Identity, Overconfidence, and
Investment Decisions
Francesco D’Acunto
∗
July 2015
Abstract
Why are men more risk tolerant than women, and why do they invest more
than women? I test whether identity stereotypes help explain this heterogeneity. I
manipulate identity in a controlled environment by priming its salience to subjects.
Men whose identity is primed take on more risk, and invest more often and more
money than controls. The salience of male identity increases men’s beliefs about
experiencing good outcomes in a game of chance. Inducing overconfidence similarly
makes men take on more risk and invest more. The effects are stronger for older
cohorts of men, consistent with the notion that gender-identity stereotypes have
become less stark over the last decades.
JEL: D81, G01, G11, J16
Keywords: Gender Identity, Stereotypes, Experimental Economics, Heterogeneous Beliefs, Individual Investors.
∗
Haas School of Business, UC Berkeley (francesco dacunto@haas.berkeley.edu). UC Berkeley CPHS
Approval Protocol number 2013-11-5805. For their invaluable help and guidance, I thank Stefano
DellaVigna, Ulrike Malmendier, Gustavo Manso, Terrance Odean, and Ross Levine. For very helpful
comments and discussions, I thank Nick Barberis, Dan Benjamin, James Choi, Xavier Gabaix, Luigi
Guiso, George Korniotis, Samuli Knüpfer, Rachel Kranton, Pete Kyle, Don Moore, Adair Morse, Muriel
Niederle, Christine Parlour, Matthew Rabin, Andrei Shleifer, Stephan Siegel, Kelly Shue, Michael Weber,
and seminar participants at the 2015 Western Finance Association, the 2014 Miami Behavioral Finance
Conference, the 2013 NBER Behavioral Economics Fall meeting, the 2014 Whitebox Advisors Graduate
Conference, and UC Berkeley. Financial support from the White Foundation is gratefully acknowledged.
All errors are my own.
1
Introduction
Men are more risk tolerant than women.1 They are more competitive (Niederle and
Vesterlund (2007)), and they invest more often and more aggressively than women when
facing financial opportunities (Barber and Odean (2001)). Researchers have proposed
several explanations for the different risk attitudes across genders. These differences have
biological roots.2 Social identity may additionally help explain these differences, because
identity stereotypes prescribe normative behaviors to men and women.3 The differences
may also stem from other characteristics that correlate with gender.
In this paper, I aim to provide a causal test for the effect of social identity on the risk
attitudes and investment behavior across genders, and to study the economic channels
that may mediate this effect. Running causal tests in the field would be hard (Camerer
and Lovallo (1999); Biais et al. (2005)), thus I randomly manipulate the salience of
identity stereotypes in a controlled environment (e.g., Steele (1997); Shih et al. (1999)),
and I compare the risk tolerance and investment behavior of treated and control subjects.
Studies have employed similar priming techniques to address economic questions for
which detecting exogenous variation in the field is hard (Benjamin et al. (2013); Cohn
et al. (2015); Coffman (forthcoming)).
Results in social psychology show that priming and threatening male identity both induce
risky behaviors in men because of salience and overcompensation (Maas et al. (2003);
Willer et al. (2013)). Salience means individuals tend to conform to the primed trait of
their own identity. Overcompensation means individuals behave more in line with their
own identity to reaffirm it when it is threatened.4
I find that men whose identity stereotypes are primed or threatened become more
risk tolerant after the manipulations, and invest more often and more money in risky
opportunities compared to control men and to women. I detect no effect of identity
1
See Eckel and Grossman (2008), Croson and Gneezy (2009), Bertrand (2011), as well as Niederle
(2015), on whether gender differences in risk attitudes are robust across elicitation methods and contexts.
2
See Kuhnen and Chiao (1972), Coates and Herbert (2008), Mastripieri et al. (2009), Cesarini and
et al. (2010), Cronqvist and Siegel (2014), Cronqvist et al. (2014). The effect of seasonal affective disorder
(SAD) on risk attitudes and empirical regularities in financial markets also speaks to these differences,
because women are affected by SAD more than men. See Kamstra, Kramer, and Levi (2003) for the first
empirical evidence, and Kamstra et al. (2014) for the first theoretical interpretation of these regularities.
3
See Mischel (1966), Williams and Bennett (1975), Eagly and Steffen (1984), Eagly (1987), Carli
(1989), Akerlof and Kranton (2000), Roccas and Brewer (2002), Stewart and McDermott (2004), Benabou
and Tirole (2011), Rueben, Sapienza, and Zingales (2014), Bertrand, Kamenica, and Pan (forthcoming).
4
This notion of identity threat is unrelated to the stereotype threat of Carr and Steele (2010). Cognitive
dissonance may explain men’s overcompensating behavior when their identity is threatened.
1
manipulation on women’s risk attitudes and investment behavior.
Previous literature in Economics and in Psychology found conflicting results on the
effect of gender identity on risk attitudes. In Meier-Pesti and Penz (2008),5 male gender
salience increases the willingness to take risks in both men and women, whereas female
gender salience decreases risk taking in men, contrary to the overcompensation literature,
but in a non-incentive-compatible setting, and with a subject pool in which men and
women reported the same masculine attributes before the experiments. To the contrary,
Benjamin, Choi, and Strickland (2010) find no effect of gender salience on risk preferences
in an incentive-compatible, between-subjects experimental design.
Compared with previous literature, this paper proposes four innovations.
First, I use a within-subjects experimental design, which controls for the heterogeneity of
subjects’ preferences before the experimental manipulations. This design allows testing
for the effect of identity on risk attitudes more precisely than between-subjects designs.
If I only use the between-subjects variation in risk attitudes after the manipulations, I
estimate effects of similar size but not statistically different from zero, which is consistent
with the non-result of Benjamin, Choi, and Strickland (2010).6 The within-subjects
design raises concerns about demand effects. Therefore, throughout the paper, I run
artefactual field experiments with varied non-student subjects that are not aware of
being part of an experiment. I also recruit additional subjects who perform the same
experimental tasks as others but guess the motivations of the tasks at the end of the
experiment.7 Less than 10% of these subjects give answers that could be compatible
with the true scope of the study, whereas I show that virtually all subjects exposed to
the treatment conditions become more risk tolerant after the manipulations.
Second, I test for the first time for the economic channels that transmit the effect of
gender identity on risk attitudes. I find beliefs are a channel of transmission, because men
whose identity is manipulated become overconfident in a pure game of chance, consistent
5
Some of the results published in Meier-Pesti and Penz (2008) were first described in two conference
abstracts (Meier-Pesti and Penz (2003) and Meier-Pesti and Goetze (2006)). Note there is no reference
to any of these abstracts in Meier-Pesti and Penz (2008).
6
The between-subjects results are in Table 5 of Appendix B. The priming procedure in this paper is
purposefully more aggressive than in Benjamin, Choi, and Strickland (2010): subjects read a text full of
male or female stereotypes, and they recall and describe in detail a situation when they behaved in line
with the stereotypes (see Appendix A). The more aggressive primes may explains the larger estimated
magnitudes of the effects compared to their test. Blanco, Engelmann, and Normann (2011) also compare
between-subjects and within-subjects data for the same pool of subjects in an experimental setting.
7
I thank Dan Benjamin for suggesting this test.
2
with the illusion of controlling a random process.8 The effect of gender identity on
men’s beliefs in a pure game of chance is a novel result that deepens our understanding
of the sources of overconfidence, which is one of the most studied behavioral biases in
Economics. Parallel to the lack of an effect of identity on women’s risk attitudes, the
manipulations do not affect women’s beliefs either.
Third, I show the effect of identity on men’s risk attitudes also obtains if subjects
make investment decisions under risk, both on the extensive margin (the decision to
invest or not), and the intensive margin (the decision of how much money to invest
conditional on investing). The results are similar if the decisions are framed as delegated
investments. The delegated setting aims to mimic the decisions of asset managers and
corporate executives in the field.
These results could therefore be interpreted as a
causal test for the large body of correlational evidence in Economics and Finance on
overconfidence and investment decisions by individual investors and corporate executives.9
Fourth, I exploit the varied pool of subjects in terms of demographics to study how the
evolution of gender-identity stereotypes shapes the effect of identity on risk attitudes
and investment decisions.
I find older men are the most affected by the identity
manipulations, and the effect decreases monotonically the younger the subjects. This
result is consistent with the notion that gender-identity stereotypes have become less
stark over the last decades, and hence their manipulation should have lower effects on
the behavior of younger cohorts of men.
Moreover, in the Online Appendix, I propose a coherent economic framework based on
Bordalo, Coffman, Gennaioli, and Shleifer (2015) to interpret the results in the paper. To
the best of my knowledge, this paper is the first to ground the priming methodology on a
theoretical framework in Economics. This framework could inspire the uprising economic
research that uses priming techniques to discipline the predictions of their tests. A novel
prediction of the framework is an effect of identity manipulation on men’s beliefs, which
I detect in the experimental analysis.
The interpretation of the evidence in the paper has caveats.
By construction, the
analysis isolates reduced-form effects: a drawback is the causal tests do not inform on
the expected size of the effects in the field. The experiments change the salience of
8
The results from an earlier non-incentive-compatible test are qualitatively similar.
See Barber and Odean (2001), Malmendier and Tate (2005), Malmendier and Tate (2008), Gervais,
Heaton, and Odean (2011), Hirshleifer, Low, and Teoh (2012), Ben David, Graham, and Harvey (2013).
9
3
identity stereotypes to gauge their causal effect on decision making, but the level of the
perception of stereotypes should affect decisions in the field. Also, the subjects I recruit
do not necessarily represent the average investor or the average CEO, and the lottery
choices and the investment decisions that the subjects perform contain no uncertainty. In
addition, the experiments and the theoretical framework in Appendix C are agnostic on
why the identity manipulations affect men’s choices and beliefs but not women’s choices
and beliefs. Neither salience nor threats to female identity affect women’s decisions,
which suggests the set of identity stereotypes attached to female identity may not be
unanimously prescribing a risk-tolerant or a risk-averse behavior, nor overconfident or
underconfident beliefs. This interpretation is consistent with the body of research that
emphasizes the inherent identity conflict of working women,10 but my paper cannot
provide any conclusive explanation for the non-effect on women.
Stressing what this paper does not show is also important. The paper does not show
gender identity is more important than biology and genetics as a determinant of risk
attitudes. Future research should be devoted to understanding how biology, genetics,
and identity interact to affect risk attitudes and investment decisions. Moreover, in this
paper, men and women react differently to the same identity manipulations. My results
are therefore inconsistent with the claim that manipulating men’s and women’s gender
stereotypes could eliminate the differences between the sexes, or that the only difference
between the sexes is their gender identity.
2
The Controlled Environment
I run the experiments on an online platform, Amazon Mechanical Turk (mTurk).
Kuziemko et al. (forthcoming) were among the first to use mTurk in Economics research.
On mTurk, Requesters post tasks, and a large pool of Workers can accept to perform
them. Requesters are often private companies and Workers are registered users. Workers
provide their fiscal address and social security number for tax purposes. Tasks are short,
and the average pay is low ($1.39 per hour).
Workers based in the United States,
whom we exclusively recruit in this paper, access mTurk mainly to spend their spare
time constructively (Paolacci et al. (2010)). The quality of answers is not lower than
10
This literature was initiated by Komarovsky (1946), and is reviewed and organized in Hays (1996).
4
in human-subjects laboratories, despite the lower pay (Casler et al. (2013)).11 Recently,
mTurk has gained interest as a means to recruit diversified subjects for artefactual field
experiments, but a concern is Workers may not properly complete the tasks. To address
the concern, (i) I restrict the subject pool to Workers with at least 95% positive rates on
all the tasks accepted in the past, (ii) I track the time they take to complete each task,
(iii) I read all essays to verify that subjects produce coherent statements, and (iv) I add
implausible options to the lotteries and I verify they are not picked.12
Advantages over Laboratory. mTurk has a set of advantages compared to humansubject laboratories (Horton et al. (2011)), especially for a study on identity priming and
for within-subjects designs:
• Subjects come from the whole United States. Figure 1 plots their IP addresses.
If subjects came from the same college town, they would live in a peculiar social
environment that is likely dissimilar from the one average Americans face. Also,
college and MBA students may sort into communities whose values they share.
