The paradox of equality policies and
meritocracy
in
female leadership
Lina Marcela Ramírez Leguizamón
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Universidad de los Andes | Vigilada Mineducación
The paradox of equality policies and meritocracy in
female leadership
Lina Marcela Ramírez Leguizamón
∗Abstract
This paper examines whether women leaders behave dierently when they be-lieve they are elected by a gender quota or by merit and if their behavior is aected by group gender composition. We conduct a laboratory experiment in which participants decide whether to invest or not in a project that presents coordination failures. Leaders can send a risky signal (their investment) to per-suade the other members of their group to invest in the project. The group can attain the cooperative equilibrium if the leader is followed. We provide experi-mental evidence that when investing is costly, the behavior of women leaders is aected by both the way they are elected and the gender composition of their group. We nd that gender quotas do not have a signicant eect on coopera-tion but meritocracy triggers selsh attitudes of female leaders when they face all-male or mixed gender followers, undermining cooperation. We argue that group identity may be the underlying explanation of all our ndings. When investing represents a social dilemma, leaders elected by merit don't identify themselves with their followers, they believe they are superior and reduce their investment. However, gender identity seems to oset this behavior and recon-nect leaders with followers, as the negative eect of meritocracy does not hold when female leaders are facing female followers.
Keywords: gender, leadership, meritocracy, quotas, group identity.
Classication JEL: D71, D91
∗Thesis for M.A in Economics - Universidad de Los Andes. Research Assistant, University of
La paradoja de las políticas de igualdad y la
meritocracia en el liderazgo femenino
Lina Marcela Ramírez Leguizamón
∗Resumen
Este trabajo evalúa si las mujeres líderes se comportan diferente cuando creen que fueron elegidas por una cuota de género o por mérito y si su comportamiento se ve afectado por la composición de género de su grupo. Realizamos un ex-perimento de laboratorio en el que los participantes deciden si invertir o no en un proyecto que presenta fallas de coordinación. Las líderes pueden enviar una señal riesgosa (su inversión) para persuadir a otros miembros de su grupo de invertir en el proyecto. El grupo puede alcanzar un equilibrio cooperativo si la lider es seguida. Proveemos evidencia experimental que soporta la idea de que cuando invertir es costoso, el comportamiento de las mujeres líderes es afectado tanto por la forma en que fueron elegidas como por la composición de género de su grupo. Encontramos que las cuotas de género no tienen un efecto signica-tivo en la cooperación pero la meritocracia desencadena actitudes egoístas en las mujeres líderes cuando están en grupos con solo seguidores hombres o de género mixto. Argumentamos que una posible explicación subyacente de estos resulta-dos es la identidad de grupo. Cuando invertir representa un dilema social, las líderes elegidas por mérito no se identican con sus seguidores, creen que son superiores y reducen su inversión. Sin embargo, la identidad de género parece contrarrestar este comportamiento y reconectar a las líderes con sus seguidores porque el efecto negativo de la meritocracia no se sostiene cuando las líderes mujeres están en grupos con mujeres seguidoras.
Palabras clave: género, liderazgo, meritocracia, cuotas, identitidad de grupo.
Clasicación JEL: D71, D91
∗Tesis de Maestría en Economía - Universidad de los Andes. Asistente de investigación,
1 Introduction
Women around the world are achieving equality in various domains. The gender gap in educational attainment has reversed and there is a clear convergence in professional development relative to men (Goldin, 2006; Azmat and Petrongolo, 2014). Despite this, women continue largely underrepresented in leadership positions such as in the political oce or corporate boards. The purpose of this paper is to examine whether the institution (e.g meritocracy, quota) through which women are elected leaders of a committee aect their behavior and investment decisions in a project, and how these decisions depend on the committees' gender composition. Understanding what mechanisms trigger eective women leadership is important from an academic and policy point of view. Higher female representation in leadership roles may accelerate gender equality in the short and long run by exposing society to female leaders and by generating changes in social norms and policy decisions (Azmat and Petrongolo, 2014; Chattopadhyay and Duo, 2004).
Equality policies, such as quotas, are perceived as necessary in order to create more opportunities for women, but they are highly controversial because both men and women may argue that they violate the principle of merit and that they raise quality loss or reverse discrimination (Van den Brink and Stobbe, 2014). There is evidence that quotas have positive eects such as giving importance to other relevant policy issues, increasing direct female participation, reducing taste discrimination, and attracting women to competition and to high positions (Beaman et al., 2009; Balafoutas and Sutter, 2012; Pande and Ford, 2012), but there is also evidence that quotas may decline operating prots and increase labor costs of companies (Ahern and Dittmar, 2012; Matsa and Miller, 2013) and that under a quota scheme women may suer from stereotype threat and become targets of sabotage from groups adversely aected by quotas (Leibbrandt et al., 2017; Balafoutas et al., 2016; Pande and Ford, 2012).
behavior positively. However, research in economics, psychology, and sociology has also shown that earned entitlement modulates the perception of fairness and self-interest (Feng et al., 2013), and that priming meritocracy may decrease perceptions of discrimination making people more selsh, less self-critical, and more tolerant of advantageous unequal outcomes (Rustichini and Vostroknutov, 2014; McCoy and Major, 2007).
Therefore, equality initiatives are usually presented in terms of a dilemma, with armative actions on one hand and merit on the other (Van den Brink and Stobbe, 2014). To study how these institutions aect dierently women leaders' behavior we take advantage of the controlled environment of a laboratory experiment. We introduce a leader-follower dynamic in a single-shot collective action game, based on Komai et al. (2011) and Grossman et al. (2015) experimental designs, in which free-riding and coordination failures can discourage ecient group cooperation.
In this context participants are assigned in groups of three and one woman is elected leader. Each group faces one of three possible investment projects (with high, median or low returns) and each member of the group decides whether to invest or not. The payment that each participant receives from the project depends on his/her decision and the decisions of the other members of the group. The leader knows the returns of the project and can send a risky signal (her investment) to persuade the other participants in her group to invest. The followers don't know the returns of the project, but observe the decision of the leader before taking their decision. The group can attain a cooperative equilibrium if the leader invests when is socially optimal to do so and the followers follow her. Before playing this game, participants complete a real eort task that is followed by the election of the leader of each group. These leaders are randomly divided in three groups. Some receive information that they are selected based on their performance in the task (Merit treatment), others receive information that they are selected because a gender quota is implemented (Quota treatment), and others receive no information about why they become leaders (Control).
assump-tion that the leader is credible. In most of the economic studies, leadership has been simplied to basic elements. Mostly, leaders are average players randomly assigned that are recognized because they have superior information and take decisions rst (Ridgeway et al., 1994; Hermalin, 1998; Komai et al., 2007, 2011; Vesterlund, 2003; Andreoni, 2006).
When personal, demographic or socioeconomic characteristics of leaders, such as gender, become salient, the committee may not attain the cooperative equilibrium. Some studies suggest that there are no dierences in how the followers perceive men and women eectiveness in leadership positions (Eagly et al., 1995; Thompson, 2000; Grossman et al., 2015), but others propose that female leaders are rated as less eective than male leaders mainly because of gender stereotypes (Rice et al., 1980; Adams et al., 1984; Ridgeway et al., 1994; Ridgeway, 2001).
