Intermarriage rates relative to diversity

Addendum: Metro-area analysis added at the end.

The Pew Research Center has a new report out on race/ethnic intermarriage, which I recommend, by Gretchen Livingston and Anna Brown. This is mostly a methodological note, which also nods at some other issues.

How do you judge the amount of intermarriage? For example, in the U.S., smaller groups — Asians and American Indians — marry exogamously at higher rates. Is that because they have fewer same-race people to choose from? Or is it because Whites shun them less than they do Blacks, which are also a larger group. To answer this, you can look at the intermarriage rates relative to group size in various ways.

The Pew report gives some detail about different groups marrying each other, but the topline number is the total intermarriage rate:

In 2015, 17% of all U.S. newlyweds had a spouse of a different race or ethnicity, marking more than a fivefold increase since 1967, when 3% of newlyweds were intermarried, according to a new Pew Research Center analysis of U.S. Census Bureau data.

Here’s one way to assess that topline number, which I’ll do by state just to illustrate the variation in the U.S. (and then I repeat this by metro area below, by popular request).*

The American Community Survey (which I download from IPUMS.org) identified people who married within the previous 12 months, whom I’ll call newlyweds. I use the 2011-2015 combined data file to increase the sample size in small states. I define intermarriage a little differently than Pew does (for convenience, not because it’s better). I call a couple intermarried if they don’t match each other in a five-category scheme: White, Black, Asian/Pacific Islander, American Indian, Hispanic. I discard those newlyweds (about 2%) who are are multiracial or specified other race and not Hispanic. I only include different-sex couples.

The Herfindahl index is used by economists to measure market concentration. It looks like this:

where s_i is the market share of firm i in the market, and N is the number of firms. It’s the sum of the squared proportions held by each firm (or race/ethnicity). The higher the score, the greater the concentration. In race/ethnic terms, if you subtract the Herfindahl index from 1, you get the probability that two randomly selected people are in a different race/ethnic group, which I call diversity.

Consider Maine. In my analysis of newlyweds in 2011-2015, 4.55% were intermarried as defined above. The diversity calculation for Maine looks like this (ignore the scale):

So in Maine two newlyweds have a 5.2% chance of being intermarried if you scramble up the marriage applications, compared with 4.6% who are actually intermarried. (A very important decision here is to use the newlywed population to calculate diversity, instead of the single population or the total population; it’s easy to change that.) Taking the ratio of these, I calculate that Maine is operating at 87% of its intermarriage potential (4.55 / 5.23). Maybe call it a diversity-adjusted intermarriage propensity. So here are all the states (and D.C.), showing diversity and intermarriage. (The diagonal line shows what you’d get if people married at random; the two illegible clusters are DC+NY and WA+KS; click to enlarge.)

How far each state is off the line is the diversity-adjusted intermarriage propensity (intermarriage divided by diversity). Here is is in map form (using maptile):

And here are the same calculations for the top 50 metro areas (in terms of number of newlyweds in the sample). I chose the top 50 by sample size of newlyweds, by which the smallest is Tucson, with a sample of 478. First, the figure (click to enlarge):

And here’s the list of metro areas, sorted by diversity-adjusted intermarriage propensity:

