Analysing your audit results

• In your employee dataset, use the ‘gender’ data provided for each employee.
• Record the percentage of employees who are women, men or people of self-described gender. The total should add up to 100%.

Looking at the gender mix of your workforce shows if some groups are over- or under-represented across your organisation. If most roles are held by one gender, it might point to issues with how people are hired, supported, or included. It can also raise safety and inclusion concerns, especially in spaces dominated by men.

A more balanced gender mix helps challenge stereotypes and brings fresh perspectives. It can be a foundation for a workplace where people feel safe and respected.

This measure also sets a baseline to compare with other measures like gender composition in leadership roles (Measure 1.3) or certain occupations (Measure 7.1).

Use these prompts to consider this measure alongside other relevant data.

Use the ‘level’ classification in your audit data and compare with your overall gender composition. You might consider grouping levels together to get a broader picture.

Are there differences in the gender composition at certain levels, compared to the overall workforce composition?

Look at the gender composition of employee type, such as ongoing, fixed term, or casual roles.

Are some genders overrepresented in certain employment types?

Compare the gender composition across different role types, like functional or support roles compared to operational roles.

Do you see certain genders over-represented within certain role types?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

Consider this measure alongside:

• Measure 1.2: Gender composition of part time workers in the duty holder organisation
• Measure 1.3: Gender composition of senior leaders in the duty holder organisation
• Measure 5.2: Gender composition of employees who were promoted
• Measure 7.1: Occupational gender segregation.

• In your employee dataset, use the ‘gender’ and ‘employment basis’ data provided for each employee.
• For each gender, record the percentage of employees who are part-time (either ‘part-time ongoing’ or ‘part-time fixed term’).

Part-time work can limit career progress, contribute to the gender pay gap, and reduce retirement savings. Knowing who works part-time in your organisation helps you identify potential barriers to gender equality and support career progression for all employees.

For example, if most part-time workers are women, it might point to a lack of support for men to work part-time and/or flexibly. Supporting men to work part-time or flexibly can help challenge uneven unpaid caring responsibilities at home and in the community.

Knowing the gender breakdown of part-time workers also helps you explore how to better help these employees to grow their careers and move to full-time if and when they want to.

Use these prompts to consider this measure alongside other relevant data.

Use the ‘level’ classification in your audit data to look at the gender composition of employees who work part-time at different levels. You might consider grouping levels together to get a broader picture.

Are there gendered patterns in part-time work at different levels?

Consider the gender composition of part-time employees on different types of contracts (such as ongoing, fixed term).

Are certain genders more likely to be in insecure, part-time work? If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

Consider this measure alongside:

• Measure 1.1: Gender composition of the duty holder organisation
• Measure 6.2: Uptake of flexible work, by gender.

• In your employee dataset, use the ‘gender’ and ‘employee type’ data provided for each employee.
• For the ‘senior leader’ employee type, record the percentage of senior leaders who are women, men or people of self-described gender. The total should add up to 100%.

Who’s at the decision-making table matters. When leadership teams reflect the people they lead (and the communities they serve) decisions are more likely to be fair, inclusive, and better for everyone. If women are under-represented in senior roles, it may mean decisions are being made without a full range of perspectives. It can also contribute to the gender pay gap, especially if women are well represented in the wider workforce but missing from the top.

These results can prompt important questions about whether your promotion pathways and recruitment practices are truly equitable.

Use these prompts to consider this measure alongside other relevant data.

Use your employment basis data in addition to your employee type data. Compare the gender composition of senior leaders who work part time with part-time work across your organisation.

Are senior leaders less likely to work part time compared to the overall workforce?

Are senior leaders of a particular gender more likely to work part time?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

Consider this measure alongside:

• Measure 1.1: Gender composition of the duty holder organisation
• Measure 1.2: Gender composition of part time workers in the duty holder organisation
• Measure 5.2: Gender composition of employees who were promoted.

• In your governing body dataset, use the ‘gender’ data provided for all members.
• Record the percentage of governing body members who are women, men or people of self-described gender. The total should add up to 100%.

When one gender dominates your governing body, it raises questions about whose voices are being heard and valued when it comes to shaping the future of your organisation. It also raises questions about how priorities are set and decisions are made. Representation at the top sends a powerful message: leadership is everyone’s business.

Governing bodies that reflect the diversity of the workforce and community they serve are more likely to build trust and strengthen legitimacy. They also show a genuine commitment to gender equality.

Use these prompts to consider this measure alongside other relevant data.

Compare the gender composition of your governing body with the gender composition of your organisation.

Are there differences in gender representation between your governing body and the rest of your organisation?

Look at the chair of your governing body and consider trends over time, if possible.

Is a particular gender over-represented in the chair role?

Where available, consider the intersection of gender and other attributes.

How diverse is your governing body when it comes to people facing intersecting inequalities?

Consider this measure alongside:

• Measure 1.1: Gender composition of the duty holder organisation
• Measure 1.3: Gender composition of senior leaders in the duty holder organisation.

