corruption, intimidation and whistleblowing
TRANSCRIPT
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