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Outgrowing the Gender Gap in Competitiveness*

Jeffrey A. Flory**, Kenneth L. Leonard**, John List***

July 8, 2011

Abstract

Several studies show consistent results that men and women have different

preferences for selecting into competitive environments. A few recent studies have

qualified these findings by showing that the gender gap in competitiveness may

changes across social settings, and possibly across the female hormonal cycle. Using

a dataset with over 700 subjects from Malawi who vary across a broad range of ages, we

find that the gender gap in competitiveness in fact disappears as men and women grow

older. We also find that culture can significantly affect both the gender gap and the

change in the gender gap with age.

Similar to Gneezy, Leonard and List (2009) we compare matrilineal and matrilocal

cultures to patrilineal and patrilocal cultures. Not only is the gender gap significantly

smaller in matrilocal societies, but there is much smaller change in the competitive

preferences of women in matrilocal societies, compared to patrilocal societies.

Gender is a combination of culture and biology and we find that the way women

react to competitive environments changes with their age (potentially determined

by hormonal changes as women age) and by their culture. These results offer crucial

insights into the underlying causes of gender differences in preferences for

competition as well as add important perspective to the implications of previous

findings on gender differences for naturally occurring labor markets and other

economic settings, and related policy-questions.

* This work was funded by the NSF Grant, SES 0922460.

**Department of Agricultural and Resource Economics, University of Maryland, College Park.

*** University of Chicago

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1. Introduction There is a growing literature on the significant gap between male and female willingness to participate

in competitive environments. Almost every study has found that the average male prefers competition

and the average female avoids it (Gneezy et al. 2003, Gneezy and Rusticini, 2004, Neiderle and

Vesterlund, 2007). Part of the literature now focuses on ways to close this gap looking at interventions

such as affirmative action (Neiderle, et al. 2007), single-gender settings (Gneezy et al. 2003, Gupta et al

2005, Grosse and Reiner, 2010) and group competition (Healy and Pate, 2011). A smaller literature has

found that this result is not consistent across all cultural settings (Gneezy, Leonard and List 2009) and

that it may not be consistent across the female hormonal cycle (Wozniak et al. 2010)1. However, to date,

all of these studies examine narrow subject groups, usually college-aged participants. Some notable

exceptions include Gneezy and Rusticini (2004) with children’s races, Sutter and Rutzler (2010) and

Anderson et al (2011) with competitiveness in children and Harbaugh et. al. (2003) with bargaining.

Similar to Charness and Villeval (2009) we explicitly addresses this lack of age-diversity, intentionally

running experiments among subjects of a broad age-range to analyze the effect of aging competitive

behaviors. In addition to age, our study examines gender and deliberately includes variation in cultural

characteristics previously shown to alter the gender gap.

We examine data on preferences from competitiveness drawn from laboratory experiments conducted

in twelve villages in rural Malawi. The experiments follow the design of Niederle and Vesterlund (2007),

in which subjects perform a task three times and choose whether to perform the task in a competitive

environment as well as whether to submit past performance to a competitive setting. The experiment

helps us to isolate the taste for competitive environments controlling for ability in the task as well as

confidence in one’s relative ability.

We chose to perform the research in Malawi because the history of successive invasions has left the

country with many highly differentiated cultural institutions in an otherwise economically similar

environment. We chose twelve villages based on a pre-study investigation of these cultural norms. We

identified 6 villages with matrilocal traditions in which men leave their home village to move to the

village of their wife. Similar to the Khasi, described in Gneezy, Leonard and List (2009) (henceforth GLL)

these men play diminished roles in their nuclear family, but often have significant roles in the families of

their sisters back in their home villages. Importantly, women who live in such cultures are raised in the

same place where they subsequently raise their own families. We similarly identified four villages with

strong patrilocal institutions. In patrilocal societies, women move to the households of their husbands

and men are more likely to live their whole lives in the villages in which they were born. In addition, we

studied two villages in which matrilocal traditions had switched to patrilocal traditions within recent

history (the last 50 years).

1 Further evidence of hormonal cycles and female labor force participation is presented in Ichino and Morietti

(2009), further discussed in Rockoff and Herrman (2010).

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Overall, the cultural variation in the gender gap in competitiveness is smaller than that found in GLL.

That study chose cultures in two very different economic and environmental settings with the objective

to maximize the difference, whereas this study hopes to control for as many other factors as possible

while examining different cultural institutions. Overall, we find the gender gap is positive and significant

in patrilocal societies and positive but insignificantly different from zero in matrilocal societies. In

addition, we found gender gap reversals in two matrilineal villages in our study, but significant positive

gender gaps in other matrilocal villages. This variation in the gender gap within matrilocal societies

suggests that matrilocal may be a necessary but insufficient condition for eliminating or reversing the

gender gap.

Figure 1 The relationship of age to competitiveness for males and females across two types of cultures

controlling for general factors

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The four lines shown in the figure are the result of a semi-parametric locally weighted regression of the subject age on the choice of the tournament in round 3, controlling for general factors (success in round 2, increase from round 1 to round 2, choice to submit past performance to tournament and belief of relative performance in round 2).

More importantly, because we have a wide variety of ages in our sample, it is clear that the gender gap

in competitiveness changes significantly with age. Figure 1 demonstrates (using semi-parametric

regressions) a summary of our findings. In patrilocal societies, the largest gender gap is found with

subjects between 18 and 22 years old. At every age after that point, the gap closes, disappearing

altogether somewhere between the ages of 40 and 50. The gap closes because women become more

competitive; we see no significant decline in men’s desire to perform in a competitive environment.

Wozniak et al. (2008) and Buser (2010) link hormones and behavior, thereby suggesting the possibility

that hormonal changes at menopause could change preferences. Although we see a strong impact

consistent with menopause, we also see continuous changes up to that age.

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On the other hand, in matrilocal societies, the gender gap for young people is much smaller initially.

There is some evidence that it continues to close as women age, but in some specifications of our

empirical model, there is no significant change with age in the desire to work in a competitive

environment for either men or women. Similar to the result we find in patrilocal societies, there is no

evidence of any gap for older men or women.

