Gender and preferences for redistribution in a dynamic production environment

Transcription

1 Gender and preferences for redistribution in a dynamic production environment Eva Ranehill, * Susanne Felder, and Roberto A. Weber Department of Economics University of Zurich February 1, 2015 A large body of evidence documents gender differences in preferences over risk, competition and the distribution of welfare. An implication of this literature is that increased female representation in decision-making bodies may yield very different types of policies and organizations. We test the impact of gender on collective outcomes in a dynamic environment with endogenous production and redistributive policies. In small laboratory societies, people repeatedly vote for a tax rate and engage in a real-effort production task. Looking at initial behavior, we observe many gender differences consistent with the previous literature. Women also vote for significantly more redistribution at the beginning of the experiment. However, over the course of the experiment, these behavioral gender differences diminish. Moreover, we find no substantive differences in terms of the redistribution policies enacted by all-female and allmale groups, nor in terms of welfare or inequality of such groups. We demonstrate that the absence of such a policy gap is natural, given the large variation in preferences among both men and women. We conclude that more work is necessary to understand the extent to which behavioral gender differences scale up to influence broader policy outcomes. Keywords: gender differences; redistributive preferences; experiment JEL codes: C91, C92 * Contact author: Department of Economics, University of Zurich, Blümlisalpstrasse 10 (eva.ranehill@econ.uzh.ch). We thank participants at the 2014 Political Economy: Theory Meets Experiment Workshop in Zurich and at the 9 th Nordic Conference in Behavioral and Experimental Economics for helpful comments and suggestions. We thank the Swiss National Science Foundation for generous financial support.

2 1. Introduction Men have traditionally dominated cultural, religious, political and economic decision making in most societies, thereby creating the institutions and structures that govern much modern behavior. This has spurred claims that the World, if ruled by women, would be different than the one we live in today (Funk and Gathmann 2008). This proposition is supported by field and laboratory experiments documenting systematic gender differences in important economic preferences and psychological traits (see Bertrand 2011; Croson and Gneezy 2009; Eckel and Grossman 2008a for overviews of this literature). In the economic literature, women are often found to be less risk taking, less confident and less competitive, than men (Eckel and Grossman 2008b; Gneezy, Niederle, and Rustichini 2003; Niederle and Vesterlund 2007; Muriel Niederle and Vesterlund 2010). Moreover, in the context of pro-sociality, women are sometimes, but not always, found to be more altruistic than men (Engel 2011). More stable differences in pro-sociality appear with respect to preferences for equality versus efficiency, with the existing evidence indicating that female adolescents and adults are more concerned with equality than males, who focus more on efficiency (Almås et al. 2010; Andreoni and Vesterlund 2001; Sutter et al. 2010). Field studies suggest that the preference gaps found in laboratory studies parallel gender gaps in decision-making outside the laboratory with respect to, for example, occupational and educational choices (Dohmen et al. 2011b; Fortin 2008; Manning and Saidi 2008; Bonin et al. 2007; Flory, Leibbrandt, and List 2010; Buser, Niederle, and Oosterbeek 2012; Zhang 2013). Empirical evidence from political surveys further suggest that women often tend to support egalitarian redistributive policies more than men (Alesina and Giuliano 2009; Funk and Gathmann 2008). In light of these gender gaps and current claims for greater gender equality in positions of influence over policy and firms, the question of how increasing female representation impacts collective decision making and outcomes is highly relevant. The key issue, in this regard, is whether the gender differences in economic preferences documented in simple laboratory choice tasks scale up to influence outcomes in more complicated and economically relevant contexts. Most of the existing literature studies gender differences in individual, one shot decisions. While such simple laboratory tasks are valuable instruments for establishing a baseline of gender differences, little is known about the stability of gender gaps in decision making in more complex environments, or across time. What happens to the gender gap in economic decision making often observed in the laboratory if we, for example, introduce 1

3 repeated interaction, collective decision making, learning, or competition in a dynamic environment? Creating such links to the more natural environments studied in policy settings and in firms is important for understanding the potential effects of gender-based preference differences on broader economic outcomes. Understanding the persistence and strength of behavioral gender gaps in such contexts is essential for validating general claims about the impacts of gender differences in decision making, and for understanding the potential consequences for firms and societies of increased female representation in economic and political decision making. In this paper, we provide a piece of this puzzle by asking whether, as one makes the environment more complex and dynamic, gender-based differences in preferences persist and whether they are manifested in substantively different outcomes. Specifically, we study an environment with repeated production and redistribution, and examine whether women support different redistributive policies e.g., more egalitarian and less competitive policies than men, and whether groups in which women hold policy control produce different outcomes than those controlled by men. For comparability with prior research, our study initially identifies the presence of behavioral gender differences in simple choice tasks, and then investigates the extent to which these differences scale up in richer laboratory societies with varying gender composition and control over policy outcomes. Our experiment employs a novel dynamic game with endogenous production and redistribution, where participants interact in stable groups for 10 periods. In each period, participants first vote for their preferred redistribution policy, analogous to a linear tax rate, before engaging in a real-effort production task. Thus, participants state their preferred redistribution rule before they know either their absolute productivity or their relative productivity within the group. In each period, the group median vote is implemented as the redistributive policy. By means of their vote, participants can implement redistributive policies that equalize the earnings differences generated through production (egalitarian institutions) or magnify these differences (competitive institutions). Egalitarian rules financially benefit participants whose production is below the mean, whereas competitive institutions benefit participants performing above the mean. Hence, our design incorporates elements of equality, competition and risk. As we note above, prior research on gender differences in preferences finds that women are less risk-seeking, tend to avoid competition, and are often more egalitarian. Hence, since voting for egalitarian institutions increases equality and decreases competition and risk, a natural extrapolation of the prior evidence on gender-based preferences yields the hypothesis that women will vote for 2

4 more egalitarian policies than men. As a consequence, if such gender differences reliably yield different policies, we might expect those societies in which policy control in the form of the median voter is held by women to have greater redistribution and equality. In measuring individual preferences in a first stage of the experiment, before the production and redistribution game begins, we confirm many of the typical results on gender differences. Women are more risk averse, and tend to shy away from competition relative to men. Women also state a higher willingness to share money in an unincentivized survey question, though not in a behavioral task measuring social preferences. Moreover, consistent with the above hypothesis, we find that women vote for significantly more egalitarian redistribution policies in the first period of the game. This is true even when controlling for the above individual differences in preferences and for beliefs about one s own relative productivity. Hence, in a first period of our experiment, we find that gender differences in preference yield voting for different types of policies. These gender differences in policy preferences decrease slightly, but remain somewhat stable throughout the 10 periods of the study. However, controlling for individual performance indicates that, already in period two, and during the remainder of the study, the gender gap in redistributive preferences is not statistically significant. Moreover, the ways in which men and women adapt their behavior across periods are quite similar. Hence, with experience and repeated interaction, we find little evidence of persistent differences in redistributive preferences based solely on gender. During the course of the study, there is also little difference in the policies implemented, the overall welfare, or distribution of earnings of male- and female-majority groups. Hence, despite finding strong differences between men and women in important preference dimensions, at least early in the experiment, our findings suggest that such gender-based differences in preferences may in some settings converge with repeated interaction, and may have limited impacts on observable policy outcomes. We demonstrate that the limited impact on policy outcomes is not entirely surprising, even despite the clear differences in mean preferences early in the experiment. While women do, on average, prefer more redistribution than men, there is a great deal of heterogeneity in both male and female preferences. Hence, a female-majority group need not look that different, in expectation, from a male-majority group under a majority-rule voting institution like the one in our experiment. Our results make a valuable contribution by studying how an observed gender gap in redistributive preferences plays out in a dynamic environment with repeated interaction and feedback. Our results hence address the degree to which connections can be made from laboratory choice data to claims about the likely impact of gender differences on broader 3

