gender pay gap australia essay

• Print current page
• Print all pages
• Print this section only

Gender pay gap guide

Learn about the gender pay gap, the key contributing factors and our recommended indicators

The gender pay gap is the difference in the earnings of men and women, expressed as a proportion of men's earnings.

There are many approaches to measuring the gender pay gap, and many factors that influence it, so no single measure can provide a complete picture. Instead, a range of measures should be considered together to understand the comparative earnings of men and women.

This guide will help you to learn more about the gender pay gap and the key ABS indicators, as well as the factors influencing the gap. Our  Earnings guide  also includes information about our various earnings measures and how to use them.

Sex and gender

The term 'gender pay gap' is commonly used when comparing the earnings of men and women. Most ABS statistics on earnings, including those used in this guide, collect and output data classified by 'sex', however it is likely that most data reported by employing businesses in payroll-based surveys more closely aligns with 'gender'.

The terms sex and gender are interrelated and often used interchangeably. However, they are two distinct concepts:

• Sex is understood in relation to sex characteristics (such as a person's chromosomes, hormones and reproductive organs).
• Gender is about social and cultural differences in identity, expression and experience.

See the ABS Standard for Sex, Gender, Variations of Sex Characteristics and Sexual Orientation Variables .

Measuring the gender pay gap

The gender pay gap describes the difference between the "average earnings" of men and women. It is not a measure of gender pay equality or equal pay - these are concepts that reflect the extent to which men and women are paid the same for performing the same or comparable work. Unequal pay is only one factor which may influence the gender pay gap.

Gender pay gap measures reflect the various social and economic factors affecting earnings and earning capacity of men and women (e.g. paid hours worked, occupation, industry, pay-setting methods, educational attainment, working arrangements, discrimination, and many more factors). There are other labour market measures where a gender gap exists including participation in paid work and hours worked.

Calculating the gap

Gender pay gap measures are presented as a percentage. They can be derived from our earnings data sources by subtracting female earnings from male earnings, dividing the result by male earnings and then multiplying by 100.

\({\text {Male earnings - Female earnings} \over \text {Male earnings}} \times 100 = \text {Gender pay gap (%)}\)

If females earn an average of $900 per week and males earn an average of $1000 per week, the gender pay gap is 10%.

\({$1000-$900 \over $1000} \times 100 = \text {10%}\)

Our gender pay gap indicators

There are four key ABS indicators derived from the two-yearly  Employee Earnings and Hours  (EEH) survey and the six-monthly Average Weekly Earnings (AWE) survey we use as a starting point for analysis of the gender pay gap:

1.   Median hourly cash earnings (EEH) 2.   Mean hourly cash earnings (EEH) 3.  Median weekly cash earnings (EEH) 4.  Mean weekly cash earnings (AWE)

These indicators, available on our Gender indicators page, provide a high-level snapshot of the gender pay gap. This is consistent with the approach used by the International Labour Organization (ILO) in their  Global Wage Report 2018/19: What lies behind the gender pay gaps . Finer population (e.g. full-time employees) can be used for more in-depth analysis.

In addition, the following two ABS indicators, based on the ordinary time earnings of full-time adult employees are also presented on Gender indicators :

5.  Mean weekly ordinary time earnings of full-time adult employees (AWE) - the most commonly cited measure of the gender pay gap 6.  Mean weekly ordinary time cash earnings of full-time adult employees (AWE) - the 'cash earnings' equivalent of the commonly cited measure (which includes amounts salary sacrificed)

Mean weekly ordinary time earnings of full-time adult employees has historically been the most cited measure and is available on a long-term comparable basis. However, it is important to note that unlike the first four indicators above, this measure excludes amounts salary sacrificed, which was first collected in 2006.

We also include an equivalent of this commonly cited measure that includes salary sacrifice ('cash earnings'). T he cash earnings series is the most comprehensive measure of earnings and is consistent with our latest underlying earnings concepts. Prior to 2006, salary sacrifice was excluded from our earnings concepts. Following a review, we implemented changes to our earnings conceptual framework to include amounts salary sacrificed. Estimates that exclude salary sacrifice are still produced in AWE to provide an uninterrupted historical time series (see Information Paper: Changes to ABS measures of employee remuneration ), alongside estimates that include salary sacrifice.

Cash earnings series generally produce smaller gender pay gaps due to the prevalence of salary sacrifice arrangements in female dominated industries, such as Health care and social assistance. Women, on average, have higher salary sacrifice amounts than men.

There are many approaches to measuring the gender pay gap, and many factors that influence it, so no single measure can provide a complete picture. Instead, a range of measures should be considered together to understand the comparative earnings of men and women. Each measure will show a different sized pay gap, reflecting the impact of differences in the distribution of earnings amongst men and women (median v mean earnings) and compositional factors related to hours worked (hourly v weekly earnings).

Our data sources

We recommend a combination of indicators from EEH and AWE - to leverage the greater detail available from EEH data with the greater frequency and timeliness of AWE data.

EEH provides detailed compositional earnings data for men and women every two years, allowing for comparison of weekly and hourly, and mean and median earnings. AWE provides a long time series of mean weekly earnings for men and women. AWE measures are published every six months (three months after the survey reference period) so provide more frequent and timely, but less detailed, indicators of the gender pay gap. For more information on comparing EEH and AWE statistics, see A guide to understanding employee earnings and hours statistics .

• Download table as CSV
• Download table as XLSX
• Download graph as PNG image
• Download graph as JPG image
• Download graph as SVG Vector image
• Based on mean weekly ordinary time earnings of full-time adult employees from AWE. These measures exclude part-time employees and overtime earnings. The commonly cited measure also excludes amounts salary sacrificed.

Source:  Employee Earnings and Hours, Australia, May 2023  (published and unpublished) and  Average Weekly Earnings, Australia, November 2023 .

Workplace Gender Equality Agency (WGEA) data

In addition to our data sources, the Workplace Gender Equality Agency (WGEA)  releases an annual scorecard on the state of gender equality in Australia , and complementary dataset on gender equality, derived from an annual employer census and release.

WGEA is an Australian Government statutory agency that is charged with promoting and improving gender equality in Australian workplaces and administering the Workplace Gender Equality Act (Cth) 2012 (the Act). Under the Act, non-public sector employers with 100 or more employees must report remuneration data to WGEA annually .

The WGEA employer census data includes employees’ total remuneration, including amounts salary sacrificed, superannuation, overtime, bonuses and other additional payments for full-time, part-time and casual employees (converted into annualised full time equivalent earnings). The census data, however, excludes salaries of CEOs, heads of business, casual managers and employees who were furloughed. Using this data, WGEA provides a national gender pay gap and gender pay gap data for industries and occupations using mean, weekly and annual earnings.   

Understanding the different measures and differences in the size of the gap

The gender pay gap differs between indicators because of differences in the composition and distribution of male and female earnings. This is why we recommend using a range of indicators together, particularly in the early stages of any gender pay gap analysis. Use this section to understand how the choice of data measure influences the size of the gap.

Advice for data users

Gender pay gap measures derived from earnings estimates for longer time periods (for example, weekly or annual) and broad groupings (for example, all employees) are more likely to be affected by compositional factors. These measures provide insight into total earnings received by males and females.

Estimates of hourly earnings and for tightly defined groupings (for example, full-time employees by occupation) provide a more common basis for comparison, reducing the impact of compositional factors including differences in the amount of paid work. These measures allow for a more direct assessment of the difference in male and female earnings.

However, these comparisons do not account for a range of other factors contributing to pay differentials such as concentration in higher or lower paying industries or occupations. They also do not necessarily reflect the overall economic position of women compared to men.

Average earnings

​​​​​​Average earnings can be derived as either a median or mean value.

The median is the most representative measure of average earnings, as it is the midpoint of earnings distributions, where half of people earn more than the median earnings value and half earn less than the median earnings value.

Mean measures are calculated by dividing total earnings by the total number of employees. They are affected by the distribution of earnings of the population. A relatively small number of highly paid employees can skew the mean higher. The Average earnings guide includes more information on these measures and how to use them.

Impact on the gap

Earnings for both men and women have a positively skewed distribution, with approximately three in five employees earning less than the mean. However, a larger proportion of men than women are highly paid.

As a result, the difference between mean and median earnings for men is larger than the difference between mean and median earnings for women. Gender pay gap measures derived using mean earnings data will usually produce a larger gap than measures derived using median earnings data.

Distribution of weekly cash earnings by sex, May 2023 (Original)

The image is a graph showing the distribution of weekly total cash earnings, split by sex. Starting with under $200, the number of employees in each earning bracket increases, then peaks at $1000 to under $1200 bracket, with a high count of females for each income bracket. The count of employees tapers off after this, but there is a higher count of males in each bracket. There is a spike in the count of males earning $4000 and over, which is not mirrored for females. The graph also shows the mean and median weekly total cash earnings is higher for males than females.

Source: Microdata and TableBuilder: Employee Earnings and Hours, May 2023

Weekly and hourly earnings

Earnings measures are generally presented on either a weekly or hourly basis. Weekly measures are more affected by differences in the overall composition of the workforce, hours worked and work patterns. Hourly measures remove the effect of differences in hours worked each week. The Weekly and hourly earnings guide includes more information on these measures and how to use them.

Gender pay gap measures derived using weekly (or annual) earnings for men and women reflect that women do less paid work on average than men. As a result, these measures show a larger gap than measures derived using hourly earnings data which provides a common basis for comparison.

Source: Employee Earnings and Hours, Australia, May 2023

Ordinary time and total earnings

Ordinary time earnings and total earnings measures are available.

• Ordinary time earnings include payments for award, standard or agreed hours of work, including allowances; penalty payments; payments by measured result; and regular bonuses or commissions. 
• Total earnings include ordinary time earnings and overtime earnings. Overtime earnings are payments for hours worked in excess of award, standard or agreed hours of work.

The  Earnings chapter  in the Labour Statistics: Concepts, Sources and Methods has more information on earnings and employee remuneration related concepts and how we produce the data.

Gender pay gaps derived from total earnings rather than ordinary time earnings provide a measure that reflects all the earnings received by men and women. Men, on average, are more likely to work overtime and have higher overtime earnings.

Source: Average Weekly Earnings, Australia, November 2023

Full-time and all employees

In addition to all employee measures, full-time and part-time status is widely used to categorise people or jobs in terms of the number of hours worked. In our business surveys, we classify employee jobs as full-time or part-time based on whether the person has been engaged by the employer on a full-time or part-time basis. In AWE, data is produced for full-time adult employees and other employees (i.e. employees who are part-time or paid at junior rates). In EEH, data can be analysed for all full-time employees or for full-time adult employees.

Our household surveys (including the monthly Labour Force Survey) define people as employed part-time if they usually work less than 35 hours per week and actually worked less than 35 hours in the reference week. People are classified as full-time if they usually work 35 hours or more per week, or actually worked 35 hours or more in the reference week (even if they usually work less than 35 hours per week). For more information, please see the Employment arrangements  chapter in Labour Statistics: Concepts, Sources and Methods.

Many women work part-time so the choice of full-time, part-time or all employee measure will affect the derived gender pay gap when weekly or annual data are used.

Earnings differentials of full-time employees have traditionally been used to provide a more  common basis for comparison,  however this results in a gender comparison excluding almost half of all working women. Measures that include all employees (regardless of working hours) will show a larger gender pay gap as women work less hours than men, on average. Full-time women also work less hours, on average, than full-time men.

Measures of weekly earnings that are limited to part-time workers show varied results given they include a broad range of hours worked, so measures of hourly earnings are preferred.

Source:  Microdata and TableBuilder: Employee Earnings and Hours ,  Employee Earnings and Hours, Australia, May 2023

I'm looking for gender pay gap by...

Use this table to find earnings data sources which can be used to measure the gender pay gap by topic (for example, additional characteristics).

Household sources are generally preferred for person characteristics with business sources preferred for job and employer characteristics. However, business sources provide more accurately reported earnings as data are obtained from employers' payrolls rather than the recall of employees or their partners. The quality of earnings data has been prioritised when assigning ratings in the table below. For more information on the strengths and limitations of different sources, please see the  Earnings chapter  of Labour Statistics: Concepts, Sources and Methods.

 ✔  Recommended for this topic in relation to gender pay gap data.   ◼  Published for this topic in relation to earnings data however limitations should be noted.   ◻  Available for this topic upon request or via TableBuilder and microdata products.

• Ratings provide guidance on the relative quality of the different sources. Business sources provide more accurately reported earnings than household sources as data are obtained from employers' payrolls. Business sources are recommended for each topic where available. For more information, please see the  Earnings chapter  of Labour Statistics: Concepts, Sources and Methods.
• Acronyms: Employee Earnings and Hours (EEH), Average Weekly Earnings (AWE), Employee earnings (EE), Personal Income in Australia (PIA), Jobs in Australia (JIA).
• Based on data from the annual Characteristics of Employment Labour Force supplementary survey.
• Changes in total wages paid only.
• Full-time adults and all employees only.

Data and resources available

This section summarises available data which can be used to measure the gender pay gap. It also includes other information which may help you to understand gender pay gap measures and factors influencing the gap.

Measuring the gap

We produce many earnings data sources which can be used to measure the gender pay gap. The most relevant data sources are included below.

Understanding the gap

We produce many data sources which provide information on labour market outcomes of men and women beyond earnings measures. The most relevant data sources are included below.

Our Gender indicators page includes a range of economic and social indicators for men and women.

Other information

Abs resources.

• Gender indicators
• Labour Statistics: Concepts, Sources and Methods
• Earnings guide - Guide for ABS labour statistics

WGEA resources

These resources provide additional insight into factors influencing the gender pay gap.

• What is the Gender Pay Gap - Workplace Gender Equality Agency
• The Gender Pay Gap Calculator
• WGEA Scorecard 2022: The state of gender equality in Australia
• She's Price(d)less - The economics of the gender pay gap
• Wages and Ages: Mapping the Gender Pay Gap by Age - Workplace Gender Equality Agency

ILO resources

• Global Wage Report 2018/19: What lies behind the gender pay gaps - International Labour Organization

Status of Women Report Card 2023

This Status of Women Report Card, released on International Women's Day, draws on the rich data available to provide a picture of what life looks like for women in Australia in 2023.

The report card shows the challenges women and girls in Australia face through youth and young adulthood, in careers and working life, through parenthood and families, and in later life. It looks at education, economic outcomes, health, safety and wellbeing, housing and gender norms.

This is just some of the data available – but we also know there are data gaps. The National Strategy to Achieve Gender Equality will look at these issues, as well on ongoing data collection and reporting to support us to track progress.

The Government will release a Status of Women Report Card every International Women's day to shine a light on where progress is slow and more effort is needed.

Australia is ranked 43rd for gender equality internationally. Note 1

• 3.9% are Aboriginal and Torres Strait Islander. Note 2
• 28.3% were born overseas. 48.5% have a parent born overseas. Note 3
• 29.4% are under the age of 25. 18.0% are 65 and over. Note 4
• 17.8% are women with a disability. Note 5
• 28.4% live in regional or remote Australia. Note 6
• 4.6% identify as lesbian, gay or bisexual. Note 7
• 79.9% of one parent families are single mothers. Note 8
• Australia has the 4th highest level of tertiary educated women in the OECD. Note 9
• On average women aged 15-64 years do 55.4 hours of work a week (2 hours more than men). 34.7 hours of these are unpaid. Note 10
• 59.9% of women over the age of 15 are employed. Note 11
• 22% of young men believe that men should take control in relationships. 36% of young men believe that women prefer the man to take control. Note 12
• 1 in 2 women and 1 in 4 men have experienced sexual harassment in their lifetime. Note 13
• 1 in 4 women and 1 in 13 men have experienced sexual violence in their lifetime. Note 14
• Approximately 1 in 9 women suffer from endometriosis. Note 15 It takes an average of 5 years to receive a diagnosis after first seeing a doctor. Note 16
• 45% are single women
• 30% are single men
• 20% are couples
• In the last 10 years, there has been a three-fold increase in intentional self-harm hospitalisations for young girls. Note 18
• A gender pay gap emerges immediately after graduation, full-time starting salaries average $69,000 for men and $67,000 for women. Note 19
• Born 1989 to 1995: 51%
• Born 1973 to 1978: 34%
• Born 1946 to 1951: 26%
• 96.6% of hours worked by child carers
• 86.9% of hours worked by registered nurses
• 79.9% of hours worked by primary school teachers
• Hourly earnings pay gap: 11.6% Note 22
• Full-time weekly pay gap: 13.3% Note 23
• Total annual taxable income gap: 29.2% Note 24
• 55% drop in earnings for the mother in the 5 years following childbirth, while men's remains unchanged. Note 25
• Women: 31.6 hours
• Men: 22.4 hours
• Women: 24.1 hours
• Men: 19.1 hours
• Women approaching retirement have 23.1% less superannuation than men of the same age. Note 28

Expanded data

Australia is ranked 43rd for gender equality internationally.

Women in Australia

Women in Australia are diverse, educated and hard-working.

• 3.9 per cent are Aboriginal and Torres Strait Islander, 28.3 per cent were born overseas and 48.5 per cent have a parent born overseas. Note 1 (expanded data) , Note 2 (expanded data)
• 29.4 per cent are under the age of 25 and 18.0 per cent are 65 and over. Note 3 (expanded data)
• 17.8 per cent are women with a disability. Note 4 (expanded data)
• 28.4 per cent live in regional or remote Australia. Note 5 (expanded data)
• 4.6 per cent identify as lesbian, gay or bisexual. Note 6 (expanded data)
• 79.9 per cent of one parent families are single mothers. Note 7 (expanded data)
• 63.3 per cent hold a qualification outside school and 35.2 per cent hold a bachelor degree or above. Note 8 (expanded data)
• Australia has the 4th highest level of tertiary educated women in the OECD. Note 9 (expanded data)
• 59.9 per cent of women over the age of 15 are employed. Note 10 (expanded data)
• On average, women aged 15 to 64 years do 55.4 hours of work a week, 2 hours more than men. 34.7 of these are unpaid. Note 11 (expanded data)

Youth and young adulthood

• In the last 10 years, there has been a three-fold increase in intentional self-harm hospitalisations for young girls. Note 12 (expanded data)
• 79.1 per cent of heterosexual women and 86.6 per cent of lesbian, gay, bisexual or women of another non-heterosexual orientation have experienced online sexual violence facilitated by dating apps. Note 13 (expanded data)
• Further, 35.4 per cent of heterosexual women and 49.5 per cent of lesbian, gay, bisexual or women of another non-heterosexual orientation have experienced in-person sexual violence facilitated by dating apps. Note 14 (expanded data)
• 37 per cent of university and 16 per cent of VET STEM enrolments are women. Note 15 (expanded data) , Note 16 (expanded data)
• 25 per cent of university and 23 per cent of VET health and education enrolments are men. Note 17 (expanded data) , Note 18 (expanded data)
• A gender pay gap emerges immediately after graduation, full-time starting salaries for women average $67,000 while salaries for men average $69,000. Note 19 (expanded data)
• Young women are more likely to report experiencing sexual violence in their lifetime: 51 per cent of women born 1989 to 1995, 34 per cent of women born 1973 to 1978 and 26 per cent of women born 1946 to 1951. Note 20 (expanded data)
• Young women are also more likely to report a recent experience of sexual harassment: 38 per cent of women aged 18 to 24, 17.4 per cent of women aged 35 to 44, and 7.1 per cent of women aged 55 and over. Note 21 (expanded data)
• 62 per cent of social housing tenants are women (38 per cent are men). Note 22 (expanded data)
• Family and domestic violence is the leading cause of homelessness for women (40 per cent of women cite it as the main reason). Note 23 (expanded data)
• 25 per cent of women who want to leave a violent partner are unable to due to a lack of financial support.
• 15 per cent of women who returned to a violent partner did so because they had nowhere else to go. Note 24 (expanded data)
• Single women are the majority of rent assistance recipients (45 per cent, compared to 30 per cent single men, and 20 per cent couples). Note 25 (expanded data)
• The fastest growing group of people experiencing homelessness is women over the age of 55 (increasing by 31 per cent from 2011 to 2016). Note 26 (expanded data)

Career and working life

• Women and men largely work the same jobs they did 35 years ago: caring and clerical professions remain dominated by women while construction trades and labouring professions are dominated by men.
• Women worked: 96.6 per cent of hours worked by child carers, 86.9 per cent of hours worked by registered nurses and 79.9 per cent of hours worked by primary school teachers.
• Men worked: 91.5 per cent of hours worked by construction managers, 96.0 per cent of hours worked by truck drivers and 82.3 per cent of hours worked by software and applications programmers. Note 27 (expanded data)
• Women are less likely to participate in the workforce (62.1 per cent) than men (71.0 per cent), and more likely to work part-time (42.9 per cent) than men (18.8 per cent). Note 28 (expanded data)
• Hourly earnings pay gap is 11.6 per cent Note 29 (expanded data)
• Full-time weekly pay gap is 13.3 per cent Note 30 (expanded data)
• Total annual taxable income gap is 29.2 per cent Note 31 (expanded data)
• Women are underrepresented in leadership: ASX200 boards are only 35.7 per cent women Note 32 (expanded data) and only 14 ASX200 CEOs are women. Note 33 (expanded data)

Health, safety and wellbeing

• 1 in 2 women and 1 in 4 men have experienced sexual harassment in their lifetime. Note 34 (expanded data)
• 1 in 4 women and 1 in 13 men have experienced sexual violence in their lifetime. Note 35 (expanded data)
• 1 woman is killed by an intimate partner every 10 days. Note 36 (expanded data)
• Police reports of sexual assault has increased 33 per cent for women in the last 5 years, with no change for men. Note 37 (expanded data)
• Rates of family, domestic and/or sexual violence are higher for Indigenous women (34 times as likely to be hospitalised as non-Indigenous women) Note 38 (expanded data) and women with disability (25 per cent experienced sexual violence since the age of 15 compared to 15 per cent without disability). Note 39 (expanded data)
• 1 in 5 women (20.7 per cent) and 1 in 6 men (16.4 per cent) live with multiple chronic conditions. Note 40 (expanded data)
• Women are more likely to experience depression, anxiety, post-traumatic stress disorder and eating disorders. Note 41 (expanded data)
• Approximately 1 in 9 women suffer from endometriosis. Note 42 (expanded data) It takes an average of 5 years to receive a diagnosis after first seeing a doctor. Note 43 (expanded data)

Parenthood and families

• Women's earnings fall by 55 per cent in the first 5 years of parenthood, while men's stay the same. Note 44 (expanded data)
• Women of all ages spend over 9 hours a week more than men on unpaid work and care (31.6 hours for women compared to 22.4 hours for men). Note 45 (expanded data)
• Women do more unpaid housework than men even when they are the primary breadwinner (24.1 hours for women compared to 19.1 hours for men, a gap of 5 hours). Note 46 (expanded data)
• Women take on the mental load of planning and coordinating activities for children in 78 per cent of families, despite only being the primary carer in 52 per cent of families. Note 47 (expanded data)
• Compared to the global average (21 per cent), more Australian men (30 per cent) believe that gender inequality doesn't really exist. Note 48 (expanded data)
• More Australian men (28 per cent) believe that women often make up or exaggerate claims of abuse or rape, compared to men from the US (17 per cent), Canada (13 per cent), and the UK (13 per cent). Note 49 (expanded data)
• Nearly a third (32 per cent) of young men believe that 'a lot of the time, women who say they were raped had led the man on and then had regrets'. Note 50 (expanded data)
• There is a continued decline in the number of Australians who understand that men are more likely than women to perpetrate domestic violence: 74 per cent in 2009, compared to 64 per cent in 2017. Note 51 (expanded data)
• Of young men aged 16 to 24 years, 22 per cent believe that men should take control in relationships and 36 per cent believe that women prefer it this way. Note 52 (expanded data)
• Fathers are less likely to feel comfortable with the idea of their sons playing with dolls, or crying when sad (75 per cent), compared to mothers (87 per cent). Note 53 (expanded data)

Later in life

• Women approaching retirement have 23.1 per cent less superannuation than men of the same age. Note 54 (expanded data)
• Initial analysis suggests that 28 per cent of postmenopausal women in Australia will have moderate to severe symptoms that impact their workforce participation, however more work needs to be done to understand barriers to women participating in the workforce when experiencing menopause. Note 55 (expanded data)

International gender equality ranking: World Economic Forum (2022). Global Gender Gap Report 2022 , released 13 July 2022.

