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.

Marriage

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 IPUMS.org), 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.

Materials

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: https://osf.io/2k86a/. 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.

The pandemic and the family, demography edition (video)

I think the big demographic story of 2021 is likely to be the very large decline in births. I think everything points that way and I think it’s going to be quite shocking when we see how big it is. — Philip Cohen, October 27, 2020

I gave a talk yesterday at the University of California, Irvine Center for Demographic and Social Analysis. The slides are here under CC-BY license. The video is below.

After reviewing the state of the economic and social shock of the pandemic, with implications for family processes and events (sex, contraception, pregnancy, birth, marriage, and divorce), I presented some new information on births and marriage.

Here is the data on births, in Florida and California, the only states I found that have monthly updated birth numbers through September (subject to revision, but probably not by much). Both show large declines in births in 2020. Which seems hard to link to the pandemic, gestation times being what they are (but maybe more reasonable than last month’s report). There may already be more miscarriages and abortions, or maybe fewer premature births? I don’t know. If these declines have nothing to do with the pandemic, and they’re just the continuation of our trend toward lower birth rates, then that’s pretty shocking, too.

This is the total births by month and year, with 2018 and 2019 compared with 2020 so far:

Here are the monthly totals compared with the annual average decline in over the previous three years, which is 2.9% in California and 0.7% in Florida, which I call “predicted.”

In September 2020 versus September 2019, births were 6.1% lower in Florida and 9.6% lower in California. There is a lot of big news in the news these days, but I think this is still pretty big news. Again, if this has nothing to do with the pandemic that’s even more shocking.

Anyway, here’s the video:

Early pandemic demographic indicators

A couple new ones and a couple updates.

Pregnancy

The pandemic could be affecting the number of abortions, miscarriages, or infant deaths, but unless those effects are large it should be too early to see effects on the total birth rate, given that we’re only about 7 months into it here. So for possible birth indicators I did a little Google search analysis using the public Google Trends data.

I found three searches that were pretty well correlated in the weekly series: “am I pregnant”, “pregnancy test,” and “morning sickness”, which all should have something to do with the frequency of new pregnancies. I ran Google Trends back five years, created an index from these searches (alpha = .68) , smoothed it a little, and this is what I got:

There was already a big drop in 2019 from the previous three years (reasonable, based on recent trends), and then 2020 started out with a further drop. But then it spiked downward in March before rebounding back to its lower level. So, maybe that implies birth rates will keep falling but not off the charts compared with recent trends.

I also checked “missed period,” which was not well correlated with the others, and got this:

Again, 2019 was already showing some decline, and 2020 started out lower than that, and now searches for “missed period” are running lower than last year, but not more in the middle of the year than they were in the beginning. So, inconclusive for pandemic effect.

Weddings

Here’s a new take on the Google trends for weddings. I took the averages of searches for “wedding invitations”, “wedding shower”, “bridal shower”, “wedding shower”, and “wedding dresses” (alpha=.94). With a little smoothing, here is 2020 compared with the average of the four previous years (unlike pregnancy searches, this one didn’t show a marked decline in 2019 compared with previous years).

March and April showed catastrophic declines in searches for wedding topics, and the rebound so far has been weak. However, weddings aren’t the same as marriages. Maybe people who had to cancel their weddings still got married down at whatever the pandemic equivalent of the courthouse is. So here’s the same analysis just for the search term “marriage license.” This shows a steep but not as catastrophic drop-off in March and April, and a stronger rebound. So maybe the decline in drop in marriages won’t be as big as the drop in weddings.

Actual marriages

I previously showed the steep decline in recorded marriages in Florida. Here’s an update.

Florida lists recorded marriages by county and month, one month behind (see Table 17). They update as they go, so as of today August marriages are probably still not all recorded. The comparison with previous years shows a collapse in March and April, and then some rebounding. August is preliminary and will come up some.

Marriages in Florida normally peak between March and May. Of course it’s too early to say how many of these were just being postponed. The cumulative trend shows that through July Florida is down 24,000 marriages, or 27%, compared with last year.

Divorce ideation

When the going gets tough, the afflicted want to get divorced, but maybe they can’t. It’s expensive and time consuming and maybe people think it will upset the children even more. (I’ve written about divorce and recessions here and here). So my initial assumption going into the pandemic was that there would be a stall in divorces even though the intent to divorce would rise, followed by a rebound when people get a chance to act on their wishes.

