Inequality heatmaps: marriage and working from home

To a kid with a hammer, everything looks like a nail. So I used the same kind of figure for two different datasets. Materials at the end.


Regardless of how you think about the causal relationship between marriage and men’s economic wellbeing, it’s an important fact that marriage in the US has become more economically polarized, with the social class gap in marriage prevalence widening.

Recently, Scott Galloway wrote a bad blog post about marriage and men, which included this truly terrible and misleading figure, which pours bad data analysis of the General Social Survey (see here) into a manipulated-axis clustermuck, which doesn’t even manage to show much of a correlation:

Anyway, Galloway also recycled a figure from bad 2012 blog post from the Hamilton Project. Bad work, but the trend is real, so I updated it and made a different kind of figure, using a heatmap with geom_tile in R, inspired by Kieran Healy’s Baby Boom heatmap. And I added women, separately.

Using the Current Population Survey (CPS) Annual Social and Economic Supplement (downloaded from, I broke men and women down into 10 income deciles in each year from 1980 to 2021, and calculated the percentage of each cell that was married (and not separated) at the time of the survey. This is men:

This shows that rich men are much more likely to be married than poor men, and the gap has grown even as marriage rates have fallen across the board. The figure for women is more complicated, and is a good way to remind yourself that the causal story here is not as simple as some people make it sound.

In 1980, women with higher incomes (their own incomes) were the least likely to be married (not get married, be married). The most likely to be married were women with just a little income. Now, women with the highest incomes are more likely to be married than all but the bottom 20 percent. The biggest drop has been among women with low incomes. (Remember, these are cross-sections, so it’s not necessarily reflecting change over time in these women’s lives.) This is an inequality story, as high income women are more likely to be married (with spouses who have incomes as well), and low income women are more likely to be single (without spouses). Cohabitation, which is not included here takes some of the edge off this, but not that much.

Working from home

Starting in May 2020, some forward-thinking people at the Bureau of Labor Statistics added a question to the monthly CPS:

At any time in the LAST 4 WEEKS, did (you/name) telework or work at home for pay BECAUSE OF THE CORONAVIRUS PANDEMIC? (Enter No if person worked entirely from home before the Coronavirus pandemic)

At the time, the great majority of workers in some occupations — especially teaching — were working from home, as their workplaces were shut down by epidemic mitigation policies. Others, such as cooks and waiters, were either unemployed or working in dangerous conditions. Since that first survey in May (through August), the pattern has changed a lot, and there is much less teleworking. But some occupations are still staying home at pretty high rates, including college teachers, programmers, lawyers, and management analysts.

There is a sharp distinction between high- and low-telework occupations. It’s not quite a map of status and income, but it’s not not that, either. As in all things, apparently, the pandemic has been a seismic inequality event. Everything has changed, but very differently for different groups of people. More and different inequalities.

Here is the heatmap, which I originally shared on Twitter.


I can’t share the CPS data I got from IPUMS, but you can get it yourself with a free account. I shared the Stata code I used to manipulate the data, and the R code I used to make the figures, on the Open Science Framework, here: My R skills are very limited so I just use it to make the figures, but if you are at a functioning beginning level the code might help.

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