Who makes a good leader? Cooperativeness, optimism, and leading-by-example.
Gachter, Simon ; Nosenzo, Daniele ; Renner, Elke 等
"Then I would look for integrity. A leader sets an example,
especially a strong leader. He or she is someone on whom people ... in
the organization model themselves." Peter Drucker on Picking a
Leader, excerpted from "The Daily Drucker" (Drucker [2004], p.
5)
I. INTRODUCTION
One of the challenges facing leaders is how to get followers to do
something they otherwise would not do, In settings where followers are
tempted to free-ride on the contributions of others, the challenge is
for leaders to somehow induce followers to eschew their narrowly defined
personal interests to promote the wider interests of the group. Such
settings are commonplace in the workplace, and also in political and
military organizations. One mechanism by which a leader may influence
her followers is through leading-by-example. Recent experimental
research has shown that followers respond strongly to the example set by
a leader (Arbak and Villeval 2007; Gachter and Renner 2003, 2007; Guth
et al. 2007; Kumru and Vesterlund forthcoming; Levati, Sutter, and Van
Der Heijden 2007; Moxnes and Van Der Heijden 2003; Pogrebna et al. 2009;
Potters, Sefton, and Vesterlund 2007; Rivas and Sutter 2008).
In this paper, we report an experiment on a simple leader-follower
game in which efficiency and self-interested behavior are in conflict.
More specifically, we study a sequential voluntary contributions game
where each player has an endowment and can choose how much of this to
contribute to a project. Joint earnings are maximized when each player
contributes their full endowment, but if subjects maximize own-earnings
they will contribute nothing. We focus on the question of who makes the
best leader, in terms of promoting efficient outcomes. We focus on two
factors: the individual's cooperativeness, as measured by her
willingness to contribute to the project when others do so, and the
individual's beliefs about the cooperativeness of others. (1)
Previous experiments with this type of game show that subjects do
make positive contributions, but, at the same time, contributions fall
short of efficient levels. Moreover, there is substantial heterogeneity in decisions across subjects in both roles. Among followers, some
maximize own-earnings but others contribute substantial amounts.
Moreover, follower contributions are heavily influenced by leader
contributions. In experiments with sequential prisoner's dilemmas
second-movers often cooperate if the first-mover cooperates, but hardly
ever if the first-mover defects (Clark and Sefton 2001), and in
experiments with sequential contributions to a public good
followers' contributions tend to increase with leader contributions
(Gachter and Renner 2003, 2007). Thus, cooperative behavior by followers
is often described as evidence of reciprocation or conditional
cooperation (Croson 2007; Fischbacher, Gachter, and Fehr 2001; Frey and
Meier 2004; Glockner et al. forthcoming; Keser and Van Winden 2000).
The experiments also reveal variability in leader decisions. Some
leaders contribute nothing, almost certainly leading the group toward
the lowest possible joint earnings. Other leaders contribute large
amounts. If matched with a conditional cooperator this leads to high
joint earnings, but there is also the possibility of being suckered when
matched with a self-interested player. Compared with follower decisions,
it is more difficult to interpret leader decisions. If a person
contributes a lot in the role of leader is it because they are somehow
cooperatively inclined, or simply because they are self-interested but
optimistic about the prospects of meeting a cooperator? If a person
contributes nothing is it because they are selfish, or are they
cooperators who are pessimistic about the prospects of meeting another
cooperator? And, what type of player is likely to set a better example
as a leader?
To answer these questions, we present a new experiment based on a
leader-follower game in which contribution decisions were elicited using
the strategy method and subjects played in both roles. Using decisions
in the role of follower we are able to classify players according to their degree of conditional cooperativeness. Correlating these measures
with their own decisions in the role of leader allows a within-subject
cross-tabulation of leader contribution against follower type. Thus, we
are able to answer whether cooperators make better or worse leaders. To
answer whether differences between leader decisions reflect differing
underlying social motivations or differing expectations about the
follower, we also have subjects predict what their opponent will do in
the role of a follower. These predictions are used to gauge how
optimistic subjects are about the chances of meeting a cooperator, and
we then ask whether leadership decisions vary across follower types
controlling for this degree of optimism. That is, we ask whether
optimistic cooperators or optimistic self-interested noncooperators make
better leaders.
From their follower decisions we classify about half of our
subjects as noncooperative and about half as cooperative to some degree.
In the role as leader we find that cooperators contribute substantially
more than noncooperators. Although several variables help predict leader
decisions--for example, economists contribute less than
noneconomists--the most useful variable for explaining leader
contributions is their degree of (conditional) cooperativeness. We find
that part (roughly, a quarter) of the difference between the leader
contributions of "Non-Cooperators (NCs)" and "Strong
Cooperators (SCs)" can be attributed to their differing beliefs.
These differences in beliefs are consistent with a false consensus
effect (Ross, Greene, and House 1977). NCs tend to expect they will be
paired with another NC and thus contribute little, whereas SCs are more
optimistic about the prospect of being paired with another SC and so
contribute more. Even so, after controlling for optimistic beliefs SC
leaders still contribute substantially more than NC leaders. Thus, we
conclude that differing leader contributions by differing types of
leader must in large part reflect social motivations. Groups perform
best when led by those who are cooperatively inclined.
Our study is related to a number of experimental papers that
explore how social preferences affect play in social dilemmas by
examining the correlations between decisions in the role of first-mover,
decisions in the role of second-mover, and beliefs about opponents, in
games where subjects play both roles (Altmann, Dohmen, and Wibral 2008;
Blanco, Engelmann, and Normann 2008; Blanco et al. 2009; Bruttel and
Eisenkopf 2009; Chaudhuri and Gangadharan 2007; Fischbacher, Gachter,
and Fehr 2001; Vyrastekova and Garikipati 2005). We discuss this related
literature in the conclusions.
