Tag Archives: demography

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.

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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.

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New working paper: The rising marriage mortality gap among Whites

I wrote a short working paper on U.S. mortality trends for the last decade. You can go straight to the paper on SocArXiv, or the code and output, if you want the full version.

The issue is that premature mortality has been rising for Whites, partly because of the opioid epidemic and also from suicide and alcohol, and also from other causes related to stress and hardship. (See, e.g., Case and Deaton, and Geronimus.) And a recent NCHS report showed that mortality nationally declined much more for married people since 2010.

So I got the Mortality Multiple Cause Files from the National Center for Health Statistics, for two years: 2007 and 2017. These are a complete set of death certificates, which include race/ethnicity, marital status, and education. I linked these to the American Community Survey, to create age-specific mortality rates by age, sex, marital status, and education, for non-Hispanic Whites, Hispanics, and Blacks, in the ages 25-74 (old enough to finished with college, but too young to die).

The basic result is that virtually all of the growth in premature death is among Whites, and further among non-married Whites. (Whites still dies less than Blacks, and more than Hispanics, at each age and marital status.)

Here is the figure of age-specific mortality rates, by race/ethnicity, sex, and marital status for 2007 and 2017. At the bottom of each column I calculated “marriage mortality ratios,” which are how much more likely single people are to die than married people. Note these death rates are deaths per 10,000, but they’re on a log scale so you can see changes where rates are very low.

f2

In the figure you can see how much the marriage mortality ratio jumped up, for Whites only. Now, at the most extreme, single White men age 35-39 are more than 4-times more likely to die than married White men (that’s in the bottom left).

Then I zoom into Whites specifically, and do the same thing for four levels of education:

f3

In the lowest education group of Whites (the far left), mortality rates for married and single people increased similarly, so the marriage mortality ratio didn’t increase. However, for the other education levels, death rates increased for single people more than married people, so the ratio increased (across the bottom). Even among White college graduates, there were increases in mortality for single people. I did not expect that.

My bottom line is that marriage is taking an ever-more prominent place in the social status hierarchy, and now we can add growing mortality inequality, at least among Whites, to that pattern.

Early version, comments welcome!

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Why we need open science in demography, and how we can make it happen

“Why we need open science in demography, and how we can make it happen” is the title of a talk I gave at the Max Planck Institute for Demographic Research yesterday, as part of an open science workshop they hosted in Rostock, Germany. (The talk was not nearly as definitive as the title.)

The other (excellent) keynote was by Monica Alexander. I posted the slides from my talk here. There should be a video available later. The organizing committee for the event is working to raise the prominence of open science discussions at the Institute, and consider practices and policies they might adopt. We had a great meeting.

As an aside, I also got to hear an excellent tutorial by E. F. Haghish, who has written Markdoc, a “literate programming” (markdown) package for Stata, which is very cool. These are his slides.

rostock talk 2rostock group shot

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AEI panel on ‘demographic decline’

I was on a panel at the American Enterprise Institute, titled, “Demographic decline: National crisis or moral panic?” The event featured Lyman Stone, who argued that “demographic decline” in the U.S. is a national crisis, and my reply. Nick Eberstadt from AEI also offered comments. The moderator was AEI’s Karlyn Bowman.

The video of the event (which was on CSPAN) is below.

In my presentation I used the projections and other material I described earlier, here (where you can also link to the data and package I used). The gist of my talk is that with immigration we don’t have an issue of declining population.

I also emphasized the political implications of catastrophic “demographic decline” talk, which are based on a combination of doomsday demographics and increasing race/ethnic diversity. For that part I included these two figures, which I worked up for the next edition of my textbook. The first shows Census Bureau projections of the U.S. population by race/ethnicity, which is the basis for the White supremacist panic. (Important caveat about this figure is the assumptions about the ethnic identity of the descendants of today’s Latinos, see Richard Alba.)

re-forecast

For the politics of immigration, which is a giant topic, I presented this very simple figure showing the rise of Latin American and Asian immigration since 1965.

imre-history

Here is the video on YouTube. If you prefer the CSPAN production style (or don’t want to give AEI click), theirs is here. My talk is 15 minutes, starting at 13:40.

Happy to hear your responses, including on the dicey issue of whether to participate in an AEI event.

(In the YouTube comments, the first person calls me a “Jewish supremacist” and demands to know my view on Israeli immigration policy, and another says, “This guy is through and through an open borders globalist.” So that’s the dialogue, too.)

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The Coming Divorce Decline, Socius edition

“The Coming Divorce Decline, ” which I first posted a year ago, has now been published by the journal Socius.  Three thousand people have downloaded it from SocArXiv, I presented it at the Population Association, and it’s been widely reported (media reports), but now it’s also “peer reviewed.” Since Socius is open access, I posted their PDF on SocArXiv, and now that version appears first at the same DOI or web address (paper), while the former editions are also available.

