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CEO GENDER AND CAREER DRAFT
CEO Gender Differences in Careers and the Moderating
Role of Country Culture: A Meta-analytic Investigation
Gang Wang, Ph.D.
Department of Management
College of Business, Florida State University
821 Academic Way, P.O. Box 3061110
Tallahassee, FL 32306-1110
Email: gwang5@business.fsu.edu
R. Michael Holmes Jr., Ph.D.
Department of Management
College of Business, Florida State University
821 Academic Way, P.O. Box 3061110
Tallahassee, FL 32306-1110
Email: mholmes@business.fsu.edu
Richard A. Devine, Ph.D.
Department of Management
Kogod School of Business, American University
4400 Massachusetts Ave NW
Washington, DC 20016-8044
E-mail: devine@american.edu
John Bishoff
224 S. 880 W.
Spanish Fork, UT 84660
***Published in OBHDP***
Reference:
Wang, G., Holmes Jr, R. M., Devine, R. A., & Bishoff, J. (2018). CEO gender differences in
careers and the moderating role of country culture: A meta-analytic investigation. Organizational
Behavior and Human Decision Processes, 148, 30-53.
CEO GENDER AND CAREER
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Abstract
How and under what conditions do female and male CEOs’ careers differ? The field lacks clear
answers to this question, as extant research has produced mixed findings, many of which also
vary across countries. In response, we examine individual- (e.g., personal career success, such as
CEO pay) and firm-level (e.g., firm performance) differences in female and male CEOs’ careers,
and also how these differences vary across countries with different cultural attributes
(specifically uncertainty avoidance and gender egalitarianism). To develop our theoretical
explanation, we draw on recent scholarship that has used the well-known demand-supply
framework from economics to synthesize extant theory on career differences between women
and men. Then, we tested our theoretical model with meta-analytic results based on 158 studies
in which differences between female and male CEOs, in the firms they lead, and in the outcomes
they receive and produce were examined. Our findings reveal that, compared to male CEOs,
female CEOs had more human capital yet led less prestigious firms. Likewise, female CEOs
received less favorable personal career success outcomes, and their firms had worse market-
based performance despite similar levels of accounting-based performance. In addition, the
country culture variables played important roles in moderating many of the career differences.
The results of our research enrich understanding of career differences between female and male
CEOs and cultural attributes that moderate such differences, suggest the importance of taking
into account both demand-side and supply-side perspectives, and offer ample implications for
theory, future research, and practice.
Keywords: CEO; gender; meta-analysis; career; culture
CEO GENDER AND CAREER
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CEO Gender Differences in Careers and the Moderating Role of Country Culture: A
Meta-analytic Investigation
Chief executive officers (CEOs) are the most visible, highly compensated, and influential
individuals in organizations. As a result, the literature on CEOs’ careers is large and important,
with some studies focused on explaining individual-level differences in CEOs’ careers (e.g.,
CEO pay) (e.g., Tosi, Werner, Katz, & Gomez-Mejia, 2000), others focused on firm-level
differences (e.g., firm risk taking) (e.g., Wang, Holmes, Oh, & Zhu, 2016), and still others
focused on cross-country differences in CEOs’ careers (e.g., Crossland & Hambrick, 2007;
2011). The extreme underrepresentation of female CEOs is a significant issue in this literature
and has attracted attention not only from academics, but also from practitioners, media, and
regulators. For example, only 5.4% of Fortune 500 companies had female CEOs in 2017.
Strikingly, this percentage is a record high in the U.S. (Pew Research Center, 2017), and there is
evidence that the percentage is even lower worldwide (Hansen, Ibarra, & Peyer, 2010). In turn, a
large and growing literature has investigated individual-level, firm-level, and cross-country
differences in female and male CEOs’ careers. This research is important, as it informs theory
about the complex impact of gender on individuals, firms, and society. It also has important
practical implications for equal opportunity efforts to address the gender imbalance in CEOs.
Despite its importance, the literature on career differences between female and male
CEOs has two critical shortcomings. First, much of the evidence is mixed, leaving equivocal
support for various theoretical arguments about the impact of gender on individual- and firm-
level differences in CEOs’ careers. At the individual-level, for example, Hill, Upadhyay, and
Beekun’s (2015) results appear to both refute and support the existence of discrimination against
female CEOs: though female CEOs were paid more, they also were more likely to exit their
CEO GENDER AND CAREER
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firms.
1
Similarly, at the firm-level, glass cliff theory posits that gender role stereotypes relegate
female CEOs to less prestigious firms that already are more likely to fail. However, whereas
some research shows that previously poorly performing firms are more likely to hire female
CEOs (Ryan & Haslam, 2005), other research shows no difference in the past performance of
firms that hire female CEOs (Adams, Gupta, & Leeth, 2009). Such discrepancies not only cloud
our understanding of career differences between female and male CEOs, but also lead to
contradictory implications for theory, executives, policy makers, and other stakeholders.
Second, and relatedly, we have little knowledge about boundary conditions that shape
career differences between female and male CEOs. In particular, mixed findings across countries
suggest that external contingencies might influence the career differences that exist. For instance,
whereas research suggests that female CEOs are paid less than male CEOs in China (Lam,
McGuinness, & Vieito, 2013), most evidence suggests that they are paid at least as well as male
CEOs in the U.S. (Bugeja, Matolcsy, & Spiropoulos, 2012; Mohan, 2014). Similarly, although
studies in both China and the U.S. have shown that firms with female CEOs take less risk than
male CEOs (Khan & Vieito, 2013; Zeng & Wang, 2015), female CEOs are associated with more
stock return volatility in China (Farag & Mallin, 2016) but less stock return volatility in the U.S.
(Martin, Nishikawa, & Williams, 2009). Thus, investors in the two countries appear to hold
different views about the suitability of, and uncertainty created by, female CEOs. These
inconsistent findings are important to understand, because they suggest that different country
contexts are likely to shape the differences between female and male CEOs in important ways.
1
Hill et al. (2015) did not differentiate voluntary and involuntary exit, but both can indicate discrimination. Dissatisfaction due to
discrimination can produce voluntary exit, and outright prejudice can produce involuntary exit. Relatedly, Becker-Blease,
Elkinawy, and Stater (2010) found that females were more likely to exit executive positions both voluntarily and involuntarily.
CEO GENDER AND CAREER
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Thus, to reconcile mixed findings and establish boundary conditions, we conduct a meta-
analysis of the literature on career differences between female and male CEOs. The overarching
research question is as follows: how and under what conditions do female and male CEOs’
careers differ? To answer this research question systematically and to identify career constructs
and potential boundary conditions theoretically, we draw from abundant scholarship that has
adapted the well-known demand-supply paradigm from economics to synthesize extant theory
about careers differences across genders into a single framework (e.g., Brands & Fernandez-
Mateo 2017; Ding, Murray, & Stuart, 2013; Fernandez-Mateo & Fernandez, 2014; Gabaldon,
Anca, Mateos de Cabo, & Gimeno, 2016). Put simply, this framework sugggests that there are
differences in the demand for, and also in the supply of, female versus male CEOs, and that these
differences have important consequences for female and male CEOs’ careers. Demand-side
forces refer to factors, such as gender role stereotypes, which reduce firms’ willingness to hire
and support female CEOs. By contrast, supply-side forces refer to factors, such as family
demands, that shape women’s experiences and preferences and, in turn, their career choices and
behaviors. Thus, the demand-supply framework identifies the different types of career obstacles
that female CEOs face and that, in turn, produce career differences between female and male
CEOs. In this way, the framework links together different theoretical arguments and has the
potential to offer a more balanced and comprehensive view than does any single theory.
Using this demand-supply framework, we identify several critical and heavily-studied
differences between female and male CEOs, in the firms that hire them, and in the outcomes that
accrue to both the CEOs themselves and to their firms. Specifically, reflecting prior literature on
female and male CEOs’ careers (e.g., Fitzsimmons & Callan 2016a, Cook & Glass, 2016), we
focus on CEO human capital (i.e., knowledge, skills, abilities, and other intellectual and
CEO GENDER AND CAREER
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psychological characteristics that contribute to orgnizational goals; Ployhart & Moliterno, 2011)
and firm prestige and predict that because of the demand-side and supply-side career obstacles
they face, female CEOs (1) must have more human capital to become CEOs and yet (2) will lead
less prestigous firms. Similarly, these same demand-side and supply-side forces tend to limit the
personal and organizational career outcomes that female CEOs achieve. Thus, again echoing
prior literature (Hill et al., 2015; Lee & James, 2007; Martin et al., 2009), we focus on personal
career success, firm risk taking, and firm performance and predict that, reflecting the demand-
side and supply-side career obstacles they face, female CEOs (3) achieve lower personal career
success, as evidenced by CEO pay and other objective indicators and also lead firms (4) that take
less risk and (5) achieve lower firm performance.
Finally, as noted, these career differences also vary across countries (e.g., Blau & Kahn,
2007; Hausmann, 2012). Such cross-country differences are important to understand, because
CEOs’ careers unfold within broader sociocultural contexts that shape not only societal
expectations about the competencies and appropriate behaviors of women and men, but also the
level of uncertainty and discomfort created when women and men violate cultural norms (Block,
1973; Wood & Eagly, 2002). By extension, it stands to reason that career differences between
female and male CEOs are culturally contingent. Specifically, research suggests that two cultural
variables, uncertainty avoidance and gender egalitarianism, shape the demand for and supply of
female CEOs and, thus, may be particularly important moderators of career differences between
female and male CEOs (e.g., House et al., 2004; Kossek, Su, & Wu, 2007; Parboteeah, Hoegl, &
Cullen, 2008). Uncertainty avoidance and gender egalitarianism are critical for understanding
CEO gender because (1) appointing female CEOs tends to challenge societal expectations about
gender, specifically the appropriateness of women in leadership, thus creating a source of
CEO GENDER AND CAREER
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uncertainty that is not present when the CEO is male, which in turn shapes not only the demand
for female CEOs, but also social structures that affect the supply of female CEOs and (2) cultural
norms and practices about gender parity impact stakeholders’ willingness to hire and support
female leaders and also the career opportunities that females believe are available to them,
further shaping the demand for and supply of female CEOs. Consistent with these views,
scholars have shown that uncertainty avoidance (Emmerik, Wendt, & Euwema, 2010) and
gender egalitarianism (Toh & Leonardelli, 2012) are critical to the emergence and development
of female leaders in a country, with uncertainty avoidance inhibiting and gender egalitarianism
enabling female leaders’ careers (Parboteeah, Hoegl, & Cullen, 2008).
Together, these predictions lead to the theoretical model in Figure 1. As the figure
suggests, two of the focal constructs—CEO human capital and firm prestige—are treated as
antecedents to firms’ decisions to hire female versus male CEOs. The other three—CEO
personal career success, firm risk taking, and firm performance—are treated as results of this
decision. Thus, in a nutshell, the model examines factors that shape decisions to hire female
CEOs, the consequences of these decisions, and how these effects vary across countries.
We test our hypotheses using meta-analytic results based on 158 primary studies that
sampled firms from 32 countries. Meta-analysis is particularly pertinent for reconciling the
disparate findings to reveal true career differences between female and male CEOs. In addition,
it is well-suited for detecting the moderating effects of some study features, such as the culture of
sampled countries, that are difficult to test in a single primary study (Post & Byron, 2015;
Schmidt & Hunter, 2014). As an added benefit, we also can test the moderating effects of certain
study characteristics, including for example, cross-sectional versus longitudinal research designs.
CEO GENDER AND CAREER
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In summary, this study sheds light on how and under what conditions CEO gender shapes
CEOs’ careers. In doing so, it makes three important contributions. First, the meta-analysis
reconciles mixed evidence on career differences between female and male CEOs and, in turn,
provides a platform for further theory development on CEO gender and its implications. In this
way, our study extends research on gender differences that has focused mostly on the careers of
employees as a whole (e.g., Ng, Eby, Sorensen, & Feldman, 2005), front-line and mid-level
managers (e.g., Koenig, Eagly, Mitchell, & Ritsikari, 2011), and boards (e.g., Post & Byron,
2015). This meta-analysis is needed, due to the importance of CEOs and the inconsistencies in
prior research.
Second, the meta-analysis enriches theory by revealing important boundary conditions. In
particular, it shows how the broader cultural context, specifically uncertainty avoidance and
gender egalitarianism, moderates individual- and firm-level differences in female and male
CEOs’ careers. Extending research on cultural contingencies that influence the choices and
impact of CEOs (e.g., Crossland & Hambrick, 2007; 2011), the current study reveals that cultural
contingencies also shape the role of CEOs’ gender in their careers. Lastly, the study provides
important practical implications for developing female leaders, reducing gender disparity in
female and male CEOs’ careers, and improving CEO selection and both CEO and firm success.
Theoretical Background
CEO Careers
Careers, which refer to accumulated work histories (Gutteridge, Leibowitz, & Shore,
1993; London & Stumpf, 1982), are complex, are multifaceted, and vary on many dimensions
(e.g., Judge, Cable, Boudreau, & Bretz, 1995; Seibert & Kraimer, 2001). The career indicators
that are relevant to CEOs include not only individual-level constructs (e.g., pay) common to
CEO GENDER AND CAREER
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people lower in the hierarchy, but also firm-level constructs (e.g., firm risk taking) that are tied
more specifically to CEOs (e.g., Hill et al., 2015; Lee & James, 2007). As the highest ranking
official, CEOs have more impact on firms, are more responsible for firm strategy and
performance, and derive more recognition from their firms’ prestige and status (e.g., Focke,
Maug, Niessen-Ruenzi, 2017; Koyuncu, Hamori, & Baruch, 2017; Quigley & Hambrick, 2015).
