Australia's legal profession and the gender income gap (and learning slopegraphs)

The R for Data Science community has been running a ‘Tidy Tuesday’ project for a few weeks. In essence they link to a data-driven paper and a somewhat tidy version of the paper’s underlying dataset. The challenge is to develop some visualisations etc from the data, all within the R for Data Science approach to working with R.

This week’s challenge is drawn from an article on Australia’s pay gap. The article’s data is sourced here.

So I took the opportunity to try figuring out how to build a slopegraph in R. As Cole Nussbaumer Knaflic puts it, slopegraphs are great for highlighting comparisons between two groups, two points in time etc. Here is my attempt at visualising some of the data:

So, what we’re looking at is a graph visualising the gender pay gap for the 100 best-paid occupations, as measured by average male income. I’ve used labels to highlight the five occupations with the worst disparities and the only three occupations where women earn 90% of men or more.

So: a critique. The labels obviously need work. They are wordy and it’s not obvious which applies where. And the basic design is flawed: if I include more data by putting more labels in, the graph becomes completely cluttered. Likewise if I add more guidance to navigate the data. And lots of information is missing, especially regarding how gendered the occupations are in the first place. I thought about placing points of varying size on each axis to signify this, but there is no tidy way to do so within a single graph.

On the data itself, it would be interesting to seek a pattern relating income equality to equality of access, but that’s for another day. Anecdotally, don’t be fooled by the futures traders: there are 281 male futures traders in the survey and only 28 women.

Likewise for members of the legal profession. Incomes are more equal where pay scales apply. But women are less likely to occupy those roles. Where occupational access is more equal, employers are freer to set salaries, and women are paid less well. When it comes to the Bar, in all likelihood the male average income is positively skewed by the male-dominated big-earners at the top:

(#tab:print_law_table)Income is more equal but women have less access where pay scales apply
Occupation Men (%) Women (%) Average taxable income (men) Women’s income as % of men’s
Judge 73 27 AUS$381,323 93%
Magistrate 63 37 AUS$246,737 105%
(#tab:print_law_table)Access is more equal but women have less income where pay scales do not apply
Occupation Men (%) Women (%) Average taxable income (men) Women’s income as % of men’s
Barrister 51 49 AUS$168,766 35%
Solicitor 43 57 AUS$138,379 72%

To my mind, this reflects the classic patterns of gender discrimination. Unless you are in a tightly regulated profession, disparities persist. And when it comes to the tightly regulated top of the profession, the career necessary for access is likely not available to enough women at all.

Gist with code here.

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Ciarán O'Kelly
Director of Graduate Studies

My research interests include distributed robotics, mobile computing and programmable matter.