles. These facilities are closed to individual con-sumers and thus are unlikely to be influenced bylocal competition. We identified fleet facilities asthe 442 facilities, owned by 257 companies, whosenames implied they were operated by municipal


Table 1 Summary Statistics

StandardVariable Observations Mean deviation Min Max

Passed inspection (dummy) 28,001,335 00930 00255 0 1Number of proximate facilities 28,001,335 10586 20178 0 20Odometer (miles) 28,001,335 79,525 50,523 0 999,999Fleet facility (dummy) 28,001,335 00005 00069 0 1Inspection year 28,001,335 2008.5 10095 2007 2010Model year 28,001,335 2002 30216 1996 2010Model year 2001 or newer (dummy) 28,001,335 00659 00474 0 1Entrant facility (dummy) 28,001,335 00040 00197 0 1Luxury vehicle (dummy) 28,001,335 00089 00285 0 1Returns next year (dummy) 21,411,677 00420 00494 0 1

Note. The last year of data for returns next year was omitted.

governments, universities, and utilities.8 The remain-ing 10,981 facilities are open facilities that serve thegeneral public; these include gasoline retailers, serviceand repair shops, and car dealers.

We created a dichotomous variable, entrant facility,to flag inspections conducted by facilities that hadonly been conducting inspections for 12 months orless; we therefore consider a facility to have tran-sitioned to incumbent status 12 months after entry.Because we do not observe the initial inspection dateof facilities that had been conducting inspections atthe beginning of our sample, we pursued a conser-vative approach and only considered facilities eligiblefor entrant status if they initially began conductinginspections in the third month or later of our sample.Although this designates firms that entered in the firstthree months as incumbents, it biases against findingentrant-specific results. Entrants make up 17.5% of thefacilities in our sample (2,004 of 11,423 facilities).

We also obtained data on several vehicle char-acteristics, including vehicle make and model (e.g.,Ford Taurus), model year, and odometer reading. Wecreated model year counter to equal the model yearminus 1996, the first year of our sample. We created adichotomous variable to flag luxury vehicles, based onthe vehicle make typology used by Gino and Pierce(2010).9 Table 1 reports summary statistics.10

8 This list is available from the authors. Examples of municipal gov-ernments or agencies include the New York City Police Departmentand the City of Canandaigua. Examples of universities includeAlfred University and Ithaca College. Examples of utilities includeCentral Hudson Gas and Consolidated Edison. The telephone com-pany Verizon operated the most fleet facilities, but its 95 facilitiesconstitute only 21% of the total number of fleet facilities. No otherfleets operated nearly as many facilities. Although a few companiesoperated between 10 and 20 facilities (primarily electric utilities),the vast majority operated just one.9 Luxury vehicles include the following vehicle makes: Acura,Alfa Romeo, Aston Martin, Audi, Bentley, BMW, Cadillac, Ferrari,Infiniti, Jaguar, Lamborghini, Lexus, Lotus, Maserati, Mercedes-Benz, Porsche, Rolls Royce, Saab, and Volvo (Gino and Pierce 2010).10 Summary statistics by facility type (fleet versus open) are avail-able from the authors.

5. Empirical AnalysisOur empirical analysis includes several steps to iden-tify the explicit mechanism through which com-petition impacts the illegal provision of quality.In a preliminary analysis presented in the onlineappendix, we confirmed that competition increasesfacilities’ incentives to exhibit leniency by showingthat vehicles were significantly less likely to returnto the same facility for future tests in markets withhigher numbers of proximate facilities. This increaseddemand under fixed prices is also consistent withour definition of quality as the value (or willingnessto pay) of leniency to the consumer. In our primaryanalysis, we first directly test whether higher num-bers of proximate facilities are associated with higherpass rates using linear probability models that con-trol for geographic, vehicle, and time characteristics.Next, we estimate the distinct effects of proximatefacilities on the pass rates of both proximate fleet andopen facilities, showing that open facilities respond tolocal competition with leniency but that fleet facilitiesdo not. Next, we exploit a model-year-based disconti-nuity in the difficulty of test manipulation to identifythe fraudulent code-clearing technique as a primarymechanism through which competition impacts passrates. We also show how competition influences theentry strategies of new facilities. Finally, we show thatcompetition has little impact on the pass rates of lux-ury cars, the owners of which are considerably lesslikely to switch facilities in the presence of alternativefacilities.

