performance incentives and the dynamics of voluntary cooperation simon gächter (university of...
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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
1 4 8 12Optimal effort (best reply)
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
1 4 8 12Optimal effort (best reply)
Phase 1 of FT
14
812
20A
ctua
l effo
rt
1 4 8 12Optimal effort (best reply)
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
1 4 8 12Optimal effort (best reply)
Phase 2 of TFT
14
812
20
Act
ual e
ffort
1 4 8 12Optimal effort (best reply)
Phase 2 of TBT
14
812
20
Act
ual e
ffort
1 4 8 12Optimal effort (best reply)
Phase 2 of TFT-R
14
812
20
Act
ual e
ffort
1 4 8 12Optimal effort (best reply)
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
Expected effort in Phase 2 (Vergleich von IC Vertraegen mit Trust, e0 immer 12)
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
Expected effort in Phase 2 (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)
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)
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