COVID-19 Baby Bust update and data

Joe Pinsker at the Atlantic has a piece out on the coming (probable) baby bust. In it he reviews existing evidence for a coming decline in births as a result of the pandemic, especially including historical comparisons and Google search data. Could we see this already?

Pinsker writes:

The baby bust isn’t expected to begin in earnest until December. And it could take a bit longer than that, Sarah Hayford, a sociologist at Ohio State University, told me, if parents-to-be didn’t adjust their plans in response to the pandemic immediately back in March, when its duration wasn’t widely apparent.

If people immediately changed their plans in February, we might see a decline in births in October, but Hayford is right that’s early. And what about September, for which I’ve already observed declining births in Florida and California? If people who were pregnant already in January had miscarriages or abortions because of the pandemic, that would result in fewer births in September, but how big could that effect be? So maybe the Florida and California data are flukes, or data errors, or lots of pregnant people left those states and gave birth elsewhere (or pregnant people who normally come didn’t arrive). Perhaps more likely is that 2020 was already going to be a down year. As I told Pinsker:

“It might actually be that we were already heading for a record drop in births this year … If that’s the case, then birth rates in 2021 are probably going to be even more shockingly low.”

Anyway, we’ll find out soon enough. And to that end I’ve started assembling a dataset of monthly births where I can find them, which so far includes Florida, California, Oregon, Arizona, North Carolina, Ohio, Hawaii, Sweden, Finland, Scotland, and the Netherlands, to varying degrees of timeliness. As of today we have October data for some of them:

As of now Florida and California remain the strongest cases for a pandemic effect. But they are also both likely to add some more births to October (in November’s report, California increased the September number by 3%).

Anyway, lots of speculation while we’re killing time. You can get the little dataset here on the Open Science Framework: https://osf.io/pvz3g/. Check the date on the .csv or .xlsx file to see what I last updated it. I’ll add more countries or states if I find out about them.

Are pandemic effects on birth rates already detectable?

As birth data approaches, maybe we can get beyond analyses like Google searches for pregnancy-related terms to see what’s happening with birth rates.

At this writing we are a few days shy of 35 weeks from February 1st. If I read this right, 10% of US births occur at 36 weeks of gestation or less. But the most recent complete data I see is from August, so it’s early. However, most fertilized human eggs do not come to term, being lost either before (30%) or after (30-40%) implantation. That’s from a paper by Jenna Nobles and Amar Hamoudi, who write:

Evidence suggests that multiple mechanisms may be involved in pregnancy survival, including those that affect placental development and function, fetal oxidative stress, fetal neurological development, and likely many others. These, in turn, are shaped by more distal processes that affect maternal nutrition, maternal exposure to biological and psychosocial stress, maternal exposure to infection, and management of chronic conditions. Pregnancy survival varies with women’s body mass index, consumption of folic acid, and in some studies, reports of stressful life events (citations removed).

The pandemic might reasonably have contributed to a higher rate of pregnancy loss from these factors. And then there are abortions, which people have probably needed more even though they had less access to them (see this report from Guttmacher). So the net effect is unclear.

Setting aside how the pandemic might have affected fertility intentions and planning (I assume this is negative, as reported by Guttmacher), there might already be fewer births, from loss and abortion.

I haven’t looked at every state, but Florida and California report births by month. In Florida, there were 9.5% fewer babies born in August 2020 than in the previous year (they revise these as they go, but the August number has been stable for a little while, so probably won’t increase much). In California there were 9.6% fewer births in August of this year compared with last year. Here are the monthly trends, including the last three years (I included Florida’s September number as of today, but that will certainly rise):

This is going to be tricky because birth rates were already falling in many places. But the average decline in the last three years was 2.9% in California and 0.7% in Florida, so these numbers clearly outpace that naïve expectation. Also, what about spring? Maybe the pandemic was already causing a decline in live births in California in March (from immigrants not coming or staying in Mexico or other countries?), but if the decline in March was unrelated, then it’s not clear how to interpret the drop in August. So it will be complicated. But this is a bona fide blip in the expected direction, so I’m posting it with a question mark.

I assume other people will be way ahead of me on this, though I haven’t seen anything. Feel free to post other analyses in the comments.

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.

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.

Demographic facts your students need to know right now (with COVID-19 addendum)

20200808-DSC_4900
PN Cohen photo / Flickr CC: https://flic.kr/p/2jw6stF

Here’s the 2020 update of a series I started in 2013. This year, after the basic facts, I’ll add some pandemic facts below.