In other words, to understand CEOs’ careers thoroughly, it is necessary to examine not only
constructs that impact them personally, but also the characteristics of the firms they lead.
Thus, the key indicators of CEO careers include both individual- and firm-level variables
(Heslin, 2005; Ng et al., 2005). Individual-level variables accrue to CEOs directly, specifically
human capital (e.g., education) and personal career success2 (e.g., pay). These career indicators
impact CEOs’ ability to do their jobs, matter to them, and thus, are important to consider
(Koyuncu et al., 2017; Seibert et al., 2003). The firm-level variables, by contrast, consist of
organizational factors, specifically firm prestige, risk taking, and performance. Firm prestige is
important because leading prestigous firms is a challenging yet admirable professional
achievement for CEOs (Chen, 2008; Focke et al., 2017). Firm risk taking and performance also
are important, as they capture not only what CEOs do in their careers, but also how well they do
it, which in turn impacts not only the CEOs, but also investors, suppliers, employees, and other
stakeholders (e.g., Dalton, Daily, Ellstrand, & Johnson, 1998; Hambrick & Mason, 1984).
Theoretical Explanations for Gender Differences in CEOs’ Careers
The literature on gender and careers is large and diverse, is multidisciplinary, and
examines a variety of research questions. As noted above, a growing chorus of scholars have
argued that both demand-side and supply-side forces contribute to differences between females’
2 We focus on objective personal career outcomes, such as CEO pay, job tiles, and tenure, because few studies have
examined CEOs’ subjective career outcomes, such as job satisfaction, self-actualization, and work-life balance.
CEO GENDER AND CAREER
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and males’ careers and, in turn, have used the well-known demand-supply framework from
economics to synthesize diverse theoretical arguments into a single paradigm (Bego, 2014;
Brands & Fernandez-Mateo, 2017; Ding et al., 2013; Fernandez-Mateo & Fernandez, 2016;
Gabaldon et al., 2016; Oxley & Fox, 2004; Reskin, 1993).
Demand-side forces limit firms’ willingness to hire and support female CEOs. Most
research in this area focuses on discrimination and bias against female CEOs from two related
sources: in-group favoritism and gender role stereotypes. First, due to in-group favoritism,
individuals tend to view similar others as more competent (Kanter, 1977). Because men tend to
dominate corporate leadership positions, for example, the credentials required for such positions
tend to be set and evaluated by men, placing women at a disadvantage (Fitzsimmons & Callan,
2016a; 2016b; Fitzsimmons, Callan, & Paulsen, 2014; see also Bourdieu, 2001). Along these
lines, because most board members and executive search firm professionals are male, they tend
to evaluate males more favorably (Dreher, Lee, & Clerkin, 2011; Post & Byron, 2015). Second,
research on gender role stereotypes suggests that “the stereotypes of leaders are decidedly
masculine” (Koenig et al., 2011, p. 634), meaning that the perceived attributes of good leaders
(e.g., aggressiveness) overlap stereotypical male attributes more than stereotypical female
attributes. Although research suggests that both males and females hold these biases, they are
stronger when males are evaluating females (e.g., Paustian-Underdahl, Walker, & Woehr, 2014;
Koenig et al., 2011; Schein, Mueller, Lituchy, & Liu, 1996). Because of this “think manager-
think male” bias, men have advantages obtaining and succeeding in leadership positions, while
women leaders are more likely to be disliked and viewed as socially inept, due to the perceived
role incongruity (Eagly & Karau, 2002; Ely, Ibarra, & Kolb, 2011; Heilman, Wallen, Fuchs, &
Tamkins, 2004; Ryan, Haslam, Hersby, & Bongiorno, 2011; Schein et al., 1996; Vial, Napier, &
CEO GENDER AND CAREER
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Brescoll, 2016). However, in this regard, glass-cliff theory posits that there is more demand for
female CEOs in less prestigious firms at risk of failing, partly because many stakeholders believe
that sterotypical female attributes, such as compassion, are needed by such firms (Ryan &
Haslam, 2005).
Supply-side forces, by contrast, refer to factors that shape the career interests, goals,
expectations, choices, and experiences of female and male CEOs differently. Research in this
area tends to highlight two factors that produce differences in the supply of female and male
leaders: family demands and socialization. First, because women shoulder most household and
childcare duties, it is more difficult for them to accumulate the developmental opportunities and
social connections to obtain and succeed in the CEO position (Hoobler, Lemmon, & Wayne,
2000; Lyness & Tompson, 1997; Nelson & Burke, 2000). Reflecting family demands, for
example, women are more likely to emphasize work-life balance and to prioritize their husbands’
careers over their own, as men tend to earn higher pay (Lyness & Thompson, 2000; Mainiero &
Sullivan, 2005; O’Neill & O’Reilly, 2010). Second, socialization processes result in different
treatment and expectations of females and males. Even as children, females are socialized to
value others’ feelings, relational intimacy, and nurturing, whereas males receive more
encouragement to assume leadership roles, especially in sports, which helps develop competitive
and risk-taking skills that are useful to CEOs (Ely & Padavic, 2007; Reskin, 1993; Ross-Smith &
Huppatz, 2010). Thus, reflecting both family demands and socialization, females may evaluate
their careers more holistically and place more value on relationships and may, in turn, be less
likely to pursue CEO or chair positions, request pay raises and promotions, or focus on prestige,
risk taking, and financial indicators of firm performance (Kish-Gephart, Harrison, & Trevino,
2010; Konrad, Ritchie, Lieb, & Corrigall, 2000; Manner, 2010; Stevens, Bavetta, & Gist, 1993).
CEO GENDER AND CAREER
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In line with these views, some have argued that, if women and men have different life
circumstances and priorities, gender imbalances in their careers are not necessarily evidence of
discrimination (Arthur, Kahapova, & Wilderom, 2005; Judge et al., 1995; Kossek et al., 2017).
Thus, both demand-side and supply-side forces create career obstacles for female CEOs,
affecting both whether females become CEOs (i.e., antecedents CEO gender) and also the
consequences of female CEOs (i.e., outcomes of CEO gender). Moreover, both demand-side and
supply-side perspectives often are necessary to paint a complete picture of the obstacles that
female CEOs confront. While gender role stereotypes can reduce the demand for female CEOs,
for example, the internalization of such stereotypes by women can affect their decisions and,
thus, the supply of prospective and actual female CEOs (Ding et al., 2013; Paustian-Underdahl et
al., 2014). As a result, focusing on either demand-side or supply-side forces alone provides only
partial explanations for the career differences between female and male CEOs. In turn, this meta-
analysis is a first-step toward understanding (1) the combined effects of demand-side and supply-
side forces on important career differences between female and male CEOs and (2) country-level
contingencies that exacerbate or diminish those differences. Relatedly, we recognize that
sometimes it is possible to construct arguments that are opposite to the hypotheses we propose.
Whereas demand-side forces hinder female CEOs’ ability to do their jobs, for example, the
supply of women who can overcome such obstacles to obtain the CEO position in the first place
might be especially competent leaders who are able to outperform their male counterparts. We
identify such contrasting predictions where necessary and, then, present the arguments and
hypotheses that we believe are most representative of themes in the bulk of the literature.
Hypotheses
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In this section, we draw on the demand-supply framework to develop hypotheses about
(a) differences in female and male CEOs’ careers and (b) how country cultural attributes (i.e.,
uncertainty avoidance and gender egalitarianism) moderate these differences. We begin by
considering differences between female and male CEOs and also the firms that hire them.
CEO Human Capital
Human capital refers to “knowledge, skills, abilities, and other characteristics (KSAOs)”
relevant for achieving organizational goals (Ployhart & Moliterno, 2011, p. 127). Due to the
complexity of CEO positions, we conceptualize human capital broadly: its indicators include not
only variables (e.g., education) that help individuals accumulate KSAOs, but also psychological
attributes (e.g., self-efficacy) that help them use their KSAOs (Fitzsimmons & Callan, 2016a;
Ployhart, Nyberg, Reilley, & Maltarich, 2014). The demand-supply framework suggests that
females must have more human capital, such as those indicators that have strong signaling effect
on the labor market (Becker, 1964; Colao, 2013; Spence, 1973; Wade, Porac, & Graffin, 2006;
2008), to overcome the obstacles they face to attain the CEO position. Thus, we argue that
female CEOs have more human capital than male CEOs.
First, various demand-side arguments suggest that there usually is less demand for female
leaders than for male leaders (Reskin, 1993). As noted, due to in-group favoritism, male-
dominated boards and executive search firms tend to evaluate males’ credentials more favorably.
Likewise, males sometimes exhibit gender role stereotypes that discriminate against women in
selection decisions (Koch, D’Mello, & Sackett, 2015; Post & Byron, 2015). Because the
selection systems usually are biased against them, females often must outshine males by a
significant margin to earn the CEO position (Foschi, 2000; Heffernan, 2002; Sharpe, 2000).
Likewise, Ding et al. (2013, p. 1448) argued that, due to the biases they confront, “an objective
CEO GENDER AND CAREER
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record of performance may matter more for women." By extension, it is possible that female
CEOs have human capital indicators (e.g., more education) superior to those of male CEOs (Hill
et al., 2015). In support, for example, there is evidence that female CEOs received their degrees
from more elite institutions than male CEOs (Miller, Xu, & Mehrotra, 2015; Schepker, 2012).
Second, from a supply-side perspective, both human capital and social capital help
individuals become CEOs (Fitzsimmons & Callan, 2016b; Ng et al., 2005; Tharenou, 2002).
However, because social activities intrude on family time and women usually have more family
duties, they can have difficulty breaking into influential male-dominated networks, especially if
they need more work-life balance. Likewise, reflecting different socialization, women and men
tend to have different social interests, yet women must engage in traditional male activities and
conversations to access male-dominated networks (Fitzsimmons & Callan, 2016b). For these
reasons, women face disadvantages obtaining social capital (Ely et al., 2011; Glass & Cook,
2016; Ibarra, 1993; Ragins & Sundstrom, 1989). Thus, to the extent that “women can rely less on
social capital … their career advancement relies more upon them having the requisite human
capital” (Duberley & Cohen, 2010, p. 189). Due to these supply-side obstacles, female CEOs
might need more human capital to substitute for a relative lack of social capital.
On a related note, the combination of demand-side and supply-side obstacles suggests
that women face many career hurdles due to their gender. Women who view themselves, their
competence, and their potential more favorably, however, are less likely to self-select out of the
career progression game when they confront setbacks (Kossek et al., 2017). By extension,
positive psychological attributes—such as positive self-concept or self-esteem—which help
individuals overcome prejudice and bias, may be more vital to the careers of women (Hoyt &
Murphy, 2015). Accumulated career successes against demand-side biases also may have
CEO GENDER AND CAREER
14
reinforced such positive psychological attributes in the supply of female CEOs. In other words,
female CEOs might be more resilient than their male counterparts. Consistent with these
arguments, Fitzsimmons et al. (2014) found that female CEOs often had positive psychological
attributes.
Thus, the demand-supply framework suggests that female CEOs score higher on both
experiential (e.g., education) and psychological aspects of human capital than male CEOs do.
Hypothesis 1: Female CEOs have more human capital than male CEOs.
Firm Prestige
One important firm-level indicator of CEOs’ careers is the prestige of their firms. Firm
prestige refers to the firm’s status, legitimacy, and stability. More prestigious firms are taken-for-
granted, experience fewer threats to their legitimacy, and are more secure (Aldrich & Fiol, 1994;
Baum, 1996; Zimmerman & Zeitz, 2002). Thus, larger, older, and better-performing firms tend
to be more prestigious than smaller, newer, and worse- performing firms (Fackler, Schnabel, &
Wagner, 2013; Hannan & Freeman, 1989; Short, McKelvie, Ketchen, & Chandler, 2009).
Whereas more prestigious firms are better positioned to hire female CEOs (e.g., such
firms’ legitimacy is more secure), prior literature suggests that both demand-side and supply-side
forces can relegate female CEOs to less prestigious firms (Ryan & Haslam, 2005; 2007).
First, the demand for female CEOs is greater in less prestigious firms, especially those at
risk of failing. Decision makers often believe that stereotypical female attributes—such as
warmth, sensitivity to feelings, and cheerfulness—can alleviate crises and poor morale in
distressed firms, thus increasing the demand for female CEOs in such firms (Ryan et al., 2011).
In support, Bruckmüller and Branscombe (2010) showed that participants in lab studies valued
stereotypical female attributes more heavily in the leadership of poorly-performing firms and, in
CEO GENDER AND CAREER
15
turn, were more likely to select female leaders for these firms. By appointing female CEOs, such
firms also can signal to stakeholders that they are moving in a new direction. Appointing male
CEOs, by contrast, is more likely to signal a commitment to the status quo (Glass & Cook,
2016). For these reasons, the demand for female CEOs tends to be higher in low-prestige firms.