	Diversity-adjusted intermarriage propensity
Birmingham-Hoover, AL	.083
Memphis, TN-MS-AR	.127
Richmond, VA	.133
Atlanta-Sandy Springs-Roswell, GA	.147
Detroit-Warren-Dearborn, MI	.155
Philadelphia-Camden-Wilmington, PA-NJ-D	.157
Louisville/Jefferson County, KY-IN	.170
Columbus, OH	.188
Baltimore-Columbia-Towson, MD	.197
St. Louis, MO-IL	.204
Nashville-Davidson–Murfreesboro–Frank	.206
Cleveland-Elyria, OH	.213
Pittsburgh, PA	.215
Dallas-Fort Worth-Arlington, TX	.219
New York-Newark-Jersey City, NY-NJ-PA	.220
Virginia Beach-Norfolk-Newport News, VA	.224
Washington-Arlington-Alexandria, DC-VA-	.224
New Orleans-Metairie, LA	.229
Jacksonville, FL	.234
Houston-The Woodlands-Sugar Land, TX	.235
Los Angeles-Long Beach-Anaheim, CA	.239
Indianapolis-Carmel-Anderson, IN	.246
Chicago-Naperville-Elgin, IL-IN-WI	.249
Charlotte-Concord-Gastonia, NC-SC	.253
Raleigh, NC	.264
Cincinnati, OH-KY-IN	.266
Providence-Warwick, RI-MA	.278
Milwaukee-Waukesha-West Allis, WI	.284
Tampa-St. Petersburg-Clearwater, FL	.286
San Francisco-Oakland-Hayward, CA	.287
Orlando-Kissimmee-Sanford, FL	.295
Boston-Cambridge-Newton, MA-NH	.305
Buffalo-Cheektowaga-Niagara Falls, NY	.305
Riverside-San Bernardino-Ontario, CA	.311
Miami-Fort Lauderdale-West Palm Beach,	.312
San Jose-Sunnyvale-Santa Clara, CA	.316
Austin-Round Rock, TX	.318
Kansas City, MO-KS	.342
San Diego-Carlsbad, CA	.343
Sacramento–Roseville–Arden-Arcade, CA	.345
Minneapolis-St. Paul-Bloomington, MN-WI	.345
Seattle-Tacoma-Bellevue, WA	.346
Phoenix-Mesa-Scottsdale, AZ	.362
Tucson, AZ	.363
Portland-Vancouver-Hillsboro, OR-WA	.378
San Antonio-New Braunfels, TX	.388
Denver-Aurora-Lakewood, CO	.396
Las Vegas-Henderson-Paradise, NV	.406
Provo-Orem, UT	.421
Salt Lake City, UT	.473

At a glance no big surprises compared to the state list. Feel free to draw your own conclusions in the comments.

* I put the data, codebook, code, and spreadsheet files on the Open Science Framework here, for both states and metro areas.

Births to 40-year-olds are less common but a greater share than in 1960

Never before have such a high proportion of all births been to women over 40 — they are now 2.8% of all births in the US. And yet a 40-year-old woman today is one-third less likely to have a baby than she was in 1947.

From 1960 to 1980, birth rates to women over 40* fell, as the Baby Boom ended and people were having fewer children by stopping earlier. Since 1980 birth rates to women over 40 have almost tripled as people started “starting” their families at later ages, but they’re still lower than they were back when total fertility was much higher.

Sources: Birth rates 1940-1969, 1970-2010, 2011, 2012-2013, 2014-2015; 2016; Percent of births 1960-1980, 1980-2008.

Put another way, a child born to a mother over 40 before 1965 was very likely the youngest of several (or many) siblings. Today they are probably the youngest of 2 or an only child. A crude way to show this is to use the Current Population Survey to look at how many children are present in the households of women ages 40-49 who have a child age 0 (the survey doesn’t record births as events, but the presence of a child age 0 is pretty close). Here is that trend:

In the 1970s about 60 percent of children age 0 had three or more siblings present, and only 1 in 20 was an only child. Now more than a quarter are the only child present and another 30 percent only have one sibling present. (Note this doesn’t show however many siblings no longer live in the household, and I don’t know how that might have changed over the years).

This updates an old post that focused on the health consequences of births to older parents. The point from that post remains: there are fewer children (per woman) being born to 40-plus mothers today than there were in the past, it just looks like there are more because they’re a larger share of all children.

* Note in demography terms, “over 40” means older than “exact age” 40, so it includes people from the moment they turn 40.

