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Are Female Auditors Still Women?
Analyzing the sex differences affecting audit quality
Kris Hardiesa, 1
, Diane Breescha, Jol Branson
a
a Faculty of Economic, Social and Political Sciences, and Solvay Business School, Free
University of Brussels, Pleinlaan 2, 1050 Brussels, Belgium
Submitted Manuscript
Abstract
Previous research has hinted a potential impact of auditor gender on audit quality. It appears
that, for example, men are less risk-averse than women. Female auditors may, therefore,
express more severe audit opinions than male auditors. This paper addresses a potential major
bias underlying the gender auditing research as it is not obvious that stereotypical believes
about men and women are true or that findings from literature about the general population can
be interpolated to the specific context of auditors.
Keywords: audit quality, gender, sex, gender differences, sex differences, psychological
research
Acknowledgments
We would like to thank Donald Wygal and Alison Woodward for comments on earlier versions
of this paper. This paper has also benefited from the comments of participants in research
seminars at the AAA Mid-Atlantic Regional Meeting, Long Branch (NJ), April 2009, and the
EAA Congress, Tampere, May 2009. Remaining errors are the responsibility of the authors.
1 Corresponding author. Tel.: +32 (0) 2 629 14 32; fax: +32 (0) 2 629 20 60.
E-mail address: [email protected] (K. Hardies).
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1
Are Female Auditors Still Women?
Analyzing the sex differences affecting audit quality
1. Introduction
As auditing is inherently a judgment and decision-making process, ultimately audit quality (i.e.
the probability that, within reasonable limits, the auditor discovers and reports a material
misstatement in the financial statements) is dependent upon the auditors judgment and
decision-making qualities (Knechel, 2000). The quality of an auditors judgment and decision-
making depends on its turn on certain auditor characteristics. The probability of discovering a
material misstatement depends, among others, on the auditors technical expertise, problem-
solving ability, risk profile, and experience. The probability of reporting a material
misstatement depends, among others, on its discovery, the auditors risk profile, and the
auditors independence from the client.
Based on psychological literature, very recently some researchers (e.g. Gold et al., 2009)
have posited that there are sex differences in personal auditor characteristics (e.g. risk-
aversion), leading to sex-differentiated audit judgments and decisions. Among other things, this
may cause the height of the audit fee to be associated with the sex of the audit engagement
partner (Ittonen and Peni, 2009).
Beliefs and speculations about whether there are fundamental differences between men and
women are pervasive, and are further augmented by the pile of scientific literature on sex
differences and by popular culture and media endorsing the existence of such differences. Our
cultural knowledge that is embedded in such stereotypes1 (as in men are from Mars and
women are from Venus) is our belief about how most people view the typical man or woman
(Ridgeway, 2009). Stereotypical beliefs relate personal characteristics to sex (so that men are
rather masculine and women rather feminine) suggesting that sex differences exist in the areas
of mathematical problem-solving, risk taking, and independence.
Math is believed to be a boy thing (Rowley et al., 2007) and, although meta-analyses
show that sex differences in mathematical performances are somewhere between non-existent
and almost non-existent (Hyde and Linn, 2006), most studies (e.g. Penner and Paret, 2008) find
men, on average, to be somewhat better mathematical problem-solvers than women. Also risk
taking is in our contemporary society associated with masculinity (Wilson and Daly, 1985).
Most studies report more risk-aversion amongst females (e.g. Jianakoplos and Bernasek, 1998),
even when these females are trained financial professionals (Olsen and Cox, 2001). There is
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also evidence that suggests that males and females differ in determinants of independence, i.e.
in ethical behaviour, empathy, and proneness to cognitive biases. Females appear to be more
ethical (OFallon and Butterfield, 2005), more empathetic (Baron-Cohen, 2004), and less prone
to cognitive biases (especially overconfidence [Beyer, 2002]).
This is an interesting field of research as the conclusions of the findings can have important
implications on the auditor choice by companies, the assignment of personnel to audit tasks,
and quality control issues. However, before expanding this line of research we need to control
for a potential major bias as it is not obvious that sex differences that may be present in the
general population are also present between female and male auditors. We need to be sure that
conclusions that stem from psychological research about the average man and woman (about
aspects that potentially influence audit quality) are also applicable for auditors. In general,
psychological research can be very useful, as well for experimental accounting research (Libby
et al., 2002) as for archival accounting research (Koonce and Mercer, 2005). For the
generalization of sex differences across populations, tasks, and settings, carefulness is however
warranted. It is not self-evident that findings from the general population can be easily
interpolated to the specific context of auditors since it is clear from the large amount of
literature on sex differences that their existence and size are context-dependent. Hence, it is far
from sure that psychological research on sex differences is predictive of sex differences within
an auditor population. The purpose of this is paper is to seek an answer to the question, Can
psychological research on sex differences be a valid basis for auditing research?
The remainder of this paper is organized as follows. In the section following this
introduction, we review scientific literature from various domains to clarify the potential
impact of auditors sex on audit quality and we define our research question. Next, we describe
the research design and sample selection. Finally, we discuss our results, limitations of our
research, and possibilities for further research.
2. Literature, Theory, and Hypothesis Development
In this section, we first discuss the topic of audit quality and auditor characteristics that could
have an impact on audit quality. We then outline why sex differences in such characteristics
would be of importance for audit quality research. We further review the existing literature on
sex differences which are most crucial for auditing (viz. problem-solving, risk profile, and
independence) and analyze what impact they could have on audit quality. Finally, we formulate
the motivation for our research and our hypotheses.
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2.1 Audit quality and auditor characteristicsIn the past, it was common to define audit quality to be the probability that the auditor
discovers and reports a material misstatement in the financial statements (DeAngelo, 1981b).
More recently however (e.g. Knechel, 2007), audit quality has been defined in terms of the
level of assurance that the auditor obtains. As the basis for the auditors opinion, professional
standards require the auditor to obtain reasonable assurance about whether the financial
statements as a whole are free from material misstatement, whether due to fraud or error.
Reasonable assurance is a high level of assurance. It is obtained when the auditor has obtained
sufficient appropriate audit evidence to reduce audit risk (that is, the risk that the auditor
expresses an inappropriate opinion when the financial statements are materially misstated) to
an acceptably low level (IFAC, 2009, ISA 200, 5).