5.1. Competition and Pass RatesTo investigate the effect of competition on illegalquality, we estimate empirical models with emissionstests as the unit of analysis, regressing passed inspec-tion on the number of proximate facilities. The detailedinformation on time and location of inspection aswell as vehicle characteristics allows us to controlfor most predictors of vehicle deterioration and likelyemissions. Although linear probability models with


Table 2 Impact of Competition on Facility Leniency

Dependent variable: Passed inspection

(1) (2) (3) (4) (5)

Number of proximate facilities 00072∗∗∗ 00122∗∗∗ 00089∗∗∗ 00088∗∗∗ 00085∗∗∗

4000245 4000275 4000265 4000265 4000265Fleet facility −40317∗∗∗

4004265Number of proximate facilities −00908∗∗∗

× Fleet facility 4002175Odometer level squared and cubed Included Included Included IncludedThree-digit ZIP code fixed effects Included Included Included IncludedYear fixed effects Included Included IncludedModel year and model year squared Included Included IncludedMake×Model fixed effects Included Included Included

Sample All All All All Openfacilities facilities facilities facilities facilities

Observations 28,001,355 28,001,355 28,001,355 28,001,355 27,868,131

Notes. Results reported are ordinary least squares coefficients and standard errors multiplied by 100. Parentheses contain standarderrors clustered at the facility level.

∗∗∗Significant at the 1% confidence level.

robust standard errors have the risk of generat-ing predicted values outside the actual data range,they are unbiased and do not suffer the potentiallysevere incidental parameters problem of logistic mod-els with many fixed effects (Katz 2001; Wooldridge2002, pp. 454–457).11 In this and all other models, wecluster standard errors by facility. These primary mod-els are presented in Table 2, which reports actual coef-ficient values and standard errors multiplied by 100.

5.1.1. Market Density. Our first model includesno controls and shows that a greater number of prox-imate facilities is associated with a higher pass rate(column (1) of Table 2). An additional facility within0.2 miles increases the probability of passing by0.07 percentage points. Although this coefficient issmall, the 93% average pass rate implies that an addi-tional competitor would lead to the passing of one outof 100 cars that should fail.12 To verify that this rela-tionship is not a product of sorting on omitted char-acteristics, the model reported in column (2) controlsfor odometer (level, squared, and cubed values) aswell as for neighborhood fixed effects (three-digit ZIPcode). Our fully controlled model in column (3) addsvehicle model year controls (model year counter andits squared value) as well as fixed effects for inspec-tion year and vehicle make and model. The coefficienton the number of proximate facilities in these modelsis similar to that in the baseline model, remains

11 With 28 million observations, the linear probability model is anextremely close approximation of logistic regression.12 Because 7% of all cars fail, or 7,000 out of 100,000, a marginaleffect of 0.07% implies that 70 fewer cars would fail annually in theaverage presence of one additional competitor within 0.2 miles.

statistically significant, and supports our hypothesisthat competition increases leniency after controllingfor physical features that should determine the testresult.13

5.1.2. Density vs. Competition. Whereas resultsfrom our primary model are consistent with compet-itive pressures to provide illegal quality, one mightbe concerned that other market mechanisms associ-ated with facility density could be generating higherpass rates. For example, pass rates in dense regionsmight occur because density provides facilities greateropportunities to learn how to manipulate inspectionsthrough knowledge spillovers across nearby facilities(Argote et al. 1990) or through explicit cooperation(McGahan 1995). To investigate this, we leverage thedifference between fleet facilities that exclusively testvehicles from dedicated fleets and open facilities thatcompete for individual and small-business vehicles.Because fleet facilities need not compete for busi-ness with other facilities, they should be unaffectedby the proximity of other facilities. In contrast, openfacilities should respond to other facilities because oftheir ability to steal unsatisfied customers from eachother. Column (4) of Table 2 reports the results ofa model akin to column (3) except that we includean interaction term between the number of proximatefacilities and fleet facility. This model yields separate

13 To explore whether our results were driven simply by the dif-ference between monopolies and facilities facing any level of com-petition, we also estimated this model on the subset of facilitiesthat faced at least one local proximate facility (i.e., excluding firmswith no local competitors). Those results were very similar to ourprimary results estimated on the full sample.


estimates of the impact of proximate facilities on openand fleet-only facilities. The results indicate that theleniency of open facilities increases significantly withthe number of local competitors, but not for fleet-only facilities. These results support our hypothesisthat competition is driving the relationship betweenfacility density and pass rates.14 As further evidenceof this, column (5) reestimates the model reported incolumn (3) but only on the subsample of open facili-ties; the positive coefficient is nearly identical to thatreported in column (3).