Australia Gender Pay Gap Report 2024

Statement from wesley walden, managing partner, mckinsey & company, australia and new zealand..

We welcome public pay gap reporting and the additional focus it will place on addressing the drivers of pay gaps at an organisational and national level. Our gender pay gap is simply not where it needs to be. It highlights the challenge we continue to face in reaching gender balance at all levels of our firm in Australia and New Zealand, particularly in our leadership, despite many years of work attracting, growing and developing women talent. Reducing and ultimately closing the gender pay gap remains a top priority – I genuinely believe this is core to our dual mission to attract and retain the best talent, and to help our clients make distinctive, lasting, and substantial improvements in their performance.

Without denying the significant work we have ahead of us, it is worth noting that we have made real progress. In 2023 in our consulting community, we achieved 54% women representation in recruiting and overall gender-balanced advancements, in a very competitive market for our senior women talent. In the last decade in Australia and New Zealand, we grew women partner representation from 7% to 20%, and women consulting representation from 25% to 40%. We’ve been experimenting with a lot of different solutions to further accelerate this progress – particularly within our leadership, given that it can take up to 15 years to develop a Partner inhouse.

McKinsey is committed to advancing diversity and inclusion in our own firm, for our clients, and in broader society. We are committed to preserving an equal and fair workplace, which includes creating opportunities for all our colleagues to grow and advance in their careers.

27th February, 2024 .

Introduction to company gender pay gaps and reporting

Australian companies with 100 or more employees are legally required to calculate and report their gender pay gap data to the Australian Government’s Workplace Gender Equity Agency (WGEA). For the first time in 2024, WGEA is reporting company-specific gender pay gap figures publicly, beginning with the median gender pay gap figures of private sector companies in February 2024. From 2025, WGEA will begin publicly reporting the mean gender pay gap of Australian companies with 100 or more employees.

The median gender pay gap is expressed as the percentage difference between women’s and men’s earnings, and describes the figure in the middle of the dataset. The mean gender pay gap is the difference in the average earnings of all men and women across a workforce, expressed as a percentage of the men’s average earnings. 1

WGEA is also publishing the gender composition and average remuneration pay per quartile of each company.

The company gender pay gap is not the same as a like-for-like pay gap, or unequal pay, which refers to paying men and women differently for performing the same work. We are committed to equal pay for equal work and have processes in place to help ensure pay equity. Central to our pay equity approach are objective benchmarking and market insights from multiple external sources, as well as robust processes that ensure all colleagues are paid equitably throughout their careers. We also use third-party pay equity tools to provide an objective evaluation of, and feedback on, McKinsey’s compensation structures.

Our commitment to diversity and inclusion

We are committed to our Diversity and Inclusion (D&I) efforts. Our collective D&I efforts include producing cutting-edge knowledge, building value-adding partnerships, ensuring supplier diversity, and conducting pro bono work. With our best-in-class partners across the globe, we share a commitment to expand economic opportunity and support communities. Our broad portfolio of D&I research continues to be a leading voice on the subject and catalyses change across industries.

We also partnered with the World Economic Forum to launch the Global Parity Alliance, a cross-industry group of companies taking action to accelerate D&I in the workplace and beyond.

In Australia and New Zealand (ANZ), we have committed to achieving an aspiration of gender balanced representation overall, and are making progress on women representation in our Partner group, which has grown from 7% to 20% in the last decade. 2

This aspiration drives our gender equality strategy in ANZ, which is underpinned by three central drivers; attraction, retention and advancement. Our strategy is achieved through five pillars of action; Transparency & Trust, Connectivity, Sponsorship & Mentorship, Flexibility and Talent Attraction, and is enabled through our local Gender Equality Leadership team.

We aim to foster an environment that is distinctive and inclusive, and measure our progress toward building teams that reflect the diversity of our clients, the communities in which we work, and society.

2023 McKinsey & Company Australia and New Zealand Office figures and drivers

For the 2022 – 2023 WGEA reporting period, our median total company pay gap is 38.3%, and our median base salary company pay gap is 33.4%.

The chart below divides the total remuneration full-time equivalent pay of all employees into four equal quartiles. The proportionately larger representation of men in the upper quartile and the proportionately larger representation of women in the lower quartile demonstrate the two core drivers of our firm’s pay gap.

WGEA gender composition by pay quartile for McKinsey & Company, 2022-23.

We recognise our gender pay gap also reflects a national trend where there is more progress to make on gender-dominated job groups, to further close the gender pay gap. For our firm, this is particularly prevalent in the high representation of women in our professional support community. We continue to work with the broader Australian corporate community, including as a Founding Member of Champions of Change, to improve societal drivers of gender inequality, which contribute to the overrepresentation of women in lower-paying job groups, and widen the gender pay gaps of many companies and organisations.

Our progress and looking ahead

Closing the gap takes a long-term commitment and achieving gender parity at all levels of our firm remains a top priority for us. Our research  has consistently demonstrated the performance benefits of a diverse workforce, and diverse perspectives in our teams help us to better understand and support our clients, innovate, and manage risk.

Each of the three central drivers in our gender equality strategy are targeted at balancing gender representation at all levels of our firm, particularly at a senior level. Over time, the continued success of this program will result in closing our gender pay gap.

Through a range of initiatives, including our Women’s Internship & Scholarship program, Associate-level female referral campaign and Women in Leadership Forum, we achieved overall gender balance in recruiting of our consultant community in 2023 .

We are also proud of our Connectivity and Flexibility programs. We hosted 35+ women’s connectivity events throughout 2023, including company-wide and cohort-based events. Our Flexibility program comprises formal and informal flexibility options, including part-time programs and support initiatives, ramp-on-ramp-off support for working parents and the integration of flexibility conversations into team kick-off meetings. This suite of offerings has helped us close the gender gap in retention of our consulting community, to a 3 percentage point gender difference, in favour of men .

Our Sponsorship and Mentorship program is designed to support all colleagues to thrive and advance, and we conduct an annual analysis of our firm-wide Mentorship, Apprenticeship and Sponsorship Survey to determine any gaps in experiences between gender. This analysis has informed a range of new initiatives in 2023, including sponsorship and mentorship learning events and sponsorship and mentorship coaching, detailing examples of how our women and men prefer to differently receive support. These initiatives have helped us achieve overall gender-balanced advancement in our consultant community in 2023 .

We will continue to build on the initiatives under our 2024 gender equality strategy as we work towards gender balance at all levels of our firm.

Australian Gender Pay Inequalities and Its Reasons Essay

Introduction, reasons for gender pay inequalities in australia, reference list.

Gender pay equity calculates the average wage of women earnings by determining the percentage of females pay in relation to male earnings. Whether a man is paid hourly, earns after a week, full time employment, or any other sort of employment, gender pay equity states that a woman should get an equal rate as that of the male worker.

Gender wage discrimination is a condition that displays the differences between the divisions of wage offers being contrary from the allocations of values of the marginal product between the males and the female workforce. In Australian continent, the situation of wage allocation and employment level among the two genders varies a lot. This paper examines three major reasons for the current gender pay inequality in Australian work force for the last two decades.

An equal remuneration for the work force should be given for equal or comparable value. Research indicates that there are various constructs that explain the gender pay disparities in Australia and that these disparities must no be the main factors that lead to multimarket disequilibrium.

The Chamber of Commerce & Industry of Western Australia and the leading business organization in the state reported that by 2008, gender pay gap stood at 37% when measured against the average wages that one earns on ordinary times. This displays the various characteristics on the female laborers in Western Australia (Chamber of commerce and industry 2008).

Several efforts have been made to establish the reason for the pay disparities. This has even contributed to Western Australia, New South Wales and Queensland pursuing several reviews on pay equity. All these efforts have yielded to almost the same conclusion and propositions.

Part time employment is a major contribution of the increased gender pay inequalities in Australia

Over the last bi-decades, the percentage of women employed at part times in Australia grew at a rate of 3.7 per cent for every year. In general, this increment of females in part time works contributed in the making of more than half of the total women who were employed in Western Australia in this particular time. Today, history has repeated itself with most female workers choosing to work at part time. Making an approximately 73% of the work force.

Recent research indicates that in 2007 most workforces in part time across WA (Western Australia) included women (News limited. 2008). When the average weekly wages of Australia men and women is measured, the results indicate that women continue to earn less because of the particularly working on given hours. This is a clear indicator that the significant number of women in part time employments contributes to the state’s gender pay disparity (Chamber of commerce and industry 2008).

According to this research, most women workers opt for this kind of employment compared to full time because it enables them to attain a balance between work and their family chores. This is as a result of most women having remained the source individuals with the assignment of caring for their families.

More so, the high earnings from the expanded Australian economy at this in these two last decades gave most families the ability to economically and financially sustain their families with either one parent working for few hours or totally abandoning employment (URCOT 2005). Secondly, Australian female workers opt for part time employment because of underemployment.

“Underemployment has three distinct related meanings. In one sense, it refers to a situation in which someone with excellent job qualifications is working in a position which requires lesser qualifications. In the second sense, underemployment means working part time when one would prefer to be working full time. Thirdly, underemployment is a form of overstaffing in which employees are not being fully utilized” (Smith 2003 1).

From the definition of underemployment, it can be concluded that some of these workers are forced by circumstances and situations that surround their natural feminine life.

Secondly, the type and nature of work contributes to the increased gender pay disparity among females and male in Australia

A general look at the kind of work that most women undertake world wide in most industries shows that females are more likely to be employed in industries that offer services as compared to their male counterparts.

More often, these industries have a low pay rate than manufacturing and building or construction sectors. High numbers of male employees dominate managerial and high- ranking positions compared to female workers. Most men also easily secure employments in mining, construction and financial companies. These positions command high compensations as compared to what females get in the service industries.

Statistics indicate that in 2007, the service industry, one of the largest industries in Western Australia, accommodated more than 88 per cent of the overall female workforce in the state. Some of the sectors in the industry include communications, insurance and finance, business and commercial property, wholesale trade, transport services, restaurants, cultural, and other services.

Among this group is the retail trade. In 2007, Chamber of Commerce and Industry in Western Australia found out that, that the industry employed over 17.2 par cent female work force in the state (Robinson, Deborah. 2010). These figures were quite high as compared to only 11.4 of male employees in the same industry.

By 2007, there was a rise in the number of females by 21,825 in relation to a decade before then, whereby the figure stood at 63,650 for female workers in the retail trade. When evaluated, the weekly standard wage income for women were lowest compared to men’s wages in the industry. A keen look at statistics of the amount of money that women earned in the year 2007 in various service industries shows that what men were earning within or without the industry greatly varied from the females’ wages (Catalyst Australia. 2008).

The minimal number of women in managerial roles in Australia is minimal

This is another factor greatly contributes to the widened disparity level between male and female remuneration in Australia. As compared to the last 10 years, the number of women in managerial positions or roles has increased although with a very minimal percentage.

In 2007, the CCI (Chamber of Commerce and Industry) established that the figure of women heading managerial positions had increased with only 7000 within a period of ten years. As a result, it can be summed up that male dominate most managerial positions in Australia (Australian Government 2007 1-6).

In Western Australia, most administrative and managerial responsibilities among female workers fall within 5.2 per cent of the national workforce (Federation election platform 2010).

A report named “Looking at the 2010 Australian Centre for Leadership for women” shows that Women sitting on boards and in management or executive positions in Australia are insignificantly small as compared to men in the same levels and positions. The body represents only a percentage of 12 being executive leaders and much fewer in the board of directors.

The research also indicates that more than 40 per cent of companies in the country have female Chief Executive Officers or even having a woman in board of directors. When the number of women in the managerial positions is minimal as compared to that of men, the end result is that, the female workers will receive normal low pay for their low positions in the services industry (Needham 2009).

There are also other arguments that attempt to say that lower wages among female workforce in Australia mainly occur among the women-dominated sectors likes family support centre, rehabilitation centre, refugee camps, and in the migrant regions. However, these sectors should also be brought to pay the female workers similar pays as it happens in similar male- dominated sectors.

From the above explanations it can be deduced that there exist major imbalances in Australian workforce’s pay that is highly dictated by ones gender. However, studies indicate that there is a great link between the reward that an employee receives and the function that the workers play or the person’s responsibility.

Employees’ reward is supposed to play three major roles. Firstly the compensation must be given to the right person for the right roles, tasks and responsibilities. With observation of this, the person’s contribution is later recognizes and rewarded accordingly.

After receiving the remuneration, the employee should feel satisfied and rightfully rewarded for having impacted on the production output. Reward is also a form of incentive on workers. The reward must motivate the workers in order to continue putting their efforts for optimum realization of the organizations goals. It is therefore, clear that Australian female workers are not sufficiently rewarded when compared to the men working for the same value.

This has further widened gender inequality cycle in the continent. It is therefore clear that Australian female employees may display certain behaviors that do not conform to the organizational behaviors expected. Hence most of them are either sucked or deliberately fail to look for permanent employment due to the dissatisfying reward.

It is also clear that these workers may have varying attitudes towards the employment they are in or the job place. This is what employment satisfaction studies indicate (Shields 2007, 35-39). Arguments have been made that when marginal product of labor for the employees is enhanced, the employees’ value in turn improves hence an increase in wages for the workers because the employers will be satisfied with the workers input.

Over the past years women have been on the forefront trying to fight social, institutional and cultural disparities so as to achieve equivalent opportunities at work place. However recent statistics indicate that the number of labor force in Australia has significantly changed in the last few decades. Females have managed to gain acceptance in the labor force and in turn a sufficiently equal pay (Human rights and equal opportunity commission 2008 1-6).

Looking at these details, it took the state a decade to create these few positions, so the question that emerges is whether Australian Government is doing enough in creating equal opportunities for the women to make effective decisions on their own and undertake leadership roles. Gender equality is a very pressing issue in almost all Australian’s initiatives and programs. This is because it is the centre of growth and stability of the governance (Sharma 1997 6-23).

Today, Australia has adopted the policy that allow both men and women, and among boys and girls to equally develop and progress in various ways including the wage rate for all earners. Gender equality program in Australia emphasizes on reducing poverty, and equally empowering women in the employment sector. The policy also aims at improving the economic status of female workers to gain access to financial and services that accompany the business world.

Women enterprises and reduction of time burdens that make women chose part time jobs are also among the concerted efforts to boost the wage rate of female employees in the continent (Australia Government 2007, 1-15). Importantly, organizations that do not conduct gender pay audits should revisit the case. Gender pay audits tools that gauge pay inequalities in an organization.

They also determine the specific duties, units of business and the departments within which one is operating. This is a tool that most organizations in the continent fail to utilize. Equal pay includes pay, allowances payments that accompany merits, bonuses, performance payments and also superannuation,

For any nation, government or state that wishes to progress economically and socially, embracing gender equality, policies that advocate for equity in compensation and rewarding of employees must be adopted. This is an essential and sound practice for the nation’s growth and development. This calls for concerted efforts from among all individuals and industries in the state to see that the female workforce is rewarded and compensated according for their value in production (Australia Government 2007, 5).

Australian Government. 2007. “Gender equality in Australia’s aid program, why and how and why gender equality is essential for development: 1-15.

Catalyst Australia. 2008. “Australian women: getting equality?”. Web.

Chamber of Commerce and Industry, 2008. “The house of representatives standing committee employment and work relations.” P ay equity . Web.

Federation election platform 2010. “The Australian centre for leadership for women”. Web.

Human rights and equal opportunity commission. 2008. “Gender equality: what matters to Australian women and men.” Web.

Needham, Kirsty. 2009. “ Women urged to sue to fix pay gap ”. Web.

News limited. 2008. “Governments can close gender pay gap”. Web.

Robinson, Deborah. 2010. “ Gender pay gap costs Australia $93 billion each year ”. Web.

Sharma. 1997.”Gender inequality in Austarlia.” Web.

Shield. 2007. Mannaging employee performance and reward: concepts, practices, strategies. Cambridge: Cambridge University press.

Smithe, S. E. 2003. “ What is underemployment? ”. Web.

URCOT. 2005. Pay Equity: “How to address the gender pay gap”. Web.

• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2019, February 7). Australian Gender Pay Inequalities and Its Reasons. https://ivypanda.com/essays/australian-gender-pay-inequalities/

"Australian Gender Pay Inequalities and Its Reasons." IvyPanda , 7 Feb. 2019, ivypanda.com/essays/australian-gender-pay-inequalities/.

IvyPanda . (2019) 'Australian Gender Pay Inequalities and Its Reasons'. 7 February.

IvyPanda . 2019. "Australian Gender Pay Inequalities and Its Reasons." February 7, 2019. https://ivypanda.com/essays/australian-gender-pay-inequalities/.

1. IvyPanda . "Australian Gender Pay Inequalities and Its Reasons." February 7, 2019. https://ivypanda.com/essays/australian-gender-pay-inequalities/.

Bibliography

IvyPanda . "Australian Gender Pay Inequalities and Its Reasons." February 7, 2019. https://ivypanda.com/essays/australian-gender-pay-inequalities/.

• Domestic Violence Against Women in Melbourne
• Privatized Kuwaiti Ministry of Electricity and Water
• Current Credit Crisis in the United States: Understanding the Trigger Factors
• Women are paid less for equal work
• Wolof Culture and Crisis of Masculinity in It
• The Masculinity Crisis in the Wolof Communities and Changes in the Women’s Status
• The Gender Wage Gap
• Equal Pay Policies: Do They Work?

Which big employers have the largest and smallest gender pay gaps? New data reveals all

The difference between what men and women are paid at the biggest employers in Australia has been revealed for the first time: from brewers and builders to banks and bakers.

Data divulged under new laws show massive gender pay gaps at some of our most well-known businesses, including many where women are their key customers.

At brewer Lion, maker of XXXX, Tooheys and Four Pillars gin, the median base pay gender gap is just 1.4 per cent.

It is higher (8.4 per cent) if you include all forms of remuneration such as bonuses and penalties, but still far below the national average of 14.5 per cent and a sliver of those at some of Australia's biggest household names.

Sarah Abbott, a human resources executive at the brewery group, understands the challenge.

"There are a number of colleagues in other organisations who are very nervous for these announcements. And my advice would be start now: The sooner you start, the sooner you're going to be able to create change," she says.

That might be too late.

More than 5 million workers get to judge if what their employer says about gender equality — and does — match up.

The median gender pay gap at airline Jetstar is 53.5 per cent, meaning that for every $1 a male worker makes at the company, women earn, on average, 46.5 cents.

It's scarcely better at Virgin (41.7 per cent), Qantas (39.3 per cent), the Commonwealth Bank (29.8 per cent), Westpac (27 per cent), insurer IAG (27.5 per cent) or Suncorp (20.5 per cent).

The figures in this article are based on "base pay". If you use a "total remuneration" figure that takes in penalties and bonuses, some gaps change. Jetstar's gap decreases to 43.7 per cent. Commonwealth Bank's increases to 29.9 per cent.

Companies have been submitting their data to the Workplace Gender Equality Agency (WGEA) for almost a decade, which created anonymised industry-by-industry reports and told companies where they sat in their sector.

Despite opposition from some business groups, the Labor government changed the law and led to this moment of radical transparency: the publication of the gender pay gaps at every company in Australia with more than 100 employees. 