5 economic and policy outcomes. This has relevance not only for the academic debate on gender differences in preferences, and their importance for economic outcomes, but also for the debate about the impact of diversity, and affirmative action, in decision-making bodies. Identifying whether gender differences in preferences actually persist in natural settings and drive policy outcomes is complicated. Some studies correlate the presence of females in decision-making positions with characteristics of outcomes. For example, Chattopadhyay and Duflo (2004) find that random political reservation of Village Council Head positions for women leads to more investments in infrastructure reflecting women s preferences in India. Further, Matsa and Miller (2013a) study the implementation of the Norwegian gender quota for corporate boards in 2006, and find that companies affected by the quota made fewer workforce reductions and experienced higher relative labor costs and lower short-time profits in comparison to unaffected companies. 1 However, these studies do not directly identify a connection between gender-based differences in preferences, the behavior of female decision makers, and policy outcomes. Rather, the preference channel is implicitly identified through the effects of increased female participation in decision making on outcomes. This approach leaves open many questions, including whether female decision makers in these settings actually differ in their preferences from males in similar positions. Hence, our point is not that these differences are not real, but rather that the direct connection between gender differences in preferences and policy outcomes needs to be better understood. Our study adds to this important issue, by showing that, at least in one context, initial differences do not scale up to produce large differences in outcomes in a dynamic production and redistribution environment. The remainder of this article is structured as follows. Section 2 describes the experiment design. In Section 3 we present the results and Section 4 concludes. 2. Experimental design The experiment consisted of three parts. In Part 1, we elicited individual preferences related to risk, competitiveness and concern for others. We also measured performance in the production task and beliefs regarding relative performance in this first part. In the main part of the experiment, Part 2, participants performed the production task with redistribution in groups of five, for 10 periods. Part 3 once again elicited individual productivity, and also included an exit 1 The result that women are less likely to lay off workers is substantiated for an analysis of US firms where no legally imposed quota exists (Matsa and Miller 2013b). See also Adams and Ferreira (2009), and Adams and Funk (2009) for additional studies on the impact of gender on leadership. 4

6 questionnaire. Full instructions for each part of the study were given at the onset of each part. 2 The participants were informed that each part was independent, such that any decision taken in one part would not influence the course of events in other parts. Subjects were paid for all incentivized tasks, including all periods of the main game in Part 2. Payments were denominated in Experimental Currency Units (ECU). Accumulated ECU were converted into monetary payments at the end of the experiment at the rate of 50 ECU to 1 Swiss Franc (CHF) Part 1: Preferences, Productivity, and Performance Beliefs We elicited preferences in the first part using both incentivized and non-incentivized measures. Specifically, we elicited risk preferences through an incentivized investment game (Gneezy and Potters 1997), as well as through a non-incentivized survey question in which participants self-reported their general risk-taking propensity on a scale from 0 to 10 (Dohmen et al. 2011a). In the incentivized investment game, participants decided how much of an initial allocation of 100 ECU to allocate to a risky investment. The investment failed with a probability of 50%, in which case the invested money was lost. With 50% probability, however, the investment succeeded, and the participant received 2.5 times the investment. We also elicited social preferences using one incentivized and one non-incentivized measure. As an incentivized measure we used the full version (15 questions) of the Social Value Orientation scale (Murphy, Ackermann, and Handgraaf 2011). This task involves subjects making 15 choices, where each choice involves selecting one outcome from a set of possible allocations between one s self and another randomly selected participant. The resulting choices allow a classification of a subject s type along different dimensions of prosociality. As a non-incentivized measure, we administered a hypothetical question about how much a participant would voluntarily donate to charity if he or she unexpectedly received a sum of CHF In Part 1, participants also performed the real-effort production task that would form the basis of the main part of the experiment. In this first part, subjects performed the task only once, under piece rate incentives. We included this to measure individual productivity in the task. The task is a computerized, slightly adapted, version of a digit-substitution task also used by Iriberry and Rey-Biel (2011). 3 Figure 1 shows an example of this task. In the task, 2 Full instructions are available in appendix B. 3 The task was originally developed and applied in neuropsychology by Wechsler (1958) as part of the Wechsler Adult Intelligence Scale. 5

7 participants are shown keys, consisting of a unique mapping of 9 letters of the alphabet to the numbers 1-9, and can decode sequences of three letters into numbers according to the 9-digit code. Keys are changed every ninth three-letter sequence, implying that the task involves both memory and codification abilities. If a sequence is decoded incorrectly, a participant must decode the same sequence until the entry is correct. In every production phase, participants had 90 seconds to decode as many sequences as possible and received a payment of 10 ECU for each correct entry. Figure 1: The digit-letter substitution task We implemented this particular production task mainly because of two characteristics: Previous research shows the task to yield (1) no significant gender differences in performance and (2) considerable variation in performance (Iriberri and Rey-Biel 2011). 4 While the first characteristic simplifies the analysis by not biasing the effect of the redistribution policy by gender, the second is a necessary condition to create a motivation for redistribution. After participants performed the task under piece rate incentives, we elicited an incentivized choice to measure willingness to engage in competition. Specifically, subjects were offered a second payment for their performance. For this payment, they chose between a piece-rate payment scheme and a competitive payment scheme. Under the piece-rate payment 4 For an overview of gender differences in this task see Majeres (1983). 6

8 scheme, a subject received a second payment identical to the one they received for their initial performance (10 ECU per correctly completed entry). Alternatively, a participant could choose a competitive payment scheme in which the participant s score would be compared to that of a randomly selected other participant, and would yield either double the original piece rate payment (20 ECU per correct entry) if the participant s performance was higher than that of the other person or, otherwise, would yield nothing. Ties were broken randomly. This binary choice is our measure of competitiveness. We also elicited subjects beliefs about their performance relative to others. At the conclusion of Part 1, participants guessed their performance ranking on the task in the experimental session. This question was incentivized such that participants received an extra payment of 50 ECU if their guessed rank was within 2 ranks of their actual rank. At the end of the study, subjects were provided information on outcomes and payment for each of the above tasks. Prior to that, subjects received no information regarding the outcomes of the different tasks in Part Part 2: A Production Game with Redistribution At the beginning of Part 2, participants were randomly assigned to five-person groups. These groups then remained the same for the ten periods of Part 2. In order to over-sample extreme group compositions in terms of gender i.e., groups in which all decisions are made by males and groups in which all decisions are made by females the software ensured at least one allmale and one all-female group in each session. The remaining groups were all constituted by randomly assigning participants to groups, irrespective of gender. 5 During Part 2, the five-person groups repeatedly engaged in the game with voting, production and redistribution for 10 periods. Below we first describe the general game, and then explain each part in detail Overview of the game Each period of the redistribution game followed the same course of events. Participants first voted for their preferred redistribution policy. Then, the computer presented the outcome of 5 Oversampling of men and women to single gender groups was made to generate groups of all types, and as a preparation for future research in which we intend to make group composition salient. In the current study, the number of groups is too small to allow for a meaningful statistical analysis, and these groups will not be analyzed separately. 7

9 the vote i.e., the group s redistribution policy in the period to the group members. Below, we describe the voting process and the ensuing redistributive policies. Group members then engaged in the real-effort production task, which was exactly the same task as in Part 1. Participants engaged in the task under the same-piece rate incentives as in Part 1 and generated an income proportional to their productivity. Once the production phase was over, this income was subject to redistribution per the policy determined by the vote at the beginning of the period. Hence, group members cast their vote for a redistribution policy before they knew the exact individual earnings obtained by themselves and others through production. At the end of the period, participants were informed about their and other group members earnings from production, the net transfers, and the final payoffs in the period The vote At the beginning of each period, all five group members simultaneously cast a vote for a redistribution parameter, t [ 1.00, 1.00], analogous to a linear tax rate. The median vote in a five-person group was implemented and applied to the group earnings at the end of the relevant period. We implemented this particular voting mechanism because it allows a flexible choice of t and thereby enables a varied set of preferences and outcomes. Using the median vote, in contrast to using, for example, the average vote, implies that each participant is incentivized to provide his or her preferred value of t, eliminating issues of strategic voting. Following the vote, all group members were informed of the resulting redistribution parameter for that period Production Subjects then worked independently on the same real-effort production task as in Part 1. Each production period lasted 90 seconds, during which each correctly completed entry by a subject generated 10 ECU of income Redistribution Following production in a period, the income generated by group members was redistributed according to the implemented redistribution policy for that period. Given a policy, t, defined by the median vote, the formula for calculating the final payoffs is given by: 8