5.1.3. Nonlinear Effects of Competition. Ourresults thus far indicate that firms facing more com-petition are more lenient, but the specifications haveestimated a linear effect. Given that New York Statehas dramatic variation in population density thatincludes both isolated markets and dense urban cen-ters, competition varies dramatically between mar-kets. In a different setting with price competition andlow differentiation, Bresnahan and Reiss (1991) founda nonlinear effect of competition on firm behaviorthat diminished almost to zero once a market con-tained three to five firms. Their results are consistentwith Bertrand models of competition, in which evena single competitor induces competitive pricing by aformer monopolist. In contrast, Olivares and Cachon(2009) found that under simultaneous price and qual-ity competition, additional competitors continued toresult in firms offering decreasing marginal increasesin service quality up to the eighth competitor. Wesimilarly investigate whether our context, with fixedpricing and competition based on leniency, also fea-tures a nonlinear effect and whether the provision ofleniency attenuates rapidly or slowly with increasesin the number of competitors.

To identify this nonlinear effect, we reestimate ourmodel from column (3) but add the squared valueof the number of proximate facilities. We report thepredicted values and 95% confidence intervals in Fig-ure 3. The predicted pass rate continues to increaseup to the sixth competitor, although only to the fifthcompetitor with 95% confidence. The results suggesta nonlinear effect, but as in Olivares and Cachon(2009), the decreased precision at higher competitionlevels means that we cannot rule out a linear relation-ship. Nor can we rule out the possibility that com-petition continues to impact quality beyond the sixthcompetitor. Our results strongly suggest, however,that firms continue to increase the provision of ille-gal quality beyond the third competitor, whose impacton price was negligible in Bresnahan and Reiss (1991).Furthermore, they suggest that the marginal impact of

14 Given that the vast majority of fleets operate just one facility (seeFootnote 8 for details), we see no reason to be concerned that ourfleet results are driven by just a few large fleet operators.

Figure 3 Pass Rate by Number of Proximate Facilities

0.935

0.930

0.925

0.940

Pass

rat

e

0 2 4 6 8 10

Number of proximate facilities within 0.2 miles

Mean pass ratefrom raw data

Regression predicted pass rate

95% confidence interval

Note. Predicted pass rates and confidence intervals are derived fromregressing passed inspection on number of proximate facilities and numberof proximate facilities squared.

the first competitor is approximately three times largerthan the coefficient estimated in our linear models.

5.2. Competition and Test ManipulationAlthough our first two models strongly suggest com-petition through fraudulent leniency, other plausibleexplanations remain. For example, lower pass ratesmight occur in less competitive markets because localmonopolists, who face little threat of customer loss,might rationally limit capacity. This decreased capac-ity, which would maximize mechanics’ labor utiliza-tion, would also create queues and provide mechanicswith little time to help vehicles pass either throughlegitimate repairs or through manipulation.

To better identify whether test manipulation drivesthe positive relationship we observe between com-petition and pass rates, we exploit a discontinuityin EPA guidelines on OBD-II testing protocols.As described earlier, all vehicles of model years1996–2000 are allowed to continue an emissions testas long as no more than two readiness indicatorsare “unset,” whereas newer vehicles (beginning with2001) are allowed only one unset indicator. Thisallowance differential makes test manipulation usingcode-clearing techniques much easier with older vehi-cles than with newer ones. For example, consider amechanic seeking to falsely pass a vehicle exhibit-ing three unset readiness indicators. The mechanicwould need to override just one of these indica-tors for older vehicles but would need to overridetwo indicators for newer vehicles. The impact ofthis policy discontinuity on pass rates is evident inFigure 4, which presents the average pass rates ofeach model year between 1996 and 2008. Pass ratesincrease monotonically for each model year except2001, for which they drop dramatically.

We explore whether this discontinuity provides evi-dence that competition has a greater impact on the


Figure 4 Pass Rate Decreases with More Stringent Tests

0.88

0.90

0.92

0.94

0.96

0.98

1996 2001 2006

Model year

Pass

rat

e

Before model year 2001:Test continues if vehiclehas up to two unsetreadiness indicators

Since model year 2001:Test continues if vehiclehas up to one unsetreadiness indicator

Note. Figures are averages from the raw data.

pass rates of older, more easily manipulated vehi-cles than on newer vehicles subject to more strin-gent test protocols. We estimate a model based onour primary specification (as reported in Table 2,column (3)) that also includes a dummy variablemodel year 2001 or newer and its interaction withnumber of proximate facilities. The results, reported incolumn (1) of Table 3, yield a significant negative