• As an online, double-blind platform, mTurk allows running artefactual field
experiments in which recruiting is simple (List (2011)). Subjects perform tasks in
their environment and are not aware they are part of an experiment. This procedure
helps me to address the concerns of demand effects that within-subjects designs
raise.
• Subjects’ demographics are well varied, whereas a sample of college students would
only include individuals between 18 and 22 years of age with some college education.
• Replicability of results is easy: any Requester accesses the same subject pool. Easy
replicability allows for a transparent comparison of results across studies.13
• Workers’ anonymity makes the priming procedure most effective, because subjects
can describe unconventional experiences with no fear of being identified.
3
Effect of Identity on Risk Tolerance
In Experiment 1, I test whether gender-identity stereotypes have a causal effect on
risk attitudes in a within-subjects experimental design, where subjects’ risk tolerance
11
Mason and Suri (2012) describe mTurk and Workers. Berinsky et al. (2012) replicate laboratory
results in political science on mTurk.
12
For instance, I add a choice between $0 for sure and a lottery that pays $0 or a positive amount.
13
On September 26, 2012, Daniel Kahnemann proposed a protocol for improving the credibility of
research on priming, which is easy to implement on mTurk.
5
is elicited before and after being exposed to the manipulation of their gender identity.
The manipulations include identity salience and threat, both of which have been shown
to induce risky behaviors in men because of salience and overcompensation (Maas et al.
(2003); Willer et al. (2013)).
Experimental Design. I recruited 340 subjects on mTurk in April 2012 (first session)
and September 2012 (second session). The description proposed a creative writing task
for which subjects would earn $.50, plus a chance to earn a bonus by picking lotteries.14
Subjects knew the bonuses were computed dividing by one hundred the nominal amounts
they earned in each of the lottery choice tasks described below, and hence bonuses could
range anywhere between $0 and $0.42. The payments were designed to be in line with the
average hourly pay of Workers on mTurk, to avoid any undue influence on Workers when
choosing between the current study and other tasks among those available on mTurk.
The nature or objectives of the study were not disclosed to ensure that (i) subjects were
not primed with their identity before the experiment; (ii) subjects would not search for
previous research on identity priming, which would raise concerns of demand effects; and
(iii) the results could be replicated in subsequent sessions and by other researchers. The
subject pool was restricted to users with a US tax identification, and with more than 95%
of lifetime tasks approved in the past.
Table 2 describes the experimental designs and samples for all the experiments in the
paper. The design of Experiment 1 was a 2 (male, female) X 3 (control, male prime,
female prime) factorial design. I excluded 20 subjects (5.8% of the full sample) because
of inconsistencies in lottery choices, leading to a final sample of 320 subjects. I assigned
subjects in proportions of 2:2:1 to the control, male-prime, and female-prime conditions.15
The aim was to maximize the power for the male-identity-salience test, because previous
research had not tested my cheating-free identity-threat test, and it was a weaker prime
than the one that Maas et al. (2003) and Willer et al. (2013) used.16
Procedure. The experimental procedure was as follows. In the first stage, subjects
answered four background questions including their country of residence, gender, age
group (18-22, 23-35, 36-45, 46-60, 60+), and education (high school or lower, some college,
college degree, postgraduate degree). Then subjects faced two screens of lottery choices
14
Although mTurk allows Requesters to not pay Workers who do not perform the tasks appropriately,
every subject received the $.50 show-up fee.
15
On mTurk, discrimination by gender is prohibited, for example, recruiting only men, or targeting the
same number of men and women. About 60% of US mTurk workers are women (Mason and Suri (2012)).
16
They gave random feedback on a gender-identity survey that suggested subjects are masculine or
feminine, which implies an act of deception on the part of the experimenter.
6
(Holt and Laury (2002)). Each choice included a degenerate lottery paying a positive
outcome for sure (certainty equivalent), and a lottery paying a positive outcome with
probability 1/2, and zero otherwise. In the second session, subjects faced three sets of
lottery choices to ensure the results did not vary with effort, which was not the case,
because the results did not differ across sections. Subjects had to complete the lottery
task in full before proceeding, but they could leave the experiment at any time. No one
left the experiment before completing it in full.17
The second stage consisted of the experimental treatments. Subjects read a short text (see
Appendix A) and recalled and described an experience in line with the text. Subjects
were asked to describe the situation and their feelings in detail in a short essay of 5
to 10 sentences. The texts were taken from online blogs, so as to be similar to the
gender-identity manipulations to which the subject pool, which consisted of internet users,
may have been regularly exposed in daily life. Note this test did not aim to give subjects a
scientific definition of identity stereotypes, but to activate their interpretation of identity
stereotypes once they faced them explicitly.
Control subjects read a text on ayurveda principles for a healthy lifestyle. Subjects in the
male-identity-prime condition read a text full of stereotypes about how a masculine person
behaves.18 Subjects in the female-identity-prime condition read a text full of stereotypes
about how a feminine person behaves.
The third stage consisted of two additional screens of lottery choices. The lottery choices
changed to ensure subjects did not merely repeat their first-stage choices. All choices
were incentive compatible: subjects knew their bonus would be calculated by picking one
screen and one line at random, running their choice (certainty equivalent or lottery), and
dividing the final amount by 100.
Manipulation Check. Across all experiments, the content of the essays the subjects
produced served as the manipulation checks. I verified that each essay reported words
related to the identity stereotypes enlisted in Table 1 in the corresponding experimental
condition (see Appendix A for essay samples). Ad-hoc manipulation-check tasks would
17
Andreoni and Sprenger (2012) discuss the problems that arise when jointly estimating the discounting
and concavity parameters of non-linear utility functions using discontinuous experimental budgets. In
this paper, I do not aim to provide point estimates of any utility parameters. Instead, I look at the
change in the lottery choices within subjects and within elicitation method.
18
Consistent with Table 1, confidence entered the text. A concern is that the treatment directly induces
overconfidence instead of priming a generic set of manly characteristics. But the male-identity-priming
text explicitly states that ”the masculine side [...] includes [...] how to accurately weigh probabilities so
that you know the most likely outcome to expect in situations you come across” (see Appendix A).
7
have made subjects aware they were part of an experiment, and hence would have
worsened the risk of demand effects. In Figure 7, I formalize this manipulation check
for Experiment 1.19
In Panel (a) of Figure 7, I show the average number of times
subjects wrote a stereotype associated with male individuals from Table 1 in their essays,
across experimental conditions.
Subjects in the male-identity-prime condition wrote
on average 2.41 male identity stereotypes.20 By contrast, subjects in the control or
female-identity-prime condition did not write masculine-identity stereotypes. In Panel (b)
of Figure 7, I report the average number of times subjects wrote a stereotype associated
with female individuals from Table 1 in their essays, across experimental conditions.
Subjects in the female-identity-prime condition indeed stereotypes associated with female
individuals, whereas others did not.
Measuring Risk Tolerance and Non-parametric Analysis.
I compute a
non-parametric measure of risk tolerance at the subject level, described in Figure 2. I
subtract the times the subject chose the certainty equivalent over the lottery from the
times a risk-neutral agent would make this decision. I average the difference across the
choices of the first stage to obtain a pre-treatment measure of risk tolerance:
RiskT olerancepre,i =
1 X
(RN choicespre,l − choicespre,l,i ),
×
L
l
where l ∈ (1, L) are the screens of lottery choices the subject faces, RN choicespre, l are
the times a risk-neutral agent would choose the certainty equivalent over the lottery in
screen l, and choicespre, l, i are the times agent i chose the certainty equivalent over the
lottery in screen l. RiskT olerancepre,i is positive when the subject, on average, chose the
lottery more often than a risk-neutral agent would. I compute the analogous measure for
choices made after the experimental treatments:
RiskT olerancepost,i =
1 X
×
(RN choicespost,l − choicespost,l,i ).
L
l
The within-subjects change in risk tolerance after the treatment compared to before the
treatment is ∆RiskT olerancei = RiskT olerancepost,i − RiskT olerancepre,i .
Figure 3 plots the estimated densities and the cumulative distributions of
RiskT olerancepre and of ∆RiskT olerance for the subsample of men, who are affected
19
The (unreported) results are similar across all other experiments.
Figure 7 only refers to the most common stereotypes, in Table 1. Subjects often reported other
masculine stereotypes that do not enter the computation.
20
8
by the manipulations. Results for women are in Appendix B. Panel A of Figure 3
plots the estimated distributions of the level of risk tolerance of men across treatment
conditions before the manipulations. The vast majority of subjects are risk averse: the
mean of all distributions and the largest part of their mass lie in the negative domain.
Reassuringly, the distributions for men that will be exposed to the control and to the
male prime conditions (green, dot-dash and blue, solid lines) are similar in terms of mean
and standard deviation. The distribution of the risk tolerance of men in the female prime
group also lies mainly in the negative domain, but the standard deviation is lower than
the one for the other experimental groups.
Panel B of Figure 3 depicts the main result of Experiment 1. The plots report the
density and cumulative distribution of ∆RiskT olerance for the male subsample across
experimental conditions. The risk tolerance of control subjects (green, dot-dash line) does
not change, on average, after exposure to the control condition. This evidence suggests
the control condition does not drive the results, and the acts of recalling and writing do
not cause any change in behavior by themselves. If the male prime and the female prime
were increasing the risk tolerance of subjects, we would expect the distributions of the
change in risk tolerance to shift to the right compared to the distribution for controls.
Indeed, the change in the risk tolerance of the male prime group (blue, solid line) is on
average positive (0.5).21 The distributions of the change in risk tolerance for the identity
threat group (dashed, red line) also peak to a positive value (0.45).
OLS Results. To assess the statistical significance of the effects, I estimate the following
OLS equation on the subsample of men:
∆RiskT oleranceiae = α + γ1 × M aleP rimeiae + γ2 × F emaleP rimeiae + ηa + ηe + iae , (1)
where ∆RiskT oleranceiae is the within-subjects change in risk tolerance for subject i, in
age group a, and education group e; M aleP rime and F emaleP rime are dummies that
equal 1 if the man is exposed to the male-identity (salience) or female-identity (threat)
prime; ηa and ηe capture subjects’ age and education groups. The identification strategy
is a difference-in-differences design: I look at subjects’ decisions before and after the
manipulations, and across subjects in the treatment and control groups. In column (1)
of Panel A of Table 3, men primed with male identity choose lotteries over certainty
21
The male group displays a fat right tail. In Table 7 of Appendix B, I show the subjects in this right
tail do not drive the results.
9
equivalents 0.51 times more often (s.e. 0.25) than controls after the prime.22 The result
is robust to averaging out age- and education-group effects in column (2) of Panel A of
Table 3. Men primed with female identity choose lotteries over certainty equivalents 0.43
times more often (s.e.0.21) after the prime compared to before it.
In Panel B of Table 3, I add the subsample of women to the analysis, and hence I use a
triple-differences estimator. I test if the results on men are robust to adding this additional
layer of subjects by estimating the following:
∆RiskT oleranceiae = α + βM aniae + γ1 × M aleP rimeiae + γ2 × F emaleP rimeiae
γ3 × (M aleP rime × M an)iae + γ4 × (F emaleP rime × M an)iae + ηa + ηe + iae , (2)
where ∆RiskT olerance is the same measure as in equation (1), and M an equals 1 if
the subject is a man. In equation (2), β does not capture the difference in average risk
tolerance between men and women ex ante, but captures the difference in the change in
risk tolerance due to the exposure to the control condition. Hence, β̂ should be zero,
unless being exposed to the control condition affects men and women’s risk tolerance
differently. I cannot reject the null that β̂ is zero at any meaningful level of significance.
The increase in risk-tolerant choices by men in the male-identity-prime condition is
significantly more positive (0.84, s.e. 0.37) than that of women, and with respect to the
control group. The result is robust to averaging out age- and education- group effects.
Men also become more risk tolerant after the identity threat, but I can only reject the
null of no effect at the 10% level of significance (0.59, s.e. 0.33).
Demand Effects? The within-subjects design raises concerns about demand effects:
subjects may be willing to conform their behavior to what they believe is the aim of the
experimenter. Demand concerns are the main reason I run artefactual field experiments,
in which subjects are not aware of the scope of the experimenter, or that they are part of
an experiment, but they perform tasks on mTurk for which they have signed up for pay.23
I also test for the scope of demand effects more directly. In June and July 2014, I recruited
100 new subjects to perform the same tasks as in Experiment 1, but also to guess the
motivations of the tasks at the end of the experiment. Subjects answered two questions:
”Why do you think we asked you to read a text and produce a short essay?” and ”Why
do you think we had you choose lotteries before and after the writing task?” The second
22
Because individual-level choices are collapsed into their average before and after the treatment, robust
standard errors are only corrected for White heteroskedasticity.
