The International Journal of Human Resource
Management
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A fair go? The gender pay gap among corporate
executives in Australian firms
Yoshio Yanadori, Jill A. Gould & Carol T. Kulik
To cite this article: Yoshio Yanadori, Jill A. Gould & Carol T. Kulik (2018) A fair go? The gender
pay gap among corporate executives in Australian firms, The International Journal of Human
Resource Management, 29:9, 1636-1660, DOI: 10.1080/09585192.2016.1255985
To link to this article: https://doi.org/10.1080/09585192.2016.1255985
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The International Journal of Human Resource Management, 2018
VOL. 29, NO. 9, 1636–1660
https://doi.org/10.1080/09585192.2016.1255985
A fair go? The gender pay gap among corporate
executives in Australian firms
Yoshio Yanadori, Jill A. Gould and Carol T. Kulik
School of Management, The University of South Australia, Adelaide, Australia
ABSTRACT
In virtually all economies, executive positions are highly male
dominated. This study examines the pay gap between male
executives and female executives in large Australian firms
from 2011 to 2014 to evaluate whether female executives are
paid equitably compared with male executives. The mean pay
comparison shows that female executives earn 80.7% of the
total pay earned by male executives. A large part of the gender
pay gap is explained by differences in positions held; female
executives are particularly underrepresented in highly paid
executive positions. After controlling for executive position
and other relevant individual and firm characteristics, there
remains a 15.1% gender gap in total pay. Our findings suggest
that to achieve the goal of gender equity, both the proportion
of women at executive level and the executive-level gender
pay gap need to be monitored.
KEYWORDS
Diversity; executive pay;
gender pay gap; female
executives
Introduction
Analyses conducted by government and non-profit organizations consistently
demonstrate that female workers earn significantly less than male workers. In
Australia, full-time female workers earn, on average, 17.3% less than full-time
male workers (Workplace Gender Equality Agency [WGEA], 2016a). Part of the
gender pay gap is explained by an occupational gender divide. Some occupations
are dominated by male workers and others are dominated by female workers, and
female-dominated occupations are valued less than male-dominated ones (Blau
& Kahn, 2007). The gender pay gap is also visible within occupations. A number
of factors have been suggested as contributing to the within-occupation gender
pay gap including line managers’ gender stereotypes (Castilla & Benard, 2010),
women’s tendency to avoid negotiation (Kulik & Olekalns, 2012), and men’s access
to better comparative salary information through high-quality networks (Seidel,
Polzer, & Stewart, 2000).
CONTACT Yoshio Yanadori
yoshio.yanadori@unisa.edu.au
© 2016 Informa UK Limited, trading as Taylor & Francis Group
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 1637
Although corporate executive positions are highly male dominated (Kulik
& Metz, 2015), some executive positions are now held by women in Australia
(WGEA, 2016b) and other countries (e.g. Catalyst, 2013). Given the enduring
evidence of the gender pay gap within occupations, it is critical to investigate
whether the pay gap is also evident at the top of the organizational hierarchy.
Indeed, researchers are beginning to investigate the gender pay gap at executive
level. Examining executive pay in US firms, Bertrand and Hallock (2001) reported
that female executives earned about 45% less than male executives. According to
the authors, two major factors account for this pay gap: (1) female executives are
likely to be appointed in small firms and (2) female executives are likely to hold
low-ranked executive positions. Once these factors are controlled, there is no
statistically significant pay gap between male executives and female executives.
However, studies that follow Bertrand and Hallock (2001) offer mixed findings
about the gender pay gap at executive level; some studies found that female executives were paid significantly less than male executives (e.g. Elkinawy & Stater,
2011), whereas others found there was no statistically significant pay gap between
male and female executives (e.g. Jordan, Clark, & Waldron, 2007). Hence, the
question whether or not female executives are paid equitably compared with male
executives remains unsettled. Further, as our subsequent literature review reveals,
empirical evidence on this topic is geographically limited. Most studies analyzed
US firms with a few exceptions examining European firms. Considering that
gender role norms and regulatory environments vary across countries (Catalyst,
2014; Kark & Eagly, 2010), findings in the US do not necessarily apply to other
countries. In short, our knowledge about the gender pay gap at executive level is
equivocal and fragmented.
To expand the geographic scope of this line of research, this study investigates the gender pay gap at executive level in large Australian firms from 2011
to 2014. The time frame of our analysis enables us to report the most recent
statistics, and also has practical implications for policy-makers and practitioners. In Australia, the Corporate Governance Principles and Recommendations
(‘the Recommendations’, hereafter) were introduced in 2010 by the Australian
Securities Exchange (ASX). The Recommendations require ASX-listed firms to
set measurable and objective gender diversity targets, including at executive level,
and to report progress in their annual reports in achieving these targets. It is too
soon to definitively evaluate the effect of the Recommendations on the proportion
of female executives in Australian organizations. However, if the proportion of
female executives increases, and female executives are paid less than their male
colleagues, the Recommendations resolve one gender equity problem (i.e. women’s underrepresentation at executive level) but produce another (i.e. gender pay
gap at executive level). To evaluate the success of the Recommendations, both
the gender composition and the gender pay gap at executive level should be
monitored.
1638
Y. YANADORI ET AL.
Theoretical background
Determinants of executive pay
Researchers have long been interested in understanding the determinants of
executive pay (Devers, Cannella, Reilly, & Yoder, 2007). The majority of studies
examined the influence of firm characteristics. Empirical studies have consistently
demonstrated that firm size has the biggest influence on executive pay level. In
their meta-analysis, Tosi, Werner, Katz, and Gomez-Mejia (2000) found that firm
size accounted for more than 40% of the variance in CEO total pay. In contrast, the
effect of firm performance on executive pay has been controversial. Early empirical studies generally reported a weak relationship between firm performance and
executive pay (e.g. Jensen & Murphy, 1990). As the proportion of equity-based
pay plans (e.g. stock options; stock grants) in total pay increased, the correlation
between firm performance (particularly when measured in terms of stock market
performance) and executive wealth, which includes cash pay and the gains from
equity-based pay plans, became stronger (Hall & Liebman, 1998; Nyberg, Fulmer,
Gerhart, & Carpenter, 2010).
Other than firm characteristics, empirical research has found that industry,
executive position, and individual executive characteristics account for variance
in executive pay. Firms typically set pay levels considering their competitors’
pay levels, and this is also true for executive pay (Fulmer, 2009). Executive
teams consist of positions with different responsibilities. Pay differences among
executives reflect differences in the type and level of contributions their roles
make to firm performance (Carpenter & Wade, 2002). Finally, certain individual
characteristics are associated with executives’ productivity and influence their
pay levels. The most widely accepted individual characteristic is position tenure,
which reflects executives’ position-specific experience (Hill & Phan, 1991). To
date, a limited number of executive pay studies have included gender in their
empirical analysis. Due to the underrepresentation of women in executive teams,
scholars have paid limited attention to the difference in pay between male and
female executives.