Table 3 Factors Moderating Competition’s Impact on Facility Leniency

Dependent variable: Passed inspection

(1) (2) (3)

Number of proximate facilities 00254∗∗∗ 00073∗∗∗ 00107∗∗∗

4000365 4000275 4000275

Model year 2001 or newer −20978∗∗∗

4000575

Number of proximate facilities× −00257∗∗∗

Model year 2001 or newer 4000255

Entrant facility −00957∗∗∗

4001665

Number of proximate 00220∗∗∗

facilities× Entrant facility 4000585

Luxury vehicle Absorbed

Number of proximate −00169∗∗∗

facilities× Luxury vehicle 4000275

Odometer level, squared Included Included Includedand cubed

Three-digit ZIP code fixed effects Included Included IncludedYear fixed effects Included Included IncludedModel year and model Included Included Included

year squaredMake×Model fixed effects Included Included Included

Sample All All Allfacilities facilities facilities

Observations 28,001,355 28,001,355 28,001,355

Notes. Results reported are ordinary least squares coefficients and standarderrors multiplied by 100. Parentheses contain standard errors clustered atthe facility level.

∗∗∗Significant at the 1% confidence level.

coefficient on this dummy, indicating that the newervehicles (beginning with model year 2001), facingtougher requirements, are indeed less likely to passinspections. More importantly, the coefficient on theinteraction term is also negative and significant; itsmagnitude is nearly identical to that of the coeffi-cient on number of proximate facilities. This indicatesthat competition impacts leniency on the older, moremanipulable vehicles but appears to have little effecton model year 2001 and newer vehicles. Given theunique policy difference between 2000 (and older)and 2001 (and newer) model year vehicles, our resultssuggest that test manipulation through code-clearingis driving the positive relationship between competi-tion and pass rates in older cars.

5.3. Market EntryNew entrant facilities’ behavior can also shed lighton whether the provision of fraudulent leniency isdriving the relationship between competition andpass rates. Successful entry and survival in our set-ting is extremely difficult. Of the 2,234 facilities thatexited during our sample period (20% of all facil-ities), 91% (2,034) had been conducting inspectionsless than two years. Among all new entrants, 40%exited in the first year, with an additional 34% exitingin the second year. Given this survival hazard, newentrants should be particularly likely to respond tocompetition with unusually high pass rates becausethey are unable to use introductory pricing strate-gies to capture customers who feel some loyalty to anincumbent facility or who face high switching costs(Klemperer 1995). Under fixed prices, new entrantsmust acquire customers by competing on qualitydimensions (White 1972), which, in our setting, meansawarding a passing result even when this requirestest manipulation. Although such entrants may havefewer established customers to lose if their fraud weredetected and their license suspended by the state,they are also less likely to survive the fines or lostbusiness that would accompany detection and sus-pension. Our manipulation story predicts that, com-pared to the pass rates of incumbent facilities, the passrates of entrant facilities will respond more stronglyto the presence of greater local competition.

To identify this effect, we interact entrant facilitywith number of proximate facilities and include all thecontrols used in our previous models. The results,presented in column (2) of Table 3, indicate thatalthough incumbents’ pass rates increase in the faceof competition (� = 00073, p < 0001), entrants’ passrates respond even more strongly (�= 00220, p < 0001).Although an entrant’s pass rate is 0.96 percentagepoints lower than other facilities when entering amarket without an incumbent, it rises dramaticallyas the number of proximate facilities increases. These


results suggest that although new entrants may onaverage be more reluctant to provide leniency to cus-tomers, their willingness is particularly strong whentrying to win new customers in more competitivemarkets.

5.4. Competition for Luxury CarsIn competitive markets, firms have higher incentivesto provide illegal dimensions of quality to thosecustomers at greatest risk of leaving. In our setting,luxury car owners are less prone to search for alter-native facilities should their vehicle fail because theyhave a greater opportunity cost of time (Hall 2009).Furthermore, only a portion of the proximate facilitieswill actually be considered by these owners, who areunlikely to patronize low-end repair facilities. Indeed,in a supplemental analysis, we find luxury car own-ers are less likely than other owners to switch facil-ities in the presence of competition (column (4) ofTable A-1 in the appendix). Facilities therefore haveless incentive to fraudulently pass luxury vehicles andare more likely to be lenient with nonluxury vehi-cles than with luxury vehicles. These factors suggestthat the effect of competition on pass rates shouldbe stronger for nonluxury vehicles than for luxuryvehicles. To test this, we add to our primary modelan interaction term between luxury vehicle and num-ber of proximate facilities. In the results presented incolumn (3) of Table 3, the coefficient on luxury vehi-cle is absorbed by the make/model fixed effects, butthe statistically significant negative coefficient on theinteraction term indicates that the positive relation-ship between competition and pass rates is attenuatedfor luxury vehicles. The sum of the coefficients for theinteraction term and number of proximate facilities beingnearly zero and nonsignificant (Wald F = −1048; p =