Second, supply conditions can relegate female CEOs to less prestigious firms. In
particular, the CEO position is more demanding in more prestigious firms, because such firms
usually are larger, have had more time to diversify, and have more stakeholders and resources to
manage (Hambrick, Finkelstein, & Mooney, 2005). Thus, family demands prevent many women
from competing for the CEO position in such firms, and many women have been socialized to
believe that they lack the leadership capabilities to lead such firms (e.g., Beyer, 1990; Paustian-
Underdahl et al., 2014). Along these lines, because fewer CEO options are available to women,
they are more likely to accept CEO positions at distressed or low-prestige firms. Male CEO
candidates, by contrast, can reject offers from such firms with the expectation that better offers
will come (Cook & Glass, 2014). Relatedly, recent evidence reveals that female executives know
that their career options are more limited and, in an attempt to bolster their profile, are more
likely to seek out poorly performing firms that need a turnaround (Glass & Cook, 2016).
Therefore, female CEO candidates are more prone to self-select into less prestigious firms.
Hypothesis 2: Female CEOs serve in less prestigious firms than male CEOs.
CEO Personal Career Success
Whereas the first two hypotheses related to differences between female and male CEOs
and the firms that hire them, the next three relate to the individual- and firm-level outcomes of
CEO gender. We begin with the personal career success of female versus male CEOs
CEO GENDER AND CAREER
16
Personal career success refers to rewards that firms provide to individuals in exchange for
their work. Examples include job titles (e.g., CEO versus CEO and Board Chair), pay, and
position tenure (Arthur et al., 2005; Heslin, 2005; Judge et al., 1995). On one hand, it is possible
that female CEOs’ personal career success is at least equal to that of male CEOs, because
apparent discrimination increases litigation risks and bad publicity. Female CEOs who have
overcome career obstacles also may display qualities—such as agency and self-promotion—
which stereotypically are linked with males and also can help secure personal outcomes in
organizations (Vial et al., 2016). Taken together, however, we believe most theoretical
arguments about the demand for and supply of female CEOs, and most empirical evidence,
suggest that female CEOs receive worse personal career success outcomes than male CEOs.
First, lower demand for female CEOs can diminish the personal career success they
achieve. Reflecting in-group favoritism and gender role stereotypes, predominantly-male boards
and investors sometimes lack complete confidence in female CEOs, are hypercritical of them,
and are more likely to attribute poor firm performance to them (rather than external
circumstances) (Bertrand & Hallock, 2011; Dixon-Fowler, Ellstrand, & Johnson, 2013; Kulich,
Trojanowski, Ryan, Haslam, & Renneboog, 2011; Lee & James, 2007). Because job titles, pay,
and tenure reflect the value that boards and investors expect CEOs to deliver, the lack of faith in
female CEOs can reduce their chances of being named both chair and CEO (Glass & Cook,
2016), their pay (Castilla & Benard, 2010), and their tenure in the position (Hill et al., 2015). In
support, there is evidence that female CEOs are less likely to serve as chair (Muller-Kahle &
Schiehll, 2013), males pay females less in general (Newton & Simutin, 2014), and female CEOs
are more likely to be fired if firm performance declines (Becker-Blease et al., 2010). Likewise,
Joshi, Son, and Roh (2013) found that females receive lower personal career success outcomes in
CEO GENDER AND CAREER
17
jobs that are complex, high-status, and male-dominated, which are attributes of CEO positions.
Further, because demand-side biases reduce the CEO opportunities available to women, they
have less bargaining power to negotiate for better personal career success outcomes.
Second, supply-side forces related to family pressures and socialization processes also
can lead to lower personal career success outcomes for women. Even among high-level
managers, females shoulder more family duties than males do (Lyness & Thompson, 1997;
Mainiero & Sullivan, 2005; Nelson & Burke, 2000). As a result, females may be less eager to
negotiate for greater job responsibilities, such as chairing the board, and may be more willing to
leave CEO positions voluntarily (Becker-Blease et al., 2010). Moreover, research on the well-
known gender wage gap suggests that female workers are paid less because women face more
social sanctions for bargaining aggressively for higher pay (Bowles, 2014; Bowles, Babcok, &
Lai, 2007). Relatedly, relative to females, males may feel more entitled to, and more likely to ask
for, better personal careers success outcomes when they become CEOs. Consistent with these
arguments, studies suggest that women tend to have lower personal career expectations than men
have (Kulich et al., 2011). Thus, we argue that the supply-side career obstacles confronting
female CEOs also reduce their personal career success outcomes.
Hypothesis 3: Female CEOs achieve less personal career success than male CEOs.
Firm Risk Taking
Female CEOs also might manage their firms differently than male CEOs, producing
differences in firm behavior. Firm risk taking is a key indicator of firm behavior, because it is
fundamental to firm survival and success (Cyert & March, 1963; Shapira, 1995) and also because
it captures other important investments, such as acquisitions, product and international
CEO GENDER AND CAREER
18
diversification, innovation, and other similar strategic actions3, that CEOs initiate (Hoskisson,
Chirico, Zyung, & Gambeta, 2017; Larraza-Kintana, Wiseman, Gomez-Mejia, & Welbourne,
2007; Li & Tang, 2010). Although risk taking is a stereotypical male attribute (Byrnes, Miller, &
Schafer, 1999; Siegrist, Cvetkovich, & Gutscher, 2002; Vial et al., 2016), it is possible that
female CEOs take at least as much risk as male CEOs do, especially if they lead distressed firms
in need of risky turnarounds (Mone, McKinley, & Barker, 1998) or if selection processes filter
out risk-averse females (Adams & Funk, 2002). Despite these possibilities, the bulk of the
literature suggests that firms with female CEOs take less risk than do firms with male CEOs.
First, demand-side forces can reduce risk taking in firms with female CEOs. As described
above, female CEOs receive more attention from the press, other executives, and investors, and
much of this attention is hypercritical, especially if evaluators are male (due to in-group
favoritism) and/or resort to stereotypes that favor male leadership and aggressiveness. Observers
who expect women to be risk averse, for example, may evaluate risky actions by firms with
female CEOs less favorably. Consistent with these arguments, there is evidence that women are
more risk averse when under more scrutiny, pressure, and stress (Beyer & Bowden, 1997;
Mather & Lighthall, 2012; see also Ely et al., 2011 and Kulich et al., 2011). Likewise, at the firm
level, Palvia, Vähämaa, and Vähämaa (2015) and Zeng and Wang (2015) found that firms with
female CEOs take less risk, as evidenced by lower default risks and larger cash holdings.
Second, from a supply-side perspective, differential treatment and experiences of females
and males affect their familiarity and comfort with risk. Previous jobs held by CEOs are a source
3 Firm strategic actions are large-scale undertakings that involve substantial and difficult to reverse investments of
firm resources (Connelly, Tihanyi, Certo, & Hitt, 2010). They differ from tactical or operational actions “such as
inventory decisions and credit policies,” which are less financially consequential, smaller in scale, and more
reversible, and “which lend themselves more to calculable solution[s]” (Hambrick & Mason, 1984, p. 195).
CEO GENDER AND CAREER
19
of valuable experiences that shape their familiarity with risk (Hambrick & Mason, 1984; Wang
et al., 2016). However, because family demands usually are higher for females, many firms do
not assign them to oversee the firm’s most challenging, expensive, and time-consuming strategic
actions (Hoobler et al., 2014; King et al., 2012). New jobs also can require relocation and, thus,
are easier on families with trailing spouses who lack or have more flexible careers, a condition
that likely favors male over female applicants (Ely et al., 2011; Kolb & Williams, 2000).
Moreover, females are socialized to be more cautious than males, due to differences in physical
strength and related safety issues (see Wood & Eagly, 2012 for a review of the effects of
differences in physical strength). Such concerns also deter firms from assigning women to jobs
with more security hazards (e.g., in unstable countries), reducing their opportunities to learn to
manage in high-uncertainty contexts (Fitzsimmons & Callan, 2016a). Thus, female CEOs may
have less experience with hostile settings, novel projects, and large investments and, in turn, may
be less familiar with many of the risky actions available to them as CEOs. This lack of
familiarity is important, as research shows that females tend to display more risk aversion on
tasks with which they are less familiar (Beyer & Bowden, 1997; Kulich et al., 2011). By
extension, firm risk taking may be lower in firms with female CEOs.
Hypothesis 4: Female CEOs’ firms take less risk than male CEOs’ firms.
Firm Performance
One of the CEO’s main responsibilities is to improve firm performance, which includes
both accounting (e.g., net income) and market metrics (e.g., shareholder return) in public firms.
Firm performance depends critically on the CEOs’ knowledge, values, and personalities, which
affect their decision making and, in turn, firm behavior (Hambrick & Mason, 1984; Quigley &
Hambrick, 2015; Wang et al., 2016). Because CEOs do not act in isolation, firm performance
CEO GENDER AND CAREER
20
also depends on their ability to mobilize stakeholders (e.g., employees and investors) to support
their decisions. Thus, firm performance is an important indicator of CEOs’ careers.
On one hand, firms with female CEOs might perform better, as female CEOs have
survived more rigorous and biased selection processes and, thus, might have superior talent,
persistence and drive, leadership and decision-making skills, and so on (van Staveren, 2014).
However, in line with the arguments above, the many demand-side and supply-side obstacles
confronting female CEOs also can hurt firm performance, primarily by undermining their ability
to mobilize and secure support from important stakeholders that are critical to firm performance.
First, demand-side forces that produce resistance to female CEOs can make it difficult for
them to lead and motivate subordinate executives. As Zhang, Li, Ullrich, and van Dick (2015, p.
1909) observed, “people tend to react to leaders in terms of gendered expectancies.” Because
most executives are male (Fernandez-Mateo & Fernandez, 2016), female CEOs face the
challenge of working with immediate subordinates who exhibit different gender-based behaviors
and expectations. Reflecting in-group favoritism, for example, at least some male executives
tend to have less respect for and confidence in female CEOs, meaning that female CEOs
probably work with a less accommodating or engaged team of subordinate executives than male
CEOs do. Similarly, Glass and Cook (2016, p. 53) argued that females receive less “support and
resources” yet more “overt or subtle resistance to their authority from subordinates.” Further,
leadership behaviors like assertiveness and control stereotypically are associated with men and,
thus, are less effective when expressed by women (Eagly & Karau, 2002; Ely & Padavic, 2007).
Consistent with these views, Vial et al. (2016) argued that subordinates who work for females
exhibit fewer positive (e.g., cooperativeness) and more negative (e.g., sabotaging) behaviors.
CEO GENDER AND CAREER
21
Likewise, Sinclair and Kunda (2000) showed that, compared to male managers, female managers
are viewed more negatively after they provide negative feedback to subordinates.
Supply-side forces also can undermine female CEOs’ standing with key stakeholders in
ways that hurt firm performance. As noted, family demands and socialization often exclude
women from social networks dominated by men. Women excluded from internal networks (e.g.,
among executives) before becoming CEO have had fewer opportunities to build connections,
solidarity, and trust with male subordinates. Likewise, women excluded from external networks
have had fewer opportunities to build relationships with alliance partners, suppliers, industrial
customers, and investors (e.g., Adler & Kwon, 2002; Leana & Pil, 2006). Moreover, many
women opt out of work before reaching executive positions (Kossek et al., 2017), reducing the
supply of female CEOs. Thus, most stakeholders have less experience with female CEOs, and
this lack of familiarity creates uncertainty, which can reduce firm performance both indirectly
(e.g., lower stakeholder support) and directly (e.g., lower demand for the firm’s stock). In
support, a recent study showed that IPO firms with female CEOs were evaluated less favorably
than those with male CEOs, partly because the female CEOs were viewed less favorably
(Bigelow, Lundmark, Parks, & Wuebker, 2012). Likewise, arguing that unfamiliarity with
female CEOs creates uncertainty about them, Lee and James (2007) found that investors reacted
more negatively to female CEO appointments than to male CEO appointments.
Hypothesis 5: Female CEOs’ firms perform worse than male CEOs’ firms.
Country Culture Moderators
As noted, mixed findings across countries suggest that country-level conditions may
moderate the differences hypothesized above. In particular, country culture is likely to shape the
demand for and supply of female versus male CEOs. Culture refers to shared social norms,
CEO GENDER AND CAREER
22
values, and beliefs which affect the preferences, expectations, and behaviors of individuals,
firms, and governments in a country (Holmes, Miller, Hitt, & Salmador, 2013). Because culture
shapes perceptions about the appropriate attributes and actions of CEOs and firms, it likely has
important implications for both individual- and firm-level aspects of CEOs’ careers (Crossland &
Hambrick, 2011; Harpaz & Fu, 1997; Heslin, 2005). We focus on two country cultural attributes
that are especially relevant to CEO gender: uncertainty avoidance and gender egalitarianism.
These cultural norms depict and reinforce socially acceptable practices regarding females’ and
males’ career options, opportunities, and barriers (e.g., Geletkanycz, 1997; Schein et al., 1996;
Parboteeah et al., 2008). As such, they are likely to shape the demand for and supply of female
CEOs in ways that affect the differences between female and male CEOs, in the firms that hire
them, and in the outcomes that accrue both to the CEOs themselves and to their firms.
