1. Beyond Pay for Performance
Financial University, Moscow
March 23, 2017
Bengt Holmström

2. Agenda The Principal-Agent Problem Single task (“effort”)
Towards a more realistic model
Linear incentives
Multitasking
Low-powered incentives
Incentives in firms
Orchestrating alternative incentives
V. Firm boundaries
The dual role of ownership of assets

3. I. Principal-Agent Problem
Employer-Employee, Client-Lawyer, Board-CEO, etc.
Two difficulties
Interests/preferences not aligned
Performance imperfectly measured
Pay-for-performance to align interests
Trade-off: incentives versus risk

4. I. P-A Problem – An “effort” model
Agent’s unobserved choice: “effort” e
Measured performance:
x = e + ,  = measurement error
Contract: Payment s(x) from P to A
Preferences:
Principal: B(e) – s(x)
Agent: s(x) – C(e)  rVar(s)
Timing: Contract, A chooses e, Outcome x realized, Payments

5. I. P-A Problem – First best and second best
First best cases: Maximize B(e)  C(e)
agent’s choice e verifiable; s(x) = constant (routine job)
agent risk neutral; s(x) = x + constant (taxi cabs)
Second best: Maximize B(e)  C(e)  rVar(s)
Note: principal has to compensate agent for cost of effort and risk
Complicated problem in general

6. I. P-A problem – The informativeness principle
When is additional information y valuable?
Whenever y contains additional information about the agent’s action e beyond x
Example where y is not of value: insurance deductibles
Information could be about effort e or external factor 
Vesting (lagged information)
Relative performance evaluation (CEO pay)
Controllability principle (filtering out risk)

7. I. P-A Problem – Lessons from informativeness
Pros:
Informativeness reveals exactly how the model “thinks” (as if this were a statistical inference problem)
Explains odd features of effort-model
Cons:
Theory says little about shape, because optimal contracts oversensitive to information

8. II. Toward a more realistic model – When are incentives linear?
A disturbing example: x = e + ,   Normal distribution.
punishing very rare failures very severely approximates first-best (tails of Normal distribution very informative)
In dynamic version
linear incentive optimal, because it provides constant incentive pressure and avoids gaming (robust)
Making the model more complex simplifies the solution.
Justifies linear incentives in the static model

9. II. Toward a more realistic model – The linear model behaves sensibly
Stronger incentive (higher commission) when:
Performance measure more precise (less risk)
Agent less risk averse
Value of agent’s effort higher
Agent’s responsiveness to incentive higher
The linear model a great “conversation partner”
But why are incentives in firms often low-powered?

10. III. Multitasking – Beyond pay-for-performance
Big change in focus when agent has many tasks
about allocation of effort rather than level of effort
Incentives for multitasking very different when
easy-to-measure and hard-to-measure tasks compete for agent’s attention
performance measure is easy to manipulate
performance measure and value are misaligned

11. III. Multitasking – “You get what you pay for”
Wells Fargo – fraudulent shell accounts
BP and oil Gulf spill – pressure to perform compromised safety
Teachers teaching to test (hard skills, not soft skills)
Atlanta teaching scandal (teachers corrected answers)

12. III. Multitasking - Lessons
Two ways to provide incentives for a task
Pay more for that task
Pay less for competing (substitute) tasks
Low powered incentives
No incentive may be best incentive even for a task that can be perfectly measured
Must consider full portfolio of tasks when designing incentives

13. IV. Incentives in firms – Many alternative instruments
Job design
define scope of jobs (Google 20% freedom)
separate easy-to-measure tasks from hard-to-measure tasks
Bureaucratic rules and constraints
Have people work in the office rather than at home
Don’t allow work for other companies
Authority and promotions (career incentives)
Control information flows (accounting system) f

14. IV. Incentives in firms – Orchestrated design
bonuses
job design
bureaucratic constraints
authority
access to information
hiring and promotions
monitoring and evaluation
Instruments need to be coordinated (Lincoln Electric)
Desire to be appreciated is a strong incentive in firms
Incentive
System

15. V. Firm boundaries – Employment versus Contracting
Employee
No ownership of assets
Low-powered financial incentives
Work is assigned and constrained by rules
Contractor
Ownership of assets
High-powered incentives (market price)
Free to organize work and choose clients
Context determines choice: Stable, complex jobs with low cost of input monitoring favor employment.

16. V. Firm Boundaries – The dual role of asset ownership
External role (power in the market)
Assets strategic chips in market
Internal role (power within firm)
Assets empower firms to set the terms of employment, structure incentives, pursue desired strategies – “set the rules of the game”
The right to exit disciplines the firm

17. Summary
A fixed wage (no pay-for-performance) is sometimes the best financial incentive because of multitasking concerns
The comparative advantage of firms lies in their ability to influence workers through a multitude of cheaper non-monetary incentives.
Incentives need to be design with the totality of tasks in mind.

18. THANK YOU!