0014) indicates that competition increases pass ratesfor standard (nonluxury) vehicles but not for luxuryvehicles. This result is consistent with our expectationthat luxury car owners, with fewer options for “auditshopping,” are less likely than other owners to ben-efit from the increased provision of illegal forms ofquality in competitive markets.

5.5. Robustness TestsBecause any geographic delineation of a market sizeis somewhat arbitrary, we conducted a series of addi-tional empirical tests to assess the sensitivity of ourresults to various definitions. We repeat our primaryspecifications, defining markets by using differentradii and by using estimated driving time rather thanEuclidean distance.

5.5.1. Market Definition. Our main results arebased on a market size defined by a 0.2-mile radiusaround the focal facility. We reestimated our primarymodel with a full set of controls (as in column (3)

Figure 5 Robustness to Market Size Definition

0

0.20

0.15

0.10

0.05

Pass

rat

e

0 0.2 0.4 0.6 0.8 1.0

Radius (miles)

Note. Predicted pass rates and confidence intervals are derived from theregression specification in Table 3, column (3), with number of proximatefacilities calculated based on varying radii.

of Table 2), using nine alternate definitions of mar-ket size, with radii ranging from 0.1 to 1 mile in0.1-mile increments. As displayed in Figure 5, whichplots each model’s coefficient on number of proximatefacilities and its 95% confidence interval, the effectof competition on pass rates is consistently positiveand statistically larger than zero. Not surprisingly,our estimates of competition entail wider confidenceintervals when using smaller radii, due to lower vari-ation in the number of competitors across facilities,and our estimate magnitudes decline when usinglarger radii because of weaker competitive effectsfrom distant competitors. As a whole, these resultsindicate that our finding that competition increasespass rates is not dependent on our choice of geo-graphical market boundaries.

5.5.2. Driving Time. Our primary measure ofcompetition follows the prior literature (e.g., Ren et al.2011) by defining competition based on a distanceradius from the focal facility. But customers in differ-ent regions may bear different costs of travelling iden-tical distances, depending on road congestion andspeed limits. To assess whether our results are robustto a more nuanced distance measure that accounts forthese differences in travel cost, we reestimated ourprimary model (column (3) of Table 2) using driving-time radii of 30, 60, and 120 seconds. We calculateddriving times between facilities using actual routesfrom the WebGIS system at the University of SouthernCalifornia Spatial Sciences Institute (Goldberg andWilson 2012). The results for each driving-time radiusare consistent with the geographic market definitions.

5.5.3. Price Differential. Although inspection testprices are fixed by the government, the fixed price inthe New York metropolitan area (approximately $27)differs from the fixed price in the upstate region ($11).Vehicle owners seeking inspections at upstate facili-ties might be especially likely to seek a second opin-ion when told that their vehicle failed because of the


Figure 6 Distribution of Unconditional Pass Rates, by Market Structure

0

5

10

15

Num

ber

of p

roxi

mat

e fa

cilit

ies

0.7 0.8 0.9 1.0

Unconditional pass rate

Bottom 10% of theoverall distribution

Middle 80% of theoverall distribution

Top 10% of theoverall distribution

No proximatefacilities (solid)

1 + Proximatefacilities (dash)

No proximate facilities: 10.9%1 + Proximate facilities: 9.1%

t-stat of difference: 3.27


t-stat of difference: –1.52


t-stat of difference: –1.31

Notes. There are 11,194 total observations. Kernel density estimation was used. “No proximate facilities” means that the facility never faced a neighboringtest center during the sample period. Number of proximate facilities calculated using a 0.2-mile ring around the focal facility.

lower price of this second test, potentially makingupstate facilities even more responsive to competi-tion than facilities in the New York metropolitan area.However, the price differential might not be a mate-rial factor if vehicle owners view the $16 differenceas trivial compared to the opportunity cost of theadditional time required to obtain the second opin-ion. Furthermore, the many other differences betweenthe New York metropolitan area and upstate regionsbesides inspection price (such as weather, road con-ditions, wealth, and population density) make it chal-lenging to isolate the effect of inspection test priceson pass rates. The cleanest approach to assess thepotential impact of inspection test prices on passrates—isolating this effect from the many other differ-ences between the metropolitan area and upstate—isto compare the pass rates of facilities near the borderdividing the two price zones. We identified the facil-ities in the five-digit ZIP codes adjacent to each sideof the metropolitan area–upstate border and found noprecisely estimated difference in the effect of competi-tion on leniency between facilities on the two sides ofthe border. This provides no evidence that the pricedifferential affected pass rates.

