Behavioral insights into unethical behavior and corruption...Many people cheat, but not to the full...

Behavioral insights into unethical behavior and corruption

Nils Köbis

CREED University of Amsterdam

14,12.2017

Structure

1. Distinguishing between different types of corruption

2. Behavioral insights into unethical behavior & corruption

3. Pathways towards reducing corruption

2

What do you think of when you hear the word

Corruption?

4

Loose use of the term in public parlour and media

Can refer to anything that is rotten, a state going fromgood to bad

Has a long history

Widely used definition:

“abuse of entrusted power for private gains”

16

One word, many meanings

https://prezi.com/view/xd4v2ZN1VA1mJSdEIfu7/

Atlas of Corruption Types

Köbis & Huss (2017) Atlas of Corruption types

17

In every society people are entrusted with shared resources

Institutions/individuals govern these common resources (Ostrom, 2000)

→ Set up ensure fair resource allocation

People are entrusted with power over resources

→ Corruption = act of impartiality violation (Kurer,

2005; Rothstein, 2011)

→ Power holder faces social dilemma:

→ Short term self-interest vs. long-term collective interest (Köbis et al. 2016)

(Köbis et al. 2016)

Commmon

Resource

Corrruption = impartiality violation

18

Insight

✓ Corruption is an umbrella term encompassing multiple corrupt behaviors

✓ Impartiality violation of entrusted resources is keyelement of corruption

✓ Gaining useful behavioral insights into corruptionrequires specfication of corruption type at hand

19

Macro

Meso

Micro

20

Zoom level

Experiments on unethical behavior and

corruption

Behavioral Ethics:

“The study of individual behavior that is subject to or judged according to generally accepted moral norms of behavior”

Trevino et al. (2006)

“A field that is primarily concerned with explaining individual behavior that occurs in the context of larger social prescriptions”

Tenbrunsel & Smith-Crowe (2008)

→ Allows to examine causal links of individual and situational factors on unethical and corrupt behavior

42

1. Group:

Hand in

A) math tests

B) report sheets

2. Group:

Hand in only

B) report sheets

➔ Allows dishonest overstating of performance

Behavioral Measures I – Matrix paradigm

Got it

43 Eisenberger (1986) Ariely (2012)

1. People roll a die in privacy

2. Report outcome

3. Get paid according to reported number

Behavioral measure II – Die rolling paradigm

1 = $1 2 = $2 3 = $3 4 = $4 5 = $5 6 = $6

Fischbacher & Fölmi-Heusi (2012) Shalvi, Dana, Handgraaf & De Dreu (2011)44

Justified unethicality

1. Single die roll

2. Roll three times but report the first

47

M = $4.45

Shalvi, Dana, Handgraaf & De Dreu (2011) 48

0

10

20

30

40

50

60

1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6

Roll + 1 (theoretical) Multiple rolls (n = 62) Single roll (n = 67)

M = $4.45

49 Shalvi, Dana, Handgraaf & De Dreu (2011)

M = $4.45


M = $4.45 M = $3.97


ShufflingObserving Choosing

“I rolled a 4!”

InventingObservingMentally simulating Choosing

“I rolled a 4!”

52

Shalvi, Dana, Handgraaf & De Dreu (2011)

✓ Shuffling facts feels legitimate,

✓ inventing facts, does not

Insight

53

Dual process meets behavioral ethics

System 1 → fast, inflexible, imprecise → intuitiveSystem 2 → slower, more flexible, calculated→ deliberate(Kahneman, 2011)

In tempting settings with complete anonymity:

Intuitive reaction → (dishonestly) serve self-interest

Deliberative reaction → calibrate to what feels justified

54

Do more people lie?

Intuition > ControlIntuition < Control

55

Köbis, Verschuere, Bereby-Meyer, Rand, & Shalvi (2018)

Yes, suggesting intuitive dishonesty

→ odds of cheating 35.4% higher in the intuition vs. control condition

Log(OR) = 0.30, 95%CI[0.10; 0.51], Z = 2.91, p = 0.003)

Do more people lie?

