1

Competition between teams vs. within a team in team production

Enrique Fatás Tibor Neugebauer LINEEX and

University Valencia University Hannover

Abstract

We report on group incentive experiments designed to study the effects of within-team

competition and between-teams competition in a voluntary contributions framework. Both settings

help to mitigate the free-rider problem. Within-team competition induces convergence to Pareto-

efficiency and promotes sustainable cooperation better than competition between teams.

JEL Classifications: C72, C92, D44, H41

Keywords: public goods, group incentives, coordination games, team production

2

A little competition goes a long, long way. (Nalbantian and Schotter, 1997, p. 332)

There is a growing interest in exploiting synergies and scale effects resulting from work in teams.

No consensus has emerged so far as to the proper way of eliciting high effort levels from individual

team members. The incentives of compensation based on team output are limited by the potential

for free-riding on the efforts of other team members, and individual piece-rates provide no

cooperative incentives.

Recent experimental research has shown that the introduction of competition between

teams (Nalbantian and Schotter 1997; van Dijk, Sonneman and van Winden 2001; Bornstein,

Gneezy & Nagel 2002) and within a team (Dickinson and Isaac 1998; Dickinson 2001) significantly

increases the effort levels exerted by experimental subjects.1 In these experiments, competition was

introduced into a team production environment by a rank order tournament structure. In the

present paper we focus on the question: what competition (within a team or between teams) elicits

greater effort levels?

We report on a laboratory study that uses the framework of voluntary contributions, which

is a standard tool in experimental economics for studying team production problems. Effort and

free-riding levels are framed as contributions to a group project and to an individual project,

respectively. Into this environment we carefully introduce a comparatively low powered

tournament structure, bearing in mind that high powered incentives might have detrimental effects

on cooperation (Lazear 1989; Harbring and Irlenbusch 2003a,b,c, 2004; Harbring, Irlenbusch,

Kräkel and Selten, 2004). In the experimental treatment on within-team competition the individual

whose contribution is lowest ranked does not receive any payoff from the group project. In the

treatment on between-teams competition the members of the group whose group project is lowest

ranked do not receive any payoff from it. The incentive structure relates to the one proposed by

the literature on exclusion from the commons (???) or by the literature on ostracism (Güth, Levati,

Sutter & van der Heijden, 2004) and can be interpreted as a formal sanction mechanism on the

1 The studies on between-teams competition involve both intra-team conflict and inter-team conflict. On one hand, individual team members have incentives to free-ride on the others; on the other hand, an individual team member might be decisive in determining the outcome of the competition and therefore has incentives to contribute. There is a bunch of related literature (cf. Bornstein 1992; Bornstein, Erev and Goren 1994; Cason and Mui forthcoming; Hausken and Ortmann 2004).

3

minimum effort. No irrational use of the authority to sanction is possible as observed in the

literature on informal sanctions (Fehr and Gächter, 2000; Bowles, Carpenter & Gintis, 2001;

Noussair & Tucker, 2002; Masclet, Denant-Boemont & Noussair, 2004; Nikiforakis, 2004).

Moreover, incentives in our experiment are not as competitive as in other experimental

tournaments (Bull, Schotter & Weigelt, 1986; Schotter & Weigelt, 1992) as all subjects receive the

same payoff if they contribute the same.

The introduction of these incentives induces a change in the equilibrium structure of the

team production game. In the voluntary contribution mechanism, there exists a strictly dominant

strategy to contribute nothing to the team project. Due to the incentive structure, dominance is

dissolved and a continuum of Pareto-ranked symmetric equilibria arises. In the treatment on

between-teams competition, additional asymmetric equilibria exist and the minimum team project

between the other teams defines the threshold of team project provision. In fact, individuals face

strategic uncertainty not only with respect to the contributions of their peers, but also with respect

to the behavior of the competitors in the other groups. Harsanyi and Selten’s (1988) theory of

equilibrium selection in games is applicable to our treatments. In the within-team competition

treatment Pareto-dominance and risk-dominance point into the same direction, in the between-

teams competition treatment individuals might face a trade-off between risk-dominance and

Pareto-dominance considerations.

Overall, this paper contributes to different research areas: From voluntary contributions,

team production, rank order tournaments and group incentives mechanism design over threshold

public goods to coordination games. We would summarize the main contributions of our paper as

follows. First, we introduce a relatively low powered incentive mechanism, which appears to us

akin to natural selection. Second, we compare the performance of between-teams competition and

within-team competition in team production in the laboratory. Third and finally, our experiment

involves tournaments with four teams of four subjects, and thus stands in contrast to the other

team incentives studies in the literature which involve usually only two competing teams. The

multiple teams setting is an important contribution to the experimental literature since existing

firms do use more than two work centers or departments on one hierarchical level; for instance,

XEL Communications (Banker & Lee, 2005).

4

The paper is organized as follows. The following section presents the experimental design

and discusses the theoretical properties of the treatments. Thereafter, the experimental results are

reported and the conclusions are discussed.

1. Experimental design and framework

The baseline treatment, hereafter VCM, is identical to the voluntary contribution mechanism used

in Croson, Fatas and Neugebauer (forthcoming). A group of four randomly assigned experimental

subjects interacts over a time horizon of ten rounds with another, followed by another ten rounds

after a surprise restart.2 In each round, a subject is endowed with an amount of 50 Eurocents and

decides privately on how much to contribute to a group project and simultaneously on the

remainder to be contributed to an individual project. The individual payoff is the sum of the

individual project and half of the group’s contributions to the group project. More formally, let ci

denote individual i’s contribution to the group project and c-i the total contribution of the other

three subjects respectively, i’s payoff function in each round is defined by equation (1).