Here I use Google search trends for four searches: “divorce lawyer”, “divorce attorney”, “get a divorce”, and “how to divorce”. The alpha for this index is .69 (when I just use the attorney and lawyer, the alpha is .86, but the result looks the same, so I’m showing the wider index). The results show a drop in divorce ideation in March into April, followed by a rebound to a level a little above the previous year average. Note this pandemic-spring drop is a lot less pronounced than the wedding and marriage collapses above.

Actual divorces

Divorces take time, of course. Like births, I wouldn’t expect to see definitive results right away. In fact, it’s hard to know how long divorces are in process before they show up as recorded. However, in my favorite real-time demography state, Florida, they have been recording divorces every month, and have a look at this:

It’s a giant plunge in recorded divorces, almost 60% in April, followed by a weaker rebound. Again, the records are not yet complete, especially for August, so we’ll see. But comparing these patterns, it might be that there was a short suspension in divorce ideation as people were distracted by the crisis, followed by a rebound which hasn’t yet translated into divorce filings. Googling about divorce seems cheap and easy (and faster) compared with pulling it off, but this might mean there is growing pent up demand for divorce, which is bad (and may imply greater risks of conflict and violence).

Young adults living “at home”

I previous wrote about young adults living with their parents and grandparents using the June and then July Current Population Survey data made available by IPUMS.org. Subsequently, the Pew Research Center did something very similar using the data through July (with additional breakdowns and historical context). Pew used living with parents, apparently including those in households where the parents are not the householders. I prefer my definition — young adults living in the home of parents (also, or grandparents) — which fits better with the popular concept of living “at home.” So if your parents come to live with you, that’s different.

Anyway, here’s the update through August, which shows the percentage of young adults living at home falling back some from the June peak. I will be very interested to follow this through the fall.

Update: By October this trend had returned almost back to pre-pandemic levels:

In October the percentage of young adults living with the parents or grandparents was almost back to normal pic.twitter.com/WTMebCV3hs

— Philip N Cohen (@familyunequal) November 17, 2020

Stata code for the living at home analysis is available here: https://osf.io/2xrhc/.

The pandemic and its attendant economic crisis is having massive effects on many aspects of family life. These early indicators are just possible targets of future analysis. There is a lot of other related work going on, which I’ve not taken the time to link to here. Please feel free to recommend other work in the comments.

Race/ethnic intermarriage trends, 2008-2018

Rising, with gender differences.

Since 2008 the American Community Survey has been asking respondents whether they got married in the previous 12 months. Using the race/ethnicity of spouses (when they are living together), you can estimate the proportion of new marriages that cross racial/ethnic lines.

Defining such “intermarriage” is not as simple as it sounds. Some people have multiple racial or ethnic identities. Some people marry across national-origin lines within panethnic groups (e.g., Mexicans marrying Puerto Ricans). Is a Black+White Dominican marrying a White Mexican, or a Black+White person marrying a Black person, “intermarriage”? In these estimates I drop people who are not Hispanic and specified more than one race, then combine Hispanic origin and race into one, mutually exclusive 5-category variable: White, Black, American Indian, Asian/Pacific Islander, Hispanic (of any race). In other words, intrapanethic marriage (Mexicans marrying Puerto Ricans, or Filipinos marrying Koreans) is not intermarriage. I’m not saying this is the best way; it combines conventional categories with convenience. I combine same-sex and different-sex marriages.

To present the results, I separate men and women (you’ll see why), and estimate predicted probabilities of intermarriage at the mean of controls for age and age-squared, using logistic regression with normalized weights. My Stata code is on the Open Science Framework; help yourself. (I previously did something very similar for states and metro areas.)

The results are in figures, with each race/ethnic group presented on its own scale (check the y-axes). I don’t present American Indians because the samples are small (about half the API sample) and the multirace group is large.

Results

Click the images to enlarge

How big will the drop in weddings be? Big

With data snapshot addendum at the end.

In the short run, people are canceling their weddings that were already booked, or not planning the ones they were going to have this summer or fall. In the long run, we don’t know.

