Lamar PierceOlin Business School

Washington University in St. Louis

Interpersonal Corruption Market and Laboratory Evidence

Corruption is a pervasive problem for organizations

ANNUAL COSTS TO AMERICAN FIRMS• Prescription drug fraud: $72.5 billion

• Employee theft: $52 billion

• Corporate fraud: $37 billion

• Mortgage fraud: still unknown

The sources of corruption are important to identify

I research how interactions between individuals and organizational environments influence corrupt behavior

SOURCES OF CORRUPT BEHAVIOR• Financial incentives and extrinsic motivation

• Social comparison and envy

• Network or peer-based intrinsic motivation

It is the role of individual relationships and interactions that makes corruption “interpersonal”

I present two papers on one source of interpersonal corruption --- comparison of wealth

1. Robin Hood under the Hood (w/ Francesca Gino)– Forthcoming at Organization Science

2. Dishonesty in the Name of Equity (w/ Francesca Gino)– Forthcoming at Psychological Science

These papers demonstrate two different approaches to understanding individual fraud and corruption

Several mechanisms may link wealth-based comparisons to corrupt behavior

• Inequity may lead to emotional distress or decreased utility – Discrepancies in wealth or income may lead to seemingly

irrational behavior – Inequity-based envy may lead to sabotage, unwillingness to help

• A real or perceived relationship with the victim or beneficiary may influence the decision to engage in helpful corrupt behavior– Empathy/sympathy may motivate economic decisions

• Favoritism toward similar others in corruption may be: – Subconscious (Bazerman and Banaji, 2004; Zitzewitz, 2006;

Price and Wolfers, 2007)– Based in emotions (Cuddy et al., 2007; Schweitzer and Gibson,

2008)

In “Robin Hood” we use large-sample econometrics to study wealth-based discriminatory fraud in a market setting

EMISSIONS TESTING MARKET• There are financial and personal motivations for

fraudulently passing cars• Firm: Customer loyalty, repeat business• Inspector: Relationships, envy/empathy, bribery

• Inspectors have broad discretion to illegally temporarily lower emissions through engine additives, removal (or addition) of parts, or adjustment of tailpipe probe

This paper is unique in observing wealth-based corruption for an entire market

Fraud in the vehicle emissions testing market is widespread

VEHICLE EMISSIONS FRAUD

• Vehicles must test below a discrete cutoff for hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxide (NOx)

• Vehicle emissions may be illegally temporarily lowered through engine additives, removal (or addition) of parts, adjustment of tailpipe probe, or clean-piping (use of bogey cars)

• Hubbard’s (1998, 2002) evidence of fraud from 1992 CA data is supported by extensive anecdotal evidence

• Testing facilities have strong incentives to keep the gross polluting cars of their customers on the road

Emission Score

Num

ber

of

Vehic

les

Pass Threshold

0 Gross Polluters

Pass Fail

The key to identifying fraud lies in the distribution of emissions test results

Discontinuity

Fraud

We observe in our data a distinct market-wide discontinuity at the pass threshold that suggests widespread fraud

Num

ber

of

vehic

les

in e

ach

bin

Distance from failure threshold

Pass Threshold

Pass Fail

A regulatory shift in April 2003 demonstrates that the discontinuity moves with policy changes

Perc

enta

ge o

f ca

rs in e

ach

bin

Hydrocarbon Pass Threshold

Before April 1st 2003After April 1st 2003

Pre 4/1/03

Threshold

Post 4/1/03 Threshold

This can be further demonstrated by comparing distributions around the new cutpoint just prior to the policy change

Perc

enta

ge o

f ca

rs in e

ach

bin

Post 4/1/03 Threshold

Before April 1st 2003After April 1st 2003

A one week window around the policy shift

05

10

15

20

Num

ber

of V

ehi

cle

s

-20 -10 0 10 20Emission Score

Tests from the first morning of the new threshold suggest preemptive repair does not cause the

discontinuity

All tests conducted before 11 AM on 4/1/03

Do “Robin Hood” inspectors discriminate based on wealth in helping vehicles pass?

