Journal of Economic Behavior & Organization
Vol. 58 (2005) 371–392
Managing diversity by creating team identity
Catherine C. Eckela,1 , Philip J. Grossmanb,∗
a
Department of Economics, Virginia Tech, 3016 Pamplin Hall, Blacksburg, VA 24061, USA
b Department of Economics, St. Cloud State University, 720 4th Avenue South,
SH 386, St. Cloud, MN 56301, USA
Received 13 August 2002; received in revised form 9 May 2003; accepted 28 January 2004
Available online 21 January 2005
Abstract
This paper explores the extent to which team identity can deter shirking and free-riding behavior in
a team production setting. Team identity is manufactured and identification is enhanced by a variety of
means. Created team identity is chosen over existing team identity to ensure that all subjects recognize
their own (and others’) team identity. Subjects then participate in a repeated-play public goods game,
framed as a team production problem. Analysis of the experiments compares aggregate team decisions
based on the strength of team identity.
© 2004 Elsevier B.V. All rights reserved.
JEL classification: C92; M54
Keywords: Diversity; Experiments; Identity; Public goods; Teams
1. Introduction
An increasingly common feature of the workplace is the use (and centrality) of teams
in the production process. Gordon (1992) reports that 82 percent of companies with 100 or
more employees use teams. Lawler et al. (1995) find more than a doubling between 1987
and 1993 in the percentage of Fortune 1000 companies reporting the use of self-managing
∗
1
Corresponding author. Tel.: +1 320 255 4232; fax: +1 320 255 2228.
E-mail addresses: [email protected] (C.C. Eckel), [email protected] (P.J. Grossman).
Tel.: 1 540 231 7707; fax: 1 540 231 5097.
0167-2681/$ – see front matter © 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.jebo.2004.01.003
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teams (from 28 percent to 68 percent). The premise motivating the growing use of teams is
that they provide an efficient and flexible way to coordinate production requiring a diversity
of skills, talents, and information.
Another increasingly common feature of the workplace is the degree of diversity in the
workforce. The concept of diversity has multiple dimensions (McGrath et al., 1995). One
dimension has been categorized as informational diversity (Jehn et al., 1999). Informational
diversity encompasses the mix of information set, talents, skills, or visions that different
workers bring to a team. As no one individual is likely to possess the full complement of
task-related characteristics necessary to achieve the desired goals, team diversity may permit
greater productivity than could be achieved by individual effort. The cross-fertilization
possible in a diverse work team leads to more creativity; diverse teams are more effective
(Northcraft et al., 1996). Furthermore, diverse work teams make each team member more
efficient; the productivity of any team member is greater as a result of the interaction
with other team members. Team members complement one another rather than serve as
substitutes for one another.
A second dimension of diversity, and the focus of this paper, is social category (demographic) diversity. Team members may be of different sexes, racial groups, or ethnic,
social, or cultural backgrounds. Social identity theory suggests that members of a team that
is heterogeneous with respect to social categories may find it difficult to integrate their diverse backgrounds, values, and norms and work together (see Jehn et al., 1999, for a review
of some of the findings). As Northcraft et al. note, “the discomfort or apprehension that
individuals experience when interacting with members of a different social category is a
natural consequence of social identification processes” (80). In general, people feel more
comfortable working with and are more likely to trust and cooperate with those whom they
identify with, and they are more likely to identify with members of their own characteristic
group.
Whether along sex, race, or ethnic lines, the degree of social category diversity in the
workplace is increasing. Over the past four or five decades, women’s labor force participation rate has steadily risen relative to men’s (from 33.9 percent versus 86.4 percent
for women and men, respectively, in 1950 to 60.0 percent versus 74.7 percent in 1999).1
At the same time, the choice of occupations open to women and minorities has widened
considerably. For example, between 1983 and 1998, women, Blacks, and Hispanics increased their representation in managerial and professional specialty occupations from
40.9, 5.6, and 2.6 percent, respectively, to 49.5, 8.0, and 5.0 percent. All three groups
almost doubled their shares in the engineering profession from 5.8, 2.7, and 2.2 percent, respectively, to 10.6, 4.6, and 3.5 percent. Similar advances were made in health
diagnosis (physicians and dentists) and legal occupations (U.S. Census Bureau, 2000b,
Table 669).
A comparison of the 1990 and 2000 Census of Population gives a further indication
of the extent of the racial diversification that the workplace has already undergone and is
likely to undergo in the future. Between 1990 and 2000, the U.S. population grew by 13.2
percent (32.7 million persons). The fastest growing major racial groups are Hispanic or
Latino, 57.9 percent (13.0 million persons); Asian, 52.4 (3.5 million persons); and Black
1
Council of Economic Advisors (2001, TableB-39).
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373
or African American, 16.2 percent (4.7 million persons). By comparison, Whites have
increased by 3.4 percent (6.4 million persons).2
If the maximum benefits are to be obtained from team production, it is imperative that
distrust, lack of cooperation, and general unwillingness to work with others created by
social category diversity be overcome. Effective teamwork requires members to recognize
the team as a unit with common goals, values, and norms (Lembke and Wilson, 1998). The
more that team members identify with one another, the more likely they are to believe they
hold similar goals, values, and norms, and the more willing they will be to cooperate and
work together as a team. An individual who perceives herself as a member of a team is
more likely to perceive the fate of the team as her own (Ashforth and Mael, 1989). This
commonality is more likely to be recognized if team members are, or perceive themselves
to be, of the same social category.