Uncertainty Avoidance. Uncertainty avoidance, which refers to the degree to which a
country’s people tend to tolerate ambiguity, unpredictability, and risk (House et al., 2004),
reduces the demand for female CEOs. The nature of CEOs’ jobs creates ambiguity, complicates
hiring, makes it difficult to assess CEO performance, and in turn, can increase in-group
favoritism (Kanter, 1977) and use of gender role stereotypes (Heilman, 2001). Thus, when
uncertainty avoidance is high, predominantly-male boards, headhunters, and investors may view
male CEOs even more favorably, because shared gender removes a source of uncertainty and
also promotes perceptions of competence and trustworthiness (e.g., Fiske, Cuddy, Glick, & Xu,
2002; Insko, Schopler, Hoyle, Dardis, & Graetz, 1990) and because gender role stereotypes that
favor males for leadership reduce uncertainty about male CEOs’ abilities (Ding et al., 2013; Lee
& James, 2007). Conversely, reflecting in-group favoritism, Brodbeck, Frese, and Kroll (2002)
argued that the key decision makers in most firms are male and, in turn, are less likely to hire
CEO GENDER AND CAREER
23
female managers in high uncertainty avoidance countries, because such hires create new sources
of uncertainty. Likewise, Parboteeah et al. (2008) found that gender role stereotypes hindering
female leaders are more pronounced in higher uncertainty avoidance countries.
Uncertainty avoidance also affects the supply of female CEOs. Over time, different
family duties and socialization create different expectations for males and females (e.g., females
are homemakers and males are breadwinners; Paustian-Underdahl et al., 2014) and, in most
cases, award higher status to males (Ding et al., 2013). Because countries high in uncertainty
avoidance resist changing the status quo, there probably is less support for social movements,
programs, and new laws and regulations to correct gender disparities in leadership, thus
preserving the higher status of males while limiting the supply of female leaders (Emmerik et al.,
2010; House et al., 2004). In addition, there is evidence that managers delegate fewer tasks in
countries that are high in uncertainty avoidance (Offerman & Hellman, 1997), further limiting
the opportunities for females to acquire leadership skills. Because female leadership creates new
sources of uncertainty, aspiring female leaders also face more sanctions and discouragement
from current power structures, thus reducing the supply of women who seek to be CEOs (Toh &
Leonardelli, 2012). In line with these arguments, Emmerik et al. (2010) found that the supply of
female managers was lower in countries higher in uncertainty avoidance.
Therefore, the career obstacles facing female CEOs can be even greater in high
uncertainty avoidance countries, which would exacerbate the relationships in Hypotheses 1-5.
Indeed, many of the demand and supply arguments above reflected the view that there is more
uncertainty about female CEOs. Fitzsimmons and Callan’s (2016b) research, for example,
suggests that CEO human capital reduces uncertainty because the performance of well-qualified
CEOs with established track records is more predictable. By extension, in high uncertainty
CEO GENDER AND CAREER
24
avoidance countries, female CEOs probably need even more human capital than they need in low
uncertainty avoidance countries. Additionally, in high uncertainty avoidance countries, it may be
that only the least prestigious firms have to take on the added uncertainty of female CEOs.
Because there is more uncertainty about low-prestige firms (Stuart, Hoang, & Hybels, 1999),
they are even less attractive in these countries, which can exacerbate the aforementioned
selection effects that sort women into, and men away from, the leadership of such firms.
By affecting demand-side and supply-side factors, uncertainty avoidance also amplifies
differences in the individual- and firm-level career outcomes that female and male CEOs
achieve. Above, we argued that female CEOs tend to attain less personal career success, at least
partly because there is more uncertainty about their performance. Thus, our framework suggests
that female CEOs will achieve even worse personal career success in countries where there is
less tolerance for uncertainty. In addition, because firm risk taking is valued less (Shane, 1993)
and adherence to the status quo is valued more (Geletkanycz, 1997) in high uncertainty
avoidance countries, firms led by female CEOs may take even less risk in these countries. If
cultural norms do not value risk, for example, there may be even more backlash and loss of
stakeholder support for women who violate such norms (e.g., Rudman & Phelan, 2008). Further,
women in such countries have even fewer opportunities to gain experience taking risks. Lastly,
to the extent that a lack of familiarity with female CEOs is more problematic when uncertainty
avoidance is high, female CEOs in such countries probably have even more difficulty garnering
the stakeholder support and resources they need to affect firm performance positively.
Hypothesis 6: Country uncertainty avoidance exacerbates the career differences in
Hypotheses 1-5. Specifically, as uncertainty avoidance increases, the career differences
between female and male CEOs in human capital, firm prestige, personal career success,
firm risk taking, and firm performance become greater in magnitude.
CEO GENDER AND CAREER
25
Gender Egalitarianism. Our final hypothesis concerns the moderating effect of gender
egalitarianism, which reflects the importance of minimizing female-male inequality and status
differences in a country. In countries higher in gender egalitarianism, females and males are
viewed more equally and steps are taken to ensure such equality (House et al., 2004). As a result,
there may be fewer differences in either the demand for, or the supply of, female versus male
CEOs (or CEO candidates) in more gender egalitarian countries (Parboteeah et al., 2008; Toh &
Leonardelli, 2012). Thus, unlike uncertainty avoidance, gender egalitarianism may reduce the
individual- and firm-level differences in female and male CEOs’ careers described above.
First, there is more demand for female CEOs in more gender egalitarian countries.
Conversely, the view that women are less effective leaders is more widely held and legitimated
in less gender egalitarian countries. For instance, Kulich et al. (2011) argued that demand-side
biases—such as undervaluing females’ competence and gender role stereotypes that women are
poor leaders—are more common when women have less social status. Similarly, Parboteeah et
al. (2008) found evidence that gender role stereotypes that associate males with leadership (and
women with non-leadership roles) were more prominent in less gender egalitarian countries. As a
result, firms in such countries are less likely to prefer female CEOs or evaluate them favorably.
Second, low gender egalitarianism also can affect the supply of female CEOs. Toh and
Leonardelli (2012), for instance, argued that gender egalitarianism is critical in determining the
supply of female leaders. Women in societies that place less value on gender equity are less
likely to believe that they are fit for leadership and, in turn, are less likely to accumulate the
credentials to compete for such positions. If they believe that the deck is stacked against them,
which is more likely in less gender egalitarianism countries, even women who believe that they
are capable may choose to not put forth the effort to try for leadership positions. In support,
CEO GENDER AND CAREER
26
McWhirter, Hackett, and Bandalos (1998) showed that more gender egalitarian attitudes related
positively to young girls’ career aspirations and commitment, which in turn led them to
formulate more ambitious educational plans. Likewise, Toh and Leonardelli (2012) found that
gender egalitarianism related positively to the number of women leaders in a country. Lastly,
Nelson and Burke (2000) argued that the relative lack of work-family conflict reported by female
managers in Finland is partly attributable to that country’s high level of gender egalitarianism,
which produces more equitable sharing of household and family duties among men and women.
Thus, the demand-supply framework suggests that female CEOs face less severe career
obstacles in more gender egalitarian countries, thus producing more similarity in female and
male CEOs and in the firms that hire them. Specifically, because female leaders are viewed as
more legitimate and because there are stronger anti-discrimination laws in countries with more
gender parity (Post & Byron, 2015), we predict that female and male CEOs’ human capital will
be similar. And when women enjoy higher status, discrimination is less acceptable and gender
role stereotypes are less prevalent and influential (Ely & Padavic, 2007). Thus, we predict that
females are less likely to be sorted into low prestige firms in more gender egalitarian countries.
Likewise, the demand-supply framework suggests that female and male CEOs’ career
outcomes should be similar in more gender egalitarian countries. Specifically, due to the stronger
norms for gender parity and more equal opportunities for men and women, we predict that
female and male CEOs’ personal career success will be similar. Similarly, when female leaders
have more legitimacy and “access to resources and opportunities in terms of education, economic
participation, employment, and political empowerment,” they affect firm strategy and
performance more positively (Post & Byron, 2015, p. 1550). With more stakeholder support and
learning opportunities, for example, female CEOs should be more comfortable with risk. Thus,
CEO GENDER AND CAREER
27
as gender egalitarianism increases, risk taking in firms with female CEOs also should increase.
Extending these arguments, when female CEOs have more voice, influence, legitimacy and
support, they also should have more positive effects on firm performance.
Hypothesis 7: Country gender egalitarianism diminishes the career differences in
Hypotheses 1-5. Specifically, as gender egalitarianism increases, the career differences
between female and male CEOs in human capital, firm prestige, personal career success,
firm risk taking, and firm performance become smaller in magnitude.
Method
We tested our hypotheses using results from meta-analysis, “the statistical analysis of a
large collection of analysis results from individual studies for the purpose of integrating the
findings” (Glass, 1976, p. 3). In the following sections, we report our effort for locating primary
studies, inclusion criteria, operationalization of constructs, specific meta-analytic procedures,
and hypothesis testing techniques.
Literature Search and Criteria of Inclusion
To locate published and unpublished primary studies, we searched electronic databases
including EBSCO, Google Scholar, Proquest Dissertations and Theses, PsychINFO, and Web of
Science using combinations and variations of the following key words: chief executive officer,
CEO, gender, sex, female, and women. We also consulted the reference lists of review pieces
(e.g., Finkelstein et al., 2009), conference proceedings (e.g., from the Academy of Management
Annual Meetings), and in-press articles in major journals (e.g., Journal of Applied Psychology)
that previously had published relevant studies. These searches produced over 300 studies. We
screened each study and included those that measured CEO gender differences (or reported the
statistical information that allowed us to compute CEO gender differences) in at least one of the
study variables. The final sample consisted of 158 studies, including 101 published studies and
57 unpublished working papers and theses. Of the 158 studies, 69 were based on U.S. firms, 64
CEO GENDER AND CAREER
28
were based on firms based in 31 other countries (e.g., China, Germany, South Korea, Sweden,
Pakistan, the Philippines, etc.), and the remaining 25 either did not specify the country or were
based on firms in a variety of countries. All of the primary studies are reported in the references.
Operationalization of Study Constructs
We operationalized each of the study constructs using conceptually similar variables (see
Appendix A for a complete list of variables that make up each study construct). Multiple authors
coded the primary studies independently and then crosschecked each other’s coding. The
convergence and accuracy of the coding was nearly perfect. In a few cases, coding discrepancies
were found and solved through discussion and by referring back to the articles.
CEO human capital. With reference to prior research (e.g., Crook, Todd, Combs, Woehr,
& Ketchen, 2011; Wright, McMahan, & McWilliams, 1994), we coded several variables,
particularly those that have strong signaling effect on the labor market (e.g., Fitzsimmons &
Callan, 2016a; Hill et al., 2015), as measures of CEO human capital since this construct was
viewed as an antecedent to firms’ selection of female versus male CEOs. First, CEO age was the
time a CEO had lived when data were collected. Younger CEOs are viewed as early achievers,
intelligent, and savvy (Colao, 2013), making age an important measure of CEO human capital.
Because youth can indicates more human capital in the CEO context, the relations of CEO age
with other variables were reverse coded. Second, formal education has long been used to proxy
human capital, as research shows that more and more elite education relates positively to
cognitive ability, cognitive complexity, and career success (e.g., Becker, 1964; Hitt & Tyler,
1991; Ng et al., 2005). Thus, we included education level, typically measured with categorical
variables and/or the number of years of post-secondary schooling, and elite education, captured
CEO GENDER AND CAREER
29
by formal schooling at elite educational institutions (Useem & Karabel, 1986), as measures of
CEO human capital.
Lastly, non-cognitive individual differences also can indicate human capital (Ployhart et
al., 2014). Specifically, personality shapes the types of knowledge and skills that individuals
choose to pursue, their motivation to grasp knowledge and skills, and their relationships with
other individuals (e.g., Barrick, Mount, & Li, 2013). In particular, positive self-concept, which
is the extent to which CEOs positively evaluate themselves and their ability, has been frequently
studied in the CEO literature. People with positive self-concept are more confident, and resilient,
and are better equipped to handle the demands of the CEO position (Judge, Erez, & Bono, 1998).
Thus, positive self-concept is an important measure of CEO human capital. Guided by Judge et
al. (1998) and Wang et al. (2016), we coded narcissism, hubris, overconfidence, optimism,
positive and negative (reverse-coded) affectivity, self-enhancement, promotion and prevention
(reverse-coded) foci, emotional stability, risk propensity, and internal locus of control as
indicators of CEO positive self-concept.
Taken together, research suggests that these measures formulate a formative construct of
CEO human capital (Edwards & Bagozzi, 2000). As suggested by the above descriptions of CEO
age, education level, elite education, and positive self-concepts, they are distinct from each other
but represent different aspects of CEOs’ knowledge, skills, abilities, and psychological
characteristics (Ployhart et al., 2014). Thus, it stands to reason that CEO human capital covaries
with each of the four measures. In terms of construct-measure causality, it is logical that CEO
human capital is caused by these measures: changes (e.g., increase in CEO elite education) in
these measures are likely to lead to changes (e.g., increase in CEO human capital) in the overall
CEO GENDER AND CAREER
30
construct, but it is less feasible that changes in CEO human capital produce changes in measures,
such as CEO age. Given this reasoning, we treated CEO human capital as a formative construct.
Firm prestige. Prestigious firms are more visible, legitimate, and stable, and in turn, are
less likely to experience crises or fail due to poor performance or lack of resources. We used
three variables for this construct. First, larger firms (e.g., S&P 500 firms) receive more attention,
have more resources and influence, and generally are regarded as more prestigious (Chen, 2008).
Thus, firm size increases firm prestige. Firm size was measured mainly with total employees,
total revenues, total assets, and market capitalization. Prior research has shown that these
measures load on a single factor that reflects firm size (Tosi et al., 2000). Second, older firms
usually are more well-known, well-established, and taken-for-granted. Hence, firm age can
signify firm prestige. Firm age was the time since the firm was founded. Third, firms with a
record of strong performance generally are regarded as more successful and prestigious (e.g.,
Focke et al., 2017). Past firm performance was operationalized with accounting (e.g., ROA)
and market (e.g., stock performance) measures. In general, increases in firm size, firm age, and
past firm performance indicate higher firm prestige4. Because these measures are likely to be
sources of (or causes of) firm prestige, we treat firm prestige as a formative construct.
