Tag Archives: marriage

Lifetime chance of marrying for Black and White women

I’m going to Princeton next week to give a talk at the Office of Population Research. It’s a world-class population center, with some of the best trainers and trainees in the business, so I figured I’d polish up a little formal demography for them. (I figure if I run through this really fast they won’t have time to figure any mistakes I made.)

The talk is about Black and White marriage markets, which I’ve written about quite a bit, including when I posted the figure below, showing the extremely low number of local same-race, employed, single men per women Black women experience relative to White women — especially when they have less than a BA degree.

This figure was the basis for a video we made for my book, titled “Why are there so many single Black women?” For years I’ve been supporting the strong (“Wilsonian“) case that low marriage rates for Black women are driven by the shortage of “marriageable” men — living, employed, single, free men. I promised last year that Joanna Pepin and I were working on a paper about this, and we still are. So I’ll present some of this at Princeton.

Predictions off

Five years ago I wrote about the famous 2001 paper by Joshua Goldstein and Catherine Kenney, which made lifetime marriage predictions for cohorts through the Baby Boom, the youngest of whom were only 30 in the 1995 data the paper used. That’s gutsy, predicting lifetime marriage at age 30, so there’s no shame that they missed. They were closer for White women. They predicted that 88.6% of White women born 1960-1964 would eventually marry, and by the age 49-53 (in the 2013 American Community Survey) they were at 90.2%, with another 2.3% likely to marry by my estimates (see below). For Black women they missed by more. For the 1960-1964 cohort, they predicted only 63.8% would ever marry, but 71.3% were already married by 2013, and I’m projecting another 7.5% will marry. (I also wrote about a similar prediction, here.) If they actually get to 79%, that will be very different from the prediction.

Their amazing paper has been cited another 100 times since I wrote about it in 2010, but it doesn’t look like anyone has tried to test or extend their predictions.

Mass incarceration

Interestingly, Goldstein and Kenney undershot Black women’s marriage rates even though incarceration rates continued to rise after they wrote — a trend strongly implicated in the Black-White marriage disparity. This issue has increased salience today, with the release of a powerful new piece by Ta-Nehisi Coates in the Atlantic (my old job), which exposes the long reach of mass incarceration into Black families in ways that go way beyond the simple statistics about “available” men. The large ripple effects implied by his analysis — drawing from his own reporting and research by Deva Pager, Bruce Western, and Robert Sampson — suggest that any statistical model attempting to identify the impact of incarceration on family structure is likely to miss a lot of the action. That’s because people who’ve been out of prison for years are still affected by it, as are their relationships, their communities — and their children in the next generation.

Some new projections

I should note that some readers unfamiliar with demographic analysis may find parts of what follows morbidly depressing.

To set up the marriage market analysis I’m doing with Joanna — which isn’t ready to show here yet — I’m going to introduce some marriage projections at the talk. These use a different method than Goldstein and Kenney, because I have a different kind of data. This is a lifetable approach, in which I use first-marriage rates at every age to calculate how many women would get married at least once before they die if they lived 2010 over and over again from birth to death. I can do this because, unlike Goldstein and Kenney in 2001, I now have the American Community Survey (ACS), which asks a giant sample of people if they have married in the previous year, and how many times they’ve been married before, so I can calculate a first-marriage rate at every age. To this I add in death rates — making what we call a multiple-decrement life table — so that there are two ways out of the birth cohort: marriage or death. (Give me marriage or give me death.)

The way this works is you start with 100,00 people, and each year some of them die and some of them get married — according to the rates you have measured at one point in time. For example, in my tables, of 100,000 Black women at the start of year 0, only 98.7% make it to age 15, the first year they can be counted as married in the data. By the time you get down to age 30, there are only 67,922 left, as 2,236 have died and 29,843 have married for the first time. And so on down to the bottom. In the last row of the table, when they are all dead, you calculate how many got married before dying.*

The bottom line: 85.3% of White women, and 78.4% of Black women born and stuck in 2010 forever are projected to marry before they die — a surprisingly small gap. The first figure shows you that basic result:

NHBW life tables 2010.xlsx

Note that my projections of 85.3% of White women and 78.4% of Black women ever marrying are lower than, for example, the roughly 96% of White women and 91% of Black that were actually ever-married at age 85+ in 2010 (reported here), for several reasons. First, I count dead people against the ever-married number (additionally, married people live longer, not necessarily because they’re married). Second, today’s 90+ year-olds mostly got married 70 years ago, when times were different; my estimates are a projection of nowadays.