The remainder of the paper is organized as follows. In the next
section, we describe our experimental design and procedures. Section HI
presents our results. We offer concluding comments in Section IV.
II. DESIGN AND PROCEDURES
A. The Experimental Game
Our experiment is based on a simple two-player Leader-Follower
game. Each player is endowed with five tokens, and must decide how many
to contribute to a joint project. Leaders move first and their
contribution decision is revealed to the Follower before the Follower
chooses her own contribution. After the Follower's choice, the game
ends and players' earnings are determined. For each token
contributed to the project both players receive 1 [pounds sterling], and
for each token not contributed to the project that player receives 1.50
[pounds sterling]. Thus, player i's earnings are given by
(1) [[pi].sub.i] = 1.5 X (5-[C.sub.i]) + [c.sub.i] + [c.sub.j]
where [c.sub.i], [c.sub.j] [member of] {0, 1, 2, 3, 4, 5} represent
the contribution decisions of player i and j, for i, j [member of]
{Leader, Follower} and i [not equal to] j.
In our experiment, we implemented a one-shot version of this game
and had subjects make decisions both in the role of Leader and Follower.
Follower's decisions were elicited using the strategy method
(Selten 1967), that is they had to specify complete strategies in the
game-theoretic sense. Thus, participants in our experiments were asked
to make in total seven contribution decisions: one contribution decision
in the role of Leader and six contribution decisions in the role of
Follower, one for each possible contribution by the Leader. Only after
all decisions had been made were subjects assigned a role in the
experiment and paid according to the decisions they made in that role:
with probability one half they were assigned the role of Leader and with
probability one half the role of Follower. Hence, all seven contribution
decisions were elicited using monetary incentives.
Subjects also had to complete a "Prediction Task." In
this task, subjects were asked to predict the contribution decisions
that the other person in their group had made in the role of Follower.
Thus, subjects made 6-point predictions, one for each contribution
decision their opponent made in the role of Follower. Subjects earned
0.50 [pounds sterling] for each correct prediction.
Immediately after having submitted their decisions, subjects were
asked to complete a short post-experimental questionnaire asking for
basic demographic and attitudinal information. This included a
self-assessment of subjects' risk attitudes, which were elicited
using the question suggested by Dohmen et al. (forthcoming). The
question reads: "Are you generally a person who is fully prepared
to take risks or do you try to avoid taking risks?" and subjects
answered on a scale from 1 (unwilling to take risks) to 10 (fully
prepared to take risk). (2) To measure and control for inherent
predisposition to self-interested behavior we also used the
"Machiavellian instrument" (Christie and Geis 1970), a
psychometric test consisting of 20 statements--such as "anyone who
completely trusts anyone else is asking for trouble" or "it is
hard to get ahead without cutting corners here and there"--to which
subjects were asked to agree or disagree using a 7-level Likert scale.
Those who tend to agree with the statements score higher on the
Machiavellian instrument, signaling a combination of selfishness,
cynicism about human nature, and manipulativeness. (3)
B. Discussion of the Design
Our main interest lies in exploring the relation between
subjects' Leader contributions, their own cooperation preferences,
and their expectations about others' cooperation preferences. We
measure subjects' cooperation preferences by the extent to which
they are conditionally cooperative in their Follower contribution
response to the Leader's contribution decisions. Note that the
Follower's decision directly determines the distribution of
earnings and thus provides a cleaner measure of cooperation preferences
than the Leader's decision. It is possible that a Leader may
contribute not because they are inherently cooperative, but rather
because they expect a cooperative response that makes contributing pay.
To measure a Follower's degree of conditional cooperation, we need
to observe a follower response to different possible leader contribution
decisions. (4) The use of the strategy method allows us to observe
subjects' follower contribution responses conditional on each
possible Leader contribution decision without either resorting to
repeated play (which might induce strategic confounds) or using
deception. Thus, from each subject we elicit in an incentive compatible
way a complete vector of conditional contribution decisions that we then
use to classify subjects into "cooperation types" according to
their revealed (conditional) cooperativeness.
Letting subjects play in both roles of the game allows us to
correlate subjects' cooperativeness (measured, as explained, by
their conditional contribution decisions) with their (unconditional)
contribution decision in the role of Leader. Thus, we can observe a
within-subject cross-tabulation of Leader contribution against Follower
cooperation types that allows us to explore whether more cooperative
types make better or worse Leaders.
Because we are also interested in how subjects' beliefs about
others' cooperation preferences relate to their leader
contributions and their cooperation types, we also have subjects predict
what their opponent will do in the role of Follower. That is, from each
subject we elicit a vector of predicted conditional contribution
decisions. This allows us to measure how optimistic subjects are about
the cooperativeness of the players they are matched with. Subjects were
given monetary incentives for correctly predicting others'
contributions and could earn up to 3 [pounds sterling] from the
prediction task. Note that this gives subjects an incentive to predict
the most likely response to each possible leader contribution, rather
than report their subjective probability distributions over possible
responses. To elicit subjective probability distributions over possible
responses in an incentive compatible manner subjects would have had to
complete a 6 x 6 matrix, and we would have had to use a different
scoring rule. This of course would only be incentive-compatible to the
extent that subjects understand the mechanism. Our simpler Prediction
Task has the advantage that it yields an operational measure of optimism
while keeping the task manageable for subjects.
Decisions in the role of Leader and Follower and beliefs were
elicited using a single computer screen so that subjects could make and
revise their choices in any order they liked. (5) This design choice was
motivated by a desire to avoid potential ordering effects which could
have arisen had we prompted subjects to complete the three experimental
tasks in a predetermined order.