10 n 1 n x i π i (x i, x j i, t) = (1 t)x i + t i=1 (Eq. 1) In this payoff equation, π i denotes the final payoff of individual i in the period, x i denotes the individual s earnings from production, x j i the earnings of other group members, and finally, t denotes the tax rate. In essence, redistribution is made by collecting a portion of the individual earnings from production, and by redistributing this amount back to the group members. The tax rate, t, has two main effects. First, depending on whether t is positive or negative, it either attenuates or amplifies the income inequalities generated in the production task. A value of t > 0 implies that redistribution decreases the earnings differences arising in the task, while a value of t < 0 increase any earnings differentials. Second, the farther t is from 0, the more redistribution takes place. To illustrate the effects of t consider first the case of t > 0. Redistribution is then done such that a share of t is collected from each group member s earnings from production. Each group member thus keeps (1 t)x i, as illustrated in the first part of Equation 1. The five collected shares are summed, divided in five equal parts, and transferred back to the group members. This is illustrated in the second part of Equation 1. Thus, each member contributes an amount proportional to his or her earnings, but gets the average of the contributed sums back. Individuals who earn more (less) than the group average in the production task thus contribute more (less) than they get back, implying that income differences arising in the real effort task are attenuated. The larger t is, the larger is the share of the income collected and redistributed. For example, when t = 1, the entire income generated in the production task is collected and income is thus fully equalized. When t < 0, (1 t) is larger than 1, and, contrary to the case when t is positive, group members receive an additional payment equal in size to tx i. Hence, producing more output initially is rewarded by redistribution. The total amount to be paid to the group members is divided in five equal shares, and collected from each member. Thus, when t is negative, group members receive a sum proportional to their earnings in the real effort task, but pay equal amounts into the redistribution. Individuals who generate earnings higher (lower) than the group average thereby receive a larger (smaller) sum than they contribute. Thus, when t is 9

11 negative, income differences arising in the real effort task are amplified by redistribution. The more negative the t, the more income inequalities are amplified. 6 Note also two special cases of redistributive policies allowed by this mechanism. The absence of redistribution corresponds to t = 0, in which case everyone simply keeps the earnings they generate through production. The mechanism also allows fully egalitarian outcomes, which occur when t = 1. The properties of the tax rate were explained to the participants, and illustrated in the instructions. The two cases of negative and positive redistribution policies were presented to the participants such that a positive t would decrease the payment differences arising in the task, while a negative t would reward those group members who generate more money in the production task. Hence, a negative transfer parameter can be understood as a redistribution rule that implements competition. Note that, in this framework, egalitarian and maximin preferences coincide at t = 1 whereas libertarian and meritocratic choices coincide at t = 0. The degree of competitiveness increases as the redistribution policy becomes more negative. Purely selfish behavior implies, for participants who perform above the mean, a vote for t = 1, whereas for participants who perform below the mean it implies a vote for t = Feedback Each period concluded with the display of the redistribution policy, together with a table indicating, for each group member in that period, the income generated from production, the member s rank in the group, the net transfers, and final earnings. Participants could also see, for each group member, the average amount of money earned through production across all previous periods. A scrollable box also provided information on the redistribution policy, as well as each group member s production and final earnings for all previous periods. 2.3 Part 3: Learning Measurement and Exit Questionnaire After the 10th and last period of the redistribution game, participants ended the study with one more round of the production task. This final round of the real-effort task was incentivized through the same piece rate as before, 10 ECU per completed entry. In this case, there was no 6 When t was negative, we limited its magnitude, such that no group member would end up with negative earnings after redistribution. Thus, in cases with strongly regressive taxes, and an unequal distribution of earnings from the production task, t was adjusted upwards such that the least productive group member received a payoff of 0. We explained this feature clearly to subjects. This constraint never proved binding in the study. 10

12 redistribution. We included this additional performance measure to get an indication of the level of learning in the task, since a participant s performance during the 10 periods of the production and redistribution game may be influenced both by varying productivity, as with learning, or by strategic responses to implemented redistribution policies. We also administered an exit questionnaire. This comprised questions about demographics and political orientation. 2.4 Implementation We took several steps to clearly explain the instructions and procedures to subjects. We particularly spent a significant amount of time explaining the redistribution mechanism. An experimenter read all instructions aloud while participants read printed instructions. The instructions provided examples in text and in tables of the impact of negative and positive redistribution coefficients, and illustrated the three special cases of t = -1, 0 and 1. In addition, after the instructions were read aloud, all participants saw a calculation screen for three minutes, in which they could test the effect of different redistribution parameters for any hypothetical distribution of earnings among the five group members. The same calculation screen was also available to the participants for 60 seconds at the onset of each subsequent period (periods 2-10), together with information about the five group members earnings from production and final earnings in all prior periods. Finally, to verify that participants had understood the procedures and how payoffs were determined in the game before it started, all participants also answered control questions pertaining to cases with both negative and positive redistribution policies. Participants had to answer the questions correctly before proceeding. We implemented the experiment using the software z-tree (Fischbacher 2007) in English at the laboratory for experimental and behavioral economics at the University of Zurich. We recruited 390 students from the University of Zurich and the Swiss Federal Institute of Technology in Zurich using the software h-root (Bock, Nicklisch and Baetge, 2014). We conducted sixteen sessions, each consisting of 20 or 25 participants. In each session, five randomly chosen men and five randomly chosen women were assigned to a same sex group for Part 2 of the study, whereas the remaining participants in each session were grouped independently of their sex. In total we have 16 single female groups, 17 single male (one single sex male group was created by chance), and 45 mixed groups. A session lasted about two hours and participants were paid their earnings from all ten periods in Part 2, their earnings from Parts 1 and 3, and a 10 CHF participation payment. Participants earned, on average, 54 CHF. 11

13 3. Results In this section, we start by looking at individual behavior in Part 1 of the experiment, to see whether we replicate the differences in preferences between men and women that are observed in much of the prior literature. We then study whether these differences yield behavioral differences in voting behavior in the first period of Part 2 the part of the experiment with voting and redistribution. Next, we examine the development of behavior across the full length of the study, as participants receive comprehensive feedback on the outcome of each period and the full game history. Finally, we study group outcomes in particular, whether the gender composition of the group influences the implemented redistribution policy and the group outcome. 3.1 Part 1: Initial gender differences in preferences We first present an overview of the gender gaps in the preference measures we elicited at the beginning of the study. We also test for any gender differences in performance on the realeffort production task. Table 1 lists the measures elicited in Part 1 of the experiment, with averages presented separately for men and women. 7 We replicate many of the gender differences observed in previous studies. Men exhibit greater risk tolerance in both the incentivized investment task and the survey question. Men are also more willing to have their payment determined through a competitive incentive scheme. On the other hand, women state a higher willingness to donate money in a non-incentivized survey question. We also note that the average gender difference in baseline performance on the realeffort production task is small, and statistically insignificant. This validates our choice of this task as one in which performance does not differ significantly by gender. Table 1 additionally provides average performance in the final instance of the task, performed at the end of the experiment under piece-rate incentives. While there is evidence of learning by both men and women performance is considerably higher in this final measure than in the initial one, by 44 percent in both cases we again observe no significant difference in performance by gender. We further find ample variation in initial task performance: the minimum performance is 5, 7 The angle of the Social Value Orientation reported here is based on the six primary items of the scale (among the 15). These items allows for the categorization of individuals as altruistic, pro-social, individualistic and competitive, on a continuous scale. The remaining 9 items essentially allow disentangling the pro-social motivations of joint payoff maximization and inequality aversion. Among the 135 individuals who expressed a pro-social preference orientation in our sample, women expressed more concern for inequality than efficiency as compared to men. However, the difference is only marginally statistically significant (p = 0.098). 12