While many aspects of a person’s social identity (age, race, sex, or ethnicity) are immutable, work in social identity theory suggests that social identity can be manufactured.
Gaertner et al. (1993) found that manipulations of seemingly irrelevant variables created
common group identity sufficient to diminish or eliminate negative bias arising from diversity in other social category factors.
This paper reports the results of a series of laboratory experiments designed to test
whether manufactured team identity can increase subjects’ tendency towards cooperative
behavior. We conduct a series of repeated-play, public goods experiments framed as a team
production problem to test the impact of team identification on cooperative behavior. Team
membership is by random assignment. Team identity is varied from none (random assignment with no team identification) to weak (random assignment with team identification),
to strong (random assignment with team identification, prior team goal attainment, and
ingroup/outgroup conflict). We find that cooperation is unaffected by simple, and artificial, team identity (i.e. assignment to an identifiable team), but does increase significantly
when team identification is enhanced by having team members cooperate on achieving an
unrelated, preproduction goal.
2. Team production and shirking
Team production “is production in which (1) several types of resources are used and
(2) the product is not a sum of separable outputs of each cooperating resource” (Alchian
and Demsetz, 1972, p. 779). The problem of shirking arises if the resources are owned
by more than one person. If the joint output is shared among the team and the marginal
product of individual team members is not observable, agents have an incentive to shirk
(i.e. to withhold their inputs from the team). Holmstrom (1982) has shown that there exists
no sharing rule that will yield an efficient outcome when the joint output is fully shared
among the agents.3
2 The figures cited represent the minimum change in population for each race between 1990 and 2000. See, U.S.
Census Bureau (1990, 2000a).
3 There is a considerable literature that examines ways to eliminate shirking via incentive mechanisms that are
designed to elicit optimal effort (see, for example, Groves, 1973; Holmstrom, 1979, 1982; Itoh, 1991, and Radner,
1986).
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Economic theory suggests that in the absence of effective monitoring, team production
methods should not be able to exist (see Holmstrom, 1982). Nevertheless, team production
is a relatively common method of production used across a wide spectrum of industries.
Among organizations employing teams, an average of 50 percent of all employees are members of a team (Gordon). The stereotypical sports team is a standard example. Katzenbach
and Smith (1994) reference 47 examples of team production from a variety of different
industries.4
The importance of team interest, the role this factor may play in team production, and
how to accentuate team orientation are considered by Alchian and Demsetz. They argue that
“[I]f one could enhance a common interest in non-shirking in the guise of a team loyalty
or team spirit, the team would be more efficient” (790). In their guide to building teams
and enhancing team production, Katzenbach and Smith continually stress the importance
of a team purpose; common purpose, performance goals, and approach; and a set of rules
and commitments plus roles and responsibilities. Their “requirements” for productive team
performance are just another way of defining the factors that social psychologists would
argue help to define a group and give a group cohesion: common attitudes, values, and
norms.
3. Team identity and cooperative behavior
The theory of team behavior from social psychology offers a possible explanation for
the presence or absence of cooperative behavior by members of teams that produce joint
goods. The basis for economic analysis is rational choice theory, which begins with a utilitymaximizing individual. Individuals interact with other individuals only if they maximize
utility by doing so. However, while, “in the final analysis, ‘individuals’ deal with ‘individuals’, they are not necessarily dealing with each other as individuals; quite often they
behave primarily as members of well-defined and clearly distinct social categories” (Tajfel,
1978, p. 27). Discrimination is an obvious example of individuals dealing with others not
as individuals but as members of a group. The landlord who refuses to rent his apartment to
a prospective tenant may balk, not because of individual characteristics, but rather because
the individual is of the “wrong” racial, religious, or social group. Group, or social, identification “is the perception of oneness with or belongingness to some human aggregate”
(Ashforth and Mael, 21). A feeling of membership in a group can create the perception that
the group’s fate and one’s own fate are the same.
A person’s social identity (how others see and react to her) is determined in part by
the different groups she is identified with and whether she is of the same group(s) as the
identifier. A person’s attitudes, values, and norms may be shaped by the groups to which she
belongs. How two people interact will be affected by a commonality in attitudes, values,
and norms and their ability to predict the existence of such a commonality. One factor
influencing the ability (actual or perceived) to predict commonality is group identity. The
behavior of a fellow group member may be perceived to be more predictable than the
4
Evidence of shirking in team production is offered by Latane et al. (1979) and Weldon and Gargano (1988).
C.C. Eckel, P.J. Grossman / J. of Economic Behavior & Org. 58 (2005) 371–392
375
behavior of someone outside one’s own group. Members of a group are thought to possess
similar beliefs, and persons are likely to be categorized into groups based on visual clues
(Wilder, 1986).
Research in social psychology offers considerable evidence that group identity conditions individuals’ interactions. Campbell (1958) suggests that one possible consequence of
sorting individuals into groups, either on a random or more formal basis, is to enhance the
cooperative tendencies of individuals. At one level, the mere act of categorizing of people
into groups leads to more positive assessments of fellow group members (see Downing and
Monaco, 1986).