More importantly for this investigation, characteristics such as gender may not only aect followers perceptions but also leaders' behavior. Multiple studies have shown that there is a behavioral gap between men and women that may aect lead-ership styles. Investigations have shown that women are less condent, less willing to lead, to compete, to take risks, and to share their ideas than men (Azmat and Petrongolo, 2014; Croson and Gneezy, 2009; Niederle and Vesterlund, 2007). Gross-man et al. (2015) show that in a collective action setting, female leaders are less likely to lead their groups to a cooperative outcome when their gender is signaled to their followers, but male leaders are unaected by the salience of their gender.
their investment. Likewise, gender quotas may trigger the stereotype threat on female leaders, potentially decreasing their investment, but may also give rise to cooperative or pro-social behavior, leading to the opposite decisions. Our experiment allows us to analyze how the entitlement produced by the institutions aect the leader's decision to favoring herself or the group, and which behavior dominates in each scenario.
In addition, our experimental design exploits how group gender composition may exacerbate or attenuate these eects. The gender composition of the group may play an important role in the attitudes of women leaders. Women with female followers may increase collective action tendencies through emotion-focused coping because of stronger group identity (Van Zomeren et al., 2008). Conversely, women in male-dominated environments may be less willing to lead because they believe they are performing in a domain in which they are negatively stereotyped (Vick et al., 2008; Steele, 1997; Born et al., 2018).
Our results show that women leaders are sensitive not only to the institutions that determine their election but also to the gender of their followers. Our main nd-ing is that in scenarios with a clear social dilemma, women leaders elected by merit are less willing to voluntarily place themselves in a vulnerable position to achieve a benecial outcome for the group. Consistent with the literature, we nd that meritoc-racy triggers selsh attitudes. However, these results only hold when women leaders face all-male or mixed-gender followers. When they face all-female followers the eect disappears. This suggests that group identity plays an important role in female lead-ers' decisions, merit disconnects the leaders from their followers, but gender identity reconnects them. In addition, we nd suggestive evidence that in a social-dilemma scenario women leaders elected under gender quota invest slightly more than those elected by meritocracy. This is consistent with the strand of research that argues that armative action policies promote women but do not aect performance (Ibañez and Riener, 2018; Balafoutas and Sutter, 2012).
leader-ship positions. Considering this limitation, an important conclusion that arises from this study is that meritocracy and equality policies represent a paradox in female leadership. The eect of these institutions in the promotion of eective women lead-ership is far from obvious. Most people believe that the world should be run on a meritocratic base and equality policies are often seen as a violation of the principle of the `even playing eld'. However, we nd evidence that in scenarios with social dilem-mas adopting meritocracy as a value may trigger non-cooperative behaviors of female leaders, and that equality policies don't undermine performance. More interestingly, we nd that the eects of these institutions on the behavior of female leaders depend on how much they identify with their followers. Meritocracy has not a negative eect on female leaders' behavior when they are in groups with only women followers.
The remainder of this paper proceeds as follows. In Section 2 we introduce a framework. In Section 3, we present the experimental design and the hypotheses. In Section 4 we present the experimental evidence. In Section 5 we discuss the results and conclude.
2 Theoretical Framework
2.1 Benchmark Model
This model is based on Komai et al. (2007) and Grossman et al. (2015) but the analysis here presented regarding to the incentives around a threshold of returns is of our own. The model follows a collective action game in which participants of a committee may have incentives to free-ride, such as contributing with money to a
public or private common good. There is a group ofpparticipants that face identical
incentives. Each player decides whether to invest or not in a project. In the basic model, without a leader-follower dynamic, all participants observe the payo of the project and take decisions simultaneously. We assume risk neutrality.
The prot for player i, ∀s−i ∈S−i, if she invests (I) is:
πi(si =I, s−i) = (r+d)η
r are the random returns of the project. Lets assume r is uniformly distributed in
the interval [−10,10]. When r < 0 the project represent losses. d > 0 is the initial
endowment and η≥0 is the share of participants that invested.
The prot of player i if she doesn't invest (N I) is:
πi(si =N I, s−i) = (αr+β)η+d
α and β are positive parameters. Specically, α < 1, β > 1 and β < d, so
that participants have incentives to free-ride and so that they are unwilling to invest
alone. In this case, player i keeps her initial endowment. With this payo structure
the payos of investing and not investing are increasing inη, but increase faster when
investing. Also, investing becomes more rewarding when r increases.
An important feature of the model is that as long as r≥0,
πi(si =I, s−i =I)≥πi(si =N I, s−i =N I)
Therefore, Pp
i=1πi(si = I, s−i = I) ≥
Pp
i=1πi(si = N I, s−i = N I). As the
payo of investing increases in η faster than the payo of not investing, it is socially
optimal that all members of the group invest in the project whenever the returns of
the project are positive (r≥0).
However, even if r ≥ 0, investing might not be the optimal strategy for each
participant i. If the other participants invest in an scenario with r ≥0, participant i
may have incentives to deviate if her prot of not investing is greater than her prot
of investing. As is proven in Appendix C the strategy prole (si = I, s−i = I) is a
Symmetric Nash Equilibrium when the returns of the project are above a threshold
ˆ
r = p−β(pα(p−−1)1). rˆrepresents the lowest level of return at which participant i is willing
to invest when all other participants invest.
Likewise, the strategy prole in which all participants don't invest,(si =N I, s−i =
N I) is also a Symmetric Nash Equilibrium if r ≤ r˜, with r˜ = d(p−1). As r >˜ rˆ,
we can conclude that when r ∈ [ˆr,r˜] full participation and null participation are
C that if r ∈ (0,rˆ), not investing is a strictly dominant strategy but the socially
optimal outcome is attained when participants invest.
Two important conclusions arise from this basic model:
1. If r is positive but bellowrˆ, participant i will never invest.
2. If r is above rˆthe game has no prediction and participant i will invest if she
believes that all other participants will invest also.
In a leader-follower dynamic, with groups of p= 3 participants with one leader
and two followers, the leader observes the returnsrof the project and decides whether
to invest or not in the project. After she takes the decision, followers observe the leader's decision and decide whether to follow or not. Only the leader knows the
returns of the project. Followers don't know r but know that r ∈[−10,10] and that
all r are equally likely to happen. In this scenario the expected payo of investing
is exactly 0 for followers. The leader has full information of the payos and takes decisions rst. The decision of the followers depends on the signal sent by the leader. It can be proven that under this dynamic there exist two Bayes-Nash equilibrium. A trivial equilibrium in which no one ever invests, and a productive equilibrium in
which the leader invests when it is socially optimal to do so (r ≥0) and the followers
mimic. Note that this game is interesting for evaluating the leaders' behavior because as investing is a risky signal, it allows exploiting how entitlement and the belief of credibility aect investment decisions of followers.
2.2 Game
The game is the single-shot leader-follower collective action game described above
with p= 3 participants. The parameters areβ= 5,α= 4
6 and the initial endowment
is d = 10. With these parameters the lowest level of return at which participant i
is willing to invest when all other participants invest is rˆ= 6. The highest level of
return at which participant iis not willing to invest when all other participants don't
invest is ˜r = 20. Each group faces one of three possible scenarios that are equally
likely to happen: Scenario 1, with high potential returns (r = 10 > rˆ), Scenario 2
(0 < r = 3 < rˆ), with average potential returns, and Scenario 3 (r = −10<0 <rˆ)
Each player i begins with an initial endowment of d= 10 Experimental Tokens
(ET) and chooses whether to invest (I) or not (NI) in the project (all the initial
endowment). With full information, in Scenario 1, (s1 = N I, s2 = N I, s3 = N I)
and (s1 =I, s2 =I, s3 =I)are Nash Equilibrium so the game has no prediction. In
Scenario 2, (s1 =N I, s2 =N I, s3 =N I) is a Nash Equilibrium and si = N I is the
dominant strategy for each participant i. In Scenario 1 and Scenario 2 investing is
socially optimal becauser >0. In Scenario 3 it is not socially optimal to invest. The
rounded payo for each scenario are shown in Table 1.