5.6. Competition and the Distribution ofFacility-level Leniency

Given the clear impact of competition on leniencyin our data, we next present the broader marketimpact of this effect across the entire state. Follow-ing Syverson (2004), we examine how competition

affects the distribution of firm-specific outcomes inNew York State. The point of this analysis is to high-light the differences in outcomes between firms fac-ing low versus high levels of competition. WhereasSyverson (2004) shows that high-competition marketsproduce substantially more high-productivity firms,we present the distribution of pass rates to exam-ine whether local markets with high competition pro-duce more high-leniency firms. Figure 6 illustrates theimpact of competition on leniency by depicting dis-tinct kernel density graphs for two groups of facilitiesin New York State: those with no proximate facilities(within 0.2 miles) and those with at least one. We trimthe top and bottom 1% of the overall distribution,leaving us with 11,197 facilities, and use vertical linesto demarcate the top and bottom 10% of the overalldistribution.15

The impact of competition on increasing leniencyis evident in both distributions. Competition primar-ily impacts the tails of the distribution, increasing thenumber of high-leniency facilities while decreasingthe number of low-leniency ones. We test these dif-ferences using the number of firms above or belowthe 10% thresholds from Figure 6. Facilities facing nocompetition are approximately 1.8 percentage pointsmore likely to fall in the bottom 10% of the distri-bution than those facing competition (t-stat = 3027;

15 Because these pass rates do not control for differences in facilities’neighborhood characteristics or vehicle portfolios, we repeat thisanalysis in the online appendix using conditional pass rates, whichyield nearly identical results.


p < 0001). Those facing no competition are also 0.8percentage points less likely to fall in the top 10% ofthe distribution, although this difference is not statis-tically significant.16

6. Discussion and ConclusionIn this paper we have shown that increased competi-tion leads firms to provide an illegal form of qualityto attract and retain customers. For a firm, the mar-ket benefit of this strategy must be weighed againstthe legal sanctions it might bring. Although we focuson one source of competitive pressure—consumerdemand for leniency—firms may face a multitude ofpressures both for and against illegal or unethicalbehavior. The calculus of firm ethics and legal com-pliance can be complicated, with both market andinstitutional stakeholders exerting complex pressures(Ostrom 2000, Stevens et al. 2005). Although ethicaland legal strategies may be in the long-term inter-est of many firms (e.g., Hosmer 1994), the plethora oflaws and regulations that govern firm behavior sug-gest this is often not the case (Schwab 1998). Manyfirms, like those in our industry, break rules becausethe market rewards them for doing so.

We find that firm misconduct appears to increasewith competitive pressure and the threat of losingcustomers to rival firms. In our context, in which pric-ing is fixed by regulation, misconduct that favors cus-tomers is a valuable quality dimension that appearsto play a large role in competition. Many emissionstesting facilities that strictly follow the law risk losingsubstantial future business, much of which involvesthe highly profitable repairs that inevitably befallolder cars. More importantly, we show that the impactof competition does not affect all firms equally. Newentrants are especially likely to respond to competi-tive pressures, as are firms that serve consumers morelikely to switch suppliers. Market structure, organiza-tional characteristics, and vehicle characteristics canhelp government agencies and private groups iden-tify the facilities most likely to violate rules and laws.

We acknowledge that several other market dynam-ics, although difficult to observe, might also influenceour results. Given the geographic agglomeration ofinspection facilities, we recognize that personal tiesand communication among inspectors and managers

16 We use 10% thresholds to be consistent with Syverson (2004).Alternative thresholds yield more favorable statistical tests. Toensure that these differences are not artifacts of data structure, werun 10,000 simulations in which we randomly assign each of the11,197 firms to a competitive or noncompetitive designation, thentest the difference in the frequency with which a firm in each groupis in the bottom 10% of the pass-rate distribution. Out of 10,000simulations, only eight produce a t-statistic exceeding the value of3.27 observed in our data.