CEO personal career success. Heslin (2005) and Judge et al. (1995) noted that
promotions and pay are among the clearest measures of personal career success. The only
promotion available to CEOs, however, is to the dual role of CEO and chair. Moreover, due to
the social status and earning potential associated with the CEO position, holding the CEO office
longer is a desirable career outcome for CEOs. Thus, we operationalized CEO personal career
4 These three measures of firm prestige are consistent with literature on the liabilities of smallness and newness,
which suggest that the risk of poor performance and firm failure declines with firm size (e.g., larger firms have more
resources) and firm age (e.g., older firms have more legitimacy) (Baum, 1996; Baum & Silverman, 2004).
CEO GENDER AND CAREER
31
success with job titles (i.e., CEO duality), pay, and position tenure. First, CEO duality is a
dummy indicating whether the CEO also chaired the board. The chair title increases power and
standing with internal and external stakeholders and, thus, is an important measure of CEO
personal career success. Second, we operationalized CEO pay with total pay and long-term
incentive pay, which accounts for the majority of CEO total pay (Devers, Cannella, Reilly, &
Yoder, 2007). We coded CEO total compensation or pay as total pay, and we coded variables
like at-risk pay, the values of stocks and stock options, and other forms of contingent pay as
long-term incentive pay. Third, CEO tenure was the time the person remained in the CEO
position. Similarly, given that changes in these measures are more likely to cause changes in the
construct of CEO personal career success, we argue that CEO personal career success is a
formative construct.
Firm risk taking. Following several scholars (see Wang et al., 2016 for a review), we
operationalized this construct with firm strategic actions. Examples include acquisitions, product
diversification, and internationalization. Firm strategic actions create risk because they require
significant resource reallocations, have long-term performance implications, and create
uncertainty (Connelly et al., 2010; Devers, McNamara, Wiseman, & Arrfelt, 2008). Given that
changes in firm risk taking decisions (e.g., increase or decrease in R&D investments) made by
CEOs tend to result in changes in firm strategic actions that are reflected in firms’ balance sheet
(e.g., more or less money spent on R&D investments), we treated firm risk taking as a reflective
construct that is indicated by firm strategic actions.
Firm performance. Firm performance refers to achievement of financial returns5.
Researchers typically assess firm performance with accounting and/or market measures. The
5 Of course, some firms prioritize other performance metrics, including social performance (see Stephan, Patterson,
Kelly, Mair, 2016 for a review). However, because each social performance metric often is unique to the particular
CEO GENDER AND CAREER
32
former are ex post indicators of effectiveness and/or efficiency, and the latter reflect investors’
ex ante valuation of firms’ future cash flows, which of course depend on effectiveness and
efficiency (e.g., Fama, 1970; Keats & Hitt, 1988; Morgan & Rego, 2009). The variables we
coded for firm accounting performance were mostly measures of firm profitability (e.g., ROA,
ROE, net income, etc.), though we also coded a few subjective and industry-specific measures in
that they also captured firm effectiveness or efficiency. Measures of firm market performance
included Tobin’s Q, shareholder return, and other indicators of stock market performance.
Researchers have long argued that both accounting- and market-based measures are
reflective indicators of firm performance (e.g., Venkatraman & Ramanujam, 1986; Whitbeck &
Kisor, 1963). Though distinct, both are outcome-based, gauge firms’ financial success, are
subject to managers’ influence, and are critical to the firm’s ongoing survival (in the case of
public firms). In addition, their individual indicators are positively correlated (sometimes highly
so), can cross-load on factor analyses, and are inter-related (e.g., investors use accounting data to
make inferences about firms’ future stock returns) (Demsetz & Villalonga, 2001; Richard,
Devinney, Yip, & Johnson, 2009). Thus, in line with prior research, we treated firm performance
as a reflective construct and combined accounting and market-based measures into a single index
of firm performance using Schmidt and Hunter’s (2014) formula. However, given the view that
accounting- and market-based indicators capture different aspects of a more general firm
performance construct, we also investigate the measures separately for informational purposes.
Theoretical moderators. In line with prior research (e.g., Holmes et al., 2013; Webb,
Campbell, Schwartz, & Sechrest, 2000), we operationalized uncertainty avoidance and gender
firm or small group of firms pursuing it (e.g., firms that focus on reducing pollution are not necessarily focused on
poverty alleviation), we lacked a sufficient number of primary studies to examine this aspect of performance.
CEO GENDER AND CAREER
33
egalitarianism using the GLOBE (House et al., 2004) cultural practice measures for these two
variables: higher scores represent greater uncertainty avoidance and gender egalitarianism. In a
few cases where the primary studies sampled firms from countries not among the 62 in GLOBE,
the mean scores for the country clusters that contained these countries were used. For example,
though Laos was not included in GLOBE, Lao is located in Southeast Asia, which is one of the
ten country clusters in the GLOBE project. In this case, we used the average scores on
uncertainty avoidance and gender egalitarianism in this country cluster as estimates of the
corresponding variables for Laos. To ensure that the moderating effects of the two cultural
dimensions were not confounded by other cultural dimensions, we controlled for an additional
cultural dimension, power distance. We selected this variable, as it is negatively correlated with
uncertainty avoidance but positively associated with gender egalitarianism (House et al., 2004)
and has been used previously in research on cross-cultural gender research. We measured this
variable similarly to the way we measured the other two GLOBE variables.
Study characteristics. To check the robustness of the main effects, we also examined
whether results differed across three study characteristics: data collection methods, potential data
overlaps, and publication status. First, data collection methods mean whether a primary study
was based on cross-sectional data (e.g., Miller et al., 2015) or longitudinal panel data (e.g., Hill
et al., 2015). Second, because publicly-traded firms often are sampled, there could be potential
data overlaps across different primary studies. Following Crook et al. (2011), we compared
sample characteristics such as sample country, industry, size, and time frame to separate studies
based on non-overlapping samples from those based on potentially overlapping samples. Third,
for publication status, we labeled published and in press articles as published studies and other
studies (e.g., dissertations, working papers) as unpublished studies. To partial out the influences
CEO GENDER AND CAREER
34
of the three study characteristics on the moderating effects of the two theoretical moderators, we
also controlled for the three study characteristics in the moderation analyses.
Meta-analytic Procedures
We used Schmidt and Hunter’s (2014) psychometric random-effect meta-analysis
methods for the d-statistic to estimate true-score CEO gender differences in the variables
(Schmidt, Oh, & Hayes, 2009). We corrected each effect size for measurement error. Notably,
most primary studies used secondary data and did not report reliability statistics. We set the
reliability of those variables to one. Following prior work, we set the minimum number of
primary studies for each meta-analysis to three. To ensure each sample contributed only once to
each meta-analytic estimate, we checked that effect sizes based on the same samples were not
duplicated. In cases where the primary studies reported multiple estimates of the same
relationship (e.g., correlations of CEO gender with different, yet, related indicators of firm
accounting performance), we computed a composite correlation using Schmidt and Hunter’s
(2014) formula and then transformed the composite correlation into a d-statistic with the formula
they provided. We used the Schmidt-Hunter meta-analysis package program (VG6 Module–
individual correction methods for d-statistics; Schmidt & Le, 2004).
Hypothesis Testing Techniques
Because meta-analysis provides accurate estimates of effect sizes, we used meta-analytic
results as inputs to test the main effect hypotheses. The demand-supply framework suggests that
female CEOs must have more human capital yet are relegated to less prestigious firms. Thus,
human capital and firm prestige are antecedents to the selection of female versus male CEOs. As
such, we tested the two hypotheses using path models in which human capital and firm prestige
were caused by their respective measures (Edwards & Bagozzi, 2000). Conversely, our
CEO GENDER AND CAREER
35
framework suggests that CEO gender may lead to differences in personal career success, firm
risk taking, and firm performance. Thus, we regarded these three constructs as outcomes of CEO
gender and tested the main effects with meta-analytic multiple regression. Because formative
constructs cannot be dependent variables (Diamantopoulos, 2006), we had to treat the individual
measures of these constructs as dependent variables. Also, because CEOs’ human capital and
their firms’ prestige may confound the effects of their gender on personal career success, firm
risk taking, and firm performance, we controlled for common measures of CEO human capital
and firm prestige—namely CEO age, firm size, and firm age—in the meta-analytic multiple
regression analyses. The meta-analytic correlations for the above analyses are in Appendix B.
To test the moderation hypotheses, we used Hierarchical Linear Modeling (HLM 7,
Raudenbush, Bryk, & Congdon, 2010) to account for nesting in the data6: study samples were
nested in countries. The HLM program has a built-in variance known (v-known) application that
can be used for meta-analytic moderation analysis (see Chapter 7 in Raudenbush and Bryk, 2002
for equations and examples). In the v-known application, weighted least-squares estimation is
used, and effect sizes (e.g., differences in female and male CEOs’ firm performance) are
weighted by the reciprocal of their sampling variance (Raudenbush & Bryk, 2002). Given their
hierarchical nature, we modeled the country-level variables (i.e., the three culture dimensions) as
level-2 variables and study-level variables (i.e., the three study characteristics) as level-1
variables. Parameters were estimated with random-effect models, due to the extensive evidence
about the advantages of random-effect over fixed-effect models in meta-analysis (e.g., Field &
Gillett, 2010; Schmidt et al., 2009). To be noted, we had to focus on lower-level measures rather
6 We thank an anonymous reviewer for suggesting treating the cultural moderators as higher-level constructs.
CEO GENDER AND CAREER
36
than higher order constructs, because the v-known application doesn’t allow us to model higher
order constructs (Raudenbush & Bryk, 2002).
To obtain reliable results, we only conducted moderator analyses when there were at least
fifteen studies at level 1and five different countries at level 2 (Ng et al., 2005; Schmidt, 1971;
Snijders & Bosker, 1999). Thus, we could not perform moderator analysis for some of the CEO
gender differences (e.g., past firm performance). In line with prior research (e.g., Liao &
Chuang, 2007), we grand-mean centered level-1 predictors to reduce potential collinearity
between level-2 intercept and slope terms and also to control for the potential influences of level-
1 predictors on the effects of level-2 variables (Hofmann & Gavin, 1998).
Results
Main Effect Hypothesis Testing Results
Table 1 presents the meta-analytic results of differences in female and male CEOs’
careers. In all of the analyses presented hereafter, female CEO is coded 1, and male CEO is
coded zero. Whereas this section reports the hypothesis testing results, we discuss the meta-
analytic results in the discussion section.
Figure 2 illustrates the results of testing Hypotheses 1 and 2. Hypothesis 1 predicted that
female CEOs have more human capital than male CEOs. As shown in Figure 2, the path model
results show that CEO human capital related positively to female CEO, supporting Hypothesis 1.
In addition, Hypothesis 2 posited that female CEOs lead less prestigious firms than do male
CEOs. The model in Figure 2 shows that the formative construct firm prestige related negatively
to CEO gender. Thus, female CEOs lead less prestigious firms, supporting Hypothesis 2.
Table 2 summarizes the results of testing Hypotheses 3 to 5. Hypothesis 3 stated that
female CEOs achieve less personal career success than male CEOs. Table 2 shows that female
CEO GENDER AND CAREER
37
CEOs were less likely to chair the board, had lower pay, and had shorter position tenure than
male CEOs. Thus, Hypothesis 3 was supported. However, Hypothesis 4—which predicted that
female CEOs’ firms take less risk than male CEOs’ firms do—was not supported. As Table 2
shows, firms with female CEOs showed no difference in firm risk taking, as operationalized by
firm strategic actions. Lastly, Hypothesis 5 suggested that firms led by female CEOs have worse
firm performance than firms led by male CEOs. In line with Hypothesis 5, firms led by female
CEOs showed lower financial performance. Thus, Hypothesis 5 was supported.
Moderation Hypothesis Testing Results
As noted, we used meta-analytic multilevel moderation analysis to test Hypotheses 6 and
7. To justify the use of HLM (e.g., Liao & Chuang, 2007), we first examined whether there were
significant between-country variances in CEO gender differences in the hypothesized variables.
HLM results based on null (i.e., outcome only) models show that there were significant between-
country variances in CEO gender differences in CEO age (χ2(18) =31.47, p < .05; ICC1 = .12
[meaning 12% of total variance is between-country]), CEO education level (χ2(14) = 36.02, p <
.01; ICC1 = .50), firm age (χ2(17) = 96.36, p < .01; ICC1 = .72), firm risk taking (χ2(12) =31.70, p
< .01; ICC1 = .38), and firm performance (χ2(17) =39.33, p < .01; ICC1 = .35). However, the
between-countries variances were not significant for CEO gender differences in CEO tenure
(χ2(10) = 11.87, p > .10; ICC1 = .07), firm size (χ2(28) = 29.62, p > .10; ICC1 = .04), CEO positive
self-concept (χ2(5) = 3.61, p > .50; ICC1 = .00), CEO pay (χ2(6) = 6.23, p > .10; ICC1 = .00), and
CEO duality (χ2(9) = 9.41, p > .10; ICC1 = .00). Although the between-country variances for
CEO gender differences in CEO tenure and firm size were not significant, we decided to test the
moderating hypotheses for these two CEO gender differences with HLM given their meaningful
ICC1 values (Bliese, 2000; Maas & Hox, 2004). However, we excluded CEO gender differences
CEO GENDER AND CAREER
38
in CEO positive self-concept, pay, and duality, because there was no sufficient evidence that the
data were nested (Hofmann & Gavin, 1998).