See below for the gaps at companies including supermarkets, football clubs, schools, healthcare providers, law firms and more — or your employer.

Power giant AGL acknowledges it has work to do. It's one of many companies with a base salary pay gap (30.3 per cent) more than double the national average. 

With a decade in the job — and distinctive rainbow-flecked hair — everyone at the Loy Yang power station in Victoria's Latrobe Valley seems to know Sarah Gilbert.

"I found that AGL in particular, there's always been a respect for people as they are rather than necessarily your gender or your cultural background," she says.

"It's always been about respect for the person."

The head of risk and assurance, she helps the company to comply with laws that govern the coal mine and power station.

Historically it has been a male-dominated field, but she's never felt uncomfortable. 

"And I've never had to queue for the toilet," she says, laughing.

As the company tries to close the gender pay gap, it includes people like Ms Gilbert in leadership programs, attracting more female apprentices to the well-paid field, and having a commitment from the CEO down to changing the company.

"In the engineering space, we're seeing more females coming through. Certainly, in terms of our operators, we're seeing more females, literally at the coalface," she says.

"There's a really clear indication that, yeah, they're serious about it."

Complex gap

The gender pay gap — the difference between what men and women are paid in the same organisation — is a  persistent problem .

WGEA, which collates the information, describes it like this:

"Gender pay gaps … show the difference between the average or median pay of women and men across organisations, industries and the workforce as a whole."

As Qantas notes in a statement, the figures show what's happening across the whole organisation: 

From Qantas Group chief people officer Catherine Walsh: "This does not mean women are paid less than men to do the same jobs at Qantas and Jetstar, but shows there is a significant under-representation of women in highly paid roles like pilots and engineers across airlines globally."

It is not about "equal pay for equal work".

Not equal pay

The fact that men and women should be paid the same for the same work has been enshrined in law for decades.

Any employer not doing that is breaking the law.

The gender pay gap is more complex and comes about through factors such as gender-dominated or previously segregated industries.

For example, teaching and "caring" professions have typically been dominated by women and paid much less than male-dominated fields such as construction.

Women are also more likely to have time out of the workforce to raise children or care for elderly relatives. Additionally, there is a history of gender discrimination against women and historic barriers to better wages and financial security, such as making up a higher proportion of part-time roles.

It's for these reasons that companies that are reducing their gaps have tended to do similar things: increasing the proportion of women in management roles, turning maternity leave provisions to "paternity leave" (and encouraging men to use it), and auditing where the lowest-paid roles in a company are. 

'Significant step'

In a statement ahead of the release, WGEA chief executive Mary Wooldridge said workers being able to see inside their own companies was a "significant step forward" for accountability.

The figures will now be released annually, meaning the public will be able to see any improvement.

"We'll be able to see if the commitments they make and articulate in relation to their understanding of the gaps and what they're going to do about them translates into the outcome of reducing their gender pay gap," she says.

The public pressure from workers inside companies — and media coverage of the data — is part of the plan. 

And it's worked elsewhere. 

Worked in the UK

The UK government first published the information in 2017, prompting an explosive response as high-profile companies were forced to defend and fix large gaps between what they paid their male and female staff.

Airline RyanAir (a 72 per cent gap) and bank Barclays (44 per cent) were among the biggest. The data made a substantial impact  in reducing the gap in future years.

Since publishing the broad data (2010) and the specific company-by-company information (2017), the gender pay gap has fallen by almost a quarter for all employees.

The reason?

Companies must submit their internal data to WGEA, with the submission signed off by a chief executive or person at a similar level.

At the same time, companies are invited to provide an "employer statement" to give context to their results.

For example, one of the reasons the airlines have large gaps is that the intake of pilots remains overwhelmingly male. Their well-paid jobs contrast with customer-facing staff at airlines who tend to be female and lower-paid. 

As Virgin Australia notes in its statement:

"The 2023 median gender pay gap … of 41.7 per cent is driven by the demographic profile of our organisation. We have a larger proportion of men occupying higher paying roles, such as pilots and aircraft engineering roles … we are focused on improving the demographic profile of key roles across our organisation over time."

Insurer IAG makes similar points about its 8,400 staff.

"While IAG has a greater representation of women than men across our workforce, there are fewer women than men at senior career levels where role pay is typically higher, and fewer women than men in higher-paying roles," its statement reads.

"While recognising we have further work to do to achieve gender pay equity … we welcome the increased focus and transparency through WGEA publishing these important metrics and look forward to reporting on our progress in future years."

It is not compulsory for companies to provide the contextual information. Much of it has  just been released and will be added to this article when it is made available. Companies mentioned in this article have been contacted for comment before publication.

The gap remains broad, even at companies that champion women.

Women undercut

Some of the most popular brands in Australia that sell goods largely to women have substantial gender pay gaps.

For every $1 a man makes working at the following companies, this is what a woman makes, on average:

• At retailer City Chic Collective, owner of City Chic and Avenue:  42.3 cents (gap is 57.7 per cent) 
• At retailer Fast Future Brands, owner of TEMT and Valleygirl:  47.9 cents (gap is 52.1 per cent) 
• At clothes chain Forever New:  49.9 cents (gap of 50.1 per cent) 
• At jeweller Pandora:  52.8 cents (gap is 47.2 per cent) 
• At swimwear retailer Seafolly:  55.5 cents (gap is 44.5 per cent) 
• At active-wear retailer Lorna Jane:  63.7 cents (gap is 36.3 per cent) 
• At clothing retailer Sussan:  73.1 cents (gap is 26.9 per cent) 
• At accessory giant Lovisa:  73.6 cents (gap is 26.4 per cent) 
• At chain store Decjuba:  79.1 cents (gap is 20.9 per cent) 

All of these companies were contacted for comment.

Forever New pointed out the implications of having a 1,436-strong workforce where the vast majority were women working in retail stores, many in entry-level positions. There are just 65 male employees in the organisation, but 62 of them work in the head office which has larger salaries.

In a statement, it defended its result.

"As a brand, Forever New has policies in place to ensure equal remuneration between women and men and offers flexible work arrangements for females to continue to progress in their careers."

Further, it says 84 per cent of the best-paid quarter of its workforce are female "and women occupy 89 per cent of manager level roles".

Decjuba has a similar-sized workforce and an even starker gender split: it's 99 per cent female.

"The pay gap at Decjuba is skewed due to the small number of male team members, many of whom are in head office roles," the company said in a statement that listed policies on parental leave, flexible work and visa sponsorship.

"We are proud to have a balanced gender representation in our executive leadership team."

City Chic says three-quarters of general manager and operational manager roles are held by women, but it doesn't employ many men in its stores because of the nature of the product.

"City Chic is committed to gender equality in remuneration and, where appropriate, gender diversity in its employment practices. Given the nature of City Chic's business (plus size women's fashion), a limited number of roles are appropriately open to gender diversity, with the majority of its personnel being in customer-facing sales roles."

Shortly before publication, lawyers for Lorna Jane sent a letter threatening legal action if the reporting of its average total remuneration gender pay gap (37.1 per cent, higher than its base salary gap) did not place the figure "in proper context".

The letter from HopwoodGanim lawyers notes that at the time the data was taken, Lorna Jane employed 1,382 women and 47 men, with women making up 97 per cent of the workforce and the entire staff of its 101 stores.

"As part of Lorna Jane’s commitment to providing flexible employment conditions for its valued team, a very large number of its female staff enjoy being able to work in casual and part-time capacities. In contrast, the vast majority of Lorna Jane’s male employees work in full-time, non-retail roles."

As the gender pay gap reflects the difference in average earnings between women and men in the workforce, the tiny proportion of men working in Lorna Jane — all in roles outside of retail service — would grow the overall gap.

Brands including 2XU, Jenny Craig, Tupperware Australia and The Body Shop were published on a list of "non-compliant" employers which did not submit a report on time.

Consumers will choose

Silvia Salazar, a senior research fellow at the Bankwest-Curtin Economics Centre at Curtin University, cites the examples of FairTrade coffee and Rainforest Alliance certification for products as she thinks about the potential impact on some of the brands listed.

"If you are somebody that cares about gender equality, you are probably going to shop elsewhere if you see that the company that you really like is offering really low pay for women, has no women in management and so forth," she says.

It comes down to values: what a company talks about and what it does.

"You expect that a company that mostly caters for women will care also to have these women enjoy equality within their companies."

Dr Salazar describes it as akin to the issue of " greenwashing " — where companies market products or services as environmentally sustainable when they're not. 

"You can have the same thing with this gender equality. I guess this publicly available data will show whether companies are actually doing what they said. Or not," she says.

Brewing change

A 2016 audit at brewer Lion exposed big gaps in how men and women were paid — and prompted action.

"We gave people changes to their base pay to say: 'What you're currently on isn't right. And we want you to be paid fairly for your role and your level'," says Ms Abbott, who runs the diversity, equity and inclusion function at Lion.

"That was done on the roles, positions that we recognised needed to be fixed.

"We needed to right the wrongs, draw a line in the sand and say: 'This is not okay on our watch'."

But the changes went further. The company banned discussion of previous salaries during job interviews and paid people according to the position they held.

"We don't want to bring on a poor behaviour that's replicated over (other) organisations into our own. So we pay for the level and the role, not for what the person was paid for before," she says.

With a gender pay gap now at 1.4 per cent, Lion is an example of both the work required inside companies that want to tackle the issue — and the change possible.

"We really welcome the opportunity for organisations to share their gender pay gap," Ms Abbott says. 

"It's just basic equity. It's black and white. To me, I don't think there's any question around why and why not we should be doing it."

Froth and bubble

"What's not to love about brewing?" laughs Celsa Wilton, the brewing manager for Castlemaine Perkins, the iconic home of XXXX near the banks of the Brisbane River.

With a background in science, Ms Wilton was drawn in by the technical challenge of the process and now helps brew millions of litres of beer every year. 

When she started 30 years ago she was the "first and only female at the table" but now has a team with a 50:50 gender split. Acknowledging that it hadn't been easy, Ms Wilton says it's now a point of pride that incoming staff don't experience a work environment dominated by one gender.

"There are so many advantages to trying to close that gender gap. Not because we've got a box to tick or a quota to meet, but you want that diversity in thinking in the brewery," she says.

"We can offer a different way of problem-solving, a different way of coming up with solutions," she says.

"There are so many benefits to having that diversity within any team."

A long list

Figures reveal the gender pay gap at some of Australia's biggest brands. Here are a few of the base pay gender gap provided to the government agency WGEA and published on Tuesday:

• Supermarket Woolworths:  5.7 per cent
• Rival Coles:  6 per cent
• Smaller rival Aldi : 5.3 per cent
• Department store Myer: 2.8 per cent
• Retailer JB HiFi: 1.9 per cent
• Parent company of carb-pusher Bakers Delight: 35 per cent
• Burger chain Grill'd: 12.5 per cent
• Optometrists Specsavers: 7.4 per cent
• Travel agents Flight Centre: 16.8 per cent
• Telco Optus: 13.5 per cent

Big banks, insurers

There's a substantial gap among the so-called "Big Four".

• Commonwealth Bank:  29.8 per cent
• Westpac: 27 per cent
• ANZ: 22.7 per cent
• NAB: 16.4 per cent

In a statement, the nation's biggest lender says the Commonwealth Bank has "a long history of promoting gender equality and working to improve the position of women in our workplace, and society more broadly" and has set clear public goals around its commitment.

"On an aggregate level, CBA has achieved gender pay equity on a 'like for like basis' – that is, men and women are paid equally for performing the same or comparable work. CBA's median pay gap calculated by WGEA reflects many factors influencing the gender pay gap more broadly, including the types of roles performed by women, the seniority of those roles and the composition of the workforce."

Women make up more than half (54 per cent) of the bank's workforce but 71 per cent of the lower-paid customer service roles in branches and call centres. 

Westpac also pointed to its leadership in gender equality and its desire to pay people fairly.

"Our gender pay gap is heavily influenced by the shape of our organisation, with many women being employed in roles in contact centres, operations and our large retail branch network," a spokesperson said in a statement.

"Our focus is on improving the gender pay gap by increasing participation of women in senior roles as well as specialist areas such as institutional banking and technology."

• Bendigo and Adelaide Bank:  24 per cent
• Goldman Sachs:  32.7 per cent
• Barrenjoey: 33.8 per cent
• IAG: 27.5 per cent
• QBE: 24 per cent
• Bupa:  33.8 per cent 
• Medibank: 17.8 per cent

Sporting clubs and associations

• Collingwood Football Club:  42 per cent
• Adelaide Football Club: 30 per cent
• Port Adelaide Football Club: 9.8 per cent
• Fremantle Football Club: 32 per cent
• Essendon Football Club:  23.1 per cent
• Richmond Football Club:  16 per cent
• North Melbourne Football Club: 2.9 per cent
• Wests Tigers Football Club:  4.8 per cent
• SANFL: 4.7 per cent
• Football Australia: 0 per cent
• AFL: 0 per cent
• Douglass Hanly Moir Pathology:  11 per cent
• Australian Red Cross Society: 10.4 per cent
• St Vincents Hospital (Melbourne):  6.6 per cent
• RSL Care RDNS: 0.9 per cent
• St John of God Health Care: -2.9 per cent

Miners, services

Rio Tinto has a negative gender pay gap, meaning women are, on average, paid more across the organisation.

• BHP Group: 18.9 per cent
• Rio Tinto:  -2.3 per cent
• Hancock Prospecting:  25 per cent
• Inpex Australia: 24.5 per cent
• Woodside Energy:  16.1 per cent
• Thiess: 8.7 per cent
• GHD: 26 per cent

Consulting firms, law firms

• PwC:  4 per cent
• KPMG:  12.9 per cent
• Deloitte:  16.7 per cent
• EY:  15.9 per cent
• Accenture: 15.8 per cent
• Boston Consulting Group: 27.2 per cent
• McKinsey Pacific Rim: 33.4 per cent
• Maurice Blackburn: 31.7 per cent
• Shine Lawyers: 25.5 per cent
• MinterEllison:  18.1 per cent
• Allens: 9.8 per cent
• Herbert Smith Freehills: 17.3 per cent
• Clayton Utz: 18.9 per cent
• Watpac Construction: 41.6 per cent
• Built Management Services: 35.6 per cent
• Schindler Lifts: 34.9 per cent
• Henley (Arch Unit Trust):  33.3 per cent
• Simonds Group: 29.7 per cent
• Hansen Yuncken:  28.6 per cent
• Lendlease: 24.7 per cent

Education and training

• South Coast Baptist College WA:  42.4 per cent
• Central Queensland University: 20.7 per cent
• Catholic Education WA:  16.7 per cent
• Methodist Ladies College WA:  33.1 per cent
• University of Technology Sydney:  11.9 per cent
• Australian National University: 7.9 per cent
• University of Melbourne: 7.4 per cent

If you're unable to load the form, click here .

Editor's note: In the above lists, law firm MinterEllison's   pay gap was initially listed as 22.2 per cent, but is 18.1 per cent.

• X (formerly Twitter)

Related Stories

The national gender pay gap has fallen to a new low, and a decision to name and shame could push it lower.

'Your gender pay gap is X, why would I go and work for you?': The revealing day coming for Australian businesses

New data reveals a gaping gender pay gap at some of Australia's biggest companies, but what about small, regional businesses?

• Business, Economics and Finance
• Corporate Governance
• Corporate Social Responsibility
• Discrimination
• Employment Statistics
• Gender Discrimination
• Gender Equality
• Industrial Relations
• Wages and Benefits

The Gender Pay Gap in Australia

Executive summary.

This paper tests the idea that a gender wage gap exists for the case study of Australian workers. The research methodology included simple and multiple linear regression models to detect differences in the significance of gender for the wage gap. Statistical analyses were conducted in MS Excel. In addition to determining the regression equations, the statistical significance of the slope coefficients was also calculated, which allowed us to judge the significance of the models as a whole. Wage and sociodemographic data, including skills, education, and professional experience in years, were collected from 1,099 Australian workers aged 20 to 74 years. The analysis showed that a gender wage gap exists, and it is amplified when additional variables are included. In particular, the gender coefficient was shown to increase for multiple regression.

Introduction

Achieving economic equality is a key need of a democratic civil society interested in developing equal rights and freedoms for all population groups without exception. One of the predictors of economic inequality is gender wage discrimination. In this case, women with identical skills and competencies to male colleagues are paid less (Cortés & Pan, 2019). According to official government data for 2022, the gender gap in Australia remains high, predominantly in the western part of the country (WGEA, 2022). Specifically, according to the Workplace Gender Equality Agency (WGEA), the wage gap between men and women can be as high as 21.2%, while the minimum level of this gap is also relatively high at 7.4%. In financial terms, it has been estimated that Australian women, on average, earn $255.30 less per week than men in similar positions. The gender gap is destructive and unmotivating because it demonstrates the traditional patriarchy of society and does not provide minimum economic freedoms for women. It should be understood that the reasons for the gender gap are multiple and cannot be reduced to a single answer.

The gap between women and men should not be perceived only as unfair wages if employees work in identical positions with similar skills. The gap should also be attributed to the historical situation in which women have fewer job opportunities and are forced to work in lower-paying jobs (Paul et al., 2022). This creates an additional problem when hiring an Australian woman for a new job, in which the final wage is based on the woman’s previous wages, creating a cycle. Among others, women are more likely than men to work in their households and/or care for a child — this labor is unpaid except for maternity leave, creating a gap. However, workplace discrimination and prejudice against women are significant predictors of this agenda (Blau, 2018). Thus, many reasons lead to the formation of a gender wage gap.

Strictly speaking, such a situation should not exist at all, nor should it be a research subject, according to the Universal Declaration of Human Rights (UDHR). Australia, which drafted this document in 1947 among a commission of UN countries, now has 75 years of experience in working to close the gender gap (Adami, 2018). This is true: the government was among the first to implement equal pay reforms back in 1969 and 1986 (Nguyen, 2022). However, observed changes do not seem sufficient, as a reference to Figure 1 shows that the average gender gap has remained a dynamic function over the past 25 years and has not shown a constant downward trend. According to The Guardian in Australia, men earn more than women on average by up to $120,000 a year (Australian Associated Press, 2022). Among the key statistics are the key predictors of this gap. In particular, WGEA (2022) reports industry differences: the maximum difference was in high-skill, scientific work, in which men’s daily wages were 24.4% higher than those of women in the same field. This does not seem surprising since science has historically provided more opportunities for men and excluded women (Astegiano et al., 2019). Although the agenda is changing, the measures taken by the government are not enough to close this gap, as can be seen.

Meanwhile, the gender gap is also mediated by the commercial orientation of labor. In the private sector, men are still paid 17.0% more, while in the public budget sector, this difference is reduced to 11.2% (WGEA, 2022). This difference may indicate government efforts to reduce the gender gap, but the difference of 11.0% is still relatively significant. Notable statistics from the WGEA report differentiating this gap by age group. In particular, the smallest gap exists for the youngest age group of workers; it was as low as 3.7% in 2021. In contrast, the gap widened with increasing age, reaching 17.8% for the 45 to 54 age groups — so adult women are discriminated against not only by gender but also by age.

The above statistics create a phenomenal agenda in which women find themselves unequal in economic and labor rights compared to men. In addition to decreased motivation to work and thus a drop in productivity, the gender gap also creates a foundation for the development of chronic stress, insecurity, effective motherhood, and health problems (Heise et al., 2019). On the other hand, there are opinions that the gender gap is not an accurate metric of a nation’s economic health because it does not consider the actual skills of men and women in the jobs they hold (Craven, 2019). One might get the impression that men’s historically richer inherited experience explains their improved competencies relative to women, with the result that the gender gap may be sparse. In this context, it is reported that “becoming a wealthy CEO is not something that happens overnight, and so expecting an immediate 50/50 gender split among CEOs is both misleading and unrealistic” (Craven, 2019, para. 18). Research also motivates the gender pay gap by punishing women for having children, creating conflicting conclusions for public debate (Kleven et al., 2018). Thus, statistics illustrate that the gender gap does exist, but different views on it can be found in academic and public discourse. While the pay gap is generally unfair and discriminates against women’s rights, there are many more variables to consider that affect this.

Purpose of the Report

The purpose of this research report, based on statistical analysis, is to examine the gender gap phenomenon for Australian men and women through linear and multiple linear regression and to demonstrate the results.

The methodological basis for the study was based on the use of publicly available published wage data for 1,099 full-time Australian men and women aged 20 to 74 years. The data collected included information on earnings, gender, educational attainment, skills, and work experience, examining different trends in the gender pay gap. MS Excel with a built-in regression statistics function was used for statistical analysis. It is essential to clarify that the variables of gender, educational degree, and availability of proficient skills were measured on a dichotomous scale and thus represented nominal variables. Table 1 below shows the coding of the values of these factors. The report included descriptive statistics and inferential statistics for prediction; a critical alpha level of.05 was used to test for statistical significance. Results were published as tables and charts presented in the appropriate section.

Table 1: Codes Used for Nominal Variables

Key Findings

The paper showed that a gender pay gap exists, amplified when additional variables are used. This included education level, skills, and professional experience. It was found that a man with the same characteristics as a woman was paid more on average by 19.8%. The analysis confirmed the existence of a gender wage gap for Australian workers.

Preliminary Linkage Analysis

It was paramount to assess the relationship between individual variables and wage levels to test the gender wage gap phenomenon and examine comprehensive data on key trends. The results showed that the average difference between men and women in terms of pay was 24.1% over one week, meaning that men were paid A$347 more than women (Table 1). A difference was also found for the education level of sample participants: it was shown that respondents with higher levels of education (“1”) were paid on average 43.1% more than participants with lower levels of education, a monetary difference of A$614. It is easy to see that the age criterion determined the wage gap almost twice as much as the gender criterion. Regarding the availability of high skills, it was found that wages were 37.1% higher for better-trained individuals than for less-skilled individuals — an equivalent financial difference of A$539. Finally, a preliminary analysis examined the difference in wages depending on the professional experience of the respondents. Since experience was not a dichotomous variable, it was appropriate to create a line chart corresponding to the relationship of pay to work experience (Figure 2). As shown in Figure 2, on average, employees’ pay increased slowly with increasing professional experience; however, serious deviations were found in this case. In particular, there were examples when less experience led to a significantly higher salary for the respondent and vice versa. Thus, the relationship between experience and salary cannot be unequivocally judged because it is too dynamic a relationship.

