Social Preferences or Collusion? Experimental
Evidence
Pablo Hernandez
UC Berkeley
Dylan B. Minor
Northwestern University
Dana Sisak
Erasmus University Rotterdam
November 2011
Abstract
We study relative performance schemes in light of social preferences. To
the extent players are other regarding, they internalize the negative externality
they impose on other players by exerting lower e¤ort. Thus, players with other
regarding preferences are more likely to initiate and sustain collusive behavior,
as traditionally de…ned. We …nd this is indeed the case experimentally. We also
…nd that when communication is allowed, a sel…sh leader may further enhance
collusion amongst an otherwise other regarding group.
Keywords: Social Preferences, Relative Performance, Leadership
1
Relative incentive pay is a common form of compensation, especially when workers’e¤orts are not perfectly observable. A large theoretical literature starting with
the work of Lazear and Rosen (1981) sheds light on the bene…ts and drawbacks of
this kind of compensation relative to …xed and piece rate pay. One problem inherent
to relative incentive pay is its proneness to collusion by the workers. Bandiera et
al. (2005) use personnel data to show that fruit pickers who were able to monitor
each others’ performance increased their productivity by 50% when compensation
changed from relative incentive pay to piece rate pay. When performance could not
be monitored, both incentive schemes fared equally well. This …nding could suggest
collusion plays a major role. Alternatively, it could suggest that workers are internalizing the negative externality their performance has on their coworkers. In other
words, workers who exhibit social preferences might choose lower e¤orts even without the threat of future punishment which characterizes a typical collusive setting.
Whether non-competitive e¤orts are due to collusion or social preferences could have
vastly di¤erent implications for a principal or policy maker.
Although Bandiera et al. (2005) are only able to …nd a suggestive answer through
their …eld experiment, the question of the source of collusive like outcomes can be
ideally tested in a controlled laboratory experiment. In such a setting, we can distinguish between classical collusion (i.e., due to purely sel…sh motives) and low, noncompetitive e¤orts arising from social preferences. To this end, we conduct an experiment where we measure social preferences a la Andreoni and Miller (2002) and then
link these di¤erent preferences back to performance in the face of relative performance
schemes. In particular, we identify people as sel…sh (i.e., they only value their own
payo¤), substitute (i.e., they value total payo¤ as if a Utilitarian), or complement
(i.e., they value equal payo¤s amongst people as a Rawlsian).
From the data, we …nd it is not so much individual social preferences but the
group composition of these that determines the amount of individual e¤ort invested.
Groups who consist of more other regarding social types invest lower e¤ort levels.
This is especially true in later periods, which suggests that these types of individuals
make sustaining lower levels of e¤ort easier. In economic terms, one additional group
member with substitute or complement preferences decreases individual e¤ort by
roughly one unit per period, which is 15% of the mean e¤ort across all treatments.
To further explore how group social composition limits competitive e¤orts, we
consider the forces of the observability of e¤ort and communication. First, we study
the emergence of non-competitive e¤orts in a communication (i.e., via chat) and observability environment. Here we …nd ample evidence of systematic e¤ort reduction.
The availability of communication reduces e¤orts by some 50%. In many groups we
observe convergence to minimum e¤ort or alternating minimum e¤ort–in fact, 62%
of groups can be classi…ed as coordinating on noncompetitive, minimum e¤orts. By
examining the chat messages of the subjects we …nd that typically low group e¤ort
is initiated through a suggestion to coordinate by a person we refer to as a leader.
Some leaders suggest coordinating on an e¤ort level other than the minimum level,
2
which is the Pareto Dominant outcome. Interestingly, it is a sel…sh subject that
emerges not only as a leader, but also as the one that suggests the strategies for a
Pareto Dominant outcome. However, if instead the majority of group members are
sel…sh, we observe maximal rent dissipation and the worst of all outcomes: expending
maximal e¤ort nearly every period.