}4,3,2,1{,2

)50(),( ∈+

+−= −− i

cccccVCM iiiiii (1)

Since each unit contributed to the individual project yields a return of one whereas each unit

contributed to the group project yields a return of one-half, i ‘s strictly dominant strategy entails a

contribution of the entire endowment to the individual project. On the other hand, Pareto-

efficiency is achieved if all subjects contribute everything to the group project. The dominance of

the free-rider solution extends to the finitely repeated game.

The second treatment involves within-team competition, hereafter WTC. Let c denote the

lowest ranked contribution within the team, i.e., c ≤ cj ∀j, cj = c for at least one j, and ∃j: c < cj and

j ∈{1, 2, 3, 4}, i’s payoff function is defined in equation (2).

2 The surprise restart technique has been widely employed in repeated public goods experiments (Andreoni 1988; Croson 1996).

5

=−

=−otherwiseVCM

ccifcccWTC

i

ii

iii,

),50(),( (2)

In WTC, every subject receives the same payoff as in VCM as long as she contributes either more

than at least one other subject or if all subjects contribute the same. Therefore, all symmetric

strategy profiles constitute Nash equilibria which can be Pareto-ranked from joint contribution of

the entire endowment to collective free-riding. The Pareto-dominant equilibrium coincides for this

game with the risk-dominant equilibrium.

The third treatment induces between-teams competition, hereafter BTC, involving four

groups of four subjects. Let Ci be the group project of i’s group (i.e., the sum of contributions of i

and the other members of i’s group), and C denotes the minimum group project between the four

groups, i.e., C ≤ Ck ∀k, C = Ck for at least one k, and ∃k: C < Ck, k ∈{1, 2, 3, 4}. The payoff of

individual i is defined in equation (3).

=−

=−otherwiseVCM

CCifcCccBTC

i

ii

iii,

),50(),,( (3)

Between groups, all symmetric group contribution vectors constitute Nash equilibria. From the

within-group viewpoint, there exist two equilibria which eventually coincide. In the first equilibrium

all subjects free-ride; in the second one the group project must weakly exceed the minimum group

project of the other groups. For a fixed minimum group project of the others C, the payoff in BTC

would be equivalent to the payoff of a subject in a public goods game with one provision point

(Marks and Croson, 1998; Croson and Marks, 1999, 2000; Broseta, Fatas & Neugebauer, 2003). For

all intermediate provision points 0 < C < 200, there exist one symmetric and an infinite number of

asymmetric equilibria if we allow for a continuous strategy space. With respect to risk dominance

considerations, for any given provision point the free-riding equilibrium is always at knife’s edge

with the other symmetric and asymmetric equilibria. In other words, (within a team) no risk

6

dominant equilibrium would exist for a fixed provision point. Since there is strategic uncertainty

about the level of the provision point, the free-riding equilibrium appears less risky. However,

Harsanyi and Selten’s (1988) theory on equilibrium selection favors the Pareto-dominant

equilibrium in both BTC and WTC. In fact, the question which equilibrium will be selected in BTC

and WCT is an empirical one and we address it in our experiment.

In all three treatments, subjects received information feedback recorded in a history table

for all past rounds. In this history table, individual contributions of all group members were

presented in increasing order; individual contributions thus could not be traced to the contributor.

Subjects were informed about their own earnings both in total and subdivided by individual project

and group project. In BTC, subjects received additional information feedback about all group

projects in increasing order; the group projects of the other three groups could not be traced.

The data the results of which are reported in the present paper were generated in eight

computerized3, experimental sessions conducted in November/December 2002 at the Laboratory

for Research in Experimental Economics LINEEX; the laboratory is jointly hosted by the

University Valencia and the University Castellón. The experiment uses between-subject variation. A

total of 112 economics undergraduates participated. Subjects were inexperienced to the extent that

they had not participated in similar experiments before. The experimental sessions of VCM and

WTC each involved 24 economics undergraduates, organized into groups of four from a room of

twelve. In BTC, 64 subjects were organized into groups of four from a room of sixteen. Average

earnings in the experiment were €13.064 (VCM), €16.56 (WTC) and €14.52 (BTC), respectively.

Experiments took less than an hour to run, hence, the earnings seemed more than sufficient to

motivate participants. Before the experiment, written instructions were read, and subjects went

through four exercises. The experiment did not start until subjects had answered all questions

correctly. Thus, we are confident that the game and the incentives were understood. After the

experiment subjects were debriefed with respect to their strategies and personal characteristics in an

ex-post experimental questionnaire. Instruction sheets and exercises are appended to the paper.

3 The software was programmed in z-Tree (Fischbacher, 1999). 4 US $1 ≈ €1.

7

2. The experimental results

This section is organized as follows: the data on group contributions is grossly surveyed in Figure 1

and Table 1; after having discussed these, we will turn to a more in-depth analysis of the observed

behavioral dynamics presented in Tables 2 – 4 and Figures 2 – 4. To warrant a better comparability

between the displayed figures, we scale contributions relative to the endowment, i.e., from 0% to

100%. In the analysis we distinguish between the first ten rounds (hereafter original game) and the

last ten rounds (hereafter restart game); we will direct our attention also to the impact of the

surprise restart.

--- Insert Figure 1 around here ---

What is the general picture?