Table 1:  Differences in Average Wages Depending on Gender, Education, and Skills

Simple Linear Regression

A simple linear regression function examined the relationship between wage level and respondent gender in Model A. The results showed that the coefficient of determination R2 was 0.028, which means that only 28% of the variance of all data in these distributions could be explained by the linear model (Fernando, 2021). The resulting regression equation was as shown in Figure 3. It can be seen that the slope coefficient was 0.347, which may indicate a positive relationship between gender and wage levels. Considering that the coding of men was marked as “1”, it means that men get higher wages in general than women. This model is statistically significant because the corresponding p -value was significantly below the critical level (Table 2). Since the null hypothesis was that the slope coefficient was zero, and it was rejected, Australian men do earn more than women.

Table 2: Step-by-step Demonstration of Hypothesis Testing with the Two-Way T-Test

Multiple Linear Regression

Multiple linear regression was run for Model B, based on the multiple effects of the independent variables. The final equation for this model was as shown in Figure 4. It can be seen that all of the slope coefficients were positive, which means that each variable in Model B increases the wage gap. The most substantial contribution comes from individuals’ education level (0.561), followed by gender (0.396), skill level (0.365), and professional experience (0.010). Thus, respondents’ experience had less of an effect on the wage level of Australian workers. All slope coefficients were statistically significant because the corresponding p -values were significantly below the critical alpha level (Table 3). To test the overall significance of this model, the null hypothesis was that each slope coefficient was zero. The calculated overall p -value in the two-sided F-test was 3.89∙10 -42 , which means that the overall model B was statistically significant.

Table 3:  Stepwise Tests of the Significance of the Slope Coefficient Using a Two-Sided T-Test for Each of the Four Variables: Gender (Blue), Educational Degree (Orange), Skills (Green), and Professional Experience (Gray)

R 2 Comparison

The calculated R 2 value in model A was 0.028, and the adjusted R2 value in model B was 0.164. Based on these data, it was concluded that Model B covers a more significant percentage of the variance of the data in the distributions, which means that this model is generally more reliable than Model A. This does not seem surprising since, in practice, the wage gap does not depend on only one variable but instead is a function of many factors, as was shown in the Introduction section. In other words, incorporating additional relevant variables to predict wage values increases reliability. Meanwhile, it should be understood that the R 2 for Model B is still not high or even moderate but rather low (Fernando, 2021). Therefore, even Model B does not perfectly describe the relationship between the variables.

Gender Coefficients

As can be seen from Figure 3 and Figure 4, the gender coefficients in the regression equations were 0.347 and 0.396 for Model A and B, respectively. In other words, the multiple regression increases the effect of gender on the wage gap. This increase was due to the combined effect of several variables, which means that gender, when combined with education, skills, and experience, has a more substantial effect on Australian workers’ wages than gender alone. For the purposes of the problem at hand, this suggests that gender discrimination increases when additional factors are included, although gender alone also affects the gender wage gap.

Predicting Earnings

The equation shown in Figure 4 should be used to predict earnings. Substituting the variables from Table 1 into it produces the following values, as shown in Figure 5. It can be seen that other characteristics being equal, men will earn on average 19.8% more than women, which is equal to 400 Australian dollars. This prediction confirms the existence of a gender gap, taking into account other unchangeable characteristics — in other words, a woman does earn less than a man, all other characteristics being equal.

Additional Variables

It would be appropriate to use an individual’s occupational focus, measured on a categorical nominal scale, as part of improving the model. This would allow us to assess how the occupational focus factor affects the wage gap and whether it might reduce it. In addition, a variable of time spent on parental leave to care for a child would be useful. This variable would be measured numerically in months or years and would allow one to assess whether maternity leave affects the earnings gap.

The existence of a gender gap is a severe problem for economic equality in a democratic society and leads to disruptive consequences. This paper tests the idea that a gender pay gap exists using Australian workers as a case study. The research methodology included simple and multiple linear regression models to detect differences in the significance of gender for the wage gap. Statistical analysis was conducted in MS Excel. In addition to determining the regression equations, the statistical significance of the slope coefficients was also calculated, which allowed us to judge the significance of the models as a whole. Wage and sociodemographic data, including qualifications, education, and professional experience in years, were collected from 1,099 Australian workers aged 20 to 74 years. The analysis showed that a gender pay gap exists, and it is amplified when additional variables are included. In particular, the gender coefficient was shown to increase with multiple regression.

Adami, R. (2018). Women and the universal declaration of human rights . Routledge.

Astegiano, J., Sebastián-González, E., & Castanho, C. D. T. (2019). Unravelling the gender productivity gap in science: a meta-analytical review. Royal Society Open Science, 6 (6), 1-20. Web.

Australian Associated Press. (2022). Gender pay gap narrows but Australian men  still twice as likely as women to earn more than $120,000 a year . The Guardian. Web..

Blau, F. (2018). The sources of the gender pay gap. In D. B. Grusky & K. R. Weisshaar (Eds.),  Social Stratification (pp. 929-941). Routledge.

Cortés, P., & Pan, J. (2019). When time binds: Substitutes for household production, returns to working long hours, and the skilled gender wage gap. Journal of Labor Economics, 37 (2), 351-398.

Craven, D. (2019). Unpopular opinions: The wage gap isn’t down to the patriarchy . B**P. Web.

Fernando, J. (2021). R-squared . Investopedia. Web.

Heise, L., Greene, M. E., Opper, N., Stavropoulou, M., Harper, C., Nascimento, M., & Gupta, G. R. (2019). Gender inequality and restrictive gender norms: framing the challenges to health. The Lancet, 393 (10189), 2440-2454. Web.

Kleven, H., Landais, C., & Søgaard, J. E. (2018) Hildren and gender inequality: Evidence from  Denmark [PDF document]. Web.

Nguyen, L. (2022). Gender wage gap in Australia . The Borgen Project. Web.

Paul, M., Zaw, K., & Darity, W. (2022). Returns in the labor market: A nuanced view of penalties at the intersection of race and gender in the US. Feminist Economics, 2 , 1-31. Web.

WGEA. (2018). Australia’s gender pay gap statistics [PDF document]. Web.

Cite this paper

• Chicago (N-B)
• Chicago (A-D)

StudyCorgi. (2023, April 26). The Gender Pay Gap in Australia. https://studycorgi.com/the-gender-pay-gap-in-australia/

"The Gender Pay Gap in Australia." StudyCorgi , 26 Apr. 2023, studycorgi.com/the-gender-pay-gap-in-australia/.

StudyCorgi . (2023) 'The Gender Pay Gap in Australia'. 26 April.

1. StudyCorgi . "The Gender Pay Gap in Australia." April 26, 2023. https://studycorgi.com/the-gender-pay-gap-in-australia/.

Bibliography

StudyCorgi . "The Gender Pay Gap in Australia." April 26, 2023. https://studycorgi.com/the-gender-pay-gap-in-australia/.

StudyCorgi . 2023. "The Gender Pay Gap in Australia." April 26, 2023. https://studycorgi.com/the-gender-pay-gap-in-australia/.

This paper, “The Gender Pay Gap in Australia”, was written and voluntary submitted to our free essay database by a straight-A student. Please ensure you properly reference the paper if you're using it to write your assignment.

Before publication, the StudyCorgi editorial team proofread and checked the paper to make sure it meets the highest standards in terms of grammar, punctuation, style, fact accuracy, copyright issues, and inclusive language. Last updated: April 26, 2023 .

If you are the author of this paper and no longer wish to have it published on StudyCorgi, request the removal . Please use the “ Donate your paper ” form to submit an essay.

Economic Inequality by Gender

How big are the inequalities in pay, jobs, and wealth between men and women? What causes these differences?

By Esteban Ortiz-Ospina, Joe Hasell and Max Roser

This page was first published in March 2018 and last revised in March 2024.

On this page, you can find writing, visualizations, and data on how big the inequalities in pay, jobs, and wealth are between men and women, how they have changed over time, and what may be causing them

Although economic gender inequalities remain common and large, they are today smaller than they used to be some decades ago.

Related topics

Women's Employment

How does women’s labor force participation differ across countries? How has it changed over time? What is behind these differences and changes?

Women’s Rights

How has the protection of women’s rights changed over time? How does it differ across countries? Explore global data and research on women’s rights.

Maternal Mortality

What could be more tragic than a mother losing her life in the moment that she is giving birth to her newborn? Why are mothers dying and what can be done to prevent these deaths?

See all interactive charts on economic inequality by gender ↓

How does the gender pay gap look like across countries and over time?

The 'gender pay gap' comes up often in political debates , policy reports , and everyday news . But what is it? What does it tell us? Is it different from country to country? How does it change over time?

Here we try to answer these questions, providing an empirical overview of the gender pay gap across countries and over time.

The gender pay gap measures inequality but not necessarily discrimination

The gender pay gap (or the gender wage gap) is a metric that tells us the difference in pay (or wages, or income) between women and men. It's a measure of inequality and captures a concept that is broader than the concept of equal pay for equal work.

Differences in pay between men and women capture differences along many possible dimensions, including worker education, experience, and occupation. When the gender pay gap is calculated by comparing all male workers to all female workers – irrespective of differences along these additional dimensions – the result is the 'raw' or 'unadjusted' pay gap. On the contrary, when the gap is calculated after accounting for underlying differences in education, experience, etc., then the result is the 'adjusted' pay gap.

Discrimination in hiring practices can exist in the absence of pay gaps – for example, if women know they will be treated unfairly and hence choose not to participate in the labor market. Similarly, it is possible to observe large pay gaps in the absence of discrimination in hiring practices – for example, if women get fair treatment but apply for lower-paid jobs.

The implication is that observing differences in pay between men and women is neither necessary nor sufficient to prove discrimination in the workplace. Both discrimination and inequality are important. But they are not the same.

In most countries, there is a substantial gender pay gap

Cross-country data on the gender pay gap is patchy, but the most complete source in terms of coverage is the United Nation's International Labour Organization (ILO). The visualization here presents this data. You can add observations by clicking on the option 'add country' at the bottom of the chart.

The estimates shown here correspond to differences between the average hourly earnings of men and women (expressed as a percentage of average hourly earnings of men), and cover all workers irrespective of whether they work full-time or part-time. 1

As we can see: (i) in most countries the gap is positive – women earn less than men, and (ii) there are large differences in the size of this gap across countries. 2

In most countries, the gender pay gap has decreased in the last couple of decades

How is the gender pay gap changing over time? To answer this question, let's consider this chart showing available estimates from the OECD. These estimates include OECD member states, as well as some other non-member countries, and they are the longest available series of cross-country data on the gender pay gap that we are aware of.

Here we see that the gap is large in most OECD countries, but it has been going down in the last couple of decades. In some cases the reduction is remarkable. In the United States, for example, the gap declined by more than half.

These estimates are not directly comparable to those from the ILO, because the pay gap is measured slightly differently here: The OECD estimates refer to percent differences in median earnings (i.e. the gap here captures differences between men and women in the middle of the earnings distribution), and they cover only full-time employees and self-employed workers (i.e. the gap here excludes disparities that arise from differences in hourly wages for part-time and full-time workers).

However, the ILO data shows similar trends.

The conclusion is that in most countries with available data, the gender pay gap has decreased in the last couple of decades.

The gender pay gap is larger for older workers

The United States Census Bureau defines the pay gap as the ratio between median wages – that is, they measure the gap by calculating the wages of men and women at the middle of the earnings distribution, and dividing them.

By this measure, the gender wage gap is expressed as a percent (median earnings of women as a share of median earnings of men) and it is always positive. Here, values below 100% mean that women earn less than men, while values above 100% mean that women earn more. Values closer to 100% reflect a lower gap.

The next chart shows available estimates of this metric for full-time workers in the US, by age group.

First, we see that the series trends upwards, meaning the gap has been shrinking in the last couple of decades. Secondly, we see that there are important differences by age.

The second point is crucial to understanding the gender pay gap: the gap is a statistic that changes during the life of a worker. In most rich countries, it’s small when formal education ends and employment begins, and it increases with age. As we discuss in our analysis of the determinants below, the gender pay gap tends to increase when women marry and when/if they have children.

The gender pay gap is smaller in middle-income countries – which tend to be countries with low labor force participation of women

The chart here plots available ILO estimates on the gender pay gap against GDP per capita. As we can see there is a weak positive correlation between GDP per capita and the gender pay gap. However, the chart shows that the relationship is not really linear. Actually, middle-income countries tend to have the smallest pay gap.

The fact that middle-income countries have low gender wage gaps is, to a large extent, the result of selection of women into employment . Olivetti and Petrongolo (2008) explain it as follows: “[I]f women who are employed tend to have relatively high‐wage characteristics, low female employment rates may become consistent with low gender wage gaps simply because low‐wage women would not feature in the observed wage distribution.” 3

Olivetti and Petrongolo (2008) show that this pattern holds in the data: unadjusted gender wage gaps across countries tend to be negatively correlated with gender employment gaps. That is, the gender pay gaps tend to be smaller where relatively fewer women participate in the labor force .

So, rather than reflect greater equality, the lower wage gaps observed in some countries could indicate that only women with certain characteristics – for instance, with no husband or children – are entering the workforce.

Why is there a gender pay gap?

In almost all countries, if you compare the wages of men and women you find that women tend to earn less than men.  These inequalities have been narrowing across the world. In particular, most high-income countries have seen sizeable reductions in the gender pay gap over the last couple of decades.

How did these reductions come about and why do substantial gaps remain?

Before we get into the details, here is a preview of the main points.

• An important part of the reduction in the gender pay gap in rich countries over the last decades is due to a historical narrowing, and often even reversal of the education gap between men and women.
• Today, education is relatively unimportant in explaining the remaining gender pay gap in rich countries. In contrast, the characteristics of the jobs that women tend to do, remain important contributing factors.
• The gender pay gap is not a direct metric of discrimination. However, evidence from different contexts suggests discrimination is indeed important to understand the gender pay gap. Similarly, social norms affecting the gender distribution of labor are important determinants of wage inequality.
• On the other hand, the available evidence suggests differences in psychological attributes and non-cognitive skills are at best modest factors contributing to the gender pay gap.

Differences in human capital

The adjusted pay gap.

Differences in earnings between men and women capture differences across many possible dimensions, including education, experience, and occupation.

For example, if we consider that more educated people tend to have higher earnings, it is natural to expect that the narrowing of the pay gap across the world can be partly explained by the fact that women have been catching up with men in terms of educational attainment, in particular years of schooling.

Indeed, since differences in education partly contribute to explaining differences in wages, it is common to distinguish between 'unadjusted' and 'adjusted' pay differences.

When the gender pay gap is calculated by comparing all male and female workers, irrespective of differences in worker characteristics, the result is the raw or unadjusted pay gap. In contrast to this, when the gap is calculated after accounting for underlying differences in education, experience, and other factors that matter for the pay gap, then the result is the adjusted pay gap.

The idea of the adjusted pay gap is to make comparisons within groups of workers with roughly similar jobs, tenure, and education. This allows us to tease out the extent to which different factors contribute to observed inequalities.

The chart here, from Blau and Kahn (2017) shows the evolution of the adjusted and unadjusted gender pay gap in the US. 4

More precisely, the chart shows the evolution of female-to-male wage ratios in three different scenarios: (i) Unadjusted; (ii) Adjusted, controlling for gender differences in human capital, i.e. education and experience; and (iii) Adjusted, controlling for a full range of covariates, including education, experience, job industry, and occupation, among others. The difference between 100% and the full specification (the green bars) is the “unexplained” residual. 5

Several points stand out here.

• First, the unadjusted gender pay gap in the US shrunk over this period. This is evident from the fact that the blue bars are closer to 100% in 2010 than in 1980.
• Second, if we focus on groups of workers with roughly similar jobs, tenure, and education, we also see a narrowing. The adjusted gender pay gap has shrunk.
• Third, we can see that education and experience used to help explain a very large part of the pay gap in 1980, but this changed substantially in the decades that followed. This third point follows from the fact that the difference between the blue and red bars was much larger in 1980 than in 2010.
• And fourth, the green bars grew substantially in the 1980s, but stayed fairly constant thereafter. In other words: Most of the convergence in earnings occurred during the 1980s, a decade in which the "unexplained" gap shrunk substantially.

Education and experience have become much less important in explaining gender differences in wages in the US

The next chart shows a breakdown of the adjusted gender pay gaps in the US, factor by factor, in 1980 and 2010.

When comparing the contributing factors in 1980 and 2010, we see that education and work experience have become much less important in explaining gender differences in wages over time, while occupation and industry have become more important. 6

In this chart we can also see that the 'unexplained' residual has gone down. This means the observable characteristics of workers and their jobs explain wage differences better today than a couple of decades ago. At first sight, this seems like good news – it suggests that today there is less discrimination, in the sense that differences in earnings are today much more readily explained by differences in 'productivity' factors. But is this really the case?

The unexplained residual may include aspects of unmeasured productivity (i.e. unobservable worker characteristics that could not be accounted for in the study), while the "explained" factors may themselves be vehicles of discrimination.

For example, suppose that women are indeed discriminated against, and they find it hard to get hired for certain jobs simply because of their sex. This would mean that in the adjusted specification, we would see that occupation and industry are important contributing factors – but that is precisely because discrimination is embedded in occupational differences!

Hence, while the unexplained residual gives us a first-order approximation of what is going on, we need much more detailed data and analysis in order to say something definitive about the role of discrimination in observed pay differences.

Gender pay differences around the world are better explained by occupation than by education

The set of three maps here, taken from the World Development Report (2012) , shows that today gender pay differences are much better explained by occupation than by education. This is consistent with the point already made above using data for the US: as education expanded radically over the last few decades, human capital has become much less important in explaining gender differences in wages.

Justin Sandefur at the Center for Global Development shows that education also fails to explain wage gaps if we include workers with zero income (i.e. if we decompose the wage gap after including people who are not employed).

Looking beyond worker characteristics

Job flexibility.

All over the world women tend to do more unpaid care work at home than men – and women tend to be overrepresented in low-paying jobs where they have the flexibility required to attend to these additional responsibilities.

The most important evidence regarding this link between the gender pay gap and job flexibility is presented and discussed by Claudia Goldin in the article ' A Grand Gender Convergence: Its Last Chapter ', where she digs deep into the data from the US. 8 There are some key lessons that apply both to rich and non-rich countries.

Goldin shows that when one looks at the data on occupational choice in some detail, it becomes clear that women disproportionately seek jobs, including full-time jobs, that tend to be compatible with childrearing and other family responsibilities. In other words, women, more than men, are expected to have temporal flexibility in their jobs. Things like shifting hours of work and rearranging shifts to accommodate emergencies at home. And these are jobs with lower earnings per hour, even when the total number of hours worked is the same.

The importance of job flexibility in this context is very clearly illustrated by the fact that, over the last couple of decades, women in the US increased their participation and remuneration in only some fields. In a recent paper, Goldin and Katz (2016) show that pharmacy became a highly remunerated female-majority profession with a small gender earnings gap in the US, at the same time as pharmacies went through substantial technological changes that made flexible jobs in the field more productive (e.g. computer systems that increased the substitutability among pharmacists). 9

The chart here shows how quickly female wages increased in pharmacy, relative to other professions, over the last few decades in the US.

The motherhood penalty

Closely related to job flexibility and occupational choice is the issue of work interruptions due to motherhood. On this front, there is again a great deal of evidence in support of the so-called 'motherhood penalty'.

Lundborg, Plug, and Rasmussen (2017) provide evidence from Denmark – more specifically, Danish women who sought medical help in achieving pregnancy. 10

By tracking women’s fertility and employment status through detailed periodic surveys, these researchers were able to establish that women who had a successful in vitro fertilization treatment, ended up having lower earnings down the line than similar women who, by chance, were unsuccessfully treated.

Lundborg, Plug, and Rasmussen summarise their findings as follows: "Our main finding is that women who are successfully treated by [in vitro fertilization] earn persistently less because of having children. We explain the decline in annual earnings by women working less when children are young and getting paid less when children are older. We explain the decline in hourly earnings, which is often referred to as the motherhood penalty, by women moving to lower-paid jobs that are closer to home."

The fact that the motherhood penalty is indeed about ‘motherhood’ and not ‘parenthood’, is supported by further evidence.

A recent study , also from Denmark, tracked men and women over the period 1980-2013 and found that after the first child, women’s earnings sharply dropped and never fully recovered. But this was not the case for men with children, nor the case for women without children.

These patterns are shown in the chart here. The first panel shows the trend in earnings for Danish women with and without children. The second panel shows the same comparison for Danish men.

Note that these two examples are from Denmark – a country that ranks high on gender equality measures and where there are legal guarantees requiring that a woman can return to the same job after taking time to give birth.

This shows that, although family-friendly policies contribute to improving female labor force participation and reducing the gender pay gap , they are only part of the solution. Even when there is generous paid leave and subsidized childcare, as long as mothers disproportionately take additional work at home after having children, inequities in pay are likely to remain.

Ability, personality, and social norms

The discussion so far has emphasized the importance of job characteristics and occupational choice in explaining the gender pay gap. This leads to obvious questions: What determines the systematic gender differences in occupational choice? What makes women seek job flexibility and take a disproportionate amount of unpaid care work?