• In your employee dataset, use the ‘gender’, ‘total remuneration’ and ‘occupation code’ data provided for all employees.
• For all employees:
  • calculate the average base salary for men, women, and people of self-described gender.
  • to find the pay gap for women in dollars, subtract the average base salary for women from the average base salary for men.
  • to show this gap as a percentage, divide this dollar gap by the average base salary for men, and then multiply by 100.
• For each occupation group:
  • calculate the average total remuneration for men, women, and people of self-described gender.
  • to find the pay gap for women in dollars, subtract the average total remuneration for women from the average total remuneration for men.
  • to show this gap as a percentage, divide this dollar gap by the average total remuneration for men, and then multiply by 100.
• Compare the percentage pay gap for all employees with the percentage pay gap for each occupation group.
• If any occupation group has more than 10 employees who self-describe their gender, you can repeat these calculations to find the gender pay gaps for people of self-described gender.
• A pay gap that is positive (i.e. >0) means that men earn more than women (or people of self-described gender).
• A pay gap that is negative (i.e. <0) means that women (or people of self-described gender) earn more than men.

Here’s an example calculation:

• In 2024, the largest gender pay gap by occupation in Australia is in the Construction Industry.
• The average total remuneration for men in ‘Construction’ is $121,000 a year. For women, it is $82,000 a year.
• Therefore, the pay gap for women is $39,000 (= $121,000 — $82,000).
• As a percentage of men’s pay, women have a total remuneration gender pay gap of 32% ($39,000 ÷ $121,000 x 100).

In 2021 and 2023, every Victorian public sector occupation had a pay gap in favour of men - including occupations that were majority-women. These gaps often reflect outdated gender stereotypes or assumptions about what types of work are ‘suitable’ for women or men. When women are over-represented in lower-paid roles, or under-represented in leadership, it’s often due to bias – not capability.

Looking at your organisation’s gender pay gaps by occupation can give you important clues. Use this information to guide where you start when promoting greater gender equality and addressing disadvantage.

Use these prompts to consider this measure alongside other relevant data.

Compare the pay gap for each occupation with the overall pay gap in your organisation.

Are there occupations where the pay gap is particularly large?

Compare the different types of pay gap calculations to help identify the root cause.

Is the pay gap larger when you look at base salary or total remuneration?

This can show if things like overtime, bonuses, or special allowances are mostly going to one gender.

Is the pay gap larger when you look at the mean (average) or the median (middle value)?

The mean can be affected by a few very high or low salaries.

A large mean gap may mean that a small number of people of a certain gender are earning much more or much less than others.

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

Consider this measure alongside:

• Measure 1.1: Gender composition of the duty holder organisation
• Measure 1.3: Gender composition of senior leaders in the duty holder organisation
• Measure 3.2: Mean total remuneration senior leader gender pay gap
• Measure 3.3: Mean base salary gender pay gap
• Measure 3.4: Median total remuneration gender pay gap
• Measure 3.5: Median base salary gender pay gap.

• In your employee dataset, use the ‘gender’, ‘total remuneration’ and ‘employment type’ data provided for all employees.
• For the senior leader employment type:
  • calculate the average total remuneration for men, women, and people of self-described gender
  • to find the pay gap for women senior leaders in dollars, subtract the average total remuneration for women from the average total remuneration for men
  • to show this gap as a percentage, divide this dollar gap by the average total remuneration of men and then multiply by 100.
• If your senior leaders include more than 10 employees who self-describe their gender, you can repeat these calculations to find the gender pay gap for people of self-described gender.

A gender pay gap in senior leadership often points to deeper issues. This might include bias in recruitment, limited promotion pathways, or outdated ideas about who belongs in top roles.

Women are still less likely to reach senior positions, and when they do, they’re often paid less. Even in workplaces covered by Awards or Enterprise Agreements, gender pay gaps often emerge through bonuses or allowances. Men are also more likely to benefit from salary negotiations. This is not because women aren’t capable negotiators. It’s because of persistent biases that reward assertiveness differently depending on who’s asking.

Closing the total remuneration gap in senior leadership is a key indicator of your commitment to gender equality and helps attract and retain top talent.

Use these prompts to consider this measure alongside other relevant data.

Look at the pay gap for senior leaders and compare it to the overall mean total remuneration pay gap in your organisation.

Is the pay gap larger among senior leaders than in the broader workforce?

Consider using your audit data to calculate the mean base salary pay gap for senior leaders as well.

Is the gender pay gap bigger for base salary or total remuneration?

This can help you understand whether additional payments, like bonuses or special allowances, are benefiting one gender more than others.

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

Consider this measure alongside:

• Measure 1.1: Gender composition of the duty holder organisation
• Measure 1.3: Gender composition of senior leaders in the duty holder organisation
• Measure 3.1: Mean total remuneration gender pay gap by occupation group
• Measure 3.3: Mean base salary gender pay gap
• Measure 3.4: Median total remuneration gender pay gap
• Measure 3.5: Median base salary gender pay gap
• Measure 5.2 Gender composition of employees who were promoted.

• In your employee dataset, use the ‘gender’, and ‘base salary’ data provided for all employees.
• Calculate the average base salary for men, women, and people of self-described gender.
• To find the pay gap for women in dollars, subtract the average base salary for women from the average base salary for men.
• To show this gap as a percentage, divide this dollar gap by the average base salary for men and then multiply by 100.
• If your workforce includes more than 10 employees who self-describe their gender, you can repeat these calculations to find the gender pay gap for people of self-described gender.

A gap in the mean base salary is about more than people of different genders being paid differently for the same, or similar, roles (although this is still common).

It reflects the combined impact of gender bias and disadvantage over time. This can include:

• unequal access to promotions or high-value projects
• limits to flexible work at senior levels, or
• the additional load women often carry in balancing work and unpaid care.