Is it true that “facts are useless in an emergency“? I guess we’ll find out this year. Knowing basic demographic facts, and how to do arithmetic, lets us ballpark the claims we are exposed to all the time. The idea is to get your radar tuned to identify falsehoods as efficiently as possible, to prevent them spreading and contaminating reality. Although I grew up on “facts are lazy and facts are late,” I actually still believe in this mission, I just shake my head slowly while I ramble on about it (and tell the same stories over and over).

It started a few years ago with the idea that the undergraduate students in my class should know the size of the US population. Not to exaggerate the problem, but too many of them don’t, at least when they reach my sophomore level family sociology class. If you don’t know that fact, how can you interpret statements like, “The U.S. economy lost a record 20.5 million jobs in April“?

Everyone likes a number that appears to support their perspective. But that’s no way to run (or change) a society. The trick is to know the facts before you create or evaluate an argument, and for that you need some foundational demographic knowledge. This list of facts you should know is just a prompt to get started in that direction.

These are demographic facts you need just to get through the day without being grossly misled or misinformed — or, in the case of journalists or teachers or social scientists, not to allow your audience to be grossly misled or misinformed. Not trivia that makes a point or statistics that are shocking, but the non-sensational information you need to make sense of those things when other people use them. And it’s really a ballpark requirement (when I test the undergraduates, I give them credit if they are within 20% of the US population — that’s anywhere between 264 million and 396 million!).

This is only a few dozen facts, not exhaustive but they belong on any top-100 list. Feel free to add your facts in the comments (as per policy, first-time commenters are moderated). They are rounded to reasonable units for easy memorization. All refer to the US unless otherwise noted. Most of the links will take you to the latest data:

Number Source
World Population 7.7 billion 1
U.S. Population 330 million 1
Children under 18 as share of pop. 22% 2
Adults 65+ as share of pop. 17% 2
Official unemployment rate (July 2020) 10% 3
Unemployment rate range, 1970-2018 3.9% – 15% 3
Labor force participation rate, age 16+ 61% 9
Labor force participation rate range, 1970-2017 60% – 67% 9
Non-Hispanic Whites as share of pop. 60% 2
Blacks as share of pop. 13% 2
Hispanics as share of pop. 19% 2
Asians / Pacific Islanders as share of pop. 6% 2
American Indians as share of pop. 1% 2
Immigrants as share of pop 14% 2
Adults age 25+ with BA or higher 32% 2
Median household income $60,300 2
Total poverty rate 12% 8
Child poverty rate 16% 8
Poverty rate age 65+ 10% 8
Most populous country, China 1.4 billion 5
2nd most populous country, India 1.3 billion 5
3rd most populous country, USA 327 million 5
4th most populous country, Indonesia 261 million 5
5th most populous country, Brazil 207 million 5
U.S. male life expectancy at birth 76 6
U.S. female life expectancy at birth 81 6
Life expectancy range across countries 51 – 85 7
World total fertility rate 2.4 10
U.S. total fertility rate 1.7 10
Total fertility rate range across countries 1.0 – 6.9 10

Sources

1. U.S. Census Bureau Population Clock

2. U.S. Census Bureau quick facts

3. Bureau of Labor Statistics

5. CIA World Factbook

6. National Center for Health Statistics

7. CIA World Factbook

8. U.S. Census Bureau poverty tables

9. Bureau of Labor Statistics

10. World Bank


COVID-19 Addendum: 21 more facts

The pandemic is changing everything. A lot of the numbers above may look different next year. Here are 21 basic pandemic facts to keep in mind — again, the point is to get a sense of scale, to inform your consumption of the daily flow of information (and disinformation). These are changing, too, but they are current as of August 31, 2020.

