Corruption, Intimidation and Whistleblowing

Sylvain Chassang

joint w. Gerard Padró i Miquel

Motivation

I Can we use reports from informed parties (monitor) totarget corrupt agents more efficiently?

DifficultyI In many environments, few competent parties able to

inform on corruptionAnonymous phone lines provide no real anonymity

I Principal’s use of information becomes signal which letsthe agent discipline monitor

Take-awayI Effective policy must garble the information content of the

monitor’s messagesI How much and how?

The ModelPlayers and Actions

1. Single agent, corrupt or not, c ∈ {0,1}2. Single monitor, observes c, reports m = 1 (corrupt), m = 0

(not corrupt)3. E.g. pair of cops, judge and clerk, boss and subordinate,

firm and accountant

4. Following message m, principal triggers interventioni ∈ {0,1} with probability σm ∈ [0,1]; σ = (σ0, σ1) policydimension of interest

5. Agent retaliates at level r ≥ 0

Timing and commitment1. Principal commits to intervention policy (σ0, σ1)

2. Agent commits to a retaliation policy r as a function ofobservables (intervention + possible leaks)

The Model – Payoffs

Reduced-form consequences of intervention

uA = c × πA + i × vA(c) − kA(r)

uM = c × πM + i × vM(c,m) − ruP = c × πP + i × vP

Assumption 1.

πA ≥ 0, vA ≤ 0vM(c,m = c) ≥ vM(c,m 6= c)

πP < 0, vP < 0

Allows for malicious monitors(benefit from intervention against honest agent)

The Model – Information

LeaksI conditional on intervention i = 1, outcome z ∼ f (z|m, c)

(i , z) observed by the agentI revelation principle does not hold

Arbitrary incomplete informationI Agent doesn’t know monitor’s preferences vM , belief ΦA

I Principal has incomplete information over types(vM , πA, vA, kA,ΦA)

I bounded support for payoffs

Exogenous vs Endogenous Information

Proposition 1 (basic trade-off).

(i) Assume messages exogenously informative, i.e. m(c) = cIf optimal policy 6= 0, then σ0 = 0 and σ1 > 0

(ii) Assume messages endogenous∃λ > 0 st whenever σ1/σ0 ≥ λ

I the agent is corrupt and commits to retaliateI the monitor sends message m = 0

IntuitionI intervention has informative contentI responsive intervention makes it easy for agent to

incentivize the monitorI should worry about silent corruption

Example: UK Accounting Authority

I Financial Reporting Review PanelI Investigates records of public companiesI Uses tips from competent informants

Likely whistleblower: auditing firm

Policy ChangeI 1999–2004, purely reactiveI 2005–2010, proactive

Example: UK Accounting Authority

Example: UK Accounting Authority

Example: UK Accounting Authority

Example: UK Accounting Authority

Inference from Unverifiable Messages

I monitor has type τM = vM

I agent type τA = (πA, vA, kA,ΦA) (where ΦA belief over τM )

I true distribution µT over types τ = (τM , τA) ∈ Tunknown to principal

I Intervention and retaliation policies induce message profile

m : τM → ∆ ({0,1})

Two Properties

Proposition 2.Given corruption decision c, message profile m(·) constantalong ray {(σ0, σ1)|σ1 = λσ0}

Proposition 3.

(i) The set of profiles (σ0, σ1) such that a given agent iscorrupt is star-shaped around 0

(ii) Fix λ = σ1σ0

. The mass of corrupt agents∫τA

cσ(τA)dµT (τA)is decreasing in σ0

Graphically

0 10

1

σ0

σ1

45o

c=1

c=0

Graphically

0 10

1

σ0

σ1

45o

Inference – Single Policy

Proposition 4 (no inference).Single policy experiment puts no restrictions on corruption

For any distribution of reports at a single policy σ, range ofconsistent corruption rates is [0,1].Take policy σ and distribution µT yielding report∫

Tm∗(σ, τ)dµT (τ)

We have that{∫TA

c∗(σ, τA)dµ̂T (τA)

∣∣∣∣∣µ̂T s.t

∫T m∗(σ, τ)dµ̂T (τ) =

∫T m∗(σ, τ)dµT (τ)

}= [0,1]

Inference – Two Policies

Benchmark policy (σB0 , σ

B1 ); New policy (σN

0 , σN1 )

σO0 < σN

0 andσO

1

σO0

=σN

1

σN0

Proposition 5 (bounds).∫T

[1− cN(τ)]dµT (τ) ≥∣∣∣∣∫

TmN(τ)dµT (τ)−

∫T

mO(τ)dµT (τ)

∣∣∣∣#honest agents at new policy ≥ drop in reported corruption

∫T

cO(τ)dµT (τ) ≥∣∣∣∣∫

TmN(τ)dµT (τ)−

∫T

mO(τ)dµT (τ)

∣∣∣∣#corrupt agents at old policy ≥ drop in reported corruption

Take-Away

I In many environments no herd-anonymity, need to provideanonymity through garbled responseCan be implemented through noisy surveys – related, butquite distinct from randomized response surveys

I Rule of thumbfirst provide sufficient anonymity that people are willing tocomplainthen scale up enforcement