These factors create long-term pay gaps that limit women’s financial security and career progression.

Addressing this gap visibly and transparently is about fairness, and it’s also good for business. That's because pay equity builds trust, boosts morale, and helps attract and keep diverse talent.

Tracking this measure over time, alongside pay gaps by occupation (Measure 3.1) and among senior leaders (Measure 3.2), helps uncover where deeper issues may exist, and where change is most needed.

Use these prompts to consider this measure alongside other relevant data.

Use the ‘level’ classification in your audit data to calculate the pay gap at different levels of your organisation. You might consider grouping levels together to get a broader picture.

How do pay gaps differ between classification levels?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

See measure 3.1 for further advice that also applies to this measure.

Consider this measure alongside:

• Measure 1.1: Gender composition of the duty holder organisation
• Measure 3.1: Mean total remuneration gender pay gap by occupation group
• Measure 3.2: Mean total remuneration senior leader gender pay gap
• Measure 3.4: Median total remuneration gender pay gap
• Measure 3.5: Median base salary gender pay gap
• Measure 5.2 Gender composition of employees who were promoted.

• In your employee dataset, use the ‘gender’, and ‘total remuneration’ data provided for all employees.
• Calculate the median total remuneration for men, women, and people of self-described gender.
• To find the pay gap for women in dollars, subtract the median total remuneration for women from the median total remuneration for men.
• To show this gap as a percentage, divide this dollar gap by the median total remuneration for men and then multiply by 100.
• If your workforce includes more than 10 employees who self-describe their gender, you can repeat these calculations to find the gender pay gap for people of self-described gender.

Even if base salaries appear equal, gaps often appear in the ‘extras’ that significantly affect overall earnings. This measure gives you a fuller picture of pay equity by looking at total remuneration. That is, base salary plus overtime pay, bonuses, allowances, and other financial benefits including superannuation.

Bonuses and overtime often appear neutral. However, who gets access to them can depend on role design, manager discretion, or assumptions around availability to ‘go the extra mile’.

If your median total remuneration gap is a lot larger than your median base salary gap (Measure 3.5), then this might indicate gendered barriers. Use these two measures together to track pay equity more accurately. This can help you address systemic issues and take action. For example, you might find that some employees don't have equal access to additional remuneration.

This measure is best understood as part of a broader analysis.

See measures 3.1 and 3.3 for advice that also applies to this measure.

Consider this measure alongside:

• Measure 1.1: Gender composition of the duty holder organisation
• Measure 3.1: Mean total remuneration gender pay gap by occupation group
• Measure 3.2: Mean total remuneration senior leader gender pay gap
• Measure 3.3 Mean base salary gender pay gap
• Measure 3.5: Median base salary gender pay gap
• Measure 5.2 Gender composition of employees who were promoted.

• In your employee dataset, use the ‘gender’ and ‘base salary’ data provided for all employees.
• Calculate the median base salary for men, women, and people of self-described gender.
• To find the gender pay gap for women in dollars, subtract the median base salary for women from the median base salary for men
• To show this gap as a percentage, divide this dollar gap by the median base salary for men and then multiply by 100.
• If your workforce includes more than 10 employees who self-describe their gender, you can repeat these calculations to find the gender pay gap for people of self-described gender.

This measure looks at the median base salary (i.e. the middle point in your salary data), which gives a useful picture of what a ‘typical’ employee earns. Unlike the mean (average), the median isn’t distorted by the highest and the lowest salaries. That makes it especially helpful for understanding pay equity in organisations with a wide range of salaries, or a small number of very high (or low) earners.

Used together with the mean base salary gender pay gap (Measure 3.3), this measure helps you get a more complete view of pay equity in your organisation.

This measure is best understood as part of a broader analysis.

See measures 3.1 and 3.3 for advice that also applies to this measure.

Consider this measure alongside:

• Measure 1.1: Gender composition of the duty holder organisation
• Measure 3.1: Mean total remuneration gender pay gap by occupation group
• Measure 3.2: Mean total remuneration senior leader gender pay gap
• Measure 3.3 Mean base salary gender pay gap
• Measure 3.4: Median total remuneration gender pay gap
• Measure 5.2 Gender composition of employees who were promoted.

• In your employee experience (People Matter or independent survey) dataset, refer to responses to the survey question: ‘During the last 12 months in your current organisation, have you experienced any of the following behaviours at work?’
• If available, record the percentage of women, men, and people of self-described gender who report experiencing any form of sexual harassment.

Anonymous reporting helps uncover the true scale of sexual harassment, because many people hesitate to report through formal channels. This may be due to fear of retaliation, stigma, or lack of trust in the process. This measure gives you a clearer picture of what’s really happening in your workplace across different gender groups.

If people of any gender report sexual harassment, and particularly if this is higher for a particular gender, it signals deeper issues in workplace culture and safety. Use these insights to:

• target prevention and support strategies
• challenge harmful behaviours, and
• build a safer, more respectful environment for everyone.

Use these prompts to consider this measure alongside other relevant data.

Look at the response rate for your employee experience survey. Compare the anonymous survey data on sexual harassment with your formal reports from workforce data.

Is there a gap between how many employees report experiencing sexual harassment in the survey and how many formally report it?

A lower number of formal reports may suggest that employees don’t know how to report or don’t feel safe doing so.