One argument usually put forward is that, to the extent that biological differences in preferences and abilities underpin gender roles, they are the main factors explaining the gender pay gap. In their review of the evidence, Francine Blau and Lawrence Kahn (2017) show that there is limited empirical support for this argument. 11

To be clear, yes, there is evidence supporting the fact that men and women differ in some key attributes that may affect labor market outcomes. For example, standardized tests show that there are statistical gender gaps in maths scores in some countries ; and experiments show that women avoid more salary negotiations , and they often show particular predisposition to accept and receive requests for tasks with low promotion potential . However, these observed differences are far from being biologically fixed – 'gendering' begins early in life and the evidence shows that preferences and skills are highly malleable. You can influence tastes, and you can certainly teach people to tolerate risk, to do maths, or to negotiate salaries.

What's more, independently of where they come from, Blau and Kahn show that these empirically observed differences can typically only account for a modest portion of the gender pay gap.

In contrast, the evidence does suggest that social norms and culture, which in turn affect preferences, behavior, and incentives to foster specific skills, are key factors in understanding gender differences in labor force participation and wages. You can read more about this farther below.

Discrimination and bias

Independently of the exact origin of the unequal distribution of gender roles, it is clear that our recent and even current practices show that these roles persist with the help of institutional enforcement. Goldin (1988), for instance, examines past prohibitions against the training and employment of married women in the US. She touches on some well-known restrictions, such as those against the training and employment of women as doctors and lawyers, before focusing on the lesser known but even more impactful 'marriage bars' that arose in the late 1800s and early 1900s. These work prohibitions are important because they applied to teaching and clerical jobs – occupations that would become the most commonly held among married women after 1950. Around the time the US entered World War II, it is estimated that 87% of all school boards would not hire a married woman and 70% would not retain an unmarried woman who married. 12

The map here highlights that to this day, explicit barriers limit the extent to which women are allowed to do the same jobs as men in some countries. 13

However, even after explicit barriers are lifted and legal protections put in place, discrimination and bias can persist in less overt ways. Goldin and Rouse (2000), for example, look at the adoption of "blind" auditions by orchestras and show that by using a screen to conceal the identity of a candidate, impartial hiring practices increased the number of women in orchestras by 25% between 1970 and 1996. 14

Many other studies have found similar evidence of bias in different labor market contexts. Biases also operate in other spheres of life with strong knock-on effects on labor market outcomes. For example, at the end of World War II only 18% of people in the US thought that a wife should work if her husband was able to support her . This obviously circles back to our earlier point about social norms. 15

Strategies for reducing the gender pay gap

In many countries wage inequality between men and women can be reduced by improving the education of women. However, in many countries, gender gaps in education have been closed and we still have large gender inequalities in the workforce. What else can be done?

An obvious alternative is fighting discrimination. But the evidence presented above shows that this is not enough. Public policy and management changes on the firm level matter too: Family-friendly labor-market policies may help. For example, maternity leave coverage can contribute by raising women’s retention over the period of childbirth, which in turn raises women’s wages through the maintenance of work experience and job tenure. 16

Similarly, early education and childcare can increase the labor force participation of women — and reduce gender pay gaps — by alleviating the unpaid care work undertaken by mothers. 17

Additionally, the experience of women's historical advance in specific professions (e.g. pharmacists in the US), suggests that the gender pay gap could also be considerably reduced if firms did not have the incentive to disproportionately reward workers who work long hours, and fixed, non-flexible schedules. 18

Changing these incentives is of course difficult because it requires reorganizing the workplace. But it is likely to have a large impact on gender inequality, particularly in countries where other measures are already in place. 19

Implementing these strategies can have a positive self-reinforcing effect. For example, family-friendly labor-market policies that lead to higher labor-force attachment and salaries for women will raise the returns on women's investment in education – so women in future generations will be more likely to invest in education, which will also help narrow gender gaps in labor market outcomes down the line. 20

Nevertheless, powerful as these strategies may be, they are only part of the solution. Social norms and culture remain at the heart of family choices and the gender distribution of labor. Achieving equality in opportunities requires ensuring that we change the norms and stereotypes that limit the set of choices available both to men and women. It is difficult, but as the next section shows, social norms can be changed, too.

How well do biological differences explain the gender pay gap?

Across the world, women tend to take on more family responsibilities than men. As a result, women tend to be overrepresented in low-paying jobs where they are more likely to have the flexibility required to attend to these additional responsibilities.

These two facts – documented above – are often used to claim that, since men and women tend to be endowed with different tastes and talents, it follows that most of the observed gender differences in wages stem from biological sex differences. But what’s the broader evidence for these claims?

In a nutshell, here's what the research and data shows:

• There is evidence supporting the fact that statistically speaking, men and women tend to differ in some key aspects, including psychological attributes that may affect labor-market outcomes.
• There is no consensus on the exact weight that nurture and nature have in determining these differences, but whatever the exact weight, the evidence does show that these attributes are strongly malleable.
• Regardless of the origin, these differences can only explain a modest part of the gender pay gap.

Some context regarding the gender distribution of labor

Before we get into the discussion of whether biological attributes explain wage differences via gender roles, let's get some perspective on the gender distribution of work.

The following chart shows, by country, the female-to-male ratio of time devoted to unpaid care work, including tasks like taking care of children at home, housework, or doing community work. As can be seen, all over the world there is a radical unbalance in the gender distribution of labor – everywhere women take a disproportionate amount of unpaid work.

This is of course closely related to the fact that in most countries there are gender gaps in labor force participation and wages .

“Boys are better at maths”

Differences in biological attributes that determine our ability to develop 'hard skills', such as maths, are often argued to be at the heart of the gender pay gap. 21 Do large gender differences in maths skills really exist? If so, is this because of differences in the attributes we are born with?

Let's look at the data.

Are boys better in the mathematics section of the PISA standardized test ? One could argue that looking at top scores is more relevant here since top scores are more likely to determine gaps in future professional trajectories – for example, gaps in access to 'STEM degrees' at the university level.

The chart shows the share of male and female test-takers scoring at the highest level on the PISA test (that's level 6). As we can see, most countries lie above the diagonal line marking gender parity; so yes, achieving high scores in maths tends to be more common among boys than girls. However, there is huge cross-country variation – the differences between countries are much larger than the differences between the sexes. And in many countries, the gap is effectively inexistent. 22

Similarly, researchers have found that within countries there is also large geographic variation in gender gaps in test scores. So clearly these gaps in mathematical ability do not seem to be fully determined by biological endowments. 23

Indeed, research looking at the PISA cross-country results suggests that improved social conditions for women are related to improved math performance by girls. 24

Not only do statistical gaps in test scores vary substantially across societies – they also vary substantially across time. This suggests that social factors play a large role in explaining differences between the sexes.

In the US, for example, the gender gap in mathematics has narrowed in recent decades. 25 And this narrowing took place as high school curricula of boys and girls became more similar. The following chart shows this: In the US boys in 1957 took far more math and science courses than did girls; but by 1992 there was virtual parity in almost all science and math courses.

More importantly for the question at hand, gender gaps in 'hard skills' are not large enough to explain the gender gaps in earnings. In their review of the evidence, Blau and Kahn (2017) concludes that gaps in test scores in the US are too small to explain much of the gender pay at any point in time. 26

So, taken together, the evidence suggests that statistical gaps in maths test scores are both relatively small and heavily influenced by social and environmental factors.

“It’s about personality”

Biological differences in tastes (e.g. preferences for 'people' over 'things'), psychological attributes (e.g. 'risk aversion'), and soft skills (e.g. the ability to get along with others) are also often argued to be at the heart of the gender pay gap.

There are hundreds of studies trying to establish whether there are gender differences in preferences, personality traits, and 'soft skills'. The quality and general relevance (i.e. the internal and external validity) of these studies is the subject of much discussion, as illustrated in the recent debate that ensued from the Google Memo affair .

A recent article from the 'Heterodox Academy ', which was produced specifically in the context of the Google Memo, provides a fantastic overview of the evidence on this topic and the key points of contention among scholars.

For the purpose of this blog post, let's focus on the review of the evidence presented in Blau and Kahn (2017) – their review is particularly helpful because they focus on gender differences in the context of labor markets.

Blau and Kahn point out that, yes, researchers have found statistical differences between men and women that are important in the context of labor-market outcomes. For example, studies have found statistical gender differences in 'people skills' (i.e. ability to listen, communicate, and relate to others). Similarly, experimental studies have found that women more often avoid salary negotiations , and they often show a particular predisposition to accept and receive requests for tasks with low promotability. But are the origins of these differences mainly biological or are they social? And are they strong enough to explain pay gaps?

The available evidence here suggests these factors can only explain a relatively small fraction of the observed differences in wages. 27 And they are anyway far from being purely biological – preferences and skills are highly malleable and 'gendering' begins early in life. 28

Here is a concrete example: Leibbrandt and List (2015) did an experiment in which they assessed how men and women reacted to job advertisements. 29 They found that although men were more likely to negotiate than women when there was no explicit statement that wages were negotiable, the gender difference disappeared and even reversed when it was explicitly stated that wages were negotiable. This suggests that it is not as much about 'talent', as it is about norms and rules.

“A man should earn more than his wife”

The experiment in which researchers found that gender differences in negotiation attitudes disappeared when it was explicitly stated that wages were negotiable, emphasizes the important role that social norms and culture play in labor-market outcomes.

These concepts may seem abstract: What do social norms and culture actually look like in the context of the gender pay gap?

The reproduction of stereotypes through everyday positive enforcement can be seen in a range of aspects: A study analyzing 124 prime-time television programs in the US found that female characters continue to inhabit interpersonal roles with romance, family, and friends, while male characters enact work-related roles. 30 In the realm of children’s books, a study of 5,618 books found that compared to females, males are represented nearly twice as often in titles and 1.6 times as often as central characters. 31 Qualitative research shows that even in the home, parents are often enforcers of gender norms – especially when it comes to fathers endorsing masculinity in male children. 32

Of particular relevance in the context of labor markets, social norms also often take the form of specific behavioral prescriptions such as "a man should earn more than his wife".

The following chart depicts the distribution of the share of the household income earned by the wife, across married couples in the US.

Consistent with the idea that "a man should earn more than his wife", the data shows a sharp drop at 0.5, the point where the wife starts to earn more than the husband.

Distribution of income share earned by the wife across married couples in the US – Bertrand, Kamenica, and Pan (2015) 33

This is the result of two factors. First, it is about the matching of men and women before they marry – 'matches' in which the woman has higher earning potential are less common. Second, it is a result of choices after marriage – the researchers show that married women with higher earning potential than their husbands often stay out of the labor force, or take 'below-potential' jobs. 34

The authors of the study from which this chart is taken explored the data in more detail and found that in couples where the wife earns more than the husband, the wife spends more time on household chores, so the gender gap in unpaid care work is even larger; and these couples are also less satisfied with their marriage and are more likely to divorce than couples where the wife earns less than the husband.

The empirical exploration in this study highlights the remarkable power that gender norms and identity have on labor-market outcomes.

Why do gender norms and identity matter?

Does it actually matter if social norms and culture are important determinants of gender roles and labor-market outcomes? Are social norms in our contemporary societies really less fixed than biological traits?

The available research suggests that the answers to these questions are yes and yes. There is evidence that social norms can be actively and rapidly changed.

Here is a concrete example: Jensen and Oster (2009) find that the introduction of cable television in India led to a significant decrease in the reported acceptability of domestic violence towards women and son preference, as well as increases in women’s autonomy and decreases in fertility. 35

Of course, TV is a small aspect of all the big things that matter for social norms. But this study is important for the discussion because it is hard to study how social norms can be changed. TV introduction is a rare opportunity to see how a group that is exposed to a driver of social change actually changes.

As Jensen and Oster point out, most popular cable TV shows in India feature urban settings where lifestyles differ radically from those in rural areas. For example, many female characters on popular soap operas have more education, marry later, and have smaller families than most women in rural areas. And, similarly, many female characters in these tv shows are featured working outside the home as professionals, running businesses, or are shown in other positions of authority.

The bar chart below shows how cable access changed attitudes toward the self-reported preference for their child to be a son. As the authors note, "reported desire for the next child to be a son is relatively unchanged in areas with no change in cable status, but it decreases sharply between 2001 and 2002 for villages that get cable in 2002, and between 2002 and 2003 (but notably not between 2001 and 2002) for those that get cable in 2003. For both measures of attitudes, the changes are large and striking, and correspond closely to the timing of introduction of cable."

To conclude: The evidence suggests that biological differences are not a key driver of gender inequality in labor-market outcomes; while social norms and culture – which in turn affect preferences, behavior, and incentives to foster specific skills – are very important.

This matters for policy because social norms are not fixed – they can be influenced in a number of ways, including through intergenerational learning processes, exposure to alternative norms, and activism such as that which propelled the women's movement. 36

How are women represented across jobs?

Representation of women at the top of the income distribution.

Despite having fallen in recent decades, there remains a substantial pay gap between the average wages of men and women .

But what does gender inequality look like if we focus on the very top of the income distribution? Do we find any evidence of the so-called 'glass ceiling' preventing women from reaching the top? How did this change over time?

Answers to these questions are found in the work of Atkinson, Casarico and Voitchovsky (2018). Using tax records, they investigated the incomes of women and men separately across nine high-income countries. As such, they were restricted to those countries in which taxes are collected on an individual basis, rather than as couples. 37

In addition to wages they also take into account income from investments and self-employment.

Whilst investment income tends to make up a larger share of the total income of rich individuals in general, the authors found this to be particularly marked in the case of women in top-income groups.

The two charts present the key figures from the study.

One chart shows the proportion of women out of all individuals falling into the top 10%, 1%, and 0.1% of the income distribution. The open circle represents the share of women in the top income brackets back in 2000; the closed circle shows the latest data, which is from 2013.

The other chart shows the data over time for individual countries. You can explore data for other countries using the 'Change country' button on the chart.

The two charts allow us to answer the initial questions:

• Women are greatly under-represented in top income groups – they make up much less than 50% across each of the nine countries. Within the top 1% women account for around 20% and there is surprisingly little variation across countries.
• The proportion of women is lower the higher you look up the income distribution. In the top 10% up to every third income-earner is a woman; in the top 0.1% only every fifth or tenth person is a woman.
• The trend is the same in all countries of this study: Women are now better represented in all top-income groups than they were in 2000.
• But improvements have generally been more limited at the very top. With the exception of Australia, we see a much smaller increase in the share of women amongst the top 0.1% than amongst the top 10%.

Overall, despite recent inroads, we continue to see remarkably few women making it to the top of the income distribution today.

Representation of women in management positions

The chart here plots the proportion of women in senior and middle management positions around the world. It shows that women all over the world are underrepresented in high-profile jobs, which tend to be better paid.

The next chart provides an alternative perspective on the same issue. Here we show the share of firms that have a woman as manager. We highlight world regions by default, but you can remove them and add specific countries.

As we can see, all over the world firms tend to be managed by men. And, globally, only about 18% of firms have a female manager.

Firms with female managers tend to be different to firms with male managers. For example, firms with female managers tend to also be firms with more female workers .

Representation of women in low-paying jobs

Above we show that women all over the world are underrepresented in high-profile jobs, which tend to be better paid. As it turns out, in many countries women are at the same time overrepresented in low-paying jobs.

This is shown in the chart here, where 'low-pay' refers to workers earning less than two-thirds of the median (i.e. the middle) of the earnings distribution.

A share above 50% implies that women are 'overrepresented', in the sense that among those with low wages, there are more women than men.

The fact that women in rich countries are overrepresented in the bottom of the income distribution goes together with the fact that working women in these countries are overrepresented in low-paying occupations. The chart shows this for the US.

How much control do women have over household resources?

Women often have no control over their personal earned income.

The next chart plots cross-country estimates of the share of women who are not involved in decisions about their own income. The line shows national averages, while the dots show averages for rich and poor households (i.e. averages for women in households within the top and bottom quintiles of the corresponding national income distribution).

As we can see, in many countries, particularly in Sub-Saharan Africa and Asia, a large fraction of women are not involved in household decisions about spending their personal earned income. And this pattern is stronger among low-income households within low-income countries.

Percentage of women not involved in decisions about their own income – World Development Report (2012) 39

In many countries, women have limited influence over important household decisions

Above we focus on whether women get to choose how their own personal income is spent. Now we look at women's influence over total household income.

In this chart, we plot the share of currently married women who report having a say in major household purchase decisions, against national GDP per capita.

We see that in many countries, notably in Sub-Saharan Africa and Asia, an important number of women have limited influence over major spending decisions.

The chart above shows that women’s control over household spending tends to be greater in richer countries. In the next chart, we show that this correlation also holds within countries: Women’s control is greater in wealthier households. Household wealth is shown by the quintile in the wealth distribution on the x-axis – the poorest households are in the lowest quintiles (Q1) on the left.

There are many factors at play here, and it's important to bear in mind that this correlation partly captures the fact that richer households enjoy greater discretionary income beyond levels required to cover basic expenditure, while at the same time, in richer households women often have greater agency via access to broader networks as well as higher personal assets and incomes.

Land ownership is more often in the hands of men

Economic inequalities between men and women manifest themselves not only in terms of wages earned but also in terms of assets owned. For example, as the chart shows, in nearly all low and middle-income countries with data, men are more likely to own land than women.

Women's lack of control over important household assets, such as land, can be a critical problem in case of divorce or the husband’s death.

Closely related to the issue of land ownership is the fact that in several countries women do not have the same rights to property as men. These countries are highlighted in the map. 40

Gender-equal inheritance systems have been adopted in most, but not all countries

Inheritance is one of the main mechanisms for the accumulation of assets. In the map, we provide an overview of the countries that do and do not have gender-equal inheritance systems.

If you move the slider to 1920, you will see that while gender-equal inheritance systems were very rare in the early 20th century, today they are much more common. And still, despite the progress achieved, in many countries, notably in North Africa and the Middle East, women and girls still have fewer inheritance rights than men and boys.

Gender differences in access to productive inputs are often large

Above we show that there are large gender gaps in land ownership across low-income countries. Here we show that there are also large gaps in terms of access to borrowed capital.

The chart shows the percentage of men and women who report borrowing any money in the past 12 months to start, operate, or expand a farm or business.

As we can see, almost everywhere, including in many rich countries, women are less likely to obtain borrowed capital for productive purposes.

This can have large knock-on effects: in agriculture and entrepreneurship, gender differences in access to productive inputs, including land and credit, can lead to gaps in earnings via lower productivity.

Indeed, studies have found that, when statistical gender differences in agricultural productivity exist, they often disappear when access to and use of productive inputs are taken into account. 41

Interactive Charts on Economic Inequality by Gender

Acknowledgements.

We thank Sandra Tzvetkova and Diana Beltekian for their great research assistance.

There are some exceptions to this definition. In particular, sometimes self-employed workers, or part-time workers are excluded.

This measure can also be negative. This means that, on an hourly basis, men earn on average less than women. It is the case for some countries, such as Malaysia.

Olivetti, C., & Petrongolo, B. (2008). Unequal pay or unequal employment? A cross-country analysis of gender gaps. Journal of Labor Economics, 26(4), 621-654.

Blau, Francine D., and Lawrence M. Kahn. 2017. " The Gender Wage Gap: Extent, Trends, and Explanations. " Journal of Economic Literature, 55(3): 789-865.

For each specification, Blau and Kahn (2017) perform regression analyses on data from the PSID (the Michigan Panel Study of Income Dynamics), which includes information on labor-market experience and considers men and women ages 25-64 who were full-time, non-farm, wage and salary workers.

In 2010, unionization and education show negative values; this reflects the fact that women have surpassed men in educational attainment, and unionization in the US has been in general decline with a greater effect on men.

The full source is: World Development Report (2012) Gender Equality and Development , World Bank.

Goldin, C. (2014). A grand gender convergence: Its last chapter. The American Economic Review, 104(4), 1091-1119.

Goldin, C., & Katz, L. F. (2016). A most egalitarian profession: pharmacy and the evolution of a family-friendly occupation. Journal of Labor Economics, 34(3), 705-746.

Lundborg, P., Plug, E., & Rasmussen, A. W. (2017). Can Women Have Children and a Career? IV Evidence from IVF Treatments. American Economic Review, 107(6), 1611-1637.

Blau, Francine D., and Lawrence M. Kahn. 2017. " The Gender Wage Gap: Extent, Trends, and Explanations. " Journal of Economic Literature, 55(3): 789-865

Goldin, C. (1988). Marriage bars: Discrimination against married women workers, 1920's to 1950's .

The data in this map, which comes from the World Bank's World Development Indicators, provides a measure of whether there are any specific jobs that women are not allowed to perform. So, for example, a country might be coded as "No" if women are only allowed to work in certain jobs within the mining industry, such as health care professionals within mines, but not as miners.

Goldin, C., & Rouse, C. (2000). Orchestrating impartiality: The impact of" blind" auditions on female musicians. American Economic Review , 90(4), 715-741.

Blau and Kahn (2017) provide a whole list of experimental studies that have found labor-market discrimination. Another early example is from Neumark et al. (1996), who look at discrimination in restaurants. In this case, male and female pseudo-job-seekers were given similar CVs to apply for jobs waiting on tables at the same set of restaurants in Philadelphia. The results showed discrimination against women in high-priced restaurants.

The full reference of this study is Neumark, D., Bank, R. J., & Van Nort, K. D. (1996). Sex discrimination in restaurant hiring: An audit study. The Quarterly Journal of Economics, 111(3), 915-941.

Waldfogel, J. (1998). Understanding the "family gap" in pay for women with children. The Journal of Economic Perspectives, 12(1), 137-156.

Olivetti, C., & Petrongolo, B. (2017). The economic consequences of family policies: lessons from a century of legislation in high-income countries. The Journal of Economic Perspectives, 31(1), 205-230.

