Tourism Analysis, Vol. 23, pp. 137–149 1083-5423/18 $60.00 + .00
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137
Address correspondence to Michael Kortt, Associate Professor, School of Business and Tourism, Southern Cross University,
Gold Coast Campus, Locked Bag 4, Coolangatta QLD 4255, Australia. Tel: 61-7-5589-3212; E-mail: [email protected]
and employed 580,200 individuals in 2015/2016
(Tourism Research Australia, 2016).
Research into tourism employment shows that:
(a) wages in the tourism sector are, on average,
lower than in other industries (e.g., Riley, Ladkin,
& Szivas, 2002; Muñoz-Bullón, 2009; Santos &
Varejão, 2007); (b) the returns to education are
lower in the tourism sector than in other industries (e.g., Lillo-Bañuls & Ramón Rodríguez,
2005; Marchante, Ortega, & Pagán, 2005); and
Introduction
The tourism sector plays an important role in
driving economic growth (e.g., Brida, CortesJimenez, & Pulina, 2016) and the creation of
employment opportunities for women (e.g., CasadoDíaz & Simón, 2016). This is particularly important
in the case of Australia, where tourism is largely
a female-dominated industry that contributed
AUD$53 billion to gross domestic product (GDP)
The gender wage gap in The Tourism indusTry:
evidence from ausTralia
MIChAEL A. KORTT,* ELISABETh SINNEwE,† AND SIMON J. PERVAN‡
*School of Business and Tourism, Southern Cross University, Coolangatta (QLD), Australia
†QUT Business School, Accountancy, Queensland University of Technology, Brisbane (QLD), Australia
‡Swinburne Business School, Swinburne University of Technology, Melbourne (VIC), Australia
This article presents an examination of the gender wage gap among tourism and hospitality employees in Australia. Data from the household, Income and Labour Dynamics in Australia (hILDA)
Survey—covering the period 2001 to 2014—are used to estimate earnings functions for prime age
(25–54) male and female workers. Conventional human capital functions are estimated using a randomeffects regression model. The principal findings suggest that, after controlling for an extensive range
of sociodemographic characteristics, female tourism and hospitality employees, on average, earned
8.5% and 7.5%, respectively, less than their male counterparts. Although human capital variables like
education and work experience play a role in the determination of wages, an employee’s gender still
continues to be a significant factor in the wage received.
Key words: earnings; gender; human capital; Tourism; wages
138 KORTT, SINNEwE, AND PERVAN
where ln(Earningsi) is the log of earnings for
individual i, Ei notes the years of education, Expi
is the number of years in paid work, εi is an error
term, and β1 provides an estimate of the returns to
the education.
In equation (1), the quadratic in work experience
suggests that earnings follow an inverted U-shape:
an individual’s earnings profile tends to rise sharply
at the beginning of their working life, plateaus in
the middle, and eventually declines towards the
end. Although the baseline model provides a useful starting point, equation (1) is often extended to
include a range of socioeconomic factors like gender, marital status, immigration status, and region
of residence. In the current context, the association between gender and earnings is of particular
relevance given that women, on average, tend to
receive lower earnings than their male counterparts
(e.g., Blau & Kahn, 2006a, 2006b; O’Reilly, Smith,
Deakin, & Burchell, 2015).
Taking this into account, considerable effort has
been devoted by scholars to explaining the reasons
underlying the gender earnings gap (e.g., Bayard,
hellerstein, Neumark, & Troske, 2003; Datta
Gupta & Rothstein, 2005). One possible explanation is that women possess less human capital than
their male counterparts. In terms of years of schooling (or educational attainment) this explanation is
not overly compelling given that the empirical evidence indicates “gender differences in education
levels do not explain a large portion of the overall
gender pay gap; and some recent examples show
women outpacing men in years of schooling” (Blau
& Kahn, 2006a, p. 42). Another possible explanation is that women accumulate work experience at
a slower rate than men because their participation
in the labor force is traditionally interrupted by
major life events like marriage, maternity leave, and
child rearing (e.g., Blau & Kahn, 2006a; Polachek,
2006). As Thrane (2008) notes, the differences in
work experience between women and men “partly
account for the overall gender gap in earnings”
(p. 515). A final explanation is that women are
subject to labor market discrimination because of
“discriminatory tastes of employers” or statistical
discrimination whereby “differences in the treatment of men and women arise from average differences between the two groups in the expected value
of productivity” (Blau & Kahn, 2000, p. 81).
(c) females in the tourism sector receive, on average, lower wages than their male counterparts (e.g.,
García-Pozo, Marchante-Mera, & Sánchez-Ollero,
2012). Thrane (2008) also makes the point that
studies “examining tourism employees’ earnings
are not abundant” (p. 516).