Now-you-know data graphic series

As I go about my day, revising my textbook, arguing with Trump supporters online, and looking at data, I keep an eye out for easily-told data short stories. I’ve been putting them on Twitter under the label Now You Know, and people seem to appreciate it, so here are some of them. Happy to discuss implications or data issues in the comments.

1. The percentage of women with a child under age 1 rose rapidly to the late 1990s and then stalled out. The difference between these two lines is the percentage of such women who have a job but were not at work the week of the survey, which may mean they are on leave. That gap is also not growing much anymore, which might or might not be good.

2. In the long run both the dramatic rise and complete stall of women’s employment rates are striking. I’m not as agitated about the decline in employment rates for men as some are, but it’s there, too.

3. What looked in 2007 like a big shift among mothers away from paid work as an ideal — greater desire for part-time work among employed mothers, more desire for no work among at-home mothers — hasn’t held up. From a repeated Pew survey. Maybe people have looked this from other sources, too, so we can tell whether these are sample fluctuations or more durable swings.

4. Over age 50 or so divorce is dominated by people who’ve been married more than once, especially in the range 65-74 — Baby Boomers, mostly — where 60% of divorcers have been married more than once.

 

5. People with higher levels of education receive more of the child support they are supposed to get.

 

New data show change in the class (identity) structure

Updating a 2013 post with the 2016 General Social Survey. Not a lot of interpretation, just some facts.

The GSS has, since 1972, asked Americans:

If you were asked to use one of four names for your social class, which would you say you belong in: the lower class, the working class, the middle class, or the upper class?

The latest data release, for 2016, confirms what I noticed before: a big rise in the percentage of people describing themselves as “lower class” since the 2008 recession, from 5% to 9%. This is striking when you zoom in on it:

Now, looking at the trend in all four classes, it’s clear there has been a decline in the proportion of people calling themselves “middle class” — which hit its lowest level ever in the series, 41%:

Is this important? I don’t know. The most common tendency in sociology these days is to use measures of education (one’s own education, or one’s parents’) to indicate social class, which is generally thought of in material terms, rather than as an identity issue (or as a question of what people actually learn in school). Of course there are sociologists who study class identity issues, but as a survey item I bet it’s more likely to show up in political science research.

Of course the political salience of “working class” was heightened by the election in 2016 (although the phrase itself was more likely used as an adjective than a noun; the noun in American politics remains “working families,” a term I dislike). And by “working class” of course most people meant White working class. A Google search of the New York Times site for [“White working class” 2016] produces 1,050 hits; [“Black working class” 2016] yields 37. But Blacks are considerably more likely to identify as “working class,” and less likely to choose “middle class,” than are Whites. Here is the breakdown for 2016 (at the mean of controls for age and sex):

Of course that doesn’t account for common correlates of class identity; it’s just a description of the groups. I looked a little more closely at income. Here is how people report class identities by family income, this time at the mean of controls for age, sex, race/ethnicity, and marital status (partly to account for family size)*:

This shows that “working class” is most common among those in the $30,000-$50,000 range, and it dominates under $75,000, while “middle class” picks up most people over $75,000. Only people in the top bracket — and only a small proportion of them — identify as “upper class.”

I did a little checking to see what difference class makes on some common political issues. Regressions holding constant sex, race/ethnicity, education, income, marital status, and region, showed that working class people were more Democratic than middle class people (on a scale from strong Dem to strong Rep); but middle class people are more pro-choice, and also more likely to think “people can be trusted.” In similar models, class didn’t do much to explain confidence in organized labor, support for same-sex marriage, attidues toward taxes on the rich, the likelihood of owning a gun, political views (liberal v. conservative), or traditional gender attitudes. Still, I think it’s worth asking.

In summary, it’s interesting that the self-identified class structure may be shifting relatively rapidly, and the implications are to be determined.