Table 1: Payo Scenarios by Project Type
Payo Scenarios
Scenario 1 (High Returns) Scenario 2 (Median Returns) Scenario 3 (Low Returns)
Investors Non-Investors Investors Non-Investors Investors Non-Investors
All invest 20 - 13 - 0
-2 invest 13 17 9 15 0 8
1 invest 7 14 5 12 0 9
Nobody invests - 10 - 10 - 10
*The rows of the table reect the possible situations that each group of three members can face: all members invest, two
invest, one invests or nobody invests.
*The columns reect the three possible payo scenarios (1, 2, and 3) by player's possible actions: Investors and Non-Investors. Each cell of the table represents the possible payos for player i given her decision and the decision of the other members of the group. For example, if a group is randomly assigned to Scenario 1, and in this situation two participants decide to invest and one decides not to, the two players that decide to participate in the project will receive a payo of 13 ET, while the player that decides not to participate will receive a payo of 17 ET. In this scenario, if all players decide to invest in the project, each participant nal earning is 20 ET and if no one invests, each participant receives 10 ET. The socially optimal outcome is attained when all participants invest in Scenario 1 (group payo of 60 ET) and 2 (group payo of 39 ET) and no one invests in Scenario 3 (group payo of 30 ET).
important in the leader-follower dynamic because adds uncertainty to followers on whether they should follow or not.
3 Experiment
3.1 Design
We study how meritocracy and gender quotas aect the decisions of female leaders in a laboratory experiment. The experiment has two parts. In the rst part, all participants complete a grid real eort task, report their gender and answer self-condence and willingness-to-lead questions. In the second part, participants play the leader-follower collective-action game described above.
In the grid task, participants are asked to count the number of ones of a 5×5
grid with randomly distributed 0s and 1s (See Figure B.1). They have one minute to
count the number of ones of the biggest amount of grids1. After they complete the
task, participants answer the following questions that seek to elicit self-condence measures about how they believe they performed in the task:
1. You counted x grids, how many do you think that you counted correctly?
2. Among the s participants on this room, do you think that you are among the
best half in terms of your performance in the task?
x is the number of grids each participant i counted, and s is the total number of
participants in each session. These questions seek to measure the overestimation of one's actual performance, and over-placement of one's performance relative to others, respectively (Moore and Healy, 2008). If the beliefs about their performance are correct, participants earn one additional Experimental Token (ET) per question. Subjects are also asked to indicate how much they want to become the leaders in the second activity on a scale between 0 (not willing) and 10 (very willing). This measure is a proxy of how strongly they would argue in favor of themselves in a leadership selection process (Born et al., 2018). It is important to stress out that up to this point participants have not been assigned to any treatment, so strategic behaviors
1Lezzi et al. (2015) compare three real eort tasks and conclude that mean performance is the
are not expected. At the end of this rst part, participants report their gender.
In the second part of the experiment, participants play the collective-action game described in Section 2.2. To make groups with one leader and two followers, we would need to assign one-third of the participants to the role of leaders, and two-thirds to the role of followers in each session. This would lead to very few observations of leaders per session. As the principal purpose of this paper is analyzing the behavior of leaders, we assign half of the participants to the role of leaders and half to the role of followers. For there to be groups of three, followers take decisions for two rounds, each one facing a dierent leader. Details are explained later. Given the limited resources of this research we focus only on women leaders, and in the dierences that may arise within women when they face dierent institutions. The experiment has a between-subjects design and treatments are randomized at the individual level. The treatments are as follow:
1. Merit information (Treatment 1): The leaders under this treatment receive the following message: Throughout the session you will play the role of leader within your group that is made up of two other participants. You were assigned to this role because of your good performance in the rst part of the activity. The followers receive the following message: The leader of your group is a woman, and she was elected because of her good performance in the rst part of the activity.
2. Gender quota information (Treatment 2): The leaders under this treatment receive the following message: Throughout the session you will play the role of leader within your group that is made up of two other participants. You were assigned to this role because a gender quota was implemented.The followers receive the following message: The leader of your group is a woman, and she was elected because a gender quota was implemented.
The gender quota that is implemented in this experiment is a simplied repre-sentation of a reserved seat in political oce, or a legislated gender quota in a corporate board (Pande and Ford, 2012).
your group that is made up of two other participants. The followers receive the following message: The leader of your group is a woman.
To guarantee that all leaders are women, at least half of the participants in each session have to be women. If the number of women is greater than half of the participants in the session, the best women in the grid task are elected leaders and are randomly assigned to the treatments. For instance, if a session has 24 participants, and 15 are women, the best 12 women are elected leaders, and the other 3 women and the 9 men are assigned to the role of followers. If the number of women is equal to half of the participants in the session, all women are elected leaders. In this case, in order not to deceive the participants the best women are assigned to the merit treatment and the others are randomly assigned to the other two treatments. Only in 3 of the 12 experimental sessions all women were elected leaders, in the other sessions the number of women was higher than the number of men, and treatments were randomly assigned. Although treatment assignment is not completely random, dierences in performance in the grid task are not associated with cognitive capacity (Charness et al., 2018) and they don't aect results as will be shown later. Participants don't know the assignment rule described above. The dierence between treatments relies on the information that participants receive about how the leader is elected. No false information is given at any moment.
After all participants are assigned to a role, leaders have the opportunity to update their answers about condence. Again, subjects can earn one additional ET for each correct answer. We implement the strategy method for leaders' decisions. The leader has to indicate whether she is willing to invest in each possible strategy combination of project type (high, median or low returns) and followers' gender (two women, two men or one woman one man). Only the decision associated to the actual scenario each leader is facing is implemented. The advantage of this method is that it allows eliciting all the information about leaders decisions for each possible information set, enabling analysis in more statistical depth. The disadvantage of this method is that it may reduce the impact of emotions, but this is not a special concern
in this experiment2. The order in which the leaders indicate their investment decisions
2Brandts and Charness (2011, 2000); Cason and Mui (1998) provide evidence that the strategy
is randomized by group gender composition to guarantee no order eects between answers. All leaders know that their gender is revealed to their followers,and leaders under merit and quota treatments know that their followers receive information about how they are elected.
The decision of the followers is a direct response to the leader's decision. After observing the decision of the leader, followers report privately and simultaneously whether they are willing to invest or not. They only know how the leader is elected, her gender, and the decision she takes. They don't know the gender of the other follower of their group, nor the returns of the project they are facing. For this reason, the actual gender composition of the group and the actual returns of the project the group is facing are not relevant to the decisions of the leaders or followers. The followers take decisions for two rounds, facing a dierent leader on each one. In both rounds, followers face leaders assigned to the same treatment and don't receive feedback between rounds so there are no order eects concerns. Appendix D explains in detail how leaders and followers are matched in each experimental session.