To look at the short run effect, I used Google Trends to extract the level of traffic for five searches over the last five years: wedding dresses, bridal shower, wedding license, wedding shower, and wedding invitations (here is the link to one, just change the terms to get the others). These are things you Google when you’re getting married. Google reports search volume for each term weekly, scaled from 0 to 100.

Search traffic for these terms is highly correlated with each other across weeks, between .45 and .76. I used Stata to combine them into an index (alpha = .92), which ranges from 22 to 87 for 261 weeks, from May 2015 to last week.

For the graph, I smoothed the trend with a 5-week average. Here is the trend, with dates for the peaks and troughs (click to enlarge):

The annual pattern is very strong. Each year people people do a lot of wedding searches for about two months, from mid-January to early-March, before traffic falls for the rest of year, until after Thanksgiving. There is a decline over these five years, but I don’t put too much stock in that because maybe the terms people use are changing over time.

But this year there is a break. After starting out with a normal spike in mid-January, searches lurched downward into February, and then collapsed to their lowest level in five years — at what should have been the height of the wedding Google search season.

Clearly, there will be a decline in weddings this spring and summer, or until we “reopen,” whatever that means. A lot of people just can’t get married. When you think about the timing of marriage, most people getting married in a given year are probably already planning to at least half a year in advance. So even if no one’s relationships are affected, and their long term plans don’t change, we’ll still see a decline in marriages this year just from canceled plans.

Beyond that, however, people probably aren’t meeting and falling in love as much. People who are dating probably aren’t as likely to advance their relationships through what would have been a normal development – dating, love, kids, marriage, and so on. So a lot of existing relationships – even for people who weren’t engaged – probably aren’t moving toward marriage. Even if they get back on track later, that’s a delay of a year or two or however long. This says nothing about people being stressed, miserable, sick (or worse), and otherwise not in any kind of mood.

In the longer term, what does the pandemic mean for confidence in the future? The crisis will undermine people’s ability to make long term decisions and commitments. Unless the cultural or cognitive model of marriage changes, insecurity or instability will mean less marriage in the future. This could be a long term effect even after the acute period passes.

What about a rebound? Eventually – again, whenever that is – there probably will be some rebound. At least, just practically, some people who put off marriage will go ahead and do it later. Although, as with delayed births, some postponed marriages probably will end up being foregone. On a larger scale, when people can get out and get together and get married again, there might well be a marriage bounce (and also even a baby boom). Presumably that would depend on a very positive scenario: a vaccine, an economic resurgence, maybe a big government boost, like after WWII. A surge in optimism about the future, happiness. That’s all possible. This also depends on the cultural model of marriage we have now, so that good times equals more marriage (and childbearing). In real life, any such effect might be small, dwarfed by big declines from chaos, fear, and uncertainty. I can’t predict how these different impulses might play against each other. However, on balance, my out-on-a-limb forecast is a decline in marriage.

Data snapshot addendum

I didn’t realize there was monthly data available already. For example, in Florida they release monthly marriage counts by county, and they have released the April numbers. These show a 1% increase in marriages year-over-year in January, a 31% increase in February, then a 31% drop in March and a 72% drop in April [Since I first posted this, Florida added 477 more marriages in April, and a few in the earlier months, changing these percentages by a couple points as on June 5. -pnc.] Here is a scatter plot [updated] showing the count of marriages by county in 2019 and 2020. Counties below the diagonal have fewer marriages in 2020 than they did in 2019. Not surprising, but still dramatic to see it happening in “real time” (not really, just in quickly available data).

Very important social scientist says — wait, Brad Wilcox did what now?

Last year Brad Wilcox gave a talk at Saint Vincent College, a monastic college just 8 minutes from the glass-lined tanks of old Latrobe. The talk was structured as a critique of Stephanie Coontz and her decadent view that marriage should be freely chosen.

I was skipping through it looking for his current definition of the “Gold Standard” family (“intact, married, biological family”), when I found this amazing piece of sophistry masquerading as incompetence (or maybe the other way around, hard to know). To lighten things up around here, I thought I’d share it.