C. Experimental Procedures
The experiment was conducted at the University of Nottingham in
Autumn 2008 using subjects recruited from a university-wide pool of
students who had previously indicated their willingness to be paid
volunteers in decision-making experiments. (6) Six sessions were
conducted, four with 18 participants, one with 16 participants, and one
with 14 participants: Thus, 102 subjects participated in total. The
average age was 19.7 yrs and 55% were male. No subject took part in more
than one session.
All sessions used an identical protocol. Upon arrival, subjects
were welcomed and randomly seated at visually separated computer
terminals. Subjects were then given a written set of instructions that
the experimenter read aloud. The instructions included a set of control
questions about how choices translated into earnings. Subjects had to
answer all the questions correctly before the experiment could continue.
The instructions also included a screenshot of the screen on which
subjects entered their decisions. The instructions did not use the
labels "Leader" and "Follower," but rather referred
to "First Movers" and "Second Movers."
The decision-making phase of the session consisted of three tasks:
two decision tasks and the prediction task. In the two decision tasks,
subjects were asked to make contribution decisions both in the role of
Leader and Follower. Subjects were informed at the beginning of the
experiment that they would have had to make contribution decisions in
both roles and that only after all decisions had been made would they
have been informed of their actual role. All decisions were made
anonymously, and neither during nor after the experiment were subjects
informed about the identity of the other person in their group. Once
everyone in the room had completed the three tasks subjects were
informed of their role. Decisions and predictions were then implemented
and subjects paid accordingly.
With our design players' earnings can range from a maximum of
15.50 [pounds sterling] (12.50 [pounds sterling] if a player contributes
zero tokens to the joint project while her opponent contributes five
tokens, plus 3 [pounds sterling] if she reports six correct predictions)
to a minimum of 5 [pounds sterling]. In the experiment, subjects'
earnings ranged from 6.00 [pounds sterling] to 15.00 [pounds sterling],
averaging 9.39 [pounds sterling] (at the time of the experiment 1
[pounds sterling] [approximately equal to] $1.50). On average, the
experimental sessions lasted about 50 min, including the completion of a
post-experimental questionnaire and the payments.
III. DATA ANALYSIS
The following analysis of data is structured around our main
research questions: What type of player makes the best Leader? And, do
different Leader contributions reflect differing underlying social
motivations, or differing expectations about the Follower? To explore
these questions:
1. We first classify subjects' cooperativeness according to
the degree of conditional cooperativeness exhibited by their
contribution responses in the role of Follower.
2. We then explore the relation between subjects'
cooperativeness and their (unconditional) contribution decisions in the
role of Leader. Thus, we will be able to answer whether more cooperative
types make better or worse Leaders.
3. In a third step, we ask whether cooperativeness is
systematically related to beliefs about the cooperativeness of others
and whether differences in beliefs are related to differences in
Leaders' unconditional contribution decisions.
A. Expressed Cooperation Preferences and Cooperation Types
We measure subjects' cooperativeness using their conditional
contribution decisions in the role of Follower. Subjects are classified
as NCs if they contribute nothing in the role of Follower irrespective
of the Leader's contribution. Forty-six percent of our 102 subjects
fall into the NC category. The remaining subjects are classified into
three different cooperation types according to the following criterion.
For each subject, we computed how a (hypothetical) self-interested
Leader would best-respond to the vector of conditional contribution
decisions submitted by this subject. (7) If even a self-interested
Leader would contribute her entire endowment as a best-response to the
subject's vector of conditional contributions, we conclude that the
subject must exhibit a strong degree of conditional cooperativeness. We
classify such a subject as an SC. Twenty-six percent of our subjects
fall into this category. A subject is classified as a Weak Cooperator
(WC) if, when matched with her, a self-interested Leader would find it
optimal to contribute some, but not all, of her tokens. Twenty-two
percent of the subjects can be classified as WC. Finally, if a subject
submitted a vector of contributions that contains positive contributions
in response to some of the Leader's contributions, but does not
give an incentive to a self-interested Leader to contribute any token to
the project, we classify the subject in the category Other. Only 6% of
subjects fall into this category. (8)
Figure 1 depicts--both separately for each preference type and
aggregated across types--the average contribution decisions subjects
made in the role of Follower as a function of the contribution level by
the Leader. (9)
[FIGURE 1 OMITTED]
B. Which Cooperation Type Makes a Better Leader?
We next move to the analysis of the relation between subjects'
cooperativeness and their contribution decisions as Leaders. Figure 2
plots the average Leader contribution decisions separately for the three
major preference types. (10) SC Leaders are those who contribute most to
the public good (about 2.8 tokens on average), whereas NC Leaders
contribute on average least (slightly more than 0.5 tokens on average).
WC Leaders' contribution decisions fall midway between the
contributions of NC and SC Leaders.
We can strongly reject the hypothesis that types contribute similar
amounts (Kruskal-Wallis test: [chi square](2df) = 38.65, p < 0.001).
Pairwise two-sided Mann-Whitney-U tests reveal that NC Leader
contributions differ significantly from WC Leader contributions (z =
4.575, p < 0.001), which in turn differ significantly from SC Leader
contributions (z = 2.065, p = 0.039).
Regression analysis of Leaders' contributions on a set of
dummy variables identifying the three Leader types shows that the
results are substantially robust to a set of controls for individual and
session effects (Table 1). (11) Models I-III build incrementally
including personal characteristics (Models II and III) and controls for
session effects (Model III). SC Leaders' contributions exceed NC
Leaders' contributions by about two tokens in all regression models
and the difference is always significant at the 1% level. WC Leaders
also contribute about one token more than NC Leaders and the difference
is highly significant in all models. Differences between the
contributions of WC and SC Leaders are significant either at the 5% or
10% level depending on the regression model specification. (12)
[FIGURE 2 OMITTED]
Among the variables controlling for individual characteristics, the
dummy identifying subjects studying Economics is highly significant in
both the regression models where it is included: Leaders who study
Economics appear to contribute significantly less than others. This
result is consistent with findings from other laboratory experiments
(e.g., Frank, Gilovich, and Regan 1993; Marwell and Ames 1981), although
there is an ongoing debate about the reasons for these differences in
other-regarding behavior (see, e.g., Frey and Meier 2003).