Certain features are common to all the experimental sessions. First, participants are informed about their endowment and the exchange rate between Colombian Pesos and ET at the beginning of the experiment before they take their decisions. Second, participants receive a show-up fee of COP 6.000 and randomly one-third of the par-ticipants of each session receive an additional payment related to their decisions in the game. Third, all participants at the end of the experiment answer a question-naire that recovers demographic and personal data. Fourth, subjects are assigned an experimental identication, that ensures that they interact anonymously. Fifth, the subjects are recruited from a student's subjects pool using ORSEE (Online Re-cruitment System for Economic Experiments) and all participants are students from the University of Los Andes. Sixth, all sessions are implemented on the computer software z-Tree (Fischbacher, 2007). Figure B.2 shows an overview of the stages of the experiment.
3.2 Hypotheses
gen-der composition (two women, one man, and one woman, or two men followers), and project type (high returns, median and low returns). The results will depend on the mechanisms that prevail given the incentives of the scenarios. As each scenario has dierent features, decisions for each one of them are analyzed separately. As the
returns in Scenario 3 are negative (r < 0) the dominant strategy and the socially
optimal decision is not to invest. Therefore, this scenario is not interesting because it represents no coordination problem or social dilemma. We expect a low or null invest-ment in this scenario with no signicant dierence between treatinvest-ments or between group gender compositions.
The incentives of the game with full information prevail with the leader-follower dynamic. In Scenario 1, if the leader invests with at least one of the followers, she obtains positives benets from investing. On the contrary, in Scenario 2 the social dilemma is salient: investing is extremely risky for the leader because she only obtains a small positive benet if both followers invest with her, otherwise she losses. For this reason, regardless of the treatment or group gender composition, we expect higher levels of investment in Scenario 1 than in Scenario 2.
Hypothesis 1 : In Scenario 1, we expect treatments and group gender composi-tions to have weak or no eect on the decisions of the leaders because regardless the treatment the project has high returns and leaders have incentives to invest.
Hypothesis 2 : As leaders face a social dilemma in Scenario 2, we expect treat-ments to aect investment decisions. Taking into account the existing evidence pre-sented in Section 1, we expect the leader under the merit treatment to increase her investment in comparison to the control group if meritocracy gives her condence that the followers will follow, but we expect her to reduce her cooperation if entitle-ment triggers her selsh attitudes. Conversely, we expect the leader under the quota treatment to increase her investment in comparison to the control group if the quota triggers her pro-social attitudes, but we expect her to reduce her investment if she feels that because of the quota she will be negatively stereotyped or sabotaged.
identity around gender.
Hypothesis 4 : Related to the Hypothesis 2, we expect the behavior of the fol-lowers to be aected by the treatments. We expect the folfol-lowers under the merit treatment to increase their investment if the meritocratic election of the leader makes them believe that they are more capable leaders that will lead them to better out-comes. Correspondingly, we expect followers under the quota treatment to decrease their investment if they belief that leaders elected under quota are less capable or less deserving of the position (sabotage).
4 Results
4.1 Descriptive Statistics
241 subjects participated in twelve sessions, and each participant played in only one session. The experiment lasted 50 minutes and subjects earned on average COP
8.500. From the 241 participants, 118 were leaders and 123 were followers.3. Among
the 118 leaders, 42 were assigned to the control group, 39 to the merit group and 37 to the quota group, and from the 123 followers, 44 were assigned to the control group, 41 to the merit group and 38 to the quota group. All the leaders were women, and 85% of the followers were men.
Panel A of Table 2 provides the descriptive statistics related to the demographic and personal characteristics of the 241 students that participated in the experiment. On average participants were almost 20 years old, 57% of participants were women, and 13% studied Economics or Business Administration. In the majority of char-acteristics participants are balanced across treatments. However, on average there are more economic students in the merit treatment than in the quota treatment, more students participated before in experiments in the control group than in the merit treatment group and more students had leadership experience under the quota treatment than under the merit treatment.
Panel B of Table 2 provides the descriptive statistics related to the performance
3In the rst session, only one-third of the participants were leaders and the other two-thirds were
Table 2: Descriptive Statistics -Leaders and Followers
(1) (2) (3) (4) (5) (6) (7)
All No Info Merit Quota Di Di Di
Mean Min Max Std. Dev. Mean Mean Mean (2)=(3) (3)=(4) (2)=(4)
Panel A. Demographics
Age 19.85 16.00 29.00 1.86 19.92 19.98 19.64 -0.06 0.34 0.28
Gender 0.57 0.00 1.00 0.50 0.57 0.55 0.59 0.02 -0.04 -0.02
Econ Related 0.13 0.00 1.00 0.34 0.14 0.19 0.07 -0.05 0.12∗∗ 0.07
Semester 5.42 1.00 11.00 2.52 5.52 5.56 5.16 -0.04 0.40 0.36
Past Participation 0.52 0.00 1.00 0.50 0.62 0.44 0.49 0.18∗∗ -0.06 0.12
Leadership Experience 0.49 0.00 1.00 0.50 0.40 0.50 0.57 -0.10 -0.07 -0.18∗∗
Panel B. Grid task and beliefs
Correct Grids 6.16 1.00 13.00 1.86 6.24 6.60 5.60 -0.36 1.00∗∗∗ 0.64∗∗
Willingness to Lead 7.22 1.00 10.00 2.37 7.40 7.08 7.19 0.32 -0.11 0.21
Self Condence 1 0.54 0.00 1.00 0.50 0.55 0.47 0.61 0.07 -0.14∗ -0.07
Self Condence 2 0.59 0.00 1.00 0.49 0.63 0.63 0.51 0.00 0.12 0.12
Observations 241 86 80 75 166 155 161
1* p<0.10, ** p<0.05, ***p<0.01
2Econ Related refers to Economics and Business Administration students. Past participation reports whether subjects participated in previous
economic experiments. Leadership Experience reports whether subjects had experience in leadership positions. Correct Grids is the number of grids participants counted correctly. Risk preference takes the value of 1 if the participant is not at all willing to take risks and 10 if is completely willing. Willingness to Lead takes the value of 1 if the participant is completely unwilling to lead and 10 if is completely willing. Self Condence 1 takes the value of 1 if the participant believes she has more correct grids than the real number. Self Condence 2 measures if participants believe they were among the best half in the session.
3Column 5, 6 and 7 report the dierence in means between No Information and Merit, Merit and Quota and No Information and Quota
respectively.
of participants and the beliefs they reported in the rst part of the experiment before they were assigned to the treatments. There are no dierences in willingness to lead before treatment. On average participants under quota, had less correct grids than participants in the control and merit treatment groups. This signicant dierence may be due to the fact that in some sessions treatment was not completely random (best participants were assigned to merit treatment when all women in the session were elected leaders as was mentioned in Section 3.1). Dierences in characteristics before treatment may bias results, so these variables are included as controls in the regressions. The dierence in correct grids between treatments may be considered a threat in the sense that it makes groups less comparable. However, as will be shown later, results hold even when these dierences are taken into account.