23
mTurk’s original and still primary function is for private businesses to outsource simple tasks, or to
test the effectiveness of advertising campaigns, and not for researchers to run experiments.
10
question directed subjects to think about the connections between the lottery and priming
tasks. Figure 8 plots the reported motivations for the lottery tasks and the writing task
across six categories. Only nine subjects in the ”Change in Risk” category in Panel B of
Figure 8 gave answers compatible with the true scope of the study, whereas in Figure 3, the
change in risk tolerance of virtually all men exposed to the treatment conditions is higher
after the manipulations than for controls. Of the nine subjects in the ”Change in Risk”
group, five were women and four were men. The men were all in the male-identity-prime
condition; three of them were more risk tolerant after the manipulations, whereas one
was not. Even if the subjects are not those in Experiment 1, the results suggest demand
effects can hardly drive the results. The generic categories ”Relate to Lotteries” and
”Relate to Text” include subjects that do not mention any scope consistent with the aims
of the experiment. For instance, the answers of a man in the identity-threat condition
are ”Perhaps to get into a focused mindset” and ”Maybe it was a part of a research and
somehow connected.” These categories also include two subjects that guessed the wrong
direction of the expected effects: a man in the control condition (”I think you wanted to
see if the regimented activity made people take less risk”) and a man in the identity-threat
condition (”It was a break from the lottery exercise, to re-orient us to feminine and less
risky decision making”). Interesting examples are for men who thought the recall task was
a distraction, and the aim was to test for the consistency of choices, but instead reacted to
the manipulations. For instance, a man in the identity-salience condition replied, ”This
was to see how cautious I was in the bets despite your distraction.” But then he made
more risk-tolerant choices after the manipulation compared to before.
Evidence against Fabricated Data. I run the test proposed by Simonsohn (2013)
against fabricated data in experimental research. This test is important in light of the
recent research-misconduct scandals commented on by Kahneman (2012). In Table 8 of
Appendix B, I verify for all the experiments in the paper that the standard deviations
of the sample averages across experimental conditions do not differ from those one would
obtain in random samples across conditions. In Experiment 1, the null that the observed
distributions come from independently drawn random samples can only be rejected above
the 99.99% significance level (99.98% in the male subsamples).
11
4
The Beliefs Channel
After having established that priming or threatening male identity increases men’s
willingness to take risk in a within-subjects, incentive-compatible research design, I move
on to test for the economic channels that might explain this effect.
The primes may act through two channels: (i) Preferences: priming identity could be a
positive shock to men’s risk tolerance. This shock would reduce the certainty equivalent
men require to give up a chance to take part in a lottery. (ii) Beliefs: priming identity
could be a positive shock to men’s subjective probability of experiencing good outcomes
if they take part in a lottery. They could believe they are more likely to obtain the good
outcome conditional on participating than what the objective probabilities suggest.
If individuals held subjective beliefs of experiencing good outcomes in risky settings, which
might differ from the objective probabilities they are told, the identity manipulations could
affect their subjective beliefs. Subjects would become overconfident, that is, believe they
are more likely than what the objective probabilities suggest to obtain the good outcome
if they participate in the lottery. This form of overconfidence involves the illusion of
controlling random processes, as opposed to overplacement or overprecision in interpreting
signals (see Moore and Healy (2008)). In this section, I propose two novel procedures to
test if a beliefs channel helps explain the effect of identity manipulations on men’s attitudes
under risk. Assessing the relative contribution of the preferences and beliefs channels is
beyond the scope of this paper.
A
Identity and Subjective Beliefs under Risk
Testing for an effect of identity manipulations on beliefs requires a procedure that does
not simultaneously elicit risk attitudes and the subjective beliefs of experiencing good
outcomes, which may bring subjects to use the beliefs-elicitation stage to hedge against
potential bad outcomes following their choices (Blanco, Engelmann, Koch, and Normann
(2010)).24 I propose such a procedure in Experiment 2.
Design and Procedure. I recruited 325 subjects on mTurk in May 2015 and invited
them to work on a survey to earn $.40, plus a bonus based on their answers. I excluded two
subjects (1% of sample) because they did not write meaningful words or sentences in the
priming task, leading to a final sample of 323 subjects. After answering four background
questions, subjects were exposed to the experimental conditions of Experiment 1 (control,
24
Note no strategic interactions occur in the experiments of this paper.
12
male-identity prime, female-identity prime). I assigned subjects in equal proportions to
the three experimental conditions (see Table 2). I elicited subjective beliefs in two steps.
First, subjects faced a lottery that won on average 5 out of 10 times. No additional details
were disclosed, including the ”winning” or ”losing” payoffs. Subjects predicted the number
of times they would win if the lottery was played 10 times. The number they reported
was a direct measure of the subjective probability of experiencing a good outcome in a
risky setting. To make the elicitation incentive-compatible, subjects knew the lotteries
would be played 10 times using a random-number generator after the experiment, and
they would receive a bonus based on the difference between the predicted number of wins
and the actual number of wins.25 This design ensured that (i) subjects were told explicitly
that the outcomes of the lotteries would be completely determined by chance, and (ii)
risk tolerance had no role in the predictions.
In a second step, subjects imagined a person taken at random from their neighborhood
participated 10 times in the same lottery. They predicted the number of times they
thought this neighbor would win, and were paid a second bonus based on the accuracy
of their predictions. The number was interpreted as a direct measure of the subjective
probability that a peer would win the lottery. The comparison to peers is relevant,
because if the primes were simply inducing a state of elation in subjects, they would reply
that both they and the peers were likely to win more than 5 times. Instead, if subjects
believed they could control a random process, and the primes made them confident they
were better than average at the lottery, then primed subjects should have thought their
peers were as likely to win the lottery as the objective probability suggested.
Results. Panel A of Figure 4 plots the subjective beliefs of winning the lottery for men,
across the control and primed conditions, where the primed condition pools together
identity salience and threat.26 Primed men think they will succeed more than five times
on average, whereas control men do not. A double-sided t-test for whether the average
predicted number of wins by primed men (5.25) equals five rejects the null at the 1% level
of significance, whereas a similar test for whether the average number of wins by control
men (4.96) equals five cannot reject the null at any plausible level.27
Panel B of Figure 4 plots the average difference between men’s predicted wins by
25
The bonus was $.10 if the subject predicted the actual number of wins, $.05 cents if the subject was
one time long or short the actual number of wins, $.02 cents for two times long or short, and $0.01 cent
for three times long or short.
26
I merge the identity-salience and -threat groups to increase the power of the test, because the two
primes have very similar effects on the willingness to take on risks in Experiment 1.
27
Testing if the averages across groups are equal only rejects the null at the 10% level of significance.
13
themselves and by peers across experimental conditions.
The difference (0.04) is
statistically and economically indistinguishable from zero for controls. It is significantly
positive for primed men (0.25).
Manipulating identity therefore does increase the
subjective probability of experiencing a winning lottery outcome for men, even if they
play a pure game of chance. This result can only be justified if subjects have the illusion
of controlling a game of chance and if priming makes subjects overconfident about their
ability at this game. In Appendix B, I show the manipulations have no effect on the
beliefs of women: both control and treated women think on average they will succeed five
times if playing the lottery 10 times, in line with the objective probabilities they are told.
The different results on the effect of the manipulations on beliefs across men and women
pair up with the asymmetric effect of identity manipulation on risk attitudes by gender
in Experiment 1.
Incidentally, the effect of identity on men’s beliefs may help to shed light on the sources of
overconfidence, which is one of the most studied behavioral biases in Economics. Gender
identity appears to causally affect beliefs in risky settings.
B
Inducing Overconfidence and Risk Tolerance
The results of Experiment 2 suggest the beliefs channel could in part explain the effect
of gender-identity manipulations on men’s willingness to take risks. I therefore propose a
further test for whether manipulating men’s beliefs about experiencing good outcomes in
choices under risk may provide an effect on risk attitudes that is similar to the effect of
the identity manipulation, and in an incentive-compatible setting.
The methodological challenge for such a test is to provide an experimental manipulation
that plausibly affects men’s confidence in a pure game of chance, and hence gives them the
illusion of controlling the outcomes. To this aim, in Experiment 3, I build on the social
psychology literature, and I induce overconfidence in the form of illusion of control by
asking subjects to recall a situation in which they had power over one or more individuals
(e.g., Anderson and Galinsky (2006); Fast et al. (2012)). Table 1 shows the stereotypes
attached to powerful individuals by a pool of 200 respondents on mTurk are strikingly
similar to those attached to men.
Recalling a situation of power over others may also induce a state of elation in subjects,
which would affect their risk attitudes for reasons unrelated to overconfidence or identity
(Kuhnen and Knutson (2011)). I propose to prime the sense of success of subjects as
14
a placebo manipulation. Recalling a situation in which subjects were successful and
describing it in detail plausibly induces a state of elation in subjects. But whereas powerful
individuals are described with stereotypes similar to those of men, successful individuals
are associated with a mix of male- and female-identity stereotypes (see Table 1).
A concern with this test for elation states is that success itself may be expected to induce
overconfidence in subjects, because confidence is a stereotype attached to successful
individuals. But (i) the social psychology literature has provided no evidence that a
success prime induces overconfidence in subjects, and (ii) if the prime was inducing
overconfidence, it would induce it in the form of overplacement as opposed to the
illusion of control of outcomes. Contrary to the illusion of control of random outcomes,
overplacement should not affect decisions under risk in a pure game of chance, because
individual ability or effort cannot affect the outcomes. Hence, Experiment 3 could also
be interpreted as a placebo test to show overconfidence in the form of overplacement does
not affect men’s risk attitudes in a pure game of chance.
Design and Procedure. I recruited 340 subjects on mTurk (mTurk) in May 2012 (first
session) and October 2012 (second session), and invited them to work on a survey on
creative writing to earn $.50, and a bonus by picking lotteries. I restricted the subject
pool to mTurk Workers in the United States and with more than 95% of lifetime tasks
approved by Requesters. Table 2 describes the experimental design, which was a 2 (male,
female) X 3 (control, overconfidence prime, success prime) factorial design. I excluded 17
subjects (5% of sample) because of inconsistencies in the lottery choices, leading to a final
sample of 323 subjects. I assigned subjects in equal proportions to the three experimental
conditions. The first and third stages of the experimental procedure were the same as in
Experiment 1. In the second stage, subjects in the control condition recalled an event in
which they felt relaxed. Subjects in the overconfidence-prime condition recalled an event
in which they had power over an individual or individuals. Subjects in the success-prime
condition recalled an event in which they felt successful. All subjects described the events
in 5-10 sentences.
Results. I measure subjects’ tolerance to risk before and after the treatment, as well as
its change, as in Experiment 1. Panel A of Table 3 reports the estimated coefficients for
the following OLS equation:
∆RiskT oleranceiae = α+γ1 ×Overconf idenceP rimeiae +γ2 ×SuccessP rimeiae +ηa +ηe +iae .
(3)
15
Column (3) of Panel A of Table 3 shows that men induced with overconfidence choose
lotteries over certainty equivalents 0.52 (s.e. 0.26) times more after the prime than before,
compared to controls. The effect is robust to adding age- and education-group fixed
effects. One subject did not report his education level. The size of the effect of the
overconfidence prime on men’s risk tolerance is similar to the size of the effect of the
identity manipulations on risk tolerance in Experiment 1. This similarity is consistent
with the similar characterizations of male and powerful individuals in Table 1. Once I
add women in Panel B of Table 3, the effect of power on men is still positive (0.44), but
I cannot reject the null that the coefficient is zero at plausible levels (s.e. 0.42).
The estimated coefficient on the elation-state treatment, SuccessP rime, is always close
to zero in magnitude and not statistically different from zero. This result suggests that
inducing a state of elation in men, or inducing overconfidence in the form of overplacement,
hardly drives the increase in risk tolerance after the identity or overconfidence primes.
