Gender pay gap guide

Guide to labour statistics

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

Released
21/02/2023

Overview

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'). The 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.

  1. 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)

Distribution graph showing weekly total cash earnings by sex

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.

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.

Impact on the gap

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.

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.

Impact on the gap

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.

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.

Impact on the gap

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.

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.

Topics available by data source (a)(b)
 EEHAWEEE (c)PIAJIACensus
Person characteristics
Age groups 
State and territory
Region   
Education    
Job characteristics 
Full-time and part-time◼ (e)   
Employment arrangement 
Occupation and skill level 
Pay setting method     
Employer characteristics
Industry
Sector 
Employer size   

 ✔  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.

  1. 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.
  2. Acronyms: Employee Earnings and Hours (EEH), Average Weekly Earnings (AWE), Employee earnings (EE), Personal Income in Australia (PIA), Jobs in Australia (JIA).
  3. Based on data from the annual Characteristics of Employment Labour Force supplementary survey.
  4. Changes in total wages paid only.
  5. 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.

ABS earnings sources with sex data available
ReleasePillarFrequencyDescription
Recommended sources
Employee Earnings and HoursBusiness surveyTwo-yearlyCompositional and distributional estimates of hourly and weekly earnings, hours paid for and methods used to set employees' pay. More detailed data is available through Microdata and TableBuilder: Employee Earnings and Hours
Average Weekly EarningsBusiness surveySix-monthlyHeadline estimates of earnings. Used extensively in legislation, and for tracking and comparing earnings by sex, industry and state/territory.
Other sources
Employee earningsHousehold surveyAnnualSourced from the Characteristics of Employment survey. Median weekly and hourly earnings as well as distribution estimates. More detailed data is available through Microdata and Tablebuilder: Characteristics of Employment
Personal Income in AustraliaAdmin dataAnnualPersonal income estimates for more than 2,200 regions sourced from personal income tax data in the Linked Employer Employee Dataset (LEED). More detailed data available through Microdata and TableBuilder: Jobs in Australia.
Jobs in AustraliaAdmin dataAnnualJob level income estimates for more than 2,200 regions sourced from personal income tax data in the Linked Employer Employee Dataset (LEED). More detailed data available through Microdata and TableBuilder: Jobs in Australia

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.

ABS labour market sources with sex data available
ReleasePillarFrequencyDescription
Labour Force, Australia; Labour Force, Australia, Detailed; Longitudinal Labour Force, AustraliaHousehold surveyMonthlyHeadline employment estimates. Labour force status over time, including short-term transitions e.g. flows into and out of employment and unemployment.
Labour Force Status of FamiliesHousehold surveyAnnualLabour force characteristics for whole families, e.g. one parent families, jobless families. Sourced from the Labour Force Survey.
Job mobilityHousehold surveyAnnualJob changes over the year and previous job details. Sourced from the annual Participation Job Search and Mobility Labour Force supplementary survey.
Potential workersHousehold surveyAnnualPotential labour supply of people who are not working, including wanting to work, available for work, job attachment and job search. Sourced from the annual Participation Job Search and Mobility Labour Force supplementary survey.
Underemployed workersHousehold surveyAnnualEmployed people who wanted more work hours or worked reduced hours. Sourced from the monthly Labour Force Survey and supplemented by the annual Participation, Job Search and Mobility supplementary survey.
Working arrangementsHousehold surveyAnnualRegularity and certainty of hours, e.g. whether guaranteed minimum hours. Sourced from the annual Characteristics of Employment Labour Force supplementary survey.
Barriers and Incentives to Labour Force ParticipationHousehold surveyAnnualFactors that influence how people participate in the labour market and the hours they work.
Trade union membershipHousehold surveyTwo-yearlyTrade union membership by employment and socio-demographic characteristics. Sourced from the annual Characteristics of Employment Labour Force supplementary survey.
Retirement and Retirement IntentionsHousehold surveyTwo-yearlyRetirement age, retirement expectations and sources of income in retirement.
Work related injuriesHousehold surveyIrregularIncluding type of injury, job details and work-related injury financial assistance.
Pregnancy and Employment TransitionsHousehold surveyIrregularLabour market participation of females during pregnancy and after the birth. 
Education and WorkHousehold surveyAnnualEngagement in work and/or study, current and recent study, qualifications, and transitions to work.
Qualifications and WorkHousehold surveyIrregularInformation about the educational qualifications people have studied and their relevance to current jobs.
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