Also important is the "Machiavellianism" of the subject.
The coefficient of the Machiavellian score (Christie and Geis 1970)--a
psychometric test where higher scores signal a combination of
selfishness and opportunism--is negative and statistically significant
in both models: Leaders with high Machiavellian scores tend to
contribute less than those who score low in Machiavellianism. This
result is consistent with Burks, Carpenter, and Verhoogen (2003), who
also find that first-movers with a high Machiavellian score send less in
a trust game where subjects played both roles. (13)
We do not observe a clear gender effect. The regressions show that,
after controlling for cooperativeness, males contribute more than
females, although the difference is insignificant. Arbak and Villeval
(2007) report a similar finding. This result compares also with findings
on first-mover's behavior in trust games where men are sometimes
found to send larger amounts than women (e.g., Buchan, Croson, and
Solnick 2008), but the effect is often not significant (e.g., Croson and
Buchan 1999). (14)
As commitment to a leadership contribution is a risky decision we
may expect that Leaders who are more willing to take risks contribute
more than those who are less prepared to make risky decisions. Our
measurement of subjects' willingness to take risks is instead
negatively correlated with leader contributions in Model II, whereas it
enters with a positive coefficient in Model III. In both cases, the
effect is not statistically significant (p = .896 in Model II; p = .992
in Model III), suggesting that risk considerations are unimportant for
leader decisions in our experiment.
Model III includes session dummies (which are jointly
insignificant) and the variable "Number of Known Others in
Session" measuring the number of other participants in the session
known to the subject. Although the overwhelming majority of participants
were strangers to one another (on average a participant only knew 0.12
other participants), knowing other participants in the session reduces
the amount a Leader is willing to contribute.
To get a sense of the importance of assigning given cooperation
types to the role of Leader, we conducted a simple accounting exercise.
For every possible pairing of subjects, we calculated total
contributions for both possible role assignments. We present the average
of these by cooperation type combination in Table 2. For example, on
average an SC Leader and NC Follower make a total contribution of 2.85
tokens. For a given Follower type, contributions increase with the
cooperativeness of the Leader, and for a given Leader type contributions
increase with the cooperativeness of the Follower (with one exception:
when an NC Leader is paired with a WC Follower contributions are higher
than when paired with an SC Follower). Note also that when types differ,
contributions are always higher when the more cooperative type occupies
the role of Leader. Using the observed distribution of cooperation types
we also compute the expected total contribution for each Leader
cooperation type. SC Leaders generate more than four times as many
contributions as NC Leaders.
[FIGURE 3 OMITTED]
C. Are SCs Better Leaders Because They Are More Optimistic About
Followers?
So far we have shown that cooperation preferences, as measured by
conditional contribution decisions, strongly correlate with leader
contributions: cooperative Leaders contribute significantly more than
noncooperative Leaders.
However, the large difference in leader contribution decisions
between SC, WC, and NC subjects observed in our experiment may not
necessarily be because of differences in the underlying social
motivations of these three types. SC, WC, and NC subjects may instead
hold different expectations about the Follower's behavior, which
may in turn drive their contribution decisions. For example, NC subjects
may believe that Followers are more likely to behave as a free-riding
"NC type," whereas SC subjects may believe that free-riding
behavior is relatively less common and hence may expect a positive
return from contributing to the project. Such a systematic bias in
beliefs (and, in particular, the tendency to estimate one's own
behavior to be more common than it is estimated by those who engage in
alternative behaviors) is called the false consensus effect (Ross,
Greene, and House 1977). (15)
To verify whether a false consensus effect might be driving our
results, we start by exploring the relation between subjects' own
preferences and their expectations about the cooperation preferences of
their opponents, as elicited in the prediction task. As a first step in
Figure 3 we draw--both separately for each preference type and
aggregated across types--the average conditional contribution decisions
that subjects predicted the other person in their group would have made
in the role of Follower.
The most remarkable feature of Figure 3 is its similarity with
Figure 1, where we depicted subjects' average own contribution
decisions by cooperation type. SC and WC contribution decisions are
almost identical to their beliefs about others' contribution
decisions. NC subjects' predictions of others' contribution
decisions differ instead from their own contribution decisions, as these
subjects seem to believe that others' contributions increase in the
Leader's contribution decisions, whereas they always choose to
contribute nothing irrespective of the Leader's decision.
[FIGURE 4 OMITTED]
Overall, Figure 3 suggests that different cooperation types hold
different beliefs about others' cooperation types. To explore this
issue further, we use subjects' predictions about their
opponent's conditional contributions to classify subjects according
to their predicted Follower type. Our classification method parallels
the one we used to classify cooperation types. If a subject predicts
that the opponent will contribute nothing to the project irrespective of
the leader contribution, we classify that subject as having a predicted
NC Follower. (16) If a subject predicts that the Follower will
contribute something in response to some Leader contribution, we
classify the subject as having a predicted Other, WC, or SC Follower
depending on whether a risk-neutral selfish Leader's optimal choice
would be to contribute zero, some, or all of her endowment to the
project. (17) Figure 4 shows--separately for each preference type--the
proportion of Leaders who predict an NC, WC, SC, or Other Follower.