Contrary to Experiment 1, the effect of the control condition on the omitted categories
is on average negative. A t-test for whether the mean is different from zero within the
omitted categories cannot reject the null at any conventional level of significance. But
the low power of a test with few observations might drive this non-result, which is why,
in the next section that looks at risky decisions framed as investment opportunities, I ask
all subjects to recall and describe a situation in which they felt relaxed, which primes the
same state as the control manipulation in Experiment 1 and Experiment 3.28
5
Identity, Overconfidence, and Investment Decisions
I move on to test whether identity and overconfidence may also explain men’s choices
in risky settings that are framed as investment decisions, instead of lotteries. Looking
at investment decisions is relevant, because doing so can provide a causal test for the
correlational evidence on the effects of gender and overconfidence on men’s investment
decisions in the field, which have been documented in a large literature in Economics and
Finance, both for individual investors and for corporate executives.29
28
Experiment 3 also differs on the gender composition of the sample. About 60% of US mTurk Workers
are women (Mason and Suri, 2012), but Experiment 3 has more men than women (see Table 2).
29
See Barber and Odean (2001), Malmendier and Tate (2005), Malmendier and Tate (2008), Gervais,
Heaton, and Odean (2011), Hirshleifer et al. (2012), Ben David et al. (2013).
16
A
Identity, Overconfidence, and Individual Investments
In Experiment 4, I test if identity and overconfidence affect the behavior of men facing
simple investment decisions.
Design and Procedure. I recruited 240 subjects on mTurk in September 2012, and
invited them to work on a survey on creative writing for $.50, plus a bonus by performing
simple investment decisions. I restricted the subject pool to mTurk workers in the United
States with more than 95% of lifetime tasks approved by Requesters. The experimental
design was a 2 (male, female) X 3 (control, male-identity prime, overconfidence prime)
factorial design.
I excluded six subjects (3% of whole sample) who did not write
meaningful words in the priming task, leading to a final sample of 234 subjects. I assigned
subjects in equal proportions to the three experimental conditions. The experimental
procedure was as follows. In the first stage, subjects answered four background questions.
To address the concern that a change in the behavior of control subjects could drive the
results, all subjects recalled a situation in which they felt relaxed, which is the condition
that the control manipulation aims to induce. In the second stage, the control and
male-prime conditions were the same as in Experiment 1. The overconfidence prime was
the induction of a sense of power in Experiment 3.
The third stage consisted of the investment decisions. I gave subjects a virtual endowment
of $100 at the beginning of each of three periods.
Each period, subjects faced an
opportunity and decided how much, if any, of their per-period endowment to invest
(Gneezy and Potters (1997)).
The first opportunity succeeded with probability 1/2
and paid off three times the invested amount in case of success.
The second and
third opportunities succeeded with probability 1/6 and paid off seven times the invested
amount. I presented the three opportunities to subjects in random order. They could not
invest less than zero, that is, pay to avoid a choice. A random-number generator calibrated
to the objective probabilities of each opportunity determined good or bad outcomes.
Subjects received feedback about the outcomes immediately after their choices. The aim
was to verify the effects of identity and overconfidence were not confined to experiences
of good outcomes but survived even after experiencing negative outcomes, which is what
I find.30 I paid subjects a scaled amount of what they were left with at the end of the
experiment in the form of a bonus payment on top of the show-up fee.
Results.
Panel A of Figure 5 shows the average take-up rate across experimental
30
The immediate feedback is also the reason Experiments 4 and 5 have a between-subjects design:
experiencing positive or negative outcomes in a first stage could have interfered with the treatments.
17
conditions. Subjects in the male-identity and power-prime conditions were more likely to
invest than controls. I estimate the following probit model to make statistical inference
on the effects of primes on the extensive margin of investment:
Pr(Invest = 1)iae = Φ(α + γ1 × M aleP rimeiae + γ2 × Overconf idenceP rimeiae + ηa + ηe ),
(4)
where Investiae equals 1 if subject i in age group a and education-level group e agreed
to invest any positive amount, and zero otherwise. Φ(.) is the normal cdf, and covariates
are defined as in Equation 1. Panel A of Table 4 reports the average marginal effects
derived from the estimated coefficients. Standard errors are clustered at the subject level,
because subjects made three investment decisions. Column (1) shows men primed with
male identity are 12 percentage points (s.e. 6.5 p.p.) more likely to invest than controls,
who invest on average 65% of the times. The effect is robust to averaging out fixed age
and education effects. Men primed with power invest 17 percentage points (s.e. 6.8 p.p.)
more often than controls. Unreported results are similar if I estimate the marginal effects
in a linear probability model.
Panel B of Table 4 refers to the intensive margin of investment. The invested amount is
censored to the right ($100) and to the left ($0); hence, I estimate a tobit specification.
Covariates are as in equation (1). Standard errors are clustered at the subject level,
because subjects make three choices.
Because estimated coefficients are within the
censoring interval, I interpret them similarly to OLS coefficients. In column (1), subjects
primed with male identity invest on average $16 dollars more in each opportunity than
controls, but the effect is not statistically significant. Averaging out age and education
effects (column (2)) increases the size of the estimated coefficient to about $21 (s.e. $10),
which is an economically and statistically significant effect. Subjects primed with power
invest about $17 (s.e. $9) more than control subjects in the baseline specification of
column (1). The size of the effect increases to about $23 (s.e. $10) once age and education
fixed effects are averaged out.
B
Identity, Overconfidence, and Delegated Investments
In Experiment 5, I test for the effects of identity on delegated investments in a setting
in which the conflict of interest between the principal and the agent is minimal. This
setting is important to ensure the overconfidence of agents predicts overinvestment, but
only a minor scope for agency-based explanations of overinvestment exists. To reproduce
18
this setting, I change the framing of the investment tasks to include a principal on whose
behalf the subjects invest, which aims to mimic the investment choices of mutual fund
managers or corporate executives in the field. If the subjects’ preferences are the same as
those of the fictitious principal,31 the interests of the principal and agent are aligned in
this setting, because the compensation of the agent is a fixed proportion of the principal’s
revenues. Because of this feature, the experiment can be interpreted as a causal test of
Malmendier and Tate (2005), where overconfident CEOs overinvest when they have access
to cheap funds, because they believe they act in the best interest of shareholders.
I also allow for money-burning investment opportunities, because the field evidence
on overconfidence and investment behavior by individual investors and executives has
shown the negative effects of overconfidence are mainly concentrated in money-burning
investment projects, such as diversifying mergers (e.g., Malmendier and Tate, 2008)
Design and Procedure. I recruited 220 subjects on mTurk in October 2012, and invited
them to work on a survey on creative writing for $.50, plus a chance to earn a bonus by
performing simple investment decisions. I restricted the subject pool to Workers in the
United States who had more than 95% of tasks approved by Requesters. The experimental
design was a 2 (male, female) X 2 (control, male prime) factorial design. Given the
strikingly similar effects of overconfidence and identity primes on investment decisions in
Experiment 4, and the similar categorization of stereotypes attached to a male individual
and a powerful individual in Table 1, I increased the power of this test by only using one
of the two treatment conditions, the male identity prime. I excluded seven subjects (3%
of whole sample) who did not write meaningful words in the priming task, leading to a
final sample of 213 subjects. I assigned subjects in equal proportions to the experimental
conditions.
The procedure was the same as in Experiment 4, except for the third stage. Investments
were framed as delegated decisions: in each of two periods, subjects had to imagine
they were given money by Sally, a lady in their neighborhood who wanted to invest
her money instead of keeping it in her checking account, but was unable to do so.
Each period, subjects faced an investment opportunity. They decided if they wanted
to invest any of Sally’s endowment, and if yes, how much. Their compensation would be
a performance-based fee, calculated as a fraction of the amount Sally ended up with after
all the investment decisions. One opportunity succeeded with probability 1/2 and paid off
31
The instructions for this experiment are in Appendix A. No deception was present, because I asked
subjects to imagine they invested for a principal.
19
2.2 times the invested amount (sound investment). The other opportunity succeeded with
probability 1/2 and paid off 1.8 times the invested amount (money-burning investment). A
risk-neutral agent would have invested her whole endowment in the first case, and nothing
in the second case. The opportunities were presented in random order to subjects. Each
period, subjects could not invest more than their endowment or less than zero.
Results. Columns (3) and (4) of Panel A of Table 4 report the marginal effects implied by
the estimated coefficients of the specification in equation (4), but with the male-identityprime category only. Men whose identity is primed are 10.7 percentage points more
likely than control men to invest (s.e. 5.0 p.p.). The effect is robust to controlling for
age and education effects, and to computing the marginal effects with a linear probability
model (untabulated). Panel B of Table 4 shows results for estimating a tobit model whose
dependent variable is the invested amount in the two opportunities, censored at $0 at $100.
Men whose identity is primed invest on average $27.40 more (s.e. $9.80) than controls.
The effect on the intensive margin of investment is robust to controlling for age and
education effects. In Panel B of Figure 4, I report the average invested amounts separately
for the sound and the money-burning investments across experimental conditions. The
difference on the intensive margin is larger for the money-burning opportunity than for the
sound opportunity, in line with the correlational evidence of Malmendier and Tate (2008).
Note the mistakes of primed men drive this difference between opportunities: whereas
controls on average shy away from the money-burning opportunity, as they should, primed
subjects invest similar amounts in the sound and the money-burning opportunities.
6
Evolution of Identity Stereotypes and Effect across Cohorts
To better understand why identity affects risk attitudes and investment decisions, I move
on to test if the effects change as the perception of gender stereotypes evolves. Alesina,
Giuliano, and Nunn (2013) show the perception of gender roles varies across countries.
Fernandez and Fogli (2009) exploit the variation in cultural stereotypes across native and
immigrant US individuals. In my setting, all subjects are from the United States, and
I do not observe the immigration status of the subjects. I cannot exploit cross-regional
variation in stereotypes within the United States because the IRB-approved protocol does
not allow me to use the IP addresses of the subjects in the analysis. I therefore exploit the
variation in gender-identity stereotypes over time: with the gradual expansion of women’s
rights, the roles of the two genders in society, and hence gender-identity stereotypes, have
20
become closer.32
One may think the identity manipulations are equivalent to manipulating biological
characteristics that are known to be associated with risk attitudes, such as testosterone.
But if the effect of identity salience is higher the higher the difference in male and female
stereotypes, the effects of male identity on subjects’ risk tolerance and investments should
be larger for older cohorts of men, whereas a large body of biological literature shows
younger men are more sensitive to variation in the biological characteristics that affect
risk taking. Since Williams (1966), evolutionary biology has argued competition to access
for mating opportunities fuels the risky behavior of male subjects of various species,
including Homo sapiens. This fact is particularly true for young individuals, who have
no established reputations within communities. A large body of neurological evidence
(e.g., surveyed by Casey, Jones, and Hare (2008)) finds risk taking increases at puberty
and decreases with age. Steinberg (2008) connects these results to the remodeling of the
dopaminergic system at the time of puberty. Psychologists also show the gender gap in
risk taking is larger around puberty and decreases over time (e.g., see Byrnes, Miller, and
Schafer (1999)).
I find the effects documented in the paper are larger for older cohorts of men. In Figure
6, I run the analyses of Experiment 1, Experiment 4, and Experiment 5 separately
for four cohorts of men. I look at subjects born before 1975, between 1976 and 1983,
between 1984 and 1989, and between 1990 and 1994.33 Panel A of Figure 6 shows the
effect of identity salience on the change in risk tolerance is highest for subjects born
before 1975, and it fades away monotonically to become insignificantly negative for men
born after 1990. A similar pattern exists for the effect of male identity on the probability
of investing (Panel B of Figure 6). Panel C of Figure 6 shows the effect of male identity
on the amount invested in Experiment 4 is also higher for those born earlier, although
the decreasing pattern is not monotonic. These results also speak to the external validity
of the effects, because older individuals are on average wealthier and are more likely to
invest than the younger cohorts.
The results across cohorts warrant two comments.
First, I cannot disentangle the
mediating effect of cohorts (change in the societal perception of identity stereotypes over
32
For example, see Fernandez, Fogli, and Olivetti (2004), citet*Goldinetal2006, Doepke and Tertilt
(2009), Glaeser and Ma (2013), Breda and Ly (2014), Olivetti, Patacchini, and Zenou (2015).
33
I designed the cohort-level analysis after running the experiments, so that cohort boundaries cannot
be changed, but are effectively exogenous for the purposes of the cross-cohort test.
