Taking an intersectional approach is to workplace gender auditing is necessary to understand whether your organisation’s systems, structures, policies, and practices are working to promote workplace gender equality for all.
"Intersectional data" (in this guidance) is data that is separated by gender as well as attributes other than gender, such as Aboriginality; age; disability; ethnicity; gender identity; race; religion; or sexual orientation.
Breaking down or “disaggregating” the data in an intersectional way is important. It helps us understand how someone’s experience of gender inequality might be compounded by the discrimination or disadvantage they may experience based on other attributes.
If available, intersectional data must be used for your workplace gender audit (s11(3)(b)). If this data is not yet available, you should consider how you can collect this in the future and include this as an action in your Gender Equality Action Plan (GEAP).
Considerations for future data collection
If intersectional data is not yet available, you should consider how you can collect this in the future and include this as a planned action in your Gender Equality Action Plan (GEAP).
You might find that your entity does not yet have the systems capabilities to collect and store some types of data. You might not yet have the culture or level of trust required to support the collection of data deemed sensitive and personal.
The best way to deal with data gaps in the immediate term is to document them in a deliberate and systematic way. Over the next reporting period, you should think about how you can improve your data collection and consultation processes to better understand intersectional gender inequality in the workplace.
The collection, analysis and reporting of data throughout the audit must be kept confidential to protect the privacy of employees.
This is particularly important when taking an intersectional approach, as the more intersectional attributes per employee are collected, the easier it becomes to identify individuals and risk their exposure.
When collecting personal information at the onboarding stage or as part of HR records, we recommend ensuring that the intention behind the collection of data is clearly articulated through a collection statement, and that questions are non-mandatory. Staff must always have the option not to disclose.
Analysing the data, disaggregated by intersectional attributes, will identify which groups of people should be the focus of your strategies. Furthermore, the analysis will provide insight into the systemic barriers to access, inclusion and equity for people who experience intersectional gender inequality. The results will also indicate what past actions have had the most positive or negative impact on groups with similar intersectional attributes.
Aggregated quantitative data often misses the nuances and specific concerns of people with intersectional attributes, who may become “lost” in the overall gender data. However, small sample sizes or false assumptions can easily lead to flawed data analysis, misinterpretation, and incorrect conclusions. Therefore, intersectional data should be supplemented by qualitative data analysis, to validate conclusions and ensure that less representative minority experiences are not overlooked.
As best practice, it is worth identifying any gaps in data for groups of people with intersectional attributes and if so, determine if these gaps need to be addressed immediately or in a future data collection exercise. Also consider what other metrics or measures can be substituted for the gaps in data and what improvements can be made to future data collection activities.
Gender disaggregated data provides one option for analysing data, but without breaking this down to other demographic attributes such as age or cultural background, the specific issues and concerns of people with intersectional attributes may not be visible.
To assist with intersectional quantitative data analysis, organisations should:
- Conduct data comparisons – comparing the data of people with intersectional attributes against the data of men and women who represent the majority. Comparisons will help you identify unusual patterns and trends in the data.
- Consider the sample size – when data is disaggregated by intersectional attributes, the sample size will decrease. Calculating the “margin of error” of the sample size, for each group being reviewed, will help avoid drawing incorrect conclusions and exaggerating effects based on a small (statistically insignificant) sample size. For example, if 1 of 2 Indian women with a disability in a department of 100 people have experienced discrimination. To present the findings as: “50% of Indian women with a disability have experienced discrimination”, would misrepresent the actual situation.
- Conversely, do not assume that there are no underlying issues if the results appear to be insignificant or within the margin of error, as the sample size may be too small.
As far as possible, use diverse sample groups that represent the many people with intersectional attributes, to ensure that different perspectives are considered. As required for the GEAP, engage in meaningful consultation with communities, peak bodies, and other external stakeholders of representative groups to gain their unique perspective and represent the voices of those with lived experiences.
Always focus on “what was said”, not “who said what” when analysing commentary to determine major themes and correlations.
Ensure psychological support is available
It is important that individuals feel psychologically and culturally safe in sharing data that reveals their intersectional attributes. (Psychologically and culturally safe means being able to represent your true self without fear of negative consequences for your mental and emotional health or career.)
In some cases, your data analysis will identify ‘good news stories’. In other cases, your analysis will identify current and/or longstanding gender and intersectional inequalities. Regardless of your findings, it is important to remember that work on gender equality can raise issues for anyone in your organisation, at any time.
Whenever you discuss issues of gender in the workplace, remind employees of your Employee Assistance Program and other local support services available to them. Having appropriately skilled staff who can respond to disclosures and refer people to services as part of your consultation process is also important. Access to psychological support for those who engage in the analysis process is also an important consideration.
Amanda, an Aboriginal queer woman, discusses the challenges of asking Aboriginal and Torres Strait Islander people to identify when collecting data.
Read the case study to understand how an organisation took steps to improve their intersectional data collection and mobilised internal communications to communicate with staff about data.
Considering their obligations of the Gender Equality Act, a large Health defined entity decided to focus on the data collection effort, as they realised that the only source of data they have, from their payroll system, only collects data with “men” and “women” identifiers.