And yet, absent the ability to communicate, we …nd a di¤erent pattern. In this
case, a leader can only emerge by example rather than by speech. Leadership is also
much more costly since one must break away by providing below average e¤ort to
signal a willingness to exert lower e¤ort. To identify leaders by example in these
treatments, we analyze which types of subjects put in minimal e¤ort and are thus
possibly trying to lead by example. Here rather than sel…sh types, leaders tend to
be other regarding types. Finally, as expected overall e¤ort is much higher in these
treatments— only some 15% of the groups succeed in coordinating on low e¤orts
without chat. Meanwhile, with the no observability treatment, no group is ever able
to collude. Thus, the relationship of social preferences and collusion is a nuanced one.
Our …nal set of treatments attempt to "turn o¤" social preferences by having
subjects play against computer rather than natural subjects. The simulated subjects
are built from actual play history of the earlier treatments.
The paper is organized as follows. The next section review the most relevant
literature. Next, we review our experimental design. Section 3 reviews a simple
theory model that informs our following empirical analysis. We conclude with a
discussion and implications.
1
Literature
Our paper is most closely related to Bandiera, Barankay and Rasul (2005). The authors conduct a …eld experiment on fruit pickers, where they implement a change in
the compensation scheme from relative incentives to a piece rate. They …nd that after
the change takes place output is around 50% higher. They interpret this as evidence
of social preferences— under relative incentives workers at least partially internalize
the externality on their co-workers. In line with this hypothesis they …nd that this
e¤ect is larger when workers are working alongside friends or family members. Our
analysis complements these …ndings in a variety of ways. We implement a similar
relative incentive scheme to Bandiera, Barankay and Rasul (2005) in the laboratory
where we can also measure individual subject social preferences. Our design allows
us to answer how much of non-competitive e¤orts is driven by (which kind of) social preferences and how much is purely due to "classical collusion." In addition, our
experimental design allows us to analyze two di¤erent facilitators of non-competitive
e¤orts, observability and communication, and how they interact with social preferences. Another novelty of our approach is that we can identify leaders, analyze their
emergence and characteristics as well as their e¤ect on group e¤orts.
In order to measure social preferences and categorize subjects we draw on work
3
by Andreoni and Miller (2002) as well as Fisman, Kariv and Markovits (2007). These
papers analyze the rationality of individual giving behavior in dictator games with
various prices of giving to self and other. They …nd that giving choices of most subjects can be represented by a CES utility function. We adopt the categorization of
Andreoni and Miller (2002) of subjects into three groups: sel…sh, substitutes (Utilitarian) and complements (Rawlsian). In addition we use the upwards sloping budget
sets in Andreoni and Miller (2002) to test for spitefulness/jealousy in our subject
population and relate that to behavior under the relative incentive scheme.
Other papers relating social preferences to cooperative behavior include van den
Assem, van Dolder and Thaler (2011), Fehr and Fischbacher (2002), Du¤y and
Munoz-Garcia (2011) as well as Dreber, Fudenberg and Rand (2011). The latter paper
is closest in spirit to our analysis. Dreber, Fudenberg and Rand (2011) relate social
preferences as measured by a giving decision in a simple dictator game to cooperative
behavior in an in…nitely repeated prisoner’s dilemma. They do not …nd a signi…cant
e¤ect of social preferences on cooperative behavior. We, in contrast, implement a
much richer set of dictator menus to construct our social preference proxies and also
a richer game environment in which cooperation can evolve. In contrast to Dreber,
Fudenberg and Rand (2011) we do …nd that social preferences predict cooperative
behavior under relative incentives.
More generally our paper also speaks to the literature analyzing the consistency of
individual social preference types across di¤erent economic settings. Some examples
in this vein include de Oliveira, Croson and Eckel (2009), Fischbacher and Gaechter
(2006), and Blanco, Engelmann and Normann (2011). However, the question this
literature is interested in is whether individual social preferences are consistent across
a range of economic games and contexts. We, instead, are considered with how
a group of individuals with potentially di¤erent social preferences relates to that
group’s relative performance outcome.