At another level, the act of categorization may have economic implications. Can group
identification suppress self-interest in favor of collective interest? Cox et al. (1991) and
Espinoza and Garza (1985) find greater cooperative play on the part of subjects from minority ethnic groups when playing with partners of the same or other minority ethnic groups
than when playing in mixed Anglo/minority groups. Leung and Bond (1984) report more
cooperative behavior among university students in Hong Kong than among American university students.5 Cox et al. and Leung and Bond ascribe the enhanced level of cooperation
to an expectation on the part of subjects that other subjects coming from the same “collectivist culture” would share a collectivist orientation, determining that it was safe to play a
cooperative strategy. Eckel and Grossman (2001) find that women are more accepting of
offers from other women, ceteris paribus, in ultimatum games, a result that is likely to be
based on gender-identified expectations.6
A second set of studies employs artificially created group identity. Often group identity
was created by no more than the random assignment of subjects into different groups.
Allen and Wilder (1975) assign subjects to two groups based on “aesthetic preference.”
Subjects are given a task of dividing rewards between in- and out-group members. In-group
members are favored under all conditions. Rabbie and Horwitz (1969) find that a simple
coin flip to decide receipt of a gift generated significant in-group bias. In a series of three
different dilemma games, Wit and Wilke (1992) find that randomly assigned subjects are
significantly more cooperative with in-group members.
In two studies of group identity in commons dilemma games (Kramer and Brewer,
1984, and Brewer and Kramer, 1986), individual restraint (cooperation) is greatest when
group identity is most salient. This is true both when group identity is defined by naturally
occurring categories and when group identity is manipulated by the researchers. Brewer
(1979) reviews a series of papers reporting such in-group bias. The reported results support
the proposal that “one effect of group identification may be that individuals attach greater
weight to collective outcomes . . . making it less likely that individuals will make sharp
distinctions between their own and others’ welfare” (Kramer and Brewer, 1984, p. 1045).
Group identity may also be defined by status. Status differentials may result in an individual acting differently towards in-group members (others of equal status) than he does
towards out-group members (others of lesser or higher status). Turner (1978) says this is
5
Only in the Leung and Bond study were subjects’ earnings a function of their decisions made in the experiment.
A study by Kachelmeier and Shehata (1992) looked at the effects of culture (China, Canada, and U.S.A.) on
competitive markets. While they observed some cultural differences in price convergence trends, there were no
cultural differences in price levels.
6
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because people attach a positive value to being able to differentiate themselves from others,
especially in a positive light. Brewer and Brown (1998) argue that conferring status on a
group legitimizes their superiority and makes them feel that they deserve, and thus should
work to obtain, better outcomes for members of their group.7
In our experiments, we induce team identity using several different procedures. From
the results of earlier research, we anticipated that it would be relatively easy to induce
team identity and to get greater cooperation among members of the teams so formed. We
also introduced an unrelated team task to enhance subjects’ team identification. Finally, we
introduce ingroup/outgroup competition by conducting the experiment under tournament
conditions.
4. Experimental design
Subjects participated in a public good experiment framed as a team production problem.8
In each decision period, a subject was provided 100 time units (TUs). TUs could be
allocated between either leisure activities, where they earned the subject $0.005 per
TU, or to teamwork. Each TU allocated to teamwork produced a unit of output, and
the total output produced by the team determined the size of the team bonus. Each
TU produced one unit of output, and each unit of output sold for $0.01. All subjects shared equally in any team bonus. Subjects played one practice decision period,
which did not count towards their final earnings, and then fifteen decision periods for
pay. (An appendix of representative experiment instructions is available on the JEBO
website.)9
The experiments were conducted under six different treatments designed to test the
impact of team identity on team production.
4.1. ID1—baseline procedure
Subjects were recruited to one room and randomly assigned to teams of five. Subjects
were told that their team consists of themselves and four other subjects. The membership
of the team was not revealed. Teams were referred to as team 1, 2, 3, or 4. Subjects then
received instructions for the experiment.
7 Studies by Commins and Lockwood (1979), Hoffman and Spitzer (1985), Sachdev and Bourhis (1987), and
Hagendoorn and Henke (1991) support these arguments.
8 Framing the experiment as an individual versus team production problem, as opposed to the individual versus
group investment framing of the more typical public goods games (see, for example, the sample instructions in
Davis and Holt, 1993, pp. 370–374), allows for a test of the impact of framing on subject behavior. In the proposed
experiment, the actions taken by subjects are identical to those taken in a typical public goods game, the only
difference being the reference to team production as opposed to group investment. This is a much more delicate
test of the framing hypothesis.
9 In treatments ID1–ID2, subjects received 80 TUs; each TU allocated to leisure activities earned the subject
$0.01, while output produced by the team sold for $0.02 per unit. Data were analyzed in terms of percent of TUs
contributed to teamwork, so this difference should have only minimal effect.
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377
4.2. ID2—team color treatment
In this treatment, subjects were randomly assigned to one of four teams (blue, gold,
green, or red) and seated at five tables with four subjects per table. One subject from each
team was seated at each table. Teams were identified by an appropriate colored tag worn
by each subject during the course of the experiment. During the experiment, experimenters
continually referred to subjects by their group color. As with treatment ID2, subjects could
identify fellow team members, but there was no interaction amongst team members.