As summarized in Fig. 1 audit quality is dependent on several personal auditor
characteristics such as the auditors technical expertise, problem-solving abilities, risk profile,
experience, and independence. Audit quality is also contingent upon several firm related
factors (e.g. the culture within an audit firm, the firm size, and the supply of non-audit
services), several audit team characteristics (e.g. the composition of the audit team and the
degree of familiarity among team members), and some factors outside the control of the
auditors (e.g. audit committees active in the reporting entity and the audit regulatory
environment) (FRC, 2008; Francis, 2004; Watkins et al., 2004). Moreover, task related
circumstances such as time pressure (DeZoort and Lord, 1997) and the complexity of
information to be processed (Bonner, 1994) might also affect audit quality. Finally, it should be
understood that all these determinants might interfere with each other.
[Insert Fig. 1 around here]
As this paper aims to identify sex differences that might affect audit quality, we will fromhereupon focus on personal auditor characteristics.2
As auditing is inherently a judgment and decision-making process, audit quality is
ultimately contingent upon the auditors judgment and decision-making qualities (Knechel,
2000). Applicable auditing standards (e.g. IFAC, 2009) and researchers (e.g. Ashton and
Ashton, 1995) have acknowledged that the work undertaken by the auditor to form an audit
opinion is permeated by judgment, in particular regarding risks of material misstatement, the
gathering of audit evidence, and the drawing of conclusions based on this evidence. The qualityof an auditors judgment and decision-making depends on personal auditor characteristics such
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as problem-solving ability (e.g. Bierstaker and Wright, 2001; Libby and Tan, 1992), technical
expertise (e.g. Bdard and Chi, 1993; Tan and Kao, 1999), risk profile (e.g. Farmer, 1993; van
Nieuw Amerongen, 2007), experience (e.g. Early, 2002; Shelton, 1999), and independence
(e.g. DeAngelo, 1981b; Moore et al., 2006).
2.2 Sex differences in auditor characteristics
In their review article on the auditors reporting model Church et al. (2008: 75) suggested that
researchers investigate whether there is a correlation or systematic relationship between
individual characteristics (e.g., age, gender, personality, and appearance) and the recommended
report. Since the recommended report is an output variable of the audit (and thus a proxy for
audit quality) such a relationship could exist because of mediating relationships between an
individual characteristic (e.g. sex) and the auditors judgment and decision-making and
personal auditor characteristics (Fig. 2). As the audit process is affected by the specific auditor
performing a task, we believe that it would indeed be interesting for auditing researchers to
know if there are such systematic associations. Systematic associations between, for example,
sex and one or more personal auditor characteristics enable an observer in possession of
information about an auditors sex to make, ceteris paribus, a statistically more accurate
prediction as to that auditors personal characteristics (and thus audit quality) than an observer
ignorant of that auditors sex. If there are indeed significant sex differences in personal auditor
characteristics, it is reasonable to expect that an auditors sex is systematically associated with
audit quality.
[Insert Fig. 2 around here]
We shortly review some of the scientific literature that supports the idea that sex
differences exist in areas which potentially affect audit quality (viz. problem-solving, riskprofile, and independence)3.
2.3 Problem-solving
In an audit context the focus should be on mathematical problem-solving since the
understanding of financial statement matters and audit reports is most likely to be influenced
by logico-mathematical abilities (Anandarajan et al., 2008). If in the population of auditors
there would be, for example, a sex difference in mathematical problem-solving favouring
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males it might be that male auditors discover more potential misstatements than female
auditors.
As noted by Rowley et al. (2007: 151) there is a fair amount of evidence that within our
culture, math is viewed as a male domain. If one, for examples, asks children to draw a
mathematician they will, almost without exception, draw a male figure (Picker and Berry,
2000, 2001). Meta-analyses have nevertheless shown sex differences in mathematical
performances to be somewhere between non-existent and almost non-existent (Hyde and Linn,
2006). Nonetheless it appears from a wide variety of studies that women vis--vis men have a
relatively negative perception of their own mathematical abilities (e.g. OLaughlin and
Brubaker, 1998; Miller and Bichsel, 2004). Such attitudes cause stress and anxiety. A special
case of this is the so-called stereotype threat. The existence of a stereotype leads to
heightened performance anxiety experienced by individuals who must perform a task for which
their group is thought to not be qualified (e.g. women and mathematics). The meta-analysis of
Signorella and Jamison (1986) concluded that in women an association exists between self-
image and cognitive performance. Self-identification as being a woman undermines the
mathematical desires, expectations, and abilities because math is thought of as being male
(Nosek et al., 2002). As it was eloquently put by Barbie (Mattel Inc., 1992) Math class is
tough! Hence, it is no surprise that it is well documented that women underestimate their
mathematical performances (Spencer et al., 1999). It also explains why Asian women score
better on tests that try to capture mathematical abilities when they identify themselves in the
first place as Asian vis--vis when they identify themselves in the first place as women
(Shih et al., 1999). Stereotypes influence the self-image and the behaviour of stereotyped
individuals. Thus, the mathematical expectations, preferences, and performances of women are
influenced by their implicit stereotyping as women (I am female thus math is not for me).
In sum, the existence of the stereotype image that women and mathematics do not accord
very well undermines (unconsciously) womens desire to pursue outstanding mathematicalperformances (Kiefer and Sekaquaptewa, 2007). Accordingly, it appears that sex differences in
math achievement are uniquely related to a nations average implicit stereotyping (Noseket al.,
2009) beyond the mere influence of generalized national gender inequality (i.e. the gender gap
in math is smaller in countries with a more gender-equal culture [Guiso et al., 2008]).
Moreover, since beliefs about math and women are widespread believed in society women see
their ideas and stereotypes about women and math being confirmed, reinforced, and stimulated
by their environments. Teachers, for example, ascribe mathematical excellence of femalesrather to effort while they ascribe the mathematical excellence of males rather to talent
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(Hamilton, 2008). Such different approach is, of course, not without consequences since
effort is, by definition, restricted to certain limits, while talent is not. So, it is no surprise
that the mathematical performances of women are negatively influenced when they are told
that sex differences in mathematical performances are caused by genetic differences while
there is no effect when women are told that such differences are due to experiential causes
(Dar-Nimrod and Heine, 2006). Males are therefore more than females stimulated to further
develop their mathematical abilities, resulting in a vicious stimulus-response-cycle. Thus,
while this remains a very complex area with many unresolved issues (i.e. unknown causes and
relationships that are not well understood, cf. Ceci et al. [2009] for a recent review) it is no
surprise that most research (e.g. Penner and Paret, 2008) finds men, on average, to be
somewhat better mathematical problem-solvers than women.