14 maximum performance is 21, and only 15 percent of observations lie at the median of 11. Such variation is necessary to motivate redistribution. However, we observe, no difference in the performance distribution between men and women (p = 0.854, Kolmogorov-Smirnov test). Table 1: The gender gap in preferences and performance 8 Variable Men Women p-value Risk (Investment task) < (incentivized 0-100, 100 = risky) (2.077) (1.941) Risk (Survey question) (non-incentivized 0-10, 10 = risk taking) (0.163) (0.165) Social Value Orientation (Angle) (Incentivized, larger angle = altruistic) (0.993) (0.958) Giving, (Survey question) (non-incentivized, , 1000 generous) (14.746) (15.500) Competitiveness < (0 or 1, 1 = competitive) (0.035) (0.032) Overconfidence (guessed rank - actual rank) (0.454) (0.520) Average performance (Initial piece rate) (0.185) (0.191) Average performance (Final piece rate) (0.230) (0.224) Observations * P-values represent Wilcoxon Mann-Whitney test. a Due to the dependent structure of the data, this p-value represents the significance of the coefficient on female in a regression of performance on female, clustering at group level. Result 1: The preference measures yield several gender differences observed in the prior literature. Men are more willing to take risk, and more willing to opt for competitive payments schemes. In a survey question, women state a larger willingness to give. 8 For a detailed description of the variables included in the analysis see Table 1A in Appendix A. 13

15 3.2 Behavior in Period 1 We next consider behavior in the first period of Part 2. We focus on gender differences in initial votes for the redistribution parameter, t, and on performance reactions to the implemented redistribution policy Initial gender differences in redistributive preferences Recall that votes for the redistribution parameter, t, may range from -1 to 1, with values above 0 corresponding to egalitarian institutions in which wealth is reallocated from those who generated income above the group mean to those performing below the mean. Negative values indicate a preference for competitive institutions with reallocation going in the opposite direction. In addition, the further from 0, the more redistribution takes place. In the first period, the result from a direct comparison of male and female votes is in line with previous research showing that women are less competitive and more egalitarian (7 and Vesterlund, 2007; Andreoni and Vesterlund 2001; Almås et al. 2010; Dickinson and Tiefenthaler 2002). Specifically, men, on average, vote for policies very close to the tax rate at which no redistribution occurs (t men = -0.02). Women, however, tend to vote for more egalitarian redistribution policies (t women = 0.17, p-value of difference < 0.001, Wilcoxon- Mann-Whitney test). A majority of the female participants, 58 percent, vote for a positive redistribution coefficient, whereas only 40 percent, of men do so; this difference of proportions is significant (two-sample test of proportion, z = , p < 0.001). Figure 2 shows the cumulative distribution functions of first period votes, separately for males and females. 9 The male distribution function is located to the left of the one for females, indicating that more men than women vote for low redistribution coefficients. The difference between the two distributions is statistically significant (Kolmogorov-Smirnov test, p = 0.003) Figure A2 in appendix shows, for men and women separately, histograms with kernel density estimates of the votes in period We also find that the distribution of votes among men has a larger variance than that of women in Period 1 (two-sample variance test, f = 2.031, p < 0.001). The same statistic is also significant comparing male and female majority groups (Wilcoxon-Mann-Whitney test comparing group variation, p = ). 14

16 Cumulative distribution functions by gender Policy vote Males Females Figure 2: Cumulative distribution functions of first period votes, for men and women separately We further analyze this initial gender difference in Table 2, which presents OLS regressions with a subject s first period vote as the dependent variable. In the first specification, we identify the overall gender effect on votes, without any additional controls. This regression shows the large and strongly significant effect of gender in Period 1 that we identify above. The second specification includes controls for the set of preferences we collected at the onset of the study: relative performance beliefs, risk aversion, social value orientation, and willingness to compete. 11 People who are more competitive vote for less egalitarian policies. The significant and negative point estimate for the variable competitiveness is in line with the previously reported finding that egalitarian preferences and competitiveness correlate negatively (Bartling, Fehr, Maréchal, & Schunk, 2009). Furthermore, individuals who are more prosocial, as indicated by the social value orientation measure, vote for more egalitarian 11 We chose a specification with the incentivized measures of the preferences we elicited. Replacing the risk measurement and the social value orientation with the survey questions pertaining to risk and altruism does not change the results with respect to the gender variable. In additional analysis (not reported here), we find that parental education, number of siblings and age do not correlate with votes. However, participants who report being more to the right of the political spectrum vote for lower redistribution policies. 15

17 policies. Finally, those who expect to be better at the task, relative to others, vote for less egalitarian policies. Including these explanatory variables as controls decreases the coefficient on the binary gender variable somewhat; however, it remains sizeable and statistically significant (p = 0.013). 12 The third specification differs from the second in that it controls for actual performance instead of performance beliefs. Also in this case, the coefficient on female remains sizeable and is significant at the 1% level. Table 2: OLS regression: Gender and first period votes Dependent variable: Vote Period 1 (1) (2) (3) Female *** ** *** (0.045) (0.046) (0.044) Risk (0-100, 100 = risk taking) (0.001) (0.001) SVO Angle, (-45-90, 90 = altruistic) *** *** (0.002) (0.002) Competition (1 = competitive) *** *** (0.048) (0.049) Performance beliefs *** (percent, 100 = best performer) (0.002) Performance *** (0.009) Constant *** *** (0.036) (0.137) (0.121) Observations R-squared Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Result 2: In the first period, women vote for more egalitarian redistribution policies than men. This gender difference persists with the introduction of individual level measures of risk taking, Social Value Orientation, willingness to compete, performance beliefs, and (expected) performance Initial gender differences in performance 12 We also ran separate regressions for women and men to study whether the relationship between our explanatory variables and the dependent variable may differ between the genders. In these regressions, relative performance beliefs, social value orientation, and competitive inclination are significant for male participants. For women, relative performance beliefs and, to a lesser extent, social value orientation matter. 16

18 As with the baseline performance measures under piece-rate incentives, from Part 1, there is no aggregate gender difference in performance in Period 1 of Part 2. Men again complete slightly more successful entries (12.5) than women (12.3). Column 1 in Table 3 presents a regression of performance in Period 1 on performance in the piece-rate baseline from Part 1 and gender. The gender coefficient is small and statistically insignificant. However, in Part 2, the production task is no longer conducted with fixed piece-rate incentive, but rather with an endogenously determined redistribution policy in place. Therefore, we can also study whether subjects performance responds to the policy in place, and whether this varies by gender. This is studied in column 2 of Table 3, where we add as explanatory variables a proxy for the median vote realized in Period 1 (remember that performance takes place after participants voted on, and were informed about, the group s redistribution policy for the current period), relative performance beliefs from Part 1, and an interaction term between these two. Due to the endogeneity of the median vote, which is influenced by a subject s won preferences, the median vote is here calculated based only on the votes of the remaining 4 group members. 13 We also add interaction terms to detect whether there are any differences in how performance responds to these variables by gender. The performance beliefs variable is centered around 0, so that a score of -50 indicates those subjects who are least confident and 50 those who are most confident in their relative ability. We are particularly interested in the interaction between performance beliefs and median vote, since this reflects potential strategic responses to redistribution policies i.e., if individuals who believe themselves to perform well on the task withdraw effort under an egalitarian policy (high t) but increase it under a competitive policy (negative t). The results indicate that performance in Period 1 does not respond significantly to the redistribution policy, neither for men nor for women. We thus find no indication that individuals adjust their effort based on whether they expect the redistribution policy to benefit them or not, at least in the first period of the game. 13 Voting is the first thing the participants do in Period 1 and participants have no prior information on others behavior prior to beginning performance in Period 1. Therefore, the other group members votes are independent from an individual s vote. Using the average vote as a measure of the group s redistribution policy, or the true median, does not change our results qualitatively. 17

19 Table 3: OLS regression. Gender and first period performance. Dependent Performance in Period 1 (1) (2) Female (0.200) (0.215) Median vote (t) (0.620) Performance beliefs (piece rate) (0.007) Median vote X Performance beliefs (0.029) Female X Median vote, t (0.982) Female X Performance beliefs (0.010) Female X Median vote X Performance beliefs (0.044) Performance in Part 1 (piece rate) *** *** (0.033) (0.035) Constant *** *** (0.412) (0.433) Observations R-squared Standard errors clustered at group level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Median vote is based on the median of the four votes by other group members in Period 1. Result 3: In Period 1, there is no performance difference by gender. Performance in Period 1 also seems not to respond to the redistribution policy in place. 3.3 Behavior in the dynamic environment In the analysis above, we find that female participants initially differ in their preferences from male participants, and vote for different redistributive policies. In particular, women vote for policies that yield more egalitarian outcomes. This section addresses how this gender gap, as well as individual votes, develop over time, and how men and women update and adjust their behavior to the environment they face. 18