A very interesting age pattern emerges here, which is relevant to the incarceration and “available men” question. If you look back at the figure, notice that the big difference in marriage opens up early — peaking at 28 points by age 33, before narrowing to 7 points at the end.The big difference in marriage is that White women marry earlier. In fact, as the next figure shows, after age 33 Black women are more likely to marry than are White women. I don’t think I knew that. Here are the number marrying at each age:

NHBW life tables 2010.xlsx

Specifically, although White women are twice as likely to marry in their mid-twenties, of our fictional 100,000 women stuck in 2010, just 15.6% of White women, compared with 36.8% of Black women end up marrying after age 33.

The other way of looking at this — and an answer to a common question about marriage rates — is to see the chances of marrying after a given age if you haven’t married yet. This figure shows, for example, that a White women who lives to age 45 without marrying has a 26% chance of someday marrying, compared with a whopping 49% for Black women.

NHBW life tables 2010.xlsx

It is surprising that Black women, with lower cumulative odds of marrying at every age in the cohort, are so much more likely to marry conditional on getting to their 40s without marrying. Maybe you’ve got a better interpretation of this, but this is mine. Black women are not against marriage, and they are not ineligible for marriage in some way (even though most of these single women are already mothers**). Rather, they have not married earlier because they couldn’t find someone to marry. That’s because of all the Black men who are themselves dead, incarcerated or unemployed (or scarred by those experiences in their past) — or married to someone else. So within their respective marriage markets (which remain very segregated), the 45-year-old single White woman is much more likely to be someone that either doesn’t want to marry or can’t marry for some reason, while the 45-year-old single Black woman is more active and eligible in the marriage market. This fits with the errors in the earlier predictions, which failed to pick up on the upward shift in marriage age for Black women — marriage delayed rather than foregone.

What do you think of that interpretation? If you have a better idea I’ll mention you at Princeton next week.

Note: I found so many mistakes as I was doing this that it seems impossible there are any more. Nevertheless, caveat emptor: This analysis hasn’t been peer reviewed yet, so consider it only as reliable the latest economist’s NBER paper you read about on the front page of the every newspaper and website on earth. (And if you’re a journalist feel free to refer to this as a new working paper.)

* Technical notes: I used death rates from 2010 (found here), and marriage rates from the five-year ACS file for 2008-2012 (which has 2010 as its midpoint), from IPUMS.org. I adjusted the death rates because never-married people are more likely to die than average (I told you this was depressing). I had to use a 2007 estimate of mortality by age and marital status for that (found here), which is not that precise because it was in 10-year increments, which I didn’t bother to smooth because they didn’t have much effect anyway. The details of how to do a multiple-decrement lifetable are nicely described (with a lot of math) by Sam Preston here (though if you really want to replicate this, note one of his formulas is missing a negative sign, so plan to spend an extra few days on it). To help, I’m sharing my spreadsheet here, which has the formulas. (Note that survival in the life table doesn’t refer to being alive, it refers to being both alive and never-married.) The mortality and marriage rates are for non-Hispanic women; the never-married adjustment is for all women. For the marriage rates I used all Black and White women regardless of what other races they also specified (very few are multiple-race when you exclude Hispanics).

** In 2010, 63% of never-married Black women who lived in their households had at least own of their own children living with them.


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Sex ratios as if not everyone is a college graduate

Quick: What percentage of 22-to-29-year-old, never-married Americans are college graduates? Not sure? Just look around at your friends and colleagues.

Actually, unlike among your friends and colleagues, the figure is only 27.5% (as of 2010). Yep, barely more than a quarter of singles in their 20s have finished college. Or, as the headlines for the last few days would have it: basically everyone.

The tweeted version of this Washington Post Wonkblog story was, “Why dating in America is completely unfair,” and the figure was titled “Best U.S. cities for dating” (subtitle: “based on college graduates ages 22-29”). This local news version listed “best U.S. cities for dating,” but never even said they were talking about college graduates only. The empirical point is simple: there are more women than men among young college graduates, so those women have a small pool to choose from, so we presume it’s hard for them to date.* (Also, in these stories everyone is straight.) In his Washington Post excerpt the author behind this, Jon Birger, talks all about college women. The headline is, “Hookup culture isn’t the real problem facing singles today. It’s math.” You have to get to the sixth paragraph before you find out that singles means college and post-college women.

In his Post interview the subject of less educated people did come up briefly — if they’re men:

Q: Some of these descriptions make it sound like the social progress and education that women have obtained has been a lose-lose situation: In the past women weren’t able to get college educations, today they can, but now they’re losing in this other realm [dating]. Is it implying that less educated men are still winning – they don’t go to college but they still get the pick of all these educated, more promiscuous women?