4.3. ID3—quiz treatment
Subjects in this treatment completed a five-question trivia quiz and were told that their
scores would determine the allocation of subjects to teams. (Subjects were not told how
their scores would be determined.) Quiz questions required numerical answers. Scores were
determined by summing the answers given with those subjects with the “highest” score being
allocated to team one, etc. Subjects could identify fellow team members by colored tags,
but there was no interaction among team members.
4.4. ID4—puzzle treatment
Subjects were randomly allocated to a team with team membership indicated by a colored
tag. To create a stronger sense of team identity, teams participated in an unpaid group task
requiring cooperation before the start of the team production problem. Each subject in a
group was given an envelope containing parts from one or more of five equal sized squares.
Group members had to leave their tables and go about the room to seek each other out and
work together, exchanging pieces until each member had constructed a square. After all
teams had completed their squares, the team production exercise began.
4.5. ID5—wage treatment
This treatment followed the same procedures of ID4, with the added incentive to allocate
units to teamwork of a nominal private return for doing so. In addition to a share in the team
bonus, a subject also received a “wage” for the teamwork performed. For every time unit
allocated to teamwork, the subject received a wage of $0.001 in addition to the subject’s
share of the team bonus.
4.6. ID6—tournament treatment
Finally, to enhance team identity even further by creating ingroup/outgroup conflict, the
ID5 treatment was repeated but under tournament conditions. The first five decision periods
of the team production problem were conducted as a tournament. Two teams were paired
off (e.g. blue against gold, green against red) with the team producing the higher total team
output receiving a team bonus of $1.00 per team member. The team with the lower total
team output was penalized $1.00 per team member. Cumulative team output was posted
after each of the first five decision periods so subjects could assess their teams’ relative
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performance. The final ten decision periods were conducted in the same manner as in the
other treatments.
5. Data analysis: strong identity versus weak identity
A total of 450 subjects were recruited from undergraduate courses in economics, anthropology, geography, sociology, and business and by word of mouth at the University of
Texas—Arlington and Saint Cloud State University. All subjects received a $5 appearance
fee and earnings averaged $13.70 per subject. Four sessions of the ID1 procedure (with
a total of 16 teams), three sessions of the ID2 procedure (with a total of six teams), two
sessions of the ID3 procedure (with a total of eight teams), six sessions of the ID4 procedure
(with a total of 12 teams), eight sessions of the ID5 procedure (with a total of 27 teams),
and six sessions of the ID6 procedure (with a total of 21 teams) were conducted.
Fig. 1 shows the mean contribution rate by decision period for each of the six individual
identity treatments. For both the three stronger (ID4–ID6) and the three weaker (ID1–ID3)
treatments, there is no consistent ordering in contribution rates. However, contributions in
the three stronger identity treatments are consistently higher than contributions for the three
weaker identity treatments. For all treatments, there is evidence of decay with time.
In Fig. 2, we combine the stronger identity (SI) treatments (ID4, ID5, and ID6) and
the weaker identity (WI) treatments (ID1, ID2, and ID3). The mean contribution rate in the
stronger identity treatments is consistently, and uniformly, higher than the mean contribution
Fig. 1. Mean contribution rate by identity treatment.
C.C. Eckel, P.J. Grossman / J. of Economic Behavior & Org. 58 (2005) 371–392
379
Fig. 2. Mean contribution rate—weak identity vs. strong identity.
rate for the weaker identity treatments. Both exhibit the usual decay in the contribution level
over time.
In Table 1, we report results from regression analysis that models the convergence process
of the contribution path. Here, the unit of analysis is the team contribution rate in each
decision period. The model of convergence is motivated by Ashenfelter et al. (1992) and
employed by Noussair et al. (1995). This model is designed to address questions about the
initial level of cooperation within a particular team, the decay in the level of cooperation
within the team, and the asymptotic level of cooperation. The model’s basic estimating
equation is given by
1
t−1
yit = β1i Di
+ β2j
+ u.
(1)
t
t
The subscripts i, t, and j denote the particular team, the particular decision period in the
experiment, and the particular treatment, respectively. The dependent variable, yit , is the
contribution rate by team i in period t. The dummy variable Di takes a value of 1 for team i
and 0 otherwise. The origin of the convergence process for team i is given by β1i . β2j is the
asymptote for treatment j’s dependent variable.
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Table 1
Convergence points for contributions to the team (standard errors in parentheses)
Model 1
ID1
Convergence
point
(standard
error)
N
Log
Likelihood
39.92 (1.28)
ID2
ID3
ID4
ID5
ID6
35.39 (2.89)
33.69 (1.48)
46.23 (1.57)
53.58 (1.23)
59.89 (1.50)
1350
−5215.53
All teams
Convergence point (standard error)
Wage (standard error)
N
Log Likelihood
Likelihood ratio test (vs. model 1)
p-Value
Likelihood ratio test (model 2A vs. model 2B)
p-Value
Model 2A
Model 2B
48.54 (0.66)
...
1350
−5253.76
76.46
<0.001
39.65 (0.85)
16.41 (1.28)
1350
−5226.73
22.40
<0.001
54.06
<0.001
Model 3A
Convergence point
(standard error)
Wage (standard error)
N
Log Likelihood
Likelihood ratio test (vs. model 1)
p-Value
Likelihood ratio test (model 3A vs.
model 3B)
p-Value
Model 3B
Weak identity
teams
Strong identity
teams
37.11 (0.97)
53.52 (0.82)
...