Initial contributions averaged around 35 Cents in BTC and WTC, respectively; the differences

between the treatments are insignificant. By round 10, the last round of the original game, average

contributions had increased to 41.8 Cents and 46.7 Cents in WTC and BTC, respectively. In the

restart game, average contributions increased in WTC by 5% from 46.6 in round 11 to 48.5 Cents

in round 20 and decreased in BTC by 13.9% from 44.0 to 37.9 Cents, respectively. Compared to

both BTC and WTC, average contributions in VCM are significantly smaller in every round of the

original and the restart game. We observe a significant treatment effect already in the first round.

Initial contributions in the original game and in the restart game averaged around 20 Cents in VCM

and declined to 9.1 Cents by round 10 of the original game and to 5 Cents in the restart game,

respectively.

Do we observe a restart effect?

Between the last round of the original game and the first round of the restart game most subjects

increased their contributions to the group project in VCM. On average, the contribution change

induced an increase of 118% from 9.1 Cent to 19.8 Cent. Significant restart effects in VCM have

been reported earlier in the literature (Andreoni 1988; Croson 1996). In WTC and BTC we observe

8

no significant restart effects; the mode observations (66.7% and 42.2%, respectively) in these

treatments involve no changes of individual contributions between rounds 10 and 11. In round 10

of WTC, all groups but one contributed 100% to the group project. Three of the four subjects who

did not contribute 100% increased and the fourth subject decreased the contribution in round 11.

Four of the subjects who contributed 100% decreased their contribution between rounds 10 and

11, and the other 16 subjects continued with a contribution of 100% of their endowment. In BTC,

29.7% and 28.1% of subjects decreased and increased their contributions between rounds 10 and

11, respectively. Half of the 28.1% of subjects who increased the contributions in that treatment

were in groups whose project was the minimum between groups in the last round of the original

game. We can only speculate why we observe a restart effect in VCM and not in WTC and BTC.

Subjects comments in the debriefings suggest that in VCM, subjects might have felt regret about

the outcome in the original game and that they tried to use the restart to try to reach at a better

outcome. In WTC and BTC, subjects’ comments suggest that they were satisfied with the outcome

of the original game and they tried to continue like this in the restart game.

Do we observe equilibrium selection?

The Pareto-efficient equilibrium strategy that involved the contribution of the entire endowment

was chosen in 65% of all individual decisions in WTC; 91.7% of participants contributed at least

once their endowment to the team project. The payoff-dominant equilibrium thus was reached 56

times (47%) in five of six groups in WTC. We observe no other equilibrium, neither in WTC nor in

the other treatments. Subjects in WTC decided contribution of the endowment significantly more

frequent than those in BTC. In BTC and VCM, 35.3% and 2.7% of subjects’ decisions (79.7% and

20.8% of subjects) involved contribution of the entire endowment, respectively. Zero contributions

occurred in 14.9% of observations in VCM and were thus significantly more frequent than in the

other treatments, where about 2% of zero contributions were recorded.

------------------ insert Table 1 about here ------------

Do we observe a trend in contributions?

9

Table 2 records the results of two random effects regressions which use individual contributions as

dependent variable. Model (1) suggests regression of contributions on rounds only; model (2) uses

two additional explicatory variables: one’s contribution of the previous round, and the difference of

one’s lagged contribution and the lagged average contribution of the others; the regressions are

stratified by the independent observation. In line with earlier experimental results on voluntary

contributions, the coefficients of regression (1) indicate that contributions decline significantly in

both the original game and the restart game of VCM. In WTC contributions increase throughout

both parts of the experiment, but the trend is only significant for the original game; the increase in

the restart game is basically caused by one group who had not reached the Pareto-dominant

equilibrium in the original game. The trends in the original game and the restart game are opposing

to each other in BTC; contributions in BTC are trend-free overall, but they increase significantly in

the original game and decline significantly in the restart game. We are going to direct our attention

to this issue further below.

How can we fit contributions?

The regression model (1) provides a poor fit to the data in WTC and in BTC as is indicated by the

correlation coefficients recorded in Table 2; the fit of the dynamics in VCM is better but not

satisfactory, either. Model (2) explains the contribution of a subject in round t, cit, with the same

subject’s contribution of the previous round ci,t-1 and its difference to the lagged average

contribution of the subject’s partners, c-i,t-1/3. The regression model (2) thus can be represented by

the following equation.

cit = b0 + b1t + b2 ci,t-1 +b3 (ci,t-1 – c-i,t-1/3) + uit

i∈{1,2,..,6} (i∈{1,2,..,4} in BTC); t ∈{1,2,…,20}.5 (4)

The results of model (2) recorded in Table 2 indicate a highly significantly positive

correlation between contributions and lagged own contributions in all treatments. The result

5 Model (2) involves two missing values of lagged contributions and lagged partners’ average contributions in rounds 1 and 11. The error term uit includes an individual random error term for each independent observation.

10

suggests some kind of path-dependence of contributions which would imply that subjects

contribute the more today the more they have contributed yesterday. The difference of one’s

lagged contribution to the average of the partners’ lagged contributions is significantly negatively

correlated with one’s contribution in all but the original game in BTC; apparently, some subjects in

this treatment “cheap-ride” on the other team members, as will be shown in the figures 3 and 4

below. The negative correlation of one’s contribution relative to the average contribution of the

others indicates what the literature has termed conditional cooperation; conditional cooperation in

the context of voluntary contribution means that people are willing to cooperate the more the more

others contribute.6 The contributions are adapted between rounds towards the average; the more

one’s previous contribution exceeded [fell short of] the previous average the smaller [greater] was

one’s contribution in the following round.7 The dependence of contributions on the observations

from previous rounds suggests a closer look at the adaptive dynamics between rounds; this will be

done in what follows.