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* Code for the income regression, using the full GSS dataset through 2016, available here.

gen rndwt=round(wtssall)
recode income16 (1/11=1) (12/15=2) (16/18=3) (19/20=4) (20/22=5) (23/25=6) (26=7) , gen(incate)
label define cl_lbl 1 "$0-<17.5k"
label define cl_lbl 2 "$17.5-<30k", add
label define cl_lbl 3 "$30-<50k", add
label define cl_lbl 4 "$50-<75k", add
label define cl_lbl 5 "$75-<110k", add
label define cl_lbl 6 "$110k-<170k", add
label define cl_lbl 7 "$179k+", add
label values incate cl_lbl
mlogit class c.age#c.age i.sex i.race i.marital i.incate [weight=rndwt] if year==2016
margins incate
marginsplot

Marriage update: less divorce, and less sex

I previously reported that divorce rates in the American Community Survey have fallen a fair amount since 2008, and especially from 2012 to 2014; and the experts at Bowling Green’s National Center for Family and Marriage Research (NCFMR) have reported that the trend continued through 2015.

Now, as I revise my book with the data through 2015, I estimated divorce rates in the American Community Survey across some key predictors to see if the decline since the first edition was concentrated in any particular group. The answer is mostly no, it’s across the board. Here, shown as the number of people in each year who said they divorced per 1000 people in that year that are married (click to enlarge).

The exception is across years married, which I show here instead of age. The 2012-2015 drops in divorce are largest among people married for the shortest duration, and for those married 35-49 years the rate actually increased. For more on this, see the great NCFMR work on “gray divorce.” Interestingly this happens as attitudes toward divorce are becoming steadily more permissive. (See the divorce tag for lots more on all this.)

Anyway, when I posted this on Twitter yesterday Jean Twenge reminded me of this article showing the trend toward less sex in America from 1989 to 2014, concentrated among married people. And that reminded me that the 2016 General Social Survey just came out, so we can update the reported sexual frequency trend.

To do this quick and dirty (no offense) I just ran a linear regression on the categorical GSS variable SEXFREQ, controlling for sex, age, and age-squared, for married people ages 18-54.* The results show the downhill trend Twenge and colleagues reported, and then a pretty dramatic drop in 2016, the biggest drop in the series. Yikes.

The variable SEXFREQ asks, “About how often did you have sex during the last 12 months?” with the options Not at all, Once or twice, Once a month, 2-3 times a month, Weekly, 2-3 times per week, and 4+ times per week. For reference, the figure is running from weekly down toward 2-3 times per month. I don’t take this literally, which is why I don’t mind treating it as linear, it’s just about the direction of change and the ballpark level. Others may disagree with this method.

So, that’s the news: divorce is down, and so is sex within marriage (as reported to survey takers). Open to interpretations.

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* Here’s the Stata code:

reg sexfreq i.year i.sex c.age##c.age if age
margins
marginsplot

(And then I tinkered with the figure.)

Here’s the distribution of SEXFREQ, weighted, in 2016, for married people age 18-54:

	Percent
not at all	3.1
once or twice	9.4
once a month	14.6
2-3 times a month	22.4
weekly	22.4
2-3 per week	22.2
4+ per week	5.9

African American marital status by age, Du Bois replication edition

At the 1900 Paris Exposition, sociologist W. E. B. Du Bois presented some the work of his students. In The Scholar Denied: W. E. B. Du Bois and the Birth of Modern Sociology, Aldon Morris writes:

Du Bois’s meticulousness as a teacher is apparent in the charts and graphs that he prepared with his students. For example, as part of his gold medal-winning exhibit for the 1900 Paris Exposition, Du Bois and his students produced detailed hand-drawn artistically colored graphs and charts that depicted the journey of black Georgians from slavery to freedom.

Some of collection is shown in this post at the Public Domain Review (shared by Tressie McMillan Cottom yesterday); the full collection is online at the Library of Congress (LOC).