EMPIRICAL APPROACH• Over 9 million individual tests from 2001-2004 in a major MSA with vehicle

characteristics, inspector, facility, date, and test results

• We identify perceived customer wealth by whether or not they own a luxury vehicle

• We identify inspector fixed-effects for each car segment (luxury/standard) while controlling for vehicle, geographic, facility, and time characteristics

• We observe whether luxury and standard fixed effects are significantly different for specific inspectors

• We compare the patterns of discrimination with counterfactual simulations

We sample the 249 (out of 18,763) largest volume inspectors

Variable Observations Mean SD Min Max

         Odometer 1,147,872 101,826 69,910 1 999,999

Age of Vehicle 1,147,872 8.84 4.27 2 21

Luxury Segment Dummy

1,147,872 0.07 0.26 0 1

Pass Rate 1,147,872 0.93 0.24 0 1

Standard Segment Pass Rate

1,067,473 0.93 0.25 0 1

Luxury Segment Pass Rate

80,399 0.97 0.15 0 1

Median Household Income

1,134,209 48,025 21,758 14,271 134,325

 

Number of Inspections per Inspector

249 4693 1283 3,502 11,917

Specification

EMPIRICAL MODEL1. Unconditional fixed-effects probit

Where X = Vehicle Controls + Inspector Luxury FE + Inspector Standard FE + Time Effects + Geographic Controls + Facility Controls

2. Run Wald test for each inspector on H0: Luxury FEi = Standard Fei

3. Compare all inspectors against Monte Carlo simulation counterfactual

Pr( 1| ) ( )Pass X X

Our data show considerably more “Robin Hoods” than counterfactual simulations

   Model 1 (Probit)

Average Placebo (33 Repetitions)

Wald TestInspector Fixed-Effects Favoring Standard Vehicles

Sig. at 10% Level 6 1.36

Sig. at 5% Level 13 0.76

Sig. at 1% Level 2 0.18

Sig. at 0.1% Level 16 0.00Inspector Fixed-Effects Favoring Luxury Vehicles

Sig. at 10% Level 13 15.39

Sig. at 5% Level 15 11.52

Sig. at 1% Level 5 1.09

Sig. at 0.1% Level 4 0.00

    74 30.30

Non-Discriminators Not Significant at 10% 175 228.52

Total Inspectors:   249 258.82

A distribution of Wald tests shows likely discriminators

-.1

-.0

50

.05

.1

Pe

rce

nta

ge

Fa

vo

ritis

m T

ow

ard

Sta

nd

ard

Ve

hic

les

0 50 100 150 200 250Each Point Reports an Inspector-Specific Estimate of Discriminatory Fraud

Discrimination significant at 1%

Discrimination not significant at 1%

Figure 1: Discrimination Measures for Each Inspector

How much of this discrimination is truly discretionary?

Inspector-specific discrimination persists as they switch facilities

-.1

-.05

0.0

5.1

Sta

ndar

d C

ar F

avor

itism

at S

econ

d F

acili

ty

-.2 -.1 0 .1 .2Standard Car Favoritism at First Facility

95% confidence interval

Fitted relationship between two jobs

Each circle represents two facilities for one inspector

Figure 2: Discrimination Measures for 86 Switchers at Two Facilities

Insufficient power to difference off facilities

“Robin Hood” suggests that customer wealth may influence employee corruptionKEY FINDINGS1. Many inspectors illegally help lower-wealth customers pass

emissions tests more than higher-wealth customers

2. This discriminatory fraud not explained by geography or facility, but correlated with per-capita income

3. We cannot fully separate intrinsic from extrinsic motivation or organizational policy from discretionary decisions

4. We also cannot control for selection in the labor market

Let’s go to the lab!

“In the Name of Equity” studies how individuals weigh financial and equity considerations in corrupt behavior

CORRUPTION IN THE NAME OF EQUITY

• Does inequity motivate corrupt behavior that hurts or helps others?

• How do individuals trade off between financial incentives and equity-based motivation?

This paper is the first to study how inequity motivates dishonest helping

Inequity (unfairness) causes psychological distress that individuals take action to relieve

TYPES OF INEQUITY• Negative inequity: Disadvantageous rewards compared to others

– Emotional distress from envy• Positive inequity: Advantageous rewards compared to others

– Emotional distress from guilt• Empathetic inequity: Disadvantageous rewards for a similar peer

compared to others– Emotional distress through empathy

Do individuals engage in helpful and hurtful corruption to relieve this distress?

