Facts and Misconceptions about 2D:4D, Social and Risk Preferences

Abstract

We study how the ratio between the length of the second and fourth digit (2D:4D) correlates with choices in social and risk preferences elicitation tasks by building a large dataset from five experimental projects with more than 800 subjects. Our results confirm the recent literature that downplays the link between 2D:4D and many domains of economic interest, such as social and risk preferences. As for the former, we find that social preferences are significantly lower when 2D:4D is above the median value only for subjects with low cognitive ability. As for the latter, we find that a high 2D:4D is not correlated with the frequency of subjects' risky choices.

Full text

ORIGINAL RESEARCH
published: 13 February 2018
doi: 10.3389/fnbeh.2018.00022
Frontiers in Behavioral Neuroscience | www.frontiersin.org 1February 2018 | Volume 12 | Article 22
Edited by:
Levent Neyse,
Institut für Weltwirtschaft, Germany
Reviewed by:
Paul Smeets,
Maastricht University, Netherlands
Erik Bijleveld,
Radboud University Nijmegen,
Netherlands
*Correspondence:
Marcello Sartarelli
marcellosartarelli@gmail.com
Received: 31 August 2017
Accepted: 25 January 2018
Published: 13 February 2018
Citation:
Alonso J, Di Paolo R, Ponti G and
Sartarelli M (2018) Facts and
Misconceptions about 2D:4D, Social
and Risk Preferences.
Front. Behav. Neurosci. 12:22.
doi: 10.3389/fnbeh.2018.00022
Facts and Misconceptions about
2D:4D, Social and Risk Preferences
Judit Alonso 1, Roberto Di Paolo 1, Giovanni Ponti 1,2,3 and Marcello Sartarelli 1
*
1Departamento de Fundamentos de Análisis Económico, Universidad de Alicante, San Vicente del Raspeig/Sant Vicent del
Raspeig, Alicante, Spain, 2Department of Economics, The University of Chicago, Chicago, IL, United States, 3Dipartimento
di Economia e Finanza, Libera Università Internazionale degli Studi Sociali Guido Carli (LUISS), Rome, Italy
We study how the ratio between the length of the second and fourth digit (2D:4D)
correlates with choices in social and risk preferences elicitation tasks by building a
large dataset from five experimental projects with more than 800 subjects. Our results
confirm the recent literature that downplays the link between 2D:4D and many domains
of economic interest, such as social and risk preferences. As for the former, we find that
social preferences are significantly lower when 2D:4D is above the median value only
for subjects with low cognitive ability. As for the latter, we find that a high 2D:4D is not
correlated with the frequency of subjects’ risky choices.
Keywords: 2D:4D, cognitive reflection, gender, risk, social preferences
JEL Classification: C91, C92, D8
1. INTRODUCTION
Research both in the hard sciences (e.g., Neurology and Physiology) and in the social sciences
(e.g., Economics and Psychology) has increasingly focused on biological markers to improve
our understanding of the biological basis of social behavior. Earlier research had claimed that
prenatal exposure to sexual hormones has an effect on brain development that, in turn, influences
individuals’ decision making routines later in life (see for a survey Manning, 2002). Motivated by
this evidence, a growing number of experimental studies has tested the relationship between the
ratio between the second and fourth hand digit (2D:4D hereafter) -a marker which has been claimed
to be negatively related to prenatal exposure to testosterone- and behavior in a wide variety of
cognitive domains, including social and risk preferences.
Social preferences are a ubiquitous phenomenon in everyday life and have gained increasing
attention in the social sciences. While there is robust evidence that shows that females exhibit more
pronounced social concerns, only few studies have looked at their relationship with 2D:4D. Within
this small set, Millet and Dewitte (2006) find a negative relationship between 2D:4D and giving in
the dictator game. Using a variety of games, such as public good and dictator, Buser (2012) finds,
instead, a positive relationship with giving. In related studies using the ultimatum game, Brañas-
Garza et al. (2013) find that the relationship with giving follows an inverted U-shape while Van den
Bergh and Dewitte (2006) find a negative relationship with rejection rates.
The relationship between 2D:4D and risk-taking has been widely studied experimentally to
quantify the role played by innate traits in this type of decisions. Again, the evidence so far is mixed,
as some studies find a negative relationship with the frequency of risky choices (e.g., Garbarino
et al., 2011; Brañas-Garza et al., 2018) while others do not find any significant correlation (e.g.,
Apicella et al., 2008; Sapienza et al., 2009).
We contribute to this literature by assembling a meta-dataset consisting of five
experimental projects involving 879 subjects in total. With this large dataset collecting

Alonso et al. Some (Mis)facts about 2D:4D
evidence on behavioral tasks of a different nature, we first assess
the relationship between 2D:4D and inequity aversion (Fehr and
Schmidt, 1999), a proxy for social preferences that identifies the
role of “envy” (i.e., negative inequity aversion) in comparison
with “guilt” (i.e., positive inequity aversion). Second, we assess
the relationship between 2D:4D and risk attitudes, which were
elicited using Multiple Price Lists (Holt and Laury, 2002). Finally,
following some recent contributions (Brañas-Garza et al., 2015;
Cueva et al., 2016), we also assess the mediating role played by
cognitive ability in the relationship between 2D:4D and subjects’
decisions in both risk and distributional tasks.