Organizational pay practices, including executive pay, are known to vary
across countries due to national differences in labor markets, government regulations, and cultures (Milkovich, Newman, & Gerhart, 2013). We point to two
major differences in executive pay between Australian firms and US firms. First,
executive pay in Australian firms is lower than that in US firms (Fels, 2010). A
report by the Australian Productivity Commission (2009) found that Australian
firms’ CEOs earn about 47% of CEOs in US firms of comparable size. Second, the
proportion of financial incentives in executive pay is smaller in Australian firms
than in US firms, although this proportion has been increasing in Australian
firms (Shields, 2007).
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 1639
The gender pay gap at executive level
In virtually all economies, women are underrepresented in top management
positions (Kulik & Metz, 2015). Women make up 9.2% of executive positions in
Australia (WGEA, 2016b), 14.6% in the US (Catalyst, 2013), and 16.4% in the
UK (Sealy & Vinnicombe, 2012). Difficulties for women in obtaining executive
positions are commonly referred to as a glass ceiling (e.g. Hillman, Shropshire,
& Cannella, 2007). Scholars explain that the glass ceiling is part of the reason for
the disparity in average pay between men and women. That is, a small number of
women have access to highly paid positions, leading to lower average pay among
women compared with men (Weinberger, 2011). Even after women break the
glass ceiling and obtain executive positions, however, their employment relationships are not necessarily comparable to those of male executives (Eagly, Karau, &
Makhijani, 1995). At the employee level, research has documented a significant
pay gap between men and women undertaking similar work (Blau & Kahn, 2007).
Bertrand and Hallock (2001) published a seminal study on the gender pay
gap at the executive level. The authors analyzed US executive pay drawing on
Standard & Poor’s ExecuComp database, which includes the five most highly paid
executives in public firms. Their data-set consisted of executive pay information
in the 1992–1997 period, and women comprised about 2.5%.1 A simple mean
comparison of total pay between male executives and female executives showed
that female executives received 45% less than male executives. However, their
analysis revealed that female executives were more likely to hold positions in
small firms. Because large firms pay their executives more than small firms (Tosi
et al., 2000), part of the gender pay gap is explained by the difference in the size
of firms employing male and female executives. When firm size was controlled
in their regression analysis, the gender pay gap decreased to 28%. The authors
also found that the proportion of women was smaller in highly paid executive
positions such as CEO (0.5%), Vice Chair (0.9%), and President (1.7%), but greater
in relatively low-paid positions such as Chief Financial Officer (6.4%) and Vice
President (4.3%). Once the model controlled for firm size and position titles, as
well as other relevant individual and firm characteristics (e.g. position, tenure, and/
or industry), the difference in pay between male executives and female executives
became non-significant.
After Bertrand and Hallock (2001), researchers have examined the gender pay
gap at executive level using different samples or different analytical strategies to
gain additional insights. We review these studies and summarize their findings
in Table 1.
The majority of prior studies on the gender pay gap at executive level analyzed
US firms. Among the 15 studies included in Table 1, only three studies analyzed
non-US firms, and all three analyzed firms in Europe (i.e. Denmark, Norway, and
the UK). Hence, despite the accumulation of research findings on this topic, our
knowledge is geographically limited. This is in stark contrast to the gender pay gap
Renner, Rives,
and Bowlin
Mohan and
Ruggiero
Jordan, Clark,
and Waldron
Skalpe
Munoz-Bullon
Elkinawy and
Stater
2003
2007
2007
2010
2011
Study
Bertrand and
Hallock
2002
2001
Publication
year
US
US
Norway
US
US
US
Country
US
Executives from
ExecuComp
Executives from
ExecuComp
CEOs included
in the yahoo.
com stock
profiles
Executives in
the Fortune
100 companies
CEOs
Non-CEO executives from
ExecuComp
Sample
Executives from
ExecuComp
1996–2004
1992–1997
2001
2001–2003
2000
1997
Data year
1992–1997
Table 1. Studies on the gender pay gap at executive level.
4.7%
5.0%
9.5%
5.8% (2003)
1.5%
Not reported (used
matched sampling
strategy)
Proportion of women
in the data-set
2.5%
(Continued)
Female/Male ratio – 77%a
The regression analysis shows that there is a significant gender gap in total pay
even after relevant individual and firm characteristics are controlled.
The gender pay gap is larger in the tourism industry.
Female/Male ratio – 53.4%a
The regression analysis shows that there is no significant gender gap in base salary
once relevant individual and firm characteristics are controlled. However, there is
a significant gender gap in financial incentives even after relevant individual and
firm characteristics are controlled.
Female/Male ratio – 71.8%a
The regression analysis shows that there is a significant gender gap in total pay
even after relevant individual and firm characteristics are controlled.
The gender pay gap is smaller in large firms.
Key findings
Female/Male ratio – 67.0%a
About 75% of the pay gap is explained by the gender differences in firm size and
positions held.
The regression analysis shows that there is no significant gender gap in total pay
once relevant individual and firm characteristics are controlled.
Female/Male ratio not reported
The regression analysis shows that there is no significant gender pay gap in base
salary once relevant individual and firm characteristics are controlled. However,
there is a significant gender pay gap in total pay after relevant individual and firm
characteristics are controlled.
Female/Male ratio – 93.7% a
The mean comparison shows that male and female CEOs receive similar levels of
base salary and bonus. However, once the value of stock options are included, the
total pay for female CEOs is lower than that for male CEOs.
Female/Male ratio – 91.6%(2003) a
The mean comparison shows that there is no significant gender gap in total pay.
1640
Y. YANADORI ET AL.
Shin
Vieito and
Khan
Li
Hill,
Upadhyay,
and Beekun
2012
2012
2015
2015
US
US
US
US
US
US
Denmark
Country
UK
CEOs from
ExecuComp
CEOs from
ExecuComp
Executives from
ExecuComp
Executives from
ExecuComp
Executives from
ExecuComp
CEOs from
ExecuComp
Executives from
the 2000 largest companies
Sample
Executives from
BoardEx
1996–2005
2002–2007
1992–2004
1998–2005
1991–2006
1998–2010
1996–2005
Data year
1998–2004
b
2.0%
Not reported
4.3%
6.0%
5.0%
49% for other executives
2.0%
10% for CEO
41% for Vice Presidents
Proportion of women
in the data-set
Not reported (used
matched sampling
strategy)
Mean comparison. Calculated using the summary statistics provided by the authors.