A: Actually, it’s the opposite. Less educated men are actually facing as challenging a dating and marriage market as the educated women. So for example, among non-college educated men in the U.S. age 22 to 29, there are 9.4 million single men versus 7.1 million single women. So the lesser-educated men face an extremely challenging data market. They do not have it easy at all.

It’s almost as if the non-college-educated woman is inconceivable. She’s certainly invisible. The people having trouble finding dates are college-educated women and non-college-educated men. By this simple sex-ratio logic, it should be raining men for the non-college women. Too bad no one thought to think of them.

Yes, the education-specific sex ratio is much better for women who haven’t been to college. That is, they are outnumbered by non-college men. But it’s not working out that well for them in mating-market terms.

I can’t show dating patterns with Census data (and neither can Birger), but I can show first-marriage rates — that is, the rate at which never-married people get married. Here are the education-specific sex ratios, and first-marriage rates, for 18-34-year-old never-married women in 279 metropolitan areas, from the 2009-2011 American Community Survey.** Blue circles for women with high school education or less, orange for BA-holders (click to enlarge):


Note that for both groups marriage rates are lower for women when there are more of them relative to men — the downward sloping lines (which are weighted by population size). Fewer men for women to choose from, plus men eschew marriage when they’re surrounded by desperate women, so lower marriage rates for women. But wait: the sex ratios are so much better for non-college women — they are outnumbered by male peers in almost every market, and usually by a lot. Yet their marriage rates are still much lower than the college graduates’. Who cares?

I don’t have time to get into the reasons for this pattern; this post is media commentary more than social analysis. But let’s just agree to remember that non-college-educated women exist, and acknowledge that the marriage market is even more unfair for them. Imagine that.***

* I once argued that this could help explain why gender segregation has dropped so much faster for college graduates.

** It was 296 metro areas but I dropped the extreme ones: over 70% female and marriage rates over 0.3.

*** Remember, if we want to use marriage to solver poverty for poor single mothers, we have enough rich single men to go around, as I showed.

A little code:

I generated the figure using Stata. I got the data through a series of clunky Windows steps that aren’t easily shared, but here at least is the code for making a graph with two sets of weighted circles, each with its own weighted linear fit line, in case it helps you:

twoway (scatter Y1 X1 [w=count1], mc(none) mlc(blue) mlwidth(vthin)) ///

(scatter Y2 X2 [w=count2], mc(none) mlc(orange_red) mlwidth(vthin)) ///

(lfit Y1 X1 [w=count1], lc(blue)) ///

(lfit Y2 X2 [w=count2], lc(orange_red)) , ///

xlabel(30(10)70) ylabel(0(.1).3)


Filed under In the news

Conservatives don’t have happier marriages

On Vice’s Munchies channel (who knew), Hillary Pollack links to an excruciating Fox News chat about how Republicans have happier marriages, which I wrote about the other day.

The Fox intro says, “According to a new study, Republicans are far happier, and more stable, than Democrats are.” (Then they inaccurately described the data as being about how “married couples who describe themselves…”, when the data are about individual spouses, not couples.)

I already showed the premise isn’t true, at least as far as expressed happiness in marriage. The two groups with the highest reported marriage happiness, with demographic controls, are strong Democrats and strong Republicans, and the difference between them isn’t statistically significant.

So we can take Brad Wilcox’s words of wisdom on the meaning of Republican marital bliss in that light — that is, not.


But there is another problem here, which is he is conflating party identification with political ideology. As when he tweeted this:


“Republican” is not the same as “conservative.” In fact, the General Social Survey — the data we’re using here — has a question on political ideology as well as party identification. (Thanks to Omar Lizardo for reminding me of this.) They ask whether you “think of yourself as liberal or conservative.” And 15% of people identifying as Democrats consider themselves conservative (Republicans are much more consistently conservative).


If you’re going to claim that “conservatives have happier marriages,” you should use the political views question.* And that is even worse for this theory than the party identification (which I’m sure has nothing to do with why Wilcox chose to use the variable he did). Here is my result from the other day, using political views instead**:


So, what was that about conservatives having happier marriages?

Extreme liberals are a small group, just 3% of the this GSS sample (compared with “strong Democrats, who are 12%). But that difference is big enough to be statistically significant, with control variables, from each of the other groups (at p<.05, except the extreme conservatives, p<.10, in two-tailed tests).