Global confirmed COVID-19 cases: 25 million

Confirmed US COVID-19 cases: 6 million

Second most COVID-19 cases: Brazil, 3.9 million

Third most COVID-19 cases: India, 3.6 million

Global confirmed COVID-19 deaths: 850,000

Confirmed US COVID-19 deaths: 183,000

Second most COVID-19 deaths: Brazil, 121, 000

Third most COVID-19 deaths: India: 65,000

Percent of U.S. COVID patients who have died: 3%

COVID-19 deaths per 100,000 Americans: 50

COVID-19 deaths per 100,000 non-Hispanic Whites: 43

COVID-19 deaths per 100,000 Blacks: 81

COVID-19 deaths per 100,000 Hispanics: 55

COVID-19 deaths per 100,000 Americans over age 65: 400

Annual deaths in the U.S. (these are for 2017): Total, 2.8 million

Leading cause of death: Heart disease, 650,000

Second leading cause: Cancer: 600,000

Third leading cause: Accidents: 160,000

Deaths from flu and pneumonia, 56,000

Deaths from suicide: 47,000

Deaths from homicide: 20,000


Sources

COVID-19 country data: Johns Hopkins University Coronavirus Resource Center

U.S. cause of death data: Centers for Disease Control

U.S. age and race/ethnicity COVID-19 death data: Centers for Disease Control

 

 

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

white intermarriageblack intermarriage

api intermarriage

hispanic intermarriage

Why there are 3.1 million extra young adults living at home

Answer: The COVID-19 pandemic.

UPDATE: A new post updates this analysis for July 2020

Catherine Rampell tweeted a link to a Zillow analysis showing 2.2 million adults ages 18-25 moving in with their parents or grandparents in March and April. Zillow’s Treh Manhertz estimates these move-homers would cost the rental market the better part of a billion dollars, or 1.4% of total rent if they stay home for a year.

We now have the June Current Population Survey data to work with, so I extended this forward, and did it differently. CPS is the large, monthly survey that the Census Bureau conducts for the Bureau of Labor Statistics each month, principally to track labor market trends. It also includes basic demographics and living arrangement information. Here is what I came up with.*

Among people ages 18-29, there is a large spike of living in the home of a parent or grandparent (of themselves or their spouse), which I’ll call “living at home” for short. This is apparent in a figure that compares 2020 with the previous 5 years (click figures to enlarge):

six year trends

From February to April, the percentage of young adults living at home jumped from 43% to 48%, and then up to 49% in June. Clearly, this is anomalous. (I ran it back to 2008 just to make sure there were no similar jumps around the time of the last recession; in earlier years the rates were lower and there were no similar spikes.) This is a very large disturbance in the Force of Family Demography.

To get a better sense of the magnitude of this event, I modeled it by age, sex, and race/ethnicity. Here are the estimated share of adults living at home by age and sex. For this I use just June of each year, and compare 2020 with the pooled set of 2017-2019. This controls for race/ethnicity.

men and women

The biggest increase is among 21-year-old men and 20-year-old women, and women under 22 generally. These may be people coming home from college, losing their jobs or apartments, canceling their weddings, or coming home to help.

I ran the same models but broke out race/ethnicity instead (for just White, Black, and Latino, as the samples get small).

white black latino

This shows that the 2020 bounce is greatest for Black young adults (below age 24) and the levels are lowest for Latinos (remember that many Latinos are immigrants whose parents and grandparents don’t live in the US).

To show the total race/ethnic and gender pattern, here are the predicted levels of living at home, controlling for age:

raceth-gender

The biggest 2020 bounce is among Black men and women, with Black men having the highest overall levels, 58%, and White women having the lowest at 44%.

In conclusion, millions of young adults are living with their parents and grandparents who would not be if 2020 were like previous years. The effect is most pronounced among Black young adults. Future research will have to determine which of the many possible disruptions to their lives is driving this event.

For scale, there are 51 million (non-institutionalized) adults ages 18-29 in the country. If 2020 was like the previous three years, I would expect there to be 22.2 million of them living with their parents. Instead there are 25.4 million living at home, an increase of 3.1 million from the expected number (numbers updated for June 2020). That is a lot of rent not being spent, but even with that cost savings I don’t think this is good news.


* The IPUMS codebook, Stata code, spreadsheet, and figures are in an Open Science Framework project under CC0 license here: osf.io/2xrhc.

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 dressesbridal shower, wedding licensewedding 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):

wedding plans searches.xlsx

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.

kissing sailor

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).

florida marriages.xlsx

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.


Overdose deaths increased more than 80 percent.

od


Chlamydia cases increased by a third.

chlamydia


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

livhome


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

histsoc


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

marbirth


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

fertage


The divorce rate has fallen 20 percent.

divorce


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

margap


International adoptions fell by more than two-thirds.

intadopt


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

refugee


The newspaper industry was cut in half.

news


Happy New Decade!

 

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.

ch 2 household diversity.xlsx

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.”

peacock family diversity update.xlsx

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.

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.

nh1

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:

nh2

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:

nh3

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.


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.