2.4 Risk profile
Just as mathematical ability, risk taking is perceived as a masculine attribute, at least in
Western cultures (Wilson and Daly, 1985). Notwithstanding the fact that there are some claims
(e.g. Schubert et al., 1999) that differences in risk attitudes between males and females are
more prejudice than fact, and that there are some claims (e.g. Roszkowski and Grable, 2005)
that sex differences in risk attitude are real but smaller than most of the time is assumed, there
seems to be evidence that women, in general, are more risk-averse than men (Barsky et al.,
1997; Byrnes et al., 1999; Damodaran, 2008; Jianakoplos and Bernasek, 1998).
Plenty of experiments on risk taking have shown that women are more risk-averse than
men in a wide variety of domains. Women are less likely to donate blood (Nonis et al., 1996),
take fewer risks in gambling (Eckel and Grossman, 2002), and are more risk-averse when
investing (Barber and Odean, 1995; Graham, 2002), even when they are financial professionals
(Olsen and Cox, 2001). However, the average effect sizes for sex differences in risk taking are
not large and vary according to context (Byrnes et al., 1999). In fact, males are no more likelythan females to have a domain-general tendency towards risk-taking. Sex differences in risk-
taking may have more to do with the fact that males are more often confronted or seek out
risky situations than females (Boyer and Byrnes, 2009). Indicative for the importance of
context, Schubert et al. (1999) showed that under controlled economic conditions females do
not make less risky financials choices than males.
Weber et al. (2002) suggested that risk-taking is indeed domain-specific. With exception
however of the social domain, their study found females to be less likely to engage in riskybehaviour than males in all domains. A pilot study conducted by Gold et al. (2009) to test
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various assumptions on which their research hypotheses and experimental manipulations
relied, suggested that also female auditors are more risk averse than male auditors.
In risk-assessment the goal of the auditor should be to identify those domains that are more
likely to contain material misstatements. However, the fact that women tend to be more risk-
averse possibly leads to a different selection of samples. The existence of the misstatements in
the financial statements of the reporting entity is unaffected by the auditors sex, but if men and
women select other or larger/smaller samples (because of differences in risk propensity), then
the probability that material misstatements will be detected might be affected by the auditors
sex. As female auditors tend to be more risk-averse, female auditors might be expected to set a
lower materiality threshold and select larger samples than male auditors. This could result in a
higher number of material misstatements detected and reported by female auditors than by
male auditors. The question then arises however to which extent female auditors might violate
the cost-benefit criterion more than male auditors, as they may desire more evidence than what
is sufficient appropriate audit evidence needed to obtain reasonable assurance.
Furthermore, even if the data considered by female and male auditors is identical, female
auditors (being more risk-averse) might assess the same type of information or misstatement as
more serious, thereby expressing more severe audit opinions, including going-concern
disclosures4, than male auditors.
2.5 Independence
Reporting material misstatements depends, among other things, on auditor independence.
Concerning independence two issues are at stake: malevolent behaviour and unconscious
behaviour. Auditor independence may not be possible at all (Moore et al., 2006), but that is an
irrelevant consideration as in the context of sex differences the question is: Are female
auditors less/more likely to be independent from their client?
If female auditors are less likely to display malevolent behaviour, there has to be evidencethat they are less often tempted to pursue some interest of their own and/or that they are bribed
less easily. Some evidence indeed points in that direction: women are less likely to be asked for
a bribe than men (Mocan, 2008) and women hold more negative attitudes toward cheating than
men (Whitley, 2001). In their review on ethical decision-making OFallon and Butterfield
(2005: 379) concluded that There are often no differences found between males and females,
but when differences are found, females are more ethical than males. Specifically with regards
to the field of accounting, Venezia (2008) found that female accounting students possessed
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higher levels of ethical reasoning than male accounting students and Eynon et al. (1997) found
far greater moral reasoning skills displayed by female accountants than by male accountants.
Rather than malevolent practice, the real problem however is an unconscious lack of
independence (Moore et al., 2006). In the context of unconscious behaviour, the primary focus
has to be on a priori-assumptions and (cognitive) biases that interfere with an objective
independent judgment.5 It is widely known and documented that prior beliefs bias the
evaluation of arguments and data. In evaluating new information, we draw upon our
background knowledge and use schemata in order to fill in details that are not there. In doing
so our thought patterns can become faulty as we suffer from cognitive distortions (i.e. our
thinking is not rational) (Gazzaniga and Heatherton, 2003).6 Cognitive biases may, for
example, hinder the issuing of a going-concern opinion, even when the auditor is aware of a
clients uncertain financial position (Kleinman et al., in press).
Some evidence points out that men and women tend to differ in this unconscious
behaviour. Psychological research indicates that men are more likely to suffer from cognitive
distortions and to exhibit dichotomous thinking than women (Chung and Monroe, 1998; Soutar
and Sweeney, 2003). The study of Chung and Monroe (1998) found evidence indicating that
male students enrolled in a third year undergraduate auditing class displayed confirmation bias,
while female students did not. Therefore, female auditors might discover more material
misstatements than male auditors. Especially with regards to overconfidence this sex difference
is well-documented (e.g. Bengtsson et al., 2005; Beyer, 2002). The overconfidence effect
describes, among others, the tendency of people to believe that their judgment is more accurate
than it really is. As a result, overconfidence can create a mismatch between ones confidence in
ones own judgments and the real accuracy of these judgments. Because women are less
overconfident than men, female auditors could be expected to be more reluctant than male
auditors when deciding to report a material misstatement.
A second issue that may unconsciously undermine auditor independence is empathy. It isbelieved that long-term relationships result in increasing empathy between parties; therefore
audit quality would get undermined when an auditor gets too familiar with a company
(Richard, 2006). To ensure auditor independence mandatory rotation of the audit firm, or of
key personnel, is therefore frequently advocated. Scientific evidence from the Empathizing-
Systemizing theory (Baron-Cohen, 2004; Chapman et al., 2006; Nettle, 2007) and from the
psychological Big Five taxonomy of personality traits (Costa et al., 2001; Feingold, 1994;
Schmitt et al., 2008) supports that women are more empathetic than men. Female auditorsmight therefore identify themselves more with their clients than male auditors and therefore
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report less material misstatements and/or append fewer going-concern disclosures to a clients
audit report than male auditors.
2.6 Hypothesis formulation
The recent finding of Ittonen and Peni (2009) (i.e. in the Nordic countries firms with female
audit partners seem to have higher audit fees) gives support to the idea that an auditors sex is
systematically associated with audit quality. Some preliminary research on sex differences in
the domain of auditor judgment and decision-making has, however, generated mixed results.