Intuition > ControlIntuition < Control

56


Do people lie more?

Control < Intuition Control > Intuition 57


Yes, suggesting Intuitive dishonesty

Hedges g = 0.24; d = 0.24; 95%CI[0.14; 0.34], Z = 4.85, p <.01

Do people lie more?

Control < Intuition Control > Intuition 58


Yes, suggesting Intuitive dishonestyHedges g = 0.24; d = 0.24; 95%CI[0.14; 0.34], Z = 4.85, p <.01

Do people lie more?

Control < Intuition Control > Intuition

→ choosing two people at random from control & intuition group,→ probability person from intuition group to cheat more than one from the control group is 57%

59

Do more people lie?

Another participant: n.s.Experimenter: intuitive dishonesty

60

Do people lie more? Others as victims

Insight

✓Meta analysis of 66 experiments suggest “intuitive dishonesty“

✓ Ethical temptations in private settings with impunity✓When people are restrained of capacities to

deliberate more people cheat and people cheat more…

✓ …as long as concrete others don’t get hurt

61

Lying for charity Lewis et al., (2012)

Legitimizes serving cheating Wiltermuth (2011)

Justifies lying all the way Conrads et al. (2013)

More beneficiaries, larger lies Gino, Ayal, & Ariely (2013)

Deception breeds trust Levine & Schweitzer (2014)

Oxytocin evokes group-serving lies Shalvi & De Dreu (2014)

Others as beneficiaries

62

Gächter & Schulz (2016)

Others as information source

External validity

Lying in die rolling paradigm is associated with:

Free-riding on buses Dai, Galeotti & Villeval

Not returning undeserved pay Stoop & Potters

Misbehavior in school Cohn & Maréchal

Nurses being late to work Hanna & Wang

Milk-sellers diluting milk with water Kröll

65

✓ Many people cheat, but not to the full extent✓ People want to maintain a positive self-view →

preference for truth telling (Abeler et al 2018)

✓ Rationalizations/ justifications are important✓ People do what they can justify to themselves and

others

✓ Social element shapes decision✓ Others play a key role (victim, beneficiary, source of

information) ✓ Most studies look at people acting in isolation

Insights

66

Dyads (A & B): privately roll a die and report the outcome If double → both A & B get the value of the double in €If no double → both A & B get nothing

€5

Collaborative cheating

A B

B: I rolled

€5€5

Weisel & Shalvi (2015)67

Expected distribution (if honest)

16.67% doubles68

Weisel & Shalvi (2015)

Observed distribution (dishonest)

16.67% doubles 82% doubles69


Round

A sets the stage; B gets the job done

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

A’s report B’s report

Rep

ort

Period70



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20


Period

Rep

ort

71



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20


Rep

ort

Period72


Period1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20


SignalingR

epo

rt

73

Insight

✓ Collaboration is a moral currency

✓ By collaborating, one can offset the costs associated with harming others by lying

✓ Research on the collaboration is needed to uncover the roots of corruption

74

Corrupt dyadPower asymmetry

Victim

Methodology

76

Behavioral measure - Bribery game I

Repeated play

User:

transfer money to

Public Official

Public Official:

accept or reject

distribution of points

Abbink, Irlenbusch Renner, (2001)77

Three conditions:

a) Control condition

b) Negative externalities

c) Sudden death

Corruption = offering/ receiving bribes + acting accordingly

Bribery game I

78

Abbink, Irlenbusch Renner, (2001)

Findings:

- reciprocity can be established even if it cannot be enforced

- no effects of externalities

- punishment has a strong and significant effect, even though the probability is low

Bribery game I

79

Abbink, Irlenbusch Renner (2001)

Banuri & Eckel (2009)

Bribery game II

80

Two treatments

citizen punishment & control

US and in Pakistan

Results punishment:

US: firms < officials,

punishment reduces bribe offered and accepted

Pakistan: firms > officials,

punishment no effect on bribe offers, but effect on bribes accepted

Bribery Game III

Institution PlayerPower holder

= awards public tender (120 credits)

Player 1 Endowment = 400 credits

Player 2Endowment = 400 credits

Several rounds competing bids

(0-50 credits)

82

Institution PlayerPower holder

= awards public tender (120 credits)

Player 1 Endowment = 400 credits

Player 2Endowment = 400 credits

Several rounds competing bids

(0-50 credits)

Bribery Game III

83

Slippery Slope vs. Steep cliff

Mild bribery?