Median comparison. Calculated using the summary statistics provided by the authors.
a
2012
Bugeja,
Matolcsy, and
Spiropoulos
Gayle, Golan,
and Miller
Study
Kulich,
Trojanowski,
Ryan, Haslam,
and Rennebood
Smith, Smith,
and Verne
2012
2011
Publication
year
2011
Table 1. (Continued).
Female/Male ratio – 99.0% b
The regression analysis shows that there is no significant gender gap in total pay
once relevant individual and firm characteristics are controlled.
Female/Male ratio – 71.3%b
The regression analysis shows that when relevant individual and firm characteristics
are controlled, women earn more than men.
Female/Male ratio – 70.5%b
The regression analysis shows that the gender pay gap is smaller when a proportion
of women in the compensation committee is higher.
Female/Male ratio – 72.8% (2004)b
The regression analysis shows that there is a significant gender gap in total pay
even after relevant individual and firm characteristics are controlled.
This gender pay gap is decreasing after 2000.
Female/Male ratio not reported.
The regression analysis shows that there is a significant gender gap in total pay
at the 0.95th quantile even after relevant individual and firm characteristics are
controlled. In other quantiles, there is no significant gender pay gap.
Female/Male ratio not reported.
The regression analysis shows that when relevant individual and firm characteristics
are controlled, female CEOs receive higher total pay than male CEOs.
Female/Male ratio – 69.1% (2005)b
The regression analysis shows that there is a significant gender gap in total earnings
even after relevant individual and firm characteristics are controlled.
Key findings
Female/Male ratio – 81.3%b
The gender pay gap is larger for bonus than for total pay.
The regression analysis shows that male executives’ pay is more sensitive to performance than female executives’ pay.
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 1641
1642
Y. YANADORI ET AL.
research at employee level, which is studied in a large number of countries and
an international comparison is straightforward (for example, see OECD, 2016).
With respect to the most critical issue, i.e. whether or not female executives are
paid less than their male counterparts, empirical evidence is mixed even within
studies that analyzed US data. As we noted, Bertrand and Hallock (2001) found
that once relevant variables (e.g. position, tenure, and/or firm size) are controlled,
there is no statistically significant gender pay gap. While some of the subsequent
studies (Bugeja, Matolcsy, & Spiropoulos, 2012; Jordan et al., 2007) supported
their findings, other studies contradicted them and found a statistically significant
gender pay gap at executive level (e.g. Elkinawy & Stater, 2011; Vieito & Khan,
2012). There are even studies that found female executives are paid more than
male executives (Gayle, Golan, & Miller, 2012; Hill, Upadhyay, & Beekun, 2015). In
contrast to studies on US firms, those that analyzed non-US firms seem to agree
on the presence of a statistically significant gender pay gap at executive level (e.g.
Skalpe, 2007; Smith, Smith, & Verne, 2011).
The divergent findings may be attributable to cross-study differences in time
frame, pay variables used,2 and control variables. We also gain two observations
through our review. First, findings differ depending on the types of executives
included in the sample. Some studies focused on CEOs (Bugeja et al., 2012; Hill
et al., 2015), while others examined a wider range of executive positions including
CEOs. Findings of studies that analyzed CEO pay exclusively are largely mixed.
Studies found that female CEOs are paid less than male CEOs (Mohan & Ruggiero,
2003), comparable to male CEOs (Bugeja et al., 2012), or more than male CEOs
(Hill et al., 2015). Findings of studies that analyzed all executives still display some
variation; however, the majority of these studies found that female executives are
paid less than male executives even after controlling for relevant variables (e.g.
Elkinawy & Stater, 2011; Vieito & Khan, 2012).
Second, findings are different depending on the pay components analyzed. In
particular, the gender pay gap appears to be more robust for financial incentives.
Munoz-Bullon (2010) found that once relevant individual and firm characteristics
were controlled, there was no statistically significant gender gap in base salary;
however, there still remained a statistically significant gap in financial incentives.
This result is consistent with Renner, Rives, and Bowlin (2002), who found a
significant gender pay gap only for total pay, but not for cash pay which excludes
long-term incentives.
There are two possible explanations for a large gender pay gap in financial
incentives. The first explanation is based on the ‘glass cliff ’ (Ryan & Haslam,
2005: 83), a phenomenon in which women are more likely to be appointed to
managerial positions when organizations experience poor performance. If firms
are not performing well, it is harder for female executives to enhance firm performance enough to qualify for their incentives, so the payment from financial
incentives is smaller among women than among men. Usually, empirical analyses
control for firm performance when estimating executive pay, so the effect of low
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 1643
firm performance on executive pay is accounted for. However, the proportion of
­financial incentives in total pay is known to be smaller in firms with a greater likelihood of failure (Beatty & Zajac, 1994). If women are more likely to be appointed in
firms with dismal performance prospects, their financial incentives will be smaller.
The other explanation is a gender difference in risk attitudes. Recent behavioral economics studies report that women are, on average, more risk-averse than
men (Bertrand, 2010). Because the payment of financial incentives is variable,
executives bear greater income risk when their pay includes a large proportion
of financial incentives. The positive performance effect of financial incentives is
smaller among risk-averse individuals, who feel greater discomfort and stress with
increased income uncertainty (Cadsby, Song, & Tapon, 2007). If female executives
are more risk-averse, firms may reduce the proportion of financial incentives in
their total pay to reduce income uncertainty. Kulich, Trojanowski, Ryan, Haslam,
and Renneboog (2011) found that the gender gap in the median bonus was 36%,
which was larger than the gap in the median total pay (19%).
In the next section, we analyze the data from Australian firms. In so doing,
we build on our literature review and adopt approaches used in previous studies.
Methods
Data
Our study drew on the Sirca Corporate Governance database (Sirca, hereafter).
Sirca compiles data about Australian domestic firms listed on the ASX (see Peat
2009 for more details about Sirca). Its Corporate Governance database includes
information about directors and executives collected from firms’ annual reports.
Our analysis focused on executives including those that serve on the board but
excluding part-time outside directors. To make our analysis comparable to prior
Australian executive pay research (e.g. Capezio, Shields, & O’Donnell, 2010), we
focused on ASX500 firms (i.e. the 500 largest public firms measured by market
capitalization). Because the primary goal of this study is to present the most
recent statistics of the gender pay gap at executive level, we analyzed executive
pay from 2011 to 2014. We did not analyze executive pay before 2011 because the
Recommendations were introduced in 2010 and firm decisions on the appointment of executives and their pay might be different before and after the introduction of the Recommendations. We also excluded executives who served less
than a year. Firm information was collected from DataAnalysis Premium. After
removing observations with missing values, our final data-set consists of 8343
executive-year observations covering 3478 distinct executives from 384 distinct
firms.3 Among our executive observations, 7527 are men and 816 are women.