20 Votes Figure 3 shows the average votes cast by men and women across the 10 periods of the redistribution game. In addition to the large and significant initial gender gap in behavior in Period 1 documented above, the figure shows that, in a direct comparison, the gender difference decreases somewhat but generally persists across the 10 periods of the game. The average vote by male participants across the 10 periods of the game is -0.04; the corresponding number for women is This yields a slightly smaller difference (0.14) than in Period 1 (0.19). Nevertheless, the persisting gender difference in policy preferences is confirmed in a regression analysis regressing, for each period, the individual vote on female, and clustering at group level. In this analysis, the gender gap is significant at the 10 percent level or lower in 8 of 10 periods (see Table A3 in appendix). Average vote by gender Average vote Period Women Men Figure 3: Average votes for redistributive policies by period and gender However, in this experimental setting we have the advantage of being able to measure performance exactly, including prior performance, which may influence preferences for redistribution. Performing the same regression analysis as above, including performance in the 19

21 previous period as a control variable, the gender gap is significant only at the 10 percent level in Period 2. From Period 3 and onwards, the coefficient on female remains small and insignificant throughout the remainder of the study (see Table A.3). Between Periods 1 and 3, the point estimate on female decreases by 57% and becomes statistically insignificant. Result 4: While women vote for more egalitarian redistribution policies than men throughout Part 2, this gender differences in policy preferences is only significant in the first two periods if we control for prior performance. Controlling for prior performance, the gender gap disappears by Period 3 and remains insignificant for all periods thereafter. We can also explore how men and women change their votes in reaction to past outcomes. 14 Table 4 presents 2SLS panel regressions with subject-level random effects. The dependent variable is a subject s vote in a period. We use the preference variables measured at the beginning of the experiment to instrument for a participant s vote in Period 1, and thereby avoid the endogeneity arising from using lagged vote. The model is estimated separately for men, in Column 1, and for women, in Column 2. In addition to the estimated initial vote, we include the individual s lagged relative performance, measured as his or her performance difference with the group mean, as explanatory variable. A few observations emerge from Table 4. First, we see that votes in Period 1 are significantly correlated with later votes. Second, there is a significant negative relationship between votes in the current period and relative performance in the prior period. This indicates that both male and female participants vote strategically they vote for significantly less redistribution as their relative performance increases and vice versa. Thus, if the prior period s relative productivity serves as a proxy for the expected relative production in the current period, both genders adjust their votes in accordance with their financial interest. Our estimates suggest that the effect is economically relevant and sizeable, and rather similar for men and women; each additional exercise solved by a participant, above the group mean, leads, on average, to a 0.08 decrease in the redistribution policy favored by a subject. 15 Hence, we again find that male and female behavior display more similarities than differences. 14 We first tested whether male or female votes display any time trend in a regression, with subject randomeffects, of the vote regressed on gender, period and the interaction of the two. We find no significant time trend in votes, neither for women nor men. 15 The results do not change qualitatively if we run separate regressions for the first half (Periods 2-5) and the second half (Periods 6-10) of the experiment. 20

22 Table 4: IV panel regression with random effects. Dependent variable is individual vote for redistribution policy. Dependent variable: Vote (t) Men Women (1) (2) Vote, t = *** ** (0.155) (0.219) Perf. diff. to mean, t *** *** (0.012) (0.010) Constant (0.035) (0.052) Observations Number of individuals R-squared Bootstrapped standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Result 5: Women and men similarly adjust their votes for redistribution policies in a similar manner that is consistent with self-interest Performance We are also interested in how participants adapt their performance over time, depending on the environment the participants experience in prior periods of the game. Recall that women and men perform equally well during the initial baseline performance under piece-rates (see Table 1) and in Period 1 (see Table 3). There also appears to be learning over time; in comparison to Period 1, average performance is higher by Period 10 for both men and women and is also higher in Part 3, when both men and women perform the task under identical piece-rate incentives as in Part 1. However, although male and female performance exhibit similar time trends and remain roughly comparable throughout the study, men persistently perform slightly better. For example, the performance of men and women in the final round of the task, in Part 3, is not significantly different, although men decode 0.6 sequences more on average (see Table A2 for an overview of performance for men and women across time, separately). Male and female effort may also be differentially influenced by the policy environment and by experience. For example, the genders may react differently to competitive payment schemes, as indicated in previous literature (Gneezy, Niederle, and Rustichini 2003). In particular, we are interested in whether men or women are equally likely to increase or withhold effort in the production task based on whether they expect the implemented redistribution 21

23 policy to benefit them or not. Since performance is endogenous to the institutional environment, we are interested in how this potentially affects the types of individual and collective outcomes that arise. For example, withholding effort in reaction to disadvantageous policies is one way through which individuals behavior can affect welfare. Table 5 below presents the results of a fixed-effects panel regression, with individual performance in each period, measured as the difference from its expected value, as the dependent variable. To calculate this difference, we compare a participant s final piece-rate performance, from Part 3, to the baseline Part 1 piece-rate performance, and assume a linear increase in productivity across the ten periods. We subtract this expected performance from the participant s actual performance in a period to create the dependent variable for the regressions. We include as explanatory variables lagged performance difference to the group mean, as well as the median vote realized in the current period (remember that performance takes place after participants voted on, and were informed about, the group s redistribution policy for the current period) and an interaction term between the individual s relative performance in the previous period and the median vote. It is of course possible that some of these variables are endogenous. We therefore interpret the results cautiously. Studying the results presented in Table 5, we see that both men and women react to their relative prior performance. When they perform above the group s mean, they decrease their performance and vice versa. Our primary interest is in how performance reacts to the redistributive policy in a period. In contrast with the insensitivity to redistribution policies that we observed in the first period, both men and women produce lower (higher) performance as the implemented policy produces more (less) egalitarian redistribution, i.e., as the median vote for t increases. The coefficient is of about the same size for both genders, indicating that men and women are equally sensitive to whether the redistribution policy produces more competitive or egalitarian incentives If we run the same regressions as in Table 5, but restrict the sample either to those experiencing competitive redistribution policies, or those experiencing egalitarian or neutral redistribution policies, the coefficient for the interaction term is somewhat higher for men, and significant only for this group. 22

24 Table 5: Fixed effects OLS panel regression. Dependent variable: Performance - predicted performance Men (1) Women (2) Perf. diff. to mean, t *** ** (0.025) (0.032) Median vote, t *** ** (0.183) (0.296) Perf. diff. to mean, t-1 * median vote, t *** *** (0.039) (0.058) Constant *** *** (0.005) (0.016) Observations R-squared Standard errors clustered at group level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Moreover, both men and women are more sensitive to the redistributive policy as their performance moves farther from the group mean indicating that they are impacted more by the redistributive policy. This is indicated by the negative and statistically significant interaction between prior relative performance and the current policy. 17 Hence, both male and female participants in our sample adjust their effort based on whether or not they expect the redistribution policy to benefit them, and we see no substantive difference in the size of this effect. Result 5: Both men s and women s performance reacts to redistributive policy in a selfinterested manner. That is, as redistribution becomes more egalitarian, those whose prior performance lies above (below) the group average decrease (increase) their current performance, and vice versa. 3.3 Gender, policy control and economic outcomes Our findings above question the extent to which seemingly strong gender differences in preferences and initial support for redistributive policies are likely to substantially impact outcomes over the course of the experiment. We do find that women show many of the differences in preferences from men that have been identified in prior literature, at least at the 17 However, analyzing men and women together, using interaction effects between female and the regressors in the model in Table 5, does not indicate any significant difference between men and women. That is, none of the gender interaction effects are statistically significant. 23