Gold et al. (2009) found the final proposed adjusting journal entry recommendation of male
auditors to be more accurate than that of female auditors. Chung and Monroe (2001) found
females audit judgments to be more accurate in more complex tasks than those of males, while
in the less complex tasks they found males to be more accurate than females. ODonnell and
Johnson (2001) found that female auditors were also more efficient than male auditors in
completing high complexity tasks, while male auditors were more efficient than female
auditors in completing tasks of low complexity. Breesch and Branson (2009) however, reported
no association between sex and the number of reported material misstatements or with the
severity of the expressed audit opinion.
The above-mentioned studies took it for granted that the sex of an auditor is somehow
systematically associated with some personal auditor characteristic (e.g. risk-aversion). While
stereotypical beliefs about sex differences in personal characteristics our affluent in our
contemporary society, it can not be safely assumed that the behaviour of auditors is biased in
the same gendered directions as the behaviour of the average man and the average woman.
There are two distinct contextual effects that may prevent this. First, because of two reasons
there might be correlational effects between certain personal characteristics and the sex of an
auditor. Firstly, there is a statistical reason, i.e. auditors are not a random sample. This makes it
impossible to interpolate findings about sex differences in personal characteristics from thewhole population to the specific context of female and male auditors. After all, considerable
self-selection might be present, i.e. people (males and females) with more or less the same
profile chose to become auditors. Secondly, certain sex differences may be absent in specific
populations as a result of (parental, educational, and occupational) socialization and the
resulting stimulus-response-cycle, i.e. people (males and females) are learned certain roles and
acquire certain abilities and since environments are not experienced randomly they reinforce
these roles and strengthen these abilities. In this respect, Smith and Rogers (2000: 76) wrotethat occupational socialization theory illustrates that differences tend to disappear as
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employees are socialized within the work environment, the same may be said for those
working in the accounting profession. Second, there might be interactional effects between
auditors gender and the organizational logic of audit firms, i.e. the way that gender stereotypes
bias behaviour and performances in gendered directions depends on how strongly a certain
setting is culturally typed as male or female (Ridgeway, 2009). Thus, even if auditors were a
random sample of the population we would not be able to conclude that sex differences in
certain personal characteristics are present between males and females in an audit context. One
would have to examine how the organizational logic interacts with the background effects of
the gender frame (Ridgeway, 2009: 156) since the prevalence of such sex differences is
context-dependent. This is true as well for sex differences in mathematical problem-solving,
risk taking, and different components of independence (ethical behaviour, cognitive biases, and
empathy).
Altogether this makes it impossible to determine a priori if an auditors sex is relevant
information when one is interested in audit quality, i.e. does knowing that an auditor is male or
female, ceteris paribus, enable an observer to make a statistically more accurate prediction as
to the audit quality supplied by that person vis--vis an observer who is ignorant of that fact.
On the one hand it appears that the answer is yes since it is believed that sex differences exist
in certain personal characteristics (viz. problem-solving, risk profile, and independence) which
potentially affect audit quality. On the other hand one will be inclined to say no since
(parental, educational, and occupational) socialization can override the influence of sex (i.e.
women and men in the same profession are more alike than men and women randomly
sampled from the whole population). Certainly in the world of auditing where there is a
longstanding tradition of the large firm quality homogeneity assumption (i.e. the assumption
that audit quality within large audit firms is reasonably homogeneous) one will be willing to
accord to the second answer. However, based on the maleness of the auditing world (cf.
Hardies et al., 2009) it would be just as good to accord to the first answer because this maylead one to believe that the background gender frame is powerfully relevant in the context of
auditing. It is however impossible to favour one position more than the other solely on
theoretical grounds. There is no reason to believe that in the context of the audit profession
women will be women in their profession (Beckmann and Menkhoff, 2008) is truer than
female auditors help their colleagues to forget that they are women (Anderson-Gough et al.,
2005).
Thus, knowing an auditors sex may enable an observer to make more accurate predictionsvis--vis an observer who is ignorant of this information in some cases, but it may just as well
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be no more relevant than the hair colour of the auditor. So before any further research in the
area of gender and auditing is undertaken we have to address the question: Are female
auditors still women? The magnitude of sex differences might be the same, smaller, larger or
even reverse for the population of auditors as it is for the general populationgender
researchers have emphasized the importance of context in creating, erasing, or even reversing
(psychological) sex differences (e.g. Bussey and Bandura, 1999; Deaux and Major, 1987;
Eagly and Wood, 1999). Based upon the stereotypical beliefs that are embedded in our
contemporary society and the enormous amount of scientific literature that has reported sex
differences in mathematical performances, risk taking, cognitive distorted thinking, and
empathy, we expect to find such sex differences between males and females randomly sampled
from the population as a whole. If auditors differ from the general population and sex
differences are, due to self-selection and/or socialization, not present in the population of
auditors this may (to some degree) also be the case in the population of business students since
auditors are only a very specific subgroup of (former) business students. Indeed it appears that
students in fields of economics are not even representative of the population of students, both
in terms of their attitudes (e.g. their appraisal of the virtues of the market system [Cipriani et
al., 2009]) as in terms of their personal characteristics (e.g. their selfishness [Frey and Meier,
2003, 2005], their tolerance to economic risk taking [Sjberg and Engelberg, 2009]). Since this
is probably due to self-selection rather than due to indoctrination (Cipriani et al., 2009; Frey,
2003, 2005), it can be assumed that people (males and females) who attend business schools
are rather similar. Thus we expect sex differences in a population of business students to be of
a magnitude somewhere in-between those of the general population and the population of
auditors.
3. Research design and sample description
The utilized data consists of a unique set of responses gathered in Belgium and the Netherlandsfrom 677 non-business students, 173 business students, and 1051 auditors. While university
students can not be expected to be representative on these traits for the general population, we
used in our research a sample of (non-business) students as a proxy for the population as a
whole because psychological research has heavily relied on the study of college students
(Peterson, 2001; Sherman et al., 1999). Hence, using such a sample yields the greatest
possibility to capture similar results, and makes it possible to argue strongly in favour of
contextual reasons for the presence or absence of sex differences (if in one or more, but not all,of our subpopulations they would be absent; since in that case they can not solely be ascribed
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to the specifics of our research instrument). For the same reason, our research instrument was
composed out of generic (i.e. non-audit related) questions. The usage of such generic
questions, that have been widely used in psychological research on sex differences, made it
possible to compare our results across our different subpopulations as well as with previous
(psychological) research (that has served as a starting point for the research on auditor gender
undertaken so far). Hence, our research instrument is well-suited to examine if psychological
research can serve as a valid foundation for hypotheses about (and explanations for) sex
differences among auditors.7
Both student groups were surveyed by means of a paper or online version of our
questionnaire (included in Appendix) in November 2008. We compared results from both
versions to make sure that our research instrument did not yield different results depending on
the fact if one answered it online or on paper. Since no such differences were found, we used
the online version for our examination of the auditor population. For subpopulations with high
levels of Internet access, Internet-based research has the potential to yield higher-quality data
with lower non-response rates and at a lower cost than traditional methods (Schmidt, 1997). In
Belgium the external auditor is chosen from the members of the professional body of Belgian
auditors IBR (Instituut van de Bedrijfsrevisoren). We distributed our survey among the Dutch
speaking Belgian auditors (which represents almost 75% of the total population of Belgian
auditors). In the Netherlands 98% of all external audits is performed by a member of the
professional body of Dutch auditors NIVRA (Nederlands lnstituut van Registeraccountants).