Clearly, subjects' predictions about others' preferences
are strongly biased toward their own preference type: More than 60% of
NC Leaders predict that they are matched with an NC Follower, more than
80% of WC Leaders predict they are matched with a WC Follower, whereas
almost 80% of SC Leaders predict that the person they are matched with
is also an SC type. We can strongly reject the hypothesis that the
distribution of predicted cooperation types is the same across the three
Leader types: [chi square](6d f) = 81.11, p < 0.001. Pair-wise Fisher
exact tests performed separately for each preference type are all
significant at the 1% level. Thus, subjects' predictions about
others' preference types are consistent with a false consensus
effect. (18)
Our next step is to explore whether such a false consensus effect
is actually driving our results about differences in leader
contributions across preference types. It may be that differences in
cooperation preferences are not the reason why SC subjects contribute
more than NC subjects in the role of leader. Instead, SC subjects may
choose to make larger leader contributions than NC subjects because they
are more optimistic that Followers will respond with positive
contributions. If this is in fact the case, we would then expect that,
for a given belief about the opponent's type, leader contributions
would not be significantly different across Leaders' preference
types.
To explore this issue, we augment our regression analysis of
Leaders' contributions developed in Models I-Ill and reported in
Table 1 above with the variable "Degree of Optimism." This
variable measures the Leader's best-response to his or her own
predictions about the opponent's conditional contribution
decisions. The higher the Leader's best-response to his or her own
beliefs, the more optimistic he or she is about the cooperativeness of
their Follower: the most optimistic Leaders are those whose
best-response is to contribute five tokens to the joint project because,
as explained above, these Leaders predict that the Follower is a SC. The
least optimistic Leaders are those whose best-response is to contribute
nothing: These subjects predict that they are matched either with a NC
or with a Follower that belongs to the category Other. Leaders whose
best-response range from 1 to 4 predict that they are matched with a WC.
(19)
The results of the regressions are shown in Table 3. (20) The
variable "Degree of Optimism" is significant and positive in
all three models: consistent with a belief-based explanation of
differing leader contributions across types, Leaders who are more
optimistic about the cooperativeness of their Follower make higher
contributions. Nevertheless, for a given degree of optimism, WC Leaders
still contribute about 1 token more than NC Leaders, and SC Leaders
contribute about 1-1/2 tokens more than NC Leaders (both coefficients
are significantly different from zero at the 1% or 5% level). Hence,
Leaders with the same degree of optimism do make different contributions
depending on their preference type.
Our controls for individual characteristics and session effects
substantially reproduce the same pattern of results observed in the
regressions reported in Table 1 : Leaders studying Economics contribute
significantly less than others, as do Leaders with high Machiavellian
scores, as do Leaders who know more other participants in the session.
Session dummies, included in Model III, are jointly insignificant, as is
the dummy controlling for gender and our measure of subjects' risk
attitudes.
Overall, these results show that Leaders' expectations about
their opponent's preference type are systematically biased toward
their own preference type (i.e., they are influenced by a false
consensus effect). However, the large differences in leader contribution
decisions between SC, WC, and NC subjects that we observed in our
experiment cannot be entirely explained in terms of systematic
differences in expectations about others' cooperation preferences,
because for a given belief about the Follower's cooperativeness,
leader contributions are still significantly different across
Leaders' preference types.
IV. DISCUSSION & CONCLUSIONS
We examine how cooperativeness and beliefs about the
cooperativeness of others affect leadership contributions in a simple
leader-follower game. The game uses the same type of earnings functions
used in experiments examining voluntary contributions to a public good.
Thus, a Follower's contribution increases group earnings at the
expense of the Follower's narrow personal interests. Our experiment
allows Leaders to attempt to induce such group-oriented behavior through
"leading-by-example": by contributing Leaders might, if the
Follower is sufficiently conditionally cooperative, induce the Follower
to contribute as well. Our focus is on the extent to which the
Leader's willingness to lead-by-example depends on her own
cooperation preferences, her beliefs about the cooperation preferences
of her Follower, and other personal characteristics.
As in previous experiments, we find that many Followers are
conditionally cooperative and are willing to reciprocate the
Leader's contribution. About half of our subjects exhibit a degree
of conditional cooperativeness such that it pays for a self-interested
Leader to contribute something, and about half of these cooperators are
classified as "SCs," as they are conditionally cooperative to
the extent that a self-interested Leader should contribute her entire
endowment. These cooperation preferences are strongly correlated with
(unconditional) Leader decisions. For example, SC Leaders contributed
around 57% of their endowments, significantly more than NC Leaders who
contributed only 12% of their endowments. Part of this effect can be
explained by subjects' personal characteristics. Economists
contribute less as Leaders, as do those who are more
"Machiavellian." However, even after controlling for these
personal characteristics, SC Leaders contribute 40% more of their
endowments than NC Leaders.
Our finding that SCs make higher Leader contributions than NCs is
in line with recent studies from trust and sequential social dilemma games where subjects play both roles. For example Altmann, Dohmen, and
Wibral (2008) and Chaudhuri and Gangadharan (2007) both find that
trustees who reciprocate more are more trusting in trust games. We see
two main differences between trust games and our leader-follower game.
First, our focus on leading-by-example has guided our choice of a game
where the leader and follower have identical choice sets and earnings
functions, and so the leader's decision can be easily viewed as an
"example" to the follower. In the trust game, there is an
asymmetry between roles that goes beyond the sequential structuring of
choices, and this asymmetry makes it less clear that the trustor can
"lead by example." Second, in our game the follower's
decision affects group earnings, whereas in a trust game the
second-mover's decision is a pure transfer, only affecting the
distribution of group earnings.
