This week’s original was an article about Australia’s wage gap that concluded with two ordered lists – one for the top 50 jobs for Australian women, and another for men. It’s easy to make a visualization that’s more engaging than an ordered list, so I focused on creating something that would enhance the article’s thesis.
The diverging bar chart in third section of my viz was my starting point. It includes jobs that appear on either top 50 list, sorted by average occupational income, and the sides are colored by % of average occupational income. If only one chart made it into the article, I would pick this one because it’s closest to the original ordered lists. This chart made me curious about jobs that look like very promising careers, but aren’t great for women once the gender gap is accounted for. Are people accounting for these differences when giving young women career advice?
The first section is a straight-up comparison of average male incomes to average female incomes by occupation, highlighting differences that are especially egregious. It’s a bit obvious, but gives a nice summary of the situation. After creating this chart, I wanted to see what the pay gaps looked like when controlling for income level. (a $1000 difference is a lot smaller to someone making 100k than someone making 10k.) That led to the middle section, which does the best job of supporting the author’s argument. Gender gaps do increase with income, even when controlling for scale.
Overall, some of the skills that went into this were: diverging bar charts, LOD calcs, sets, custom colors, and setting up a 45 degree reference line.