The proportion of women in our sample (9.8%) is comparable to the statistics in
2012 reported by WGEA (9.2%).4 This figure is larger than the figure in previous
1644
Y. YANADORI ET AL.
studies that drew on US data (e.g. 2.5% in Bertrand and Hallock (2001), 5.1% in
Munoz-Bullon (2010)).
Measures
Executive pay
Our analysis primarily used total pay, which is the sum of base salary, bonuses and
other short-term incentives, long-term incentives, benefits, and other payments.
We also analyzed base salary and financial incentives separately considering the
potential differences in gender pay gaps across pay components. Financial incentives include bonuses and other short-term incentives, and long-term incentives.
In Sirca, some executives’ long-term incentives take negative values as a result of
adjustments from previous years’ long-term incentive payments. These executives
receive payments in other pay forms (e.g. base salary; benefits); consequently, no
executive’s total pay takes a negative value. Because our data span four years, we
adjusted for inflation using the Consumer Price Index (CPI).
Because executive pay observations exhibit a highly skewed distribution, we
applied a log transformation to executive pay. Some executives’ financial incentives take a negative value due to the negative long-term incentive values and a
log transformation is impossible for these observations. We applied a hyperbolic
sine function (Burbidge, Magee, & Robb, 1988), which allows the transformation
of negative values, to our pay variables.5 We also winsorized the top and bottom
1% of observations in our data-set to reduce the influence of extreme observations
(Dixon, 1960).
Female executive
Sirca has gender information for most executives included in its database. When
gender information was missing in the database, we tried to identify gender information using other sources (e.g. documents available on the firms’ websites).
Observations with missing gender information were removed from our final
sample.6 We created a female executive indicator variable with female executives
coded 1 and male executives coded 0.
Position category
Sirca includes information about the position titles of executives. Using this information, we sorted executive titles into seven position categories: (1) CEO, (2)
Managing Director, (3) Chief Officer of a functional department, (4) Executive
Director, (5) General Manager, (6) Company Secretary, and (7) others (e.g. former
CEO). We created corresponding categorical variables. Company Secretary is a
reference category.
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 1645
Tenure
Sirca includes appointment and resignation dates of executives, although appointment dates are missing for some executives (about 18% of the observations in our
final data-set). Based on this information, we calculated each executive’s position
tenure (in years) as of 31 December of the observation year. We then created four
tenure categories: (1) less than three years, (2) three–five years, (3) five years or
longer, and (4) tenure information missing. We use less than three years as the
reference category and include three indicator variables.7
For executives who resigned mid-year, we calculated the length (in years) the
executive did not hold the position. For instance, if an executive resigned on 30
September 2011, the length this executive did not hold the executive position was
0.25 years (i.e. 3 months out of 12 months). We refer to this variable as resigned
during the year. This variable is expected to be negatively associated with executive pay.
Firm characteristic variables
To identify firm characteristic variables to include in our model, we draw on past
research on executive pay (e.g. Tosi et al., 2000) and gender inequity (e.g. Mohan,
2014). Empirical analyses on executive pay consistently include firm size and performance. We used total assets (in million Australian dollars) as the measure of
firm size (e.g. Elkinawy & Stater, 2011). For firm performance, we used firm return
on assets (ROA). For ROA, we winsorized the top and bottom 1% of observations
to reduce the influence of extreme observations. Previous research also shows that
firm risk is associated with executive pay, especially financial incentives (e.g. Core,
Holthausen, & Larcker, 1999). Following Bugeja et al. (2012), we used the standard
deviation of ROA over the previous three years. Because the distribution of total
assets and firm risk is highly skewed, we applied a logarithmic transformation.
We also controlled for firms’ corporate governance structure as it is known to
influence both the level and mix of executive compensation (Misangyi & Acharya,
2014). We included the proportion of outside directors on the board (Bugeja
et al., 2012). In addition, we included the presence of women on the board because
women may be more vigilant about gender inequity, including gender pay gaps
in their firms (Hirsch, 2013). Considering that about half of the firms in our final
data-set do not have a woman on their board, we created an indicator variable,
woman on the board. This variable takes 1 for a firm if its board includes at least
one woman, and 0 if not. We then created an interaction between this variable
and the female executive indicator variable to see if the presence of a woman on
the board reduces the size of the gender pay gap at executive level. As a robustness
check, we also ran a model using the proportion of women on the board.
Industry
Because executive pay differs systematically according to industry (Fulmer, 2009),
our model included industry variables. Using the Global Industry Classification
1646
Y. YANADORI ET AL.
Standard, we categorized firms into 10 industry sectors (e.g. energy, materials,
industrials, consumer discretionary and financials). We created indicator variables
for each industry.
Year
Because our data span four years, we included three-year indicator variables. The
reference year is 2011. We were also interested in examining whether the gender
pay gap at executive level changed over time, so we included interaction terms
between the female executive indicator variable and year indicator variables.
Results
Summary statistics
Table 2 summarizes the mean pay level by gender for base salary, financial incentives, and total pay, presented in 2011 Australian dollars. The mean total pay for
female executives ($800,109) is significantly lower than the mean total pay for
male executives ($991,991) (p < .001 based on a paired t-test). On average, female
executives’ total pay is 80.7% of male executives’. The magnitude of the gap is
slightly smaller for financial incentives (83.4%) than base salary (78.8%), although
the difference is statistically significant for both pay components.
The size of the gender gap in total pay in Australian firms (i.e. 80.7%) is smaller
than the pay gap observed in US studies (67.0% in Bertrand and Hallock (2001),
53.4% in Munoz-Bullon (2010)). To understand the nature of the gender pay
gap at executive level in Australian firms, we compare our statistics with those
of Bertrand and Hallock (2001) in Table 3. Their study is the seminal study on
the gender pay gap at executive level and has served as a good reference for
subsequent studies (e.g. Gayle et al., 2012; Munoz-Bullon, 2010). In addition,
their study provides detailed information about the analyses they conducted (e.g.
statistics according to pay components and firm characteristics), which enables
us to undertake a parallel investigation. Because Bertrand and Hallock (2001)
analyzed US firms, our comparison highlights the similarities and differences
between Australian firms and US firms.
