At first I found this fascinating -- "data shows that CEOs marry assistants" is a good narrative about gender attitudes towards marrying. But as I played with it, I was kind of bummed to learn: most male professions tend to marry assistants, since there are a lot of married female assistants (second only to teachers for females).
I did a quick search for who male Programmers, Executives, and Janitors marry (tried to pick a diverse set), and then filtered out the top five female occupations in this data (Teachers, Assistants, Nurses, Misc Managers, Salespersons). The remaining results were: male Programmers marry female Programmers, male Executives marry female Executives, male Janitors marry female Janitors or female Maids.
So I whipped up a script that, instead of measuring the absolute frequency, normalizes the data set against how often the target occupation gets married to. ie instead of just counting common pairings, it measures "how much more likely is profession X (compared to the general population) to marry profession Y?".
For male CEOs, the result is: they marry other CEOs. Male CEOs are 12x more likely to marry a female CEO than males in other professions are. They are also 12.6x more likely to marry embalmers! 7x more likely to marry Announcers, 5.3x more likely to marry "Dancers and Choreographers", and 4.9x more likely to marry "Public Relations and Fundraising Managers". They are only 1.2x more likely to marry a Secretary than other males are.
For male programmers, it is similar: male programmers are 20x more likely to marry female programmers than other male occupations are. This is because only 0.2% of men marry female computer programmers, but 3.4% of male programmers do. Male programmers also marry female Materials Engineers (20x more likely), Information Security Analysts (13x), "Surveyors, Cartographers, and Photogrammetrists" (10x), and "Architects, Except Naval" (8x).
I also picked one final way of slicing it: "how many more of these marriages did we see than expected" (expected based on the relation frequencies of the two occupations). This is another way of removing "well we would expect a lot of these marriages", but in a way that is less geared towards low-occurrence matches like CEO-Embalmer. For CEOs, the results are: CEOs, Misc Managers, Elementary and Middle School Teachers, Secretaries, Accountants and Auditors. For Programmers, it's: Programmers, Misc Managers, Other Teachers and Instructors, Software Developers, Accountants and Auditors.
Anyway, this isn't to say that these other ways of looking at the data are any "better", they're just answers to different questions.
I did a quick search for who male Programmers, Executives, and Janitors marry (tried to pick a diverse set), and then filtered out the top five female occupations in this data (Teachers, Assistants, Nurses, Misc Managers, Salespersons). The remaining results were: male Programmers marry female Programmers, male Executives marry female Executives, male Janitors marry female Janitors or female Maids.
So I whipped up a script that, instead of measuring the absolute frequency, normalizes the data set against how often the target occupation gets married to. ie instead of just counting common pairings, it measures "how much more likely is profession X (compared to the general population) to marry profession Y?".
For male CEOs, the result is: they marry other CEOs. Male CEOs are 12x more likely to marry a female CEO than males in other professions are. They are also 12.6x more likely to marry embalmers! 7x more likely to marry Announcers, 5.3x more likely to marry "Dancers and Choreographers", and 4.9x more likely to marry "Public Relations and Fundraising Managers". They are only 1.2x more likely to marry a Secretary than other males are.
For male programmers, it is similar: male programmers are 20x more likely to marry female programmers than other male occupations are. This is because only 0.2% of men marry female computer programmers, but 3.4% of male programmers do. Male programmers also marry female Materials Engineers (20x more likely), Information Security Analysts (13x), "Surveyors, Cartographers, and Photogrammetrists" (10x), and "Architects, Except Naval" (8x).
I also picked one final way of slicing it: "how many more of these marriages did we see than expected" (expected based on the relation frequencies of the two occupations). This is another way of removing "well we would expect a lot of these marriages", but in a way that is less geared towards low-occurrence matches like CEO-Embalmer. For CEOs, the results are: CEOs, Misc Managers, Elementary and Middle School Teachers, Secretaries, Accountants and Auditors. For Programmers, it's: Programmers, Misc Managers, Other Teachers and Instructors, Software Developers, Accountants and Auditors.
Anyway, this isn't to say that these other ways of looking at the data are any "better", they're just answers to different questions.
I threw up the code in a gist -- sorry it's messy https://gist.github.com/kmod/ee6ac3c029641b39d0b6