------------------ insert Table 2 about here ------------

What dynamics do we observe?

Table 3 (all treatments) and Table 4 (only BTC) record the regression outcomes for the individual

adjustments between rounds. Before we turn to the outcomes of these statistical analyses, we direct

our attention to the graphical representation of the observed dynamics in Figures 2 (WTC), 3 and 4

(BTC). Figure 2 displays the individual contributions in WTC; a lower bar marks the minimum

contribution within the group for each round of the original and the restart game. To remind the

reader, subjects whose contributions in a round were both smaller than the endowment8 and

6 C.f. Croson, 1998; Sonnemans, Schram and Offerman, 1999; Ockenfels, 1999; Keser and van Winden, 2000; Fischbacher, Gächter and Fehr, 2001; Brandts and Schram, 2001; Levati and Neugebauer, 2004; Croson, Fatas and Neugebauer, in press; Neugebauer, Perote and Schmidt, 2005; Fischbacher and Gächter, 2005. 7 As the analysis includes censored data involving an upper bound on contributions at 50 Cents and a lower bound at 0 Cents (e.g., WTC contains only #134/#432 uncensored observations), a random-effects tobit model might be preferable for the estimation. Besides an adjustment of the coefficients, the random-effects tobit model involves the same conclusions as model (2); we preferred a presentation of model (2) to the tobit model due to its easy interpretability. 8 We remind the reader that the Pareto-dominant equilibrium was the only observed equilibrium in the entire experiment.

11

corresponding to the lower bars in Figure 2 did not receive any payoff from the group project in

that round. In four of the six groups in WTC we observe a quick convergence to the full

contribution equilibrium. All subjects in WTC, but the one with the maximum contribution, appear

to increase their contributions between rounds in the original game. Once the equilibrium is

reached it seems to be ‘locked-in’ as eventual individual deviations from it do not affect decreasing

contributions. An exception to this is group WTC#1 (see Figure 2) as group contributions do not

reach the equilibrium in any round; the plot shows that contributions continually increased from

round to round, and thus suggests that the equilibrium would have been in-reach within a couple of

more repetitions. Besides WTC#1, not much dynamics persist in the restart game to that treatment

due to the apparent lock-in effect. When we look at averages in WTC, we must have in mind that

much of the dynamics in the restart game of WTC is represented by the behavior of the outlier

group WTC#1.

---- insert Figure 2 about here -----

Figure 3 represents the group dynamics observed in BTC; each depicted point corresponds

to one group project in some session. The lower bars mark the minimum group project between

groups in each round; the corresponding groups did not receive a payoff from their group projects

in the particular round. The initial adjustments of group projects in BTC look similar to those of

the individual contributions in WTC, though more clumsy. Almost all groups start by increasing

their group project, but the dynamic of the convergence towards the Pareto-dominant equilibrium

appear to slow down from round to round. By the restart game, three of the four BTC sessions (i.

e., including all groups except BTC#9-#12) involve decreasing minimum group projects. A

decrease of the minimum group project induces lower competitive pressure between groups,

because the threshold level of group project provision decreases for the other groups. Lower

provision points affect again the incentives to ‘cheap-ride’ within the group; we designate people as

cheap-riders if they are motivated to exert less effort than the other group members (see

contributions of BTC#3d and BTC#4b in Figure 3). Cheap riding has been observed in

experiments on threshold public goods games (cf. Isaac et al 1988).

12

---- insert Figure 3 about here -----

Figure 4 plots the individual contributions of selective groups in BTC. The bars illustrate

the minimum group projects of the others (i.e., the threshold of group project provision) and the

bold squares represent the group’s project; both variables are scaled relative to the group’s

endowment. Three graphs plot the contributions of the groups BTC#2-#4 who interacted in the

first session; group BTC#4 caused the decline of the minimum group project in the restart game.

Why? - The sudden decline of the group project was caused by an apparent chain reaction; by

round 14, BTC#4 had lost the competition between groups three times in a row. In the rounds 12-

14, the most cooperative contributor of that group, BTC#4a, had experienced in every round a loss

of his endowment. BTC#4a’s zero contribution submitted in round 15 exerted an almost chaotic

appearing effect on the other group members’ contributions; thereafter, subjects alternated their

contributions between 0 and 50. Successively, the individual contributions in BTC#2-#3 also

decreased presumably upon the observation of a lower threshold of group project provision.

The fourth graph plots the individual contributions of group BTC#5 whose members

experienced a collapse of their group project in the restart game. Where did this collapse come

from? – By the beginning of the restart game, group competition had grown to an extreme in that

session; almost every Cent was contributed to the group project in all groups. Group BTC#5 lost

the first three competitions between groups in the restart game, because only three subjects

contributed the entire endowment while always one subject (with alternating identity) contributed

one to four Cents less than the endowment. In round 14, two subjects in that group contributed

less than 100 Cents, while all other groups contributed basically the entire endowment. In round

15, the other two subjects, who had lost their endowment in the former four rounds, decreased

their contributions to zero and did not contribute any further Cent thereafter. These examples

suggest that cheap-riding by some group members might have produced a foregone payoff from

the group project inducing high costs to all but particularly to the more cooperative members of

the group. Such losses might cause frustration of cooperative subjects, which can affect a collapse

of contributions within the group. Our data seem to suggest that the success of between-teams

13

competition depends crucially on a team’s unified keen interest to beat another team, and that even

small deviations can affect a tremendous decline of cooperation.