Against this background, this article aims to
examine and quantify the gender wage gap among
employees in the Australian tourism industry. The
rationale for this study resides in human capital
theory, which is the cornerstone of contemporary
labor economics. This study contributes to the
tourism gender wage gap literature by: (1) providing the first results for Australia (where the tourism industry makes a significant contribution to
GDP); and (2) exploiting 14 waves of data from the
household, Income, and Labour Dynamics in Australia (hILDA) Survey, which is a large and representative panel survey, to ensure that the results are
statistically robust across time.
Theoretical and Empirical Context
The study of earnings differentials in the tourism
sector traditionally makes reference to human capital theory (Becker, 1962), where “human capital” is
defined as “the amount of knowledge and technical qualifications that workers in the tourism sector have acquired as a result of their investment
in formal education and training for the jobs they
hold” (Lillo-Bañuls & Ramón Rodríguez, 2005,
p. 120). The theory contends that individuals with
higher levels of human capital are more productive
and, as a result, are rewarded by employers in the
form of higher earnings. One important implication
of human capital theory is “the existence of wage
differentials among workers with different levels
of education” (Lillo-Bañuls & Ramón Rodríguez,
2005, p. 121), which gives rise to a voluminous literature devoted to understanding and explaining the
reasons for earnings differentials in the labor market (e.g., Blau & Kahn, 2016; Lazear, 2000). within
a human capital framework, the baseline empirical
model (Mincer, 1958; Mincer & Polachek, 1974)
is typically specified in a quadratic equation as
follows:
ln(Earningsi) = α + β1Ei + β2Expi + β3Expi2 + εi
(1)
GENDER wAGE GAP IN AUSTRALIAN TOURISM INDUSTRY 139
et al. (2012) and García-Pozo, Campos-Soria, and
Sánchez-Ollero (2012) reported that returns to education in the Spanish hospitality and travel agency
sectors are consistently lower than for almost all
other occupations. Other research identifies disparities between the return to earnings in the hospitality
sector and the private service sector (e.g., CamposSoria, García-Pozo, Sanchez-Ollero, & BenavidesChicon, 2011; Casado-Díaz & Simón, 2016; GarcíaPozo, Campo-Soria et al., 2012).
Turning to the second suite of studies on the
gender earnings gap in the tourism and hospitality
sector, the literature’s findings commonly show that
women receive, on average, lower earnings than
men. For example, successive studies on the Spanish tourism sector show monthly wage discrepancies for men of 10.5% (Marchante et al., 2005) and
6.7% (Muñoz-Bullón, 2009) higher than those of
their female counterparts. Further, Campos-Soria,
Ortega-Aguaza, and Ropero-García (2009) reported
that men in the Spanish hospitality industry receive
an hourly wage premium of between 7.9% and
11.1%. These figures are reinforced by studies in
Portugal and Norway, which estimate that men
in the tourism sector receive an hourly wage premium of 8.4% (Santos & Varejão, 2007) and 20%,
respectively (Thrane, 2008). In South Korea the
median earnings for women in the tourism sector is
30% lower than the median earnings of their male
counterparts (Lee & Kang, 1998). More recently,
Ferreira Freire Guimarães and Silva (2016) estimated that men in the Brazilian tourism sector
receive an hourly wage premium of 35.3%.
Some of these studies have also been able to exploit
matched employer–employee data sets in order to
decompose the gender wage gap into endowment
and discriminant differentials. For example, Santos
and Varejão (2007) used the Oaxaca–Blinder technique (Blinder, 1973; Oaxaca, 1973) to decompose
the gender wage gap in the Portuguese tourism sector to find that 45% of the gap is due to “endowment differentials” while the remaining 55% is due
to discrimination. Similarly, Muñoz-Bullón (2009)
suggested that 88% of the gender wage gap in the
Spanish tourism sector can be explained by “endowment differentials” while the remaining 12% can
be attributed to discrimination. Casado-Díaz and
Simón (2016) extended the Spanish analysis to
examine wage disparities between the hospitality
A related yet relevant strand of Australian literature identifies four main drivers behind the gender
wage gap (Kennedy, Rae, Sheridan, & Valadkhani,
2017). First, discriminatory hiring practices have
limited the number of women appointed to managerial positions (e.g., Barón & Cobb-Clark, 2010).
This driver appears to be more common in the private sector (e.g., Booth, 2016) than in the public
sector where enterprise agreements have helped to
lower the gender wage gap (Kennedy et al., 2017).