We briefly summarize here our main results, that have been
obtained by defining right hand 2D:4D high if it is greater
than the gender-specific median value. When we look at social
preferences, we find that for subjects with high 2D:4D the
relationship with guilt is negative but not significant, whereas
the relationship with envy is only significant and negative for
subjects with low cognitive ability. If we, instead, use directly
2D:4D measures we find no significant association with social
preferences. When we look at risk preferences, we find that
the association between high 2D:4D and the frequency of risky
choices is negative but not significant, with similar results holding
if we use the raw 2D:4D index as a covariate. Overall, our
empirical findings cannot but confirm some recent literature
(discussed in section 2) which downplays the link between 2D:4D
and behavior in experimental domains of interests, such as social
and risk preferences.
The remainder of the paper is structured as follows. Section 2
reviews the related literature while section 3 describes the
layout of our meta-dataset. In section 4, we report correlations
between 2D:4D, gender and cognitive ability distilled from the
debriefing questionnaire. In section 5 we report our findings on
the relationship between 2D:4D and inequity aversion and in
section 6 we look at risk attitudes. Finally, section 7 discusses
our results and concludes, followed by an appendix collecting
additional statistical evidence.
2. LITERATURE REVIEW
The ratio between the length of the second (“index” finger) and
fourth (“ring” finger) digit, also called second-to-fourth digit
ratio (2D:4D), has been claimed to be a proxy for prenatal
exposure to testosterone, with a lower ratio indicating higher
exposure both for children and for adults (Manning et al.,
1998). Related studies find a positive correlation between sex
hormones at birth and 2D:4D measured at age 2 (Lutchmaya
et al., 2004; Ventura et al., 2013). More recently Hollier et al.
(2015) have challenged this view by providing evidence that
the relationship between a measure of exposure to testosterone
obtained using umbilical cord blood and 2D:4D measured at
age 19-22 is not significant1. However, this result may be due
by the fact that testosterone peaks between 12 and 18 weeks of
gestation and decreases thereafter (Xie et al., 2017). In addition,
in a replication study, (Hönekopp et al., 2007) find no systematic
evidence of a relationship between 2D:4D and circulating sex
1See Kaltwasser et al. (2017) for analogous findings.
hormones in adults. On the one hand, this result suggests
that estimating the relationship between 2D:4D and proxies for
decision-making without accounting for circulating testosterone
does not lead to omitted variable bias. On the other, it suggests
that additional research is awaited to obtain conclusive evidence
on the relationship between 2D:4D and testosterone subjects are
exposed to from gestation to adulthood.
Several studies have also shown that 2D:4D is a sexually
dimorphic measure with, on average, males having lower 2D:4D
than females (Putz et al., 2004). Moreover, earlier studies
have reported that 2D:4D varies not only by gender, but also
by ethnicity (Manning, 2002). It has also been found that
these differences emerge prenatally and are stable during the
developing years (Trivers et al., 2006). Voracek et al. (2007)
carry out a wide replication study of published results on the
relationship between 2D:4D and a variety of outcomes and,
overall, confirm the results.
The literature on the relationship between 2D:4D and social
preferences is scant and, again, results are mixed. Buser (2012)
finds that in public good, dictator, trust and ultimatum games
subjects with higher 2D:4D are more generous. By contrast,
Brañas-Garza and Kovárík (2013) argue that, since 2D:4D
measures in Buser (2012) are self-reported, his results may
be affected by measurement error and biased if the error is
correlated with one or more subjects’ characteristics.
As for the experimental evidence on the dictator game,
Millet and Dewitte (2006) find, instead, a negative relationship
between 2D:4D and giving. In related experimental studies using
ultimatum games, Van den Bergh and Dewitte (2006) find a
negative relationship between 2D:4D and rejection rates while
Brañas-Garza et al. (2013) find evidence of non-linearities in the
relationship, with subjects with either high or low 2D:4D giving
less. A non-linear relationship is also found by Sanchez-Pages and
Turiegano (2010) for the one-shot prisoner’s dilemma, with men
with intermediate 2D:4D being more likely to cooperate2.
As for the relationship between 2D:4D and risk-taking
behavior, results are mixed (see for a survey Apicella et al., 2015).