---- insert Figure 4 about here ----

In this paragraph, we discuss Tables 3 and 4 which record the statistical outcomes of

individual contribution changes between rounds. The individual contribution changes between

rounds are regressed on dummy variables of lagged order statistics. We remind the reader, that after

each period subjects learned whether their contribution was maximum, second greatest, third

greatest or minimum within their group. Equation (5) represents the estimated random effects

regression model.

∆cit = b1maximumi,t-1 + b2 secondi,t-1 +b3 thirdi,t-1 +b4 minimumi,t-1 + uit

i∈{1,2,..,6} (i∈{1,2,..,4} in BTC); t ∈{1, 2,…10,..,12,..,20}.9 (4)

In equation (4), ∆cit denotes subject i’s contribution change between rounds t and t-1, i.e.,

∆cit = cit - ci, t-1; maximumi,t-1, secondi,t-1, thirdi,t-1, minimumi,t-1 denote dummies taking the value one

if the subject’s contribution was the maximum, second greatest or minimum within the group in

the previous round.10 The results recorded in Table 3 confirm our above comments to Figures 2-4;

contribution changes are more upward directed in WTC than in the other two treatments.11

---- insert Table 3 about here -----

9 Model (2) involves two missing values of lagged contributions and lagged partners’ average contributions in rounds 1 and 11. The error term uit includes an individual random error term for each independent observation. 10 If all contribute the same strictly positive amount, we designate all contributions as maxima. In case of a tie at the maximum contribution or at the minimum contribution we designate contributions maxima or minima, respectively. If not all members contribute the same, we always designate at least one contribution as maximum and at least one contribution as a minimum. If second and third greatest contributions are the same and they are strictly greater [smaller] than the minimum [maximum], we designate these contributions second greatest. 11 The contribution changes in WTC are significantly different to both BTC and VCM in the restart game and to VCM also in the original game. We obtained these results when we ran the regressions on two treatments including a treatment dummy variable.

14

The model proposed in equation (4) neglects the information feedback on between teams

competition which subjects received in BTC. The regression, the results of which are recorded in

Table 4, includes four additional explicatory variables. First, a dummy variable named ‘lagged

foregone group project dummy’ is added; it takes the value one for the subjects who did not reach

the threshold of group project provision in the previous round, and the value zero otherwise.

Second, the variable ‘lagged foregone group project run’ indicates the number of times running a

subject’s group has experienced without having reached the threshold of group project provision;

the variable takes the value zero for all subjects who received a payoff from the group project in

the previous round. Third, the variable ‘lagged distance threshold from below’ measures the

amount of money a group must have contributed more to reach the threshold of group project

provision in the previous round; for all subjects who received a payoff from the group project this

variable takes the value zero. Finally, we added the variable ‘lagged distance from above’ which

measures the amount a group could have contributed less in order to just reach the threshold of

group project provision in the previous round, i.e. this variable states how much more cheap-riding

the individual group members could have exerted without losing the payoff from the group project.

As indicated in Table 4 the latter variable is the only insignificant variable for individual

contribution changes. This evidence suggests that cheap-riding, although some subjects might

eventually practice it, does not characterize the average behavior in BTC. The coefficients further

indicate that subjects who lost the between teams competition in the previous round (i.e., the

members of a group that did not reach the threshold of group project provision) increase their

contribution significantly in the next round. However, the more often a group has lost the

between-teams competition in a row the less inclined will its group members be to contribute more

in the following round. In contrast to this, it is indicated that increases of contributions within the

losing group increase with the distance from the threshold of group project provision.

---- insert Table 4 about here -----

15

3. Conclusions

In this paper we have reported on experiments that introduced a team incentive mechanism, which

establishes competition in a team production environment by sanctioning the minimum effort

within a team. The experiments used a voluntary contribution mechanism setting and a framing of

standard public goods experiments; shirking was framed as a contribution to an individual project

and effort was framed as a contribution to a team project. We compared the effect of competition

on the individual contribution to the team project in two experimental treatments; on one hand, we

induced competition between teams and, on the other, we introduced competition within a team.

Our results suggest that both settings are effective at eliciting great effort levels; relative to the

benchmark of the voluntary contribution mechanism initial cooperation is significantly enhanced in

both treatments and contributions do not decline in the repeated setting. In the within-team

competition treatment, voluntary contributions to the team project converged quickly to an amount

equal to the subjects’ endowment, and there they got ‘locked in’; contribution of the entire

endowment in that treatment is both Pareto-dominant and risk-dominant. In the between-teams

competition treatment we initially observed increasing contributions but the intrinsic motivation of

subjects to increase contributions further towards the Pareto-dominant equilibrium seemed to

cease after a few rounds. After a surprise restart contributions declined significantly in the between-

teams competition setting; this decline was tremendous in two groups who were repeatedly unlucky

in the team competition. We do not exclude that the emotions of some subjects might have caused

the contributions to ‘crash’. As the between-teams competition setting appears more vulnerable to

individual members’ actions,12 the results of the within-teams competition setting seem most

encouraging with respect to successful implementation of the Pareto-efficient cooperation level in

the voluntary contribution mechanism.

We cannot be sure to which degree our results can be obtained in different team

production environments. The literature on incentive structures in work environments is

particularly concerned about collusion and sabotage activities that might be unintentionally elicited

with an incentive instrument. Although we believe that our proposed incentive mechanism is ‘low

12 Nalbantian & Schotter (1997) suggested that incentives in production should be designed to warrant that the outcomes are “relatively riskless or not ‘vulnerable’ to slight mistakes by ones colleagues.”