We use a standard experimental design, but manipulate equity between participants

EXPERIMENTAL PROCEDURE1. Approximately 120 participants publicly flip a fair digital coin:

heads=$20, tails=$02. Each participant is randomly assigned to a role, “solver” or

“grader”, then paired off3. Solvers complete four rounds of anagrams4. Graders evaluate how many rounds the goal was reached by

their partner5. Graders then paid the solvers $2/round and received their

own payment

Solvers’ cash wealth is always visible in transparent lanyards

Initial lottery creates four equity-based conditions

Do financial and equity considerations motivate graders to dishonestly help or hurt others?

Wealthy ($20) Poor ($0)

Wealthy ($20)

Equity with partnerEmotion: HappinessPrediction: Honesty

Negative inequity with partner

Emotion: EnvyPrediction: Hurt but

not help

Poor ($0)

Positive inequity with partner

Emotion: GuiltPrediction: Help but

not hurt

Empathetic negative inequity

Emotion: EmpathyPrediction: Help but

not hurt

GraderS

olv

er

We run three studies with different financial incentives

FINANCIAL INCENTIVES1. Grader paid flat rate

– No financial incentive to misrepresent

2. Grader pay equal to solver pay– Financial incentive to dishonestly help

3. Grader residual claimant on money not earned by solver– Financial incentive to dishonestly hurt

Is equity worth real money?

Grader counts with no financial incentive

Poor Solver Wealthy Solver

Poor Solver Wealthy Solver

02468

1012141618

HurtHonestHelp

Poor Grader Wealthy Grader

Fisher’s Exact = Motivation Supported:

.000 Positive Inequity.003 Empathetic Inequity

.026Dyadic inequity >

Empathetic inequity

.018 Negative Inequity

Poor Solver

Wealthy Solver

Poor Solver

Wealthy Solver

0

5

10

15

20

25

HurtHonestHelp

Poor Grader Wealthy Grader

Grader counts with incentive to help

Fisher’s Exact = Motivation(s) Supported:

.000 Positive inequityNegative inequity

Empathetic inequity

.000 Negative inequity.021

Against financial interest

Grader counts with incentive to hurt

Poor Solver Wealthy Solver

Poor Solver Wealthy Solver

0

5

10

15

20

25

HurtHonestHelp

Poor Grader Wealthy Grader

Fisher’s Exact = Motivation(s) Supported:

.000Negative inequity.000 Positive inequity.001

Empathetic inequity

Negative inequity

Against financial interest

Both financial motivation and emotional distress from inequity drive corrupt behavior

MOTIVATORS OF CORRUPT BEHAVIOR• Financial motivation: Individuals on average respond to

incentives• Equity

– Positive inequity: Guilt increases dishonest helping– Negative inequity: Envy increases dishonest hurting and eliminates

dishonest helping– Empathetic inequity: Empathy with similar others increases

dishonest helping

• Personal ethics and fear of detection/punishment are other important, unmeasured factors

People will trade off absolute payoffs for relative payoffs

Collectively, these papers suggest that levels of equity in personal interaction motivate corrupt behavior

• “Robin Hood”: Workers discriminate in fraud based on perceived wealth– How much of the motivation is extrinsic or intrinsic?

• “Abundance”: Individual corruption is motivated by the proximity of wealth– Does this effect generalize to organizations and non-monetary wealth?

• “Equity”: Equity considerations influence decisions to dishonestly help or hurt others– Do managers or other workers dishonestly report information in

response to inequity?

The empirical evidence still leaves many questions unanswered

The next step empirically is to study organizations where extrinsic and intrinsic motivation can be separated

STUDIES IN DEVELOPMENT• How compensation systems influence peer effects and theft

among department store salespeople• How relationships between pharmacists and doctors influence

fraud• How interactions between doctors and patients influence

unnecessary drug prescriptions• Factory-based field experiments on how comparisons of

performance-based pay influence effort, sabotage, and turnover• Field experiments on how employee-customer interactions

influence cheating• Compensation changes on cruise ships• Interpersonal corruption in traffic stops

The overall goal of this research is to help firms and policy makers reduce corrupt behaviorORGANIZATIONAL APPLICATIONS• How do we better design compensation systems to account for both

economic and psychological mechanisms?– Sorting on ability and fit– Extrinsic and intrinsic motivation

• How do we better predict when discretionary behavior will be counter to firm interests– Decentralized decision-making yields information and adaptation

benefits– Moral hazard and intrinsically-motivated actions

• How do we design organizations to minimize perceptions of inequity or associated adverse reactions?– Better communication of performance or contribution– Better control of compensation information– Better design of organizational environment