Second, indirect discrimination like the failure to
offer a flexible work environment can adversely
influence female employment and labor force participation given that women “most often assume
the role of primary caregiver for their children”
and are subsequently pushed into “lower-paying,
less secure forms of employment’” (Kennedy et al.,
2017, p. 16). Third, the degree to which men and
women are dispersed across different occupations
can also lead to a widening of the gender wage gap,
especially when female-dominated occupations like
childcare and nursing are typically paid less than
male-dominated professions like financial services
and mining (Kennedy et al., 2017; Miller, 1994).
Finally, differences in work experiences among
men and women, especially the fact that women are
more likely to be employed on a part-time basis, are
likely to exacerbate the gender wage gap, given that
“part-time employment is associated with lower
quality jobs, limited access to training, and fewer
promotion and career opportunities” (Kennedy et al.,
2017, p. 16).
Successive empirical contributions to the literature can be broadly divided into two camps: studies
that focus on the returns to education, and studies
that focus on the gender earnings gap. with respect
to the first suite of studies, evidence suggests that
the returns to education are traditionally lower in
the tourism and hospitality sector than in other sectors of the economy (e.g., Fernández, Pena-Boquete
& Pereira, 2009; Lillo-Bañuls & Ramón Rodríguez,
2005; Lillo-Bañuls & Casado-Díaz, 2010, 2012,
2015; Marchante et al., 2005). In an interesting study, Thrane (2010) estimates the so-called
sheepskin effect—that is, the return to educational
degrees net of the return to years of schooling—
for the Norwegian tourism sector to find that the
returns to educational degrees exceeded the returns
to years of schooling. In a similar vein, García-Pozo
140 KORTT, SINNEwE, AND PERVAN
Statistics [ABS], 2006). hospitality employees are
classified as those individuals working in “accommodation services,” while tourism employees are
classified as those individuals working in “cafés,
restaurants, pubs, taverns, bars and clubs.” Unfortunately, respondents who were employed as travel
agents and tour operators are not able to be identified (and included in the analysis). As such, the
findings may underestimate the magnitude of the
wage penalty experienced by women in the Australian tourism industry.
The dependent variable is the log of the hourly
wage rate adjusted for inflation (to adjust for inflation, CPI data sourced from the Australian Bureau
of Statistics is used to convert the hourly wage
rate to 2014 dollars). The analysis includes controls for the number of work hours, number of siblings, number of children, marital status, whether
the respondent was born overseas, whether the
respondent is Indigenous, the occupational status
of the respondent’s father at the time when he or
she was aged 14, an indicator for state of residence,
plus an indicator for year. The father’s occupational status scale, developed by researchers at the
Australian National University, ranges from 0 (the
lowest status occupations) to 100 (highest status
occupations). As the scale takes into account the
average income and educations levels in an occupation (for more details, see Ganzeboom, DeGraaf,
& Treiman, 1992; Jones & McMillan, 2001), it is
employed as a proxy for the respondent’s socioeconomic background.
Either the level of educational attainment or
the number of years of education is used as a
measure of education. Five variables (high school
leaver, high school graduate, holder of an advanced
diploma, holder of an undergraduate degree, and
holder of a postgraduate degree) are used to capture
educational attainment (with the first group used
as the excluded reference category). Years of education are also coded as the highest year of completed schooling (if the respondent had no postschool qualification). Postschool qualifications are
coded as follows: (1) masters/doctorate = 17 years;
(2) graduate diploma/certificate = 16 years; (3) bachelor degree = 15 years; (4) diploma = 12 years; and
(5) certificate = 12 years. The analysis also includes
a quadratic in work experience (the number of years
in paid work).
and private sector over the period 2002 to 2012.
One important finding from this research is that
“the wage disadvantage of hospitality presents
an increasing profile along the wage distribution
so that it is particularly relevant for those earning comparatively higher salaries” (Casado-Díaz &
Simón, 2016, p. 96).
Data and Empirical Strategy
The data used in this study derive from the
hILDA Survey, which is Australia’s first nationally
representative household panel (wooden & watson,
2007). hILDA commenced in 2001 (wave 1) and
was based on a large national probability sample of
Australian households with a major emphasis on
families, income, employment, and subjective wellbeing. wave 1 sampled 7,696 households and 13,696
individuals. households were selected using a multistage sampling strategy and a 66% response rate
was obtained. within each household, information
was collected from each household member aged 15
and over, using face-to-face and self-assessed questionnaires. In wave 1, 92% of adults provided an
interview and, in each subsequent wave the previous
wave-on-wave response rates were between 87%
and 95%. Additional information on the hILDA
Survey can be found at http://melbourneinstitute.
unimelb.edu.au/hilda.