25 beginning of the experiment. Moreover, consistent with behavioral gender gaps, women initially vote for significantly more redistribution than men. This would suggest that women, where they hold decision-making power, might implement very different policies than men. But, we also find that the behavioral gaps over the remaining 9 periods differ little between men and women, and that few statistical differences persist beyond the first period or two. In addition, men and women seem to update and react to their environment similarly during the course to the experiment. This calls into question whether the initial differences are likely to produce long-term differences in outcomes such as wealth and inequality. In this section we study group level outcomes, comparing groups in which either males or females are in the majority, which corresponds to one gender holding control over policy. This brings us back to one of our central motivation questions, of whether male- and femalecontrolled policy yields different kinds of outcomes. Redistribution policy and gender majority Period Fem. majority Male majority Figure 4: Redistribution Policy and gender majority Figure 3 shows the average redistribution policy implemented in groups, based on whether either males or females are in the majority. In the first period, there is a slight tendency 24

26 toward more egalitarian redistribution in female-majority groups, consistent with the Period 1 behavioral difference we observe at the individual level, though the difference is only marginally statistically significant (p = 0.070, Wilcoxon Mann-Whitney test). For the remaining periods, differences are negligible and far from statistically significant. 18 Hence, group composition seems to have some weak effects on policy outcomes, at least initially. But, in general, we observe very little difference between groups based on whether men or women hold policy-making power. To provide an interpretation for the lack of sizable gaps in policy outcomes (Figure 4) despite the larger gaps in preferences (Figure 3), consider that the distributions of male and female policy preferences, while different, both have considerable variation (Figure 2). Hence, while men and women may differ significantly from one another in terms of their mean and median preferences, it may simply be that a group consisting of 3 men and 2 women may not have a very different median preference from one consisting of 2 men and 3 women, or of one consisting of 1 man and 4 women. To explore this interpretation, we simulated 10,000 groups, composed of five individuals selected at random from our data set, and compared the median policy vote in these synthetic groups in Period 1. We separate those groups with a male majority from those with a female majority. In these simulated groups, the difference in median policy votes between male and female majority groups is on average ((t male majority = 0.057, t female majority = 0.120; note that this difference is close to the difference indicated in the first period of Figure 4, based on the actual data.). This difference is statistically significant, due to the large number of observations, but considerably smaller than the difference in average votes between men and women. Cumulative density functions of median policy votes for the simulated male and female majority groups are shown in Figure 5. Comparing Figures 2 and 5 gives an indication of how the initial gender gap in preferences, while robust, may fail to scale up into substantively different policy outcomes in our experimental setting. 18 Excluding the gender mixed groups and comparing only all-male and all-female groups, the tendency of more egalitarian redistribution policies implemented in female groups is somewhat reinforced, but still not significant across the board (the difference is significant, or marginally significant, in 4 of the 10 periods). 25

27 Cumulative distribution functions by gender majority Median vote Male majority Female majority Figure 5: Cumulative density functions of median policy votes in Period 1, based on random draws of five individuals from our sample. Male and female majority groups reported separately. Lastly, we look at the overall welfare generated by the two different types of groups. In total, male and female majority groups generate similar payoffs from the production and redistribution game. Male majority groups earn average payoffs of CHF 30.3, while female majority groups generate a payoff of CHF 29.5 (p = 0.376, Wilcoxon-Mann-Whitney test). 19 Hence, aggregate welfare differs little between men and women, despite substantial differences in preferences and initial policy votes. We also calculate Gini coefficient for each group, based on the individual payoffs generated from the redistribution game, as a measure of group inequality. This provides us with a test of whether the distributions of earnings differ between male and female majority groups. Again, we fail to reject the null hypotheses of equal levels of inequality between male and female majority groups (p=0.915, Wilcoxon Mann-Whitney 19 Men earn slightly more than women on average, but this difference is far from significant. These comparisons also yield statistically insignificant differences when we compare single-gender groups. 26

28 test). At the group level, we thus find virtually no substantive differences with respect to implemented policies and outcomes between groups with a different gender majority. Result 6: Groups in in which men and women comprise the majority are statistically indistinguishable, beyond the first period, in enacted policies, aggregate earnings and the distribution of earnings. 5. Conclusion We develop a novel laboratory experiment to study whether gender gaps in preferences influence policy and outcomes in a repeated game with production and redistribution. Prior literature suggests significant gender gaps in preferences and behavior, including several that suggest women will favor less competitive and more egalitarian policies. In a first part of our experiment, we replicate many of these gaps. Following much of the earlier literature, we use simple one-shot, individual choice tasks. In these first measurements, we corroborate the gender gaps generally found in previous literature. Women prefer less risk and less competition, and report a greater willingness to share wealth. Hence, our initial results support the possibility that men and women may yield very different types of societies when they hold primary control over policy. We then introduce the same participants into a repeated game in which they can vote on redistributive policies ranging from highly competitive and unequal to highly egalitarian and then engage in production under the policy determined by the group. In the first period of this main part of our study, we find that voting behavior supports the preferencebased idea that women prefer more egalitarian and less competitive environments. Specifically, women vote for higher, more egalitarian, tax rates. Again this supports the idea that women may produce substantially different societies where they hold policy control. However, the repeated game results show that the initially strong gender difference in the preferred redistributive policy diminishes somewhat, and disappears almost immediately when we control for individual performance. By the third period of the game, and for the remainder of the experiment, women vote insignificantly differently from men. Moreover, they update their voting behavior and production in response to incentives in the same manner as 27

29 do men. Hence, we show that gender gaps, as often measured in the lab, may look very different in broader economic settings with repetition, feedback and competition. 20 We believe the discrepancy between individual behavior and group outcomes is driven by variation in male and female preferences. While, as in much of the prior literature, there are systematic differences in how men and women behave, there is also great variance in the behavior and preferences of men and women. Hence, a group comprising 60 percent men and 40 percent women that makes decisions through majority rule may not look that different in terms of policies and outcomes than one that is 40 percent male and 60 percent female. Hence, when people argue that the World would look different if run by women, they potentially neglect the large variation and overlap in preferences and behavior between men and women, and the fact that female policy control will not always produce female outcomes. Moreover, gender gaps may interact with contexts, and may be exacerbated in some settings and may dissipate in others. 21 In our study, participants get full feedback on the outcome of the redistribution game at the end of each period. Thus, participants directly observe their productivity relative to that of the other group members. A growing body of research indicate that good information about one s relative position may influence gender gaps in economic decision making in other settings (Kuhnen and Tymula 2011; Wozniak, Harbaugh, and Mayr 2014). In addition, providing group members with information about their relative rank and earnings may increase competitive concerns (see, for example, Dijk, Holmen, and Kirchler 2014). While it is, of course, possible that such an effect differs between men and women, our study suggests that gender gaps in preferences for competition and responses to competition at least as reflected in policy votes and production do not persist far into the experiment. The main result of this paper, that behavioral gender gaps in preferences, including initial preferences for group redistribution policies, fail to translate into substantively different policy or group outcomes, addresses the important question of how gender based preference gaps may influence real world policy outcomes. In contrast with arguments that, as the share of female decision makers rises, groups will begin to yield very different outcomes, we find this not to be the case. The outcome of this study suggests that, at least in some situations, the 20 Further evidence on the effects of repetition on gender based preference gaps is found by Cotton, McIntyre and Price (2013), who study the gender gap in competition in a series of math contests. In the first competition, but in no contest thereafter, a male advantage is present. In later rounds of the experiment males even perform worse than women of similar ability. 21 For example, Heinz, Juranek and Rau (2012) show that windfall versus earned money influence male and female dictators differently. Female dictators, but not male, act more reciprocally and reduce taking rates when the pot to be shared is generated through recipients effort in comparison to windfall money. 28