Although this study was specifically designed to test the role of culture in Malawi, we did implement the

same instrument with a sample of men and women in the US, recruiting subjects from a University

setting, flea markets, swimming pools and finally a farmer’s market. This sample of 84 participants

shows the same strong pattern of changes in female preferences with age. There is a very large gender

gap for young participants, which disappears by the age of 50.

The fact that female preferences for competitive environments change with age and culture suggest

that the very construct of gender is a combination of both biology can culture. As women age, their roles

in society change and their preferences for competitive environments to change as well; what it means

to be female is changing. It is possible that these changes are driven by biology, but our work with the

matrilocal societies in Malawi shows that society, not biology determines how females behave as they

experience such changes.

These results suggest a missing dimension in the analysis of the gender gap. On the one hand,

competiveness may be an important determinant of career choice and labor market outcomes in

developed countries. Under such a view, it does not matter what 50-year old men and women do, it

matters what young people making career decisions do. Our finding in patrilocal societies (that age

matters), could be seen as unnecessarily clouding the emerging wisdom on competitiveness and job

choice. However, it appears that matrilocal societies do not have the same age pattern. Women in

matrilocal societies have preferences that appear more consistent across their lifetimes. If learning,

imitation or other social effects affect preferences for competitive environments, matrilocal societies

are populated with women who learn faster. This evidence adds to the evidence in Gneezy et al, (2009)

that preferences for competiveness are not innate, but are heavily dependent on cultural settings and

the behavioral models that women from which women can learn.

In the following section, we outline the design of our laboratory setting and discuss the basic findings for

the whole sample and broken down by gender. Section 3 discusses the institutional variation in the

study and looks at data collected on these institutions in each village. In particular, we discuss the fact

that, in three of the villages we studied, there is little evidence that institutional features are as strong

as we had originally hypothesized. This section also looks at the gender gap, village by village. Section 4

examines the results from the experiment separating the analysis between the villages with strong

matrilocal and patrilocal institutions (having dropped the three indeterminate villages). Section 5

concludes.

2. Experimental Design

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We ran a version of Niederle and Vesterlund’s 2007 (henceforth NV07) experiment, adjusted to fit the

environment and restrictions imposed by a low human resource setting. Each of the 12 villages in our

study was visited by a member of the team well in advance of our study. These meetings allowed us to

choose a location for the study, meet important officials in the village and talk obtain permission to do

studies in the village. These officials were given a “recruitment script” and asked to talk to everyone in

their community about our study. The recruitment script stated only that it was a study of decision-

making and that people would earn a show up fee. Within a week of our experiments, we contacted

these officials and told them the day our team would be arriving and asked them to tell everyone in the

village that we would be there. In all cases, the numbers of people who came to participate in the

experiment was far greater than our capacity, allowing us to select participants randomly from among

those who showed up.

The structure of each session was identical to that of NV07: Round 1 was a piece-rate, paying the

equivalent of 32 cents per successful task completed. Round 2 was competition against three other

people selected at random from among all the subjects in the village participating (about 60 subjects),

with the winner earning the equivalent of $1.28 (4 times the piece rate payout) per successful task

completed if wins and $0 if they lose.2 This gave subjects the chance to experience both pay schemes.

For round three, subjects first chose their pay-scheme (compete vs. piece-rate) and then performed the

task. For round four, they did not actually perform a task but decided whether to submit their piece-rate

performance to a competition or keep it as piece-rate. Payment was determined through a random

draw of one round from among the four rounds experience by each subject. The draw was done by each

participant individually3, in front of them and at the end of the experiment. This process was explained

to the entire set of subjects before the experiments, with the aid of several examples and a

demonstration of randomly picking a card from a bag. Also, just as in NV07, the room was equally

balanced between men and women (there were a few villages where this was not the case if too few of

one sex happened to be present). Finally, we also ask subjects how they think they performed relative to

their opponents in R1 and R2 and gave monetary rewards for accurate guesses.

The task was different: NV07 use solving math problems since all of their subjects are university

students. Due to concerns over education levels and our desire to sample from the full distribution of

individual types in the village4, we opted for a simpler cognitive task – arranging shapes in a row from

smallest to largest. Each subject had a set of 6 blocks. Each side of the block had one of six shapes and

the relative location of the shapes on each of the six blocks was different. The task was to arrange all six

2 With few exceptions, the male-female distribution within each session was equal. While NV07 have opponents

drawn from within the same session, the intimacy of village settings complicates this – people in villages often have better information about each other than subjects in a typical lab. So we took steps to reduce knowledge about competitors’ likely abilities by expanding the pool of potential opponents and removing them from view. 3 We deliberately choose individual draws of four marked cards from a bag, since this eliminated any possibility

that we would choose the lowest possible payment. Although many subjects received less than the maximum possible payout, it was clear to them that they “could” have received a higher payout and that the experiment was, therefore, ex ante, fair. Choice by a computer or choice for the entire session would have generated mistrust contaminating choices. 4 We did have to exclude a few very elderly who were blind or deaf.

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blocks such that a given shape (e.g. star) appeared facing up and align the six versions of that shape (e.g.

all 6 stars) in order from smallest to largest. Upon completing one shape, the subject had to move

immediately to the next randomly selected shape. The blocks were specifically designed so that the

order of the blocks for one shape did not confer any advantage to arranging the blocks for the next

shape. Subjects were paid for the number of shapes completed within a 3-minute interval. All subjects

worked with identical blocks and faced the same randomly selected order of shapes to fill.

In order to insure that all subjects understood what they were to do, a facilitator 1) showed the subject

the link between patterns shown on the block and patterns shown on a stack of index cards, 2)

demonstrated the correct process for solving the first set of shapes and then 3) waited until the subject

demonstrated the correct completion of the next set of shapes. Each facilitator worked with two

subjects, separated by a plywood barrier. In each session, there was a script reader, who explained the

instructions and kept time. In order to insure comprehension, subjects were never asked to read any

material; all instructions and examples were explained from the script.