1350
−5227.87
24.68
<0.001
...
Weak identity
teams
37.11 (0.97)
Strong identity
teams
56.23 (1.57)
9.83 (1.83)
1350
−5222.06
13.06
<0.025
11.62
<0.001
Model 4
Convergence point (standard error)
N
Log Likelihood
Likelihood ratio test (vs. model 1)
p-Value
Likelihood ratio test (vs. model 3A)
p-Value
Weak identity teams
ID4
ID5
ID6
37.11 (0.97)
1350
−5217.99
4.92
<0.09
19.68
<0.001
46.23 (1.57)
53.28 (1.23)
59.89 (1.50)
Note: Estimated starting points for each team are suppressed.
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381
We use this technique to examine the impact of the treatment (team identity) on the
ending point on the convergence path.10 We estimated the starting point for each team and:
(1) asymptotes (convergence points) for each of the identity treatments (model 1), (2) a
common convergence point for all six identity treatments (models 2A and 2B), (3) common
convergence points for the combined weak identity (WI) treatments and for the combined
strong identity (SI) treatments (models 3A and 3B), and (4) common convergence points
for the combined weak identity (WI) treatments and separate convergence points for each
of the strong identity treatments.11 Table 1 suppresses the starting points and reports only
the convergence values.
Models 2A and 2B and 3A and 3B differ only in the inclusion of a dummy variable,
WAGE, indicating whether a wage was paid to subjects contributing work units to teamwork
(WAGE = 1 if yes, 0 otherwise). Inclusion of WAGE controls for the private incentive to
contribute to teamwork beyond the team solidarity incentive.
The results reported for model 1 indicate that the convergence points for the three
weak identity treatments are consistently below the convergence points for the three
strong identity treatments. While there is little variation in convergence points among
the WI treatments, the convergence points for the SI treatments are directly related
to the strength of team identity. The results suggest that minimal team identification
(i.e. assignment of a team label) is alone insufficient to overcome self-interest. An
enhanced sense of identification created by working together on an unrelated and unpaid
project significantly enhances the subjects’ cooperative tendencies relative to their
self-interest.
A Likelihood ratio test of the unrestricted model 1 to model 4, which restricts the ID1,
ID2, and ID3 treatments to a common convergence point, cannot reject, at the 95 percent
confidence level, the null hypothesis of a common convergence point (χ2 (2) = 4.92, pvalue = 0.085). A Likelihood ratio test, unreported, of the unrestricted model 1 to one that
restricts the ID4, ID5, ID6 treatments to a common convergence point rejects the null
hypothesis (χ2 (2) = 19.78, p-value < 0.001).
To highlight the importance of a strong sense of team identity, we compared model 2A
(common convergence point for all treatments) to model 3A (separate common convergence
points for WI treatments and for SI treatments). The convergence level of contributions by
teams in the WI treatments averaged approximately 30 percent less than the convergence
level of contributions by teams in the SI treatments (37.1 percent versus 53.5 percent). A
Likelihood ratio test rejects the null hypothesis of a common convergence point for the WI
and SI treatments (χ2 (2) = 51.78, p-value < 0.001).
Finally, in models 2B and 3B, we control for the impact of the private wages paid to
contributors of work units to teamwork. Wages were only paid to teams in the ID5 and
ID6 treatments. In both models, WAGES significantly increases contributions to teamwork.
10 As Noussair et al. note, “analysis of the data . . . encounters some classical problems that exist in the analysis
of almost all data produced in experimental markets . . . a convergence process that is not understood theoretically
[and] this means that serial correlation is present, and heteroscedasticity may be present” (472). The method of
correction employed is Kmenta (1986) cross-sectionally heteroskedastic and time-wise autoregressive model (see
pages 618–622). SHAZAM is the statistical package used to estimate the model.
11 Other models were estimated, but the results have been suppressed as they added nothing to the findings.
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382
Table 2
Pair-wise Mann–Whitney test statistics
Treatment
ID1
ID2
ID3
ID4
ID5
ID2
ID3
ID4
ID5
ID6
0.40
1.52
0.56
2.40*
2.86*
0.86
0.81
2.10*
2.46*
1.91
3.52*
3.87*
1.57
2.03*
0.66
*
p-Value < 0.05.
However, as evident from a comparison of models 3A and 3B, the payment of wages does
not significantly alter the convergence point for SI teams.
Finally, we conducted nonparametric tests that confirm our regression results.12 After
estimating Eq. (1) separately for each team and generating individual team convergence
points, we conducted a Kruskal–Wallis test of the null hypothesis that the population distribution functions are identical for all six treatments, ID1–ID6. The test statistic χ2 = 19.64
(critical value χ2 (5) = 11.07, p-value < 0.05) indicates the rejection of the null hypothesis.
We then conducted pair-wise Mann–Whitney tests (see Table 2 for the calculated z statistics). The null hypothesis that the pair-wise samples are drawn from the same population
can only be rejected for ID5 teams and ID1–ID3 teams and ID6 teams and ID1–ID4 teams.