Figure 1: Leaders' Investment by Project Type
78.6 81.0 78.6 82.1 84.6 82.1 78.4 70.3 91.9 0 20 40 60 80 100
% Leaders investing
No Info Merit Quota
Panel 1. High Returns
64.3 59.5 52.4 38.5 38.5 48.7 45.9 43.2 54.1 0 20 40 60 80 100
% Leaders investing
No Info Merit Quota
Panel 2. Median Returns
4.8 4.8 4.8 2.6 2.6
0 20 40 60 80 100
% Leaders investing
No Info Merit Quota
Panel 3. Low Returns
Confidence level at 90%
Two Males Mixed Two Females
are not signicant, it can be observed that under the merit treatment leaders invest slightly more for all group gender compositions than in the control group. Under the quota treatment the leaders invest slightly less in comparison with the control group when they are in groups with male followers (78.4% and 70.3% of the leaders invest with only male followers and mixed followers respectively), but invest slightly more when they are with only female followers (91.9%).
than participants in the control group: 41.9% and 47.7% respectively in comparison with 58.7% that invest in the control group. In the control group the investment de-creases when the female composition of the group inde-creases, but this trend is reversed when leaders are informed that they are elected by merit or quota. This gives the rst insight that the treatments and the group gender composition may aect the decisions of the leaders and that the way leaders are elected may change their beliefs about their credibility and their pro-social preferences.
Figure 2: Followers' Investment by Round
57.1
47.8
3.8 58.3
0
20
40
60
80
% of followers investing
No Info Merit Quota
Panel 1. Round 1
7.1 34.6
12.5 47.6
4.3 46.2
0
20
40
60
80
% of followers investing
No Info Merit Quota
Panel 2. Round 2
Confidence level at 90%
Leader doesn’t invest Leader invests
Figure 2 shows the mean investment of followers. Followers take two decisions (each one in response to a dierent leader). Panel 1 shows the rst decision and
Panel 2 shows the second. On average, 27.11% of followers invested. In both rounds,
investment when the leader invested fall to 42.8 % on Round 2. This is driven by the behavior of participants in the control group. However, there is no signicant dierence between the mean rate of investment in Round 1 and Round 2.
No matter the round or treatment, on average only 48.6% of the followers in-vested after the leader inin-vested. This shows that less than half of the followers cooperate when they see that the leader leads to cooperation. Excluding the groups randomly assigned to the low returns project, from the 91 groups assigned to the
projects with high and median returns, only 14 (≈ 15%) attained the socially
opti-mal outcome. This result opposes to theoretical and experimental evidence of Komai et al. (2007) and Komai et al. (2011), who argue that a leader with private informa-tion can solve the coordinainforma-tion and free riding problems attaining the socially optimal outcome. In the three treatment arms the participants knew the gender of the leader. Although we cannot compare to how would have been with a male leader the average participation in the project, well below of full participation, gives an insight that personal and socio-demographic characteristics of leaders may aect their credibility, nding that is in line with (Grossman et al., 2015) results.
4.2 Experimental Evidence
4.2.1 Leaders' investment decisions
As was shown in Section 4.1, Scenario 3 has no interesting results and Scenario 1 and 2 present dierent behaviors. For this reason,we estimate the following equation separately for Scenario 1 and 2:
Ii =β0+β1M eriti+β2Quotai+β3M ixedF ollowersi+β4F emalleF ollowersi
+β5M eriti×M ixedF ollowersi+β6M eriti×F emaleF ollowersi+
β7Quotai×M ixedF ollowersi+β8Quotai×F emaleF ollowersi
+θXi+ui
(1)
Ii reports whether the leader decides to invest or not. M eriti andQuotai report
whether the participantireceives the merit or the quota treatment, respectively. The
base category is the group that receives no information. M ixedF ollowersi reports
and F emaleF ollowersi reports whether the decision was made for a group with two women followers. The base category is the case were the two followers are men. We also include all the interaction terms between treatments and group gender
composi-tions. Xi is a vector of individual characteristics that includes age, whether the leader
istudied a degree related to Economics, whether she participated before in a decision
committee, and the number of correct grids that she had on the real eort task at the beginning of the experiment. We include these variables because they have dierent means in the leaders' sub-sample and to improve eciency (See Appendix A.2). In addition, in all regressions we control for possible order eects with a variable that takes the value of 1 if the leader took decisions rst responding to a group with two male followers, then to a group with one man and one woman and nally a group with two female followers, or 0 if the leader took decisions rst responding to a group with two female followers, then to a group with mixed followers and nally to a group with two male followers.
Equation 1 is estimated with a Linear Probability Model4 and results are
pre-sented in Table 3 for the high returns scenario and in Table 4 for the Median Returns Scenario. Columns (1) and (2) show the estimation of Equation 1 with and without controls respectively. Columns (3) and (4) present the same specications but with the interaction terms between treatments and group gender compositions. For each estimation we have 354 observations because with the strategy method each leader takes three decisions per project type. We clustered standard errors by leader.
Table 3 shows that neither the institution nor the group gender composition af-fects the investment decision of the leaders when they are in the high returns scenario. The coecients are not only statistically insignicant but also small in magnitude and sensitive to the specication. This is consistent with the idea that although this scenario presents coordination problems, it represents no social dilemma. However, results show that in this scenario the probability of investing is on average 5.4 per-centage points higher under the quota treatment when facing two female followers than when facing mixed followers. This result reinforces the idea that group identity, in this case driven by gender identity, aects decision making. Leaders invest more when they are in groups with which they identify. All other interaction terms and
Table 3: All Leaders' Decisions - High Returns
Dep Var: Investment Decision (1) (2) (3) (4)
β1: Merit 0.035 0.041 0.035 0.040
(0.063) (0.065) (0.090) (0.091)
β2: Quota 0.008 0.010 -0.002 -0.000
(0.063) (0.066) (0.094) (0.097)
β3: Mixed Followers -0.008 -0.008 0.024 0.024
(0.048) (0.048) (0.080) (0.081)
β4: Two Female Followers 0.042 0.042 -0.000 -0.000
(0.046) (0.046) (0.090) (0.091)
β5: Merit×Mixed Followers 0.002 0.002 (0.112) (0.113)
β6: Merit×Female Followers 0.000 0.000
(0.117) (0.117)
β7: Quota×Mixed Followers -0.105 -0.105 (0.121) (0.121)
β8: Quota×Female Followers 0.135 0.135
(0.114) (0.114) Constant 0.782*** 0.785** 0.786*** 0.788** (0.050) (0.327) (0.064) (0.331) Observations 354 354 354 354 R-squared 0.005 0.006 0.018 0.019
Controls NO YES NO YES
p-valH0
H0:β1=β2 0.672 0.663 0.693 0.684
H0:β3=β4 0.205 0.207 0.710 0.712
H0:β5=β6 0.983 0.983
H0:β7=β8 0.0198 0.0205
H0:β5=β7 0.372 0.375
H0:β6=β8 0.183 0.186
Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Merit
reports one when the leader is under the merit treatment. Quota reports one when the leader is under the quota treatment. The base category is no information. Two Female Followers equals 1 if the leader decision was made in the scenario with two female followers, and Mixed Followers equals 1 if the leader decision was made in the scenario with mixed followers. The base category is male followers. The controls in (2) and (4) include age, whether the leader studied a degree related to Economics, whether she had participated before in a decision committee, the number of correct grids that she had on the real eort task at the beginning of the experiment.
coecients are not statistically signicant and small in magnitude. The results in the high returns scenario support Hypothesis 1 and Hypothesis 3.
