session 2 2012 ima presentation compensation system
TRANSCRIPT
Management Compensation System:
-- Adding Tournament to Tournament: The Interactive Effect of Individual and Team IncentivesYu TianKenneth G. Dixon School of AccountingUniversity of Central Florida
IMA Carolinas Winter Conference
February 17, 2012
Background: Incentive Systems Incentive systems design: an important aspect
of a management control system.
Organization can incentivize effort based on: Individual performance Team performance A combination of both.
2
Background: Incentive Systems Individual incentive systems
Lack of cooperation Team incentive systems
Need for cooperation Increasing use of teams Encourage cooperation Problem: free-riding
Tournament (RPE) used to mitigate free-riding Increasing use in corporate world Problem: uncooperative & collusion
Background: Incentive Systems
Individual compensation (within-team)
Tournament (No)
Tournament
(Yes)
Team
Com
pens
atio
n (b
etw
een -
team
)
Tournament
(No)
Low Effort
Uncooperative& Collusion
Tournament
(Yes)
Free riding
???
Research Question
Can both free-riding and collusion problems be simultaneously mitigated, when a combination of individual and team incentive systems are used?
Will we get the best or the worst of both worlds?
Figure 1
Individual compensation (within-team)
Tournament (No)
Tournament
(Yes)
Team
Com
pens
atio
n (b
etw
een -
team
)
Tournament
(No)
NONE
WITHIN
Tournament
(Yes)
BETWEEN
BOTH
The Model - Extension
Extend Nitzan’s (1991) nested contest model Add a group (team) reward component
More generalizable in practice Encourage cooperation
Introduce output functions .ijijindividual cxxfOutput
i
n
j ij
n
j ijteam cxcxxfOutput ii 11
ijx is effort level of member j in team i.
The Model – Group Contest
(1)
otherwise 2/1
0,max if ......
2121
xxxx
x
xpi
iji
0ijx
.1
in
j iji xx
: probability that team i wins the contest and receives both group and individual rewards
: the effort contribution of member j in team i
: total effort in team i
ip
Step 1: Inter-team contest success function:
The Model – Individual Share
(2)
otherwise 1
0......max if 1
1
i
ijii
ij
ij
n
xnx
x
q
is a share of individual reward that member j in team i receives.
α = 1: equal share within a team
α = 0: distribution of individual reward based on “merit”
ijq
Step 2: Distribution of individual reward ( ) within a teamIV
The Nitzan’s Model - Payoff
Total reward ( )
= Group reward ( ) + Individual reward ( )
ijIijiGi
iijij xVqpVn
px 1
......
ii n
j ij
n
j Iijii
Giiii xVqpn
Vpnx11
)1
(......
V
GV IV
The payoff function of individual j in group i is:
Expected group reward
Expected individual reward
Cost of individual
effort
Expected team payoff
Expected individual
payoff
Model Prediction (Baseline)(maximizing joint payoff: joint NE)
Model Prediction (Baseline)(maximizing individual payoff: symmetric NE)
Social Identity Theory (SIT) A theory of the role of self-conception in group
members, group process, and intergroup relations.
Positive distinctiveness (PD) from other teams prevails in intergroup relations.
Promote PD to enhance self-esteem.
Optimal distinctiveness
SIT Prediction (Hypotheses)
Within: NO Within: YES
Effort Between: NO
Between: YES
Between: between team tournament Within: within team tournament
Design
Participants: 144 senior and graduate business students
Multi-period 2 X 2 X 2 design Between-subject factors: team & individual incentive
systems Within-subject factor: 2 different incentive systems
P(4,2) = 12 ordered combinations (e.g. NONE&WITHIN, NONE&BETWEEN, NONE&BOTH…)
Each individual participates in 2 conditions 12 team observations (6 individual decisions for 10
periods in each team observation) Payoff: $5 participation fee + decision income
ProcedureRandomly assign participants into a team.
Assign to one combination of incentive systems
Communicate & make decisions (10 periods)
Participants are mixed up and
randomly assigned to new teams.
Decide a team name.
Part I
Notify individual payoff after each period
Forced manipulation check (quiz)
Instructions
Part II
Calculate payoff and pay participants.
Z-tree: Welcome
Z-tree: Chat
Z-tree: Decision
Z-tree: Feedback
Average Effort LevelsTable 1 Average Effort
Equilibrium Equilibrium Standard effort effort Actual deviation Actual
Condition (maximize (maximize average within average ind. payoff) joint payoff) effort condition profit NONE (N = 36) * (equal share between and within teams) 0 0 9.47 7.75 110.53 WITHIN (N = 36)
(equal share between teams,
tournament within team) 20 0 25.07 7.84 94.93 BETWEEN (N = 36)
(tournament between teams,
equal share within team) 10 30 40.28 10.22 79.72 BOTH (N = 36) (tournament between and within teams) 30 30 47.15 4.00 72.35
Effort Levels: Comparisons
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Table 2 Main Effect and Pairwise Comparisons (Effort)
Panel A: Main effect Mean Conditions N Difference t Value p-value Team Incentive (between-team tournament)
with vs. without 72 26.45 9.36 <0.0001 Individual Incentive (within-team tournament)
with vs. without 72 11.24 2.48 0.017 Panel B: Pairwise comparisons Mean Conditions N Difference t Value p-value BOTH vs. NONE 24 37.68 14.96 <0.0001 BOTH vs. WITHIN 24 22.08 8.69 <0.0001 BOTH vs. BETWEEN 24 6.87 2.17 0.041 WITHIN vs. NONE 24 15.61 4.90 <0.0001 BETWEEN vs. WITHIN 24 15.21 4.09 0.0005 BETWEEN vs. NONE 24 30.82 8.32 <0.0001 The average effort of all six subjects in paired teams is considered as one independent unit of observation.