Dreber and Hoffman (2007); Garbarino et al. (2011); Brañas-
Garza et al. (2018) find a negative relationship for both genders,
with Brañas-Garza et al. (2018) also finding that the relationship
with a self-assessed and subjective measure of risk attitudes is
not significant. Similarly, Ronay and von Hippel (2010); Brañas-
Garza and Rustichini (2011); Stenstrom et al. (2011) find a
negative relationship although only for males, with Brañas-Garza
and Rustichini (2011) also finding that this result is mediated
by a negative relationship between 2D:4D and abstract reasoning
2Related studies manipulate experimentally hormones levels and estimate their
relationship with proxies for social preferences. Zak et al. (2009) increase the
level of circulating testosterone and find that it decreases giving in ultimatum
games. Kosfeld et al. (2005); Zak et al. (2007) increase, instead, levels of oxytocin, a
hormone that is hypothesized to increase empathy in humans, and find that it has
a positive impact on giving in ultimatum games but not in dictator games, which
they interpret as evidence of generosity. In addition, neuroeconomic evidence
shows that exposure to prenatal hormones (testosterone or estrogen) may affect
the activity in specific brain areas that are associated with individuals’ behavior
in several settings and with their personality (Fehr and Camerer, 2007; Lee, 2008;
Fehr and Krajbich, 2009).
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Alonso et al. Some (Mis)facts about 2D:4D
ability, an aspect of cognitive ability that was measured using the
Raven Progressive Matrices task. In contrast, a number of studies
find that the relationship is not significant at any conventional
level (Apicella et al., 2008; Sapienza et al., 2009; Schipper, 2012;
Aycinena et al., 2014; Drichoutis and Nayga, 2015)3.
3. DATA AND METHODS
We collect data from five experimental projects that were
carried out at the Laboratory of Theoretical and Experimental
Economics (LaTEx) of the Universidad de Alicante, from 2014
to 2017. The objects of these studies include, among others, risk
and social preferences, which will be discussed in section 5 and 6
respectively. All experimental protocols are also endowed with a
debriefing questionnaire from which we obtained information on
subjects’ gender and cognitive ability. Table 1 lists the projects in
our meta-dataset and summarizes their structure4.
3.1. Behavioral Evidence
The behavioral content of the five projects is as follows. Social
preferences are elicited in projects 3 and 4 (432 subjects) and risk
preferences are elicited in projects 1–5 (497 subjects).
3.1.1. Social Preferences
As for social preferences, the elicitation protocol consists in a
sequence of 24 distributional decisions, whose basic layout is
borrowed from Cabrales et al. (2010). Subjects are matched in
pairs and must choose one out of four options, as shown in
Figure 1. An option corresponds to a pair of monetary prizes,
one for each subject within the pair. At the beginning of each
round t=1, ..., 24, subjects are informed about the option set
Ct= {bk},k=1, ..., 4. Each option bk=(bk
1,bk
2) assigns a
monetary prize, bk
i, to player i=1, 2, with bk
1≥bk
2for all
k. In other words, player 1 (player 2) looks at the distributive
problem associated with the choice of a specific option kfrom the
viewpoint of the advantaged (disadvantaged) player, respectively.
Once choices are made, a “Random Dictator” protocol
(Harrison and McDaniel, 2008) determines the payoff relevant
decision, that is, an i.i.d. draw fixes the identity of the
subject whose choice determines the monetary rewards for that
pair and round. This design feature is particularly efficient
when estimating inequity aversion in that, for roughly half
of the observations we can identify separately, within-subject,
3In a non-experimental setting Coates et al. (2009) find a negative relationship
between 2D:4D, profitability and tenure on the job for a sample of 49 financial
traders in the City of London. In a related although different experimental
setting that involves strategic interactions among subjects, Pearson and Schipper
(2012) find no significant association between 2D:4D, bids in sealed bid first-price
auctions and subjects’ total payoffs. A positive relationship is also found between
2D:4D, risky choices and criminality using field data, although with a low number
of observations in Hanoch et al. (2012).
4Approval for the experiment was given by the LaTEx Ethics Committee.
Participants gave their consent to participate in social experiments when they
signed up in ORSEE (Greiner, 2004), the online recruitment tool used at LaTEx.
When, before the experiment started, instructions about its content were read
aloud to all participants, they were informed that they could leave the experiment
at any stage. Separate approvals were obtained for each of the five experimental
studies used in the paper.
individuals’ attitudes toward envy (i.e., social preferences from
a disadvantageous position) and guilt (i.e., social preferences
from an advantageous position), respectively. After subjects have
selected their favorite options, all payoff relevant information is
revealed, and round payoffs are distributed.
3.1.2. Risk Preferences
Risk preferences have been elicited with a Multiple Price List
(MPL, Holt and Laury, 2002) protocol in all projects, for a total
of 497 subjects. In projects 2–5 our MPL protocol consists of a
sequence of 21 binary choices. As Figure 2 shows, “Option A”
corresponds to a sure payment whose value increases along the
sequence from 0 to 1000 pesetas in steps of 50 while “Option B”
is constant along the sequence and corresponds to a 50/50 chance
to win 1,000 pesetas. In project 1, instead, the list consists of 16
binary choices: “Option A” is increasing from 0 to 15 euros in
steps of 1 while “Option B” is a fixed lottery over three prizes
drawn from Hey and Orme (1994). Subjects are asked to elicit
their certain equivalent for 50 such lotteries. In both protocols
one of the binary choices is selected randomly for payment at the
end of the experiment5.