are likely, which could lead to knowledge transfersof techniques for fraud or manipulation (Obloj andSengul 2012) that would tend to increase with firmdensity. Although this may explain some of the dif-ference in leniency between new entrants and incum-bents, we find it unlikely to explain differences amongincumbents, given the ease of implementing fraudtechniques and the high labor mobility in this indus-try (Pierce and Snyder 2008). We also cannot ruleout the possibility of collusion among proximatefacilities (McGahan 1995), although we expect thatincreased collusion by densely located firms wouldlower pass rates (similar to the effect of monopolies)and therefore bias against our hypothesized results.Similarly, density might allow better monitoring bythe government or by competitors, but this too shouldlead to reduced leniency in areas of high competi-tion. Furthermore, we note that our results cannot bedefinitively intrepreted as causal because competitionis endogenous.

Finally, we recognize that facilities with localmonopolies might restrict capacity, limiting the timethey could spend on fraudulent clean-scanning tech-niques.17 However, our evidence that code clearing isan important technique makes this explanation lesslikely, because codes can be cleared quickly, withreturn tests scheduled for subsequent days. Further-more, given the fixed price of inspections, facilitieswould be unable to install monopoly prices to benefitfrom capacity constraints. We also believe that monop-olists constraining capacity are unlikely to be drivingthe effects we identify, given that we find significantmarginal impacts on leniency well beyond the firstcompetitor.

We believe that our results have serious implicationsfor policy makers and managers. For policy makerschoosing between the potential inefficiencies of localmonopolies (or even state-run facilities) and those offree-market competition, the calculus must includethe erosion of monitoring stringency that accom-panies competition. In 1980, when the New Jerseygovernment was deciding whether to switch fromits government-operated emissions testing system toa system relying on private-sector facilities, “criticsfeared the move would weaken enforcement of theprogram” (Lazare 1980, p. S21). Several decades later,the National Research Council (2001, pp. 189–190) con-cluded that “motorists, testing personnel, and techni-cians have found many ways to avoid compliance”with vehicle emissions testing protocols, and that“decentralized programs have come under partic-ular scrutiny because, it is argued, they presentmany opportunities for testing fraud 0 0 0 0 Motorists

17 Schneider (2012) found that facility capacity, based on time ofday, predicts some automotive repair decisions.


therefore shop around to find stations most likely torespond to incentives.” Government agencies havetaken enforcement actions against emissions testingfacilities exhibiting leniency in part because fraudu-lent inspections create “an unfair competitive advan-tage” over facilities “conducting lawful emissionsinspections” (States News Service 2009, 2010a, b).

Policymakers must consider how to obtain theefficiency benefits of competition while mitigatingthe risk of companies gaining competitive advan-tage through corruption, fraud, and other unethicalbehaviors. If customers indeed demand corrupt busi-ness practices, firms may feel compelled to cross eth-ical and legal boundaries simply to survive, often inresponse to the unethical behavior of just a few oftheir rivals. In markets with such potential, concen-tration with abnormally high prices and rents butwith reduced corruption may be preferable (Shleifer2004). The good intention to fix prices at low lev-els may eliminate legitimate ways in which firms cancompete, unintentionally creating incentives for themto cross legal boundaries. Alternatively, policy mak-ers can increase regulatory monitoring and enforce-ment efforts for highly competitive markets to ensurethat incentives for illegality do not dictate survivalin these markets. An alternative approach to reduc-ing the impact of competition on emissions testingfraud is improved monitoring technology. Althoughthe replacement of older tailpipe-based testing withnewer OBD-II technology has likely reduced fraud,competition has motivated firms to innovate withnew techniques for test manipulation, such as cleanscanning and code clearing.

The magnitude of our estimated effect, althoughsmall, is not trivial. Using our estimated 0.089%marginal impact of an additional proximate facilitywithin 0.2 miles, we roughly estimate how manyfewer cars would have passed had each firm beena monopoly within this radius, rather than havingthe observed average of 1.59 proximate facilities. Thisis a conservative test, given the willingness of con-sumers to drive far beyond 0.2 miles for a betterresult. Applying this coefficient to the 28 million testsconducted over the four-year sample period impliesthat at least 39,000 more tests would have resultedin failing outcomes.18 The magnitude of this effecton pollution levels, however, is likely much larger.The nonlinear model presented in Figure 3 suggeststhat the marginal effect of the first competitor islikely three times larger. Furthermore, widespreadremote emissions-sensing studies have revealed thatthe worst 5% of cars produce half of all emissions,and the worst 1% of cars produce 20% of all emis-sions (Stedman 2002, Stedman et al. 2009). This sug-gests that each of the 39,000 fraudulent passes implied

