Performance Incentives and the Dynamics of Voluntary Cooperation

Performance Incentives and the Dynamics of Voluntary

Cooperation

Simon Gächter (University of Nottingham)Esther Kessler (University College London)Manfred Königstein (University of Erfurt)

2

Motivation

• Many employment contracts are incomplete

• “Voluntary cooperation” of the agent is important:

– “Managers claim that workers have so many opportunities to take advantage of employers that it is not wise to depend on coercion and financial incentives alone as motivators” (Bewley, 1999)

– “work morale”, “creativity”, “loyalty”, “initiative”, “Good will”, etc. (Williamson 1985; Simon 1997; Bewley 1999)

– “Organizational citizenship behaviour” (Organ 1988)

• Explicit performance incentives quite popular

3

• A simple model: adapted from Fehr, Kirchsteiger & Riedl (QJE 1993)

• Participants are randomly assigned to the roles of “employer” and “worker”, respectively.

• Incomplete contract, because effort not specified• Worker payoffs: w – c(e) (costs increasing in effort)• Employer payoffs: ve – w (revenues increasing in effort)

1. Employer:

Wage offer [0,700]

2. Worker:

– Accept/reject offer– Choose costly effort [1, 2, …, 20]

3. Payoffs realised

Motivation (2)

4 There is reciprocity-based voluntary cooperation

Fehr, Kirchsteiger & Riedl (QJE 1993):

Motivation (3)

5

Motivation (4)

• Starting ideas for our experimental study:– Do explicit incentives crowd out voluntary cooperation?

– Can voluntary cooperation be re-established after experiencing incentive pay?

– Since we know from other experiments that framing of incentives and repeated game effects are also potentially relevant for behavior, these should be studied as well

6

• We investigate in a unified framework:– 1. Existence of voluntary cooperation

– 2. Effectiveness of monetary incentives

– 3. Crowding out effects

– 4. Framing effects (Bonus vs Fine)

– 5. Repeated game effects

Motivation (5)

7

• Principal-agent game:– Principal offers work contract– Agent can accept or reject– Agent chooses effort– Contract and effort determine payoffs

Experimental Game

8

Experimental Game (2)

Trust Fine BonusWage:Desired effort:Incentive:

Effort cost: c(e) = 7e – 7Payoff if contract rejected: 0 for both

Payoff Principal

Payoff Agent

w [-700, 700]

ê [1, 20]-

35e – w

w – c(e)

w [-700, 700]

ê [1, 20]

f {0,24,52,80}

w [-700, 700]ê [1, 20]

b{0,24,52,80}

35e–w if e≥ê 35e–w+f if e<ê

35e–w–b if e≥ê 35e–w if e<ê

w –c(e) if e≥ê w –c(e)–f if e<ê

w –c(e)+b if e≥ê w –c(e) if e<ê

9

Standard Theoretical Predictions

• Trust Contract: – e = 1 (minimal effort)

• Fine Contract, Bonus Contract: – e = ê if fine is sufficiently large: f c(ê)

(“incentive compatibility”)

– Otherwise, e = 1

– Equivalent for bonus (framing of incentives)

– Higher fine/bonus induces higher effort: f ,b {0, 24, 52, 80} enforceable effort levels: {1, 4, 8, 12}

– limited possibility for sanctions/rewards

11

A Comprehensive Experimental Design (1)

A. Baseline Treatments: No experience of Trust before Fine/Bonus

Treatment label

Phase 1(Period 1-

10)

Phase 2(Period 11-

20)

Phase 3(Period 21-

30)

No. Independent matching

groups

FT FINE TRUST - 6

BT BONUS TRUST - 6

TTT TRUST TRUST TRUST 6

B. Trust experience before Fine/Bonus

TFT TRUST FINE TRUST 6

TBT TRUST BONUS TRUST 6

Random matching in each period to minimize strategic effects

12

A Comprehensive Experimental Design (2)

C. Repeated game and Trust experience before Fine/Bonus

Treatment label

Phase 1(Period 1-

10)

Phase 2(Period 11-

20)

Phase 3(Period 21-

30)

No. of pairs

TTT Partner

TRUST TRUST TRUST 12

TFT Partner

TRUST FINE TRUST 18

TBT Partner

TRUST BONUS TRUST 17

13

Procedures

1. Experiments at the University of St. Gallen

2. Computerised, z-Tree (Fischbacher 1999)

3. 456 participants

4. CHF 45 (€30) for 1.5 – 2 hours

14

Results

15

Period 1-10

Period 11-20

Period 21-30

Voluntary cooperation exists and is stable over time

16

14

812

20A

ctua

l effo

rt

1 4 8 12Optimal effort (best reply)

Phase 1 of FT

14

812

20A

ctua

l effo

rt


Phase 1 of BT

Higher incentives induce higher effort

• 68% of all contracts are incentive compatible

• Most principals (about 90%) choose maximal fine, bonus

171

3

5

7

9

11

13

15

17

19

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

Period

Bonus_ST

Bonus_P

1

3

5

7

9

11

13

15

17

19

Fine_ST

Fine_P

1

3

5

7

9

11

13

15

17

19

Trust_ST

Trust_P

Phase 1 Phase 2 Phase 3

TRUST

Partner vs.

Stranger

FINE

Partner vs.

Stranger

BONUS

Partner vs.

Stranger

18

Results From These Graphs

1. Trust contracts can induce high effort (“trust-

and-reciprocity” is an important mechanism)

2. Monetary incentives are effective

3. Repeated interaction has strong effect

4. Framing (Bonus vs Fine)?

5. Crowding out of voluntary cooperation?

19

• But, take a look at the distribution of data again

How to proceed?

• Evaluate these effects within a unifying

statistical model

• Convincing structural model?