Table 3 compares the mean total pay in our data-set and the mean total pay in
Bertrand and Hallock (2001). We converted the mean total pay in their study into
Australian 2011 dollars by (1) adjusting for inflation and representing total pay in
2011 price and (2) converting into Australian dollars using the annual exchange
rate in 2011. We calculated the mean total pay for executives in Australian
firms drawing on two samples: our final sample, which included all executives
(N = 8343), and the sample of five most highly paid executives (N = 6155). The
ExecuComp database used by Bertrand and Hallock (2001) included only the five
most highly paid executives, so the statistics that focus on the five most highly
paid Australian executives may make a more accurate comparison.
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 1647
Table 2. Summary statistics on executive pay in 2011–2014.
Base salary
Financial incentives
Total pay
N
All executives
$ 455,394
$ 401,382
Male
executives
$ 465,027
$ 408,007
Female
executives
$ 366,538
$ 340,265
Female/Male
ratio (%)
78.8
83.4
paired t-test
***
*
$ 973,223
8343
$ 991,991
7527
$ 800,109
816
80.7
***
Note: All pay data are reported in 2011 Australian dollars.
*p < .05; ***p < .001.
Table 3. Comparison of executive pay between Australian firms and US firms.
Total pay in Australia
(2011–2014)
(All executives)a
Total pay in Australia
(2011–2014)
(Five most highly paid executives)b
Total pay in the US (1992–1997)
in Bertrand and Hallock
(2001)c
US total pay/AU total pay (All
executives)
US total pay/AU total pay (Five
most highly paid executives)
(A)
All executives
$973,223
Male executives
$991,991
Female
executives
$800,109
Female/Male
ratio (%)
80.7
(B)
$1,080,010
$1,094,469
$909,832
83.1
(C)
$1,782,527
$1,796,944
$1,204,654
67.0
(C)/(A)
1.83
1.81
1.51
–
(C)/(B)
1.65
1.64
1.32
–
N = 8343.
N = 6155.
c
In 2011 Australian dollars. Inflation was adjusted using US CPI. Converted to Australian dollars using the 2011
annual average exchange rate.
a
b
The comparison between total pay in Australia (all executives) and the US
shows that total pay for female executives in the US is 1.51 times larger than that
for female executives in Australia, whereas total pay for male executives in the
US is 1.81 times larger than that for male executives in Australia. The comparison
between Australia (five most highly paid executives) and the US shows a similar
pattern. Total pay for female executives in the US is 1.32 times larger than that for
female executives, whereas total pay for male executives in the US is 1.64 times
larger than that for male executives in Australia. In both comparisons, US male
executives received more than Australian male executives to a greater extent than
US female executives received more than Australian female executives. Although
female executives are paid less than male executives both in US and Australian
firms, a larger gender pay gap in US firms appears to be caused by the fact that
male US executives receive considerably larger pay.
Table 4 compares firm size (total assets) and performance (ROA) between
firms that employ at least one female executive (564 firm-years) and those that
do not (823 firm-years). While Bertrand and Hallock (2001), as well as others
(e.g. Munoz-Bullon, 2010), reported that female executives were more likely to
1648
Y. YANADORI ET AL.
be found in small firms, our analysis revealed that, in Australia, female executives
are more likely to be found in large firms. The mean total assets is much greater
among firms with at least one female executive than among firms without female
executives. While this result diverges from the US findings, it is consistent with
a government report that found large Australian firms were more likely to hire
female executives than small Australian firms (Equal Opportunity for Women
in the Workplace Agency, 2009). With respect to performance, the mean ROA is
higher among firms with at least one female executive than among firms without
female executives. Hence, the glass cliff explanation does not seem to apply to the
appointment of female executives in Australian firms.
Next, we examined gender composition according to position category, which
is summarized in Table 5. In our sample, 9.8% of executives are women. Table 5
reveals that the proportion of women is lower in highly paid executive position
categories (e.g. 3.7% for CEO and 5.4% for Managing Director). In contrast, the
proportion of women is very large in the Company Secretary role (30.6%), whose
pay level is much lower than that of CEO or Managing Director. The average
total pay of Company Secretary ($493,825 for all executives) is less than half of
the average total pay of CEO ($1,804,835) and Managing Director ($1,321,765).
This finding is in line with Bertrand and Hallock (2001), which reported that part
of the gender pay gap is explained by the fact that females are less likely to hold
highly paid executive positions.
Interestingly, in the highly paid executive positions (i.e. CEO; Managing Director),
the average pay for women is higher than the average pay for men, although the differences are not statistically significant. Examination of the data shows that the standard
deviation of total pay is large for female CEOs and female Managing Directors. The
number of women holding these positions is very small, so a few highly paid female
CEOs and Managing Directors raised the average total pay for female executives.
Regression analysis
Building on these findings, we regressed executive pay variables on gender and
other relevant predictor variables. Because our data span four years and include
multiple observations from the same executives, error terms are unlikely to be
independent (Greene, 2008). Previous studies on the gender pay gap at executive level typically handled this problem by estimating the model by clustering
Table 4. Summary statistics on total assets and return on assets.
Total assets (in million dollars)a
Return on assets
Nb
a
In 2011 Australian dollars.
Number of firm-years.
b
Firms with at least one female
executive
$ 22,117
3.7%
564
Firms with no female executive
$ 4627
1.8%
823
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 1649
Table 5. Summary statistics on executive total pay according to position category.
Number and proportion
CEO
Managing Director
Chief Officer of a
Functional Department
All
Male
executives executives
1345
1295
96.3%
971
919
94.6%
2366
2103
Executive Director
439
General Manager
2676
Company Secretary
444
Female
executives
50
3.7%
52
5.4%
263
88.9%
413
94.1%
2393
89.4%
308
69.4%
11.1%
26
5.9%
283
10.6%
136
30.6%
Mean total pay
All
executives
$ 1,804,835
Male
executives
$ 1,790,854
Female
executives
$ 2,166,938
$ 1,321,765
$ 1,318,530
$ 1,378,927
$ 881,135
$ 887,457
$ 830,579
$ 823,649
$ 815,690
$ 950,081
$ 606,572
$ 609,926
$ 578,214
$ 493,825
$ 506,947
$ 464,108
Note: All pay data are reported in 2011 Australian dollars.
around executives and reported robust estimators, known as White estimators
(e.g. Elkinawy & Stater, 2011; Munoz-Bullon, 2010; Smith et al., 2011). We follow
these previous studies and report robust estimators using generalized estimating
equations (Ballinger, 2004). Some researchers employed other approaches such
as pooled ordinary least square (OLS) regression (e.g. Bugeja et al., 2012) and
random effects model (Shin, 2012). Results are substantively consistent across
these methods, although their estimates of the gender pay gaps are slightly larger
than the results of the analysis we present in this section.