Are there gender differences in who does or doesn’t make a formal report, when compared to the survey data?

Review other parts of your employee experience data related to sexual harassment. For some organisations, data may be suppressed to protect respondent privacy. Where possible, break the data down by gender.

Look for gendered patterns in:

• the types of sexual harassment reported
• reasons employees gave for not making a formal complaint
• the roles or seniority levels of people accused of harassment
• how confident employees feel to speak up about inappropriate behaviour
• employee views on whether the organisation takes effective action to prevent bullying, harassment, and discrimination.

Also review your workforce data on employee exits:

Are more people leaving from certain levels or job types?

Could sexual harassment be a contributing factor in these exits?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

Consider this measure alongside:

• Measure 1.1 Gender composition of the duty holder organisation
• Measure 4.2 Number of formal reports of sexual harassment
• Measure 4.3 Survey respondents who said they reported sexual harassment
• Measure 4.4 Reasons for not making a formal sexual harassment complaint
• Measure 4.5 Satisfaction with handling of workplace sexual harassment complaint
• Measure 7.1 Occupational gender segregation

• In your sexual harassment complaints dataset, use the ‘gender (complainant)’ data.
• Record the number of complaints where complainants are women, men, people of self-described gender, mixed genders, or prefer not to say their gender.

Tracking formal sexual harassment reports, along with anonymous reports from Measure 4.1, gives a more accurate picture of what’s happening in your workplace. A low number of formal reports doesn’t mean the problem doesn’t exist. Instead, it may mean people don’t feel safe or supported enough to speak up.

If there is a large gap between the number of people who say they’ve experienced harassment (Measure 4.1) and those who formally report it (Measure 4.2), it may be a sign employees don’t want to use formal reports. This could signal fear of retaliation, lack of trust in the process, or cultural norms that discourage people from speaking up or ‘making a fuss’.

And remember: a short-term increase in formal reports after rolling out new training, policies, or communications is not necessarily a worsening problem. It can be a sign of progress. Employees might have more trust in the system and greater awareness of what constitutes unacceptable behaviour.

Use these prompts to consider this measure alongside other relevant data.

See measure 4.1 for advice that also applies to this measure.

Consider this measure alongside:

• Measure 1.1 Gender composition of the duty holder organisation
• Measure 4.1 Anonymous experience rate of sexual harassment
• Measure 4.3 Survey respondents who said they reported sexual harassment
• Measure 4.4 Reasons for not making a formal sexual harassment complaint
• Measure 4.6 Satisfaction with handling of formal workplace sexual harassment complaint
• Measure 7.1 Occupational gender segregation.

• In your employee experience (People Matter or independent survey) dataset, refer to responses to the survey question: ‘How did you respond to the harassment?’.
• Note that respondents only see this survey question if they report they have experienced sexual harassment in a previous survey question.
• If available, record the percentage of women, men and people of self-described gender who respond that they ‘submitted a formal complaint’.

This measure shows what happens after a sexual harassment incident. It tells you if people feel safe and supported enough to submit a formal complaint.

If there is a gap between participants who said they reported sexual harassment and the number of formal reports in your workforce data (Measure 4.2), this highlights issues with your reporting mechanisms. This is because you are not capturing all the ways in which complaints are being reported and who they are being reported to. This gap also means you do not have an understanding of how complaints of sexual harassment are being addressed. For example, whether staff receiving reports of sexual harassment:

• understand the causes and impacts of sexual harassment
• understand the laws and policies on sexual harassment
• know how to prevent and respond to workplace sexual harassment in a victim-centred way
• can refer victims to appropriate psychological support and other services as required.

You need to capture all the ways people report sexual harassment in your organisation. You also need to know all the people it is being reported to. This will help you to improve quality control and consistency in recording incidents.

If you are not currently doing this, you should include strategies in your new GEAP. These strategies should address gaps in your data collection capability. Improving data collection will help you undertake more nuanced analysis in the future. It may also help you to demonstrate reasonable and material progress on this indicator. This is a requirement of the Gender Equality Act 2020.

Use these prompts to consider this measure alongside other relevant data.

Compare the proportion of survey respondents who said they made a formal complaint with the number of formal complaints recorded by your organisation (measure 4.2).

Is there a difference between these two numbers?

If the survey shows more people reported harassment than are formally recorded, investigate where those reports are going. Consider:

• Reviewing how reports are received and documented
• Include strategies to ensure that all reports are centrally recorded, no matter where or how they are made.

Centralising your reporting process helps you:

• make the system safer and more consistent
• ensure quality control
• make sure every report of sexual harassment receives an appropriate and timely response.

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

See measure 4.1 for further advice that also applies to this measure.

Consider this measure alongside:

• Measure 4.1 Anonymous experience rate of sexual harassment
• Measure 4.2 Number of formal reports of sexual harassment
• Measure 4.5 Satisfaction with handling of workplace sexual harassment complaint
• Measure 7.1 Occupational gender segregation.

• In your employee experience dataset (People Matter or independent survey), refer to responses to the survey question: ‘What was your reason for not submitting a formal complaint?’.
• Note that respondents only see this survey question if they report they have experienced sexual harassment and do not report that they submitted a formal complaint.
• If available, record the percentage of employees who select each different reason for not submitting a complaint. This may not add up to 100% as respondents can select multiple reasons.