3.2. Individual Characteristics
In all studies, we scanned both hands and we measured 2D:4D
following the protocol set up by Neyse and Brañas-Garza
(2014). By using this procedure, we avoid measurement errors
usually associated with self-reported statements (Brañas-Garza
and Kovárík, 2013). The 2D:4D measure reported in what follows
is a dummy equal to 1 for subjects with a right hand 2D:4D
above the gender-specific median value, high 2D:4D hereafter,
and equal to 0 otherwise. This choice is based on the non-
linear relationship between 2D:4D and behavioral outcomes that
is reported in Brañas-Garza et al. (2013) among others. Gender
difference in 2D:4D, with men exhibiting a lower 2D:4D as
shown in Figure 3, have been taken into account by defining
our binary measure of high or low 2D:4D by computing median
values separately by gender. An additional advantage of using
a dummy to discriminate between high and low 2D:4D rather
than 2D:4D, that takes values in a very small interval around 1,
is that it tends to simplify the interpretation of coefficients
of interactions between the high 2D:4D dummy and other
covariates in regressions6.
The Cognitive Reflection Test (CRT hereafter, Frederick,
2005) was administered in our debriefing questionnaire. It is a
simple test of a quantitative nature especially designed to elicit
the “predominant cognitive system at work” in respondents’
reasoning:
CRT1. A bat and a ball cost 1.10 dollars. The bat costs 1.00
dollars more than the ball. How much does the ball cost?
(Correct answer: 5 cents).
5The interested reader in the estimation of risk preferences in a setting with several
identical rounds, in which subjects may learn over rounds, can refer to Albarran
et al. (2017).
6In section 5 we discuss the advantages and disadvantages of using the high 2D:4D
dummy rather than 2D:4D itself. For the sake of robustness, we also report results
of our analysis with 2D:4D in Appendix A (Supplementary Material).
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Alonso et al. Some (Mis)facts about 2D:4D
TABLE 1 | Summary of experimental projects in the meta-dataset.
Project Reference N Topic Social preferences Risk preferences 2D:4D
1Albarran et al., 2017 279 Risk and uncertainty No Yes (89) Yes
2Cueva et al., 2016 96 Behavioral finance No Yes Yes
3Ponti et al., 2014 288 Entrepreneurship Yes Yes (96) Yes
4Ponti et al., 2017 144 Agency Yes Yes Yes
5Zhukova, 2017 72 Investment No Yes Yes
879 432 497 879
FIGURE 1 | User interface for distributional decisions in projects 3 and 4.
CRT2. If it takes 5 machines 5 min to make 5 widgets, how
long would it take 100 machines to make 100 widgets?
(Correct answer: 5 min).
CRT3. In a lake, there is a patch of lily pads. Every day, the patch
doubles in size. If it takes 48 days for the patch to cover
the entire lake, how long would it take for the patch to
cover half of the lake? (Correct answer: 47 days).
The CRT provides not only a measure of cognitive ability, but also
of impulsiveness and, possibly, other individuals’ unobservable
characteristics. In this test, the “impulsive” answer (10, 100, and
24, respectively) is shown to be the modal answer (Frederick,
2005). These answers, although incorrect, may have been selected
by those subjects who do not think carefully enough. Following
Cueva et al. (2016), we partition individuals into three groups.
Impulsive subjects answer the erroneous intuitive value at least
in two questions, reflective ones answer correctly at least two
questions, and others are the residual group.
4. RESULTS I: DESCRIPTIVE STATISTICS
In this section we report descriptive statistics of 2D:4D and
estimates of its correlation with the CRT score and with CRT
categories dummies, our proxies for cognitive ability by way of
pairwise correlations.
Figure 3 reports the distribution of 2D:4D in our meta-dataset
for the full sample and separately for subsamples by gender.
The distribution tends to be symmetric and the median value
is slightly smaller than one for the full sample as well as for
subsamples by gender. In addition, Figure 3 shows that 2D:4D
tends to be smaller for males, in line with evidence that 2D:4D is
sexually dymorphic in related studies.
Table 2 shows the correlations between 2D:4D, gender and
proxies of cognitive ability. In addition, it report correlations
using as a measure of prenatal exposure to testosterone a dummy
equal to 1 if 2D:4D is greater than the gender-specific median
and, also, a dummy equal to 1 if 2D:4D is either in the top or
in the bottom tercile of the 2D:4D distribution by gender. The
correlation between 2D:4D and the female dummy is positive and
highly significant for both hands. 2D:4D is, instead, negatively
and highly significantly correlated with the CRT reflective group
dummy for the left hand when using the top-bottom tercile
dummy. In addition, Table 2 shows that correlations between
2D:4D and the frequency of risky choices, our proxy for risk
attitudes, are negative and, hence, qualitatively in line with results
in related studies. However, estimates are not significant, even
when using binary measures of prenatal exposure to testosterone.
Since our proxies for social preferences are estimated parameters
of Fehr and Schmidt (1999) model, the estimation procedure
and their relationship with prenatal exposure to testosterone are
reported in section 57, 8.