Altmann, Dohmen, and Wibral (2008) speculate that a false consensus
effect, whereby selfish subjects believe others are selfish and
reciprocal subjects believe others are reciprocal, could explain why
reciprocal trustees trust more in their experiment. The same effect may
also explain why SCs make higher Leader contributions in our
experiment--they may be more optimistic about the cooperativeness of
Followers. Similarly, this could explain the positive correlation between decisions as first-mover and second-mover reported in sequential
social dilemma game experiments where subjects play both roles (see,
e.g., Blanco et al. 2008; Bruttel and Eisenkopf 2009; Fischbacher,
Gachter, and Fehr 2001). Differently from these studies, our design
allows us to control for differences in beliefs, and we do indeed find a
strong correlation between own cooperation preferences and beliefs about
the cooperation preferences of others: SCs are more optimistic about the
chances of being paired with another SC. However, even after controlling
for optimism, SCs contribute about 30% more of their endowment than NC
Leaders. Thus, differing degrees of optimism can only explain part of
the difference between the Leader contributions of NCs and SCs and most
of the difference reflects their differing social motivations.
Our findings are comparable with those reported in a recent
experiment by Blanco et al. (2009). Their extensive design uses several
treatments to examine the relationship between cooperativeness and
beliefs in a sequential prisoner's dilemma game experiment where
subjects play both roles. In their Baseline treatment, they do not
elicit beliefs about second-movers' cooperativeness, and find, like
the studies cited above, that a majority of subjects make the same
choice as a first-mover and as a second-mover. The correlation between
first- and second-mover decisions persists in a second treatment where,
as in our experiment, they elicit subjects' beliefs about
second-movers' cooperativeness. They find that beliefs about second
movers' cooperativeness are positively related to subjects'
own cooperativeness, which is consistent with a consensus effect. In a
third treatment, Blanco et al. (2009) provide subjects with feedback
about the true distribution of second-movers' cooperativeness
before eliciting their first-mover decisions. They find that the
correlation between first-mover and second-mover decisions persists even
when accurate feedback about second-movers' cooperativeness is
provided, suggesting that a consensus effect can only provide a partial
explanation for the positive correlation between first-mover and
second-mover decisions observed in their experiments. (21) Our findings
are also consistent with those of Vyrastekova and Garikipati (2005):
Using a trust game, they correlate trustor decisions with their beliefs
about the trustee's decision and with their distributional
preferences as measured using the "Decomposed Game technique,"
an instrument developed by sociologists and social psychologists to
assess individual value orientations (see, e.g., Liebrand et al. 1986).
They find a strong relation between distributional preferences and
trustor decisions even after controlling for beliefs. They also find
that beliefs are strongly correlated with distributional preferences.
We only address a narrow aspect of leadership: leading-by-example.
Nevertheless our results are suggestive that effective leadership will
depend on the leader's cooperative preferences and beliefs. To the
extent that a large part of the variation in Leader contributions can be
explained by cooperation preferences, even after controlling for
optimism, this suggests that groups may perform better when led by
individuals who are willing to sacrifice personal benefit for the
greater good. Furthermore, because beliefs are highly correlated with
cooperation preferences, such individuals are more likely to have
optimistic views about Followers that will reinforce their propensity to
contribute. Although noncooperative Leaders could, in principle, do
anything that an optimistic cooperator does, their cooperation
preferences and expectations about others may make them less likely to
provide effective leadership.
A natural question that follows from our findings and that may be
particularly relevant in settings that involve repeated interactions is
whether it is more beneficial for the group that the most cooperative
individuals set an example by committing to an initial contribution, or
whether it can be better to have other, less cooperative individuals
move first and let SCs observe their contributions and discipline them.
Rivas and Sutter (2008) report on an experiment where they let leaders
move after other subjects have made a contribution and find that this
does not affect cooperation rates in a simple public good setting.
However, they do not selectively choose the most cooperative types as
leaders in their experiment. Moreover, leaders in their study can only
discipline first-movers through their own contribution decisions. An
interesting development, which we leave for further investigation, would
be to assess how cooperativeness is affected when second-movers are
given some form of sanctioning or rewarding power such that they can
effectively discipline early contributors' behavior.
doi: 10.1111/j.1465-7295.2010.00295.x
ABBREVIATIONS
LRM: Linear Regression Model
NC: Non-Cooperator
OLS: Ordinary Least Square
ORM: Ordered Regression Model
SC: Strong Cooperator
WC: Weak Cooperator
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online
version of this article:
Appendix S1: Experimental instructions.
Please note: Wiley-Blackwell are not responsible for the content or
functionality of any supporting materials supplied by the authors. Any
queries (other than missing material) should be directed to the
corresponding author for the article.
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SIMON GACHTER, DANIELE NOSENZO, ELKE RENNER and MARTIN SEFTON *
* This work was funded by the British Academy under small research
grant SG-44918. We gratefully acknowledge the helpful comments by
anonymous referees and Yah Chen.