We conducted the experiment in a dozen villages, each of which had 3-4 sessions and each session

lasting about an hour each5. We conducted the experiment in an isolated location of each village – often

the inside of a church or schoolhouse. Each session had 16 stations, each consisting of a sheltered spot

on the ground or at a table with a set of blocks and a pile of shape-indicator cards. We used facilitators

to fill many of the functions of a typical economics lab computer: they gave subjects silent indication

when their task was completed and they could move to the next, kept track of the number of successes,

and recorded subjects’ choices and beliefs. The facilitator sat facing the subject, handling two subjects at

a time (with a barrier between the two subjects). Communication between facilitators and subjects was

non-verbal, aided by the use of gestures and pictures (e.g. pointing to a card displaying the shape for the

next task). The only speaker during the session was the script-reader, who read the instructions for the

experiment translated into the local language.

3. Malawi and Cultural Institutions GLL examined two carefully chosen communities representing the extremes of matrilineal-

matrilocal institutions (the Khasi of India) and patrilineal-patrilocal institutions (the Maasai of Tanzania).

In matrilineal societies wealth and prestige of family name is inherited by daughters or follows the

female line. In contrast, in patriarchal societies, sons inherit wealth. In matrilocal societies, a husband

joins the household of his wife’s family, whereas in patrilocal societies, a wife joins the household of her

husband’s family. Using experiments performed with the Khasi and the Maasai, the authors find strong

evidence that matrilineal, matrilocal practices increase women’s preferences for competition.

The central African nation of Malawi and its specific cultural and historical circumstances provide

unusual opportunities to build on the initial findings of GLL and provide further insight into the

underpinnings of this phenomenon. Malawi, because of the coexistence of patrilineal/local and

5 The sessions were very long because reading the instructions out loud, having all questions answered by one

person and making sure each subject could complete the first demonstration arrangement was time consuming.

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matrilineal/local groups in the same general area, offers the chance to dramatically reduce the factors

differing between the two groups. We can observe the differences in preferences for competition in

groups that differ by cultural institutions but are otherwise similar. This enables us to more confidently

attribute any observed differences in competitiveness to the lineage-location system.

Malawi is small (about the size of Pennsylvania), and divided into three administrative regions.

The tribes of the north are firmly patrilineal and patrilocal; inheritance is traced through sons, marriage

involves much higher levels of bride wealth, and polygyny is more prevalent. Most parts of the southern

region are matrilineal and matrilocal; inheritance of property (including land) is traced through

daughters. The central region is in flux, with a mixture of the two systems. The Chewa (the predominant

ethnic group) are historically matrilineal and matrilocal, but they have been transitioning toward

patriliny and patrilocation practices slowly over the last 100 – 150 years.

The most widely accepted explanation for this pattern seems to be the varying degrees of

success the different indigenous matrilineal groups had in resisting aggressive patrilineal migrants –

primarily the Ngoni. Pushed northward in the early nineteenth century by the violent mfecane caused by

the Zulu’s conquests in southern Africa, the Ngoni entered Malawi around the 1870s. They were

predominantly pastoral, and had impressive military organization and tactics that some scholars

attribute to their emulation of the Zulu warriors they fled. As they pushed westward into Malawi from

modern-day Zambia, they attacked and subjugated much of the country, pressing their cultural

institutions onto their subjects. The northern Tumbuka tribe was fully conquered and subsequently

assimilated the patrilineal-patrilocal practices of their conquerors. The southern Yao, while they fought

with the Ngoni, were never completely brought under Ngoni control, and therefore preserved their

cultural autonomy, including matrilineal-matrilocal institutions.6 The Ngoni tribe lies at the patriarchal

extreme, having always been patriarchal, while the Tumbuka adopted patrilineal and patrilocal practices

around 150 years ago. The Chewa are currently in a state of transition – some having switched to

patrilineal/patrilocal practices, others retaining matrilineal/local institutions. The Yao have always been

matrilineal/matrilocal.

We visited Malawi and explored villages from these tribes, asking questions about the culture and

history at the village level. Based on these conversations we choose 12 villages to represent a variety of

institutions. To validate these first impressions we interviewed a random sample of experimental

participants, asking questions about their own history with respect to marriage and location.

6 The above description is perhaps an overly schematic representation of the history and transformation of cultural

institutions in the region. The reality may be more complex, with mixtures of both systems within given ethno-linguistic groups, and perhaps even competing practices within the same community. Phiri (1983) mentions additional causes of moves towards patrilocation among the Chewa–including the slave trade’s influence from the early 19

th century, the influence of missionaries and colonialism (from the early 20

th century), the growth of

tobacco (farmed by men) as a cash crop, and other immigrant patrilineal groups. Some of these might have affected other tribes as well. Whatever the historical process, the evidence seems quite clear that there is a significant degree of variation in practices at both the group-level, and perhaps the individual level, within a relatively small area.

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4. Results

4.1 Basic Experimental Results Table 1 reports the averages for all twelve villages, reporting both overall results and results

differentiated by gender. The average participant completed just over 6 tasks in the first round,

improved to almost 7.5 tasks in the second round and improved again to 8.3 tasks in the third round.

There was a significant difference between the performance of men and women in the study, with men

completing almost one more task per round than women. This difference is partially driven by a number

of older women who were unable to complete the task at all. We discovered in the first few villages that

some women were effectively blind and unable to see the differences in shapes on the blocks. However,

even when we remove the people who were unable to complete any tasks in the three rounds, there is

still a significant difference of 0.7, 0.67 and 0.55 in the three rounds respectively. Importantly, the

performance gap between men and women does not vary across cultures.

Figure 2 Average number of successes for men and women over three rounds

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Table 2 shows the distribution of choices to compete in round 3 and the choice to submit previous tasks

to competition in round 4. Figure 3 shows these same numbers. Overall, there is a difference of almost 8

percentage points between the willingness of men and women to compete, but there is no gap between

men and women on the willingness to submit previous tasks to competition.

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Figure 3 Percentage of Men and Women choosing the tournament in Rounds 3 and 4

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Table 3 examines the stated beliefs of relative rank and the actual performance of individuals. Overall,

almost 50 percent of participants believe they were the best in round 1, whereas 32% believed they

were the best in round 2. Men were much more optimistic about their performance: 57% of men

believed they were best in round 1 versus 38% of women, and 41% and 22% believed they were best in

round 2. Overall, the more successes they experienced, the better they believed they did. The

relationship between success and belief is stronger for men than for women.