We then combined the weaker identity teams and the stronger identity teams and conducted
a Mann–Whitney test. The test statistic, z = 3.77 (p-value < 0.001) indicates the rejection of
the null hypothesis that the population distributions are identical.
6. Data analysis: tournaments
Nalbantian and Schotter (1997) have shown that [T]ournament-based group incentive
mechanisms that create competition between subgroups . . . determine higher mean outputs
. . .” (315). They, however, applied the tournament incentives to all decision periods. In
this study, we were concerned with whether or not initial cooperation fostered by tournament incentives would create sustained team identification even after these incentives were
removed.
Fig. 3 shows the mean contribution rate by decision period for just the ID5 and ID6
treatments. These two treatments are identical with the exception that the ID6 treatment
included a tournament format for the first five decision periods. Consistent with Nalbantian
and Schotter, the contribution rate for teams in the ID6 treatment is consistently higher than
that for the teams in the ID5 treatment. However, this difference disappears immediately
following the end of the tournament decision periods. From decision period 6 on, the
contribution rates for the ID5 and ID6 treatments track one another closely.
Table 3 reports results for the model estimated just for the ID5 and ID6 treatments.
Models 5A, 6A, and 7A report results for the full 15 decision periods; models 5B, 6B, and
12
We would like to thank David M. Grether, the coeditor, for this suggestion.
C.C. Eckel, P.J. Grossman / J. of Economic Behavior & Org. 58 (2005) 371–392
383
Fig. 3. Mean contribution rates—ID5 treatment vs. ID6 treatment.
7B report the results estimated for just the last 10 periods, after the tournament decision
periods. Starting values (not reported) again vary across teams. Regressing across all 15
decision periods, we find the convergence point for the ID6 teams to be 6 percentage points
higher than the convergence point for the ID5 teams. A Likelihood ratio test rejects the null
hypothesis of no differences in the two convergence points (χ2 (1) = 8.20, p-value < 0.005).
This difference appears, however, to be a function of the tournament decision periods.
Regressing across the final ten, non-tournament, decision periods, we find the ID6 teams
have a lower convergence point than their ID5 counterparts. A Likelihood ratio test cannot
reject the null hypothesis of no differences in the two convergence points (χ2 (1) = 0.28,
p-value < 0.60).
Finally, we considered whether winning teams and losing teams in the tournament sessions behaved differently in subsequent decision periods. Did the winning teams (high
producers) develop a stronger sense of team identity and solidarity and continue to be high
producers even after the tournament ended? Did the losing teams (low producers), fail to
develop a sense of team identity and solidarity, or see their sense of team diminished, by
losing the tournament? Results from regressing across all 15 decision periods suggests that
the answer to both questions is only a weak “yes”. High producer teams had a higher convergence point than low producer teams but the difference is not significant (χ2 (1) = 0.22,
p-value < 0.64). Results from regressing across the last ten decision periods suggest a slightly
stronger “no” to both questions. Low producers have a higher convergence point than high
producers, though insignificantly so (χ2 (1) = 2.70, p-value < 0.11).
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Table 3
Convergence points for contributions to the team: tournament vs. no tournament sessions (standard errors in
parentheses)
Convergence point (standard error)
N
Log Likelihood
All decision periods
Last 10 decision periods
Model 5A
Model 5B
ID6
ID5
ID6
ID5
Teams
Teams
Teams
Teams
59.89 (1.50)
720
−2775.87
53.28 (1.23)
41.38 (2.58)
480
−1758.29
43.39 (2.25)
Convergence point (standard error)
N
Log Likelihood
Likelihood ratio test (vs. model 5)
p-Value
Model 6A
Model 6B
All teams
All teams
56.05 (0.95)
720
−2779.95
8.20
<0.005
42.54 (1.69)
480
−1758.43
0.28
<0.60
Model 7A
Model 7B
ID6
ID6
ID5
ID6
ID6
ID5
Teams: high
producers
Teams: low
producers
Teams
Teams: high
producers
Teams: low
producers
Teams
Convergence point
63.28 (2.34) 56.49 (1.98) 53.28 (1.23)
37.54 (3.35) 45.69 (3.85) 43.39 (2.26)
(standard error)
N
720
480
Log Likelihood
−2775.76
−1756.94
Likelihood ratio test
0.22
2.70
(vs. model 5)
p-Value
<0.64
<0.11
Note: Estimated starting points for each team are suppressed. Standard errors are corrected for heteroscedasticity
and first-order autocorrelation.
7. Conclusion
Team identification may suppress an individual’s private interest relative to the team interest. High degrees of identification may limit individual shirking; well-defined, cohesive
teams may be more successful in deterring free-riding behavior. This paper explores the extent to which team identity deters shirking and free-riding behavior in a team production setting. Team identity is conferred and identification is enhanced by a variety of means. Subjects
then participate in a repeated-play public goods game, framed as a team production problem.
Our results provide no evidence that overt means of identifying subjects with a team
generate greater cooperation on the part of subjects than do random, anonymous team
assignments. Our results suggest that just being identified with a team is, alone, insufficient
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385
to overcome self-interest. We do find, however, that actions designed to enhance team identification contribute to higher levels of team cooperation. Working together on an unrelated
and unpaid project prior to the team production task significantly enhanced the subjects’
cooperative tendencies relative to their self-interest. We also find evidence, consistent with
that reported by Nalbantian and Schotter (1997) that tournament-based incentive mechanisms significantly improved team production. This increase was, however, only temporary,
lasting only as long as the tournament-based incentive mechanisms were in effect. Once the
incentives returned to normal, team production returned to a level similar to that for teams
that had never operated under the tournament-based incentives.
