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MrBookman_LibraryCop OP t1_jbi6ajh wrote

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

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WaterstarRunner t1_jbi92ks wrote

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

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MrBookman_LibraryCop OP t1_jbibska wrote

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

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WaterstarRunner t1_jbidfq5 wrote

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.

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toastyroasties7 t1_jbpwau5 wrote

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

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