2
Experimental Design
In total, we conducted 8 experimental sessions of 21 subjects. The 168 participants
were students from UC Berkeley. Sessions lasted approximately 60 minutes from
reading instructions to subject payment, which averaged approximately $15 per subject. Subjects were not allowed to participate more than one time. The experiments
were programmed and conducted with the software z-Tree developed by Fischbacher
(2007).
We had the twin purpose of identifying people’s social preferences and measuring
their choices when faced with relative performance contracts. To achieve this, the
experiment was divided into three stages. For the …rst stage, we randomly matched
subjects into anonymous groups of three. Participants were then given 100 tokens
for each of nine di¤erent periods to allocate across their group members (including
themselves). However, each period participants faced a di¤erent price vector of giving
4
Treatment
Chat & Observability
No Chat & Observability
No Chat & No Observability
Robot (No Chat & Observability)
Total
Subjects
63
63
21
149
Table 1: Summary of Treatments
to each group member. The price vector is reported below in table 3 . Prices were
varied such that we could both identify one’s willingness to give to others (i.e., other
regarding) and one’s willingness to give between others when facing di¤erent prices
of giving (i.e., concern for equity).
Subjects did not learn their other group members’choices, but did know that one
of the group’s choices would be randomly selected and implemented for payo¤s. From
this …rst stage we categorize players as Sel…sh, Rawlsian, or Utilitarian. In particular,
we label them as one of these based on their choices being nearest (in Euclidian space)
to the theoretical choices of each of these di¤erent stereotypical social types. This
approach is similar to Andreoni and Miller (2002), hereafter AM.
For the second stage, participants were again randomly matched with two other
players and administered a relative performance game modelled after Bandiera et al.
(2005). The purpose of this stage was to give players the possibility to collude by
jointly providing low levels of e¤ort. Thus, we simulated an in…nitely repeated game:
players did not know how many periods the second stage would last, but instead knew
the continuation probability was 95%: We randomly drew the number of periods exante to be 29 periods for stage two.
We also varied factors considered important in the creation and sustaining of
collusion. In particular, one treatment (“Chat and Observability”) allowed chat via
computer terminals during each period and players observed all players payo¤s and
e¤ort choices after every period. A second treatment (“Observability”) did not allow
for chat but did continue with observability after each period. A …nal treatment (“No
Observability”) disallowed both chat and observability. Subjects only learned of their
own payo¤ after each period. Table 1 provides a summary of these treatments, as
well as our …nal treatment labeled “Robot,”which is described below.
A subject’s payo¤ was calculated as follows. Note these …gures are in Berkeley
Bucks $, converted at $66.6 Berkeley Bucks to 1 US$, as this is how it was presented
to subjects. Each player received an endowment of $12 (i.e., Berkeley Bucks $) each
period from which they could choose costly e¤ort. E¤ort cost $1 for each unit of
e¤ort. Total payo¤ was then
5
= 12 +
xi
xj
3
15
xi
x
where 3 j is the average e¤ort across i0 s group and i chooses e¤ort xi 2 [1; 12]:1
Hence, above (below) average e¤ort yielded an above (below) average payo¤. Note
this setup is theoretically isomorphic to a tournament.
For the …nal stage, subjects were again given the same allocation price menus as
in the …rst stage. However, now division was done within the same group they just
played the relative performance game with for 29 periods. The purpose of keeping the
same group was to allow for organic acts of reciprocity. Critically, subjects did not
know they were going to have this …nal stage allocation opportunity. Instead, they
were told at the beginning of the experiment they would have a …nal stage with some
additional opportunities to increase their payo¤. We also administered this second
allocation to explore if any subjects would change their social preferences category.
That is, this was a way to explore where social preferences might come from: positive
or negative experience with their immediate community.
After the allocation decisions, subjects completed a risk aversion test a la Holt
and Laury (2002), and a basic demographic questionnaire.