We used the member lists of the IBR and the NIVRA and invited 7400 auditors in this manner
to participate in our investigation, 1241 of them responded (17% response rate). 8 It is not very
likely that our results are affected by non-response bias (which occurs when the individuals
responding to a survey differ from nonrespondents on variables relevant to the survey topic
[Rogelberg and Luong, 1998: 6061]) since we are dealing with a very homogeneous group.
Hence, traditional variables that cause non-response bias are identical (e.g. occupation) or atleast very similar (e.g. amount of education) for the whole population under examination and
can not be relevant. Approximately 18% of all the auditors in the Low Countries are female;
our sample is representative in this respect (p = .514).
[Insert Table 1 around here]
Table 1 presents descriptive statistics of our entire sample. We found female auditors, onaverage, to be younger and less experienced than their male counterparts. The proportion of
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female and male auditors that were interns at a B4 audit firm did not significantly differ.
Finally, we found that female auditors hold lower positions in audit firms than male auditors.
Altogether we generated 1901 valid questionnaires. The questionnaire was designed to
examine three dimensions: problem-solving ability (including mathematical abilities), risk
profile (risk-aversion), and independence (through empathy and cognitive biases). To measure
problem-solving ability we constructed a scale ( = .609) counting the number of correct
answers on the ten questions regarding problem-solving. Eleven Likert-items were cumulated
into a Likert-scale ( = .720) to measure risk-aversion. To evaluate empathy we used one item
of Baron-Cohen and Wheelwrights (2004) Empathy Quotient. Independence was further
examined with seven questions testing for cognitive biases in thinking. In the population of
non-business students sex differences were expected to be found in all these areas. These sex
differences would or would not be replicated throughout the other two populations. The results
of the survey are discussed below.
4. Results
It is now well-established and broadly acknowledged that statistical significance testing is often
misused and misinterpreted (Nickerson, 2000; Thompson, 2007). Following the advices of
Cohen (1990) and Feingold (1995), therefore, when we present univariate comparisons we
supplement the reporting of statistical significance with confidence intervals (CI.975), and with
standardized effect sizes (Cohens dor odds ratios, depending on the appropriateness) and their
confidence intervals (CI.975). Since estimates of effect-size provide a means for interpreting
statistical results regardless of statistical significance (Robey, 2004), we report effect sizes
even when a statistical test failed to achieve statistical significance. Furthermore, to enhance
comparability, we also computed standardized effect sizes (Cohens d) when regression
coefficients (i.e. unstandardized measures) are given. We derived 95% confidence intervals for
(based on Fishers z-transformation) from partial point-biserial correlations, which we
converted into Cohens ds (using Friedmans [1968: 246] formula).
4.1 Problem-solving
In the population of non-business students we found men to be better problem-solvers than
women (Table 2).
[Insert Table 2 around here]
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We also found the sex difference in problem-solving to narrow when considering more
specified population categories: the difference was smaller for the business students (Cohens d
= 0.23 0.30) (p = .133) than for the non-business students (Cohens d= 0.45 0.17) (p =
.000), and negligible for the auditors (Cohens d= 0.02 0.22) (p = .811) (Table 3).
[Insert Table 3 around here]
To control for mediating influences we incorporated the demographic and career
characteristics (Table 1) in a multivariate framework (Table 4). Controlling for these
demographic and career characteristics confirmed the results from the univariate analysis:
while in the populations of non-business students (Cohens d= 0.34 0.17) (p = .001) and
business students (Cohens d= 0.20 0.30) (p = .001) (p = .065) females scored lower on the
problem-solving test than their male counterparts, this was not true for the female auditors
(Cohens d= 0.13 0.16) (p = .273). As could be expected (cf. Verhaeghen and Salthouse,
1997) problem-solving ability was negatively influenced by age, although the effect was
small.9
[Insert Table 4 around here]
4.2 Risk profile
As expected, women were, on average, found to be more risk-averse than men. Female non-
business students (Cohens d= 0.31 0.16) (p = .000) as well as female business students
(Cohens d = 0.47 0.31) ( p = .008) were found to be more risk-averse than their male
counterparts. We found, however, no difference in risk-aversion between female and male
auditors (Cohens d= 0.08 0.16) (p = .354) (Table 5).
[Insert Table 5 around here]
In order to control for possible mediation of demographic and career characteristics, we
analyzed sex differences in risk-aversion in a multivariate framework (Table 6) similar to the
above. The multivariate framework confirmed the results of our univariate analysis only partly:
in the populations of non-business students (Cohens d= 0.31 0.16) (p = .000) and business
students (Cohens d= 0.47 0.31) (p = .005), females were indeed found to be significantlymore risk-averse than males. However, as could be expected, older individuals were found to
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be more risk-averse than younger ones (cf. Steinberg, 2007). It was also found that Dutch
auditors indicated a greater willingness to take economic risks than their Belgium colleagues.
When controlling for these mediating characteristics, female auditors were found to be
significantly more risk-averse than male auditors (Cohens d= 0.14 0.12) (p = .025).
[Insert Table 6 around here]
4.3 Independence
In none of our three subpopulations statistical testing revealed women to be significantly more
empathetic than men (Table 7) and the effect sizes were small as well in the population of non-
business students (Cohens d = -0.12 0.18), as in the population of business students
(Cohens d= -0.21 0.32), as in the population of auditors (Cohens d= -0.18 0.19).
[Insert Table 7 around here]
As expected, our experiment revealed significant differences between men and women for
some of the cognitive biases (Table 8). However, in the population of non-business students,
we found females to be somewhat more cognitive biased than their male counterparts: they
tended to be somewhat more influenced by the conjunctive fallacy (OR = 1.91) (p = .009),showed greater insensitivity to sample size (OR = 1.42) (p = .098), and suffered more from the
information bias (OR = 1.51) ( p = .073) than there male counterparts. In the population of
business students such differences were only found with regards to the conjunctive fallacy (OR
= 2.12) ( p = .077). On the other hand, as expected, in the non-business students population
females were found to be somewhat better calibrated (i.e. less overconfident)10 than males (OR
= 0.60) (p = .025). This result was replicated throughout the population of business students
(OR = 0.38) (p = .012), but not in the auditor subpopulation (OR = 1.23) (p = .338). In the
auditor population females were also found to be somewhat more influenced by the
conjunctive fallacy (OR = 1.99) (p = .063), and showed greater insensitivity to sample size
(OR = 1.61) (p = .055) than there male counterparts.