Gachter: Professor of the Psychology of Economic Decision Making,
Centre for Decision Research and Experimental Economics (CeDEx),
University of Nottingham, School of Economics, Sir Clive Granger Building, University Park, Nottingham NG7 2RD, United Kingdom. Phone +44
(0) 115 846 6132, Fax +44 (0) 115 951 4159, E-mail
[email protected]
Nosenzo: PhD Candidate, Centre for Decision Research and
Experimental Economics (CeDEx), University of Nottingham, School of
Economics, Sir Clive Granger Building, University Park, Nottingham NG7
2RD, United Kingdom. Phone +44 (0) 115 846 7492, Fax +44 (0) 115 951
4159, E-mail
[email protected]
Renner: Lecturer in Economics, Centre for Decision Research and
Experimental Economics (CeDEx), University of Nottingham, School of
Economics, Sir Clive Granger Building, University Park, Nottingham NG7
2RD, United Kingdom. Phone +44 (0) 115 951 5399, Fax +44 (0) 115 951
4159, E-mail
[email protected]
Sefton: Professor of Economics, Centre for Decision Research and
Experimental Economics (CeDEx), University of Nottingham, School of
Economics, Sir Clive Granger Building, University Park, Nottingham NG7
2RD, United Kingdom. Phone +44 (0) 115 846 6130, Fax +44 (0) 115 951
4159, E-mail
[email protected]
(1.) of course, there are many other aspects of leadership that we
do not address in this paper. See Yukl (1989) for a comprehensive
treatment. In natural settings, the role of a leader may encompass a
broad range of activities-coordinating and organizing efficient
allocation of individual tasks, mediating conflicts, designing incentive
schemes, disciplining deviators, maintaining group relations, and so
on--and these activities may require different (psychological)
qualities. See Van Vugt and De Cremer (2002) for a social psychological
perspective on aspects of leadership in social dilemma situations.
(2.) The average response was 6.05 (SD 2.10).
(3.) Higher Machiavellian scores are generally associated with less
generous offers in dictator games, but not in ultimatum games (see,
e.g., Carpenter, Burks, and Verhoogen 2005; Carpenter, Verhoogen, and
Burks 2005; Spitzer et al. 2007). Meyer (1992) shows that subjects
scoring high on the Machiavellian instrument are less likely to reject
unfair offers in a one-shot ultimatum game with hypothetical payoffs.
Burks, Carpenter, and Verhoogen (2003) find that subjects with higher
Machiavellian scores send less in trust games, while Gunnthorsdottir,
McCabe, and Smith (2002) find that subjects with higher Machiavellian
scores are less likely to reciprocate trust.
(4.) For example, observing a Follower that contributes zero tokens
in response to a leader contribution of zero tokens does not reveal
whether the subject is a conditional cooperator (and hence responds with
low contributions to low leader contributions) or whether he or she is
motivated by own-profit maximization. What we need to observe is the
Follower's contributions in different subgames.
(5.) A screensbot of the computer screen used to elicit
subjects' decisions and beliefs was included in the instructions
that were given to subjects, which are reproduced in the online
supplementary materials.
(6.) Subjects were recruited through the online recruitment system
ORSEE (Greiner 2004). The experiment was programmed and conducted with
the software z-Tree (Fischbacher 2007).
(7.) Should the Leader be indifferent between two or more
contribution decisions, the largest contribution is used for computing the Leader's best-response.
(8.) Half of these subjects are "unconditional
cooperators" who contribute the same (nonzero) amount irrespective
of the Leader's contribution. The other half contributes one or two
tokens only if the Leader contributes four or five tokens.
(9.) Note that the patterns of contribution decisions of NC, SC,
WC, and Other closely resemble the average contribution patterns
typically found in linear public goods games for "Free Rider,"
"Conditional Cooperator," "Hump-shaped," and
"Other" according to the classification system introduced by
Fischbacher, Gachter, and Fehr (2001). In fact, the two classification
systems are highly consistent with one another: All NC and Other would
be classified as Free Riders and Other, respectively, 85% of SC subjects
as Conditional Cooperators, and 64% of WC subjects as Hump-shaped
Contributors.
(10.) In the remainder of the paper, we will focus our analysis on
the three major groups and ignore the subjects we classified as Other.
With only six subjects in the Other category, we would not be able to
draw any valid inference from their behavior and their inclusion in the
analysis would only unnecessarily complicate the exposition of our
results. All our findings are robust to whether we include or exclude
these six subjects.
(11.) Long (1997) (pp. 115-119) discusses the costs and benefits of
using a linear regression model (LRM) instead of ordered regression
models (ORMs) when using ordinal dependent variables and concludes that
in general "... the results of the LRM only correspond to those of
the ORM if [the cut-points of an ORM] are all about the same distance
apart" (p. 119), that is, if the intervals between adjacent
categories of the dependent variable are equal, which is in fact the
case for the variable "Leader's contribution." Given
their simpler interpretation, OLS estimates are reported hereafter. Any
inference based on such estimates can be also derived using ORM
estimation.
(12.) The p-values from the F-test for equality of coefficients on
SC and WC are 0.045 (Model I), 0.069 (Model II), and 0.051 (Model III).
(13.) Across the whole sample scores ranged from 67 to 136. The
average score was 98.92 with a standard deviation of 14.1 l, which is
similar to that reported in other experimental studies (e.g., Burks,
Carpenter, and Verhoogen 2003; Carpenter, Burks, and Verhoogen 2005;
Flues and Gachter 2008; Gunnthorsdottir, McCabe, and Smith 2002).
(14.) See Bohnet (2007) for a discussion of gender effects in
trusting behavior. Croson and Gneezy (2009) provide a general review of
gender effects in experiments.
(15.) In the context of cooperation, a seminal paper is Kelley and
Stahelski (1970). Recent experimental studies finding evidence of a
false consensus effect are Selten and Ockenfels (1998), Charness and
Grosskopf (2001), and Van Der Heijden, Nelissen, and Potters (2007).
Engelmann and Strobel (2000) and Engelmann and Strobel (2007) discuss
whether the consensus effect is "truly" false and show that
the bias mitigates with the presentation of representative information
(see also Offerman, Sonnemans, and Schram 1996).
(16.) One might worry that subjects may report biased beliefs in
the Prediction Task to hedge against risk. For example, a Leader who
contributes five tokens will receive a low payoff if the Follower
contributes zero. Even if the Leader expects the Follower to reciprocate
he may predict the Follower will contribute zero to insure against the
worst possible case. If this were indeed the case, Leaders who
contribute more would report more pessimistic beliefs about the
Follower's cooperativeness. In fact, Figure 3 suggests the
opposite: more cooperative Leaders predict higher contribution by the
Follower. More generally, we note that there is very limited evidence of
hedging in sequential prisoner's dilemma experiments when
first-movers predict second-mover's choices (see, e.g., Blanco et
al. 2008).