Descriptive statistics and a correlation matrix for the data are presented in
Table 6. Table 7 presents the results of our analyses on total pay, base salary, and
financial incentives for all executives including CEOs. For all pay variables, we ran
four models. Model 1 regressed executive pay on the female indicator variable.
The only control variables included are year indicator variables. Model 2 added
position category variables to Model 1. Model 3 included other control variables.
Model 4 adds interaction terms to see if the gender pay gap at the executive level
differs (1) between firms with a woman on the board and those without and (2)
over data years. Because Model 4 includes interaction terms, we primarily focus
on the estimate of the gender pay gap (i.e. the beta coefficient of the female executive indicator variable) in Model 3 to confirm if female executives are paid less
than their male counterparts.
In our analysis of total pay, the beta coefficient of the female executive indicator
variable in Model 1 is −.260 (p < .001). It suggests that female executives’ pay is
lower than male executives’ by 22.9%.8 When executive positions were controlled
in Model 2, the gap narrowed considerably to 14.8% (β = −.160, p < .001). Thus,
about one-third of the gender pay gap in total pay is accounted for by the difference
in executive positions held by men and women. In Model 3, after controlling for
relevant variables, there remains a 15.1% gender gap (β = −.164, p < .001) in total
pay. Finally, Model 4 adds the interaction terms to Model 3. For the interaction
Variable
Total paya
Base salarya
Financial incentivesb
Female executive
Tenure 3–5 years
Tenure 5 years or more
Tenure information missing
Resigned during the year
Total assetsa
Return on assets
Firm riska
Proportion of independent directors
Woman on the board
M
13.305
12.769
10.165
.098
.185
.402
.183
.062
20.503
.033
−3.833
.587
.509
Note: N = 8343. Correlations = |.03| or greater indicate p < .01.
a
In natural logarithm.
b
In hyperbolic sine function.
1
2
3
4
5
6
7
8
9
10
11
12
13
Table 6. Descriptive statistics and correlations.
SD
.932
.757
6.058
.297
.388
.490
387
.197
2.072
.153
1.426
.226
.500
.856
.505
−.090
−.002
.167
−.089
−.077
.619
.148
−.316
.275
.305
1
.338
−.103
−.010
.169
−.105
−.092
.516
.164
−.282
.234
.274
2
−.026
.001
.010
.004
−.225
.270
.062
−.124
.168
.159
3
.019
−.058
.036
.007
.015
.023
−.032
.040
.083
4
−.390
−.225
.027
−.007
−.036
.036
.017
−.006
5
−.387
−.028
.037
.056
−.027
.032
.020
6
−.001
−.004
.033
−.052
−.010
−.018
7
−.015
−.051
.022
.003
−.005
8
.215
−.602
.413
.427
9
−.275
.103
.087
10
−.353
−.309
11
.294
12
1650
Y. YANADORI ET AL.
Woman on the board
Proportion of independent directors
Firm risk
Return on assets
Total assets
Resigned during the year
Tenure information missing
Tenure 5 years or longer
Tenure 3–5 years
Others
General Manager
Executive Director
Chief Officer of a Functional
Department
Managing Director
Chief Executive Officer
Female executive
Model 1
−.260***
(.051)
Model 3
−.164***
(.032)
.998***
(.047)
.433***
(.047)
.296***
(.042)
.381***
(.055)
.160*
(.042)
.394***
(.101)
.089***
(.019)
.149***
(.021)
.042
(.026)
−.188***
(.042)
.277***
(.007)
.109†
(.058)
.023**
(.008)
.063
(.039)
.065***
(.018)
Model 2
−.160***
(.048)
.829***
(.059)
.524***
(.058)
.282***
(.050)
.299***
(.061)
.123*
(.049)
.371***
(.094)
Total pay
Table 7. Results of analysis estimating executive pay.
(.042)
.377***
(.055)
.155***
(.041)
.391***
(.101)
.089***
(.019)
.148***
(.021)
.042
(.026)
−.189***
(.042)
.277***
(.007)
.105†
(.058)
.023**
(.008)
.063
(.039)
.056**
(.019)
Model 4
−.220***
(.050)
.995***
(.047)
.430***
(.047)
.294***
Model 1
−.234***
(.042)
(.044)
.328***
(.057)
.143**
(.044)
.227*
(.096)
(.041)
.353***
(.053)
.154***
(.041)
.213*
(.100)
.043**
(.016)
.100***
(.019)
.001
(.024)
−.181***
(.037)
.191***
(.006)
.227***
(.056)
.017*
(.007)
.060†
(.035)
.071***
(.017)
Model 3
−.143***
(.030)
.946***
(.044)
.350***
(.045)
.286***
Base salary
Model 2
−.128***
(.039)
.913***
(.052)
.496***
(.050)
.318***
(.040)
.348***
(.052)
.150***
(.040)
.209*
(.100)
.043**
(.016)
.099***
(.019)
.001
(.024)
−.181***
(.037)
.191***
(.006)
.223***
(.056)
.017*
(.007)
.061†
(.035)
.058***
(.017)
Model 4
−.226***
(.052)
.942***
(.044)
.347***
(.045)
.283***
Model 1
−.516†
(.307)
(.344)
−.778
(.488)
−.192
(.342)
−2.808***
(.786)
(.318)
−.559
(.475)
−.195
(.317)
−1.967*
(.798)
−.159
(.198)
−.332†
(.182)
.115
(.213)
−6.250***
(.469)
.737***
(.049)
.930†
(.492)
.282***
(.074)
1.510***
(.354)
.561***
(.161)
Model 3
−.572*
(.268)
1.080**
(.357)
.324
(.361)
.427
Financial incentives
Model 2
−.375
(.309)
1.354***
(.390)
1.172**
(.394)
.803*
(.317)
−.572
(.474)
−.207
(.316)
−1.978*
(.799)
−.159
(.199)
−.335†
(.182)
.111
(.213)
−6.253***
(.469)
.738***
(.049)
.926†
(.493)
.281***
(.073)
1.502***
(.354)
.536**
(.166)
Model 4
−.765
(.498)
1.063**
(.356)
.306
(.360)
.416
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 1651
Not
included
Included
8348.4
Not
included
Included
8357.9
Not
included
Included
8408.3
.101†
(.053)
−.059
(.041)
.036
(.049)
−.002
(.054)
Not
included
Included
8410.0
Notes: N = 8343 executive-years. Robust standard errors are reported in parentheses.