This study focuses on prime age (25–54) male
and female workers from 14 waves of the hILDA
Survey covering the period 2001 to 2014. Excluded
from the study are those respondents who were not
employed, self-employed, or enrolled in full-time
education. wage observations below half the federal minimum wage were also excluded, because
these values can be regarded as improbably low.
The subsequent regression analysis is based on a
final analytic sample of 2,460 individuals, comprising 1,494 women and 966 men. The major advantage of using the hILDA Survey is that it is one
of the largest surveys in Australia to collect data
on the wages of men and women employed in the
tourism and hospitality sectors as well as detailed
sociodemographic and economic information on
its respondents. The identification of participants
employed in the tourism and hospitality sectors
is based on the Australian and New Zealand Standard Industrial Classification (Australian Bureau of
GENDER wAGE GAP IN AUSTRALIAN TOURISM INDUSTRY 141
proportion of women held advanced diplomas
(35% of women versus 48% of men). The average number of years in paid work for men was
17.01 years (SD = 9.56) while for women it was
15.95 years (SD = 8.48). Thus, on average, men
had an additional year of work experience compared to their female counterparts. Another clear
pattern emerges with regard to the number of
working hours per week: as the number increases,
the proportion of women decreases. In the first
category, <20 hr, the proportion is substantially
higher (25% for women vs. 6% for men); in the
second, 20 to <30 hr, the proportion is less but still
higher than men (17% for women vs. 7% for men);
and in the third, ≥30 hr, the proportion has dropped
to be substantially lower (58% for women vs. 88%
for men).
Hospitality Sector
Individuals employed in the hospitality sector,
on average, earned $23.53 per hour over the study
period. The principal categories of educational attainment among hospitality employees were high school
leavers (44%), high school graduates (18%), and
holders of advanced diplomas (27%). Conversely,
10% of hospitality employees held an undergraduate degree while only 1% held a postgraduate qualification. The average number of years of schooling
was 11.45 (SD = 1.97), while the average number of
years in paid work was 16.35 (SD = 8.82). Looking
across Table 1, it is evident that, as in the tourism
sector, men and women employed in the hospitality sector have different profiles, particularly in
relation to the hourly wage rate, educational attainment, the number of years in paid work, and the
number of working hours per week.
with respect to the mean hourly wage rate,
men received $25.81 (SD = $10.02) while women
received $22.42 (SD = $9.51), approximately 13%
lower over the study period. with respect to educational attainment, a similar pattern emerges: a
higher proportion of women were high school leavers (52% for women vs. 29% for men) and a lower
proportion of women held advanced diplomas (23%
for women vs. 35% for men). The average number of years in paid work for men was 18.62 years
(SD = 10.00) while for women it was 15.24 years
(SD = 7.97). Thus, on average, men have an additional
Thus, the following regression model is estimated
separately for “persons” (i.e., both men and women),
for men, and for women in the Australian tourism
and hospitality sectors:
Wi = α + β1Ei + β2Expi + β3Expi2 + β4X + εi (2)
In equation (2), W is the log of the respondent’s
hourly wage adjusted for inflation, E is the level of
education, Exp is work experience in years, X is a
vector of the controls (i.e., work hours, number of
siblings, number of children, marital status, and so
on), and εi is an error term. All regression results
are estimated using a random effects model with
the standard errors clustered at the individual level
to account for within-person serial correlation.
The summary statistics for the sample in Table 1
clearly show that there is a greater proportion of
women than men employed in the tourism (58%
women) and hospitality (67% women) sectors. This
finding is consistent with those of Lucas (2004),
Santos and Varejão (2007), and Thrane (2008), and
reinforces the view that tourism is largely a femaledominated industry.
Tourism Sector
Individuals in the tourism sector, on average,
earned $22.65 per hour over the period 2001 to
2014 (Table 1). The major categories of educational
attainment among tourism employees were: high
school leavers (31%), high school graduates (20%),
and holders of advanced diplomas (40%). By contrast, 8% of tourism employees held an undergraduate degree while only 1% held a postgraduate
degree. The average number of years of schooling
was 11.68 (SD = 1.57) while the average number of
years in paid work was 16.38 (SD = 8.96). Looking
across Table 1 it is evident that men and women
have different profiles, particularly in relation to
the hourly wage rate, educational attainment, the
number of years in paid work, and the number of
working hours per week.