H1
H2
H3
Actual Effort (Figure)
Actual Effort (Figure)
Effort – TSCS Analysis
25
Table 3 Time Series Cross-Sectional Regression Results
Panel A: Main Effects Independent variables DF Coefficient p-value Intercept (NONE) 1 15.07 < 0.0001 Team (between-team) Tournament 1 26.45 < 0.0001 Individual (within-team) Tournament 1 11.24 < 0.0001 Period 1 -0.62 0.0155 Panel B: Hypothesis 3 (each condition compared with BOTH condition) Independent variables DF Coefficient p-value Intercept (BOTH) 1 50.57 < 0.0001 NONE 1 -37.68 < 0.0001 WITHIN 1 -22.08 < 0.0001 BOTH 1 -6.87 0.0045 Period 1 -0.62 0.0155 Number of observations: 2880. Dependent variable: Individual effort in each period.
H1H2
H3
Actual Effort (Within-subject Comparison)
26
Table 4 Within Subject Comparisons (Effort)
Mean Differences (within subject) N Difference t Value p-value BOTH - NONE 24 33.60 14.52 <0.0001 BOTH - WITHIN 24 25.85 7.13 <0.0001 BOTH - BETWEEN 24 13.54 5.76 <0.0001 WITHIN - NONE 24 20.94 5.60 <0.0001 BETWEEN - WITHIN 24 12.76 2.33 0.0290 BETWEEN - NONE 24 36.23 8.88 <0.0001 The average effort of all 10 periods for a subject is considered as one independent unit of observation.
Free-riding (zero effort)
Free-riding (< 1/3 endowment)
Messages: descriptive
7,671 messages recorded. Each experimental session:
NONE: 289 WITHIN: 288 BETWEEN: 350 BOTH: 353
80 messages (on average) within a single team in each part of each experimental session.
Messages: coding
For each team and each period,
“1” – if a statement or argument showed up in a given period and chat
“0” – otherwise.
960 observations in total.
Messages: categoriesTable 6 Analysis of Communication
Panel A: Categories for coding messages
Category Description Relative frequency of coding "1"
NONE WITHIN BETWEEN BOTH
Cooperation
C1
Ask for the opinions of other team members (may or may not specifically refer to an effort level) 0.188 0.333 0.379 0.396
C2
Proposal to choose high efforts within team or state own choice of high efforts 0.129 0.167 0.654 0.692
C3 Agree on team members’ proposals (high effort) 0.058 0.075 0.554 0.571
C4 Give reasons why need to choose high efforts 0.025 0.008 0.104 0.146
Overall cooperation 0.263 0.413 0.725 0.750
Collusion
C5
Proposal to choose low efforts within team or state own choice of low efforts
0.529
0.558
0.221
0.129
C6 Agree on team members’ proposals (low effort) 0.371 0.392 0.146 0.063
C7 Proposal to take turns in winning the tournament 0 0.142 0 0.021
C8 Give reasons why need to choose low efforts 0.233 0.167 0.050 0.038
Overall collusion 0.567 0.600 0.221 0.146 All messages within each team in each period are taken as one observation unit for coding, resulting in 960 observations in total. Within each observation, each category is coded as “one” if present and “zero” otherwise.
Messages: p-valuesTable 4.6 Analysis of Communication (continued) Panel B: Cooperation and collusion
Category Description Pairwise comparisons between condition coefficients from logistic regressions (p-values)
NO
NE
vs.
BO
TH
WIT
HIN
vs.
BO
TH
BE
TW
EE
N
vs.
BO
TH
NO
NE
vs.
BE
TW
EE
N
WIT
HIN
vs.
BE
TW
EE
N
NO
NE
vs.
WIT
HIN
Cooperation C1
Ask for the opinions of other team members
***
0.131
0.690
*** 0.266
**
C2
Proposal to choose (or state own choice) high efforts within team
*** *** 0.371
*** ***
0.242
C3
Agree on team members’ proposals (high effort)
*** *** 0.637
*** ***
0.463
C4
Give reasons why need to choose high efforts
*** *** 0.157
**
**
0.173
Overall cooperation
***
*** 0.519 *** *** **
Collusion
C5
Proposal to choose (or state own choice of) low efforts within team
***
***
*
***
***
0.517
C6
Agree on team members’ proposals (low effort)
***
***
*
***
***
0.636
C7
Proposal to take turns in winning the tournament
N/A
***
N/A
N/A
N/A
N/A
C8
Give reasons why need to choose low efforts
***
***
0.504
***
***
0.067
Overall collusion *** *** * *** *** 0.456
The p-values reported in this table are based on Wald Chi-Square statistics. ***: p-value < 0.0001 **: p-value < 0.001 *: p-value < 0.05
Overall cooperation
Overall collusion
Implications
MA incentive system design: one of few studies that examine interactive effect of individual & team incentive systems. Answer the call to examine incentive system combinations
(Bonner & Sprinkle 2002) Practical implication
Management control system: mitigate moral hazard problems. Multi-agent setting
Extend original contest model More generalizable in practice
Thank you !