Table 3 presents the moderation analysis results. Hypothesis 6 posited that uncertainty
avoidance strengthens the CEO gender differences in Hypotheses 1-5. Supporting Hypothesis 6,
CEO gender differences in CEO age, firm size, and CEO tenure became larger as uncertainty
avoidance increased. In contrast to Hypothesis 6, however, CEO gender differences in firm age,
firm risk taking, and firm performance became smaller as uncertainty avoidance increased.
Lastly, CEO gender difference in CEO education level was not significantly moderated by
uncertainty avoidance. Taken together, Hypothesis 6 received mixed support.
In addition, Hypothesis 7 predicted that gender egalitarianism would weaken the CEO
gender differences in Hypotheses 1-5. As shown in Table 3, only CEO gender differences in
CEO age and firm size declined as gender egalitarianism increased. By contrast, CEO gender
difference in firm performance became larger as gender egalitarianism rose. The other CEO
gender differences were not moderated by gender egalitarianism. Thus Hypothesis 7 received
only weak support. The results for all of the hypothesis tests are summarized in Table 4.
Empirical Study Characteristics
We also tested whether the results for gender differences in CEOs’ careers were affected
by the three study characteristics above. Given their dichotomous nature, we used Schmidt and
Hunter’s (2014) subgrouping methods, which use lack of overlap between subgroups’ 95% CIs
to detect moderation. As Table 1 shows, the meta-analytic results are rather robust across the
three study characteristics: data collection methods moderated only one CEO gender difference
out of fourteen testable cases, potential data overlaps moderated only two out of sixteen testable
cases, and publication status moderated only three out of sixteen testable cases.
CEO GENDER AND CAREER
39
Notably, among cases where the three study characteristics had no significant moderating
effects, five previously significant CEO gender differences become insignificant under some
sub-group conditions. Specifically, although female and male CEOs differed significantly in
positive self-concept when all samples were considered, this CEO gender difference was not
significant in the cross-sectional samples only. Likewise, CEO gender differences in CEO
duality, firm age, and firm size were significant in the non-overlapping samples but not the
potentially overlapping samples. Lastly, although CEO gender difference in firm performance
was significant when all samples were considered, it was not significant in the cross-sectional,
longitudinal panel, potentially overlapping samples, published, and unpublished subsamples.
Discussion
This study enriches understanding of CEO gender in several ways. Prior literature
suggests that demand-side and supply-side forces create obstacles for female CEOs, potentially
creating both individual- and firm-level differences between female and male CEOs’ careers.
However, to date, much of the research on these differences has produced conflicting results,
including variation across countries. To advance knowledge, we used meta-analysis to synthesize
extant research on career differences between female and male CEOs, and we examined country
cultural attributes as boundary conditions of these differences. The results synthesize and
advance the literature on CEO gender and lay a foundation for future research on CEOs, the
firms that hire them, and the surrounding cultural context. We now discuss the study’s theoretical
implications, its limitations, future research opportunities, and its practical implications.
Theoretical Implications and Future Research Opportunities
This meta-analysis identifies several differences between female and male CEOs’
careers, most of which are consistent with the argument that female CEOs confront demand-side
CEO GENDER AND CAREER
40
and supply-side obstacles that male CEOs do not. First, consistent with our first hypothesis, the
results for the formative CEO human capital construct suggest that female CEOs have more
human capital than male CEOs. Likewise, the meta-analytic results show that female and male
CEOs differ in three of the four individual measures of CEO human capital (CEO age, elite
education, and positive self-concept) in the predicted direction, providing further support for the
hypothesis. Although we found few effects for education level, this result may reflect limited
variance: CEOs nearly always have at least bachelor degrees. Despite this one non-finding, the
results suggest an overall pattern. Specifically, women able to overcome the career obstacles to
become CEOs may be early achievers who are more talented, driven, and “invested in the career
progression game than men” (Dick, 2008, p. 339) and also tend to have personality attributes,
such as confidence and self-esteem, that help them overcome the in-group favoritism, unequal
family demands, and other challenges they tend to face. The findings that female CEOs were
even younger than male CEOs in high uncertainty avoidance countries, yet were closer in age to
male CEOs’ in more gender egalitarian countries, further support the argument that females must
be early achievers when the deck is stacked more heavily against them. Although we could not
analyze more specific aspects of the CEOs’ human capital, such as the particular jobs they have
held (because the primary studies did not do so), examining such differences is important for
future research. Despite their youth, for instance, female CEOs might have rarer and more
valuable human capital that helps them reach the CEO suite faster (Hill et al., 2015).
Hypothesis 2 was derived from the well-known glass cliff theory that female CEOs are
more likely to lead distressed firms, which we operationalized with firm age, firm size, and prior
firm performance and referred to collectively as firm prestige. The findings suggest that the
formative firm prestige construct and the individual indicators firm size and firm age were lower
CEO GENDER AND CAREER
41
for firms with female CEOs. These results could suggest that the demand for stereotypical
female attributes (e.g., nurturing) is greater in less prestigious firms and/or that the supply of
female CEO candidates are more likely to take CEOs positions at such firms because fewer CEO
positions are available to them. Because smaller and younger firms fail more often (Baum, 1996;
Carroll, 1984), this sorting process could reduce the demand for female CEOs further, while also
discouraging other aspiring female leaders. In this way, the career obstacles facing female CEOs
could become self-reinforcing.
However, the result for prior firm performance was not significant. One possibility is that
although some stereotypical female attribute are favored in poorly performing firms, other
stereotypical male attributes—such as dominance and decisiveness—are preferred if boards seek
a turnaround (Ryan et al., 2011). Also, the country culture variables moderated the firm prestige
measures in complex ways. Consistent with expectations, female CEOs led smaller firms as
uncertainty avoidance increased, yet they led larger firms as gender egalitarianism increased.
Conversely, they led older firms as uncertainty avoidance increased. It may be that firm prestige
is associated more closely with firm size than firm age. For instance, some younger firms are
founded to commercialize promising innovations or become famous precisely because of their
novelty, both of which can attract investors and reduce uncertainty (e.g., Baum & Silverman,
2004; Pollock & Rindova, 2003 ). The finding that female CEOs led larger firms, yet not
necessarily older firms, in more gender egalitarian countries supports this view that firm prestige
is more closely associated with firm size. Thus, future research should focus more on the
determinants of firm prestige, on CEO selection in low-prestige firms, and on the effects of
uncertainty avoidance on smaller versus newer firms.
CEO GENDER AND CAREER
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We predicted that lower demand for female CEOs, and differences in the supply of
female versus male CEOs, would result in lower personal career success for female CEOs. The
results for CEO pay, CEO duality, and CEO tenure supported this hypothesis. These findings are
interesting, given that many countries have laws to promote equality between the sexes. It may
be that criteria used to make decisions about pay, appointment of chairs, and termination are
sufficiently ambiguous that discrimination is hard to enforce (e.g., Mohan & Ruggiero, 2007).
Our finding that female CEOs’ tenure is even shorter in high uncertainty avoidance countries
supports a complementary explanation. Due to their rarity, there simply is more uncertainty
about female CEOs, increasing boards’ willingness to terminate them in such countries.
Alternately, female CEOs might make some choices that reduce their personal career
success. If females are socialized to use more participative decision styles (Paustian-Underdahl
et al., 2014; Zhang et al., 2015), perceive less support from others (especially subordinate
executives and investors), and/or care less about status, for instance, they may opt for less pay
(e.g., perhaps to pay other executives more) or at-risk pay (the largest portion of pay; Devers et
al., 2007) and be willing to share authority with non-executive chairs. Likewise, shorter tenure
can result from leaving the firm voluntarily to pursue other interests, including caring for family
members (e.g., Mainiero & Sullivan 2005). Thus, future research should tease out the different
explanations for differences in female and male CEOs’ personal career success.
The fourth hypothesis concerned CEO gender differences in firm risk taking. Though we
predicted that firms with female CEOs take less risk, the main effect analysis revealed no CEO
gender differences on this construct. This is surprising, because gender role stereotypes suggest
that females are risk averse (Kulich et al., 2011) and a meta-analysis (not focused on managers
necessarily) documented that females take less risk than males (Byrnes et al., 1999). Perhaps
CEO GENDER AND CAREER
43
female CEOs try to counteract gender stereotypes by behaving more aggressively. Alternately,
perhaps CEO selection processes, which are shaped by in-group favoritism, filter out the most
risk averse women, leaving a supply of female CEOs with risk preferences more akin to those of
male CEOs. Lastly, in line with the view that women and men approach ethics differently (Pan &
Sparks, 2012), perhaps female CEOs take well-calculated risks because extreme risk aversion
harms shareholders (e.g., Eisenhardt, 1989). All three arguments are consistent with the finding
that firms with female CEOs took more risk in high uncertainty avoidance countries. Because
there is more uncertainty about female CEOs, which is more problematic in such countries, they
may compensate by trying to initiate risky actions in the firm that stereotypically are associated
more with men. In any event, though our findings challenge gender role stereotypes that women
are risk averse, they are consistent with Kulich et al.’s (2011, p. 315) observation that risk
aversion “should not necessarily be treated as a stable trait of women in general.”
The final hypothesis concerned the performance of firms with female versus male CEOs.
Results support the prediction that firms with female CEOs perform worse (probably due to the
many demand-side and supply-side career obstacles that female CEOs face). However, analysis
of the individual measures reveals that this effect is driven mostly by firm market performance.
Importantly, though firms with female versus male CEOs had comparable accounting
performance, firms with female CEOs had lower market performance. Thus, despite the similar
accounting performance, investors discount the stock of firms with female CEOs. We have
argued that women able to overcome biased selection processes to reach the CEO position might
be especially competent. Thus, it may be that they can marshal the support of stakeholders with
whom they interact regularly (e.g., subordinate executives) at least as well as male CEOs do
(Adams & Funk, 2012), thus explaining the similar levels of firm accounting performance.
CEO GENDER AND CAREER
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The results for the culture moderators support this interpretation. In high uncertainty
avoidance and less gender egalitarian countries, both of which create more significant career
obstacles for female CEOs, firms with female CEOs actually performed better. Conversely, stock
market investors and analysts (most of whom are male; van Staveren, 2014) usually have less
direct experience with, and fewer opportunities to observe the competence of, particular female
CEOs. Lacking more direct evidence, the demand-side (in-group favoritism and gender role
stereotypes) and supply-side forces (family duties and socialization) above might lead such
investors to conclude that female CEOs are less competent and committed, thus explaining the
lower market performance of firms with female CEOs. In fact, research suggests that
discrimination usually is stronger among individuals who have no direct contact with the target
of the discrimination. Koch et al. (2015), for example, found evidence that experience with
female leaders lowers uncertainty about them and leads to better assessments of their
capabilities. Thus, it is vital for future research to investigate how to reduce demand-side biases
against female CEOs and also how to enrich the supply of female CEOs in ways that do not
involve biased selection systems.
Finally, the robustness test revealed few CEO gender differences that could be attributed
to study characteristics. For example, the findings for firm age, firm size, and CEO duality might
be relevant only for a small, albeit important, population of Fortune 500 firms, which are studied
heavily due to data availability. Likewise, the findings for firm performance held for the entire
sample of studies but for few of the subsamples. Thus, consistent with our arguments that the
supply of female and male CEOs are equally competent, future researchers might find fewer
differences in firm performance in firms with female versus male CEOs. Of course, readers
should pay attention to study characteristics when interpreting the findings of research on career
CEO GENDER AND CAREER
45
differences based on gender. Also, the subsamples of studies with certain characteristics were
relatively small in many cases, suggesting that readers should interpret the findings with caution
(Schmidt & Hunter, 2014). In this regard, our findings further underscore the need for future
research on contingencies that moderate career differences between female and male CEOs.
Limitations and Additional Future Research Opportunities
This study has limitations that suggest further future research opportunities. First, some
of the meta-analytic results were based on a limited number of studies. For similar reasons, we
could not analyze the moderators for some of the CEO gender differences. Thus, we encourage
future researchers to replicate and extend our findings when more primary studies become
available. Second, most of the effects had large and significant Q statistics, indicating
heterogeneity in the effect sizes reported in primary studies. Thus, we encourage scholars to
devote more attention to identifying additional theoretical boundary conditions of the differences
between female and male CEOs’ careers.
For example, as noted, more information about female leaders can reduce discrimination
and uncertainty. The opportunity to interact with female CEOs and observe their competence
firsthand, in other words, probably reduces the prevalence and negative effects of gender-based
biases. This interpretation is consistent with an overall pattern in results for country culture: the
country culture variables were more likely to shape the effects of constructs that influence who is
appointed to the CEO position (e.g., CEO age and firm size) than constructs that represent the
outcomes of CEO gender (e.g., CEO pay and CEO duality). Thus, whereas demand-side and
supply-side obstacles and cultural norms may shape boards’ decisions before female CEOs are in
place, the female CEOs’ competence and decision making may become more influential
thereafter. This argument is consistent with heuristic-systematic (Chaiken, 1980), intuitive-
CEO GENDER AND CAREER
46
rational (Dane & Pratt, 2007), and behavioral (Kahneman, 2003) perspectives, which suggest
that cognitive shortcuts substitute for more deliberate cognitive processing when information is
limited and uncertainty is greater. Likewise, Leicht, de Moura, and Crisp (2014) found that
exposure to successful female leaders mitigated negative gender-based biases. Thus, future
research should examine factors that influence stakeholders’ exposure to female CEOs.