Table 4 shows that in the median returns scenario for all the specications, β1,
the coecient associated with the merit treatment is large in magnitude and highly signicant. Our preferred specication, in column (2) shows that being in the Merit-treatment group reduces the probability of investing on average by 16.2 percentage
points in comparison with the control group. β2, the coecient associated with the
quota treatment is also large in magnitude but not statistically signicant. Results in column (4) show that the negative eect of merit on the probability of investing is larger (25.2 percentage points) when leaders are facing male followers. As the
experiment may have not enough power5 the probability of Type II error is bigger
and so we may reject the null hypothesis when it is false. For this reason, even though the coecients are not statistically signicant, their magnitude and sign give us some insights about the potential eect. The quota treatment may have a negative eect on the probability of investing in comparison to the control group, regardless of the group gender composition. However, when compared to merit, quota leads to more investment.
Table 4: All Leaders' Decisions -Median Returns
Dep Var: Investment Decision (1) (2) (3) (4)
β1: Merit -0.168** -0.162** -0.258** -0.252**
(0.080) (0.080) (0.109) (0.112)
β2: Quota -0.110 -0.075 -0.183 -0.149
(0.081) (0.081) (0.112) (0.115)
β3: Mixed Followers -0.025 -0.025 -0.048 -0.048
(0.051) (0.051) (0.068) (0.068)
β4: Two Female Followers 0.017 0.017 -0.119 -0.119
(0.059) (0.060) (0.109) (0.110)
β5: Merit×Mixed Followers 0.048 0.048 (0.119) (0.120)
β6: Merit×Female Followers 0.222 0.222
(0.150) (0.151)
β7: Quota×Mixed Followers 0.021 0.021 (0.120) (0.121)
β8: Quota×Female Followers 0.200 0.200
(0.142) (0.142) Constant 0.590*** 0.131 0.643*** 0.184
(0.063) (0.290) (0.075) (0.293) Observations 354 354 354 354 R-squared 0.021 0.067 0.029 0.075
Controls NO YES NO YES
p-valH0
H0: β1=β2 0.483 0.290 0.516 0.383
H0: β3=β4 0.440 0.442 0.444 0.447
H0: β5=β6 0.160 0.163
H0: β7=β8 0.211 0.214
H0: β5=β7 0.846 0.847
H0: β6=β8 0.875 0.876
Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Merit
reports one when the leader is under the merit treatment. Quota reports one when the leader is under the quota treatment. The base category is no information. Two Female Followers equals 1 if the leader decision was made in the scenario with two female followers, and Mixed Followers equals 1 if the leader decision was made in the scenario with mixed followers. The base category is male followers. The controls in (2) and (4) include age, whether the leader studied a degree related to Economics, whether she had participated before in a decision committee, the number of correct grids that she had on the real eort task at the beginning of the experiment, and order as a dummy variable identifying sessions where the decisions were made in the order two male, mixed, two female followers compared to the default that was two female, mixed, two male followers.
Regarding to group gender composition, Table 4 shows thatβ3 , coecient
asso-ciated to having mixed followers and β4, coecient associated to having two female
followers are not statistically signicant. The coecients of the interaction terms
between treatments and group gender composition are also not signicant. However, the magnitudes and signs of these coecients suggest that under the merit and quota treatment the probability of investing is higher when the leader faces two women than when she faces two men.
The rst important conclusion that is drawn from Table 4 is that meritocracy reduces the investment by female leaders in scenarios that present a social dilemma. Under this scenario female leaders are less willing to place themselves in a vulnerable position to achieve a cooperative outcome. Consistent with a strand of the literature, meritocracy seems to trigger the selsh attitudes of the leaders. Although results suggest that quotas may also reduce the probability of investing by female leaders, it is important to highlight that this possible negative eect of quotas is smaller than the negative eect of merit. In scenarios where what is needed is cooperation, our results contradict the strand of literature that argues in favor of meritocracy and against any form of armative action because it represents a quality loss.
To deepen in the analysis regarding to the gender composition of the group, the following regression is estimated separately for each project type (high and median returns) and for each group gender composition (two women followers, one man and one woman, and two men):
Ii =β0+β1M eriti+β2Quotai+θXi+ui (2)
Ii reports whether the leader decides to invest or not. M eriti andQuotai report
whether the participant i receives the merit or the quota treatment, respectively, in
comparison with the control group. Xi is the same vector of individual characteristics
that was included in Equation 1.
Table A.3 presents the results for the High Returns Scenario and Table A.4 presents the results for the Median Returns Scenario. Figure 3 plots the coecients of all estimations with controls.
Figure 3: LPM Coecients by Treatment, Project Type and Group Gender Compo-sition
Merit
Quota
−.4 −.2 0 .2 −.4 −.2 0 .2
High Returns Median Returns
Male Followers Mixed Followers Female Followers
no signicant, the quota treatment seems to increase the probability of investing compared to control when the leader is facing two female followers. Conversely, the quota seems to decrease the probability of investing when the leader is facing one man and one woman followers. This is in line with the idea that group identity may increase investment.
Estimates of Figure 3 also evidence that in the Median Returns Scenario, merit decreases the probability of investing in comparison with the control group. However, we can observe that merit has no negative eect on investment when the leader is facing a group of only female followers. The negative eect of merit only happens with mixed and male followers. Although no statistically signicant, we can observe the same trend for the quota treatment. Quota leads to lower investment when leaders are in groups with male and mixed followers, but this eect is also reversed when leaders are in groups with female followers.
are the sessions that have a selection problem. We also trim the sample discarding the leaders with correct grids above percentile 95 or below percentile 5. This proce-dure eliminates 21 observations. This sample has no signicant dierences on average on the number of correct grids between treatments. We then estimate Equation 2 with controls. Results of this estimation are in columns (1), (4) and (7) in Appendix A.5 for the high returns scenario and in Appendix A.6 for median returns scenario. Second, without dropping any session, we trim the sample again. This procedure eliminates 7 observations. With this sample, we re-estimate the equation including a quadratic form of the variable Correct grids to control for more exible forms. The results of this estimation are in columns (2), (5) and (7). Finally, again for the trimmed sample, we re-estimate the equation including a xed eect of the number of correct grids. The results of this estimation are in columns (3), (6) and (9). Controls are not signicant and the magnitude and signicance of the coecients hold for both high and median returns scenarios for all specications.
A second important conclusion arises from the previous analysis: female leaders are sensitive to the gender composition of their group. This is consistent with the existing literature that shows that women act dierently in male or female dominated environments. Both treatments, merit and quota, seem to have a negative eect on investing. However, this eect disappears when women are facing two female followers. Our ndings are in line with Van Zomeren et al. (2008), which argument that women with female followers may increase collective action tendencies because of group identity, and with Vick et al. (2008), that argue that women may feel negatively stereotyped with mixed or all-male followers, but this stereotype threat disappears with female followers.
4.2.2 Potential mechanism: self-condence
in the rst part of the experiment:
U pdatei =β0+β1M eriti+β2Quotai+γSelf Conf idence0i+θXi+ui (3)
U pdatei is the response leaders give to the two questions of self-condence
af-ter knowing they are leaders and how they are elected. Leaders' answer the self-condence questions before treatment, but after treatment, we give them the pos-sibility to update their answers and receive extra Experimental Tokens if they are accurate. The rst measure of self-condence is related to the dierence between the
number of correct grids subjecticompletes in the rst activity of the experiment and
the number of correct grids she believes she completed correctly. This variable is a
dummy that takes the value of one if subjectibelieves she has more correct grids than
what she actually gets and zero otherwise. This is a measure of the overestimation of one's actual performance. The second measure of self-condence takes the value of
one if the participant i beliefs she is among the best half participants present in the
room. This is a measure of the over-placement of one's performance relative to others.