[Insert Table 8 around here]
5. Conclusion
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Throughout this paper we analyzed a number of personal auditor characteristics that are
potentially associated with the auditor being male or female. For a non-biased gender auditing
research, we need however to be sure that conclusions for the average men and women also
apply to auditors. It is not straightforward to determine a priori if auditors are a random sample
of the general population with respect to relevant characteristics (see supra) for audit quality.
Therefore, we conducted a survey whereby different groups of males and females were asked
to fill in a questionnaire. Three groups of people were distinguished: non-business students,
business students, and auditors. The questionnaire examined three dimensions: problem-
solving ability (including mathematical abilities), risk profile (risk-aversion), and independence
(through empathy and cognitive biases). The results of our experiment were somewhat mixed
(Table 9).
[Insert Table 9 around here]
On the basis of our experiment we can not unambiguously answer the question Are female
auditors still women? We found, in accordance with Gold et al. (2009), female auditors to be
somewhat more risk-averse than male auditors, but we did not find sex differences in other
personal auditor characteristics. To a great degree our results accord with those of Beckmann
and Menkhoff (2008) who found female fund managers to be more risk averse, but not less
overconfident than their male colleagues. Our conclusion, however, differs from theirs. Since
female auditors differ less from their male colleagues than the average women from the
average men and since female auditors are more alike than different from their male
colleagues, we conclude that women will not be women in the audit profession: being an
auditor dominates doing gender. Limitations of our study have however to be taken into
account. It is without doubt that our research instrument is a source for criticism, and it can not
be ruled out that we would have found some significant sex differences if we had used anotherresearch instrument; for example, no sex differences in empathy were found, but we measured
empathy by a measure that is self-report while it is not yet clear how self-report of empathizing
relate to actual performance (Wakabayashi et al., 2006). More important, while our research
points to the importance of context when examining sex differences, and indicates that sex
differences may not be present between male and female auditors (even when they exist in the
general population), it can not rule out that there are sex differentiated audit quality differences
(due to differences in problem-solving ability, risk-aversion, and/or independence betweenmale and female auditors). After all, the prevalence of sex differences depends on the way that
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behaviour and performances are biased in gendered directions, which depends on how strongly
a certain setting is culturally typed as male or female (Ridgeway, 2009). Therefore, the priming
of one aspect of someones identity (e.g. women stereotype) may undermine performance
while another aspect of someones identity (e.g. accountant stereotype) may enhance it. We
can only speculate about which aspect of their identity was primed when auditors took our
survey. Thus, it may be that stereotypical gender-roles are made salient in an audit context,
while they were not activated in our experimental setting.
Furthermore, one should be very careful to jump to conclusions based on this experiment
alone. It is wise to remember Schmidts (1996: 127) admonition that meta-analysis has
revealed how little information there typically is in any single study. It has shown that, contrary
to widespread belief, a single primary study can rarely resolve an issue or answer a question.
Any individual study must be considered a data point to be contributed to a future meta-
analysis. More research into this topic is certainly necessary. Further research could also focus
on more traditional measures of audit quality. Chin and Chi (2008) found for example an
association between the sex of the auditor and the likelihood that a going-concern opinion was
issued. Finally, even if auditor gender does not differentiate actual audit quality it might have
an impact on how audit quality is perceived. After all, it is a well-known fact that performance
judgments (cf. Bauer and Baltes 2002) and perceptions on ethical behaviour (cf. McCabe et al.,
2006) are gender-biased. Female and male auditors might therefore not be perceived as
supplying audits of the same quality (cf. Hardies et al., 2009).
Notes
1Stereotypes distinguish a particular group from other groups; they need not be negative or inaccurate (Judd
and Park, 1993).
2
We acknowledge that it would be interesting to study auditor gender in relationship with task related
circumstances and audit team characteristics, ifthere are differences between male and female auditors. Such
relationships are, however, not discussed in this paper since the central claim of this paper is that we do not yet
know if there are differences between male and female auditors.
3As technical expertise and experience are individual characteristics that are rather acquired during lifespan
than sex differentiated characteristics we omit them from our analysis here and incorporate them back in our
empirical analysis (see infra) as independent variables.
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4If a client is nearing bankruptcy, it is less costly for auditors to be conservative since the type II error cost (i.e.
misclassification of a failing company as a non-failing company) is typically larger than the type I error cost
(i.e. misclassification of a non-failing company as a failing company) (Matsumara et al., 1997).
5Auditor independence is the conditional probability that an auditor reports a discovered breach in a clients
accounting system (DeAngelo, 1981a). Therefore, we regard any cognitive bias that may undermine the
probability that an auditor reports a discovered material misstatement as a potential threat to auditor
independenceauditor independence is broader than the ability to withstand clients pressure when forming
judgments and making decisions.
6The widely cited review of Smith and Kida (1991: 485) concluded that although the evidence indicates that
the heuristics and biases common to many experiments using student subjects and generic tasks are also
present in the judgments of professional auditors performing familiar, job-related tasks, the nature of these
heuristics or the extent of their presence is often notably different. From the more recent review of Koch and
Wstemann however (2009) it appears that auditors make as much use of heuristics as laymen, and
consequently, are also prone to cognitive biases, even when conducting audit tasks.
7Clearly, this would not be the case if we made use of audit related questions or an actual audit task
comparisons with non-audit subjects and non-auditing literature would be meaningless.
8600 Belgian and 6800 Dutch auditors were invited. This big numerical difference is partly due to a difference
in country size (approximately 6 million people live in the Dutch speaking part of Belgium compared to more
than 16 million in the Netherlands), and partly due to the fact that the member list of the IBR only contains
practicing external auditors while the member list of the NIVRA also contains individuals who are legally
qualified to perform statutory audits but do not work as external auditor.
9These results, as well as those reported later, are robust to the exclusion of different variables from the
regression.
10Calibration is a criterion for evaluation of probability judgments. Overconfident individuals have been found
to miscalibrate by giving too narrow confidence intervals (e.g. Beckmann and Menkhoff, 2008).
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Fig. 1. An overview of the determinants of audit quality.