with respect to the mean hourly wage rate,
men received $23.99 (SD = $7.93) while women
received $21.69 (SD = $8.45), approximately 10%
lower over the study period. with regard to educational attainment, a clear pattern emerges: a higher
proportion of women were high school leavers
(38% of women versus 22% of men); and a lower
142 KORTT, SINNEwE, AND PERVAN
Table 1
Summary Statistics
Tourism [Mean (SE)] hospitality [Mean (SE)]
Variable Definition Persons Men women Persons Men women
hourly wage Inflation adjusted gross
income (in AUD)
$22.65 ($8.31) $23.99 ($7.93) $21.69 ($8.45) $23.53 ($9.80) $25.81 ($10.02) $22.42 ($9.51)
Log of hourly wage Natural log of annual wage 3.07 (0.31) 3.13 (0.31) 3.03 (0.30) 3.10 (0.33) 3.19 (0.33) 3.05 (0.32)
educational attainment
Year 11 or below (ref.) high school leaver 0.31 0.22 0.38 0.44 0.29 0.52
Year 12 Graduated from high school 0.20 0.20 0.19 0.18 0.21 0.16
Adv. diploma/Cert III or IV holds advanced diploma,
certificate III or IV
0.40 0.48 0.35 0.27 0.35 0.23
Bachelor degree or Grad cert holds undergraduate degree
or equivalent
0.08 0.09 0.08 0.10 0.12 0.09
Master or Ph.D. holds postgraduate degree 0.01 0.01 0.00 0.02 0.04 0.01
education (years)
Years of schooling No. of years in education 11.68 (1.57) 11.94 (1.48) 11.50 (1.61) 11.45 (1.97) 12.09 (1.88) 11.14 (1.93)
work experience
work experience (in years) No. of years in paid work 16.39 (8.96) 17.01 (9.56) 15.95 (8.48) 16.35 (8.82) 18.62 (10.00) 15.24 (7.97)
work experience squared (No. of years in paid work)2 348.74 (343.22) 380.48 (385.62) 326.03 (307.51) 344.98 (340.55) 446.02 (413.81) 295.68 (286.19)
working hours per week
20 hours or less (ref.) works 20 hours or less per
week
0.17 0.06 0.25 0.12 0.05 0.16
> 20 and < 30 hours works between 20 and
30 hours per week
0.13 0.07 0.17 0.16 0.02 0.24
≥ 30 hours works 30 or more hours per
week
0.70 0.88 0.58 0.71 0.94 0.60
(continued)
GENDER wAGE GAP IN AUSTRALIAN TOURISM INDUSTRY 143
Table 1 (Continued)
Tourism [Mean (SE)] hospitality [Mean (SE)]
Variable Definition Persons Men women Persons Men women
other controls
Children Number of resident children
under 15 years
0.69 (1.00) 0.48 (0.88) 0.84 (1.04) 0.58 (0.92) 0.56 (0.86) 0.60 (0.95)
Siblings Number of siblings 2.92 (2.02) 2.84 (1.70) 2.98 (2.22) 3.26 (2.04) 2.93 (1.54) 3.43 (2.23)
Marital status 1 = married; 0 = otherwise 0.43 0.39 0.47 0.46 0.45 0.46
Born overseas 1= overseas; 0 = otherwise 0.27 0.30 0.25 0.27 0.31 0.25
indigenous
No (ref.) Not indigenous 0.71 0.68 0.73 0.68 0.66 0.69
Yes Indigenous 0.02 0.02 0.02 0.05 0.04 0.06
DK/NA Don’t know/not asked 0.27 0.30 0.25 0.27 0.31 0.25
Father’s occupational status Father’s occupational status
when respondent was 14
40.57 (22.15) 41.87 (22.26) 39.62 (22.04) 44.49 (23.89) 48.40 (25.45) 42.68 (22.94)
state indicator
NSw (ref.) New South wales 0.33 0.39 0.29 0.33 0.27 0.36
VIC Victoria 0.20 0.19 0.22 0.14 0.14 0.14
QLD Queensland 0.24 0.21 0.27 0.30 0.34 0.28
SA South Australia 0.07 0.10 0.05 0.06 0.08 0.06
wA western Australia 0.10 0.06 0.12 0.08 0.08 0.08
TAS Tasmania 0.04 0.02 0.05 0.04 0.04 0.04
NT Northern Territory 0.01 0.01 0.01 0.04 0.03 0.04
ACT Australian Capital Territory 0.02 0.02 0.01 0.01 0.01 0.00
gender 1 = Female; 0 = Male 0.58 – – 0.67 – –
N (observations) 1,786 745 1,041 674 221 453
144 KORTT, SINNEwE, AND PERVAN
less than their male counterparts. (The estimated
coefficient is interpreted as [(exp(β) – 1)*100]).