Third, relatedly, we suspect that industry characteristics may explain some of the
heterogeneity in the differences between female and male CEOs’ careers. Because most of the
primary studies sampled firms in various industries, we were unable to explore this possibility.
We think it is important that future research considers the role of industry in CEO gender
differences. Fourth, it is plausible that female CEOs in high uncertainty avoidance countries take
more strategic actions, partly because their firms are smaller or younger that need bolder moves.
In other words, there might be a three-way interaction such that the interaction between
uncertainty avoidance and firm size may predict the relationships of CEO gender with firm
outcomes. Although we cannot perform such analyses, we think this is an interesting venue for
future research. Last, although we theoretically differentiate antecedents from outcomes of CEO
gender, we acknowledge that we are unable to make conclusive causal inferences given that the
primary studies were not experimental (Bergh, Sharp, Aguinis, & Li, 2017). We encourage
future research to adopt designs that facilitate causal inferences (Rubin, 1974; 2008).
Implications for Practice
The findings also have many managerial implications. To begin, an unfair but realistic
implication for women who aspire to become CEOs is to plan ahead. Elite educations, for
example, could be important for women who want to be a CEO (Miller et al., 2015). Likewise,
the finding that female CEOs were younger suggests that getting early and fast promotions is
CEO GENDER AND CAREER
47
another important implication. Likewise, women with CEO aspirations should note that low-
prestige, especially smaller and younger firms are more likely to provide such opportunities.
Relatedly, the study sheds light on ways to develop future female leaders to reduce the
gender disparity in the CEO suite. Firms might benefit by identifying exceptionally talented and
driven supply of female CEO candidates and crafting career paths, including challenging
assignments, aimed at retaining and developing them. Also, stakeholders who interact with
female CEOs regularly might make more accurate judgments and decisions about them. Thus,
promoting interactions among female leaders and important stakeholders could be useful for
alleviating the demand-side and supply-side obstacles that women face. Moreover, the findings
about firm outcomes suggest important implications for firm decision makers and investors,
particularly regarding CEO selection and firm strategy. For instance, the results for firm
accounting performance suggest that CEO selection committee members should be aware that
discrimination toward female leaders are without merit and, in turn, should provide female CEO
prospects with equal opportunities. Because firms with similar accounting performance and risk
profiles should perform comparably on the stock market, boards also should consider potential
discrimination when assessing firms’ market performance.
Lastly, firm decision-makers and policy makers should be aware that country culture
shapes the careers of female and male CEOs in different ways. Uncertainty avoidance can
exacerbate the obstacles that female CEOs face, while gender egalitarianism can reduce them.
These findings can inform career planning and expectations. Aspiring female leaders may find,
for example, that they are relegated to even smaller firms in high uncertainty avoidance countries
yet have opportunities to lead larger firms in more gender egalitarian countries.
Conclusions
CEO GENDER AND CAREER
48
In sum, this meta-analysis provides evidence about CEO gender differences in individual-
and firm-level indicators of CEOs’ careers, reveals cultural contingencies that shape these
differences, and deepens understanding of extant research on these important phenomena. Our
findings suggest that both “pushed out” and “opting out” factors might have contributed to the
underrepresentation of female CEOs and their career differences with male CEOs’.
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CEO
Gender
(Female
CEO)
CEO individual-level career
indicators
−Human capital
−Age
−Education level
−Elite education
−Positive self-concept
−Personal career success
−Pay
−Duality
−Position tenure
(−H3)
Cultural
attribute:
Uncertainty
avoidance
Cultural
attribute:
Gender
egalitarianism
(−H7)
(+H7)
(+H7)
(+H6)
(−H6)
(−H6)
Country Level
CEO Level
CEO GENDER AND CAREER
70
Figure 1. Proposed Theoretical Model. The signs in the parentheses indicate hypothesized
positive (+) or negative (−) relationships. H=Hypothesis
Hypothesis 1 Testing Results:
Harmonic mean = 11,099. PSC = positive self-concept.
Hypothesis 3 Testing Results:
CEO firm-level career indicators
−Firm prestige
−Firm age
−Firm size
−Past firm performance
−Accounting-based measures
−Market-based measures
−Firm risk taking
−Firm strategic actions
−Firm performance
−Accounting-based measures
−Market-based measures
CEO age
CEO education
CEO PSC
CEO Human
capital
Female CEO
.22**
.84**
-.15**
**
.58**
Firm size
Firm age
Past firm
performance
Firm prestige
Female CEO
-.08**
.70**
.04
**
.70**
CEO GENDER AND CAREER
71
Harmonic mean = 15,476
Figure 2. Hypotheses 1 and 2 Testing Results
CEO GENDER AND CAREER
71
Table 1
Meta-analytic Effect Sizes of Gender Differences in CEOs’ Careers
Variable
k
N
d
d+
95% CI
Q
CEO Human capital
CEO age
107
471,578
-.41
-.42
-.52
-.31
23,417.13**
Cross-sectional studies
53
21,703
-.23
-.23
-.31
-.15
435.77**
Longitudinal panel studies
54
449,875
-.42
.42
-.58
-.25
17,393.49**
Non-overlap samples
64
276,802
-.41
-.41
-.57
-.18
18,065.34**
Potential overlap samples
43
194,776
-.42
-.42
-.54
-.30
5,893.44**
Published
68
244,441
-.50
-.50
-.68
-.33
19,000.59**
Unpublished
39
227,137
-.32
-.32
-.41
-.23
4,165.75**
CEO education level
57
254,521
-.04
-.04
-.08
.00
1,448.74**
Cross-sectional studies
34
13,544
-.09
-.09
-.19
.00
255.78**
Longitudinal panel studies
23
240,977
-.03
-.04
-.10
.02
1,183.41**
Non-overlap samples
44
182,272
-.06
-.07
-.12
-.02
1,144.55**
Potential overlap samples
13
75,549
.02
.02
-.04
.08
201.64**
Published
38
53,356
.00
.00
-.07
.07
522.72**
Unpublished
19
201,165
-.05
-.05
-.11
.01
901.23**
CEO elite education
4
5,324
.23
.23
.01
.45
50.26**
CEO positive self-concept
25
38,241
.22
.25
.12
.38
833.34**
Cross-sectional studies
17
3,117
-.03
-04
-.17
.09
40.67*
Longitudinal panel studies
8
35,124
.24
.27
.04
.51
740.78**
Non-overlap samples
18
25,468
.05
.07
.00
.14
106.16**
Potential overlap samples
7
12,773
.55
.62
.40
.85
207.13**
Published
18
27,063
.09
.08
.03
.14
81.91**
Unpublished
7
11,178
.54
.70
.47
.92
173.39**
Firm prestige
Firm size
139
596,771
-.12
-.12
-.18
-.06
15,777.19**
Cross-sectional studies
69
43,693
-.22
-.22
-.29
-.16
746.63**
Longitudinal panel studies
70
553,078
-.11
-.11
-.19
-.02
14,895.66**
Non-overlap samples
79
238,075
-.17
-.17
-.26
-.08
7,695.61**
Potential overlap samples
60
358,696
-.08
-.08
-.16
.01
7,877.26**
Published
89
344,113
-.16
-.18
-.26
-.11
9,628.56
Unpublished
50
252,658
-.02
-.01
-.11
.08
5,375.23
Firm age
58
269,329
-.03
-.03
-.06
-.00
653.45**
Cross-sectional studies
39
14,967
-.13
-.13
-.19
-.06
158.00**
Longitudinal panel studies
19
254,362
-.02
-.02
-.06
.02
459.95**
Non-overlap samples
46
175,462
-.05
-.05
-.08
-.02
458.91**
Potential overlap samples
12
93,867
.00
.00
-.05
.05
157.61**
Published
39
115,588
.03
.03
-.00
.06
288.75**
Unpublished
19
153,741
-.07
-.07
-.11
-.04
186.40**
CEO GENDER AND CAREER
72
Table 1 (cont.)
Variable
k
N
d
d+
95% CI
Q
Past firm performance
20
45,989
-.11
-.11
-.27
.05
1,242.39**
Cross-sectional studies
9
1,377
.05
.05
-.15
.25
26.39**
Longitudinal panel studies
11
44,612
-.11
-.11
-.32
.10
1,210.29**
Non-overlap samples
13
22,609
-.24
-.24
-.47
-.01
903.38**
Potential overlap samples
7
23,380
.02
.02
-.11
.16
161.00**
Published
11
22,971
-.00
.01
-.11
.13
183.22**
Unpublished
9
23,018
-.21
-.21
-.49
.08
949.59**
Past accounting-based firm
performance
17
39,112
-.13
-.13
-.28
.02
1,060.47**
Cross-sectional studies
9
1,642
.08
.08
-.11
.26
26.53**
Longitudinal panel
studies
8
37,470
-.14
-.14
-.39
.11
1,011.85**
Non-overlap samples
12
22,777
-.24
-.24
-.48
.00
903.16**
Potential overlap samples
5
16,335
.02
.02
-.03
.06
8.48
Published
11
17,762
-.00
-.00
-.06
.06
40.53*
Unpublished
6
21,350
-.24
-.24
-.60
.12
888.82**
Past market-based firm
performance
6
20,597
-.02
-.02
-.19
.15
178.76**
CEO personal career outcome: Personal career success
CEO duality
47
310,677
-.12
-.12
-.20
-.04
4,425.49**
Cross-sectional studies
14
8,863
-.14
-.14
-.23
-.04
62.72**
Longitudinal panel studies
33
301,814
-.12
-.12
-.21
-.02
4,362.43**
Non-overlap samples
22
102,815
-.10
-.10
-.16
-.04
544.00**
Potential overlap samples
25
207,862
-.13
-.13
-.25
.00
3,859.00**
Published
27
177,100
-.07
-.07
-.19
.06
3,652.46**
Unpublished
20
133,577
-.19
-.19
-.24
-.13
555.73**
CEO position tenure
63
303,619
-.16
-.16
-.22
-.11
3,458.39**
Cross-sectional studies
23
5,274
-.03
-.03
-.21
.15
231.35**
Longitudinal panel studies
40
298,345
-.17
-.17
-.23
-.10
3,205.98**
Non-overlap samples
33
77,310
-.13
-.13
-.19
-.07
571.26**
Potential overlap samples
30
226,309
-.17
-.17
-.26
-.09
2,858.29**
Published
38
203,652
-.20
-.20
-.26
-.13
2,156.42**
Unpublished
25
99,967
-.10
-.10
-.18
-.01
1,141.89**
CEO pay
45
274,953
-.05
-.05
-.10
-.01
1,463.01**
Cross-sectional studies
35
8,778
-.16
-.16
-.29
-.04
113.67**
Longitudinal panel studies
30
266,175
-.05
-.05
-.10
.01
1,322.34**
Non-overlap samples
25
168,167
-.04
-.04
-.11
.02
865.63**
Potential overlap samples
20
106,786
-.07
-.06
-.14
.01
592.23**
Published
27
189,438
-.03
-.02
-.08
.03
862.65**
Unpublished
18
85,515
-.10
-.10
-.18
-.03
514.95**
CEO GENDER AND CAREER
73
Table 1 (cont.)
Variable
k
N
d
d+
95% CI
Q
CEO organizational career outcome: Firm risk taking
Firm strategic actions
78
422,158
-.05
-.01
-.05
.03
2,513.44**
Cross-sectional studies
33
28,659
-.07
-.11
-.19
-.03
168.83**
Longitudinal panel studies
45
393,499
-.04
-.00
-.06
.05
2,311.93**
Non-overlap samples
37
166,818
-.04
-.01
-.05
.03
590.20**
Potential overlap samples
41
255,340
-.05
-.01
-.08
.06
1,923.02**
Published
42
235,952
-.06
-.02
-.08
.04
1,614.23**
Unpublished
36
186,206
-.02
.00
-.06
.06
893.66**
CEO organizational career outcome: Firm performance
H5: Firm performance
107
664,401
.05
-.06
-.11
-.00
7,476.15**
Cross-sectional studies
39
17,943
.02
.02
-.09
.12
425.10**
Longitudinal panel studies
68
646,458
-.05
-.06
-.12
.01
7,038.18**
Non-overlap samples
58
343,534
-.06
-.06
-.12
-.01
2,761.37**
Potential overlap samples
49
320,867
-.04
-.04
-.14
.06
4,669.73**
Published
67
301,811
-.08
-.09
-.19
.00
6,784.59**
Unpublished
40
362,590
-.02
-.02
-.05
.01
600.48**
Accounting-based firm
performance
88
610,800
.01
.01
-.02
.03
1,706.64**
Cross-sectional studies
33
18,955
.04
.04
-.07
.15
416.37**
Longitudinal panel
studies
55
591,845
.01
.01
-.02
.03
1,286.90**
Non-overlap samples
52
350,485
.02
.02
-.01
.06
1,277.99**
Potential overlap samples
36
260,315
-.02
-.01
-.04
.02
389.96**
Published
57
267,438
.00
.01
-.03
.04
908.50**
Unpublished
31
343,362
.01
.01
-.03
.05
797.39**
Market-based firm
performance
52
279,594
-.13
-.14
-.28
-.00
11,673.50**
Cross-sectional studies
17
2,832
-.04
-.04
-.18
.10
55.69**
Longitudinal panel
studies
35
276,762
-.13
-.14
-.31
.03
11,615.17**
Non-overlap samples
24
164,059
-.18
-.18
-.36
.01
6,855.10**
Potential overlap samples
28
115,536
-.06
-.08
-.30
.15
4,588.00**
Published
29
131,449
-.27
-.32
-.58
-.05
10,440.55**
Unpublished
23
148,145
-.01
-.01
-.06
.04
462.91**
Note. Positive effect sizes mean female CEOs had higher scores than male CEOs, whereas
negative effect sizes mean male CEOs had higher scores than female CEOs. H = hypothesis; k =
number of effect sizes; N = combined sample size; d = mean unweighted effect sizes; δ =
estimated corrected mean effect sizes; CI = confidence interval of δ; Q = homogeneity of ds.