David Leonhardt and other journalists covering “reports” from Brad Wilcox should consider the merits of peer review or, absent that, checking around a little before serving up this bologna. I understand there isn’t time for our peer review system to vet every little partisan claim, and I’ve served up some non-peer-reviewed reports to the news media, too. I would always encourage journalists to at least check around before running with a splashy claim.


* This doesn’t mean ideology is always a better measure, of course. For example, when it comes to attitudes toward health care spending, this paper by Stephen Morgan and Minhyoung Kang shows that party identification is a strong predictor even controlling for ideology. But in this case the issue is conservative values, not some partisan policy matter.

** Use the code I posted the other day, but with POLVIEWS instead of PARTYID


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Why it’s rotten to tell someone they’re poor because of when they had their kids

The “success sequence” is an idea from Ron Haskins and Isabel Sawhill at the Brookings Institution. They want to balance government investment and “personal responsibility” to reduce poverty. By personal responsibility, they mean adherence to what they call “the three norms”: complete high school, work full time, wait until you’re 21 and married to have children. If you do that — and smile while doing it — they’re willing to spot you a little welfare and some education.

I’ll describe it, and some criticism of the idea, then show a little data analysis.

Haskins and Sawhill claim to have analyzed data to show that when you follow all three of these norms, you have a 98% chance of not being poor. This is how they illustrate it (from this slideshow):

success-sequence-slideMatt Bruenig at Demos has an important post explaining how misleading — and wrong — this is. There are three main problems, briefly:

  1. The high school degree and full-time job is doing almost all of the work. With those two hurdles complete, you’re already down under 4% poverty. So the marriage stuff is mostly moralizing for political purposes.
  2. The data they used does not include the information necessary to see whether people were married when they had their children — it doesn’t have marital history. So they didn’t even do the analysis they said they did.
  3. Family complications mess this up badly. In particular, if a person (say, a man), has children with a partner and never lives with them, he shows up as having met the “norms” because the data don’t show him having any children — it’s a household survey, so absent parents aren’t parents in the data.

So if someone gives you the “success sequence” thing, just remember, the analysis is baloney, and the bottom line is decent full-time jobs are what keep people out of poverty (by the official poverty measure, of course).


Anyway, I can go a little further using the American Community Survey, which includes data on the year of each person’s most recent marriage, and the number of times they’ve been married. So, limiting the data to first-time married parents, I can check the age of their oldest child and see whether it was born before they were married, and before the parent was age 21. Some of the above problems still apply, but this is something. And it enables me to underscore Bruenig’s point that step three of the success sequence is not pulling its weight.

(Note this analysis is just about the timing of births for people who are currently married. Single parents of course have higher poverty rates that you can’t attribute to the timing of their births without more information than the ACS has.)

Using the 2013 ACS provided by IPUMS, I took all married parents, living in their own households, age 18 or older, married for the first time, with a child under 18 in the household. Then I used the job norm (self or spouse full-time employed), the education norm (high school complete), and the parent norm in two parts (child born after marriage, child born after age 21), as well as other variables, to see their relative contribution to not being poor. The other variables were additional education (BA degree), race/ethnicity, age, sex, disability, and nativity

This figure shows the marginal effects. That is, how much does the chance of being in poverty change with each of these conditions, holding all the others constant at their means? Click to enlarge:

success sequence acs 2013.xlsx

If the oldest child in the family was born before the year of the parents’ marriage, the chance of being in poverty is increased by 0.4%. If the child was born before the parent was 21, the chance goes up by 0.6%. This seems reasonable to me, given the potential hardships associated with single and early parenthood. But compare: Not having a high school diploma increases the chance of poverty by 2.2%, and neither spouse having a full-time job increases the chance by 6.4%.

Remember, these are all effects holding constant everything else in the model. If you just look at the difference between those who fulfill the parenting “norm” and those who don’t, it’s much bigger. Among people with a full-time job in the family and a high school degree, the poverty rate is 2.8% for people whose oldest present child was born after they were married and 21, versus 9.1% for the people who let us all down on the childbearing norm. But that big difference is mostly because of education and race/ethnicity and disability, etc.

In short, this exposes how rotten it is to tell someone they are poor because of when they had their kids. A decent job and some education would mean a lot more than your sermon.


Here is the IPUMS codebook for my download, and the Stata .do file for the analysis.


Filed under In the news

Marriage equality is official now that it’s in The Family textbook

Well, actually, it’s in a special addendum to the textbook that W. W. Norton is just releasing.

The book I wrote, The Family: Diversity, Inequality, and Social Change, hit the streets a year ago today. Marriage equality plays a significant part in the story, much larger than the proportion of the population that is directly affected by the changing law. That’s because of the high-stakes nature of the debate for so many people, and because of its symbolic acceptance of rising family diversity — the main theme of the book.