16

powered’ (Lazear, 1989), we can imagine that it also might be susceptible to undesired motivations.

We will study these issues further.

17

References

Banker, Rajiv D., Lee, Seok-Young, 2005. Mutual monitoring and team pressure in teamwork.

Mimeo, University of Texas, Dallas.

Bornstein, Gary (1992): “The free rider problem in intergroup conflict over step-level and

continuous public goods.” Journal of Personality and Social Psychology 4, 597-606.

Bornstein, Gary, Ido Erev and H. Goren (1994): “The effect of repeated play in the IPG and

IPD team games.” Journal of Conflict resolution 38, 690-707.

Bornstein, Gary, Uri Gneezy & Rosemarie Nagel (2002): “The effect of intergroup competition

on intragroup coordination: An experimental study,” Games and Economic Behavior, 41, 1-25.

Brandts, J. and Schram, A. (2001): “Cooperation and Noise in Public Goods Experiments:

Applying the Contribution Function Approach.“ Journal of Public Economics 79 (2): 399-427.

Broseta, Bruno, Fatás, Enrique and Neugebauer, Tibor (2003): Asset Markets and Equilibrium

Selection in Public Goods Games with Provision Points: An Experimental Study. Economic

Inquiry 41 (4), 574-591.

Bull, Clive; Andrew Schotter; Keith Weigelt (1987): Tournaments and Piece Rates: An

Experimental Study, Journal of Political Economy 95, 1-33.

Cason, Timothy N. and Vai-Lam Mui (forthcoming): “Uncertainty and resistance to reform in

laboratory participation games.” European Journal of Political Economy.

Croson R. T. A. (1998): “Theories of Commitment, Altruism and Reciprocity: Evidence from

Linear Public Goods Games.“ OPIM Working Paper, Wharton School of the University of

Pennsylvania, Philadelphia.

Croson, R. T. A. (2000): “Feedback in Voluntary Contribution Mechanisms: An Experiment in

Team Production.” Research in Experimental Economics 8, 85-97.

Croson, Rachel, and Melanie Marks (1999): "The Effect of Incomplete Information in a

Threshold Public Goods Experiment", Public Choice, 99, 103-118.

18

Croson, Rachel and Melanie Marks (2000): "Step Returns on Threshold Public Goods: A Meta-

and Experimental Analysis", Experimental Economics, 2, 3, 239-259.

Croson, Rachel, Fatás, Enrique and Neugebauer, Tibor (in press). Reciprocity, Matching and

Conditional Cooperation in Two Public Goods Games. Economics Letters.

van Dijk, Frans; Joep Sonnemans, Frans van Winden (2001): Incentive Systems in a Real Effort

Experiment, European Economic Review 45, 187-214.

Dickinson, David L. (2001) “The Carrot vs. the Stick in Work Team Motivation” Experimental

Economics, 4(1): 107-124.

Dickinson, David L. and Isaac, Mark R. Absolute and Relative Rewards for Individuals in Team

Production.” Managerial and Decision Economics, 19: 299-310.

Fehr, E., Gächter, S., 2000. Cooperation and punishment in public goods experiments.

American Economic Review 90(4), 980-994.

Fischbacher, U., Gächter, S. and Fehr, E. (2001): “Are People Conditionally Cooperative?

Evidence from a Public Goods Experiment.“ Economics Letters 71 (3): 397-404.

Fischbacher, U., and Gächter, S. (2005): “Heterogeneous motivations and the dynamics of free

riding in public goods.“ mimeo.

W. Güth, M. V. Levati, M. Sutter and E. van der Heijden, 2004. Leadership and cooperation in

public goods experiments, Discussion Paper 28-2004, Papers on Strategic Interaction, Max

Planck Institute for Research into Economic Systems, Jena.

Harbing, Christine and Irlenbusch, Bernd, (2003a) Zur Interaktion von Arbeitgeber und

Arbeitnehmern in Turnieren mit Sabotage. Zeitschrift für Betriebswirtschaft, Ergänzungsheft

4/2003, Personalmanagement 2003, 19-41.

Harbing, Christine and Irlenbusch, Bernd, (2003b) Anreize zu produktiven und destruktiven

Anstrengungen durch relative Entlohnung. forthcoming in Zeitschrift für

betriebswirtschaftliche Forschung.

19

Harbing, Christine and Irlenbusch, Bernd, 2003c. How Many Winners are Good to Have? – On

Tournaments with Sabotage. Discussion Paper, University of Bonn and University of Erfurt.

Harbing, Christine and Irlenbusch, Bernd, 2004. Incentives in Tournaments with Endogenous

Prize Selection. Department of Economics, University of Bonn.

Harbing, Christine and Irlenbusch, Bernd, Kräkel, Matthias, and Selten, Reinhard, 2004.

Sabotage in Asymmetric Contests – An Experimental Analysis. Discussion Paper, University of

Bonn and University of Erfurt.

Hausken, and Andreas Ortmann (2004): “A First Experimental Test of Multilevel Game

Theory: The PD Case.” Mimeo.

Isaac, Marc R., David Schmidtz and James M. Walker (1989): "The assurance problem in a

laboratory market", Public Choice 62: 217-236.

Kandel, E. and E. Lazear, 1992. "Peer Pressure and Partnerships," Journal of Political

Economy, 100, 801-817.