21
time) from an effect of age (individual-level change in the perception of identity stereotypes
from youth to adulthood). This caveat does not affect the interpretations of the results
as long as individuals become, if anything, more conservative over time.34
But the two mechanisms have different implications for the identity effect over time.
If cohort effects drive the results, the aggregate effect of identity on risk tolerance and
investment decisions should fade away over time as more individuals with less stark views
on gender roles replace the older cohorts (e.g., see Guiso et al. (2008) for a cross-country
argument). If aging partly drives the results, the aggregate effects would fade away
more slowly over time. Second, one may be concerned the results capture some form of
regression to the mean. Young men are more likely to engage in risky behaviors than
older men. Young people may be so risk tolerant that the manipulations have no effect
on them. But the average risk tolerance of men before the manipulations, the average
likelihood of investing, and the average amounts invested by controls do not decrease
monotonically with age, which the regression-to-the-mean explanation requires.
7
Discussion and Conclusions
I test whether social identity contributes to an explanation of the systematic heterogeneity
in risk attitudes and investment behavior across genders, because gender identity
prescribes normative behaviors to men and women. Previous literature found conflictive
answers to this question.
In a set of artefactual field experiments, priming or threatening the identity of men
increases their risk tolerance.
Primed men invest more often and more money in
risky opportunities, even when they act as agents of a principal, and especially in
money-burning investments. The primes have no effect on the behavior of women.
To the best of my knowledge, this paper is the first that tests for the economic channels
that drive the effect of identity on risk attitudes. Manipulating identity salience affects
men’s beliefs about experiencing positive outcomes in a game of chance, through the
illusion of controlling random outcomes. Moreover, the causal effects of identity on risk
tolerance and investments decrease monotonically from older to younger cohorts of men,
consistent with the attenuation of gender-related stereotypes over time.
The results can be interpreted as a causal test of Barber and Odean (2001), who use male
gender as a proxy for overconfidence. Results on delegated investments when the interests
34
Biology may mediate the sensitivity to the priming: Samanez-Larkin et al. (2010) find nucleus
accumbens activity explains the varying quality of financial choices by age.
22
of principal and agent are aligned may be interpreted as a causal test of Malmendier and
Tate (2005), but also as an empirical test of Gervais, Heaton, and Odean (2011), who
show overconfident agents are less conservative than other agents when investing in sound
opportunities, consistent with Hirshleifer, Low, and Teoh (2012).
In line with the trade-off between the positive effects of self-serving beliefs and the
risks of overconfidence (Benabou and Tirole (2002)), the quality of the opportunities
individual investors and CEOs face drives the welfare implications of the results.35 If
the opportunities have a positive net present value, male identity and overconfidence
benefit men by increasing their willingness to invest. But they cause men to overinvest
in money-burning opportunities if not tied by liquidity constraints. That overconfidence
makes investors better off in many states of the world, and hence is a trait that might
survive over time, is also consistent with Kyle and Wang (1997).
These results can be the basis for research efforts that better characterize the sources
of biased beliefs. Understanding the roots of biased beliefs has relevant implications for
hiring firms that need to screen potential managers and employees, as well as design
policies that reduce the emergence of distorted beliefs if they hurt the decision makers.
A fruitful avenue for future research is also to study the interactions between identity and
biology to better understand financial decisions. mTurk does not allow reliable collection
of information on the biological characteristics of subjects, but laboratories or field settings
that the experimenter can physically reach may allow for such analysis.
The priming techniques this paper employs show identity and overconfidence can be
manipulated. Male identity and overconfidence cues may be used to design policies or
compensation contracts that foster individual investors’ and executives’ willingness to
take on financial risks. Figure 13 of Appendix B shows financial firms seem to believe
in the effects of priming the identity and overconfidence of potential clients to boost the
take-up rates of their risky products. Future research should be devoted to testing for the
causal effects of cues and priming on financial decisions in the field.
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26
25
30
Latitude
35
40
45
50
Figure 1: Location of Subjects
-120
-110
-100
Longitude
Experiment 2
-90
-80
-70
Experiment 3
Figure 1 depicts the location of the subjects in Experiment 2 and Experiment 3 based on
their IP address.
Figure 2: Risk Tolerance Elicitation and Measure
Risk Neutral Agent choice:
RN choices = 10
Subject’s choice:
# choices = 13
Risk Tolerancei =
10-13 = -3
Figure 2 describes the construction of the Risk Tolerance measure based on the subject’s
choices. Subjects face a set of screens, each including a variable number of choices between
a decreasing certainty equivalent and a fixed lottery. The Risk Tolerance measure is the
difference between the number of times a risk neutral agent would choose the certainty
equivalent in the screen, and the number of times the subjects chooses the certainty
equivalent in the screen.
27
Figure 3: Changes in Risk Tolerance induced by the Identity Manipulations
Density Risk Tolerance before manipulations
0
.05
.1
.15
.2
Cumulative Distribution Risk Tolerance before manipulations
0
.2
.4
.6
.8
1
(a) Distribution of Risk Tolerance before the manipulations (male subsample)
-10
-5
0
Control
Female Prime
5
Male Prime
-10
-5
0
5
risklevpre
Control
Female Prime
Male Prime
Cumulative Change in Risk Tolerance
.2
.4
.6
.8
.4
.3
.2
0
.1
-4
0
Estimated Density Change in Risk Tolerance
1
(b) Distribution of ∆ Risk Tolerance induced by the manipulations (male subsample)
-4
-2
0
2
4
6
-2
0
Control
Female Prime
2
4
6
Male Prime
Figure 3 plots the estimated densities and cumulative distributions for the risk tolerance
before the manipulations (panel A) and the subject-level change in risk tolerance before and
after exposure to the experimental conditions (panel B) in Experiment 1 for male subjects.
The subject-level change in risk tolerance is measured as follows: ∆RiskT olerancei =
RiskT olerancepost,i − RiskT olerancepre,i , where RiskT olerancepre,i and RiskT olerancepost,i
are elicited via lottery choices a la Holt and Laury (2002). The smooth density plots use
an Epanechnikov kernel with bandwidth of 0.7. The cumulative distribution plots interpolate
the medians of 10 vertical bands with cubic splines. The plots of the distributions for the female
subsample are in Appendix B.
28
Figure 4: Identity Manipulations and Beliefs under Risk
4.6
Predicted Wins if Lottery played 10 times (p=0.5)
4.8
5
5.2
5.4
5.6
(a) Subjective beliefs of experiencing the good outcome in a lottery that succeeds on average
5 out of 10 times
Control Men
Primed Men
-.4
Own minus Neighbor Predicted Wins (p=0.5)
-.2
0
.2
.4
.6
(b) Better-than-average beliefs across experimental conditions (men only)
Control Men
Primed Men
Panel (a) of Figure 4 plots the subjective beliefs of experiencing the good outcome by gender
for subjects who imagine to play 10 times a lottery whose good outcome obtains on average 5
out of 10 times. Bandwidths are 95% confidence intervals for estimated means. Panel (b) of
Figure 4 plots the average subjective better-than-average beliefs for men in the control group
and in the primed group, which collects the male identity (salience) and female identity (threat)
primes.
29
Figure 5: Identity, Extensive and Intensive Margins of Investment
1
Number of Times Invested (out of 3)
2
3
(a) Times invested out of 3 opportunities (men only)
Control
Male Prime
Power Prime
30
Amount Invested (out of $100)
40
50
60
70
(b) Dollars Invested in Delegated Opportunities (men only)
Control Sound
Male Prime Sound Control Bad Inv. Male Prime Bad Inv.
Panel (a) of Figure 5 reports the average number of times that male subjects invest out of three
potential opportunities in Experiment 3, where all the investment opportunities have a positive
NPV, across experimental conditions. Panel (b) of Figure 5 reports the average amount of
money (in experimental dollars) the subjects invest in Experiment 5 across the experimental
conditions, and separately for sound and money-burning investment opportunities.
30
Figure 6: Identity vs. Biology: Effects of Identity across Cohorts
-.2
Effect of Male prime on Change in Risk Tolerance
0
.2
.4
.6
.8
(a) Effect of Male Identity prime on Risk Tolerance by cohorts (men only)
Born before 1975 Born 1976-1983
Born 1984-1989
Born 1990-1994
Marginal Effect of Male Prime on Probability to Invest
-.1
0
.1
.2
.3
(b) Effect of Male Identity prime on Probability to Invest by cohorts (men only)
Born before 1975 Born 1976-1983
Born 1984-1989
Born 1990-1994
-20
Effect of Male Prime on Amount Invested
0
20
40
60
(c) Effect of Male Identity prime on Amount Invested by cohorts (men only)
Born before 1975 Born 1976-1983
Born 1984-1989
Born 1990-1994
Panel (a)-(c) of Figure 6 plot the effect of the male identity manipulations on the change in
risk tolerance in Experiment 1, the likelihood that subjects invest in Experiment 4, and the
average amount invested in Experiment 4, across four cohorts of subjects based on their year
of birth.
31
Figure 7: Manipulation Check for Experiment 1
0
Number of Male Types from Table 1 in Essay
1
2
3
(a) Average Number of Male Types from Table 1 reported in Experiment 1
Control
Male Prime
Female Prime
0
Number of Female Types from Table 1 in Essay
1
2
3
(b) Average Number of Female Types from Table 1 reported in Experiment 1
Control
Male Prime
Female Prime
Figure 7 reports the results for the manipulation check for Experiment 1. Panel (a) shows the
average number of times each subject reported any of the types related to male individuals
enlisted in Table 1 in their essays, by experimental conditions. Panel (b) shows the average
number of times each subject reported any of the types related to female individuals enlisted in
Table 1 in their essays, by experimental conditions. The bars represent 95% confidence intervals
for the arithmetic mean within each group of subjects.
32
Figure 8: Testing for the Scope of Demand Effects
(a) Reported Motives for the Reading and Essay Tasks (100 subjects)
35
28
21
14
7
0
No Idea
Distraction
Collect
Opinions
Check if
Human
Relate to Manipulation
Lotteries
(b) Reported Motives for the Lottery Tasks before and after Essay (100 subjects)
30
25
20
15
10
5
0
No Idea
For Bonus Won't Use Measure
Risk
Relate to Change in
text
Risk
Figure 8 reports results for 100 subjects recruited in June and July 2014 to perform the tasks
in Experiment 1 following the same recruitment procedures as described in the paper. These
subjects were also asked to guess the motivations of the tasks at the end of the study. Panel (a)
reports the distribution of answers for why subjects had to read a text and write a short essay,
split into six broad categories. The dark histogram to the right (8 subjects) shows the answers
compatible with the aim of the study. Panel (b) shows the distribution of answers for why
subjects had to make lottery choices before and after the writing task. The emphasis on before
and after aimed to instill the idea that the order of tasks was related to their motives. The
only category compatible with the true motives includes 9 subjects (dark, right), 8 of which
are those depicted in dark in Panel (a).
33
Table 1: Characterization of Stereotypes
MALE
POWERFUL
SUCCESSFUL
FEMALE
34
Strong
37
Strong
50
Confident
46
Caring
40
Loud
27
Confident
45
Smart
36
Emotional
34
Aggressive
26
Arrogant
19
Happy
26
Flirty
16
Confident
18
Aggressive
18
Proud
19
Passive
15
Arrogant
15
Assertive
15
Determined
15
Smart
13
Dumb
12
Dominant
13
Focused
12
Loving
13
Stubborn
11
Greedy
9
Motivated
11
Happy
12
Assertive
11
Cocky
8
Assertive
10
Gossipy
12
Proud
10
Proud
8
Ambitious
10
Helpful
10
Dominant
10
Smart
7
Strong
10
Kind
10
1 reports the words mentioned most frequently by 200 survey respondents recruited on mTurk to describe how
Table
they think a typical male individual, a powerful individual, a successful individual, or a female individual behave.
Respondents wrote 3 to 5 words for each type of individual. Types were presented in a random order. The numbers
next to each stereotypical semantic area are the number of times respondents reported a word in that area.
Table 2: Experimental Designs and Sample Descriptions
Treatment
Design
Sample
Experiment 1
Identity Salience,
2 (male-M, female-F)
Risk Attitudes
Threat
X 3 (control-C, male identity-MI, female identity-FI)
MC:55, FC:74, MMI-61,
FMI:64, MFI:32, FFI:34.