5. RESULTS II: SOCIAL PREFERENCES
This section frames Dictators’ behavior in projects 3 and 4 within
the realm of Fehr and Schmidt (1999), one of the most popular
models of social preferences. According to it, the Dictator’s
utility associated to option k,u(k), does not only depend on
7The interested reader can find additional statistical evidence on the relationship
between 2D:4D and personality traits in Alonso et al. (2017), the working paper
version of this manuscript.
8Out of our 879 subjects CRT reflective, with 2 or more correct answers are 149
(16.7%), CRT impulsive, with at least one incorrect and impulsive answers, are 531
(60.4%) and the residual group contains 199 (22.6%).
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Alonso et al. Some (Mis)facts about 2D:4D
FIGURE 2 | User interface for the multiple price list in projects 2–5.
FIGURE 3 | 2D:4D histograms.
the Dictator’s own monetary payoff, xk
D, but also on that of the
Recipient, xk
R, as follows:
u(k)=xk
D−αmax[xk
R−xk
D, 0] −βmax[xk
D−xk
R, 0], (1)
where the values of αand βdetermine the Dictator’s envy (i.e.,
aversion to inequality when receiving less than the Recipient) and
guilt (i.e., aversion to inequality when receiving more than the
Recipient), respectively.
In what follows we shall estimate by maximum likelihood, for
each participant, the two coefficients of Equation (1) by way of a
standard multinomial logit model.
Figure 4 reports the estimated coefficients of equation (1) for
each subject participating in the experiment, disaggregated by
gender and by whether the right hand 2D:4D is above the gender-
specific median. By conditioning on the gender-specific median,
we control for the correlation between gender and 2D:4D that we
detected in Table 2. As Figure 4 shows, (i) estimates for males are
less dispersed with respect to the origin (corresponding to more
“selfish” preferences) and (ii) inequity aversion appears to be
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Alonso et al. Some (Mis)facts about 2D:4D
TABLE 2 | Correlations.
2D:4D in level Above median dummy Top-bottom tercile dummy
L2D:4D R2D:4D LH2D:4D HR2D:4D TBL2D:4D TBR2D:4D
L2D:4D 1.000 0.628*** 1.000 0.456*** 1.000 0.185***
Female 0.177*** 0.208*** −0.001 −0.001 −0.026 0.005
CRT −0.066** −0.049 −0.015 −0.002 −0.073** −0.023
CRT Impulsive 0.047 0.037 0.001 0.008 0.052 0.046
CRT Reflective −0.068** −0.068** −0.036 −0.029 −0.092*** −0.026
CRT Other 0.014 0.027 0.039 0.022 0.035 −0.032
Freq. of risky choices −0.043 −0.034 −0.004 −0.026 −0.011 0.029
**p<0.05,***p<0.01.
FIGURE 4 | Social preferences: individual estimates.
the modal distributional type, with specific reference to females
with low 2D:4D. The pooled estimates of αand βfor the full
sample (clustered at the subject level) are 0.288 (std. err. 0.001,
p=0.000) and 0.684 (std. err. 0.008, p=0.000), respectively9.
In order to quantify the relationship between 2D:4D and
inequity aversion, we follow a semi-parametric approach. First,
for both αand β, we partition our subject pool into three
subsets, depending on whether the corresponding individual-
level estimates are significantly smaller than zero (53 and 28
for αand βrespectively), not significantly different (130 and
160), or significantly greater (159 and 154). We then set up an
ordered probit regression by which the probability of falling in
each category is a function of high 2D:4D dummy, gender and
the CRT groups, with the reflective group as omitted category.
Our choice of using a dummy equal to 1 if 2D:4D is above
the gender-specific median, rather than 2D:4D itself, may be
subject to problems, such as a lower statistical power and a higher
probability of type I or II errors (Irwin and McClelland, 2003;
McClelland et al., 2015). However, by using non-linear models to
estimate the relationship between 2D:4D and social preferences
9These figures are consistent with previous results (take, e.g., Cabrales et al., 2010).
in this section, our estimates are unlikely to suffer from such
problems10.
Table 3 reports the estimated coefficients, with alternative sets
of covariates being used. We start estimating the relationship
between social preferences and the high 2D:4D dummy
(HR2D:4D) in model (1) without adding any additional control
and then, in model (2) and (3) we add female and CRT categories
dummies to assess if they play a mediating role. In model (4)
we use an interaction term between HR2D:4D and the female
dummy to account for the positive correlation between gender
and 2D:4D we observed in Table 2. Finally, in model (5) we
use an interaction term between the CRT categories dummies
and HR2D:4D. In addition, we report in Table 3 marginal effects
(MFX) of HR2D:4D, evaluated at the sample mean, while MFX
with respect to gender and CRT are shown in Appendix A
(Supplementary Material)11.
10We also set up a bivariate ordered probit estimation in which we allow error
terms in the equations of αand βto be jointly distributed. We find that the
covariance parameter is not significant.