4.2 Learning As can be seen from Table 1, people got better at the task over time, improving the number of tasks

completed by slightly more than one success from round 1 to round 2, and slightly less than one success

from round 2 to round 3. In addition to recording the number of successes, we recorded the time at

which each task was completed. This allows us to measure the rate of learning within rounds. To

generate numbers on the rate of success, we extrapolate the rate of success for each thirty-second

interval in the full 9 minutes, measuring the percentage of total items performed in each interval.

Anyone who had a perfectly steady rate would complete 1/9th of their total tasks in each minute

independent of how many total successes they achieved.

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Figure 4 Rate of Improvement over the course of three rounds by choice in Round 3

Figure 4 shows the rate of improvement over the three rounds. The average actual rate of success for

every half minute is shown on the graph, differentiated by whether they choose tournament or piece

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rate in round 3. There are three overall phenomena in this graph. First, in the last 30 seconds of each

round, the average person is much faster than their average. This is because the enumerators were

naturally flexible about defining the timing of a success. If the alarm rang to end a round and the person

finished 2 seconds later, the enumerator gave them full credit. They were not instructed to do this, but

it strikes us as an unavoidable problem with using humans to judge other humans. Second, people are

much faster in the first 30 seconds of each round. This is probably because, although they cannot touch

the blocks until the round begins, they can study them and plan their first couple of moves. Third,

people get significantly better over time as well as within each round.

The fact that we can observe overall learning as well as learning within a round allows us to measure

“reaction to the tournament setting” in round 2. People who do better in round 2 (when they are forced

to compete) than can be predicted by their learning across the whole three rounds can be said to have a

positive reaction to the competitive setting. Figure 4 shows no significant different between those who

chose the tournament setting in round 3 and those who chose the piece rate, either in round 2 or round

3. Thus, it is not clear that people who react positively to the tournament are those who are more likely

to choose the tournament in round 3. The only visible differences (which are not significant) suggest

that those who eventually choose the tournament accelerate at the end of the second round and

perform worse overall in the third round.

In order to examine the role of learning and the competitive response at the individual level we take the

data on the rate of success (independent of success) and run a regression for each individual. The line

“predicted rate” shows the result of a regression on the whole sample which includes cubic polynomial

for time elapsed, dummy variables for rounds 2 and 3 and dummy variables for the end and beginning of

each round. This regression is solved then for each individual and we generate two scores from these

results; the response to the competitive environment (coefficient on the dummy variable for the second

round) and the rate of learning (full impact of time variables and coefficients).

4.3 Explaining the choice to compete in round three and four Table 4 examines the basic pattern of the choice of tournament incentives in round 3 and the choice to

submit the round 1 performance to tournament incentives in round 4. Columns 1 and 3 examine the

overall pattern for round 3 and round 4 respectively, and Columns 2 and 4 include a coefficient that

measures the gender gap. Overall, the only significant explanatory factor is the decision to submit past

performance to the tournament (in Column 1) and the decision to perform under tournament incentives

(in Column 3). The gender gap is significant in the decision to perform under competitive incentives, but

it is not significant (or economically meaningful) for the decision to submit past performance.

The lack of significance for the total successes, learning and the competitive response is not particularly

surprising; it is not clear what impact these variables should have. People who learn fast do not know

that they learn faster than others and learning demonstrates the high variability in potential skills. Thus,

people who learn may be excited to test their ability, or they may believe that they are way behind

where others might be. Total success so far can only be a predictive variable if people knew something

about the distribution of abilities. The competitive response coefficient should indicate those who do

better under competitive incentives, but that does not mean they prefer them. It is possible to do well

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at something and still consider it unpleasant. The three variables successes so far, learning and

competitive response are all derived from the same data, the performance of participants. The

collection of these three variables is jointly correlated and a factor analysis of the three variables reveals

that there are only two underlying factors. Thus, for each regression we include the p-value on the

restriction that all three variables are jointly zero. For the decision to perform under tournament

incentives, the test fails to reject the hypothesis that they not different from zero. However for the

decision to submit past performance we do reject this hypothesis.

4.4 Age and Competitiveness Table 5 shows the link between age and gender in the sample. Whereas there is no pattern by age for

men, there is a marked increase in the proportion of females choosing the tournament for in round 3

with age. Column 2 shows that the coefficients for age and age squared are significantly different from

zero, whereas neither of these two is significant for Column 1. The implied maximum probability of

choosing the tournament is 48 years old.

Column 3 shows the same regression but with a dummy variable for women over the age of 50. We ran

similar regressions for women using values between 45 and 55 years old, the full range of probably

onset of menopause; a cutoff of 50 provides the greatest explanatory power. The coefficient for age

over 50 is not significant in Column 3, but when we include both the quadratic form for age and the

discrete variable for age older than 50, all three coefficients are significant for women (Column 4).

The combination of coefficients suggest that there may be a chemical or biological feature occurring at

menopause which changes the way that women feel about competition, but additional changes are

observed gradually as women get older. The coefficients measure the change from the age of 18. In

Column 4, the maximum propensity to compete for women is at 51 years of age, but the quadratic form

implies a local maximum is also obtained at 32 years of age. An 18 year-old woman is 36 percentage

points less likely than a 51 year-old woman to choose competition.

Table 6 examines the gap between males and females (combining the two genders in one regression)

allowing competitiveness to vary by female age, but not by male age. The gap between males and

females at the age of 18 is almost 16 percentage points. At the age of 51, this gap closes by 38

percentage points and 51-year old females are more likely than all males to choose to compete.

If this were a random sample of communities in Malawi and Malawi had been randomly selected for all

countries in the world, we could use these results to hypothesize that the significant gender gap

between men and women exists only for college-aged men and women, and that there is an opposite

and equally significant gap for 50 year olds. However, this is not a random sample of communities; we

choose Malawi because of its history and we chose several of the villages because we believed they

represented matrilocal marriage institutions. We turn now to examining this data with reference to

these institutions.