Acknowledgements
Grossman was supported by the National Science Foundation, SBR#97-14943. We would
like to thank Mark Isaac and the coeditor David M. Grether, for their helpful comments and
suggestions.
Appendix A
The following instructions are for the ID1—baseline procedure. The additional instructions received by subjects in the strong identity treatment are included in Italics.
Instructions
Each of you has been given, at random, a packet containing pieces of cardboard. These
pieces of cardboard will be used for forming squares. When the experimenter gives the
signal to begin, the task of your team is to form five squares of equal size. The task will not
be complete until each team member has before him/her a perfect square of the same size
as that held by all other team members.
The following rules must be obeyed during the course of this exercise:
1. Team members may give pieces to other team members but may not take pieces from
other team members.
2. You may not simply throw pieces into the center for others to take; you must give the
piece directly to one other team member.
3. It is permissible for a team member to give away all the pieces to his/her puzzle, even if
he/she has not already formed a square.
Please observe these rules
You are asked to participate in a study of team and individual behavior. The instructions
are simple and if you follow them carefully and allocate your time units wisely, you may
earn a considerable amount of money. You are free to make as much money as you can.
You will be paid in cash in private at the end of the session.
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This study has been designed to maintain the anonymity of each subject’s decision and
each subject’s cash earnings. Only the proctors running the experiment will know a subject’s
decisions and cash earnings. To preserve this anonymity, we ask that from this point on,
there be no talking among the subjects and that all subjects take precautions to maintain the
confidentiality of their materials.
Before we begin, please verify that you have the following items before you on your
desk.
1 Subject’s instructions packet
21 Time unit allocation forms
1 Time unit record form
1 Earnings receipt form
The allocation problem
You and four other people in this room have been assigned to one team. The composition
of your team will be the same for every decision period. In this study, we will conduct up to
20 decision periods in which you and your fellow team members will be asked to allocate
your time between leisure activities and working for the team.
You and four other people with the same colored tags have been assigned to one team.
The composition of your team will be the same for at least the first five decision periods.
At the end of the fifth decision period, teams may be reconstituted for the remainder of the
session. In this study, we will conduct up to 20 decision periods in which you and your fellow
team members will be asked to allocate your time between leisure activities and working
for the team.
In each decision period, every team member will begin with 100 time units (TUs). Your
task is to decide how many of your TUs to allocate to LEISURE ACTIVITIES and how
many TUs to allocate to TEAM WORK. You are free to allocate some TUs to LEISURE
ACTIVITIES and some to TEAM WORK. Alternatively, you can allocate all of your TUs
to LEISURE ACTIVITIES or all to TEAM WORK.
1. TUs allocated to LEISURE ACTIVITIES have a value of $0.005 (½ cent per TU)
2. (a) For every TU you allocate to TEAM WORK you will be paid a wage of $0.001 (1/10
cent per TU). (b) In addition, all team members will share in a bonus earned by the
team. All team members will share equally in the bonus, regardless of how many TUs
they contributed to TEAM WORK. The bonus earned will be determined by the units of
output (we will not specify what the output is) produced by the team. TUs allocated to
TEAM WORK are used to produce units of output. Each TU allocated to TEAM WORK
produces one (1) units of output. Units of output produced by the team will be sold at
the end of each decision period at the price of $0.01 (1 cent) per unit of output. Thus,
the more the team is able to produce, the higher will be the team bonus.
Performance bonus/penalty
At the end of the first five decision periods, the team with the higher total team output will
receive a team bonus and the team with the lower total team output will be penalized. Each
C.C. Eckel, P.J. Grossman / J. of Economic Behavior & Org. 58 (2005) 371–392
387
member of the team with the lower total team output will be penalized $1.00 (i.e. earnings
will be reduced by $1.00 per team member). Each member of the team with the higher total
team output will receive a bonus of $1.00 (i.e. earnings will be increased by $1.00 per team
member).
Sample allocations of time units
Example 1: Suppose each team member (including you) allocates 100 TUs to team work.
(Note: the amount of TUs allocated to team work by others depends on their allocation
decisions—we are just assuming they contribute 400 units for this example)
Value of leisure activities
Value of team work
Your allocation (TUs)
@ Value per unit
Value
0
$.005
$0.00
Value
$0.00
Your allocation (TUs)
@ Wage per unit
Your wages
Other team members’ allocation
Total allocation to team work
@ Value per unit
Value of team work bonus
Your share of bonus (1/5)
Value (wages + bonus)
100
$.001
$.10
400
500
$.01
$5.00
$1.00
$1.10
Total earnings (value of leisure + value of team work) = ($0.00 + $1.10) = $1.10
Example 2: Each team member (including you) allocates 100 TUs to leisure activity.
(Note: the amount of TUs allocated to team work by others depends on their allocation
decisions—we are just assuming 0 units for this example.)