† p < .10; * p < .05; ** p < .01; Two-tailed tests.
CEO GENDER AND CAREER
74
Table 2
Meta-analytic Multiple Regression Results of Testing Hypotheses 3, 4, and 5
Variables
CEO duality
CEO position
tenure
CEO pay
Firm risk taking
Firm performance
Step 1
Step 2
Step 1
Step 2
Step 1
Step 2
Step 1
Step 2
Step 1
Step 2
Control variables
CEO age
.18**
.18**
.34**
.34**
.07**
.07**
-.00
-.00
-.02**
-.02**
Firm size
.02**
.02**
-.03**
-.03**
-.05**
-.06**
.15**
.15**
.06**
.06**
Firm age
-.08**
-.08**
-.01**
-.01**
-.11**
-.11**
.07**
.07**
.11**
.11**
Focal variable
CEO gender
-.03**
-.02**
-.02**
-.00
-.02**
R2
.034**
.035**
.114**
.115**
.017**
.018**
.030**
.030**
.015**
.016**
ΔR2
.001**
.001**
.001**
.00
.001**
Harmonic N
176,332
138,085
157,942
217,346
223,280
Note. CEO gender was coded such that positive effect sizes mean female CEOs had higher scores than male CEOs, whereas negative
effect sizes mean male CEOs had higher scores than female CEOs. Standardized regression coefficients were reported. Appendix B
contains the meta-analytic correlations used as inputs for the meta-analytic multiple regression analyses.
* p < .05; ** p < .01; Two-tailed tests.
CEO GENDER AND CAREER
75
Table 3
Moderating Effects of Uncertainty Avoidance and Gender Egalitarianism Based on Two-level HLM Models
CEO gender
differences in
Intercept
Level 1 Variables
Level 2 Variables
n1/n2
Pseudo
R2
Panel
data
Overlapping
samples
Published
studies
Power
distance
Uncertainty
avoidance
Gender
egalitarianism
CEO age
-.00
.26
.01
.02
.03
-.17**
.12†
92/19
.70
CEO education
level
-.04
-.03**
.02**
.02
.17
.02
.23
51/15
.04
CEO position
tenure
-.01
-.12**
.11**
-.37*
-.06
-.15**
-.06
52/11
.35
Firm age
-.06*
.01
.04
.06*
.03
.09**
-.03
50/18
.88
Firm size
-.11*
-.09†
.11**
.04
.15**
-.05*
.19**
117/29
.76
Firm risk taking
-.18*
.01
-.02
.04
-.06
.18**
-.06
71/12
.45
Firm
performance
-.07*
-.03
-.03
-.04*
-.29**
.13**
-.28**
87/19
.55
Note. n1 = level-1 sample size (the number of studies); n2 = level-2 sample size (the number of countries). Level-1 variables were
grand-mean centered. Pseudo-R2 was computed as proportional reduction of error variance due to adding predictors to the null model
(Snijders & Bosker, 1999).
The mean and standard deviation (entry after /) of uncertainty avoidance and gender egalitarianism (see values in parentheses) in the
above analyses from CEO gender difference in CEO age to CEO gender difference in firm performance are as follows: 4.30/.56
(3.23/.38); 4.29/.53 (3.27/.36); 4.35/.51 (3.25/.32); 4.16/.46 (3.18/.34); 4.43/.61 (3.29/.39); 4.56/.57 (3.29/.37); and 4.41/.58 (3.33/.41).
The United States scores 4.15 for uncertainty avoidance and 3.34 for gender egalitarianism respectively (House et al., 2004).
† p < .10; * p < .05; ** p < .01; Two-tailed tests.
CEO GENDER AND CAREER
76
Table 4
Summary of Hypothesis Testing Results
Hypothesis
Results
Hypothesis 1: Female CEOs have more human
capital than male CEOs
Supported
Hypothesis 2: Female CEOs serve in less
prestigious firms than male CEOs.
Supported
Hypothesis 3: Female CEOs achieve less
personal career success than male CEOs.
Supported
Hypothesis 4: Female CEOs’ firms take less
risk than male CEOs’ firms.
Not supported
Hypothesis 5: Female CEOs’ firms perform
worse than male CEOs’ firms.
Supported
Hypothesis 6: Country uncertainty avoidance
exacerbates the career differences in
Hypotheses 1-5. Specifically, as uncertainty
avoidance increases, the career differences
between female and male CEOs in human
capital, firm prestige, personal career success,
firm risk taking, and firm performance become
greater in magnitude
Supported for CEO gender differences in CEO
age, firm size, and position tenure; Not
supported for CEO gender differences in firm
age, firm strategic actions, education level, or
firm performance.
Hypothesis 7: Country gender egalitarianism
diminishes the career differences in Hypotheses
1-5. Specifically, as gender egalitarianism
increases, the career differences between female
and male CEOs in human capital, firm prestige,
personal career success, firm risk taking, and
firm performance become smaller in magnitude.
Supported for CEO gender differences in CEO
age and firm size; Not supported for CEO
gender differences in education level, CEO
tenure, firm age, firm strategic actions, or firm
performance.
CEO GENDER AND CAREER
77
Appendix A
Constructs Operationalization
Overarching
constructs
Specific
indicators
Operationalization of specific indicators
CEO human capital
CEO age
-CEO age (105)
-CEO age at appointment (4)
-CEO 64 years or older (1)
CEO education
level
-Education level (49)
-MBA degree (9)
-Years of education (1)
-Professional degree (3)
-University education (1)
CEO elite
education
-Elite/ivy league education (4)
CEO positive self-
concept
-Narcissism/hubris/overconfidence/self-focus/self-
enhancement (12)
-Self-efficacy (4)
-Emotional stability/neuroticism (reverse-coded) (4)
-Locus of control (1)
-Optimism (3)
-Promotion focus/prevention focus (reverse-coded)
(2)
-Positive affectivity (PA)/negative affectivity (NA;
reverse-coded) (2)
-Risk propensity (1)
Personal career
success
CEO total pay
-CEO total compensation, pay (11)
CEO long-term
incentive pay
-CEO stock, share, option holdings/ownership/equity
in business/subsidiary incentive compensation/long-
term, at-risk compensation/firm specific
wealth/contingent pay ration/share value to total
compensation (38)
CEO duality
-CEO duality, chair, chair/CEO structural power (47)
CEO position
tenure
-CEO tenure/position tenure (63)
CEO GENDER AND CAREER
78
Appendix A (cont.)
Overarching
constructs
Specific
indicators
Operationalization of specific indicators
Firm prestige
Firm age
-Firm/company/business/venture/bank age (58)
Firm size
-
Firm/company/business/organization/subsidiary/bank
size (94)
-Assets/total assets (25)
-Number of employees (11)
-Sales revenue/net sales/sales turnover/value of
sales/company sales (16)
-Market value/capitalization (5)
Past accounting-
based firm
performance
-Past ROA/ROS/net income (funds) (14)
-Past sales performance/(subjectively rated)
organizational, market performance (3)
Past market-based
firm performance
-Past stock performance/abnormal return/shareholder
return (6)
Firm risk taking
Strategic actions
-Leverage/debt to equity ratio/debt (46)
-R&D expenditure/R&D intensity/product
innovation/patent/invention patents/firm
innovativeness/technological diversity/patent
application/patent stock value/product under
development & on the market/degree of
innovativeness(23)
-Diversification/firm
diversification/divestiture/related divestiture/number
of segments (10)
-Strategic risk taking/firm risk taking/portfolio at
risk/market exploration & exploitation/corporate
entrepreneurship/entrepreneurial orientation (9)
-Capital expenditure/capital investment intensity (7)
-Number of acquisitions, deals/merger and
acquisition completion/acquisition value, size,
premium/percentage of unrelated acquisitions (7)
-Strategic change/strategic actions/strategic
orientation (4)
-Advertising intensity (3)
-Internationalization/foreign sales as percentage of
total sales (2)
CEO GENDER AND CAREER
79
Appendix A (cont.)
Overarching
constructs
Specific
indicators
Operationalization of specific indicators
Firm performance
Accounting
measures
-ROA/ROE/ROS/ROI/net
income/profitability/operating profit before
tax/economic value added/total accruals/loss
(reverse-coded) (76)
-Subjective rated firm performance/market (share)
performance/ sales per person/loan growth (7)
Market measures
-TOBINQ/market to book/cumulative abnormal
return/abnormal return/total shareholder, stock, share
return/market, risk adjusted return/earning per
share/cash pay-out ratio (52)
-IPO value per share/the gross proceeds of the
offering in millions from IPO date to first closing
price (2)
Note. The values in parentheses refer to the number of studies from which the variables were
coded.
CEO GENDER AND CAREER
80
Appendix B
Meta-analytic Correlations for Testing Hypotheses 1 to 5
Relationships
k
N
r
SD r
ρ
SD ρ
95% CI
Var
CEO age
CEO education
47
140,836
-.05
.09
-.05
.09
-.16
.06
-.08
CEO PSC
22
35,529
.03
.06
.04
.05
-.02
.11
.02
Firm size
84
326,879
.07
.07
.07
.07
.05
.08
5%
Firm age
33
82,532
.20
.12
.20
.12
.16
.24
2%
CEO gender
107
471,578
-.18
.18
-.18
.18
-.21
-.14
1%
CEO duality
29
250,880
.17
.06
.17
.06
.14
.19
3%
CEO pay
24
136,599
.04
.06
.04
.06
.02
.07
5%
CEO position tenure
45
182,495
.33
.14
.34
.15
.29
.38
1%
Firm risk taking
43
245,380
.01
.06
.02
.07
-.08
.11
.00
Firm performance
56
317,978
.01
.08
.01
.09
-.11
.12
-2%
Firm size
Firm age
45
130,272
.14
.17
.14
.17
.09
.19
1%
Past firm performance
18
44,603
-.01
.14
.01
.16
-.20
.22
-7%
CEO gender
139
596,771
-.05
.15
-.06
.16
-.08
-.03
1%
CEO duality
39
267,803
.02
.08
.02
.08
.00
.05
2%
CEO pay
39
186,309
-.07
.12
-.06
.16
-.11
-.02
1%
CEO position tenure
50
217,720
-.01
.08
-.01
.07
-.03
.01
4%
Firm risk taking
73
377,658
.14
.17
.16
.22
-.12
.45
11%
Firm performance
88
426,319
.08
.18
.07
.23
-.22
.36
2%
Firm age
Past firm performance
5
3,062
-.02
.07
-.02
.06
-.09
.06
32%
CEO gender
58
269,329
-.01
.03
-.01
.03
-.02
-.00
25%
CEO duality
14
64,885
-.04
.08
-.04
.08
-.08
.00
3%
CEO long term pay
9
62,891
-.10
.05
-.10
.05
-.13
-.07
6%
CEO position tenure
18
34,836
.05
.13
.05
.13
-.01
.11
3%
Firm risk taking
22
116,061
.08
.18
.09
.18
-.13
.32
2%
Firm performance
30
106,811
.12
.18
.11
.25
-.20
.43
2%
CEO gender
CEO PSC
25
38,241
.10
.15
.12
.15
-.07
.31
2%
CEO education
57
248,550
-.02
.08
-.02
.07
-.04
.00
4%
Past firm performance
22
42,509
-.05
.17
-.05
.17
-.12
.02
2%
CEO duality
47
310,677
-.06
.11
-.06
.11
-.09
-.03
1%
CEO pay
45
274,953
-.02
.07
-.03
.08
-.05
-.00
3%
CEO position tenure
63
303,619
-.03
.04
-.03
.03
-.04
-.02
17%
Firm risk taking
78
422,158
-.02
.08
-.01
.09
-.03
.01
2%
Firm performance
107
664,401
-.02
.10
-.02
.11
-.01
-.00
5%
CEO GENDER AND CAREER
81
Appendix B (cont.)
Relationships
k
N
r
SD r
ρ
SD ρ
95% CI
Var
CEO education
CEO PSC
11
2,114
.04
.08
.04
.04
-.01
.09
82%
Note. k = number of effect sizes in each meta-analysis; N = combined sample size; r = sample-
size weighted mean uncorrected correlation; SDr = standard deviation of the sample-size
weighted mean uncorrected correlation; ρ = estimated corrected mean correlation; SD ρ =
standard deviation of the estimated corrected mean correlation; CI = confidence interval; Var =
percentage of observed variance accounted for by statistical artifacts. CEO gender was coded
such that higher scores always indicate female CEOs. PSC = positive self-concept. Education
include education level and elite education.