Knowing how many employees do not formally report sexual harassment is just as important as knowing how many do. If people of certain genders are more likely to stay silent, it may point to deeper issues. These could include fear of retaliation, stigma, lack of trust in the process, or a belief that nothing will change. These are cultural warning signs that need attention. If some groups don’t submit a complaint because they don’t know how or who to talk to, you may need to improve awareness of your complaints system.

By better understanding the reasons for not submitting a complaint, you can focus on creating an environment where people of all genders feel safe. Encouraging reporting, and making sure people feel protected and supported when they do report, is essential to creating a workplace free from harassment.

Use these prompts to consider this measure alongside other relevant data.

Look at complainant satisfaction using both your employee experience data (measure 4.5) and your workforce data (measure 4.6).

Are people who make a formal sexual harassment complaint satisfied with the process?

If not, this could be discouraging others from making a formal report.

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

You may also want to review this measure alongside other sexual harassment data for a more complete picture.

Most importantly, use this measure to shape your strategies in GEAP. For example:

• If the data shows employees don’t know how to report, consider running awareness and education campaigns.
• If employees don’t feel safe making a report, review your processes to make them more victim-centred, trauma-informed, and safe.

Make sure you communicate these changes clearly to all staff.

Consider this measure alongside:

• Measure 4.1 Anonymous experience rate of sexual harassment
• Measure 4.2 Number of formal reports of sexual harassment
• Measure 4.5 Satisfaction with handling of workplace sexual harassment complaint
• Measure 4.6 Satisfaction with handling of formal workplace sexual harassment complaint.

• In your employee experience (People Matter or independent survey) dataset, refer to responses to the survey question: ‘Were you satisfied with the way your formal complaint was handled?’.
• Note that respondents only see this survey question if they report they have experienced sexual harassment and that they ‘submitted a formal complaint’.
• If available, record the percentage of women, men and people of self-described gender who responded ‘yes’ to this question.

When someone experiences sexual harassment and makes a formal complaint, how the organisation responds matters deeply. Negative experiences can be harmful for victims and respondents. They can also discourage others from coming forward. This can create a culture of silence, where harmful behaviours are not addressed.

If employees are not satisfied with how their complaint is handled, it helps reveal gaps in your systems. It also shows where trust needs rebuilding. Look for gendered patterns in the levels of satisfaction. This helps you understand and respond effectively. When people feel heard, supported, and safe, it improves your workplace culture. It sends a clear message that harassment is taken seriously.

Check if there is a gap between satisfaction levels reported in your People Matter survey and satisfaction levels reported in workforce data (Measure 4.6). This may indicate lack of consistency in how managers respond to complaints of sexual harassment and how HR handle more serious complaints procedures.

Use these prompts to consider this measure alongside other relevant data.

Compare complainant satisfaction in your employee experience data with the results in your workforce data (measure 4.6).

Where possible, break down both sets of data by gender.

Do both measures tell the same story, or are there differences between them?

Are satisfaction rates different for different genders?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

Look at other parts of your employee experience data that relate to sexual harassment. Keep in mind that for some organisations, survey responses may be suppressed to protect privacy.

Where possible, break down the data by gender to better understand different experiences.

Use what you learn to shape strategies in your GEAP that will improve how your organisation handles reports of sexual harassment.

Make sure your reporting processes are:

• victim-centred
• safe and supportive
• clearly communicated to all staff.

These steps can help build trust and encourage people to report when they experience or witness inappropriate behaviour.

Consider this measure alongside:

• Measure 4.1 Anonymous experience rate of sexual harassment
• Measure 4.3 Survey respondents who said they reported sexual harassment
• Measure 4.4 Reasons for not making a formal sexual harassment complaint
• Measure 4.6 Satisfaction with handling of formal workplace sexual harassment complaint.

• In your sexual harassment complaints dataset, use the ‘gender (complainant)’ and ‘complainant satisfaction’ data provided for each complaint.
• Record the percentage of complaints where the complainant was satisfied or very satisfied with how their complaint was handled.
• If you have more than 10 complainants for any gender, record the percentage of complainants of this gender who were satisfied or very satisfied with how their complaint was handled.

This measure shows how well your formal complaints process works for those who use it. When employees who make a formal complaint are satisfied with how it’s handled, it suggests that the process meets their expectations.

Low satisfaction suggests a lack of trust in how complaints are managed and how well the system protects those who come forward. If a particular gender is dissatisfied, it could indicate a lack of safety for that group. A workplace culture where people trust the complaints process is essential to foster safety and inclusion at work.

Track this measure alongside satisfaction levels reported in employee experience data (Measure 4.5). This may show differences in how HR manage formal complaints and how managers respond to complaints of sexual harassment.

This measure is best understood as part of a broader analysis.

See measure 4.5 for advice that also applies to this measure.

Consider this measure alongside:

• Measure 4.2 Number of formal reports of sexual harassment
• Measure 4.4 Reasons for not making a formal sexual harassment complaint
• Measure 4.5 Satisfaction with handling of workplace sexual harassment complaint.

• In your employee dataset, use the ‘gender’ and ‘recruited’ data provided for all employees.
• Among recruited employees, record the percentage of employees who are women, men and people of self-described gender. The total should add up to 100%.

Recruitment shapes the future of your workforce. If your hiring practices are fair and inclusive, you’re more likely to attract a broader, more diverse pool of talent. But if certain genders are consistently underrepresented among new recruits, it may point to (conscious or unconscious) bias in how roles are advertised, shortlisted, or filled.