This is the clip (It’s here on the full video), followed by the transcript:

Stephanie Coontz, who is a very prominent, progressive author, writer and scholar, has argued that ‘marriage has become more joyful, more loving, more satisfying.’ So what’s the evidence tell us about what actually happened? Well, looking just at those folks who were married, from the 70s to the 80s, and the 90s, and actually the 2000s [he’s looking up at his own graph here], there’s no evidence that marriage is becoming more joyful or more loving among those folks who managed both to get married, and in this case, to remain married. So we see a decline. It’s actually particularly precipitous for women, from the 70s to the 2000s. And when you factor in the fact that fewer and fewer folks were getting married, that’s particularly interesting. What’s this is telling us is that a smaller and smaller share of American adults both men and women were in a very happy marriage from the 70s to the 2000s. So, I don’t think there’s a lot of evidence to sort of back up her idea that sort this newer, post-70s model, this more contingent model, was more successful.

Here’s a screengrab of the slide he’s looking at:

Wow, that sure makes it seem like the percent of adults in a very happy marriage is a lot lower than it used to be, although rebounding strongly this decade. It also just doesn’t look anything like the many graphs of marital happiness I have seen, and made, from the General Social Survey, which is the data he’s using here. So, how exactly is Brad Wilcox completely wrong this time?

Two amazingly incompetent and/or disingenuous ways. First, he included people who aren’t married in his denominator, so the decline in “happy marriage” he shows is due to the decline in marriage altogether, not to any change within marriage. That’s like saying the percentage of people who love reading handwritten parchment scrolls has plummeted since the Middle Ages.

Second, that huge drop in the 2000s he shows is entirely due to the fact that most people weren’t asked the question in survey years 2002, 2004, and 2006, and he included them as not in “very happy” marriages. Brad could just as accurately have said the 19,000 happily married people in the GSS are the only happily married people on earth.

Here is the actual trend, with what he showed (which is trivially easy to reconstruct), showing the individual years instead of the decade grouping he used:

I can’t think of something better to say than just showing this. (I didn’t bother with the gender breakdown, which isn’t the point.)

I hope this helps. Please stay at home if you can, and support those who can’t stay home as best you can.

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Update: Stephanie Coontz writes on Facebook:

Thanks to Philip Cohen for showing how Brad Wilcox misuses data to criticize my claim that modern marriages are “more loving, more satisfying” than in the past. What I actually say is that when they work WELL, modern marriages are more intimate, fair & loving than marriages of the past. When they DON’T work well, they are more disappointing and seem less bearable because of our higher expectations. This makes our current crisis especially challenging, because we have to expect more of our partners, and can expect less from others, than usual.

11 trends for your New Decade’s holiday party

There’s a lot to do this decade, and only a few days to do it. You need to look smart doing it. The best way to look smart is to be smart, and that means ingesting meaningful bits of data and turning them into useful knowledge. When you display data bits at a holiday party, they merge with those from the other people there, to become the common knowledge we need to get things done in the next decade, which we will do.

So here are a few meaningful bits of demographic data, presented with trend lines and easy-to-memorize fact statements. These aren’t the most important or most interesting demographic trends of the decade, but they’re all meaningful and readily interpretable — plus I was able to gather them on short notice between other last-minute decadal deadlines. Feel free to add your own in the comments.

Notes: We don’t have data through the end of the decade for all of these, so I just present the latest data. And I extend them back toward 1999 as far as I can for context. And I scaled them to show the change as clearly as possible, so watch out for y-axes that are compressed to the active range rather than starting at zero (file complaints here). If I don’t specify the time frame in the text, it refers to the last 10 years of data.

So just memorize the facts that interest you, and remember the associated images. Here goes.

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Overdose deaths increased more than 80 percent.

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Chlamydia cases increased by a third.

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One-in-six 25-34 year-olds live with their parents

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The share of college graduates majoring in sociology or history fell by more than a third.

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The percentage of new mothers who are married has risen back over two-thirds.

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For the first time in decades women over 40 may soon be more likely to have a baby than teenagers.

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The divorce rate has fallen 20 percent.

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People with college degrees are 19 percent more likely to be married than people without.

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International adoptions fell by more than two-thirds.

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Refugee admissions are at their lowest level since before 1980, and falling fast.

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The newspaper industry was cut in half.

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Happy New Decade!

