AU Gender Pay Stats

Built by Anna Syme and Claude (AI). Uses real data from the Workplace Gender Equality Agency (WGEA).

Pre-generated results

Click any file to read now — no setup needed.

Category	Top 20	Top 100
Worst pay gap %	top20_worst_pay_gap.md	top100_worst_pay_gap.md
Worst hourly gap ($/hr)	top20_worst_hourly_gap.md	top100_worst_hourly_gap.md
Fewest women in leadership	top20_fewest_women_in_leadership.md	top100_fewest_women_in_leadership.md

All tables cover companies with 500+ employees.

What a company lookup shows

• Pay gap % — median and average gender pay gap for 2024-25
• Approx. hourly gap — estimated dollar difference per hour (men vs women)
• Senior management — breakdown across CEO, executive, and senior manager roles

Pay figures use full-time-equivalent annual salary, so part-time workers are fairly compared to full-time.

Setup (one-time, ~5 minutes)

You need Python 3. To check: open Terminal and run python3 --version. If you get a version number, you're good. If not, download from python.org.

1. Download this project

Click the green Code button → Download ZIP. Unzip it, open Terminal, and navigate to the folder:

cd ~/Downloads/gender-stats-main

2. Install the required library

pip3 install openpyxl

3. Download the WGEA data (~200 MB, one-time only)

python3 fetch_wgea_data.py

Look up a company

python3 wgea_analyze.py "Woolworths"

Replace Woolworths with any Australian company name. If there are multiple matches, you'll pick from a numbered list. Here's what the output looks like (Qantas example):

════════════════════════════════════════════════════════════
  Qantas Airways Limited
  Transport, Postal and Warehousing  |  5000+ employees
════════════════════════════════════════════════════════════

────────────────────────────────────────────────────────────
  PAY GAP 2024-25 (FTE-equivalent annualised salary)
────────────────────────────────────────────────────────────
  Positive % = men earn more.  Negative % = women earn more.

  Metric                                   2024-25
  Median total remuneration GPG (%)         +32.2%
  Median base salary GPG (%)                +37.0%
  Average total remuneration GPG (%)        +40.1%
  Average base salary GPG (%)               +40.8%
  Approx. hourly gap (men earn more)     ~$49.06/hr
    (based on avg remuneration, 38hr/52wk)

────────────────────────────────────────────────────────────
  SENIOR MANAGEMENT — gender breakdown
────────────────────────────────────────────────────────────
  Role                      Women     Men
  CEOs                          0%   100%
  Other Executives              22%    78%
  Senior Managers               28%    72%
  ─────────────────────────────────────
  All senior roles combined     25%    75%   ⚠ 75% men

Other commands:

python3 wgea_analyze.py "Commonwealth Bank"   # search by name
python3 wgea_analyze.py                       # browse interactively
python3 wgea_analyze.py --industry            # national industry summary

Regenerate the result tables

The results/ folder already has pre-built tables. To regenerate them after downloading the data:

python3 generate_results.py

Loading data...
  8,617 employers, 1,652 with senior management data

Generating tables...
  Written: results/top20_worst_pay_gap.md       ← Top 20 by median pay gap %
  Written: results/top100_worst_pay_gap.md      ← Top 100 by median pay gap %
  Written: results/top20_worst_hourly_gap.md    ← Top 20 by estimated $/hr gap
  Written: results/top100_worst_hourly_gap.md   ← Top 100 by estimated $/hr gap
  Written: results/top20_fewest_women_in_leadership.md   ← Top 20 fewest women in senior roles
  Written: results/top100_fewest_women_in_leadership.md  ← Top 100 fewest women in senior roles

Done. Results saved to results/

Open .md files in a text editor, or view on GitHub where they render as formatted tables.

Limitations

• Large employers only — only companies with 100+ employees are required to report. Smaller companies not included.
• Private sector focus — covers private sector employers; some Commonwealth public sector employers included, but not state/territory government.
• No individual salaries — figures are aggregated per company, not per person.
• Pay gap %, not raw hourly rate — WGEA uses full-time-equivalent annual salary. The $/hr figure shown is an estimate derived from this.
• Self-reported — employers report their own data; not independently audited.
• Australia only.

Data sources

All data is publicly available under Creative Commons Attribution 3.0 Australia.

Data	Source
Employer pay gap percentages	WGEA Employer Gender Pay Gaps Report
Workforce composition by gender & role	data.gov.au — WGEA Dataset

Data files are not stored in this repo — run fetch_wgea_data.py to download them.

Other scripts

• analyze_pay.py — analyse a CSV of your own employee data
• generate_sample_data.py — generate fake employee data for testing