18 This number is calculated as 28 million × 1059 × 0000089.

by our results might result in a 20-fold increase inmobile emissions compared to the average vehicle.Under these assumptions, these fraudulent passesresult in excess emissions equivalent to an additionalone million cars on the road,19 which is a conservativenumber given much larger marginal effect of the firstcompetitor observed in Figure 3. Furthermore, elim-inating this fraud could reduce total emissions evenmore if failing cars were sold to drivers in unregu-lated states. Past studies have shown that exportedgross polluters typically replace even worse pollutingcars, improving air quality in both locations (Davisand Kahn 2010).

Given the major health impacts of even small reduc-tions in carbon monoxide emissions from mobilesources (e.g., Currie et al. 2009), the social impact ofcompetition is significant. These results are particu-larly troubling in light of recent work by Fowlie et al.(2012) showing that mitigating mobile emissions is themost cost-effective way to reduce airborne pollution.Similarly, given the extreme cost of alternative pro-grams designed to remove gross polluters, such as“Cash for Clunkers” (Knittel 2009), reducing emis-sions testing fraud appears relatively cost-effective.Furthermore, if local competition geographically con-centrates the illegal passing of grossly polluting cars,the health threat may be particularly severe in theseareas (Currie and Walker 2011).

The implications for managers are also important,because managers, their employees, or their suppliersmay be under considerable pressure to cross legaland ethical lines when market competition is high.Top managers and owners under intense competitionmust strengthen monitoring and governance mecha-nisms to ensure their own legal compliance if theyfear government sanctions. Similarly, managers mustunderstand that using internal competition amongemployees or divisions to improve performance mayincrease the likelihood that managers and otheremployees will engage in behavior that puts the firmat risk for legal sanctions or lost reputation. Thecost of such behavior may be even greater, however,if it creates cultures of corruption that spill over toother workers (Ashforth and Anand 2003, Pierce andSnyder 2008).

Perhaps the most important implication for man-agers is that intense supply chain competition mightlead suppliers to engage in illegal and unethical activ-ities. Intensifying price pressure on suppliers in coun-tries that lack regulatory enforcement regimes can

19 Because new vehicles have incorporated improved emissions-minimizing technologies, the share of pollution coming from theworst-polluting vehicles has increased over time (as evidenced inlongitudinal studies such as Stedman et al. 2009). This exacerbatesthe impact of fraud on increasing pollution levels. At the same time,these newer technologies have made emissions repairs more expen-sive, generating higher incentives for customers to seek fraud.


lead these suppliers to weaken safety conditions,employ child labor, and violate minimum wage laws,all of which pose reputational risks to the buyingorganizations. Similarly, intense pressure on suppli-ers or partners to win access to local markets andresources might motivate bribery and other corrup-tion, making those firms (and managers) liable underthe U.S. Foreign Corrupt Practices Act. Finally, man-agers must understand that government policy, firmdecisions, or exogenous factors that increase marketrivalry may necessitate the monitoring of competitors’behavior. The failure to do so may allow these rivalsto gain advantage through corrupt strategies, particu-larly under institutional regimes in which regulatorymonitoring or enforcement is weak.

We acknowledge the value of the recent focus in themanagement and strategy literature on the impact ofethical and socially responsible behavior on firm per-formance, but, like Devinney (2009), we caution that apositive relationship between these measures shouldnot be assumed. Our market setting provides a strik-ing example that firms do not always “do well bydoing good.” We therefore encourage future work tofocus on identifying circumstances in which we mightexpect market mechanisms to encourage compliantbehavior in firms, whether those mechanisms comefrom consumers, partners, interest groups, or otherstakeholders. In some markets, the market mecha-nism of competition may indeed simultaneously yieldimproved social welfare and financial returns forfirms. But where market mechanisms such as com-petition pressure firms to cross legal boundaries toprofit—or just survive—the solution to socially harm-ful strategies may necessarily be institutional.

AcknowledgmentsThe authors acknowledge helpful comments from RyanBuell, John Elder, Andrei Shleifer, Bill Simpson, and atten-dees of the meeting of the Strategy Research Initiative,as well as the assistance of Dan Goldberg at the Univer-sity of Southern California Spatial Sciences Institute, JeffBlossom at the Harvard Center for Geographic Analysis,and Melissa Ouellet at the Harvard Business School. Anonline appendix is available at http://nrs.harvard.edu/urn-3:HUL.InstRepos:9667364.

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