Culture within the firmFirm sizeNon-audit services
CompositionFamiliarity
Technical expertiseProblem-solving abilityRisk profileExperienceIndependence
Firm RelatedFactors
Factors Outside
Control of AuditorsAudit CommitteesAudit regulation
Task Related
CircumstancesComplexityTime pressure
Personal Auditor
Characteristics
Audit Team
Characteristics
Audit quality
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Fig. 2. Schematic overview of the relationship between individual characteristics, personal
auditor characteristics, auditor's judgment and decision-making, and audit quality. Solid lines
indicate a direct relationship, dashed lines indicate moderation.
Table 1. Descriptive statistics of non-business students, business students, and auditors characteristics a
Audit quality
Personal auditor characteristics(e.g. risk profile)
Individual characteristics(e.g. sex)
Auditors judgment and decision-making(e.g. risk assessments)
Socio-cultural and
biological factors
Task relatedfactors
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Characteristics split by subpopulation
and sex Non-business students Business students Auditors
Men Women Men Women Men Women
Sex 241(35.6%) 436(64.4%) 97(56.1%) 76(43.9%) 862 (82%) 189 (18%)
56.167 p= .000 2.549 p= .110 4.31 p= .000
Age22.85 21.26 20.70 20.47 47.66 38.57
3.053 p= .002 1.015 p= .311 15.699 p= .000
Experienceb 17.01 9.95
13.006 p= .000
B4 542 (62.9%) 121(64.4%)Internshipc
NB4 320 (37.1%) 67(35.6%)
0.146 p= .702
B4 related 151(17.5%) 28(15.1%)
NB4 related 219(25.4%) 52(28.1%)
0.927 p= .336
Current statusd
Self-employed 79(9.2%) 10(5.4%)
Othere 413(47.9%) 95(51.4%)
Employee 82(22.2%) 26(32.5%)
Co-worker 135(36.5%) 32(40.0%)
Office positionf
Partner 153(41.4%) 22(27.5%)
6.393 p= .041
aThe table gives the mean value for age and experience for males and females in comparison as well as t-value of
the respective Independent Samples Test and respective p-value regarding sex specific differences. For the other
variables, the distribution per value is given for males and females as well as the Chi-value of the respective Chi
Test regarding sex specific differences.b
Experience is derived from the year one took the oath to be appointed as an external auditor (in years).
cIn Belgium and the Netherlands, auditor trainees must complete a minimum of three years of practical training in
an audit office.d
The external auditor can be self-employed or employed by an audit office.
e Members of the NIVRA do not exclusively work as external auditors, a majority works in other professions (e.g.
internal auditor).
fAuditors can have one of the following three statuses in an audit firm: partner, co-worker or employee.
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Table 2. Men and women as problem-solvers
Population category Sex N Meana Std. Dev.
Men 213 7.35 [7.06 ; 7.64] 2.13Non-business students
Women 372 6.33 [6.09 ; 6.57] 2.35
Men 96 7.61 [7.26 ; 7.96] 1.77Business students
Women 76 7.18 [6.75 ; 7.61] 1.93
Men 482 7.38 [7.23 ; 7.53] 1.71Auditors
Women 100 7.34 [6.95 ; 7.73] 1.97
aMean problem-solving score is derived from the number of correct answers on the ten questions regarding
problem-solving.
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Table 3. Men and women as problem-solvers (Independent Samples Test)
t-test for Equality of Means
Levenes
Test for
Equality of
Variances 0H : no sex difference
95%
Confidence
Interval of the
Difference
Population category F Sig. t df
Sig.
(2-
tailed)
Mean
Difference
Std. Error
Difference Lower Upper
Equal variances
assumed2.348 .126 5.175 583 .000 1.01 .20 .63 1.40
Non-business
students
Equal variances
not assumed
5.314 477.603 .000 1.01 .19 .64 1.39
Equal variances
assumed.314 .576 1.510 170 .133 .43 .28 -.13 .98
Business students
Equal variances
not assumed1.495 153.902 .137 .43 .29 -.14 .99
Equal variances
assumed3.716 .054 .239 580 .811 .046 .19 -.33 .42
Auditors
Equal variances
not assumed.218 131.685 .828 .046 .21 -.37 .46
Table 4. Problem-solving in a multivariate frameworka
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Non-business students Business students Auditors
Female -.313660 (p= .001) -.296691 (p= .065) -.031750 (p= .273)
More math educationb .181193 (p= .000) .107205 (p= .044)
More mathematical studyc .751234 (p= .000)
Time pressured -.744891 (p= .000)
Older -.00265089 (p= .010)
NL dummye
-.047655 (p= .125)
aThe table gives the coefficients of the Poisson regression models with p-values in parentheses.
bHours of mathematical education per week at the end of secondary education.
cA dummy variable distinguished between natural sciences and social sciences.
dFor 39 business students the time available to finish the questionnaire was limited.
eA dummy variable was used to code for nationality (Belgian = 0, Dutch = 1).
Table 5. Sex differences in risk-aversion
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Population category Sex N Meana Std. Dev.
0H : no sex difference
Asymp. Sig.
Z (2-tailed)
Men 224 19.19 [18.58 ; 19.80] 4.63Non-business students
Women 383 17.71 [17.23 ; 18.19] 4.76-4.003 .000
Men 92 21.18 [20.18 ; 22.18] 4.90Business students
Women 76 19.11 [18.28 ; 19.94] 3.71-2.651 .008
Men 818 18.93 [18.56 ; 19.30] 5.44Auditors
Women 175 18.47 [17.68 ; 19.26] 5.34-.928 .354
aMean is derived from the Likert-scale measuring risk-attitude. A higher score indicates a higher willingness to take
risks (i.e. a less risk-averse attitude).
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Table 6. Risk-aversion in a multivariate frameworka
Non-business students Business students Auditors
Female -.336868 (p= .000) -.453570 (p= .005) -.200110 (p= .025)
Older -.00794270 (p= .339) .042745 (p= .431) -.016367 (p= .000)
NL dummyb .618536 (p= .000)
aThe table gives the coefficients of the linear ordered probit regressions with p-values in parentheses.
bA dummy variable was used to code for nationality (Belgian = 0, Dutch = 1).
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Table 7. Sex differences in empathy
Population category Sex N Meana Std. Dev.
0H : no sex difference
Asymp. Sig.
Z (2-tailed)
Men 181 1.25 [1.15 ; 1.35] .78Non-business students
Women 317 1.33 [1.26 ; 1.40] .62-1.325 .185
Men 84 1.17 [1.01 ; 1.33] .75Business students
Women 67 1.33 [1.15 ; 1.51] .74-1.588 .112
Men 620 1.30 [1.25 ; 1.35] .65Auditors
Women 128 1.41 [1.30 ; 1.52] .63-1.751 .080
aMean is derived from the question on empathy. A higher score indicates a higher level of empathy.