In terms of educational attainment, the finding
of the analysis is that the return to education for
individuals with an advanced diploma is 6.5%
higher than for individuals without a high school
diploma. Similarly, the return to education for individuals with a bachelor’s degree is 12.5% higher
than for individuals without a high school diploma.
work experience has a concave relationship with
the hourly wage rate, a finding that is consistent
with previous research (e.g., Lillo-Bañuls & Ramón
Rodríguez, 2005; Marchante et al., 2005; Santos &
Varejão, 2007; Thrane, 2008). The average hourly
wage rate reaches its highest level after 29 years of
work experience, a finding that is broadly consistent with the estimate for the Norwegian tourism
industry (Thrane, 2008). There is no association
between the number of working hours for an individual and the hourly wage rate.
Looking at the male and female regressions
for tourism employees in Table 2, a number of
3.4 years of work experience compared to their
female counterparts. Once more, a clear pattern
emerges with regard to the number of working
hours per week, where the proportion of women is
higher for fewer hours worked and lower for more
hours worked: for <20 hr, the proportion of women
is substantially higher (16% for women vs. 5% for
men); for 20 to <30 hr, the proportion of women is
even higher (24% for women vs. 2% for men); and
for ≥30 hr, the proportion of women is substantially
lower (60% for women vs. 94% for men).
Results
Tourism Employees
The results from the regression analysis, which
includes controls for an extensive array of sociodemographic characteristics, are reported in Table 2.
For tourism employees, the female coefficient is
statistically significant at the 1% level (β = -0.089).
This means that women, on average, earned 8.5%
Table 2
wage Functions for Australian Tourism and hospitality workers Aged 25 to 54, 2001–2014
(Random Effects Regressions)
Tourism hospitality
Variables
Persons
β (SE)
Men
β (SE)
women
β (SE)
Persons
β (SE)
Men
β (SE)
women
β (SE)
Female -0.089** (0.021) – – -0.078* (0.035) – –
education
Year 12 0.010 (0.029) 0.029 (0.053) 0.015 (0.035) 0.032 (0.052) -0.008 (0.074) 0.067 (0.063)
Adv. diploma 0.063** (0.024) 0.079 (0.045) 0.060* (0.029) 0.067 (0.038) -0.064 (0.056) 0.145** (0.047)
Bachelor 0.118** (0.044) 0.146* (0.068) 0.102 (0.056) 0.190** (0.064) 0.020 (0.084) 0.229** (0.083)
Master/Ph.D. 0.114 (0.108) 0.268** (0.092) -0.338 (0.181) 0.400** (0.130) 0.121 (0.184) 0.651** (0.088)
work experience
Experience (in years) 0.011** (0.004) 0.014* (0.006) 0.009 (0.006) 0.011 (0.007) 0.024 (0.013) 0.015 (0.009)
Experience-squared -0.000* (0.000) -0.000 (0.000) -0.000 (0.000) -0.000 (0.000) -0.000 (0.000) -0.000 (0.000)
work hours
>20 and <30 hr 0.007 (0.032) 0.171* (0.076) -0.032 (0.035) -0.051 (0.058) 0.107 (0.154) -0.086 (0.057)
30+ hr per week -0.014 (0.032) 0.142* (0.072) -0.060 (0.036) -0.024 (0.061) 0.292* (0.146) -0.077 (0.060)
Constant 2.964** (0.063) 2.705** (0.111) 2.992** (0.079) 2.822** (0.099) 2.391** (0.200) 2.799** (0.127)
R2 (within) 0.045 0.053 0.056 0.072 0.156 0.084
R2 (between) 0.098 0.145 0.093 0.157 0.412 0.161
R2 (overall) 0.084 0.129 0.085 0.171 0.445 0.171
N (observations) 1,786 745 1,041 674 221 453
Note. Education reference category: year 11 and below. Work hours reference category: less than 20 hr per week. All regressions
control for the number of siblings (plus an indicator for whether the sibling variable was missing), marital status, whether the
respondent was born overseas, whether the respondent is Indigenous, the occupational status of the respondent’s father at the
time when he or she was aged 14 (plus an indicator for whether the occupational status variable was missing), an indicator if
the respondent completed schooling in the same year as when the wage was reported, an indicator for state of residence, plus
an indicator for year.
*p < 0.05; **p < 0.01.
GENDER wAGE GAP IN AUSTRALIAN TOURISM INDUSTRY 145
counterparts. Once again, this finding is consistent
with previous studies (e.g., Thrane, 2008).
