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Corruption, Intimidation and Whistleblowing
Sylvain Chassang
joint w. Gerard Padró i Miquel
Motivation
Can we use reports from informed parties (monitor) to
target corrupt agents more efficiently?
Difficulty
In many ...
The Model
Players and Actions
1. Single agent, corrupt or not, c ∈ {0, 1}
2. Single monitor, observes c, reports m = 1 (co...
The Model – Payoffs
Reduced-form consequences of intervention
uA = c × πA + i × vA(c) − kA(r)
uM = c × πM + i × vM(c, m) −...
The Model – Information
Leaks
conditional on intervention i = 1, outcome z ∼ f(z|m, c)
(i, z) observed by the agent
revela...
Exogenous vs Endogenous Information
Proposition 1 (basic trade-off).
(i) Assume messages exogenously informative, i.e. m(c...
Example: UK Accounting Authority
Financial Reporting Review Panel
Investigates records of public companies
Uses tips from ...
Example: UK Accounting Authority
Example: UK Accounting Authority
Example: UK Accounting Authority
Example: UK Accounting Authority
Inference from Unverifiable Messages
monitor has type τM = vM
agent type τA = (πA, vA, kA, ΦA) (where ΦA belief over τM)
tr...
Two Properties
Proposition 2.
Given corruption decision c, message profile m(·) constant
along ray {(σ0, σ1)|σ1 = λσ0}
Prop...
Graphically
0 1
0
1
σ0
σ1
45o
c=1
c=0
Graphically
0 1
0
1
σ0
σ1
45o
Inference – Single Policy
Proposition 4 (no inference).
Single policy experiment puts no restrictions on corruption
For an...
Inference – Two Policies
Benchmark policy (σB
0 , σB
1 ); New policy (σN
0 , σN
1 )
σO
0 < σN
0 and
σO
1
σO
0
=
σN
1
σN
0
...
Take-Away
In many environments no herd-anonymity, need to provide
anonymity through garbled response
Can be implemented th...
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Corruption, Intimidation and Whistleblowing

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Presentation by Sylvain Chassang "Corruption, Intimidation and Whistleblowing" at the SITE Corruption Conference, 31 August 2015.

Find more at: https://www.hhs.se/site

Published in: Economy & Finance
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Corruption, Intimidation and Whistleblowing

  1. 1. Corruption, Intimidation and Whistleblowing Sylvain Chassang joint w. Gerard Padró i Miquel
  2. 2. Motivation Can we use reports from informed parties (monitor) to target corrupt agents more efficiently? Difficulty In many environments, few competent parties able to inform on corruption Anonymous phone lines provide no real anonymity Principal’s use of information becomes signal which lets the agent discipline monitor Take-away Effective policy must garble the information content of the monitor’s messages How much and how?
  3. 3. The Model Players 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 intervention i ∈ {0, 1} with probability σm ∈ [0, 1]; σ = (σ0, σ1) policy dimension of interest 5. Agent retaliates at level r ≥ 0 Timing and commitment 1. Principal commits to intervention policy (σ0, σ1) 2. Agent commits to a retaliation policy r as a function of observables (intervention + possible leaks)
  4. 4. The Model – Payoffs Reduced-form consequences of intervention uA = c × πA + i × vA(c) − kA(r) uM = c × πM + i × vM(c, m) − r uP = c × πP + i × vP Assumption 1. πA ≥ 0, vA ≤ 0 vM(c, m = c) ≥ vM(c, m = c) πP < 0, vP < 0 Allows for malicious monitors (benefit from intervention against honest agent)
  5. 5. The Model – Information Leaks conditional on intervention i = 1, outcome z ∼ f(z|m, c) (i, z) observed by the agent revelation principle does not hold Arbitrary incomplete information Agent doesn’t know monitor’s preferences vM, belief ΦA Principal has incomplete information over types (vM, πA, vA, kA, ΦA) bounded support for payoffs
  6. 6. Exogenous vs Endogenous Information Proposition 1 (basic trade-off). (i) Assume messages exogenously informative, i.e. m(c) = c If optimal policy = 0, then σ0 = 0 and σ1 > 0 (ii) Assume messages endogenous ∃λ > 0 st whenever σ1/σ0 ≥ λ the agent is corrupt and commits to retaliate the monitor sends message m = 0 Intuition intervention has informative content responsive intervention makes it easy for agent to incentivize the monitor should worry about silent corruption
  7. 7. Example: UK Accounting Authority Financial Reporting Review Panel Investigates records of public companies Uses tips from competent informants Likely whistleblower: auditing firm Policy Change 1999–2004, purely reactive 2005–2010, proactive
  8. 8. Example: UK Accounting Authority
  9. 9. Example: UK Accounting Authority
  10. 10. Example: UK Accounting Authority
  11. 11. Example: UK Accounting Authority
  12. 12. Inference from Unverifiable Messages monitor has type τM = vM agent type τA = (πA, vA, kA, ΦA) (where ΦA belief over τM) true distribution µT over types τ = (τM, τA) ∈ T unknown to principal Intervention and retaliation policies induce message profile m : τM → ∆ ({0, 1})
  13. 13. Two Properties Proposition 2. Given corruption decision c, message profile m(·) constant along ray {(σ0, σ1)|σ1 = λσ0} Proposition 3. (i) The set of profiles (σ0, σ1) such that a given agent is corrupt is star-shaped around 0 (ii) Fix λ = σ1 σ0 . The mass of corrupt agents τA cσ(τA)dµT (τA) is decreasing in σ0
  14. 14. Graphically 0 1 0 1 σ0 σ1 45o c=1 c=0
  15. 15. Graphically 0 1 0 1 σ0 σ1 45o
  16. 16. 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 of consistent corruption rates is [0, 1]. Take policy σ and distribution µT yielding report T m∗ (σ, τ)dµT (τ) We have that TA c∗(σ, τA)dµT (τA) µT s.t T m∗(σ, τ)dµT (τ) = T m∗(σ, τ)dµT (τ) = [0, 1]
  17. 17. Inference – Two Policies Benchmark policy (σB 0 , σB 1 ); New policy (σN 0 , σN 1 ) σO 0 < σN 0 and σO 1 σO 0 = σN 1 σN 0 Proposition 5 (bounds). T [1 − cN (τ)]dµT (τ) ≥ T mN (τ)dµT (τ) − T mO (τ)dµT (τ) #honest agents at new policy ≥ drop in reported corruption T cO (τ)dµT (τ) ≥ T mN (τ)dµT (τ) − T mO (τ)dµT (τ) #corrupt agents at old policy ≥ drop in reported corruption
  18. 18. Take-Away In many environments no herd-anonymity, need to provide anonymity through garbled response Can be implemented through noisy surveys – related, but quite distinct from randomized response surveys Rule of thumb first provide sufficient anonymity that people are willing to complain then scale up enforcement

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