Total:320
Experiment 2
Identity Salience,
2 (male-M, female-F)
Threat
X 3 (control-C, male identity-MI, female identity-FI)
Experiment 3
Induction of
2 (male-M, female-F)
Risk Attitudes
Overconfidence
X 3 (control-C, overconfidence-O, success-S)
Experiment 4
Identity Salience,
2 (male-M, female-F)
Investments
Induction of Overconfidence
X 2 (control-C, male identity-MI, overconfidence-O)
Experiment 5
Identity Salience
2 (male-M, female-F)
Beliefs
Investments
35
X 3 (control-C, male identity-MI, overconfidence-O)
MC:50, FC:58, MMI:49,
FMI:53, MFI:53, FFI:60.
Total:323
MC:56, FC:51, MMI-54,
FMI:53, MO:59, FO:50.
Total:323
MC:35, FC:43, MMI:38,
FMI:41, MO:32, FO:45.
Total:234
MC:48, FC:60,
MMI:49, FMI:55.
Total:212
Table 2 describes the treatments, experimental designs, and sample compositions for all the experiments in the paper. On mTurk,
one cannot discriminate by gender at the recruitment stage, and about 60% of U.S. mTurk Workers are women (Mason and Suri
(2011)).
Table 3: Identity, Overconfidence, and Risk Taking
Experiment 1:
Identity
Experiment 3:
Overconfidence
(1)
(2)
Male Prime
0.507
(0.246)**
0.577
(0.264)**
Overconfidence Prime
Female Prime
0.431
(0.205)**
0.564
(0.258)**
Mean omitted
Median omitted
Session f.e.
Age group f.e.
Education f.e.
Observations
R2
-0.212
0
148
0.03
-0.212
0
X
X
X
148
0.09
(1)
(2)
Male Prime
-0.337
(0.280)
-0.286
(0.268)
Female Prime
-0.163
(0.262)
-0.089
(0.271)
Man*
Male Prime
0.844
(0.373)**
Man*
Female Prime
Man
Panel A. Men Only
Panel B. Full Sample
Mean omitted
Median omitted
Session f.e.
Age group f.e.
Education f.e.
Observations
R2
(3)
(4)
0.519
(0.259)**
0.595
(0.270)**
Success Prime
0.014
(0.260)
-0.005
(0.262)
Mean omitted
Median omitted
Session f.e.
Age group f.e.
Education f.e.
Observations
R2
-0.332
-0.167
169
0.03
-0.332
-0.167
X
X
X
168
0.12
(3)
(4)
Overconfidence Prime
0.077
(0.331)
0.122
(0.348)
Success Prime
0.263
(0.321)
0.274
(0.328)
0.836
(0.375)**
Man*
Overconfidence Prime
0.442
(0.420)
0.394
(0.444)
0.594
(0.333)*
0.659
(0.344)*
Man*
Success Prime
-0.249
(0.414)
-0.292
(0.417)
-0.257
(0.212)
-0.329
(0.223)
Man
0.048
(0.320)
0.103
(0.331)
0.045
0
0.045
0
X
X
X
320
0.10
Mean omitted
Median omitted
Session f.e.
Age group f.e.
Education f.e.
Observations
R2
-0.536
-0.333
-0.536
-0.333
X
X
X
322
0.08
320
0.02
323
0.02
Columns (1) and (2) of Table 3 report the estimated coefficients from:
∆RiskT oleranceiae = α + γ1 × M aleP rimeiae + γ2 × F emaleP rimeiae + ηa + ηe + iae
Columns (3) and (4) report the estimated coefficient from:
∆RiskT oleranceiae = α + γ1 × Overconf idenceP rimeiae + γ2 × SuccessP rimeiae + ηa + ηe + iae
where ∆RiskT oleranceiae is the within-subject change in risk tolerance for subject i, in age
group a and education group e, before and after being exposed to the treatments in Experiment
1 and Experiment 2. ηa and ηe are fixed effects for subjects’ age and education-level groups.
Hubert-White s.e. are reported in brackets. Statistical significance is reported as follows: **
5%, * 10%.
36
Table 4: Identity, Overconfidence, and Investment Decisions
Experiment 4:
Individual
Investments
Experiment 5:
Delegated
Investments
(1)
(2)
(3)
(4)
Male Prime
0.123
(0.065)*
0.136
(0.063)**
0.107
(0.050)**
0.105
(0.048)**
Overconfidence Prime
0.170
(0.068)**
0.173
(0.069)**
0.65
1
0.84
1
315
0.03
0.65
1
X
X
X
315
0.09
194
0.05
0.84
1
X
X
X
194
0.06
(1)
(2)
(3)
(4)
Male Prime
15.66
(9.92)
20.59
(9.72)**
Overconfidence Prime
16.97
(9.32)*
22.77
(9.77)**
41.7
29.0
41.7
29.0
X
X
X
315
0.03
Panel A. Probability
to Invest
Mean omitted
Median omitted
Success prob. f.e.
Age group f.e.
Education f.e.
Observations
pseudo-R2
Panel B. Amount
Invested
Mean omitted
Median omitted
Success prob. f.e.
Age group f.e.
Education f.e.
Observations
pseudo-R2
315
0.01
Male Prime
Mean omitted
Median omitted
Success prob. f.e.
Age group f.e.
Education f.e.
Observations
pseudo-R2
Male Prime
Mean omitted
Median omitted
Session f.e.
Age group f.e.
Education f.e.
Observations
pseudo/R2
27.35
(9.82)***
24.99
(9.57)**
53.3
50.0
53.3
50.0
X
X
X
194
0.02
194
0.01
All the results in Table 4 refer to the subsample of male subjects. The corresponding
(non-)results for the female subsample are reported in Table 6 of Appendix B. Panel A
of Table 4 reports the estimated coefficients from:
Pr(Invest = 1)iae = α + γ1 × M aleP rimeiae + γ2 × Overconf idenceP rimeiae + ηa + ηe )
where ηa and ηe are fixed effects for subjects’ age and education-level groups. Panel
B of Table 4 reports the estimated coefficients from a tobit regression of the amount
invested in each opportunity (censored at $0 and $100) on the Male identity prime and
Overconfidence prime indicators from Experiment 3 and Experiment 4. S.e. are clustered
at the subject level. Statistical significance is marked as follows: ***1%, ** 5%, * 10%.
37
Appendix A - Experimental Materials
This Appendix provides part of the materials used in Experiments 1 through 4. Figure 8
shows the screenshot subjects faced any time they had to perform a risk aversion elicitation
lottery task à la (Holt and Laury (2002)). For each line, subjects were asked to choose
between a lottery paying a strictly positive amount with probability 50%, zero otherwise,
and a certain amount. The certain amount decreases from one line to the other, while
the lottery is fixed. The number of times a subjects chooses the certainty equivalent is
subtracted from the number of times a risk neutral agent would pick the certain amount
over the lottery to obtain a measure for the subject’s risk tolerance (see Section 5.B). In
Experiments 1 and 2, subjects perform lottery tasks before and after being exposed to
the experimental condition, so as to obtain a within-subject measure of the change in risk
tolerance due to the experimental treatment.
Figure 9 shows the screenshot subjects faced if assigned to the male identity priming
condition. It is an excerpt from an internet blog, which makes the treatment as close
as possible to daily-life experiences by subjects, who are regular internet users. The
excerpt describes a series of characteristics attributed to the “masculine side” of life,
which are similar to the traits survey responders in Table 1 think best describe how a
typical male individual behaves. Figure 10 shows an analogous screenshot subjects faced
if assigned to the female identity priming condition. The excerpt describes a series of
characteristics attributed to the “feminine side” of life. Finally, Figure 11 shows the
screenshot subjects faced if assigned to the control condition. The excerpt, also from a
blog, describes lifestyle according to the “ayurveda principles”. In all three cases, after
reading the excerpt, subjects were asked to recall a situation when the behaved in line
with the principles presented in the text, and describe the situation, their thoughts and
feelings in detail in a short essay (5 to 10 sentences). Computing a within-subject measure
of change in risk tolerance helps to ensure results are not driven by individuals exposed
to the control condition. To further address this concern, all subjects were asked to recall
a situation when they felt relaxed, and describe it in detail before being exposed to any
experimental condition in Experiment 3 and Experiment 4.
44
Survey | Qualtrics Survey Software
Ignore Validation
Click Here to Start Over
Do Not Show Hidden Questions
Previewing Survey
Figure 8: Set of lottery pairs choice
This picture reports the screenshot of one of the sets of lottery pair choices subjects were faced with in Experiment 1 and
Experiment 2. Lottery pairs are a degenerate version of those introduced by Holt and Laury (2002).
For each line, please pick one choice by filling the corresponding circle:
$6 for sure 50% -- $7
$5.7 for sure 50% -- $7
$5.4 for sure 50% -- $7
$5.1 for sure 50% -- $7
$4.8 for sure 50% -- $7
$4.5 for sure 50% -- $7
$4.2 for sure 50% -- $7
$3.9 for sure 50% -- $7
$3.6 for sure 50% -- $7
$3.3 for sure 50% -- $7
$3 for sure 50% -- $7
$2.7 for sure 50% -- $7
$2.4 for sure 50% -- $7
$2.1 for sure 50% -- $7
$1.8 for sure 50% -- $7
$1.5 for sure 50% -- $7
$1.2 for sure 50% -- $7
$0.9 for sure 50% -- $7
$0.6 for sure 50% -- $7
$0.3 for sure 50% -- $7
$0 for sure 50% -- $7
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45
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Figure 9: Male Identity Priming text
This picture reports the screenshot of the text subjects in the male identity prime treatment were asked to read. It is an
excerpt from a blog entry on the internet. After reading the excerpt, subjects were asked to recall a situation when the
behaved in line with the “masculine side” as presented in the text, and describe the situation, their thoughts and feelings
Please read the following short
textincarefully.
In (5
the
screens you will be asked three
in detail
a short essay
to next
10 sentences).
easy questions based on the text.
It should take you about 10 minutes to complete this section.
The Masculine Side
The Masculine Side deals with the strength of the self. It is what causes you to act either
timidly or self-confidently. The thing that is most important in determining the strength of
the masculine side, is the value that you, at a deep level, place on yourself. This is a value
you know within yourself that you have really and truly earned. It could be thought of as a
sort of self esteem. Placing a high value on yourself affects your whole being and helps you
feel strong and confident in operating your life. And, in the reverse direction, when you are
able to operate your life confidently, things can really turn around for you because you get
more out of life, and this automatically makes you place a higher value on yourself. You can
build the masculine side through progress and small wins, through positive reinforcement,
by practicing, and by doing things and generally taking an active part in operating your life.
If you have a strong masculine side, you are in charge of your own life because you are
internally controlled. You tend to look people in the eye. You stand straight, and you usually
command attention when you walk into a room, whether you say anything or not. This
happens because of the strength within. […] if you have a strong masculine side you are
self-confident, and don't feel it is necessary to show off.
The masculine side is full of things that you have to be strong and self-confident in order to
do. These include being able to claim your basic rights, such as the right to feel free to
operate independently of others, and the right to belong or fit into society in any way you
please. Claiming your rights also includes being able to stand up to people who try to take
away your rights, either by force or intimidation, or by manipulation, or by trying to hinder
you in choosing your own direction in life. The masculine side also includes the ability to
take risks when appropriate, to be decisive when necessary, and to focus or concentrate in
order to get something done. In addition, it includes being able to figure out how to
accomplish things so you can get more of what you want out of life. Part of this is being
able to figure out how to operate your life in a responsible manner, how to reason without
distorting reality and without fooling yourself, and how to accurately weigh probabilities so
that you know the most likely outcome to expect in situations you come across.
Extract from http://www.lovesedona.com/02.htm.
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Figure 10: Female Identity Priming text
This picture reports the screenshot of the text subjects in the female identity prime treatment were asked to read. It is an
excerpt from a blog entry on the internet. After reading the excerpt, subjects were asked to recall a situation when the
Please
thethe
following
text
carefully.
In the
screens
you will
asked three
behaved
in read
line with
“feminine short
side” as
presented
in the text,
andnext
describe
the situation,
theirbe
thoughts
and feelings in
easy questions based on the text.
detail in a short essay (5 to 10 sentences).
It should take you about 10 minutes to complete this section.
The Feminine Side
[…] the Feminine Side is based, […] on a value that you place on others. It could be thought
of as a sort of other esteem. The value you place on others affects your whole being. If you
have a strong feminine side and place a high value on others, you are often giving and
unselfish. You usually know what is good for people, and you tend to operate in ways that
help others get what they want out of life. You happily let people operate their own life
without interference from you, but when asked, you are also willing to help by supporting,
cooperating, and giving advice. People feel comfortable with you because you
give them who you are without pushing yourself on others.