The payroll system has not been set up to collect any other intersectional identifiers, i.e., there is no demographic data on Aboriginality, age, disability, ethnicity, gender identity, race, religion, sexual orientation or other attributes. Based on the data available the defined entity has 75% of their staff, identifying as women and 25% identifying as men. Men are disproportionately in the most senior roles while there has been gender balance achieved in executive and leadership roles, as a result of deliberate succession planning and advancement. There is a recognition in the industry that a health care worker is gender stereotyped as a woman. While the industry is seeing growth in workers who identify as women who are culturally and linguistically diverse and Aboriginal and Torres Strait Islanders, this defined entity recognises that these women with intersectional attributes are not represented in management and leadership, as is evidenced from their organisational charts.
Challenges and Complications
A key challenge identified by this entity when approaching their data collection; is resourcing and senior stakeholder buy-in, considering the impact of the months of lockdown and disruption to the public health system, in Victoria. The resourcing and capability for an intersectional data collection and analysis is a major concern, as the D&I team is limited to one senior consultant, a graduate, and a trainee – no one has this expertise. An added contextual complication is that intersectionality has been addressed within the Diversity and Inclusion policy with distinct plans per intersectional attribute. There are separate plans for Aboriginality, Cultural and Linguistic Diverse People, LGBTIQA+, Age and Disability. As the Gender Equality Act requires the defined entity to address Intersectional Gender Inequality, the Diversity and Inclusion Policy will need to be amended.
A further complexity is the definition of “employee” for this defined entity, as it has an ‘internal’ workforce and an ‘external’ workforce delivering the service - made up for a range of employee types (full-time, part-time, casual or fixed term basis as well as apprentices and trainees). The act shares that the scope of the data collection should go across all these employee types, making the intersectional data collection a challenge.
There has been an attempt to collect intersectional data but on a volunteer basis via staff surveys. (In 2019, data collection efforts focused on disability and LGBTIQA+ identities; in 2020, data collection focused on ethnicity and Aboriginality.) All previous attempts yielded a low response rate. The reasons for the low response rates were investigated through staff-led network (formal staff networks of people with similar intersectional attributes) consultations, revealing a mix of survey fatigue and low levels of trust and concerns of psychological and cultural safety. The defined entity recognised that the People Matters Survey had a better response rate, being anonymous.
This defined entity decided to mitigate the low resources in D&I team by forming two Gender Equality Action working groups, one focussing internally and one focussing externally. There was an expression of interest campaign endorsed by an Executive Director to form the two working groups - resulting in a representative from every department. These two working groups have designed a communication plan with a ten-minute regular time slot in every executive’s meeting agenda, to share information on what is needed to prepare for and execute the data collection. Early wins included securing a full-time data scientist, through joint funding across department heads and securing project-specific capacity from others skilled in data collection and analysis.
Using the seven measures table provided by the Commission for Gender Equality in the Public Sector, the working groups and data scientist, initially assessed whether data was available on gender composition and by occupation, including the Board; gender pay gap, sexual harassment complaints, attrition data, promotion data, training data, proportion of employees with formal flexible work arrangements, and those accessing parental leave, family violence leave, carers leave. While this gender data was available, very limited data was available on intersectional attributes of Aboriginality, age, disability, ethnicity and race, religion and sexual orientation.
This defined entity has now provisioned in its budget for its payroll and other data systems to capture intersectional attributes to help future data collection. To understand intersectional gender inequality at this time, the defined entity hired an expert in qualitative data gathering (focus groups and interviews) and together with Pride, Women of Colour, Aboriginal and Torres Strait Islander and Ability Networks, arrived at key themes to address in its Gender Equality Action Plan, such as, cultural safety, gender ethnic pay gaps, downward carer mobility, underemployment, pigeon-holing in certain roles (e.g. community or specialist roles, like Indigenous Specialist).
- The skilled data project resources have ensured that the defined entity’s data storage and security is compliant and of a high quality, accuracy and confidentiality.
- The internal communications team was engaged to create a fortnightly email to communicate topics like, “why the defined entity is undertaking the gendered data collection process”, “how the data collection will help with the GEAP”, and “how to get involved in the consultation process”, once data has been analysed. These messages were communicated via senior leaders and staff-led networks Chairs to the relevant forums.
- The D&I Policy has been reviewed and provisionally edited to ensure that there is a placeholder for the outputs of the gender equality audit and intersectional qualitative data. This will ensure that this policy is up to date.
- The data collection phase has been completed and the data has been provisionally analysed by the two working groups, revealing gender inequalities (including, but not limited to):
- All women with intersectional attributes, in permanent part time roles suffered downward career mobility more than any other group.
- Women of colour rarely took up flexible leave for fear of backlash to their careers.
- Aboriginal women were segregated into community and indigenous specialist roles that are lowly paid.
- Women with a disability and gender diverse women experienced bullying and harassment more often than other demographics.
- Older women were disproportionately represented in casual roles, as they needed to work past retirement.
The working groups and the executive leadership team are meeting to finalise the results of the workplace gender audit and will use the insights and analysis as the basis of their Gender Equality Action Plan.
Reviewed 23 November 2022