In terms of educational attainment, the returns to
education for individuals with a bachelor’s degree
were 21% higher (β = -0.190; p < 0.01) than for
individuals without a high school diploma. Similarly, the returns to education for individuals holding a postgraduate qualification (e.g., a masters or
doctorate) were 49% higher (β = -0.400; p < 0.01)
than for individuals without a high school diploma.
however, no statistically significant returns to education for individuals who hold either a high school
or advanced diploma are evident. with respect to
work experience, a concave relationship with the
hourly wage rate is not shown, which may be due
to the relatively smaller number of hospitality workers
in the sample. In other words, a larger sample of
hospitality workers would a priori indicate a concave relationship. Finally, the result does not represent a statistically significant association between
the number of working hours for an individual and
the hourly wage rate.
Looking at the male and female regressions
for hospitality employees in Table 2, a number of
differences are worth noting. To begin with, no
statistically significant association between educational attainment and the hourly wage rate for
men can be found. In contrast, there is a statistically significant association between educational
attainment and the hourly wage rate for women.
First, the returns to education for women with an
advanced diploma were 15.6% higher than for
women without a high school diploma. Second, the
returns to education for women with a bachelor’s
degree were 25.7% higher than for women without
a high school diploma. Third, the returns to education for women with a postgraduate qualification
were 91% higher than for women without a high
school diploma. In addition, a concave relationship
between work experience and the hourly wage rate
for either men or women is not evident. Lastly, for
men, there is a statistically significant association
between working 30 or more hours a week and the
hourly wage rate. however, no such association is
evident for women.
In the above analyses, the regression specifications include a set of categorical variables to
capture educational attainment. To shed further
light on the returns to education for tourism and
differences are worth noting. First, the return to
education for women with an advanced diploma
was 6.2% higher than for women without a high
school diploma, but higher levels of educational
attainment (i.e., holding an undergraduate or postgraduate degree) were not associated with higher
earnings. In contrast, higher levels of educational
attainment for men were associated with higher
earnings. For example, the returns to education
for men holding a bachelor’s degree were 15.7%
higher than for men without a high school diploma.
Furthermore, the returns to education for men with
a postgraduate qualification were 30.7% higher
than for men without a high school diploma.
Stratifying the regression analysis by gender
does not result in a statistically significant concave relationship between work experience and the
hourly wage rate, which may be due to the relatively smaller sample sizes for men and women.
Put differently, if the sample sizes for men and
women were larger, a concave relationship would
be expected to appear. To examine this issue further, the regression models were reestimated by
including both tourism and hospitality employees.
Adopting this sector-wide approach enabled an
increase in the sample size for men and women to
966 and 1,496, respectively. The revised estimates
(available upon request) now suggest that there is
some evidence of a concave relationship between
work experience and the hourly wage rate. For men,
a statistically significant concave relationship can
be seen, although the squared term is only significant at the 10% level (experience, p < 0.01; experience2, p = 0.06). For women, a concave relationship
is also evident, which is statistically significant at
the 10% level but “borderline significant” at the 5%
level (experience, p = 0.05; experience2, p = 0.049).
These relationships are plotted in Figure 1 and
serve to illustrate that men experience a more pronounced and steeper experience–earnings profile
than women.
Hospitality Employees
The results from the regression analysis are
shown in Table 2. For hospitality employees, the
female coefficient is statistically significant at the
1% level (β = -0.078). This means that women,
on average, earned 7.5% less than their male
146 KORTT, SINNEwE, AND PERVAN
hospitality sector there appear to be no returns to
education while for women the returns to education are 5.3%. Overall, the estimates are similar
to those of Lillo-Bañuls and Ramón Rodríguez
(2005), who estimated that the returns to education
for the Spanish tourism sector were 3.3% (i.e., for
an additional year of education, the hourly wage
rate, on average, increases by 3.3%).
Discussion and Conclusion
The principal findings indicate that female tourism and hospitality employees, on average, earn
hospitality workers, the regression models are reestimated by replacing “educational attainment” with
“years of education.” The results from this analysis
are reported in Table 3. For individuals employed
in the tourism sector, the returns to education are
2.4%. This means that for each additional year of
education, the hourly wage, on average, increases
by 2.4%. For men employed in the tourism sector,
the returns to education are 3.8% while for women
the returns to education are 1.6%, which is only statistically significant at the 10% level. For individuals employed in the hospitality sector, the returns
to education are 4.4%. For men employed in the
Table 3
hourly wage (Logged) by Years of Education for Australian Tourism and hospitality Employees Aged 25 to 54,
2001–2014 (Random-Effects Regression Analysis)
Tourism Employees hospitality Employees
Persons
β (SE)
Men
β (SE)
women
β (SE)
Persons
β (SE)
Men
β (SE)
women
β (SE)
Years of education 0.024** (0.007) 0.037** (0.010) 0.016* (0.009) 0.043** (0.010) 0.014 (0.015) 0.052** (0.013)
N (observations) 1,786 745 1,041 674 221 453
Note. All regressions control for the independent variables reported in Table 2, except for the educational dummy variables.