30 impact may be less important than suggested by snapshot measures of preferences. Additional studies are, of course, needed to understand more about how gender differences in preferences matter for policy outcomes out in natural, broader, economic settings. References Adams, Renée, and Daniel Ferreira Women in the Boardroom and Their Impact on Governance and Performance. Journal of Financial Economics 94 (2): doi: /j.jfineco Adams, Renée, and Patricia Funk Beyond the Glass Ceiling: Does Gender Matter?. Working Paper. Barcelona: Universitat Pompeu Fabra. Department d Economia i Empresa. Alesina, Alberto, and Paola Giuliano Preferences for Redistribution. NBER Working Paper NBER. Almås, I., A. W. Cappelen, E. Ø. Sørensen, and B. Tungodden Fairness and the Development of Inequality Acceptance. Science 328 (5982): doi: /science Andreoni, J., and L. Vesterlund Which Is the Fair Sex? Gender Differences in Altruism. The Quarterly Journal of Economics 116 (1): doi: / Bartling, Björn, Ernst Fehr, Michel André Maréchal, and Daniel Schunk Egalitarianism and Competitiveness. American Economic Review 99 (2): Bertrand, Marianne New Perspectives on Gender. In Handbook of Labor Economics, 4: Elsevier. Bonin, Holger, Thomas Dohmen, Armin Falk, David Huffman, and Uwe Sunde Cross-Sectional Earnings Risk and Occupational Sorting: The Role of Risk Attitudes. Labour Economics, Education and Risk Education and Risk S.I., 14 (6): doi: /j.labeco Buser, Thomas, Muriel Niederle, and Hessel Oosterbeek Gender, Competitiveness and Career Choices. w Cambridge, MA: National Bureau of Economic Research. Chattopadhyay, Raghabendra, and Esther Duflo Women as Policy Makers: Evidence from a Randomized Policy Experiment in India. Econometrica 72 (5): doi: /j x. Cotton, Christopher, Frank McIntyre, and Joseph Price Gender Differences in Repeated Competition: Evidence from School Math Contests. SSRN Scholarly Paper ID Rochester, NY: Social Science Research Network. Croson, Rachel, and Uri Gneezy Gender Differences in Preferences. Journal of Economic Literature 47 (2): doi: /jel David Wechsler The Measurement And Appraisal Of Adult Intelligence. The Williams & Wilkins Company. 29

31 Dickinson, David L., and Jill Tiefenthaler What Is Fair? Experimental Evidence. Southern Economic Journal 69 (2): 414. doi: / Dijk, Oege, Martin Holmen, and Michael Kirchler Rank matters The Impact of Social Competition on Portfolio Choice. European Economic Review 66 (February): doi: /j.euroecorev Dohmen, Thomas, Armin Falk, David Huffman, Uwe Sunde, Jürgen Schupp, and Gert G. Wagner. 2011a. INDIVIDUAL RISK ATTITUDES: MEASUREMENT, DETERMINANTS, AND BEHAVIORAL CONSEQUENCES. Journal of the European Economic Association 9 (3): doi: /j x b. Individual Risk Attitudes: Measurement, Determinants, and Behavioral Consequences. Journal of the European Economic Association 9 (3): doi: /j x. Eckel, Catherine C., and Philip J. Grossman. 2008a. Differences in the Economic Decisions of Men and Women: Experimental Evidence. SSRN Scholarly Paper ID Rochester, NY: Social Science Research Network b. Men, Women and Risk Aversion: Experimental Evidence. In Handbook of Experimental Economics Results, 7: Engel, Christoph Dictator Games: A Meta Study. Experimental Economics 14 (4): doi: /s Fischbacher, Urs Z-Tree: Zurich Toolbox for Ready-Made Economic Experiments. Experimental Economics 10 (2): doi: /s Flory, Jeffrey A., Andreas Leibbrandt, and John A. List Do Competitive Work Places Deter Female Workers? A Large-Scale Natural Field Experiment on Gender Differences in Job-Entry Decisions. National Bureau of Economic Research Working Paper Series No Fortin, Nicole M The Gender Wage Gap among Young Adults in the United States. Journal of Human Resources 43 (4): doi: /jhr Funk, Patricia, and Christina Gathmann Gender Gaps in Policy Making: Evidence from Direct Democracy in Switzerland. Working Paper. Barcelona: Universitat Pompeu Fabra. Department d Economia i Empresa. Gneezy, U., M. Niederle, and A. Rustichini Performance in Competitive Environments: Gender Differences. The Quarterly Journal of Economics 118 (3): doi: / Gneezy, Uri, and Jan Potters An Experiment on Risk Taking and Evaluation Periods. The Quarterly Journal of Economics 112 (2): Heinz, Matthias, Steffen Juranek, and Holger A. Rau Do Women Behave More Reciprocally than Men? Gender Differences in Real Effort Dictator Games. Journal of Economic Behavior & Organization 83 (1): doi: /j.jebo Iriberri, Nagore, and Pedro Rey-Biel Let s (not) Talk about Sex: The Effect of Information Provision on Gender Differences in Performance under Competitiion. 30

32 Barcelona: Universitat Pompeu Fabra. Department d Economia i Empresa. Kuhnen, Camelia M., and Agnieszka Tymula Feedback, Self-Esteem, and Performance in Organizations. Management Science 58 (1): doi: /mnsc Majeres, Raymond L Sex Differences in Symbol-Digit Substitution and Speeded Matching. Intelligence 7 (4): doi: / (83) Manning, Alan, and Farzad Saidi Understanding the Gender Pay Gap: What s Competition Got to Do with It? Industrial & Labor Relations Review 63 (4). Matsa, David A, and Amalia R Miller. 2013a. A Female Style in Corporate Leadership? Evidence from Quotas. American Economic Journal: Applied Economics 5 (3): doi: /app Matsa, David A., and Amalia R. Miller. 2013b. Workforce Reductions at Woen-Owned Busineses in the United States. Industrial and Labor Relations Review, Forthcoming. Miller, Luis, and Paloma Ubeda Are Women More Sensitive to the Decision-Making Context? Journal of Economic Behavior & Organization, Gender Differences in Risk Aversion and Competition, 83 (1): doi: /j.jebo Murphy, R. O., K.A. Ackermann, and M. J. J Handgraaf Measuring Social Value Orientation. Judgment and Decision Making 6 (8): Niederle, Muriel, and Lise Vesterlund Explaining the Gender Gap in Math Test Scores: The Role of Competition. Journal of Economic Perspectives 24 (2): doi: /jep Niederle, M., and L. Vesterlund Do Women Shy Away From Competition? Do Men Compete Too Much? The Quarterly Journal of Economics 122 (3): doi: /qjec Rodriguez-Lara, Ismael An Experimental Study of Gender Differences in Distributive Justice. Discussion Papers in Economic Behavior. Valencia: Univerrsitat de Valencia and ERI-CES. Sutter, Matthias, F. Feri, M. G. Kocher, P. Martinsson, K. Nordblom, and D. Rützler Social Preferences in Childhood and Adolescence - A Large-Scale Experiment. Working Paper Economics and Statistics. University of Innsbruck. Wozniak, David, William T. Harbaugh, and Ulrich Mayr The Effects of Free and Costly Feedback on Gender Differences in Competitive Choices. SSRN Scholarly Paper ID Rochester, NY: Social Science Research Network. Zhang, Y. Jane Culture and the Gender Gap in Competitive Inclination: Evidence from the Communist Experiment in China. MPRA Paper University Library of Munich, Germany. 31

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34 Appendix A Table 1A: List of Variables List of variables Female Performance beliefs (percent, 100 = best performer) Interaction female X beliefs Risk (0-100, 100 = risk taking) SVO Angle, (-45-90, 90 = altruistic) Binary variable for gender (1 = female, 0 = male). Participants' subjective guess of their relative performance. Participants provided a guess of their rank within their experimental session. Calculated as 100-(guesses rank *100/n). Thus, higher values indicate higher confidence. Interaction term between female and performance beliefs. Sum invested in the risky option. ECU 100 indicates the most risky option, the largest investment possible. Social value orientation, ranging from -45 to 90 degrees, with altruists having angles greater than 57.15, pro-socials between and 57.15, individualists between and 22.45, and finally competitive types angles lower than Competition (1 = competitive) Vote, t-1 Median vote, t Median vote, t-1 Performance, t-1 Perf. diff. to mean, t-1 Perf. diff. to mean, t-1 * median vote, t Binary variable for the choice of the competitive payment scheme in part 1. The participant's vote in the previous period- The group's median vote, the implemented redistribution policy, in the current period. The group's median vote, the implemented redistribution policy, in the previous period. Number of correctly decoded sequences in the previous period. The individual participant's performance difference compared to the group mean in the previous period. Interaction term between the individual performance distance to the mean in the previous period, and the median vote in the current period. Perf. diff. to mean, t-1 * median Interaction term between the individual performance distance to vote, t-1 the mean, and the median vote in the previous period. Period The period, ranging from 1 to

