Tag Archives: marriage

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:

H =\sum_{i=1}^N s_i^2

where si 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.)

State intermarriage

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):

State intermarriage

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.


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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:

du bois marstat replication.xlsx

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):

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


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Two examples of why “Millennials” is wrong

When you make up “generation” labels for arbitrary groups based on year of birth, and start attributing personality traits, behaviors, and experiences to them as if they are an actual group, you add more noise than light to our understanding of social trends.

According to generation-guru Pew Research, “millennials” are born during the years 1981-1997. A Pew essay explaining the generations carefully explains that the divisions are arbitrary, and then proceeds to analyze data according to these divisions as if are already real. (In fact, in the one place the essay talks about differences within generations, with regard to political attitudes, it’s clear that there is no political consistency within them, as they have to differentiate between “early” and “late” members of each “generation.”)

Amazingly, despite countless media reports on these “generations,” especially millennials, in a 2015 Pew survey only 40% of people who are supposed to be millennials could pick the name out of a lineup — that is, asked, “These are some commonly used names for generations. Which of these, if any, do you consider yourself to be?”, and then given the generation names (silent, baby boom, X, millennial), 40% of people born after 1980 picked “millennial.”

“What do they know?” You’re saying. “Millennials.

Two examples

The generational labels we’re currently saddled with create false divisions between groups that aren’t really groups, and then obscure important variation within the groups that are arbitrarily lumped together. Here is just one example: the employment experience of young men around the 2009 recession.

In this figure, I’ve taken three birth cohorts: men born four years apart in 1983, 1987, and 1991 — all “millennials” by the Pew definition. Using data from the 2001-2015 American Community Surveys via IPUMS.org, the figure shows their employment rates by age, with 2009 marked for each, coming at age 26, 22, and 18 respectively.


Each group took a big hit, but their recoveries look pretty different, with the earlier cohort not recovered as of 2015, while the youngest 1991 group bounced up to surpass the employment rates of the 1987s by age 24. Timing matters. I reckon the year they hit that great recession matters more in their lives than the arbitrary lumping of them all together compared with some other older “generations.”

Next, marriage rates. Here I use the Current Population Survey and analyze the percentage of young adults married by year of birth for people ages 18-29. This is from a regression that controls for year of age and sex, so it can be interpreted as marriage rates for young adults (click to enlarge).


From the beginning of the Baby Boom generation to those born through 1987 (who turned 29 in 2016, the last year of CPS data), the marriage rate fell from 57% to 21%, or 36 percentage points. Most of that change, 22 points, occurred within the Baby Boom. The marriage experience of the “early” and “late” Baby Boomers is not comparable at all. The subsequent “generations” are also marked by continuously falling marriage rates, with no clear demarcation between the groups. (There is probably some fancy math someone could do to confirm that, with regard to marriage experience, group membership by these arbitrary criteria doesn’t tell you more than any other arbitrary grouping would.)

Anyway, there are lots of fascinating and important ways that birth cohort — or other cohort identifiers — matter in people’s lives. And we could learn more about them if we looked at the data before imposing the categories.


Filed under Research reports

Poverty, marriage, and single mother update

With the annual Census report on poverty out, here are two quick updates.

First, updating this post, the share of all poverty (using official rates) found in single-mother families remains lower than it was from 1974 to 2000. Since 1995, as the poverty rate has gone up and down between 10 and 15 percent, the share of poor people in single-mother families has fallen. As of 2015, 34% of poor people are found in single-mother families.


Marriage has declined, and single motherhood has increased, but that has not produced a poverty population more dominated by single-mother families. Of course these families are more likely to be poor than married-couple families, but they’re not the main poverty story.

Second, updating this post a little, it’s important to keep two major trends in the back of your mind when thinking about social change. The first is that marriage has declined precipitously since 1960. It’s unremitting decline is one of the major social facts of our time. The other trend to keep in mind is that poverty rates fell a lot after the 1960s, but since then have bounced around at an atrocious 10-15%. Now try to keep them both in mind at once: marriage falls, poverty goes up and down. This year’s update puts those together (sorry people who hate this kind of figure), as change in the percentage of women married, and change in the percentage of the population poor.


For a recent op-ed on poverty and marriage, here’s the unpaywalled version of my essay in the Washington Post‘s Post Everything.

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Interview: Independence, uncertainty, defamilialization


My photo from Flickr: https://flic.kr/p/Fet9Dc

I had an hour-long discussion about the decline of marriage on WYPR, Baltimore public radio, a couple weeks ago. You can listen to it here. I transcribed a short section that summarizes some of the points I find myself making in different contexts. This is a light edit (including taking out a couple things I disagree with myself on).


Q: Tell us more about some of the factors that are at work here. Some people say, well, back in the day my grandmother of course got married because she wasn’t going to have much of a job anyway, but now women have great jobs, so, that’s why they’re not getting married. True or false?

A: That is probably the biggest factor. Not just employment, but really independence. For women especially but for young adults overall. And that is, increased educational opportunities, increased employment opportunities, and the extended young adulthood, or what some people call extended adolescence. Just going to college into your 20s, and delaying that entry into marriage.