YesNo

Severe bribery?0% advantage

Costs: 0$

Yes 25% advantageCosts: 10.000$

100% advantageOverall costs: 40.000$

No

Severe bribery?

100% advantageCosts: 40.000$

0% advantageCosts: 0$

Yes No

Köbis, Van Prooijen, Righetti, & Van Lange (2017)84

Study name Statistics for each study Odds ratio and 95% CI

Odds Lower Upper

ratio limit limit Z-Value p-Value

Study 1 0,233 0,100 0,539 -3,398 0,001

Study 3 0,241 0,103 0,566 -3,268 0,001

Study 2 0,396 0,214 0,734 -2,945 0,003

Study 4 0,644 0,386 1,072 -1,693 0,091

0,377 0,230 0,617 -3,875 0,000

0,01 0,1 1 10 100

Steep Cliff Slippery Slope

Meta Analysis

Köbis, Van Prooijen, Righetti, & Van Lange (2017)

Total:OR = 2.65N =Mage = 35.9 (11.9) ♀= 52.3%

Study name Statistics for each study Odds ratio and 95% CI

Odds Lower Upper

ratio limit limit Z-Value p-Value

Study 1 0,233 0,100 0,539 -3,398 0,001

Study 3 0,241 0,103 0,566 -3,268 0,001

Study 2 0,396 0,214 0,734 -2,945 0,003

Study 4 0,644 0,386 1,072 -1,693 0,091

0,377 0,230 0,617 -3,875 0,000

0,01 0,1 1 10 100

Steep Cliff Slippery Slope

Meta Analysis

Steep cliff → Slippery slope

Slippery Slope vs. Steep cliff

85

Insight

✓ Bribery games provide insights into corrupt behavior

✓ Large collection of bribery games exists (Wantchekon & Serra, 2012)

✓ A lot of heterogeneity (Köbis et al forthcoming)

✓ Mixed effects→ some indication for effectiveness of punishment

However:

✓ Vast majority of corruption games do not model corrupt behavior as a social dilemma

86

Systemic corruption

87

1. Culture Repeated corruption becomes normal =

acceptable

More corruption in cultures withhigher…

…masculinity, power-distance and uncertainty avoidance (Husted, 1999)

…collectivism (Mazar and Aggarwal, 2011)

88

Explanations

Köbis, Soraperra, & Troost (2018)


acceptable




Diplomats from “corrupt” countriesabuse immunity more (Fisman & Miguel,

2008)

Frequency dependent equilibrium

(Bardhan, 1997; Fisman & Golden,

2017)

Corruption corrupts (Andvig &

Moene, 1990)

89

Explanations



acceptable




Diplomats from “corrupt” countriesabuse immunity more (Fisman & Miguel,

2008)

90

Explanations

2. Coordination Frequency dependent equilibrium

(Bardhan, 1997; Fisman & Golden, 2017)

Corruption corrupts (Andvig & Moene, 1990)

Self-reinforcing Corruption (Stephenson,

2018)


91

Explanations

2. Coordination Frequency dependent equilibrium

(Bardhan, 1997; Fisman & Golden, 2017)

Corruption corrupts (Andvig & Moene, 1990)

Self-reinforcing Corruption (Stephenson,

2018)

Payoffs for being „not corrupt“

Payoffs for being„corrupt“


Social norms of corruption

Social norms: Shared understandings about actions that are obligatory, permitted, or forbidden within a society (Ostrom, 2000: 143-144)

Two elements:

Injunctive (personal/ social) → acceptability

Descriptive → frequency

Cialdini et al. (1990), Bicchieri, (2016)