M eriti and Quotai report whether the participant i receives the merit or the quota
treatment, respectively, in comparison with the control group. Self Conf idence0i
reports the rst response that leaders give to the questions of self-condence, before
treatment. Xi is the same vector of individual characteristics that was included in
Equation 1.
Figure 4 and Table A.7 report the estimates of Equation 3. Panel A of Figure 4 shows the mean overestimation of one's actual performance per treatment group. There are no signicant dierences between treatments neither before nor after treat-ment. However, on average leaders in the control group and the merit-treatment group updated their beliefs. On the contrary, leaders under quota didn't update on average the beliefs of their performance.
treat-Figure 4: Self Condence Measures - Leaders ____________p>0.1 ____________p>0.1 ___________________p>0.1 ____________p>0.1 ____________p>0.1 ___________________p>0.1 50.0 43.6 59.5 54.8 48.7 59.5 0 20 40 60 80 100 Mean Overestimation
Before Treatment After Treatment
Panel A. Overestimation of one’s actual performance
____________p>0.1
____________p>0.1
___________________p>0.1
____________p=0.010____________ p=0.000 ___________________p=0.028 54.8 56.4 48.6 66.7 92.3 48.6 0 20 40 60 80 100 Mean Overplacement
Before Treatment After Treatment
Panel B. Over−placement relative to others
Confidence level at 90%
No Info Merit Quota
ment, signicant dierences arise between groups. The probability that the leader believes that she is among the best half is 21.4 percentage points higher if she is in the merit group in comparison to the control group and 40.4 percentage points higher in comparison to the quota group. Being in the quota group reduces the probability that the leader believes that she is among the best half in 19 percentage points in comparison to the control group. These dierences are signicant with condence of 95%.
they perform better than the others in the task. Leaders in the quota group are less self-condent than all the other leaders.
Experimental evidence shows that the merit treatment boosts the belief of the leaders that they are better than the others and that this has a direct eect on their selsh attitudes in scenarios that require cooperation, because the probability of investing signicantly decreases. This is consistent with Rustichini and Vostroknutov (2014), who nd that individuals that believe that are more skilled than the others support less the distribution of prizes. On the contrary, the quota treatment decreases the probability that the leaders believe they are better than the others and in scenarios that represent a social dilemma we nd suggestive evidence that the probability of investing of leaders under the quota-treatment decreases in comparison with the control group. We may argue that the lack of self-condence of the leaders under quota is a mechanism that aect their belief that the followers will follow, and this self-distrust may be reected in lower investment. Under this argument, both treatments seem to disconnect leaders from followers. Results suggest that leaders elected by merit don't identify with their followers because they believe they are superior, and leaders elected by quota also don't identify with their followers because they believe they are inferior, and this may explain why investment is reduced in both treatments in comparison with the control group. However, gender identity seems to counteract these eects because in groups with only women the negative eect of merit and quota on investment disappears.
4.2.3 Followers' investment decisions
Figure 2 shows that the decisions of the followers depend on the decisions of the leaders of their group. We estimate the following equation separately for followers that decide after observing that the leader of their group invests, and for followers that decide after observing that the leader doesn't invest.
Ii,t =β0+β1M eriti+β2Quotai+γRoundt+θXi+ui,t (4)
Ii,t reports whether the followeridecides to invest or not in roundt. M eriti and
respectively, in comparison with the control group. Roundt is a dummy indicator
for Round 1 and Round 2. Xi is a vector of individual characteristics that include if
followeriparticipated before in a decision committee and the number of correct grids
that he/she had on the real eort task at the beginning of the experiment. We include
these variables because they have dierent means in the followers' sub-sample. ui,t is
the error term. All results are for pooled OLS estimates with robust standard errors.
Figure 5: Followers' Investment Decisions
_____________p>0.1
_____________p>0.1 _____________________p>0.1
_____________p>0.1
_____________p>0.1 _____________________p>0.1
3.3 5.9 4.1
46.3 47.7 52.0
0
20
40
60
80
Mean Investment − Followers
Leader doesn’t invest Leader invests
No Info Merit Quota
Figure 5 and Table A.8 show the estimates of Equation 4. Results show that treatments have no signicant eect on the decisions of the followers. These results reject our Hypothesis 4 and support the idea that the institution through which the leader is elected has behavioral eects on leaders but not on followers. Followers don't believe more in leaders elected by merit and there is no evidence of backlash or sabotage against women elected by quota because followers don't act dierently.
4.3 Payos, welfare and social eciency
social optimal is attained when all participants invest and the individual payo of investing is increasing in participation. Therefore, the social and individual payos will be higher if the leader invests.
Table 5: Individual and Social Payo by Project
(1) (2) (3) (4) (5) (6) (7)
All No Info Merit Quota Di Di Di
Mean Min Max Std. Dev. Mean Mean Mean (2)=(3) (3)=(4) (2)=(4)
Panel A. High Returns
Leaders' Payo 11.92 7.00 20.00 4.22 11.79 12.67 10.55 -0.88 2.12 1.25
Social Payo 40.42 30.00 60.00 9.73 40.08 42.21 37.27 -2.13 4.94 2.81
Observations 59 24 24 11 48 35 35
Panel B. Median Returns
Leaders' Payo 9.81 5.00 13.00 1.84 9.62 9.00 10.64 0.62 -1.64∗∗ -1.02
Social Payo 31.91 29.00 36.00 2.31 32.23 31.00 32.18 1.23 -1.18 0.05
Observations 32 13 8 11 21 19 24
1* p<0.10, ** p<0.05, ***p<0.01
2Column 5, 6 and 7 report the dierence in means between No Information and Merit, Merit and Quota and No Information and Quota
respectively.
For this reason, the negative eect of meritocracy on the probability that female leaders invest in scenarios that present a social dilemma may translate into a loss of social eciency and a loss of welfare. Table 5 show the leaders' average payo and the social payo separately for the high and median returns scenario. Panel A shows that the average payo of the leaders assigned to the high returns scenario is 11.92 ET and there is no signicant dierence in means between treatment groups. The average social payo of the groups assigned to this scenario is 40.42 ET, and there are no signicant dierences between treatments.
for these results is that the leaders are risk averse and prefer the safe action or that they have incorrect beliefs about how the followers will answer to their signal.
5 Discussion and policy implications
In this paper, we investigate whether women leaders behave dierently when they believe they are elected by merit or by a gender quota, and if the gender composition of their group matters. To do so we implement a collective-action game with a leader-follower dynamic in a laboratory experiment, and we introduce two treatments: merit and quota. Participants in the merit treatment are informed that the leaders are elected because of their performance in a real eort task they complete in the rst part of the experiment, and participants in the quota treatment are informed that they are elected because a gender quota is implemented. Participants in the control group receive no information about how the leaders are elected.
Our experimental evidence shows that in scenarios that present no social dilemma, merit and quotas do not aect women leaders' behavior. However, in scenarios that involve a social dilemma, women leaders elected by merit are less willing to put them-selves in a vulnerable position in favor of a collective improvement when compared with leaders that receive no information about how they are elected. Besides, we nd suggestive evidence that leaders elected by a gender quota are less willing to cooperate than leaders uninformed about how they are elected. Both results only hold when women leaders are facing all-male or mixed-gender followers. We believe that a possible explanation for all our results is group identity. When female leaders face social dilemmas, merit disconnects them of their followers, they believe they are superior and as they don't identify with followers they don't cooperate. Similarly, quotas also disconnect leaders of the followers because they believe they are inferior, and this has a potential negative eect on investment. Counteracting these eects, gender identity seems to reconnect leaders with followers. The negative eect of merit and quotas on investment does not hold when female leaders are facing female followers.
equality policies and meritocracy on female leadership. In scenarios that represent social dilemmas, we nd that the common belief that meritocracy should be the only leading social ideal of society and that armative actions are made in the detriment of good performance is contradicted. Simply holding meritocracy as a value leads to less cooperative outcomes. Meritocracy is a way to break the glass ceiling for women leaders, but this gain on equality may represent a cost in terms of social eciency.
