I ran a linear regression model to estimate the % difference in income levels between men and women by occupation based on the variables listed in the next paragraph. The model also includes Unpaid domestic work, but it was insignificant. I then used the model to predict the difference in income levels if all independent variables were equal - this is the equivalised difference in the chart (in blue).

Data used for the graph and linear model are from the Australian 2021 Census of Population and Housing. They are Total personal income (weekly), Occupation at the 4-digit ANZSCO level, Unpaid child care, Level of highest educational attainment, Hours worked and Volunteer Status.

Note that the model has an intercept of -0.018 which is highly significant (p < 6.08e-07), suggesting that there remains a structural difference in mean income levels after accounting for the factors listed above; even if domestic duties, hours worked etc. were all the same, there would still be a difference in income levels due to other factors.

Plot generated in R using ggplot2

Could you make the font smaller?

Hmmm I don't think I can go into the negatives so sorry but no

[deleted]

I mean the data seems meaningful, but honestly it is certainly not beautiful to me.

Averages please? Ideally weighted by population size?

For each group, and overall? Thanks in advance.

Saying "there is a difference" without giving the amount is maddeningly incomplete.

You should also include possible confounding variables that were not controlled for.

Can you explain the domestic hours worked and the correction for it?

[removed]

That's unpaid work done in the home, which women usually do more often so may negatively impact participation

I don't know what those variables would be so it's difficult to include them

Sounds interesting, but it is painful to try to read and digest. I think even if you just break it up a bit, that would be a good start.

That's what I assumed, but was more curious about the correction methodology.

Given the presumably high inverse correlation between hours worked and domestic hours worked, I'd assume that there's a risk of double-correction.

However it'd be awesome to see whether the impact of domestic hours worked has a bigger impact in certain job types over others, based on "uncounted working hours" which typically accompany higher paid roles.

What about the first part of the question--what IS the difference?

I thought the x axis would help with that. On average, it's about 2% after equivalising

The lines are a little misleading, because it looks like a "range". And the visual weight is hugely below zero, so it appears to show that the wage gap is hugely negative.

Separating the "equivalized" numbers into their own chart would make clear that they dance pretty evenly around both sides of zero.

(Exactly how close to zero, we are evidently supposed to guess, as you mysteriously did not include the overall average!?)

A possible graph title:

Actual Gender Wage Gap is Only 2% (Or possibly zero, being within the margin of error)

That 2% number is the end result of the entire exercise, and should have been front and center.

The unadjusted one is about 3% on average, so you can't state it's close to zero without mentioning the participation issue

Someone needs to forward this to J. Peterson.

What's the data source?

Remember when the first comment below a viz was a link to the data source? The good ol' days...

It's in my top level comment

He would misinterpret it like apparently many others do

So red is the observed difference and blue the observed difference after it has been adjusted?

Is linear regression the right analysis?

How did you determine which adjustment factors to use and the weight to assign to them?

People need to upvote the source to have it appear on top. Alternatively, I suppose you could sort the oldest ones to the top.

thanks! unfortunately the OP hasn't linked to the data, even in their oldest comment. :(

Darn it… well, it was worth a try!

[deleted]

Surely you have simultaneity issues doing that since the number of hours worked are chosen based on wages.