(17.) We thought it natural to convert the vector of predictions
into a type using the same method as that used to convert the vector of
Follower choices into a cooperation type. However, the predicted
Follower type labels should be interpreted with caution. The optimal
contribution of a risk-neutral selfish Leader depends on the expected
responses to the six possible leader contributions and, as noted
previously, our belief elicitation procedure gives subjects an incentive
to reveal the most likely response to each leader contribution. Thus, a
risk-neutral selfish Leader may not necessarily find it optimal to
contribute five tokens against a predicted SC Follower type.
(18.) One may argue that such a strong bias toward the own
preference type may be because of the fact that subjects do not report
their beliefs truthfully but rather in a way that satisfies the need to
see oneself behaving "as others do" and hence behaving
appropriately. In fact, one potential explanation for the false
consensus effect is based on such a "motivational" mechanism.
However, if this is the case, one would also expect the bias to
disappear or to be mitigated in the presence of financial incentives and
to be stronger for answers to questions about socially
desirable/undesirable activities. However, the false consensus effect
has also been reproduced in the presence of monetary incentives, as in
the present experiment. Moreover, a false consensus effect has been
found also in studies employing morally neutral questions (see, e.g.,
Engelmann and Strobel 2000, 2007).
(19.) Again, the caveat noted in footnote 17 applies.
(20.) Because Leaders' type and degree of optimism are
correlated it may be difficult to identify the contribution of each
factor to leader contributions if there is a co-linearity problem.
However, checks for multicollinearity (based on variance inflation
factor values) suggest that this is not the case for the Models reported
in Table 3.
(21.) Note that their subjects are classified as cooperative or not
according to whether, as second-mover, they cooperate or defect in
response to cooperate. Our second-movers fill in a contribution schedule
indicating bow may tokens they contribute (up to 5) for each possible
contribution decision by the first-mover (again, a number from 0 to 5).
We measure a subject's degree of cooperativeness based on the slope
of this contribution schedule.
TABLE 1
Determinants of Leader Contributions
I II
1 if SC 2.235 *** (0.345) 2.078 *** (0.364)
1 if WC 1.428 *** (0.320) 1.400 *** (0.286)
1 if male 0.287 (0.284)
1 if area of study
is economics -0.841 *** (0.263)
Willingness to take risks -0.010 (0.077)
Machiavellian score -0.021 ** (0.010)
Number of known others
in session
Constant 0.617 *** (0.180) 2.867 *** (1.323)
Session dummies No No
96 96
F-statistic F(2,93) = 24.55 F(6,89) = 16.16
Prob > F 0.000 0.000
[R.sup.2] 0.360 0.447
III
1 if SC 1.928 *** (0.384)
1 if WC 1.187 *** (0.324)
1 if male 0.401 (0.301)
1 if area of study
is economics -0.876 *** (0.287)
Willingness to take risks 0.001 (0.081)
Machiavellian score -0.021 * (0.010)
Number of known others
in session -0.507 ** (0.237)
Constant 2.882 ** (1.386)
Session dummies Yes
96
F-statistic F(12,83) = 10.60
Prob > F 0.000
[R.sup.2] 0.489
Ordinary least squares (OLS) regressions. Dependent variable is
Leader's contribution. Robust standard errors are in parentheses.
* .05 [less than or equal to] p [less than or equal to] .10; ** .01
[less than or equal to] p < .05; *** p < .01.
TABLE 2
Total Contributions by Cooperation Types
Type of Follower
Type of NC WC SC
Leader (n = 47) (n = 22) (n = 27) Expected *
NC 0.63 1.24 1.17 0.92
WC 2.05 3.66 3.70 2.87
SC 2.85 4.40 5.32 3.89
* The expected total contribution takes into account that
a subject cannot be matched with oneself. For instance, for
an NC Leader the expected total contribution is calculated
as (0.63 x 46/95) + (1.24 x 22/95) + (1.17 x 27/95).
TABLE 3
Leader Contribution and Degree of Optimism
I II
1 if SC 1.712 *** (0.497) 1.503 *** (0.462)
1 if WC 1.228 *** (0.344) 1.193 *** (0.295)
Degree of optimism 0.167 * (0.097) 0.175 ** (0.084)
1 if male 0.265 (0.266)
1 if area of study is
economics -0.913 *** (0.265)
Willingness to take risks -0.023 (0.078)
Machiavellian score -0.019 * (0.010)
Number of known others in
session
Constant 0.414 ** (0.175) 2.594 * (1.345)
Session dummies No No
N 96 96
F-statistic F(3,92) = 20.16 F(7,88) = 14.30
Prob > F 0.000 0.000
[R.sup.2] 0.390 0.479
III
1 if SC 1.427 *** (0.524)
1 if WC 0.966 ** (0.375)
Degree of optimism 0.159 * (0.095)
1 if male 0.349 (0.280)
1 if area of study is
economics -0.925 *** (0.286)
Willingness to take risks -0.011 (0.084)
Machiavellian score -0.019 * (0.011)
Number of known others in
session -0.417 * (0.224)
Constant 2.523 * (1.465)
Session dummies Yes
N 96
F-statistic F(13,82) = 10.54
Prob > F 0.000
[R.sup.2] 0.510
OLS regressions. Dependent variable is Leader's contribution. Robust
standard errors are in parentheses.
* .05 [less than or equal to] p [less than or equal to] .10; ** .O1
[less than or equal to] p < .05; *** p < .01.