*p < 0.1, **p < 0.01.
Figure 1. Earnings profile of work experience.
GENDER wAGE GAP IN AUSTRALIAN TOURISM INDUSTRY 147
2005). Similar findings have also been observed
for the tourism industry. For instance, Santos and
Varejão (2007) reported that the gender wage gap
for the Portuguese tourism sector is “due both to
occupational segregation within the industry and to
discrimination” (p. 237). Concurring with Thrane
(2008), further studies using matched employer–
employee data are required in order to unravel the
extent of occupational segregation within the tourism industry and its attendant impact on the gender
wage gap.
The second explanation put forth is that women
possess less human capital than their male counterparts, with women’s education being a noteworthy
factor. There is evidence to suggest that investment
in education has a direct payoff in terms of higher
earnings, as the returns to education for individuals
employed in the tourism and hospitality sectors are
2.4% and 4.4%, respectively (Table 3). Although
the returns to education for women in the hospitality sector are 5.3%, for men there appear to be
no returns to education. These findings differ from
Lillo-Bañuls and Ramón Rodríguez (2005), but are
broadly consistent with Thrane (2008). Thus, differences in the returns to education do not appear
to be a particularly compelling explanation of the
gender wage gap.
The third explanation put forth is that women
accumulate work experience at a slower rate because
their participation in the labor force is traditionally interrupted by major life events like marriage,
maternity leave, and child rearing (e.g., Blau &
Kahn, 2006a; Polachek, 2006). This relationship
is borne out in the sector-wide analysis (Fig. 1)
where the earnings profile for work experience is
noticeably steeper for men than for women. These
findings, which again are broadly consistent with
Thrane (2008), highlight that a portion of the gender wage gap may be due to the slower accumulation of human capital by women.
Other potential explanations are that women
in the Australian labor market are more likely to
face discriminatory hiring practices (e.g., Barón
& Cobb-Clark, 2010), inflexible work environments (Kennedy et al., 2017), and be employed
in female-dominated professions that traditionally
pay less than male-dominated professions (Kennedy
et al., 2017; Miller, 1994). women are also more
likely to be employed on a part-time basis, which
8.5% and 7.5%, respectively, less than their male
counterparts, even after controlling for an extensive
range of demographic, economic, and social characteristics. Evidence of a gender wage gap among
Australian tourism and hospitality employees is
broadly consistent with other international studies. For example, Marchante et al. (2005) estimated
that men in the Spanish tourism sector receive a
monthly wage premium of 10.5% while Santos
and Varejão (2007) estimated that men in the Portuguese tourism sector benefit from an hourly wage
premium of 8.4%. In a similar vein, Thrane (2008)
estimated that men in the Norwegian tourism sector
receive an annual wage premium of 20% while Ferreira Freire Guimarães and Silva (2016) estimated
that men in the Brazilian tourism sector receive an
hourly wage premium of 35.3%.
The findings of this study follow those of Thrane
(2008) in that the independent variables in the tourism (hospitality) regression models account for
approximately 88% (95%) of the tourism (hospitality) gender wage gap, and the remaining 12%
(5%) can be attributed to discrimination. Although
additional insights may be gleaned by undertaking an Oaxaca–Blinder decomposition, this analysis was not pursued because to “actually grasp
the discrimination portion (if any) of the gender
wage gap, one needs matched employer–employee
data” (Thrane, 2008, p. 520). Moreover, the use of
matched employer–employee data to decompose
the gender wage gap has become increasingly commonplace among tourism scholars (e.g., CasadoDíaz & Simón, 2016; Muñoz-Bullón, 2009; Santos
& Varejão, 2007).
A number of possible explanations have been put
forth in an effort to explain these gender wage gaps.
The first explanation is that men are overrepresented in occupations that have, on average, higher
wages and in occupational positions that offer
greater financial rewards (e.g., Blau & Kahn, 2006a;
Gunderson, 2006). Thus, the inclusion of “occupational” control variables into conventional earnings functions may partially (or fully) attenuate the
gender wage gap coefficient. however, as Thrane
(2008) notes, a number of studies that include these
“occupational controls” still observe a statistically
significant “gender coefficient,” which “indicates
discrimination against women” (p. 522) (see, e.g.,
Bayard et al., 2003; Datta Gupta & Rothstein,
148 KORTT, SINNEwE, AND PERVAN
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