11The number of observations shown at the bottom of Table 3 is lower than the
total number of subjects in projects 3 and 4 since we dropped those subjects for
whom maximum likelihood estimation of αand βdid not converge.
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Alonso et al. Some (Mis)facts about 2D:4D
TABLE 3 | Ordered probit regressions of social preferences individual estimates.
(1) (2) (3) (4) (5)
α β α β α β α β α β
HR2D:4D (HR) −0.064 −0.235* −0.066 −0.236* −0.068 −0.213* 0.018 −0.185 0.395 −0.562**
(0.123) (0.125) (0.124) (0.125) (0.124) (0.126) (0.168) (0.172) (0.249) (0.254)
Female (F) 0.376*** 0.097 0.326*** 0.062 0.423** 0.093 0.326** 0.069
(0.124) (0.125) (0.126) (0.127) (0.180) (0.181) (0.127) (0.127)
CRT Imp (CRTI) 0.359** 0.333** 0.355** 0.332** 0.604*** 0.111
(0.149) (0.151) (0.149) (0.151) (0.208) (0.214)
CRT Others (CRTO) 0.269 −0.120 0.269 −0.121 1.034*** −0.441
(0.215) (0.214) (0.215) (0.214) (0.351) (0.330)
HR ×F−0.189 −0.060
(0.249) (0.251)
HR ×CRTI −0.494* 0.440
(0.294) (0.300)
HR ×CRTO −1.298*** 0.579
(0.449) (0.433)
MFX P(α > 0) of HR −0.025 −0.026 −0.027 −0.029 −0.034
S.e. 0.049 0.049 0.049 0.049 0.050
MFX P(β > 0) of HR −0.093* −0.093* −0.084* −0.084* −0.082*
S.e. 0.049 0.049 0.050 0.050 0.050
N 342 342 342 342 342
Standard errors in parentheses. *p<0.10,**p<0.05,***p<0.01.
Table 3 shows that the relationship between HR2D:4D and
negative inequity aversion, i.e., envy, is negative and the same
holds for the relationship with positive inequity aversion, i.e.,
guilt. MFX, which are reported at the bottom of the table, show
that the relationship with envy or with guilt is not significant.
The table also shows that envy is higher for females while the
impulsive group (CRTI) is characterized by higher envy and
higher guilt than the reflective group, which is the excluded CRT
category. These estimates are significant as shown by MFX in
Appendix A (Supplementary Material). These results hold for the
five econometric specifications reported in Table 3, as shown by
MFX in Appendix A (Supplementary Material). Finally, when we
interact the HR2D:4D dummy with CRT categories to assess if
the influence of 2D:4D differs by subjects’ cognition, we find that
subjects with high 2D:4D and low cognitive ability, proxied by the
CRT impulsive dummy, do not exhibit significantly lower envy
than subjects with high 2D:4D in the CRT reflective group, while
the relationship is significant when considering the CRT residual
group dummy12, 13.
12Marginal effects are the same when we estimate them using, as an alternative
measure, 2D:4D in levels, except the estimated relationship with guilt.
13When we replicated our main experimental results by using a dummy equal
to 1 if 2D:4D is either in the bottom tercile of the distribution or in the top
one, as a sensitivity analysis, we obtained similar results, except a positive and
significant relationship between envy and the top-bottom tercile dummy, as shown
in Appendix A (Supplementary Material). Most of the results shown in this section
on the relationship between 2D:4D and social preferences tend to lose significance
6. RESULTS III: RISK ATTITUDES
In this section we study the relationship between 2D:4D and
proxies for risk preferences by using data on 497 subjects from
all projects. Risk preferences are elicited by way of a Multiple
Price List (MPL, Holt and Laury, 2002), in which individuals
have to choose between two alternatives: a list of increasing sure
payments and a lottery. Since the same protocol has been used in
projects 2 to 5 while the number of decisions, lottery prizes, the
experimental currency and their probability distribution differ
in project 1, we choose two proxies for risk preferences that we
believe are not affected by these differences.
Following Cueva et al. (2016), we define consistent those
individuals whose decisions satisfy two conditions: (i) start by
choosing the lottery option, as it stochastically dominates the sure
payment of 0, and (ii) switch only once at some point along the
price list to the sure payment and stick to it up to the end. We
can use data from all projects in our empirical analysis as none
of the differences between our MPL protocols has an impact on
the consistency definition. We also define a dummy equal to 1
if the proportion of risky choices made by a subject, i.e., the
ratio between the number of lotteries chosen in the list and the
total number of decisions, is greater than the median value. By
using the proportion rather than the number of risky choices, we
control for the difference in the design of the MPL in project 1.
when they are obtained with the high 2D:4D dummy defined using left hand
2D:4D, as shown in Appendix A (Supplementary Material).
Frontiers in Behavioral Neuroscience | www.frontiersin.org 7February 2018 | Volume 12 | Article 22

Alonso et al. Some (Mis)facts about 2D:4D
TABLE 4 | Subjects’ consistency in risky choices.