As we show above, in several nations, such as Sweden and Denmark, a “motherhood penalty” in earnings exists, even though these nations have generous family policies, including paid family leave and subsidized child care.

For a discussion of this mechanism, see page 814, Blau, Francine D., and Lawrence M. Kahn. 2017. The Gender Wage Gap: Extent, Trends, and Explanations. Journal of Economic Literature, 55(3): 789-865.

Hard skills are abilities that can be defined and measured, such as writing, reading, or doing maths. By contrast, soft skills are less tangible and harder to measure and quantify.

Also importantly: If we focus on gender differences for average , rather than top students, we find that there is not even a clear tendency in favor of boys. ( This interactive chart compares PISA average math scores for boys and girls ).

For more on this see Pope, D. G., & Sydnor, J. R. (2010). Geographic variation in the gender differences in test scores. Journal of Economic Perspectives, 24(2), 95-108.

Guiso, L., Monte, F., Sapienza, P., & Zingales, L. (2008). Culture, gender, and math. SCIENCE-NEW YORK THEN WASHINGTON-, 320(5880), 1164.

A number of papers have documented the narrowing of gender gaps in test scores. See, for example, Hyde, J. S., Lindberg, S. M., Linn, M. C., Ellis, A. B., & Williams, C. C. (2008). Gender similarities characterize math performance . Science, 321(5888), 494-495.

Blau, Francine D., and Lawrence M. Kahn. 2017. The Gender Wage Gap: Extent, Trends, and Explanations. Journal of Economic Literature, 55(3): 789-865.

Blau and Kahn write: "While findings such as those in table 7 ['Selected Studies Assessing the Role of Psychological Traits in Accounting for the Gender Pay Gap'] are informative in elucidating some of the possible omitted factors that lie behind gender differences in wages as well as the unexplained gap in traditional wage regressions, in general, the results suggest that these factors do not account for a large portion of either the raw or unexplained gender gap."

For a discussion of 'gendering' see West, C., & Zimmerman, D. H. (1987). Doing gender. Gender & Society, 1(2), 125-151.

Leibbrandt, A., & List, J. A. (2014). Do women avoid salary negotiations? Evidence from a large-scale natural field experiment. Management Science, 61(9), 2016-2024.

Lauzen, M. M., Dozier, D. M., & Horan, N. (2008). Constructing gender stereotypes through social roles in prime-time television. Journal of Broadcasting & Electronic Media, 52(2), 200-214.

McCabe, J., Fairchild, E., Grauerholz, L., Pescosolido, B. A., & Tope, D. (2011). Gender in twentieth-century children’s books: Patterns of disparity in titles and central characters. Gender & Society, 25(2), 197-226.

Kane, E. W. (2006). “No way my boys are going to be like that!” Parents’ responses to children’s gender nonconformity. Gender & Society, 20(2), 149-176.

Bertrand, M., Kamenica, E., & Pan, J. (2015). Gender identity and relative income within households. The Quarterly Journal of Economics, 130(2), 571-614.

More precisely, the authors find that in couples where the wife’s potential income is likely to exceed her husband’s (based on the income that would be predicted for her observed characteristics), the wife is less likely to be in the labor force, and if she does work, her income is lower than predicted.

Jensen, R., & Oster, E. (2009). The power of TV: Cable television and women's status in India . In  The Quarterly Journal of Economics , 124(3), 1057-1094.

Regarding intergenerational transmission of gender roles, see Fernández, R. (2013). Cultural change as learning: The evolution of female labor force participation over a century. The American Economic Review, 103(1), 472-500.

For a discussion regarding social activism and its link to the determinants of female labor supply, see for example this study by Heer and Grossbard-Shechtman (1981).

Atkinson, A.B., Casarico, A. & Voitchovsky, S. Top incomes and the gender divide . J Econ Inequal (2018) 16: 225.

The authors produced results for 8 countries, and included earlier results for Sweden from Boschini, A., Gunnarsson, K., Roine, J.: Women in Top Incomes: Evidence from Sweden 1974-2013, IZA Discussion paper 10979, August (2017).

World Bank. (2011). World development report 2012: gender equality and development . World Bank Publications.

The map from The World Development Report (2012) provides a more fine-grained overview of different property regimes operating in different countries.

For more discussion of the evidence see page 20 in World Bank (2011) World Development Report 2012: Gender Equality and Development. World Bank Publications.

Cite this work

Our articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:

BibTeX citation

Reuse this work freely

All visualizations, data, and code produced by Our World in Data are completely open access under the Creative Commons BY license . You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

The data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.

All of our charts can be embedded in any site.

Our World in Data is free and accessible for everyone.

Help us do this work by making a donation.

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

The persistence of pay inequality: The gender pay gap in an anonymous online labor market

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected] (LL); [email protected] (LB)

Affiliation Department of Psychology, Lander College, Flushing, New York, United States of America

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

Affiliation Department of Computer Science, Lander College, Flushing, New York, United States of America

Roles Formal analysis, Writing – original draft, Writing – review & editing

Affiliation Department of Health Policy & Management, Mailman School of Public Health, Columbia University, New York, New York, United States of America

Roles Conceptualization, Writing – review & editing

Affiliation Department of Clinical Psychology, Columbia University, New York, New York, United States of America

Roles Formal analysis

Affiliation Department of Computer Science, Stern College for Women, New York, New York, United States of America

Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Affiliation Department of Epidemiology, Mailman School of Public Health, Columbia University New York, New York, United States of America

• Leib Litman, 
• Jonathan Robinson, 
• Zohn Rosen, 
• Cheskie Rosenzweig, 
• Joshua Waxman, 
• Lisa M. Bates

• Published: February 21, 2020
• https://doi.org/10.1371/journal.pone.0229383
• Reader Comments

Studies of the gender pay gap are seldom able to simultaneously account for the range of alternative putative mechanisms underlying it. Using CloudResearch, an online microtask platform connecting employers to workers who perform research-related tasks, we examine whether gender pay discrepancies are still evident in a labor market characterized by anonymity, relatively homogeneous work, and flexibility. For 22,271 Mechanical Turk workers who participated in nearly 5 million tasks, we analyze hourly earnings by gender, controlling for key covariates which have been shown previously to lead to differential pay for men and women. On average, women’s hourly earnings were 10.5% lower than men’s. Several factors contributed to the gender pay gap, including the tendency for women to select tasks that have a lower advertised hourly pay. This study provides evidence that gender pay gaps can arise despite the absence of overt discrimination, labor segregation, and inflexible work arrangements, even after experience, education, and other human capital factors are controlled for. Findings highlight the need to examine other possible causes of the gender pay gap. Potential strategies for reducing the pay gap on online labor markets are also discussed.

Citation: Litman L, Robinson J, Rosen Z, Rosenzweig C, Waxman J, Bates LM (2020) The persistence of pay inequality: The gender pay gap in an anonymous online labor market. PLoS ONE 15(2): e0229383. https://doi.org/10.1371/journal.pone.0229383

Editor: Luís A. Nunes Amaral, Northwestern University, UNITED STATES

Received: March 5, 2019; Accepted: February 5, 2020; Published: February 21, 2020

Copyright: © 2020 Litman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Due to the sensitive nature of some of the data, and the terms of service of the websites used during data collection (including CloudResearch and MTurk), CloudResearch cannot release the full data set to make it publically available. The data are on CloudResearch's Sequel servers located at Queens College in the city of New York. CloudResearch makes data available to be accessed by researchers for replication purposes, on the CloudResearch premises, in the same way the data were accessed and analysed by the authors of this manuscript. The contact person at CloudResearch who can help researchers access the data set is Tzvi Abberbock, who can be reached at [email protected] .

Funding: The authors received no specific funding for this work.

Competing interests: We have read the journal's policy and the authors of this manuscript have the following potential competing interest: Several of the authors are employed at Cloud Research (previously TurkPrime), the database from which the data were queried. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

The gender pay gap, the disparity in earnings between male and female workers, has been the focus of empirical research in the US for decades, as well as legislative and executive action under the Obama administration [ 1 , 2 ]. Trends dating back to the 1960s show a long period in which women’s earnings were approximately 60% of their male counterparts, followed by increases in women’s earnings starting in the 1980s, which began to narrow, but not close, the gap which persists today [ 3 ]. More recent data from 2014 show that overall, the median weekly earnings of women working full time were 79–83% of what men earned [ 4 – 9 ].

The extensive literature seeking to explain the gender pay gap and its trajectory over time in traditional labor markets suggests it is a function of multiple structural and individual-level processes that reflect both the near-term and cumulative effects of gender relations and roles over the life course. Broadly speaking, the drivers of the gender pay gap can be categorized as: 1) human capital or productivity factors such as education, skills, and workforce experience; 2) industry or occupational segregation, which some estimates suggest accounts for approximately half of the pay gap; 3) gender-specific temporal flexibility constraints which can affect promotions and remuneration; and finally, 4) gender discrimination operating in hiring, promotion, task assignment, and/or compensation. The latter mechanism is often estimated by inference as a function of unexplained residual effects of gender on payment after accounting for other factors, an approach which is most persuasive in studies of narrowly restricted populations of workers such as lawyers [ 10 ] and academics of specific disciplines [ 11 ]. A recent estimate suggests this unexplained gender difference in earnings can account for approximately 40% of the pay gap [ 3 ]. However, more direct estimations of discriminatory processes are also available from experimental evidence, including field audit and lab-based studies [ 12 – 14 ]. Finally, gender pay gaps have also been attributed to differential discrimination encountered by men and women on the basis of parental status, often known as the ‘motherhood penalty’ [ 15 ].

Non-traditional ‘gig economy’ labor markets and the gender pay gap

In recent years there has been a dramatic rise in nontraditional ‘gig economy’ labor markets, which entail independent workers hired for single projects or tasks often on a short-term basis with minimal contractual engagement. “Microtask” platforms such as Amazon Mechanical Turk (MTurk) and Crowdflower have become a major sector of the gig economy, offering a source of easily accessible supplementary income through performance of small tasks online at a time and place convenient to the worker. Available tasks can range from categorizing receipts to transcription and proofreading services, and are posted online by the prospective employer. Workers registered with the platform then elect to perform the advertised tasks and receive compensation upon completion of satisfactory work [ 16 ]. An estimated 0.4% of US adults are currently receiving income from such platforms each month [ 17 ], and microtask work is a growing sector of the service economy in the United States [ 18 ]. Although still relatively small, these emerging labor market environments provide a unique opportunity to investigate the gender pay gap in ways not possible within traditional labor markets, due to features (described below) that allow researchers to simultaneously account for multiple putative mechanisms thought to underlie the pay gap.

The present study utilizes the Amazon Mechanical Turk (MTurk) platform as a case study to examine whether a gender pay gap remains evident when the main causes of the pay gap identified in the literature do not apply or can be accounted for in a single investigation. MTurk is an online microtask platform that connects employers (‘requesters’) to employees (‘workers’) who perform jobs called “Human Intelligence Tasks” (HITs). The platform allows requesters to post tasks on a dashboard with a short description of the HIT, the compensation being offered, and the time the HIT is expected to take. When complete, the requester either approves or rejects the work based on quality. If approved, payment is quickly accessible to workers. The gender of workers who complete these HITs is not known to the requesters, but was accessible to researchers for the present study (along with other sociodemographic information and pay rates) based on metadata collected through CloudResearch (formerly TurkPrime), a platform commonly used to conduct social and behavioral research on MTurk [ 19 ].

Evaluating pay rates of workers on MTurk requires estimating the pay per hour of each task that a worker accepts which can then be averaged together. All HITs posted on MTurk through CloudResearch display how much a HIT pays and an estimated time that it takes for that HIT to be completed. Workers use this information to determine what the corresponding hourly pay rate of a task is likely to be, and much of our analysis of the gender pay gap is based on this advertised pay rate of all completed surveys. We also calculate an estimate of the gender pay gap based on actual completion times to examine potential differences in task completion speed, which we refer to as estimated actual wages (see Methods section for details).

Previous studies have found that both task completion time and the selection of tasks influences the gender pay gap in at least some gig economy markets. For example, a gender pay gap was observed among Uber drivers, with men consistently earning higher pay than women [ 20 ]. Some of the contributing factors to this pay gap include that male Uber drivers selected different tasks than female drivers, including being more willing to work at night and to work in neighborhoods that were perceived to be more dangerous. Male drivers were also likely to drive faster than their female counterparts. These findings show that person-level factors like task selection, and speed can influence the gender pay gap within gig economy markets.

MTurk is uniquely suited to examine the gender pay gap because it is possible to account simultaneously for multiple structural and individual-level factors that have been shown to produce pay gaps. These include discrimination, work heterogeneity (leading to occupational segregation), and job flexibility, as well as human capital factors such as experience and education.

Discrimination.

When employers post their HITs on MTurk they have no way of knowing the demographic characteristics of the workers who accept those tasks, including their gender. While MTurk allows for selective recruitment of specific demographic groups, the MTurk tasks examined in this study are exclusively open to all workers, independent of their gender or other demographic characteristics. Therefore, features of the worker’s identity that might be the basis for discrimination cannot factor into an employer’s decision-making regarding hiring or pay.

Task heterogeneity.

Another factor making MTurk uniquely suited for the examination of the gender pay gap is the relative homogeneity of tasks performed by the workers, minimizing the potential influence of gender differences in the type of work pursued on earnings and the pay gap. Work on the MTurk platform consists mostly of short tasks such as 10–15 minute surveys and categorization tasks. In addition, the only information that workers have available to them to choose tasks, other than pay, is the tasks’ titles and descriptions. We additionally classified tasks based on similarity and accounted for possible task heterogeneity effects in our analyses.

Job flexibility.

MTurk is not characterized by the same inflexibilities as are often encountered in traditional labor markets. Workers can work at any time of the day or day of the week. This increased flexibility may be expected to provide more opportunities for participation in this labor market for those who are otherwise constrained by family or other obligations.

Human capital factors.

It is possible that the more experienced workers could learn over time how to identify higher paying tasks by virtue of, for example, identifying qualities of tasks that can be completed more quickly than the advertised required time estimate. Further, if experience is correlated with gender, it could contribute to a gender pay gap and thus needs to be controlled for. Using CloudResearch metadata, we are able to account for experience on the platform. Additionally, we account for multiple sociodemographic variables, including age, marital status, parental status, education, income (from all sources), and race using the sociodemographic data available through CloudResearch.

Expected gender pay gap findings on MTurk

Due to the aforementioned factors that are unique to the MTurk marketplace–e.g., anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect a gender pay gap to be evident on the platform to the same extent as in traditional labor markets. However, potential gender differences in task selection and completion speed, which have implications for earnings, merit further consideration. For example, though we expect the relative homogeneity of the MTurk tasks to minimize gender differences in task selection that could mimic occupational segregation, we do account for potential subtle residual differences in tasks that could differentially attract male and female workers and indirectly lead to pay differentials if those tasks that are preferentially selected by men pay a higher rate. To do this we categorize all tasks based on their descriptions using K-clustering and add the clusters as covariates to our models. In addition, we separately examine the gender pay gap within each topic-cluster.

In addition, if workers who are experienced on the platform are better able to find higher paying HITs, and if experience is correlated with gender, it may lead to gender differences in earnings. Theoretically, other factors that may vary with gender could also influence task selection. Previous studies of the pay gap in traditional markets indicate that reservation wages, defined as the pay threshold at which a person is willing to accept work, may be lower among women with children compared to women without, and to that of men as well [ 21 ]. Thus, if women on MTurk are more likely to have young children than men, they may be more willing to accept available work even if it pays relatively poorly. Other factors such as income, education level, and age may similarly influence reservation wages if they are associated with opportunities to find work outside of microtask platforms. To the extent that these demographics correlate with gender they may give rise to a gender pay gap. Therefore we consider age, experience on MTurk, education, income, marital status, and parental status as covariates in our models.

Task completion speed may vary by gender for several reasons, including potential gender differences in past experience on the platform. We examine the estimated actual pay gap per hour based on HIT payment and estimated actual completion time to examine the effects of completion speed on the wage gap. We also examine the gender pay gap based on advertised pay rates, which are not dependent on completion speed and more directly measure how gender differences in task selection can lead to a pay gap. Below, we explain how these were calculated based on meta-data from CloudResearch.

To summarize, the overall goal of the present study was to explore whether gender pay differentials arise within a unique, non-traditional and anonymous online labor market, where known drivers of the gender pay gap either do not apply or can be accounted for statistically.

Materials and methods

Amazon mechanical turk and cloudresearch..

Started in 2005, the original purpose of the Amazon Mechanical Turk (MTurk) platform was to allow requesters to crowdsource tasks that could not easily be handled by existing technological solutions such as receipt copying, image categorization, and website testing. As of 2010, researchers increasingly began using MTurk for a wide variety of research tasks in the social, behavioral, and medical sciences, and it is currently used by thousands of academic researchers across hundreds of academic departments [ 22 ]. These research-related HITs are typically listed on the platform in generic terms such as, “Ten-minute social science study,” or “A study about public opinion attitudes.”

Because MTurk was not originally designed solely for research purposes, its interface is not optimized for some scientific applications. For this reason, third party add-on toolkits have been created that offer critical research tools for scientific use. One such platform, CloudResearch (formerly TurkPrime), allows requesters to manage multiple research functions, such as applying sampling criteria and facilitating longitudinal studies, through a link to their MTurk account. CloudResearch’s functionality has been described extensively elsewhere [ 19 ]. While the demographic characteristics of workers are not available to MTurk requesters, we were able to retroactively identify the gender and other demographic characteristics of workers through the CloudResearch platform. CloudResearch also facilitates access to data for each HIT, including pay, estimated length, and title.

The study was an analysis of previously collected metadata, which were analyzed anonymously. We complied with the terms of service for all data collected from CloudResearch, and MTurk. The approving institutional review board for this study was IntegReview.

Analytic sample.

We analyzed the nearly 5 million tasks completed during an 18-month period between January 2016 and June 2017 by 12,312 female and 9,959 male workers who had complete data on key demographic characteristics. To be included in the analysis a HIT had to be fully completed, not just accepted, by the worker, and had to be accepted (paid for) by the requester. Although the vast majority of HITs were open to both males and females, a small percentage of HITs are intended for a specific gender. Because our goal was to exclusively analyze HITs for which the requesters did not know the gender of workers, we excluded any HITs using gender-specific inclusion or exclusion criteria from the analyses. In addition, we removed from the analysis any HITs that were part of follow-up studies in which it would be possible for the requester to know the gender of the worker from the prior data collection. Finally, where possible, CloudResearch tracks demographic information on workers across multiple HITs over time. To minimize misclassification of gender, we excluded the 0.3% of assignments for which gender was unknown with at least 95% consistency across HITs.

The main exposure variable is worker gender and the outcome variables are estimated actual hourly pay accrued through completing HITs, and advertised hourly pay for completed HITs. Estimated actual hourly wages are based on the estimated length in minutes and compensation in dollars per HIT as posted on the dashboard by the requester. We refer to actual pay as estimated because sometimes people work multiple assignments at the same time (which is allowed on the platform), or may simultaneously perform other unrelated activities and therefore not work on the HIT the entire time the task is open. We also considered several covariates to approximate human capital factors that could potentially influence earnings on this platform, including marital status, education, household income, number of children, race/ethnicity, age, and experience (number of HITs previously completed). Additional covariates included task length, task cluster (see below), and the serial order with which workers accepted the HIT in order to account for potential differences in HIT acceptance speed that may relate to the pay gap.

Database and analytic approach.

Data were exported from CloudResearch’s database into Stata in long-form format to represent each task on a single row. For the purposes of this paper, we use “HIT” and “study” interchangeably to refer to a study put up on the MTurk dashboard which aims to collect data from multiple participants. A HIT or study consist of multiple “assignments” which is a single task completed by a single participant. Columns represented variables such as demographic information, payment, and estimated HIT length. Column variables also included unique IDs for workers, HITs (a single study posted by a requester), and requesters, allowing for a multi-level modeling analytic approach with assignments nested within workers. Individual assignments (a single task completed by a single worker) were the unit of analysis for all models.

Linear regression models were used to calculate the gender pay gap using two dependent variables 1) women’s estimated actual earnings relative to men’s and 2) women’s selection of tasks based on advertised earnings relative to men’s. We first examined the actual pay model, to see the gender pay gap when including an estimate of task completion speed, and then adjusted this model for advertised hourly pay to determine if and to what extent a propensity for men to select more remunerative tasks was evident and driving any observed gender pay gap. We additionally ran separate models using women’s advertised earnings relative to men’s as the dependent variable to examine task selection effects more directly. The fully adjusted models controlled for the human capital-related covariates, excluding household income and education which were balanced across genders. These models also tested for interactions between gender and each of the covariates by adding individual interaction terms to the adjusted model. To control for within-worker clustering, Huber-White standard error corrections were used in all models.

Cluster analysis.

To explore the potential influence of any residual task heterogeneity and gender preference for specific task type as the cause of the gender pay gap, we use K-means clustering analysis (seed = 0) to categorize the types of tasks into clusters based on the descriptions that workers use to choose the tasks they perform. We excluded from this clustering any tasks which contained certain gendered words (such as “male”, “female”, etc.) and any tasks which had fewer than 30 respondents. We stripped out all punctuation, symbols and digits from the titles, so as to remove any reference to estimated compensation or duration. The features we clustered on were the presence or absence of 5,140 distinct words that appeared across all titles. We then present the distribution of tasks across these clusters as well as average pay by gender and the gender pay gap within each cluster.