 

The arriving divorce decline

In “The Coming Divorce Decline” I showed the U.S. divorce rate falling from 2008 to 2017, and predicted that, because the married population was being stocked with increasingly non-divorce-prone marriages, the rate would continue to fall. After the first draft (based on 2016 data), divorce fell in 2017, providing the first support for my prediction before the paper was even “published” (accepted for Socius). Now the 2018 data is out, and divorce has become less common still.

Here’s a quick update.

Based on the number of divorces reported in the survey each year, by sex, and the number of married people, I calculate the refined divorce rate, or the number of divorces per 1,000 married people. That fell another 3% for both women and men in 2018, to 15.9 and 14.3 respectively (the rates differ because these are self reports and women report more).

When I run the model from the paper again on the new data (on women only), I can show the drop in the adjusted odds of divorce, updating Figure 1 of the paper (the 2018 change in an unadjusted model is significant at p=.06; adjusted is p=.14, the adjusted change from 2016 is significant at p=.002).

For other takes on the latest data, see this report on the marriage-divorce ratio from Valerie Schweizer, and this on geographic variation from Colette Allred, both at the National Center for Family and Marriage Research.

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• The data and code for the paper are available here. This update uses the same code with one new year of data.
• If you like my new Stata figure scheme (modified from Gray Kimbrough’s Uncluttered) you’re welcome to it: here.
• Slides from my presentation this fall at the European Divorce Conference are here.
• Divorce posts are gathered under this tag.

Family diversity, new normal

Family diversity is not just a buzzword (although it is that), and it’s not just the recognition of diversity that always existed (although it is that). There really is more actually-existing diversity than there used to be.

In The Family, I use a figure with five simple household types to show family conformity increasing from 1900 to a peak in 1960 — and then increasing diversity after that. I’ve updated that now for the upcoming third edition of the book.

In 2014, I wrote a report for the Council on Contemporary Families called “Family Diversity is the New Normal for America’s Children,” which generated some news coverage and a ridiculous appearance with Tucker Carlson on Fox & Friends. A key point was to demonstrate that the declining dominant family arrangement after 1960 — the male-breadwinner-homemaker family — was replaced by a diversity of arrangements rather than a new dominant form. Here I’ve updated one the main figures from that report, which shows that “fanning out from a dominant category to a veritable peacock’s tail of work-family arrangements.”

For this update, I take advantage of the great new IPUMS mother and father pointers to identify children’s (likely) parents, including same-sex couple parents who are cohabiting as well as those who are married. Census doesn’t collect multiple parent identifiers in the Decennial Census or American Community Survey, and IPUMS has tackled the issue of how to best presume or guess about these with a consistent and well-documented standard. In this figure, 0.42% of children ages 0-14 (about 250,000) are living in the households of their same-sex couple parents. I also rejiggered the other categories a little, but the basic story is the same.

I published a version of this figure for K-12 educators in Educational Leadership magazine in 2017. I wrote:

Today, teachers need to have a more inclusive mindset that recognizes the diversity of family structures. Although there are reasons for concern about some of the changes shown in the data, the driving factors have often been positive. For example, changes in family roles reflect increased educational and occupational opportunities for women and greater gender equality within families. Fathers are expected to play an active role in parenting—and usually do—to a much greater degree than they did half a century ago.

My advice to teachers is:

The key points of diversity in family experiences that teachers should watch for are family structure (such as who the student lives with), family trajectories (the transitions and changes in family structure), and family roles (who cares and provides for the student). Using principles from universal design, teachers can promote language and concepts that work for all students. Done right, this is an opportunity to broaden the learning experience for everyone—to teach that care, intimate relationships, and family structures can include people of different ages, genders, and familial connections.

So that’s my update.

Man woman couple height, updated

I had a popular post in 2013 called, “Why taller-wife couples are so rare,” a title given it by my old Atlantic editor, who ran it under a picture of Nicole Kidman (5′ 11″) with her second shorter husband. I also put a version of it in my book Enduring Bonds, and reference it in The Family. In it I used data from the 2009 PSID to show that people are more likely to pair up as taller-man-shorter-woman than would be expected by chance. I’ve now updated it with the 2017 PSID data. This is a revised version of that post with the new data.