A …nal treatment consisted of having subjects face the same treatment as Observability but instead of playing against two other subjects, they played against computer
simulated subjects. We call this session the “Robot”treatment. The purpose of this
treatment was to "switch o¤" social preferences. In all of the other treatments, to
the extent a player values other group members’payo¤s, she internalizes the negative externality she imposes on others when she exerts higher e¤ort. However, when
playing against a computer, she has no such externality since the computer receives
no payo¤.
To create the simulated players, we took actual play history of subjects in the
Observability treatments, representing 1827 choices. We assumed players follow a
Markov process, best responding to the most recent period’s play (i.e., the realized
state). Thus, states simply transition from last period’s e¤ort choices to the next.
This Markov process is in the spirit of the dynamic models found in Bajari et al.
(2009). In addition, individual level regression analysis suggests actual subjects do
base play only on the history consisting of the last period’s e¤orts.2 We then built a
"best" response to any realized state— last period’s observed e¤orts. However, actual
subjects did not face every possible state in the state space. Thus, to …ll these best
response "holes," we enter a probabilistic response based on the actual response of
all subjects that actually faced that given subject’s missing state. Additionally, for
tractability and to assure complete state space coverage, we made the state space
more coarse by dividing it into thirds as opposed to elevenths (recall subjects could
enter e¤ort from 1 to 12). Thus, in the end, each of the historical subjects are
1
2
Although subjects were not told to do so, almost all entered e¤ort choices as an integer.
These regression are available form the authors upon request.
6
given a complete strategy set as a function of each possible state space realization,
based on the empirical distribution of actual e¤ort choices, and free of any parametric
assumptions.
The full simulation then proceeds as follows during a treatment. First, an actual
subject is paired with two simulated subjects drawn randomly with equal chance.
This match remains …xed throughout the 29 periods, as it was with natural subject
matching. The subject chooses her e¤ort for the …rst period and the computer chooses
the randomly drawn subjects’e¤ort that they actually chose in the …rst period of their
historical experiment. Second, for the next period, after observing all e¤ort choices
for the previous period, the subject enters her e¤ort choice. The computer chooses
e¤ort choices for each of the two other simulated group members based on the realized
state, which is again all three e¤orts realized in the previous period (i.e., the e¤ort
chosen by the actual subject and the two simulated subjects in the previous period).
This process now continues until completing the 29th period.
To test the e¢ cacy of our “Robot”we then ran simulations of having one subject
enter e¤ort of 1 every period against two other subjects generated from our simulation.
In particular, we ran a monte carlo simulation with 7,000 draws. The end result was
the subject entering 1 every period successfully colluded with the computer generated
subjects 34% of the time. However, in no case did anyone in these actual sessions
play 1 every period. In addition, we ran 7,000 draws randomly matching computer
simulated subjects and we …nd collusion on 11% of the cases. This corresponds to
the actual history of subjects in the “Observability” treatment colluding roughly
15% of the time (i.e., 3 of 21 groups). In short, these many simulations suggested we
had recreated a similar play experience to playing actual subjects. To maximize the
chance of subjects playing as if they were otherwise playing natural players, we also
stated the following in the subject instructions
"In the second stage you will be playing against your computer, which
will represent two other players in your group. However, the computer will
draw e¤ort choices from decisions of actual participants that previously
played this very same game. In particular, over 140 participants have now
played this game in the Xlab, making many thousands of e¤ort choices.
Thus, your outcome today based on your e¤ort choices, will be similar to
as if you played with two of these past subjects live."
3
Model
The unit of analysis is the three agent group. We label each member as member
i = 1; 2; 3 whose material payo¤ is given by
i
=
xi
w
x
7
xi + E
Type
Preferences
Sel…sh
i
Complements (Rawlsian) minj=1;2;3 f j g
Substitutes (Utilitarian)
j j
Table 2: Overview of Social Types
where xi denotes individual i’s e¤ort and x the group’s average e¤ort. The parameters
w and E denote the wage and the endowment constant across agents.