5. The Gender Gap and Institutions

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Table 7 examines the gender gap (over all ages) village by village with and without key controls. There

are twelve villages in the study and the regression shows the gap in each of these twelve villages

(constant terms for each village are estimated, but not shown in the table.) Each village was chosen

because we believed it would demonstrate matrilocal or patrilocal marriage customs or the transition

from matrilocal to patrilocal. All villages except three demonstrate a significant gender gap between

male and female preferences for competitiveness. Village 8 has no significant gap and villages 7 and 10

exhibit reversals in which the average women is more likely to compete than the average man.

The labels in Table 7 reflect the definitions of customs based on our initial study of the villages. Both of

the villages that exhibit reversals were initially identified as villages with matrilocal customs.

5.1 The link between institutions and marriage practices Our pre-experimental research in each of the 12 communities involved conversations with key

individuals about the current marriage customs in the village. Using these interviews we decided to

work in 6 matrilocal communities, 4 patrilocal communities and 2 villages that had recently undergone a

transition from matrilocal to patrilocal. To verify these initial assessments, we interviewed a random

sample of experimental participants and asked them about their own marriage history.

Table 8 shows the probability that a married woman was born in the village in matrilocal, patrilocal and

transition villages. Of the 6 villages that were identified as having matrilocal customs, four have

statistically significant marriage patterns that confirm to our hypothesis that married women should be

more likely to live in the village of their birth than men. Of the 5 villages that were identified as having

patrilocal customs, four have significant marriage patterns that conform to our hypothesis that married

women should be less likely to live in the village of their birth than men. Both of the transition villages

look like they are observing patrilocal customs, though village 6 does not have a statistically significant

positive pattern.

Therefore we drop villages 1 and 12 (from matrilocal), 4 (from Patrilocal) and 6 (from transition) and

redefine village 5 as patrilocal. This leaves us with four matrilocal and four patrilocal villages for the

further analysis of the impact of institutions on competitive preferences.

5.2 Comparing the link between gender and age in across marriage customs Table 9 shows the relationship between age, gender and preferences for competition across the 4

villages with matrilocal customs and the 4 villages with patrilocal customs as defined above. The table is

setup to test the differences across the two types of villages, estimating separate coefficients for each

explanatory variable. Chow tests for the coefficients with errors clustered at the village and unique set

level (most villages had four sets) reject the hypothesis that the coefficients are the same across each

institution.

The most important differences between village that observe matrilocal and patrilocal institutions are

the overall gender gap and the differential relationship between age and preferences for competitive

environments. The gender gap in patrilocal villages is 30 percentage points at the age of 18, but this gap

falls significantly as women age. In addition, being over the age of 50 has a very large effect on the

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willingness of women to perform in a competitive environment. Although the gender gap and its change

with age are similar in matrilocal villages, it is both smaller and statistically insignificant. In matrilocal

villages, 18 year-old women are not less likely to choose the competitive environment than either men

or 51 year-old women.

Furthermore, men and women in matrilocal villages are more likely to choose the competitive

environment if they also choose to submit to competition and they are more likely to submit to the

competitive environment if they believe they were not the worst in their group. On the other hand, the

impact of being willing to submit on being willing to perform is about half as large for men and women

in patrilocal villages and they are less likely to submit to competition if they believe they were the best.

Table 10 examines the differences across the two cultures examining only women. The gender gap is not

estimated, only the differences across women of different ages. The age curve is less precisely estimated

for women in patrilocal villages, but is still significant at the 10% level using standard errors clustered at

the unique set level. The impact of being older than 50 is still about twice the size for women in

patrilocal villages compared to women in matrilocal villages, but in one of the specifications the gap is

significant for women in matrilocal villages. Thus, there is some evidence that being older than 50 is

important in matrilocal villages, only the impact is much smaller than for patrilocal villages. As with the

previous two tables, there is no age continuous age pattern for women in matrilocal villages. Women

who are willing to submit to competition are significantly more likely to be willing to perform in a

competitive environment.

On the other hand, the difference in the importance of self-ranking between participants in matrilocal

and patrilocal villages does change. The role of the “rationality” variables leads us to examine the overall

patterns for people who signed their name. Error! Reference source not found. examines the pattern

for women who sign their names comparing matrilocal and patrilocal villages. As with Error! Reference

source not found., the role of self-assessment and performance changes significantly; people who sign

their name seem to make more nuanced decisions. However, the role of age (particularly being over the

age of 50) in patrilocal villages and the willingness to submit to competition in matrilocal villages

becomes stronger. Rather than attempt to interpret these variables (based on a much smaller set of

individuals) we take Error! Reference source not found. as confirmation that the important patterns of

gender, age and institutions and not driven by differential ability to understand the game.

5.3 Testing the findings in the US In order to test the basic findings from Malawi—that age changes the preferences for competition in

women—we replicated the laboratory in the US, following the format of Malawi as closely as possible.

The instructions were in English and the basic payouts were $1 per success and $4 per success for round

1 and 2 respectively. We recruited participants in three sessions. The first was at a major university

campus at 4:30 in the afternoon; we recruiting staff, graduate students and undergraduates using flyers.

The second session was on a Saturday afternoon on campus, with participants recruited from a farmer’s

market, a flea market and a local swimming pool. The third session was at farmer’s market with subjects

recruited from the market, the nearby commuter rail station and the community. We recruited 84

subjects of which 44% were under 30, 31% between 30 and 50 and 25% 50 or older. There were 46

15

females and 39 males. Subjects in the US were better at the task, completing approximately 12, 13 and

14 sets for each of the three rounds. There were no differences between men and women.

Table 11 and Figure 5 show the properties of the sample in the US. Table 11 specifically compares the

findings in the US and in Malawi (from Table 7). Although not all of the variables are equally significant,

the coefficients for gender, and female age are almost identical. Competitiveness increases for women

with age and women over the age of 50 are much more likely to choose the competitive environment.