Look at this data alongside your overall workforce composition (Measure 1.1). This can show whether recruitment is helping shift the balance, or reinforcing the status quo. Greater gender-balance helps build a stronger, more inclusive workplace over time.

Use these prompts to consider this measure alongside other relevant data.

Consider measure 5.3 (perceptions of recruitment by gender).

Are there gendered patterns in how employees perceive the recruitment process?

If so, does this help explain the gender composition of people your organisation is hiring?

If you have the data, review the gender composition of applicants for roles at different levels of seniority.

Are certain genders more or less likely to apply for roles at specific levels?

Compare the gender composition of applicants with the gender composition of successful candidates.

Are there significant differences between who applies and who gets hired?

These insights can help identify potential biases or barriers in your recruitment process. They can also inform actions in your GEAP.

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

Consider this measure alongside:

• Measure 1.1 Gender composition of the duty holder organisation
• Measure 5.3 Perceptions of recruitment, by gender
• Measure 6.3 Perceptions of flexible work culture, by gender
• Measure 7.1 Occupational gender segregation.

• In your employee dataset, use the ‘gender’ and ‘promoted’ data provided for all employees.
• Among promoted employees, record the percentage of employees who are women, men, and people of self-described gender. The total should add up to 100%.

Fair and transparent promotion practices ensure everyone has a chance to move into leadership or higher-paid roles. If one gender is consistently promoted more than others, it may point to (unconscious) bias. This can result in missed opportunities to recognise and keep talented people.

Tracking the gender breakdown of promotions and comparing it to your overall workforce (Measure 1.1). This helps you spot gaps, challenge assumptions about who gets promoted, and ensure potential is recognised and rewarded fairly.

Use these prompts to consider this measure alongside other relevant data.

Use the ‘level’ classification in your audit data to analyse promotions at each level of your organisation. You might consider grouping levels together to get a broader picture. You can also use ‘employee type’ to identify senior leaders.

What gendered patterns in promotions emerge at each level, particularly in senior roles?

Consider employment basis (e.g. full-time vs part-time) when reviewing promotions data.

Do part-time employees get promoted at the same rate as full-time employees?

If available, look at the average time employees spend at a level before being promoted, broken down by gender:

What are the average timelines for career progression?

Are there significant differences between genders?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

Consider this measure alongside:

• Measure 1.1 Gender composition of the duty holder organisation
• Measure 1.2 Gender composition of part time workers in the duty holder organisation
• Measure 1.3 Gender composition of senior leaders in the duty holder organisation
• Measure 6.3 Perceptions of flexible work culture, by gender.

• In your employee experience (People Matter or independent survey) dataset, refer to responses to the survey question: ‘I believe the recruitment processes in my organisation are fair’.
• Record the percentage of women, men and people of self-described gender who agree (‘strongly agree’ or ‘agree’) with this statement.

If certain groups see recruitment as unfair or biased, it can erode trust. This limits access to opportunities, especially in roles where one gender is already underrepresented. When employees feel hiring is fair and inclusive, they are more likely to feel valued, motivated, and committed.

This measure helps you understand how different genders experience your recruitment practices. Alongside workforce data from Measure 5.1 and 5.2, it shows not just who is being hired, but how fair the process feels. If a much higher proportion of a particular gender feels that hiring practices are unfair, you may need to explore this issue further during staff consultation.

Use these prompts to consider this measure alongside other relevant data.

Look at your employee experience data in relation to workplace culture and inclusion.

Are there any trends in how your employees feel about the safety and inclusiveness of your organisation?

Could these be impacting perceptions of recruitment?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

Consider this measure alongside:

• Measure 1.1 Gender composition of the duty holder organisation
• Measure 5.1 Gender composition of recruited employees
• Measure 5.2 Gender composition of employees who were promoted.

• In your employee experience (People Matter or independent survey) dataset, refer to responses to the survey question: ‘I believe the promotion processes in my organisation are fair’.
• Record the percentage of women, men and people of self-described gender who agree (‘strongly agree’ or ‘agree’) with this statement.

When promotion processes are seen as unfair or biased, it can reinforce existing gender imbalances. It can limit opportunities for diverse talent to progress. It can also fuel stereotypes about what a leader ‘looks like’ in your organisation, and who gets access to growth opportunities.

If a higher proportion of a particular gender feel that promotion practices are unfair, you may need to explore this issue further during staff consultation. Address perceptions of unfairness or lack of transparency in promotion to build trust. This will also improve retention, and ensure employees of all genders see leadership as a path open to them. Over time, this can help break down stereotypes and strengthen your talent pipeline. It can also foster a more inclusive workplace culture.

Use these prompts to consider this measure alongside other relevant data.

Use the ‘level’ classification in your audit data to look at the gender composition of your organisation at different levels. You might consider grouping levels together to get a broader picture.

Is one gender more concentrated in senior levels?

Could this be impacting perceptions of promotion in your organisation?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

See measure 5.3 for further advice that also applies to this measure.

Certain performance measures can help shed light on each other. Consider this measure alongside:

• Measure 1.1 Gender composition of the duty holder organisation
• Measure 1.2 Gender composition of part time workers in the duty holder organisation
• Measure 1.3 Gender composition of senior leaders in the duty holder organisation
• Measure 5.1 Gender composition of recruited employees
• Measure 5.2 Gender composition of employees who were promoted
• Measure 6.3 Perceptions of flexible work culture, by gender.