If you have a strong feminine side, people also feel comfortable being around you because
there is no selfishness for them to detect. [… ]
If you have a strong feminine side, you often behave in ways that are considered feminine in
nature. You do things you have to be giving and unselfish in order to do. These include
recognizing the basic right of all people to use their own will to operate their own life, for
example, by allowing them freedom to operate independently, freedom to fit in where and
how they want, and freedom to choose what things to confront or face up to in life. Allowing
people their basic rights also includes allowing them to control their own life without
interference from you, to choose their own obligations in life without being manipulated by
you, and to choose their own path or direction in life without hindrance from you. The
feminine side also includes having enthusiasm and zest for life, and recognizing what things
are worth getting enthusiastic about. And it includes having the persistence and tenacity to
stay with things to the end, while still knowing when to give up on something if your energy
is better used elsewhere.
In addition, the feminine side also includes being kind, compassionate, patient, responsive
to the needs of others, and it includes knowing how much energy you can put into each of
these without hurting yourself by draining your own energy.
Extract from http://www.lovesedona.com/02.htm.
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47
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Figure 11: Control Condition text
This picture reports the screenshot of the text subjects in the control condition were asked to read. It is an excerpt from a
blog entry on the internet. After reading the excerpt, subjects were asked to recall a situation when the behaved in line
with
the “ayurveda
principles”
as presented
in the
text, and describe
the situation,
their
thoughts
andasked four
feelings in detail in
Please
read the
following
short text
carefully.
In the next
screens
you
will be
a short essay (5 to 10 sentences).
easy questions based on the text.
It should take you about 10 minutes to complete this section.
Dincharya [Daily Routine] In Sanskrit, the word 'dincharya' means daily routine. According to Ayurveda, one should
follow the dincharya in order to lead a healthy and disease-free life. Everyday, two cycles of
change pass through the human body, each bringing a Vata, Pitta, or Kapha predominance.
Based on the cycles of vata, pitta and kapha, our daily routine should be divided into
morning, noon, evening/twilight, dinner and bedtime. In the Ayurvedic texts, it is written that
a person should wake up two hours prior to the sunrise, if he/she is not suffering from any
diseases such as fever or diarrhea. Very young, very old and sick people are some of the
exceptions.
According to dincharya, the day should be kick-started by eliminating the colon and the
bladder, followed by a through cleaning of the senses - ears, eyes, mouth etc. This should be
followed by an oil self massage. Exercise in the morning, just after the massage, helps
rejuvenate the body and soul. After bathing, one should head towards the dining table for
breakfast. The day follows by activities like studying, working or traveling. During the lunch,
one should consume nutritious meal […]. Dinner should consist of a light meal. Before going
to bed, one should sit back and relax. By following the dincharya of Ayurveda, one can
ensure a healthy life.
Though it is difficult to follow a stringent dinacharya in this fast moving life, it is highly
recommended by Ayurvedic physicians, because a number of health benefits are associated
with it. The dinacharya makes one to lead a healthy and disciplined life. According to the
latest studies in the field of medical science, people who stick to the daily routine are more
fit than those, who do not have a particular time to perform their everyday activities. It is
said that dinacharya reduces the stress level to a great extent. In addition to this, the
person's body is purified and detoxified. Therefore, barring a few exceptions like sickness,
very old and young age, Ayurvedic dinacharya is recommended for everyone.
Extract from http://ayurveda.iloveindia.com/dincharya/index.html
I am now ready to answer four questions based on the text above.
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48
This page reports sample essays written by subjects in the four conditions
across the experiments presented in the paper: priming relax, male identity,
female identity or power over other individuals (any typos as in the subjects’
entries.
Control condition:
I was at our lake house. The kids were reading and taking care of themselves. The lake
was calm and the bugs were scarce. All you could hear was the occasional boat, which to
me is relaxing. The sound of the tiny waves lapping at the shore in the twilight was music
to my ears. I had a book, but spent most time looking out at the mountains and clear water.
Priming male identity:
It was a typical weekend after work. [...] I was with a group of friends in a bar and in
walked the hottest group of girls that night. Every guy immediately turned their heads
to look, including me, and the girls knew it and loved it. However, everyone quickly
averted their glances thinking they were out of league. However, I remained calm and
kept my steady gaze until the one I was eyeing saw me in her sweep of the room. She
looked back, cocked her head slightly, and I saw a faint smile forming at the edge of her
lips. After about half an hour, I confidently walked up to the group of girls, straight
to the girl I picked out, and asked her to join me at the bar for a drink. The rest is history.
Priming female identity:
My best friend was recently laid off from his job and his lease to his apartment was set
to expire. Although I live with 3 other roomates, I allowed my friend to stay in my
apartment for 4 months until he got back on his feet with a full-time job. I felt a sense of
responsibility to be compassionate and help him in this situation.
Priming Power over other individuals:
When I became the executive director of my actual company, my attributions were many.
I was almost the most powerful person. I could do whatever I wanted, but I did only what
was fair for every employee. Everyone got what they deserved and they seemed satisfied
49
with me. I felt very capable and skillful. I did exactly what was right.
50
Appendix B - Additional Results
51
Table 5: Experiment 1 - Using Post-manipulation Risk Tolerance only (No
Within-subject Design)
The left panel shows results for the level of risk tolerance after the experimental
manipulations in the male subsample in Experiment 1. The right panel shows results for
the level of risk tolerance after the experimental manipulations for the all subjects in
Experiment 1. Statistical significance is marked as follows: *** 1%, ** 5%, * 10%.
(1)
Male Prime
Female Prime
(2)
Men only
(3)
(4)
(6)
0.803
0.821
0.918
-0.134
-0.138
-0.008
(0.488)
(0.501)
(0.526)*
(0.474)
(0.474)
(0.462)
0.937
0.907
0.934
-0.337
-0.407
-0.371
(0.431)**
(0.491)*
(0.500)*
(0.399)
(0.396)
(0.400)
Man*M. Prime
Man*F. Prime
Man
0.894
0.866
0.733
(0.681)
(0.689)
(0.689)
1.034
1.039
1.040
(0.596)*
(0.587)*
(0.594)*
0.087
(0.483)
Mean omitted
Median omitted
Session f.e.
Age group f.e.
Education f.e.
Observations
R2
(5)
Full Sample
X
148
0.08
-2.01
-1.67
X
X
148
0.08
X
X
X
148
0.09
52
X
320
0.06
0.080
(0.482)
-2.25
-2.5
X
X
320
0.07
0.074
(0.484)
X
X
X
320
0.11
Experiment 2
20 40 60 80 100 120
Experiment 1
61+
18-22 23-28 29-35 36-45 46-60
61+
Experiment 4
0
20 40 60 80 100 120
Experiment 3
0
20 40 60 80 100 120
18-22 23-28 29-35 36-45 46-60
0
0
20 40 60 80 100 120
Figure 12: Age Distribution of Subjects across the Experiments
18-22 23-28 29-35 36-45 46-60
61+
18-22 23-28 29-35 36-45 46-60
53
61+
Figure 13: Identity and Overconfidence cues in financial products ads
The set of pictures below are examples of financial products ads across countries and for different types of products with male identity
and overconfidence cues.
54
Figure 14: Risk Tolerance Change and Beliefs in the Female Subsamples
Panel A depicts estimated densities for the change in risk tolerance measured
at the subject level across experimental conditions in Experiment 1 (Identity
prime) for female subjects. The change in risk tolerance is measured as follows:
∆RiskT olerancei = RiskT olerancepost,i − RiskT olerancepre,i , where RiskT olerancepre,i
and RiskT olerancepost,i are elicited via lottery choices a la Holt and Laury (2002) before
and after exposure to the experimental conditions, respectively. Panel B depicts the
average measure of “better-than-average” beliefs constructed in Experiment 4 for the
female subsample across experimental conditions.
Estimated Density Change in Risk Tolerance - Women
0
.1
.2
.3
.4
A. Distribution of ∆Risk Tolerance (female subsample)
-6
-4
-2
0
2
4
x
Control
Female Prime
Male Prime
-.6
-.4
-.2
0
.2
.4
.6
B. Better-than-Average Beliefs (female subsample)
Control
55
Male and Power Prime
Table 6: Investment Decisions by the Female Subsamples
Panel A shows results for the investment decisions made by women in Experiment 3. The
left panel reports marginal effects computed from the following probit model:
P r(Invest = 1)iae = Φ(α + β × M aleP rime + γ × Overconf idenceP rime + ηa + ηe )
where Invest is 1 if subject i in age group a and education level group e invests
any positive amount, zero otherwise. Φ(.) is the normal cdf. M aleP rime and
Overconf idenceP rime equal one for subjects exposed to the male identity or power
prime; ηa and ηe are fixed effects for subjects’ age and education level groups for
female subjects. The right panel reports results for a tobit model whose dependent
variable is the amount of money a subject invests in each opportunity, which is
censored at $0 and $100. Standard errors are clustered at the subject level, and computed
with the delta method. Statistical significance is marked as follows: *** 1%, ** 5%, * 10%.
A. Experiment 3: Investment Decisions (female subsample)
Female
subsample
(1)
(2)
(3)
(4)
(5)
Probability of Investing
Male Prime
0.113
Overconfidence
Prime
0.137
0.135
(7)
(8)
Amount Invested
0.136
3.554
5.638
5.961
5.763
(0.076)
(0.074)*
(0.074)*
(0.073)*
(5.667)
(5.321)
(5.384)
(5.359)
-0.026
-0.017
-0.004
-0.003
-7.751
-6.813
-6.616
-6.778
11.94
14.87
-13.54
2.803
(4.48)***
(5.64)***
(15.82)
(15.32)
X
X
X
X
X
X
387
0.01
384
0.01
384
0.03
(0.073)
(0.073)
(0.074)
(0.073)
Constant
Age group f.e.
Education f.e.
Success prob. f.e
Observations
(pseudo-)R2
(6)
387
0.01
X
X
X
X
X
X
387
0.02
384
0.02
384
0.09
(6.060)
387
0.01
(5.851)
(5.864)
(5.848)
B. Experiment 4: Investment Decisions (female subsample)
Female
Subsample
(1)
(2)
(3)
(4)
Probability of Investing
Male Prime
0.005
(0.055)
0.001
(0.053)
(6)
Amount Invested
0.004
(0.052)
Constant
0.54
(5.37)
0.50
(5.22)
0.84
(2.18)
X
X
X
230
0.01
228
0.01
38.26
(3.92)***
Age group f.e.
Education f.e.
Observations
(pseudo-)R2
(5)
230
0.00
X
X
X
230
0.03
228
0.06
56
230
0.00
Table 7: Experiment 1 - Contribution of right tail and average to results
Panel A shows results for the change in risk tolerance in the male subsample in Experiment
1. Panel B shows results for the change in risk tolerance for the full sample of subjects
in Experiment 1. The outcome variable (∆RiskT olerance) is winsorized at the 5-95
percentiles in the left Panels, at the 10-90 percentiles in the right panels. Hubert-White
s.e. are reported in brackets. Statistical significance is marked as follows: ** 5%, * 10%.
A. Experiment 1: Winsorizing outliers (male subsample)
Male Only
(1)
M. Prime
F. Prime
(4)
(5)
(6)
Winsorize 10-90 perc.
0.319
0.336
0.335
0.293
0.306
0.304
(0.197)
(0.195)*
(0.195)*
(0.165)*
(0.161)*
(0.163)*
0.481
0.447
0.450
0.419
0.377
0.373
(0.208)**
(0.215)**
(0.221)**
(0.189)**
(0.194)*
(0.199)*
Mean omitted
Median omitted
Session f.e.
Age group f.e.
Education f.e.
Observations
R2
(2)
(3)
Winsorize 5-95 perc.
-0.170
0
X
X
X
148
0.03
148
0.09
X
X
X
X
148
0.09
148
0.04
-0.133
0
X
X
148
0.09
X
X
X
148
0.09
B. Experiment 4: Winsorizing outliers (full sample)
Full Sample
M. Prime
F. Prime
Male*M. Prime
Male*F. Prime
Male
(1)
(2)
(3)
Winsorize 5-95 perc.