The demographics of the analytic sample are presented in Table 1 . Men and women completed comparable numbers of tasks during the study period; 2,396,978 (48.6%) for men and 2,539,229 (51.4%) for women.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0229383.t001

In Table 2 we measure the differences in remuneration between genders, and then decompose any observed pay gap into task completion speed, task selection, and then demographic and structural factors. Model 1 shows the unadjusted regression model of gender differences in estimated actual pay, and indicates that, on average, tasks completed by women paid 60 (10.5%) cents less per hour compared to tasks completed by men (t = 17.4, p < .0001), with the mean estimated actual pay across genders being $5.70 per hour.

https://doi.org/10.1371/journal.pone.0229383.t002

In Model 2, adjusting for advertised hourly pay, the gender pay gap dropped to 46 cents indicating that 14 cents of the pay gap is attributable to gender differences in the selection of tasks (t = 8.6, p < .0001). Finally, after the inclusion of covariates and their interactions in Model 3, the gender pay differential was further attenuated to 32 cents (t = 6.7, p < .0001). The remaining 32 cent difference (56.6%) in earnings is inferred to be attributable to gender differences in HIT completion speed.

Task selection analyses

Although completion speed appears to account for a significant portion of the pay gap, of particular interest are gender differences in task selection. Beyond structural factors such as education, household composition and completion speed, task selection accounts for a meaningful portion of the gender pay gap. As a reminder, the pay rate and expected completion time are posted for every HIT, so why women would select less remunerative tasks on average than men do is an important question to explore. In the next section of the paper we perform a set of analyses to examine factors that could account for this observed gender difference in task selection.

Advertised hourly pay.

To examine gender differences in task selection, we used linear regression to directly examine whether the advertised hourly pay differed for tasks accepted by male and female workers. We first ran a simple model ( Table 3 ; Model 3A) on the full dataset of 4.93 million HITs, with gender as the predictor and advertised hourly pay as the outcome including no other covariates. The unadjusted regression results (Model 4) shown in Table 3 , indicates that, summed across all clusters and demographic groups, tasks completed by women were advertised as paying 28 cents (95% CI: $0.25-$0.31) less per hour (5.8%) compared to tasks completed by men (t = 21.8, p < .0001).

https://doi.org/10.1371/journal.pone.0229383.t003

Model 5 examines whether the remuneration differences for tasks selected by men and women remains significant in the presence of multiple covariates included in the previous model and their interactions. The advertised pay differential for tasks selected by women compared to men was attenuated to 21 cents (4.3%), and remained statistically significant (t = 9.9, p < .0001). This estimate closely corresponded to the inferred influence of task selection reported in Table 2 . Tests of gender by covariate interactions were significant only in the cases of age and marital status; the pay differential in tasks selected by men and women decreased with age and was more pronounced among single versus currently or previously married women.

To further examine what factors may account for the observed gender differences in task selection we plotted the observed pay gap within demographic and other covariate groups. Table 4 shows the distribution of tasks completed by men and women, as well as mean earnings and the pay gap across all demographic groups, based on the advertised (not actual) hourly pay for HITs selected (hereafter referred to as “advertised hourly pay” and the “advertised pay gap”). The average task was advertised to pay $4.88 per hour (95% CI $4.69, $5.10).

https://doi.org/10.1371/journal.pone.0229383.t004

The pattern across demographic characteristics shows that the advertised hourly pay gap between genders is pervasive. Notably, a significant advertised gender pay gap is evident in every level of each covariate considered in Table 4 , but more pronounced among some subgroups of workers. For example, the advertised pay gap was highest among the youngest workers ($0.31 per hour for workers age 18–29), and decreased linearly with age, declining to $0.13 per hour among workers age 60+. Advertised houry gender pay gaps were evident across all levels of education and income considered.

To further examine the potential influence of human capital factors on the advertised hourly pay gap, Table 5 presents the average advertised pay for selected tasks by level of experience on the CloudResearch platform. Workers were grouped into 4 experience levels, based on the number of prior HITs completed: Those who completed fewer than 100 HITs, between 100 and 500 HITs, between 500 and 1,000 HITs, and more than 1,000 HITs. A significant gender difference in advertised hourly pay was observed within each of these four experience groups. The advertised hourly pay for tasks selected by both male and female workers increased with experience, while the gender pay gap decreases. There was some evidence that male workers have more cumulative experience with the platform: 43% of male workers had the highest level of experience (previously completing 1,001–10,000 HITs) compared to only 33% of women.

https://doi.org/10.1371/journal.pone.0229383.t005

Table 5 also explores the influence of task heterogeneity upon HIT selection and the gender gap in advertised hourly pay. K-means clustering was used to group HITs into 20 clusters initially based on the presence or absence of 5,140 distinct words appearing in HIT titles. Clusters with fewer than 50,000 completed tasks were then excluded from analysis. This resulted in 13 clusters which accounted for 94.3% of submitted work assignments (HITs).

The themes of all clusters as well as the average hourly advertised pay for men and women within each cluster are presented in the second panel of Table 5 . The clusters included categories such as Games, Decision making, Product evaluation, Psychology studies, and Short Surveys. We did not observe a gender preference for any of the clusters. Specifically, for every cluster, the proportion of males was no smaller than 46.6% (consistent with the slightly lower proportion of males on the platform, see Table 1 ) and no larger than 50.2%. As shown in Table 5 , the gender pay gap was observed within each of the clusters. These results suggest that residual task heterogeneity, a proxy for occupational segregation, is not likely to contribute to a gender pay gap in this market.

Task length was defined as the advertised estimated duration of a HIT. Table 6 presents the advertised hourly gender pay gaps for five categories of HIT length, which ranged from a few minutes to over 1 hour. Again, a significant advertised hourly gender pay gap was observed in each category.

https://doi.org/10.1371/journal.pone.0229383.t006

Finally, we conducted additional supplementary analyses to determine if other plausible factors such as HIT timing could account for the gender pay gap. We explored temporal factors including hour of the day and day of the week. Each completed task was grouped based on the hour and day in which it was completed. A significant advertised gender pay gap was observed within each of the 24 hours of the day and for every day of the week demonstrating that HIT timing could not account for the observed gender gap (results available in Supplementary Materials).

In this study we examined the gender pay gap on an anonymous online platform across an 18-month period, during which close to five million tasks were completed by over 20,000 unique workers. Due to factors that are unique to the Mechanical Turk online marketplace–such as anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect earnings to differ by gender on this platform. However, contrary to our expectations, a robust and persistent gender pay gap was observed.

The average estimated actual pay on MTurk over the course of the examined time period was $5.70 per hour, with the gender pay differential being 10.5%. Importantly, gig economy platforms differ from more traditional labor markets in that hourly pay largely depends on the speed with which tasks are completed. For this reason, an analysis of gender differences in actual earned pay will be affected by gender differences in task completion speed. Unfortunately, we were not able to directly measure the speed with which workers complete tasks and account for this factor in our analysis. This is because workers have the ability to accept multiple HITs at the same time and multiple HITs can sit dormant in a queue, waiting for workers to begin to work on them. Therefore, the actual time that many workers spend working on tasks is likely less than what is indicated in the metadata available. For this reason, the estimated average actual hourly rate of $5.70 is likely an underestimate and the gender gap in actual pay cannot be precisely measured. We infer however, by the residual gender pay gap after accounting for other factors, that as much as 57% (or $.32) of the pay differential may be attributable to task completion speed. There are multiple plausible explanations for gender differences in task completion speed. For example, women may be more meticulous at performing tasks and, thus, may take longer at completing them. There may also be a skill factor related to men’s greater experience on the platform (see Table 5 ), such that men may be faster on average at completing tasks than women.

However, our findings also revealed another component of a gender pay gap on this platform–gender differences in the selection of tasks based on their advertised pay. Because the speed with which workers complete tasks does not impact these estimates, we conducted extensive analyses to try to explain this gender gap and the reasons why women appear on average to be selecting tasks that pay less compared to men. These results pertaining to the advertised gender pay gap constitute the main focus of this study and the discussion that follows.

The overall advertised hourly pay was $4.88. The gender pay gap in the advertised hourly pay was $0.28, or 5.8% of the advertised pay. Once a gender earnings differential was observed based on advertised pay, we expected to fully explain it by controlling for key structural and individual-level covariates. The covariates that we examined included experience, age, income, education, family composition, race, number of children, task length, the speed of accepting a task, and thirteen types of subtasks. We additionally examined the time of day and day of the week as potential explanatory factors. Again, contrary to our expectations, we observed that the pay gap persisted even after these potential confounders were controlled for. Indeed, separate analyses that examined the advertised pay gap within each subcategory of the covariates showed that the pay gap is ubiquitous, and persisted within each of the ninety sub-groups examined. These findings allows us to rule out multiple mechanisms that are known drivers of the pay gap in traditional labor markets and other gig economy marketplaces. To our knowledge this is the only study that has observed a pay gap across such diverse categories of workers and conditions, in an anonymous marketplace, while simultaneously controlling for virtually all variables that are traditionally implicated as causes of the gender pay gap.

Individual-level factors

Individual-level factors such as parental status and family composition are a common source of the gender pay gap in traditional labor markets [ 15 ] . Single mothers have previously been shown to have lower reservation wages compared to other men and women [ 21 ]. In traditional labor markets lower reservation wages lead single mothers to be willing to accept lower-paying work, contributing to a larger gender pay gap in this group. This pattern may extend to gig economy markets, in which single mothers may look to online labor markets as a source of supplementary income to help take care of their children, potentially leading them to become less discriminating in their choice of tasks and more willing to work for lower pay. Since female MTurk workers are 20% more likely than men to have children (see Table 1 ), it was critical to examine whether the gender pay gap may be driven by factors associated with family composition.

An examination of the advertised gender pay gap among individuals who differed in their marital and parental status showed that while married workers and those with children are indeed willing to work for lower pay (suggesting that family circumstances do affect reservation wages and may thus affect the willingness of online workers to accept lower-paying online tasks), women’s hourly pay is consistently lower than men’s within both single and married subgroups of workers, and among workers who do and do not have children. Indeed, contrary to expectations, the advertised gender pay gap was highest among those workers who are single, and among those who do not have any children. This observation shows that it is not possible for parental and family status to account for the observed pay gap in the present study, since it is precisely among unmarried individuals and those without children that the largest pay gap is observed.

Age was another factor that we considered to potentially explain the gender pay gap. In the present sample, the hourly pay of older individuals is substantially lower than that of younger workers; and women on the platform are five years older on average compared to men (see Table 1 ). However, having examined the gender pay gap separately within five different age cohorts we found that the largest pay gap occurs in the two youngest cohort groups: those between 18 and 29, and between 30 and 39 years of age. These are also the largest cohorts, responsible for 64% of completed work in total.

Younger workers are also most likely to have never been married or to not have any children. Thus, taken together, the results of the subgroup analyses are consistent in showing that the largest pay gap does not emerge from factors relating to parental, family, or age-related person-level factors. Similar patterns were found for race, education, and income. Specifically, a significant gender pay gap was observed within each subgroup of every one of these variables, showing that person-level factors relating to demographics are not driving the pay gap on this platform.

Experience is a factor that has an influence on the pay gap in both traditional and gig economy labor markets [ 20 ] . As noted above, experienced workers may be faster and more efficient at completing tasks in this platform, but also potentially more savvy at selecting more remunerative tasks compared to less experienced workers if, for example, they are better at selecting tasks that will take less time to complete than estimated on the dashboard [ 20 ]. On MTurk, men are overall more experienced than women. However, experience does not account for the gender gap in advertised pay in the present study. Inexperienced workers comprise the vast majority of the Mechanical Turk workforce, accounting for 67% of all completed tasks (see Table 5 ). Yet within this inexperienced group, there is a consistent male earning advantage based on the advertised pay for tasks performed. Further, controlling for the effect of experience in our models has a minimal effect on attenuating the gender pay gap.

Task heterogeneity

Another important source of the gender pay gap in both traditional and gig economy labor markets is task heterogeneity. In traditional labor markets men are disproportionately represented in lucrative fields, such as those in the tech sector [ 23 ]. While the workspace within MTurk is relatively homogeneous compared to the traditional labor market, there is still some variety in the kinds of tasks that are available, and men and women may have been expected to have preferences that influence choices among these.

To examine whether there is a gender preference for specific tasks, we systematically analyzed the textual descriptions of all tasks included in this study. These textual descriptions were available for all workers to examine on their dashboards, along with information about pay. The clustering algorithm revealed thirteen categories of tasks such as games, decision making, several different kinds of survey tasks, and psychology studies.We did not observe any evidence of gender preference for any of the task types. Within each of the thirteen clusters the distribution of tasks was approximately equally split between men and women. Thus, there is no evidence that women as a group have an overall preference for specific tasks compared to men. Critically, the gender pay gap was also observed within each one of these thirteen clusters.

Another potential source of heterogeneity is task length. Based on traditional labor markets, one plausible hypothesis about what may drive women’s preferences for specific tasks is that women may select tasks that differ in their duration. For example, women may be more likely to use the platform for supplemental income, while men may be more likely to work on HITs as their primary income source. Women may thus select shorter tasks relative to their male counterparts. If the shorter tasks pay less money, this would result in what appears to be a gender pay gap.

However, we did not observe gender differences in task selection based on task duration. For example, having divided tasks into their advertised length, the tasks are preferred equally by men and women. Furthermore, the shorter tasks’ hourly pay is substantially higher on average compared to longer tasks.

Additional evidence that scheduling factors do not drive the gender pay gap is that it was observed within all hourly and daily intervals (See S1 and S2 Tables in Appendix). These data are consistent with the results presented above regarding personal level factors, showing that the majority of male and female Mechanical Turk workers are single, young, and have no children. Thus, while in traditional labor markets task heterogeneity and labor segmentation is often driven by family and other life circumstances, the cohort examined in this study does not appear to be affected by these factors.

Practical implications of a gender pay gap on online platforms for social and behavioral science research

The present findings have important implications for online participant recruitment in the social and behavioral sciences, and also have theoretical implications for understanding the mechanisms that give rise to the gender pay gap. The last ten years have seen a revolution in data collection practices in the social and behavioral sciences, as laboratory-based data collection has slowly and steadily been moving online [ 16 , 24 ]. Mechanical Turk is by far the most widely used source of human participants online, with thousands of published peer-reviewed papers utilizing Mechanical Turk to recruit at least some of their human participants [ 25 ]. The present findings suggest both a challenge and an opportunity for researchers utilizing online platforms for participant recruitment. Our findings clearly reveal for the first time that sampling research participants on anonymous online platforms tends to produce gender pay inequities, and that this happens independent of demographics or type of task. While it is not clear from our findings what the exact cause of this inequity is, what is clear is that the online sampling environment produces similar gender pay inequities as those observed in other more traditional labor markets, after controlling for relevant covariates.

This finding is inherently surprising since many mechanisms that are known to produce the gender pay gap in traditional labor markets are not at play in online microtasks environments. Regardless of what the generative mechanisms of the gender pay gap on online microtask platforms might be, researchers may wish to consider whether changes in their sampling practices may produce more equitable pay outcomes. Unlike traditional labor markets, online data collection platforms have built-in tools that can allow researchers to easily fix gender pay inequities. Researchers can simply utilize gender quotas, for example, to fix the ratio of male and female participants that they recruit. These simple fixes in sampling practices will not only produce more equitable pay outcomes but are also most likely advantageous for reducing sampling bias due to gender being correlated with pay. Thus, while our results point to a ubiquitous discrepancy in pay between men and women on online microtask platforms, such inequities have relatively easy fixes on online gig economy marketplaces such as MTurk, compared to traditional labor markets where gender-based pay inequities have often remained intractable.

Other gig economy markets

As discussed in the introduction, a gender wage gap has been demonstrated on Uber, a gig economy transportation marketplace [ 20 ], where men earn approximately 7% more than women. However, unlike in the present study, the gender wage gap on Uber was fully explained by three factors; a) driving speed predicted higher wages, with men driving faster than women, b) men were more likely than women to drive in congested locations which resulted in better pay, c) experience working for Uber predicted higher wages, with men being more experienced. Thus, contrary to our findings, the gender wage gap in gig economy markets studied thus far are fully explained by task heterogeneity, experience, and task completion speed. To our knowledge, the results presented in the present study are the first to show that the gender wage gap can emerge independent of these factors.

Generalizability

Every labor market is characterized by a unique population of workers that are almost by definition not a representation of the general population outside of that labor market. Likewise, Mechanical Turk is characterized by a unique population of workers that is known to differ from the general population in several ways. Mechanical Turk workers are younger, better educated, less likely to be married or have children, less likely to be religious, and more likely to have a lower income compared to the general United States population [ 24 ]. The goal of the present study was not to uncover universal mechanisms that generate the gender pay gap across all labor markets and demographic groups. Rather, the goal was to examine a highly unique labor environment, characterized by factors that should make this labor market immune to the emergence of a gender pay gap.

Previous theories accounting for the pay gap have identified specific generating mechanisms relating to structural and personal factors, in addition to discrimination, as playing a role in the emergence of the gender pay gap. This study examined the work of over 20,000 individuals completing over 5 million tasks, under conditions where standard mechanisms that generate the gender pay gap have been controlled for. Nevertheless, a gender pay gap emerged in this environment, which cannot be accounted for by structural factors, demographic background, task preferences, or discrimination. Thus, these results reveal that the gender pay gap can emerge—in at least some labor markets—in which discrimination is absent and other key factors are accounted for. These results show that factors which have been identified to date as giving rise to the gender pay gap are not sufficient to explain the pay gap in at least some labor markets.

Potential mechanisms

While we cannot know from the results of this study what the actual mechanism is that generates the gender pay gap on online platforms, we suggest that it may be coming from outside of the platform. The particular characteristics of this labor market—such as anonymity, relative task homogeneity, and flexibility—suggest that, everything else being equal, women working in this platform have a greater propensity to choose less remunerative opportunities relative to men. It may be that these choices are driven by women having a lower reservation wage compared to men [ 21 , 26 ]. Previous research among student populations and in traditional labor markets has shown that women report lower pay or reward expectations than men [ 27 – 29 ]. Lower pay expectations among women are attributed to justifiable anticipation of differential returns to labor due to factors such as gender discrimination and/or a systematic psychological bias toward pessimism relative to an overly optimistic propensity among men [ 30 ].

Our results show that even if the bias of employers is removed by hiding the gender of workers as happens on MTurk, it seems that women may select lower paying opportunities themselves because their lower reservation wage influences the types of tasks they are willing to work on. It may be that women do this because cumulative experiences of pervasive discrimination lead women to undervalue their labor. In turn, women’s experiences with earning lower pay compared to men on traditional labor markets may lower women’s pay expectations on gig economy markets. Thus, consistent with these lowered expectations, women lower their reservation wages and may thus be more likely than men to settle for lower paying tasks.

More broadly, gender norms, psychological attributes, and non-cognitive skills, have recently become the subject of investigation as a potential source for the gender pay gap [ 3 ], and the present findings indicate the importance of such mechanisms being further explored, particularly in the context of task selection. More research will be required to explore the potential psychological and antecedent structural mechanisms underlying differential task selection and expectations of compensation for time spent on microtask platforms, with potential relevance to the gender pay gap in traditional labor markets as well. What these results do show is that pay discrepancies can emerge despite the absence of discrimination in at least some circumstances. These results should be of particular interest for researchers who may wish to see a more equitable online labor market for academic research, and also suggest that novel and heretofore unexplored mechanisms may be at play in generating these pay discrepancies.

A final note about framing: we are aware that explanations of the gender pay gap that invoke elements of women’s agency and, more specifically, “choices” risk both; a) diminishing or distracting from important structural factors, and b) “naturalizing” the status quo of gender inequality [ 30 ] . As Connor and Fiske (2019) argue, causal attributions for the gender pay gap to “unconstrained choices” by women, common as part of human capital explanations, may have the effect, intended or otherwise, of reinforcing system-justifying ideologies that serve to perpetuate inequality. By explicitly locating women’s economic decision making on the MTurk platform in the broader context of inegalitarian gender norms and labor market experiences outside of it (as above), we seek to distance our interpretation of our findings from implicit endorsement of traditional gender roles and economic arrangements and to promote further investigation of how the observed gender pay gap in this niche of the gig economy may reflect both broader gender inequalities and opportunities for structural remedies.

Supporting information

S1 table. distribution of hits, average pays, and gender pay gaps by hour of day..

https://doi.org/10.1371/journal.pone.0229383.s001

S2 Table. Distribution of HITs, average pays, and gender pay gaps by day of the week.

https://doi.org/10.1371/journal.pone.0229383.s002

• 1. United States Equal Employment Opportunity Commission, Lily Ledbetter Fair Pay Act of 2009 (2009), available at https://www.eeoc.gov/eeoc/publications/brochure- equal_pay_and_ledbetter_act.cfm, accessed on 11/12/2018.
• 2. United States Department of Labor (DOL), Office of Federal Contract Compliance Programs (OFCCP), Pay Transparency Nondiscrimination Provision, available at https://www.dol.gov/ofccp/PayTransparencyNondiscrimination.html , accessed on 11/12/2018.
• View Article
• Google Scholar
• 4. United States Department of Labor (DOL), Bureau of Labor Statistics (BLS) (2016) Women’s earning 83 percent of men’s, but vary by occupation. TED Econ Dly , available at https://www.bls.gov/opub/ted/2016/womens-earnings-83-percent-of-mens-but-vary-by-occupation.htm , accessed on 11/12/2018.
• 5. Davis A (2015) Women still earn less than men across the board (Economic Policy Institute, 2015), available at http://www.epi.org/publication/women-still-earn-less-than-men-across-the-board/ , accessed on 11/12/2018.
• 6. “Gender Pay Inequality: Consequences for Women, Families and the Economy” (Joint Economic Committee, 2016). [no author]
• 7. Hartmann H, Hayes J, Clark J (2014) “How Equal Pay for Working Women would Reduce Poverty and Grow the American Economy” (Institute for Women’s Policy Research, 2014).
• 8. OECD (2015) In it together: Why Less Inequality Benefits All (OECD Publishing, Paris) available at http://www.oecd.org/els/soc/OECD2015-In-It-Together-Chapter1-Overview-Inequality.pdf , accessed on 11/12/2018.
• PubMed/NCBI
• 16. Litman L, Robinson J (In Press) Conducting Online Research on Amazon Mechanical Turk and Beyond. Sage Publications.
• 17. Farrell D, Greig F (2016) Paychecks, paydays, and the online platform economy: Big data on income volatility. JP Morgan Chase Institute.
• 18. Kuek SC, Paradi-Guilford C, Fayomi T, Imaizumi S, Ipeirotis P, Pina P, Singh M (2015) The global opportunity in online outsourcing (World Bank Group, 2015) Available at http://documents.worldbank.org/curated/en/138371468000900555/pdf/ACS14228-ESW-white-cover-P149016-Box391478B-PUBLIC-World-Bank-Global-OO-Study-WB-Rpt-FinalS.pdf , accessed on 11/12/2018.
• 23. Bureau of Labor Statistics, U.S. Department of Labor, Labor Force Statistics from the Current Population Survey, Household Data Annual Averages. Employed persons by detailed occupation, sex, race, and Hispanic or Latino ethnicity, on the Internet at https://www.bls.gov/cps/cpsaat11.htm (visited 9/3/18).