• In your employee dataset, use the ‘gender’, ‘weeks of paid parental leave’ and ‘weeks of unpaid parental leave’ data provided for each employee.
• Focus on the employees who have taken any number of weeks of parental leave (paid or unpaid).
• For these employees who have taken parental leave, calculate the average number of weeks taken by women, men and people of self-described gender.

Looking at the length of parental leave can reveal underlying expectations about the roles and responsibilities of carers. If women consistently take more leave than men or employees of self-described gender, your parental leave policies (and workplace culture) may not equally support carers of all genders.

Parental leave matters not just for women’s career progression and pay, but for employees of other genders who want to be more involved at home. Normalising shared caregiving helps shift outdated gender stereotypes at work and beyond.

Use these prompts to consider this measure alongside other relevant data.

Use the ‘level’ classification in your audit data and compare this with your parental leave data. You might consider grouping levels together to get a broader picture.

Are there patterns in how long employees take parental leave for at different levels?

Focus on the average length of parental leave taken by men.

Are there patterns in how long employees take parental leave for at different levels?

What proportion of men use the full parental leave they’re entitled to?

Use your employee experience data and focus on questions 86 and 87. These look at perceived barriers to success in your organisation. If possible, disaggregate the data by gender.

Do employees see caring responsibilities or flexible work arrangements as a barrier to career success?

Are there gendered differences in these perceptions?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

In consultation, you can explore further:

• What are the perceived barriers to accessing caregiving leave (i.e. parental or carer’s leave), by gender?
• Do employees experience changes to their role after they return from parental leave?
• Do employees believe their career progression opportunities changed after returning from parental leave?

Consider this measure alongside:

• Measure 1.1 Gender composition of the duty holder organisation
• Measure 1.3 Gender composition of senior leaders in the duty holder organisation
• Measure 6.4 Gender composition of parental leave takers.

• In your employee dataset, use the ‘gender’ and ‘formal flexible work arrangement’ data provided for each employee.
• For each gender, record the percentage of employees who had a formal flexible work arrangement during the reporting period.

If flexible work is mostly taken up by one gender (often women), it can point to gendered expectations around caregiving. Or, it could indicate concerns that accessing flexible work will hurt career prospects. This can reinforce stereotypes about who should be balancing work and care, and who is considered ‘committed’ to their job.

When flexible work is normalised and taken up across all genders, it improves retention. It can also boost resilience and build a more inclusive workplace. It challenges outdated gender roles and encourages fairer sharing of paid and unpaid work. This helps to close the gender pay gap and improve gender equality.

Use these prompts to consider this measure alongside other relevant data.

Use the ‘level’ classification in your audit data and compare this with your parental leave data. You might consider grouping levels together to get a broader picture.

Are there patterns in flexible work uptake across different levels of your organisation?

Use the ‘employment type’ field to identify senior leaders. Compare their flexible work usage with other employees.

Is there a difference in flexible work uptake in senior roles, compared to other employees?

Use the occupational codes in your audit data.

Is there a difference in flexible work uptake across different occupational groups?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

In consultation, you can explore further:

What are the perceived barriers to accessing flexible leave, by gender?

See measure 6.1 for further advice that also applies to this measure.

Consider this measure alongside:

• Measure 1.1 Gender composition of the duty holder organisation
• Measure 1.2 Gender composition of part time workers in the duty holder organisation
• Measure 1.3 Gender composition of senior leaders in the duty holder organisation
• Measure 6.3 Perceptions of flexible work culture, by gender
• Measure 7.1 Occupational gender segregation.

• In your employee experience (People Matter or independent survey) dataset, refer to responses to the survey question: ‘I am confident that if I requested a flexible work arrangement, it would be given due consideration.’
• Record the percentage of women, men and people of self-described gender who agree (‘strongly agree’ or ‘agree’) with this statement.

Your flexible work policies may seem inclusive on paper. But it is important to understand if employees believe their requests will be taken seriously. If perception levels differ by gender, it may signal underlying cultural bias. Or there could be barriers discouraging people from seeking flexibility. This information can also help you design strategies to address the issue.

Understanding these perceptions helps uncover where support is lacking or uneven. When all employees feel confident to request flexible work, it’s a sign of a genuinely inclusive culture. This is a culture that supports better balance and reduces stigma. It helps shift outdated ideas about who can (or should) access flexibility.

Use these prompts to consider this measure alongside other relevant data

If available, use demographic data from your employee experience survey to break down responses to questions about flexible work by different attributes, not just gender.

Do different social groups experience flexible work culture differently?

For example, what do the responses show for employees from diverse cultural backgrounds? Or those who identify as a person with disability?

Do perceptions of flexible work differ across types of roles?

Consider whether responses vary based on factors like management level, salary range, or work area.

See measure 6.1 for further advice that also applies to this measure.

Consider this measure alongside:

• Measure 1.1 Gender composition of the duty holder organisation
• Measure 1.2 Gender composition of part time workers in the duty holder organisation
• Measure 1.3 Gender composition of senior leaders in the duty holder organisation
• Measure 6.2 Uptake of flexible work, by gender
• Measure 7.1 Occupational gender segregation.

This measure uses workforce data to see who is more likely to take parental leave.