(1) (2) (3) (4) (5)
HR2D:4D 0.071* 0.072* 0.069* 0.047 −0.039
(0.037) (0.037) (0.036) (0.049) (0.055)
Female (F) −0.057 −0.025 −0.048 −0.023
(0.037) (0.038) (0.056) (0.038)
CRT Imp. (CRTI) −0.164*** −0.163*** −0.240***
(0.039) (0.039) (0.053)
CRT Other. (CRTO) −0.152*** −0.152*** −0.202***
(0.052) (0.052) (0.073)
HR2D:4D ×F 0.047
(0.073)
HR2D:4D ×CRTI 0.149**
(0.074)
HR2D:4D ×CRTO 0.095
(0.104)
Project 1 0.066 0.069 0.066 0.066 0.067
(0.044) (0.044) (0.044) (0.044) (0.045)
Constant 0.737*** 0.764*** 0.879*** 0.889*** 0.934***
(0.029) (0.032) (0.034) (0.038) (0.036)
MFX of F −0.025
S.e. 0.039
MFX of CRTI −0.166***
S.e. 0.039
MFX of CRTO −0.155***
S.e. 0.052
MFX of HR 0.069* 0.069*
S.e. 0.037 0.036
N 497 497 497 497 497
Robust standard errors in parentheses. *p<0.10,**p<0.05,***p<0.01.
Table 4 shows linear probability estimates of subjects’
consistency dummy. In addition to the high 2D:4D dummy,
our covariates include dummies for females and for the CRT
groups, as well as for the interaction between the high 2D:4D
dummy, female and CRT groups dummies. The top panel of
the table shows regression estimates while the bottom one
marginal effects (MFX) for those specifications in which we
used interaction terms, evaluated at the sample mean. Because
of the differences in the experimental protocol of project 1
with respect to the others, we also include a dummy equal to
1 for subjects in project 1 in order to absorb project-specific
effects.
When we look at estimates in Table 4, we find that the
probability of being consistent in their decisions is higher for
subjects with a high 2D:4D but the difference is not significant,
that there is no significant gender difference and that it is
significantly lower for subjects in the impulsive (CRTI) or
in the residual (CRTO) group than for the reflective group.
We see no changes when we include the interaction between
female and the high 2D:4D variable, suggesting that they do
not play any mediating role. When we add interaction terms
TABLE 5 | Consistent subjects’ relative frequency of risky choices above median.
(1) (2) (3) (4) (5)
HR2D:4D (HR) 0.005 0.006 0.006 −0.009 −0.041
(0.017) (0.017) (0.017) (0.020) (0.031)
Female (F) −0.058*** −0.056*** −0.073*** −0.056***
(0.017) (0.018) (0.025) (0.018)
CRT Imp. (CRTI) −0.007 −0.006 −0.036
(0.020) (0.020) (0.028)
CRT Other. (CRTO) 0.007 0.007 −0.030
(0.025) (0.025) (0.037)
HR2D:4D ×F 0.033
(0.034)
HR2D:4D ×CRTI 0.058
(0.038)
HR2D:4D ×CRTO 0.072
(0.050)
Project 1 −0.064*** −0.058*** −0.058*** −0.057*** −0.056***
(0.019) (0.019) (0.019) (0.019) (0.019)
Constant 0.453*** 0.478*** 0.480*** 0.487*** 0.503***
(0.012) (0.013) (0.018) (0.019) (0.023)
MFX of F −0.057***
S.e. 0.018
MFX of CRTI −0.007
S.e. 0.020
MFX of CRTO 0.006
S.e. 0.025
MFX of HR 0.007 0.008
S.e. 0.017 0.017
N 390 390 390 390 390
Robust standard errors in parentheses. ***p<0.01.
between the high 2D:4D dummy and the female dummy,
we find no significant gender differences in the relationship
between 2D:4D and consistency. When we add interactions
between high 2D:4D and cognitive ability dummies, the high
2D:4D dummy coefficient is no longer significant while the
coefficient of the interaction with the CRTI dummy is positive
and significant, suggesting that subjects in the CRT impulsive
group and with high 2D:4D are more consistent. When
looking at MFX, we find that consistency is significantly
lower for subjects with low cognitive ability, it is higher for
subjects with a high 2D:4D although the difference is not
significant14.
Table 5 shows linear probability estimates for consistent
subjects of a dummy equal to 1 if the proportion of
risky choices is greater than the median. We find no
14Estimates of the same regression except for using, rather than the high 2D:4D
dummy, 2D:4D itself or the top-bottom tercile dummy are reported in Appendix A
(Supplementary Material). We can see some differences depending on the measure
used: the probability of consistency is lower for females when we use 2D:4D and
also when we use the top-bottom tercile dummy, although the estimates are not
significant.
Frontiers in Behavioral Neuroscience | www.frontiersin.org 8February 2018 | Volume 12 | Article 22

Alonso et al. Some (Mis)facts about 2D:4D
significant relationship with the high 2D:4D dummy while
the probability is significantly lower for females. Results
are unchanged when using 2D:4D or the top-bottom
tercile dummy, as shown in Appendix A (Supplementary
Material)15,16,17.