Value of leisure activities
Value of team work
Your allocation (TUs)
@ Value per unit
Value
100
$.005
$0.50
Value
$0.50
Your allocation (TUs)
@ Wage per unit
Your wages
Other team members’ allocation
Total allocation to team work
@ Value per unit
Value of team work bonus
Your share of bonus (1/5)
Value (wages + bonus)
0
$.001
$.0
0
0
$.01
$.00
$.00
$.00
Total earnings (value of leisure + value of team work) = ($0.50 + $0.00) = $0.50
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Example 3: You allocate 20 units to leisure activities and 80 to team work; everyone
else allocates a total of 300 units to team work. (Note: the amount of TUs allocated to team
work by others depends on their allocation decisions—we are just assuming 300 units for
this example.)
Value of leisure activities
Value of team work
Your Allocation (TUs)
@ Value per unit
Value
20
$.005
$0.10
Value
$0.10
Your allocation (TUs)
@ Wage per unit
Your wages
Other team members’ allocation
Total allocation to team work
@ Value per unit
Value of team work bonus
Your share of bonus (1/5)
Value (wages + bonus)
80
$.001
$.08
300
380
$.01
$3.80
$.76
$.84
Total earnings (value of leisure + value of team work) = ($0.10 + $.84) = $.94
Example 4: You allocate 70 units to leisure activities and 30 to team work; everyone
else allocates a total of 300 units to team work. (Note: the amount of TUs allocated to team
work by others depends on their allocation decisions—we are just assuming 300 units for
this example.)
Value of leisure activities
Value of team work
Your allocation (TUs)
@ Value per unit
Value
70
$.005
$0.35
Value
$0.35
Your allocation (TUs)
@ Wage per unit
Your wages
Other team members’ allocation
Total allocation to team work
@ Value per unit
Value of team work bonus
Your share of bonus (1/5)
Value (wages + bonus)
30
$.001
$.03
300
330
$.01
$3.30
$.66
$.69
Total earnings (value of leisure + value of team work) = ($0.35 + $.69) = $1.04
Example 5: Use this space to figure out an example for yourself. Assume the other
members of your team have allocated a total of 250 units to team work. Decide how many
units you wish to allocate to leisure activities and how many to team work. Fill in the
appropriate blanks and then compute the value of leisure activities, your wages from team
work, and your share of the team bonus. Calculate your total earnings. If you need help,
please raise your hand.
C.C. Eckel, P.J. Grossman / J. of Economic Behavior & Org. 58 (2005) 371–392
Value of leisure activities
Your allocation (TUs)
@ Value per unit
Value
Value
389
Value of team work
$.005
$
$
Your allocation (TUs)
@ Wage per unit
Your wages
Other team members’ allocation
Total allocation to team work
@ Value per unit
Value of team work bonus
Your share of bonus (1/5)
Value (wages + bonus)
$.001
$
250
$.01
$
$
$
Total earnings (value of leisure + value of team work) = ($ + $) = $
These are only examples. You may allocate any amount of TUs between 0 and 100
to LEISURE ACTIVITIES. You may allocate any amount of TUs between 0 and 100 to
TEAM WORK. The only restriction is that the sum of your allocation to LEISURE ACTIVITIES plus your allocation to TEAM WORK must equal 100 TUs. Your earnings depend
on your allocation decisions and the allocation decisions of the other members of your
team.
The production decision
At the beginning of each decision period, you will be given an endowment of 100 TUs.
You are to decide how you wish to allocate your TUs between the LEISURE ACTIVITIES
and the TEAM WORK. You are to record your decision using a TIME UNIT ALLOCATION FORM. Be sure that your allocation to of TUs to LEISURE ACTIVITIES plus your
allocation of TUs to TEAM WORK equals the number of TUs in your endowment, 100.
You must make your allocation decision without knowing what the others in your team are
deciding. Do not discuss your decision with any other participant.
After you have made your decision and recorded it on your TIME UNIT ALLOCATION
FORM, the proctor will collect the form.
You have been provided a TIME UNIT RECORD FORM. You may keep a record of
your TU allocations and earnings for each decision period on this form. In column C,
enter your allocation of TUs to LEISURE ACTIVITIES. In column D, enter your allocation of TUs to TEAM WORK. In column E, calculate the value of the TUs allocated to
LEISURE ACTIVITIES (equal to $0.005 × number of TUs in column C). Calculate your
wages earned from TUs allocated to TEAM WORK in column F (equal to $0.001 × number
of TUs in column D). Your share of the TEAM BONUS POOL (to be provided by the
proctor) should be entered in column G. The sum of columns E–G gives you your total
earnings for the decision period. Record your total earnings from each decision period in
column H.
Please do not mark this form with any identifying marks.
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After all TIME UNIT ALLOCATON FORMS have been collected, the proctor will
record the allocation decisions. The TEAM BONUS POOL and team member shares
will be calculated and posted. The proctor will maintain a record of each subject’s total
earnings.
A practice decision period (for which your earnings will not count) plus up to 20 decision
periods (for which your earnings will count) will be conducted.
Cash payments
At the end of the study, please complete your EARNINGS RECEIPT FORM. Collect
all other materials and place them in the folder.
You will be called out into the hallway where you will receive her earnings. After
confirming that the amount is correct, please complete and sign your EARNINGS RECEIPT
FORM. You may now leave.
This procedure will be repeated for the remaining subjects. The study is then completed.
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