92


Insight

✓ Corruption tends to reinforce itself→ social trap

✓ Decision to engage in corruption is often a social dilemma

✓ Two main explanations for the persistence: culture & coordination

✓ Social norms frameworks allow deriving testablehypotheses about the interplay of both elements(descriptive and injunctive) norms

93

Lab Experiments

• Barr & Serra (2010): bribery game @Oxford

• Abbink, Freidin, Gangadharan, & Moro (2018): emphasis on descriptive norms

• Köbis et al. (2015); Schramm et al. (2018): descriptive norms → corrupt behavior

“I bribe because others are doing it too – even though I think it’s wrong”

94

00%

25%

50%

75%

100%

low control high

Fair

Bribe

N = 259, Mage = 35.6 (11.5), ♀= 42.1%

Social nudges to reduce corruption

B = -.93, Wald = 16.2, p < .001, Exp(B)=0.39

“Almost nobody bribes”

“Almost everybody

bribes”

Köbis et al. (2015)95

00%

25%

50%

75%

100%

low control high

Fair

Bribe

N = 259, Mage = 35.6 (11.5), ♀= 42.1% B = -.93, Wald = 16.2, p < .001, Exp(B)=0.39

“Almost nobody bribes”

“Almost everybody

bribes”


Social nudges to reduce corruption

00%

25%

50%

75%

100%

low control high

Fair

Bribe



Social nudges to reduce corruption in the lab

00%

25%

50%

75%

100%

low control high

Fair

Bribe




• Can a descriptive norms message on posters succesfullychange participants’

a. beliefs about corrupt behavior of others (perceived descriptivenorms)?

b. bribery levels in a corruption game?

Köbis, Soraperra, & Troost (2018) 100

Social nudges to reduce corruption in the field

Set-up

• Baseline: N = 187 • Treatment: N = 124• Mobile lab in a mall in Manguzi,

South Africa• Duration: 27.6 minutes• Payoffs:

show-up fee: R25(± €1,50) + R25-45,- (± €1,50-2,73)

• Payment via Instant Money transfer to phone number byRA3

• Pre-registration on OSF

Week 1:RA1 conducts lab study = BASELINE 1

Week 2:RA2 distributes poster

RA1 conducts lab study = TREATMENT

Week 3:RA2 takes down posters = COOL DOWN

Week 7:RA1 conducts lab study = BASELINE 2

101Köbis, Soraperra, & Troost (2018)

https://osf.io/akp24/settings/#createVolsAnchor

MeasuresIn the mobile lab

1. Incentivized norms assessment ▪ 3 items (1 bribery transaction)

▪ Descriptive & injunctive norms

2. Bribery Game▪ Social dilemma framework

▪ Matching groups of 10

▪ Strategy method

3. Demographics▪ Age, gender, education, phone number, date of

salary payment, recognition of the poster

Outside of the lab

1. Missing stock ▪ In pharmacy drugs are frequently sold under the

counter

▪ Although decreased after recent introduction of findes, still exists

▪ Stock inventory every evening



Social Dilemma Bribery Game

Social cost for each succesful briberytransaction = -2 R



Pre-registered Hypotheses

• H1: Perceived descriptive social norms about bribery (Item 1) are lower in the period of the Poster treatment compared to the Baseline treatment.

• H2: Perceived injunctive social norms about bribery (Item 2) do not statistically differ between both treatments.

• H3: The levels of bribe offers in the bribery game are lower in the period of the Poster treatment compared to the Baseline treatment.


Results


Results

Probit regression reveals significant shift in perceived descriptive norms duringposter treatment (B = -0.341)***






• H4: Missing stock decreases in the period of the poster treatment compared to the baseline treatment.