Men are bigger and stronger than women. That generalization, although true, doesn’t adequately describe how sex affects our modern lives. In the first place, men’s and women’s size and strength are distributions. Strong women are stronger than weak men, so sex doesn’t tell you all you need to know. Otherwise, as retired colonel Martha McSally put it with regard to the ban on women in combat positions, “Pee Wee Herman is OK to be in combat but Serena and Venus Williams are not going to meet the standard.”

Second, how we handle that average difference is a matter of social construction: We can ignore it, minimize it, or exaggerate it. In the realm of love and marriage, we so far have chosen exaggeration.

Consider height. The height difference between men and women in the U.S. is about 6 inches on average. But Michael J. Fox, at five feet, five inches, is shorter than almost half of all U.S. women today. On the other hand, at five-foot-ten, Michelle Obama is taller than half of American men. So how do people match up romantically, and why does it matter?

Because everyone knows men are taller on average, straight couples in which the man is shorter raise a problem of gender performance. That is, the man might not be seen as a real man, the woman as a real woman, if they don’t (together) display the normal pattern. To prevent this embarrassment, some couples in which the wife is taller might choose to be photographed with the man standing on a step behind the woman, or they might have their wedding celebrated with a commemorative stamp showing her practically on her knees—as the British royals did with Charles and Diana, who were both the same height.

They were the same height pic.twitter.com/I9YVAcU4hn

— Philip N Cohen (@familyunequal) March 18, 2017

But the safer bet is just to match up according to the height norm. A study from Britain measured the height of the parents of about 19,000 babies born in 2000. They found that the woman was taller in 4.1 percent of cases. Then they compared the couples in the data to the pattern found if you scrambled up those same men and women and matched them together at random. In that random set, the woman was taller in 6.5 percent of cases. That means couples are more often man-taller, woman-shorter than would be expected by chance. Is that a big difference? I can explain.

For illustration, and to compare the pattern with the U.S., I used the 2017 Panel Study of Income Dynamics, a U.S. survey that includes height reported for 4,666 married couples. These are the height distributions for those spouses, showing a median difference of 6 inches.

Clearly, if these people married (and didn’t divorce) at random we would expect the husband to be taller most of the time. And that is what we find. Here is the distribution of height differences from those same couples:

The most common arrangement is the husband six inches taller, and a small minority of couples—2.7 percent—are on the left side of the line, indicating a taller wife.

But does that mean people are seeking out taller-husband-shorter-wife pairings? To answer that, we compare the actual distribution with a randomized outcome. I made 10 copies of all the men and women in the data, scrambled them up, and paired them at random. They I superimposed the two distributions — observed and random — which allows us to see which arrangements are more or less common in the actual pairings than we would expect by chance:

Now we can see that from same-height up to man-8-inches-taller, there are more couples than we would expect by chance. And below same-height—where the wife is taller—we see fewer in the population than we would expect by chance. There also are relatively few couples at the man-much-taller end of the spectrum—at 9 inches or greater—where the difference apparently becomes awkward, a pattern also seen in the British study. In the random distribution, we would expect 10 percent of couples to have a taller wife, but we only see 7 percent in that range in the observed data.

You could look at this as people marrying to conform more closely to a norm of husbands being 6 inches taller, because there is reduction in both tails. But that left tail just happens to be pulled down right after you get to taller wives, so you could also call it a taller-man norm.

Humans could couple up differently, if they wanted to. If it were desirable to have a taller-woman-shorter-man relationship, it could be much more common. In this sample, 27 percent of women could marry a shorter man, if they all insisted on it. Instead, people seem to exaggerate the difference by seeking out taller-man-shorter-woman pairings for marriage (or maybe the odd taller-woman couples are more likely to divorce, which would produce the same result).

What difference does it make? When people—and here I’m thinking especially of children—see men and women together, they form impressions about their relative sizes (and related capacities). Because people’s current matching process reduces the number of woman-taller pairings, our thinking is skewed that much more toward assuming men are bigger.

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I put the Stata code for this analysis up here.

Thanks to Sebastian Karcher, who figured out for me that 34 percent of women could marry a shorter man.

Special thanks to Jeff Spies, who talked me down on Twitter the other night when I thought the results were all different, and then went and got the data and showed me that I was wrong, which led me to fix it, in the process restoring my faith in the resilience of patriarchy.