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Table 8. Sex differences in cognitive biases (Test Statistics)
0H : no sex difference
Population
category
Conjunctive
fallacy a
Confirmation
bias 1 b
Confirmation
bias 2 b
Insensitivity
to sample
size c
Information
bias d
Disjunctive
fallacy e
Overconfidence
(miscalibration) f
Z -2.626 -1.136 -.720 -1.655 -1.792 -1.027 -2.244
Non-
business
students
Asymp.
Sig. (2-
tailed)
.009 .256 .471 .098 .073 .305 .025
Odds
ratio
1.91
[1.17; 3.10]
1.61
[0.70; 3.70]
1.31
[0.63; 2.76]
1.42
[0.94; 2.16]
1.51
[0.96; 2.37]
1.33
[0.77; 2.27]
0.60
[0.38; 0.94]
Z -1.768 -.247 -2.215 -.514 -1.181 -.100 -2.501
Business
students
Asymp.
Sig. (2-
tailed)
.077 .805 .027 .607 .237 .920 .012
Odds
ratio
2.12
[0.92; 4.90]
1.15
[0.38; 3.52]
0.12
[0.14; 1.05]
1.22
[0.57; 2.63]
0.62
[0.29; 1.37]
0.09
[0.20; 4.38]
0.38
[0.18; 0.82]
Z -1.859 -.072 -.595 -1.919 -.1064 -1.378 -.959
Auditors Asymp.
Sig. (2-
tailed)
.063 .943 .552 .055 .287 .168 .338
Oddsratio
1.99[0.95; 4.12]
1.03[0.45; 2.38]
1.57[0.35; 7.03]
1.61[0.99; 2.61]
1.30[0.80; 2.11]
2.07[0.72; 5.96]
1.23[0.80; 1.90]
aConjunctive fallacy: false belief that specific conditions are more probable than a single general one.
bConfirmation bias: the tendency to look for believes-supporting evidence and to attach relatively more value to
evidence that confirms believes than to evidence that contradicts believes.
cInsensitivity to sample size: ignoring sample size when assessing the likelihood of certain results.
dInformation bias: believing that the more information that can be acquired to make a decision, the better, even if
that extra information is irrelevant for the decision.
eDisjunctive fallacy: false belief that the probability of a general event is less than the sum of the probabilities of
its separate components.
fOverconfidence: the tendency to believe that judgments are more accurate than they really are.
Table 9. Summary
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Non-business students Business students Auditors
Problem-solving
F < M F < M F = M
Risk-aversion
F > M F > M F > M
Empathy F = M F = M F = M
Independence
Cognitive biases Overconfidence: F < M
Conjunctive fallacy +
Insensitivity sample size +
Information bias: F > M
Overconfidence: F < M
Other biases: F = M
Conjunctive fallacy +
Insensitivity sample size:
F > M
Other biases: F = M
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Appendix
This questionnaire examines three dimensions: risk profile (risk-aversion and empathy),
problem-solving ability (including mathematical abilities), and independence (through a priori-
assumptions and cognitive biases). The purpose of this questionnaire is to examine if sex
differences present at the level of the general population are replicated throughout different
subpopulations of men and women.
This questionnaire was produced based on the following sources:
Baron-Cohen, S. and Wheelwright, S. (2004) The Empathy Quotient: An Investigation of
Adults with Asperger Syndrome or High Functioning Autism, and Normal Sex
Differences,Journal of Autism and Developmental Disorders, 34(2), pp. 163175.
Goldstein, E. B. (2008) Cognitive Psychology: Connecting Mind, Research, and Everyday
Experience (Belmont: Wadsworth Publishing).
Tversky, A. and Kahneman, D. E. (1973) Availability: A Heuristic for Judging Frequency and
Probability, Cognitive Psychology, 5(2), pp. 207232.
Tversky, A. and Kahneman, D. E. (1974) Judgment Under Uncertainty: Heuristics and Biases,
Science, 185(4157), pp. 11241131.
Weber, E. U., Blais, A. R. and Betz, N. E. (2002) A Domain-specific Risk-attitude Scale:
Measuring Risk Perceptions and Risk Behaviors, Journal of Behavioral Decision
Making, 15(4), pp. 263290.
Wickelgren, W. A. (1995) How to Solve Mathematical Problems (New York: Dover
Publications).
Risk profile (11 questions on risk-aversion)
For each of the following statements, please indicate the likelihood of engaging in each
activity. Provide a rating from 1 to 5, using the following scale:
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1. ____ Betting a days income at a sports game.
2. ____ Co-signing a new car loan for a friend.
3. ____ Investing 10% of your annual income in a blue chip stock.
4. ____ Investing 10% of your annual income in a very speculative stock.
5. ____ Investing 10% of your annual income in government bonds (treasury bills).
6. ____ Investing in a business that has a good chance of failing.
7. ____ Lending a friend an amount of money equivalent to one months income.
8. ____ Spending money impulsively without thinking about the consequences.9. ____ Taking a days income to play the slot-machines at a casino.
10.____ Taking a job where you get paid exclusively on a commission basis.
11.____ Betting one months income at a double or nothing coin toss game.
Problem-solving capacities (10 questions)
1. What is the value of the expression (x y) when x = 5 and y = 1
A) 4
B) 6
C) 16
D) 24
E) 36
2. Which of the following is equivalent to (4x)
A) 864x
B) 664x
C) 612x
D) 512x
E)
6
4x
1 2 3 4 5
Extremely unlikely Not sure Extremely likely
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3. Which of the following is an equivalent simplified expression for 2( x4 + 7) 3( x2 - 4)
A) 2+x
B) 22 +x
C) 262 +x
D) 103 +x
E) 113 +x
4. Ms. Cox plans to drive 900 miles to her vacation destination, driving an average of 50
miles per hour. How many miles per hour faster must she average, while driving, to
reduce her total driving time by 3 hours.
A) 5
B) 8
C) 10
D) 15
E) 18
5. Evi cuts a board in the shape of a regular hexagon and pounds in a nail at an equal
distance from each vertex, as shown in the figure below. How many rubber bands will
she need in order to stretch a different rubber band across every possible pair of nails?
A) 15
B) 14
C) 12
D) 9
E) 6
6. There are 280 runners registered for a race, and the runners are divided into 4 age
categories, as shown in the table below.
Age category: Under 16 16-25 26-35