The one that caught my eye was this, showing marital status (“conjugal condition”) by age and sex for the Black population. I can’t find the source details in the LOC record, so I don’t know if it’s Georgia or national, but I presume it’s from tabulations of 1890 decennial census or earlier:

It’s artistic and meticulous and clearly informative, beautiful. So I tried to make a 2015 update to complement it. I used data from the 2015 American Community Survey via IPUMS.org, and did it a little differently.* Most importantly, I added two more conjugal conditions, cohabiting and separated/divorced. Second, I used five-year age groupings all the way up, instead of ten. Third, I detailed the age groups up to age 85. Here’s what I got:

Some very big differences: Much smaller proportions of African Americans married now. Also, much later marriage. In the 1900 figure more than 30% of men and 60% of women have been married by age 25; those numbers are 5-6% now. I don’t know how they counted separated/divorced people in 1900, but those numbers are high now at 31% for women and 24% for men at age 60-64. Widowhood is later now, as 42% of women were widowed before age 65 in 1900, compared with only 13% now (of course, that’s off a lower marriage rate, and remarried people are just counted as married). And of course cohabitation, which the chart doesn’t show for 1900. Note I included people in same-sex as well as different-sex couples.

So, thanks for indulging me. I hope you don’t think it’s frivolous. I just love staring at the old charts, and going through the (very different) steps of replicating it was really satisfying. (I also just love that in another 100 years someone might look back on this and say, “Wait, which one was Earth again?”)

Note: If you want to compare them side-by-side, here’s a go at that. The age ranges don’t line up perfectly but you can get the idea (click to enlarge):

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* SAS code, ACS data, images, and the spreadsheet used for this post are shared as an Open Science Framework project, here.

Kids these days really know how to throw off a narrative on gender and families

The most important thing is that Stephanie Coontz has written another very good, and very important, New York Times essay. It describes a “slippage” in support for gender equality among young people these days, and warns that without improved work-family policies, progress toward egalitarian family arrangements may be imperiled. The piece also announced a package of short papers in a Council on Contemporary Families symposium, which provided the supporting evidence. (This kind of work, incidentally, is why I’m a proud member, and board member, of CCF.) If you haven’t read Stephanie’s essay, I recommend reading it now, and if you forget to come back here that’s fine.

Anyway, an unfortunate confluence of events created some chaos after the piece came out. First, the NYTimes wrote a headline, “Do Millennial Men Want Stay-at-Home Wives?”, that emphasized only one piece of the evidence. It referred to a figure showing General Social Survey data on the trend in very young men and women (ages 18-25) disagreeing with the statement, “It is much better for everyone involved if the man is the achiever outside the home and the woman takes care of the home and family.” (That is the classic FEFAM question, to GSS fans, asked since 1977. I’ve used it myself, and it figures in the key analysis of stalled gender progress by Cotter, Hermsen, and Vanneman.)

This was the figure, showing a marked divergence between men and women:

The second event was the unfortunate timing: between the time Stephanie wrote the piece and the day it appeared, the General Social Survey released its 2016 round of data (it’s been running every two years). The survey is fickle. It’s very good quality and has many great demographic and attitude items running for 40 years, making it the best source for analyzing many social trends. But it’s not that big. In 2014 it had 2,867 respondents, of whom only 141 were ages 18-25. So it wasn’t surprising that the 2016 numbers were different from the 2014 numbers, but the scale of the blip was shocking, as reported independently by Emily Beam and Neal Caren. Here is what the updated trend looks like:

Yikes. As exciting as it is for survey analysts to see such a wild swing, it’s not what anyone wants to see the day after their NYTimes piece drops. We can’t know yet what happened, but on further inspection, at least we can say that it’s not limited to the youngest group and its small sample. Among men ages 26-54, the percentage disagreement with FEFAM also jumped, from 73.7 to 78.3 (women 26-54 were up one point).* In fact, 2014 may have been as big a blip as 2016, you just wouldn’t notice because it continued the trend.