Results


Results

Probit regression reveals neither a significant shift in perceived social injunctive norms duringposter treatment (B = 0.075)…


Results

… nor in personal injunctive norms (B = -0.073)


Results


Results Bribery Game



***


Consistency


Insight

✓ Descriptive norms contribute to persistence of corrupt systems

✓ Perceptions of descriptive norms rather than injunctive normsdrive corrupt behavior→ Coordination > culture

✓ Experimental evidence indicates: descriptive norms are malleable

✓ in the lab and in the field

✓ Changing descriptive norms can reduce corrupt behavior (at leastshort term) → window for opportunity

137

Corruption in the education sector:

Immense financial resources are invested in education: National governments allocate 20%-30% of their national budgets to education

(UNDP, 2012)

Global Partnership for Education (GPE) spends $110 billion 2018-2020

Corruption in the education sector marks a severe obstacle (Heyneman et

al., 2008; UNDP, 2011)

Most pervasive: bribery transactions between teaching staff and students (up to 80% surveyed students report paying bribes)

Educational degrees become high-price low-quality commodities

155

How to change systems of corruption?

Side-payments = mutually beneficial & socially costly

156


N = 480; 14,400 observations

Location: Bogotá, Colombia

Subject pool familiar withcoruption

Matching groups : 2 teachers & 8 students

Students wtih different effort costs β to reflectdifferent incentives forbribery

1. Create a system of corruption→ default

157


A) Fixed salary increase

One of the most popular interventions (Fisman &

Golden, 2017)

1. more effort (gift exchange)

2. less need for corruption

3. higher opportunity cost (van Reickegem and Weder, 2001)

→ Systematic literature review: mixed results

→ Especially, in countries with ineffective policing institutions fixed salary increase is ineffective

Intervention

158

2. Change a system of corruption→ default

six-fold salary increase of salary for teachers

159


2. Change a system of corruption→ default

six-fold salary increase of salary for teachers

160


B) Piece rate scheme

Pay teachers according to the number of students they attract

Competition for students can help to lower extraction of bribes

Empowering parents and students to choose their school

Market for integrity by restructuring incentives

Intervention

161

2. Change a system of corruption

Introduction of piece rateIntroduction of piece rate, payment per student in class

162


Social welfare

Piece rate increasesoverall welfare

Reduces the erosion of

educational degrees due

to bribery

Proof of concept

163

Insight

✓ Corruption in endemic contexts best modeled as social dilemma

✓ Creating a corrupt system before testing interventions✓ Studying corruption in the education sector ✓ Fixed salary increase ineffective✓ Piece rate scheme reduces corrutption→ proof of

context

164

167

Anti-corruption through a social norms lens

168


169


Köbis, Jackson & Iragorri-Carter (2018)


170

Not a single corruption norm

“I am a policeofficer and

thus deservethis gift.”

“All mycolleagues are

faking theirtravel

expenses.”

“My superior is suggestingI invite theclient for a

fancy dinner.”

“I need to provide myfamily withthe best.”

171


“I am a policeofficer and

thus deservethis gift.”

“All mycolleaguesare faking

their travelexpenses.”

“My superior is suggesting I

invite theclient for a

fancy dinner.”

“I need to provide myfamily withthe best.”

172


https://www.youtube.com/watch?v=dDAbdMv14Is

https://www.youtube.com/watch?v=y6GxIuljT3w

https://www.youtube.com/watch?v=4jmScyq3GSY

https://www.youtube.com/watch?v=BgRoiTWkBHU

https://www.youtube.com/watch?v=GHG3ySO40Gk

Diagnosing social norms:

Self-report

Survey

Interview

Vignette

Behavioral

Experiment

Social network analysis

173



Changing social norms:

Media / cultural campaigns→ Radio, TV, social media, children’s books

Transformative dialogue techniques→ collective deliberation

Small-torches approach / lighthouse approach

Trendsetters → social network approach

(Expatriate) outsiders → introduction of new norms

Reward integrity→ islands of integrity/ integrity idol

Credible information → reduce pluralitic ignorance

➔ Field research needed

174



Overall Summary

✓ Identification of corruption type marks important first step ✓ Rationalizations and social forces shape dishonesty✓ Bribery games can provide behavioral insights into corruption✓ Systemic corruption best modeled as social dilemma✓ Creating a corrupt system before testing interventions✓ Emerging trend: social norms to change collective corrupt

behavioral patterns→ descriptive norms✓ Behavioral field research for eviendece based anti-coruption

175

Behavioral insights into unethical behavior and corruption...Many people cheat, but not to the full...

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