We assume that preferences are linear in money and the action space is [xl ; xh ] with
xh > 23 w. Following Andreoni and Miller (2002), we consider three types of agents in
the population: Sel…sh, Complements (Rawlsian) and Substitutes (Utilitarian). They
di¤er in the way they care about other agents payo¤s. Table 2 summarizes the three
stereotypical social preference types.
Notice these preferences are a particular case of more general speci…cations in
Fisman et al. (2009) and Della Vigna (2009). Speci…cally, we are considering that
agents assign equal weights to themselves as to the other agents. This assumption is
made only to simplify the exposition of the results.
If we follow the simplest case in which agents of a given type believe the others
are also of the same type (Iriberri and Rey-Biel 2010) and this is invariant over time,
then the Nash equilibrium of the stage game is xi = 23 w if sel…sh, xi = xj = x and
x 2 [xl ; 23 w] if Complement and xi = xl if Substitute, for i; j = 1; 2; 3. In each case,
there exists a Nash equilibrium of the in…nitely repeated game (with high enough
discount rate) such that the outcome is the Pareto Optimal e¤ort decisions xi = xl
for all i.3 Of course there are many other equilibrium strategies and in…nitely many
equilibrium outcomes. There is no reason a priori to consider the Pareto optimal
outcome as the focal one. However, as Kreps (1986) points out, a leader could assume
that role. This is what we test in our …rst treatment, assuming that communication
could generate the cooperative outcome as in Cooper et al (1992).
In our second treatment, which involve no communication, agents will use the
information about past outcomes to update their beliefs about others’ types. We
conjecture that reciprocity plays a role in this case. That is, even if agents are
Rawlsian or Utilitarian, they are willing to punish sel…sh behavior in earlier rounds.
Cooperation in this case is then very hard to obtain, and it is bounded above by the
8
.
probability there is no Sel…sh agent in the group 27
In sum, communication anchors beliefs in such a way agents are more likely to
3
This is a consequence of the Folk Theorem. In particular, the presence of a Sel…sh type in
the group is what may decrease the incentives to coordinate, since it is the only type who does
not incorporate the negative externality imposed on others. In this model, however, a Sel…sh type
will mot be willing to cooperate on the Pareto Optimal outcome if he beliefs with high enough
probability there is a Utilitarian type in the group.
8
100
80
60
40
20
0
0
10
20
30
40
50
Period
Effort
Effort
keep
Effort
keep
keep
Treatment: Chat (G roup: S3G1)
Figure 1: Example of "perfect collusion"
coordinate. No communication and observation of others’behavior can lead to coordination in a rather small number of cases.
4
4.1
Empirical Analysis
Examples of Decisions
We begin with some examples of actual giving and e¤ort rates per group to illustrate
the behavior of our subjects. The following graphs illustrate the progression of the
three stages. In periods 1-9 we can observe how many of a hundred tokens each
player in the group keeps for himself. From period 12-40 we observe the choice
of e¤ort ranging from 1 to 12. From period 41-50 we again observe how many of a
hundred tokens subjects keep for themselves, this time making the allocation decision
in their respective groups from stage 2.
Figure 1 shows an example of "perfect collusion" in the chat treatment (Session 3,
Group 1). Subjects coordinate on minimal e¤ort from the very …rst period. Whereas
we observe initially a lot of heterogeneity in the allocation decision (one subject keeps
everything to himself, while the others share almost equally), after stage 2 all subjects
exhibit very similar and very high giving rates.
Figure 2 shows another group from the chat treatment (Session 1, Group 5). Here
we can see another kind of collusion. Subjects alternate between one providing maximal e¤ort, and the other two minimal e¤ort. With the help of the chat, they perfectly
9
100
80
60
40
20
0
0
10
20
30
40
50
Period
Effort
Effort
keep
Effort
keep
keep
Treatment: Chat (G roup: S1G5)
Figure 2: Example of other collusion
coordinate on this synchronized play. Although this does not allow the subjects to
reach the maximal group payo¤, this form of collusion still leads to relatively high
payo¤s. Also in this group we observe heterogeneous giving rates, even though it
doesn’t seem like they are signi…cantly di¤erent in the …rst and third stage.