†
p < .10; *p < .05; **p < .01; ***p < .001.
Year
Quasi-likelihood information
criterion
Industry
Year 2014 × Female executive
Year 2013 × Female executive
Woman on the board × Female
executive
Year 2012 × Female executive
Not
included
Included
8348.8
Not
included
Included
8361.7
Not
included
Included
8408.6
.131*
(.052)
−.006
(.040)
.003
(.047)
.015
(.045)
Not
included
Included
8411.0
Not
included
Included
8349.4
Not
included
Included
8360.5
Not
included
Included
8394.3
.263
(.520)
−.393
(.484)
.628
(.551)
−.108
(.543)
Not
included
Included
8398.2
1652
Y. YANADORI ET AL.
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 1653
between woman on the board and female executive indicator variable, the sign
of the beta coefficient is positive and its effect is marginally significant (β = .101,
p < .10). Hence, it appears that the presence of a female director is associated with
a smaller gender pay gap at executive level. In contrast, none of the interaction
terms between the female executive indicator variable and year indicator variables
are statistically significant. Our analysis does not suggest that the gender pay gap
at executive level has changed during the four years following the introduction
of the Recommendations.
Next, we estimated base salary and financial incentives separately. The results of
the analysis on base salary are very similar to the results of the total pay analysis.
The beta coefficient for female executive decreased by almost half (from −.234 to
−.128) when executive positions were controlled in Model 2. Inclusion of all other
control variables in Model 3 slightly increased the beta coefficient (from −.128 to
−.143). After controlling for relevant variables, there remains a 13.3% gender gap
in base salary. In Model 4, the interaction between the female executive indicator
variable and woman on the board is positive and significant (β = .131, p < .05).
When we ran a model using the proportion of women on the board, the interaction term remains positive and marginally significant for the analysis on total
pay. In the analysis on base salary, the interaction term is positive and marginally
significant. Hence, the gender gap in base salary is smaller in firms when at least
one woman sits on their board. None of the interaction terms between the female
indicator variable and year indicator variables are statistically significant.
The results from the analysis on financial incentives are different from those
of the analysis on base salary. The results of Model 1 and Model 2 show that the
effect of the female executive indicator variable is marginally significant in Model 1
(β = −.516 in Model 1, p < .10) and becomes non-significant in Model 2 (β = −.375,
p > .10), although its magnitude is greater than that in the analysis on base salary.
When we controlled for other relevant variables, the effect of the female indicator
variable is significant (β = −.572, p < .05). Overall, the results of the analysis on
financial incentives are somewhat unstable, and thus we are unable to come to a
definite conclusion about the presence of the gender gap in financial incentives.
However, the large beta coefficient (β = −.572), which suggests that the gender
gap in financial incentives is 43.6%, is consistent with Munoz-Bullon (2010), who
found that the gender pay gap was larger for financial incentives than for base
pay. None of the interaction terms included in Model 4 are statistically significant.
Because our literature review suggests that studies often failed to find a statistically significant gender pay gap at CEO level, we ran an analysis for a sample of
CEOs and a sample of non-CEO executives separately. In this analysis, we drew
on Model 3 in the previous analysis, which included all control variables but did
not include interaction terms. In Table 8, we present only the beta coefficient of
the female executive indicator variable and its significance level. We also included
the results from the all executive sample, which we presented in Table 7, to facilitate comparison. Results in Table 8 show that the effect of the female executive
1654
Y. YANADORI ET AL.
Table 8. Effects of female executive indicator variable on executive pay.
Female executive
(CEO sample, N = 1345)
Female executive
(Non-CEO sample, N = 6998)
Female executive
(All executive sample, N = 8343)
Total pay
−.134
(.112)
−.158***
(.033)
−.164***
(.032)
Base salary
−.136
(.092)
−.140***
(.031)
−.143***
(.030)
Financial incentives
−1.484
(1.274)
−.514†
(.269)
−.572*
(.268)
Notes: Robust standard errors are reported in parentheses. Control variables are included but not reported.
†
p < .10; *p < .05; ***p < .001.
indicator variable is highly consistent between the all executive sample and the
non-CEO sample. For total pay and base salary, the effect of the female executive
indicator variable is negative and highly significant; the effect is marginally significant for financial incentives. In contrast, in the analysis of the CEO sample, the
effect of the female indicator variable is non-significant for all pay components,
although its sign is negative. Hence, we do not obtain evidence that female CEOs
in Australia are paid less than male CEOs.
Discussion
This study confirms a significant pay gap between male executives and female
executives in large Australian firms. A mean comparison of total pay shows that
female executives earn 80.7% of male executives’ pay. After controlling for individual and firm characteristics that are associated with executive pay, there remains a
15.1% gender gap in total pay. When we analyzed CEOs and non-CEO executives
separately, the gender pay is statistically significant only for non-CEO executives,
not for CEOs.
Our analysis revealed that gender differences in positions held within executive
teams have important implications for the gender pay gap at executive level. In
our data-set, 9.8% of executives are women. However, once we focus on highrank, and thus highly paid executive positions, the proportion of women is much
smaller; women comprise only 3.7% of CEO roles and 5.4% of Managing Director
roles. Thus, while executive teams are already male dominated, women face challenges particularly in obtaining these high-ranked executive positions. As such,
the gender pay gap at executive level is associated with the promotion of women
to high-ranked executive positions. While the proportion of women in executive
teams serves as a useful indicator to understand gender equity at executive level,
examination by position helps us better understand the progress of gender equity.
The gender pay gap in Australian firms is smaller than the gender pay gap
reported in US studies. A comparison of our data with Bertrand and Hallock’s
(2001) data suggests that the difference between Australian male executives’ and
US male executives’ pay is larger than the difference between Australian female
executives’ and US female executives’ pay. One interpretation of this is that the
gender pay gap is smaller in Australia because Australian firms maintain a lower
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 1655
pay ceiling. Pay at the top of the pay range (where male executives are more likely
to sit) does not get as high as in the US.
Results regarding gender differences in firm size are different between the US
and Australia. While US studies found that female executives were more likely
to hold positions in small firms, our study on Australian firms found that female
executives are more likely to hold positions in large firms. We suspect that US
studies’ findings that female executives were more likely to be appointed in small
firms may result from database constraints. All of the US studies we reviewed
drew on Standard & Poor’s ExecuComp database, which includes only the five
most highly paid executives. It is possible that female executives hold positions
below these five most highly paid positions in large firms, and are, therefore, not
reported in ExecuComp. In contrast, Sirca reports all executives identified in the
annual report of ASX-listed firms, not just the five most highly paid executives.