The continuous trend for age is not as marked in the US as it was in Malawi. Overall, the results for the

US are not as strong as for Malawi (due in large part to the smaller sample), but there is no evidence

that the pattern is different. The data from the US is almost exactly the same as the combined sample

from Malawi.

Note that the data from Malawi contains two identifiably different cultures; if two such cultures exist in

our data from the US, we do not have the information that would be necessary to identify them.

Figure 5 Age, Gender and Competitiveness, US sample

.4.5

.6.7

.8

Pro

ba

bili

ty o

f C

ho

osin

g C

om

petitio

n

20 40 60 80Age (max 75)

female male

Overall Age and Competitiveness, No Controls

Conclusion

16

References Anderson, S., Ertac, S., Gneezy, U, List, J.A. and Maximiano, S. “Gender, Competitiveness and

Socialization at a Young Age: Evidence from a Marilineal and a Patrilineal Society.” Mimeo.

Charness and Villeval (2009) “Cooperation and Competition in Intergenerational Experiments in the Field and the Laboratory” AER.

Datta Gupta, N., A. Poulsen and M.C. Villeval (2005) “Male and female competitive behavior – experimental evidence.” http://ideas.repec.org/p/iza/izadps/dp1833.html.

Gneezy, U., K. Leonard and J. List (2009) “Gender Differences in Competition: Evidence from a Matrilineal and a Patrilineal Society”, Econometrica

Gneezy, U., M. Niederle and A. Rustichini (2003) “Performance in competitive environments: Gender differences,” Quarterly Journal of Economics, August, 1049-1074.

Gneezy, Uri, and Jan Potters, 1997, An Experiment on Risk Taking and Evaluation Periods. Quarterly Journal of Economics, 631-645.

Gneezy, U. and A. Rustichini (2004) “Gender and competition at a young age,” American Economic Review Papers and Proceedings, May, 377-381.

Gneezy, U. and A. Rustichini (2005) “Executives versus teachers: Gender, competition and selection.”

Goldberg, J. “Savings Behavior and Social Norms in Malawi: Evidence from a Field Experiment”, mimeo University of Michigan. 2008.

Harbaugh, William T., Kate S. Krause, and Steven J. Liday. (2003). “Bargaining by Children.”

Heale, A. and Pate, J. “Can Teams Help to Close the Gender Competition Gap?” Economic Journal, 2011.

Herrmann, M.A. and Rockoff, J.E. (2010) “Does Menstruation Explain Gender Gaps in Work Absenteeism,” NBER Working Paper.

Ichino, A. and Moretti, E. (2009) “Biological Gender Differences, Absenteeism and the Earnings Gap,” American Economic Journal: Applied Economics 1(1): 183-218

Niederle, M. and L. Vesterlund (2007) “Do women shy away from competition? Do men compete too much?” Quarterly Journal of Economics 122 (3): 1067-1101.

Niederle, Muriel, Carmit Segal, and Lise Vesterlund, “How Costly is Diversity? Affirmative Action in Competitive Environments,” mimeo June 2007.

Phiri, Kings. 1983. “Some Changes in the Matrilineal Family System among the Chewa of Malawi since the Nineteenth Century”. Journal of African History 24:257-274.

Sutter, M. and Rutzler, D. “Gender Differences in Competition Emerge Early in Life,” IZA D.P. 5015, 2010.

Vandegrift, D., A. Yavas and P. Brown (2004) “Men, women and competition: An experimental test of labor market behavior.” Mimeo.

Wozniak, D., Harbaugh, W.T. and Mayr, U. “The Menstrual Cycle and Performance Feedback Alter Gender Differences in Competitive Choices,” mimeo 2010.

17

18

Tables Table 1 Summary Statistics, Successes per Round

Number of Successes

Obs Mean SD Min Max Median

Round 1

Overall 731 5.88 2.54 0.00 14.00 6.00

Male 364 6.32 2.42 0.00 14.00 7.00

Female 367 5.44 2.59 0.00 11.00 6.00

Round 2

Overall 731 7.19 2.97 0.00 17.00 8.00

Male 364 7.60 2.81 0.00 17.00 8.00

Female 367 6.78 3.07 0.00 14.00 7.00

Round 3

Overall 731 8.08 3.13 0.00 16.00 9.00

Male 364 8.45 2.97 0.00 16.00 9.00

Female 367 7.72 3.25 0.00 15.00 8.00

19

Table 2 The Choice of Tournament or Piece Rate in Round 3 and 4

Percentage that Choose to Compete

Obs Percentage

Round 3

Overall 715 43.08

Male 358 46.93

Female 357 39.22

Round 4

Overall 714 45.38

Male 358 45.53

Female 356 45.22

20

Table 3 Summary Statistics, Self Rank in Round 1 and 2

Successes

Rank Obs Proportion Mean SD

Round 1

Overall

Rank 1 347 48% 6.31 2.56

Rank 2 197 27% 5.99 2.42

Rank 3 112 15% 5.30 2.58

Rank 4 72 10% 4.47 2.06

Male

Rank 1 207 57% 6.82 2.43

Rank 2 91 25% 6.00 2.25

Rank 3 42 12% 5.40 2.02

Rank 4 24 7% 4.75 2.36

Female

Rank 1 140 38% 5.54 2.56

Rank 2 106 29% 5.98 2.57

Rank 3 70 19% 5.24 2.88

Rank 4 48 13% 4.33 1.91

Round 2

Overall

Rank 1 231 32% 8.26 2.82

Rank 2 247 34% 6.92 2.88

Rank 3 160 22% 6.72 2.95

Rank 4 90 12% 6.19 2.82

Male

Rank 1 151 41% 8.62 2.60

Rank 2 117 32% 7.35 2.79

Rank 3 64 18% 6.44 2.68

Rank 4 32 9% 6.03 2.32

Female

Rank 1 80 22% 7.58 3.09

Rank 2 130 36% 6.54 2.91

Rank 3 96 26% 6.91 3.12

Rank 4 58 16% 6.28 3.08

21

Table 4 Probit Results on the Choice to Compete in Round 3 and 4

Dep Var: Decision to submit to the tournament incentives in Round 3 or 4 round 3 3 4 4 (1) (2) (3) (4)