The following …gure, Figure ?? shows an example of successful collusion in the No
Chat/ Observability treatment (Session 5 Group 3). Subjects very slowly coordinate
on lower e¤orts. Also in this group the giving rates are highly heterogenous and
seem to change between the …rst and third stage. Two subjects seem to keep less to
themselves, while a third actually keeps slightly more.
Our last example, Figure 3 illustrates failed collusion. In this group from the
Chat treatment (Session 1 Group 3) subjects almost in all rounds choose the maximal
e¤orts. Only one subject tries to deviate from this strategy once, without success.
Note that two of these subjects are perfectly sel…sh and keep everything to themselves,
in the …rst as well as third stage menus.
4.2
Categorizing Social Preference Types from Giving Menus
In order to identify social preference types we follow Andreoni and Miller (2002) and
conduct dictator menus at di¤ering prices of giving. Table 3 summarizes the mean
choices of our subjects under all 9 price vectors.
We see that regardless of the price of giving, subjects keep around 70% of their
endowment for themselves. Next, using these choices, we sort our subjects into social
10
100
80
60
40
20
0
0
10
20
30
40
50
Period
Effort
Effort
keep
Effort
keep
keep
Treatment: Chat (G roup: S1G3)
Figure 3: Example of failed collusion
Period
1.
2.
3.
4.
5.
6.
7.
8.
9.
Price vector
(1; 1; 1)
1; 21 ; 12
1; 43 ; 34
1; 45 ; 54
1; 23 ; 32
1; 1; 32
1; 1; 43
1; 43 ; 12
1; 45 ; 34
Keep (min; max)
70.66 (33,100)
73.39 (0,100)
71.82 (0,100)
72.29 (20,100)
71.03 (20,100)
71.80 (0,100)
73.46 (0,100)
77.09 (0,100)
72.72 (0,100)
Give to 1
15.21
13.24
13.98
14.13
14.67
15.90
15.03
12.33
16.18
Table 3: Giving Rates
11
Give to 2
14.13
13.37
14.20
13.59
14.30
12.30
11.51
10.58
11.10
Social Preference Ty pes
17.01%
20.41%
62.59%
selfish
substitute
complement
Figure 4: Social preference types
Soc ial Preferenc e Ty pes by Gender
Female
15.96%
Male
12.77%
20.83%
31.25%
47.92%
71.28%
selfish
substitute
complement
Figure 5: Social preference types by gender
preference type categories. The procedure is described in section 2. Diagram 4 shows
the distribution of social preference types in our subject population.
We …nd that for a little more than 60% of our subjects, preferences of the complement type minimize the absolute errors between choices and stereotype giving. The
other subjects are best categorized in equal shares to either substitutes or sel…sh.
Diagram 5 shows the distribution of social types by gender.
We see that females seem far more likely to be categorized a complement type
while males are especially likely to be a sel…sh type. These di¤erences are statistically
signi…cant (add statistic). This points to an interesting question. Are past studies
controlling for gender just picking up a noisy proxy for social preferences? Our design
will help us address this question later. Figure 6 illustrates giving behavior by social
preference type.
4.3
Relating Social Preferences to E¤orts - A First Pass
In Figure ?? we …rst provide a summary of e¤ort choices over time as a function of
treatment type. As expected, with Chat, there is a strong tendency to coordinate
on lower e¤ort over time. Without chat but with observability there is still a slight
tendency to coordinate on lower e¤ort over time. However, with neither chat nor
12
25
20
15
10
5
0
0
2
4
6
8
10
Period
Avg given to 1 (selfish)
Avg given to 1 (substitute)
Avg given to 1 (complement)
Avg given to 2 (selfish)
Avg given to 2 (substitute)
Avg given to 2 (complement)
Figure 6: Overview over giving rates by social preference types
observability there does not seem to be any change in e¤ort.
Now we consider some additional statistical analysis. We want to …nd out to what
extent the composition of a group’s social preferences facilitates collusive behavior.