Anyway, back for a minute to the main point. Joanna Pepin, who co-wrote one of the symposium pieces with David Cotter (and who is also an advisee of mine), has pointed out that the divergence between men and women is secondary to the main trend, which is the reversal of progress on FEFAM for both men and women since the mid-1990s. They used the Monitoring the Future survey, and find a big drop in FEFAM disagreement among high school seniors — regardless of gender. Here’s their key figure, with the FEFAM trend shown in green (their full paper is available on SocArXiv):

So that is the most important news: a big reversal among young adults on attitudes toward homemaker-breadwinner family arrangements.

Now, If you’ve now read Stephanie’s piece, and Joanna’s, and you’re back, here’s a little more on the minor kerfuffle that arose over the new data.

When to call a trend a trend

I don’t think Stephanie was wrong to use the GSS trend, although it might have been better to widen the age range, or pool the data over several years. The bigger problem was the headline selling that divergence as the main story, which it wasn’t in the grand scheme. (The fact that so many jumped on the story shows how good they are at headline writing.) But even that wasn’t really wrong, given the information they had. The Op-Ed staff checked the facts, and the facts were the facts. Until yesterday.

To confirm this, I ran some tests on the gender divergence in the data they used (I started with code that Neal shared; it’s at the bottom). I started at 1994, the last peak of the trend, to look for the divergence after that, which is what Stephanie referred to. First, here is what you get if you run a logistic model that controls for race/ethnicity and individual years of age (two things that changed over the last two decades), and enters the years individually in an interaction with gender (those are 95% confidence intervals).

If you stop at 2014, it looks like men are pulling away from women (in the direction of “traditional” attitudes), but it’s not definitive. And obviously 2016 is an issue. To help with the small samples, I ran a linear test of the year trend, that is, entering year as a continuous variable instead of individual years. I did it ended at 2014 and then through 2016. Here are the results:

In the 1994-2014 model, the Male*Year interaction is statistically significant at conventional levels, which in my opinion means it’s legit to say men were pulling away from women. Of course 2016 ruined that; if you had 2016 and didn’t use it, that would be really wrong. There are other ways to slice it, but at some point we have to call a trend a trend and deal with it. It was a reasonable decision. Of course, new data always comes along (until the last trend of all, whatever that is), no trend lasts forever; it’s just a shame when it comes along the next day. In addition, though I’m not showing it because it’s boring, if you didn’t disaggregate the trends by gender, you would also see a significant decline in FEFAM disagreement after 1994, which gets to Joanna’s point.

Anyway, score one for sociology Twitter. People came up with the data, shared code and results, and discussed interpretations. It got back to Stephanie and the NYTimes editors, and within a day they added an addendum to the original piece:

Update: After this article was posted, 2016 data from the General Social Survey became available, adding some nuance to this analysis. The latest numbers show a rebound in young men’s disagreement with the claim that male-breadwinner families are superior. The trend still confirms a rise in traditionalism among high school seniors and 18-to-25-year-olds, but the new data shows that this rise is no longer driven mainly by young men, as it was in the General Social Survey results from 1994 through 2014.

This is pretty much how it’s supposed to work. As the Car Guys used to say, if you never stall you’re wearing out your clutch (sorry, Millennials). If you never overshoot an analysis of trends you’re probably waiting too long to get the information out.

* Note: I originally accidentally described this as “over 25.” 

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You can get the data here. Here’s the STATA code:

/* recodes */

recode fefam (1/2=0) (3/4=1), gen(fefam_d)
gen young=age>=18&age<=25
recode sex (2=0), gen(male)

/* the model for the figure */

logit fefam_d i.year##i.male i.age i.race if year>=1994 & young==1 [pweight = wtssall]
margins year##male
marginsplot

/* the models for the table */

logit fefam_d c.year##i.male i.age i.race if year>=1994 & young==1 & year<=2014 [pweight = wtssall]
logit fefam_d c.year##i.male i.age i.race if year>=1994 & young==1 [pweight = wtssall]