Occupation Matchmaker

By Nathan Yau  /  Posted to Data Underload  /  Tags: ,

People typically gravitate towards others who can relate or live a similar lifestyle, which is often reflected in choice of occupation. If you’re into mathematics or science, you might have more to talk about with someone in a similar field. It’s why doctors often marry other doctors. Similar story with farming. Or the food industry.

How people with different occupations match up can say something about how personalities are compatible.

In the chart below, select an occupation to see who those with that occupation are more likely to match up with.


The visual was inspired by Adam Pearce and Dorothy Gambrell’s chart for Bloomberg, which looked at the five most common matchups for each profession. However, because of the wide array of job choices (close to 500 classified by ACS), an occupation can end up in the top five with a fraction of a percentage. I was interested in the wider distributions.

I also wanted a mode of comparison that accounted for occupations that are way more common than others. For example, there’s the cliche of the CEO dating the secretary or assistant, and this shows up when you look at the absolute scale. However, a lot of the CEO and secretary relationships come about because a lot of people are secretaries.

It goes the other way around too. Less common occupations overall, such as a stucco mason, are less likely to show up near the top anywhere.

So I used a relative scale that compares occupation-specific rates with the overall married population. (You can also still see the data on an absolute scale.) How does marriage choice for people with a given occupation differ from how everyone marries?

Marriage within the entertainment industry is much more prominent. Family businesses in farming and construction are also more obvious. Mathematicians and statisticians are more likely to match up with financial examiners, social scientists, statistical assistants, and…cabinetmakers?

How do those with your job match up?


  • The data comes from the American Community Survey from 2015, and I downloaded the data using the IPUMS-USA extraction tool maintained by the University of Minnesota.
  • When looking at the relative scale, some of the less common jobs really blow up sometimes, because the denominator is so small (e.g. textile knitting machine operators). I thought about placing a maximum threshold on the radius size, but opted to keep it as-is. I suspect less noise with the ACS 5-year sample.
  • Many who work, have a spouse who is not in the labor force. I didn’t look at that here, but I did look a bit a while back.
  • For both scales, I placed minimum thresholds to show labels and decrease opacity, which highlights the more prominent occupations. The labels were unreadable otherwise.
  • I analyzed the data in R and visualized it with d3.js.

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