As a …rst pass we only look at sel…sh vs. non-sel…sh subjects, later we will use
our …ner measure discussed in section 2. We control for the possibility to chat and
for observability of e¤orts and payo¤s, as these are likely to in‡uence the scope
for non-competitive e¤orts. The next regressions show how group social preference
composition relates to group e¤ort choices. The dependent variable is group e¤ort
averaged over all rounds of play (stage 2). Because these are independent observation,
we use a standard OLS regression with robust standard errors. Table 4 shows the
regression coe¢ cients and p-values.
In the …rst column we see the overall e¤ect of the fraction of sel…sh subjects in
a group, without any further controls. The coe¢ cient is positive but insigni…cant.
Adding controls for chat and observability increase the explanatory power of the
model to .623 and show a signi…cant and positive e¤ect of the fraction of sel…sh subjects in a group. Each sel…sh subject increases group average e¤ort by nearly one unit.
Furthermore adding the possibility to chat decreases average e¤ort signi…cantly by
over 6 units, while observability increases average e¤ort by one unit. Adding further
interactions of the fraction of sel…sh group members with our treatments doesn’t add
much explanatory power, and the marginal overall e¤ect of group composition becomes statistically insigni…cant (p-value of .19) even though the economic magnitude
stays similar (2.86).
13
(1)
(2)
(3)
Avg Group E¤ort Avg Group E¤ort Avg Group E¤ort
Fraction of Sel…sh
0.888
2.767
2.580
(0.678)
(0.067)
(0.000)
Chat
Observability
-6.151
(0.000)
-6.273
(0.000)
0.945
(0.034)
0.913
(0.079)
Chat
Fraction of Sel…sh
0.574
(0.911)
Observability
Fraction of Sel…sh
0.0351
(0.977)
Constant
Observations
Adjusted R2
7.044
(0.000)
49
-0.018
8.487
(0.000)
49
0.623
8.540
(0.000)
49
0.605
p-values in parentheses
p<0.1,
p<0.05,
p<0.01
Table 4: OLS Group Social Preferences and Group E¤ort
14
The next table, Table 5 breaks down the e¤ect of social preferences by complement
and substitute type group members. We see that overall the results are quantitatively
similar in magnitude to table 4, but the marginal e¤ects are mostly insigni…cant.
Without interactions only the fraction of complements is statistically signi…cant at
the 10% level. With interactions we get signi…cance for certain treatments but the
overall marginal e¤ect is insigni…cant (-2.28 and -3.59 with p-values of 0.289 and 0.198
respectively for fraction of complements and substitutes).
Overall these regressions o¤er some …rst evidence in favor of our hypothesis that
groups with subjects with social preferences exhibit lower e¤orts. In the next section
we pursue this hypothesis further and examine the aspect of leadership and how it
relates to social preferences.
4.4
Leadership
Leadership in our experiment takes two di¤erent forms. In the Chat treatment, a
subject can become a leader through the chat, asking the group members to jointly
reduce e¤orts. We use the chat protocols to identify this form of leadership. We
decided to identify two forms of leaders: "First Leader" and "Right Leader". A First
Leader is the …rst subject to propose coordination on low e¤orts, no matter what
the exact form. A Right Leader is the …rst to propose coordinating on minimum
e¤orts. We identify 18 First Leaders and 13 Right Leaders amongst the 63 subjects
in the Chat treatment. A second form of leadership is leadership by example. This
form of leadership is the only form of leadership possible in the No-Chat treatments.
Here we …nd 15 leaders out of 63 in the Observability treatment. In this section we
examine how social preferences relate to these two forms of leadership. We start by
giving an overview over the characteristics of leaders in terms of social preferences.
In Figure ?? we show the distribution of social preference types in the population of
First Leaders and in the population of Non-First Leaders, while Figure ?? shows the
distribution for Right Leaders and Non-Right Leaders.