So when the law suddenly, and fundamentally, changed this summer, we decided we needed an update for instructors teaching this fall. The three-page supplement reviews the political and legal events leading up to the June 26 Obergefell decision, and the logic of the legal questions addressed — along with a little context on the place of marriage equality in the story of family change. I hope it’s helpful for you.

The update is now available on the Norton website, here, and on my teaching page. While you’re at it, you should visit the book’s homepage, and see what we have in store for you if you teach family sociology (and request an exam copy), here.


  • A symposium with 12 writers and researchers addressing the concept, “After marriage equality,” which Syed Ali and I edited for Contexts.
  • My whole series of blog posts on marriage equality is archived under the homogamy tag.


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No, you should get married in your late 40s (just kidding)

Please don’t give (or take) stupid advice from analyses like this.

Since yesterday, Nick Wolfinger and Brad Wilcox have gotten their marriage age analysis into the Washington Post Wonkblog (“The best age to get married if you don’t want to get divorced”) and Slate (“The Goldilocks Theory of Marriage”). The marriage-promotion point of this is: don’t delay marriage. The credulous blogosphere can’t resist the clickbait, but the basis for this is very weak.

Yesterday I complained about Wolfinger pumping up the figure he first posted (left) into the one on the right:

wolfbothToday I spent a few minutes analyzing the American Community Survey (ACS) to check this out. Wolfinger has not shared his code, data, models, or tables, so it’s hard to know what he really did. However, he lists a number of variables he says he controlled for using the National Survey of Family Growth: “sex, race, family structure of origin, age at the time of the survey, education, religious tradition, religious attendance, and sexual history, as well as the size of the metropolitan area.”

The ACS seems better for this. It’s very big, so I can analyze just the one-year incidence of divorce (did you get divorced in the last year?), according to the age at which people married. I don’t have family structure of origin, religion, or sexual history, but he says those don’t influence the age-at-marriage effect much. He did not control for duration of marriage, which is messed up in his data anyway because of the age limits in the NSFG.

So, in my model I used women in their first marriages only, and controlled for marriage duration, education, race, Hispanic ethnicity, and nativity/citizenship. This is similar to models I used in this (shock) peer-reviewed paper. Here are the predicted probabilities of divorce, in one year, holding those control variables constant.


Yes, there is a little bump up for the late 30s compared with the early 30s, but it’s very small.

Closer analysis (added to the post 7/19), generated from a model with age-at-marriage–x–marital duration interactions, shows that the late-30s bump is concentrated in the first five years of marriage:


This doesn’t much undermine the “conventional wisdom” that early marriage increases the risk of divorce. Of course, this should not be the basis for advice to people who are, say, dating a person they’re thinking of marrying and hoping to minimize chance of divorce.

If you want to give advice to, say, a 15-year-old woman, however, the bottom line is still: Get a bachelor’s degree. You’ll likely earn more, marry later, and have fewer kids. If you or your spouse decide to get divorced after all that, it won’t hurt that you’re more independent. For what it’s worth, here are the education effects from this same model:


(The codebook for my IPUMS data extraction is here, my Stata code is here.)

Anyway, it’s disappointing to see this in the Wonkblog piece:

But the important thing, for Wolfinger, is that “we do know beyond a shadow of a doubt that people who marry in their thirties are now at greater risk of divorce than are people who wed in their late twenties. This is a new development.”

That’s just not true. I wouldn’t swear by this quick model I did today. But I would swear that it’s too early to change the “conventional wisdom” based only on a blog post on a Brad-Wilcox-branded site.


One interesting issue is the problem of age at marriage and education. They are clearly endogenous — that is, they influence each other. Women delay marriage to get more education, they stop their education when they have kids, they go back to school when they get divorced — or think they might get divorced. And so on. And, for the regression models, there are no highly-educated people getting married at really young ages, because they haven’t finished school yet. On the other hand, though, there are lots of less-educated people getting married for the first time at older ages. Using the same ACS data, here are two looks at the women who just married for the first time, by age and education.

First, the total number per year:


Then, the percent distribution of that same data:

age-ed-mar-distInteresting thing here is that college graduates are only the majority of women getting married for the first time in the age range 27-33. Before and after that most women have less than a BA when they marry for the first time. This is also complicated because the things that select people into early marriage are sometimes but not always different from those that select people into higher education. Whew.

It really may not be reasonable to try to isolate the age-at-marriage effect after all.


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