Dierently, the gender quota doesn't reduce cooperation when compared to mer-itocracy. The quota is very criticized because it is a mechanism that imposes gender equality by force and critics argue that it induces a loss in productivity and in social eciency. Our results suggest that in scenarios with and without social dilemmas the quota is a mechanism that increases direct female representation, and it doesn't reduces investment compared to merit. However, policymakers interested in imple-menting a quota should take into account its possible psychological eects and give it a correct framing in order to eliminate the negative consequences, probably via stereotype threat, it can have in women's self-condence.
Related to the followers, our analysis suggests that their behavior is unaected by how the leader is elected. This is important from a policy perspective because it puts forward that the success of policies will depend principally on how women leaders react to the mechanisms that seek to foster female leadership. Policymakers and recruiting teams in companies should focus more in how these institutions, quotas and meritocracy, aect the behavior of the leaders than in how it aects followers.
Appendices
A Tables
Table A.1: Number of Leaders and Followers per Session
(1) (2) (3) (4) (5) Session Num. of Leaders Num. of Followers Women Men Total 1 5 10 9 6 15 2 7 7 7 7 14 3 12 12 14 10 24 4 11 11 12 10 22 5 6 6 6 6 12 6 12 12 16 8 24 7 9 9 10 8 18 8 8 8 8 8 16 9 12 12 15 9 24 10 12 12 13 11 24 11 12 12 13 11 24 12 12 12 14 10 24 Observations 118 123 137 104 241
Table A.2: Descriptive Statistics -Leaders' Sub-sample
(1) (2) (3) (4) (5) (6) (7) All No Info Merit Quota Di Di Di Mean Min Max Std. Dev. Mean Mean Mean (2)=(3) (3)=(4) (2)=(4) Demographics
Age 19.72 17.00 29.00 1.99 19.86 19.97 19.30 -0.12 0.68∗ 0.56
Econ Related 0.09 0.00 1.00 0.29 0.10 0.15 0.03 -0.06 0.13∗ 0.07
Semester 5.31 1.00 11.00 2.52 5.00 5.82 5.11 -0.82 0.71 -0.11 Born in Bogota 0.55 0.00 1.00 0.50 0.60 0.54 0.51 0.06 0.02 0.08 Mother with High Education 0.76 0.00 1.00 0.43 0.74 0.74 0.81 -0.01 -0.07 -0.07 Father with High Education 0.62 0.00 1.00 0.49 0.67 0.59 0.59 0.08 -0.00 0.07 Past Participation 0.53 0.00 1.00 0.50 0.60 0.49 0.51 0.11 -0.03 0.08 Leadership Experience 0.52 0.00 1.00 0.50 0.50 0.51 0.54 -0.01 -0.03 -0.04 Decision Committee 0.48 0.00 1.00 0.50 0.62 0.44 0.38 0.18 0.06 0.24∗∗
Part 1: Grid task and beliefs
Correct Grids 6.39 2.00 13.00 1.62 6.38 6.97 5.78 -0.59∗ 1.19∗∗∗ 0.60∗
Risk Preference 6.89 1.00 10.00 1.74 6.95 6.69 7.03 0.26 -0.33 -0.07 Willingness to Lead 7.00 1.00 10.00 2.51 7.33 6.72 6.92 0.62 -0.20 0.41 Self Condence 1 0.51 0.00 1.00 0.50 0.50 0.44 0.59 0.06 -0.16 -0.09 Self Condence 2 0.53 0.00 1.00 0.50 0.55 0.56 0.49 -0.02 0.08 0.06 Observations 118 42 39 37 81 76 79
1* p<0.10, ** p<0.05, ***p<0.01
2Econ Related refers to Economics and Business Administration students. Mother with High Education and Father with High Education report whether the participant's mother and father have a high education degree. Past participation reports whether subjects participated in previous economic experiments. Leadership Experience reports whether subjects had experience in leadership positions. Decision Committee reports whether the participant was part of a decision committee before. Correct Grids is the number of grids participants counted correctly. Risk preference takes the value of 1 if the participant is not at all willing to take risks and 10 if is completely willing. Willingness to Lead takes the value of 1 if the participant is completely unwilling to lead and 10 if is completely willing. Self Condence 1 is the absolute dierence between the real correct number of grids and the belief of the participant. Self Condence 2 measures if participants believe they were among the best half in the session.
Table A.3: High Returns: Leaders' Decisions by Group Gender Composition
(1) (2) (3) (4) (5) (6) Dep Var: Investment Decision Male Male Mixed Mixed Female Female
β1: Merit 0.035 0.058 0.037 0.039 0.035 0.032
(0.089) (0.091) (0.085) (0.087) (0.089) (0.095)
β2: Quota -0.002 0.037 -0.107 -0.138 0.133* 0.135
(0.094) (0.098) (0.098) (0.102) (0.079) (0.083) Constant 0.786*** 0.641* 0.810*** 1.040** 0.786*** 0.845*
(0.064) (0.362) (0.061) (0.497) (0.064) (0.480) Observations 118 118 118 118 118 118 R-squared 0.002 0.098 0.021 0.054 0.023 0.036
Controls NO YES NO YES NO YES
β3=β1−β2 0.0367 0.0207 0.143 0.178* -0.0984 -0.103
Std.Err (0.0926) (0.102) (0.0960) (0.103) (0.0771) (0.0834) p-val 0.692 0.839 0.138 0.0869 0.204 0.221
1 Robust standard errors in parentheses. 2 *** p<0.01, ** p<0.05, * p<0.1
3 Merit reports one when the leader is under the merit treatment. Quota reports one when the leader is under
Table A.4: Median Returns: Leaders' Decisions by Group Gender Composition
(1) (2) (3) (4) (5) (6) Dep Var: Investment Decision Male Male Mixed Mixed Female Female
β1: Merit -0.258** -0.261** -0.211* -0.224** -0.037 -0.000
(0.109) (0.114) (0.110) (0.111) (0.113) (0.115)
β2: Quota -0.183 -0.187 -0.163 -0.093 0.017 0.054
(0.112) (0.116) (0.113) (0.118) (0.114) (0.115) Constant 0.643*** 0.919* 0.595*** -0.325 0.524*** -0.209
(0.075) (0.477) (0.077) (0.410) (0.078) (0.432) Observations 118 118 118 118 118 118 R-squared 0.049 0.087 0.034 0.106 0.002 0.084
Controls NO YES NO YES NO YES
β3=β1−β2 -0.0748 -0.0740 -0.0478 -0.132 -0.0534 -0.0542
Std.Err (0.115) (0.123) (0.114) (0.116) (0.116) (0.123) p-val 0.515 0.548 0.676 0.260 0.646 0.661
1Robust standard errors in parentheses. 2*** p<0.01, ** p<0.05, * p<0.1
3Merit reports one when the leader is under the merit treatment. Quota reports one when the leader is under