• In your employee dataset, use the ‘gender’, ‘weeks of paid parental leave’ and ‘weeks of unpaid parental leave’ data provided for each employee.
• Focus on the employees who have taken any number of weeks of parental leave (paid or unpaid).
• For these employees who have taken parental leave, record the percentage of women, men, or people of self-described gender. The total should add up to 100%.Looking at who takes parental leave helps shine a light on expectations around caregiving. If parental leave is mostly taken by women, it can reinforce the idea that caring is mainly a woman’s role. This often limits women’s career progression and contributes to the gender pay gap.

Looking at who takes parental leave helps shine a light on expectations around caregiving. If parental leave is mostly taken by women, it can reinforce the idea that caring is mainly a woman’s role. This often limits women’s career progression and contributes to the gender pay gap.

When more men (especially those in heterosexual partnerships) take parental leave, this can signal more equal sharing of care and a workplace culture that supports all parents. Understanding this measure also helps you to think about why some employees might avoid taking leave. This insight can help you create a more inclusive, supportive culture for all.

Use these prompts to consider this measure alongside other relevant data.

Use the ‘level’ classification in your audit data and compare this with your parental leave data. You might consider grouping levels together to get a broader picture.

Is parental leave used less often at certain levels?

Use ‘employment type’ to identify senior leaders. Compare senior leaders and other employees.

Is there a difference in parental leave uptake in senior roles, compared to other employees?

Use the occupational codes in your audit data.

Is there a difference in parental leave uptake across different occupational groups?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

See measure 6.1 for further advice that also applies to this measure.

Consider this measure alongside:

• Measure 1.1 Gender composition of the duty holder organisation
• Measure 1.3 Gender composition of senior leaders in the duty holder organisation
• Measure 6.1 Average weeks of parental leave, by gender.

This measure uses workforce data to see if employees of different genders are equally likely to take carer’s leave.

• In your employee dataset, use the ‘gender’ and ‘accessed carers leave’ data provided for each employee.
• For each gender group, record the percentage of employees who accessed carer’s leave within the reporting period.

A gender gap in carer’s leave can reveal whether employees of different genders feel equally able to take time off to care for others. If men are significantly less likely to access carer’s leave, it may reflect unspoken cultural norms or stigma. Or, there may be workplace barriers that discourage them from doing so.

Encouraging and normalising carer’s leave for all genders creates a more inclusive workplace. Everyone should feel supported to balance work and care without fear of judgement. It also plays a part in reducing the ‘motherhood penalty,’ improving gender equity in career progression, and promoting more equal participation in caregiving.

Use these prompts to consider this measure alongside other relevant data.

Use the ‘level’ classification in your audit data and compare this with your carer’s leave data. You might consider grouping levels together to get a broader picture.

Is carer’s leave used less often at certain levels of the organisation?

Use ‘employment type’ to identify senior leaders. Compare senior leaders and other employees.

Is there a difference in carer’s leave uptake in senior roles, compared to other employees?

Use the occupational codes in your audit data.

Is there a difference in carer’s leave uptake across different occupational groups?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

See measure 6.1 for further advice that also applies to this measure.

Consider this measure alongside:

• Measure 1.1 Gender composition of the duty holder organisation
• Measure 1.2 Gender composition of part time workers in the duty holder organisation
• Measure 1.3 Gender composition of senior leaders in the duty holder organisation
• Measure 6.1 Average weeks of parental leave, by gender
• Measure 6.4 Gender composition of parental leave takers.

• In your employee dataset, use the ‘gender’ and ‘occupation code’ data provided for each employee.
• Use the first digit of your occupation code to categorise employees into eight major occupational groups (i.e. 1-Managers, 2-Professionals, 3-Technicians & Trade Workers, 4-Community and Personal Service Workers, 5-Clerical and Administrative Workers, 6-Sales Workers, 7-Machinery Operators and Drivers, 8-Labourers).
• For each occupational group, record the percentage of employees who are women, men and people of self-described gender. The total for each group should add up to 100%.

Use these prompts to consider this measure alongside other relevant data.

Look at measure 3.1 (mean total remuneration gender pay gap by occupation group).

What is the pay gap between occupation groups?

Are majority-men occupations generally paid more than majority-women occupations?

Consider your data in relation to gender stereotypes.

Does the gender composition of different occupations reflect traditional gender roles?

For example, are women concentrated in caring or administrative roles? Do men dominate technical or trade roles?

Look at your employee experience data related to workplace safety and culture. Where possible, break the results down by gender.

Are there gendered differences in how safe and included employees feel at work?

Look at employee experience questions related to bullying, harassment, and discrimination. If possible, disaggregate the data by gender and work group.

Are there specific areas of the organisation where employees of a particular gender feel unwelcome?

Do these areas align with occupations that are heavily gendered?

Look at measure 5.4 (perceptions of recruitment by gender).

Do employees of different genders view the fairness of recruitment differently?

If you have the data, look at how gender intersects with other factors, like cultural identity or disability status.

Do you see different trends for groups facing intersecting inequalities?

Consider this measure alongside:

• Measure 1.1 Gender composition of the duty holder organisation
• Measure 3.1 Mean total remuneration gender pay gap by occupation group
• Measure 3.3 Mean base salary gender pay gap
• Measure 3.4 Median total remuneration gender pay gap
• Measure 3.5 Median base salary gender pay gap
• Measure 5.3 Perceptions of recruitment, by gender.