Female -0.0764** -0.00533 (0.0304) (0.0289)

Total successes so far 0.00224 0.00160 -0.0138*** -0.0139***

(0.00456) (0.00457) (0.00390) (0.00398)

Learning coefficient -0.419 -0.353 0.131 0.135 (0.958) (0.974) (0.719) (0.714)

Competitive response coefficient

0.112 0.122 0.136 0.137 (0.112) (0.113) (0.171) (0.171)

Submit to tournament in R4

0.386*** 0.386*** (0.0480) (0.0475)

Choose tournament in R3 0.384*** 0.383*** (0.0455) (0.0451)

R2 Relative rank 1 0.0569 0.0375

(0.0961) (0.100)

R2 Relative rank 2 0.0415 0.0331

(0.0888) (0.0903)

R2 Relative rank 3 -0.0365 -0.0387 (0.0893) (0.0892)

R1 Relative rank 1 -0.0941 -0.0953 (0.0883) (0.0877)

R1 Relative rank 2 -0.0717 -0.0723 (0.0823) (0.0814)

R1 Relative rank 3 -0.115 -0.116

(0.0929) (0.0929)

Number of Observations 712 712 712 712 p-value* 0.25 0.19 0.005 0.006

*A test of the restriction that the coefficients for successes so far, learning and competitive response are

jointly equal to zero.

22

Table 5 Gender, Age and the Choice to Compete

Dep Var: Decision to choose the tournament incentives in

Round 3

(1) (2) (3) (4)

male female female female

Age -0.00413 0.00881* 0.00866*

(0.00516) (0.00483) (0.00472)

Age squared 8.02e-05 -0.000146** -0.000299***

(8.10e-05) (6.22e-05) (9.88e-05)

Female age above 50 years 0.127 0.398***

(0.102) (0.146)

Total successes so far 0.00790 -0.00364 -0.00209 -0.00605

(0.00852) (0.00505) (0.00417) (0.00546)

Learning coefficient 0.299 -0.0289 -0.0940 -0.0296

(0.213) (0.183) (0.174) (0.205)

Competitive response coefficient -0.143 -0.327 -0.290 -0.0682

(2.083) (0.541) (0.531) (0.544)

Submit to tournament 0.436*** 0.321*** 0.331*** 0.324***

(0.0559) (0.0636) (0.0597) (0.0622)

Relative rank 1 0.0407 0.0228 0.0149 0.0152

(0.176) (0.0810) (0.0818) (0.0856)

Relative rank 2 0.0535 0.00864 0.0125 -0.00360

(0.178) (0.0692) (0.0716) (0.0675)

Relative rank 3 -0.0871 0.00548 0.00729 0.00599

(0.139) (0.0812) (0.0779) (0.0793)

Number of Observations

362 349 350 349

Reported coefficients are the marginal effects from probit regression. Clustered Standard errors (at the village) are

reported in parenthesis.

Age and age squared is for years above 18. Maximum value for females in (2) is obtained at 48 years

(0.00881/(2*0.000146)+18) .

23

Table 6 Gender, Female Age and choosing the tournament

Dep Var: Decision to choose the tournament incentives in Round 3

Female (0/1) -0.156** (0.0661)

Female age 0.0106*

(0.00556)

Female age squared -0.000304***

(0.000113)

Female age above 50 years 0.366** (0.151)

Total successes so far 0.00269

(0.00600)

Learning coefficient 0.142

(0.121)

Competitive response coefficient -0.332 (0.961)

Submit to tournament 0.386*** (0.0438)

Relative rank 1 0.0386 (0.101)

Relative rank 2 0.0305

(0.0868)

Relative rank 3 -0.0306 (0.0852)

Number of Observations 711

24

Table 7 Probability of choosing tournament by village and gender

(1) (2) (3)

Simple Perform. Complete

Age 0.000702 0.000970 0.000693

(0.00117) (0.00166) (0.00192)

Male Female Gap in Village 1 Matri 0.132*** 0.168*** 0.120***

(0.00797) (0.00776) (0.0273)

Male Female Gap in Village 2 Matri 0.182*** 0.162*** 0.206***

(0.00339) (0.0123) (0.0189)

Male Female Gap in Village 3 Patri 0.0630*** 0.0834*** 0.0970***

(0.00261) (0.0147) (0.0178)

Male Female Gap in Village 4 Patri 0.0616*** 0.0318*** 0.0724***

(0.00129) (0.0104) (0.0115)

Male Female Gap in Village 5 Trans 0.0927*** 0.110*** 0.135***

(0.00167) (0.00730) (0.0130)

Male Female Gap in Village 6 Trans 0.161*** 0.178*** 0.181***

(0.00431) (0.0116) (0.0159)

Male Female Gap in Village 7 Matri -0.159*** -0.127*** -0.157***

(0.000679) (0.0111) (0.0151)

Male Female Gap in Village 8 Patri 0.000552 0.00214 0.0169

(0.000904) (0.00894) (0.0172)

Male Female Gap in Village 9 Patri 0.205*** 0.195*** 0.131***

(0.00482) (0.0104) (0.0197)

Male Female Gap in Village 10 Matri -0.0338*** -0.0513*** -0.0626**

(0.00118) (0.00518) (0.0282)

Male Female Gap in Village 11 Matri 0.0606*** 0.0788*** 0.0748***

(0.00295) (0.00588) (0.0152)

Male Female Gap in Village 12 Matri 0.203*** 0.202*** 0.118***

(0.00520) (0.00824) (0.0329)

Total successes so far 0.00169 0.00401

(0.00520) (0.00636)

Learning coefficient 0.190* 0.140

(0.112) (0.121)

Competitive response coefficient -0.480 -0.615

(1.181) (1.131)

Submit to tournament in Round 4 0.390***

(0.0517)

Round 2 Relative rank 1 0.0613

(0.117)

Round 2 Relative rank 2 0.0519

(0.0985)

Round 2 Relative rank 3 -0.0442

(0.0862)

Number of Observations 729 712 711

Reported coefficients are the marginal effects from probit regression.

Clustered Standard errors (at the village) in parenthesis.