• Effort is bounded below and above

Tobit-Regression

20

Period 1-10

Period 11-20

Period 21-30

Distribution of effort conditional on wage

Two groups of data: • e=1 independent of fixed wage

• e>1 positively correlated with fixed wage

21

05

1015

20

Act

ual e

ffort

0 100 200 300 400Fixed wage

bandwidth = .8

Trust0

510

1520

Act

ual e

ffort

0 100 200 300 400Offered compensation (w-f)

bandwidth = .8

Fine

05

1015

20A

ctua

l effo

rt

0 100 200 300 400Fixed wage

bandwidth = .8

Trust

05

1015

20

Act

ual e

ffort

0 100 200 300 400Fixed wage

bandwidth = .8

Trust

05

1015

20

Act

ual e

ffort

0 100 200 300 400Offered compensation (w)

bandwidth = .8

Bonus

05

1015

20

Act

ual e

ffort

0 100 200 300 400Fixed wage

bandwidth = .8

Trust

TBT

TFT0

510

1520

Act

ual e

ffort

0 100 200 300 400Fixed wage

bandwidth = .8

Trust

05

1015

20

Act

ual e

ffort

0 100 200 300 400Fixed wage

bandwidth = .8

Trust

05

1015

20

Act

ual e

ffort

0 100 200 300 400Fixed wage

bandwidth = .8

Trust

05

1015

20

Act

ual e

ffort

0 100 200 300 400Fixed wage

bandwidth = .8

Trust

05

1015

20

Act

ual e

ffort

0 100 200 300 400Offered compensation (w-f)

bandwidth = .8

Fine

05

1015

20

Act

ual e

ffort

0 100 200 300 400Fixed wage

bandwidth = .8

Trust

TFT-Partner

TTT-Partner

05

1015

20

Act

ual e

ffort

0 100 200 300 400 500Fixed wage

bandwidth = .8

Trust

05

1015

20

Act

ual e

ffort

0 100 200 300 400Offered compensation (w)

bandwidth = .8

Bonus

05

1015

20

Act

ual e

ffort

0 100 200 300 400Fixed wage

bandwidth = .8

Trust

TBT-Partner

05

1015

20

Act

ual e

ffort

0 100 200 300 400Fixed wage

bandwidth = .8

Phase 2 of FT

05

1015

20

Act

ual e

ffort

0 100 200 300 400Fixed wage

bandwidth = .8

Phase 2 of BT

BTFT

Robustness of Data Pattern

22

How to proceed?

Hurdle Model

1. Estimate p = prob(e>1)

2. Estimate ê = f(x|e>1)

For Step 2 use Tobit with upper bound 20

• But, take another look at the distribution of data

23

14

812

20A

ctua

l effo

rt


Phase 1 of FT

14

812

20A

ctua

l effo

rt


Phase 1 of BT

Distribution of effort conditional on best reply effort

Three groups of data:• e=1 independent of best reply effort

• e=e*

• other choices

24

14

812

20

Act

ual e

ffort


Phase 2 of TFT

14

812

20

Act

ual e

ffort


Phase 2 of TBT

14

812

20

Act

ual e

ffort


Phase 2 of TFT-R

14

812

20

Act

ual e

ffort


Phase 2 of TBT-R

TFT (left), TBT (right) TFT-Partner (left), TBT-Partner (right)

Robustness of Data Pattern

25

How to proceed?

Double Hurdle Model

1. Estimate p = prob(e>1)

2. Estimate q = prob(e=e*|e>1)

3. Estimate ê = f(x|e>1 and e≠e*)

For Step 3 use Tobit with upper bound 20

26

Can trust contracts do better than incentive contracts?

• Applying this structure we evaluate effectiveness of trust

contracts, monetary incentives, repeated game, framing,

crowding out

• Important question: Can trust contracts perform better than

incentive contracts (cet. par.)?

• We need to compare trust contracts with equally expensive

incentive contracts; i.e., holding total compensation

constant

• Use estimates of p, q and ê to determine expected effort for

payoff-equivalent contracts

27

Expected effort in Phase 1 (Vergleich von IC Vertraegen mit Trust, e0 immer 12)

0

2

4

6

8

10

12

14

16

18

20

50 100 150 200 250 300 350 400 450 500 550 600 650 700

compensation

Exp

ecte

d e

ffo

rt

P*e(Trust)

P*e(Fine)

p*e(Bonus)

Yes! Trust contracts can do better

than incentive contracts

Data: FT, BT, only incentive compatible contracts

28


0

5

10

15

20

25

50 100 150 200 250 300 350 400 450 500 550 600 650 700

compensation

Exp

ecte

d e

ffo

rt

P*e(Trust)

P*e(Fine)

p*e(Bonus)

Data: TFT, TBT, only incentive compatible contracts

Robustness: 3-Phases-Data Stranger

29

Data: TFT-Partner, TBT-Partner, only incentive compatible contracts


0

2

4

6

8

10

12

14

16

18

20

50 100 150 200 250 300 350 400 450 500 550 600 650 700

compensation

Exp

ecte

d e

ffo

rt

P*e(Trust)

P*e(Fine)

p*e(Bonus)

Robustness: 3-Phases-Data Partner

30

Summary

• Trust contracts and monetary incentives are both effective in inducing effort

• We find substantial crowding out of voluntary cooperation due to incentives; if the contract is incentive compatible most subjects exactly choose rational effort

• Trust contracts may be more beneficial for a principal than an incentive compatible contract with bonus or fine

• Other results: Repeated game important, framing relatively unimportant

• Interestingly, non-incentive compatible contracts perform relatively well (further analyses needed)

Documents

Performance Incentives and the Dynamics of Voluntary Cooperation