A Fisher’s exact test shows that the distribution of social preferences is not statistically di¤erent between …rst leaders and non-…rst leaders (p-value 0.151) while it
is statistically di¤erent for right leaders and non-right leaders (p-value 0.066). Comparing the fractions of sel…sh and complements we also …nd statistical di¤erences for
right leaders (two-sided Fisher’s exact test, p-values 0.062 and 0.057) . The fraction
of substitutes is statistically insigni…cant in both populations.
Finally, Figure ?? shows the distribution of social preferences between leaders
and non-leaders in the No Chat / Observability treatment. Here we see a qualitative
di¤erence to the previous two …gures. Substitutes seem more likely to be leaders
by example. We do not …nd a statistical di¤erence in the two distributions though
(Fisher’s exact test, p-value = 0.439).
15
(1)
(2)
(3)
Avg Group E¤ort Avg Group E¤ort Avg Group E¤ort
Fraction of Complements
-0.484
-2.616
-2.758
(0.812)
(0.066)
(0.000)
Fraction of Substitutes
-3.368
(0.333)
CHAT
OBS
-3.400
(0.147)
-1.812
(0.010)
-6.087
(0.000)
-6.113
(0.156)
0.836
(0.075)
1.043
(0.312)
Chat
Fraction of Complements
0.838
(0.868)
Observability
Fraction of Complements
0.140
(0.912)
Chat
Fraction of Substitutes
-2.658
(0.686)
Observability
Fraction of Substitutes
-0.757
(0.734)
Constant
Observations
Adjusted R2
8.101
(0.000)
49
-0.017
11.33
(0.000)
49
0.616
p-values in parentheses
p<0.1,
p<0.05,
p<0.01
Table 5: OLS Group E¤ort and Social Preferences - Detail
16
11.02
(0.000)
49
0.586
To further disentangle the e¤ect of social preferences we now add leadership to the
regression in Table 5. Table 6 reports the results when we add as a control whether
there exists a leader in a group. We di¤erentiate as above between First Leader,
Right Leader and No Chat Leader.
We …nd that adding the leadership controls yields economically similar marginal
overall e¤ects for the fraction of substitutes and the fraction of complements (2.239 and -2.406 respectively), both highly statistically signi…cant (p-values 0.037
and 0.001). This means that social preferences lead to lower group e¤orts even conditional on the existence of a leader. Furthermore, the Chat leadership controls are
highly signi…cant, implying a negative correlation between leadership through communication and group e¤orts. The coe¢ cient on the Chat treatment dummy becomes
insigni…cant, suggesting that communication facilitates collusion solely through the
possibility to coordinate on low e¤orts. If this coordination does not arise, the possibility of chatting does not lead to lower e¤orts.
4.5
Individual-Level Regressions
to be added
4.6
Gender vs. Social Preferences
to be added
4.7
The E¤ect of Reciprocity
to be added
5
Conclusion
to be added
6
Bibliography
References
[1] James Andreoni & John Miller, 2002."Giving According to GARP: An Experimental Test of the Consistency of Preferences for Altruism," Econometrica, vol.
70(2), pages 737-753, March.
17
(1)
(2)
(3)
Avg Group E¤ort Avg Group E¤ort Avg Group E¤ort
Fraction of Complements
-0.484
-2.616
-2.406
(0.812)
(0.066)
(0.003)
Fraction of Substitutes
-3.368
(0.333)
Chat
Observability
-3.400
(0.147)
-2.239
(0.043)
-6.087
(0.000)
0.177
(0.880)
0.836
(0.075)
1.080
(0.007)
Right Leader Exists
-2.892
(0.000)
First LeaderExists
-4.757
(0.000)
No Chat Leader Exists
Constant
Observations
Adjusted R2
0.705
(0.292)
8.101
(0.000)
49
-0.017
11.33
(0.000)
49
0.616
10.35
(0.000)
49
0.863
p-values in parentheses
p<0.1,
p<0.05,
p<0.01
Table 6: OLS Group E¤ort/Social Preferences/Leadership - Detail
18
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19