Our analysis drew on executive pay data from 2011 to 2014, immediately after
ASX’s introduction of the Recommendations. Hence, even if the Recommendations
are effective in increasing the proportion of women in executive teams, as long
as the gender pay gap at executive level remains, the goal of gender equity is not
achieved. Several countries (e.g. Canada, Norway, and the Netherlands) have introduced policies to improve women’s representation at executive level; however, the
gender pay gap at this level has not been examined after the introduction of these
policies. Given our findings, we call for the examination of the gender pay gap
at executive level in these countries. In addition, our empirical analysis focused
on large firms (i.e. ASX 500 firms). Considering that firm size is associated with
both executive pay (Tosi et al., 2000) and the gender composition of boards, our
findings do not necessarily apply to all firms in Australia. We call for gender pay
gap research that examines a larger sample that includes small- and medium-sized
firms.
Our analysis did not identify a statistically significant gender pay gap for CEOs.
As our review suggests, prior studies that examined the gender pay gap at CEO
level provided mixed findings. In this sense, our finding of the non-significant
pay gap at CEO level is not surprising. However, the results of these CEO level
analyses, including ours, need to be interpreted with care because the proportion
of female CEOs is very small (3.7% in our sample). Because the beta coefficient of
the female CEO indicator variable in our analysis is still negative (e.g. β = −.134
for the analysis of CEO total pay), the non-significant pay gap may simply be due
to the fact that we do not have enough female CEO observations to statistically
confirm the CEO gender pay gap. Expecting that more women will be appointed
as CEOs in the near future, a follow-up study is necessary on the gender pay gap
at CEO level to ensure there is no gender pay gap at CEO level.
In addition to emphasizing the importance of examining the gender pay gap
at executive level, our study lays a good foundation for future research on this
topic. Building on our findings as well as previous studies we reviewed in Table 1,
we identify several research questions. First, a future accumulation of empirical
1656
Y. YANADORI ET AL.
evidence on the gender pay gap at executive level in different countries will allow
us to undertake a systematic international comparison. Cross-national examinations of executive pay (Greckhamer, 2016) highlight the presence of country-level
institutional and cultural factors that influence the size and variance of executive
pay. Similarly, we expect certain institutional (e.g. women’s workforce participation) and cultural (e.g. femininity) factors may explain cross-national differences
in the gender pay gap at executive level. Identification of these factors also informs
policy-makers of potential strategies to reduce the gender pay gap at executive
level. To this end, more studies outside the US are encouraged.
Second, beyond estimating the size of the gender pay gap at executive level,
research needs to identify contextual variables that influence the size of the gender
pay gap. Our study shows that when a firm’s board includes at least one woman,
the executive-level gender pay gap becomes smaller. A few other studies (Elkinawy
& Stater, 2011; Shin, 2012) also found that firm characteristics are associated with
the magnitude of the gender pay gap. Likewise, certain industry characteristics
(e.g. gender composition of industry workforces) and individual characteristics
(e.g. female executives’ past professional experience) may be associated with the
size of the gender pay gap. Identification of these contextual variables is practically
meaningful as they inform us of potential strategies to reduce the gender pay gap.
Third, the implications of increasing the proportion of female executives on the
gender pay gap should be investigated. As we noted, policies to increase women’s
representation at executive level have been introduced in many countries. Our
analysis of large Australian firms did not show an increase in the proportion of
female executives after the introduction of the Recommendations. Nor did our
analysis show that the gender pay gap at executive level changed in this time
period. Admittedly, our data-set spans only four years (i.e. 2011–2014) and a longer
time span may be needed to fully capture the effect of the Recommendations.
Future research is strongly encouraged to analyze the effect of the change in the
proportion of female executives on the gender pay gap at executive level because
an increase in female executives is likely to influence dynamics within executive
teams, as well as perceptions of external stakeholders (e.g. compensation committees; shareholders) about the executive teams, leading to changes in executive pay.
Finally, assuming that there is an unexplained gender pay gap at executive level,
the consequences of the gender pay gap should be examined. When female executives find they are paid less than their male counterparts, how will they react? If
women are aware of this pay gap, but unable to change it, what is the impact on
their workplace experience? Through employee-level research, we know that one
potential reaction to underpayment is quitting the job (Barbar & Bretz, 2000). Will
underpaid female executives quit and give up the positions they have achieved?
Pay dispersion research (Conroy, Gupta, Shaw, & Park, 2014) also demonstrates
that pay differentials not explained by performance evoke a sense of unfairness,
causing dysfunctional behaviors. If so, the gender pay gap at executive level may
THE INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT 1657
have a negative impact on the functioning of executive teams, contributing to
decreased organizational performance.
To conclude, our study makes an important addition to the gender pay gap
literature. Further research is encouraged to identify the factors that account for
the size of the gender pay gap at executive level. Given the statistically significant
gender pay gap in Australia, the consequences of the gap, both at the individual
level and the firm level, need to be researched. A wider scope of international
comparison will also enhance our understanding of the gender inequality at corporate executive level.
Notes
1. The proportion of women in the data-set was calculated based on the number of
observations included in the data-set, and thus may not represent the proportion of
women in all executive roles in a given time period. This is also true for other studies
that analyze ExecuComp data.
2. For instance, while most studies analyzed direct payment made in a particular year,
Gayle et al. (2012) analyzed executive wealth, which included not only direct pay but
also changes in the value of shares held by executives.
3. Sirca includes 408 ASX500 firms’ executive pay information in the 2011–2014 period.
Twenty-four firms were excluded from our analysis due to missing information. The
list of firms included in our final data-set is available upon request.
4. WGEA’s recent report (2016c) shows that women hold 27.4% of key management
personnel positions. This figure is not directly comparable to our figure because
WGEA’s analysis includes medium-sized firms and organizations in the not-for-profit
sector.
5. It is represented by the following equation: sinh−1(x) = log [x + (x2 + 1)1/2].
6. We excluded 41 executive observations from our final sample because we were unable
to identify their gender. For these executive observations, executives’ first names were
not disclosed (e.g. only the initials of their first names were disclosed) or their first
names were gender neutral. These companies’ documents did not use pronouns that
allow us to identify their gender (e.g. he or she).
7. We also ran a model using a continuous tenure variable. For executive observations
whose appointment date information was missing, we examined Sirca and assumed
that the executive was appointed on 1 January of the year the executive first appeared
in the database. The results are substantively the same.
8. The gender pay gap is calculated by (1 – exp β).
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was funded by a University of South Australia Business School Research Grant.
1658
Y. YANADORI ET AL.
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