Keser, C. and van Winden, F. (2000): “Conditional Cooperation and Voluntary Contributions to

Public Goods.“ Scandinavian Journal of Economics 102: 23-39.

Lazear, Edward P. (1989) “Pay Equality and Industrial Politics,” Journal of Political Economy

97(3), pp. 561-80.

Lazear, Edward P.; Sherwin Rosen (1981): Rank-Order Tournaments as Optimum Labor

Contracts, Journal of Political Economy 89, 841-864.

Levati, M. Vittoria and Neugebauer, Tibor, 2004. An Application of the English Clock Market

Mechanism to Public Goods Games. Experimental Economics 7, 153-169.

Marks, Melanie, and Croson, Rachel (1998): "Identifiability of Individual Contributions in a

Threshold Public Goods Experiment", Journal of Mathematical Psychology, 42, 167-190.

Nalbantian, H. and A. Schotter, 1997. "Productivity Under Group Incentives: An Experimental

Study," American Economic Review, 87, 314-341.

20

Neugebauer, T., Perote, J., Schmidt, U., 2004. Selfish-biased conditional cooperation: On the

decline of contributions in repeated public goods experiments. Mimeo.

Ockenfels, A. (1999). Fairness, Reziprozität und Eigennutz – ökonomische Theorie und

experimentelle Evidenz. Die Einheit der Gesellschaftswissenschaften, Bd. 108, Tübingen Mohr

Siebeck.

Schotter, Andrew; Keith Weigelt (1992): Asymmetric Tournaments, Equal Opportunity Laws,

and Affirmative Action: Some Experimental Results, Quarterly Journal of Economics 107, S.

511-539.

Sonnemans, J., Schram, A., and Offerman, T. (1999): “Strategic Behavior in Public Good

Games: When Partners Drift Apart.“ Economics Letters 62: 35-41.

21

Figure 1. Average contribution relative of endowment

0%

25%

50%

75%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

roundBTC WTC VCM

Table 1. Average contribution & p-values of two-tailed tests VCM WTC BTC p-value

VCM vs

WTC|BTC

WTC vs

BTC

Round 1a) 20.2 34.9 35.6 .000 .977

Round 10 b) 9.1 46.7 41.8 <.010 .048

Original game b) 15.9 40.9 40.5 <.010 >>.100

Round 11 b) 19.8 46.2 44.0 <.010 .105

Round 20 b) 5.0 48.5 37.9 <.010 .019

Restart game b) 14.7 46.6 42.0 <.010 .039

#zero contribution % b) .142 .019 .026 <.048 >>.100

#full contribution % b) .027 .650 .353 <.018 .076

Restart effect

- round 10 vs round 11c)

.063

.750

1

- original vs restart game c) 1 .031 .250 a) Mann-Whitney test NVCM=NWTC=24, NBTC=64. b) two sample randomization test NVCM=NWCT=6, NBTC=4 c) one sample randomization test NVCM=NWCT=6, NBTC=4; H0: #increases = #decreases

22

Table 2. Random effects regression of contributions independent VCM WTC BTC

(lagged) variable model (1) (2) (1) (2) (1) (2)

Original R2 0.097 0.475 0.041 0.410 0.001 0.269

Game constant 23.886** 3.766 35.617** 7.721* 38.234** 18.286**

(3.636) (2.446) (3.730) (3.215) (1.314) (2.605)

Round t -1.445 -0.424 0.967** 0.124 0.411** -.113

(.229) (.278) (.252) (.287) (.142) (.150)

(own contribution) 0.853** 0.822** .581**

(.070) (.075) (.064)

(difference from other’s average) -0.518** -0.385** -0.084

(.078) (.078) (.059)

Restart R2 0.169 0.377 0.002 0.326 0.032 0.456

Game constant 41.780** 14.840** 44.838** 10.106** 53.345** 7.393*

(4.140) (5.770) (3.253) (4.535) (2.667) (3.542)

Round t -1.745 -0.785** 0.113 0.124 -0.733** -.238

(.228) (.297) (.143) (.183) (.139) (.149)

(own contribution) 0.752** 0.745** .900**

(.099) (.074) (.049)

(difference from other’s average) -0.408** -0.526** -0.348**

(.090) (.083) (.052) * p<.05 ** p<.01 significance level; standard errors in parenthesis

Table 3. Random effects regression of contribution changes between rounds independent VCM WTC BTC

(lagged)

variable

coefficient

(std. error)

coefficient

(std. error)

coefficient

(std. error)

Original R2 0.238 0.173 0.089

Game (maximum dummy) -8.984** -2.793* -2.448**

(1.368) (1.093) (.640)

(second dummy) -2.750 0.846 0.143

(1.506) (2.309) (1.158)

(third dummy) 1.419 4.321 2.732*

(1.950) (2.225) (1.122)

(minimum dummy) 5.686** 10.087** 4.715**

(1.298) (1.736) (.791)

Restart R2 0.279 0.166 0.074

Game (maximum dummy) -10.515** -1.090 -3.286**

(1.841) (.620) (.604)

(second dummy) -0.979 1.400 -0.709

(2.255) (2.608) (1.093)

(third dummy) 1.061 0.556 0.366

(1.841) (2.749) (1.153)

(minimum dummy) 5.072** 11.500** 2.958**

(1.273) (1.844) (.752) * p<.05 ** p<.01 significance level

23

Table 4. Random effects regression of contribution changes in BTC Independent Original Game Restart Game