Gender Pay Gap - Free Essay Samples And Topic Ideas

The Gender Pay Gap refers to the relative difference in the average earnings of men and women within the workforce. Essays on this topic could explore the historical evolution of the gender pay gap, its current status across different countries or sectors, and the societal and economic factors contributing to it. Moreover, discussions could extend to the impact of the gender pay gap on economic inequality and suggestions for policies and practices to alleviate the gap and promote gender equality in the workplace. We have collected a large number of free essay examples about Gender Pay Gap you can find in Papersowl database. You can use our samples for inspiration to write your own essay, research paper, or just to explore a new topic for yourself.

The Gender Pay Gap Situation

Despite numerous feminist movements and policies put in place to promote gender inequality, women still do not get paid as much as men. The gender pay gap is the difference between what the average man and woman makes. Wade (2018) found that full time working women make $0.82 for every dollar that a full time working man makes. The gap has slowly gotten smaller since women were first allowed to work, however, it still persists. According to the Institute for […]

Americanah: Gender Pay Gap in Nigeria and North America

In the book Americanah by Chimamanda Adichie, women's earning potentials are vividly shown based on experiences that Ifemelu and her Aunty Uju have in both Nigeria and North America. These earning potentials affect gender roles and expectations in Nigeria and North America because women are expected more to be the house keepers and mothers rather than ever having a job themselves. Nowadays it is much different as the feminist movement continues to grow across the world. This is presented throughout […]

Gender Wage Gap and Gender Equality

Although men and women have made great strides for gender equality in recent years, the economic pay gap between men and women still persists. The Gender Wage Gap refers to the general gap between what similarly qualified men and women are paid for the same job. It is most commonly measured in the median annual pay of all women who work full time compared to a similar group of men. However, whichever way it is measured, the gender pay gap […]

We will write an essay sample crafted to your needs.

The Gender Pay Gap in Sport

The gender pay gap, within the United States, is an issue across all places of work and negatively impacts the lives of all women, but the question comes into play in the sports industry. A place where women are encouraged to participate in the same activity as the male competition yet prevented from excelling due to the overbearing male presence. This multi-billion dollar industry is giving the majority of its money to the male athletes and has been since the […]

The Gender Pay Gap

Living in the year 2019 and you would think that after centuries of women being oppressed, there would be some sort of change, a progression that is long overdue. However, the wage gap between men and women is still an ongoing issue that will not be acquired for another hundred years to come. With that in mind, the state of the gender pay gap in America is explained, along with the wage gap in various occupations, and the structural barriers […]

Why do Women Deserve to be Paid Equally?

In the age of freedom, this is a harsh reality that we are still facing the unequal pay gap in United States. Women are the only bread earners in many American families. Despite their equal work hours, they are paid unequal as compared to their male coworkers. The United States is the largest economy in the world, yet we are struggling for equal income for women. In the age where we are taking stand for LGBTQ’s rights, we must be […]

Gender Pay Gap is a Myth

There have been ongoing arguments about the gender way gap and what are the factors to it. Many assume that it has a lot to do with race or ethnicity and this is not the case at all. Gender wage pay has nothing to do with race or ethnicity because when looking at the graphs from the article “ The Gender Wage Gap: 2017 Earning differences by Race and Ethnicity” from the Institute for Women’s Policy Research it has been […]

The Gender Pay Gap and the Equality

Introduction The gender pay gap and the equality of pay rates have always been topics of discussion in today’s society. Equal pay means that individuals accomplishing the same work should be compensated equivalently in regards to completion. Issues are raised between the earning differences between men and women due to the lack of equal pay between the two genders. By referring back to U.S. history on the subject, we found that the issue dates back over 100 years ago and […]

The Gender Gap in Political Ambition

The gender gap in political ambition has been a topic extensively researched by political analysts and professors for years. The focus of this essay will be to examine why this gender gap exists and how it directly affects the underrepresentation of women who hold public office in the United States. This essay will explore the ways in which young women are politically socialized and factors in early childhood through high school which affect one’s political motivations. This research also seeks […]

What is the Gender Pay Gap

Women have suffered greatly in their bid to be considered equals and this has been from the basic right to exist, vote and to lead companies. Cultures and societies have played a role in how stigmatized the opposite sexes are. Even today, we see many who claim that weaknesses in women prevent them from leading or doing the same work and hence why should they get paid equally. Case and point are the Billie Jean King vs Bobby Riggs, in […]

The Gender Pay Gap Women Face

Income by gender, women in the labor market system versus men.The gender pay gap women face is a result of inequality based on gender, resulting in lower income. An independent variable impacts the dependent variable. As a result, an individuals gender- the independent variable-impacts income-dependent variable. The gender pay gap is an issue and it’s real. Nowadays, being a female in the current U.S culture means that the income I receive will be less than a mans. This culture has shaped […]

Sexism in Shakespeare’s Play Othello

"In the book, Othello written by Shakespeare, there is a main theme of sexism present throughout the book, Although the book was written in the 1600s, and there have been great decreases in sexism around the world, many of these ideas and scenarios are still present to this day. Sexism is defined as prejudice, stereotyping, or discrimination, typically against women, on the basis of sex. Sexism has been present for centuries, in many different forms, such as wage gaps, gender […]

Gender Pay Gap Situation in Bangladesh

Introduction: The difference between the average gross earnings of female and male employees is known as the 'gender pay gap'. ("Gender Equality: Gender Pay Gap", 2019) Most commonly, it refers to the median annual pay of all women who work full time and year-round, compared to the pay of a similar cohort of men. Other estimates of the gender pay gap are based on weekly or hourly earnings, or are specific to a particular group of women. (Vagins, 2019) In […]

The Right to Equality

Imagine a community or city where everyone is treated equally and no one is discriminated against. Everyone in this world deserves to be treated as an equal and nothing less. Equality is the absence of legal discrimination against any one individual, group, class, gender or race. Till this day many races, groups and class are being discriminated. Race and ethnicity is one of the biggest things that cause people to discriminate against others.The global community is not doing enough to […]

Should Everyone Attend College?

For decades there has been an ongoing debate on whether or not everyone should attend College, and if it’ll be beneficial for their overall gross income. On one hand, it is argued that College is a crucial essential path that should indeed be completed in order to reach a designated level of success. While on the other hand, others maintain the belief that it is possible to earn a sufficient income without a College degree. I, however, do believe that […]

Essay about Gender Wage Gap Analysis

Men and women are both capable of successfully occupying the same positions or jobs. Both genders are capable of attaining the same education, working at the same firm, and moving up the ladder while being productive in their occupation. Even though men and women are equal in theses aspects, the wages they are being paid do not show that equality. Men make more than women in the same occupations. This is a significant issue as it allows discrimination to continue […]

Men and Women in the Workplace

The gender wage gap in the economy, as a whole, is defined by “the relative earnings difference between men and women. His female counterparty makes about seventy-seven cents for every dollar a man makes.” Even if a man and a woman have the same background in education and work history, the man will go home with a paycheck higher than the woman. In society, there is a gender wage gap that cannot be remedied by increasing education alone. Inequality results […]

Looking Beyond the Numbers

Mathematicians, scientists, doctors, and countless other professions validate theories by producing a factor that is then analyzed for accuracy. Similarly, in 2019, economists analyzed median income data in the United States and determined what is the most commonly used figure to measure the economic phenomena of gender pay gap, 80.5%. Compared to Estonia at 25.3%, this figure, 80.5%, is frequently misinterpreted as an economic achievement for America. Although most states have implemented laws against gender discrimination and the 1964 Civil […]

Pay Gap by Gender and Race in Seattle WA

Seattle is deeply unsettled the past ten years once a national study unconcealed that the railway line space has one among the biggest genders pay gaps within the country. the foremost goal of this text is to know the sources of the convergence in men’s and women’s earnings within the public and personal sectors similarly because the stagnation of this trend in the new millennium. For this purpose, we tend to delineate temporal changes within the role vie by major […]

A Problem of Social Justice in World

Multiple people are discriminated for their race, their religion, or their sexuality. The idea of entitlement has been an issue in the United States for centuries. Even before the United States became a country in 1776, racial prejudice existed. At first it was the Native Americans' who were looked down on and forced to do the new white settlers dirty work. Then it became African Americans. Whites have been seen to be superior to African Americans for many years, more […]

Women and Men Pay Gap

Imagine you have been working the same job for years, but you learn that your paycheck is less than your co-workers that do the same job as you. Wouldn't that would make you upset? Well, that is what much of women experience with their male co-workers. This is the gender pay gap, and it is sexist. Because the gender pay gap is sexist, the government should put more laws in place for employers, so they pay each gender the same […]

Gender Discrimination in Hollywood

The gender pay gap is also one of the major issues that contribute to discrimination in the industry. This has been a long-standing issue in Hollywood, despite the progression of women right and equality. In the article "The Gender Wage Gap: Cause, Consequences, and Remedies," Yaveline Aly explains some of the major elements that lead to the discrimination between male and female pay gap. In 1963, the Equal Pay Act banished the sperate pay between men and women in the […]

Pink Capitalism for LGBTQ Community

Pink Capitalism, plainly, is the incorporation of the LGBTQ movement and sexual diversity to capitalism and the market economy. It is a targeted inclusion of the LGBTQ community to generate a market focused specifically on them. And even though pride parades sweep away the world and legal turnarounds change our perspectives, it’s hard to deny that discrimination against the LGBTQ community exists, especially in the workplace. Pride parades are about celebrating diversity and inclusion. And while we do celebrate the […]

Hunger Games and in Real Life

The novel The Hunger Games by Suzanne Collins presents themes that are real even in today's society such as inequality. The themes make what would qualify for good writing research topic proposal. This paper presents my writing research topic proposal based on the social issues presented in The Hunger Games. My general/broad topic focusses on inequality The subtopics directly related to the broad topic include income inequality between men and women in the United States, the effects of political inequality […]

Equal Pay Act Analysis

This is a plea to action. The gender wage gap is silent but on going debate. Employees are told to not discuss pay and salary by threat of job security, the threat keeps everyone silent so the pay differnce isn't seen or noticed. In 1963, the Equal Pay Act was introudced. It promised to close the wage gap by essentislly making gender discrimination in wages illegal. For the past 50 years, in the presence of The Equal Pay Act , […]

Education and Women’s Right

In Mary Wollstonecraft’s book, “A Vindication of the Rights of Women”, she covers a broad range of topics in each chapter concerning the equality of women compared to men. To be clear, Wollstonecraft is not indicating that male and female are made mentally and physically the same but wants her readers to understand that both are equal when it comes to acquiring an education and their position in the workplace. This is a point that she debated with opposing theorists’ […]

Equal Rights Ammendment

A potential constitutional reform is to implement the Equal Rights Amendment. If the constitution implemented the Equal Rights Amendment, inequality based on gender would be deemed unconstitutional. The idea of the equal rights amendment is not new and has been thought about for years, but has still not been passed. The Equal Rights Amendment was introduced by Alice Paul in 1923, and many supporters fought for the amendment to be implemented through protests and acts of civil disobedience from 1972 […]

Radical Feminism: when People Go too Far

"Feminism, according to The Merriam Webster Dictionary is, “The theory of the political, economic, and social equality of the sexes” (Merriam Webster Dictionary). It is a noble cause that the majority of people agree with. Sadly, in the present, feminism has a radical group of women convinced that they are oppressed in today's society and demand to be treated above men. It is clear that this group no longer want the equality of the sexes, but rather they desire the […]

France: New Gender Equality Obligations Established

Article Summary In this article, Marion Le Roux and Ji Eun Kaela Kim clarify a set of new obligations that are enforced on employers that aim to promote professional equality between men and women in the workplace. Le Roux and Kim (2019) raise the argument that there are about one-fourth of pay gaps between men and women employees, and they also add that numerous female employees also undergo further kinds of disparate treatment at the workplace (Le Roux and Kim, […]

Sexism in the Workplace Among Minority Women

Women make up less than a quarter of the system. The leadership gap is universal; gender gaps across industries in the U.S have the biggest leadership gap ever found in staffing. This is proven because there is a 15% gap versus leadership representation. Women, specifically, face unequal treatment and pay in comparison to men. The treatment and job positions in the workplace should not be influenced by gender. Gender Is an identification that determines what role an individual will have […]

Additional Example Essays

• Gender Inequality in the Workplace
• About Gender Discrimination in Sports
• Logical Fallacies in Letter From Birmingham Jail
• How the Roles of Women and Men Were Portrayed in "A Doll's House"
• Analysis of Letter from Birmingham Jail
• Personal Narrative: My Family Genogram
• The Road not Taken Poem Analysis
• The Importance of Professional Bearing in the Military
• Run or Stay: A Streetcar Named Desire
• Silence in Their Eyes Were Watching God
• Character Heathcliff in Emily Bronte’s "Wuthering Heights"
• How is the Crucible an Allegory for Mccarthyism

1. Tell Us Your Requirements

2. Pick your perfect writer

3. Get Your Paper and Pay

Hi! I'm Amy, your personal assistant!

Don't know where to start? Give me your paper requirements and I connect you to an academic expert.

short deadlines

100% Plagiarism-Free

Certified writers

Case studies

Read about what people and organisations are doing to improve gender equality in their own workplaces and Australia. 

Want to share your own story of how you or your organisation is creating change in this area? Get in touch with us!

Laing O'Rourke on targeting and tracking promotions

14 September 2022

Laing O'Rourke undertook a significant review transforming their promotions process and communications to accelerate the progression of women into more senior roles. They introduced a sponsorship program, in partnership with Cultivate Sponsorship, matching Executive and Senior Leaders with high potential women. The program focussed on women who are progressing through project delivery and engineering streams into Construction Manager and Project Leader roles.

Kimberly-Clark Australia Pty Ltd: Gender segregated industries

15 July 2022

Kimberly-Clark Australia made changes in their advertising and recruitment processes to remove the gender bias in operational roles in their Millicent Mill site. The manufacturing sector is largely male dominated.

Motorola: Zero Eligibility Paid Parental Leave

4 June 2021

Motorola Solutions Australia Pty Ltd is determined to attract and retain the best talent to their business. As  a male-dominant business in a solutions-based industry, the attraction and retention of talented women and men is a fundamental strategic imperative. Motorola wants to ensure their employee value proposition is consistent with the challenges faced by their workforce, reflects their customer base and continues to deliver a distinct, competitive advantage.  

Download the full case study below to find out more:

Motorola: Zero Eligibility Paid Parental Leave in a Male Dominated Industry (PDF, 278.51 KB)

Aurecon: elevating women in stem.

24 February 2020

Demand for technical and STEM skills is high in the current market and as Aurecon seeks to solve  problems with clients that are complex and multi-dimensional, this demand will continue to grow. Aurecon has recognised the importance of increasing diversity to strengthen its business operations and connect with untapped talent. Concurrently, Aurecon is building an inclusive culture to support all employees to feel valued, have a sense of belonging and have equal access to opportunities. 

Aurecon: elevating women in STEM (PDF, 3.44 MB)

Viva energy: tackling pay gaps.

7 November 2018

Helen Karatasas, Education Delivery Manager at the Workplace Gender Equality Agency, and Jodie Haydon, Executive General Manager, People and Culture at Viva Energy Australia, discuss how Viva Energy addresses gender pay gaps within their organisation.

Mercy Health: introducing men to a career in caring

4 July 2017

Mercy Health recognises the importance of having a workforce that is as diverse as its client base and this means attracting more male workers. With almost 60% of the industry’s direct care workers being 45 years or older and 36% of the current workforce within 10 years of retirement, there is also a need to attract younger people to healthcare. 

Mercy Health: introducing men to a career in caring (PDF, 248.43 KB)

Griffith university: developing female leaders.

To address a persistent gender imbalance in senior management roles, Griffith University is working to increase the number of women entering leadership roles by developing the skills of existing staff.

Griffith University: developing female leaders (PDF, 598.68 KB)

St barbara: attracting women to a male-dominated industry.

St Barbara has been working to increase the number of women in its male-dominated workplace for several years, with the aim of achieving a more equal balance of women and men across the organisation.

St Babara: attracting women to a male-dominated industry (PDF, 686.95 KB)

Hesta: conducting a gender pay gap analysis.

In 2016 HESTA began the process of applying for the Employer of Choice for Gender Equality citation. To support this process, an in-depth gender pay gap analysis was required to understand the state of gender pay equity within the organisation.

HESTA: conducting a gender pay gap analysis (PDF, 406.85 KB)

Stockland: supporting parents and carers.

For many years, Stockland has been focused on providing competitive parental benefits to attract and retain employees. Understanding that the caring requirements of employees vary widely, the company’s priority has been on reviewing its existing offering and looking for improvements that provide employees with a greater degree of flexibility in how they structure their parental leave benefits.

Stockland: supporting parents and carers case study (PDF, 679.83 KB)

Myob: recruiting women into it.

Faced with a shortage of female IT graduates, and a lack of women at all levels of the company, MYOB wanted to find an alternative recruitment process. To address this issue, MYOB devised an experimental program to see if it was possible to teach people with no previous IT experience basic coding in 16 weeks. 

MYOB: recruiting women into IT case study (PDF, 653.71 KB)

Benetas: challenging male stereotypes.

Health Care and Social Assistance is the fastest growing sector in Australia, yet to meet increasing demand for care, the workforce needs to triple by 2050. That is an enormous task. Benetas is striving to achieve cultural change in its workplaces and create greater gender balance by challenging gender stereotypes.

Benetas: challenging male stereotypes case study (PDF, 439.48 KB)

Industrial segregation: challenging stereotypes.

2 August 2016

Across Australia, women and men tend to work in different industries and occupations. Health care, social assistance and education are dominated by women; scientific and technical roles are dominated by men. 

Let these stories inspire you about the kind of work you might enjoy: 

women's work | men's work

A series of profiles of women and men in non-traditional roles, designed to challenge stereotypes about work.

Small business: pay gap analysis

15 March 2015

Small businesses in Australia employ a large number of employees and face a unique set of issues when it comes to managing and improving gender equality in their workplace. Gender pay equity is one of these key challenges for small businesses. Through two fictional case studies, learn how a gender pay gap analysis can be undertaken.

Worked example: pay gap analysis (PDF, 407.92 KB)

This document provides two worked examples for conducting a payroll analysis within a small business.

Home — Essay Samples — Social Issues — Social Inequality — Gender Wage Gap

Essays on Gender Wage Gap

The issue of gender wage gap in america, the impact of gender on income inequality, made-to-order essay as fast as you need it.

Each essay is customized to cater to your unique preferences

+ experts online

The Need for Eliminating The Gender Wage Gap to Improve Society

The reasons for the disparity in wages between men and women, gender wage gap issue: equal pay for equal work, impact of experience and education on womens wages, let us write you an essay from scratch.

• 450+ experts on 30 subjects ready to help
• Custom essay delivered in as few as 3 hours

A Study of The Different Aspects of Gender Gap in Society

A history of the issue of the gender wage gap in america, the causes, consequences and solutions of income inequality, gender pay discrimination in the us soccer, get a personalized essay in under 3 hours.

Expert-written essays crafted with your exact needs in mind

The Issue of Pay Gap in The Women's U.s. Soccer Team

Result of the feminization of poverty, gender pay gaps on the example soccer`s team, a study of gender inequality in hong kong: review of literature, the effects of gender inequality on society and the economy, the legal dilemma behind equal pay for equal work in india, reflection of gender inequality in different spheres, gender discrimination in the workplace: challenges and solutions, gender hierarchies, stereotypes, and the fight for equality.

The gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally considered to be paid less than men.

Differences in pay are caused by occupational segregation (with more men in higher paid industries and women in lower paid industries), vertical segregation (fewer women in senior, and hence better paying positions), ineffective equal pay legislation, women's overall paid working hours, and barriers to entry into the labor market (such as education level and single parenting rate).

The gender pay gap can be a problem from a public policy perspective because it reduces economic output and means that women are more likely to be dependent upon welfare payments, especially in old age.

The pay gap exists in nearly every profession. Mothers face an even wider pay gap than women without kids. Women with bachelor’s degrees working full time are paid 26% less than their male counterparts. Women face an income gap in retirement.

Relevant topics

• Gender Equality
• Gender Inequality
• Pro Life (Abortion)
• I Have a Dream
• Pro Choice (Abortion)
• Cultural Diversity
• Gay Marriage

By clicking “Check Writers’ Offers”, you agree to our terms of service and privacy policy . We’ll occasionally send you promo and account related email

No need to pay just yet!

We use cookies to personalyze your web-site experience. By continuing we’ll assume you board with our cookie policy .

• Instructions Followed To The Letter
• Deadlines Met At Every Stage
• Unique And Plagiarism Free