Observations
R2
(5)
(6)
Winsorize 10-90 perc
-0.265
-0.269
-0.237
-0.189
-0.188
-0.169
(0.194)
(0.196)
(0.196)
(0.154)
(0.155)
(0.156)
-0.117
-0.162
-0.153
-0.088
-0.127
-0.122
(0.202)
(0.209)
(0.209)
(0.162)
(0.167)
(0.167)
0.590
0.588
0.566
0.489
0.484
0.469
(0.276)**
(0.277)**
(0.278)**
(0.225)**
(0.225)**
(0.227)**
0.628
0.650
0.643
0.543
0.557
0.553
(0.276)**
(0.280)**
(0.281)**
(0.235)**
(0.239)**
(0.241)**
-0.253
-0.262
-0.271
-0.244
-0.248
-0.252
(0.179)
Mean omitted
Median omitted
Session f.e.
Age group f.e.
Education f.e.
(4)
X
320
0.03
(0.179)
0.050
0
X
X
320
0.04
(0.182)
(0.151)
X
X
X
X
320
0.06
320
0.03
57
(0.151)
0.072
0
X
X
320
0.05
(0.154)
X
X
X
320
0.10
Table 8: Just Post It - Evidence vs. Fabricated Data
Panel A refers to the full sample, Panel B to the male subsample. For each cell the
sample mean is reported, the standard deviation is reported in round parentheses, and
the number of subjects in curly parentheses. Panel C shows results for running the test
of Simonsohn (2013) for each experiment and sample. The test consist of: (i) estimating
100,000 samples randomly drawn from a normal distribution with mean equal to the one
in the condition, and standard deviation equal to the average across conditions in each
experiment (samples are drawn from a binomial distribution with probability equal to
the mean of the extensive margins in Experiment 3 and Experiment 4); (ii) Computing
the standard deviation of the standard deviations in each drawn sample; (iii) reporting
the ratio of times that the simulated standard deviation of standard deviations are lower
than the actual one in the data.
A. Summary Statistics (full samples)
Experiment 1
Experiment 2
Experiment 3
(extensive)
Experiment 3
(intensive)
Experiment 4
(extensive)
Experiment 4
(intensive)
Experiment 5
Male Identity
Prime
Female Identity
Prime
-0.01
(1.74) {125}
0.05
(1.07) {66}
Overconfidence
Prime
-0.21
(1.43) {107}
0.680
(0.468) {231}
23.34
(28.55) {231}
0.747
(0.436) {237}
27.95
(29.81) {237}
0.904
(0.296) {208}
49.93
(32.17) {208}
0.079
(1.711) {214}
Success
Prime
-0.38
(1.37) {109}
Control
-0.06
(1.22) {129}
-0.51
(1.62) {107}
0.624
(0.485) {234}
23.41
(29.40) {234}
0.852
(0.356) {216}
42.04
(30.07) {216}
-0.090
(1.743) {110}
B. Summary Statistics (male subsamples)
Experiment 1
Male Identity
Prime
Female Identity
Prime
0.30
(1.54) {61}
0.22
(0.81) {32}
Experiment 2
Experiment 3
(extensive)
Experiment 3
(intensive)
Experiment 4
(extensive)
Experiment 4
(intensive)
Experiment 5
0.781
(0.416) {114}
34.92
(35.42) {114}
0.949
(0.221) {98}
60.88
(35.03) {98}
0.405
(1.407) {84}
Overconfidence
Prime
0.03
(1.32) {54}
0.823
(0.384) {96}
35.47
(33.97) {96}
Success
Prime
-0.47
(1.39) {59}
Control
-0.21
(1.09) {55}
-0.49
(1.40) {56}
0.648
(0.480) {105}
26.99
(33.41) {105}
0.844
(0.365) {96}
44.96
(32.75) {96}
-0.024
(1.968) {41}
C. P-values for random draw of samples across conditions
Full
Sample
Male
Subsample
Experiment 1
0.99991
0.99982
Experiment 2
0.82831
0.10523
Experiment 3 (ext. margin)
0.45736
0.41635
Experiment 3 (int margin)
0.20449
0.17365
0.50212
0.47808
Experiment 4 (int. margin)
0.67175
0.49040
Experiment 5
0.17617
0.98543
Experiment 4 (ext. margin)
58
Appendix C - Economic Interpretation of Priming and Threatening
Identity Stereotypes
59
In this section, I sketch an economic interpretation of the effects of priming identity on
men based on Bordalo, Coffman, Gennaioli, and Shleifer (2015), which helps to organize
the results in the paper in a coherent setting.
Table 1 in the paper reports the most common types a pool of 200 anonymous respondents
associate with four identities: male, powerful, successful, and female individuals. The lists
for male and female individuals are similar to those in the pioneering work of Williams and
Bennett (1975). I interpret the enlisted characteristics as the most likely types associated
with each identity. Because the most likely types for the male and female identities do
not overlap, we can interpret them as the most representative types of each gender. In
Bordalo et al. (2015), a type is representative of a group if a Bayesian decision maker
that observes the type thinks it was more likely drawn from the group than from its
complement.
I call M the set of representative types for men, and −M the set of representative types
for women, where −M = Ω\M , and Ω, the union of male and female representative types,
covers the universe of gender-related representative types, where gender-related types can
only refer to men or women. Note that the set of types in M needs not include the
opposite of the types in −M .
I follow Bordalo et al. (2015), and define the stereotype for a group as a truncated
probability distribution over types, where the most representative types are assigned a
positive probability weight, and the decision maker neglects all other possible types. Thus,
the stereotype is a set of beliefs decision makers hold about the probability distribution
of types associated with the assessed group.
The novelty of this paper is interpreting the act of priming as an exogenous shock to the
probability distribution over the types the subjects associate with their identity. Before
the act of priming, subjects assess their type based on all the social groups to which they
belong, such as gender, social class, or religious affiliation. Subjects belong to several and
possibly different social groups, as implied by the concept of social identity complexity first
described by Roccas and Brewer (2002). Hence, absent any external manipulation to the
self-perception of identity stereotypes, we should observe that subjects have heterogeneous
risk attitudes and beliefs. But after the gender identity prime, subjects’ beliefs about their
own types are determined by the most representative types of their gender identity, and
the non-gender-related types for the other social groups to which the subject belongs are
neglected.
Importantly, I also interpret the act of threatening subjects’ gender identity as an
exogenous shock to the probability distribution over the types the subjects associate
with their own gender identity. This interpretation follows from the fact that the threats
in the experiments consist of priming the other gender’s most representative types, hence
for the case of men, −M . Making −M salient to men cannot have them relate to the
60
types in −M , because those types are not part of men’s gender identity. Instead, priming
−M makes it salient to subjects that they should assess their type based on the social
group ”gender,” which, for the case of men, includes the representative types in M , and
not in −M . This interpretation of gender identity threats is crucial to rationalize the
same directional results of priming and threatening male identity on men’s willingness to
engage in risky behaviors, which is documented in the social psychology literature (Maas
et al., 2003; Willer et al., 2013), and is documented in this paper for the case of risk
attitudes and investment decisions.
Akerlof and Kranton (2000) argue that stereotypes prescribe normative behaviors to
individuals. Hence, a link exists between gender-related types and the behaviors to which
individuals conform. Suppose that any possible types associated with gender identities
could be classified in a type associated with a ”winning behavior” or with a ”losing
behavior.” Subjects will then form beliefs about the likelihood of winning as follows:
P r(win) = p = f (πt )
P
where πt = πt(n) / πt(n) for n ∈ {1, ..., N } and N are all possible types associated with
the subject’s identity, across all the social groups to which he belongs. If a man faces
a lottery with two possible outcomes, the definition above could be interpreted as his
subjective probability of winning the higher payoff when entering the lottery. If the
types associated with winning and losing have a similar weight in a man’s identity, this
subjective probability would coincide with the objective probability of winning the higher
payoff when entering the lottery. Based on the interpretation of priming described above,
if a man is primed with male or female identity, he will assess his probability of winning
as follows:
P r(win|M ) = p̂ = f (π̂t )
where π̂t is now the male identity stereotype, that is, the probability distribution of
P
the most representative types that belong to the male identity: π̂t = π̂t(m) / π̂t(m) for
m ∈ {1, ..., M } ⊂ {1, ..., N }, and 0 otherwise. The set {1, ..., M } only includes the
most representative types associated with the male identity group. The two subjective
probabilities differ in general: P r(winner|M ) will be higher or lower than P r(winner),
based on whether the most representative types for the male identity are associated with
winning or losing. I propose the following hypothesis:
Hypothesis 1. If the set of most representative male types {1, ..., M } includes more
types associated with winning behaviors than with losing behaviors compared to the full
set of possible types {1, ..., N }, then p̂ > p.
If the subset of types {1, ..., M } is more likely to include types associated with winning
than with losing, primed men become overconfident about their performance even in a
pure game of chance, because they are more likely to believe they will experience high
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outcomes than what the objective probabilities for each state of the world suggest. This
distortion of beliefs due to the priming is the crucial and novel hypothesis to interpret
the results in this paper. I test directly for this hypothesis in Experiment 2, where I find
that men whose identity is primed believe they are more likely than a group of peers
to experience the winning outcome of a lottery, even if the objective probability of each
future state of the world is known.
Moreover, if men primed with their identity stereotypes become overconfident in a game of
chance, they will choose lotteries over certainty equivalents more often after being primed
than before. Intuitively, every time a subject decides whether to choose a lottery over a
certainty equivalent, she will compare the expected utility from entering the lottery with
the utility from the certain amount. Because p̂ > p, the subjective expected utility from
entering the lottery after the prime is higher than the expected utility implied by the
objective probabilities, as long as the subjects’ expected utility is strictly monotone:
Hypothesis 2. If {1, ..., M } includes more types associated with winning than {1, ..., N },
a primed man will require a higher certainty equivalent to give up the chance to take part
in the same lottery compared to a non-primed man.
I test Hypothesis 2 with a within-subject incentive-compatible design in Experiment 1,
where I find that men primed either with salience or threat of their identity are more
likely to choose lotteries over certainty equivalents after the manipulations compared to
before them.
Moreover, if priming male identity increases men’s subjective beliefs of experiencing higher
outcomes, primed men should be more likely to invest in risky opportunities whose
objective probabilities are known than non-primed men, because their expected utility
from investing is higher than the one implied by the objective probabilities:
Hypothesis 3. If {1, ..., M } includes more types associated with winning than {1, ..., N },
a primed man will be more likely to invest in a risky opportunity whose objective
probability of success is known than a non-primed man.
I test Hypothesis 3 in Experiment 4 for individual investment decisions, and in Experiment
5 for delegated investment decisions. In both cases, primed men invest more often and
more money than controls.
So far, the set of types associated with male identity, {1, ..., M }, was the same across men.
Bordalo, Gennaioli, and Shleifer (2014) show how the change in the representativeness of
types for each identity over time modifies the stereotypes associated with genders. I exploit
the fact that identity stereotypes have become less stark over the last decades (Goldin et
al., 2006; Doepke and Tertilt, 2009; Glaeser and Ma, 2013), to develop a hypothesis on
the predicted magnitude of the effects of priming across cohorts with different perceptions
of stereotypes:
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Hypothesis 4. If {1, ..., MOLD } includes more types associated with winning than
{1, ..., MY OU N G }, the effects of priming identity on the behavior of men should be larger
for older cohorts of men than younger cohorts of men.
In Figure 6, I show evidence in line with Hypothesis 4 for the risk attitudes and investment
decisions of men.
In the paper, I find no effect of priming or threatening gender identity on women. The
non-result for women can be consistent with the setup above if the most representative
types for female identity do not include more types associated with losing than with
winning. The types enlisted in Table 1, as well as the more comprehensive lists of Williams
and Bennett (1975), seem to include characteristics of both winners (smart, flirty, happy)
and losers (passive, emotional, kind). But it is surely impossible to categorize the types,
and there is no guidance on how to associate types to winning or losing behaviors in the
theoretical framework.
References
P. Bordalo, K. Coffman, N. Gennaioli, and A. Shleifer. Stereotypes. Working Paper, 2015.
S. Roccas and M. Brewer. Social identity complexity. Personality and Social Psychology
Review, 6(2):88–106, 2002.
J. Williams and S. Bennett. The definition of sex stereotypes via the adjective check list.
Sex Roles, 1(4):327–337, 1975.
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