25

Table 8 Marital Location Customs in Matrilocal, Patrilocal and Transition Villages

Dep Var: Whether a currently married individual was born in this village

Village All Matri 2 11 10 7 1 12

Female 0.297*** 0.675*** 0.423*** 0.381** 0.349** 0.0952 -0.0568

(4.17e-05) (0.00273) (0.00390) (0.0240) (0.0285) (0.390) (0.691)

Constant 0.446*** 0.143 0.286*** 0.333** 0.533*** 0.810*** 0.737***

(0) (0.353) (0.00716) (0.0186) (3.82e-05) (0) (2.08e-08)

Obs. 179 18 45 40 32 42 44

All Patri

Villages

9 8 3 4

Female -0.506*** -0.712*** -0.438** -0.582*** -0.215

(3.56e-10) (4.55e-07) (0.0444) (0.000138) (0.191)

Constant 0.859*** 0.947*** 0.750*** 0.895*** 0.778***

(0) (0) (0.000188) (0) (5.70e-08)

Observations 129 36 24 35 34

Both Transition Villages 5 6

Female -0.122 -0.212* -0.0581

(0.176) (0.0575) (0.684)

Constant 0.796*** 0.920*** 0.690***

(0) (0) (7.50e-10)

Observations 97 49 48

p-val in parentheses *** p<0.01, ** p<0.05, * p<0.1

26

Table 9 Comparing the Choice to Compete across Matri and Patri Villages

Dep Var: Decision to choose the tournament incentives in Round 3

(1) (2)

Matrilocal -0.500*** -0.500***

(0.105) (0.170)

Matri Patri Matri Patri

Female -0.104 -0.306*** -0.104 -0.306***

(0.0968) (0.114) (0.118) (0.0901)

Male age -0.00288 -0.00202 -0.00288 -0.00202

(0.00182) (0.00322) (0.00323) (0.00257)

Female age 0.00351 0.0164* 0.00351 0.0164**

(0.0113) (0.00875) (0.0143) (0.00800)

Female age squared -6.90e-05 -0.000505*** -6.90e-05 -0.000505***

(0.000315) (0.000134) (0.000304) (0.000130)

Female age above 50 years 0.241 0.559*** 0.241 0.559***

(0.239) (0.102) (0.285) (0.0844)

Total successes so far 8.27e-05 0.000905 8.27e-05 0.000905

(0.00339) (0.0116) (0.00631) (0.00769)

Learning coefficient 0.136 0.211 0.136 0.211

(0.167) (0.310) (0.239) (0.275)

Competitive response coefficient 0.826 1.109 0.826 1.109

(0.997) (1.059) (2.235) (1.927)

Submit to tournament 0.539*** 0.259*** 0.539*** 0.259***

(0.0369) (0.0530) (0.0550) (0.0703)

Relative rank 1 0.284** -0.237*** 0.284*** -0.237**

(0.119) (0.0865) (0.107) (0.0938)

Relative rank 2 0.301** -0.157*** 0.301** -0.157*

(0.120) (0.0429) (0.126) (0.0944)

Relative rank 3 0.249*** -0.117 0.249** -0.117

(0.0423) (0.0832) (0.108) (0.117)

Number of Observations 226 254 226 254

clustered at village clustered at set

Chow test Chi-square statistic 165.65 44.08

Chow test p-value <0.001 <0.001

27

Table 10 Choosing the Tournament for Women Across Matri and Patri Villages

Dep Var: Decision to choose the tournament incentives in Round 3

(1) (3)

Matrilocal -0.280 -0.280

(0.251) (0.329)

Matri Patri Matri Patri

Female age 0.00487 0.0141 0.00487 0.0141*

(0.00997) (0.00871) (0.0120) (0.00756)

Female age squared -0.000123 -0.000488*** -0.000123 -0.000488***

(0.000251) (0.000112) (0.000245) (0.000137)

Female age above 50 years 0.291* 0.597*** 0.291 0.597***

(0.170) (0.0916) (0.262) (0.107)

Total successes so far -0.00174 -0.00839 -0.00174 -0.00839

(0.00433) (0.00992) (0.00742) (0.0142)

Learning coefficient 0.00124 -0.194 0.00124 -0.194

(0.334) (0.332) (0.501) (0.347)

Competitive response coefficient 0.121 0.561 0.121 0.561

(0.721) (1.098) (3.553) (2.309)

Submit to tournament 0.492*** 0.193*** 0.492*** 0.193*

(0.0938) (0.0199) (0.0860) (0.113)

Relative rank 1 0.0908 -0.186 0.0908 -0.186

(0.0892) (0.126) (0.162) (0.145)

Relative rank 2 0.160 -0.0435 0.160 -0.0435

(0.121) (0.0579) (0.172) (0.126)

Relative rank 3 0.222*** -0.0712 0.222** -0.0712

(0.0693) (0.0610) (0.107) (0.152)

Number of Observations 110 126 110 126

clustered at village clustered at set

Chow test Chi-square statistic 110.37 17.05

Chow test p-value <0.001 0.0733

28

Table 11 Competitiveness with the US Sample

Dep Var: Decision to choose the tournament incentives in Round 3

US Malawi

Female -0.168** -0.156**

-(0.0716) -(0.0661) Female age 0.00856 0.0106*

-(0.0082) -(0.0056) Female age squared -0.000371** -0.000304***

-(0.0002) -(0.0001) Female age above 50 years

0.381*** 0.366**

-(0.0796) -(0.1510) Total successes so far -0.0112 0.00269

-(0.0092) -(0.0060) Learning coefficient 0.223 0.142

-(1.3190) -(0.1210) Competitive response coefficient

-2.876 -0.332

-(9.9010) -(0.9610) Submit to tournament 0.392*** 0.386***

-(0.0803) -(0.0438) Relative rank 1 -0.988*** 0.0386

-(0.0190) -(0.1010) Relative rank 2 -0.998*** 0.0305

-(0.0025) -(0.0868) Relative rank 3 -0.948*** -0.0306

-(0.0218) -(0.0852)

Number of Observations 84 711 Standard errors clustered by day (US) and village (Malawi)