Improvement: Last time I posted about it here I had a crude measure of divorce risk with one point each for various risk factors. For the new version I fixed it up, using a divorce prediction model for people married less than 10 years in 2017 to generate a set of divorce probabilities that I apply to the newly-wed women from 2008 to 2017:

…the coefficients from this model are applied to newly married women from 2008 to 2017 to generate a predicted divorce probability based on 2017 effects. The analysis asks what proportion of the newly married women would divorce in each of their first 10 years of marriage if 2017 divorce propensities prevailed and their characteristics did not change.

The result looks like this, showing the annual probability falling from almost 2.7% to less than 2.4%:

divprobnewlyweds

The fact that this predicted probability is falling is the (now improved) basis for my prediction that divorce rates will continue to decline in the coming years: the people marrying now have fewer risk factors. (The data and code for all this is up, too).


Prediction aside: The short description of study preregistration is “specifying your plan in advance, before you gather data.” You do this with a time-stamped report so readers know you’re not rejiggering the results after you collect data to make it look like you were right all along. This doesn’t always make sense with secondary data because the data is already collected before we get there. However, in this case I was making predictions about future data not yet released. So the first version of this paper, posted last September and preserved with a time stamp on SocArXiv, is like a preregistration of the later versions, effectively predicting I would find a decline in subsequent years if I used the same models — which I did. People who use data that is released on a regular schedule, like ACS, CPS, or GSS, might consider doing this in the future.


Rejection addendumSociological Science rejected this — as they do, in about 30 days, with very brief reviews — and based on their misunderstandings I made some clarifications and explained the limitations before sending it to Socius. Since the paper was publicly available the whole time this didn’t slow down the progress of science, and then I improved it, so I’m happy about it.

Just in case you’re worried that this rejections means the paper might be wrong, I’m sharing their reviews here, as summarized by the editor. If you read the current version you’ll see how I clarified these points.

* While the analyses are generally sensible, both Consulting Editors point out the paper’s modest contribution to the literature relative to Kennedy and Ruggles (2014) and Hemez (2017). The paper cites both of these papers but does not make clear how the paper adds to our understanding derived from those papers. If the relatively modest extension in the time frame in this paper is sociologically consequential, the paper does not make the case clearly.

* There is more novelty in the paper’s estimates of the annual divorce probability for newly-married women (shown in Table 3 and Figure 3), based on estimating a divorce model for the most recent survey year, and then applying the coefficients from that model to prior years. This procedure was somewhat difficult for the readers to follow, but issues were raised, most notably the question of the sensitivity of the results to the adjustments made. As one CE noted, “Excluding those in the first year of marriage is problematic as newlyweds have a high rate of divorce. Also, why just married in the last 10 years? Consider whether married for the first time vs remarried matters. Also, investigate the merits of an age restriction given the aging of the population Kennedy and Ruggles point to.”

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Against the generations, with video

I had the opportunity to make a presentation at the National Academies to the “Committee on the Consideration of Generational Issues in Workforce Management and Employment Practices.” If you’ve followed my posts about the “generation” terms and their use in the public sphere you understand how happy this made me.

The committee is considering a wide array of issues related to the changing workforce — under a contract from the Army — and I used the time to address the uses and misuses of cohort concepts and analysis in analyzing social change.

In the introduction, I said generational labels, e.g., “Millennials”:

encourage what’s bad about social science. It drives people toward broad generalizations, stereotyping, click bait, character judgment, and echo chamber thinking. … When we give them names and characters we start imposing qualities onto populations with absolutely no basis, or worse, on the basis of stereotyping, and then it becomes just a snowball of clickbait confirmation bias. … And no one’s really assessing whether these categories are doing us any good, but everyone’s getting a lot of clicks.

The slides I used are here in PDF. The whole presentation was captured on video, including the Q&A.

From my answer to the last question:

Cohort analysis is really important. And the life course perspective, especially on demographic things, has been very important. And as we look at changes over time in the society and the culture, things like how many times you change jobs, did you have health insurance at a certain point in your life, how crowded were your schools, what was the racial composition of your neighborhood or school when you were younger — we want to think about the shadow of these events across people’s lives and at a cultural level, not just an individual level. So it absolutely is important. … That’s a powerful way of thinking and a good opportunity to apply social science and learn from it. So I don’t want to discourage cohort thinking at all. I just want to improve it… Nothing I said should be taken to be critical of the idea of using cohorts and life course analysis in general at all.

You know, this is not my most important work. We have bigger problems in society. But understanding demographic change, how it relates to inequality, and communicating that in ways that allow us to make smarter decisions about it is my most important work. That’s why I consider this to be part of it.

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