(lagged)

variable

coefficient

(std. error)

coefficient

(std. error) R2 0.196 0.120

(maximum dummy) -2.282* -3.297**

(.927) (.791)

(second dummy) -0.252 -0.999

(1.294) (1.171)

(third dummy) 2.201 0.340

(1.262) (1.219)

(minimum dummy) 4.480** 3.047**

(.997) (.894)

(lost previous competition dummy) 3.631* 3.514**

(1.522) (1.169)

(length of run of lost competitions) -2.977 -1.865**

(.530) (.365)

(distance threshold from below) 0.178** 0.079**

(.053) (.028)

(distance threshold from above) -0.018 -0.002

(.021) (.011) * p<.05 ** p<.01 significance level;

24

Figure 2. Individual contributions in WTC

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

9 10 11 12 MIN

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

13 14 15 16 MIN

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

17 18 19 20 MIN

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

21 22 23 24 MIN

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

1 2 3 4 MIN

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

5 6 7 8 MIN

25

Figure 3. Group contributions in BTC

Figure 4. Individual contributions in BTC: Groups BTC#2-#5

Figure 4. Individual contributions in BTC – Groups BTC#2 - #5

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

BTC.4a BTC.4b BTC.4c BTC.4d threshold TP4

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

BTC.5a BTC.5b BTC.5c BTC.5d threshold TP5

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

BTC.2a BTC.2b BTC.2c BTC.2d threshold TP2

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

BTC.3a BTC.3b BTC.3c BTC.3d threshold TP3

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

1 2 3 4 MIN

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

5 6 7 8 MIN

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

9 10 11 12 MIN

0

25

50

75

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

period

13 14 15 16 MIN

26

Instructions

The aim of this experiment is to study how individuals take decisions in certain contexts. The instructions are simple and if you follow them carefully you will confidentially earn an amount of money at the end of the experiment, since nobody will know the amount of money received by the rest of participants. Should you have any questions please raise your hand and ask us. It is prohibited to comunicate with the other participants during the experiment. If you violate this rule, we shall have to excluded you from the experiment.

1 The experiment consists of 10 rounds. At the beginning of the experiment you will be divided into two independent sections of 8 participants each. You will never know the identity of the members of each section.

2 In each 8 members section, there will be 2 groups of 4 participants, Group 1 and Group 2.

3 At the beginning of each round, each participant receives a lump sum payment of 50 EUROCENT. You only have to decide how much of it to assign to a collective account. The remaining part will automatically be assigned to a private account.

3 The private account payoff is equal to your assignment to this account and it does not depend on the other members’ decisions.

4 The size of the collective account payoff is determined by using the total amount of money assigned to the collective account by the group members (that is, your assignment to the collective account plus the assignments to the collective account of the other three members of your group). This total assignment to the collective account will be multiplied by two and then splitted into four equal parts among the group members.

5 Therefore, your earnings in one round will be computed as follows: Private A. Payoff + Collective A. Payoff = Individual Earning

(50 Eurocent –your assignment to the collective account) +(4

2 sum of the assignments of

the group members to the collective account)

6 The composition of the groups in the first round is ramdomly determined at the beginning of the experiment. The group with the highest total assignment to the collective account in round 1 will be named Group 1, and the other group will, therefore, be named Group 2. If both groups assign the same amount, the numbers will be ramdomly determined.

7 The composition of the groups will change according to the following mechanism: a. The member with the lowest assignment to the collective account of the

Group 1 will be moved to the Group 2 in the following round. b. The member with the highest assignment to the collective account of the

Group 2 will be moved to the Group 1 in the following round. c. If two or more subjects have the lowest assignments in the Group 1 (the

highest assignments in the Group 2), the transfer will be ramdomly determined.

8 After each round, you will receive the following information in your computer screen a. The assignments of the members of your group to the collective account in

each round, ranked from the maximum to the minimum one. But, you will not know where each assignment comes from.

b. Your earnings in each round, divided into those coming from the collective account and those coming from the private account.

c. The group you are assigned to in each round, and if your group has been changed from one round to another.

9 At the end of the experiment the total amount of money you have earned during the ten rounds will be privately paid in cash.

27

Questionaire

Choose 4 numbers between 0 and 50 and write them down: N° 1 _________________ N° 2 _________________ N° 3 _________________ N° 4 _________________ Suppose these numbers are the assignments to the collective account of your group (all the amounts are given in EUROCENT). Taking into account these assignments to the collective account, you have to calculate

the earnings of each member, by following the next steps:

Step 1: Write the sum of the assignments to the collective account: ________ Step 2: As mentioned in the instructions, each member will receive an equal part of the double of the collective account. Write the result of doubling the collective amount and dividing into 4 parts:

Collective Account Payoff = (2 x Collective Account) / 4 : ________

Step 3: The assignment to the private account is equal to the difference between the 50 EUROCENT and the individual assignment to the collective account. In order to calculate it, firstly, copy the assignments to the collective account of each member in the first column of the table below. Secondly, substract the assignment to the collective account from the inicial endowment of 50 EUROCENT and write the result in the second column of the same table. Step 4: Copy the assignments to the private account (the private account payoff) in the third column. Step 5: Copy the collective account payoff (the same payoff for each group member,

calculated in the step 2) in the fourth column. The individual payoff is the sum of the

collective account payoff (4º column) and the private account payoff (3º column). Write

this sum for each member in the fifth column.

Assignment to the Collective Account

Assignment to the Private Account

Private Account Payoff

Collective Account Payoff

Individual Payoff

1

2

3

4