Closing the Data Gap

Project Info

Team Name


Team Members

Louie , Bryan Pajarito , Ahira Martin , Ryan Macalandag , Regina Macalandag

Project Description

Women experience more barriers to achieving better financial outcomes than men. On every occupation group, women earn less than men. On low-income occupation groups, women make up majority of the workforce. Among these women, those with a specific visa stream consistently earn less than all the others. Why are there more women in these occupations? Are there visa restrictions that make some women more vulnerable than others? What are the challenges they are facing, where can we find them, and how can we best help them?

To answer these questions, we first created a dashboard to visualize the following:
• The geographic distribution of financial outcomes (total income or loss, wage or salary, reportable employer superannuation contribution, and number of employing businesses) per female population (15 years old and above)
•The average number of businesses per industry on the user-selected geographical location
•Slice the financial outcomes per female population by geographical remoteness and state/territory.

Our dashboard contributes to understanding how women’s financial outcomes (income sources and amounts, superannuation) compare across geographical locations. Users can quickly focus on a local government area (LGA) of low financial outcome per female population and determine which industries are predominantly present. Our dashboard can point to where LGAs can target programs to improve women’s participation in higher paid industries and improve their financial welfare.

During data preparation and analysis for our dashboard, we encountered challenges due to inconsistent and unavailable data. Identifying women based on location and socio-economic status using diversity and inclusion criteria such as occupation, education, ethnicity, and migration status was difficult. Combining datasets from ATO and ABS was also challenging due to missing and mismatched spatial fields. Because of this, we are proposing a policy to close the data gap. Available government data should be reviewed to find the gaps in diversity and inclusion data. For example, on every job application form, the government asks for diversity data to help design and improve inclusion in hiring practices. Imagine what we could achieve if we have diversity data as well for every tax file lodged.

Next, to improve the financial outcomes of women in a way that complements government efforts, we wanted to assess available government programs and services in the regions where women had lower financial outcomes. To get the list of programs and services, we had to dig for it and navigate through the web of links available online. This led us to a solution that simplifies the discovery of government programs and services that a person is eligible for.

Finally, by looking at the intersectionality of data, we have also developed a policy recommendation for visa restrictions encountered by women migrants.

#data gap #policy #esg #gender #women #intersectionality #diversity #inclusion

Data Story

Improving the financial outcomes for women require an intersectional and integrated approach. Our product covers three solutions: a dashboard for agencies and researchers, a policy brief to address the challenges we encountered, and a platform that could have immediate and direct impact to women.

In preparing our dashboard, we first combined Australian Taxation Office’s (ATO) dataset on taxation statistics (individual, 2020-21) with several Australian Bureau of Statistics (ABS) datasets (population estimates, business indicators, digital boundary files, and geographic correspondence). We selected total income or loss, wage or salary, reportable employer superannuation contribution, and number of employing businesses as indicators of financial outcomes. The financial indicators were then normalized using census data (population of females aged 15 years old and above). We used Power BI to analyze and visualize data.
In the dashboard, red regions indicate LGAs of low financial outcome per female population while blue regions indicate otherwise. Slicers allow users to select specific geographical remoteness and state/territory to focus on specific LGAs of interest. Selecting an LGA in the map visuals will show the distribution of businesses by industry as a tool tip. The dashboard is also accessible from our prototype web-based support platform.
In the next version of the dashboard, we intend to analyze and visualize data from the other financial years. This will allow users to view the financial outcomes of women across time. The dashboard is in

Policy Brief
This brief presents our recommendations for closing the gaps in diversity data across all agencies, facilitating better integration and support for migrants in the workforce, and intervening with women who have provisional visa status. The policy document is in

Our purpose is to make it easier for everyone to discover the government support that they need and are eligible for. Rather than going through various websites to research on various government programs and services, the platform will connect you to the right service based on your needs. The platform identifies your needs through a short questionnaire.

For this delivery, we built a prototype for a web-based support platform. With this version, the user can discover a fixed set of services from various departments depending on what they need.

If we had more time, we would perform user research. We want to understand whether this is a real problem or not for the audience we intend to serve, understand the usage of current services and understand how best to reach them.

The current version is in

Evidence of Work



Project Image

Team DataSets

NSW gender pay gap

Description of Use visualisation of gender pay gap release by the NSW gov

Data Set

WGEA Dataset | Datasets | - beta

Data Set

Local Government Areas - 2021 – Shapefile, Digital Boundary Files, Australian Statistical Geography Standard (ASGS) Edition 3

Data Set

Data cube 10: Businesses by industry division by Local Government Area (LGA) by employment size ranges

Data Set

Population estimates by age and sex, by LGA (ASGS2021), 2021

Data Set

ASGS Geographic Correspondences (2021) Edition 3

Data Set

Employment in the 2021 Census | Australian Bureau of Statistics (

Data Set

Personal Income of Migrants, Australia, 2016-17 financial year | Australian Bureau of Statistics (

Data Set

Taxation Statistics 2020-21 - Individuals - Table 15 -

Data Set

Taxation Statistics 2020-21 - Individuals - Table 7 -

Data Set

Taxation Statistics 2020-21 - Individuals - Table 6: Selected items, by taxable status, state/territory and postcode, 2020–21 income year

Data Set

Challenge Entries

Best Creative Use of Data in Response to ESG

How can you showcase data in a creative manner to respond to ESG challenges? How can we present and visualise data to stimulate conversation and promote change?

Go to Challenge | 33 teams have entered this challenge.

Helping women succeed financially

How can financial outcomes for women be improved through increasing diversity and inclusion across industries?

Go to Challenge | 6 teams have entered this challenge.