35 Table A2: Performance over time Period Men Women p-value Piece rate Stage Period Period Period Period Period Period Period Period Period Period Piece rate Stage Observations P-values denotes the significance of the coefficient on female in the per period OLS regression of performance on female, clustering at group level Male Performance over time Female Period Figure A1. Performance over time 34

36 Histogram: Policy votes in period Males 2 Females.5 1 Density.5 Density Policy vote Policy vote Figure A2. Histograms of Period 1 votes, for men and women separately, with kernel density estimates. Table A3: Votes over time, by gender Period Men Women p-value p-value (Average vote) (Average vote) (no control) (control perf., t-1) Period < <0.000 Period Period Period Period Period Period Period Period Period N P-values denotes the significance of the coefficient on female in the per period OLS regression of performance on female (and performance), clustering at group level. 35

37 Appendix B : Instructions Initial Instructions The experiment comprises three parts. We will provide you with detailed instructions before each part. In addition to the CHF 10 payment that you receive for your participation, you will be paid an additional amount of money that you accumulate from decision tasks in the three parts of the study. The exact amount you receive will be determined during the experiment, and will depend on your decisions, and the decisions of others. Please note that the decisions you make in any part of the experiment will have no effect on what happens in other parts. All monetary amounts you will see in this experiment will be denominated in experimental currency units (ECU). At the end of the experiment, your earnings in ECU will be exchanged into CHF at a rate of 50 ECU = 1 CHF Note that all your interactions in the study are anonymous. This means that you will not know the identity of any other participant with whom you interact and no other participants will know your identity. If you have any questions during the experiment, please raise your hand and wait for an experimenter to come to you. Please do not talk, exclaim, or try to communicate with other participants during the experiment. Participants intentionally violating the rules may be asked to leave the experiment with only their participation payment. Please click Continue now to see the instructions for the first part of the study. 36

38 Instructions to Part 1 In Part 1 you will make a few simple economic decisions and answer some questions. You will receive detailed instructions before each decision. Please note that each of the decisions you make in this part is independent and does not influence the future course of the study. For each decision you will be informed about the outcome, and the earnings you received, at the end of the study. Please press the ''Continue'' button now to see the instructions for the first decision. Investment Decision The first decision is an investment decision. This decision is for real money; the result of your decision will be added to your account and paid to you at the end of the experiment. You start the investment task with a balance of 100 ECU. You choose how much of this amount (from 0 ECU to 100 ECU) you wish to allocate to the investment. The ECU that you choose not to invest will be saved in your account and cannot be lost. You will receive these ECU at the end of the experiment. The value of the ECU you choose to invest depends on the success or failure of the investment. The success or failure of the investment will be determined by a computerized random draw, similar to a coin flip. There are two possible outcomes: With 50% probability the investment fails and you lose the amount invested. With 50% probability the investment succeeds and you receive 2.5 times the amount invested. So, for any amount X that you invest, you will keep 100 X, regardless of what happens with the investment. If the investment fails, which happens with 50% probability, your earnings from this decision will be 100 X, since you lose the amount that you invested. If the investment succeeds, which also happens with 50% probability, your earnings from the decision will be 100 X + 2.5*X = *X Please enter the amount you wish to invest on your screen now. If you have any questions about the investment task please raise your hand and an experimenter will come to you. Your Decision: Please enter the amount of money (from ECU 0 to ECU 100) you wish to invest: ECU Once you enter a number, please submit your investment by clicking "Continue". If you have any questions about the investment task please raise your hand and an experimenter will come to you. Allocation Decisions In the second task, you will make 15 decisions in which you allocate ECU between yourself and an anonymous other participant. You are again making decisions for real money; the payoff from this task will be added to your account and paid to you at the end of the experiment. Any money you allocate to another participant will be paid to another randomly selected participant at the end of the experiment. For each of the 15 decisions, you will see a range of possible allocations. Your task in each decision is to choose your preferred allocation among the alternatives. 37

39 After you have made your allocation decisions, one randomly chosen decision (among the 15) will be chosen for each participant and implemented. This means that you will receive payment for two randomly chosen decisions. This is because each decision involves a decision-maker and a receiver. You will be paid once for a randomly selected decision in which you chose, and once as a receiver for the allocation chosen by another participant. The participant you interact with in these two cases will not be the same. That is, the receiver from your implemented decision will not be the decision-maker in the decision for which you are the receiver. Below you see a sample of what your screen will look like for each of the 15 allocation decisions. The numbers in this example are used only to illustrate the task and do not correspond to the numbers in the actual decisions. In the upper row, you will see the allocation for you, and in the bottom row the allocation for the randomly chosen receiver. In the example below, if you choose the leftmost allocation, you receive 1 and the other participant receives 9. If you choose the rightmost allocation, you receive 9 and the other participant receives 1. Notice that each time you select an allocation the corresponding payments for you and the receiver will be displayed to the right of the table. You will see 15 decisions, with varying allocations for you and another randomly selected participant. In each case, click on the button corresponding to the decision you would like to implement. Once you are ready, please start the task by clicking "Continue". If you have any questions about the allocation task please raise your hand and an experimenter will come to you. Questionnaire Before Part 1 ends, we would like you to answer a few additional questions. Please fill in your answers to the questions on the screen. If you have any questions raise your hand and an experimenter will come to you. You confirm your entries by clicking the "Continue" button. Once everybody has answered these questions, Part 2 will begin. 1. First, state, in general, how willing or unwilling you are to take risks on a scale from 0 to means you are "completely unwilling to take risks" and a 10 means you are "very willing to take risks". You can also use any integer number between 0 and 10 to indicate where you fall on the scale. Possible choices are: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, Second, imagine the following situation: Today you unexpectedly received CHF 1,000. How much of this amount would you donate to a good cause? Values between 0 and 1,000 are allowed for the donation. 3. How old are you? 4. Are you male of female? 38

40 Instructions to Part 2 In Part 2 you will work on a task for 90 seconds. You will be paid, as explained in more detail below, based on your performance on the task. You will also make a few decisions in which you can make additional money. Your actions in Part 2 are independent of the other parts of the study, and do not in any way influence the future course of the study. The Production Task The task for Part 2 consists of a coding task. During the task, your screen will display a key, which consists of a series of unique matches between letters and numbers. More precisely, each key will show the numbers 1-9, and a series of 9 letters of the alphabet, displayed such that one letter corresponds to one number. An example of such a key is in the picture below. In this example, M corresponds to 1, U to 2, O to 3, and so on. During the task, you will be shown sequences of three letters. Your task is to enter the corresponding three numbers on your computer. For example: if the sequence you are asked to code is SGO, as indicated in the picture, the correct answer for the key above is 963. You obtain this sequence of numbers by exchanging the S for a 9, the G for a 6, and the O for a 3. The sequences of three letters will appear on your screen, one at a time. Once you enter the corresponding three digits, you confirm your answer with the OK button. If the entered sequence is correct, a new sequence will appear on your screen, otherwise you will be asked to recode the three-digit sequence until it is entered correctly. For each key of letters and digits, you will see nine three-digit sequences to code into numbers. Then a new key appears, for which you will get nine consecutive three-digit codes, and so on. You will have 90 seconds to enter as many sequences as you can. Your payment from the task depends on the number of sequences you code correctly. In particular, you will be paid 10 ECU per correctly coded sequence. At the end of the experiment, you will be informed about your resulting earnings. On the next screen, you will see an example screen similar to the one you will see during the actual task. In the upper half of the screen you will see a picture of the key, consisting of nine letter-number pairs. Below that you will see the sequence of three letters that you are to recode into a three-digit number, as well as the box where you enter your answer. Remember that you will receive 10 ECU for each sequence that you correctly type in during the 90 seconds. Please click the Continue button on your screen now. You may then try the task on the example screen by filling in the correct three-digit number in the empty box. If the cursor is not positioned within the box, you will need to click inside the box to allow you to type in the box. Confirm your entry with the OK button. You will then be able to click to start the actual task for Part 2. 39