I’m not sure that if people are marrying less or marrying later we should equate this with a decline in respect or the importance of marriage. In some ways marriage is more important now that it’s more often a choice. That is people elevate it in their minds or in the culture because – when everybody had to be married, and it was virtually universal around 1960, it wasn’t something that that people personally chose. And so, yes it was important in the sense that everyone was doing it, but now it’s reached the point where people are much more likely to say: This marriage is not good, it’s not working, it’s not what I imagine a marriage should be, therefore we’re going to divorce. Or: We have a vision for marriage which is exalted, and we want to have our marriage take place when we have arrived, and we’re ready to own a house or a decent place to live, we have good jobs, to provide something for our children – and therefore because of that high view of marriage, we’re going to delay marriage. And so that may end up reducing the numbers of married people also, but not because people don’t value marriage.


One way to think about the high divorce rate – which people are aware of – is it’s a kind of uncertainty that hangs over people. But it’s only one kind. In many ways life is less predictable and more uncertain than it was a few decades ago. And that just makes it difficult for a person to make long-term plans and commitments. We see this in the economic sphere, definitely, where people change careers and jobs more often than they did in the past. In housing, where they may change where they live more often. In a variety of ways our lives are less predictable. And when you don’t know what the future holds in one arena it’s very hard to make a commitment in another. You wouldn’t want to pick your job and make a lifetime commitment to it before you know what your college major is going to be. And in the same way, it’s difficult to make a commitment in marriage before you know what career you’re going to have, or how long you’re going to spend in school. So the uncertainty in one realm translates into cautiousness in others.

[Here I recommended All the Single Ladies by Rebecca Traister and Going Solo by Eric Klinenberg.]

There are different kinds of freedom in play here, and they’re somewhat contradictory. If you have a long-term commitment, that gives you one kind of freedom, for example the freedom to experiment, to make changes in your lifestyle, to change jobs, to take time off from work. Or things that you can do with the security of knowing that the other person is there to back you up. On the other hand, of course, the freedom of being single is a different kind of freedom, is the freedom to not have the set of burdens and obligations that do come from marriage or any kind of long-term commitment. So I do think it’s possible to consider the pros and cons that go in both ways, and it does get back to that idea of uncertainty in life, and the idea of tying oneself down to a long-term commitment in the absence of predictability in all the other aspects of life just seems increasingly disjointed to people. It doesn’t resonate with a lot of people.

The economic argument for marriage has always been that – like contracts, in the economy in general – when you make a commitment, it increases predictability, and you can make long-term plans and investments. For example, you can take a year off to invest in some training, and not worry that you’re going to end up losing income in the long run. And then you also have the economies of scale, two people sharing one refrigerator and one car is more efficient. And then there also are effects of marriage on people’s behavior. The fact that people are relying on you may make people, especially men, behave more responsibly. That may not have to happen within marriage, but the idea of having people depend on you may make people, for example, focus on their career advancement more than other kinds of ambitions.


So it’s a challenge for our economy and our welfare state to think about: how can we ensure the wellbeing of people who do not have the two-person marriage – if we can’t assume people have that to back them up, economically speaking, and especially their children. But we’ve been going in that direction for a long time. The introduction of Social Security, retirement for older people, the public education system, we’ve been making investments in people to make them less reliant for their survival on their families for a long time, and in the long run that’s an important part of modern society. There’s a downside and an upside to that. The upside is people can act according to their own ambitions and desires individually, with more freedom than they could in the past. The downside is the expense for state institutions of caring for them and their children. It’s a complicated set of tradeoffs, and I think the important thing to realize is we can’t build our policies around the assumption that everybody and their parents are going to be married forever. And if we do that we’re going to leave a lot of people out, and put a lot of people at risk for real hardship.


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Explain to me again how marriage is the problem here

This is one of those things you share with all your friends on social media.


Black married parents are 2.4-times more likely to be in poverty, are 2.1-times more likely to be unemployed, and have one-ninth the median net worth compared with White married parents. So explain to me again how marriage is the problem here.


The other day I picked on someone’s fact meme, and wondered what makes these things work, without offering a constructive alternative. I can’t answer the question I asked in that post (how old are the fathers of teen mothers’ children?), but I can answer some other questions about families and Black-White inequality. So that’s what I did.

Feel free to take these facts (or any others) and make something better.


Here are my sources:

Poverty: 2014 American Community Survey from IPUMS.org. It’s Black and White, non-Hispanic, householders who are married and have their own children in the household. The poverty rates were 5% for White married parents and 11.9% for Black married parents. The poverty variable goes from 0 to 501, with 0-99 being below the poverty line, so you specify the recode like this: poverty(r:0-99 “poor”; 100-501 “not poor”). Here’s how you fill out the boxes in the online analysis tool:


Unemployment: Again, 2014 American Community Survey from IPUMS.org. It’s Black and White, non-Hispanic, householders who are married and have their own children in the household. For this one you limit it to people in the labor force (empstat(1-2)) to get the unemployment rate. I did it for men and women combined, getting unemployment rates of 3.1% for White married parents and 6.6% for Black married parents. The numbers are higher for women (3.7% versus 7.3%) but the Black/White ratio is a little worse for men (2.6% versus 5.8%). Here’s how:


Median net worth: I used the Survey of Consumer Finances from 2013, available here. These are also non-Hispanic Black and White parents living with children. The median net worths were $150,500 for Whites and $16,000 for Blacks (Hispanics, incidentally, have $18,750, and the rest are just coded “other”). This data set combines married people with those who are “living with partner,” so this comparison includes cohabitors. (I don’t know how that affects the results, but I’m sure there’s still lots of inequality.) I put my STATA code in an Open Science Framework project here, so feel free to play with it yourself.


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