7. DISCUSSION
When we look at social preferences, we contribute to the
literature that has almost entirely focused on giving as a proxy
for social preferences in a variety of experimental settings
(e.g., Buser, 2012; Brañas-Garza et al., 2013) by isolating
two aspects underlying the incentives to give, that is, envy
and guilt. Finding a negative and significant relationship
between 2D:4D and envy, i.e., less generous behavior by
subjects when they play in the disadvantaged role, only for
subjects with low cognitive ability and non-significant results
for guilt suggests that individual heterogeneity may play a
role in reconciling the mixed evidence on the relationship
between 2D:4D and giving in the literature. However, giving
and inequity aversion are not fully comparable proxies for
social preferences as they are used in different experimental
settings.
Although evidence of heterogeneity by ability in the
relationship between 2D:4D and subjects’ decision-making has
been documented in risky choices (Brañas-Garza and Rustichini,
2011), we are the first to do so in the realm of social preferences,
to the best of our knowledge. Finding that subjects with high
2D:4D and low cognitive ability exhibit significantly lower envy
than subjects with low 2D:4D and high cognitive ability shows
evidence of heterogeneity by ability in the relationship between
social preferences and 2D:4D. This result, by suggesting an
attenuating role of low cognitive ability and high 2D:4D on
inequity aversion contributes to related studies, for example
Cueva et al. (2016) and Ponti and Rodriguez-Lara (2015), who
find that the CRT impulsive category exhibits higher inequity
aversion.
When we look at risk attitudes, we find that the relationship
between 2D:4D and the probability that the number of
risky decisions is above the median, shows a mixed sign, it
is quantitatively small and never significant. These results
contribute to the related literature as the sign and significance
15Estimates of Table 5 obtained using the full sample are not reported as they are
in line with those obtained using only observations of consistent subjects.
16Results are qualitatively unchanged when using a logit model or when the
dummy equal to 1 if the frequency of risky choices is above the median, one of
the dependent variables, is defined using median values separately for projects 1
since the certain equivalent is different from projects 2 to 5. They are not reported
although they are available upon request. As a sensitivity analysis, we replicated
our main experimental results by using 2D:4D and a dummy equal to 1 if 2D:4D
is either in the bottom tercile of the distribution or in the top one and obtained
similar results and obtain similar results. This seems to suggest that, at least in our
case, estimates of regressions using the high 2D:4D dummy are not severely biased,
as suggested by Irwin and McClelland (2003); McClelland et al. (2015).
17Most of the results shown in this section on the relationship between 2D:4D and
risk attitudes do not hold when they are obtained with the high 2D:4D dummy
defined using left hand 2D:4D, as shown in Appendix A (Supplementary Material).
of the relationship is not conclusive. Overall, this may be
due to the fact that there is genuinely no relationship
between 2D:4D and risky decisions or, alternatively, to
differences across studies. The composition of the subject
pool may play a role if the willingness to participate in
an experiment correlates with subjects’ socio-economic
background and risk aversion. In addition, the type of
risk preferences elicitation task may also matter. For
example, studies that, including ours, use a task in which
subjects can choose a risk-free option tend to find a non-
significant association while studies in which subjects choose
between two lotteries tend to find a negative and significant
association.
After discussing our results relative to those in related
studies, we now critically assess them in the light of potential
methodological issues, that we believe all researchers wanting
to contribute to this interdisciplinary literature should bear
in mind. Studies in hard sciences of the relationship between
direct measures of prenatal exposure to testosterone and
2D:4D find mixed results, whose sign and significance
seem to depend critically on whether direct measures are
obtained in an early stage in utero or, instead, close to the
birth. Studies in social sciences on the relationship between
2D:4D and decision-making find mixed results that may
depend on the accuracy of 2D:4D measurement and, in
addition, to the experimental tasks used to elicit subjects’
preferences. Overall, this suggests both that additional
research is awaited to reconcile existing differences across
studies in the literature and that caution is used in the
interpretation of results before these differences are better
understood.
AUTHOR CONTRIBUTIONS
All authors listed have made a substantial, direct and intellectual
contribution to the work, and approved it for publication.
FUNDING
Instituto Valenciano de Investigaciones Económicas (IVIE).
ACKNOWLEDGMENTS
Financial support from the Spanish Ministerio de Economía
y Competitividad (ECO2013-43119, ECO2015-65820-P and
ECO2016-77200-P), Universidad de Alicante (GRE 13-04),
Generalitat Valenciana (Research Projects Grupos 3/086) and
Instituto Valenciano de Investigaciones Económicas (IVIE) is
gratefully acknowledged.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fnbeh.
2018.00022/full#supplementary-material
Frontiers in Behavioral Neuroscience | www.frontiersin.org 9February 2018 | Volume 12 | Article 22

Alonso et al. Some (Mis)facts about 2D:4D
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